esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Microstructure of Ferrite and Martensite
#### 3.2.3 Texture and Crystallographic Orientation  
Figure 7 shows a comparison between the macroscopic textures of the orientation distribution function (ODF) map on the RD-ND plane and the standard ODF. The results show that the main texture types contained throughout the thickness of the samples are {223}<110>, {113}<110>, {112}<110>, {332}<113>, and {001}<110>. Figure 8 shows the ODF and inverse pole figure (IPF) maps based on EBSD data at typical locations on the RD-ND plane. The quantitative statistics for texture types and contents at three positions on the RD-ND plane are listed in Table 3. It is discovered that the main types of micro-texture structures are basically the same as the macrotexture structures of Figure 7.  
According to the results of quantitative statistics (Table 3), it is observed that the content of ([223]\(<\)110\(>\), [113]\(<\)110\(>\), and [112]\(<\)110\(>\)) of \(\alpha\)-fiber texture gradually increases from the top surface to the bottom surface, the content of [001]\(<\)110\(>\) and [110]\(<\)110\(>\) of austenite recrystallization phase transformation texture remain relatively low and have nearly the same values at three typical positions, the content of the [332]\(<\)113\(>\) texture at the core position is significantly higher than those at both surfaces, while the content of [332]\(<\)113\(>\) texture at the top and bottom surfaces have almost the same values.
## 3 Results  
### Model Validation  
#### 3.1.1 Physical model experiment verification  
According to the similarity criterion, the experimental data are converted into prototype data and compared with the numerical simulation results. The measured data and simulation results are analyzed by the root mean squared error (RMSE) and coefficient of determination (R\({}^{2}\)). The closer the coefficient of determination (R\({}^{2}\)) is to 1, the better the fit between the measured values and simulation values.  
A comparison between the numerical simulation values and experimentally measured values (converted into prototype values) of the load is shown in Figure 7. When the water blocking ratio is 0.8264, the maximum relative error of the load is less than 13%, the average relative error is 7%, the RMSE is less than 12 kN, and the R\({}^{2}\) is greater than 0.96. Under the condition of a water blocking ratio of 0.716, the maximum relative error is less than 11%, the average relative error is 6.27%, the RMSE is less than 4.3 kN, and the R\({}^{2}\) is greater than 0.94. The numerical simulation results fit the experimentally measured data well, which means that the numerical simulation results are dependable. The simulation model can predict the load, and the following research results are accurate and reliable.
# Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data
## 3 Approach
### Core Components  
#### 3.3.1 Configuration and Input Feed  
Before the system can perform in an applicable way several configurations have to be set up accordingly. Due to the high amount of different settings a configuration file was built up to maintain them easily at a single place. It is a simple JSON-file with a single (root) node. This file format was picked to be independent about the development platform, is easy to adapt and widely used. On start-up, the system tries to read and parse the configuration file and initializes itself. In Table 1, each of these settings are described in more detail and are subsequently used in order to create a configuration file in JSON format. It mainly consists of values for the detection framework, which input feed is used and how the input feed should be processed. Furthermore, there are some more flags available to show additional information on the screen.  
As described in Table 1, the configuration file can be adapted for usage of local (offline) or remote (online) video sources. On the one hand, local video sources are just video files, e.g., in a mp4 file format, at a given path which are fed to the system. This fact makes debugging and offline testing of the system feasible. On the other hand, remote video sources are only web addresses. The system requests the site and tries to parse the image. This option is usually used for analysing live traffic on certain highways or roads for production usage. For both local and remote video sources the request interval behaves the same, hence restricts the system to only load new frames at a specified frequency.
## 5 Model Simulation and Validation by Experiments
### Comparison of Simulation and Experiment Results  
On the premise of no steering assist power, it can be known from Newton's Third Law that the steering wheel torque and equivalent resistance torque on the steering column are equal and opposite in direction when turning the steering wheel at a constant speed, as shown in Equation (18):  
\[T_{s}=-T_{r}=-\frac{M_{r}}{i_{\omega 0}\cdot\eta_{+}} \tag{18}\]  
where \(T_{s}\) is the steering wheel torque and \(T_{r}\) is the equivalent resistance torque on the steering column.  
Thus, the measured steering wheel torque and the simulated equivalent resistance torque on the steering column can be contrasted to verify the accuracy of the low-speed steering resistance torque model. The comparative results are shown in Figure 10. For the pivot steering experiment, the driver turned the measurement steering wheel with a constant speed when the vehicle was under a static state, causing the steering wheel to reach the limit positions on two sides. As a result, there are two mutations on the curve in Figure 10a. For the low-speed steering experiments, the driver turned the measurement steering wheel when the vehicle maintained a state of uniform linear motion at different speeds (1 km/h, 2 km/h, 3 km/h, 4 km/h, 5 km/h, 10 km/h, 15 km/h and 20 km/h). The steering wheel did not reach the limit position on either side for safety. Therefore, it is round at the corner of the curves from Figure 10b-h. In addition, the decreasing height of these curve rings from Figure 10a-h, which means the steering resistance torque decreases with the vehicle speed. It can be seen from the figures that the simulation results are consistent with the experimental results, indicating that the established model of the steering resistance torque has a high accuracy.  
Figure 9: The experimental scene.  
Figure 10: Simulation and experimental results of the steering wheel torque at different speeds.  
Furthermore, the surface plot of the low-speed steering resistance torque with the vehicle speed and steering wheel angle can be finally drawn based on the simulation results, as shown in Figure 11. It can be clearly seen from the figure that the change laws of the low-speed steering resistance torque are as follows:  
1. The low-speed steering resistance torque increased with the steering wheel angle at a certain speed. When the vehicle speed was below 10 km/h, the steering resistance torque almost changed linearly with the steering wheel angle. When the vehicle speed was between 10 km/h and 20 km/h, the steering resistance torque varied nonlinearly with the steering wheel angle;  
2. The low-speed steering resistance torque decreased with the vehicle speed at a certain steering wheel angle. When the vehicle speed was in the range of 0 km/h to 5 km/h, the steering resistance torque decreased rapidly with the vehicle speed. When the vehicle speed exceeded 5 km/h, the change of the steering resistance torque was not obvious.
# Construction of a Geological Fault Corpus and Named Entity Recognition
## 3 Fault Corpus Construction  
### Data Preprocessing and Labelling  
The data utilized in this study are derived from GeoCloud result reports, a platform that integrates the extensive geological survey data formed by China's national-level geological work since the establishment of the country. The identification of fault entities is achieved by the selection of six typical fault investigation reports, all of which originate from basic geological survey projects issued by the China Geological Survey of the Ministry of Natural Resources. These reports contain information regarding faults with varying geological backgrounds, including the Bayan Kra Massif, the North China Land Massif, and the Hong Kong-Zhuhai-Macau Bay Area.  
The methodology of this study is as follows: firstly, the original fault survey report is OCR recognized by ABBYY FineReader, and the initial text library is formed by manual sampling and checking to ensure the accuracy of text recognition. Secondly, the text of the reports is spliced in an orderly manner through Python3.7 to create a uniformly encoded.txt text. Subsequently, the contents that may affect the quality of data annotation are eliminated, such as pictures, tables, special characters, and other non-text information in the report. Finally, some redundant formatting contents are removed to complete the preprocessing of the data. The process is illustrated in Figure 1a.  
In order to ensure the accuracy of the annotation, a combination of manual and computer software is used, and the annotation process is shown in Figure 1b. Firstly, two professional researchers carry out manual annotation, and the annotation results are proof-read and audited by subject experts, and the results are qualified for Doccano1.8.4 software annotation. Fault semantic information annotation is unified using <BIO> annotation, the first Chinese character of the fault entity field is labelled with 'B-label', the other Chinese characters of the fault entity field except the first character are labelled with 'I-label', and the non-fault entity field is labelled with 'O-label', the annotation process is based on the unit of the sentence, under the premise of ensuring the basic integrity of the semantic information of the fault entity, the smallest semantic unit of the fault entity is annotated, and the sample annotation is shown in Figure 2.  
Figure 1: Flowchart of fault corpus collection and annotation.
## 2 Photovoltaic Modeling and Problem Formulation
### Two Commonly Used Objective Functions  
The main objective for solar cell parameter extraction is to find a set of parameter values to minimize the overall errors between the simulated current \(I_{sim}\) and measured current \(l\) under the same terminal voltage \(V\). In this paper, the sum of square errors (SSE) is chosen as the objective function and the optimization goal is set to minimize Equation (4) with respect to the current error function\(f(X)\). For ease of comparison with the reported results, the root mean square error (RMSE) is also calculated with Equation (5). It is evident  
Figure 1: Equivalent circuit models of a real solar cell: (**a**) SDM, (**b**) DDM and (**c**) TDM.  
from Equations (4) and (5) that the smaller the SSE or RMSE values, the more accurate the parameter values extracted from the measured \(I\)-\(V\) data of the solar cells/modules.  
\[F(X)=SSE(X)=\min_{X\in[\text{LB,UB}]}\sum_{j=1}^{N}f(X)^{2}=\min_{X\in[\text{LB,UB}]}\sum_{j=1}^{N}[I_{sim}(X)-I]^{2} \tag{4}\]  
\[RMSE=\sqrt{F(X)/N} \tag{5}\]  
where the model parameter vector \(X\) is constrained within a lower bound (LB) and an upper bound (UB) and \(N\) is the number of measured \(I\)-\(V\) data points. To simplify the calculation of \(I_{sim}\), the most commonly used implicit exponential function (IEF) method expresses/replaces it with the measured current \(I\). By the direct application of Equations (1)-(3), it can be formulated as:  
\[I_{sim}(X)=I_{ph}-\sum_{i=1}^{N_{d}}I_{0i}\bigg{[}\exp\bigg{(}\frac{V+IR_{s}}{ n_{i}V_{th}}\bigg{)}-1\bigg{]}-\frac{V+IR_{s}}{R_{sh}} \tag{6}\]  
where \(N_{d}\) denotes the number of parallel diodes in Figure 1a-c and can be seen as a pointer mapping to SDM when \(N_{d}\) = 1, DDM when \(N_{d}\) = 2 and TDM when \(N_{d}\) = 3, respectively.  
It is clear from Equation (6) that \(I_{sim}\) is characterized by the fact that the measured current \(I\) and voltage \(V\) appear simultaneously. This implicit expression increases the complexity and difficulty of parameter extraction, thereby making Equation (6) less accurate than the Lambert \(W\) function (LWF)-based approximate explicit expression [3; 4]:  
\[I_{sim}(X)=\frac{I_{ph}+\sum_{i=1}^{N_{d}}I_{0i}-V/R_{sh}}{1+R_{s}/R_{sh}}- \frac{V_{th}}{R_{s}}\sum_{i=1}^{N_{d}}n_{i}W_{0}(SU_{i}) \tag{7}\]  
where \(W_{0}\) is the principal branch of Lambert \(W\) function and  
\[SU_{i}=\frac{I_{0i}R_{s}}{n_{i}V_{th}(1+R_{s}/R_{sh})}\exp\bigg{[}\frac{R_{s}I _{ph}+R_{s}I_{0i}+V}{n_{i}V_{th}(1+R_{s}/R_{sh})}\bigg{]} \tag{8}\]
## 3 Results
### Motion Trajectory Analysis
#### 3.1.2 Further Conclusion  
According to the conclusions above, we can further deduce that if the electron's initial energy is increased or the laser pulse amplitude is reduced, there should be a critical condition where the electron's velocity at the end of the first phase just decreases to 0. That is, when the radial velocity of the electron decreases to 0, it will just cross the central axis of the laser pulse. After crossing the central axis, the electron begins to interact with the falling edge of the laser pulse, and the electron experiences the negative \(z\)-axis ponderomotive force, causing its velocity to increase from 0 in the original direction, thus directly transitioning from the first phase to the fourth phase. If the initial energy exceeds this critical value, the velocity will behave similarly, transitioning directly from the first phase to the fourth phase, but the electron's velocity will not drop to 0.  
Through testing, it was found that when the normalized initial energy of the electron is increased to \(gm_{0}=2.85\), the electron does not undergo reversal. Its axial velocity reaches 0, while the radial radius continues to change under the influence of the pulse, resulting in a trajectory envelope with a radial ring-shaped protrusion in the middle, resembling a gong shape. As the electron's initial energy continues to increase, the deceleration of radial motion becomes less noticeable, and the trajectory exhibits the typical spindle shape. For example, at \(gm_{0}=5\), the trajectory appears as shown in Figure 5. Therefore, the above analysis and conclusions are verified to be correct.  
Figure 5: Collision trajectories under different initial energies. The upper image is the main view, and the lower image is the front view.
## 2 Materials and Methods
### Physiological Measurement and Signal Preprocessing  
The ECG signals were measured using a wearable device developed in-house, as described in [35]. Ag/AgCl patches were placed on the below the subject's left and right collar bones and on the lower left rib in a lead II configuration. The ECG signals were sampled at a frequency of 500 Hz. The measured signals were divided into different time sections. These time sections are Rest 1, Game 1, Rest 2 and Game 2&3. The time for Game 2 and Game 3 were combined as they were played in succession and there was no resting period in between.  
To ensure the quality of the ECG signal, it underwent signal preprocessing before feature extraction. The raw ECG signal was detrended to remove the linear trend, then filtered using a Butterworth band-pass filter with cutoff frequencies from 5 to 40 Hz. The filtering was carried out to remove the baseline drift, white noise and motion noise.  
Artifact removal is vital in the calculation of HRV, as a minor artifact can severely skew the extracted HRV features. Artifacts such as ectopic beats, arrhythmic events, missing data and other noise can affect the calculation of the power spectral density (PSD) of HRV. The software ARTiiFact [36] was used to remove these artifacts. It first extracts the interbeat interval (IBI) or the R-R interval from the ECG. After the IBI extraction, artifact detection was implemented using either or both of these two algorithms: the median  
Figure 4: Hit-the-Ball Game. The subject has to distinguish between the two ball types and press the correct button when the football reaches the target area.  
Figure 3: Whack-a-Mole Game.  
absolute deviation detection and an algorithm proposed by [PERSON] et al. [37]. It also allows for manual artifact detection, in which the user can visually check the ECG signal. To ensure the accuracy of the IBI calculations, the automated methods were used first, then verify the results from these algorithms through manual detection. The detected artifacts were then processed through interpolation. Two methods were used, namely cubic spline and linear interpolation. Both methods have their own pros and cons. Linear interpolation is simple to use, however it is ineffective when dealing with nonlinear data. The cubic spline interpolation can produce smooth interpolants, but it would not work well with sample points that are near together while having large value variations.
## 2 Methodology
### Optimization Algorithms
#### 2.2.1 Grey Wolf Optimizer
Since \(f(t)=0\) at both \(t=0\) and \(t=T\), and \(f(t)\) is strictly increasing on \([0,T]\), it follows that  
\[\mathbf{A}=2\cdot\mathbf{a}\cdot\mathbf{r}_{1}-\mathbf{a}. \tag{6}\]7  
This implies the following:  
\[\mathbf{A}=2\cdot\mathbf{a}\cdot\mathbf{r}_{1}-\mathbf{a}. \tag{6}\]8  
Equivalently,  
\[\mathbf{A}=2\cdot\mathbf{a}\cdot\mathbf{r}_{1}-\mathbf{a}. \tag{6}\]9  
This completes the proof. To further validate the impact of different decay strategies for the parameter \(a(t)\) in the Grey Wolf Optimizer (GWO), we conducted experiments using the Sphere function, a widely recognized benchmark function for optimization problems. This comparison focuses on four decay strategies: Linear, Quadratic, Exponential, and Cosine. The experimental setup is defined as follows:  
* Problem dimensionality (\(D\)): 30;
* Number of wolves (\(N\)): 30;
* Maximum iterations (\(T\)): 100;
* Number of independent runs: 30. The Sphere function, a standard benchmark for optimization, is mathematically defined as  
\[\mathbf{C}=2\cdot\mathbf{r}_{2}. \tag{7}\]0  
The objective is to minimize \(f(\mathbf{x})\), where the global minimum is achieved at \(f(\mathbf{0})=0\). The comparative results of different decay strategies in GWO are summarized in \[\mathbf{C}=2\cdot\mathbf{r}_{2}. \tag{7}\]1  
The performance comparison of different decay strategies in the Grey Wolf Optimizer (GWO) reveals distinct characteristics in terms of convergence speed and optimization efficiency. Among the four tested approaches, the _Cosine decay_ strategy exhibits the slowest convergence, requiring 48 iterations, which is significantly higher than the Exponential (35), Linear (40), and Quadratic (38) strategies. While slower convergence may seem disadvantageous in simple optimization problems, it indicates a longer exploration phase, making _Cosine decay_ particularly suitable for problems that require extensive global search before transitioning into exploitation. In contrast, _Exponential decay_ converges the fastest, achieving the lowest mean fitness value (\(9.0\times 10^{-4}\)) in just 35 iterations, demonstrating its aggressive reduction in the parameter \(a(t)\). This rapid convergence is effective for straightforward optimization tasks where the early exploitation of promising solutions is preferred. However, in scenarios where the solution space is highly complex or contains multiple local optima, premature convergence may lead to suboptimal results. The _Cosine decay_ strategy mitigates this risk by maintaining a slower reduction in \(a(t)\) at the beginning of the optimization process, enabling the algorithm to explore a broader range of potential solutions before committing to exploitation. This characteristic makes it particularly well suited for _Wireless Sensor Networks (WSNs)_, especially in scenarios where sensor nodes are randomly deployed.
# Quantitative Assessment of Upper- Limb Volume: Implications for Lymphedema Rehabilitation?
## 4 Methods for Volumetric Assessment
### Magnetic Resonance Imaging (MRI)  
Magnetic resonance imaging (MRI) is a powerful imaging technique that might be a valuable tool for volumetric assessment in upper-limb lymphhedema. This non-invasive method utilizes a magnetic field and radio waves to generate detailed images of the affected limb, allowing for the accurate measurement and visualization of tissue volumes [60].  
The principle behind MRI involves the interaction of hydrogen atoms within the body with the applied magnetic field. By manipulating the magnetic field and detecting the radiofrequency signals emitted by the hydrogen atoms, MRI scanners can construct high-resolution images that provide insights into tissue composition and fluid distribution [61].  
MRI offers several advantages in the volumetric assessment of upper-limb lymphhedema. According to BIS, MRI allows for the precise and comprehensive evaluation of the limb's volume and the different tissue types within the limb, further characterizing muscle, fat, and edematous tissue [62]. This capability enables clinicians to assess the extent and distribution of edema, providing valuable information for treatment planning and monitoring [63].  
In recent years, non-contrast magnetic resonance lymphography (NC-MRL) has been introduced as a non-invasive imaging technique that does not require the injection of contrast agents into the body [59]. NC-MRL provides high-resolution images of the lymphatic system, allowing for a detailed visualization of the lymph nodes, lymphatic vessels, and lymphatic flow. It identifies abnormalities, such as lymphatic obstruction, dilation, or lymph node enlargement [59].  
In addition, the multiplane imaging allows for a comprehensive assessment of the lymphatic system, providing functional information about lymphatic flow dynamics. Indeed, by employing specialized imaging sequences and techniques, it is possible to evaluate the direction and velocity of lymphatic flow, detect areas of flow obstruction or reflux, and assess lymphatic pump function [59; 63].  
Despite its advantages, there are specific considerations when using MRI for volumetric assessment. MRI scans are relatively time-consuming and may require the patient to remain still within the scanner for an extended period [64]. Additionally, the cost and availability of MRI scanners may limit their widespread use, especially in resource-limited settings. Careful consideration of contraindications, such as the presence of metallic implants or claustrophobia, is necessary before performing an MRI scan [65; 66].  
Taken together, MRI and NC-MRL are extremely precise techniques providing several advantages in lymphhedema assessment. Technological advantages might simplify these techniques with potential implication for further integration into the routine management of upper-limb lymphhedema.
# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
## 5 Experimental Results and Discussion
### Evaluation Parameters  
The Semantic Segmentation i.e., per pixel classification is done with the help of SVM. The performance of the proposed technique is measured through parameters listed below.  
* **Overall Accuracy (OA)**:- It measures the percentage of correctly classified samples. It is summation of accurate recognition of samples divided by total number of samples. \[OA=\frac{\sum_{i=1}^{C}M_{ii}}{N}\] (16) where \(C\) is the total number of labels/classes. \(M_{ii}\) represents the samples that actually belonged to _ith_ class and were predicted to belong to _ith_ class.
* **Average Accuracy (AA)**:- It measures the average percentage of correctly classified samples for an individual class. \[AA=\frac{\left(\sum_{i=1}^{C}(M_{ii}/\sum_{i=1}^{C}M_{ij})\right)}{C}\] (17) where \(C\) is the total number of labels/classes. \(M_{ij}\) represents the samples that actually belonged to _ith_ class and were predicted to belong to _ith_ class.