esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Modeling
### Series Capacitive Compensation  
Series capacitive compensation (SCC) plays a key role in the operation of electrical networks, since it modifies the inductive reactance (\(x_{ij}\)) of the transmission lines, and consequently, its power flow. As power flows are redistributed, in some cases, there might be an alleviation of lines close to overload. In this paper, we propose to modify the transmission line inductive reactance in a given percentage. This is carried out aiming to have identical power flows among parallel lines in a right-of-way. It is assumed that if a line of right-of-way \(i\)-\(j\) is compensated, then all the other lines in that same right-of-way should be equally compensated.  
When SCC devices are installed in a transmission line, then the total inductive reactance of the circuit changes; that is, if the inductive reactance of the original circuit is \(x_{ij}\) and the reactance associated with the SCC is \(x_{ij}^{sec}\), then the total reactance of the circuit will be as shown in (13), where \(x_{ij}^{sec}\) is calculated as a percentage of \(x_{ij}\), and \(\lambda_{ij}\) is a value between 0 and 1 that corresponds to the compensation percentage of the SCC devices installed in the right-of-way \(i\)-\(j\).  
\[x_{ij}^{total}=x_{ij}-x_{ij}^{sec}=x_{ij}-\lambda_{ij}x_{ij}=x_{ij}(1-\lambda_ {ij}) \tag{13}\]  
The total inductive susceptance of the compensated right-of-way can be calculated as:  
\[\gamma_{ij}^{total}=\frac{1}{x_{ij}(1-\lambda_{ij})}=\gamma_{ij}\bigg{(}\frac {1}{1-\lambda_{ij}}\bigg{)};\quad\forall(i,j)\in\Omega_{l}\quad\forall\lambda _{ij}\in[0,1]; \tag{14}\]  
Note that if \(\lambda_{ij}=0\), indicates that the circuits of the right-of-way \(i\)-\(j\) are not compensated; therefore, \(\gamma_{ij}^{total}\) and \(\gamma_{ij}\) are equal. Otherwise, if \(\lambda_{ij}\
eq 0\), it indicates that circuits in the right-of-way \(i\)-\(j\) are compensated.  
The cost associated with the optimal placement of SCC devices in the right-of-way \(i\)-\(j\) is a percentage of the original network circuit investment cost and can be calculated as follows:  
\[c_{ij}^{sec}=\beta_{ij}c_{ij} \tag{15}\]  
where \(\beta_{ij}\) is a value between 0 and 1, and represents the cost percentage of the SCC devices in the right-of-way \(i\)-\(j\).
# Sales Forecasting with LSTM, Custom Loss Function, and Hyperparameter Optimization: A Case Study
## 4 Discussion  
The experimentation results show us that for the lumber mill case study, the conditions of the products observed made it possible for the custom loss function, the network structure, and the genetic algorithm proposed to address the forecasting sales trends in the lumber mill. In this sense, with the initialization of weights with the proposed configuration C, it was possible to improve the convergence and final performance of the LSTM network, which improved the cost of the production and storage of the lumber products.  
Similarly, we observed that standardizing and balancing product instances is appropriate to improving the network's forecast results. Moreover, we noticed that the personalized loss cost function proposed in this paper for considering the cost of manufacturing and storage products was essential to adapting the LSTM network's learning to the specific needs of the lumber mill.  
Although we observed higher volatility in the sales behavior of some products, despite working with the lumber mill's five most sold products, our preliminary tests showed good forecasting performance.  
We established the range of the LSTM network hyperparameters and the parameters of the genetic algorithm implemented to explore the hyperparameter solution space; it was possible to train and test the LSTM network with each of the lumber products considered for this paper, to obtain models that allowed an adequate forecast of lumber sales.  
The results obtained in the experimentation show that the proposed LSTM network and the genetic algorithm for optimizing its hyperparameters improve the forecasts for all products. Here, product 1 was the most complicated for the network, with an improvement of 5.81% compared to other products. We believe that this behavior came from the higher volatility of this particular product during all test time series in contrast with other products that, at specific periods, did have a more stable sales ratio. This behavior suggests that the sales forecasting between products is not the same and emphasizes the implication of producing a specific network for each product.
## 3 Feasibility of Construction Plan
### Establishment of Numerical Model
#### 3.1.2 Calculation Parameters  
The geological environment at the site is notably intricate. Based on the geological survey report for the shield tunneling area, the strata are simplified into three categories: plain fill soil, gravel, and silty clay, which are regarded as nonlinear materials. The Mohr-Coulomb model is uniformly applied for the strata and grouting soils. The parameters for the grouting reinforcement areas are determined based on relevant data and literature. The pertinent parameters for strata are outlined in Table 1. It is worth noting that the cohesion of gravel is set to 0 according to the geological survey report of this project and previous studies [33; 46; 47]. Both the shield tunnel and rectangular pipe-jacking tunnel structures are regarded as elastic materials and modeled with linear elastic model. They have a unit density of 24 kN/m\({}^{3}\), an elastic modulus of 31 GPa, and a Poisson's ratio of 0.2. The shield tunnel is typically made of C50 concrete with a thickness of 0.35 m. The shield shell and tunnel segments can be simulated using plate elements. Steel supports are modeled using beam elements. Detailed calculation parameters for the structure are provided in Table 2.  
Figure 5: Three-dimensional numerical model. (**a**) Model overview; (**b**) spatial relationships.
# Digital Twin-Driven Virtual Network Architecture for Enhanced Extended Reality Capabilities
## 2 Background and Related Work
### In-Space Communications and Tolerant Networks
\(N_{Round}\) follows a geometric distribution and takes into account the probability that a bundle is not delivered with success due to the BER or SNR, while \(T_{Trans\text{-}last}\) is the average of the maximum bundle time. In the proposed ON-DTN scenario, delay metrics can be implemented based on Equation (1). \[T_{delivery}=N_{Round}T_{Bundle}+(N_{Round}-1)T_{RTO}+T_{Prop}+T_{Trans\text{-} last}+T_{Disrupt} \tag{1}\]
## 2 Description of the Proposed Approach
### Forwarding Area of FLRAF
Since the vertex of the \"cone\" forwarding area is at \(\text{A}(x_{0},y_{0},z_{0})\), the axis is along \(\overrightarrow{v}=\overrightarrow{\text{AB}}=(x_{1}-x_{0},y_{1}-y_{0},z_{1}- z_{0})\), and the semi vertex angle is \(\gamma\), we only need to ensure that the angle \(\beta\) between the vector \(\vec{\omega}\) from the center of the sphere \(\text{A}(x_{0},y_{0},z_{0})\) to the point \(\text{C}(x,y,z)\) and the \"cone\" axis vector \(\vec{v}\) is less than or equal to \(\gamma\). We have  
\[\cos(\beta)=\frac{\vec{\omega}\cdot\vec{v}}{\left|\vec{\omega}\right|\times \left|\vec{v}\right|} \tag{16}\]  
Figure 3: Schematic diagram of the forwarding area in the fuzzy logic reasoning adaptive forwarding (FLRAF) routing protocol. and let \(\cos(\gamma)\leq\cos(\beta)\), finally let  
\[(x-x_{0})^{2}+(y-y_{0})^{2}+(z-z_{0})^{2}\leq R^{2}. \tag{17}\]  
That is to say, A needs to be confined within the sphere. In addition, to determine the scope of the optimal forwarding area in the future, it is agreed here that \(\alpha=\frac{\gamma}{90^{\circ}}\).
# Investigating the Performance of FixMatch for COVID-19 Detection in Chest X-rays
## 3 Materials and Methods
### Preparing the Dataset  
In order to prepare the dataset for the training, a stratified split was conducted to assign a proportion of the imbalanced data to serve as the unlabeled data for the semi-supervised learning process, while the rest would serve as the labeled data. The proportion of labeled data was varied, with the tests being conducted with the labeled proportion being 80%, 60%, 40%, 20%, 10% and, finally, an extreme case of 5%. Finally, the images were resized to 224 \(\times\) 224, as this is the standard input for the underlying ResNet-18 model [15] used within the FixMatch algorithm.
## 4 Strategies for Sensor Fault Diagnosis
### Distributed Strategy  
Due to the small number of base stations in agricultural scenarios, weak signals, and low network coverage, agricultural sensor data cannot be transmitted to the central node or base station in some areas, making it challenging to implement centralized fault diagnosis. In addition, the centralized fault diagnosis strategy increases network traffic, resulting in network congestion and an inability to meet real-time fault diagnosis requirements. The distributed fault diagnosis strategy effectively resolves this issue. Distributed fault diagnosis is located between sensor nodes, eliminating the need to send agricultural sensor data to a central node or base station for diagnosis and satisfying real-time specifications. [PERSON] et al. [80] proposed a distributed sensor fault diagnosis system based on a machine learning algorithm, which implemented fault detection in the sensor and performed the fault diagnosis on the central node, thereby ensuring the real-time performance of the fault diagnosis and reducing the number of calculations performed on the sensor.  
The greater the number of decisions the sensor node makes, the less information is transmitted to the central node, thereby accelerating the detection rate. However, the hardware requirements of the sensor node are high, and the node for fault diagnosis requires specific memory, computing, and battery resources. The memory resources of wireless sensor nodes are on the order of kilobytes, and the operation speed is on the order of MHz. Conversely, due to the limited resources carried by the sensor nodes, the fault diagnosis method must meet the requirements of being lightweight and having high performance, allowing for fast and accurate diagnosis despite limited computing and storage resources without excessive battery consumption. Data-driven fault detection methods require fewer resources than model-driven ones [81]. Some researchers [82] investigated using lightweight fault diagnosis algorithms to reduce sensor resource consumption during the fault diagnosis procedure.
## 4 Case Study  
### Data Collection  
A buffer zone in the lane adjacent to the dedicated lane for AVs can be set, where AVs can make preparations to change to the dedicated lane, and only once this is complete can the compulsive lane-changing procedure happen in the buffer zone. Since the dedicated lane for AV has not been implemented, it is difficult to collect the real lane-changing behavior trajectory data of AVs at the present time. For this reason, vehicle trajectory from the NGSIM data set was used to investigate the factors that influence the longitudinal driving distance of lane-changing behavior, so as to build a foundation to analyze the lane-changing behaviors in the buffer zones. The NGSIM trajectory data set for the I-80 highway in the United States was chosen for exploration in this experiment. The selected road section and direction includes a high occupancy vehicle lane, five regular lanes, and an on-ramp, which gives the highway section more lane-changing behaviors and facilitates the study of lane-changing behaviors. As indicated in Figure 3, each lane is approximately 3.5 m wide and the weather and pavement are in good condition. It could be deduced that the majority of lane-changing behaviors in the chosen section are forced because of the ramp, which corresponds to the lane-changing behavior features that were researched in the experiment. Only passenger vehicles are included in the research classification. MATLAB was used to extract the vehicle trajectory data to only include one-time lane changes.  
There may be anomalous values and variations in the NGSIM because the indicators are instantaneous values recorded by frame. Preprocessing the original data, such as denoising and smoothing, is required to assure the reliability of the input data. First, data with null values in important indicators such as frame, speed, acceleration, vertical and horizontal position should be removed. Second, samples with excessive missing values or continuous missing indications are unfit for analysis and should be deleted. Finally, data smoothing can be adopted to deal with the circumstances where deviation is large. Data smoothing methods, such as the Kalman filter method [27], the moving average method [28], and the median filter method [29] are extensively employed. The moving average method is a statistical method that highlights trend characteristics by suppressing noise in the original data and dividing the change of the variable into trend and residual sections. The following is its general mathematical model:  
\[\widehat{Y}_{i}=\sum_{i=1}^{n}W_{i}Y_{i} \tag{17}\]  
where \(\widehat{Y}_{i}\) is the calculated moving average value of the \(i\)th value in a group of data, \(Y_{i}\) is the \(i\)th data's adjacent observation value, \(W_{i}\) is each observation value's weighting coefficient, and \(n\) is the length of the window. Figure 4 shows the smoothing results of the vehicle's acceleration. In addition, the same processing procedure is done for speed, relative lateral and longitudinal position, headway, and spacing.
# Interpreted Petri Nets Applied to Autonomous Components within Electric Power Systems
## 1 Introduction
The modeling methodology for a deterministic system specified by an interpreted Petri net is proposed in [28], distinguishing between strong and weak determinism. There are several methods that allow verification of the control algorithm in power systems [29], including simulations, model checking (both symbolic and statistical), hardware in loop (HiL), and experiments. The specification of the control algorithm in a power and energy system is not only for its documentation but can also be used for validation before implementation. Formal specification by means of interpreted Petri nets offers some support by existing analysis and verification methods, which allows the checking of the basic properties at an early stage of development. Hence, possible errors related to the structure of a formal model may be resolved. Additionally, model checking can be performed to verify some behavioral properties. This aspect is, however, not explored further in this article. This work continues our previous research on the use of interpreted Petri nets for the specification of cyber-physical systems (in particular their control parts) [23]. We now extend the application area and show that they can also be successfully used for autonomous components within power and energy systems. The presented example describes an autonomous system with electricity storage for the provision of a renewable energy source (RES) stabilization system service or one having highly variable loads. The analysis by the use of interpreted Petri nets takes into account measurement signals from the power system, diagnostic signals from the battery management system (BMS), and the relationships specified in the control algorithm for the power stabilization system service. The main contributions of the paper can be summarized as follows:  
1. We show how interpreted Petri nets can be applied for autonomous components within power and energy systems;
2. A novel modeling methodology for control algorithms in the power area with the use of interpreted nets is proposed that allows verification of basic properties at an early stage of system development;
3. The presented idea is illustrated with a case study of an energy storage system;
4. The possibilities of interpreted Petri nets in the energy domain are indicated, as are some implications for practical projects. The remainder of the paper is structured as follows. Section 2 presents some background on interpreted Petri nets. Section 3 introduces a novel method for modeling power and energy system components with interpreted Petri nets. Section 4 illustrates the proposed approach with a case study. Section 5 presents experimental verification. Section 6 highlights the benefits and implications for practical projects. Finally, Section 7 summarizes and concludes the paper.
# Energy Evolution Analysis of Coal Fracture Damage Process Based on Digital Image Processing
###### Abstract  
Coal rocks often contain calcite, which has a significant effect on the mechanical properties of coal and the energy evolution during rupture damage. In this study, the meso-scale of rock is considered, and the spatial distribution of the internal structure of coal is characterized by digital image technology. Uniaxial compression tests were conducted using RFPA on coal rocks containing calcite veins with diverse dip angles. The research results show that the different azimuth angles of the calcite veins change the internal stress distribution of the coal, resulting in higher coal compressive strength at low dip angles (0\({}^{\circ}\), 15\({}^{\circ}\) and 30\({}^{\circ}\)). Under high dip angles (45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\) and 90\({}^{\circ}\)), coal has lower compressive strength. The fracture mode of coal is significantly affected by calcite. At low dip angle, the fracture mode of coal and rock is complex, which are inclined _Z_-type (0\({}^{\circ}\)), V-type (15\({}^{\circ}\)) and inverted V-type (30\({}^{\circ}\)), respectively. At high dip angle, the fracture mode of coal and rock is single, which is type I failure mode. The destruction process of coal rocks is influenced by calcite veins. Under low dip angle, the internal stress distribution of coal is relatively uniform, the weak cementation between matrix and calcite vein in coal is not easy to be damaged, the stress required for coal failure is large and the input energy, accumulated elastic energy and impact energy index are large. Under high dip angle, the internal stress distribution of coal is uneven, the weak cementitious material between matrix and calcite vein in coal is easy to be damaged and the input energy, accumulated elastic energy and impact energy index are small.  
coal rock; RFPA; digital image; calcite vein; energy evolution
# Towards a Real-Time Description of the Ionosphere:
## 3 Measured and Modeled Data Used for Validation
### Observations from Ground-Based Ionosondes
The considered dataset includes the last two solar cycles as depicted by the F10.7\({}_{81}\) solar index, i.e., the 81-day running mean of the F10.7 solar index, in Figure 2b. F10.7 is the solar radio flux at 10.7 cm wavelength (2800 MHz) [60] and represents one of the most used solar activity proxies for ionospheric modeling. In particular, its 81-day running mean was used in order to smooth its short-time variability. F10.7 daily data were downloaded at the NASA's OMNIWeb Data Explorer website ([[https://omniweb.gsfc.nasa.gov/form/dx1.html](https://omniweb.gsfc.nasa.gov/form/dx1.html)]([https://omniweb.gsfc.nasa.gov/form/dx1.html](https://omniweb.gsfc.nasa.gov/form/dx1.html)), accessed on 30 July 2021). Three solar activity ranges are considered here to evaluate the solar activity dependence:  
Figure 2: (a) Percentage of available ionosonde observations per year and ionosonde station. (b) F10.7\({}_{81}\) time series from 2000 to 2019. Red horizontal dashed lines identify the three ranges chosen for the solar activity description. * Low solar activity (ISA): F10.7\({}_{81}\) < 80 s.f.u. (solar flux units, 1 s.f.u. = 10\({}^{-22}\) Wm\({}^{-2}\) Hz\({}^{-1}\)). * Mid solar activity (MSA): 80 s.f.u. \(\leq\) F10.7\({}_{81}\) < 120 s.f.u. * High solar activity (HSA): F10.7\({}_{81}\)\(\geq\) 120 s.f.u. The F10.7\({}_{81}\) solar index thresholds were selected by considering the solar activity level experienced in the last two solar cycles, and on the basis of the available datasets of ionosonde and COSMIC derived observations.
## 3 Research Background
### The XCS  
The XCS is a system that is commonly employed to perform ID, and the FLAME algorithm is typically used for feature filtering. Thus, the XCS is the most common learning classifier system (LCS). They are broadly employed in various applications, including IDSs. [PERSON] introduced this classifier system in 1998. It is categorized as a rule-based system, in which rules are designated as classifiers [33]. Each rule consists of two parts: a condition and an action. The condition (\"the body of the rule\" [34]) is represented using binary format as {0,1,#}, where # refers to \"do not care.\" The action part is \"the fired classifier\" [34], which is often represented as a variable (0,1) [34].  
The XCS was composed from two concepts: reinforcement learning (RL) and GA. RL, or reinforcement learning, is a method used to determine the optimal behavior of a machine in a given environment. This behavior is learned through continuous interaction with the environment while observing how it responds. To maximize the reward, the machine will adjust its behavior over time, learning from each interaction. The XCS is a reward-driven classifier system that allocates rewards from the classifier environment [35]. It is designed to recognize how the classifier will be beneficial in the future and provide feedback for improving the rules [36]. Additionally, GA can discover new elements in the search space by creating new rules [34]. After creating a new rule, the population is inspected to determine whether the new classifier is a duplicate. Otherwise, it is added to the current population, and the total number of rules is increased by one. The primary components of the XCS, as shown in Figure 1, are [33; 34; 35; 36; 37]:  
* Detector  
The detector receives input from messages coming from the environment, which reveals the features of the network traffic. Subsequently, the features that play a significant role in the detection of attacks are designated as important features and selected. The irrelevant features were substituted with #.  
* Match set [M]  
Match set [M] is a classifier composed of conditional and action parts. A part of the condition must correspond to the feature input of the classifier from the environment.  
* Action set [A]  
Action set [A] supports an action selected by the [M] classifier.  
* Prediction array The prediction array consists of [M] actions depending on the suitability-weighted average of the prediction rules in [A].
* Effector The effector fires the rule action in the environment. The outcome can be normal activity/behaviour or an attack.