esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results
### Packet Loss Rate  
In Figure 5 we investigate the packet loss rate for different pairs of erasure probabilities \([\epsilon_{1},\epsilon_{2}]\) in the case of \(K=2\) satellites. In each subfigure, similarly to what was done for the throughput in Figure 3, we fixed the erasure probability \(\epsilon_{1}\) at the first satellite, while we considered different erasure probabilities \(\epsilon_{2}\) at the second satellite. Pairs of low erasure probabilities led to low packet loss rates only in the case of relatively low channel load, and in the case of high channel loads, any pair of erasure probabilities led to a high packet loss rate. Moreover, it is very interesting to analyze Figure 5 in conjunction with Figure 3. Note that pairs of erasure probability that led to high throughput in the case of high channel load, such as \([\epsilon_{1}=0.01,\epsilon_{2}=0.9]\) in Figure 3, led to high packet loss rates. Therefore, it is important to note that high erasure probabilities, although favorable in terms of throughput at high channel loads, may lead to inefficient usage of the available resources. Another look at the packet loss rate as a function of the erasure probabilities can be seen in Figure 6, where we assumed a low channel load. In this case, the error floor effect of the on-off fading is apparent--i.e., the erasure probabilities. Very low packet loss rates are only achievable with very low erasure probabilities and reduced channel loads.  
Figure 4: Throughput surface versus \(\epsilon_{1}\), \(\epsilon_{2}\), for the case of \(K=2\) satellites and a high channel load of \(G=12\) packets per time slot.
## 3 Method
### Tracking Technique  
Detection and tracking are implemented with the algorithm YOLOv7 (2024) [34], an open-source software, consisting of an arrangement of convolutional neural networks, trained to detect objects, based on the public dataset MS COCO [35]. Images from a video are fed into it, returning bounding boxes and tracking information of persons and trains. The software is able to assign an identification number to different persons and keep them until they disappear from the scene. Bounding boxes are characterised by four vertices, defined in pixels, from where the precise location of the object can be defined. One approach to perform analysis such as location, speed, or other statistics is to declare the location of the object in a single point. One approach is to select the middle point between the two lower vertices, and others could be the centroid, or middle point between the upper vertices.  
Data delivered by YOLO are then processed to obtain specific information about the passengers or trains. A smoothing preprocessing step is advisable to reduce slight variations in the bounding box coordinates. Based on [36], a Kalman filter is applied to the position coordinates to obtain smoothed position and speed, which also allows for  
Figure 6: Calibration points on the platform using the cameras in Valparaiso Metro.  
gaps in the data to be filled with estimated data when a person leaves the frame for a few sample times.  
An important processing step consists of determining the number of passengers in different sectors of the station in real time. This is obtained by defining sectors of interest, such as platforms, rails, and crossings, in the images before running the YOLO algorithm. As the camera remains in a static position, this is conducted just once, manually or automatically, and returns the fixed pixel coordinates of all sectors. Then, during operation, a logic based on geometry and trigonometry is used to determine the presence of all detected persons in each of the sectors, which in turn allows us to keep a counting number. This is very useful to provide station managers and safety people with quick information on the accumulated number of persons and whether or not it could surpass a given threshold. Therefore, the LOS was used as an indicator to identify the congestion and conflicts on the platform or inside the train carriage. The inputs used to determine the LOS were based on the speed and the number of passengers detected. These variables were obtained from the new model defined in the Results section using video cameras (see Figure 7).
## 3 Materials and Methods
### PC-CRITIC Combined Assignment Method
The main steps are as follows:  
The maximum value method is expressed as follows:  
\[N_{i}=\frac{x_{i}-min(x_{i})}{max(x_{i})-min(x_{i})} \tag{3}\]  
The minimum value method is expressed as follows:  
\[N_{i}=\frac{max(x_{i})-x_{i}}{max(x_{i})-min(x_{i})} \tag{4}\]  
The correlation coefficient is:  
\[\rho_{XY}=\frac{\sum_{i=1}^{N}\left(X_{i}-\overline{X}\right)\left(Y_{i}- \overline{Y}\right)}{\sqrt{\sum_{i=1}^{N}\left(X_{i}-\overline{X}\right)^{2} \sqrt{\sum_{i=1}^{N}\left(Y_{i}-\overline{Y}\right)^{2}}}} \tag{5}\]  
The standard deviation of the variables is:  
\[\sigma=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(X_{i}-\overline{X}\right)^{2}} \tag{6}\]  
\[E_{j}=\sigma_{j}\sum_{i=1}^{n}\left(1-\rho_{ij}\right) \tag{7}\]  
The objective weight of the \(j\)th variable \(w_{j}\) is:  
\[w_{j}=\frac{E_{j}}{\sum_{j=1}^{n}E_{j}} \tag{8}\]Here, \(N_{i}\) represents the quantitative value after normalising the \(i\)th master control factor attribute data, and \(x_{i}\) represents the quantified value of the \(i\)th master control factor attribute data before normalization. The minimum (\(min(x_{i})\)) and maximum (\(max(x_{i})\)) values of the \(i\)th master control factor attribute data before normalization are also accounted for in the normalization process. Furthermore, \(X_{i}\) and \(Y_{i}\) denote the value of the \(i\)th evaluation point of the \(X\) and \(Y\) evaluation indicators, respectively;\(\overline{X}\) and \(\overline{Y}\) represent the average of the values taken by \(X\) and \(Y\) evaluation indicators, respectively; The number of values taken by each evaluation index is denoted as \(N\). The quantitative result (\(\sum_{i=1}^{n}\)(\(1-\rho_{ij}\))) reflecting the conflictability of the \(j\)th indicator with respect to other \(n\) primary control factors is then calculated, where \(m\) represents the total number of evaluation indicators. By integrating different assignment methods, the OOM method was employed to calculate the subjective weights (\(\alpha\)), while the CRITIC method was utilized to derive the objective weights (\(\beta\)). These two approaches follow distinct principles of weight determination: the OOM method relies on the data size information, whereas the CRITIC method considers data volatility and the correlation between variables. Due to these differing data characteristics, the final comprehensive weights were calculated by applying Equation (9), combining both subjective and objective elements to achieve a balanced evaluation. \[\omega_{i}=\frac{\alpha_{i}\beta_{i}}{\sum_{i=1}^{n}\alpha_{i}\beta_{i}} \tag{9}\]  
Here, \(\omega_{i}\) denotes the combined weight of the \(i\)th indicator.
# M-Healthcare Model: An Architecture for a Type 2 Diabetes Mellitus Mobile Application
## 4 Proposed Architecture  
### Mobile Application  
The development of the architecture (Figure 2) for the Type 2 Diabetic Mellitus Mobile Application is broadly divided into three major parts:  
* Medical Sensor and Exercise Equipment  
Figure 1: Research method.  
Medical sensors in the architecture section include supporting devices (inputs) in the form of wearable bands, glucose meters, and heart rate sensors. The exercise equipment consists of a gym cycle and a treadmill. In this section, the device is Bluetooth- and RFID-compatible. In this section, the device will work for the next stage of data acquisition before going to the transmission section.
* Transmission  
Transmission in the architecture section consists of smartphone applications and cloud storage. In this section, the smartphone application receives input data from the Medical Sensor and Exercise Equipment section, which is referred to as the data acquisition process. The processed data are stored in cloud storage, and in this part of the process, the entire process is supported by the Internet network.
* Information  
Information on the architecture is the final part (output), namely the process after the data are processed in the transmission section. The process at this stage is called \"real-time exercise data\", where the processed data can be received at the same time by the user (patient) with an Internet connection as network support.
# Laser Welding on Cerclage Wire Joining in Femur Surgery
## 3 Welding, Inspection and Mechanical Testing
### Inspection of Weld Appearance  
In the experiment, two laser parameters were adjusted including the charge voltage (V) and the pulse frequency (Hz), while the pulse width was set to be constant at 3 ms, as shown in Figure 6. The purpose was to find the upper and the lower bound of the weld appearance which indicated that the weld process was properly performed on stainless steel wire 316L. Figure 6 shows that the upper bound of charge voltage is at 110 V, while the pulse frequency is at 12 Hz. The weld appearance contained oxidation and burns due to overheating at high charge voltage, faster heat accumulation  
Figure 4: The schematic experimental setup.  
Figure 5: Cerclage wire joints with laser welded.  
at high pulse frequency adjustment, as well as the heat from the welding with inappropriate wire dimension. The charge voltage below the lower bound at 80 V resulted in the lack of fusion due to insufficient heat to melt the wire. Therefore, for acceptable range of laser parameters, the charge voltage is in the range of 80-110 V and the pulse frequency is in the range of 6-12 Hz. Notably, at the pulse frequency of 3 Hz, there were many gaps between weld beads that will cause the cavity defect, which might affect the joint strength.  
In this study, three laser parameters were chosen including charge voltage, pulse frequency, and pulse width in order to evaluate the effects of metal melting. The concept was to design the laser parameters for welding on the stainless steel wire with desired weld width and depth. This was implemented by adjusting the rank of laser parameters for the experiment with the feedback from the inspection of weld appearance. The experimental results are listed in Table 1.
# Enhancing Railway Network Safety by Reproducing Wheel-Rail Electrical Contact on a Laboratory Scale
## 2 Bench Conception  
### General Principle  
The developed setup, presented in Figure 1, consists of a reduced-scale wheel running on a 1 m full-scale rail. An electrical actuator applies a normal force on the wheel axis inducing a pressure on the contact area equivalent to that produced by a real loaded train. The linear motion axis allows the wheel carriage to move along the rail at a controlled, scaled-down speed.  
Figure 1: Schematic diagram of the test bench.  
The bench was designed in order to be the most representative as possible of the devices most likely to be involved in deshunting problems. Electrically, the aim was to simulate the most extensively used track circuit on the French rail network (FRN).  
The sizing of the reduced scale wheel (hereinafter referred to as the roller), the load applied and the speed of the displacement were estimated as explained in Section 2.2.  
Our major concern was to reproduce the physical behavior of the contact interface. Consequently, we had to carefully study the impact of the downscaling on the dimensions of the setup.
## 3 Analysis of Dynamic Performance  
### Dynamic Analysis Based on Lumped Mass Method  
The SBG transmission dynamic equation (Equation (6)) is built based on the 8-DOF dynamic model with the lumped mass method (Figure 2) [39]. The 2-DOF dynamic model was established by simplifying the 8-DOF dynamic model, and the dynamic equation is built as Equation (7). Finally, the RMA is counted by applying the MS excitation, LTE excitation and MI excitation to the dynamic model. The MS and the LTE are counted using the LTCA method. The MI is counted with the MI theory [40].  
Figure 1: The perfect surface.  
Figure 2: Dynamic model of the SBG transmission.  
\[\left\{\begin{array}{l}m_{1}\ddot{X}_{1}+c_{1x}\dot{X}_{1}+k_{1x}X_{1}=-F_{x}\\ m_{1}\ddot{Y}_{1}+c_{1y}\dot{Y}_{1}+k_{1y}Y_{1}=-F_{y}\\ m_{1}\ddot{Z}_{1}+c_{1z}\dot{Z}_{1}+k_{1z}Z_{1}=-F_{z}\\ J_{1}\ddot{\theta}_{1}=T_{1}-F_{n}r_{1}+F_{s}r_{1}\\ m_{2}\ddot{X}_{2}+c_{2x}\dot{X}_{2}+k_{2x}X_{2}=F_{x}\\ m_{2}\dot{Y}_{2}+c_{2y}\dot{Y}_{2}+k_{2y}Y_{2}=F_{y}\\ m_{2}\ddot{Z}_{2}+c_{2z}\dot{Z}_{2}+k_{2z}Z_{2}=F_{z}\\ J_{2}\ddot{\theta}_{2}=-T_{2}+F_{n}r_{2}-F_{s}r_{2}\end{array}\right. \tag{6}\]  
where \(F_{s}\) represents the impact force, and it is obtained based on the impact theory.  
\(F_{n}\) represents the normal load, and  
\(k_{n}\) represents the meshing stiffness. The LTE and MS are obtained by LTCA.  
\(\lambda_{n}\) represents the normal relative displacement of the gear meshing point.  
\(F_{x}\), \(F_{y}\), \(F_{z}\) represent the components of a normal load \(F_{n}\) along the \(x\), \(y\) and \(z\) axes, respectively.  
\(k_{1x}\), \(k_{1y}\) and \(k_{1z}\) represent the supporting stiffness of the driving gear along \(x\), \(y\) and \(z\) axes, respectively.  
\(k_{2x}\), \(k_{2y}\) and \(k_{2z}\) represent the supporting stiffness of the driven gear along \(x\), \(y\) and \(z\) axes, respectively.  
\(c_{1x}\), \(c_{1y}\) and \(c_{1z}\) represent the damping of the driving gear along \(x\), \(y\) and \(z\) axes, respectively.  
\(c_{2x}\), \(c_{2y}\) and \(c_{2z}\) represent the damping of the driven gear along \(x\), \(y\) and \(z\) axes, respectively.  
\(c_{n}\) represents the meshing damping.  
\(T_{1}\) and \(T_{2}\) represent the torque of the driving gear and driven gear, respectively.  
\(M_{1}\) and \(M_{2}\) represent the mass of the driving gear and driven gear, respectively.  
\[\left\{\begin{array}{l}I_{1x}\theta_{1x}=T_{1}-F_{n}r_{1}+F_{s}r_{1}\\ I_{2y}\theta_{2y}=-T_{2}+F_{m}r_{2}-F_{s}r_{1}\end{array}\right. \tag{7}\]  
where \(I\) represents the moment of inertia.
# Error Mitigation Methods for FSM Using Triple Modular Redundancy
## 3 Triple Modular Redundancy in FSMs
### Model 3--Triplicated Transition Block \(\delta\) and Independently Registered  
In the case of Model 3, the TMR circuit appears primarily on the transition blocks \(\delta\) and each state is latched in a separate state register DFF. The situation is shown in Figure 11.  
1. If the error occurs in the output block \(\lambda\), it will be mitigated due to voter on the output.
2. If the error is injected into one of the three transition blocks \(\delta\), the voter at the output of this block will correct the value based on the data from the other two next state logic blocks and each FSM will move to the correct state.
3. Conversely, with an error on the state register DFF, two of the three transition block \(\delta\) still operate correctly, which will cause the third FSM to return to the correct cycle as well. The output is subject to voting, so this will also be appropriate.  
The output block \(\lambda\) can, of course, be replaced by a single one--analogous to the voter of the state register DFF in Model 2.
## 4 Experiment and Result Analysis
### Rotational Angle Comparison Experiment  
According to Equation (7), the distances d1 and d2 are critical parameters for angle measurement. Therefore, they need to be calibrated before angle measurement. Calibration was performed using the angle measurement result of the Renishaw interferometer, and the final calibration value of d1 was 1.750 mm, and d2 was 1.910 mm. In this angle measurement experiment, the measuring mirror M2 of DPMI and the measuring reflector of the Renishaw interferometer were mounted back to back on an M-038.DG1 stage. As the inherent rotation angle of most linear stages is within 0.01\({}^{\circ}\), the M-038.DG1 stage drove the two measuring reflectors to rotate from 0\({}^{\circ}\) to 0.01\({}^{\circ}\) with a step increment of 0.0001\({}^{\circ}\), and the rotational angle  
Figure 4: Experimental results of differential displacement stability and environment parameter drift within 100 min: (**a**) displacement; (**b**) air temperature; (**c**) air pressure; (**d**) humidity.  
was measured simultaneously via DPMI and Renishaw interferometer. The experiment was performed twice, and the stage was placed vertically to generate pitch motion and was placed horizontally to generate yaw motion.  
The experimental results of pitch and yaw angles are shown in Figure 5 and Table 1. In Figure 5, the red dots are the angle measurement results of DPMI, the blue dots are the angle measurement results of the Renishaw interferometer shifted by 0.001\({}^{\circ}\) for clear display, and the yellow dots are the deviations of the measurement results of the two interferometers. The experimental results are summarized in Table 1; for pitch angle measurement, the maximum deviation was \(-4.74\times 10^{-5}\), the average deviation was \(-5.71\times 10^{-6}\), and the standard deviation was \(1.68\times 10^{-5}\). For yaw angle measurement, the maximum deviation was \(4.96\times 10^{-5}\), the average deviation was \(9.71\times 10^{-6}\), and the standard deviation was \(1.86\times 10^{-5}\). The results of comparison experiments showed that the proposed DPMI can accurately measure the rotational angle and is suitable for compensating for the displacement measurement of linear stages.
## 4 Determination of Full Anchor Cable Support Parameters
### Simulation Result Analysis
#### 4.2.2 Surrounding Rock Displacement Analysis  
As seen in Figure 11, the vertical displacement of the roadway floor is small. The unit in the figure is in m. However, the vertical displacement of the roadway roof is substantial. The largest roadway displacement is on the roof surface. By comparing the maximum displacement of the roadway roof surface, we can derive the control effects of different support schemes on the roof displacement of the roadway.  
From Figure 11A-I, the displacement of the roadway roof was 189.4, 189.8, 194.6, 182.5, 190.6, 187.4, 187.4, 185, and 181.8 mm, respectively. Figure 11A-C,E had relatively large rock displacements, while the other schemes had relatively small displacements and better control of the roadway roof.  
Figure 10: Vertical stress cloud maps of the roadway under different schemes.  
Figure 11: Cloud maps of vertical displacement of roadway under different schemes.
## 3 Proposed SLM PAPR Scheme
### Derived General Form of SLM in the Time Domain  
From Equation (4), according to the circular convolution property, the \(n\)th element of candidate signal vectors \(\mathbf{x}^{(u)}\) can be given as follows:  
\[x^{(u)}\ (n)\ =\ \sum_{m\ =\ 0}^{N-1}p^{(u)}(m)x((n-m))_{N},\ 0\leq n\leq N-1 \tag{11}\]  
where \(p^{(u)}(m)\) is the \(m\)th element of \(\mathbf{p}^{(u)}\ =\ \mathbf{F}\mathbf{P}^{(u)}\).  
By comparing Equations (7) and (8), it can be seen that the generated time domain candidate signals of the SLM scheme is equivalent to passing the original signal through a tap-delayed wireless channel. Figure 1 shows the equivalent structure in generating candidate signals of the SLM scheme in the time domain. As shown in Figure 1, the parameter of the related channel in the SLM scheme depends on the used phase rotation vector \(\mathbf{P}^{(u)}\) and can be given as  
\[p^{(u)}\ (n)\ =\ \sum_{l\ =\ 0}^{N-1}p^{(u)}(l)\delta(n-l),\ 0\leq n\leq N-1 \tag{12}\]where \(\delta(\cdot)\) denotes the Dirac-delta function.  
It should be pointed out that the wireless channel parameter characterized by Equation (12) does not exist in most practical cases due to its length \(N\) if \(\mathbf{p}^{(u)}~{}=~{}[p^{(u)}(0),p^{(u)}(1),\cdots,p^{(u)}(N-1)]^{\mathrm{T}}\) has relatively small support, i.e., \(p^{(u)}(n)~{}=~{}0\) for a certain small \(n\). Let the support of \(\mathbf{p}^{(u)}\) be denoted as \(L_{u}\) and \(\mathbf{p}^{(u)^{\prime}}~{}=~{}[p^{(u)}(0),p^{(u)}(1),\cdots,p^{(u)}(L_{u}-1) ]^{\mathrm{T}}\). If \(L_{u}+L-1\) is smaller than the length of CP, the general form of the SLM scheme in the time domain can be described by Figure 2.  
As shown in Figure 2, the final equivalent channel of the SLM scheme can be given as  
\[\mathbf{h}_{eq}~{}=~{}\mathbf{p}^{(u)^{\prime}}\otimes\mathbf{h} \tag{13}\]  
where \(\otimes\) is the circular convolution operator. The channel frequency response of \(\mathbf{h}_{eq}\) can be given as  
\[\mathbf{H}_{eq}~{}=~{}\mathbf{Q}^{(u)}\mathbf{H} \tag{14}\]  
where \(\mathbf{H}~{}=~{}[H(0),H(1),\ldots,H(N-1)]^{\mathrm{T}}\) is the original channel frequency response vector.  
According to Equation (13), when the support of \(\mathbf{h}_{eq}\) is smaller than the number of pilot subcarriers in every OFDM symbol, \(\mathbf{h}_{eq}\) can be estimated in the same way as \(\mathbf{h}\). In the following, it will be seen that the general form of the SLM scheme in the time domain can be used to realize low complexity without SI transmission.
## 3 Deep-Learning-Based Channel Estimation Methods
### Overview of Neural Networks
#### 3.1.1 Dnn  
A basic structure of DNN is shown in Figure 7, which consists of one input layer, \(L\) hidden layers, and one output layer, with several neurons in each layer and full connectivity  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Advantages** & **Disadvantages** \\ \hline Pilot-based channel estimation & Low computational complexity & Waste of spectrum resources \\  & Easy to implement & Low transmission efficiency \\  & Pilot can improve estimation performance & \\ \hline  & Efficient use of spectrum resources & High computational complexity \\  & Able to adaptively track the dynamic changes of the channel & Slow convergence speed \\  & & Presence of phase ambiguity \\  & & Long observation time \\ \hline  & Relatively high bandwidth efficiency & Relatively large amount of calculation \\  & Able to solve phase ambiguity & Relatively difficult to implement \\  & Relatively fast convergence speed & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of advantages and disadvantages of conventional channel estimation methods.  
between layers. A DNN can be considered a neural network that contains numerous hidden layers, and the purpose of adding multiple hidden layers is to enhance the learning and mapping capabilities of the network. Additionally, to introduce the nonlinearity, activation functions like the sigmoid, rectified linear unit (ReLU), or hyperbolic tangent (Tanh) are applied after the outputs of each layer.
## 2 State of Art of Fractances' Implementation
### Equivalent Active Circuits  
An alternative solution to the equivalent passive circuits is the synthesis of fractances by means of networks involving active components. Most of them are based on operational amplifiers [29]. Some implementations also try to use the non-linear behavior of transistors characteristic in order to reproduce a fractional behavior. However, so far these solutions are not as popular as the ones based on passive networks.