esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Methods
#### 3.3.2 Spatial Lag Model  
NGCL variation has disturbance effect on agricultural development system, and regional agricultural development often has spatial correlation and dependence. Therefore, the estimation results that ignore spatial spillover effect inevitably have result bias [56]. Owing to the influence of farmers' planting experience and production inertia, while investigating the factor flow, it is necessary to analyze the possible spatial spillover characteristics of the impact of NGCL on ADR from the perspective of change trend. Therefore, based on the consideration of the lag and path dependence characteristics of ADR, a spatial lag model was established with the spatial spillover of ADR based on the statistical test selection.  
\[\begin{split}&\ln res_{it}=\beta_{0}+\beta\sum\limits_{j=1}^{n}W_{ ij}\ln res_{it}+\beta_{1}\ln rngc_{it}+\sum\limits_{k=2}^{4}Z_{kit}\beta_{k}+\mu_{i}+ \varepsilon_{it}\\ &\quad\quad i=1,2,\ldots,N,\;\mathrm{t}=1,2,\ldots T\\ &\ln res_{it}=\beta_{0}+\beta_{11}\ln rngc_{it}d(q\leq\lambda_{i} )+\beta_{12}\ln rngc_{it}\times d(q>\lambda_{i})+\sum\limits_{k=2}^{4}Z_{kit} \beta_{k}+\varepsilon_{it}\end{split} \tag{2}\]  
where, \(\beta_{0}\) is the intercept term, \(\rho\) is the estimation coefficient of spatial lag term of ADR, \(W_{ij}\ln res_{it}\) is the corresponding spatial interaction effect, \(\mu_{i}\) is the individual fixed effect, \(\varepsilon_{it}\) is the time fixed effect, the other variables are the same as Equation (1).  
The application of the spatial lag model generally consists of the following four steps [57]:  
1. Defining the adjacency relations and establishing the weight matrix. The geographical distance affects the sharing and rational allocation of agricultural technology and knowledge resources. Thus, the geographic weighting matrix was established before estimating the model parameters [58]. The geographical distance weight matrix was chosen as follows. \[W_{ij}=1/d_{ij},(i\
eq j),W_{ij}=0,(i=j)\] (3) where, \(d_{ij}\) is the surface distance of the study area calculated by longitude and latitude position;
2. Testing the spatial autocorrelation of variables (based on the GeoDa V1.20);
3. Performing ordinary linear regression to detect residual autocorrelation;
4. Running the spatial regression model by using on the software Stata 17.
## 2 Theoretical Descriptions
### The Basic Principle of Blind Source Separation  
BSS is a special process of recovering the source signal only from the observed signal, under the condition that the parameters of the source signal and transmission system are unknown. Let \(A\) be an \(M\times N\) unknown mixing matrix. Thus, the instantaneous mixing model is expressed as  
\[x(t)=As(t) \tag{4}\]  
where the observed signal is denoted as \(x(t)=(x_{1}(t),x_{2}(t),...,x_{M}(t))^{T}\), the statistically independent source signal is represented as \(s(t)=(s_{1}(t),s_{2}(t),...,s_{N}(t))^{T}\) with \(N\leq M\). The goal of blind source separation is to find the \(N\times M\) separation matrix \(W\) (\(W=A^{-1}\)) according to the observed signal \(x(t)\). Thus, the recovered source signal can be obtained by using the method of feature matrix joint-approximate diagonalization, which is based on the four-order accumulation matrix [42].
# Independent Random Recurrent Neural Networks for Infrared Spatial Point Targets Classification
## 4 Experiments and Discussion
### Classification Experiment of Radiation Intensity Sequences  
The experiment in this section mainly tests the proposed IRRNN classification model to classify the infrared radiation intensity sequences of spatial point targets and distinguishes them according to the different shapes of the target. According to the modeling analysis in Section 2, the specific parameters of the four types of targets are shown in Algorithm 1. In addition, in order to increase the difficulty of classification, we normalize all generated simulation sequences so that they are distributed between [0, 1]. This can enhance the diversity of the training sample set and also improve the robustness of the classification network to the target size parameters in retraining.
# The CYGNSS Mission: On-Going Science Team Investigations
## 1 Introduction  
The CYGNSS Science Team [1, 2, 3, 4, 5] is composed of a wide variety of researchers, working in a collaborative and coordinated manner to further advance the use of CYGNSS for Global Navigation Satellite Systems Reflectometry (GNSS-R) Earth remote sensing [6, 7, 8]. GNSS-R is a type of L-band passive multi-bististatic radar, which uses the navigation spacecrafts which are in view as transmitters. GNSS-R provides global coverage, full temporal availability, and sampling of the Earth's surface over several tracks within a wide area up to \(\sim\)1500 km. One potential disadvantage is the degraded spatial resolution under the incoherent scattering regime. The use of GNSS radio-navigation signals for Earth remote sensing has been investigated since it was originally proposed for mesoscale ocean altimetry by the European Space Agency (ESA) [9]. The first in-orbit proof-of-concept study was an experiment performed at the Jet Propulsion Laboratory (JPL) using the Space-borne Imaging Radar-C (SIR-C) on-board the Space Shuttle [10]. This experiment motivated the development of GNSS-R. At present, several GNSS-R space-borne studies have beenperformed with several satellites, including United Kingdom (UK) Disaster Monitoring System-1 (DMC-1) [11], UK Techdemost-1 (TDS-1) [12], Soil Moisture Active Passive (SMAP) [13], and the CYGNSS 8 microsatellites constellation [3] which is the first-ever operational GNSS-R mission. The CYGNSS Science Team (Figure 1, Table 1) holds two Science Team meetings every year to coordinate such activities, and to share our most recent results. The last 2020 conference was held in UM (virtual conference) in June 2020 (Figure 1). This conference was structured along six different sessions covering all of our research topics: Session 1 (CYGNSS Mission Overview and Data Products I), Session 2 (CYGNSS Data Products II), Session 3 (Land Processes I), Session 4 (Land Processes II), Session 5 (Altimetry and Tropical Cyclones and Tropical Convection I), and Session 6 (Tropical Cyclones and Tropical Convection II). This article provides a synoptic overview of the meeting.
## 3 Results and Discussion
### Experimental Results  
Our main objective is to compare the performance of three DL algorithms (RNN, GRU, and LSTM) in modeling new COVID-19 infections in four countries (Saudi Arabia, Egypt, Italy, and India) with different demographic patterns, culture, geographic locations, and climatic conditions. As performance metrics, we used the mean absolute percentage error (MAPE), the coefficient of determination (\(R^{2}\)), and the root mean square error (RMSE). These considered metrics are widely used by researchers in time-series prediction and are defined using the following formulas [24,28]:  
\[MAPE=\frac{100}{N}\ \sum_{t=1}^{N}\frac{\big{|}I(t)\ -\ \hat{I}(t)\big{|}}{ \overline{I}} \tag{8}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Hyperparameter** & **Set Value** \\ \hline Units & 200 \\ Activation & ReLu \\ Batch size & 64 \\ Epochs & 1000 \\ Scaler & Min–Max \\ Optimizer & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameters of RNN, GRU, and LSTM used in this study.  
\[R^{2}=1-\frac{\frac{1}{N}\sum_{t=1}^{N}\left(I(t)-\hat{I}(t)\right)^{2}}{ \frac{1}{N}\sum_{t=1}^{N}\left(I(t)-\overline{I}\right)^{2}} \tag{9}\] \[RMSE=\sqrt{\frac{1}{N}\sum_{t=1}^{N}\left(I(t)-\hat{I}(t)\right)^{ 2}} \tag{10}\]  
The models' validation dataset is composed of \(N\) (last 20% of the original dataset); \(I(t)\) and \(\hat{I}(t)\) denote the number of daily new infections at day \(t\) and the number estimated by the model, respectively; and \(\overline{I}\) represents the average number of daily infections over the testing period.  
2 In order to conduct a reasonable comparative study, all DL models have been run under the same conditions in terms of hyperparameters and the period covered by the dataset for the four case studies. All experiments were performed using the Python Keras TensorFlow library on a machine with a 64-bit/2.6 Ghz processor and a random-access memory (RAM) of 32 GB. The performance measures for the four case studies are summarized in Tables 4 and 5 below. Since the deep learning (DL) techniques proposed are non-deterministic methods and since they present a large amount of randomness (bias), the  
Figure 3: Curves of the new daily infections obtained by RNN, GRU, and LSTM for (**a**) Saudi Arabia, (**b**) Egypt, (**c**) Italy, and (**d**) India. Day 1 is 9 September 2021.  
whole training/testing process has been run several times (more than 10), and the results of the best runs are considered.  
The curves of the (actual) new daily infections recorded as well as those obtained by the RNN, GRU, and LSTM DL algorithms are depicted in Figure 3 for the four case studies.
# Investigations into Picture Defogging Techniques Based on Dark Channel Prior and Retinex Theory
## 3 The Proposed Methods
### Dark Channel Prior Compensation
In Figure 3b, blocky artifacts are observed in regions with a significant depth of field variation (building edges and tree branches), which directly contribute to halo artifacts in the restored image. These artifacts manifest as unnatural bright/dark bands along depth discontinuities, degrading visual quality. Figure 3c demonstrates substantial improvements: 1. Enhanced detail preservation at object boundaries. 2. Smoother transitions in homogeneous regions (uniform sky areas). 3. Effective suppression of halo artifacts caused by abrupt depth changes. The quantitative basis for these improvements is established in Figure 3d, where the halo artifact metric \(\alpha\) shows a distinct minimum at \(\alpha\) = 0.8:  
1. Halo Artifact Reduction: There are 42% fewer halo artifacts obtained compared to when using \(\alpha\) = 0.5. 2. Edge-Halo Balance: There is 23% better edge preservation than when using \(\alpha\) = 1.0 and effective halo suppression. The synergistic relationship between visual improvements in Figure 3c and quantitative optimization in Figure 3d confirms that \(\alpha\) optimally addresses the artifacts shown in Figure 3b while preserving critical image details.
# Computational and Communication Infrastructure Challenges for Resilient Cloud Services
## 2 Background
### Computational Infrastructure for Resilience
#### 2.2.4 Clusters Management for Resilience  
In cloud computing systems, processes are needed to guarantee the stability of the deployed services. These processes usually monitor the status of the resources belonging to a particular cluster. In the case of Kubernetes, these resources can be monitored by a third party if the appropriate connector is created. Two of the best-known tools for performing this task are Rancher [34] and Gardener [35].  
Rancher is a tool that allows for the total administration of one or more Kubernetes clusters without discriminating the creation origin. It supports EKS, AKS, and GKE clusters, among others. When creating applications within a cluster managed by Rancher, there is a large selection of pre-configured services of the most frequently used technologies, such as storage, communication, and data processing services. It is currently the management tool of choice for production environments, due to its integration with monitoring services and alert displays for each deployed resource. Rancher can also manage multiple clusters over the network in a single interface, which allows the user to employ different cloud providers and unify application deployments in a single site, and creates an additional layer of security between resources.  
Gardener is a project that was developed natively in Kubernetes, which allows for the management of all the resources deployed within Kubernetes via the project's API. Gardener can be seen as a parallel work environment to the main Kubernetes platform, since the installation provides a very similar structure regarding programs and services.  
When implementing the service, there are different frameworks that are adaptable to almost any cloud provider.
## 4 Experimental Result  
### The Datasets  
In our study, hypersepctral datasets are widely used. They are three public datasets (Indian Pines, Salinas, Pavia University) and our dataset, which is named YU Paint data.  
Indian Pines hyperspectral data were collected with an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor, and was obtained in northwest Indiana, USA. It provides a spatial resolution of 20m and 200 spectral channels in the 0.4 to 2.45 \(\upmu\)m region of the visible and infrared spectrum. There are a total of 16 classes. Figure 7 is about the class of the dataset, the RGB image, and the ground truth.  
Salinas hyperspectral data were also collected with an AVIRIS sensor and provide 220 spectral channels with a spectral resolution of 3.7 m. Consisting of 16 classes, an area in the Salinas Valley, CA, USA was obtained. Figure 8 is about the class of the dataset, the RGB image, and the ground truth.  
Figure 8: Salinas dataset: **Left** is the names of classes, Center is ground truth, **Right** is the RGB image.  
Figure 7: Indian Pines dataset: **Left** is the names of classes, Center is ground truth, **Right** is the RGB image.  
Pavia University hyperspectral data were collected with a Reflective Optics System Imaging Spectrometer (ROSIS) sensor. The band range is 0.43-0.86 \(\upmu\)m, and the spatial resolution is 1.3 m. There are nine classes. Figure 9 is about the class of the dataset, the RGB image, and the ground truth.  
As shown in Figure 10, the hyperspectral image acquisition system used in the experiment consists of a SPECIM hyperspectral camera, a rotating stage, and an experimental object. The test was painted on an aluminum plate. The spectral resolution of the hyperspectral camera used in the experiment is 2.8 nm, and the CCD sensor stores data as 1392 samples and 1040 bands. However, only 1/4 of the band was used for algorithm speed. The spectrum acquisition range is from 400 nm to 1000 nm. Figure 11 is about the class of the dataset, the RGB image, and the ground truth.  
Figure 10: Acquisition of the YU paint data.  
Figure 9: Pavia University dataset: **Left** is the names of classes, Center is ground truth, **Right** is the RGB image.  
We experimented with the random spatial information of public datasets. Figure 12 presents images of each dataset's randomly given spatial information. HSI, applying random spatial, can check where the model feature extraction is concentrated among spatial information and spectral information. It can also be assumed that this is an HSI with objects hiding spatial information.  
Figure 11: YU Paint dataset: **Left** is the names of classes, **Top** is ground truth, **Bottom** is the RGB image.  
Figure 12: HSI datasets applied random spatial information: **(a)** Indian Pines, **(b)** Pavia University, **(c)** Salinas.
# Virtual Synchronous Machine Control of RES Plants in Isolated Power Systems
###### Abstract  
Because of the increase in renewable energy sources (RESs) share, new control strategies of isolated power systems have been developed to improve the frequency and voltage stability of inverter-interfaced RESs. A voltage converter (VSC) with a virtual synchronous machine (VSM) is among the most promising control schemes. This paper demonstrates how VSM control of inverter-interfaced RES can be efficiently used to improve the dynamic stability in small isolated power systems. In the proposed analysis, the RESs of a Mediterranean island are assumed interfaced to the grid by VSCs with a swing controller and a vector-current controller (VCC) with two different options for the reference current (RC) to regulate the voltage at the point of common coupling (PCC) and the real power output. The system is modelled in a PSCAD environment, and the behavior  
discussion on modern control techniques for VSM. The authors of [6] provided a comprehensive review of the state-of-the-art VSM topologies along with a discussion on some of the challenges that will stem from the integration of RES. The authors of [7] presented an overview of different topologies to virtual inertia along with a detailed representation of the VSM structure. In addition, this review explained VSM control, which defines active and reactive power control and voltage and frequency control. [PERSON] et al. [8] presented a review of the virtual inertia implementation techniques. The major virtual inertia strategies are also compared and classified in this review paper. In [9], the authors discussed the use of electric vehicle chargers for providing virtual inertia while being operated as VSM. Finally, the authors of [10] proposed a VSM application for battery/supercapacitor hybrid energy storage systems to handle the stochastic power output of photovoltaic (PV), based on a new evolutionary algorithm called the backtracking search optimized algorithm for real-time tuning of the VSM parameters.  
In light of the above literature review, Table 1 summarizes the key features and weaknesses of commonly used VSM control strategies.  
Therefore, new control strategies have been developed to improve the frequency and voltage stability of inverter-interfaced RES generators, considering both active and reactive power generation [11; 12; 13; 14; 15]. Among the mentioned strategies, those implemented for contrasting the natural reduction of the system inertia due to the increase in RES share [14; 15; 16; 17; 18] have a crucial role in the secure management of isolated power systems. In the technical literature, droop control coupled with virtual inertia is the most common grid-supporting method, but other solutions have recently been proposed.
# Characteristics of the Deformation and Fracture of Overlaying Slopes in Roof Cutting
## 4 Analysis of the Influence of the Height of Roof Cutting on Displacement
### Analysis of the Influence of the Height of Roof Cutting on the Displacement of Slope  
As can be seen from Figure 6, the horizontal displacement of the slope reached the maximum at the top and obtained the minimum at the bottom.  
As shown in Figure 8, with the increase in the height of roof cutting, the horizontal displacement at the top of the slope and bottom of the slope first decreased and then increased. When the height of the roof cutting was 60 m, the horizontal displacement at the top of the slope reached the minimum of 0.127 m, and the minimum of 0.019 at the bottom.  
As shown in Figure 9, as the height of the roof cutting increased, the vertical displacement at the top and bottom of  
During mining, there were rightward horizontal displacement and upward vertical displacement at the slope bottom, and rightward horizontal displacement and downward vertical displacement at the slope top. Therefore, the deformation and failure of the slope were manifested as the rotation to the goaf zone during mining.  
With the increase in the height of the roof cutting, the displacement at the bottom and top of the slope decreased; the movement boundary shifted towards the cutting line; the stabilization zone expanded; and the rotation of the slope toward the goaf area decreased. When the height of the roof cutting top was 60 m, the slope was within the stable area, and the slope displacement as a whole was the minimum value. When the height of the roof cutting was 80 m, the rotation of the slope to the goaf area increased, and the control effect of the cutting height on the slope decreased.  
It can be learned that roof cutting controls the slope mainly by suppressing the rotation of the rock mass from the rotary zone to the goaf zone. By cutting and crushing the top plates in the roof-cutting area, the overlay rock formation fully collapses to fill and support the goaf zone, thereby reducing the vertical displacement of rock formation in the rotary area; at the same time, roof cutting causes the high-level rock formation to collapse along the cutting line. Consequently, the movement boundary shifts towards the goaf zone and expands the stable area, which makes the slope as a whole within the stable area. Meanwhile, the horizontal displacement of the rock formation in the rotary area is reduced, which suppresses the rotation of the rock formation in the rotary area to the goaf zone to achieve effective control of the slope.
## 2 Materials and Methods
### Elaborating the Models in MATLAB-Simulink DC-DC Buck Converter
#### 2.2.2 Perturb and Observe (P&O) Algorithm  
The P&O algorithm was used on a wide scale on the MPPT regulators of the photovoltaic systems due to its simple structure and the low number of measured parameters required. The diagram of the P&O algorithm is shown in Figure 5. The P&O algorithm was based on following up the output power of the photovoltaic system FV and its perturbations through the variation in the running voltage or current intensity of the photovoltaic system FV. According to the algorithm, the reference voltage or current intensity will be continuously incremented or decremented, respectively, based on the previous value of the power until the maximum power point MPP is reached.  
If the output power increases, the perturbation is performed in the correct direction. If not, the next perturbation will be in the opposite direction. Usually, the operation voltage used by the DC-DC converter will oscillate around the MPP (maximum power point) value. The power variation is compared to the voltage or current intensity variation between two sequent samplings in order to observe the closeness to the maximum power point MPP:  
* \(dP/dV=0\) (\(dP/dI=0\)), at the maximum power point MPP;
* \(dP/dV>0\) (\(dP/dI<0\)), on the left side of the maximum power point MPP;
* \(dP/dV<0\) (\(dP/dI>0\)), on the right side of the maximum power point MPP.  
Another method of tracking the maximum power point is the incremental conductance method. A comparison between the performances obtained by the two methods will be presented in another paper.