esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Computational Experiments
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Greedy**} & \multicolumn{2}{c}{**Random (Beta = 0.3)**} & \multicolumn{2}{c}{**BR-DES**} & \multicolumn{2}{c}{**SA**} \\ \cline{2-10}
**Instance** & **Quantity** & **Quality** & **Quantity** & **Quality** & **Quantity** & **Quality** & **Quantity** & **Quality** \\  & **Mismatch** & **Mismatch** & **Mismatch** & **Mismatch** & **Mismatch** & **Mismatch** & **Mismatch** & **Mismatch** \\ \hline
1 & 2373 & 3.70 & 9219 & 16.40 & 1326 & 9.59 & 2881 & 40.21 \\
2 & 1890 & 8.42 & 6084 & 36.89 & 860 & 14.91 & 1421 & 28.91 \\
3 & 1134 & 14.19 & 7470 & 16.72 & 931 & 21.95 & 1721 & 22.21 \\
4 & 3468 & 16.94 & 23,205 & 112.86 & 3809 & 67.58 & 5580 & 93.21 \\
5 & 8880 & 15.78 & 12,432 & 56.66 & 3143 & 12.28 & 5772 & 88.28 \\
6 & 2418 & 29.62 & 7832 & 84.69 & 2539 & 50.36 & 4103 & 18.22 \\
7 & 4896 & 38.63 & 19,296 & 13.96 & 4752 & 20.17 & 5193 & 33.22 \\
8 & 9610 & 15.42 & 21,204 & 55.61 & 3640 & 41.71 & 3698 & 17.03 \\
9 & 8525 & 13.27 & 4850 & 146.91 & 5225 & 42.46 & 4433 & 31.99 \\
10 & - & - & - & - & 6272 & 19.15 & 9003 & 198.02 \\
11 & - & - & 12,936 & 15.44 & 6006 & 14.14 & 8633 & 92.26 \\
12 & - & - & - & - & 7189 & 120.45 & 8874 & 6.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mismatches in quantity (kilograms) and quality levels between the required and retrieved items for each approach (greedy, random, BR-DES, and SA) and instance (row). Figure 8: Makespan comparison of different approaches.
## 4 Results  
### Data Collection and Segmentation  
In this study, training was conducted using PTA and speech audiometry data from 12,972 patients, as shown in Table 3. As shown in Table 3, the dataset includes age, representing the patient's age, and SA, which denotes speech audiometry results. The PTA data consist of AC and BC threshold values at 250 Hz, 500 Hz, 1000 Hz, 2000 Hz, and 4000 Hz. A higher SA value indicates better speech audiometry performance, whereas higher PTA threshold values indicate worse hearing ability. The threshold represents the minimum intensity required for the patient to perceive sounds. Speech audiometry consists of three classes (0 to 2), where class 0 indicates excellent speech audiometry, class 1 represents poor speech audiometry, and class 2 signifies very poor speech audiometry. This classification serves as an indicator for determining the presence or absence of sound recognition and was used for model training. The PTA dataset used in this study was collected from 12,972 patient records at Ajou University Hospital, Asan Medical Center, Konyang University Hospital, Soonchunhyang University Hospital, Sookmyung Women's University Hospital, Seoul National University Hospital, and Yonsei University Hospital. The speech audiometry scores range from 100 to 0. The dataset used in this study was reviewed and approved by the Institutional Review Board (IRB) of Soonchunhyang University Cheonan Hospital (2021-06-040).
# Offline-Online Change Detection for Sentinel-1 InSAR Time Series
## 2 Methods
### Changes in Displacement Time Series
#### 2.2.2 Gradient Change Detection  
In this case we are interested in detecting changes in the local displacement gradient within a given temporal window. Therefore we aim to detect peaks in the second derivative of the series in local windows. To improve the signal-to-noise ratio of the derivative calculations we first filter each time series with a rolling mean function over a small window [37], which should be a fraction of the gradient window size. The gradient window can be specified depending on the types of potential changes of interest, but must cover at least 2 data points in time (in order to be able to calculate a gradient). Ideally the window will cover at least 5 measurement points in time to get a more accurate measurement of the local gradient.  
\[g_{t}=\frac{d^{2}Y_{\Delta t}}{d\Delta t^{2}} \tag{8}\]  
where \(g_{t}\) is the second derivative series determined by calculating the local second derivative of the displacement time series in rolling windows of size \(\Delta t\) (in days), with the result of the calculation in each window placed in the centre. In practice this involves first determining the slope of all displacement measurements within moving windows of size \(\Delta t\) to get the first derivative series. And then repeating this step with the same window size to get the second derivative series. The choice of window size will determine the types of gradient changes that can be detected. Larger windows will be able to detect gradient changes that occur over a longer time period. Smaller windows are more sensitive to noise in the time series.  
We perform the same standard deviation estimation and Student's \(t\)-test described in the offset change detection above but in this case we only do the hypothesis test once on the second derivative time series. Unlike the offset detections, the gradient change points represent windows of size \(\Delta t\) within which a change in gradient has occurred (Figure 5).
# Fuzzy Frankot-Chellappa Algorithm for Surface Normal Integration
## 4 Fuzzy Frankot-Chellappa Surface Normal Integration
### Adaptive Strategy and Parameter Selection Strategy  
Unlike the additive noise modeling in [8], we also introduce uncertainty in the gradient fields by scaling the perturbation relative to the magnitude of the gradient. For each pixel \((u,v)\), we construct a fuzzy gradient as a symmetric triangular fuzzy number:  
\[\tilde{p}_{i,j}=(p_{i,j}-\delta_{p},p_{i,j},p_{i,j}+\delta_{p}),\quad\delta_{p}= \lambda|p_{i,j}|\ \ \,\quad\tilde{q}_{i,j}=(q_{i,j}-\delta_{q},q_{i,j},q_{i,j}+\delta_{q}),\quad \delta_{q}=\lambda|q_{i,j}|\]  
The scaling factor \(\lambda\) is selected based on the estimated noise level, allowing the fuzzification process to adaptively reflect the local uncertainty in the gradients.  
In contrast to [8], where fixed averaging of fuzzy bounds used (i.e., \(d(\tilde{z})=0.5(z^{-}+z^{+})\)), here we introduce a context-aware defuzzification that dynamically adjusts weights \((w_{l},w_{m},w_{u})\) depending on the local spread of the fuzzy surface solution. The defuzzified result is  
\[d_{new}(\tilde{z}_{i,j})=w_{l}z_{i,j}^{-}+w_{m}z_{i,j}+w_{u}z_{i,j}^{+}, \tag{13}\]  
where weights \(w_{l},w_{m},w_{u}\) are dynamically determined by the local spread \(spread=z_{i,j}^{+}-z_{i,j}^{-}\), with thresholds \(T_{1}\) and \(T_{2}\) defined as functions of the noise level.  
This dual improvement leads to a fuzzy integration method that is more responsive to local variations and noise characteristics in the input data.  
The adaptive defuzzification strategy employs threshold parameters \(T_{1}=a_{1}+\beta_{1}\cdot\sigma_{n}\) and \(T_{2}=a_{2}+\beta_{2}\cdot\sigma_{n}\), where \(\sigma_{n}\) is the estimated noise level, and \(\beta_{1},\beta_{2},a_{1},a_{2}\) are tunable constants.  
In this study, we determined these parameters empirically through grid search experiments over a validation set consisting of synthetic benchmark surfaces. The goal was to identify values that minimize RMSE while maintaining consistent performance across varying noise intensities. Note that using the noise level \(\sigma_{n}\) directly as a threshold (e.g., \(T=\sigma_{n}\)) is not suitable due to differences in scale between the normalized noise metric and the actual spread in the fuzzy output. Therefore, we define adaptive thresholds \(T_{1},T_{2}\) as affine functions of \(\sigma_{n}\), incorporating a base level \(a\) and a sensitivity coefficient \(\beta\), allowing better alignment with the expected range of spread values across different datasets and noise intensities.
## 2 Background and Related Work
### Sampling-Based Path Planning Methods  
Sampling-based methods include the Probabilistic Road Map (PRM) [61], Rapidly Exploring Random Trees (RRTs) [62], and their refined versions. These methods have demonstrated effectiveness at quickly generating near-optimal/optimal global solutions. Their algorithmic simplicity makes sampling-based methods applicable to solving both real-time and single-query PP problems. The RRT PP method and its subsequent versions such as informed RRT\({}^{\star}\)[63], Transition-aware RRT (T-RRT) [64], RRT-connect [65], and AnyTime-RRT (AT-RRT) [66] are all complete probabilistically. Most RRT-based methods yield slow convergence rates in complex environments, and they mostly fail to resolve the trade-off between length and time while finding reasonable-quality paths. [PERSON] et al. [67] designed a better version of the original RRT, named RRT\({}^{\star}\). This method has a fast rate of convergence compared to RRT, and it has an ability to find a quasi-optimal path with minor post-processing. However, computing issues such as pre-mature convergence, high space complexity, path searching from a whole map, and discarding beneficial samples while converging into a solution pre-maturely make it unreliable for solving practical missions. [PERSON] et al. [68] designed a new variant of RRT\({}^{\star}\) named RRT\({}^{\star}\)-Smart. It has a faster convergence rate, compared to the traditional RRT\({}^{\star}\) algorithm, by using smart-sampling and optimization techniques. However, the main limitations of RRT\({}^{\star}\)-Smart are higher sensitivity to the operating environment, too many iterations, and extensive memory consumption. [PERSON] et al. [69] suggested a sampling-based PP method with improved convergence rate. [PERSON] et al. [70] presented a concept relatively closer to our PP method, called RRT\({}^{\star}\)-AB (Adjustable Bounds), to determine low-cost paths. It shows better resultsthan the traditional RRT* method. However, it yields time performance issues due to the near-neighbor search and extensive rewiring operations while optimizing the path lengths. Hence, the shortest paths determined by the existing methods have higher time complexity. Accordingly, a constrained space complexity analysis with in-depth complexity parameters and an obstacle's geometry information has not been simultaneously explored to find a good-quality path with the least time cost.
## 3 Simulation Framework
### Link Performance Model  
In order to determine the packet loss probability for a given packet, one needs the mapping from the effective SNR to the packet error probability. In the simplest setting, these curves can be directly used to determine the packet error ratio if the fading in the channel can be regarded as approximately constant during the transmission of one block (quasi-static assumption), and the channel magnitude response is approximately constant over the transmission bandwidth (frequency-flat fading). If the latter assumption is not valid, the effects of the frequency selectivity need to be taken into account, which also depends on the signal processing algorithms employed in the respective receiver to cope with, or even exploit, the distinct multipath echos in the channel. This presents an additional non-trivial step in the performance evaluation for IEEE 802.15.4-like, relatively wideband, spread spectrum-based air interfaces. In cellular context, this challenge is usually tackled by applying various semi-empirical SNR penalties to the instantaneous SNR.  
Figure 3: (**a**) Sample frequency correlation functions; (**b**) Sample pair of small-scale fading realizations at carriers spaced 50 MHz apart (dashed: High correlation; dotted: Low correlation).  
At this point, it should be noted that finding the packet loss probability, given an instantaneous channel realization and co-channel interference value, can be done in a more general way if the communication scheme employs coded orthogonal frequency division multiplexing (OFDM), as, for example, certain transmission modes of 802.11 do. OFDM, which uses low-rate parallel transmission on multiple subcarriers, \"converts\" the frequency-selective wireless channel into a set of parallel frequency-flat fading channels, and it is relatively simple to find an compression function that yields a scalar effective signal-to-noise ratio based on the per-subcarrier signal-to-noise and interference ratios (SINRs). If the compression function is chosen appropriately, the resulting effective signal-to-noise ratio marks a point on the AWGN packet error ratio curve (as in Figure 6 shown in Section 5), which approximates well the real packet error ratio that the multipath fading channels were exhibiting. Such mappings include exponential effective SINR mapping and mean instantaneous capacity mapping [4].
# Development and Testing of Copper Filters for Efficient Application in Half-Face Masks
## 1 Introduction
Respiratory, half masks, and masks belonging to the FFP3 category must meet the requirements of European norms EN 143, EN 149, and EN 1827, as well as the relevant standards applicable to respirators protecting against particles. The basic tests of filters include the tests for the particle penetration through filters, an analysis of a CO\({}_{2}\) concentration in the inhaled air, tests for respiratory resistance, filter clogging with dolomite dust, total penetration through the mask, flammability, skin tolerance of the mask, etc. [13, 14, 15]. The insufficient number of accredited laboratories, as well as the lack of certified testing equipment available on the market, led the authors to develop their own measuring devices, which enabled simple and less expensive testing of half mask filters and their accessories before the certification process. In order to ensure sufficient antiviral protection of the airways, the filtering face piece (FFP 3) respirators, which are practically all made of a filtration material, are required to achieve 99% filtration efficacy to comply with the EN 149 standard. Half masks without inhalation valves and with replaceable filters against FMP 3 (face mask particles) must comply with the EN 1827 standard under which their filtration efficiency must be as much as 99.95%. In addition to the filter's function of minimizing the penetration of particles, it seems beneficial to modify its properties and use self-disinfecting/self-cleaning antibacterial materials containing microparticles that produce ions of copper, silver, or zinc. Such materials contribute to the degradation of viruses captured in the filter.
## 2 Materials and Methods
### Machine Learning Algorithms and Statistical Analysis  
In this study, we used three Machine Learning algorithms for classification namely multilayer perceptron (MLP), support vector machine (SVM), and Random Forest. MLP is a feedforward neural network that consists of an input layer, hidden layers, and an output layer. Each layer comprises interconnected neurons through adjustable weights, which can be optimized through training using the backpropagation technique and an activation function to learn complex data patterns. MLP excels in non-linear classification tasks [20]. SVM is a statistical learning technique used for data classification by creating the most optimal hyperplane to separate data groups. It utilizes a subset of data points known as support vectors. A key feature of SVM is its ability to handle high-dimensional data and non-linear data patterns using the kernel trick, which transforms the data into a higher-dimensional space where it becomes linearly separable [21]. Random Forest is an ensemble learning algorithm that consists of multiple decision trees. Each tree is trained  
Figure 3: Example of data stored in the MySQL database.  
using a randomly selected subset of the data (bootstrap sampling) and employs majority voting to produce accurate predictions. A key strength of Random Forest is its robustness against noisy data and its ability to reduce overfitting [22].  
To validate data from wearable devices and medical devices, we employed Pearson correlation and linear regression analysis. Correlation analysis is a statistical method used to assess the relationship between two variables. The Pearson correlation coefficient ranges from \(-1\) to \(+1\), indicating the strength and direction of the relationship. Additionally, R-squared (R\({}^{2}\)), known as the coefficient of determination, is a statistical measure used in regression analysis to represent the proportion of variance in the dependent variable that can be explained by the independent variable(s). All significance tests were conducted as two-tailed tests.
# Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies
###### Abstract  
Energy consumption is a critical issue for the communications network operators, impacting deeply the cost of the services, as well as the ecological footprint. Network slicing architecture for 5G mobile communications enables multiple independent virtual networks to be created on top of a common shared physical infrastructure. Each network slice needs different types of resources, including energy, to fulfill the demands requested by each application, operator, or vertical market. The existing literature on network slicing is mainly targeted at the partition of network resources; however, the corresponding management of energy consumption is an unconsidered critical concern. This paper analyzes the requirements for an energy-aware 5G network slicing provisioning according to the 3 GPP specifications, proposes an architecture, and studies the strategies to provide efficient energy consumption in terms of renewable and non-renewable sources. NFV and SDN technologies are the essential enablers and leverage the Internet of Things (IoT) connectivity provided by 5G networks. This paper also presents the technical 5G technology documentation related to the proposal, the requirements for adaptive energy management, and the Integer Linear Programming (ILP) formulation of the energy management model. To validate the improvements, an exact optimal algorithmic solution is presented and some heuristic strategies.  
energy efficiency; energy management; network slicing; NFV; SDN; workload scheduling; renewable energy +
Footnote †: journal:  
_Article_  
Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies
## 3 Methods
### The AQM-PROBA Algorithm
#### 3.1.1 Pre-Processing  
PROBA-V NIR daily reflectance values, with spatial resolution of 300 m, are georeferenced based on coordinate information contained in the metadata, rejecting pixels:  
1. containing solar zenith angles greater than 60\({}^{\circ}\) and/or viewing zenith angles of NIR channel greater than 40\({}^{\circ}\);
2. classified as cloudy in the PROBA-V Quality assurance layers;
3. containing low radiometric quality;
4. containing reflectance values higher than 0.5.
## 3 Results
### Chemical Properties  
Based upon OM measurement results, application of FYM was observed to enhance SOM under both tillage systems (Figure 1). A comparison of mean values among different fertilizer treatments showed significantly different organic matter content (\(p<0.05\)) with different rates of FYM application. In the meantime, FYM exhibited more remarkable impacts under the RT system than the CT system. Maximum SOM was obtained when FYM was applied at 40 t\(\cdot\)ha\({}^{-1}\) in the RT system (27 and 41 g\(\cdot\)kg\({}^{-1}\) in the first and second years, respectively). A small increase in SOM was observed with chemical fertilization, as compared to control.  
Nitrogen content measurements indicated that, irrespective of NPK fertilizer dose, application of NPK fertilizer enhanced the nitrogen content of the soil. Generally speaking, higher nitrogen contents were obtained with the RT system than the CT system. The highest nitrogen content was achieved in the second year when 50% of the recommended dose of chemical fertilizer was applied in combination with FYM in the RT system (Table 2). Commonly, the nutrient content of plots subjected to CT was more perceptible, partly because of the deficit in substantial soil displacement and mixing. Maximum K and P contents were observed when FYM was applied in combination with chemical fertilizer in the RT system (Table 2). Maximum K content in the RT system was 13% higher than that in the CT system. Applying FYM in combination with chemical fertilizer in the CT and RT systems enhanced K content by 48% and 59% over the control, respectively. Although the soils of semi-arid areas are known to contain relatively high K contents, the use of FYM and an RT system can improve the content of K available to the plant, thereby increasing its resistance to abiotic stresses.  
P content measurements made it clear that maximum P contents in the CT and RT systems were achieved with chemical fertilization and combined FYM/chemical fertilizer application, respectively. The results indicated that further organic P was available in the RT system than the CT system.
# 2D Gaze Estimation Based on Pupil-Glint Vector Using an Artificial Neural Network
###### Abstract  
Gaze estimation methods play an important role in a gaze tracking system. A novel 2D gaze estimation method based on the pupil-glint vector is proposed in this paper. First, the circular ring rays location (CRRL) method and Gaussian fitting are utilized for pupil and glint detection, respectively. Then the pupil-glint vector is calculated through subtraction of pupil and glint center fitting. Second, a mapping function is established according to the corresponding relationship between pupil-glint vectors and actual gaze calibration points. In order to solve the mapping function, an improved artificial neural network (DLSR-ANN) based on direct least squares regression is proposed. When the mapping function is determined, gaze estimation can be actualized through calculating gaze point coordinates. Finally, error compensation is implemented to further enhance accuracy of gaze estimation. The proposed method can achieve a corresponding accuracy of 1.29\({}^{\circ}\), 0.89\({}^{\circ}\), 0.52\({}^{\circ}\), and 0.39\({}^{\circ}\) when a model with four, six, nine, or 16 calibration markers is utilized for calibration, respectively. Considering error compensation, gaze estimation accuracy can reach 0.36\({}^{\circ}\). The experimental results show that gaze estimation accuracy of the proposed method in this paper is better than that of linear regression (direct least squares regression) and nonlinear regression (generic artificial neural network). The proposed method contributes to enhancing the total accuracy of a gaze tracking system.  
human-computer interaction; gaze tracking; gaze estimation; pupil-glint vector; direct least squares regression; improved artificial neural network +
Footnote †: journal: Applied
# Can Biochar Improve the Sustainability of Animal Production?
## 2 Summary of Biochar-Production Techniques
### Activation of Biochar  
The raw biochar obtained from any of the thermal processes described above is usually activated to further enhance its physical and chemical properties [49]. In general, two types of activation are employed: physical and chemical, which are summarized as under: