esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Method  
### Geological Data  
A total of 1506 scanned files of investigation data recording borehole points and corresponding geotechnical reports was collected in the modeled area. In total, 614 files came from the Tongzhou District Urban Construction Archives, while the remaining 892 files were obtained from the Tongzhou District Architectural Survey and Design Institute. Most of these boreholes were shallow (30 m or less) and clustered around  
Figure 1: (a) Distribution map of alluvial fans in the Beijing plain area (adapted from [41]); (b)distribution of original and new added boreholes in the study area; (c) boreholes selected for modeling in the study area.  
local construction sites in the north part of the study area, some having relatively poor lithological descriptions. To provide more information about the subsurface lithology and define the standard layer in the modeled area, 40 new deep boreholes (Figure 1a,b) (maximum depth 100 m, such as 1-11 on Figure 1a) were drilled in the south part of the Tongzhou area. In total, 476 boreholes (Figure 1c) with deeper depth (typically more than 30 m) and detailed descriptions were finally honored for modeling, containing information concerning position, borehole identifications, depth, elevation, coordinates, top, bottom, and lithology of layers.  
We also included faults as an important factor in the modeling procedure. A total of five faults with upper breakpoints located in the shallow sediments (50 m or less) was considered, including Nankou-Sunhe fault, Nanyuan-Tongxian fault, Zhangjiawan fault, Xiadian fault, and Yaoxinzhuang fault [43; 44; 45]. Fault data were derived from the technical report of the project titled \"Investigation of major geological problems in the sub-center of Tongzhou area\", which was implemented during 2016-2017 by the Beijing Institute of Geological Survey. However, few records document exact locations of upper breakpoints in shallow Quaternary sediments (<50 m); fault plane was hence modeled based on an average fault throw, which was around 1-2 m.
# A State Recognition Approach for Complex Equipment Based on a Fuzzy Probabilistic Neural Network
## 2 Literature Review
### State Recognition  
In recent years, many qualitative and quantitative state recognition methods have quickly developed both at home and abroad, promoted development of science and technology, and have been widely applied in the engineering field.  
In order to accurately discriminate the real-time operating status of crude oil tanks, ref. [15] proposed a new method based on an expert system and associated cluster analysis. In [16], a method of equipment degradation state recognition based on wavelet correlation feature scale entropy and hidden semi-Markov models was proposed to prevent the equipment from further degradation and failure. The authors take a roller bearing as an example, and the experiment results showed that the proposed method was effective. In [17], a new state recognition method based on a hierarchical hidden Markov model was presented. Based on the method, the computational complexity and the inference time were decreased, and state of gearbox could be recognized accurately. In [18], a high-speed train running state monitoring technique was presented based on a fuzzy grey correlation degree. In [19], a novel approach for screw compressor state recognition and fault diagnosis based on wavelet theory was proposed. According to tested rotor and valve vibration signal energy changing characteristics under different working conditions of the compressor, the working state could be obtained clearly, and an experiment validated the accuracy of the proposed method. In [20], to recognize the state of viscoelastic sandwich structure, an approach based on the adaptive redundant second-generation wavelet packet transform, permutation entropy and the wavelet support vector machine (SVM) was proposed. In [21], a novel state recognition method combining kernel principal component analysis and support vector machine was proposed, and experimental results demonstrated the effectiveness of the proposed algorithm. In [22; 23], an artificial neural network was applied to wear state recognition. In [24], aiming at the non-stationary and time-variation characteristics of the gear-box signal, a new method that combined wavelet analysis and a neural network was proposed to recognize the state. To verify the proposed approach, three conditions of a gear-box were simulated by experiments, and the result indicated that the method could recognize the three conditions effectively. In [25], a multi-fractal analysis was applied in state recognition by extract nonlinear features from complex systems.
# DRL-Based Dynamic SFC Orchestration Algorithm for LEO Satellite Networks
## 3 System Model
### Delay Modeling
Setting packet length to 1, with link bandwidth \(B_{i,j}\) and total traffic \(B_{u}\), the actual transmission delay on link \(l_{i,j}(t)\) is as follows:  
\[dq_{i,j}(t)=\frac{B_{i,j}}{2\cdot(B_{i,j}-B_{u})}\cdot\frac{l}{B_{i,j}} \tag{8}\]  
This formula shows that link delay is proportional to bandwidth utilization--higher traffic results in greater queuing delay. However, calculations indicate that in this scenario, transmission delay on physical links has minimal impact on service request delay when traffic remains below 80% of total bandwidth. At the same time, to facilitate mathematical modeling and computation, we assign a delay value exceeding the upper limit of latency for all user service requests to interstellar links that cannot be connected, thereby rejecting virtual link deployment on unconnectable links.
# A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion
###### Abstract  
Higher-level autonomous driving necessitates the best possible execution of important moves under all conditions. Most of the accidents in recent years caused by the AVs launched by leading automobile manufacturers are due to inadequate decision-making, which is a result of their poor perceivance of environmental information. In today's technology-bound scenarios, versatile sensors are used by AVs to collect environmental information. Due to various technical and natural calamities, the environmental information acquired by the sensors may not be complete and clear, due to which the AVs may misinterpret the information in a different context, leading to inadequate decision-making, which may then lead to fatal accidents. To overcome this drawback, effective preprocessing of raw sensory data is a mandatory task. Pre-processing the sensory data involves two vital tasks, namely data cleaning and data fusion. Since the raw sensory data are complex and exhibit multimodal characteristics, more emphasis is given to data preprocessing. Since more innovative models have been proposed for data cleaning, this study focused on data fusion. In particular, this study proposed a generic data fusion engine, which classifies different formats of sensory data and fuses them accordingly to improve accuracy. This study proposed a generic framework to fuse the text, image, and audio data. In the first stage of this research, an innovative hybrid model was proposed to fuse multispectral image and video data. Simple and efficient models to extract the salient image features were also proposed. The hybrid image fusion model that was proposed did not yield satisfactory outcomes when combining 3D point cloud data, and its performance declined when evaluating large datasets. To address this issue, the study expanded by introducing an advanced generative adversarial network (GAN) to transform the hybrid image fusion model into a machine learning model capable of handling substantial datasets. Additionally, customized kernel functions were suggested to fuse 3D point cloud data effectively. The performance of the proposed models was assessed using standard metrics and datasets, comparing them with existing popular models. The results revealed that the proposed image fusion model outperformed the other models.  
autonomous vehicles (AVs); data fusion; situation awareness; data-preprocessing; machine learning (ML) +
Footnote †: journal: Applied Sciences
# A Tour Recommendation System Considering Implicit and Dynamic Information
## 3 Research Methodology
### Implicit POI Feature Extraction  
This study uses the implicit feature extracted from the textual description of the POI as its representation. First, the textual description of POIs is collected. Second, a set of text-preprocessing steps, including tokenization, stop word removal, stemming, and lemmatization, are applied for each textual description [31]. Then, a bag of words (i.e., a set of terms) for each POI can be generated. For each POI, the appearance frequency of each term that appears in the textual description will be counted. Let a set of terms be \(\{l_{1},\;l_{2},\ldots,\;l_{n}\}\). Therefore, each POI will be represented as a feature vector formed as \(poi_{i}=<c_{1},c_{2},\ldots,c_{tn}>\) where \(c_{tn}\) indicates the count of term \(n\). Note that some terms frequently appearing in textual descriptions are common words. Therefore, the word count will be converted by the TF-IDF (Term Frequency-Inverse Document Frequency) approach. TF-IDF is a statistical method used to evaluate the importance of a word to a document set. The Term Frequency (TF) of term \(t_{i}\) in textual description \(d_{j}\) can be represented as \(tf_{i,j}=n_{i,j}/\sum_{k}n_{i,j}\) where \(n_{i,j}\) is how many times that \(t_{i}\) appears in \(d_{j}\). The Inverse Document  
Figure 1: The framework of the proposed tour recommendation system.  
Frequency (IDF) of term \(t_{i}\) can be evaluated by \(idf_{i}=\log\lvert D\rvert/\left|\left\{j:t_{i}\in d_{j}\right|\right.\) where \(\lvert D\rvert\) is the total number of textual descriptions. Finally, the feature vector of \(poi_{j}\) measured by TF-IDF is \(poi_{j}=<tf_{1,j}idf_{1},tf_{2,j}idf_{2},\ldots,tf_{n,j}idf_{n}>\) where \(tf_{i,j}idf_{i}\) is the TF-IDF of \(ct_{i}\).  
The feature vector of \(poi_{j}\) measured by TF-IDF is a high-dimensional vector and tends to cause sparsity problems. The Autoencoder (AE) mechanism is adopted to reduce the feature vector dimension and find the implicit features [32] to make later recommendations easier and achieve higher prediction accuracy. The AE trains the network by constraining the output values to be equal to the input values, indicating that the nodes in the output layer are equivalent to the ones in the input layer. The reconstruction error between the network's input and output is used to adjust the weights of each layer. Therefore, the features learned by AE can represent the input data well.  
Moreover, the training of AE is unsupervised since it does not require label information. The structure of AE is illustrated in Figure 2. After conducting the AE, the implicit feature for \(poi_{j}\) can be retrieved from the hidden layer and is represented as \(poi_{j}=<l_{1,j},l_{2,j},\ldots,l_{n,j}>\).
# A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges
## 2 Types of Fault in the Li-Ion Battery System
### Internal Battery Faults  
Internal battery faults are difficult to detect since the operation within a Li-ion cell is still not fully understood [11]. Some examples of internal battery faults are overcharge, overdischarge, internal and external short circuit, overheating, accelerated degradation, and thermal runaway. All these faults affect the battery operation, but accelerated degradation and thermal runaway are the most dangerous since they can significantly affect the Li-ion battery application or directly harm the users [12]. Internal faults are often identified from abnormal responses from the battery operation, which include voltage drop, SOC drop, temperature rise, increase in internal resistance, and physical transformation, such as swelling. These responses are discussed further throughout this section.
## 7 System Prototype  
The developed wireless motion capture system is composed by three main components: a personal computer (PC), a base station and one wireless sensor node for each segment of the body requiring monitoring.  
The main component of the wireless sensor node is the CC2430 [10], from Texas Instruments, a SoC (System on Chip) that integrates an 8051 based microcontroller and an IEEE 802.15.4 compliant transceiver in the same chip. A printed circuit antenna compatible with CC2430 radio was also implemented [16], effectively reducing the size of the sensor node and making it less obtrusive. The sensor node is powered by a 300 mAh rechargeable lithium-ion battery.  
Each sensor node also contains a 3-axis accelerometer and a 3-axis magnetometer, that are used to obtain the pitch, roll and yaw angles through a process described in patent WO 2008/018810 A2 [17]. Both the gravitational force and the Earth's magnetic field are used to detect the angles of the segments. The former is used to detect inclination while the later is used to measure the rotation of the body about the axis perpendicular to the gravity field. Fifth order low pass elliptical filters are used to minimize noise from sensor readings. A DAC (Digital-to-Analog Converter) with adjustable current enables calibration of the magnetic sensors, which are sensitive to magnetic field variations. Each sensor node is able to provide resolution around 1 degree. Figure 11 shows the sensor node prototype board.  
Figure 11: Wireless sensor node prototype board.  
The implementation of the base station is based on the SmartRF04 EB and CC2430 EM boards provided by the CC2430 Development Kit from Texas Instruments. The CC2591 RF range extender can be used to increse the base station's output power. The base station is powered by the PC through a USB cable.  
The PC application receives the data acquired from the sensor nodes, calculates the angles of the segments of the user's body, generates a 3D model compliant with the BVH (Biovision Hierarchy) file format [18] and displays the movement of the user's body in real-time.
# Hybrid Edge-Cloud-Based Smart System for Chatter Suppression in Train Wheel Repair
## 4 Industrial Internet of Things (IIoT) Platform for Hybrid Edge and Cloud Computing  
An Industrial Internet of Things (IIoT) platform (see Figure 9) is a form of middleware that sits between the layers of Internet of Things (IoT) devices (hardware, sensors) and IoT gateways (and thus, data) on the one hand, and applications (software) on the other hand. The reality of IIoT platforms is complex because applications and solutions come with different architectures, ways of connecting and managing devices, possibilities for managing and analyzing data, capabilities to build applications, and options to leverage IIoT in a meaningful way for any given use of IIoT in any given context.  
Figure 8: Vibration spectra with (**a**) continuous spindle speed variation (CSSV) off and (**b**) CSSV on at amplitude = 5%, frequency = 0.5 Hz, on a 36′′ wheelset machining.  
An IIoT platform needs hardware, such as sensors or devices (the \"physical\" part). These sensors and devices collect data from the machine/components, machining process, and performed actions. Three requirements can be established for the design of an IIoT platform:  
_An IIoT platform needs connectivity._ The hardware needs a way to transmit all the collected data to the cloud, or needs a way to receive commands from the cloud. There is usually an intermediate step between hardware and the connection to the cloud, such as an IIoT gateway.  
_An IIoT platform needs software_ (the \"cyber\" part). This software is hosted in the cloud and is responsible for storing and analyzing the vast amount of data collected from the sensors, and for making decisions based on learned patterns due to the high computing power of the cloud. Nevertheless, the analysis, processing, and transmission of some types of data can be critical in contexts where fast actions need analytics and intelligence at the so-called edge (where the devices/assets and IIoT gateways are). This is what edge computing and fog computing are partially about and where edge platforms come into play.  
Finally, _a complete IIoT platform needs a user interface._ To make all this useful, there must be a way for users to interact with the IIoT platform (for instance, a web-based app with dashboards for visual data analytics or live remote data monitoring).
## 2 Governing Equations
### Solution Technique  
According to the locations of the different nodes, different equations are obtained. For the inner nodes, Equation (2) can be converted into a finite difference, yielding Equation (4):  
\[\begin{array}{l}20\phi_{i,j}-8\big{(}\phi_{i+1,j}+\phi_{i-1,j}+\phi_{i,j+1}+ \phi_{i,j-1}\big{)}+2\big{(}\phi_{i+1,j+1}+\phi_{i-1,j+1}+\phi_{i+1,j-1}+\phi_ {i-1,j-1}\big{)}+\\ \big{(}\phi_{i+2,j}+\phi_{i-2,j}+\phi_{i,j+2}+\phi_{i,j-2}\big{)}=0\end{array} \tag{4}\]  
where \(\varphi_{i,j}\) is stress function of node \((i,j)\).  
For the boundary nodes, a basis point must be chosen. For convenience, node (41, 11) is chosen as the basis point. The stress function and first-order partial derivative of the stress function should be equal to 0, as follows in Equation (5):  
\[\varphi_{41,11}=\left(\frac{\partial\varphi}{\partial x}\right)_{41,11}=\left( \frac{\partial\varphi}{\partial y}\right)_{41,11}=0 \tag{5}\]  
According to the basic theory, the stress functions and first-order partial derivatives of the stress functions of the other boundary nodes can be calculated by Equation (6):  
\[\left\{\begin{array}{l}\left(\frac{\partial\varphi}{\partial x}\right)_{A} =-\int_{O}^{A}F_{y}ds\\ \left(\frac{\partial\varphi}{\partial y}\right)_{A}=\int_{O}^{A}F_{x}ds\\ \phi_{A}=\int_{O}^{A}\left(y_{A}-y\right)F_{x}ds+\int_{O}^{A}\left(x-x_{A} \right)F_{y}ds\end{array}\right. \tag{6}\]  
where \(O\) is the basis point, and \(A\) is any boundary node that must be solved.  
The stress functions of all the boundary nodes can be obtained according to Equation (6). In addition, from the first-order partial derivatives of the stress functions, the following equation can be obtained:  
\[\left\{\begin{array}{l}\frac{\phi_{2,j}-\phi_{0,j}}{B/10}=\left(\frac{ \partial\varphi}{\partial y}\right)_{1,j}(j=1-21)\\ \frac{\phi_{2,j}-\phi_{40,j}}{B/10}=\left(\frac{\partial\varphi}{\partial y} \right)_{1,j}(j=1-21)\\ \frac{\phi_{1,2}-\phi_{1,0}}{B/10}=\left(\frac{\partial\varphi}{\partial x} \right)_{i,1}(i=1-41)\\ \frac{\phi_{1,2}-\phi_{1,20}}{B/10}=\left(\frac{\partial\varphi}{\partial x} \right)_{i,21}(i=1-41)\end{array}\right. \tag{7}\]  
The stress functions of the four corner nodes are meaningless to the problem. Based on Equations (4), (5) and (7), the stress function of each node can be solved. The linear systems of Equations (4), (5) and (7) are solved using the scientific computation package Scipy through the Python language.
# Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy
## 2 Materials and Methods
### Data
#### 2.2.3 LiDAR Data Derivatives  
We obtained a DTM at 2.5 m spatial resolution that was generated from airborne Light Detection and Ranging (LiDAR) data acquired in 2006 [40]. The DTM was resampled to the resolution of PlanetScope and Sentinel-2 using a bilinear resampling technique. Additionally, we used a canopy height model (CHM) (2.5 m) derived from the same LiDAR data, which we resampled to the two satellite sensor pixel sizes (3 m and 10 m).
## 1 Introduction
### Organization  
The rest of this paper is organized as follows. The system model and user association strategy are described in Section 3. In Section 4, we deduce the closed-form expression of network EE. A low complexity heuristic algorithm is proposed to optimize pico CRE bias, power reduction factor, and PBS density jointly for the network EE maximization in Section 5. Numerical results and discussions are presented in Section 6. Concluding remarks are given in Section 7.
# Mouth Sounds: A Review of Acoustic Applications and Methodologies
## 4 Main Findings
### Acoustic Feature Analysis
#### 4.3.2 Machine Learning (ML) Techniques for Pattern Recognition  
ML algorithms, including DL, performed well when used to classify features from audio recordings [55; 64; 113]. Most of these techniques were used to differentiate between control and experimental groups [87; 114]. For this purpose, correlation models, such as the identification of autocorrelation between variables [10] and correlation-based feature subset algorithms [78], have proven useful to identify features for a better classification of data in ML algorithms.  
The most common algorithms used in studies included Support Vector Machine (SVM) [40; 49; 72; 78; 80; 94; 96; 101; 107; 108; 111; 115; 121; 122], Neural Networks (NNs) [38; 39; 42; 45; 62; 67; 72; 100; 123; 124], the Gaussian Mixture Model (GMM) [63; 65; 72; 109; 112] combined with the Hidden Markov Model (HMM) [51; 60; 76; 90; 110], and Random Forest (RF) [80; 93; 115].  
One of the most pursued differentiations is the detection of apneas/hypopneas presented in snoring during sleep [68; 113], showing promising results in classifying apneas and hypopneas compared with a gold standard. The logistic regression model was used to classify snoring sounds as apnea/hypopnea and non-apnea sounds.  
Another very commonly pursued differentiation refers to emotion recognition. In [125], a classification performance of 73.1% was achieved when analyzing cries from babies containing frequent short noises. Furthermore, a classification algorithm named SemiEager reported accuracies of 88.08% and 90.66% [56] in classifying emotional states. This learning algorithm was intended to reduce computational cost while receiving perceptual-based features, such as MFCC and BFCC, and time features, such as ZCR and STE. The highest accuracy scores were observed when feature fusion was employed [49; 56].  
Overall, a trend was observed in research articles, including the diagnosis of pathologies or disorders. The majority of them used either ML algorithms, smartphone applications, or both [10; 78; 96; 125; 126; 127].
## 3 Results
### The Effect of EO Concentration on the DON Level in Maize and Wheat Grain  
DON contamination in cereal samples also depended on the concentration of EOs, however, the types of cereals and EOs likewise influenced the level of toxin reduction (Figure 6). It is worth underlining that _F. graminearum_ produced a low amount of DON compared to ZEA, both in maize and wheat grain (the level of DON concentration in control samples reached 3.49 and 3.19 \(\upmu\)g/g, respectively). In wheat grain samples impregnated with tested EOs in concentrations from 1% to 10%, DON was not detected (100% of toxin reduction was noticed compared to control). At the concentration of 0.1%, no significant reduction in DON content was observed, while in samples treated with cinnamon and palmarosa EOs, a significantly higher content of the toxin was observed. In maize grain, the highest efficacy in DON reduction was demonstrated by cinnamon EO, decreasing toxin content in the whole tested range of concentration. The highest percentage reduction of the toxin was observed at the range of concentration 1% to 5% with lower efficacy at the concentration 10% (72.4% of DON reduction). Verbena and palmarosa EOs effectively reduced DON content at a concentration of 10%, while at lower concentrations, their effectiveness was much weaker. It is worth underlining that at a concentration of 0.1%, only cinnamon EO showed a significant reduction, while in samples treated with verbena and palmarosa, significantly higher content was observed.  
Figure 5: Effects of EOs on ZEA contamination in maize and wheat samples after inoculation with the _F. graminearum_. nd-not detected, below the limit of quantification (<0.01 \(\upmu\)g/g). Averages with different letters (a–i) for each cereal are significantly different at the \(p\) < 0.05.