esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of the Causes of Grassland Fires
#### 3.5.2 Anthropogenic Factor  
Among the identified causes of fires, the proportion of anthropogenic grassland fires (93.33%) was much higher than that of natural grassland fires (6.67%), and thus, this study concluded that human activities were the main factor contributing to the occurrence of grassland fires in Qinghai Province.  
1. High-voltage line contact: These fires accounted for 40.40% of the anthropogenic grassland fires. Qinghai Province is a key hub for new energy development and a major energy-exporting province in China's \"West-to-East\" power transmission strategy. As a result, rapid expansion of the power grid has led to an increase in high-voltage transmission lines. The long span between high-voltage transmission poles makes the wires susceptible to swaying in strong winds. This movement can cause wires to come into contact, generating electric sparks that ignite dry vegetation and trigger grassland fires. For example, in 2019, a high-voltage line contact incident in Delong Village, Youganning Township, Henan County, led to a grassland fire that burned 89.01 ha and resulted in an estimated economic loss of approximately CNY 224,800.
2. Improper heating: These fires accounted for 22.22% of the anthropogenic grassland fires. For example, in 2018, herders in Xiawute Village, Toyama Township, He'nan County, unintentionally ignited a grassland fire while using fire during grazing activities. The fire burned 7.99 ha of grassland and caused an estimated economic loss of approximately CNY 11,935.
3. Firecrackers: Grassland fires caused by firecrackers, whether set off by children or herders attempting to drive away wolves or bears, accounted for 19.19% of the anthropogenic grassland fires. For example, a grassland fire in Xiazhidawa Village, Dosong Township, He'nan County, in 2010 burned 21.01 ha and resulted in an estimated economic loss of approximately CNY 30,000.
4. Grassland fires resulting from domestic fires accounted for 13.13% of the anthropogenic grassland fires. For example, in 2011, a grassland fire in Hezheheng Village, Yougan'ning Township, He'nan County, burned 13.87 ha.
5. Simmering: Grassland fires resulting from improper handling of simmering activities by herders accounted for 12.12% of the anthropogenic grassland fires. For example,in 2010, a grassland fire in Kesheng Township, Henan County, near the edge of Twin Fish Lake, burned 21.68 ha.
6. Smoking: Grassland fires resulting from smoking accounted for 8.08% of the anthropogenic grassland fires. For example, in 2010, a grassland fire in Langjia Village, Bao'an Township, Tongren County, caused an estimated economic loss of approximately CNY 20,000 due to improperly discarded cigarette butts.
7. Industrial fires: Grassland fires resulting from industrial activities accounted for only 2.02% of the anthropogenic grassland fires. For example, in 2011, during the construction of a mobile base station in Xiujia Village, Yougan'ning Township, He'nan County, improper fire use by workers led to a grassland fire that burned 33.6 ha, resulting in an estimated economic loss of up to CNY 300,000.
# Survey on Power-Aware Optimization Solutions for MANETs
## 2 Related Work
### Power-Aware Routing Metrics
Nonetheless, the idea of ensuring a similar power drain rate metric amongst critical nodes is a tough task, because such a metric is directly dependent on the packet size. Therefore, we cannot blindly choose an optimal path without having all the information about the size of future arriving data packets. However, if we assume that all the future arriving data packets have the same size, we can ensure an equivalent rate of power consumption amongst such crucial mobile nodes. However, maximizing network segregation time is challenging, especially when we expect high throughput and high performance by reducing end-to-end delay. * _Minimize High Variance in Mobile Node Battery Energy Level_: The basis of this metric is that all mobile nodes are highly essential in the network and the untimely shut-down of any node is not good for the network's throughput and delay performance. Along with that, while designing any forwarding scheme, we have to take full care that no one mobile node is overloaded more than any of the others [61]. This metric tries to ensure that all the available mobile nodes can live and work for a longer period. Nevertheless, again, the idea of ensuring a similar power drain rate metric amongst nodes is a challenging task, since such a metric is directly dependent on the packet size. Nonetheless, if we assume that all the future inward data packets are of the same size, we can unquestionably guarantee an equivalent rate of power consumption amongst mobile nodes. Although, we cannot make such an infeasible assumption while designing any forwarding strategy. * _Minimizing Price-Per-Packet (PPP)_: According to [61], if we need to prolong the lifetime of all the nodes, then we need to utilize the PPP metric, other than the power disbursed per packet metrics. The PPP metric is the total price of transmitting a packet along some selected routes. The main advantages of the PPP metric are: (1) it can commendably assist in integrating the battery-related features unswervingly in designing part of the routing scheme; (2) it directly reflects the congestion level along a selected path. [PERSON] and [PERSON] [74] effectively instigated the Energy Exhaustion per Packet Transmission and Reception and the PPP metric in their proposed scheme, called Power-Aware Multi-Access Protocol with Signalling (PAMAS). Specifically, the authors believe that incorporating the suggested PPP metric in PAMAS directly reflects the optimization in other metrics (i.e., Period to Network Segregation and Battery Energy-level Variance metric, respectively, as well). Furthermore, it is noteworthy that such metrics for forwarding do not need to be utilized constantly. Rather, initially, when the nodes are configuring and have an ample amount of battery power in the network, we can stick with a conventional forwarding scheme (i.e., minimum hop-count). Nevertheless, after a certain time, when battery power starts going down by a certain amount, nodes can switch to these mentioned power-aware metrics.
# Intrusion Detection for Industrial Control Systems Based on Improved Contrastive Learning SimCLR
## 4 Experiments and Results Analysis
### Experimental Results and Analysis
#### 4.4.3 Comparison of Experimental Results of Different Classification Models  
On top of the trained contrast learning model, the feature extraction network is obtained, a linear classification layer is added and retrained using a small amount of labeled data, and the classification experiment is completed by fine-tuning the model parameters. CNN and LSTM are commonly used deep learning algorithms. The results of this model are compared with three algorithms after improvement: ResNet, CNN-LSTM, and Attention-LSTM, which are deep learning methods without using comparison learning models for training assistance. The loss values and accuracy curves of the models in the training phase are shown in Figures 15 and 16, the confusion matrix of the test set results is shown in Figure 17, and the evaluation metrics of the four models are shown in Table 4.  
Figure 14: Comparison of the four evaluation metrics of the improved data enhancement approach with the other three enhancement approaches on the test set.  
Figure 15: Accuracy curves of the four models on the training set as the number of iterations increases.  
From Figures 15 and 16, it can be seen that the model training on the training set, as the number of training iterations increases, has the highest accuracy and the smallest loss value. From the confusion matrix in Figure 17; it can be seen that most of the results are located  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Acc** & **Precision** & **Recall** & **F1** \\ \hline Our Model & 0.957 & 0.981 & 0.940 & 0.960 \\ ResNet & 0.932 & 0.947 & 0.915 & 0.930 \\ Attention-LSTM & 0.947 & 0.953 & 0.923 & 0.937 \\ CNN-LSTM & 0.928 & 0.939 & 0.896 & 0.916 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Four evaluation indicators for the natural gas pipeline control system dataset on four different models.  
Figure 16: Loss curve with increasing number of iterations.  
Figure 17: Natural Gas Pipeline Control System Dataset Confusion Matrix.  
on the diagonal of the matrix, and a small number of results are located on both sides of the diagonal, indicating that the model is able to detect most of the results. As can be seen from Table 4, using the test set in the trained model for model testing, the method in this paper achieves 95.7%, 98.1%, 94.0%, and 96.0% in the four indexes of accuracy, precision, recall, and F1, respectively. The results are better than ResNet, CNN-LSTM, and Attention-LSTM. The model training time is shown in Table 5:
# Streetlight Control System Based on Wireless Communication over DALI Protocol
## 4 Implementation of the Wireless Node
### Hardware Node: UCODALI Board
#### 4.1.5 Push Button and LEDs  
The board contains two push buttons. One of them is the Reset button, which the technician can push if the node is in any anomalous state for any reason. The other button was included for manual tests, so that the node connects to the coordinator when this button is pushed.  
On the other hand, some LEDs have been included to monitor the power supply's voltages, the transmission and reception signals of the DALI bus with the microcontroller and the state of the  
Figure 14: DALI frames.  
node. The state LEDs show the technician the state of the node, such as if the wireless node is scanning or if it has been associated with another node.
# Reference Broadcast-Based Secure Time Synchronization for Industrial Wireless Sensor Networks
## 1 Introduction
RSTS aims to obtain a valid time difference in receiving packets from the same sender. Unlike the direct communication between master and slaver in existing protocols, the slaver in RSTS employs the public neighbor to acquire the time of the master indirectly. Subsequently, based on the recorded time and calculated time difference in receiving packets from the public neighbor, a least square linear regression is utilized to estimate the compensation relative to the master node. Hence, RSTS can filter the fake time information automatically. The main contributions of this study are summarized as follows:  
1. We propose a novel protocol RSTS aimed at improving the security of time synchronization in centralized IWSNs. A novel time information transmission mechanism is designed to obtain reliable time information with the verification of packet sequence numbers. 2. RSTS employs the time difference when the master node and slave one receive packets from the public neighbor to block the interference of malicious messages from manipulators and masqueraders automatically instead of coping with large amounts of data to filter illegal messages or verify the node identity. 3. We provide an effectiveness analysis and conduct simulations to verify the feasibility and efficiency of RSTS. The simulation results demonstrate that RSTS can defend against both Sybil attacks and message manipulation attacks. The rest of the paper is organized as follows. Section 2 describes the related work of secure time synchronization protocols. In Section 3, we introduce the network model, clock model, attack model, and problem formulation. The RSTS protocol is detailed in Section 4, including protocol design, multi-hop network synchronization, security analysis, and communication energy cost. The simulations to assess the performance of the RSTS protocol are represented in Section 5. Finally, Section 6 concludes the article.
# Hydraulic Vibration and Possible Exciting Sources Analysis in a Hydropower System
## 3 Materials and Mathematical Model
### Transfer Matrix and Hydraulic Impedance Methods
#### 3.2.2 Point Matrix  
The point transfer matrix relates the left and right state vectors of local discontinuity, such as a junction, valve, turbine, etc. The point matrices of several common hydraulic elements are presented below.  
For a series junction connecting two pipes, neglecting the minor losses and obeying the relationship \(Q_{D1}=Q_{U2}\), \(H_{D1}=H_{U2}\), the point matrix is,  
\[P_{S}=\left(\begin{array}{cc}1&0\\ 0&1\end{array}\right) \tag{7}\]The equations for a hydraulic machine operating at a fixed speed can be expressed in a simplified matrix form if there is no excitation pressure head and flow rate at the turbine point,  
\[P_{\text{T}}=\left(\begin{array}{cc}1&-M\\ 0&1\end{array}\right) \tag{8}\]  
where \(M\) is the slope of the head-discharge curve, and the value of \(M\) is usually assumed to be a real constant for pumps and turbines if the opening of guide vanes remains constant in the transient regime.  
For a throttled surge tank installed in the system shown in Figure 4, the equation of motion is applied to the fluid in the pipeline between the upstream and downstream ends, and the friction term is linearized while the mean flow conditions are subtracted.  
\[h_{u}(t)-h_{d}(t)-2 kq(t)=\frac{l}{gA_{s}}\frac{dq(t)}{dt} \tag{9}\]  
where \(k\), \(A_{s}\), and \(l\) are the head loss coefficient, cross-sectional area, and water depth in the surge tank, respectively.  
Based on the continuity equation \(\frac{dh_{d}(t)}{dt}=\frac{q}{A_{s}}\), the Laplace transformation of \(h_{d}\) can be derived, \(\hat{h}_{d}(s)=\frac{\hat{q}(s)}{sA}\).  
Applying the Laplace transformation to Equation (9), the impedance formula can be written,  
\[Z_{u}=\frac{\hat{h}_{u}(s)}{\hat{q}(s)}=\frac{1}{sA_{s}}+2k+\frac{sl}{gA_{s}} \tag{10}\]  
Therefore, the matrix of a throttled surge tank is  
\[P_{st}=\left(\begin{array}{cc}1&0\\ -\frac{1}{Z_{u}}&1\end{array}\right) \tag{11}\]  
For the parallel system in Figure 5 with no forcing function, the field matrix of path \(j\) is expressed as \(\left[F\right]_{j}=\left[\begin{array}{cc}e_{11}&e_{12}\\ e_{21}&e_{22}\end{array}\right]_{j}\). Then, the overall field transfer matrix of the whole parallel system is  
\[\left[F\right]_{\text{loop}}=\left[\begin{array}{cc}\frac{\xi}{\eta}&\frac {1}{\xi}\\ \frac{\xi\xi}{\eta}-\eta&\frac{\xi}{\eta}\end{array}\right] \tag{12}\]  
where \(\eta\), \(\zeta\) and \(\xi\) are \(\eta=\sum\limits_{j=1}^{n}\left(\frac{1}{e_{12}}\right)\), \(\zeta=\sum\limits_{j=1}^{n}\left(\frac{e_{11}}{e_{12}}\right)\), and \(\xi=\sum\limits_{j=1}^{n}\left(\frac{e_{22}}{e_{12}}\right)\), respectively.  
Figure 4: Sketch of the throttled surge tank.
## 2 Methodology
### Data Pre-Processing Module
#### 2.1.2 Data Segmentation  
In all proposed forecasting models, the dataset is divided into training and testing datasets. Each dataset is arranged into seven segments based on the day of the week. For instance, the training dataset consisting of Monday's load can only forecast the load for Monday. There are 104 data points for the training dataset and 53 pairs for testing in 2021. The proposed models use 104 whole training datasets to test the first Monday of 2021. Then, the data slides to the next 104 training datasets to test another day and perform the same procedure until the end of the testing dataset. This is called \"walk-forward\", as shown in Figure 5. The training dataset from 2019-2020 is applied to train both the proposed ensemble and DL models. The empirical results are compared between the bagging ensemble and three DL models.  
Figure 4: Relationship between original data and smoothed data.  
Figure 5: Sample dataset for walk-forward testing routine.
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 4 Framework and Formal Definition of the Problem
### Acoustic Channel Noise  
The ocean medium is different than the air medium in WSNs in terms of impedance. In the acoustic channel, the signal is affected by different noises. Primarily, we take into account turbulence (\(N_{l}\)), shipping (\(N_{s}\)), wind (\(N_{w}\)) and thermal noise (\(N_{th}\)). [PERSON] _et al._[19] modeled these noises by Gaussian statistics, given as:  
\[N(f)=N_{t}(f)+N_{s}(f)+N_{w}(f)+N_{th}(f) \tag{23}\]  
where,  
\[10 logN_{t}(f) =17-30 logf\] \[10 logN_{s}(f) =40+20(s-0.5)+26 logf-60 log(f+0.03)\] \[10 logN_{w}(f) =50+7.5\sqrt{w}+20 logf-40 log(f+0.4)\] \[10 logN_{th}(f) =-15+20 logf\]  
Here, \(s\) is the shipping activity factor, and it ranges \(0\leq s\leq 1\) and \(0\leq w\leq 10\) (m/s) in wind velocity.
# Fast Connectivity Construction via Deep Channel Learning Cognition in Beyond 5G D2D Networks
###### Abstract  
Along with the recent advance in wireless networking and data processing technologies, demands for low latency communication (LLC) are increasing in a wide variety of future-driven autonomous applications such as a smart factory, self-driving cars, and so on. The fifth generation of cellular mobile communications (5G) will cover this need as one of three key capacities in their usage scenarios: enhanced mobile broadband (eMBB), massive machine type communication (mMTC), and ultra-reliable low-latency communications (URLLC). The 5G systems are composed of mobile devices and various internet of things (IoT) devices for sensing, acting, and information services; they configure diverse networking topologies such as direct mobile-to-mobile, also known as device-to-device (D2D). In the 5G D2D network systems, the network topologies are easily broken because of the mobile devices such as smartphones, IoT devices, and so on. Thus, for the highly flexible and extensible 5G D2D network systems, mobility support for the mobile devices is necessary. In this paper, we first explore the mobility issues in beyond 5G D2D. Since there are static and mobile elements in the 5G application domains such as the smart factory, overall mobility would lead to highly frequent topology reconfiguration or connectivity reconstruction. Thus, latency-related problems derived from topology changes and connectivity failures due to the mobility are addressed. To handle the problems, a fast connectivity construction scheme, denoted by LMK, is proposed with a deep neural network dealing with learning on radio signal information in order to achieve the LLC. Evaluation results demonstrate that the proposed framework can provide reliable connectivity for the MAC layer link with a low latency data transmission.  
low latency communication; out-band device-to-device; 5G 20222111580. [[https://doi.org/10.3390/electronics11101580](https://doi.org/10.3390/electronics11101580)]([https://doi.org/10.3390/electronics11101580](https://doi.org/10.3390/electronics11101580))
# Fall Detection Approaches for Monitoring Elderly HealthCare Using Kinect Technology: A Survey
## 3 The Microsoft Kinect
### Kinect Software Tools
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Kinect V1** & **Kinect V2** & **Azure Kinect** \\ \hline
**Number of body tracked** & 2 & 6 & N/A a \\
**simultaneously** & 2 & 6 & N/A a \\ \hline
**Number of joint** & 20 & 25 & 32 \\ \hline
**per person** & & & 0-Pelvis \\  & & & 1-Spine\_Naval \\  & & & 2-Spine\_Chest \\  & & 0-Spine\_Base & 3-Neck \\  & & 1-Spine\_Mid & 4-Clavicle\_Left \\  & & 2-Neck & 5-Shoulder\_Left \\  & 0-Hip\_Center & 3-Head & 6-Elbow\_Left \\  & 1-Spine & 4-Shoulder\_Left & 7-Wrist\_Left \\  & 2-Shoulder\_Center & 5-Elbow\_Left & 8-Hand\_Left \\  & 3-Head & 6-Wrist\_Left & 9-Handtip\_Left \\  & 4-Shoulder\_Left & 7-Hand\_Left & 10-Thumb\_Left \\  & 5-Elbow\_Left & 8-Shoulder\_Right & 11-Clavicle\_Right \\  & 6-Wrist\_Left & 9-Elbow\_Right & 12-Shoulder\_right \\  & 7-Hand\_Left & 10-Wrist\_Right & 13-Elbow\_Right \\  & 8-Shoulder\_Right & 11-Hand\_Right & 14-Wrist\_Right \\
**Enumerates of Joints** & 9-Elbow\_Right & 12-Hip\_Left & 15-Hand\_Right \\
**in SDK** & 10-Wrist\_Right & 13-Knee\_Left & 16-Handtip\_Right \\  & 11-Hand\_Right & 14-Ankle\_Left & 17-Thumb\_Right \\  & 12-Hip\_Left & 15-Foot\_Left & 18-Hip\_Left \\  & 13-Knee\_Left & 16-Hip\_Right & 19-Knee\_Left \\  & 14-Ankle\_Left & 17-Knee\_Right & 20-Ankle\_Left \\  & 15-Foot\_Left & 17-Knee\_Right & 21-Foot\_Left \\  & 16-Hip\_Right & 18-Ankle\_Right & 22-Hip\_Right \\  & 17-Knee\_Right & 19-Foot\_Right & 23-Knee\_Right \\  & 18-Ankle\_Right & 20-Spine\_Shoulder & 24-Ankle\_Right \\  & 19-Foot\_Right & 21-HandTip\_Left & 25-Foot\_Right \\  & 22-Thumb\_Left & 26-Head \\  & 23-HandTip\_Right & 27-Nose \\  & 24-Thumb\_Right & 28-Eye\_Left \\  & & 29-Ear\_Left \\  & & 30-Eye\_Right \\  & & 31-Ear\_Right \\ \hline
**Development** & & & \\
**language** & C++, C\#, VB & C++, C\#, VB & C, C++ \\ \hline \hline \end{tabular}  
* The official documentation does not specify the exact number, stating only that “Azure Kinect body tracking can track multiple human bodies at the same time.”  
\end{table} 
Table 2: Comparison of representations of the human body in different Kinect SDK versions.
## 2 Materials and Methods
### FEM Simulation  
The experimental data of the last tension cycle (7 th cycle, \(\lambda=1\Longleftrightarrow 2.0\)) were fitted to nonlinear constitutive models using the inbuilt nonlinear fitting capabilities available in ABAQUS software package. The hyperelastic models considered for this approach were Ogden (Equation (1)) [27; 28], Polynomial model (Equation (2)) [29; 30] and finally Reduced Polynomial (RP) model (Equation (3)) [31].  
\[U=\sum_{i=1}^{N}\frac{2\mu_{i}}{\alpha_{i}^{2}}\big{(}\bar{ \lambda}_{1}^{\alpha_{i}}+\bar{\lambda}_{2}^{\alpha_{i}}+\bar{\lambda}_{3}^{ \alpha_{i}}-3\big{)}+\sum_{i=1}^{N}\frac{1}{D_{i}}\Big{(}J^{el}-1\Big{)}^{2i} \tag{1}\] \[\mu_{i},\alpha_{i},D_{i} > 0,\ i=1,2\ldots,N\]  
\[U=\sum_{i+j=1}^{N}C_{i,j}(\bar{I}_{1}-3)^{i}(\bar{I}_{2}-3)^{j}+ \sum_{i=1}^{N}\frac{1}{D_{i}}\Big{(}J^{el}-1\Big{)}^{2i} \tag{2}\] \[C_{i,j},D_{i} > 0,\ i+j=1,2\ldots,N\]  
\[U=\sum_{i=1}^{N}C_{i,0}(\bar{I}_{1}-3)^{i}+\sum_{i=1}^{N}\frac{1 }{D_{i}}\Big{(}J^{el}-1\Big{)}^{2i} \tag{3}\] \[C_{i,0},D_{i} > 0,\ i=1,2\ldots,N\]  
where U is the strain energy potential, \(\bar{\lambda_{1}}\), \(\bar{\lambda_{2}}\), \(\bar{\lambda_{3}}\) are the deviatoric principal stretches, \(\mu_{i}\), \(\alpha_{i}\), D\({}_{i}\) and C\({}_{i,j}\) are temperature-dependent material parameters, \(J^{el}\) is the elastic volume ratio and \(\bar{I_{1}}\) and \(\bar{I_{2}}\) are the first and second deviatoric strain invariants [27; 29].  
The fitting results are an estimation of the parameters associated to each model, i.e., the mechanical properties under a nonlinear framework. They constitute the inputs for the FEM simulations conducted to compare the experimental and numerical results. Several FEM simulations were carried out considering C3 DBH elements mesh (8-node hybrid hexahedral element quadratic approach with 8 nodes [29]).  
The FEM model was assembled using boundary conditions and imposed displacements similar to the experimental procedure. The model is comprised of 7000 elements and 8946 nodes. A mesh convergence study was performed to make sure that the element size is adequate to achieve an accurate solution (global mesh seed of 0.5 mm). A nonlinear static analysis was conducted. Figure 5 shows the hexahedral element mesh with the imposed displacement and boundary condition.
## 3 Sensor Technologies in Chemical and Elemental Detection
### Multispectral Imaging  
Multispectral cameras capture images with multiple wavelength bands; these images offer wider spectral information than RGB images. The cameras are cheaper and usually smaller than hyperspectral cameras, which helps with being more widely used in remote sensing [14]. More specifically, these cameras are widely used for environmental monitoring, where they identify vegetation health, soil conditions, and water quality with high precision by leveraging their ability to capture data across narrow spectral bands [14]. With a light weight and lower cost, they are a primary choice to be spaceborne and airborne. Multispectral camera technology is continuously advancing, enabling the development of smaller devices with a wider range of bands and more affordable prices.