Browse Topic: Telecommunications

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J1979 DBCJ1979DBC_202609To be published on 09/07/2026
The SAE J1979 DBC file contains decoding rules for converting raw J1979 data to 'physical values' (Mph, %, etc.). This file lets you easily decode data from heavy duty vehicles (trucks, buses, tractors, etc.). This DBC file download includes: The SAE J1979 DBC file with Includes 2,400+ Parameter Group Numbers (PGNs) and 16,000+ Suspect Parameter Numbers (SPNs), derived from the J1979-2 released in September 2026. One legal license (1 user, 1 PC) matching the DA license DECODE J1979: Convert J1979 data in wide range of software/API tools REVIEW FIRST: Use our CAN ID converter to check if your PGNs are covered CROWD INPUT: Benefit from free corrections based on large user base SAVE HOURS: Avoid manually constructing the DBC file from scratch Improved Accuracy & Reliability A fully standardized DBC file ensures precise signal decoding, eliminating errors and ensuring reliable data interpretation. Interoperability Seamlessly compatible with many different software stacks, enabling frictionless adoption and significantly expanding market reach. Partnership with Vector Informatik GmbH Works seamlessly with Vector’s free software (CANdb++), used by over 90% of the industry, with free download link provided on SAEI’s J1979DBC file landing page. What is a DBC file? A DBC file is a standardized method for storing the "rules" on how to interpret raw CAN bus data. It contains details on what 'signals' (e.g. RPM, Vehicle Speed, …) are contained within which 'messages' (i.e. CAN IDs). In the J1979 standard, messages are referred to as Parameter Group Numbers (PGN) and signals as Suspect Parameter Numbers (SPN). Further, a DBC file includes names, descriptions, positions, and lengths of the signals - as well as how to offset & scale them.
The vigorous rate of new spacecraft being launched has made the accurate estimation of in-orbit environmental disturbances torques paramount to reducing attitude control performance corrosion. Leveraging telemetry from an asset in low-earth-orbit, we present a novel Adaptive Super-Twisting Sliding-Mode Observer, which interlinks three techniques heretofore decoupled: 1) saturation-constrained angular-acceleration adaptation; 2) Kalman-filter preconditioning of angular velocity; and 3) state-weighted logarithmic gain with dual leakage. Denoising of raw Euler angle sequences and detection of quasi-steady epochs are achieved with a customized Kalman update, while an adaptive band-pass stage isolates the torque-related acceleration signature. Casting these filtered data into the super-twisting form, we update the log gain on-the-fly, and twin leakage terms remove excess energy with accompanying chatter rejection—without compromising bandwidth. Head-to-head telemetry tests show a positive margin headroom on noise attenuation that has to be compared with the power-gain type counterpart and that increases with the signal roughness, thereby validating the fact that this technique refines environment torque estimates and hence strengthens robustness design envelopes in next-generation attitude-control systems.
Yin, XuDeng, YuhuiChi, Dongxiang
High-performance composite materials are widely used in the main structural components of satellites. Various cylindrical components will hit the satellite at the time. The cartridge is fixed and unchanging. Honeycomb structure and ceramic honeycomb structure are the same, the cylindrical component is lighter and can attack satellites at a greater speed under the same conditions as the propellant. In terms of mass quality, the speed can reach over 1.6 times, and ceramic honeycomb structure can have minimal kinetic energy loss and can face enemies in combat with thicker steel plates, allowing for continuous destruction. Cylindrical component remaining kinetic energy (∆E) is 3.2968e3 J, but ceramic cylindrical component ∆E is 2.082e3 J, and ceramic cylindrical component have more advantages, and there are fewer arbitrary steel balls flying.
Xu, JianYang, ZhenChang, XuefangLi, QiangZhang, KunQu, Dandan
This SAE Information Report defines the diagnostic communication protocol TP1.6. This document should be used in conjunction with SAE J2534-2 in order to fully implement the communication protocol in an SAE J2534 interface. Some Volkswagen of America and Audi of America vehicles are equipped with ECU(s), in which a TP1.6 proprietary diagnostic communication protocol is implemented. The purpose of this document is to specify the requirements necessary to implement the communication protocol in an SAE J2534 interface. This SAE Information Report describes how a tester can be connected to a vehicle to perform diagnostics using the TP1.6 protocol. Details regarding ECU to ECU communication have been left out.
Vehicle E E System Diagnostic Standards Committee
In complex urban environments, vehicle positioning based on Global Navigation Satellite Systems (GNSS) is prone to failure or accuracy degradation due to signal blockage and multipath effects. To address this issue, this article proposes a vehicle–road cooperative positioning method based on factor graph optimization for GNSS-denied environments and evaluates its performance through both simulation and real-vehicle experiments. In the proposed approach, road codes deployed on the road surface serve as absolute position references on the road surface, and high-precision vehicle position estimates are obtained in real time by fusing roadside positioning information with onboard sensor measurements using factor graph optimization. Furthermore, to reduce the experimental cost and development cycle of the vehicle–road cooperative positioning method, a performance simulation platform is developed based on the CARLA simulator, RoadRunner, and the CARLA-ROS (Robot Operating System) bridge for real-time communication between simulation and positioning modules. The platform supports road code generation and deployment, customized scenario construction, and positioning performance simulation, and a multi-objective optimization approach is employed to obtain an optimal deployment scheme for road code spacing. Finally, the effectiveness of the proposed vehicle–road cooperative positioning method is validated through both simulation and real-vehicle experiments. Real-vehicle experiments show that the proposed method reduces the average RMSE (Root Mean Square Error) by 26.6% compared with the ESKF (Error State Kalman Filter) baseline, achieving an RMSE of 0.25 m and a maximum error of 0.95 m at a vehicle speed of 60 km/h and a 10 m code spacing, thereby confirming decimeter-level continuous accuracy in GNSS-denied environments.
Shen, ChuanfuZhao, ZhiguoYan, DanshuLing, Yubin
The primary objective of this recommended practice is to confirm that vehicles can communicate a minimum set of information in accordance with diagnostic test services specified in SAE J1979-2, or the equivalent ISO 14229-1:2013 (Ed. 2) and SAE J1979-3 documents. A successful result using the SAE J1699-5 Automotive Alliance for Innovation (AAI) software for OBD-II testing indicates that the main aspects of UDS communication have been examined, but it does not provide assurance of complete compliance with all aspects of SAE J1979-2 or SAE J1979-3. SAE J1699-5 tests run with a SAE J2534 device hardware and API that is not fully compliant will result in a test failure. This specification takes precedence over all conflicts in the documents cited in this section.
Vehicle E E System Diagnostic Standards Committee
Connected and Automated Vehicles (CAVs) represent a transformative innovation poised to revolutionize roadway transportation by leveraging automated driving systems equipped with advanced sensors, high-performance computing, and communication technologies. While urban areas are the primary focus of current CAV developments, rural transportation systems risk being left behind despite the significant benefits that CAVs can bring to these regions. This article, therefore, explores the physical and digital infrastructure requirements for the safe deployment of CAVs in rural areas, drawing insights from standards, recommendations, and guidelines developed by leading standard organizations. The study highlights the specific design of physical infrastructure, including traffic signs, traffic signals, and pavement markings, and digital infrastructure, including communication, sensing, and mapping, to ensure rural communities are effectively prepared to benefit from the potential of CAVs. As its primary contribution, this article provides a comprehensive review of existing standards and guidelines relevant to rural CAV deployment. By synthesizing guidance across multiple standard-setting organizations, this review delivers a structured analytical assessment of existing standards, revealing their limitations and misalignment with rural transportation contexts while highlighting emerging good practices. The article clarifies the applicability of current guidance to rural infrastructure, identifies systemic infrastructure-related failure modes, and informs context-aware planning considerations for efficient and scalable CAV deployment in rural areas.
Zakaria, MohammedGetahun, TesfamichaelTavasoli, MahsaPandey, VenkteshSarrafzadeh, AbdolhosseinKarimoddini, Ali
Aiming at problems such as low efficiency and poor accuracy in fault identification for traditional small satellites, this paper proposes a multi-model fusion method based on machine learning. By constructing a telemetry data preprocessing module based on the Data Generation Adversarial Network, it effectively deals with outliers and fills missing values. Combining single model methods such as polynomial curve fitting, the grey model, and the ARMA model, and introducing the Long Short-Term Memory network and Gated Recurrent Unit to fuse with these models enhance the ability to process complex data features. The prediction results of each model are fused using machine learning methods, and finally, the fused value is taken as the final prediction result. The numerical simulation results show that this prediction method can predict the anomalies of different types of satellite telemetry parameters and has achieved good results.
Liu, BiyanChen, YeGuo, Qi
This paper solves the problem of resource and energy constraints on orbit computing for LEO satellites. By combining MADDPG reinforcement learning and Lyapunov optimization, the paper proposes a computing framework and implements an adaptive task offloading model for space flight using a multi-agent deep actor critic algorithm, MADDPG. The joint optimization mechanism is implemented by multi-agent dynamic task offloading. Through the transformation from the state with long-term constraints into optimization of the status of queue stability, the load scheduling under threshold energy in accordance with the characteristics of energy constraints was realized by introducing Lyapunov virtual queues into the process of policy evaluation of deep reinforcement learning. The experimental results show that the proposed framework enables a lightweight preliminary calculation, balanced energy consumption to reduce resource allocation, and realizes the stable queues through adaptability of tasks under energy balance conditions, which can provide high-efficiency computing assistance and support for space orbit tasks such as monitoring remote sensing of Earth.
Yan, MingZhao, LiangXu, LexiZhou, XiaofeiHawbani, AmmarSun, Yunhe
In this paper, we focus on satellite production lines and design and implement a digital twin simulation and verification system for them. This is to improve manual documentation efficiency and provide sufficient process controllability in the small satellites’ batch production and assembly testing. We built a layered architecture. This allows the system to dynamically interact with AIT data management systems, structured process systems, and equipment data by fusing multi-source data. We also develop functional modules that combine lightweight 3D model visualization, dynamic simulation engines, and hybrid scheduling optimization algorithms. These modules can perform twin simulation, execute processes, intelligently schedule production, manage work reporting, conduct intelligent analysis, trigger anomaly alarms, and perform system management. We also dynamically simulate complex workflows like satellite transfer and automated assembly. These workflows are then verified using 3D virtual scene modeling and physical engines. We use time-series analysis to improve scheduling accuracy and multidimensional dynamic monitoring and hierarchical response to enhance production stability. In practice, the system can provide visualized control over the full process of satellite production. This greatly improves assembly efficiency and process controllability. It can also be an extensible digital way for aerospace manufacturing. The use of hierarchical architecture design and multimodal data fusion can be further applied in the complex equipment intelligent manufacturing.
Zhao, Fenghua
This paper investigates the tracking of highly maneuverable targets during flight and the corresponding satellite scheduling problem in a space-based observation system. Based on dual-satellite measurements, a nonlinear observation equation was formulated. The J2 perturbation model and the current statistical model were utilized within an Interacting Multiple Model filtering framework to achieve adaptive estimation of the target states of the boost-glide vehicles. Building upon this framework, a greedy satellite scheduling algorithm based on IMM model probabilities is proposed. This method dynamically selects the optimal measurement set within a given prediction window to maximize observation performance. The proposed strategy is compared against rolling-horizon scheduling and fast-slow timescale scheduling approaches. Simulation results demonstrate that the proposed method effectively adjusts model weights in response to target maneuvers, enhancing adaptability during highly maneuverable phases. Meanwhile, it reduces the number of satellite switches while maintaining estimation accuracy, significantly improving scheduling efficiency and tracking continuity.
Deng, SiruiLiu, ChengzheWang, Yandong
To solve a problem that ignition anomaly can’t be detected in time, based on the thermal equilibrium equation, the space heat flow, heater heating, propellant combustion, and thermal radiation to cryogenic space are considered to build an accurate ignition temperature method for the 10 N thruster by using on-orbit true temperature. Further, considering the error of measuring the thermistor, an envelope model for the 10 N thruster ignition temperature is established. Based on the above, a detection method for the 10 N thruster ignition anomaly of on-orbit satellites is proposed. The accuracy of the method is relatively high, and the absolute error is less than 3 degrees Celsius. An anomaly can be quickly detected when the 10N thruster ignition temperature deviates from the normal trend by 3–5 degrees celsius. The method is applied to a DFH-3 satellite, and the maximum difference of 10 N thruster ignition temperature between the theoretical values calculated by the proposed method and the measured values is only 2.72 degrees celsius. It has been proven that the prediction accuracy of the proposed method is high. It plays an important role in discovering the 10N thruster ignition anomaly in time and ensuring the success of satellite orbit or attitude control.
Li, LilingTian, HuadongWei, YuboFei, DiXing, Chao
This paper presents the development of a speed controller for e-bikes, designed as part of an energy-adaptive assistance system. The controller provides riders with appropriate support along planned routes, based on the available battery capacity. The control concept is intended for integration into existing commercial e-bikes without requiring extensive modifications to the drive system. Therefore, the rider remains part of the control loop, adjusting the support mode according to instructions from the controller. The speed controller is implemented as a rule-based state machine, enabling comprehensible design and parameterization. Since the rider must manually switch between support modes while riding, the control logic incorporates hysteresis and dead times to ensure stability, prevent oscillations, and avoid frequent mode switching. The user interface is a smartphone application that issues visual and audio instructions for switching support modes. An initial, system-independent version that relied on GPS-based speed measurement was found to be insufficiently accurate for the control task. Furthermore, it was found that detection of the pedaling state was essential for proper operation. To address these issues, a Bluetooth-based hardware adapter was developed to access relevant signals from the e-bike’s CAN bus communication system. These include pedal power, cadence and speed, which are made accessible through reverse engineering of the CAN bus. The proposed concept is evaluated in a chassis dynamometer study with 13 participants on two test profiles: a synthetic gradient profile for assessing control stability and a realistic elevation profile for dynamic evaluation. Additional measurements taken with one of the test riders at different speeds demonstrate the system’s reliability and its potential to improve the energy efficiency. The results show that, with approximately the same power brought in by the rider, only 27% more electrical energy is required to increase the average speed by 45%.
Rauch, YannickSimmann, GabrielSchneider, ManuelGoss, ChristianKriesten, Reiner
Electrical/Electronic Architectures (EEAs) are continuously evolving to meet newly emerging demands. In recent years, major drivers of this evolution have been the increasing software-defined nature of vehicles and the push toward automated driving. Key technologies such as edge-enhanced functions, vehicle-to-vehicle communication, and service-oriented architectures are therefore the focus of current research efforts. This paper presents a vision of how these technologies can be used to enable cooperation between vehicles, illustrated by using parked vehicles as edge nodes. These are typically seen as obstructions, as they significantly increase the risk of missing or misinterpreting vulnerable road users such as pedestrians or cyclists. Our proposed approach to counteract this problem is the use of the parked vehicles themselves as edge nodes that support object detection or even trajectory planning. Current research primarily considers smart traffic infrastructure, roadside units, and other vehicles as potential edge nodes. Including parked vehicles as edge nodes means that, instead of acting solely as obstacles, we leverage their built-in sensors to contribute to cooperative awareness. While such cooperation will enhance the safety of automated vehicles in urban areas, several challenges arise. In this paper, we discuss how data traceability, decision-making in the presence of conflicting information, and incentive mechanisms for owners of parked vehicles can be addressed. Based on these challenges, the paper outlines requirements for future cooperative architecture and highlights the role of edge-enhanced functions, Vehicle-to-Vehicle (V2V) communication, and service-oriented architectures in enabling fully automated driving.
Lüntzel, VitusLukezic, NikolaKraus, DavidSeidel, LucaBeck, MaximilianSchindewolf, MarcSax, Eric
This article presents a cross-layer framework that integrates realistic vehicle-to-network-to-vehicle (V2N2V) delay characterization with a rigorous stability analysis of automated vehicle steering control. Both constant and network-induced time-varying delays modeled via deterministic bounds are addressed. For constant delays, delay-independent stability regions within the controller gain space are analytically derived. For time-varying delays with stochastic network origins, modeled using deterministic bounds, a refined Lyapunov–Krasovskii functional (LKF) incorporating augmented single- and double-integral terms is constructed. To establish delay-dependent linear matrix inequality (LMI) conditions, a reciprocally convex combination approach is employed to handle the delay interval partitioning, and the second-order Bessel–Legendre inequality is applied to tighten the integral quadratic bounds. The resulting LMI conditions explicitly capture the coupled effects of delay magnitude, delay variation rate, and control gains on closed-loop stability. Simulations of a lane-keeping scenario confirm that the predicted stability boundaries accurately match the closed-loop system behavior. Notably, incorporating a realistic time-varying V2N2V delay profile into the controller design reduces the lateral-state root-mean-square error (RMSE) by over 54% and decreases the settling time by a factor of 10 compared to designs relying on an average-delay assumption. However, high packet loss rates are shown to still induce residual oscillations due to information scarcity. Ultimately, these results elucidate delay-induced instability mechanisms and provide practical guidelines for designing delay-robust steering controllers for connected and automated vehicles.
Li, JialinLu, JianweiWei, HengAo, Di
This SAE Information Report SAE J2836/6 establishes use cases for communication between plug-in electric vehicles and the EVSE for wireless energy transfer as specified in SAE J2954. It addresses the requirements for communications between the on-board charging system and the wireless EV supply equipment (WEVSE) in support of detection of the WEVSE, the charging process, and monitoring of the charging process. Since the communication to the charging infrastructure and the power grid for smart charging will also be communicated by the WEVSE to the EV over the wireless interface, these requirements are also covered. However, the processes and procedures are expected to be identical to those specified for V2G communications specified in SAE J2836/1. Where relevant, the specification notes interactions that may be required between the vehicle and vehicle operator, but does not formally specify them. Similarly, communications between the on-board charging sub-system and the on-board vehicle electronics is not formally specified in this document. This document will be published as a set of steps. The intent of step 1 was to record as much information on “what we think works” and publish. The intent of step 2 is to provide refinement and missing pieces to step 1, with a an eye to early testing. This version is step 2, with the aim of providing a communication protocol for home chargers.
Hybrid - EV Committee
Noise pollution is a major environmental and health challenge, yet its strong spatial and temporal variability makes comprehensive mapping highly complex. Current approaches under the European Noise Directive (END) provide only partial coverage and often lack temporal dynamics. The NoiseSphere project, funded by the Austrian Research Promotion Agency FFG, develops an AI-based methodology for dynamic, large-scale noise prediction and mapping. A machine learning model is trained on heterogeneous data sources, including semantically enriched open Sentinel-2 satellite imagery, OpenStreetMap road data and existing noise maps. The model is refined through integration of noise emission data and validated using targeted in-situ measurements. A case study in an urban environment (Graz, Austria) demonstrates the model’s applicability. By combining remote sensing, traffic dynamics, and machine learning, NoiseSphere enables predictive noise mapping even in regions not covered by current legislation. This approach provides a scalable tool for evidence-based environmental planning, health risk assessment, and policy support.
Girstmair, Josef
The integration of Electric Vehicles (EVs) as active grid resources represents a pivotal shift towards decarbonization. However, the implementation of effective Vehicle-to-Everything (V2X) services faces technical challenges regarding interoperability, predictive management, and battery health preservation. This work presents a comprehensive system design and research methodology developed within the framework of the FLEXV2X project, aimed at addressing interdependencies within a unified bidirectional charging ecosystem. The proposed scientific framework addresses two complementary timescales. At the device level, the study details the modelling and optimization of bidirectional converters, focusing on control algorithms designed to ensure robust dynamic response and efficiency. Building upon this hardware foundation, the paper describes a system-level optimization strategy. By employing open-source cyber-physical modelling, the architecture simulates complex EV-grid interactions. This layer integrates Artificial Intelligence (AI) algorithms to forecast stochastic variables such as renewable generation and fleet availability, driving a rule-based optimization engine. This dispatching logic will also be constrained by novel battery aging models, calibrated through experimental cycling stress-tests, balancing grid flexibility services with the preservation of the vehicle’s asset value. The effectiveness of this multi-layered design is assessed through a validation roadmap involving real-world deployment of a corporate mobility hub connected to a 10 MW wind farm, and a large-scale urban car-sharing fleet.
Lutzemberger, GiovanniBarater, DavideCeraolo, MassimoFera, CesareLeaver, IanPasini, Gianluca
Emergency evacuation slides (EVAC slides) are critical safety devices used on aircraft to enable rapid egress during emergencies. While these slides provide a quick and reliable escape route, communication between separated slides during evacuation remains a challenge. Often, during raft deployment over water, slides may drift apart impeding communication among evacuees and rescue personnel potentially compromising safety. Existing aircraft EVAC systems lack integrated wireless communication relying on visual or voice signals that are unreliable in chaotic conditions. This paper explores the integration of wireless IoT technology into EVAC slide systems to facilitate inter-slide communication and monitor critical parameters such as slide air pressure and the floating weight of stranded passengers through embedded sensors. It proposes the adoption of Long Range (LoRa) modulation technology for wireless communication chosen for its low-power, long-range performance and license-free operation in emergency evacuation scenarios. In addition, the usage of this proposed technology can be further extended to locate the aircraft when other existing locating mechanisms fail.
Sengodan, RajkumarTalore, Suresh
In today’s global aviation industry, passenger experience is strongly influenced by effective communication. In-flight announcements, often limited to English and a single local language, can create confusion and stress for international travelers who may not be fluent in either. This communication gap not only impacts passenger comfort but also poses potential risks in conveying time-sensitive or safety-critical information. Recent advances in Generative Artificial Intelligence (GenAI), particularly in speech recognition, neural machine translation, and naturalistic text-to-speech, provide a pathway to overcome these challenges. This paper explores the concept of real-time multilingual in-flight announcements delivered in each passenger’s preferred language through connected headphones or personal devices. The proposed system architecture integrates speech-to-text conversion, language translation, and speech synthesis with aircraft infotainment platforms. Potential applications range from pre-generated multilingual safety messages to long-term visions of fully personalized, real-time translations with minimal latency. Benefits include improved inclusivity, accessibility for hearing-impaired passengers, and enhanced brand differentiation for airlines. Challenges such as regulatory certification, translation accuracy, latency constraints, and hardware integration must be addressed. Beyond aerospace, this capability has cross-domain relevance in automotive, railways, and public services, making it a promising area for future customer experience innovations.
Mishra, AshwiniKature, KartikPatil, Ashish
Commercial and military aircraft increasingly rely on Integrated Vehicle Health Management (IVHM) as a critical enabler for predictive maintenance, operational efficiency, and mission availability. The evolution of IVHM data communication architecture- from legacy wire-based networks to more wireless based architecture involving onboard wireless sensor networks (WSN) and IP-based air to ground communication networks introduces multidomain cyber-physical attack surfaces that challenge both functional safety and continued airworthiness. DO-326A/ED-202A and DO-356A/ED-203A standards define aviation cybersecurity requirements within a safety-driven assurance context, and IEC 62443 standard offers a defense-in-depth, lifecycle-based control framework for industrial systems. A unified approach by mapping and harmonizing the complimentary aspects of these two standards has the potential to simplify and expedite the security assurance and certification process for the IVHM and other digital avionic systems. The proposed work is a unified, standard-aligned cybersecurity assurance framework for aircraft IVHM systems covering both airborne ground segments as a single integrated entity. The novel methodology evaluates risks by performing Fundamental Requirements (FR) analysis, mapping IEC 62443 to DO-326A/ED-202A requirements through qualitative risk assessment, protocol vulnerability analysis, and attack tree modelling on zone-conduits as per the Security Level (SL) requirements. The resulting risk treatment plan demonstrates a verifiable and auditable method to achieve security assurance and certification of aircraft IVHM system which is extendable to other digital avionic systems.
Samudrala, RamakrishnaRamamurthy, Prasanna
Global Navigation Satellite System (GNSS) receivers are widely being used in aerospace as well as automotive applications primarily for navigation applications. ISRO uses indigenously developed GNSS receivers in its Launch vehicles (LV) mainly for POD (Preliminary Orbit Determination) and for INS aiding in long duration missions. Advanced GNSS receivers are being developed and used in ISRO’s new generation launch vehicles for closed loop guidance (CLG) applications. Being used in CLG, continuous solution availability and robustness of GNSS solutions are of paramount importance. From April 2023 onwards, GNSS receivers on-board ISRO’s LV missions have shown degraded performance in terms of reduction in no. of satellites tracked and in some cases loss of GNSS solution as well. This was seen in multiple missions and was analyzed in detail. It was observed that there is nearly 3-4dB reduction in carrier to noise density (C/No) ratio and corresponding change in RF AGC gain is also observed. The issue is seen when the LV’s ground trace is over a particular terrestrial area (latitude 7degN-10degN longitude 95degE-110degE). A survey on internet indicated presence of GPS interference/jamming signals from nearby region and aircraft pilots have also reported similar observations. This paper addresses the performance degradation observed due to terrestrial jamming in ISRO’s GNSS receiver and analysis of observation w.r.t reduction in carrier to noise ratio and RF AGC gains. Further details on anti-jamming techniques implemented in LV GNSS receivers using trajectory based jamming signal isolation using RF switch is also addressed in this paper. Future developments being pursued towards building resilience towards different threats including jamming and spoofing using dual RF-front end & other software techniques are also discussed in this paper.
A, Mohammed BasimO T, Anand ShankaraV S, BijuV Gopal, BijuV S, VinojK, BalanC, Radhakrishna Pillai
As aerospace platforms adopt increasingly interconnected architectures for avionics, telemetry, and predictive diagnostics, lightweight publish–subscribe protocols have become integral to communication efficiency. The Message Queuing Telemetry Transport (MQTT) protocol is widely employed due to its small footprint and low network overhead. The release of MQTT 5.0 introduces new control features—reason codes, session expiry, user properties, topic aliasing, shared subscriptions, and improved error feedback—aimed at enhancing scalability and diagnostic reliability. However, these benefits come with trade-offs in complexity and potential overhead, particularly in real-time and resource-constrained environments typical in aerospace. This paper evaluates MQTT 3.1 and MQTT 5.0 within aerospace IoT contexts using a Raspberry Pi–based experimental framework. The analysis is done using practical throughput benchmarks implemented via popular open-source tools like Eclipse Mosquitto Clients. Realistic aerospace communication scenarios are modeled for inter-module messaging, under varying QoS levels and payload conditions. Comparative throughput, latency, and broker resource utilization benchmarks were conducted under multiple QoS levels and payload sizes to quantify the trade-offs between functionality and efficiency. This research aims to empirically validate the theoretical improvements of MQTT 5.0 on realistic embedded hardware and under controlled network constraints, replicating operational aerospace environments. Results show that MQTT 5.0 provides measurable advantages in complex, multi-tenant environments but introduces moderate processing overhead. Recommendations are proposed for selecting the optimal MQTT version for aerospace deployments and strategies for seamless migration from legacy systems [8].
Bhuyar, PrabhudevM, MeghanaKaniraja, ChristinaThomas, Tinto
Space vehicle and satellite development programs are driving demand for new small- and medium-sized satellites across commercial and defense imaging, data collection, and other space-based applications.
As satellites take on more onboard processing - from Earth imaging to autonomy - spacecraft computing designers are pushing for higher performance under tight thermal and radiation constraints. Here's how suppliers are approaching heat removal, radiation mitigation and production-scale space-grade computing for LEO and beyond.
Researchers discover texts, phone calls, military communication, internal corporate networks all easily eavesdropped on using off-the-shelf equipment. University of California San Diego, La Jolla, CA With $800 of off-the-shelf equipment and months' worth of patience, a team of U.S. computer scientists set out to find out how well geostationary satellite communications are encrypted. And what they found was shocking. Close to half of the communications beamed from satellites to the ground that the researchers were able to listen in on were not encrypted. This included sensitive data including cellular text messages, voice calls, as well as sensitive military information, data from internal corporate and bank networks, and the in-flight online activity of airline passengers.
The successful launch of the final GPS-III satellite into orbit makes 32 total satellites in the GPS-III constellation, and paves the way for production and launch of GPS-IIIF satellites. Space Systems Command, El Segundo, CA With the successful launch of the 10th Global Positioning System III satellite on April 21 from Cape Canaveral Space Force Base, Space Systems Command is celebrating the start of a new era for the world's premier GPS constellation. “This milestone satellite launch completes GPS Block III,” said Erin Carper, Acting Portfolio Acquisition Executive for Satellite Communications and Positioning, Navigation, and Timing (PNT) at SSC. “Providing critical military and civil signal accuracy 24/7, GPS continues to underpin global military operations for our warfighters.”
Mercury Systems, Inc. Andover, MA
With the successful launch of the 10th Global Positioning System III satellite on April 21 from Cape Canaveral Space Force Base, Space Systems Command is celebrating the start of a new era for the world’s premier GPS constellation.
With $800 of off-the-shelf equipment and months’ worth of patience, a team of U.S. computer scientists set out to find out how well geostationary satellite communications are encrypted. And what they found was shocking.
As an emerging innovative mode of public transportation, electric modular buses (EMBs) offer a novel solution to the problems of existing public transportation systems, due to the coupling-decoupling processes. In this paper, we study the energy consumption characteristics of EMBs by joining vehicle-to-vehicle (V2V) charging and reduction in aerodynamic drag due to coupling. For the pursuit of energy economy, ride comfort, and operational efficiency, we constructed an optimization scheme based on the simulated annealing (SA) algorithm to facilitate the coupling-decoupling process. The simulation results show that EMBs can meet 82.5 % of service requests compared with 61.8 % for the benchmark group, and V2V presents a significant contribution to energy efficiency, especially at low battery state of charge (SOC). Additionally, sensitivity analysis is conducted to study the impact of initial SOC, operation interval, and route type. The results provide insights for optimizing EMBs’ operations and emphasize the potential role of EMBs in supporting low-carbon and sustainable urban mobility systems.
Liao, PengGuo, JiaheNing, DonghongLi, SijiaWang, Tao
Researchers at Lawrence Livermore National Laboratory (LLNL) have optimized and 3D-printed helix structures as optical materials for Terahertz (THz) frequencies, a potential way to address a technology gap for next-generation telecommunications, non-destructive evaluation, chemical/biological sensing and more.
The race to develop sixth-generation (6G) mobile networks is accelerating, with commercialization expected by 2030. According to the International Telecommunication Union (ITU), 6G will introduce new usage scenarios such as “integrated artificial intelligence (AI) and communication” and “ubiquitous connectivity.” In this context, a recent article published in Engineering titled “Space– Ground Fluid AI for 6G Edge Intelligence” explores the integration of edge AI and space–ground integrated networks (SGINs) to extend AI services globally.
Sixth-generation, or 6G, cellular networks are the next step in wireless communication, and electromagnetic terahertz waves are seen as crucial to its development. However, terahertz waves, with their higher frequency and shorter wavelength, are subject to greater interference from electromagnetic noise, making clear and secure transmission a challenge.
The convergence of highly capable edge AI models and advanced commercial-off-the-shelf (COTS) edge AI accelerators is reshaping how computation is deployed across defense, aerospace, and commercial platforms. Mission-critical decisions increasingly must be made at the edge, onboard vehicles, satellites, and infrastructure nodes, where latency, connectivity, and power availability are constrained.
SAE J1979-2 describes the communication between the vehicle’s OBD systems and test equipment required by OBD regulations. OBD regulations require passenger cars and light-, medium-, and heavy-duty trucks to support a minimum set of diagnostic information to external (off-board) “generic” test equipment.
Vehicle E E System Diagnostic Standards Committee
SAE J1978-1 specifies a complementary set of functions to be provided by an OBD-II scan tool. These functions provide complete, efficient, and safe access to all regulated OBD (on-board diagnostic) services on any vehicle which complies to SAE J1979. The content of this document is intended to satisfy the requirements of an OBD-II scan tool as required by current U.S. OBD regulations. This document specifies: A means of establishing communications between an OBD-equipped vehicle and an OBD-II scan tool. A set of diagnostic services to be provided by an OBD-II scan tool in order to exercise the services defined in SAE J1979. In addition, SAE J1978-1 covers first generation protocol functionality defined in SAE J1979 plus automatic protocol determination for all SAE J1979/J1979-2/J1979-3 application content. The presentation of the SAE J1978 document family, where SAE J1978-1 covers first generation protocol functionality defined in SAE J1979 and protocol determination for SAE J1979, SAE J1979-2, and SAE J1979-3, and SAE J1978-2 covers second generation protocol functionality defined in SAE J1979-2. The SAE J1978 document family does not preclude the inclusion of additional capabilities or functions in an OBD-II scan tool. However, it is the responsibility of the OBD-II scan tool designer to ensure that no such capability or function can adversely affect either an OBD-equipped vehicle, which may be connected to the OBD-II scan tool, or to the OBD-II scan tool itself.
Vehicle E E System Diagnostic Standards Committee
The scope of this standard is Automated Vehicle Marshalling (AVM) of vehicles to enable remote control functionality for achieving SAE Level 4 (High Driving Automation according to the Surface Vehicle Recommended Practice SAE J3016) driving capabilities at controlled speeds within geofenced private controlled environments utilizing infrastructure-assisted sensing. It specifies a concept of operations which includes a reference-system architecture and use cases, system functional and performance requirements, multiple wireless communications protocols, and associated wireless messages to support AVM. AVM use cases such as plant marshalling, depot marshalling, valet parking, electric vehicle charging, etc. The Automated Vehicle Marshalling Central Server (AVM CS) infrastructure does detect objects, vehicles, vulnerable road users, and any obstructions that help guide the Automated Vehicle (AV) starting from uninitiated, activation, identification, automated control, unavailable and deactivation states of the respective feature lifecycle of AVM use cases by using only two wireless messages named Infrastructure Marshalling Message (IMM) and Vehicle Marshalling Message (VMM). This standard specifies the minimum requirements for the Vehicle-to-Everything (V2X) messages, such as IMM and VMM, and the corresponding data frames and elements which are defined to support Infrastructure guided AVM use cases over Direct (LTE-V2X) and Network-based wireless communications technologies. These messages are utilized to achieve desired interoperability, safety, and data integrity. This standard focuses on an infrastructure-led implementation of an AVM system analogue to a Type 2 Automated Vehicle Parking (AVP) system implementation as described in International Organization for Standardization (ISO) 23374-1; the functional allocations listed below are part of the Type 2 AVP System where AVM CS of infrastructure carries out most of the operation functions including AV Identification and Emergency Stopping: Destination Assignment Route Planning Object and Event Detection and Response (OEDR) AV Localization Path Determination Trajectory Calculation Vehicle Motion Control (VMC) NOTE 1: Functional Safety rating Automotive Safety Integrity Level (ASIL) related requirements are outside the scope of this standard. NOTE 2: This standard could be utilized as a base for human operator assisted AVM. The implementation details from human operator assistance without Infrastructure assistance is outside the scope of this standard. NOTE 3: Unless otherwise marked as Informative, all material in this standard is to be considered normative.
V2X Core Technical Committee
Accurate identification of Productive and Non-Productive States or tractor duty cycles—comprising working, idle, and transport states—is critical for performance analysis, fuel optimization, and emissions modeling in agriculture machinery and fleet monitoring. This study explores the application of integrated unsupervised machine learning (ML) techniques to classify duty cycles using GPS-derived parameters such as speed, location variance, and temporal patterns. Unlike supervised approaches, the proposed method does not rely on several labeled engine and vehicle parameters, making it scalable and adaptable across diverse operational contexts. Clustering algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) in integration with hybrid rule-based and a road feature is employed to segment GPS data into distinct behavioral states. Feature engineering focuses on extracting motion signatures and spatial-temporal features that correlate with operational modes. Validation against manually annotated datasets demonstrates high accuracy in distinguishing idle, working, and transport phases. Furthermore, the present study demonstrates that by accurately determining the operational status of the tractor, unnecessary idling can be prevented through an idle avoidance system. Additionally, after assessing transport and working conditions, a movement-based control system for tire pressure adjustment is proposed. Both strategies have the potential to reduce fuel consumption by approximately 5-7%; however, this lies outside the scope of the present work. The framework offers a robust, data-driven solution for duty cycle monitoring and can be integrated into telematics systems for predictive maintenance and operational efficiency of the tractors.
Maharana, Devi prasadGangsar, PurushottamDharmadhikari, NitinPandey, Anand Kumar
Distributed battery management systems (BMS) are critical for scaling electric vehicle packs to hundreds of cells, but reliable high-speed communication between modules remains a challenge. Daisy-chained SPI and CAN FD are widely deployed today, while Ethernet is being evaluated for next-generation systems that require higher bandwidth, synchronization, and diagnostics. This paper examines the signal integrity (SI) challenges facing distributed BMS communication, including skew, jitter, crosstalk, and electromagnetic interference (EMI) across PCB traces and wiring harnesses. HyperLynx and SPICE-based simulations are combined with experimental results on a 192-cell test platform to quantify the impact of layout constraints, impedance mismatches, and harness parasitic. Results show that poor SI design can reduce signal margins by more than 18 dB, leading to data corruption and diagnostic failures. Results show poor SI design can reduce signal margins by 18 dB, causing data errors. Measured BER is ≤1×10-12, jitter decreases up to 30%, and Ethernet latency stays below 120 ns under worst-case EMI. Additional testing confirmed SPI and Ethernet maintain stable communication across 192-cell BMS platform. Co-design strategies for PCB routing, termination, and shielding are proposed, achieving up to 30% reduction in jitter and error rates under worst-case EMI conditions. By addressing both current SPI-based systems and future Ethernet implementations, this paper provides practical guidelines for engineers developing distributed BMS architectures that meet ISO 26262 functional safety while enabling scalable and reliable next-generation EV platforms.
Abdul Karim, Abdul Salam
Design for durability in the automotive industry depends on a clear understanding of how road surfaces and driving characteristics affect structural road loads and fatigue. Traditionally, road surface classification has been subjective (e.g., city, highway, rural), and done through driving instrumented vehicles over a small selection of roads. The variations in driving characteristics that are often consequent to the road surface quality are rarely accounted for in designing vehicle level durability tests. This makes it difficult to establish targets for durability testing that accurately match the wide variations in real-world roads and driving. This paper presents a data-driven approach to objectively classify road surface and driving characteristics using metrics derived from existing road response metrics like Vibration Dose Value (VDV) and statistical estimates of vehicle speed and acceleration. Data collected at the proving grounds on gravel roads, smooth roads, city-like roads, etc., is used to identify classifiers that categorize road-driving combinations into groups correlating with structural fatigue damage. This correlation between fatigue damage and road-driving classification is developed using Wheel Force Transducer (WFT) measurements from instrumented vehicles. This method shows promise to develop structural fatigue estimates directly from telemetry data. The method provides a path to replacing subjective road classification with a vehicle-sensor and signal-based, objective classification for developing durability targets and tests. This method is also scalable in terms of application on vehicle fleet data in uncontrolled environments, to develop an accurate understanding of real-world use of vehicles by customers.
Shaurya, ShubhamRamakrishnan, SankaranDemiri, AlbionKhapane, Prashant
The electrification of drayage fleets offers potential economic and operational benefits, but the financial viability of electrified vehicles remains sensitive to battery cost, energy price, and fleet usage patterns. While total cost of ownership (TCO) is a useful benchmark, fleet operators and investors are equally concerned with investment performance metrics such as payback period (PB) and Internal Rate of Return (IRR), which better reflect financial risks and investment return timelines. This study develops a unified techno-economic framework that jointly evaluates TCO, PB, and IRR to determine when electrified trucks become cost-effective alternatives to diesel trucks. Building on a previously developed cost modeling tool and using real-world telematics data from a Class 8 drayage fleet at the Port of Savannah, the analysis incorporates projected battery cost trajectories, electricity and diesel price trends, vehicle efficiency improvements, and multiple battery capacities. Parameter ranges reflect widely cited projections and observed drayage-duty-cycle variability. A surrogate-modeling method approximates economic performance across thousands of battery cost–electricity price combinations, enabling high-resolution identification of conditions that achieve TCO parity, acceptable PB thresholds, and target IRR levels. Additionally, the study estimates the evolving share of the fleet that can feasibly electrify over time under multiple economic metrics. This integrated framework offers a novel, data-driven approach to inform risk-aware decision-making for fleet electrification and supports investment planning under evolving cost and operational conditions.
Sun, RuixiaoSujan, VivekGoulet, NathanWang, Qixing
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