esa-sceva/satcom-chunk-collection huggingface dataset:
# VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security
## 5 Discussion
### Limitations of Our Model  
While the results are promising, several limitations should be acknowledged. Firstly, the dataset was generated using SUMO for a specific urban environment (Gangnam, Seoul); although realistic, real-world V2X data can exhibit greater diversity and noise, and the synthetically injected anomalies might differ from actual attack signatures. Secondly, the study focused on anomalies in fundamental BSM kinematics, meaning that more sophisticated attacks, such as Sybil attacks, advanced DoS, or 'ghost vehicle' generation [39], might require analysis of additional message fields or data correlation. Furthermore, while the concept of adaptive retraining was proposed, its effectiveness under dynamically evolving data distributions was not experimentally validated in this work due to dataset limitations, and the optimal triggers for retraining warrant further investigation. Lastly, the defined attack model covers common data falsification; however, highly sophisticated attackers with a deep understanding of the detection system might devise strategies to evade it.
## 3 Methodology  
Time-series feature representations using contrastive learning were employed to improve parcel-based crop mapping using multi-temporal Sentinel-2 images, as illustrated in Figure 3. This procedure consisted of four major steps: (1) pixel-wise spectral features, (2) parcel-based spectral features, (3) time-series feature representation, and (4) time-series crop classification.  
Figure 3: Flowchart of contrastive-learning-based time-series feature representation for parcel-based crop classification.  
Before the main process, data preprocessing was performed, including atmosphere calibration, the cloud/shadow-based masking of Sentinel-2 images, the geographic registration of experimental data (including Sentinel-2 images, farmland parcel maps, and survey samples), and the generation of farmland parcel maps. First, at the pixel scale, time-series composition and band calculation were applied to Sentinel-2 images to generate spectral features and vegetation indices. Second, cloud/shadow-masked Sentinel-2 feature images (including spectral bands and indices) were overlaid onto parcel maps to generate parcel-based incomplete time-series spectral features (with missing values). Third, at the parcel scale, a contrastive learning framework was enhanced to map time-series spectral features into their inherent feature representation (without missing values). Finally, using the feature representation and time-series classifiers, parcel-based crop classification maps were generated.
# Design of a Wideband Antenna for Wireless Network-On-Chip in Multimedia Applications
## 1 Introduction
Moreover, [56] declares that, although implementations of on-chip antennas have been proposed and studied in literature, there are still many challenges in manufacturing and integrating them in terms of area, performance, and energy overheads, especially as the number of on-chip cores will be scaling up in the future. Within the above scenario, this work presents the design of a mm-wave antenna, with bow-tie topology, which ensures a compact size implementation and a high bandwidth, suited for WiNoC applications. The large bandwidth of the WiNoC antenna is important to achieve high data-rate connections also in the case of simple modulation schemes with low spectrum efficiency. After this introduction, the rest of the paper is organized as follows. Section 2 presents the antenna architecture design, whereas Section 3 discusses the design of the layout and the performance characterization of the antenna. A comparison of the proposed antenna vs. the state-of-the-art is addressed in Section 4. Section 5 discusses the impact of the proposed antenna on the performance of the NoC infrastructure. Conclusions are drawn in Section 6.
## 5 Experimental Results
### PTR Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Annealing Temperature [\({}^{\circ}\)C]**} & **Thermal Diffusivity** & **Thermal Conductivity** & **Thermal Effusivity** \\  & \(\mathbf{\alpha_{c}}\) **[cm\({}^{2}\) s\({}^{-1}\)]** & \(\lambda_{c}\) **[Wcm\({}^{-1}\)K\({}^{-1}\)]** & \(\mathbf{\epsilon_{c}}\times\mathbf{10^{-3}}\) **[Ws\({}^{1/2}\)cm\({}^{-2}\)K\({}^{-1}\)]** \\ \hline
25 & 2.52\(\cdot 10^{-3}\) \(\pm\) 1.31\(\cdot 10^{-4}\) & 3.02\(\cdot 10^{-3}\) \(\pm\) 2.15\(\cdot 10^{-4}\) & 6.01\(\cdot 10^{-2}\) \(\pm\) 3.72\(\cdot 10^{-3}\) \\
90 & 2.66\(\cdot 10^{-3}\) \(\pm\) 1.37\(\cdot 10^{-4}\) & 3.33\(\cdot 10^{-3}\) \(\pm\) 2.20\(\cdot 10^{-4}\) & 6.46\(\cdot 10^{-2}\) \(\pm\) 3.81\(\cdot 10^{-3}\) \\
120 & 2.68\(\cdot 10^{-3}\) \(\pm\) 1.06\(\cdot 10^{-4}\) & 3.38\(\cdot 10^{-3}\) \(\pm\) 1.71\(\cdot 10^{-4}\) & 6.53\(\cdot 10^{-2}\) \(\pm\) 3.00\(\cdot 10^{-3}\) \\
150 & 2.70\(\cdot 10^{-3}\) \(\pm\) 1.06\(\cdot 10^{-4}\) & 3.40\(\cdot 10^{-3}\) \(\pm\) 1.68\(\cdot 10^{-4}\) & 6.55\(\cdot 10^{-2}\) \(\pm\) 2.88\(\cdot 10^{-3}\) \\
180 & 2.78\(\cdot 10^{-3}\) \(\pm\) 1.57\(\cdot 10^{-4}\) & 3.48\(\cdot 10^{-3}\) \(\pm\) 2.46\(\cdot 10^{-4}\) & 6.61\(\cdot 10^{-2}\) \(\pm\) 4.23\(\cdot 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The thermal diffusivity, thermal conductivity, and thermal effusivity values of the investigated coatings obtained at different annealing temperatures using the PTR method. Figure 11: Values of the determined thermal diffusivities of investigated PBMA–siloxane coatings annealed at different temperatures. The squares are experimental data, and the dashed line is a trend line. for thermal diffusivity, conductivity and effusivity, respectively. These results coincide with those obtained in the literature [44].
# Lane following Learning Based on Semantic Segmentation with Chroma Key and Image Superposition
## 3 Materials and Methods
### Experimental Setup
#### 3.2.4 Comparisons  
To verify the correct operation of the system and its improvement with respect to the state of the art, comparisons with other systems have been made.  
First, the proposed model is compared with another end-to-end model that is not based on semantic segmentation; that is, no module is trained to explicitly identify the lines of the road. In this case, a convolutional network with the same structure of the proposed encoder + MLP is used, similarly to the proposal in Reference [14]. This model is trained using the images at the input and the steering value at the output.  
Second, the same model as in Reference [18] is used on the original images; in this case, the Autoencoder is trained to approximate the identity function using the same image at both the input and the output. The final model is built using the encoder and an MLP to determine the needed steering value.  
Finally, a comparison is made with a road line detection system based on traditional image processing. In this case, the processing is integrated with the use of a filtered Canny edge detector, the Hough Transform, and a regression to reconstruct the lines. A discussion on this approach can be found in Reference [36]. The result will generate images that will be used to train the same CNN+MLP as in the first alternative comparison. This provides the benefit of having the semantic labeling for the initial images, which will facilitate the evaluation of the line detection.  
The results of the experiments are shown in the next section.  
To carry out the work, a computer with an i5-9400f processor, 32 Gb ram, and two rtx2070 spus was used, with tensorflow 1.13.1 and keras 2.3.1 on ubuntu 18.
## 4 Distributed Optimal Dispatching Model and Algorithm of MGs and ESP Based on ADMM
### Optimization Solution of MGs in the Iteration Process  
According to (20), the optimal dispatching model of MG needs to be solved during each iteration of the distributed optimization of MGs in coalition. As mentioned before, in order to simplify the calculation, we conclude that the cost of BESS can be calculated according to (4) during the discharging stage, while the cost of BESS can be seen as zero during the charging stage. In order to find the optimal exchange power of MGs in the coalition, it is necessary to determine the state of charge and discharge of BESS effectively. According to (4)-(22), the scheduling strategy of MG \(n\) can be solved as follows:  
Figure 2: The bidirectional interaction flowchart between MGs and ESP.  
\[\begin{split}\mathbf{x}_{n}^{k+1}&=\arg\min_{\mathbf{x}}(C_{ gridn}(x_{gridn})+C_{BESSn}(x_{BESSn})\\ &+\frac{\rho}{2}\left\|\left[\begin{array}{c}x_{n}^{in}\\ x_{gridn}\end{array}\right]+\left[\begin{array}{c}(\mathbf{B}\mathbf{w}^{k})_{n} \\ (\mathbf{B}\mathbf{w}^{k})_{N+n}\end{array}\right]+\left[\begin{array}{c}\mathbf{u}_{n}^{ k}\\ \mathbf{u}_{N+n}^{k}\end{array}\right]\right\|_{2}^{2})\\ s.t.&\max(P_{charge}^{max},\frac{Q(S_{OCinit}-S_{OCmax})}{\Delta T})\leq\eta x_{ BESSn}\leq 0\\ &0\leq\frac{x_{BESSn}}{\eta}\leq\min(P_{discharge}^{max},\frac{Q(S_{OCinit}-S_{OC min})}{\Delta T})\end{split} \tag{28}\]  
Figure 3 shows the flowchart of the optimization solution for individual MGs. First, we assume that the output power of BESS is negative during the charge state, then we have \(C_{BESSn}(x_{BESSn})=0\). Combined with (11) and (12), the optimal scheduling strategy can be made by solving (26) and (27). Although the cost of BESS during the charge stage can be ignored, if BESS still adopts the charging scheme in the case of the power shortage of WT and PV, the power purchased from the main grid will increase, which adds to the cost of MG. Therefore, it is obvious that the charging power after optimization must be zero under the circumstances of the power shortage of WT and PV. If \(x_{BESSn}<0\) in the optimization results, we can conclude that the output power of WT and PV is surplus. Therefore, it is necessary to charge the BESS to absorb excess energy, the solution \(\mathbf{x}_{n}^{k+1}\) under which case is the optimal scheduling result of the current iteration.  
As for the charge stage, if \(x_{BESSn}=0\) in the optimization results, we can infer that the output power of WT and PV may be insufficient, which leads to the scheduling strategy in the charge state not being optimal. Thus, the scheduling scheme in the discharge state of BESS needs to be further calculated. Combined with (12), the optimal scheduling scheme can be formulated by solving (26) and (28). The optimal solution \(\mathbf{x}_{n}^{k+1}\) is the optimal scheduling strategy of the current iteration.
## 2 \(p\)-\(q\) Method Using Clarke Transform for Three-, Five-, and Two-Phase Systems
### Determination of Power Components and Total Power Factor of PES  
Instantaneous power defined by Equation (1) can be decomposed into a mean value \(P_{av},\ Q_{av}\) as DC components and \(p_{ac}(t),\ q_{ac}(t)\) as AC components, in each \(p,q\) phase  
\[\begin{bmatrix}p(t)\\ q(t)\end{bmatrix}=\begin{bmatrix}u_{\alpha}(t)i_{\alpha}(t)&u_{\beta}(t)i_{ \beta}(t)\\ u_{\alpha}(t)i_{\beta}(t)&-u_{\beta}(t)i_{\alpha}(t)\end{bmatrix}=\begin{bmatrix} P_{av}&p_{ac}(t)\\ Q_{av}&q_{ac}(t)\end{bmatrix}. \tag{9}\]  
Our aim is now to determine individual components of power as we know them from the classical power theory [4, 6]. So  
\(S_{av}\)--apparent power (proportional to \(U_{rms}\times I_{rms}\))  
\(P_{av}\)--active power  
\(Q_{av}\)--reactive blind power  
\(D_{av}\)--reactive distortion power  
\(PF\)--power factor (ratio of \(P_{av}/S_{av}\)),  
and those as quasi-instantaneous quantities. So, the first goal is the determination of  
\(P_{av}\) and \(Q_{av}\) components. There are more ways to conduct it:  
- using continuous-time filter [15],  
- using digital filtering (DF) [15, 16],  
- using integral calculus [17],  
- using discrete Fourier transform (DFT) [19],using artificial neural networks (ANN) [20].  
The use of the continuous-time filter seems to be the fastest way to process a signal but requires the use of auxiliary hardware for conversion and processing (DAC-ADC converter, multiplier, integrator, etc.). So, the time of calculation also depends on settling time, time of conversion of DAC, and similarly. The last two items are not directly bound to the \(p\)-\(q\) method.  
From Equation (1), using integral calculus, we can obtain the active and blind power components as  
\[P_{av}(k) \Leftrightarrow \frac{1}{T}\int\limits_{0}^{T}\big{[}u_{\alpha}(t)i_{\alpha}(t)+u_{ \beta}(t)i_{\beta}(t)\big{]}\mathrm{d}t, \tag{10}\] \[Q_{av}(k) \Leftrightarrow \frac{1}{T}\int\limits_{0}^{T}\big{[}u_{\alpha}(t)i_{\beta}(t)-u_{ \beta}(t)i_{\alpha}(t)\big{]}\mathrm{d}t. \tag{11}\]  
But it needs integration over the whole period. Therefore, a moving average filtering method (MAF) should be used to provide the results in each computation (integration) step \(k\). The use of the MAF method is shown in Figure 1 for a single-phase AC linear system (\(\alpha\)) with the following parameters:  
\[U_{max}\ =\ 325\ \mathrm{V},\ f=50\ \mathrm{Hz},\ Z\ =\ 23\ \Omega,\ \cos(\varphi)=0.8.\]  
The length of the sliding window was taken as \(T/2\), and the number of window points \(N=20\).
# Towards a Collection of Security and Privacy Patterns
## 3 Security Patterns
### Integrity
#### 3.2.4 Hash Check  
A direct implementation of the Safe Channel pattern is the Hash Check pattern. Let us assume a sequential orchestration P with two activity placeholders, A and B, whereby B is executed after A.  
We assume that for each x in {P, A, B}, the following hold:  
* IN\({}^{\text{x}}\) and OUT\({}^{\text{x}}\) are the sets of inputs and outputs of x;
* D\({}^{\text{x}}\) (i) are the data of x at the given time t;
* Hash(D\({}^{\text{x}}\) (i)) are the cryptographic hash function results applied to data D\({}^{\text{x}}\)(i).  
Further conditions that define P include:  
* The inputs of A are the inputs of the orchestration P;
* The inputs of B are the outputs of A;
* The outputs of the orchestration P are the outputs of B.  
Based on the above, a pattern for preserving integrity for data that are in processing and in transit on the service orchestration P can be defined as follows:  
* Hash (IN\({}^{\text{P}}\)) = Hash (IN\({}^{\text{A}}\));
* Hash (IN\({}^{\text{B}}\)) = Hash (OUT\({}^{\text{A}}\));
* Hash (OUT\({}^{\text{P}}\)) = Hash (OUT\({}^{\text{B}}\)).  
Interpreting the pattern above, we have to--for all data that are transmitted, not only through datalinks but also through inter-process communication--evaluate that the data that activity A sends to activity B are not changed.  
Moreover, based on the above specification. we can define a generic pattern for integrity of data at rest as the following:  
\[\text{Hash (D\({}^{\text{x}}\) (i))}=\text{Hash (D\({}^{\text{x}}\) (i \(-\) 1))} \tag{1}\]which means that whenever we check data at rest, that data must not be changed.
## 3 Results
### Energy Evolution
The dissipated energy exhibits an approximately linear increase, while the elastic energy gradually decreases and tends to stabilize. The external work is primarily used to overcome the internal friction within the rock, resulting in approximately constant external work during the unit deformation process of the rock. The cumulative damage dissipated energy curve under different confining pressures is presented in Figure 5, where curves of the same confining pressure are depicted in the same color. The figure reveals that the cumulative damage dissipated energy of the rock undergoes four distinct stages: low-speed increase, accelerated increase, rapid increase, and uniform increase. (1) Before the elastic stage, the internal damage and energy dissipation rate is small, resulting in a slow increase in the cumulative damage dissipated energy. (2) Upon entering the elastic and plastic stages, the rate of damage and energy dissipation increases, leading to an accelerated growth trend in the cumulative damage dissipated energy. The rock damage transitions into the stage of accelerated evolution. (3) After entering the post-peak softening stage, the elastic energy inside the rock is released, and all the external work is converted into the damage dissipated energy. (4) At the residual stress stage, as the internal damage evolution comes to a halt, the elastic energy within the rock remains relatively constant. At this point, all the external work is used for the friction work inside the rock, resulting in a linear increase in dissipated energy. Figure 5: The cumulative damage dissipated energy under different confining pressures. The cumulative damage dissipated energy curves under different confining pressures exhibit several noteworthy observations. (1) The variation patterns of the cumulative dissipated energy of rock are roughly the same under different confining pressures, experiencing slow increase, accelerated increase, rapid increase, and uniform increase stages. (2) During the initial slow increase stage of rock damage energy dissipation, the influence of confining pressure on the rate of increase in rock damage energy dissipation is relatively insignificant. (3) Under different confining pressures, at the same strain level, the cumulative damage dissipated energy of rock subjected to higher confining pressure surpasses that of rock under lower confining pressure, with the disparity becoming more prominent as strain increases. This suggests that as the confining pressure rises, the energy dissipation of rock gradually intensifies for a given strain level. (4) After the residual stress stage, the residual stress of rock escalates with increasing confining pressure, thereby amplifying the rate of increase in dissipated energy. (5) With the augmentation of confining pressure, the rapid increase stage of rock damage energy dissipation displays a higher energy dissipation rate, implying faster and more vigorous energy release.
## 3 Drone Routing Algorithms
### Charging
#### 3.2.2 Collaboration between Public Transport and Charging Station (CBPTCS)  
In [11], the authors proposed CBPTCS considering collaboration between public transport and CSs to execute drone-based delivery in rural areas. From the depot, a drone takes on suitable public transport to reach the best possible closest location of the targeted customers located in distant areas. Next, drones leave the public transport to reach the customers and based on the flight time constraint, drones can utilize CSs to swap their batteries. The main objective of CBPTCS is to minimize the average service delivery time to achieve customer satisfaction, which is carried out by optimally placing CSs. Based on the precise computation of drone flight distance, CSs are initially deployed to construct a fully connected topology considering the connectivity between CSs and all customers. Next, a minimum spanning tree (MST) is constructed from this connected topology. Subsequently, the locations of CSs are updated sequentially to reduce the average flight distance of the affected customers while maintaining the MST topology to avoid disconnection with any customer.
# Harris Hawks Sparse Auto-Encoder Networks for Automatic Speech Recognition System
## 3 Methodology
### Harris Hawks Sparse Auto-Encoder Networks (HHSAE)-ASR Framework  
This system aims to reduce the computation complexity while investigating the loud and noisy environment speech signal. The HHSAE-ASR framework utilizes the learning concepts that continuously train the system using speech patterns. Then, metaheuristic techniques, specifically the Harris Hawks (HH) algorithm, are applied to the encoder network to fine-tune the network parameters that minimize the error-rate classification problem. Here, the HH algorithm allows for recognizing the sequence of speech patterns, learning concepts, and the network parameter updating process, and improves the precise rate, robustness, and reliability of the ASR. The HHSAE-ASR framework is then illustrated in Figure 1.  
The working process illustrated in Figure 1 consists of several stages, such as the collection of speech signals, preprocessing, feature extraction, and the recognizer. The collected speech signals generated a lot of noisy and inconsistent information that completely affects the quality and precision of the ASR system. Therefore, modulations and changes should be suspected at all frequencies, and irrelevant details should be eliminated.
# Optimal Routing for Time-Driven EH-WSN under Regular Energy Sources
## 1 Introduction
* At most, data aggregation is considered at the intra-cluster level in the case of hierarchical topologies. This is consistent with the trend of deploying sensor networks over larger and larger areas, a fact that reduces correlation among data from different sub-regions. * Homogeneous distribution of the traffic workload generated by nodes (offered traffic). This means that all sensor nodes generate the same amount of packets per unit of time. * The transmit power of nodes is set to the maximum, that is, power control is disabled. This implies that the energy wasted by a node to transmit a packet does not depend on distance. Accordingly, the distance between a node and its receiver is not relevant, except for the fact that it must be lower than the transmission range. On the basis of these assumptions, this paper demonstrates that the MHC criterion should be prioritized in the design of routing strategies and protocols for time-driven EH-WSN under regular energy sources. More specifically, the detailed contributions of this paper can be outlined as follows:  
* Based on a previous result regarding the energy consumed by TinyOS sensor nodes [14], a comprehensive model is derived to characterize the energy consumption of nodes in generic time-driven duty-cycled wireless sensor networks. * general formulation is then obtained which relates duty cycle and traffic load for time-driven duty-cycled EH-WSN. * It is mathematically shown that, in addition to the obvious minimization of path delay, the minimum hop count criterion also minimizes the average traffic load over the network, and thus it maximizes the average duty cycle of nodes. In turn, this contributes to minimizing the link-level delay and maximizing the average network throughput that can be sustained by the whole network. The rest of this paper is organized as follows. In Section 2, from the result obtained in [14], a generic energy consumption model for time-driven duty-cycled sensor networks is developed. Though the analysis performed in this paper is valid for any regular or quasi-periodic energy source, special attention is dedicated to solar radiation, as it is the most representative example. Accordingly, a review of solar-based energy-harvesting models is provided in Section 3. By assuming a regular or quasi-periodic energy source, the condition for energy neutral operation is formulated in Section 4. In Section 5, it is shown that the MHC metric minimizes the average traffic load over the network and maximizes the average duty cycle of nodes. In Section 6, numerical results are obtained and comparedwith those from other routing strategies. Finally, Section 7 concludes the paper with suggestions for further research.