esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Reliable and Secure Probability Analyses
### Analysis of Error Probability in Source-Relay Links
In the case of a Gaussian source, the relationship is given by the following:  
\[R_{\text{S}_{1}}^{\text{D}}(\epsilon_{1})\hat{R}_{\text{S}_{1}} =\frac{1}{2}\log_{2}\frac{\sigma^{2}}{\epsilon_{1}}\hat{R}_{\text{S }_{1}}\leq C(\gamma_{\text{S-UR}_{1}})=\log_{2}(1+\gamma_{\text{S-UR}_{1}}), \tag{16}\] \[R_{\text{S}_{2}}^{\text{D}}(\epsilon_{2})\hat{R}_{\text{S}_{2}} =\frac{1}{2}\log_{2}\frac{\sigma^{2}}{\epsilon_{2}}\hat{R}_{\text{S }_{2}}\leq C(\gamma_{\text{S-UR}_{2}})=\log_{2}(1+\gamma_{\text{S-UR}_{2}}). \tag{17}\]  
Let \(\mathcal{E}_{\text{S-UR}_{1}}\) and \(\mathcal{E}_{\text{S-UR}_{2}}\) be the signal strength of the source and \(\mathcal{E}_{\text{D-UR}_{1}}\) and \(\mathcal{E}_{\text{D-UR}_{2}}\) be the jamming signal strength of the destination, respectively. The received SNRs at UR\({}_{1}\) and UR\({}_{2}\) are  
\[\frac{\mathcal{E}_{\text{S-UR}_{1}}}{\mathcal{E}_{\text{D-UR}_{1 }}+N_{0}} =\frac{\gamma_{\text{S-UR}_{1}}}{\gamma_{\text{D-UR}_{1}}+1}, \tag{18}\] \[\frac{\mathcal{E}_{\text{S-UR}_{2}}}{\mathcal{E}_{\text{D-UR}_{2 }}+N_{0}} =\frac{\gamma_{\text{S-UR}_{2}}}{\gamma_{\text{D-UR}_{2}}+1}, \tag{19}\]  
where \(\mathcal{E}_{\text{D-UR}_{1}}\) and \(\mathcal{E}_{\text{D-UR}_{2}}\) are considered as cooperative jamming (interference) [40].
## 3 Interpretation of Experimental Results
### Relation of Dynamic Shear Strain and the Damping Ratio  
Based on the existing studies on rockfill materials [19], the relationship of \(\lambda\) of soil and \(\gamma_{d}\) can be described as follows:  
\[\lambda=\gamma_{\text{d}}/(c+d\gamma_{\text{d}}) \tag{2}\]  
\[1/\lambda=c/\gamma_{d}+d \tag{3}\]  
where \(c\) and \(d\) represent experimental parameters, with the inverse of the linear intercept \(d\) defining the maximum damping ratio \(\lambda_{max}\).  
Figure 7a-c delineate the relationship of \(\lambda\) of soil and \(\gamma_{d}\) samples S1 to S3 under varying relative densities. The curves reveal considerable disparities in the \(\lambda\)-\(\gamma_{d}\) relationships across different relative densities. For shear strains below \(10^{-2}\), \(\lambda\) progressively increases with rising \(\gamma_{d}\). The rate of increase is more pronounced in samples exhibiting lower relative densities. Notably, the sample with a relative density (\(D_{r}\)) of 0.3 exhibits the most rapid augmentation in damping ratio, significantly outpacing those with \(D_{r}\) = 0.5, 0.7, and 0.9. These observations underscore the profound impact of compaction on the soil's energy dissipation capacity. Looser soils demonstrate easier energy dissipation, leading to higher damping ratios.
## 4 Discussion  
### Water Level versus Sediment Concentration  
Based on the analysis of the simultaneous water level and sediment concentration data, it can be concluded that an unequivocal relationship cannot be established between the water level (or discharge) and the sediment concentration. The equivocal relationship can be traced back to a number of reasons, which may be more on a watershed scale. The sediment concentration during a flood wave may depend on the factors triggering it (snowmelt, precipitation). The concentration may also depend on which subbasin the flood wave comes from and the characteristics of this subbasin. In addition to the above, the amount of suspended sediment is also influenced by the level of the flood wave: the flood wave can descend at such a high level that it washes out the silt deposited in the side-branches or by reaching the floodplain, and the deposited silt may be washed into the bed as well. The arriving flood may also increase the amount of sediment being transported by tearing up the bed armor formed between floods. The succession of the floods may also affect the sediment concentration, since the first flood wave can wash out the deposited silt from the side-branches or tear up the bed armor (thus increasing the sediment concentration); subsequent flood waves can no longer do this. Because of these, the sediment regime of a river can usually not be described with the traditional relationship (G = f(Q)) due to its complexity [26].  
There is an amount of international literature on the direction of hysteresis of water level and suspended sediment concentration (or discharge and turbidity) [27; 28; 29]. In the research presented in [27], simultaneous flow and turbidity data of several flood waves were examined in medium-sized river basins, where several hysteresis samples (clockwise, counter-clockwise, eight-shaped, and complex) were distinguished, and the different hysteresis patterns were explained by the factors triggering the flood wave, the number of sediment sources, the distance of sediment sources from the examined cross-section, and the mobility of individual sediment sources.
# Optimizing Healthcare Delivery: A Model for Staffing, Patient Assignment, and Resource Allocation
## 5 Results and Discussions
### Staff Assignment  
\[S_{ij}=\begin{cases}1&\text{if }j\in\text{staff\_assignment}(i+1)\\ 0&\text{otherwise}\end{cases} \tag{1}\]  
Let \(S_{ij}\) be a binary matrix, where \(i\) represents the index of staff members, and \(j\) represents the index of time slots. \(S_{ij}=1\) if the staff member \(i+1\) is assigned to the time slot \(j+1\), and 0 otherwise.  
Figures 5 and 6 show the visualizations of the staff members across the slated time slots for the two scenarios in a hospital setting. The heatmaps with right-hand scales in the figures illustrate matrices with staff members on rows and time slots on columns. The darker blue cells indicate the assignment of staff members to specific time slots. The scale on the heatmap's right side, ranging from 0.0 to 1.0, with 1.0 being the highest, indicates the percentage of staff members assigned at each time slot. For example, if the scale shows 0.4 at a specific time slot, this means that 40% of the staff members are assigned at that time slot.  
Figure 4: Patient distribution over time slots for the second model procedure.  
Figure 3: Patient distribution over time slots for the first model procedure.  
These visualizations aid in optimizing staff assignment and scheduling by providing better decision-making in healthcare management. In the two heatmaps, the staff assignments vary depending on the time slots and the demand for services. In Figure 5, the peak hours are between the 4 h and the 10 h periods, and the lowest for staff assignments are the 5 h and 8 h periods. Also, in Figure 6, the peak hour is the 5 h period, and the lowest for staff assignments is the 2 h period.
## 3 Materials and Methods
### Vortex Search Algorithm (VSA)
Subsequently, in the hyper-ellipsoid, the effective radius is reduced to \(r_{t}\), so it must readjust the new radio to suit the process of searching for neighboring solutions through the inverse incomplete gamma function during each iteration. Equation (12) represents the incomplete gamma function. \[\Gamma(a)=\int\limits_{0}^{x}e^{-t}t^{a-1}\cdot dt+\int\limits_{x}^{\infty}e^ {-t}t^{a-1}\cdot dt,\ \ a>0,\ \ \ x\geq 0 \tag{12}\]  
In MATLAB, \(gammaincincinciv\)\((giniv)\) is the inverse incomplete gamma function, which computes the opposite of the incomplete gamma function concerning the integration limit \(x\) as \(gammaincinciv(x,a)\). For the case under study, \(x=0.1\) and \(a\)\(e[0,1]\) where Equation (13) explains \(a\). \[a=\frac{tr}{t_{\max}} \tag{13}\]  
where \(t_{\max}\) is the maximum number of iterations, and \(tr\) is a number that initializes equal to the maximum number of iterations but decreases by one unit as the iteration concludes. On the other hand, it is defined that \(t_{\max}\) can vary depending on the size of the system, which implies that the variation of \(a\) will change in the steps determined by \(t_{\max}\). According to the above, Equation (14) defines the new radius of the new hyper-ellipsoid. \[r_{t}=\frac{1}{x}\cdot gammaincinv(x,a)\cdot\frac{x^{\max}-x^{\min}}{2} \tag{14}\]  
From the new radius \(r_{t}\)[36], a new set of solutions is generated stored in \(C_{i}^{t}(x)\), which implies that the value of the ensemble of neighboring solutions will be closer and closer as the radius decreases by each iteration. Finally, the process of calculating the new radius, new center, and new solution spaces will successively repeat until it accomplishes one out of two termination conditions; if it reaches the maximum number of proposed iterations \(t_{\max}\) or if, after some consecutive iterations \(\tau\), it reaches the maximum number of sequential iterations \(\tau_{max}\) in which the center of the hyper-ellipsoid remains constant.
# Image Dehazing and Enhancement Using Principal Component Analysis and Modified Haze Features
## 4 Experimental Results
### Comparison of the Estimated Atmospheric Light  
This subsection demonstrates the performance of the atmospheric light estimation using a pair of 34 haze and ground-truth images proposed in reference [20]. [PERSON] et al. manually extracted the specified region of the ground-truth atmospheric light. Objective assessments are summarized in Table 1. Figure 7 shows estimated atmospheric lights using the existing and proposed methods given the ground-truth of atmospheric light.  
Since [PERSON]'s method estimated the atmospheric light using the top 0.1% brightest pixels of the DCP, the estimated atmospheric light may be inaccurate because of the brightest object in a haze image [1]. As shown in Figure 7c, [PERSON]'s method provided darker atmospheric light because this method does not compensate the magnitude of the atmospheric light vector when estimating the atmospheric light [11]. [PERSON]'s method estimated the atmospheric light located at the end of the haze-line [15]. For that reason, the [PERSON]'s method showed a bright atmospheric light value, as shown Figure 7d.  
On the other hand, since the proposed method estimates the atmospheric light using the candidate pixels in a specified region, the estimated atmospheric light should be similar to the ground-truth. Table 1 shows that the proposed method provided the lowest \(l_{2}\)-error in terms of the atmospheric light estimation compared with existing methods. In terms of the processing time, the proposed method provided the best and second best processing times with and without parallel processing, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**[1]**} & \multicolumn{2}{c}{**[11]**} & \multicolumn{2}{c}{**[15]**} & \multicolumn{2}{c}{**Proposed**} \\ \cline{2-10}  & **Error** & **Time** & **Error** & **Time** & **Error** & **Time** & **Error** & **Time** & **Time** (Parallel)** \\ \hline Sweden & 0.24 & 0.85 & 1.03 & 8.41 & 0.19 & 4.40 & 0.28 & 0.18 & 0.15 \\ Train & 0.82 & 0.67 & 0.45 & 5.43 & 0.03 & 2.25 & 0.30 & 0.34 & 0.29 \\ Swan & 0.23 & 1.72 & 0.68 & 8.36 & 0.21 & 5.10 & 0.02 & 0.22 & 0.21 \\ Schechner & 0.65 & 0.96 & 0.32 & 11.44 & 0.14 & 4.26 & 0.01 & 2.61 & 1.56 \\ Forest & 0.07 & 1.63 & 1.17 & 14.16 & 0.74 & 5.49 & 0.16 & 1.50 & 0.93 \\ \hline
**Avg.** & 0.40 & 1.17 & 0.73 & 9.56 & 0.26 & 4.30 & 0.15 & 0.97 & 0.63 \\ \hline \end{tabular}
\end{table}
Table 1: Objective assessments of atmospheric light estimation using the \(l_{2}\)-error and processing time (seconds) [20].  
Figure 7: Estimation of the atmospheric light using the proposed and existing methods. The first and second rows respectively show the input haze image and the estimated atmospheric light [20], reproduced with permission from [20], IEEE, 2016. The third row shows the R, G, and B pixel values of estimated atmospheric light: (**a**) ground-truth, (**b**) [PERSON]’s method [1], (**c**) [PERSON]’s method [11], (**d**) [PERSON]’s method [15], reproduced with permission from [15], IEEE, 2017 and (**e**) the proposed method.
###### Abstract  
Substrate integrated waveguides reduce the losses and increase the quality factor of resonators in communication filters when compared with traditional planar technologies, while maintaining their low-cost and low-profile characteristics. Empty substrate integrated waveguides go one step further, removing the dielectric of the substrate. One of these transmission lines is the empty substrate integrated coaxial line (ESICL), which has the advantage of being a two-conductor structure. Thus, it propagates a transversal electric and magnetic (TEM) mode, which reduces the dispersion and the bandwidth limitation of other one conductor empty substrate integrated waveguides. Continuous profile filters, at the cost of being long structures, are very easy to manufacture and design (usually no optimization is needed), and they are highly insensitive to manufacturing tolerances. In this work, a simple continuous profile filter, with a stop band response, is designed for the first time using novel ESICL technology. The influence of the design parameters on the insertion losses and fractional bandwidth is discussed. A prototype has been successfully manufactured and measured. A sensitivity analysis shows the high tolerance of the proposed stop band filter to manufacturing errors.  
empty substrate integrated waveguides; empty substrate integrated coaxial line; ESICL; continuous profile filters; stop band filters 2018 8 2176 1
# High-Efficiency Quantum Dot Lasers as Comb Sources for DWDM Applications
## 1 Introduction
### DFB Arrays  
The archetype wavelength division multiplexing (WDM) light source, DFB arrays, are implemented in long haul, metro, and data center applications [9]. DFBs are commercially available across the entire telecom range, with wavelengths available anywhere between 760 and 14,000 nm [10]. The WDM source is formed by combining the optical outputs of many individual lasers. DFB arrays have two aspects that NOFCs and MLLs do not; namely, the ability to scale the power in each channel independently to the desired level, and showing no reduction in efficiency as the number of channels increases. They do, however, have the largest footprint, and require an MUX to combine individual channels into onefiber. Additionally, the large temperature variation inside data center racks requires control electronics to keep each individual laser aligned to the DWDM grid/MUX, increasing their energy cost per bit [11].
# Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network
## 4 Experiments
### Ablation Experiments
#### 4.5.2 Effectiveness Analysis of Color Correction Module  
In order to verify and analyze the effectiveness of the Color Correction Module proposed in this paper, two sets of comparison experiments were conducted. The two comparison networks are: (a) LACCNet (w/o CCM), which involves removing the network structure of the entire Color Correction Module and the decoder directly. The enhanced image results are output, and other network structures remain unchanged; and (b) LACCNet, a network that adds a complete Color Correction Module. Figure 8 shows the enhancement results of these two networks on two sample images in the LOL-Blur dataset. It can be seen that the enhanced image obtained by network (b) is closest to the corresponding ground truth color.
# Steel Surface Defect Classification Based on Small Sample Learning
## 2 Principles and Methodology
### Nearest Neighbor Algorithm  
Once the feature extraction network \(f_{\alpha}\) is trained, the subsequent operations are performed on the images in the feature space. \(I=f_{\alpha}(input)\) is defined as the image after feature extraction. N-way K-shot settings are adopted, where \(N\) represents the number of classes in the training set and \(K\) represents the number of samples in each class. In the feature space, a certain distance metric, \(d(I,I)\in R\), is used for nearest neighbor classification. For the one-shot setting, there is only one picture for each category in the training set \(D_{train}\), \(D_{train}=\{(I_{1},L_{1}),(I_{2},L_{2}),\ldots,(I_{N},L_{N})\}\), where \(L\) represents the category label. The nearest neighbor rule is to calculate the distance between the test image and the training image, and then assign the label of the training image with the smallest distance to the test image.  
\[y(I)=argmin_{n\in\{1,\ldots,N\}}d\big{(}I,I_{n}^{\prime}\big{)}, \tag{7}\]  
For the multi-shot setting, the prototype network is adopted. In the feature space, the average value of the sample feature vectors of each class in the training set is used as its class prototype, and then the distance between the test sample and class prototype is calculated according to Formula (7).
## 2 Materials and Methods
### Spatial Analysis  
Using QGIS software applied to a digital terrain model (DTM) prepared by the Spanish Center of Geographic Information in 2016 (resolution of 5 \(\times\) 5 m), a spatial analysis was carried out to calculate the values of land slope and terrain roughness at the hillslope scale. As exposed by [PERSON] et al. (2018), terrain roughness is defined as the unevenness of the terrain surface (including rocks and low vegetation) at scales of several meters. This analysis was targeted to identify possible relationships between vegetation regeneration and land characteristics (that is, land slope and terrain roughness) for the different soil conditions. In order to analyze VARI index and its relationships with land slope and terrain roughness at the hillslope scale, a total of 100 plots were randomly selected on the DTM prepared by the Spanish Center of Geographic Information in 2016 (40 plots in LEB, 20 in CFD, 20 in BNA, 20 in UB.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**VARI** & **LANDSAT8** & **UAV** \\ \hline Minimum & \(-\)0.215 & \(-\)0.599 \\ Maximum & 0.058 & 0.173 \\ Range & 0.273 & 0.771 \\ Class width (range/5) & 0.055 & 0.154 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calculation of the class width for the VARI classification in ranks in Sierra de Los Donceles forest (Castilla-La Mancha, Spain) (Source: our processing).  
Figure 4: Examples of satellite (**a**) and drone (**b**) images caught in Serra de Los Donceles forest (Castilla-La Mancha, Spain).
## 3 Materials and Methods
### Other Data Analysis Methods  
#### 3.4.1 Clustering Method  
The \(k\)-means clustering algorithm is one of the most popular clustering algorithms, which is used to find groups that are not explicitly labeled in the data. It uses iterative refinement to produce a final result. The inputs of the algorithm are the dataset and the number of clusters \(k\). A cluster is a collection of data points that have been aggregated together because of certain similarities, and the dataset is a collection of features for each data point. The algorithms start with initial estimates for the \(k\) centroids, which can either be randomly initialized or randomly selected from the dataset. Then, the algorithm iterates between two steps:  
* Data assignment: each data point is assigned to its nearest centroid, based on the squared Euclidean distance. If \(c_{i}\) is the collection of centroids in set \(C\), then each data point \(x\) is assigned to a cluster based on: \[\operatorname*{arg\,min}_{c_{i}\in C}\,dist(c_{i},x)^{2}\] (6) where \(dist(\cdot)\) is the standard Euclidean distance. \(S_{i}\) is the set of data point assignments for each \(i\)th cluster centroid.
* Centroid update: centroids are recomputed by taking the mean of all data points assigned to that centroid's cluster. \[c_{i}=\frac{1}{|S_{i}|}\sum_{x_{i}\in S_{i}x_{i}}\] (7)  
The algorithm iterates between those two steps until convergence. Convergence is reached when the computed centroids do not change or the centroids and the assigned points oscillate back and forth from one iteration to the next one. The result may be a local optimum, so assessing more than one run of the algorithm with randomized starting centroids may give a better outcome [41].
# Efficient Human Activity Recognition Using Machine Learning and Wearable Sensor Data
###### Abstract  
With the rapid advancement of global development, there is an increasing demand for health monitoring technologies. Human activity recognition and monitoring systems offer a powerful means of identifying daily movement patterns, which helps in understanding human behaviors and provides valuable insights for life management. This paper explores the issue of human motion state recognition using accelerometers and gyroscopes, proposing a human activity recognition system based on a majority decision model that integrates multiple machine learning algorithms. In this study, the majority decision model was compared with an integer programming model, and the accuracy was assessed through a confusion matrix and cross-validation based on a dataset generated from 10 volunteers performing 12 different human activities. The average activity recognition accuracy of the majority decision model can be as high as 91.92%. The results underscore the superior accuracy and efficiency of the majority decision model in human activity state recognition, highlighting its potential for practical applications in health monitoring systems.  
Keywords:human activity recognition; machine learning; majority decision model; accelerometers and gyroscopes +
Footnote †: journal: Applied Sciences
## 3 Results
### Potassium Adsorption Isotherms  
Based on the significant coefficient of determination (R\({}^{2}\)) and lower \(\chi^{2}\) values, K adsorption data on soil alone, B1, and B2 fit Freundlich (Table 17) whereas K adsorption data on CLB and B3 fit Langmuir type 4 isotherm. Chicken litter biochar only showed lower Langmuir bonding energy constant (K\({}_{\text{L}}\)) compared with that of B3 (Tables 17 and 18). The maximum adsorption capacity (q\({}_{\text{m}}\)) of B3 was higher than with CLB (Table 19). \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multicolumn{2}{c}{**Langmuir**} & \multicolumn{2}{c}{**Freundlich**} \\ \cline{2-5}  & **K\({}_{\text{L}}\) (\(\mu\)g g\({}^{-1}\))** & **q\({}_{\text{m}}\)** & **MBC (\(\mu\)g g\({}^{-1}\))** & **K\({}_{\text{F}}\) (\(\mu\)g g\({}^{-1}\))** & \(\frac{1}{n}\) \\ \hline Soil & 0.015 & 5000 & 75 & nd & nd \\ CLB & nd & nd & nd & 0.22 & 1.809 \\ B1 & 0.003 & 10,000 & 30 & nd & nd \\ B2 & 0.003 & 10,000 & 30 & nd & nd \\ B3 & 0.002 & 10,000 & 20 & nd & nd \\ \hline \hline \end{tabular} Note: nd: not determined.  
\end{table} 
Table 16: Variables from Langmuir and Freundlich isotherms for phosphorus adsorption of different treatments. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{**Treatment**} & **Regression Equation** & **R\({}^{2}\)** & **\(\chi^{2}\)** \\ \hline \multicolumn{4}{c}{**Langmuir-4**} \\ \hline Soil & y = 56.484 \(-\) 0.008x & 0.5825 ns & 14.057 \\ CLB & y = 8.453 \(+\) 0.002x & 0.6296 ns & 2.700 \\ B1 & y = 28.447 \(-\) 0.003x & 0.7526 * & 1.069 \\ B2 & y = 23.716 \(-\) 0.002x & 0.7643 * & 0.476 \\ B3 & y = 21.484 \(-\) 0.001x & 0.8879 * & 0.119 \\ \hline \multicolumn{4}{c}{**Freundlich**} \\ \hline Soil & y = 2.289 \(+\) 0.574x & 0.9868 ** & 1.07 \(\times\) 10\({}^{-3}\) \\ CLB & y = \(-\)0.651 \(+\) 1.809x & 0.8494 * & 0.019 \\ B1 & y = 1.833 \(+\) 0.745x & 0.9984 ** & 1.54 \(\times\) 10\({}^{-4}\) \\ B2 & y = 1.675 \(+\) 0.806x & 0.9991 ** & 9.30 \(\times\) 10\({}^{-5}\) \\ B3 & y = 1.573 \(+\) 0.846x & 0.9997 ** & 2.67 \(\times\) 10\({}^{-5}\) \\ \hline \multicolumn{4}{c}{**Temkin**} \\ \hline Soil & y = \(-\)3779.082 \(+\) & 0.8630 * & 853 \\ CLB & y = \(-\)24024 \(+\) 5298.410x & 0.9710 ** & 115 \\ B1 & y = \(-\)5819.288 \(+\) & 0.9040 ** & 729 \\ B2 & y = \(-\)6552.631 \(+\) & 0.9036 ** & 789 \\ B3 & y = \(-\)7057.821 \(+\) & 0.9202 ** & 637 \\ \hline \hline \end{tabular} Note: ** significant at P = 0.01, * significant at P = 0.05, ns not significant at P = 0.05.  
\end{table} 
Table 15: Regression equations, Coefficient of determination (R\({}^{2}\)), and Chi-square analysis (\(\chi^{2}\)) for the fit of the Langmuir type 4, Freundlich, and Temkin isotherms to the phosphorus adsorption data of the treatments.