esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel GBSM for Non-Stationary V2V Channels Allowing 3D Velocity Variations
## 4 Time-Variant Parameter Computations
### Time-Variant AoAs and AoDs  
By considering the displacements of the terminals and clusters, the AoAs and AoDs excessively vary with time. Both the azimuth and elevation angles can be represented by the random parameters \(\varphi\) and \(\alpha\) and can be characterized by a definite PDF. A small number of preceding works already proposed that under various scenarios, the AoA and AoD may follow the Gaussian uniform distribution. Nevertheless, the measured and analyzed outcomes in [28; 32] showed that the VMF distribution is responsive and can describe such angle allocations. Therefore, in this paper, it was presumed that the AoA and AoD carry out the following distribution as:  
\[p(\alpha,\varphi)=\frac{\cos\alpha\exp\left(\kappa^{i}\left(\cos(\varphi^{i}- \bar{\varphi}^{i})\cos\alpha^{i}\cos\bar{\alpha}^{i}+\sin\alpha^{i}\sin\bar{ \alpha}^{i}\right)\right)}{\left(2\pi\right)^{m/2}I_{m/2-1}(\kappa)(\kappa^{1-m /2})} \tag{18}\]  
where \(\kappa^{1-m/2}\) represents the element correlated with the distribution concentration, \(I_{m/2-1}(\cdot)\) denotes the zeroth-order modified Bessel function of the first kind, and \(\bar{\varphi}^{i}(t)\), \(\bar{\alpha}^{i}(t)\) represent the marginal value of the AoAs and AoDs, respectively. Moreover, in the order \(m/2-1\), the factor \(m\) represents the 3D and 2D VMF distribution, while \(m=3\) and \(m=2\), respectively. The value of the distribution factor \(\kappa\) remains constant, while the simulation time is very short. Therefore, when \(\kappa=0\), this means a special case of the isotropic scattering scenario. For the time-variant AoAs and AoDs, the mean angles are also time variant and can be denoted by \(\bar{\varphi}^{i}(t)\) and \(\bar{\alpha}^{i}(t)\) under non-stationary scenarios, while \(\kappa\) can be obtained by measurement. Accordingly, we can obtain the geometrical relationship as:  
\[\bar{\varphi}_{n}^{i}(t)=\left\{\begin{aligned} &\arccos\left(\frac{\mathbf{r}_{n,x}^{C_{n}^{i}}(t)- \mathbf{r}_{n,x}^{i}(t)}{\left\|\mathbf{D}\right\|_{n}^{i}(t)}\right),\mathbf{ r}_{n,y}^{C_{n}^{i}}(t)-\mathbf{r}_{n,y}^{i}(t)\geq 0\\ &-\arccos\left(\frac{\mathbf{r}_{n,x}^{C_{n}^{i}}(t)-\mathbf{r}_{n,x}^{i}(t)}{\left\|\mathbf{D}\right\|_{n}^{i}(t)}\right),\mathbf{r}_{n,y}^{C_ {n}^{i}}(t)-\mathbf{r}_{n,y}^{i}(t)<0\end{aligned}\right. \tag{19}\]and:  
\[\bar{\alpha}_{n}^{i}(t)=\arcsin\left(\frac{\mathbf{r}_{n,z}^{i}(t)-\mathbf{r}_{n,z }^{i}(t)}{\|\mathbf{D}\|_{n}^{i}(t)}\right) \tag{20}\]  
where \([\mathbf{r}_{n,x}^{i}(t),\mathbf{r}_{n,y}^{i}(t),\mathbf{r}_{n,z}^{i}(t)]\) and \([\mathbf{r}_{n,x}^{i}(t),\mathbf{r}_{n,y}^{i}(t),\mathbf{r}_{n,z}^{i}(t)]\) represent the location vectors of the x-, y-, and z-axes, respectively, and \(\|\mathbf{D}\|_{n}^{i}(t)\) denotes the distance over time.
## 4 Results
### Analysis Monitoring--Adoption of Monitoring Methods  
The monitoring of the implementation of GPP in European countries presents considerable heterogeneity in terms of the tools, methodologies and areas observed. In most cases, monitoring is carried out through electronic procurement platforms, online forms, periodic questionnaires and statistical analyses of public contracts. Some countries, such as Poland, Portugal and Malta, have implemented automated and standardized systems, with mandatory annual reports that include specific indicators on the inclusion of environmental criteria in tenders and contracts.  
Monitoring activities generally involve central, regional and local public authorities and concern both calls for tenders and contracts as well as organizational and strategic practices within the entities. The criteria monitored vary between European GPP criteria and national criteria, and they affect a plurality of product groups, including ICT products, paper, transport, construction and cleaning and catering services.  
Some countries, such as Norway, France and Germany, are adopting multi-level monitoring models, which not only verify the application of green criteria but also assess the environmental, economic and social effects of sustainable procurement. In others, such as Italy or Austria, the monitoring systems are still being consolidated or show critical issues related to data collection and quality.  
In particular, in Italy, the monitoring of Green Public Procurement is regulated by the Public Procurement Code (Legislative Decree 36/2023), which entrusts the ANAC (National Anti-Corruption Authority) with the task of collecting and managing data relating to the inclusion of Minimum Environmental Criteria (MEC) in tenders. Contracting authorities are required to communicate this data through the SIMOG system and the National Database of Public Contracts, tools that allow a first level of tracking and verification.  
However, in practice, monitoring is often used almost exclusively to detect the amount of green procurement carried out, neglecting the assessment of the impacts generated in different sectors. This approach limits the strategic potential of monitoring, which should instead be understood as a fundamental tool not only to verify the effective implementation of MEC or GPP criteria but also to direct corrective strategies, assess the effectiveness of public policies and measure the environmental, economic and social effects produced by sustainable procurement on the economic and production system.
# On Alpha-Expansion-Based Graph-Cut Optimization for Decoder-Side Depth Estimation
## 3 The Proposed Method  
This paper presents the results of the first implementation of hierarchical alpha-expansion [13] for depth estimation performed for immersive video. Moreover, two novel further improvements of this scheme are proposed, to provide even faster estimation and improve the quality of depth maps in the demanding task of decoder-side depth estimation.
## 2 Methods
### Measurement and Procedure
#### 2.3.2 Defensive Reactive Agility Test (DRAT)  
The defensive reactive agility test was performed on the test field presented in Figure 1. The subject stood in front of the starting line formed by an electronic timer. He startedrunning backward across the starting line, and once he passed the infrared (IR) beam (Photocell), the timing was started. At the same time, a direction indicator in the form of an arrow in random order (left, right, down) was displayed without delay on the LED light placed in front of the player, and the corresponding LED light turned on at the same time. The athlete had to react to the indicator placed in front of him by moving according to the direction shown and approaching the specific light with an open palm to a distance 0.2 m from the glowing LED light, thus initiating its extinguishing and thereby ending the measurement of the time interval. The person was then supposed to return to the starting position in the same direction of movement, while the time of return was not registered. The delay between consecutive stimuli was 3 s. In the lateral direction (left and right), a person could only move in a defensive slide by running backward. One test series consisted of six repetitions, with the lights coming on in a random order, two times in each direction. The better of the two attempts in each direction was recorded, thus eliminating possible errors in execution.  
The total time (TT) of the whole test, as well as the time of three components: left-side slide (LS), right-side slide (RS), and back slide (BS) were registered. The results were then outcomes.  
The study was carried out in two phases in December 2024. Measurements consisted of body height (BH), body weight (BM), body mass index (BMI), and the defensive agility test itself. The reliability of the new defensive agility test was determined by having participants perform the test twice, with an interval of one week between the two tests (test-retest). Both tests were conducted by experienced experts specializing in agility research in the presence of the trainer of the researched group. Testing took place at the training unit, always at the same time (6:00 pm). Each player was instructed and verbally encouraged to perform the test to the maximum. The tests were carried out in a sports hall with a wooden deck floor; subjects wore training clothes, and times were recorded with an accuracy of 0.01 s using an electronic timer (WITTY photocell system from Microgate, ITA). Before the actual testing, the subjects were asked about their health and any recent injuries, as only healthy players could participate in the study. During the test period, the air temperature varied from 21 to 24 \({}^{\circ}\)C. Before testing, the players warmed up under the coach's guidance (5-10 min of easy movement activities, 5 min of static stretching, and 7-10 min of dynamic flexibility exercises used to improve coordination, balance, proprioception and speed of movement, and finally 5 min of special high-intensity agility exercises).  
Figure 1: Defensive reactive agility test (DRAT).
# Cloud Computing in Free Route Airspace Research
## 2 FRA Mathematical Models--Graphs and Networks  
The complex concept of FRA will be modelled by using graph theory--more precisely, using the networks. First, we define some basic concepts of graph theory.  
Graph is a pair \(G=(V,E)\), where \(V\) is a set of _vertices (nodes)_, and \(E\) is a set of _edges (arcs)_, connecting some pairs of vertices (nodes). Two vertices \(u\) and \(v\) of a graph are adjacent if they are connected by an edge \(r=(u,v)\). For our purposes, we will consider no multiple edges (several edges are called multiple if they connect the same pair of vertices) and loops (edges beginning and ending at the same vertex). A graph is said to be _simple_ if it contains no loops or multiple edges. _Adjacency matrix_\(M\) of simple graph \(G(V,E)\) is a 0-1 matrix (a matrix with elements 0 or 1) defined by \(M(i,j)=1\Longleftrightarrow(i,j)\in E\) and \(M(i,j)=0\Longleftrightarrow(i,j)\
otin E\) for each \(i,j\in V\). An _adjacency list_ is a set of all edges of a simple graph (we can enter graphs in the computer program using the adjacency list).  
_Directed graph_ or _digraph_ is a pair \(D=(V,E)\), where \(V\) is a finite set of vertices, and \(E\) is a relation on \(V\). Elements of the set \(E\) are called _directed edges_ or _arcs_. A _simple digraph_ contains no loops or multiple arcs. _Path_\(p=(v_{0},v_{1},\ldots,v_{n})\) of length \(n\) in a digraph \(D=(V,E)\) is a sequence of various vertices \(v_{0},v_{1},\ldots,v_{n}\in V\), each pair \((v_{i-1},v_{i})\in E\)\((i=1,\ldots,n)\) of which forms an arc.  
A _weighted graph_ is a graph in which a number (the weight) is assigned to each edge. Such weights might represent, for example, costs, lengths, or capacities, depending on the problem.  
Further definitions and characteristics of graphs can be found in publication [11].  
In a basic definition, a network is defined as a collection of points joined together in pairs by lines. The points are referred to as vertices or nodes, and the lines are referred to as edges or arcs. Any system that is composed of individual parts or components linked together in some way can be called a network. It can be, for example, the internet, a collection of computers linked by data connections or human societies, collections of people linked by social interactions, or flights in air transport [12].
# Discrimination of Aroma Characteristics for Cubeb Berries by Sensomics Approach with Chemometrics
## 2 Results and Discussion  
### Optimization of SPME  
The volatile compounds in cubeb fruits were extracted using HS-SPME and the highest peak area response was selected in order to optimize the main parameters. These different desorption times, incubation times, extraction temperatures, and extraction times were optimized based on the total ion response in the GC-MS [19]. As shown in Figure 1a, study of desorption time including 1, 2, 3, and 4 min was tested. The peak areas of different volatiles were not significantly affected by the desorption time (\(p<0.05\)). In order to clean the fiber sufficiently, 3 min was chosen as the desorption time in the present work.  
Figure 1b shows the effect of incubation time (10, 15, 20, and 25 min) on the detection of total volatiles. The total volatile amounts significantly rose with increasing incubation time. However, there was no significant difference between 15, 20, and 25 min (\(p<0.05\)), which indicated that a 15 min incubation time would allow distributions between the fiber, the vial headspace, and the analytes to reach an equilibrium.  
Study of the extraction temperature including 40, 50, 60, and 70 \({}^{\circ}\)C was investigated as illustrated in Figure 1c. The peak areas of volatiles were significantly affected by the extraction temperature. In order to avoid aroma changes cause by higher temperature, 60 \({}^{\circ}\)C was chosen as the extraction temperature.  
Similarly, Figure 1d showed that there was no significant difference between 30 min and 40 min (\(p<0.05\)). In order to avoid the fiber desorption caused by the long time exposure in the vial, 30 min was chosen as the optimum extraction time.  
Therefore, the optimal extraction conditions were as follows: desorption time, 1 min; incubation time, 15 min; extraction temperature, 60 \({}^{\circ}\)C; and extraction time, 30 min. These conditions were applied during the headspace extraction of volatile compounds from cubeb fruits.
# Integrative Factors of E-Health Laboratory Adoption: A Case of Indonesia
###### Abstract  
Around the world, the adoption of digital health applications is growing very fast. The use of e-health laboratory systems is increasing while research on the factors that impact users to use e-health laboratory systems in Indonesia has not been done much. The objective of this study is to analyze the behavioral factors of e-health laboratory users. This study includes a survey conducted on Indonesian users, and data analysis was carried out thoroughly. Based on the Technology Acceptance Model, this research framework explores a combination of variables consisting of task-driven, technology-driven, human-driven, and adoption variables to form the model proposed in this study. This model was verified using the Structural Equation Modeling (SEM) method for factor analysis, path analysis, and regression. A total of 163 respondents were collected to evaluate this research model empirically and the level of this study were individuals. These three problems are all essential in affecting usage intentions in adopting an e-health laboratory system. Specifically, task technology fit, information quality, and accessibility show a direct effect on both perceived usefulness and perceived ease of use factors perceived by the user, and have an indirect influence on the adoption of an e-health laboratory system through these two factors. The design of an online laboratory system affects perceived ease of use and personal innovativeness factors affect the perceived usefulness that users feel when adopting a laboratory system, while task technology fit and personal innovativeness factors do not affect the perceived ease of use. However, overall technology characteristic and perceived usefulness followed by design are the main predictors of adopting an e-health laboratory system on e-health systems in Indonesia.  
e-health laboratory; task technology fit; technology acceptance; adoption intention +
Footnote †: journal: _Future Internet_
## 3 Results
### Quantitative Assessment
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & & & & & **Cocoa** & \\ \cline{3-6}
**Dataset** & **Input Data** & **OA (\%)** & & **UA (\%)** & **PA (\%)** & **F1 (\%)** \\ \hline \multirow{4}{*}{Refined} & 4 features 1  & 80.6 \(\pm\) 1.6 & 82.4 \(\pm\) 1.2 & 77.9 \(\pm\) 2.8 & 80.1 \(\pm\) 1.9 \\  & 4 features 1  & 85.1 \(\pm\) **2.0** & 87.5 \(\pm\) 0.8 & **81.9 \(\pm\) 3.9** & **84.6 \(\pm\) 2.4** \\  & 25 features 2  & 82.3 \(\pm\) 1.8 & 87.0 \(\pm\) 0.8 & 75.9 \(\pm\) 3.6 & 81.1 \(\pm\) 2.3 \\  & 25 features 2  & 84.7 \(\pm\) 1.9 & **88.9 \(\pm\) 0.9** & 79.3 \(\pm\) 3.5 & 83.8 \(\pm\) 2.3 \\  & 12 Sentinel-2 bands & 84.3 \(\pm\) 2.4 & 87.8 \(\pm\) 1.1 & 79.9 \(\pm\) 4.6 & 83.6 \(\pm\) 2.9 \\ \hline \multirow{4}{*}{Raw} & 4 features 3  & 82.1 \(\pm\) 1.8 & 80.4 \(\pm\) 1.4 & 84.7 \(\pm\) 3.4 & 82.4 \(\pm\) 2.2 \\  & 4 features 3  & 83.4 \(\pm\) 2.2 & 85.0 \(\pm\) 1.1 & 81.2 \(\pm\) 4.1 & 83.0 \(\pm\) 2.5 \\  & 25 features 4  & 80.7 \(\pm\) 3.2 & 85.5 \(\pm\) 1.4 & 73.9 \(\pm\) 6.2 & 79.2 \(\pm\) 4.1 \\  & 25 features 4  & 83.9 \(\pm\) 2.6 & 87.7 \(\pm\) 1.2 & 78.8 \(\pm\) 5.0 & 83.0 \(\pm\) 3.2 \\  & 12 Sentinel-2 bands & 83.7 \(\pm\) 2.3 & 85.8 \(\pm\) 1.2 & 80.9 \(\pm\) 4.2 & 83.2 \(\pm\) 2.7 \\ \hline \hline \end{tabular}  
* 1 NDVI + TCI + B12\_save + B8\_save. 2 Eleven VIs + 14 textural features selected from hierarchical clustering (see Figure 2c,d). 3 TCI + NDVI + GNDVI + B6\_save. 4 Eleven VIs + 14 textural features selected from hierarchical clustering (see Figure 2a,b).[ENDFOOTNOTE]  
\end{table} 
Table 4: The accuracy measures evaluated on different input configurations for the refined dataset and the raw dataset. The overall accuracy (OA), user’s accuracy (UA), producer’s accuracy (PA) and F1 were calculated from ten bootstrapping iterations of the refined test data. The mean accuracy \(\pm\) one standard deviation intervals are reported. Bold values indicate the highest accuracy per measure. For the UA, PA and F1 of the non-cocoa class, the reader is referred to Table 10 in Appendix A.
## 4 Results and Discussion
### Ablation Study  
To strengthen the validation of our proposed three-channel fusion-based CNN + GRU model, we conducted an ablation study focusing on the impact of different activation functions and pooling layers. Specifically, we compared the performance of the model using two activation functions, ReLU and Leaky ReLU, along with two types of pooling layers, average pooling and max pooling. Table 2 presents a performance comparison of a CNN + GRU model on two datasets: a three-class dataset (MIT-BIH + BIDMC) and a five-class dataset (MIT-BIH). The metrics used are accuracy and loss, with variations depending on the activation functions (ReLU and Leaky ReLU) and pooling layers (average pooling and max pooling). A comparative analysis of average pooling and max pooling within the proposed model demonstrated that average pooling consistently outperformed max pooling. Furthermore, the ReLU activation function exhibited marginally better performance than Leaky ReLU. As summarized in Table 2, ReLU with average pooling consistently performs the best across both datasets. Max pooling generally results in lower accuracy and higher loss, particularly for the five-class dataset. Leaky ReLU + max pooling shows the worst performance, especially on the five-class dataset, with accuracy dropping sharply. The combination of ReLU + average pooling seems to be optimal for this CNN + GRU model at each block of both the FEM and GRU networks across both datasets.  
To further highlight the advantages of our proposed three-channel fusion-based CNN + GRU model, we conducted another ablation study to analyze the impact of each input channel (spectrogram, LBP, and HOG images) on the overall model performance. Table 3 illustrates the comparison between the proposed three-channel fusion-based CNN + GRU model and the one-channel CNN + GRU models for the three-class dataset(MIT-BIH + BIDMC), where the latter utilizes only one of the three input images. While the training and average prediction times for the fusion-based model are longer than those for the one-channel CNN + GRU models, this increased duration should not pose a significant concern, as the training is a one-time offline process. Conversely, the three-channel fusion-based model demonstrates superior performance, achieving improved loss rates and accuracy.  
Overall, this ablation study reinforces the effectiveness of our proposed model by emphasizing the importance of careful architectural design in achieving optimal results for ECG classification tasks.
# Approaches to Parameter Estimation from Model Neurons and Biological Neurons
## 3 Biological Neurons: Estimating Parameters from Guessed Models  
The lack of knowledge on the exact biological model presents the principal challenge to the aim of characterizing ion channels. When the cost function only measures the fitting error between the time series data and one or more state variables, the optimum fit cannot represent the optimum parameters which we are seeking. Instead, some parameters will hit the boundaries of their search intervals in an attempt to compensate for model error. A model error term must therefore be added to the cost function to eliminate this compensation. The weight of the model error term must be carefully balanced with the data error term. This is done via their respective covariance matrices as we shall see below. If the model error is omitted from the cost function, parameter solutions will hit search interval boundaries to compensate for model error. Conversely, if the model error term carries a disproportionate weight, parameter solutions will be loosely constrained. This section discusses strategies which have been implemented, with various degrees of success, to obtain biological parameters from models whose error is known to be small. Models with residual error are defined here as models that (i) predict neuronal dynamics to a very high degree of accuracy under any current stimulation, and (ii) assign biologically realistic values to the thresholds and recovery times associated with the largest ionic conductances.