esa-sceva/satcom-chunk-collection huggingface dataset:
# Unmanned Aerial Traffic Management System Architecture for U-Space In-Flight Services
## 4 UTM System Architecture
### Tactical Deconfliction
The riskiness is determined by the minimum distance between the alternative route and any geofence, or by the length of the route portions that remain within a geofence, in case that the solution goes through any geofence partially. In  
Figure 9: Iterative procedure to solve a conflict in the case of a loss of separation (from **left** to **right**). The flight plans of the two lower UAS are in conflict and need to be separated. Then, the middle UAS enters in conflict with the upper UAS, so these two get separated again. As the plan of the middle UAS gets modified, the lowest UAS is also adapted to achieve a final solution without loss of separation. case of a UAS out of its Operational Volume, the riskiness of the solution is determined by the length of the route portion where the UAS stays out of its Operational Volume. Finally, an alternative route where the UAS travels to its closest landing site can also be computed in some cases, for instance, if the UAS presents a technical problem like a lack of battery. In those cases, the riskiness is determined by the distance of the route that goes through any geofence in the airspace.
# Depletion-of-Battery Attack: Specificity, Modelling and Analysis
###### Abstract  
The emerging Internet of Things (IoT) has great potential; however, the societal costs of the IoT can outweigh its benefits. To unlock IoT potential, there needs to be improvement in the security of IoT applications. There are several standardization initiatives for sensor networks, which eventually converge with the Internet of Things. As sensor-based applications are deployed, security emerges as an essential requirement. One of the critical issues of wireless sensor technology is limited sensor resources, including sensor batteries. This creates a vulnerability to battery-exhausting attacks. Rapid exhaustion of sensor battery power is not only explained by intrusions, but can also be due to random failure of embedded sensor protocols. Thus, most wireless sensor applications, without tools to defend against rash battery exhausting, would be unable to function during prescribed times. In this paper, we consider a special type of threat, in which the harm is malicious depletion of sensor battery power. In contrast to the traditional denial-of-service attack, quality of service under the considered attack is not necessarily degraded. Moreover, the quality of service can increase up to the moment of the sensor set crashes. We argue that this is a distinguishing type of attack. Hence, the application of a traditional defense mechanism against this threat is not always possible. Therefore, effective methods should be developed to counter the threat. We first discuss the feasibility of rash depletion of battery power. Next, we propose a model for evaluation of energy consumption when under attack. Finally, a technique to counter the attack is discussed.  
Keywords:wireless sensor networks; security; depletion-of-battery attack +
Footnote †: journal: Sensors
## 2 Materials and Methods
### MHC-PSPNet Network
#### 2.2.2 Attention Mechanism  
The PSPNet network model can enhance image segmentation quality; however, when dealing with blurry images in complex backgrounds, important details may be lost during the segmentation process, resulting in blurred segmentation boundaries. To address this issue and create a more effective and detailed semantic segmentation model, the Convolutional Block Attention Module (CBAM) is incorporated into the network. This module can be seamlessly integrated into existing network architectures, enhancing feature extraction capabilities without significantly increasing the number of parameters or computational complexity. The structure of the CBAM is illustrated in Figure 4.  
Figure 3: Inverted residual structure.  
Figure 2: MHC-PSPNet network architecture.  
The attention module integrates spatial attention and channel attention to enhance the convolutional neural network's focus on both spatial and channel-wise relationships. Spatial attention emphasizes spatial correlations within the input feature maps by applying pooling operations across spatial dimensions, thereby capturing features at each location. Fully connected layers are then used to learn the importance of each spatial location, assigning appropriate weights. To allow the network to selectively adjust feature weights at different spatial locations, the spatial attention weights are element-wise multiplied with the feature maps via broadcasting. Typically, global average pooling and max pooling strategies are employed to capture diverse spatial information.  
In contrast, channel attention highlights important channels within the input feature maps. It begins by applying global average pooling along the channel dimension to extract global features for each channel. Two fully connected layers are then used to learn channel-specific weights, determining their importance. Finally, the channel attention weights are multiplied with the feature maps, enabling the network to adaptively adjust the relative importance of different channels [24]. This process is illustrated in Figure 5.  
The CBAM integrates spatial and channel attention, enhancing the convolutional neural network (CNN) by emphasizing spatial and channel correlations. This mechanism enables the network to focus on essential information from the input features while minimizing the influence of irrelevant details, thereby improving the model's expressiveness and generalization ability.
# BNS: A Framework for Wireless Body Area Network Realistic Simulations
###### Abstract  
Simulation is a useful and common technique to evaluate the performance of networks when the implementation of a real scenario is not available. Specifically for Wireless Body Area Networks (WBAN), it is crucial to perform evaluations in environments as close as possible to the real conditions of use. To achieve that, simulations must include different protocol layers involved in WBAN and models close to reality to create realistic simulation environments for e-health applications. To satisfy these needs, this work presents the BNS framework, a flexible tool for WBAN simulations. The proposal is an extension of the Castalia framework, which includes: (1) a new wireless channel model considering real radio-propagation over the human body; (2) an updated implementation of the WBAN MAC protocol in Castalia, with functionalities and requirements in accordance with the IEEE 802.15.6 standard; (3) a new comprehensive and configurable mobility model for simulating intra-WBAN communication; (4) a temperature module based on the Pennes bioheat transfer equation, to model the temperature of a WBAN node based on the activity of the node; and (5) a Healthcare Application Layer that implements data representation and a communication protocol between Personal Health Devices (PHD) following the ISO/IEEE 11073 standard. Three use cases are presented, where WBAN scenarios are simulated and evaluated using the proposed BNS framework. Results show that BNS is a valid and flexible tool to evaluate WBAN solutions through simulation.  
Keywords:WBAN; personal health devices; ISO/IEEE 11073; IEEE 802.15.6; e-health +
Footnote †: journal: Sensors
## 1 Introduction
[15] concluded that the extent of grouting defects affects the maximum pullout load. Moreover, researchers also studied the influences of the bolt shape and grout properties on the pullout load [24; 25; 26; 27; 28]. A certain number of fractures generally occur in the rock mass, which also affects the mechanical properties of bolt supports. [PERSON] et al. [29] used discrete element software to study the influences of intact and fractured rock mass on the axial force of bolts in compression tests on anchored rock mass and found that fractures greatly influence the stress distribution of bolts. [PERSON] et al. [30], [PERSON] et al. [31], and [PERSON] et al. [32] investigated the influences of fractures on the anchoring effect through numerical simulation while not ascertaining the influence of fractures on stress distribution. By conducting pullout tests, [PERSON] et al. [33] explored the influence of rock integrity on the failure mode of bolts and grouts. [PERSON] et al. [34] conducted laboratory compression tests on anchored rock mass and found that bolts with a negative Poisson's ratio (NPR) exert a good reinforcement effect on a soft rock mass. In summary, the research methods deployed in the investigation of the mechanical properties of bolt supports mainly include pullout tests on bolts, and a few researchers also used compression tests on anchored rock mass. Firstly, due to the different objects to which loads are applied and different loading modes in the two tests, the bolts and rock mass show different boundary conditions, which may affect the mechanical properties of bolt supports. Secondly, in the compression tests on anchored rock mass, predecessors used this test to study the influence of bolts on improving the strength of rock mass and reducing the deformation degree of surrounding rock, but few scholars used this test to study the stress distribution law of bolt and the change in anchorage force. Finally, the rock mass in most of the existing research is intact, while research into the mechanical properties of bolt support in a fractured rock mass is rare. Compression tests of anchored rock mass are suitable for the topic. Considering this, it is necessary to study the mechanical properties of bolt supports in compression tests on an anchored rock mass. At first, the difference between pullout tests on bolts and compression tests on anchored rock mass were compared and analyzed, and finds the superiority of compression tests on anchored rock mass. Then, discrete element software PFC2D was adopted to simulate compression tests on anchored rock mass. Finally, influences of factors, including the bolt diameter and length, the strength of the rock, and the fracture angle of rock mass on the mechanical properties of bolt supports, were studied. The results provide guidance for the design of anchorage works and the subsequent safety monitoring of an anchored rock mass.
# Path Planning Based on Artificial Potential Field with an Enhanced Virtual Hill Algorithm
## 3 Virtual Hill Concept and Open Path Indicator, _New e\({}_{b}\)_
### Open Path Indicator, New \(\mathbf{e}_{b}\)  
_new \(\mathbf{e}_{b}\)_ is proposed, in which the obstacle form is considered. As soon as the robot is trapped in the LM, it is necessary to select in which of the two directions to move. This is divided into the \((+)\) direction and \((-)\) direction based on \(\mathbf{P}(t_{0})\). For each direction, the rate of change in the obstacle contour is calculated. The direction with the larger rate of change is considered the more open path, that is, the driving direction. This direction setting helpsthe robot avoid a closed path and reach the destination with a shorter moving distance. More efficient path planning becomes possible when the driving direction is determined in consideration of the shape of the obstacle.
# Predictive Model for the Optimized Mixed-Air Temperature of a Single-Duct VAV System
## 3 Development of the ANN Model
### Development and Prediction Models  
A building load and cooling energy prediction model was developed using an ANN to develop the predictive model-based economizer control. At this time, the built-in Matlab function was used. For the optimization of the ANN prediction model, it was important to determine the number of neurons in the hidden layer and the number of hidden layers. In order to derive the optimal number of hidden layer neurons and hidden layers, each CV(RMSE) evaluation was performed after developing a predictive model while changing the number of hidden layer neurons and the number of hidden layers. Table 2 shows the final prediction model developed through the optimization process. The load prediction model consisted of 1 input layer, 4 hidden layers, and 1 output layer, and each node had 2, 5, and 1 nodes; the energy prediction model consisted of 1 input layer, 4 nodes, 1 hidden layer, and 4 nodes, as well as 1 output layer and 1 node. Moreover, 70% of the total data were used for training, 15% of the data not used as training data was used for validation, and the remaining 15% was used for testing.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Load Prediction Model** & **Energy Prediction Model** \\ \hline \multirow{3}{*}{Input} & Outdoor-air temperature (\({}^{\circ}\)C) & Outdoor-air temperature (\({}^{\circ}\)C) \\  & Supply air flow rate (kg/h) & Indoor Load (kJ/h) \\ \cline{1-1}  & Indoor load (kJ/h) & Outdoor-air intake ratio (-) \\ \cline{1-1}  & Indoor load (kJ/h) & Mixed-air temperature (\({}^{\circ}\)C) \\ \cline{1-1}  & Indoor load (kJ/h) & Cooling energy (kWh) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input and output variables of the ANN model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Category** & **Load Prediction Model** & **Energy Prediction Model** \\ \hline \multirow{3}{*}{Function} & Activation & Sigmoid & Sigmoid \\  & Performance & Mean squared error & Mean squared error \\  & Epoch & 1000 & 1000 \\  & Input layer & Number of layers & 1 & 1 \\  & Number of neurons & 2 & 4 \\ Structure & Hidden layer & Number of layers & 4 & 1 \\  & Number of neurons & 5 & 4 \\  & Output layer & Number of layers & 1 & 1 \\  & Number of neurons & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter composition of the ANN model.  
To verify the ANN model developed in this study, the CV (RMSE; Coefficient of Variant Root Mean Square Error)--which are the error rate analysis methods suggested by the ASHRAE Guidelines--and R\({}^{2}\) were used.
## 1 Introduction
Decentralized load frequency controls [17] and fault-tolerant mechanisms [18] could be utilized in these situations. Although existing works have characterized EV charging behavior in various public locations, the focus of this research is on the analysis of charging behavior during the COVID-19 pandemic. Therefore, this research aims to analyze and quantify public charging behavior during lockdown restrictions which have not been addressed by the existing body of literature. The proposed research work has several implications. First, the analysis presented can be utilized by researchers, the public energy sector, and policymakers to make better decisions during a future lockdown or similar public restrictive scenarios. This would enable the energy sector to adjust the time of use electricity price to match the changing demand without compromising on the user's flexibility. Moreover, the key insights identified, such as the increase in energy consumption despite a decrease in charging activity due to the lockdowns, can help public charging providers to better manage their electricity demand. This would perhaps lead to incentivizing users to charge their cars at home or locally over a period of time to distribute the increasing energy demands. Finally, the clustering results provide a high-level understanding of the different groups of charging behaviors during the lockdown period. The clustering results indicate that despite the lockdown, users in the same group follow a routine in their daily charging activities. Following are the key contributions of this paper:  
It analyzes the charging data from a public charging facility in the state of California, USA during the COVID-19 pandemic. To the best of our knowledge, this is the first research addressing the pandemic EV charging behavior. It utilizes K-means, hierarchical, and Gaussian mixture models to find clusters of charging behavior during the pandemic. It analyses and quantifies the clustering results and provides a discussion on the future work. The rest of the paper is organized as follows: Section 2 presents a concise review of the existing works in the literature as well as a description of clustering algorithms. The dataset description, preprocessing, and in-depth analysis of charging behavior during COVID-19 are presented in Section 3. In Section 4, the clustering results are presented and discussed. Section 5 concludes the paper and outlines the future research directions.
## 3 Results
### Sensor Sample Reading Times and 12C Bus Frequencies  
In the first measurement phase, we aimed to determine how the read times of the sensors depend on the speed of the I\({}^{2}\)C bus, which is defined by the clock frequency, and to identify the maximum stable frequency of the I\({}^{2}\)C interface for each tested device.  
We performed a series of measurements of sensor read times at I\({}^{2}\)C frequencies ranging from 100 kHz to 2000 kHz. We measured each device five times at each bus frequency. As this phase is used solely to determine the stable bus frequency, we believe that no further measurements are required. Table 1 lists the mean cumulative sensor read times for four different devices at I\({}^{2}\)C bus frequencies from 100 to 1800 kHz. Attempts to operate at 2000 kHz were unsuccessful for all devices. Figure 7 illustrates the mean sensor readout times of all devices as a function of the I\({}^{2}\)C bus frequency, shown separately for the magnetometer (LIS3 MDL) and the combined accelerometer and gyroscope sensor (LSM6 DS33). On the Pololu MinIMU-9 v5 board, the accelerometer and gyroscope are integrated in a single chip, while the magnetometer is a separate component. Therefore, we report a combined read time for the accelerometer and gyroscope as they are read simultaneously. We do not show standard deviation results, as they fall within a few percent of the mean values and would not be visually distinguishable in the graph.  
It can be seen from Table 1 and Figure 7 that higher I\({}^{2}\)C bus frequencies reduce read times and indicate stable bus operation up to 1600 kHz. **To ensure reliability, we selected an I\({}^{2}\)C bus frequency of 1500 kHz for all subsequent tests.** (For both critical parameters (i.e., reliable I\({}^{2}\)C bus frequency at 1500 kHz and maximum sampling frequency in Section 3.3), we observed 20 out of 20 successful trials, which corresponds to a 95% Clopper-Pearson confidence interval lower bound of approximately 0.83, indicating reasonable reliability despite the small sample size. We believe the results remain meaningful for two main reasons. First, they provide practical guidelines for developers of similar low-cost, self-made systems. Second, they establish a conservative lower bound on achievable performance, which is especially relevant when considering higher-quality, industrial-grade manufacturing).  
Figure 7: Mean sensor readout times across all devices versus I\({}^{2}\)C bus frequency for the magnetometer (Mag), accelerometer and gyroscope (Acc + Gyro) that are acquired together and their sum.  
The I\({}^{2}\)C bus frequency is a critical parameter defined within a range of 100 to 3400 kHz. As shown in Table 1, the highest bus frequency achieved was 1800 kHz with Devices A and D, resulting in a sensor reading time of 0.286 ms. With improved device assembly and optimization, attaining even higher bus frequencies and lower sensor reading times is feasible, enabling increased sampling frequencies for the wearable device.
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 5 Experiments
### Experimental Setup  
We implemented Adaptive-DecayRank in C++ and ran experiments on a MacOS(r) with 3.2 GHz Intel 8-Core(tm) 64-bit processor running at 3.2 GHz, a 7-Core(tm) GPU, 16-Core(tm) Neural Engine, and 8 GB unified memory (RAM).  
We compared Adaptive-DecayRank to four state-of-the-art distance-based methods for anomaly detection: **SedanSpot**[18], **AnomRank**[20], **DynAnom**[23], and **De  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Nodes (\(|V|\))** & **Edges (\(|E|\))** & **Timespan** & **Snapshots (\(|\mathcal{G}_{\mathbf{n},\mathbf{T}}|\))** & **Anomalies** \\ \hline DARPA & 33,221 & 4,616,321 & 2 months & 1463 (256 _init_) & Network traffic attacks \\ CTU-13 & 256 & 1,416,971 & 7 days & 301 (50 _init_) & Botnet-related \\ RTM Synthetic & 512 & 19,683,000 & 1000 timestamps & 101 (10 _init_) & Injected (10\% and 30\%) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset statistics and characteristics. Graphs are read and processed as directed, with snapshots \(|\mathcal{G}_{\mathbf{0},\mathbf{T}}|\) generated over time. Anomalies are detected after the specified initial snapshot (_Init. t_).  
cayRank[38]. Our approach extends DecayRank and is closely related to AnomRank; however, we introduce a more robust and dynamic scoring function incorporating an adaptive decay factor and Bayesian updating, enabling faster detection of micro-changes at each timestamp. The average precision and algorithm runtimes are presented in Table 4. The precision-recall curve for the top-k (50-800) anomalous snapshots is shown in Figure 4, the AUC-ROC for DARPA is depicted in Figure 5, and the scalability analysis is illustrated in Figure 6. However, due to constraints in algorithmic design and dataset compatibility, we were unable to evaluate SedanSpot and DynAnom on the CTU-13 and RTM datasets.
# Large Field-Size Elliptic Curve Processor for Area-Constrained Applications
## 4 Implementation Results and Performance Comparison
### Comparison to State of the Art
Similarly, as shown in Table 4, our architecture can operate on maximum 311 MHz while the design of [16] can operate on 274 MHz which is comparatively 1.13 (ratio of 311 over 274) times lower than our work. But, the latency values calculated for [16] are lower than our work (shown in Table 4). There is always a tradeoff. Comparison to Edward model of ECC.As shown in Table 4, the Edward model of ECC on \(GF(p)\) and \(GF(2^{m})\) field lengths are implemented in [20; 21; 22; 23; 30]. These Edward curve implementations utilize 256-bit and 233-bit field lengths. But in our work, we utilize only the higher field lengths of 409 and 571. In short, due to different field lengths, a fair comparison is impossible to provide. As seen in Table 4, for higher field lengths of 409-bit and 571-bit, our architectures on Virtex 6 and Virtex 7 achieves higher operational frequency as compared to Edward designs of [20; 21; 22; 23; 30]. Similarly, as compared to [20; 21; 22; 23], our architectures take lower computational time (i.e., latency) on Virtex 6 and Virtex 7 FPGA devices. The design of [23] is more efficient as compared to our work in terms of latency as their architecture utilizes 233-bit field length while in our work we use 409 and 571 bits field length. The area comparison is non-trivial as lower field lengths are targeted in designs of [20; 21; 22; 23; 30] when compared to our work. Consequently, the proposed large field length designs of this work are competent for recent Edward-based FPGA solutions. Comparison to Huff model of ECC.Table 4 provides that the Huff model of ECC on \(GF(2^{233})\) field length is implemented in [24; 25]. Due to higher field lengths (409-bit and 571-bit) in our work, a reasonable comparison is impossible to provide. On Virtex 7 FPGA,the 4-stage pipeline designs of [24; 25] over \(GF(2^{233})\) takes 1.22 (ratio of 7123 over 5797) and 1.21 (ratio of 7017 over 5797) times higher FPGA slices as compared to our non-pipelined architecture over \(GF(2^{571})\). On a similar Virtex 7 device, the comparison to latency reveals that the proposed architecture over \(GF(2^{571})\) is more efficient than the design of [25] over \(GF(2^{233})\). As the field lengths are different, the design of [24] over \(GF(2^{233})\) takes 2.19 (ratio of 66.08 over 30.08) times lower computational time as compared to our Virtex 7 implementation of \(GF(2^{571})\).