esa-sceva/satcom-chunk-collection huggingface dataset:
# Multidimensional Assessment of Lake Water Ecosystem Services Using Remote Sensing
## 2 Materials and Methods
### Principal Components Regression  
Principal components regression (PCR) is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates are unbiased, but their variances are large so they may be far from the true value. The theory of PCR is to eliminate multicollinearity of original variables using principal component analysis. The principal component variables are used as independent variables in the regression model, and then they are substituted with the original variables according to the score coefficient matrix to the regression model obtained. The principal components regression model with n sets of observations and \(k\) original variables \(X_{k\times n}\) can be outlined as follows:  
\[X_{k\times n}=\left[\begin{array}{ccc}x_{11}&\cdots&x_{1n}\\ \vdots&\ddots&\vdots\\ x_{k1}&\cdots&x_{kn}\end{array}\right] \tag{5}\]  
First, complete a principal components analysis of the \(X\) matrix and save the principal components in \(Z\). We then transform the original variables to their principal components as:  
\[X^{\prime}X=PDP^{\prime}=Z^{\prime}Z \tag{6}\]  
where \(D\) is a diagonal matrix of the eigenvalues of \(X^{\prime}X\), \(P\) is the eigenvector matrix of \(X^{\prime}X\), and \(Z\) is a data matrix made up of the principal components. \(P\) is orthogonal so that \(P^{\prime}P=I\).  
Second, fit the regression of \(Y\) on \(Z\) obtaining least squares estimates of \(A\).  
\[\hat{A}=\left(Z^{\prime}Z\right)^{-1}Z^{\prime}Y=D^{-1}Z^{\prime}Y \tag{7}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**z-Score (Standard Deviation)** & **\(p\)-Value (Probability)** & **Confidence Level** \\ \hline z \(<-1.65\) or z \(>+1.65\) & \(p\) \(<0.1\) & \(90\%\) \\ z \(<-1.95\) or z \(>+1.95\) & \(p\) \(<0.05\) & \(95\%\) \\ z \(<-2.58\) or z \(>+2.58\) & \(p\) \(<0.01\) & \(99\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Critical \(p\)-value and z-score with various confidence levels.  
Third, set the last element of \(A\) equal to zero and transform back to the original coefficients using \(B=PA\). The two sets of regression coefficients, \(A\) for principal components and \(B\) for original variables, are related using the formulas  
\[A=P^{\prime}B \tag{8}\]  
\[B=PA \tag{9}\]  
Last, the F test is adopted to test the statistical significance of the principal component regression model in this study.
# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 3 Test Cases
### Sesia River--Italy 2020  
The second study area focuses on the Sesia River, situated in the northern part of Italy. In October 2020, the area experienced an exceptionally severe meteorological event. Regional and National Civil Protection authorities responded by issuing red and orange alerts, respectively [44].  
The northern and western parts of the Piedmont region were particularly affected with significant damage. Heavy rainfall has led to the overflow of multiple rivers and subsequent flooding of numerous areas [45]. In particular, the Sesia River experienced a breach that resulted in road closures, bridge collapses, and the submergence of various regions.
## 2 Smart Grids System
### Power Supply
#### 2.2.2 Renewable Energy (RE)
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Algorithm \& Tools** & **Battery Categories** & **Ref.** \\ \hline Constant current/Constant voltage (CC/CV) & Several types of batteries, Lithium-ion Battery & [99,100] \\ \hline Arbitrage optimization algorithm & & & \\ \hline CubeSat battery algorithm (CubeSat) & Non-Available (NA) & & \\ Maximum efficiency tracking (MEET) & (NA) & [22,27,101,102] \\ \hline MOA & & & \\ \hline First access first charge (FAFC) & & & \\ scheduling & & & \\ \hline Flat feeder profile & & & \\ \hline Battery Management & (GA) & Lithium-ion Battery & [72,103,104] \\ \hline JAYA algorithm & & & [72,103,104] \\ \hline Pontryagin’s minimum principle (PMP) & & & \\ \hline PSO & Electric vehicles batteries, & & \\ Lithium-ion Battery & [28,73,75] & \\ \hline Orthogonal least squares algorithm & Lithium-ion Battery & [86] & \\ \hline MATLAB algorithm & Variety of batteries & [105] & \\ \hline Liquid cold plate control equation & LiFePO4 battery & [106] & \\ \hline Stochastic algorithm & Electric vehicles batteries & [107] & \\ \hline \multirow{4}{*}{Renewable energy} & (GA) & NA & [108,109] \\ \cline{2-4}  & Markov decision process (MDP) & & \\ \cline{1-1} \cline{2-4}  & Levenberg–Marquardt algorithm & & \\ \cline{1-1} \cline{2-4}  & (LMA) & & \\ \cline{1-1} \cline{2-4}  & Gaussian algorithm & Several of batteries & [110] \\ \cline{1-1} \cline{2-4}  & Forgetting factor algorithm & & \\ \cline{1-1} \cline{2-4}  & Trust-region reflective & & \\ \hline \multirow{4}{*}{Cloud Computing} & BMS-Master and BMS-Slave & Lithium-ion and & [18] \\ \cline{1-1} \cline{2-4}  & The home energy management & Electric vehicles batteries & \\ \cline{1-1} \cline{2-4}  & system (HEMS) & Branch and bound algorithm & [111,112] \\ \cline{1-1} \cline{2-4}  & Smart home energy management & & \\ \cline{1-1} \cline{2-4}  & system (SHEMS) & NA & [113,114] \\ \cline{1-1} \cline{2-4}  & Energy-performance trade-off multi & & \\ \cline{1-1} \cline{2-4}  & resource cloud task scheduling & & \\ \cline{1-1} \cline{2-4}  & algorithm (ETMCTSA) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the literature algorithms of battery management, renewable energy, and cloud computing.
# Diagenesis of Deep Low Permeability Reservoir in Huizhou Sag and Its Influence on Reservoirs
## 5 Discussion  
### Impact of Compaction on Reservoir Quality  
Previous studies have established a quantitative relationship between the primary pore volume of sandstone (\(V_{1}\)) and the sorting coefficient (\(S_{0}\)) to determine the primary pore volume (initial intergranular volume or initial depositional porosity) of clastic sediments [58]. The grain size statistics based on the clastic particle size classification show that the distribution of the sorting coefficient of Enping Formation sandstone ranges from 1.45 to 14.8 with an average value of 2.35. Therefore, we can deduce that the initial porosity of deep, low-permeability Enping Formation sandstone spans from 24.6 to 36.1% with an average value of 26.8%. Because of the reservoir effect caused by mechanical compaction, the sandstone particles were packed together, and the initial porosity decreased or even disappeared. However, the pores that were not affected by mechanical compaction stayed the same. Mechanical compaction in the Enping Formation affected the residual porosity (\(V_{2}\)), which primarily consists of the remaining primary intergranular pores and early cementation-preserved pores.  
\[V_{2}=\frac{P_{1}+P_{c}}{P_{t}}\times\ Q_{m}+W \tag{3}\]  
\[V_{mc}=V_{1}-\ V_{2} \tag{4}\]  
The formula shown above uses a thin slice of the cast material under a microscope to calculate the percentage of the remaining intergranular pore surface, represented as \(P_{1}\) and expressed in %. By examining a thin slice of the cast material under a microscope, we can determine the ratio of the pore face area occupied by carbonate cement dissolution, which we express as \(P_{C}\) in %. We represent the content of cement that controls the reservoir property analyzed by a thin slice of the cast material under a microscope, expressed as \(W\) in %. \(P_{t}\) represents the total face percentage obtained from analyzing a thin slice of the cast material under a microscope, expressed in %; \(Q_{m}\) represents the measured porosity within the core samples, expressed in a percentage; \(V_{1}\) represents the original porosity level before mechanical compaction occurred, expressed in %; and \(V_{2}\) represents the porosity level after mechanical compaction and is expressed in %, while \(V_{mc}\) represents the amount of porosity lost due to mechanical compaction processes, also expressed in %. Microscopic observations reveal a loss range of 12.5-28.2% due to compaction processes, with an average value of 18.9.
## 4 Experimental Results and Comments
### Experimental Methods and Results Analysis
Figure 9: Prediction results for validation data (Boston housing price data, P = 5, C = 5, m = 2, flexible generation method). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Databases** & **Models** & **m** & **Context** & **P** & **C** & **Rules** & **PI** & **Time (s)** \\ \hline \multirow{4}{*}{Auto MPG} & \multirow{4}{*}{MGFM} & \multirow{4}{*}{1.5} & Uniform & 6 & 3 & 18 & 0.4602 & 0.0133 \\ \cline{3-8}  & & & Flexible & 5 & 3 & 15 & 0.4505 & 0.0132 \\ \cline{3-8}  & & & Uniform & 6 & 3 & 18 & 0.4737 & 0.0127 \\ \cline{3-8}  & & & Flexible & 6 & 3 & 18 & 0.4909 & 0.0104 \\ \cline{2-8}  & & & Uniform & 6 & 3 & 18 & 0.3986 & 0.0400 \\ \hline \multirow{4}{*}{Boston housing} & \multirow{4}{*}{MGFM} & \multirow{4}{*}{1.5} & Uniform & 6 & 4 & 24 & 0.4603 & 1.2834 \\ \cline{3-8}  & & & Flexible & 6 & 3 & 18 & 0.4053 & 1.2634 \\ \cline{3-8}  & & & Uniform & 6 & 6 & 36 & 0.4353 & 1.3044 \\ \cline{3-8}  & & & Flexible & 5 & 6 & 30 & 0.4253 & 1.2937 \\ \cline{2-8}  & & & Uniform & 5 & 6 & 30 & 0.3043 & 1.3427 \\ \hline \multirow{4}{*}{Energy efficiency} & \multirow{4}{*}{MGFM} & \multirow{4}{*}{1.5} & Uniform & 5 & 6 & 30 & 0.4585 & 0.5562 \\ \cline{3-8}  & & & Flexible & 5 & 5 & 25 & 0.4774 & 0.5472 \\ \cline{3-8}  & & & Uniform & 5 & 4 & 20 & 0.4824 & 0.2561 \\ \cline{3-8}  & & & Flexible & 6 & 4 & 24 & 0.4952 & 0.2834 \\ \cline{2-8}  & & & Uniform & 4 & 3 & 12 & 0.4561 & 1.9887 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results of performance index and processing time (validation data).
## 3 Results and Discussion
### Tests with Termites  
Repeated experiments with termites showed that although termites damaged the surface of both impregnated and untreated wood, the damage was significantly greater in untreated wood. Furthermore, feeding on impregnated wood caused extinction of all termite colonies within 1.5 months. The termite colonies feeding on untreated specimens were fully viable during the entire test period. According to the literature, humic substances act as natural pesticides and repellents for wood-damaging insects [43]. Humic substances are known for their fungicidal and bactericidal effects [44]. Salts of humic acids with Ca and Mg are insoluble in water [36] and precipitate in wood veins, acting as sustainable natural wood preservatives and making them more resistant to insect attack. Moreover, the build-up of crystalline (Ca, Mg)O\(\cdot\)xSiO\({}_{2}\cdot\)yH\({}_{2}\)O phases in the wood veins (see Section 2.4) additionally inhibits the leaching out of humic substances and provides a physical barrier to wood-eating insects to reach organic wood fibers as well as a barrier to wood-degrading fungi and bacteria. Additionally, alkaline pore water may contribute to the antifungal, antibacterial, and insect-repelling effects of impregnation. The pH of the fresh impregnation solution exceeded 13, but after crystallization had completed, the pH value had decreased to approximately 9.  
Figure 7: Heat release rates of untreated (**a**) and treated (**b**) black alder wood.
## 1 Introduction
It is known that liftoff point of intersection (LOI), as a desirable signal feature, is independent of liftoff distance [13]. The LOI is a common intersection point of PEC signals when liftoff distance changes. For the permeability effect on PEC signals, a similar method can be considered in the conductivity measurement to reduce the influence of permeability. Thus, similarly, a common intersection invariance point feature is needed in PEC signals of ferromagnetic samples for reducing the permeability effect when sample permeability changes or the environmental magnetic field changes. Therefore, in the measurement of conductivity, it is important and significant to propose a method to obtain a permeability invariance (PI) feature for reducing permeability effect or environmental magnetic field influence. The main contribution of this paper is that a thrice subtraction method is proposed to obtain a PI point feature in PEC signals of ferromagnetic samples for reducing the permeability effect or environmental magnetic field influence. Correspondingly, the availability of the thrice subtraction method and the obtained PI feature is analyzed and discussed. The rest of this paper is organized as follows: in Section 2 the PEC analytical model is formulated from a harmonic eddy current model, the thrice subtraction method is introduced in Section 3, the simulation results are analyzed in Section 4, in Section 5 the experiments are performed for verifying the simulation results and finally, Section 6 presents the concluding remarks.
# An Adaptive Neuro-Fuzzy Propagation Model for LoRaWAN
###### Abstract  
This article proposes an adaptive-network-based fuzzy inference system (ANFIS) model for accurate estimation of signal propagation using LoRaWAN. By using ANFIS, the basic knowledge of propagation is embedded into the proposed model. This reduces the training complexity of artificial neural network (ANN)-based models. Therefore, the size of the training dataset is reduced by 70% compared to an ANN model. The proposed model consists of an efficient clustering method to identify the optimum number of the fuzzy nodes to avoid overfitting, and a hybrid training algorithm to train and optimize the ANFIS parameters. Finally, the proposed model is benchmarked with extensive practical data, where superior accuracy is achieved compared to deterministic models, and better generalization is attained compared to ANN models. The proposed model outperforms the nondeterministic models in terms of accuracy, has the flexibility to account for new modeling parameters, is easier to use as it does not require a model for propagation environment, is resistant to data collection inaccuracies and uncertain environmental information, has excellent generalization capability, and features a knowledge-based implementation that alleviates the training process. This work will facilitate network planning and propagation prediction in complex scenarios.  
propagation modeling; adaptive-network-based fuzzy system; LoRa & LoRaWAN; radio wave propagation; artificial neural networks; subtracting clustering +
Footnote †: journal:
# Research on Variable Universe Fuzzy Multi-Parameter Self-Tuning PID Control of Bridge Crane
## 4 Simulation Analysis  
This section presents an evaluation of the performance of three control methods for anti-sway and positioning control of a bridge crane. The methods being evaluated are the traditional PID controller, the FUZZY-PID controller, and the VUFMS-PID controller. To conduct this comparison, we establish a simulation model of the bridge crane using the Simulink module in Matlab. We then subject the three control methods to the same operating conditions and compared their control effects.  
In the Matlab simulation, we use a step signal as the control signal and the initial position of the trolley is \(X_{o}=0\) m, and the target position is \(X_{d}=0.6\) m. We set the simulation time \(T\) to be 30 s and the sampling time \(T\)s to be 0.01 s. The length of the wire rope is \(L=1\) m, the mass of the trolley is m\({}_{1}=10\) kg, the load mass is m\({}_{2}=5\) kg, and the gravitational acceleration is \(g=9.8\) m/s\({}^{2}\)[10]. The basic universe for the controller parameters e and ec is [\(-5\), \(5\)] and [\(-1.5\), \(1.5\)], respectively. The output universe of the parameters \(\Delta\)K\({}_{\rm P}\), \(\Delta\)K\({}_{\rm I}\) and\(\Delta\)K\({}_{\rm D}\) are [\(-200\), \(200\)], [\(-0.2\), \(0.2\)] and [\(-300\), \(300\)], respectively. Both input and output functions use trigonometric functions. The gains of the positioning PID controller are K\({}_{\rm IFD}=16\), K\({}_{\rm I10}=0.1\) and K\({}_{\rm IDD}=40\), while the gains of the anti-sway PD controller are K\({}_{\rm ZPO}=320\) and K\({}_{\rm ZDO}=100\).
# Automatic Recognition of Mexican Sign Language Using a Depth Camera and Recurrent Neural Networks
## 3 Materials and Methods
### Classification Architectures  
For classification, we evaluated three different architectures: recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU). We selected these architectures due to the data characteristics, consisting of temporal sequences. As indicated in the work of [PERSON] et al. [33] unlike feedforward networks, RNNs have cyclic connections that make them powerful for modeling sequences. However, it is well known that this model has problems updating weights, resulting in gradient vanishing and gradient exploding errors. If the weight is too small, the gradient will disappear, i.e., it will not continue learning, and in the opposite case, if large gradients accumulate, they will result in large weight updates causing the gradient to explode [34]. The LSTM and GRU variations of the RNN were developed to address the problems above.  
Figure 4 shows the model architectures used in this work. The input is conformed by a vector containing the hands, body, and face keypoints' x, y, and z coordinates. We either used an RNN, an LSTM, or a GRU with recurrent dropout and ending with a dense layer.  
In the following, we describe these three architectures.
# Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets
### ML with Low Quality Datasets: State-of-the-Art and Recent Progress
#### 1.1.1 Classical Data Augmentation  
Artificial generation of training samples for machine learning has been used for many years, for example, in the form of virtual examples for training support vector machines in supervised learning [7, 8]. In these papers, training data is augmented so that the learned model will be invariant to known transformations or perturbations. By using this technique, in [8], it was reported the lowest test error (0.6%) until that moment in 2002 on the well-known MNIST digit recognition benchmark task. Since then, data augmentation has been considered essential for the efficient training of neural networks, specially on images where it is typically performed in an ad-hoc manner by using class preserving transformations such as random cropping and rotations. Data augmentation is crucial to achieve nearly all state-of-the-art results, for example, in 2010 a new record on the best test error on MNIST dataset was reported (0.35%) by using deep neural networks [9]. Data augmentation is also fundamental to attain very good performance results on discriminative unsupervised feature learning based on convolutional neural networks [10]. While it is usually easy for domain experts to specify the involved transformations, for example the cropping and rotations in images, applications in other domains may require a non trivial choice of transformations. Motivated by this, in [11], the authors proposed a method for automating data augmentation by learning a generative sequence model over user-specified transformation functions using a generative adversarial approach, which was successfully applied to image and text datasets.  
Data augmentation was also applied in machine applications other than image classification, for example, in music source separation [12]. In that paper, the authors augmented the training datasets by randomly swapping left/right channels for each instrument, chunking into sequences for each instrument and mixing them from different songs sources, which demonstrated to boost the performance of deep neural networks for this task. Other application domains where data augmentation improved the discriminative power of classifiers include: biometrics [13], fault diagnosis in industry [14], radio frequency fingerprint identification [15], synthetic aperture radar (SAR) target recognition [16], and others.  
From the theoretical point of view only very recently some understanding of the underlying theoretical principles involved in data augmentation procedure was provided. In [17], the authors provide a general model of augmentation as a Markov process and show that, combined with a \(k\)-nearest neighbor (\(k\)-NN) rule, is asymptotically equivalent to a kernel classifier. This result provides novel connections between prior work on invariant kernels, tangent propagation and robust optimization, giving an illustrative view on how augmentation affect the learning model.
# An Efficient Geometric Search Algorithm of Pandemic Boundary Detection
## 3 Materials and Methods
### The Process of Infection  
The set of infected individuals is determined by the following random process. Time is discrete (think of each step as a day) and the infection process proceeds for \(T\) time steps. On day 0, patient 0 becomes infected. From that point on, during the first \(m\) time steps that an individual, say in cell \(c\), is infected, that individual can infect all individuals in cell \(c\) and in the other adjacent eight cells with the probability of \(p\). We assume that the probability of an infected individual infecting a non-infected individual in the same or in an adjacent cell during one of the \(m\) time steps that they are infectious is \(p\), that such events are independent, and that an individual becomes infected only once. In addition, we have multiple patients 0 in the entire grid and each one of them follows the same random process.  
**Definition 1**.: _A cell is infected if it contains at least one infected individual. Otherwise, it is uninfected._  
**Definition 2**.: _A cell \(c\) is called a potential uninfected boundary cell if:_  
_(a) The cell \(c\) is uninfected;_  
_(b) At least one of the 8 neighboring cells of \(c\) (\(N\), \(NW\), \(W\), \(SW\), \(S\), \(SE\), and \(E\)) is infected;_  
_(c) There is a contiguous path of uninfected cells from \(c\) to the end of the world._  
**Definition 3**.: _We say that a set \(S\) of uninfected cells encloses a set \(X\) of infected cells if every path from an infected node in \(X\) to the end of the world must pass through at least one cell of \(S\)._  
**Definition 4** (Boundary).: _A set of uninfected cells is called a boundary if it is a rectilinear closed path consisting only of potential uninfected boundary cells and it encloses all infected cells. See Figure 2. We call the minimal such boundary the \"true boundary\", where minimal means that it contains the lowest number of cells._  
Since the boundary partitions the cells into two parts, with one part containing all infected cells, the following claim is immediate:  
**Claim 1**.: _A rectilinearly aligned connected and closed path of uninfected cells encloses all infected cells if and only if it encloses at least one infected cell._  
Our goal is to efficiently trace and map out the boundary while performing a minimum number of tests.
# A Privacy-Preserving Routing Protocol Using Mix Networks in Opportunistic Networks
###### Abstract  
This paper focuses on the problem of providing anonymous communications in opportunistic networks. To that end, we propose an approach using Mix networks that enables a relatively simple solution. Opportunistic networks present some constraints that make the deployment of typical network anonymity solutions difficult or infeasible. We show, utilizing simulations on the basis of real mobility traces, that the proposed solution is feasible for some scenarios by introducing a tolerable penalty in terms of message delay and delivery. To investigate the impact of routing strategies, we offer two different methods to select Mix nodes. From the experiment results, we show the trade-off between network performance and security.  
opportunistic networks; mix networks; privacy Article