esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Test and Prediction Model of Soil Thermal Conductivity in Pernafrost Regions
## 1 Introduction
[27] and [PERSON] et al. [28] proposed an inverse modeling method to recover soil thermal conductivity using time series of measured data and physics-based numerical models. Likewise, other researchers also proposed some improved empirical models for wider application by introducing more soil parameters, such as soil texture-dependent parameters, quartz content, fluid network connectivity, etc. [6; 29; 30; 31; 32; 33]. Considering the effects of freeze-thaw process on the thermal conduction behavior, many scholars discussed the influence of multiple freeze-thaw cycles on the thermal conductivity of frozen soil and developed some useful models to estimate thermal conductivity under temperature and phase transition condition [34; 35; 36; 37]. Meanwhile, the statistical-physical method [4; 38], artificial neural network method [39] and mesoscopic predictions method [40; 41] were also applied for the prediction of soil thermal conductivity. The Qinghai-Tibet Engineering Corridor (QTEC) is defined as the engineering channel from Golmud to Lhasa in the western China, which includes five major linear projects: Qinghai-Tibet Highway, Qinghai-Tibet Railway, Golmud-Lhasa oil pipeline, Lanzhou-Xining-Lhasa optical cable communication project and high-voltage power transmission and transformation project [42]. It is the widest continues permafrost distribution region in the high altitudes around the world, and with changeable terrain structure, frequent geological disasters, cold and hypoxic climate, its engineering geological conditions are extremely harsh [43; 44]. With global warming, the permafrost in the corridor is in a dynamic equilibrium or even gradual degradation [1; 2; 3; 45]. However, due to the complex geological distribution and sampling restrictions, the research on the thermal conductivity of various soil types of permafrost regions in the QTEC is not systematic and comprehensive, which needs to be further investigated. Therefore, in the present work, the variation features of the thermal conductivity of massive drilling specimens along the QTEC have been analyzed. Then, based on the cluster analysis of typical soils, the distribution characteristics of dry density, water content and thermal conductivity of three soil clusters have been investigated, and three prediction models have been developed and compared to obtain the optimal calculation models of soil thermal conductivity.
# Assessment of Electromagnetic Exposure to a Child and a
## 2 Specific Absorption Rate (SAR)  
Concerning the exposure of humans to EM fields, guidelines have been established to prevent potential health risks. The parameter used to quantify these exposure limits is quantified by the Specific Absorption Rate (SAR), and it measures the rate at which biological tissues absorb electromagnetic energy deposited in biological tissue per unit mass. The spatial distribution of specific absorption rate is given by the following equation:  
\[\mathrm{SAR}=\frac{\sigma\,|\,\mathrm{E}\,|^{2}}{\rho}[\mathrm{W}/\mathrm{kg}] \tag{1}\]  
where \(\sigma\) (Si/m) represents the tissue's electrical conductivity, \(\rho\)(kg/m\({}^{3}\)) denotes the mass density of the biological tissue and \(\mathrm{E}\) (V/m) represents the effective induced electric field.  
The specific absorption rate at the whole body is evaluated by the following equation:  
\[\mathrm{SAR}_{\mathrm{WB}}=\frac{\mathrm{P}_{\mathrm{abs}}}{\mathrm{m}}[ \mathrm{W}/\mathrm{kg}] \tag{2}\]  
where \(\mathrm{P}_{\mathrm{abs}}(\mathrm{W})\) denotes the power that is absorbed, while m (kg) corresponds to the absorber's entire mass  
According to international standards for safety, the maximum permissible spatial peak SAR, averaged over 10 g of biological tissue, is 2.0 W/kg, and the whole-body averaged SAR is constrained to 0.08 W/kg. These values are typically computed for a continuous exposure duration of 6 min [61; 62].
# Enhanced Path Planning by Repositioning the Starting Point +
## 3 Proposed Method
### Distance Analysis
#### 3.1.1 Starting Point Distance  
To quantify the impact of starting point optimization, we establish appropriate performance indicators. The most direct measure involves calculating the sum of all distances connected to the starting point. We define starting point distance (SPD) in Equation (3) as follows:  
\[\textit{Starting Point Distance (SPD)}=\sum_{i=1}^{n}(d_{i,<\textit{first>}}+d_{i,< \textit{last>}}) \tag{3}\]  
where \(n\) represents the number of paths (routes) in a mission, \(d_{first}\) denotes the distance from the starting point to the first station in each path, and \(d_{last}\) represents the distance from the last station back to the starting point.  
Applying this definition to our examples, the SPD in Figure 3 equals the sum of dotted line distances: \(d(1)+d(5)=398.2\). For the multi-drone scenario in Figure 4, which comprises three distinct paths, the SPD calculation becomes \(d(1,1)+d(1,6)+d(2,1)+d(2,6)+d(3,1)+d(3,6)=1953\), representing the sum of all starting point connections.
## 3 Results and Discussion
### Landscape Metrics Analysis
The following table (Table 13) summarizes the results from the MLR modeling, which were obtained on the basis of the data set and the MLR modeling approach that was applied to determine the relationships between landscape metrics and the mean soil erosion rate per land cover class. The mean soil erosion rate per land cover class for the RUSLE model implemented with R-factor values derived from in situ data and C-factor values derived from _NDVI_ data (Scenario 6) with the landscape metrics analysis for the year 2020 exhibited highly positive correlations with most of the landscape metrics; thus, higher PD intensifies soil erosion and lower PD causes more erosion. In fact, for cropland (2) and woodland and forest (3) classes, the PD increased between 2010 and 2020 by almost 50%. Although these classes were undergoing expansion, they were highly fragmented and their patches were scattered and disconnected. This finding was confirmed by the results of the study by [PERSON] et al. (2020). Their results showed that the overall level of soil erosion declined between 2010 and 2020, while the estimated PD metric values for the same period decreased by almost 50%. Over one decade, between 2010 and 2020, the PD values of grassland (4) and heathland and shrub (5) classes increased, indicating that PN increased and LPI decreased. [PERSON] et al. (2020) explained that grassland patches mitigate soil erosion mainly at the edge of the patch, not across the whole patch area. Consequently, it seems that the landscape pattern influences soil erosion. In fact, higher interspersion (SPLIT metric) and patch numbers (_NP_ metric) for land cover classes proved to significantly accelerate soil erosion.
## 3 Results and Discussion
### Ecological Compensation Mechanism for the Tourism Industry during the COVID-19 Pandemic  
#### 3.3.1 Calculation of Effective Ecological Compensation in the Tourism Industry  
In 2019 and 2020, the carbon emissions of the tourism industry were 277.12 and 70.08 million tons, respectively, indicating a decrease of 20,704.61 million tons. This represents a reduction of 74.71% compared to the previous year. The carbon sequestration of the scenic areas in 2019 and 2020 was 2247.67 and 2324.30 million tons, respectively, showing an increase of 766.27 million tons, corresponding to a growth of 3.4% compared to the previous year. From the perspective of carbon balance, adequate ecological compensation in the tourism industry during 2019-2020 includes a reduction in carbon emissions in the tourism sector and an increase in carbon sequestration in scenic areas. Therefore, the total adequate ecological compensation in the tourism industry during this period amounted to 28,367.32 million tons.  
Figure 4: Carbon sinks of scenic spots in 31 provinces (cities) of China from 2019 to 2020.  
Figure 3: (a) Vegetation carbon sinks in scenic spots in 2019; (b) Vegetation carbon sinks in scenic spots in 2020.  
In most provinces, the total amount of effective ecological compensation during 2019-2020 was positive (Figure 5). This indicates that despite the prevalence of the COVID-19 pandemic, which led to the suspension of the tourism industry in China, when not considering the differences in climate conditions between 2019 and 2020, the shutdown of the tourism industry reduced carbon emissions. Simultaneously, the decrease in human activities temporarily improved vegetation growth conditions in tourist areas, enhancing their carbon sequestration capacity. Regarding the comprehensive carbon emissions and carbon sink volume, the tourism industry possesses considerable ecological compensation in their carbon budget.  
From a spatial distribution perspective, regions with more significant effective ecological compensation are primarily located in the central and southern parts of the country. Among them, Henan, Guangdong, and Hubei have compensation amounts reaching 20,000 tons. The compensation amounts in the northwest region are generally lower, as negative compensation occurs in the northern and northeastern regions due to a significant decrease in overall vegetation carbon sequestration.
## 2 Materials and Methods
### Related Works  
Suppose that the training set samples are represented as \(X=[X_{1},X_{2},\ldots,X_{n}]^{T}\in\Re^{n\times d}\), where \(n\) is the number of categories contained in the training set samples and d is the dimension of any sample. Suppose the training sample of the \(i\)-th subspace is \(X_{i}=[x_{i1},x_{i2},\ldots,x_{in_{i}}]^{T}\in\Re^{n_{i}\times d}\), denoted as \(x_{i}\in\Re^{d}(i=1,2,\ldots,n)\), with \(x_{ij}\) denoted as the \(j\)-th sample of the \(i\)-th class, \(n_{i}\) denoted as the number of the \(i\)-th class of samples, and the test sample denoted as y.  
The Linear Regression Classification algorithm is based on the assumption of subspace, and the data containing n categories are represented as n different subspace vectors, in which the samples belonging to the \(i\)-th category are represented as \(X_{i}\). The following is the specific principle of the LRC [18] algorithm when classifying the test samples.  
Assuming that the test sample y belongs to the \(i\)-th class, it can be approximately represented as a linear combination of the training samples of the same category.  
\[y=X_{i}\beta_{i},i=1,2,\ldots,n, \tag{1}\]  
where \(\beta_{i}\) is the representation coefficient of the \(i\)-th category of training samples.  
Face recognition is expressed as a regression problem in the above formula, and the representation coefficient is obtained through the pseudo-inverse matrix.  
\[\hat{\beta}_{i}={(X_{i}^{T}X_{i})}^{-1}X_{i}^{T}y, \tag{2}\]  
where \(y\) is the test sample vector.  
The projection and projection surface of the test sample y in each subspace can be expressed as:  
\[\hat{y}_{i}=X_{i}\hat{\beta}_{i},i=1,2,\ldots,n, \tag{3}\]  
\[\hat{y}_{i}=X_{i}{(X_{i}^{T}X_{i})}^{-1}X_{i}^{T}y, \tag{4}\]  
\[\hat{y}_{i}=P_{i}y, \tag{5}\]  
where \(\hat{\beta}_{i}\) is the predicted regression coefficient of the \(i\)-th category training sample, \(\hat{y}_{i}\) is the linear model reconstruction vector, and \(P\) is the projection matrix.  
The distance between the test sample vector and the projection of y on the \(i\)-th subspace can be expressed as:  
\[d_{i}(y)=\left\|y-\hat{y}_{i}\right\|_{2},i=1,2,\ldots,n. \tag{6}\]  
Selection of the category with the smallest Euclidean distance as the discrimination result obtains:  
\[\text{identity}(y)=\operatorname*{argmin}_{i}d_{i}(y),i=1,2,\ldots,n. \tag{7}\]
# [PERSON] SOM Architecture for Intrusion Detection on In-Vehicle Communication Networks
## 3 Control Area Network (CAN)  
The CAN bus is a message-based protocol designed for in-vehicle networks. It enables numerous electronic components, throughout the in-vehicle system, to communicate with each other through a single/dual-wire bus [29]. Consequently, all the nodes in a CAN bus can receive all packet transmissions and a frame is defined as a structure [30]. Figure 1 shows the standard structure, a sequence of CAN data (bytes) in the network.  
The meaning of each field in a CAN frame is: SOF (start of frame), which specifies the start of a new message with a dominant bit to synchronize all the nodes for CAN message transmission; Arbitration includes 11 bits and can be extended up to 29 bits. The ID field for each transmitted CAN frame indicates the packets' priority. The lower the ID bits, the higher the priority of the packet the value signifies, and the single remote transmission request (RTR) bit is dominant when information is required from another node; Control provides information in order to verify that all the expected packets are received successfully by the receiver. It consists of three fields: the Identifier Extension (IDE) must be recessive for extended format and dominant for standard format, Reserved Bit (RB0) must be dominant for standard format, and DLC (data length code) contains the number of data bytes; Data contains information for CAN nodes to perform actions; CRC (cyclic redundancy check) contains the checksum of the previous data for error detection and in CRC Delimiter (CRC Del) the single bit must be recessive; ACK (acknowledge) ensures that all the nodes involved in the message receive everything correctly, and in case of error, that the transmitter is immediately alerted to send the data packets again. In ACK Delimiter (ACK Del) the single bit must be recessive; EOF (end of frame) indicates the end of a current CAN Message.
## 3 Data Compression Technology-Based Intelligent Data Acquisition (IDAQ) System
### Two-Way Wireless Communication System (TWCs)  
For bi-directional wireless communication between the cDAQ-9139, the data logging and controller system, and the host PC, the central control two-way wireless control communication system (TWCs) for the IDAQ system was the SWK-3121 module from Moxa, shown in Figures 6c and 7c. This module uses industrial LAN, guaranteeing a stable wireless connection and streaming performance. The communication speed is approximately 11-54 Mbps and the basic communication distance is around 100 m. Furthermore, since it can secure up to several kilometers of additional communication distance by simply changing the RF antenna, it can be flexibly applied to the SHM systems of large civil structures.
# Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM
## 1 Introduction
Several techniques have been studied to select relevant features, such as the Laplacian score (LS), which evaluates the importance of features calculating its power of locality preserving [25]; the distance evaluation technique (DET) that indicates the degree of variation between samples from each feature provided [46]; scatter matrices (SM), as a supervised extension of the well-known PCA approach [47]; and the self-weight (SW) algorithm that adaptively evaluates the contribution of each attribute without class labels [45]. Recently, a feature ranking and subset selection based on Euclidean distance was proposed, employing a one-vs-one regulation between classes and features [48]. In turn, to efficiently extract fault feature information and improve fault diagnosis accuracy, an improved multiscale dispersion entropy and max-relevance min-redundancy feature selection is proposed in [49]. Authors in [50] integrated dimension reduction step to improve the efficiency of bearing fault detection. Though different feature-embedding and selection approaches have been proposed in the state-of-the-art, the coding of non-stationary and interpretable patterns for vibration-based bearing fault diagnosis and severity evaluation remains as an open issue [51]. In this work, a stochastic feature selection approach based on HMM is introduced to reveal relevant multi-domain features from vibration signals in bearing fault diagnosis and severity evaluation tasks. Our knowledge-based framework calculates time, frequency, and time-frequency domain-based features. Afterward, each feature relevance's ranking is computed under a windowing scheme through a supervised cost function. In this sense, our feature ranking employs a local dissimilarity criterion using the relief-F method aiming to reveal local relationships among samples under a stochastic perspective governed by the windowing strategy and the HMM classifier. Further, we built a learning curve by adding one-by-one the features ranked, to code non-stationary and interpretable patterns concerning multiple health conditions in bearings. The results obtained in a public database of bearing diagnosis and severity evaluation, prove that our strategy is competitive compared to state-of-the-art approaches regarding the classification accuracy and the number of selected features. The rest of the paper is organized as follows: In Section 2, we describe the theoretical framework. Then, in Section 3 we describe the experimental set-up. After, the obtained results and discussions are presented in section 4. Finally, we outline the main remarks in Section 5.
# Applications of Probabilistic Forecasting in Demand Response
## 2 Materials and Methods
### Demand Response and Physically Based Load Models
#### 2.5.2 Aggregated Models  
For a practical participation of the load flexibility potential in electricity markets or services, flexibility must reach some thresholds (usually, some hundreds of kW), and this involves load aggregation. Literature usually uses two alternatives: Monte-Carlo and Stochastic Equations. Monte-Carlo is the older and most common option [48], and it has been selected for this work. Another possibility was first discussed by [PERSON] et al. in [49]. Authors use probability density functions obtained through the resolution of a Stochastic Partial Differential Equation but for a one-zone water tank (this elemental model is only accurate if the appliance performs near its steady state). If the elemental model of the WH tank requires several zones, this method becomes very complex because it involves higher order equations and becomes computationally more expensive.  
Two aggregated load groups are simulated for testing: one for EH loads and another for WH loads. To consider the actual diverse characteristics of different end-uses within a large population of loads (perhaps thousands of appliances and customers) and simplify the aggregation methodology, a quasi-homogeneous control group (QHCG) is established for each end-use. The QHCG involves the consideration of different load characteristics (e.g., elemental model parameters, performance, or the size of each appliance) and customer uses (e.g., small variations in thermostat settings, water and heating usage patterns). As an example, the tank capacity of WH loads in the QHCG has been considered a uniform distribution between 80 L and 110 L (a standard size for 2-3 inhabitants, see Section 2.1). The electrical parameters can be found in manufacturers' catalogs and by considering the size of loads in a specific country (e.g., in Spain, the average size of a WH is around 90 L).  
For those interested in PBLM aggregation methodologies, additional models, validation databases, individual end-use profiles for residential customers, and some Matlab scripts with elemental models for solving PBLMs can be found in [46].
## 3 Experiment and Analysis
### Sensitivity Analysis of the Proposed DBSCAN-Based Alternative Model's Parameters
#### 3.4.3 Sensitivity Analysis of the Merging Distance Threshold  
The sensitivity of the proposed DBSCAN-based alternative model to the merging distance threshold \(\lambda\) is evaluated using \(\varepsilon=0.250\) m, \(\delta=0.110\) m, \(\alpha=0.055\), and \(\tau=0.001\). As shown in Figure 24, the model achieves the highest recall of 89.6% when \(\lambda\geq 0.350\) m. Although with this configuration the model does not yield the highest precision, it results in the highest F1 score of 92.7%.
## 3 Results and Discussion
### Temperature Uniformity of Aquatic Products  
To assess the temperature uniformity in products at different stacking modes, the temperature coefficient of variation (\(TCOV\)) was selected as a studied parameter [36]. The \(TCOV\) is the ratio of the standard temperature deviation to the average temperature. At lower ratios, the temperature gradients are smaller, so the temperature uniformity is better. The calculation formula for \(TCOV\) is as follows:  
\[TCOV=\frac{1}{T_{ave-t}}\sqrt{\frac{1}{n-1}\sum_{i=1}^{n}(T_{i-t}-T_{ave-t})^{ 2}} \tag{10}\]  
The centers of each package were chosen as the measurement points. As depicted in Figure 9, the trends of \(TCOV\) at UPF-PSMs and the corresponding NSMs are similar. For a residence time of 10 h, two peaks of \(TCOV\) occur, amounting to \(1.10\times 10^{-3}\) and \(1.07\times 10^{-3}\). The latter is due the fact that the gaps between adjacent package piles at these two stacking modes are narrower (0.1 m), which makes it difficult for the cold air to pass through the space at the beginning. The values of \(TCOV\) at UPF-PSMs and the corresponding NSMs are also increasing first, and then decreasing with the time variation, but the peak times are 2 and 16 h, respectively. In addition, the peaks of \(TCOV\) are smaller, i.e., \(2.76\times 10^{-4}\) and \(9.15\times 10^{-4}\), respectively. In all cases, the temperature uniformity gradually improves after the peak times. Wider gaps facilitate the forced convection in the refrigerated room, so the product temperature uniformity can thus be improved. The peak of \(TCOV\) at UPF-PSMs is the smallest one. Moreover, the \(TCOV\) at UPF-PSMs reaches its lowest value for a residence time of 24 h. Therefore, the best temperature uniformity of aquatic products is obtained by using UPF-PSMs.
## 2 Micro-Strip Patch Antenna and DR Process
### Feature Extraction (FE)
#### 2.2.1 Principal Component Analysis (PCA)  
To draw independent and uncorrelated features from heavy correlation, PCA employs orthogonal transformation, which is represented by \(Y=PX\). The transformed matrix, \(Y\), is explained by a transformation matrix, \(P\), and the original data set, \(X\). In order to conduct a linear transformation, \(X\) and \(Y\) will contain numerous examinations and variables, which can be described as \(m\) and \(n\), respectively. Specifically, PCA requires a decomposition of eigenvectors [26] to truncate the data dimension. A covariance matrix, \(C_{Y}\), can be acquired by  
\[C_{Y}=\frac{1}{n-1}PAP^{T}\ =\ \frac{1}{n-1}P(P^{T}DP)P^{T}\ =\frac{1}{n-1}D. \tag{8}\]  
In Equation (8), \(A=XX^{T}\), which implies a symmetric condition. \(A\) consists of the eigenvectors in the rows of the matrix, \(P\), and is denoted by \(P^{T}DP\). Here, \(D\) stands for a diagonal matrix that should be connected to equivalent eigenvectors of matrix \(A\). The property of an orthonormal matrix allows the inverse of the matrix \(P\) to be equal to its transpose. Thus, principal components corresponding to \(X\) will act as the eigenvectors of \(PC_{x}=(n-1)^{-1}XX^{T}\), and the diagonal variables of \(C_{Y}\) should be the \(X\)'s variance.