esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model Concepts and Dynamic Routing Setup
### Influence Matrix  
The base score of a waypoint exerts influence on its neighbor waypoints as a function of distance. Waypoints that are spatially \"close\" to each other will more likely have the same attribute of being damaged or undamaged. The influence of one waypoint on another is a function of the distance between the waypoints. Let an influence matrix \(\mathbb{I}\) be a \(nn\) matrix that can be referenced as the influence waypoint \(j\) has on waypoint \(i\). The value of any element \(\mathbb{I}_{ij}\) is in the range of 0 to 1. For this paper, we used a linear relationship of influence; however, other functions (say, logarithmic or exponential) can be used. The objective is to inform the solver of the likelihood a nearby waypoint will share the same state (damaged or undamaged). We use the parameters minimum influence range (the distance from a  
Figure 3: Decision tree for determining the base score of a waypoint; once the waypoint has been visited (i.e., the right, orange part of the, the score is fixed for the remainder of the scenario).  
waypoint where the influence is 1.0, typically \(r_{scan}\)) and maximum influence range (where waypoints beyond have an influence of 0.0). Samples of the symmetric case can be found in Figure 4.  
In addition to the previous \"symmetric influence matrix\", we have also developed a data driven influence matrix where the samples can be seen in Figure 5. Here, we use the historical data set of tornadoes to create an elongated matrix (along the 35-215 to 50-230 degree directions, south-west to north-east) to better reflect the typical direction a tornado might travel. The idea behind this data driven influence matrix is that since most tornadoes traverse along this half, there will be a greater chance of waypoints along this path to have identical conditions and attributes. Samples of this matrix can be found in Figure 5.  
This is generated by using the frequency of the direction of tornadoes from the historical tornado database from 1950 until 2021. The radial histogram of this can be found in Figure 6. Here, the direction of a tornado is simply what is recorded as the start point and the end point, and the direction between those two points. This is the same direction used in this data driven influence matrix. It will not necessarily capture the path the tornado might take that may resemble an S or something else.  
Figure 4: Visual samples of an “influence matrix” for a random waypoint (in blue) where yellow dots indicate high influence (close to 1.0), red dots indicate a lower influence (close to 0.0), and black dots indicate no influence (that is, 0.0).
## 4 Discussion  
Previous studies have used different devices, such as hand-held dynamometer, Cyhex Norm Dynamometer and Biodex, to measure the lower limb muscles [18; 19; 22; 23]. In this present study, a FS device was used to measure the limb and joint strengths, and all lower and upper limb joints were measured. It is the first study to complete all measurements for the TCC group. It should be noted that the measuring ways in this study were done when participants stood or sat naturally and thus different from the traditional ways using isokinetic dynamometers. It is also understood that various muscles other than the primary agonist(s) may contribute to a specific movement and affect the readings.
# Low Delay Inter-Packet Coding in Vehicular Networks
## 3 Packet Recovering Codes
### Binary Wyner-Ash codes  
#### 3.1.1 Code Description and Distance Properties  
A binary convolutional code of rate \(R=k/n\) can be characterized by its semi-infinite parity-check matrix  
\[H=\begin{pmatrix}H_{0}&\mathbf{0}&\cdots&&&&\\ H_{1}&H_{0}&\mathbf{0}&\cdots&&&\\ H_{2}&H_{1}&H_{0}&\mathbf{0}&\cdots&&&\\ \cdots&\cdots&\cdots&\cdots&&&\\ H_{m}&H_{m-1}&H_{m-2}&H_{1}&H_{0}&\mathbf{0}&\cdots\\ \mathbf{0}&H_{m}&H_{m-1}&\cdots&H_{1}&H_{0}&\mathbf{0}&\cdots\\ \cdots&\cdots&\cdots&\cdots&\cdots&\cdots&\cdots&\cdots\\ \end{pmatrix}, \tag{2}\]  
where \(m\) denotes the code syndrome memory [18], and for each \(i=0,1,...,m\), the entry \(H_{i}\) is of size \((n-k)\times n\).  
Figure 2: Coding for recovering packet losses.  
Let us consider one block-column submatrix  
\[H^{(m)}=\begin{pmatrix}H_{0}\\ H_{1}\\ H_{2}\\ \cdots\\ H_{m}\end{pmatrix}. \tag{3}\]  
If \(H^{m}\) is a suitably chosen parity-check matrix of the \(R_{b}=(n-m-1)/n\) extended Hamming code of length \(n=2^{m}\) with minimum distance \(d_{\min}=4\), then Equation (2) determines a subclass of Wyner-Ash codes [16] of rate \(R=(n-1)/n\) with syndrome memory [18]\(m\) and free distance \(d_{\text{free}}=3\).  
The subblock codes of the rate \(R=3/4\) and \(R=7/8\) Wyner-Ash codes with \(m=2,3\), respectively, are determined by parity-check matrices  
\[H^{(2)} = \begin{pmatrix}1&1&1&1\\ 1&0&1&0\\ 1&1&0&0\end{pmatrix}, \tag{4}\] \[H^{(3)} = \begin{pmatrix}1&1&1&1&1&1&1&1\\ 1&1&1&1&0&0&0&0\\ 1&1&0&0&1&1&0&0\\ 1&0&1&0&1&0&1&0\end{pmatrix}. \tag{5}\]  
The corresponding polynomial parity-check matrix of the convolutional code for \(m=2\)  
\[H(D)=\left(1+D+D^{2}\ \ 1+D^{2}\ \ 1+D\ \ 1\right) \tag{6}\]  
has systematic form. Its generator matrix can be written as  
\[G(D)=\begin{pmatrix}1&0&0&1+D+D^{2}\\ 0&1&0&1+D^{2}\\ 0&0&1&1+D\end{pmatrix}. \tag{7}\]  
Generalization to an arbitrary \(m\) is obvious.  
Close connection to the Hamming codes allows to compute the exact spectrum of the convolutional code determined by Equation (2) through the known spectra of Hamming codes and their cosets. This analysis was performed in [28]. For example, in case \(m=2\), the spectrum generating function is  
\[g(D)=D^{3}\frac{6+5D-D^{2}-2D^{3}-3D^{4}+3D^{6}-D^{7}}{1-3D-2D^{2}-D^{3}+D^{5}- D^{7}}.\]  
In Table 1, we present the first 10 coefficients of \(g(D)\) from this paper to further use them for bounding the error probability.
# Design of a Compact 1-MV-Class Radial Closing Switch for Tesla Type Generator
## 4 Optimization Techniques
### Global Field Distribution and Optimization Targets  
Figure 4 presents the potential and the electric field distribution for the radial gas switch for the initial design. The LV electrode is designed as a sphere with a diameter of 16 mm, mounted perpendicular to the inner (central) HV conductor of the Tesla transformer. The HV electrode length along axis is 230 mm. The thickness for both insulators, inserted into the inner conductor is 35 mm, which is ascertained by mechanical simulation loaded with the largest pressure. Polyethylene (PE) is chosen as insulator material for the gas chamber with the dielectric constant (\(\varepsilon_{\mathrm{r}}\)) of 2.2 while \(\varepsilon_{\mathrm{r}}\) of the transformer oil is 2.3 and \(\varepsilon_{\mathrm{r}}\) of the deionized water at the room temperature the system is operated is considered as 81. Figure 4a shows that the high voltage is concentrated along the inner surface area of the generator, while Figure 4b shows that the high electric field area is concentrated in the inner of the gas chamber, both results being expected.  
Optimization techniques were next applied to all switch components, as follows:  
1. For the HV and the LV surfaces of the two electrodes, the optimization target was to generate a uniform electric field and to ensure that breakdown of the SF\({}_{6}\) will be possible in the range of pressure and voltage considered;
2. For the gas-insulator interface toward the Tesla transformer secondary winding filled with oil and for the gas-insulator interface toward the water-filled load, the optimization target was to prevent SF\({}_{6}\)-insulator surface flashover;
3. Same as (2) for both the oil-insulator and for the water-insulator interface;
4. For the gas chamber, the optimization target is not only to minimize the volume but also to reduce its axial length as much as possible.  
Figure 4: Initial design of the radial SF\({}_{6}\)-insulated main switch. Overall potential distribution (**a**) and electric field distribution (**b**) for \(1\) MV applied.  
It is noted that during simulation, the size of mesh is controlled smaller than one-third of the characteristic size of the grooves; the size of regions near cathode triple junctions (CTJ) is smaller than one-fifth of the critical size.  
In order to assess the results obtained from the optimization effort, the corresponding electric field distributions along all six surfaces were analyzed after each cycle of optimization, as shown in Figure 5.
# A Road Defect Detection System Using Smartphones
## 1 Introduction
This system is adept at distinguishing between common road defects, such as potholes, and exceptional conditions, including speed bumps and manholes, thereby generating a rich dataset for analysis. For the purposes of clarity and simplicity within this paper, all aforementioned exceptional road conditions, including but not limited to speed bumps, manholes, and potholes, will collectively be categorized and referred to as \"road defects\". Leveraging accelerometer data sourced from smartphones and video footage from dashcams, our system enables the efficient collection of high-quality, labeled datasets. These datasets are crucial for the training of deep learning models aimed at road defect detection. The ease of data collection and the quality of the resulting dataset underscore the system's utility in generating valuable inputs for deep learning algorithms. The implications of our collected data and the automated collection system are significant for the advancement of research in road defect detection using vibration sensors. Additionally, we present a deep learning model that, utilizing the automatically collected data, accurately classifies three specific road defects: speed bumps, manholes, and potholes. The remainder of this paper is organized as follows: In Section 2, we introduce conventional vibration sensor-based road defect detection technologies and derive the problems in collecting data. Section 3 explains the proposed road defect detection system, dividing it into two parts: an automatic data collection part and a road defect classification part. Section 4 covers the collection of training data using smartphones and vehicles and evaluates accuracy through a test dataset. Finally, Section 5 discusses the conclusions.
# Texture and Materials Image Classification Based on Wavelet Pooling Layer in CNN
###### Abstract
Based on the results from our previous work, we now present a wavelet pooling approach to improve the learning of the classification model with the following contributions:  
1. We present a CNN architecture with a combination of regularization methods (DropOut, Data Augmentation, and Batch Normalization) to evaluate the performance of each pooling method: MaxP, AveP, and wavelet pooling (DWTP, DWTaP, and DWTdP). The objective is to have a reference of the learning behavior. 2. We present a complete evaluation of the classification performance of textures and materials in images, in addition to a state-of-the-art benchmark dataset. The idea is to evaluate the adaptability of deep learning with wavelet pooling. Furthermore, we argue that the methodology is ideal for this type of dataset, where it is recurrent to have repetitive patterns. 3. We show that the method eliminates the overfitting created by pooling methods while reducing features using an approach based on level-based decomposition, and it is more compact than pooling by using neighboring regions. Hence, we offer three configurations: DWTP = approximation and detail information; DWTaP = approximation; and DWTdP = detail information. The goal is to preserve the most information for each texture and material. 4. We demonstrate that a correct inference of texture or material can be obtained if we determine the type of pooling to be used during learning. We have conducted several experiments, but now we can choose the best pooling method depending on the dataset. Our experiments indicate that this is also useful for future object detection applications, focusing on physical features such as texture. In order to present our approach, this paper has been organized as follows: Section 2 shows related work; Section 3 introduces the methodology to address the texture and material classification problem. Section 4 shows the proposed methodology. After that, Section 5 shows the results with the three data sets, which have been used to test our approach and the experimental part. Finally, Section 6 discusses the results, and Section 7 presents the conclusions and future work.
## 2 Materials and Methods
### Hydrogen Production  
The overall energy efficiency of a hydrogen production system, whether connected or not, is closely linked to the performance of its key components, namely the electrolyzers and hydrogen storage tanks, which fundamentally impact the final quantity of produced hydrogen as well as the production cost [33]. [PERSON] et al. demonstrate the close relationship between electrolyzer performance and the energy efficiency of the hydrogen production process [34]. Improving electrolyzer efficiency leads to a reduction in energy losses and an increase in the final hydrogen output, offering significant energy savings.  
On the other hand, [PERSON] et al.'s study [35] emphasizes the importance of enhancing hydrogen storage tanks in reducing production costs. Enhancing the efficiency of these tanks can minimize energy losses and improve the economic efficiency of the hydrogen production process. Hence, the combined improvement of electrolyzers and hydrogen storage tanks holds paramount importance for the integrated energy and economic performance of the hydrogen production system.  
The typical performance in hydrogen production and storage varies depending on the technologies used and operational conditions. Electrolyzers, the primary subsystems for hydrogen production, often exhibit efficiencies ranging between 60% and 80% [36]. This percentage is a crucial parameter for process efficiency, as higher efficiencies lead to reduced energy losses and increased hydrogen output.  
However, hydrogen storage tanks also impact the overall system efficiency. Typically, the performance of these tanks hovers around 60%, resulting in losses during hydrogen storage [37]. Nevertheless, technological advancements in recent years have allowed improvements in these figures, potentially increasing the efficiency of the storage tanks.
## 3 Residual Displacement Ratios
### Residual Displacements for Mainshock-Aftershock Sequences  
Under intense sequential ground motions, structures typically retain unrecoverable residual displacements following either the mainshock or the complete seismic sequence. Figure 5 exemplifies the displacement response of a typical elastoplastic SDOF oscillator with the natural period \(T=1.0\) s and peak displacement ductility \(\mu=4\) subjected to three earthquake sequence samples (as previously shown in Figure 1). Three typical relationships between the mainshock-induced residual displacement (\(d_{\rm r,ms}\)) and the sequence-induced residual displacement (\(d_{\rm r,seq}\)) are summarized as follows:  
1. \(d_{\rm r,seq}>d_{\rm r,rms}\): indicates that the aftershock further deteriorates the structural repairability. In such cases, \(d_{\rm r,seq}\) serves as the governing parameter for post-earthquake rehabilitation assessments.
2. \(d_{\rm r,seq}=d_{\rm r,rms}\): suggests negligible aftershock impact on repairability thresholds. In this case, both \(d_{\rm r,rms}\) and \(d_{\rm r,seq}\) must comply with prescribed allowable limits.
3. \(d_{\rm r,seq}<d_{\rm r,rms}\): while partial displacement recovery may occur, immediate post-mainshock residual displacement \(d_{\rm r,rms}\) remains critical for emergency response operations (personnel evacuation, supply delivery) and secondary disaster mitigation.  
Under sequential seismic excitation, a more reliable and conservative approach for residual displacement estimation involves prioritizing the maximum offset recorded during either the mainshock or the subsequent aftershock. Consequently, it is desirable to define the residual displacement for mainshock-aftershock sequences as follows:  
\[d_{\rm r}=\max\{d_{\rm r,rms},\,d_{\rm r,seq}\} \tag{2}\]
## 4 Results and Discussion
### Evaluating Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Confusion Matrix**} \\ \hline \multirow{2}{*}{Dumping} & 18 & 3 & 0 & 1 & 81.80\% \\  & 22.50\% & 3.80\% & 0.00\% & 1.30\% & 18.20\% \\ \hline \multirow{2}{*}{Excavation} & 1 & 17 & 4 & 0 & 77.30\% \\  & 1.30\% & 21.30\% & 5.00\% & 0.00\% & 22.70\% \\ \hline \multirow{2}{*}{Hauling} & 0 & 0 & 16 & 0 & 100.00\% \\  & 0.00\% & 0.00\% & 20.00\% & 0.00\% & 0.00\% \\ \hline \multirow{2}{*}{Swing} & 1 & 0 & 0 & 19 & 95.00\% \\  & 1.30\% & 0.00\% & 0.00\% & 23.80\% & 5.00\% \\ \hline \multirow{2}{*}{Output Class} & 90\% & 85\% & 80\% & 95\% & 87.50\% \\  & 10.00\% & 15.00\% & 20.00\% & 5.00\% & 12.50\% \\ \hline \multirow{2}{*}{Target Class} & \multirow{2}{*}{Dumping} & \multirow{2}{*}{Excavation} & \multirow{2}{*}{Hauling} & \multirow{2}{*}{Swing} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix of the test set. The results of our study have been compared with some previous research studies in Table 6. These studies have used different algorithms and datasets. A CNN-based deep bidirectional long short-term memory (CNN-DBLSTM) was used in the study [20] for human action recognition on the UCF-101, YouTube 11 Actions, and HMDB51 datasets and similar results are reported for activity recognition on the HMDB51 dataset. In another study [17], the pre-determined starting point and duration of each activity has been used for classifying actions of construction workers and equipment. The study [11] used a Faster R-CNN detector + Deep SORT tracker + 3.D ResNet classifier for the excavator activity recognition and productivity analysis from construction and showed a similar kind of result. Furthermore, the study considered hauling and swinging as one activity. Meanwhile, this study implemented the pre-trained CNN model \"Googlenet\" for feature extraction and considered each frame of the video sequence. Furthermore, this study considered hauling and swing as different activities. Compared with these studies, the results of the proposed studies are promising. Figure 9: Example of successful activity recognition from the test dataset (**a**) Excavation (**b**) Hauling (**c**) Dumping (**d**) Swing.
# Thermogenic Supplementation and Fat Loss in Resistance-Trained Males: A Randomized Controlled Trial
## 3 Results  
A total of 34 individuals completed the study, with 18 in the TS group and 16 in the placebo group. The mean age and height for the TS group were 21.5 \(\pm\) 5.0 years and 177.8 \(\pm\) 6.3 cm, while the placebo group had a mean age of 21.0 \(\pm\) 2.0 years and a height of 176.2 \(\pm\) 6.4 cm. Post-intervention descriptive statistics and raw change scores for all measured variables are summarized in Table 3.  
There were no statistically significant differences between groups for changes in body mass (TS: 0.50 kg, 95% CI: \(-\)0.24, 1.24; placebo: 1.23 kg, 95% CI: 0.42, 2.05; \(p\) = 0.147) or lean body mass (TS: 1.15 kg, 95% CI: 0.53, 1.77; placebo: 1.12 kg, 95% CI: 0.29, 1.95; \(p\) = 0.965). However, significant differences were observed for fat mass and body fat percentage. Figure 2 illustrates the change in fat mass over the intervention for both groups. Fat mass decreased more in the TS group compared to the placebo group (TS: \(-\)0.65 kg, 95% CI: \(-\)1.15, \(-\)0.14; placebo: 0.12 kg, 95% CI: \(-\)0.40, 0.64; \(p\) = 0.029), and the TS group also experienced a larger reduction in body fat percentage (TS: \(-\)0.89%, 95% CI: \(-\)1.47, \(-\)0.32; placebo: \(-\)0.08%, 95% CI: \(-\)0.67, 0.52; \(p\) = 0.039).  
Figure 2: Individual fat mass (kg) responses for TS and placebo.  
Both groups demonstrated increases in squat and bench press strength following the resistance training intervention, but there were no statistically significant differences between groups for changes in squat strength (TS: 16.87 kg, 95% CI: 12.48, 21.26; placebo: 18.47 kg, 95% CI: 12.46, 24.48; \(p=0.312\)) or bench press strength (TS: 7.80 kg, 95% CI: 6.09, 9.50; placebo: 7.13 kg, 95% CI: 3.33, 10.94; \(p=0.883\)). Similarly, no group differences were observed for relative squat strength (TS: 0.21, 95% CI: 0.15, 0.27; placebo: 0.21, 95% CI: 0.14, 0.28; \(p=0.719\)) or relative bench strength (TS: 0.09, 95% CI: 0.06, 0.12; placebo: 0.07, 95% CI: 0.03, 0.12; \(p=0.643\)).
## 2 Materials and Methods
### FEMM Model  
All numerical calculations related to the magnetic field and interactions between the magnets were made using the Finite Element Method Magnetics (FEMM 4.2, [PERSON]) program [40]. This open-source tool is used worldwide for magnetic field numerical calculations in particle accelerators. Despite some limitations, this program is a simple and time-saving way of conducting precise 2D and axisymmetric calculations, using low computational solvers [41]. The magnetic model used for FEMM calculations is a doubled full Halbach matrix with additional partial magnets at its ends (Figure 5). This solution reflects the boundary conditions of the real THz undulator analyzed in this study.  
The model used for the FEA simulation (Figure 6) was split into 3 sections: the main magnetic array, the surrounding magnets, and the outer region. This approach enabled better management of the mesh quality used in different model sections. The finest mesh was used for the magnet array and its surroundings, whereas the outer region was less accurate. Each modelled magnet array takes into account some inaccuracies in the geometry. The 0.1 mm gap was created between each full magnet. This gap also reflects real conditions assumed in the THz undulator. Such gaps are necessary to allow the integral calculations of the magnetic field around all side walls of the magnet and calculations of the forces acting on the magnet. The magnet material is N42 UH grade (Table 1), and the Halbach array assembly is settled in air. No additional mechanical fastening system was taken into consideration. The calculations were conducted for three different cases reflecting three different gaps between jaws with the Halbach arrays (Figure 5): the maximum opening (600 mm), the minimum opening (100 mm), and the operational position (200 mm). This position is established relative to the space created by the distance between the inner surfaces of the magnets.  
Figure 5: Boundary conditions for FEMM simulations.