esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things (IoT) and the Energy Sector
## 3 Enabling Technologies
### Communication Technologies
NB-IoT is a low-cost solution that has long battery life and provides enhanced coverage. According to the authors of [81], due to the latency features of NB-IoT, this technology is a potential solution for smart energy distribution networks by providing low-cost communications for smart meters. In addition, the study in [82] demonstrates the NB-IoT technology for smart metering. As another application of NB-IoT in the energy sector, the work in [83] introduces NB-IoT as a potential solution for smart grid communications by comparing NB-IoT with other communication technologies in terms of data rate, latency, and communication range. _Long Term Evolution for Machine-Type Communications (LTE-M)_ is the 3 GPP (the third-generation partnership project) standardization, which is designed to reduce the device complexity for machine-type communication (MTC) [84]. LTE-M supports secure communication, provides ubiquitous coverage, and offers high system capacity. LTE-M also offers services of lower latency and higher throughput than NB-IoT [85]. In addition, this technology offers energy efficiency resource allocation for small powered devices, making it to be a potential solution for smart meter [86] and smart grid communications [87]. _Weightless_ is LPWAN open wireless standard that is developed to establish communication among great number of IoT devices and machines ([[http://www.weightless.org/](http://www.weightless.org/)]([http://www.weightless.org/](http://www.weightless.org/))). Weightless is a potential solution for smart metering in the energy sector [88]. Based on the study in [89], Weightless is a suitable wireless technology can be used in smart home IoT applications for smart metering and smart grid communications. _Satellite_ is another communication technology that has a very wide-area coverage and can support low data rate applications in machine-to-machine (M2M) fashion [90]. Satellite technology is suitable for supporting IoT devices and machines in remote places. The study in [91] presents an IoT-based machine-to-machine satellite communication that is applicable to the smart grid, particularly for the transmission and distribution (T&D) sector. A similar study highlights the importance of using satellite-based IoT communications in energy domain such as solar and wind power plants [92].
## 1 Introduction
The range profile provides the reflected wave at the front and back ends of the box. The front provides the initial reflected wave, which is then compared with the second peak, the reflected wave at the back of the box. Depending on the material inside the box, attenuation differs. If a light, low-density material is enclosed in the box, the attenuation between the two reflections at the front and back end of the box will be low, whereas a dense or heavy material that has generally high permittivity or high conductivity produces larger attenuation over the same distance. Accordingly, we use a range profile to classify the box with a material that is to be avoided. A theoretical approach is also conducted to further verify the obtained measurement results. Plane-wave propagation/reflection provides a theoretical background for later comparison with measurements made using the FMCW radar. After checking the feasibility of classifying different densities of the papers, we also investigate other materials: bubble wrap, clothes, water bottles, garden soil, much, and granite pebbles. The range profiles of these different materials are analyzed to further investigate the feasibility of determining the potentially dangerous materials on the road. This paper is organized as follows: Section 2 defines the theoretical approach, focusing on deriving the equations for reflections in the front and back ends of the box. Section 3 compares measurements obtained from the radar measurements with calculations made using the theoretical approach. Section 4 focuses on other materials, and Section 5 provides a discussion. Section 6 offers our conclusion.
## 2 Background
### Lidar System Optimization for Control Applications  
Although much of the literature on LAC focuses on controller design rather than the remote sensing aspect, a significant amount of research has been conducted specifically on the optimization of lidar systems for LAC. This section provides a review of existing research on the optimization of lidars for control applications, organized into four categories: (1) lidar optimization via the analysis of controller performance; (2) scan pattern optimization by minimizing measurement error using simulated lidar measurements; (3) scan pattern optimization by minimizing measurement error using computationally efficient frequency domain calculations; and (4) lidar configuration optimization to address practical considerations.
## 3 Results
### Capture of GPP by VIs and SIF in Dry Years  
#### 3.4.1 SIF Responds Better Than VIs to Drought  
The drought event started in May 2001 and ended in July 2001. These three months were characterized by a severe drop in precipitation compared to the reference year and higher temperatures than the reference year, resulting in a severe lack of soil moisture, which affected the growth of vegetation (Figure 4).  
The SPEI drought index indicated that the SPEI for both sparse vegetation and grassland was less than the reference year value from May to July, and drought conditions recovered in August (Figure 9). The cropland, forest-shrub-grass vegetation mosaic belt, deciduous forest, and evergreen forest were all smaller than the multi-year average from May to August before drought conditions subsided in September. The SSMI drought index indicated that sparse vegetation was smaller than the multi-year average from the beginning of the year for SSMI values, and other land classes were less than the multi-annual average from March. Sparse vegetation, grassland, and cropland showed relief in September; forest-shrub-grass vegetation mosaic belts, deciduous forests, and evergreen forests showed relief in August. Both SPEI and SSMI had their lowest values in May, with the largest differences occurring almost simultaneously (Figure 9).  
Figure 9: Trends in drought index for 2001 and reference year. Where mean-SSMI and mean-SPEI are monthly averages of SSMI and SPEI from 2000 to 2020; 2001-SSMI and 2001-SPEI are monthly values of SSMI and SPEI for 2001; SSMI and SPEI are the difference between the monthly values of SSMI and SPEI for 2001 and the multi-year average.  
The response of VIs and SIF to drought is reflected by the difference between the VIs and SIF drought year and the reference year. Both VIs and SIF were essentially lower than multi-year averages in 2001, which differed from the drought index performance. During the drought event, the maximum difference between VIs and SIF for sparse vegetation and grassland occurred in May; the maximum difference for forest-shrub-grass vegetation mosaic belt, deciduous and evergreen forests occurred in June (Figure 10), one month after the maximum difference in the drought index. The drought event was caused by a sharp drop in precipitation in March and May 2001. At this time, only SIF showed a clear downward trend, while the change in VIs was much less significant than SIF (Figure 10). In this drought event, both VIs and SIF were subjected to drought stress at the stage of the year when vegetation growth was at its peak, but SIF captured the drought signals and drought initiation stage more accurately.
# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
Ontology-Based Stock Selection: A systematic method for classifying stocks using financial indicators like liquidity, profitability, debt, and growth to effectively identify high-performing assets. 2. Non-Stationary Markov Chain (NMC): A model for dynamic market state transitions incorporating \(t\)-copulas and exogenous variables such as the Illiquidity Index (ILLIQ) ajnd Sentiment Index (SI). 3. Sentiment Analysis with BERT: Use of the Bidirectional Encoder Representations from Transformers (BERT) model to extract market sentiment from financial news and predict future market states. 4. Prediction with LSTM: An approach that captures temporal dependencies in market states by integrating outputs from the NMC, sentiment scores, and external variables like volatility (VIX) and GARCH. By integrating these components, the proposed framework offers a comprehensive solution that unites technical, fundamental, and sentiment analyses. This approach surpasses previous efforts where these methods were applied separately. Empirical evaluations highlight its effectiveness with an impressive prediction accuracy of 97.20% and an average cumulative return of 13.67%, significantly outperforming the 8.62% accuracy of the buy-and-hold strategy. This study contributes to the field of quantitative trading through the following:
* Presenting a unified framework that combines ontological financial analysis, machine learning, and sentiment modeling;
* Demonstrating the effectiveness of integrating financial ontologies with predictive models for dynamic stock selection and market state forecasting;
* Providing empirical evidence of robust performance across diverse market conditions. The remainder of this paper is structured as follows: Section 2 presents the theoretical framework underlying the proposed approach. Section 3 details the methodology, including ontology-based stock selection and market state prediction. Section 4 discusses the results, including model evaluations, computational costs, and strategy comparisons. Finally, Section 5 highlights the implications and contributions of the study.
## 5 Results and Discussion
### Economic Benefit  
To analyse the economic benefit of each scenario, the day-ahead Iberian Electricity Market price for the year 2022 was chosen. As an example, Figure 15 shows the hourly day-ahead price on the Iberian Electricity Market and the energy generated for 16 April 2022. The product of these two curves determines the economic benefit on that particular day.  
Figure 16 shows the monthly economic benefit of the Rebolluelo micro-hydropower plant with both technologies. The micro-hydropower plant operated with fixed speed technology in 2016 and 2017, and it operated with variable-speed technology in 2021 and 2022.  
The year 2016 was used as the basis for comparison to determine the _EBR_. Compared with 2016, the economic benefit ratio increased by 108.72% and 29.31% in 2021 and 2022, respectively. Therefore, the implementation of variable-speed control significantly increased the economic benefit.  
Figure 14: Annual energy generated by the Rebolluelo micro-hydropower plant with both technologies.  
Figure 15: Hourly day-ahead price on the Iberian Electricity Market and the energy generated for 16 April 2022.
## 7 Results and Performance Evaluation  
The results of the system are highly encouraging. We discuss the effects of various parameters on the result one by one while keeping the other parameters unchanged.  
Figure 6: Schematic diagram of one of the 720 simulation set-ups, where the inter-nodal distance is 20 m, the jammer is a constant jammer with high output power, the jammer is located inside the WSN grid at coordinates (30,45) with the sink node located at coordinates (85,85), and the WSN has a total of 25 nodes, excluding the sink.
# Fast Real-Time Data Process Analysis Based on NoSQL for IoT Pavement Quality Management Platform
## 4 Experiment and Result
### Data Read (=Search) Operation Speed Experiment  
Table 2 shows the results of the read operation experiment. In this experiment, a data reading operation was performed, in which specific values were searched for among existing stored data. The measurements obtained herein can be used as a performance index for analysis, statistics, and data searches. It took approximately 14.306 s for NoSQL to search and read 50,000 data points from 100,000 data points, and 0.299 ms (0.000299 s) on average to read 1 data point. By comparison, it took approximately 13.663 s for MySQL to search and read 50,000 data points from 100,000 data points, and 0.306 ms (0.000306 s) on average to read 1 data point. It can be observed that the search and read performance of the proposed system, the NoSQL-based system, was highly similar to that of the conventional RDBMS-based system.  
Figure 12 shows a graph of the read experiment results for RDBMS and NoSQL. This graph also shows that the two systems did not have a large difference in their performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**NoSQL**} & \multicolumn{2}{c}{**RDBMS**} \\  & \multicolumn{2}{c}{**MongoDB**} & \multicolumn{2}{c}{**MySQL**} \\  & **Total Time** & **Unit Time** & **Total Time** & **Unit Time** \\  & **(ms)** & **(1 Time per ms)** & **(ms)** & **(1 Time per ms)** \\ \hline
10 times & 9.4 & 0.9400 & 210.0 & 21 \\
50 times & 16.8 & 0.3360 & 942.0 & 18.840 \\
100 times & 34.6 & 0.3460 & 2135.6 & 21.356 \\
1000 times & 351.4 & 0.3514 & 22,487.2 & 22.487 \\
5000 times & 1576.4 & 0.3152 & 109,876.0 & 21.975 \\
10,000 times & 3042.2 & 0.3042 & 214,680.8 & 21.468 \\
20,000 times & 6152.6 & 0.3076 & 429,847.0 & 21.492 \\
50,000 times & 17,074.8 & 0.3414 & 1,027,717.6 & 20.554 \\ \hline \end{tabular}
\end{table}
Table 1: Results of write experiment.  
Figure 11: Results of write performance experiment (\(x\)-axis: number of data points, \(y\)-axis: write speed in ms).
# State of Charge Estimation for Lithium-Ion Power Battery Based on H-Infinity Filter Algorithm
## 3 SOC Estimation Based on the HIF Algorithm  
The Kalman series of filtering algorithms have been supported, widely researched and applied owing to their high accuracy, low computational complexity, and good robustness. However, they have two limitations that are difficult to overcome: (1) It is necessary to ensure that the input noise is white noise, and the statistical characteristics of the noise are approximately known. In practice, the noise is often not a white noise that satisfies the Gaussian distribution, and the statistical characteristics are difficult to obtain. (2) The accuracy of the estimation is very dependent on the accuracy of the established model. When the accuracy of the model is insufficient or the accuracy of the model gradually decreases with the dynamic changes of the working process, the filtering error becomes increasingly larger.  
To overcome the Kalman series filtering algorithm's requirements for white noise and its dependence on accurate modeling, an HIF with poor model accuracy, uncertain SOC initial value, and stronger robustness of various colored noise interference is introduced.
## 5 Conclusions and Future Work  
During space scientific load position transfer control, the joint load inertia and transmission stiffness change nonlinearly due to the manipulator arm shape, leading to flexible deformation of the joint harmonic reducer and nonlinear variation in joint transmission stiffness. These changes perturb the joint dynamics model and affect the control system's key parameters, resulting in decreased damping and stiffness, which increase joint position tracking error and flexible vibration. To address this issue, we first established a full-element flexible joint dynamic model and used a Luenberger state observer to estimate joint flexible deformation, which cannot be directly measured, and to identify joint load inertia disturbance and transmission stiffness disturbance. Then, we designed a disturbance compensation control strategy based on a simplified model that is convenient for engineering application, which maintains the joint control system's desired damping and stiffness under internal and external disturbances and improves control quality. Finally, we  
Figure 16: Physical joint position tracking error curve using CDHS controller.  
Figure 15: Physical joint position command tracking curve using CDHS controller.  
compared the new algorithm with the traditional three-loop feedback controller through simulation and physical experiments. The results show that the CDHS algorithm has more stable motion characteristics than the three-loop feedback control algorithm, reducing the joint's flexible deformation by 97% and improving joint dynamic tracking performance, with an 88% reduction in position tracking error. These results are consistent with the theoretical analysis.  
In the future, experiments will be conducted on an air-bearing platform to validate the effectiveness of the algorithm in controlling the real load displacement of space manipulators in microgravity, especially in the presence of multi-joint coupling disturbances. The research and experiments on the constant damping and high stiffness control method of flexible space manipulators based on the Luenberger state observer have positive implications for the future of space manipulators. These implications include the ability to capture, transfer, and maintain large spacecraft in orbit, and the capability to perform on-orbit servicing missions with high precision and reliability. The findings of this research will serve as a valuable guide for the development of advanced space manipulator technologies and contribute to the progress of space exploration and technology.  
Conceptualization, T.Y.; Methodology, F.X.; Validation, S.Z. ([PERSON]); Formal analysis, Y.L.; Resources, T.Y. and Y.W.; Writing--original draft, T.Y.; Project administration, S.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 1975566.  
Not applicable.  
The authors declare no conflict of interest.
# Reduction of Coupling Interface Degrees of Freedom in Mixed-Interface Component Mode Synthesis
## 1 Introduction
Thus, it is necessary to establish an approach to reduce the number of interface DOFs without too much accuracy diminished, and this will result in incomplete boundary conditions [28]. To date, different interface reduction methods have been proposed by various researchers, and most of them can be classified by two categories. One is to replace full size of constrain modes by a subset of interface modes via performing a second modal analysis related to the interface DOFs [29; 30; 31]; the other is to introduce additional variables such as Lagrange multipliers that represent the interface generalized vectors [32; 33; 34; 35; 36]. However, in general, the validity of these methods are heavily rely on a good selection of special interface modes, and they may be time-consuming by performing supplementary modal analysis for the interface modes. In this work, a new coupling interface degrees of freedom reduction technique for the MCMS method (referred to as the MCMS-rid method) is established properly. In MCMS-rid, a set of interpolation functions via the linear interpolation (LI) [37; 38] suitable for both nodal displacements and nodal forces are built, and then the corresponding coordinate transformation matrices for constrained interfaces and free interfaces are derived respectively. Different from the methods aforementioned, the interpolation functions representing the reduced interface DOFs are established based on the initial interface DOFs of substructures, in which no additional variables or supplementary modal analysis are needed. Therefore, the corresponding coordinate transformation can be carried out within each substructure before assembling or even in individual processors of a parallel computer. The other advantage of this method is that the number of the retained interface DOFs are distributed between the separate substructures in an optimal sense. It should be noted that we seek a consistently reduced model for MCMS, whose size depends exclusively on the physics of the problem rather than the FE models. Thus, the presented work is aimed to propose a coupling interface DOFs reduction procedure which has to satisfy the following two conditions: (i) the numerical precision with respect to the full interface dimension model should be kept in an acceptable level, and (ii) the interface modes selection shall be based on a robust criterion. The efficiency of the approach is demonstrated by eigenvalue analyses of three typical numerical examples, including a rectangular beam with two ends fixed, a non-rectangular beam with the button fixed and a simplified dam-foundation system. The optimal number and distribution of the interpolation nodes are discussed on structural dynamic analysis as well.
## 4 Experimental Results
### Comparison with DMKMSVM-2, CNN, RBF-SVM and NB
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Datasets** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & \(F_{1}\)**-Score (\%)** \\ \hline \multirow{6}{*}{DRKMSVM} & MR & **78.91** & **78.91** & **78.96** & **78.94** \\  & CR & 79.37 & 77.96 & **77.79** & 77.89 \\  & Subj & 92.10 & 92.24 & 91.90 & 92.03 \\  & MPQA & **89.16** & **88.57** & **86.36** & **87.32** \\  & TREC & **96.61** & **97.26** & **95.64** & **96.36** \\  & Average & **87.23** & **86.99** & **86.13** & **86.51** \\ \hline \multirow{6}{*}{DMKMSVM-2} & MR & 78.83 & 78.90 & 78.77 & 78.78 \\  & CR & 80.77 & 79.93 & 77.79 & **78.49** \\ \cline{1-1}  & Subj & **92.44** & **92.43** & **92.45** & **92.43** \\ \cline{1-1}  & MPQA & 86.92 & 85.95 & 82.82 & 84.08 \\ \cline{1-1}  & TREC & 87.20 & 89.40 & 83.776 & 85.88 \\ \cline{1-1}  & Average & 85.23 & 85.32 & 83.12 & 83.93 \\ \hline \multirow{6}{*}{CNN} & MR & 77.24 & 72.38 & 72.27 & 72.19 \\  & CR & 77.72 & 77.58 & 73.70 & 74.65 \\ \cline{1-1}  & Subj & 90.83 & 90.78 & 90.83 & 90.79 \\ \cline{1-1}  & MPQA & 87.66 & 86.14 & 85.68 & 85.93 \\ \cline{1-1}  & TREC & 89.42 & 89.29 & 90.26 & 89.72 \\ \cline{1-1}  & Average & 84.57 & 83.23 & 82.54 & 82.66 \\ \hline \multirow{6}{*}{SVM} & MR & 77.21 & 77.22 & 77.20 & 77.28 \\  & CR & 79.47 & 78.11 & 76.77 & 77.27 \\ \cline{1-1}  & Subj & 90.90 & 90.90 & 90.90 & 90.89 \\ \cline{1-1}  & MPQA & 86.63 & 85.67 & 82.41 & 83.72 \\ \cline{1-1}  & TREC & 81.60 & 83.95 & 81.89 & 82.64 \\ \cline{1-1}  & Average & 83.16 & 83.17 & 81.83 & 82.36 \\ \hline \multirow{6}{*}{NB} & MR & 73.66 & 73.65 & 73.66 & 73.65 \\ \cline{1-1}  & CR & **83.60** & **81.38** & 76.57 & 78.36 \\ \cline{1-1}  & Subj & 91.30 & 91.32 & 91.36 & 91.30 \\ \cline{1-1}  & MPQA & 86.15 & 87.28 & 79.28 & 81.85 \\ \cline{1-1}  & TREC & 77.80 & 67.07 & 66.12 & 66.10 \\ \cline{1-1}  & Average & 82.50 & 80.14 & 77.39 & 78.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy, precision, recall and \(F_{1}\)-score of four algorithms on five datasets.
## 5 Performance Analysis and Results
### Encryption Quality Metrics
#### 5.2.5 Entropy Analysis  
The unpredictability of the received image is calculated using entropy, which is a measure of uncertainty in the cyphered image. Strong randomness and strong confidentiality are signs that the encoded image has high entropy [13]. One definition of entropy in an information system reads like follows:  
\[H(m)=-\sum_{i=0}^{2^{N}-1}p(m_{i})log_{2}p(m_{i}) \tag{32}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Image** & **Original Image** & \multicolumn{2}{c}{**MPFrFT**} \\ \cline{3-4}  & **Histogram** & **Encryption** & **Decryption** \\ \hline \hline \end{tabular}
\end{table}
Table 13: Histogram analysis for the proposed MPFrFT-coded OFDM using Cameraman, Peppers and Boat standard gray-scale test images.  
where \"\(m\)\" is the information source, the symbol \"\(m_{i}\)\" is represented by \(N\) total bits, it has a probability of \(p_{(m_{i})}\), and the optimal information entropy value is close to 8. The entropy result based on the proposed algorithm is 7.9999.