esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Selection of Materials for Each Layer
### Selection of Panel Materials  
Ceramic armor, as a face panel material, possesses characteristics of high hardness, high strength, high toughness, and low density. Table 1 presents the main performance data of commonly used ceramic materials for armor.  
From Table 1, it is evident that boron carbide exhibits the lowest density and the highest Vickers hardness, whereas boron-titanium carbide demonstrates the highest density and elastic modulus. Due to its relatively high elastic modulus, silicon carbide ceramic causes substantial plastic deformation in the projectile, consequently diminishing its kinetic energy during penetration. However, its high cost, ranging from USD 110 to 220 per kilogram, and incapacity to withstand multiple hits limit its extensive application in armored vehicles. Fractures in this ceramic material during protection can readily propagate to adjacent ceramic components, thereby reducing the overall structural protection capability. Titanium boride demonstrates the highest density and elastic modulus, along with a relatively high Vickers hardness. This renders it suitable for use in intermediate armor. Companies such as Ceradyne and Cercom in the United States have employed titanium boride to defend against large-caliber projectiles in the M2 Infantry Fighting Vehicle (IFV). It exhibits strong resistance to violence and minimal armor fragmentation. Boron carbide showcases the best performance parameters, being one of the hardest materials globally and among the most stable compounds. Additionally, it boasts a high melting point, excellent wear resistance, and corrosion resistance, rendering it an ideal material for ceramic armor. It effectively contributes to reducing tank weight and energy consumption. However, its unstable processing and high production costs contribute to its expensive price. Due to the mature sintering process, abundant raw materials, and low production costs, alumina ceramics find wide application in various types of armored vehicles, seats, and belly panels due to their low price and stable material performance. Alumina ceramics contain varying proportions of Al\({}_{2}\)O\({}_{3}\), resulting in different compressive strength and fracture toughness characteristics. Table 2 provides the ceramic performance parameters for different levels of Al\({}_{2}\)O\({}_{3}\) content. Ceramics with a 99.5% Al\({}_{2}\)O\({}_{3}\) content exhibit higher compressive strength and fracture toughness. Impact tests conducted by Liang Yanyuan revealed that higher Al\({}_{2}\)O\({}_{3}\) content correlates with better resistance to projectiles and higher fracture toughness. Thus, this study selected ceramics with a 99.5% Al\({}_{2}\)O\({}_{3}\) content as the panel material.
## 4 Data Collection System  
The modules responsible for collecting data on the current state of a patient with Parkinson's disease can be divided into background and interactive.  
In an interactive module, the user manually enters their state data. Some of this data is sent by postal services directly to the doctor's computer, and all data is collected on a server to create an intelligent component of the monitoring system.  
The interactive part also includes various tests that the user can do. The test results involve both unprocessed data (e.g., hitting accuracy and keystroke frequency), memory and attention scores, and generalized estimates (e.g., the accuracy of figure outlining and voice sound parameters). This data is then sent by the application to the server, for analysis and improvement of the general model for predicting the condition of a patient with PD, and is analyzed locally to train the neural network for a specific patient who owns the mobile phone.  
The background part is the collection of data from a mobile device in the background (without any involvement of the patient). In particular, these include:  
* Data from the sensors of devices that are responsible for the movement of the phone (e.g., changing the coordinates along three axes, the angle of inclination, activity). These data allow for the tracking of the patient's activity, tremor, and dyskinesia.  
Data collected from sensors responsible for controlling the telephone (e.g., pressing buttons, keys, and moving fingers on the screen). These data allow for the assessment of the general condition of patients.
* On the server, the main components are a data pre-processing module and a module for applying data mining algorithms to obtain knowledge about a patient. This knowledge is necessary to understand the characteristic signs of phone use in patients with Parkinson's disease, and whether the patient is holding the phone in their hands or if another person is doing it.  
In addition to mobile devices and a server, the data is intelligently processed on the doctor's computer, where it is first converted into a special format, then distributed to various folders and analyzed using artificial intelligence methods, visualization tools, and other assistive technologies.  
The interface is designed in such a way that the patient requires as little effort as possible to learn and use the application.
## 5 Analysis of Test Results
### The Influences of Mudstone Content on the Permeability of Specimens  
It can be seen from Figure 16 that the Klinkenberg permeability increases with the increase of mudstone content for both kinds of specimens. There is a strong exponential relationship between Klinkenberg permeability and mudstone content of rock salt. We also find that for both kinds of specimens, when the mudstone content is below 40%, the permeability increases only slightly with mudstone content, whereas above this threshold, the permeability increases rapidly with mudstone content. When the mudstone content of rock salt is below 40%, the rock salt particles are closely arranged between each other, while the mudstone particles are relatively less, so the mudstone content has little impact on the Klinkenberg permeability. However, when the mudstone content ranges from 40%-100%, more particles of mudstone embed around rock salt particles, making the connection between the particles of rock salt less dense, so that some pore channels are formed and the pore throat radius is larger. Hence, with the increase of mudstone content, the Klinkenberg permeability increases rapidly when the mudstone content exceeds 40%, and when the mudstone content is 80%, the Klinkenberg permeability is close to the synthetic pure mudstone specimen value.
# Turn-to-Turn Fault Diagnosis on Three-Phase Power Transformer Using Hybrid Detection Algorithm
## 2 Hybrid Algorithm in TTF Diagnosis System
### Conventional Differential Protection  
The first sub algorithm is the conventional differential protection. This protection is a conventional method, which measures the differential current between the primary and secondary side for each phase. This protection is unit-type for a specified zone or piece of equipment. It is only in the case of faults to the zone that the differential current (the difference between input and output currents) is high [21]. Differential protection schemes are mainly used for protection against phase-to-phase faults and phase-to-earth faults. Typically, the operating coil carries no current as they are balanced on both of the PT's sides. When the fault occurs in the PT windings, the balance is disturbed. The differential relay's operating coils carry a current corresponding to the different current among the transformers' two sides. Thus, the relay trips the main circuit breakers on both sides of the PT. The first sub algorithm is mainly used to distinguish between the normal and external fault conditions in a PT.
## 4 Materials and Methods
### Model Selection Approach
#### 4.3.1 Lasso Regression  
While the baseline approach relied on the linear regression model implementing the L2 regularization norm, due to the large number \(n\)-gram features present in the feature set, it is possible that a model implementing L1 regularization can yield better results [108]. While L2 regularization introduces the squared magnitude of coefficients as a penalty function, the L1 uses the absolute value--making it more robust to outliers. As a result, the L1 regularization can impact and potentially eliminate some less important features from the numerous \(n\)-grams used in the feature set. A regression model that uses the L1 regularization norm is also known as Lasso regression (Least Absolute Shrinkage and Selection **O**perator).  
The difference between these two models can be mathematically formulated in the following way. If we have \(m\) features and \(n\) observations in our data, with \(x_{i,j}\), we can mark the \(j\)-th feature of the \(i\)-th observation. Next, if we use \(w\) to represent the weight of our function, for the \(i\)-th feature of the observation that we are interested in predicting (which we mark with \(y\)), the basic regression formula can be written as Equation (1). For the L2 regularization with the regularization parameter \(\alpha e[0,1]\) that we multiply with the sum of squared weights \(w\), the regression formula takes the form of Equation (2). Finally, if we take the absolute value of weights instead of squaring them, we obtain the L1 regularization as shown in Equation (3).  
\[\sum_{i=1}^{n}(y_{i}-\sum_{j=1}^{m}x_{i,j}w_{j})^{2} \tag{1}\]  
\[\sum_{i=1}^{n}(y_{i}-\sum_{j=1}^{m}x_{i,j}w_{j})^{2}+\alpha\sum_{j=1}^{m}w_{j} ^{2} \tag{2}\]  
\[\sum_{i=1}^{n}(y_{i}-\sum_{j=1}^{m}x_{i,j}w_{j})^{2}+\alpha\sum_{j=1}^{m}|w_{j}| \tag{3}\]
## 2 Signal and Frequency Offset Model
### DFT and DTFT of Complex Exponential Signals  
First, the \(M\)-point DFT of \(x(n)\) can be obtained by  
\[X(k)=\sum_{n=0}^{M-1}x(n)e^{-j\frac{2\pi\text{mk}}{M}}. \tag{3}\]  
It is assumed that \(M\)-point data are generated by padding an integral multiple of \(M-N\) zeros after \(N\) signal samples; for example, \(M=3N\) means that \(N\) signal samples are padded with \(2N\) zeros. (3) can be written as  
\[X(k)=\sum_{n=0}^{N-1}Ae^{j\phi}e^{j2\pi\frac{f}{f}n}e^{-j\frac{2\pi\text{mk}}{ M}}. \tag{4}\]  
In (3), noise is not considered for simplifying the estimation process. The spectral line can be found by locating the maximum of \(|X(k)|\) and is denoted as \(k_{m}\), \(k_{m}\in[1,M]\). The corresponding frequency estimation is \(k_{m}\Delta f\). \(\Delta f=f_{s}/M\) is the frequency resolution of the spectrum of the input complex exponential signal. Since the actual spectral peak usually lies between spectral lines \(f=(k_{m}\pm\delta)\Delta f\), where \(\delta\in[-0.5,0.5]\) is the residual frequency offset between the true frequency and the coarse frequency estimate \(k_{m}\Delta f\), a smaller frequency resolution is helpful for obtaining a higher frequency estimation accuracy. Thus, frequency resolution has an important impact on the accuracy of frequency estimation. To reduce this influence, it is necessary to perform a second stage to refine the frequency estimation process. This is an efficient method for performing fine estimation using DTFT samples around the DFT peak \(k_{m}\). DTFT samples are usually regarded as interpolations between DFT samples. The DTFT samples around \(k_{m}\) can be expressed as  
\[X(k_{m}+p)=\sum_{n=0}^{N-1}Ae^{j\phi}e^{j2\pi\frac{f}{f_{s}}n}e^{-j\frac{2 \pi\text{mk}}{M}}\mid_{k=k_{m}+p,f=(k_{m}+\delta)\Delta f}. \tag{5}\]  
As shown in Figure 1, the two neighboring green lines of \(k_{m}\) are the DTFT samples used to assist with the estimation of \(\delta\). \(-1<p<1\) is the offset between the assistant DTFT samples and the coarse frequency estimate \(k_{m}\Delta f\). The meanings of \(\Delta f\), \(\delta\), \(k_{m}\), and \(p\), and their relationships are also shown in Figure 1.  
After some rearrangement, (5) can be simplified as  
\[X(k_{m}+p)=Ae^{i\phi}e^{j\pi\frac{N-1}{M}(\delta-p)}\frac{\sin(\pi(\delta-p))}{ \sin(\frac{\pi}{M}(\delta-p))}. \tag{6}\]
## 3 Problem Statement and Formulations
### Graphical Formulation
#### 3.2.4 Arcs  
In graph \(G\), arc \((i,j)\) represents a potential truck trip beginning at node \(i\) and concluding at node \(j\). This truck trip has two possible states: either the truck travels with an empty container or it travels without any container. As shown in Figure 2, the arcs leading from pickup to delivery requests/depots represent truck trips with an empty container, while all the other arcs indicate truck trips without a container. If arcs encounter the following situations, they correspond to infeasible truck trips and can be deleted from the graph. Firstly, since the retrieval and deposition of empty containers at the depot are explicitly modeled as visits to nodes in \(N_{E}^{P}\), \(N_{IF}^{P}\), and \(N_{IF}^{D}\), respectively, arcs connecting two pickup requests/depots, as well as those connecting two delivery requests/depots, are not feasible. Secondly, arcs linking pickup depots with delivery depots are also not feasible. Thirdly, arc \((i,j)\) is infeasible if time windows of node \(i\) and node \(j\) are conflicting. Each feasible arc \((i,j)\) has two attributes, namely the travel time \(T_{ij}\) and travel cost \(C_{ij}\) from node \(i\) to node \(j\).  
In summary, the studied problem is perfectly represented by the directed graph. The purpose of the problem consists of finding feasible routes fulfilling all inland container transportation requests with minimum total cost. A feasible route starts at the source node, visits some nodes of pickup/delivery requests and depots, and finally end at the sink node. Note that if an arc \((i,j),i\in N_{IF}^{P},j\in N_{IF}^{D}\) is traversed by a truck, the depot's inventory will increase by one empty container. Contrarily, if an arc \((i,j),i\in N_{IF}^{P}\cup N_{E}^{P},j\in N_{OF}\cup N_{OE}\) is traversed by a truck, the depot's inventory will decrease by one empty container. Therefore, the critical issue of the problem is to decide whether and when to go through such arcs while avoiding out-of-stock or overstock occurrences in the depot.
## 2 Materials and Methods
### Data Acquisition  
We used two Kinect V2 sensors that were mounted behind the evaluators' seat (Figure 1a) to automatically track the touches' hand and the evaluators' face positions. We collected their data on 3D positions of each joint of the touchers (including the center of their hands) and the 3D head position of the evaluators. Simultaneously, we also recorded the timing of the mouse clicks by the evaluators that signaled the touchers to stop getting their hand any closer to evaluators' face. In order to calculate the minimum comfortable distance between the touchers' hand and the evaluators' face, we subtracted the size of the touchers' hand (measured prior to the commencement of the experiment) from the average Japanese face size (i.e., 9.0 cm for female and 10.0 cm for males) [25].  
[PERSON] et al. [26] suggested that Kinect sensors are suitable for applications in which the joint position accuracy does not exceed a few cm. However, in their study, they focused on the accuracy of a whole body tracking algorithm in a upper-limb rehabilitation scenario. Our experiment differed from their setting in which we considered the interaction space between the touchers' hand and the evaluators' face. Therefore, we employed (unlike [PERSON] et al. [26]) two Kinect sensors for data acquisition, thereby bypassing the use of markers on touchers' hand and the evaluators' face to prevent their potential confounding effect on participants' pre-touch feelings. To increase the accuracy of the detected joint positions, we further calibrated the relative positions of these two sensors and used their absolute positions to integrate their joint positions data. In an event that one of these Kinect sensors failed to estimate the joint positions, we used the other sensor if its estimates were continuous and stable.
## 4 Results and Discussion
The significant difference comes from the computational time: PSO identified the optimal value 50 times faster than the grid search. The MSE's in scenario B2 are smaller in the experiments with 51 stations than those in the experiments with only ten main stations. In scenario B1, when taking into account only the results for the main stations, they were as follows: 30.3704 vs. 29.22245 for IDW*, 30.37178 vs. 29.22245 for OIDW and 31.022 vs. 30.876 (Table 7, rows 3 and 6). This was to be expected because the algorithms received far more information in the scenario B2 than in B1. On the other hand, from the same reason, \(\beta\) st.dev in OIDW increased, while remaining very small. All the results from Table 7 that summarizes the average MSE in all experiments show similar performances of IDW*, OIDW and KG. None of the algorithms were the best in all situations, confirming the literature findings [39]. Therefore, one can remark that OIDW yields competitive results in terms of MSE's of estimated values when compared to KG. This is important because it proves OIDW to be a reliable method.
## 4 Case Study
### Results and Analysis  
It focuses on a scenario of the open circuit failure of a node as the initial failure, that is, the initial failure is the physical equipment failure in the CEPR-CPS. The multi-agent method [11] is selected as a control method. Comparison diagram of CCF initiated from a node failure in the PN and in the CN are shown in Figures 5 and 6, respectively.  
According to Figures 5 and 6, the PCMD proposed in this paper can block the occurrence of CCF in the CEPR-CPS compared with the existing multi-agent method. Meanwhile, the conclusion that the convergence of PCMD could be guaranteed by the parameters adjustments is verified. The conclusion that a local optimal PCP can be constructed under the comparability condition among a fitted \(Q\) function and a fitted policy function is also verified. The PCMD is better than the multi-agent method in terms of reducing the number of failure nodes and avoiding the state space explosion problem. So, an existing problem of the multi-agent method is the curse of dimensionality (feature dimension), and the multi-agent method is not suitable for large-scale networks due to its poor scalability. The PCMD overcome the curse of dimensionality and can be extended to large-scale networks.  
The PCMD is compared with the other two kinds of methods: theoretical analysis methods and physical experimental methods in terms of time, cost, reliability, and accuracy. The specific comparison results are shown in Table 3. It can be shown that the PCMD is better than theoretical analysis methods and physical experimental methods in terms of time and cost.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Case** & & \multicolumn{6}{c}{**Parameters**} \\ \hline  & _stopTrainV_ & \(cl\_RB\) & \(V_{ni}\) & \(r_{1,ini,1}\) & \(N_{3}.var\) & \(T_{s}\) & \(\gamma\) & \(\alpha\) \\  & \(20.0\times 10^{17}\) & False & \(0.2\) & \(10.0\) & \(4.5\) & \(1.0\times 10^{-6}\) & \(0.96\) & \(5.0\times 10^{-5}\) \\  & \(r_{1,ini,2}\) & \(w_{ni}\) & \(I_{ni}\) & \(r_{2,ini,1}\) & \(N_{3}.decR\) & \(T_{f}\) & \(\delta\) & \(N_{2}\) \\  & \(-50\) & \(1.0\) & \(0.2\) & \(0.0\) & \(5.0\times 10^{-5}\) & \(3.0\) & \(3.0\times 10^{-6}\) & \(100.0\) \\  & \(r_{2,ini,2}\) & \(c_{fnf}\) & \(r_{3,fnf,1}\) & \(r_{3,fnf,2}\) & \(cp_{c}\) & \(r_{4,c\_1}\) & \(r_{4,c,2}\) & \(r_{nf,1}\) \\  & \(-50\) & \(0.2\) & \(0.0\) & \(-50\) & \(20.0\) & \(10.0\) & \(-50.0\) & \(10.0\) \\  & N\({}_{1}\) & \(N_{5}\) & \(N_{4}\) & \(gr.Th\) & \(r_{n\prime 2}\) & \(isD\) & & \\  & \(30.0\times 10^{6}\) & \(1024.0\) & \(3.0\times 10^{6}\) & \(1.0\) & \(10.0\) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used in the PCP construction method based on deep deterministic policy gradient idea (PCMD) for the CEPR-CPS.  
Figure 5: Comparison diagram of CCF initiated from a node failure in the PN.  
Figure 6: Comparison diagram of CCF initiated from a node failure in the CN.
# Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks
## 6 Performance Analysis
### Throughput  
Network throughput is defined as the average successful data rate over the communications link. The maximum throughput can be achieved in the network when exactly one node transmits the data to the destination. The data communication during the shared slot in the superframe is through the slotted CSMA/CA mechanism. The throughput of the IEEE 802.15.4e network can be calculated as:  
\[Throughput=L_{pkt}P_{success}PHY_{rate} \tag{22}\]  
where \(L_{pkt}\), \(P_{success}\), \(PHY_{rate}\) are the payload duration, the probability of successful packet transmission and the physical data rate of IEEE 802.15.4e, respectively.
## 1 Introduction
[PERSON] et al. (2018) utilized 3D electrical capacitance tomography (ECT) measurement data to establish and train artificial neural network models, with experimental results indicating that the fuzzy pattern recognition accuracy of the horizontal case ranged from 85% to 99%, and in the vertical case, the accuracy fluctuated from 65% to above, with the average correct recognition rate for fuzzy reasoning based on raw ECT data without images being approximately 90%. [PERSON] et al. (2019) proposed a method for identifying flow patterns based on Gramian angular field (GAF) and densely connected network (DenseNet), with experimental results demonstrating that GAF images effectively reflected the characteristics of different flow patterns, achieving an average flow pattern recognition accuracy of 98.3%. [PERSON] et al. (2019) introduced an intrusive robust CNN flow pattern recognition method, based on flow-induced vibration (FIV) analysis, with the accuracy rate being above 90% when using different axis data to predict flow patterns. [PERSON] et al. (2019) compared and analyzed machine learning (ML) and deep learning (DL), concluding that extreme gradient boosting is the optimal model for predicting the two-phase flow states of inclined or horizontal pipelines. In conclusion, the application of intelligent learning algorithms in classifying flow patterns of gas-liquid two-phase flow has reached a notable level of accuracy, and promising advancements have also been made in flow measurement. However, current methods for combining sensors with intelligent learning models for measurement pose challenges in installation and susceptibility to signal disturbances. Similarly, the integration of images with intelligent learning models faces hurdles in achieving precise measurements solely based on a single image due to the intricate nature of the gas-liquid two-phase flow. Therefore, it is crucial to explore the feasibility of combining video with intelligent learning models for flow measurements, aiming to achieve convenient flow rate measurements through camera-based approaches. This article delves into a novel approach for flow measurements using gas-liquid two-phase flow videos, focusing on extracting pertinent information from the video via optical flow methods. It investigates the integration of this extracted data with machine learning models, aiming to enhance measurement accuracy. Through an exploration of diverse machine learning models, this study identifies and optimizes the most effective model, thereby improving measurement outcomes. This method facilitates more convenient and real-time measurements of two-phase flows, offering a fresh avenue for daily monitoring of two-phase flow rates in the oil and natural gas industry through camera-based acquisition.