esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomous Obstacle Avoidance with Improved Deep Reinforcement Learning Based on Dynamic Huber Loss
## 3 A DRL Framework Based on Dynamic Huber Loss and Soft Updates
### Loss Function  
#### 3.2.1 Mean Squared Error  
Mean Squared Error (MSE) is a commonly used loss function in regression problems, defined as the average of the squared differences between the predicted and true values. The formula is as follows:  
\[L_{MSE}(\theta)=\frac{1}{n}{\sum_{i=1}^{n}\varepsilon^{2}} \tag{9}\]  
where \(\varepsilon\) is the error between the true value and the predicted value. For the neural network parameter \(\theta\), the gradient is as follows:  
\[\
abla_{\theta}L_{MSE}=\frac{\partial L_{MSE}}{\partial\varepsilon}=\frac{1} {n}{\sum_{i=1}^{n}2\varepsilon} \tag{10}\]  
The MSE is sensitive to outliers because the error is squared, and outliers can lead to large gradients, making the training process unstable and the speed of convergence affected.
# Effect of Process Parameters on Distortions Based on the Quantitative Model in the SLM Process
## 2 Materials and Methods
### Numerical Method  
The thermo-structural model is utilized to investigate residual stresses and distortions of SLM. The thermal simulation model is established using the FLUENT, a computational fluid dynamics (CFD) software, to calculate temperature fields in the SLM process. During the process, the governing equation can be expressed as:  
\[\rho c\bigg{(}\frac{\partial T}{\partial t}+\mathbf{u}\cdot\
abla T\bigg{)}=\frac {\partial}{\partial x}\bigg{(}k_{x}\frac{\partial T}{\partial x}\bigg{)}+\frac {\partial}{\partial y}\bigg{(}k_{y}\frac{\partial T}{\partial y}\bigg{)}+\frac {\partial}{\partial z}\bigg{(}k_{z}\frac{\partial T}{\partial z}\bigg{)}-q_{l}+Q \tag{1}\]  
where \(\rho\) is the density, \(c\) is the specific heat, \(T\) is the temperature of the material, \(t\) is time, \(\mathbf{u}\) is the velocity of the liquid metal, \(k_{x}\), \(k_{y}\), and \(k_{z}\) are the thermal conductivities along the \(x\), \(y\), and \(z\) directions, \(q_{l}\) is the heat loss through the surface of the part, and \(Q\) is the heat source. To accurately simulate the thermal effect, a volumetric Gaussian distributed heat source is used in this model.  
After the thermal simulation, the temperature field is loaded into the mechanical model built using the FEA method to calculate thermal stresses, strains, and distortions. For example, the total strain, \(\varepsilon_{tot}\), can be defined as:  
\[\varepsilon_{tot}=\varepsilon_{e}+\varepsilon_{p}+\varepsilon_{th} \tag{2}\]  
where \(\varepsilon_{e}\) is the elastic strain, \(\varepsilon_{p}\) is the plastic strain, and \(\varepsilon_{th}\) is the thermal strain.  
More details of the thermo-mechanical model and the experimental verification of it can be found in our recent work [23].
## 4 Experiments
### Result Analysis
#### 4.5.2 Electrodes Attention Analysis  
The Standard 10-20 system EEG electrode layout and brain region division map, as presented in Figure 7, is foundational for understanding the variations in electrode attentiveness across the different frequency bands depicted in Figure 8.  
Figure 7: Standard 10–20 system EEG electrode layout and brain region division map.  
To delve into the model's attention allocation patterns across different frequency bands, we performed a row-wise averaging of the model's attention weight matrix and normalized the results to a range of 0 to 10. This process enabled us to quantitatively assess the GAAE's degree of attention to interactions between EEG electrodes across different frequency bands.  
In the \(\delta\) band, the model particularly focuses on the frontal lobe electrodes Fp1 and Fp2, closely related to frontal lobe activity. In the \(\theta\) band, attention is mainly on the occipital (O1, O2) and frontal (F3, F4) regions, possibly reflecting these areas' unique roles in this frequency band. \(\alpha\) band's attention distribution is more concentrated, especially in the posterior temporal (T5) and occipital regions, closely related to age-associated brain activity. As for the \(\beta\) band, the model shows broad attention, covering multiple areas from the mid-temporal (T3, T4) to posterior temporal (T5, T6), emphasizing the close relationship between beta band EEG signals and age.  
Overall, the model's attention in the \(\alpha\) and \(\beta\) bands was significantly higher than in the \(\delta\) and \(\theta\) bands. This observation aligns with existing research, confirming that delta and theta bands dominate brain electrical activity in young ages. As people grow older, activity in the \(\alpha\) and \(\beta\) bands increases, especially from adolescence to middle age, showing a significant upward trend [3; 4; 5; 6]. These insights not only enrich our understanding of age-related changes in brain electrical activity but also provide new perspectives for future neuroscience research.
## 5 Results
The number of parameters is calculated for different combinations of the approximator network and segmentation models with different backbones. The evaluation time per a batch size of 20, which was taken by each combined model \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference Method** & **U-Net** & **Linknet** & **FPN** \\ \hline Segmentation model only & 0.3870 (0.008) & 0.3970 (0.012) & 0.4615 (0.006) \\ Approximator trained & 0.4084 (0.004) & 0.4367 (0.005) & 0.4587 (0.007) \\ separately & 0.6418 (0.026) & 0.6962 (0.034) & 0.7089 (0.027) \\ Proposed method & \multicolumn{1}{c}{} & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Effects of the proposed method (mean IoU with standard dev.). Figure 10: Results (IoU score) obtained from the proposed approximator network and FPN. Images are named 1 to 5 for cross referencing them respectively between other figures. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Segmentation Model** & **Backbone** & \
\begin{tabular}{c} **Segmentation Model** \\ **Only** \\ \end{tabular} &
\begin{tabular}{c} **Approximator Trained** \\ **Separately** \\ \end{tabular} & **Proposed Method** \\ \hline Unet & Resnet18 [34] & 0.7022 (0.015) & 0.6238 (0.002) & 0.7749 (0.0004) \\  & SeResnet18 [41] & 0.7109 (0.017) & 0.7229 (0.020) & 0.7873 (0.0011) \\  & DenseNet121 [42] & 0.6433 (0.004) & 0.5423 (0.001) & 0.7139 (0.0015) \\  & InceptionV3 [23] & 0.3541 (0.050) & 0.4881 (0.008) & 0.7065 (0.0022) \\  & MobileNetV2 [43] & 0.4024 (0.031) & 0.5283 (0.002) & 0.7330 (0.0004) \\  & EfficientNetB0 [44] & 0.4836 (0.009) & 0.5773 (2 \(\times\) 10\({}^{-6}\)) & 0.7689 (0.0002) \\ \hline Linknet & Resnet18 [34] & 0.7133 (0.017) & 0.5866 (6 \(\times\) 10\({}^{-5}\)) & 0.7706 (0.0002) \\  & SeResnet18 [41] & 0.6759 (0.009) & 0.6426 (0.004) & 0.7752 (0.0004) \\  & DenseNet121 [42] & 0.6946 (0.013) & 0.6109 (0.001) & 0.7569 (9 \(\times\) 10\({}^{-6}\)) \\  & InceptionV3 [23] & 0.4850 (0.008) & 0.5034 (0.005) & 0.7252 (0.0008) \\  & MobileNetV2 [43] & 0.4009 (0.031) & 0.4426 (0.018) & 0.6289 (0.0156) \\  & EfficientNetB0 [44] & 0.6847 (0.011) & 0.6132 (0.001) & 0.7586 (2 \(\times\) 10\({}^{-5}\)) \\ \hline FPN & InceptionV3 [23] & 0.7327 (0.023) & 0.7086 (0.016) & 0.9189 (0.0272) \\ MobileNetV2 [43] & 0.4293 (0.022) & 0.5128 (0.004) & 0.7357 (0.0003) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of results for different backbones (mean IoU with standard dev.). entity, was measured and included. Larger combined models with backbones, such as InceptionV3, took a longer time to evaluate. Some combined models with backbones such as DenseNet121 took a longer amount of time because of the relatively higher internal mathematical operations which are needed to perform the evaluation.
# Research and Analysis of MEMS Switches in Different Frequency Bands
## 3 The Research Status of MEMS Switches in Different Frequencies
### Research Regarding Insertion Loss and Isolation of MEMS Switches in Different Bands  
Insertion loss and isolation are two critical performance parameters that are determined by the structures and materials of MEMS switches [6]. Insertion loss means the ratio of the input signal to the output signal when the switch is ON. Isolation means the ratio of the input signal to the output signal when the switch is OFF. There have been numerous studies to achieve high isolation and low insertion loss in different frequency bands.
## 2 MAC Design Issues in CRBANs
### Spectrum Access  
The most important issue pertaining to the MAC in CRBANs is to prevent SUs from colliding and interfering with PUs. Self-coexistence is difficult to achieve, because of the non-deterministic activities of PUs and other noise. Effective spectrum access can be attained in underlay or overlay approaches. In the underlay approach, the SUs employ a combination of lower power transmission and wider frequency bandwidth to generate a signal that appears as noise to the PUs. The spectrum handovers are not issued, because the transmissions of SUs are perceived to be noise by the PUs. On the other hand, overlay access generally uses higher transmission power and a narrower transmission bandwidth. This approach requires accurate and appropriate sensing and signaling mechanisms to handle PU behavior. Coordinated spectrum access provides better spectrum utilization and less interference than uncoordinated spectrum access, because of the cooperation between radios through the exchange of real-time spectrum information.
## 6 Simulation Results
### Robustness to Geometric Attacks
#### 6.4.1 Robustness to a Translation by Circular Shifting  
In this test, an EEG-like signal called \"chb01_01_edf\" was circularly shifted to the right and then to the left by the factors \(k=+20\) and \(k=-20\), respectively. The proposed method was then used to check the copyright of the shifted signal. The results of the test are shown in Figure 21. From the results obtained in this figure, it is noticeable that the BER values tended to be zero, which indicates good resistance of the suggested method to signal-shifting attacks. This outcome can be explained by the fact that our method extracts feature vectors from large-sized frames. Therefore, signal shifting by a few signal samples presents a low influence on the extracted feature vector. In the present test, the used frames are of size L = 4096. Therefore, a shift in \(k=\pm 20\) samples led to a slight variation in the coefficients of the extracted feature vectors. However, if the circular shifting is significant (\(k=\pm 200\) samples), the signal copyrights could not be deleted by our scheme. This is a weak point of our scheme to be addressed in future work.  
Figure 20: Original ECG signal and its filtered versions with the corresponding BER values.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Latency Aware and Energy-Efficient Routing
#### 3.9.2 Multipath  
As explained previously in this survey, this category of protocols is based on multiple routing paths instead of a single path to transmit data between communicating nodes to balance the traffic load in the network. Multipath routing protocols are able to find the path with the lowest cost while meeting the network latency in the network.
# Surrogate-Based Robust Design for a Non-Smooth Radiation Source Detection Problem
## 2 Radiation Transport Model and Surrogate Formulations
### Surrogate Statistical Model  
The analysis of interpolation error relies on smoothness properties of the map being approximated. In our case, such properties are not directly available nor are error bounds. Instead, we assume that the response surrogate models errors associated with the true source can be modeled as normal random variables \(\varepsilon_{i}^{m}\sim\mathcal{N}(0,(\sigma_{i}^{m})^{2})\), yielding the statistical model  
\[F_{i}(\boldsymbol{\theta}_{0})=\tilde{F}_{i}(\boldsymbol{\theta}_{0})+ \varepsilon_{i}^{m},\ i=1,\ldots,N, \tag{11}\]  
with \(\sigma_{i}^{m}\) being the standard deviation of the response surrogate models errors.  
A statistical model incorporating both model errors \(\varepsilon_{i}^{m}\) and observation errors \(\varepsilon_{i}^{o}\) introduced in (9), is  
\[\mathrm{Y}_{i}=\tilde{F}_{i}(\boldsymbol{\theta}_{0})+\varepsilon_{i}^{o}+ \varepsilon_{i}^{m},\ i=1,\ldots,N. \tag{12}\]  
Assuming the independence of model errors and observation errors, and exploiting the fact that the sum of independent normal random variables is also a normal random variable, (12) can be expressed as  
\[\mathrm{Y}_{i}=\tilde{F}_{i}(\boldsymbol{\theta}_{0})+\tilde{\varepsilon}_{i},\ \mathrm{Y}_{i}\sim\mathcal{N}(\tilde{F}_{i}(\boldsymbol{\theta}_{0}), \sigma_{i}^{2})\ i=1,\ldots,N, \tag{13}\]  
where \(\tilde{\varepsilon}_{i}\sim\mathcal{N}(0,\sigma_{i}^{2})\), and \(\sigma_{i}^{2}=(\sigma_{i}^{o})^{2}+(\sigma_{i}^{m})^{2}\).
# Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning
## 2 Related Work
### Deep Learning Models
#### 2.3.2 TCN  
A temporal convolutional network (TCN) [36] is a network based on a convolutional neural network, which can be used to model time series data. TCNs exhibit good performance when applied to time series data such as text and video, and have the advantage of reduced memory consumption for training. Beyond convolutional networks, TCNs incorporate causal convolution [37; 38], dilated convolution, and residual networks [39]. Briefly, causal convolution sets the causal relationship between each layer of the network and is suitable for data involving time series characteristics; dilated convolution increases the receptive field while reducing the network depth; and the residual block inside a residual network alleviates the vanishing gradient problem using skip connections. These features are described in further detail below.  
1. Causal convolution  
As shown in Figure 2, causal convolution performs convolution operations in chronological order. For instance, for the time series data modeling task \(f\), assuming that the input is \(x^{(0)},\dots,x^{(T)}\) and the target output is \(y^{(0)},\dots,y^{(T)}\), the data before time \(t\) is used to compute the output \(\hat{y}^{(t)}\) at time \(t\), such that  
\[\hat{y}^{(t)}=f(x^{(0)},\dots,x^{(t)}). \tag{2}\]  
Dilated convolution is incorporated to expand the TCN receptive field. In Figure 2, the convolution kernel has size 3 and the expansion coefficients \(d\) of the first to third convolution layers are 1, 2, and 4, respectively. As the layer number deepens exponentially, the receptive field of the network expands under the premise of the controllable number of calculations, and more historical data can be used for time series data modeling.  
Figure 2: Schematic of the TCN.  
If the input sequence is \(x=\left\{x^{(0)},\ldots,x^{(T)}\right\}\in\Re^{n}\) and the convolution kernel is \(f:\{0,\ldots,k-1\}\rightarrow\Re\) then, for any data sample \(x^{(t)}\), the convolution can be expressed as  
\[F_{d}(x^{(t)})=\sum_{i=0}^{k-1}f(i)\cdot X_{x^{(t)}-d\cdot i^{\prime}} \tag{3}\]  
where \(k\) is the convolution kernel size, \(d\) is the expansion factor, and \(x^{(t)}-d\cdot i\) represents the past direction. The larger the expansion coefficient, the larger the receptive field of the convolutional network. When \(d=1\), the expanded convolution degenerates to an ordinary convolution.  
3. Residual connection  
When the TCN depth increases, a residual module is introduced to improve the network stability. When \(x^{(t)}\) is the input, the network output is expressed as follows:  
\[o=\text{Activation}(x^{(t)}+F_{d}(x^{(t)})). \tag{4}\]
# On the Impact of Multiple Access Interference in LTE-V2X and NR-V2X Sidelink Communications +
## 5 Results and Discussion
### Impact of the Distance between Transmitter and Receiver  
The packet success probability is also examined for the separation of the distance between the transmitter and receiver for a fixed number of resources in the selection window, which is set to 8. Different platoon sizes are considered by setting the number of vehicles to 8, 12, and 16, respectively. The validation of the proposed approximation model is illustrated in Figures 9 and 10, while the comparison between LTE-V2X and NR-V2X is shown in Figure 11.  
Figure 8: Packet success probability as a function of the number of resources.  
Figure 7: Packet success probability as a function of the number of resources.  
Figure 11: Packet success probability as a function of distance.  
Figure 10: Packet success probability as a function of distance.  
Figure 9: Packet success probability as a function of distance.  
The probability of success decreases by increasing the distance between the transmitting vehicle and the platoon head. This is primarily caused by the impact of high attenuation, which worsens the received SNR, and the reduced carrier sensing efficiency, which induces a higher packet error rate. Because of the higher interference level, we can see that performance deterioration is more pronounced when the number of communicating vehicles is higher than the number of resources. We can also deduce that the impact of channel sensing failures may have a greater influence on performance than an increase in the number of vehicles. Indeed, the performance degradation observed when switching from a platoon size of 12 to a platoon size of 16 is very low if compared to a change in distance. Finally, from Figure 11, we can appreciate the performance comparison between the two considered communication modes, and NR-V2X is shown to provide better performance than LTE-V2X.
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 3 System Model
### Proposed Scheme-I(Key Recovery System Using Signcryption)
#### 3.2.3 Security Requirements of Proposed Scheme-I  
The security requirements of the key recovery system using signcryption are as follows:  
* KRF integrity: No participant in key recovery can maliciously transform \(KRF\) information from the device and \(KRF\) information required for key recovery cannot be changed.
* Data confidentiality: It should be possible for only authorized devices to decrypt encrypted data.
* Non-repudiation: The device should not be able to reject the fact that it generated the \(KRF\). In addition, the fact that device-generated \(KRF\) should be clear after transmission, exchange, communication, and processing.
* Attack on group authentication detection: If a malicious third-party \(KRA\) pretends to be a lawful member of the key recovery group, \(KRA\) should be detected through group verification.
* Single point of failure protection: In \(M\)-\(KRA\), some \(KRA\)s should be able to recover session keys even if another \(KRA\) fails to operate properly.
## 3 LPWAN Technologies and Design Goals
### Contention Mechanism  
LPWAN devices use lightweight and simple MAC protocols to conserve power. LoRa and Sigfox are based on the ALOHA protocol and do not utilize carrier sense multiple access collision avoidance or collision detection (CSMA CA/CD) mechanism as being used in other wireless technologies. Also, as both work in unlicensed bands, the number of devices is not controlled, thus CSMA may become ineffective due to the increase in the number of devices.
# Fuzzy-Based Dynamic Time Slot Allocation for Wireless Body Area Networks
## 4 Tree Formation and Cost Function Evaluation
### Minimum Cost Parent Selection Algorithm  
To find the best parent node, the proposed minimum cost parent selection (MCPS) algorithm is used whenever a node receives an announcement from the neighboring nodes. According to this algorithm, the best parent node will be the one with a minimum hop, minimum cost, and the shortest distance from the child node. Since it utilizes the minimum link cost, minimum hop, and the shortest distance between nodes, it satisfies the required QoS for WBANs. The selection of best parent nodes from the tentative parent list is depicted in Algorithm 1.  
```
1:Initialization:\(LC_{ij}\)--link cost function between sensor nodes \(i\) and \(j\)\(C_{m}\)--maximum link cost = 1\(N_{id}=-1\)\(h_{n}\)--highest number of hops nid--node Identifier of node \(j\)\(C_{n}id\)--link cost of node \(j\)NNi s1, s2,....., sm--set of neighboring nodes of node \(i\), \(1\leq i\leq N,1\leq m\leq N\)
2:BNH--best parent node of \(NN_{i}\)\(N_{md}\)--node with minimum distance from child node
3:for each node in the list \(NN_{i}\)do
4: compute link cost: \(LC_{ij}\) using Equation (9)
5:endfor
6:for each node, \(j\), in the list \(NN_{i}\)do
7: nid = node Id of \(j\)
8:if\(h_{n}\) > \(h_{nid}\)then
9: C\({}_{m}=C_{nid}\)
10:\(h_{n}=h_{nid}\)
11:\(N_{id}=nid\)
12:else
13:if\(h_{n}==h_{nid}\)then
14:if\(C_{m}\) > \(C_{nid}\)then
15:\(C_{m}=C_{nid}\)
16:\(N_{id}=nid\)
17:else
18:if\(C_{m}=C_{nid}\)then
19:\(N_{id}=N_{md}\)
20:endif
21:endif
22:endif
23:\(BNH_{i}=N_{id}\)
24:endfor
```  
**Algorithm 1** Best parent node selection algorithm.
## 1 Introduction
According to [PERSON] et al., [19] PSHA depends on fault length and depth, which causes uncertainty when estimating PGA. Several MCDM methods have been developed for seismic hazard assessment in GIS, such as the analytical hierarchy process (AHP), Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), technique of order preference similarity to the ideal solution (TOPSIS), and multiple attribute utility approach (MAUT) methods [20]. In Odisha, a limited number of studies have been conducted for site-effect estimation and there have been no recorded studies for EPA using artificial intelligence techniques [1; 21]. The problem statement pinpoints that India's existing seismic zonation map does not count the detailed seismic parameters, including local site effects. Many assumptions have been made regarding PSHA, such as earthquakes occur randomly through space and time; there is no proper method for identifying length and depth in major faults; faults with branched lineaments are one; ground attenuation relation for probabilistic seismic hazard assessment can be adopted. However, in this study, we did not assume that earthquakes occurred randomly with a global attenuation relation that estimates PGA and intensity. Previous studies in Odisha primarily considered soil thickness. However, in this study geological, topographical, structural information, and historical events were integrated into GIS to identify seismic amplification zones and implement the RNN technique to map the probable sites for future events. The specific objectives of this study were as follows: (1) to locate the potential areas of sediment deposit; (2) to identify the site of high PGA and intensity with the spatial variation; and (3) to develop an RNN model to identify the probable locations for future events. The addressed major research questions, such as: (1) is it possible to achieve good accuracy in seismic wave amplifications (SSA) and EPA? If so, how efficient are the obtained results? ; (2) what are the possible reasons behind high probable earthquake areas? ; and (3) is the proposed model powerful enough for future earthquake studies?