esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis  
As in previous studies [32], performance was defined as the average skiing speed during the entire race distance. To determine the contribution of biomechanical and physiological parameters to skiing speed, random forest analysis was employed to assign the importance scores of the aforementioned variables. Variables with higher importance scores were considered to have a greater impact on speed. Initially, random forest analysis on biomechanical parameters during flat terrain skiing was conducted, selecting variables with importance scores greater than 0.1 for inclusion in the final random forest analysis. Subsequently, a similar analysis was performed for biomechanical parameters during uphill skiing, again selecting variables with importance scores greater than 0.1 for the final analysis. In the final random forest analysis, physiological parameters were also incorporated alongside the significant variables identified from flat and uphill terrain analyses to determine the biomechanical and physiological parameters with the most substantial impact on XCS speed. This analytical process was designed to identify key variables for subsequent inclusion as input parameters in a neural network model for predicting skiing speed.  
The model consisted of a feed-forward neural network with two neurons in a single hidden layer. Before training, each parameter was normalized using standard procedures. Model training and optimization of performance were carried out by minimizing the objective function  
\[J=\frac{1}{2N}{\sum}_{k=1}^{N}{\left(y(k)-\hat{y}(k)\right)}^{2} \tag{1}\]  
where \(N\) is the number of values, \(y\) the actual output value, and \(\hat{y}\) the value predicted by the neural network.  
In the construction of the neural network model, the dataset was randomly divided to ensure a robust training and validation process. Specifically, 70% of the samples were randomly selected as the training set, utilized for training the neural network. The remaining 30% of the samples were allocated as the test set, designed to evaluate the model's performance. Throughout the model training phase, a 4-fold cross-validation method was implemented. To ensure a balanced representation in the test set, it was ensured that the test set maintained a comparable ratio of classical and freestyle XCS. Additionally, an appropriate gender ratio within the test set was preserved. This selection strategy was adopted to create a diversified and representative test environment, facilitating a more comprehensive evaluation of the model's performance across different dimensions of the dataset.  
The error was defined as the difference between the predicted and the actual skiing speeds. Pearson correlation analysis was performed to assess the agreement between the predicted and actual values. Furthermore, as an indicator of consistency, Bland-Altman analysis was applied by calculating the mean difference and 95% limits of agreement (mean \(\pm\) 1.96 \(\times\) standard deviation) between the two sets of results.
## 4 Results and Discussion
### Analysis of Excavation Methods  
#### 4.2.1 Plastic Zone Analysis  
The final equivalent plastic strain distribution of the surrounding rock is shown in Figure 16. The plastic zone of the surrounding rock caused by the two methods of construction was roughly similar and particularly evident on the rock between the arches at the top of the middle partition wall, on both sides of the tunnel, near the foot of the outer arch of the tunnel, and at the bottom of the middle partition wall. This was related to the force form of the double-arch tunnel. The effective plastic strain in the surrounding rock, when excavated using the Center Diaphragm Excavation Method, is shown in Figure 16a. Compared with Three-bench Excavation Method, the distribution characteristics of the plastic zone of the surrounding rock at the top of the center wall were more dispersed and gentle due to the side guide holes and temporary supports, while the radial depth was not large. The effective plastic strain of the surrounding rock when the Three-bench Excavation Method was used for excavation is shown in Figure 16b. It can be seen that the radial depth of the plastic area of the surrounding rock at the top of the middle diaphragm wall under the three-step excavation method was larger. This was because the Three-bench Excavation Method involved excavating from top to bottom, layer by layer, without side guide holes and temporary support. The plastic zone generated in the surrounding rock of the tunnel sidewalls was more pronounced when using the three-step excavation. Numerically, the maximum plastic strain generated by using Center Diaphragm Excavation Method was \(4.538\times 10^{-3}\), while the maximum plastic strain generated by using Three-bench Excavation Method was \(5.456\times 10^{-3}\), making the Center Diaphragm Excavation Method superior to the Three-bench Excavation Method. The Center Diaphragm Excavation Method was also slightly better than the Three-bench Excavation Method in terms of the area of the region where plastic strain occurred.  
Figure 16: Effective plastic strain: (**a**) center Diaphragm Excavation Method; (**b**) Three-bench Excavation Method.
###### Abstract
Figure 1: Offloading to MEC-BS, offloading to MEC-NBS, and executing in C.  
As shown in Figure 1, the mobile edge computing can enhance the performance of multiple computation-intensive and delay-sensitive applications, such as virtual reality, augmented reality, pattern recognition, automatic pilot, intelligent transportation, video acceleration, video surveillance, smart home, indoor positioning, remote medical surgery, unmanned aerial vehicle control, online live and interaction, smart building, etc. The users can experience higher real-time performance of applications, and run more complex applications in their resource-constrained MTs. The steps for computing offloading and data caching under the mobile edge computing environments are as follows: (1) when MT has a new computing task, it will upload the offloading request (OL-REQ) to its MEC-BS; (2) if the task is determined to be executed in MEC-BS by the Cooperative Resource Management Algorithm and MEC-BS has cached data of the task, the task will be executed directly in MEC-BS, then the offloading response (OL-RSP) will be returned to MT; (3) if the task is determined to be executed in MEC-BS and there is no cached data in it, MEC-BS will send the data request (DA-REQ) of the task to C, then C will return the data response (DA-RSP) to MEC-BS. The next process of executing and delivering is the same as 2); (4) if the task is decided to be executed in MEC-NBS by the algorithm, MEC-BS will send the offloading request to a MEC-NBS. If MEC-NBS has cached the computing data before, it will execute the task and return the offloading response to MEC-BS, and then MEC-BS will return the offloading response to MT; (5) if MEC-NBS is decided to execute the task and it has not cached the computing data of the task in advance, it will send the data request to C, then C will return the data response to it. The next process of executing and delivering is the same as (4); (6) if the task is resolved to be offloaded to C by the algorithm, MEC-BS will send the offloading request to C, then C will execute the task and return the offloading response to MEC-BS, and then the offloading response will be returned to MT. Our research work can be described briefly by a block diagram, as shown in Figure 2.  
Figure 2: Brief introduction of the research work in our paper.
## 4 Simulation Results Using FEA Method
### Influence of Coupling of Shaft Diameter and Tailstock Stiffness  
According to the results in Section 4.3, the stiffness determines the natural-frequency curve as the shaft diameter decreases. Therefore, in this section, the variation of the natural frequency with decreasing diameter is analysed for different support stiffnesses and constant axial forces.  
As shown in Figure 8, when the support stiffness was very small, the natural frequency of the transverse vibration decreased with a decrease in diameter; this is similar to the results of the cantilever beam model and is consistent with the theoretical results. However, when the support stiffness increased to approximately \(1\times 10^{5}\) N/m, the natural frequency of the shaft first decreased but then increased with decreasing diameter. This is because the mass of the workpiece continued to decrease as the diameter decreased during turning process, and the tailstock support stiffness \(Kt\) was much larger compared with the shaft stiffness \(Ks\), and tailstock support stiffness influence exceeded that of the shaft itself. Consequently, when the diameter decreased to a certain value, it increased. As the support stiffness continued to increase, its effect became greater than that of the diameter, and the natural frequency exhibited little change as the diameter decreased. The natural frequency decreased sharply only when the diameter reached such a small value that the shaft became unstable, and at the same time, therefore, the ideal boundary conditions of fixed-fixed model [9] is no longer suitable under such large support stiffness.  
In conclusion, the support stiffness determines the curve trend of the natural frequency change with the diameter. For \(P=0\) N, \(Kt=0\) N/m, the natural frequency is computed as 139.0-511.6 Hz, which is the natural frequency in the cantilever beam model.
# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 2 Systematic Review Methodology  
### Formulation of Research Questions  
The following research questions are designed to guide a comprehensive literature review and analysis, focusing on both the technological advancements and practical applications of sitting posture recognition systems:  
RQ1: What are the current methodologies and technologies employed in sitting posture recognition systems, and how do they compare in terms of accuracy, cost, and ease of implementation?  
RQ2: How effective are machine learning algorithms in enhancing the accuracy of sitting posture recognition systems compared to traditional methods?  
RQ3: How do recent advancements in deep learning and computer vision contribute to the development of more effective sitting posture recognition systems?  
RQ4: What are the major challenges and limitations faced by existing sitting posture recognition systems, and what are the potential solutions proposed in recent literature?  
These questions aim to thoroughly explore the current state of sitting posture recognition systems, identifying areas for improvement and potential future research directions.
# Characterization of Chirality in Diffractive Metasurfaces by Photothermal Deflection Technique
## 3 Results
### Heat Sources Localization from PTD Data
Once the k vector is calculated in the xy plane, the internal inclination angle in substrate \(\theta_{m}\) can be easily obtained by imposing the conservation of the wavevector at surface z = 0: \(k_{xy,m}=\sqrt{k_{x,m}^{2}+k_{y,m}^{2}}=\frac{2\pi}{\lambda}n\sin\theta_{m}\), which gives:  
\[\theta_{m}=\arcsin\left(\frac{1}{n}\sqrt{\left(\frac{2}{\sqrt{3}}\frac{ \lambda}{a}\right)^{2}-2\left(\frac{2}{\sqrt{3}}\frac{\lambda}{a}\right)\cos \left(m\frac{\pi}{3}\right)n_{air}\sin\theta_{air}+\left(n_{air}\sin\theta_{ air}\right)^{2}}\right) \tag{5}\]  
from internal angle \(\theta_{m}\), one may calculate the distance (\(R_{m}\)) between the location of the heat source corresponding to the m-th diffracted beam and the incident beam, as follows (see Figure 5b):  
\[R_{m}=2L\tan\theta_{m} \tag{6}\]  
Table 2 shows that the quantities (\(R_{m}\)) are different from each other. A small group of beams for \(m=1\), 5 and 6 are absorbed very close to the incident beam, due to the small internal angle. The pump-probe offset, \(d_{m}\), during the probe beam scan can be calculated from the previous results, looking at the sketch in Figure 5b, and according to the following formula:  
\[d_{m}=R_{m}\cos(\beta_{m}+\alpha) \tag{7}\]  
where quantity \(\alpha=15\) deg is the misalignment angle calculated from Equation (2). Table 2 summarizes all the geometrical results. A good agreement is found for the experimental data shown in Figure 3b,d. The diffracted beam for \(m=1\) is hidden by the heat source of the pump beam located at \(d=0\). The diffracted beams for \(m=5\) and 6 are clearly visible for \(d>0\). The orders \(m=2\), 3, 4 fall at \(d<0\), but are too far from the center and cannot be seen. In Figure 3b,d, one can find an additional heat source that is always at \(d=-0.47\) mm, corresponding to the specular reflection, which is wavelength independent. Figure 5: (a) Geometric representation of the diffracted beams in the k space; (b) Location of the heat sources in the real space (xy plane).
# Exploiting Concurrent Wake-Up Transmissions Using Beat Frequencies
## 3 Concurrent Wake-Up Message
### More than Two Concurrent Senders
Furthermore, mitigating the destructive interferences of the beats needs to be considered, which could be achieved by randomizing the frequencies of node pairs similar to the concept introduced in Zippy [8], although randomizing on the node level, not on the bit level. Figure 12a,b visualizes exemplary beats on the real and imaginary axis. Figure 12a visualizes two sinusoidal signals with frequencies \(f_{1}\) and \(f_{2}=f_{1}+\Delta f\) and with amplitudes \(A_{1}=1\) and \(A_{2}=0.6\). It can be seen that the amplitude of the first signal is larger than that of the second, in accordance with Equation (3) and Figure 8. Figure 12b graphically depicts four sinusoidal signals with frequencies \(f_{1}\), \(f_{2}=f_{1}+\Delta f\), \(f_{3}=\alpha f_{1}\) and \(f_{4}=f_{3}+\Delta f\) as given in Equation (6). Here, the amplitudes were \(A_{1}=1\), \(A_{2}=0.6\), \(A_{3}=0.3\), \(A_{4}=0.2\). The figure demonstrates the additional frequencies as expected from the discussion above. Figure 13 visualizes amplitudes \(A\) over the number of nodes \(n\) for concurrently-sending nodes as calculated by MATLAB. The black solid curves are the averaged amplitudes \(Y(n)\) for \(n\) sinusoidal signals (transmitted by \(n\) nodes) all having the same frequency \(f\), a random phase-shift \(\phi_{n}\), amplitude \(A=1\) averaged over \(i=100\) runs, as calculated by:  
\[Y(n)=\frac{1}{i}\sum_{1}^{i}\sum_{1}^{n}max(A\sin(f+\phi_{n})) \tag{7}\]  
where the function \(max()\) extracts the magnitude of the amplitude of each sinusoidal signal. The black dashed curve shows \(Y(15)\), exemplary for one sample run. The black dotted curve depicts the expectation value \(E(Y(n))\). Assume the phases \(\phi\) and amplitudes \(a\) of the sums of \(n\) sinusoidal signals are normally distributed random variables with \(\mu=0\) and \(\sigma^{2}=n/2\). Then, \(R=\sqrt{\phi^{2}+a^{2}}\) is Rayleigh distributed with \(E(R)=\sigma\sqrt{\pi/2}\). From \(\sigma=\sqrt{n/2}\) follows \(E(Y(n))=\sqrt{\pi n/4}\). The red solid curve shows \(Y_{b}(n)\) calculated with Equation (7), but each second signal had frequency \(f+\Delta f\) to generate beat frequencies according to Equation (3). All signals had a random phase-shift \(\phi_{n}\) and amplitudes \(A=1\) and were averaged over \(i=100\) runs. The red dashed curve depicts the result of one exemplary run (\(Y_{b}(15)\)), and the red dotted curve illustrates the expectation value \(E(Y_{b}(n))=\sqrt{\pi n/2}\). The expectation value \(E(Y_{b}(n))\) increased by a factor of two as the variance \(\sigma^{2}\) of the beat amplitudes also increased by a factor of two. Figure 13 does not consider frequency inaccuracies originating from the crystal oscillators, but it demonstrates the advantage of concurrent wake-up signals.
# UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health
## 2 Methods
### Tree Health Data  
The method for field-based, manual tree health monitoring was first described in detail in [12]. Tree canopies of all study trees were assessed for needle discolouration and canopy density from the ground by an experienced assessor who is regularly engaged in forest health monitoring. The assessor undergoes regular training, auditing, and calibration and carries photographic reference material to improve consistency. To reduce the variance associated with assessment subjectivity the same expert assessor was used for all assessments for the duration of the trial. Each tree was assessed from two separate angles, approximately 180\({}^{\circ}\) from each other, so that a comprehensive assessment of the tree canopy could be made. Assessment locations were permanently marked and trees were assessed from these positions at approximately the same time of day at each time. The field assessor followed a commonly used approach to classifying stress symptom expression by scoring percentage canopy discolouration and density into increments of 5. Wherever possible, ground assessments were made on the same day that UAV data were collected, the longest gap between ground and UAV data collection was three days.
# Digital Pre-Distortion of Carrier Frequency Offset for Reliable Wi-Fi Enabled IoTs
## 6 Performance Evaluation  
In this section, the evaluation methodology and results are presented. The proposed digital pre-distortion (DPD) scheme for CFO correction was evaluated using a computer simulation. To evaluate the performance of the proposed scheme, the simulation model was built on a C simulator with the IEEE 802.11 reference channel model [20].  
Figure 2 shows signal analysis results, which indicate fairly significant impairments of the lower index subcarriers. This can arise from excessive CFO and as a result of cutting off the low frequency subcarriers by use of a high pass filter with too high of a corner frequency. Figure 3 shows the signal  
Figure 1: (a) flowchart and (b) algorithm for digital carrier frequency offset (CFO) predistortion.  
qualities over the CFOs in additive white Gaussian noise (AWGN) and Fading channel. The average SINR was 30 dB for both channel conditions; there was a 50 ns root mean square (RMS) delay spread channel condition in the Fading channel condition.  
Figure 4 shows the packet error ratio (PER) evaluation results for the proposed digital predistortion scheme compared with results for the conventional design. \"w/ DPD\" refers to the proposed digital predistortion, and \"w/o DPD\" is the conventional design. The simulation is performed with various CFO conditions. As a result, if the proposed digital predistortion scheme was enabled, the PER was significantly improved for the high frequency offset condition, which is higher than 25 ppm. This is because of the reduced signal distortion in the low index subcarriers near the DC tone.  
Figure 3: EVM over CFO for channel conditions.  
Figure 2: error vector magnitude (EVM) over subcarriers.  
Figure 5 shows the training evaluation results for the proposed scheme compared with those for the conventional design. The simulation was performed in two average SINR regions: 20 dB (high SINR) and 3 dB (low SINR). As a result, if the proposed digital CFO predistortion scheme is adopted, it can reduce the residual CFOs by tracking the offsets.
## 5 Discussion
### Ethical Considerations
#### 5.4.1 Bias and Fairness  
To address potential biases in synthetic datasets, we employed a multi-step validation process. First, we designed region-specific prompts with neutral and inclusive language to prevent reinforcement of stereotypes. Second, we analyzed the linguistic diversity of the generated datasets using statistical methods, such as type-token ratio and keyphrase extraction, to ensure they reflect a broad and representative sample of regional language variations. Third, we compared classification performance across multiple regions to detect any systematic biases in model predictions. Any observed discrepancies were addressed by refining prompt engineering strategies to enhance fairness and balance.
## 2 Materials and Methods
### Outcome Measures
#### 2.4.3 Dynamic Balance  
1. Berg Balance Scale (BBS)  
Dynamic balance was evaluated using the BBS, which is used to determine the effects of pre-training trunk stabilization on postural strategy and reactive control in patients with stroke [6]. The BBS comprises 14 items categorized into three domains: sitting, standing, and postural transitions. Each item is rated on a scale of 0 to 4, with a total possible score of 56. High scores indicate better dynamic balance ability. The tasks include functional activities, such as rising from a seated position, sitting down, transferring between chairs, reaching forward while standing, retrieving an object from the floor, turning to look behind, turning 360\({}^{\circ}\), and alternating foot placement on a stool [34]. The BBS has demonstrated excellent reliability, with an intra-rater reliability of \(r\) = 0.99 and an inter-rater reliability of \(r\) = 0.98 [35].  
2. Limit of Stability (LOS)  
The LOS was assessed using the BioRescue system to evaluate the maximum displacement of the body's COP during voluntary weight shifting. The platform (610 mm \(\times\) 580 mm \(\times\) 10 mm) was embedded with approximately 1600 pressure sensors and, data were sampled at 100 Hz, which were transmitted to the analysis software in real time. During testing, the participants were seated with their hips and knees at 90\({}^{\circ}\) and feet flat on the ground. They were instructed to follow the on-screen directional arrows by shifting their COP toward the indicated direction without lifting their feet or losing balance. The trials were repeated if the participant lost stability. Each measurement was performed three times, and the average value was used for the analysis. The LOS test has shown high test-retest reliability, with an intraclass correlation coefficient of 0.84 [33].