esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 New Generation Sensor Web Enablement
### KVP and SOAP Binding Introduced
#### 3.3.4. Eventing and Alerting  
This section covers the specifications which have been developed in context of the SWE initiative to realize push-based and asynchronous communication. This enables _eventing_, _i.e._, the automatic publication of data that is of interest for the user (without him having to repeatedly pull for that data), and _alerting_, which incorporates the publication of more significant data to the user who is supposed to react in a domain or application specific way upon receipt of this kind of data. In contrast to classic SWE services, e.g., the SOS (Section 3.3.2), which use the request-response communication pattern, the services described in this section are based on the _publish/subscribe_ pattern. This allows disseminating data (events) as soon as possible and without the need to periodically request them. Especially _in situ_ations when the update rate of the data source is unknown, such push-based systems can be of high value. The basic functionality of such services is allowing consumers to subscribe for notifications and the ability to send proper notifications to subscribed consumers [83]. Enhanced services may also be able to act as a notification broker and thus be notification consumers themselves.  
The Sensor Alert Service (SAS) [65] and Web Notification Service (WNS) [67] were the first services in SWE 1.0 to enable alerting. This functionality was revised and enhanced during the development of the SWE 2.0 standards. The table below and the following sections describe the advancements and changes which have been achieved within the development to the new SWE framework.
# AT-BOD: An Adversarial Attack on Fool DNN-Based Blackbox Object Detection Models
## 4 The Proposed Model
### Patch-Based AT_BOD
#### 4.1.1 Problem Definition  
The problem was defined as follows: in the sample space \(\mathcal{S}\), an input image \(\chi\), find an adversarial example (AE) \(\chi^{\prime}\) that can mislead the object detection model M, such that \(\chi^{\prime}\in\ \mathcal{S}\) and \(\chi\) and \(\chi^{\prime}\) are very similar. Let us assume that \(\left(\mathsf{P}_{i},\ \mathsf{P}_{i}\right)\) is the image ground truth where \(i\) is an object in the real image input and \(C_{i}\) is the class label and \(\mathsf{P}_{i}\) is the position such that M detects \(i\) for a given \(\chi\) and outputs \(\left(c_{i},\ \mathsf{P}_{i}\right)\) while for a given \(\chi^{\prime}\), M detects \(i\) and outputs \(\left(c^{\prime}_{i^{\prime}},\ \mathsf{P}^{\prime}_{i}\right)\). Which means that \(\left(c_{i},\ \mathsf{P}_{i}\right)\) is replaced by \(\left(c^{\prime}_{i^{\prime}},\ \mathsf{P}^{\prime}_{i}\right)\), making the object \(i\) to be ignored in \(\chi^{\prime}\) or replaced by \(i^{\prime}\) where \(i^{\prime}\) is a patch.
# MIMO Relaying UAVs Operating in Public Safety Scenarios
## 1 Introduction
### Sidelink Communications  
One communication tool that is possibly applicable in public safety scenarios is the new radio sidelink (SL), a device to device (D2D) protocol introduced in the 3 GPP Release 16 [25] that assists vehicle to everything communications (V2X) and provides road safety services under the Ultra-Reliable low latency communications paradigm (URLLC). The architecture supports both out-of-coverage and in-network coverage situations for broadcast, groupcast, and unicast communications, as presented in Figure 2.  
The idea of D2D communication in public safety scenarios is not new. In Fact, Release 12 presents a feature that supports communication when the base station malfunctions or is the target of cyber-attacks [26]. Release 12 prioritizes the issues of implementing broadcast communication where there is no channel state information (CSI) and reception acknowledgment in D2D protocols. Furthermore, according to [PERSON] et al. [27], in Release 12, SL communication has synchronization issues, i.e., UE nodes that are not, allocated inside the D2D network should not send time reference signals, but they do and the opposite is also true, i.e., UE nodes inside the network should send time reference signals, but they don't. Another problem is that the SL protocol is enabled in the UE even though there are no other devices to communicate.  
In Release 13, SL incorporates a Layer 3 (network) feature that extends coverage called the UE-to-Network relay. The main objective is connecting out-of-coverage UEs to the LTE evolved NodeBs (eNB) through the in-coverage UE. The integration with V2X scenarios in Release 14, introduces CSI and improves the physical sidelink control channel (PSCCH) multiplex schemes because of the low latency and a large amount of connection requirements.  
Release 15 adds carrier aggregation, transmission diversity to reduce delay, and increases the capacity of the network. The concept of scheduling in the UE was introduced and Release 16 prioritized use cases related to car communication [28].  
In the next Release 17 [29], SL relaying features will be studied, including user equipment (UE)-to-network relay for network coverage extension. The physical sidelink feedback channel (PSFCH) will be incorporated to inform the transmitting status of the UE. Besides, the UAV communication will be incorporated in the standard, and there are some discussions on introducing public safety and gaming scenarios in D2D communications.
## 3 Experimental Setup and Result Analysis
### Experimental Results
#### 3.4.4 Confusion Matrix
The confusion matrix for MobileNetV3_Strategy, which employs the multi-layer feature information fusion strategy, is shown in Figure 7e. This matrix illustrates that the model achieved a more balanced recognition across different lesion categories with this strategy. Adenopathy was correctly recognized in all instances, and the misclassification rate for ductal carcinoma was significantly reduced. This suggests that the multi-stage information fusion strategy effectively enhances feature representation, accommodating both simple and complex pathological samples. The confusion matrix for MobileNetV3_All, the fully constructed model in this paper, is shown in Figure 7f. This model integrated various modules and strategies, leading to improved recognition performance compared to the benchmark MobileNetV3. Notably, while ductal carcinoma (DC) was often misclassified as lobular carcinoma (LC) due to the subtle differences between subtypes, the addition of attention mechanisms and multi-layer information fusion enabled the model to capture finer lesion features, thus distinguishing breast cancer subtypes more accurately. The enhanced MobileNetV3 model demonstrated superior recognition results, significantly reducing the misclassification rate. Furthermore, the model exhibited a more balanced recognition across all breast cancer histopathology image types, markedly improving its ability to identify rare subtypes.
## 3 System Design
### Telemetry Decoding
#### 3.8.3 TOW Estimation  
The receiver estimates the TOW using the TOA of the telemetry synchronization patterns and the local embedded OS clock. The TOW of the Galileo synchronization patterns corresponds to the exact timing of the telemetry page frame boundaries [41]. The receiver takes advantage of the fact that, in the Galileo E1b/c signals, the synchronization patterns are transmitted in sync with the second. The estimation of the TOW involves measuring the exact TOA of the telemetry sync patterns and rounding this time to the nearest second. The TOA is computed in Galileo System Time (GST) as follows: In the first step, the local time is converted to UTC considering the local time zone. In the second step, the computed UTC time is converted to GST considering the leap seconds between UTC and GST. Assuming that the GNSS signal travel time, plus the error induced by delays in the receiver detecting the preambles, added to the receiver's local clock offset is in total below \(\pm 500\) ms, the computed TOW estimation is deemed correct. This is because rounding the TOA to the nearest second provides the TOW. Achieving the required accuracy in the local OS clock is performed by synchronizing the receiver's OS clock to the true local time using the Network Time Protocol (NTP) [42]. The NTP protocol offers a nominal accuracy of tens of milliseconds on Wide Area Networks (WANs) [42], which is sufficient for the proposed TOW estimation process. When in the TOW estimation state, the telemetry decoder continuously verifies that a minimum percentage of preambles is accurately detected at the expected timings. If the receiver fails to detect the telemetry preambles, it assumes a synchronization issue and reverts to the frame sync detection state. This allows the receiver to resynchronize with the received preambles in case of an incorrect sync frame detection.  
When working in post-processing mode using recorded GNSS signals, the receiver local OS clock time does not correspond to the recorded signal time. Therefore, the receiver embedded OS clock cannot be used to estimate the TOA of the telemetry sync patterns. In this case, the telemetry decoder estimates the TOW of the telemetry messages using the receiver sample counter and a predetermined relationship between the sample counter and the recorded signal time that shall be provided by the user. The sample counter counts up the number of processed samples and is automatically reset when the user starts the GNSS-SDR software receiver.
## 4 Conclusions  
This study aimed to investigate fixed traffic loads, focusing on stress prediction for different axle configurations, including single, tandem, and tridem axles; the conclusions are as follows:  
* There is an inverse relationship between the generated stress and the depth of the pavement for single, double, and triple axles. The greater the depth, the lower the stress.
* The spacing between tires in the X and Y directions has a significant effect on the stress. As the spacing decreases, the stress notably increases due to the overlap.
* The use of response surface methodology (RSM) successfully developed models for the generated stress from single, tridem, and triple axles. However, applying a base-10 logarithmic transformation significantly improved the accuracy of the models.
* The quadratic model showed the best performance in predicting stress across all axle configurations. Moreover, it fulfilled all essential statistical assumptions, including normality, homoscedasticity, independence, and linearity, confirming the validity and reliability of the results.
* The analysis of machine learning models showed that gradient boosting showed better performance than the other models in predicting stress.
* Combining RSM and machine learning offers a better understanding of pavement performance. This integrated approach can lead to more effective design strategies, enhancing durability and overall performance.  
Future work should focus on multi-layer validation, dynamic loading, and real-world applicability to transition from theoretical models to practical engineering solutions. In particular, future research should investigate both horizontal and vertical stress responses under traffic loads. Collaborations with transportation agencies to acquire field data would add significant value.  
Conceptualization, A.M.A. ([PERSON]); Methodology, A.M.A. ([PERSON]) and M.B.R.; Software, A.M.A. ([PERSON]), M.B.R. and and A.M.A. ([PERSON]); Validation, A.M.A. ([PERSON]), M.B.R., A.O.B. and and A.M.A. ([PERSON]); Formal analysis, A.M.A. ([PERSON]) and A.O.B.; Investigation, A.M.A. ([PERSON]) and A.M.A. ([PERSON]); Writing--original draft, A.M.A. ([PERSON]); Writing--review & editing, A.M.A. ([PERSON]), M.B.R., A.O.B. and A.M.A. ([PERSON]); Supervision, M.B.R.; Project administration, A.O.B.; Funding acquisition, M.B.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data generated or analyzed during this study are included in this published article.  
The authors would like to thank A'Sharqiyah University, Oman, for its support and encouragement of this research. They also wish to extend their heartfelt thanks to the Department of Civil Engineering at NUST Balochistan Campus for its invaluable support.  
The authors declare no conflicts of interest.
# Artificial Intelligence-Based Autonomous UAV Networks: A Survey
### AI-Based UAV Networks
#### 1.1.1 Security and Privacy Issues  
Security and privacy are always a concern when dealing with a wireless network. This concern even gets more robust when the wireless network we are dealing with has an ever-changing topology. Many studies are being conducted to highlight the security and privacy issues in UAV networks and address these issues using AI techniques. The authors in [4] highlighted various security issues for UAVs with traditional cellular networks. AI-based security solutions are being suggested for various cyber-attacks, including cyber-physical attacks. The authors have proposed using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to identify and classify high-risk areas and various motion characteristics of UAVs.  
An interesting study is presented based on communicating only local information among the neighboring UAVs in a UAV swamp [5]. The simulation results show that a UAV swamp can effectively blanket terrestrial coverage by using local information and voiding various cyber security threats. The simulation is conducted in OPNET Modeler, considering MANET connectivity for all UAVs.  
Federated learning (FL) is a new ML technique proposed for distributed Internet of Things (IoT) devices. FL's critical feature is providing a secure communication channel among IoT devices. Reference [6] proposed a UAV-based FL technique based on the 5G heterogeneous networks concepts. Introducing UAVs in the FL enhanced communication efficiency, hence resulting in more reliable and secure network coverage.
# Convergence Check Phase-Field Scheme for Modelling of Brittle and Ductile Fractures
## 5 Numerical Examples
### Brittle Fractures
#### 5.1.3 Asymmetric Three-Point Bending Test  
The last verification example of brittle fractures is the standard problem of an asymmetrically notched beam with holes, which demonstrates the ability of the CCPF scheme to capture curvilinear crack paths. The material of the specimen was Plexiglas, according to experimental testing [63], with [PERSON]'s modulus \(E=20.8\) GPa and [PERSON]'s ratio \(\
u=0.3\). The phase-field parameters used in the problem were critical fracture energy density \(G_{\mathrm{c}}=1\) N/mm and length scale parameter \(l=0.025\) mm. Geometry and boundary conditions are presented in Figure 10.  
Figure 8: Crack pattern of a single-edge notched shear test.  
Figure 9: Number of iterations utilised through loading increments for a single-edge notched shear test.  
The specimen was loaded by the force \(F=500\) N in 10 loading increments and discretised by 321,154 finite elements in the displacement layer, with refinement in the expected FPZ. However, to avoid unwanted crack initiation at the point where loading is imposed, the yellow shaded zone in Figure 10 was discretised by 320,601 phase-field finite elements. Analogous to the previous examples, mesh discretisation was downloaded from the RCTRL download site. The results obtained by two approaches are compared in Figure 11.  
In this problem, the Riks solution method was utilised, because according to Figure 11, the force-displacement dependency represents a snap-back problem which cannot be dealt with using the Newton-Raphson solver. As can be seen, both approaches give identical results. The crack path obtained is shown in Figure 12.  
Figure 11: Force-displacement diagram of an asymmetric three-point bending test.  
Figure 10: Geometry and boundary conditions of an asymmetric three-point bending test.  
As in previous examples, both simulations were conducted at the same workstation. Herein, the parallelisation option with 10 CPUs was used. The RCTRL scheme completed the simulation in 24,384 s (6.77 h) while the CCPF scheme completed the simulation in 13,190 s (3.66 h), 46% faster. Furthermore, Figure 13 represents iterations per increment.  
As in previous examples, it can be seen that the CCPF provides faster solutions, and less iterations are required for finding the equilibrium and crack path. From the presented examples, it can be concluded that the CCPF scheme provides correct solutions with shorter computational time compared to the RCTRL scheme. After verification of the CCPF algorithm on brittle fracture problems, the ability of the proposed method to handle ductile fractures is presented.
## 2 Experimental Model Considering CSI Effect  
### Time-Delay Sources of a Control System  
According to the work flow of an AMD control system, the time-delay shown in Figure 1 mainly results from: (1) Structural response \(d_{1}\); (2) Sensors monitoring \(d_{2}\); (3) Feedback signals are transmitted from the sensors to the controller \(d_{3}\); (4) Control force calculation \(d_{4}\); (5) Control signals are transmitted from the controller to the actuator \(d_{5}\); and (6) Actuator response \(d_{6}\). The minimal delays (\(d_{3}\) and \(d_{5}\)) of the transmission of signals in wires can be ignored, in terms of the delays (\(d_{1}\), \(d_{2}\), and \(d_{4}\)) that are basically unchanged. In this paper, the actuator response time-delay \(d_{6}\) constitutes the important research content.  
An AMD control system that considers the CSI effect can be described in Figure 2. \(v\), \(u\), and \(w\) are the control voltage, control force, and external excitation load, respectively. \(H_{vy}\) denotes the transfer function from the structural response to the control voltage, \(H_{uv}\) denotes the transfer function from the control voltage to the control force, and \(H_{yu}\) denotes the transfer function from the control force and the external excitation load to the structural response. A CSI effect occurs at the stage of \(H_{uv}\).
## 5 Roles of SDN for Cloud-Fog Enabled 5G and Beyond
### Resource Provisioning in Cloud-Fog  
SDN can support reliable communication management for IoT delay-intensive applications. The application layer in IoT introduces continuous data packets, while the link disruption, due to various reasons, widely occurs when a resource-rich cloud serves these applications. It incurs the QoS and QoE challenges for diverse IoT edge applications. On the other hand, when IoT applications are served by near computational resources (fog nodes), any sort of failure or generic service unavailability requires abrupt flow detouring to meet the expected latency. To enable intelligent, reliable communication, in [63; 64],the authors employed an ML algorithm (i.e., k-nearest neighbor regression) in the SDN control function layer for predicting the failure behavior, based on the failure event history. Collecting the network statistical, failure, and resources utilization data is a grace of the SDN controller. So, the objective to confine the ML-aware applications at the controller is to serve the IoT applications over the most reliable communications paths (reliable links and intermediate forwarding devices), ensuring end-to-end latency, failure resiliency and providing better QoS.  
[PERSON] et al. [65] proposed the traffic prediction based resource allocation in 5G infrastructure for IoT applications. The proposed method uses long short term memory (LSTM) at the SDN controller, employed in remote Cloud. To deal with 5G major application use cases (uRLLC), a prediction model supports resource provisioning at the edge of the network. If a local or edge controller is overloaded, based on the predicted load, the cloud controller configures the supported forwarding devices to meet the QoS.
## 4 Results and Discussion  
### Grid Division and Its Independence Analysis  
As shown in Figure 2, the simulated object is a horizontal pipe (the orange marker A in Figure 1) with a length of 2.4 m, which is meshed by GAMBIT 2.4.6 software.  
To enhance the computational accuracy and efficiency of simulation, under typical experimental conditions with supplementary gas \(Q_{s}\) = 0.8 m\({}^{3}\)/h, the horizontal pipe has been divided into four different mesh specifications (Mesh A, B, C, and D, as illustrated in Table 5). Furthermore, the simulation has been conducted using the modified Berzi frictional stress model to investigate mesh-independent boundaries and determine appropriate mesh sizes.  
In Table 5, it can be observed that as the grid size decreases, the simulated pressure drop in horizontal pipe gradually converges toward the experimental value. Particularly, when the total number of grids exceeds 460, 800 (e.g., Mesh C and Mesh D), the simulated pressure drop in the horizontal pipe reaches a near-constant state. Mesh C was therefore ultimately selected as the mesh type for simulation calculations.
## 5 Discussion
### Resource Utilization and Processing Speed  
This study aimed to enhance recognition accuracy and model interpretability; however, computational efficiency is also vital for the practical implementation of HAR systems, especially on resource-limited wearable devices.  
Our ConvResBiGRU-SE model, with its multiple specialized components, requires more computational resources than simpler architectures such as basic CNN or LSTM models. During our experiments on the Google Colab Pro+ environment with Tesla L4 GPU, the average training time per epoch was approximately 5.85 s for the UCI-HAR dataset and 6.39 s for the WISDM dataset. The inference time for a single activity sample was approximately 0.5352 ms for the UCI-HAR dataset and 0.5171 ms for the WISDM dataset, which is acceptable for most real-time applications.  
The model size is approximately 1.1 MB, which might present challenges for deployment on extremely resource-limited devices without optimization. However, several approaches could mitigate these constraints, including model compression techniques (pruning, quantization), knowledge distillation to smaller models, or on-device optimization frameworks.  
Figure 12: Visualization of the Grad-CAM method using WISDM dataset: (**a**) Downstairs, (**b**) Jogging, (**c**) Sitting, (**d**) Standing, (**e**) Upstairs, and (**f**) Walking. The white lines in the figures represent the original raw sensor signals from the accelerometer channels, allowing for direct visual comparison between the model’s activation patterns (shown by the colored heatmap regions) and the underlying sensor data.