esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultra-Low-Power Wide Range Backscatter Communication Using Cellular Generated Carrier +
## 2 Background
### Application of Backscatter Communications  
Many applications based on traditional BC were studied, such as tracking devices, remote switches, medical telemetry, and sensor networks. Recently, with the studies and development on AmBC, many applications are emerging in various industries, such as smart life, medical and healthcare, retail and logistics, and transportation:  
(i) Smart Home: ambient BDs can be deployed in a home to improve a life quality. A large amount of backscatter sensors can be placed at home [23], which can operate for a long time without dedicated power sources. These backscatter sensors can detect normal environmental indices (e.g., temperature, humidity) and harmful gases (e.g., CO, smoke) to prevent potential risks at home.  
(ii) Medical and Healthcare: tiny-scale and long-lasting smart devices are required for medical and healthcare applications, such as wearable and implantable health monitoring, where ambient BDs can be implemented [5]. [PERSON] et al. [24] developed a battery free platform for wearable devices such as smart sneaker, which embedded sensors and ambient backscatter modules in the shoes. Sensors can count steps and heart rate, and two shoes are coordinated by using the ambient backscatter modules. [PERSON] et al. [25] proposed a smart fabric, where a BD inside a shirt monitors vital signs of human body, such as heart rate and breathing rate.  
(iii) Retail and Logistics: ambient BDs can be deployed in retail and logistics. In the study of [PERSON] et al. [22], each item in a grocery store is equipped with a BD that has a unique identification number (ID). One BD broadcasts its ID in every five-second interval, and all BDs in the same network periodically listen and store their neighbour BDs. By this means, the existence of an item can be indicated by comparing its ID with the information stored in the neighbours. Furthermore, this technique has been implemented in self-service retail. For example, each item in a vending machine is stuck with a BD label. Buyers are required to register a payment method (e.g., credit card, PayPal) before purchase. The payment is automatically executed when the item is taken out of the vending machine. The similar solution can also be implemented in logistics, e.g., warehouse and container.  
(iv) Transportation: backscatter communication enables reliable, accurate, and low-cost vehicular positioning in high-density cites, tunnels, and underground scenarios, where the global navigation satellite system (GNSS) has an unacceptable performance due to signal shielding. [PERSON] et al. [26] proposed a novel BC assisted vehicular positioning system, where a backscatter reader is deployed on a vehicle and backscatter tags with location information are deployed along roadside. Thus, the vehicle can be located by BC between the reader and tags. This technique can achieve high positioning accuracy and it can be easily expanded. In the high-speed railway (HSR) industry, [PERSON] et al. [27] developed a backscatter aided wireless transmission scheme.
# Point Cloud Segmentation from iPhone-Based LiDAR Sensors Using the Tensor Feature
## 1 Introduction  
The point cloud is a universal spatial information acquisition format and plays an important role in indoor and outdoor environment understanding [1]. In conventional point processing methods, the point cloud is usually defined as a spatial location, or as having textural information, and geometric features are computed from the unstructured point cloud; then, the data are segmented into different geometric or semantic structures for further processing, such as in object detection, classification, and scene understanding [2, 3].  
In recent years, with different kinds of **L**ight **D**etection **A**nd **R**anging (LiDAR) sensors included in consumer electronic devices, such as the iPhone or Kinect, it has become increasingly convenient to acquire point cloud data. However, it also raised many challenges for data processing, due to the low accuracy and high noise of the point cloud data collected by these unprofessional LiDAR devices. To address these issues, some processing techniques are applied [4, 5, 6, 7, 8], such as point cloud filter, denoising, normal computation, and resampling. Then features are extracted from the point cloud, and the data are segmented. To achieve this goal, many kinds of point cloud segmentation methods were proposed that extract the feature in different ways, including deep learning-based approaches.  
To deal with these kinds of problems in a universal framework, this work proposes a tensor feature-based point cloud segmentation method, and the point cloud data in an actual scene is obtained from the iPhone LiDAR sensor. The contributions are as follows: (1) we conduct the theoretical derivation of the N-d tensor voting, which helps the high-dimensional normal vector computation and structure decomposition, and design therelated algorithms (2) we compute the normal vectors from the noisy point cloud based on the two-step procedure of tensor encoding and aggregating; (3) we construct the tensor feature decomposing and clustering framework to segment the point cloud from the noisy scenes acquired by the iPhone LiDAR sensor, and make a comparison between several related methods.  
The remainder of this paper is structured as follows. The next section gives an overview of works related to this paper. Section 3 provides a detailed description of the point cloud segmentation method based on the tensor feature, including normal vector computing, tensor aggregation, tensor feature decomposition, and point cloud clustering. Experiments are conducted and the results are discussed in Section 4, followed by the conclusions in Section 5.
# Neighbor-Aware Non-Orthogonal Multiple Access Scheme for Energy Harvesting Internet of Things
## 4 Constrained Stochastic Game
### Constraint Function  
For keeping a low energy outage probability, we define the constraint function \(c(S_{i},A_{i})\). The situation where there is no energy in the battery of IoT device \(i\) represents the energy outage. Therefore, the constraint function \(c(S_{i},A_{i})\) can be defined as  
\[c(S_{i},A_{i})=\left\{\begin{array}{ll}1,&\text{if }S_{i}=0\\ 0,&\text{otherwise}.\end{array}\right. \tag{11}\]
# Indoor Occupancy Sensing via Networked Nodes (2012-2022): A Review
###### Abstract  
In the past decade, different sensing mechanisms and algorithms have been developed to detect or estimate indoor occupancy. One of the most recent advancements is using networked sensor nodes to create a more comprehensive occupancy detection system where multiple sensors can identify human presence within more expansive areas while delivering enhanced accuracy compared to a system that relies on stand-alone sensor nodes. The present work reviews the studies from 2012 to 2022 that use networked sensor nodes to detect indoor occupancy, focusing on PIR-based sensors. Methods are compared based on pivotal ADPs that play a significant role in selecting an occupancy detection system for applications such as Health and Safety or occupant comfort. These parameters include accuracy, information requirement, maximum sensor failure and minimum observation rate, and feasible detection area. We briefly describe the overview of occupancy detection criteria used by each study and introduce a metric called \"sensor node deployment density\" through our analysis. This metric captures the strength of network-level data filtering and fusion algorithms found in the literature. It is hinged on the fact that a robust occupancy estimation algorithm requires a minimal number of nodes to estimate occupancy. This review only focuses on the occupancy estimation models for networked sensor nodes. It thus provides a standardized insight into networked nodes' occupancy sensing pipelines, which employ data fusion strategies, network-level machine learning algorithms, and occupancy estimation algorithms. This review thus helps determine the suitability of the reviewed methods to a standard set of application areas by analyzing their gaps.  
s _future internet_ 2023, 15, 116. [[https://doi.org/10.3390/fi5030116](https://doi.org/10.3390/fi5030116)]([https://doi.org/10.3390/fi5030116](https://doi.org/10.3390/fi5030116))
# Assessment of Grassland Degradation on the Tibetan Plateau Based on Multi-Source Data
## 4 Discussion
### Grassland Quality Indexes Analysis  
The consistencies between the six vegetation indicators and observed grassland degradation points were low and varied widely. These differences in consistency may be real or may be caused by the analysis process. During the pre-processing of the six vegetation indexes, the spatial resolution was unified at 1 km reducing the problem of mixed pixels as well as introducing errors. In addition, the six vegetation indexes represented the quality changes in constant grassland on the TP, and the observed points were sampled for grassland degradation on the TP. The consistency of the six vegetation indexes with observed points was underestimated when the observed sampling points were outside the constant grassland.  
The vegetation indexes underestimated grassland degradation to some extent. Venom grass and shrub invasive grasslands are not monitored by vegetation indexes [56]. The six vegetation indexes with different ecological meanings reflect different degrees of grassland degradation. The NDVI, EVI and LAI represented vegetation greenness indexes, and the SIF, NPP and GPP represented carbon sequestration capacity. In this study, we found that the carbon sequestration capacity of grasslands on the TP declined more rapidly than greenness. Grassland degradation is the process of quality decline to area reduction. The decline of grassland carbon sequestration capacity is a reflection of the degradation of ecological functions. The decline in greenness is a symptom of degradation and the reduction in area is an extreme degradation of grassland.
## 3 Results
### Exafs
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \(\mathbf{D_{0}}\) & \(\mathbf{C_{1}}\) & \(\mathbf{D_{1}}\) & Component 1 & Component 2 & Component 3 \\ \hline Fe-C/Å & 1.857(3) & 1.894(6) & 1.878(2) & 1.864(2) & 1.900(2) & 1.895(2) \\ \(\sigma^{2}\) Fe-C/Å\({}^{2}\) & 0.0031(5) & 0.0033(5) & 0.0027(4) & 0.0022(2) & 0.0030(4) & 0.0029(5) \\ C\(\equiv\)N/Å\({}^{2}\) & 1.190(6) & 1.172(7) & 1.181(2) & 1.187(2) & 1.172(3) & 1.170(3) \\ \(\sigma^{2}\) C\(\equiv\)N/Å\({}^{2}\) & 0.005(1) & 0.009(1) & 0.0087(5) & 0.005(1) & 0.012(1) & 0.007(1) \\ Mn-N\({}_{\rm eq}\)/Å\({}^{2}\) & 2.10(2) \({}^{**}\) & 1.982() \({}^{**}\) & 2.150(4) \({}^{*}\) & 2.09(1) \({}^{**}\) & 2.10(1) \({}^{**}\) & 1.986(6) \({}^{**}\) \\ \(\sigma^{2}\) Mn-N\({}_{\rm eq}\)/Å\({}^{2}\) & 0.010(2) & 0.008(2) & 0.011(2) & 0.010(1) & 0.002(1) & 0.008(2) \\ Mn-N\({}_{\rm ax}\)/Å\({}^{2}\) & 2.34(2) \({}^{***}\) & 2.19(2) \({}^{***}\) & / & 2.35(2) \({}^{***}\) & 2.17(1) \({}^{***}\) & 2.18(1) \({}^{***}\) \\ \(\sigma^{2}\) Mn-N\({}_{\rm eq}\)/Å\({}^{2}\) & 0.018(5) & 0.018(3) & / & 0.014(3) & 0.014(4) & 0.018(3) \\ \(\sigma^{2}\) Fe-C-N/deg\({}^{2}\) & 12(5) & 14(8) & 44(20) & 19(5) & 16(10) & 9(5) \\ \(\sigma^{2}\) Mn-N-C/deg\({}^{2}\) & 4(3) & 4(3) & 4(3) & 4(3) & 28(15) & 4(3) \\ Fe-C-N/deg\({}^{2}\) & 180 & 180 & 180 & 180 & 180 & 180 \\ \(\theta\) Mn-N-C/deg\({}^{2}\) & 180 & 180 & 180 & 180 & 180 & 180 \\ E\({}_{0}\) Mn & 6540(1) & 6546(1) & 6542.4(8) & 6540(1) & 6541(2) & 6547.6(8) \\ E\({}_{0}\) Fe & 7116.1(6) & 7117.7(4) & 7117.4(2) & 7116.6(3) & 7117.7(5) & 7117.8(5) \\ S\({}_{0}\)\({}^{2}\) Mn & 0.65(3) & 0.65(3) & 0.65(3) & 0.65(3) & 0.65(3) & 0.65(3) \\ S\({}_{0}\)\({}^{2}\) Fe & 0.80(3) & 0.80(3) & 0.80(3) & 0.80(3) & 0.80(3) & 0.80(3) \\ \(\chi^{2}\)-like & 1.29 & 1.38 & 2.65 & 4.76 & 7.53 & 4.26 \\ \hline \end{tabular} \({}^{*}\)CN = 6, \({}^{**}\)CN = 4, \({}^{***}\)CN = 2, CN = coordination number.  
\end{table} 
Table 1: Relevant extended X-ray absorption fine structure (EXAFS) refined parameters for the D\({}_{0}\), C\({}_{1}\), and D\({}_{1}\) states of charge, as well as the three components retrieved by the MCR-ALS analysis.
# Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique
## 2 Trends of Track Condition Monitoring  
The monitoring of railway track geometry from an in-service vehicle became increasingly attractive over the past decade [3]. Track geometry measurement systems using in-service vehicles have been developed worldwide, although they are still in the developing stage. The repeated verification of the same track provides an opportunity to record track geometry degradation. The obtained information is fed back to the track maintenance section to implement necessary steps.  
Several types of track faults are detected by measuring the acceleration of bogies. [PERSON] et al. demonstrated track irregularity monitoring by using bogie-mounted sensors [4; 5]. [PERSON] et al. proposed a technique to estimate long wavelength track irregularities from on-board measurements [6]. If track faults are detected using car-body mounted sensors, it is easier to perform the condition monitoring of track irregularities. The distinctive signal of track faults is hidden in car-body vibrations, and thus signal processing of the acceleration measured in-cabin is necessary to detect the track faults. [PERSON] et al. demonstrated the possibility of estimating the track geometry of Shinkansen tracks using only car-body motions [7; 8]. A Kalman filter was applied to estimate the track irregularity from the car-body motions.  
[PERSON] et al. developed a system that attempted to identify vertical and lateral track irregularities by using accelerometers placed on the car-body of in-service vehicles [9]. Furthermore, the system provides a function to listen for corrugation faults by using an acoustic sensor (a microphone). An approach was tested to detect squats by listening to rolling noise using microphones [10]. [PERSON] et al. demonstrated the possibility of estimating the track irregularities of conventional railway tracks by using only car-body acceleration [2]. The Kalman filter-based estimation technique was proposed for conventional railways and evaluated from data obtained with the track condition monitoring system developed by [PERSON] et al. [1; 11].
## 4 Proposed Analytical Expressions of the Maximum-Rating Constraints
### RMS Current Supported by \(L_{SH}\)  
The RMS current through the inductance \(L_{SH}\) is given by  
\[I_{L_{SH}RMS}=\sqrt{\frac{1}{T}\int_{0}^{T}(i_{L_{SH}}(t))^{2}dt}. \tag{36}\]  
Solving (36), the analytical solution is  
\[I_{L_{SH}RMS}=h_{m}(q,D)\left(\frac{2V_{DD}\cdot g_{x}(D,q)}{R_{L}}\right), \tag{37}\]  
where \(h_{m}\) is an extensive analytical function plotted in Figure 7a. Therefore, a curve-fitting process similar to the one discussed in Section 4.6 was used to simplify the expression. This process employed 2500 points, which corresponds to a 50 \(\times\) 50 grid for the \(D\) and \(q\) variables. The \(D\) and \(q\) values were linearly swept from 0.09 to 0.92 and 0.09 to 1.92, respectively. Then, the curve-fitting of the \(h_{m}\) data were developed using a non-linear least-squares method and the custom function defined by  
\[h_{m}(q,D)\approx\frac{f_{3}(q,D)}{f_{4}(q,D)}, \tag{38}\]  
where \(f_{3}\) and \(f_{4}\) use the following equation  
\[\begin{split} f_{x}(q,D)&=b_{x0}+b_{x1}q+b_{x2}q^{2 }+b_{x3}D+b_{x4}D^{2}+b_{x5}qD+b_{x6}q^{2}D+b_{x7}qD^{2}\\ &\quad+b_{x8}q^{2}D^{2}.\end{split} \tag{39}\]  
The coefficients of the mathematical expression found by the curve-fitting tool were summarized in Table 2. The \(h_{m}\) fitting process has an \(R^{2}\) of 0.9956, and its related residuals are shown in Figure 7b. The residuals are characterized by an absolute mean error of 0.0325 and a maximum absolute error of 0.3833, which is located at \(D=0.1916\) and \(q=0.9863\). The graph illustrated that the fitting error is not focalized in a specific region, whichindicates a low discrepancy in the overall design space between fitted values and the values expected under the ideal Class-E PA with FDI model.
# Double-Bend and Semi-Spherical Energy Deflectors' Blast Load Efficiency--A Comparative Study
## 4 Results and Discussion
### Numerical Analyses of the Semi-Spherical Deflector  
In the case of a semi-spherical deflector, the effect of the arc radius forming the working part of the deflector was considered. Radii from 10 to 50 mm were adopted, with gradations of 5 mm. Figure 21 shows the deflections of the ballistic pendulum for the geometries mentioned above and all three distances of the explosive charge. Analyzing the graphs below, we can see that the difference between the maximum and minimum deflection values is 7.4%, 6.9%, and 10% for distances of 30, 40, and 50 cm, respectively.  
The maximum pendulum swing value depending on the applied geometry of the semi-spherical deflector and the distance from the explosive charge is presented in Figure 22a. We can see that it has a linear character, while the only slight deviations are observed for deflectors with radii of 35 and 40 mm. It is worth noting that at a distance of 30 cm, the maximum deflection for 15, 20, and 25 mm deflectors are very close to each other (the difference is less than 1%), and it is only for greater distances that the results begin to diverge more in favor of the deflector with a larger radius.  
Figure 21: Swing of the pendulum—semi-spherical deflector. (**a**) Stand-off of 30 cm; (**b**) Stand-off of 40 cm; (**c**) Stand-off of 50 cm.  
Figure 22: Change of the maximum pendulum swing in the function of the: (**a**) stand-off distance; (**b**) deflector radius.  
Figure 22b presents the change in pendulum swing as a function of deflector radius for all distances considered. It shows that for a stand-off of 30 cm and radii from 10 to 25 mm, the changes in the values are small, and only from 30 mm does a significant decrease in the deflection begin, which indicates the ability of the deflector to dissipate energy.  
The exact results of the ballistic pendulum deflections for semi-spherical deflectors are shown in Table 8.  
Figure 23 compares energy dissipation capacities for the best variants of double-bend and semi-spherical deflectors. For comparison, values for a double-bend 80-15 deflector, which is the same as a typical V-shaped deflector, are also included. All the deflectors shown have the same height at their highest point. The use of double-bend deflectors is advantageous. The difference between semi-spherical and V-shaped deflectors ranges from 12.5% to 14.5% (for distances of 50 cm and 30 cm, respectively). This deflector provides much better energy dissipation capability, especially when the distance between the explosive charge and the protected object is short. The semi-spherical deflector also proved superior to the V-shaped variant. However, the differences are much smaller and range from 3.4% (distance of 50 cm) to 4.9% (distance of 30 cm). As with the double-bend deflector, we observe a decrease in the difference as the distance from the explosive increases.
## 4 Applications to Real Data
### Data set
Lastly, we study the subset metal and use daily data on gold (GOL), silver (SIL), platinum (PLA), ruthenium (RUT), and palladium (PAL). The time period ranges from November 1994 until March 2019. In [40], the authors motivate the attractiveness of dynamic time warping in the context of economics by different time periods until the same action has an impact. When an economic factor is released on the basis of its expectations, investors take a determined position, which leads to movements in share prices. At a certain point in the life of the financial markets, an announcement like the one mentioned above can have an impact within a short time frame, such as a week. Conversely, if you are in the presence of a high-volume trading period, the same news may have an impact for only an hour. Our approach is the same for all three data subsets: We first find the common time period that all time series of one subset provide data for. Note that due to this approach, the length of the time series varies per subset. Within one subset, however, all time series have the same number of entries, which ensures comparability and facilitates interpretation. In a second step, we apply the generalized causality algorithm to all pairs and find the one with minimal local cost--details are discussed for this combination. We normalize the data before applying the algorithm to be able to reasonably compare the time series. To be more specific, we follow the vast majority of literature and determine the returns of each time series [9; 67; 68; 69]. Following [70], returns can more easily be assumed to be invariant than prices.
## 4 Bigram and Traditional Results
### Forecasting Water Levels
#### 4.3.1 Bigram Document Analysis  
The main concepts have been examined in the bigram analysis, as shown in Figure 11. For the topic of forecasting water levels, it is evident that the primary applications are related to water management, water supply, water levels, groundwater, and irrigation. Regarding techniques, regressions and artificial neural networks appear among the first. Moreover, the development of remote sensing is a critical point.  
In the thematic map analysis, four main groups have been observed. The first group, in light green, is related to water management, lakes, water quality, and machine learning applied to these types of issues. A cluster in the lower right quadrant concerns ground water level, irrigation, and prediction models using regression techniques. The light blue cluster associates water level with water supply, performing forecasting through artificial neural networks. Finally, the last cluster in the lower left quadrant is related to groundwater resources.  
Upon analyzing the conceptual structure map and the dendrogram in Figure 12, two large groups are observed. The blue group is related to water conservation, environmental issues, water quality, and lakes. In red, a cluster involves water reservoirs, groundwater, and predicting levels through various techniques, with SVM, ANN, Cluster analysis, and Gaussian process regression standing out.  
Figure 11: Tree and thematic map for a forecasting water levels topic.
## 2 Preliminaries and Problem Formulation  
### Graph Theory  
In this paper, the multi-agent directed communication network is depicted. A directed connected graph is defined as \(G(V,E,A)\), where \(V=\{v_{1},v_{2},\cdots,v_{n}\}\) represents a finite non-empty set of nodes, \(E\subseteq V\times V\) is the ordered set of nodes pairs and \(A=[a_{ij}]\) is the adjacency matrix. If node \(v_{i}\) can receive the information from node \(v_{j}\), then the node pairs \(\overline{v}_{ij}=(v_{i},v_{j})\in E\), and node \(v_{j}\) is called a neighbor of node \(v_{i}\). The neighbor set of node \(v_{i}\) is represented by \(N_{i}=\big{\{}\begin{array}{l}v_{j}[(v_{i},v_{j})\in E\end{array}\big{\}}\). Correspondingly, the adjacency matrix element \(a_{ij}=1\) when \(v_{j}\in N_{i}\), otherwise \(a_{ij}=0\). The graph Laplacian matrix is defined as \(L=D-A\), whose row sums are equal to zero [25]. Diagonal matrix \(D=diag(d_{i})\) is the in-degree matrix, where \(d_{i}=\sum_{j\in N_{i}}a_{ij}\) is the in-degree of node \(v_{i}\).  
**Definition 1**.: _A directed graph is called as strongly connected if there is a directed path for any pair of distinct nodes \((v_{i},v_{j})\), where the directed path is the edge sequence \((v_{i1},v_{i2})\), \((v_{i2},v_{i3})\),..., \((v_{ik},v_{j})\)._  
**Definition 2**[17].: _A directed tree is a connected graph where every node except the root node, has an in-degree equal to one. The graph is called to have a spanning tree if a subset of the node pairs constructs a directed tree._  
In this paper, \(\overline{\lambda}(B)\) and \(\underline{\lambda}(B)\) represent the maximum and minimum singular values of the matrix \(B\), respectively.