esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition
## 3 Methods
### KNN Model
#### 3.2.3 Segmentation  
The segmentation process isolates specific regions of interest within the images, reducing noise and allowing for more accurate classification. We achieved this using LAB color space segmentation, which provides a more uniform representation of color differences than the RGB color space. The resized images are first converted to the LAB color space, and the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique is applied to the L channel to enhance the image's contrast. After that, the images are segmented into skin and non-skin regions based on a predefined color range, focusing on the regions containing skin color and effectively removing irrelevant areas within the images. Illustrated are Steps 2 and 3 in Figure 6.
## 4 Case Study
### Element Maintenance Decision Model
#### 4.2.4 Cost Matrix and Discount Factor  
Based on the estimated unit prices \(UP_{x}\) for the maintenance measures in Table 3, the repair costs for different defects in each element could be calculated based on attributes such as area and length, as shown in Table 5. By substituting the results from Table 5 into Equation (6), the cost matrix \(R\) can be obtained, as shown in Equation (23). When the bridge had defects that could not be repaired by any of the three maintenance strategies, the maintenance cost was defined as the unit price for cleaning and washing, which was USD  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{\begin{tabular}{c} **Number of** \\ **Concrete** **Cracks** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Number of** \\ **Exposed** \\ **Reinforcement** \\ **Corrosion** \\ \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{c} **Number of** \\ **Surface** \\ **Defects** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **State before** \\ **Maintenance**  
1.09. For the same type of defect, different repair measures can be adopted. For example, for element R-4-1, according to Table 4, there were three concrete cracks in different positions of the box girder. Different repair actions, \(a_{1}\) and \(a_{2}\), can be chosen to repair the cracks based on construction conditions.  
\[R=\begin{array}{c|cccc}A_{0}&0&0&0&0&0\\ -7.80&-7.80&-486.16&-778.78&-2792.08\\ A_{2}&-7.80&-195.18&-7.80&-715.27&-7262.24\\ A_{3}&-7.80&-7.80&-272.69&-252.47&-281.78\end{array} \tag{23}\]  
In the case of a limited maintenance budget, the initial value of the discount factor \(\gamma\) was set to 0.1, thus indicating the need to save future maintenance costs.
# Key-Recycling Strategies in Quantum-Key-Distribution Networks
## 2 QKD Networks with QKP
### Point-To-Point QKD System
#### 2.1.2.  
Measuring qbit  
[PERSON] uses a single photon detector to receive quantum signals on the quantum channel and measures quantum polarization states independently. [PERSON] generates a sequence of random numbers called named base sequence B, which determines the measurement base according to its value. Similar to the mechanism of base sequence A, when the value is 0, the X base is selected by [PERSON] to measure the quantum state, and when the value is 1, the Z base is selected (step \(\mathbb{I}\)). After the measurement, [PERSON] records the photon polarization state of each measurement and its corresponding bit information. When the measurement result is |H> or |->, the classic bit is 0, and when the measurement result is |V> or |+>, the classic bit is 1 (step \(\mathbb{I}\)). Due to the inevitable attenuation problem of the quantum channel and the detection deviation of the device, the number of quantum states received and measured by [PERSON] may not necessarily equal to the number of quantum states sent by [PERSON]. A clock channel is needed here to achieve the accurate synchronization of the qubit transmission and reception.
## 3 Results and Discussion
### Vibration Response of the Bearers  
Table 2 compares the natural frequencies of an FFU bearer, experimentally [20,21] and numerically. It should be noted that the bearer properties are adjusted to the one in the experiments while solving the natural frequencies since the length of the bearers in the current model is different from the experiment and a limited data on the FFU bearers. As indicated in Table 2, the numerical results seem to be consistent with the experimental ones. The maximum discrepancy between results is 13% at the third mode. In the experiment, the location of the springs that is used to provide free-free condition are not indicated, which is believed to be the origin of the increasing error through the higher modes. In other words, the discrepancy between two cases is inevitable. As a result, it could be assumed that experimental and numerical bearer models are similar and the discrepancy is acceptable. Using the same methodology, natural frequencies of a bearer under the crossing nose are estimated (Table 2). As can be seen from Table 2, the length of the bearer influences the natural frequencies significantly. Regarding the vibration theory, it is expected that impact forces over the crossing nose could excite the resonance frequencies of bearers, particularly unsupported bearers, due to high frequency high  
Figure 2: Comparison between the current model and parent model.  
magnitude impact forces. Hence, acceleration of rail seat close to impact location is analyzed in frequency domain.  
In Figure 3, indications of resonance behavior could be detected. However, it seems that only first (37 Hz) and fourth (251 Hz) modes are excited below impact frequency. Several factors could lead to this result such as skipping some resonance frequencies quickly, contribution of random track surface causing noise in the signal or limitation in numerical method. It is noteworthy that the dominant frequencies are higher than natural frequencies of free-free condition due to the different boundary conditions. Furthermore, two peaks at higher frequencies result from track irregularities. For instance, the peak at 970 Hz is close to so called pinned-pinned frequency of track with the rail profile used in this study (54E1) [22].
# A Proposal for a Zero-Trust-Based Multi-Level Security Model and Its Security Controls
## 1 Introduction
### Background
The MLS model provides a security framework based on data sensitivity; however, it operates under the assumption of trust towards internal users, relying on static access controls, which lack continuous verification. The abovementioned limitations can be addressed by integrating the Zero Trust model, which offers dynamic access control and continuous verification of access to data and resources. A security framework that combines MLS with Zero Trust can continuously validate the identity of users, devices, and systems attempting access and provide differentiated access controls based on the sensitivity of the data. This integrated approach ensures a security model that considers the protection of data, the reliability of access behaviours, and data utilisation.
## 5 Assessment of Human Health Risk of Environmental Pollutants Using DGT  
Environmental pollutants can enter the human digestive and circulatory systems through environmental media and food chains. It is necessary to more reasonably assess the human health risk of exposure to environmental pollutants consumed by the human  
Figure 3: Determination of organic pollutants in environmental media by DGT.  
body; the bioavailability of environmental pollutants is mainly evaluated through the in vivo test and in vitro test. In vivo methods generally used mice, minipigs, and other animals to directly detect the concentration of the targets in the blood after the animal ingests the pollutants and calculate the concentration of the target entering the blood [121; 122]. The in vivo method is relatively reliable, but the methods have some disadvantages, such as high experimental cost, long experimental period, and differences among different individuals. In different in vitro methods, there are great differences among the measured results due to the different physiological parameters of the simulated gastrointestinal tract. DGT can be used to evaluate the effectiveness and toxicity of organic pollutants and heavy metals in soil or aquatic systems [104; 123], and it has also confirmed the possibility of its application in human health risk assessment. The health risk index (HI) and ecological RQ are commonly used environmental risk assessment methods [100]. By combining the effective concentration of pollutants monitored by DGT methods, the toxic effects and potential risks of pollutants in environmental media can be accurately characterized to assess the environmental risk. At present, there have been reports on the monitoring of PAEs [100] and three antiviral corona virus disease 2019 (COVID-19) drugs [124] in water and the risk assessment of human health and the ecological environment using DGT, while the environmental risk assessment in soil mainly focuses on heavy metals [48]. The concentration and toxicity of heavy metals in contaminated soil [125], the accumulation in crops [45; 126] and earthworms [79], and even human health risks [127] were accurately and effectively evaluated using DGT technology, the DIFS model [128] or combined with BCR [62], biogeochemical fractionation [129], whole-cell bioreporter [125], and physiological-based extraction tests [127]. Thus, the risk assessment of organic pollutants in soil using DGT needs to be further explored.
## 3 Results and Discussion
It is necessary to determine its value when designing and optimizing refrigeration technologies [47; 48; 49]. The ice cream containing 2.5% (LW2.5) of MDB had a lower conductivity coefficient (\(1.06\) W\(\cdot(\mathrm{m}\cdot\mathrm{K})^{-1}\)) than ice cream with the share of 1.0% (LW1.0). The highest value of heat capacity (C = 2.72 MJ\(\cdot(\mathrm{m}^{3}\cdot\mathrm{K})^{-1}\)) was recorded for the sample with 1% addition of MDB (LW1.0). [PERSON] et al. [50] reported that thermal conductivity of ice cream ranged from 1.039 W\(\cdot(\mathrm{m}\cdot\mathrm{K})^{-1}\) to 1.071 W\(\cdot(\mathrm{m}\cdot\mathrm{K})^{-1}\) and heat capacity values from 2.516 MJ\(\cdot(\mathrm{m}^{3}\cdot\mathrm{K})^{-1}\) to 2.542 MJ\(\cdot(\mathrm{m}^{3}\cdot\mathrm{K})^{-1}\). Thermal diffusivity for ice cream containing 3.0% (LW3. 0) MDB was significantly higher than the other tested samples and amounted to 0.50 mm\({}^{2}\cdot\mathrm{s}^{-1}\). The main factor influencing the thermophysical properties is the amount of frozen water, which also depends on the final temperature of the product. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Properties** & **LW1.0** & **LW1.5** & **LW2.0** & **LW2.5** & **LW3.0** \\ \hline First drop [min] & \(8.65\pm 0.812\)\({}^{\mathrm{c}}\) & \(11.19\pm 1.021\)\({}^{\mathrm{a}}\) & \(11.53\pm 0.491\)\({}^{\mathrm{a}}\) & \(12.20\pm 0.954\)\({}^{\mathrm{ab}}\) & \(13.94\pm 0.961\)\({}^{\mathrm{b}}\) \\ Complete melting & \(34.74\pm 0.672\)\({}^{\mathrm{a}}\) & \(35.56\pm 0.391\)\({}^{\mathrm{a}}\) & \(35.83\pm 0.474\)\({}^{\mathrm{ab}}\) & \(37.35\pm 0.744\)\({}^{\mathrm{bc}}\) & \(38.00\pm 0.620\)\({}^{\mathrm{c}}\) \\ Hardness [N] & \(9.25\pm 0.173\)\({}^{\mathrm{b}}\) & \(11.60\pm 1.232\)\({}^{\mathrm{c}}\) & \(15.77\pm 0.901\)\({}^{\mathrm{a}}\) & \(15.74\pm 0.463\)\({}^{\mathrm{a}}\) & \(28.76\pm 0.681\)\({}^{\mathrm{d}}\) \\ Adhesiveness & \(-15.72\pm 0.732\)\({}^{\mathrm{e}}\) & \(-21.63\pm 0.773\)\({}^{\mathrm{d}}\) & \(-27.46\pm 0.502\)\({}^{\mathrm{c}}\) & \(-32.89\pm 1.221\)\({}^{\mathrm{b}}\) & \(-44.47\pm 1.420\)\({}^{\mathrm{a}}\) \\ \hline \hline \end{tabular} \({}^{\mathrm{a}}\)\({}^{\mathrm{a}}\)\({}^{\mathrm{b}}\)\({}^{\mathrm{a}}\)Means in the same line indicated by different letters were significantly different (\(p\) value < 0.05). The results are expressed as mean \(\pm\) SD (\(n=3\)).  
\end{table} 
Table 5: Physical properties of the tested samples.
## 2 Methodology
### Dynamic Allocation of Satellite Resources for Emergency Scheduling
#### 2.4.2 Dynamic Scheduling of Newly Arrived Emergency Missions
\begin{table}
\begin{tabular}{l|l} \hline \multicolumn{1}{c}{**Hybrid Genetic Tabu Search Algorithm**} \\ \hline  & (6) Perform crossover operation on the population. Chromosomes are crossed by single point \\  & crossover according to crossover probability \(P_{c}\). \\  & (7) Perform mutation operation for the population. According to the mutation probability \(P_{m}\), individuals in the population are mutated to produce a new generation. \\  & (8) Perform a tabu search for the new generation and determine whether the termination criterion is satisfied. If the criterion is satisfied, the best solution is output; otherwise, the candidate solution is determined by the neighborhood function \(F(x)\), which generates the neighborhood of the current solution. \\  & (9) Assess the merit of the candidate solution based on the fitness function and whether it \\  & satisfies the aspiration criterion. If the criterion is satisfied, it is assigned to the current solution \\  & and the best solution found-so-far, the tabu length is set again, and the tabu length of other \\  & elements is updated. Otherwise, the sub-best solution is taken as the current solution and added \\  & to the tabu table to replace the object that first entered the tabu table. The lengths of other \\  & elements in the tabu table are updated. \\ \hline Output & When the maximum iteration number \(i_{bs\_max}\) is reached, the best solution is output. \\ \hline \end{tabular}
\end{table} 
Table 4: \(Cont\). At this point, the process proceeds to step 1 until all new missions in the set \(T\)' are scheduled; otherwise, the satellite resource number plus 1 and the process proceeds to step 2.
# Biosignals Monitoring of First Responders for Cognitive Load Estimation in Real-Time Operation
## 3 Materials and Methods
### Physiological Variables Selection
Therefore, we propose to include eye gaze monitoring to complement biosignals measurements. Finally, the sensitivity of our face to emotions and to systemic changes is well known. Some examples of this behavior are sweat on the forehead or when the face turns red from embarrassment. Indeed, the face is a primary region for the expression of emotional states, and the surface temperature is constantly dependent on changes in the autonomous nervous system [62]. These changes cause variations in the blood perfusion on the facial skin that can be measured through different techniques. One of them is the use of thermal imaging. Another is the use of a contact-based devices, such as thermometers. The first technology poses some advantages as it is non-invasive and the temperature can be easily obtained from a certain distance. However, it also implies some issues when dealing with motion tracking and it is less reliable when trying to measure the temperature of a specific location on the face's skin, such as the nose or the cheeks. In this regard, the use of thermometers, such as a thermocouple, produces a higher spatial resolution, is able to measure the changes in a very specific area of the face, and also provides higher robustness in dynamic contexts. In this context, there are already some studies that relate the variances in facial temperatures to the cognitive burden [63; 64; 65], which pose the measurement of the facial temperature of the cheek as a relevant variable for the cognitive load assessment. On one hand, the measurement of facial temperature as a variable that can be correlated with CL has already been studied and reported in the literature [66]. The approach proposed in this paper is to measure the temperature on the cheek's skin using a contact-based sensor. Following that approach, the temperature of the cheek's skin can be continuously tracked. On the other hand, the hypothesis that the electrical activity of the masseter muscle--one of the most relevant muscles involved in the movement of the jaw--may significantly change during stressful or especially cognitive-demanding situations has been already posed in the literature [63]. Therefore, as a second source of facial information, the electrical activity of the masseter muscle was proposed to be measured using superficial EMG (sEMG) sensing techniques. sEMG provides insightful information on the behavior and activation of the muscle under measurement.
# Advanced Progress of Optical Wireless Technologies for Power Industry: An Overview
## 3 Wireless Power Transfer of Power Industry Optical Wireless Communications
### Optical Wireless Power Transmission with Mirror  
The OWPT system consists of a power transmitting unit (light source), a power receiving unit (solar battery), and a DC operating circuit. Generally, a light source such as a laser converts electrical energy into a highly collimated beam, which can travel within a few meters with very little loss. Besides, solar cells convert light energy into electrical energy. The light source is driven by a DC working circuit to output a specific and stable solar battery voltage [106]. On this basis, a compact system can be used to achieve long-distance transmission, and no electromagnetic waves will leak into the environment outside the beam. By using this method, when the irradiated area is larger than the solar cell, light will be wasted and the power transmission efficiency will be reduced. In addition, when the solar cell area is larger than the illuminated area, the conversion efficiency will be reduced  
Figure 26: Diagram of the proposed wireless energy transfer system [90].  
because the solar cell is not fully irradiated at this time. Therefore, to maintain high efficiency, an appropriate irradiation pattern is required [107].  
To improve the efficiency of the transfer system OVPT in remote and steep, authors propose a new system image OWPT numerical and experimental investigation and analysis that put forward the divergence angle of the power generation efficiency and system variables in the model space of the beam [106]. Furthermore, the OWPT system with multiple mirrors was considered, and the number of mirrors required for rooms of different sizes was studied. In this paper, the beam can be transmitted through the mirror to a device far away from the light source and with a small inclination angle, which avoids the energy waste caused by the beam shining on the outside of the solar cell and improves the energy transmission efficiency [106]. Moreover, the experimental results show that the WPT system using mirrors can effectively extend the application range of OWPT system and improve the flexibility of room effective charging [106].
## Appendix A Properties of Local Mean Envelope of a Stationary Stochastic Process  
A discrete-time envelope \(z[n]\) is generated by a stationary stochastic process with mean \(\mu_{z}\) and variance \(\sigma_{z}^{2}\). The first-order moment is \(m_{1}=\mathbb{E}\{z[n]\}=\mu_{z}\), and the second-order moment is \(m_{2}=\mathbb{E}\{z[n]^{2}\}=\sigma_{z}^{2}+\mu_{z}^{2}\). The length \(T\) (\(T\in\{0,1,2,\ldots\}\)) sliding local average of the envelope \(z[n]\) can be obtained as  
\[u[n]=h[n]*z[n], \tag{1}\]  
where \(h[n]\) is the square window Finite Impulse Response (FIR) filter normalized with the length \(T\) to get the average,  
\[h[n]=\begin{cases}1/T,&\text{for }n=1,\ldots,T\\ 0,&\text{else}.\end{cases} \tag{2}\]  
The LME is obtained as \(u[n]\) sub-sampled by a factor \(T\). The moments of \(u[n]\) are hence the moments for the LME. The first-order moment (mean) of the LME is the mean of \(u[n]\), which is \(\mu_{z}\). The second-order moment depends on the length \(T\). To obtain it, we study the correlation function for \(u[n]\), the filtered stochastic signal  
\[r_{u}[k]=\mathbb{E}\{u[n]\cdot u[n+k]\}=r_{h}[k]*r_{z}[k], \tag{10}\]  
where \(r_{z}[k]\) is the correlation for the envelope \(z[n]\), and  
\[r_{h}[k]=h[k]*h[k]=\begin{cases}\dfrac{T-|k|}{T^{2}},&\text{for }-T\leq k\leq T \\ 0,&\text{else}.\end{cases} \tag{11}\]  
As \(z[n]\) is real valued and \(z[n]\geq 0\) then \(r_{z}[k]=r_{z}[-k]\) and \(r_{u}[k]\geq 0\). The second-order moment for \(u[n]\), denoted \(m_{2}^{T}\), is obtained as \(r_{u}[0]\)  
\[m_{2}^{T}=r_{u}[0]=r_{h}[k]*r_{z}[k]|_{k=0}=\sum_{k=-\infty}^{ \infty}r_{h}[k]\cdot r_{z}[0-k]\] \[= \dfrac{1}{T}\Bigg{\{}r_{z}[0]+\dfrac{2}{T}\sum_{k=1}^{T}(T-k) \cdot r_{z}[k]\Bigg{\}}. \tag{12}\]  
For \(T=1\), we obtain \(m_{2}^{1}=m_{2}\), which is the power of the signal. Hence, the LME has the mean \(\mu_{u}=\mu_{z}=m_{1}\) and the variance \(\sigma_{u}^{2}=m_{2}^{T}-(m_{1})^{2}\), where the second-order moment is given in Equation (12).  
By normalising the LME second-order moment by the power of the signal, we obtain the moment ratio as  
\[\dfrac{m_{2}^{T}}{m_{2}}=\dfrac{1}{T}+\dfrac{2}{T^{2}}\sum_{k=1}^{T}(T-k) \cdot\dfrac{r_{z}[k]}{r_{z}[0]}. \tag{13}\]  
The ratio starts at \(1\) for \(T=1\) and converges to \(\dfrac{(m_{1})^{2}}{m_{2}}\) for large \(T\), as for a sufficiently large \(T\) the LME is equal to the mean of the envelope. How fast the ratio converges depends on the correlation function \(r_{z}[k]\). If the correlation function falls off fast, the \(m_{2}^{T}\) will converge fast. We use the coefficient of variation to evaluate the convergence of the LME.
## 6 Results and Discussion
### One-Sample t-Test  
A one sample t-test was conducted to statistically verify whether the mean scores of each item differed significantly from a neutral test value of 3. The choice of this parametric test was justified as the data met the normality assumption [82] (Shapiro-Wilk test, \(p>0.05\)), and the sample size (N = 12) was adequate for the central limit theorem to apply [81]. As detailed in Table 13, all items showed statistically significant deviations from neutrality (\(p<0.05\)). Positive t-values indicate strong agreement with the statements, with the largest t-values observed in items related to standardized terminology, technology integration, and decision-making support. Notably, the negative phrasing of items regarding complexity and flexibility also yielded significant results, reaffirming that respondents did not perceive these as limitations.  
Although slightly lower in t-values compared to the highest-rated components, the following aspects still reflect strong agreement among respondents as follows: the framework's effectiveness in addressing common industry challenges (t = 7.340, \(p\) = 0.000, Mean Difference = 1.417), capturing key dependencies and interactions (t = 5.745, \(p\) = 0.000, Mean Difference = 1.250), and presenting sufficient detail through diagrams and models (t = 7.091, \(p\) = 0.000, Mean Difference = 1.333). These results suggest a consistently positive perception, albeit with slightly more variation in participant responses. The 95% Confidence Intervals (CIs) for all statements do not include 0, confirming that the differences from the test value of 3 are statistically significant. Specifically, positive mean differences, reflected in the confidence intervals, with both bound above zero, reinforce the framework's perceived strengths across various dimensions. Conversely, negative mean differences, where both CI bounds are below zero, indicate a clear rejection of concerns related to the framework's  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multicolumn{6}{c}{**Test Value = 3**} \\ \cline{2-7}
**Question** & **t** & **df** & **Sig. (tailed)** & **Mean Difference** & \multicolumn{2}{c}{**95\% Confidence Interval of the Difference**} \\ \cline{2-7}
1 & 7.000 & 11 & 0.000 & 1.167 & 0.80 & 1.53 \\
2 & 7.000 & 11 & 0.000 & 1.167 & 0.80 & 1.53 \\
3 & 4.841 & 11 & 0.001 & 1.1  
complexity and lack of flexibility. This one-sample \(t\)-test provides strong evidence that the framework is well perceived across multiple dimensions.