esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on a Wi-Fi RSSI Calibration Algorithm Based on WOA-BPNN for Indoor Positioning
## 3 Experiments and Results
### Hidden Layer Structure Setting  
Setting the hidden layer structure of the BPNN model is one of the conditions for establishing a satisfactory WOA-BPNN calibration algorithm model. In this study, we set the number of hidden layers of the BPNN model as one or two layers. According to Equation (18), we determine the number of neurons in each hidden layer. When the number of hidden layers of the BPNN model is two, the number of neurons in the first hidden layer and second hidden layer is the same.  
\[H_{hidden}=\sqrt{I_{input}+O_{output}}+R \tag{18}\]  
where \(H_{hidden}\) represents the number of neurons in the hidden layer, \(I_{input}\) represents the number of input layers, \(O_{output}\) represents the number of output layers and \(R\) is a constant between 1-10. As both the number of neurons in the input layer and the output layer in this experiment are 1, and too many or too few neurons in the hidden layer will cause the over-fitting or under-fitting of the experimental data, thus we determined that the number of neurons ranges from 5 to 11.  
To compare and analyse the influence of the different BPNN models on the final calibration effect of the WOA-BPNN calibration algorithm to select the optimal BPNN model for the subsequent indoor positioning experiment, we chose the 12 modelling points  
Figure 8: Results of regional division.  
Figure 7: The number of clusters \(L\) and the corresponding BWP results.  
(as shown in Figure 4) and the best number of Wi-Fi APs (six APs: AP1\(\sim\)AP6) to build a stable WOA-BPNN model. Then, we conducted the experiment in the experimental area described in Section 3.2. In addition, we add 19 test fingerprint points (purple triangles), which are in the middle of the two reference points, to detect the positioning accuracy of the model. The distribution of the test fingerprint points is shown in Figure 9.  
At the offline phase, we establish an offline fingerprint database according to the method described in Section 3.2. At the online phase, we hold two test mobile terminals, collect the Wi-Fi RSSI test fingerprint data at each test fingerprint point at a frequency of 30 s/time for 3 min, and average all the collected RSSI data of each AP as the test input data of the WOA-BPNN model. Then, according to the mobile terminal models, calling the corresponding model for the RSSI calibration to obtain the predicted output RSSI data after calibration.
# A Comprehensive Study on Deep Learning-Based 3D Hand Pose Estimation Methods
## 3 Deep Learning Methods
### RGB-Based Approaches  
The RGB-based methods are classified into two sub-categories, model-free and model-based methods. The model-free category includes methods that directly predict 3D joint positions or heatmaps, whereas the model-based category describes methods that predict parameters of a hand model, such as pose and shape.
## 4 Experimental Results
### Results of Dynamic Accuracy  
We also evaluated the dynamic accuracy of both the networks inferred from the original and the synthetic data on the multi-level discretization dataset (Figure 4) and on the Boolean discretization dataset (Figure 5). Contrary to precision or recall metrics, the dynamic accuracy tends to decrease as the number of time steps increases. We believe this is due to the increase in the number of data samples to be fitted by the inferred network. Moreover, the incorporation of additional time points may introduce deviations from the patterns already learned by the model, particularly if the new data include noise, nonlinear dynamics, or previously unobserved system behaviors. In addition, the dynamic accuracy of the synthetic data-driven networks is inferior to that of the original data-driven networks in most cases. We note that the latter is investigated over \(K\) time steps, whereas the former is investigated over \(T\) time steps, which is longer than \(K\). Considering this difference between the examination time steps, the performance difference in terms of the dynamics accuracy may not be considerably significant.
## 5 Orbit Evaluation  
The GNSS satellite orbits and ERP were estimated simultaneously, thus the accuracy of the orbits could also reflect the performance of the ERP estimates. Since the B1C/B2a combination has higher ambiguity fixing percentages and smaller ambiguity fixing residuals, both in the ambiguity resolution of WL and NL, we would like to further investigate the performance of B1C/B2a-based BDS-3 orbit estimation by using orbit overlapping, comparing the orbits with external products and SLR validation. We evaluated the accuracy of the BDS-3 MEO orbits for CAST satellites and SECM satellites, separately because their shapes and the materials of the surfaces used are different, which could present different suitability for the dynamical models for orbit determination [9; 10].
# Improving IoT Data Security and Integrity Using Lightweight Blockchain Dynamic Table
## 5 Proposed Solution
### Smart Contract  
To keep the data management process running smoothly, the smart contract was created using the solidity programming language. The proposed solution uses a simple implementation of a smart contract on the server side. This implementation replicates the real-world method for collecting sensed data. The contract is composed of three parts:  
* The data processing tasks that are called at each event include the production of sensed data records, data date and time, and IoT device ID, which is represented by a dynamicTable 256-bit.
* A database with a dynamic table from an Internet of Things device to store the detected data. ID, IoT ID, sensed data, and sensed data date/time are among these data.
* Functions, which contain all the functions needed to proceed with the event. Which are: 1. Data creation: add the sensed data to the array with the basic information mentioned before.
2. Block verification: when the dynamic table has confirmed the validity of the data with the previous dynamic table.  
Figure 3: Dynamic blockchain table architecture.  
The smart contract solution is used mainly to confirm and store transaction data. However, the proposed solution has focused on the security of transferring data using blockchain. Nevertheless, the standard smart contract has been integrated on the server side to generate smart contract information for each IoT device information.
## 5 Experimental Results
### Simulation Results  
Figure 9 shows the curves of ITAE in the iterations of optimization for the four methods. Among them, PSO has the fastest decline, but IWO eventually approaches a lower value. Table 4 shows the optimal learning parameters corresponding to the PSO, IWO, BBO, and GWO methods. Common optimal parameters are employed in each simulation result, and the tracking errors are compared and summarized. Table 5 shows the comparison of the simulation results for the optimization algorithms. Taking an average of all four contours, the IWO method achieved a higher accuracy, with an ATE of 1.7852 \(\upmu\)m, TESD of 6.4548 \(\upmu\)m, and ITAE of 692.6948.
# Joint Energy Supply and Routing Path Selection for
## 5 Conclusions  
To explore the routing and energy problems of rechargeable wireless sensor networks, a joint algorithm of the routing and energy replenishment based on a single wireless charging device with limited energy supply capacity is proposed in this paper. We not only establish a two-stage energy replenishment strategy which preferentially meets the energy demand of those nodes with faster energy consumption, but also consider a variety of factors, that include the charging, to define a routing selection metric of the next-hop node. The simulation results show that the algorithm can prolong the network lifetime effectively, and obtain different delay and energy consumption by adjusting parameters such as harmonic coefficient, fusion index and charging duration. In future work, we will focus on studying the cooperation of multiple wireless charging devices, in which the sensor nodes are mainly powered by an initiative power supply method and supplemented by an energy-harvesting method.  
Conceptualization, L.T. and J.C.; Methodology, L.T., J.C. and J.Y.; Software, J.C.; Validation, L.T. and J.C.; Formal Analysis, L.T., J.C. and Z.Z.; Investigation, J.C.; Resources, L.T. and J.Y.; Data Curation, J.C.; Writing-Original Draft Preparation, L.T. and J.C.; Writing-Review & Editing, L.T., J.C. and Z.Z.; Visualization, L.T. and J.C.; Supervision, L.T., J.C. and Z.Z.; Project Administration, L.T. and J.C.; Funding Acquisition, L.T. and J.Y.  
This research was funded by the National Natural Science Foundation of China (No. 51677065).  
The authors declare no conflict of interest.  
Figure 12: Network lifetime of different strategies with varying charging duration. (**a**) Packet size = 2000 bits; (**b**) packet size = 3000 bits.  
Figure 13: Average travel time of different strategies with varying charging duration.
## 4 Discussions
### Limitations and Prospects  
As far as we know, this study is the first to analyze the performances of different biophysical products in the TRSR region both in terms of LSP detection and long-term changes; however, it must be emphasized that the regression model and Spearman correlation coefficient used in this study are statistical analyses, whereas statistical analyses are methods to objectively calculate the linear relationship between time variables and vegetation growth. Several studies [62; 63] give evidence that the correlations and responding mechanisms between ecosystem and natural variation are nonlinear, which highlights the potential defect of linear statistical analysis. This limitation may be overcome by correctly and appropriately adopting process-based ecosystem models in further studies.  
Our study also indicates the high mismatch of LSP metrics observed in areas with highly rugged surfaces or landscape fragmentations. Given the highly surface hetero geneity and topographic complexity, the inversion accuracy of GLASS products should be enhanced. Particularly, the high uncertainties of surface reflectance data limit latent development of the algorithms of biophysical products, and subsequently influence the performance of LSP retrievals of GLASS products over mountain areas. The improvement of retrieval algorithms with topography and heterogeneous surface in consideration will help to improve the inversion accuracy [32]. Furthermore, coarse-resolution pixels will greatly increase the uncertainties of spectral information, which would propagate into the production of global products. Paradoxical conclusions may arise from integrated analysis by end-users using multiple time-series satellite products, which poses a challenge for reliable monitoring and attribution in LSP and vegetation dynamics over some special environments. Thus, it is an imminent study to develop various satellite products with finer resolution (e.g., 30 m or even higher) over heterogeneous surfaces and complex terrains.  
Overall, the disproportionate increase between vegetation greening trend, represented by FVC, with vegetation carbon sequestration indicates that the climatic conditions and human activities in the TRSR may hinder the conversion of vegetation greening to a carbon sink. Therefore, we emphasize that the existing natural vegetation in the TRSR needs to be further protected. The time-series analysis based on a single product may not be directly considered as a convincing result when exploring the long-term changes of vegetation, carbon, and water cycles in the TRSR. Hence, we propose the application of a complementary combination of simulation data based on climate and human impacts with multiple products as a new attempt for tendency analysis, which may greatly improve the interpretation and attribution of vegetation trends.
## 4 Discussion
### Relationships of Agro-Pellets Characteristics and Raw Material Properties  
The relationship of pellet characteristics and some chemical and physical properties of oil palm residues:pine sawdust mixtures was contrasting. Bulk density of agro-pellets was related with the chemical components cellulose, hemicellulose, lignin, and extracts on oil palm residues:pine sawdust biomass mixtures, as was seen (Figure 1a,b) when increasing the amount of cellulose and lignin, increasing pellets bulk density, both are considered as natural binderies and responsible for bonding [42]. On the one hand, cellulose is the main (and the strongest) mass compound on lignocellulosic matrix; this increases friction, providing density of pellets during pelletizing. On the other hand, lignin provides hardness after pelletizing, where biomass is heated at high temperature and pressure, its low melting point becomes soft and melts with the particles; after that, pellets cool [13; 43]. In contrast to the previous, hemicellulose considered as the weakest parties on polymer structure in wood matrix reduces the bulk density; while extracts, as a mixture of different compounds, migrate during pelletizing to the pellet surface and reduce friction, producing weak pellets [44].  
The increment of bulk density on agro-pellets was also seen when the proportion of pine sawdust increased in the mixture, the same was observed when increasing the bulk density of raw material (Figure 1a,b). This is consistent with the study carried out by [PERSON] and [PERSON] [45], attributing it to the bonding strength between small and long particles that stick together strongly.  
Gross calorific values of pellets varied in the range 18.84-22.31 MJ Kg\({}^{-1}\); values showed a reduction when increasing the pine sawdust proportion in the mixture (Figure 3a). The same trend (17.02-22.72 MJ/kg) was presented by [PERSON] et al. [46] after add glycerin on percentage of 15, 30, and 45% to oil palm froms, they ascribed this to major biochemical components, such as cellulose, hemi-cellulose, lignin, extractives, and other ash forming minerals [47].
## 3 Classifier  
### Support Vector Machine  
SVM technology was devised for handling data in space. A hyperplane in the space is found which separates the data into two different groups. Suppose we have a bunch of points and the rendezvous point is expressed as (5)  
\[\{x_{i},y_{i}\},i=1,\ldots,n\,\text{and}\,x_{i}\in R^{d},\,y_{i}\in\{+1,-1\} \tag{5}\]  
An attempt is made to find a straight line \(f(x)=w^{T}x-b\) that allows all the \(y_{i}=-1\) points to fall on the \(f(x)<0\) side, and all the \(y_{i}=+1\) to fall on the \(f(x)>0\) side. Therefore, it is possible to distinguish to which side a point belongs by the sign (\(+\) or \(-\)) of \(f(x)\). This spatial plane is called the separatinghyperplane, and the greatest distance from the margin is called the optimal separating hyperplane (OSH). Solving OSH is equivalent to finding the support hyperplane with the farthest distance.  
The support hyperplane is defined as in (6)  
\[\begin{array}{l}w^{T}x=b+\xi\\ w^{T}x=b-\xi\end{array} \tag{6}\]  
The margin between the two separating hyperplanes is naturally double \(d\). Where the margin = 2d = 2/\(\|w\|\), the smaller the \(\|w\|\), the larger the margin. Knowing that the distance between the support hyperplane and the optimal separating hyperplane is within \(\pm 1\), so the constraint conditions are written as in Equations (7) and (8)  
\[y_{i}\big{(}w^{T}x_{i}-b\big{)}-1\geq 0 \tag{7}\]  
The Lagrange multiplier is then used for transformation to a quadratic Equation (8) and to find \(w\), \(b\), and \(\alpha\) that allows \(L\) to be a minimum, as in Equation (8)  
\[L(w,b,\alpha)=\frac{1}{2}\|w\|^{2}-\sum_{i=1}^{N}\alpha_{i}\Big{[}y_{i}\big{(} w^{T}x_{i}-b\big{)}-1\Big{]} \tag{8}\]  
To solve the minimum value \(L\), find the partial differential of \(w\) and \(b\) respectively to get (9)  
\[L_{D}=\sum_{i=1}^{N}\alpha_{i}-\frac{1}{2}\sum_{i,j}\alpha_{i}\alpha_{j}y_{i} y_{j}x_{i}^{T}x_{j} \tag{9}\]  
However, the solution for nonlinear data is to project the data to a space of higher dimension or a feature space. The mapping of \(x\) to the feature space through \(\varphi\), is shown in (10)  
\[x_{i}^{T}x_{j}\rightarrow\varphi(x_{i})^{T}\varphi(x_{j}) \tag{10}\]  
However, the mapping function \(\varphi\) is very complicated and it is not easy to obtain the value, but its inner product type may become very simple. Take the radial based function (RBF) as an example. Although RBF is a complex function, it can be changed to an inner product and simplified as shown in (11)  
\[k\Big{(}x_{i},x_{j}\Big{)}=\varphi(x_{i})^{T}\varphi(x_{j})=\exp\left(\frac{- \|x_{i}-x_{j}\|^{2}}{2\sigma}\right) \tag{11}\]  
The function obtained by the mapping function from the inner product is the SVM kernel.
## 4 Methodology
### Fuzzy TOPSIS  
Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) was used to calculate the ACI for the selected routes. The ACI values range between 0 and 1, with 0 indicating no accessibility and 1 indicating the highest level of accessibility. Fuzzy TOPSIS is a multi-criteria decision analysis method, initially developed by [PERSON] and [PERSON] in 1981, with further developments by [PERSON] in 1987 and [PERSON], [PERSON], and [PERSON] in 1993. The method objectively and systematically evaluates alternatives on multiple criteria, relying on determining the closest distance to the fuzzy positive ideal solution (FPIs)  
Figure 3: Membership function of triangular fuzzy number [56]. and the furthest distance to the fuzzy negative ideal solution (FNIS) [57]. Under many conditions, crisp data are inadequate to model real-life situations, while human judgments, including preferences, are often vague and cannot be estimated with an exact numerical value. In this study the linguistic scale was used to assess the ratings and weights of the criteria. The selection of the accessibility criteria was based on several important factors directly related to the study objectives. Participants also quickly understood them and contributed to the decision-making process. The following are the steps of Fuzzy TOPSIS:  
Step 1: The fuzzy normalized decision matrix of the alternatives (\(\widetilde{A}\)) and the criteria weight matrix (\(\widetilde{W}\)) are developed as follows:  
\[ \begin{array}{c|cccc}C&C_{1}&C_{2}&\ldots&C_{p}\\ \widetilde{W}&\widetilde{w}_{1}&\widetilde{w}_{2}&\ldots&\widetilde{w}_{p}\\ \widetilde{\times}_{1}&\widetilde{\times}_{1}&\widetilde{\times}_{12}&\ldots &\widetilde{\times}_{1p}\\ \widetilde{\times}_{1}&\widetilde{\times}_{1}&\widetilde{\times}_{12}& \ldots&\widetilde{\times}_{1p}\\ \widetilde{A}_{2}&\widetilde{\times}_{21}&\widetilde{\times}_{22}&\ldots &\widetilde{\times}_{2p}\\ \ldots&\ldots&\ldots&\ldots&\ldots\\ \widetilde{A}_{n}&\widetilde{\times}_{n1}&\widetilde{\times}_{n2}&\ldots &\widetilde{\times}_{np}\\ \end{array}  \tag{3}\]  
where \(\widetilde{W}_{j}\) is the importance weight of \(j\)th criteria, \(C_{j}(j=1,\ldots,n)\) estimated by the survey's participants using FRS's weights and linguistic variables \(\widetilde{\times}_{ij}\). These linguistic variables can be described by triangular fuzzy numbers, \(\widetilde{\times}_{ij}=(l_{ij},\ m_{ij},\ u_{ij})\) and \(\widetilde{w}_{j}=(l_{j},\ m_{j},\ u_{j})\), where \(l<m<u\). In mathematical terms, the fuzzy set \(A\) of a universe \(X\) is defined by a membership function \(\widetilde{\times}(x)\) such that \(X\rightarrow(0,1)\), where \(\widetilde{\times}(x)\) is the membership value of \(\widetilde{\times}\) in \(\widetilde{A}\) as defined below, where \(\widetilde{A}=(l,\ m,\ n)\) is a triangle fuzzy number. Here, \(x\) represents a criterion that belongs to \(\widetilde{X}=[x_{1},\ x_{2},\ldots,x_{n}]\). \[\widetilde{\times}(x)=\left\{ \begin{array}{ll}0,&x\leq l\\ \frac{x-l}{m-l},&l\leq x\leq m\\ \frac{u-x}{u-m},&m\leq x\leq u\\ 0,&d>u\\ \end{array}
# Prediction of Transient Temperature Distributions for Laser Welding of Dissimilar Metals
## 2 Definition of the Model
As long as \(t\) is not equal to 0, the position vectors with respect to one frame are different from the position vectors with respect to another frame. At \(t=0\), \(d(0)=d0\). The vector \(v_{no}\) has three components; Among them, \(d/dt(d_{x})=0\), \(d/dt(d_{y})=0\), and \(d/dt(d_{y})=2\), which means that for any value of t, \(d_{x}(t)=d_{x}0\) and \(d_{z}(t)=d_{z}0\). The starting position of the laser source is assumed to be 10 mm on x axis and 2 mm on y axis. The laser is moving at a velocity of 2 mm/s along the y axis and the total moving time for a sample is 17 s. The global parameters used for this model are given in Table 1. The model arguments with upper and lower limit is mentioned in Table 2.Temperature  
Figure 2: Profile of Hermit–Gaussian heat source function. dependent thermal parameters (Table 3) are plotted from material properties [49; 50]. These plots are important for obtaining the property values of intermediate temperatures within the working range, as shown in Figure 3. Some essential constant parameters are presented as well (Table 4). \begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Value** & **Details** \\ \hline x0 & 10 [mm] & x coordinate \\ y0 & 2 [mm] & y coordinate \\ Sigx & 0.200 [mm] & Deviation along x \\ Sigy & 0.200 [mm] & Deviation along y \\ Rc & 0.001 & Reflection coefficient \\ Ac & 5 [1/cm] & Absorption coefficient \\ Q0 & 300 [W] & Laser power \\ L1 & 10 [mm] & Material size 1 \\ L2 & 10 [mm] & Material size 2 \\ LZ & 2.5 [mm] & Thickness of the sheet \\ Time step & 0.2 & Time step for storing solution \\ End time & 17 [s] & End time step \\ V & 2.0 [mm/s] & Laser velocity \\ L & 40 [mm] & Length of sheet \\ \hline \end{tabular}
\end{table} 
Table 1: Global parameters. \begin{table}
\begin{tabular}{c c c} \hline
**Arguments** & **Upper Limit** & **Lower Limit** \\ \hline a & (L1 + L2) & 0 \\ a0 & x0 & x0 \\ Siga & Sigx & Sigx \\ b & 2(L1 + L2) & 0 \\ b0 & y0 & y0 \\ sigb & sigy & Sigy \\ \hline \end{tabular}
\end{table} 
Table 2: Model arguments with limits. \begin{table}
\begin{tabular}{c c c} \hline
**Name** & **Value** & **Unit** \\ \hline Density & rho(T) & kg/m\({}^{3}\) \\ Thermal conductivity & k\_iso(T) & W/(m\(\cdot\)K) \\ Heat capacity at constant & Cp(T) & J/(kg\(\cdot\)K) \\ pressure & & \\ \end{tabular}
\end{table} 
Table 3: Temperature dependent properties with units. distribution in the direction of laser irradiation (z axis) and across the weld joint (x axis), the probe points were considered. From the probe points, the transient thermal responses in the two coordinates were probed at various points, since the thermal profiles as obtained from surface temperature and thermal contours were directly exposed the temperature distribution of entire weld joint.
# A Novel Adaptive Cluster Based Routing Protocol for Energy-Harvesting Wireless Sensor Networks
###### Abstract
Therefore, reasonable data transmission and data fusion mode of the WSNs nodes would guarantee energy efficiency. 2. A data transmission adjustment mechanism is proposed for the EH-WSNs and forms the proposed routing protocol (HCEH-UC). The unique modes of EH-WSNs nodes, including energy harvesting (sleeping-mode) and data transmission (operation-mode), require a suitable succession method. Thus, a corresponding routing mechanism is proposed for EH-WSNs to sustain the uninterrupted coverage of the target area. The remainder of this paper is organized as follows. Section 2 introduces the proposed HCEH-UC routing protocol, including the environment-adaptive hierarchical clustering algorithm and the distributed data transmission mode adjustment method. Verification simulations are conducted in an emulation WSNs using MATLAB, and an analysis of the comparison results is given in Section 3.