esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 State of Charge (SOC) Estimation  
State of charge (SOC) is essential for Lead Acid (LA) battery to declare its well-being operation and to eliminate over-charging/deep-discharging issues. However, the complexity of the electrochemical responses marks the SOC estimation as a challenging subject [29; 30; 31]. In case of nonlinear systems, BPNN is the most employed technique because of its self-learning and feedback distinction [25; 32]. Thus, BPNN based SOC assessment method is adopted in this paper as shown in Figure 2. As mentioned in Figure 3, SOC is estimated for every single battery independently by collecting its voltage and current parameters where V\({}_{\text{T}}\) is the total voltage of the battery bank. The root mean square error (RMSE) of the SOC estimation is minimized by adjusting the weights. In BPNN, total input of the hidden layer can be calculated from,  
\[neti_{n}\ =\ \sum\
olimits_{i\ =\ 1}^{3}x_{i}w_{in}+b_{n} \tag{1}\]  
where \(neti_{n}\) represents the total input to the hidden layer neuron \(n\), \(x_{i}\) symbolizes the value of input neuron, \(w_{in}\) deliberates the weight between the input layer and hidden layer neurons \(i\) and \(n\), respectively, bias of the hidden layer neuron \(n\) is mentioned as \(b_{n}\).  
Figure 2: Structural design of the proposed backpropagation neural network (BPNN) algorithm.  
A tangent function is the utilized activation function for the hidden layer neuron \(n\). The valuation of the output layer neuron can be found from (3).  
\[h_{n}\ =\ f(neti_{n})\ =\ \frac{1-exp(-2 neti_{n})}{1+exp(-2 neti_{n})} \tag{2}\]  
\[neto\ =\ \sum\
olimits_{i\ =\ 1}^{n}h_{i}g_{i}+p \tag{3}\]  
where \(neto\) represents the overall product of the output layer neuron \(o\), the value of hidden layer neuron to output layer neuron for \(i\) is \(h_{i}\), weight between hidden layer neuron \(i\) and output layer neuron \(o\) is \(g_{i}\), bias of the output layer neuron \(o\) is \(p\).  
Sigmoid function is used as the applied activation function of the output layer neuron where output \(y_{n}\) can be calculated from (4)  
\[y_{n}\ =\ f(neto)\ =\ \frac{1}{1+exp(-neto)} \tag{4}\]  
\[e_{i}\ =\ f_{Ref}-f_{Est} \tag{5}\]  
\[\frac{\partial Error}{\partial w_{ij}^{n}}\ =\ \frac{\partial Error}{\partial f _{Est}}\ \frac{\partial f_{Est}}{\partial w_{ij}^{n}} \tag{6}\]  
\[RMSE\ =\ \sqrt{\left(\sum\
olimits_{i\ =\ 1}^{n}e_{i}^{2}\right)/n} \tag{7}\]  
where \(e_{i}\) represents the output error, \(f_{Ref}\) and \(f_{Est}\) are the reference output function and estimated output function; respectively. Using gradient descent algorithm (GDA) as given by (5) to (7), weights are restructured repeatedly to attain the expected output and reduce the error. From (7) the RMSE is considered from which the improved weights are achieved while the error is diminished.
## 4 Experiments and Analysis
### Impact on the Constellation Rotation and Translations
The subplots from top to the bottom correspond to the RMS in the along, cross, and radial directions, respectively. It can be seen that, after the Helmert transformation, the orbital accuracy using the broadcast ephemeris as the a priori orbit has improved in all three directions, almost the same as the ones with precision ephemeris. However, the degree of the improvement varies among different directions. To provide a more intuitive demonstration, Table 8 shows the average RMS of the MEO and IGSO satellites in the three directions. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Item**} & \multicolumn{3}{c}{**Translation (mm)**} & \multicolumn{3}{c}{**Rotation (mas)**} & \multirow{2}{*}{**Scale (ppb)**} \\ \cline{2-2} \cline{4-7}  & **TX** & **TY** & **TZ** & **RX** & **RY** & **RZ** & **SC** \\ \hline prec & \(-\)78.5 & 31.28 & 47.33 & 0.31 & \(-\)0.59 & \(-\)1.35 & \(-\)0.29 \\ brdc & \(-\)94.35 & 43.05 & 47.81 & 2.74 & \(-\)2.48 & \(-\)5.33 & \(-\)0.29 \\ \hline \end{tabular}
\end{table} 
Table 7: Helmert transformation parameters. From Table 8, we can see that after Helmert transformation, the RMS of the IGSO satellites is better than 20 cm in three directions. For the MEO satellites, the RMS is better than 15 cm, 10 cm, and 5 cm in the along, cross, and radial directions, respectively. In addition, the difference of the orbit accuracy between different a priori orbits is significantly reduced after the Helmert transformations.
## 4 Simulation Study
### Effect of the Focal Length  
In this section, we demonstrate the impact of focal length on performance via simulation results. The experiments encompassed 2.8 mm, 3.6 mm, and 6.0 mm focal lengths.  
Analogous to the sensor size, the focal length notably influences the detection area and quality.  
In Figure 8a, the detection time, denoted as \(T_{suc}\), was monitored while increasing the number of surveillance cameras with varying focal lengths. When considering a focal length of 2.8 mm, the smallest of the focal lengths studied, \(T_{suc}\), exhibited an increase from 8.15 s to 257.12 s as the number of surveillance cameras increased. In the case of focal lengths of 3.6 mm and 6.0 mm, \(T_{suc}\) demonstrated an increase from 1.18 s to 59.59 s and from 0.0 s to 7.96 s, respectively, as the number of surveillance cameras increased.  
Figure 7: Performance analysis of the surveillance system according to various sensor widths and heights in terms of (**a**) detection time and (**b**) surveillance resolution.  
That is, similar to the impact of sensor size on performance, as highlighted in the preceding section, a noteworthy decrease in detection time performance is observed as the surveillance area diminishes with an increase in focal length.  
In accordance with Figure 8, the surveillance resolution per ms is depicted as we increase the quantity of surveillance cameras with varying focal lengths.  
Upon utilizing the smallest focal length of 2.8 mm, an increase in surveillance resolution per ms performance is observed, escalating from 55.98 to 87.31 kilopixels/ms as the number of surveillance cameras increases.  
Similarly, for a focal length of 3.6 mm, the surveillance resolution per ms increases from 131.77 to 146.15 kilopixels/ms as the number of surveillance cameras increases. In contrast, for the longest focal length of 6.0 mm, the surveillance resolution per ms exhibits a fluctuation between 559.90 and 576.45 kilopixels/ms as the number of surveillance cameras is heightened.  
In a manner akin to the influence that sensor size yields on performance, as was discussed in the above section, it is noteworthy that a heightened focal length leads to a reduction in detection time owing to the decreased size of the surveillance area. It is, however, relevant to report that the surveillance resolution per millisecond may escalate by a factor of 10 or more.
# RSS-Based Localization and Mobility Evaluation Using a Single NB-IoT Cell+
## 5 NB-IoT Mobility Evaluation Setup  
In traditional RSS-based localization algorithms, the position of an MT is determined by combining the estimated distances to multiple base stations in a multilateration algorithm. For example in LTE, the location of a UE is estimated based on timing information to multiple eNBs, leading to an increased localization accuracy. Similarly, an OTDoA-capable UE is able to request information from neighboring cells, due to the presence of a location server in the OTDoA-capable NB-IoT network. However, only a single (serving) cell of an eNB can be used for RSS-based localization in NB-IoT [3]. Therefore, in this paper, we study the mobility of NB-IoT in a localization context. In order to assess the performance of the cell reselection process, we collected messages at a constant velocity and update rate around a single eNB with three cells, as discussed in Section 3 and shown in Figure 3. The azimuths of the three directional antennas are separated 120 degrees, pointing to 0 deg, 120 deg and 240 deg. The trajectory around the eNB was chosen with the objective to force a handover between each cell of the eNB. Consequently, we are able to evaluate two mobility parameters:  
* Sector reliability, i.e., whether the UE is located inside the correct sector of the serving cell.
* Cell reselection time, i.e., how long it takes to switch to another antenna.
## A Contextual Fire Detection Algorithm for Simulated HJ-1B Imagery  
**[PERSON] 1, [PERSON] 1,*, [PERSON] 1,2, [PERSON] 3**  
1 State Key Laboratory of Remote Sensing Science, School of Geography, State Key Laboratory of Remote Sensing Science, Beijing Normal University, 100875, Beijing, P.R. China; E-Mail: [EMAIL_ADDRESS] (Y.Q)  
2 Institute of Advanced Computing and Digital Engineering, Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences, 518054, Shenzhen, P.R. China  
3 The Geography and Planning College of Ludong University, 264025, Yantai, P.R. China  
*Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]  
_Received: 20 November 2008; in revised form: 29 December 2008 / Accepted: 6 January 2009 / Published: 13 February 2009_  
**Abstract:** The HJ-1B satellite, which was launched on September 6, 2008, is one of the small ones placed in the constellation for disaster prediction and monitoring. HJ-1B imagery was simulated in this paper, which contains fires of various sizes and temperatures in a wide range of terrestrial biomes and climates, including RED, NIR, MIR and TIR channels. Based on the MODIS version 4 contextual algorithm and the characteristics of HJ-1B sensor, a contextual fire detection algorithm was proposed and tested using simulated HJ-1B data. It was evaluated by the probability of fire detection and false alarm as functions of fire temperature and fire area. Results indicate that when the simulated fire area is larger than 45 m\({}^{2}\) and the simulated fire temperature is larger than 800 K, the algorithm has a higher probability of detection. But if the simulated fire area is smaller than 10 m\({}^{2}\), only when the simulated fire temperature is larger than 900 K, may the fire be detected. For fire areas about 100 m\({}^{2}\), the proposed algorithm has a higher detection probability than that of the MODIS product. Finally, the omission and commission error were evaluated which are important factors to affect the performance of this algorithm. It has been demonstrated that HJ-1B satellite data are much sensitive to smaller and cooler fires than MODIS or AVHRR data and the improved capabilities of HJ-1B data will offer a fine opportunity for the fire detection.
## 3 Industrial Case Study  
### Industrial Pilot  
The method presented in Chapter 2 has been applied in an industrial pilot in the production line of a copper tube plant. In Figure 2, the process plan of the copper tube production under study is presented. The process starts with copper billets being preheated before entering the hot extrusion press to produce the \"mother\" tube. These tubes, which have an initial length of 15 to 30 m, are then reduced in diameter and wall thickness by either drawing or cold rolling processes. Further reductions are then performed at Breakdown lines and Spinner Blocks by drawing the tube further with successive passes, till the final or semi-final dimension is reached. In the spoolers' workstations, the copper pipe is formed in coils, to be forwarded for annealing to the Bright Annealing Furnaces and then for insulation with polyethylene foam. The final step of the production is the packaging and storing of tubes.  
Figure 2: Copper Tube Plant process plan.  
The workstation where the proposed solution has been applied is the Spoolers' workstation, due to the amount of scrap these machines are producing. More specifically, the Spoolers produce level wound coil (semi-) final products and the scrap generated is collected in containers. A container may be used to collect scrap for a group of Spooler machines. In Figure 3 samples of the bins located close to the Spoolers are depicted.
# High-Resolution Multi-Scale Feature Fusion Network for Running Posture Estimation
## 4 Experiments
### Ablation Study
#### 4.6.1 The Main Modules of RHPNet  
In order to gain a better understanding of the benefits of the proposed modules, we conducted detailed ablation studies on each module. The experimental results are presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **SimSPPF-R** & **RFB** & **RFB-S** & **AP** & **AP\({}^{\text{M}}\)** & **AP\({}^{\text{L}}\)** \\ \hline HRNet-w32 & & & & 93.3 & 81.1 & 94.0 \\ RHPNet & ✓ & & & 95.2 & 89.5 & 95.6 \\ RHPNet & ✓ & ✓ & & 95.4 & 90.5 & 95.7 \\ RHPNet & ✓ & & ✓ & 94.9 & 87.6 & 95.2 \\ RHPNet & ✓ & ✓ & ✓ & 95.7 & 92.5 & 95.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study of three main components: SimSPPF–R, RFB, and RFB – S. We report the AP, AP\({}^{\text{M}}\), and AP\({}^{\text{L}}\) for predicted poses from the Running Human validation set. All results were obtained with the backbone HRNet –W32 and input images of size 256 \(\times\) 192.  
Figure 8: Results of visualization of some example images from the MPII Val dataset.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Method** & **Head** & **Shoulder** & **Elbow** & **Wrist** & **Hip** & **Knee** & **Ankle** & **Mean** & **[EMAIL_ADDRESS]** \\ \hline Simple Baseline [3] & 97.0 & 95.9 & 90.0 & 85.0 & 89.2 & 85.3 & 81.3 & 89.6 & 35.0 \\ HRNet-W32 [4] & 97.1 & 95.9 & 90.3 & 86.4 & 89.1 & 87.1 & 83.3 & 90.3 & 37.7 \\ DarkPose [29] & 97.2 & 95.9 & 91.2 & 86.7 & 89.7 & 86.7 & 84.0 & 90.6 & 42.0 \\ UDP-Pose [24] & 97.4 & 96.0 & 91.0 & 86.5 & 89.1 & 86.6 & 83.3 & 90.4 & 42.1 \\ SimCC [33] & 97.2 & 96.0 & 90.4 & 85.6 & 89.5 & 85.8 & 81.8 & 90.0 & - \\ TokenPose [31] & 97.1 & 95.9 & 90.4 & 86.0 & 89.3 & 87.1 & 82.5 & 90.2 & - \\ \(4\times\) RSN-50 [12] & 96.7 & 96.7 & 92.3 & 88.2 & 90.3 & 89.0 & 85.3 & 91.6 & - \\ ASDA [34] & 97.3 & 96.5 & 91.7 & 87.9 & 90.8 & 88.2 & 84.2 & 91.4 & - \\ PCT [7] & **97.5** & **97.2** & **92.8** & 88.4 & **92.4** & **89.6** & **87.1** & **92.5** & - \\ \hline RHPNet & **97.5** & 96.8 & 92.2 & **88.9** & 90.7 & 89.4 & 86.1 & 92.0 & **44.3** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparisons on the MPII validation set.
## 2 Materials and Method
### Principal Component Regression (PCR) Method
#### 2.2.5 Principal Components (PC)  
Principal components, as eigenvectors of the covariance matrix derived from preprocessed multiparameter data, enable a physically interpretable coordinate transformation in PCA [30]. By projecting high-dimensional experimental observations onto orthogonal eigenvector axes--termed principal component (PC) axes--this process generates PC scores for each sample, as defined in Equation (4). For example, in datasets tracking lime-cement-soil amendment effects (e.g., strength evolution across curing times and stabilizer ratios), PC scores distill complex covariance patterns among chemical, mechanical, and microstructural variables into simplified composite indices. These indices consolidate correlated responses into interpretable dimensions, preserving multidimensional reconstructability while isolating dominant variance sources. Selection of significant PCs, guided by cumulative explained variance thresholds, scree plots, or cross-validation, ensures dimensionality reduction without sacrificing critical information. This transformation bridges multivariate empirical data and mechanistic insights, providing a structured basis for regression modeling that links stabilization mechanisms to engineering performance.  
\[Z=X^{\prime}W \tag{4}\]  
where  
\(Z=\) matrix of principal components score  
Each column \(Z_{i}=\) scores for principal component i  
\(X^{\prime}=\) matrix of standardized data  
\(W=\) matrix of eigenvectors
## 2 Prototype
### Hardware Component  
The following hardware was used to implement the prototype with the systems that make up the architecture described. The sensor node was implemented on a Raspberry Pi 4 Model B with 4 GB of RAM [26], with a 32 GB Micro-SD and Bluetooth 5.0 support. A Raspberry Pi Camera Module 3 [27] camera will be connected to this single-board computer via a ribbon cable, as shown in Figure 5.  
The bridge nodes, responsible for communicating via Bluetooth with the sensor node to collect information and then send these data to a database hosted on a server on the Internet, are Android smartphones belonging to the users of the trails. They will have installed a mobile application described in a later section of this document.  
The remote database and application server are hosted on a machine with an Intel-Core I7-11370H processor, 16 GB of RAM and an NVIDIA RTX 3050 graphics card.
# Pattern-Based Decoding for Wi-Fi Backscatter Communication of Passive Sensors
## 5 Pattern-Based Decoding Algorithm
The absolute value of slope[3] is larger than that of slope[2]; thus, bit[2] is determined as the first value of bit pair (1, 0), i.e., data one. The calculated slope for bit[4] corresponds to bit pair (0, 1) and is obtained as positive, thus resulting in bit[4] as data one. Since slope[3] and slope[4] produce bit pairs (1, 0) and (0, 1), respectively, a comparison between two does not change the result of bit[3]. The same procedure is applied to bit[5]-bit[7]. However, for bit[8], slope[8] is obtained positive, which is not aligned with the true bit pair (1, 0) due to signal fluctuation. The algorithm assumes that the corresponding bit pair is (0, 1) and judges bit[8] as data one, which is corrected by the decision of slope[9], since slope[9] is obtained larger than slope[8].
# Separation of Iron Oxide from Harbor Waste Using Selective Flocculation
## 3 Results
### Selective Flocculation Tests
#### 3.4.2 Selective Flocculation Tests Depending on Particle Size Classes  
Further research went in the direction of performing a selective flocculation test on three different particle size classes (\(-450+250\)\(\mu\)m, \(-250+25\)\(\mu\)m, and \(-25+0\)\(\mu\)m), samples labeled K1, K2, and K3. The goal was to determine content of the basic components depending on the size classes. The results are presented in Table 7.  
As can be seen from Table 7, in the finest size class (\(-25+0\)\(\mu\)m), there is the highest content of Fe in the input but also by products compared to the other size classes in the settled part. Also, in the finest class, there is a higher content of other tested components.  
We observed that there are some differences between the input and output compositions. These discrepancies can arise due to various factors, and we are still investigating what the cause was. However, from a qualitative perspective, the results can be confirmed.  
According to the obtained results, different particle sizes do not play a significant role in the distribution of masses between the settling and floating parts, especially in the case of the finer class. An increase in iron ore content after flocculation by comparison to the initial sample can be seen in the settling part. In the floating part, the Fe content decreases compared to the initial sample. Considering the mass distribution, the iron concentration in the settling part is quite satisfactory compared to the Fe content in the initial sample (34.18%) for all three size classes and especially in the finest class (38.05%). The best Fe recovery was achieved in the medium-sized class, but it was also satisfactory in other classes.  
At the same time, there is an increase in the content of other elements in the floating part (and thus a decrease in the settling part), which was one of the goals of this research and indicates the possibility of selective flocculation of iron ore from other impurities in such complex systems. Theoretically, selective flocculation depends on many parameters, such as surface chemistry of particles, particle size distribution, degree of dispersion of particles, functional groups of the polymer, etc. According to the obtained results, we can consider this method as suitable.
## 5 Conclusions  
In this paper, we simulated ocean surface images from a single SAR satellite at different wind speeds and analyzed the effects of the different wind speeds on the images. The main methods used in this paper were the rough sea model, electromagnetic scattering model, time-domain echo algorithm and imaging algorithm. Aimed at the problem of the azimuth cutoff of ocean waves observed by SARs, networked SAR satellites with three SARs were simulated, and multiview SAR wave synchronization data were obtained, which is the innovation and difficulty of this paper. MPI algorithm was used to verify the accuracy of the SAR multiview wave synchronization data. Although the calculations of complex original echo signals are not avoided, the accuracy of the obtained sea surface SAR images is high. The conclusions that we have reached are as follows:  
Firstly, wind speed is the key factor affecting ocean surface fluctuation and backscatter coefficients, and the change trend of stripe can be seen from SAR images.  
Secondly, ocean surface SAR images and the backscatter coefficient images are horizontally symmetric. This situation causes the observed SAR spectrum to be horizontally flipped.  
Thirdly, azimuth cutoff has a non-linear effect on SAR images, and this effect is irreversible. From the fit SAR spectrum obtained from the MPI algorithm inversion, the influence of the azimuth cutoff can be seen. SAR can only image the ocean waves within a certain wavenumber range, and waves with wavelengths less than the azimuth cutoff wavelength cannot be imaged.  
To sum up, the simulation of the multiview SAR ocean wave synchronization data in this paper has important significance in the imaging algorithm research and SAR system scheme design, and we believe that the results in this paper can provide some reference for marine remote sensing. The acquired data can also be used as the basic data source for related research, such as the azimuth cutoff wavelength compensation.  
All authors contributed to the research in this paper. [PERSON] searched the literature, designed the study, analyzed the data and modified the paper; [PERSON] designed the study, analyzed the data and wrote the paper; [PERSON] designed the study and analyzed the data; and [PERSON] analyzed the data.  
This research was funded by the National Key R&D Program of China (Grant No. 2017 YFC1405600) and the Fundamental Research Funds for the Central Universities, China (Grant No. 17 CX02079).  
The authors declare no conflict of interest.