esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Suggestions for Pollution Remediation  
Remediation of heavy metal contamination in soil refers to the process of removing heavy metals from soil or immobilizing them in soil to reduce their mobility and bioavailability, thereby reducing the health and environmental risks of heavy metals. Common methods include physical, chemical, and biological methods. According to the heavy metal pollution elements and the pollution degree of the soil around the mining area, combined with the actual situation, the following two suggestions are given:  
(1) Clean up the pollution source by using stabilization repair technology. The pollution in the Dachang mining area and its surrounding areas is related to the discharge of waste slag, wastewater, and exhaust gas in the early stage. Therefore, the remediation of the soil in this area must start with cleaning up the pollution sources. Due to the large area of contamination, the guest soil and soil replacement methods are obviously not economically feasible. Considering the advantages and disadvantages of various remediation technologies and the actual situation in the field, it is recommended that stabilization reagents be added in the soil to reduce the mobility and bioavailability of heavy metals in the soil and prevent the transfer of heavy metals to the food chain. Referring to the study results of [36], it is suggested to use calcium dihydrogen phosphate to reduce the bioavailability of Pb and Cd in the soil. After soil remediation is completed, it is useful to plant Pb and Cd resistant crops, such as corn.  
(2) There are serious Cd, As, and Pb pollution issues in the industrial and mining wasteland and some unused land in the study area. Studies have shown that _Typha orientalis_ Presl can absorb As, Cd, and Pb in the soil, accumulate, and fix them in the root [37]. Therefore, it is suggested to plant _Typha orientalis_ Presl in these regions to achieve the goal of remediating and beautifying the environment.
# Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD)
## 3 Singular Value Decomposition (SVD)
### SVD Properties  
#### 3.2.1 Energy Packaging  
SVD has the property of maximum energy packing. This property is usually used in compression [29], and it is a stable method to split the system into a set of linearly independent components, each of them bearing their own energy contribution. SVD offers a low-rank approximation which could be an optimal sub-rank approximation by considering the largest singular value that packs most of the energy contained in the signal and representing the matrix \(A\) as truncated matrix \(k\), which allows storing matrix \(A\) as an approximation of matrix \(A_{k}\).
## 5 Results and Discussion
### Discussion  
The above sections clearly depicted that all the three flavors of MITM attacks have high impact on VANET, where the network achieves high E2E delays, low content delivery ratios, higher packet loses and the propagation of high number of compromised messages. It can be seen that the distributed malicious attackers affect the network in terms of E2E delays and CDR. The fleet MITM attackers, on the other hand, influence the VANET in terms of compromised messages and PLR. Therefore, in order to design a security solution in VANET, these characteristics of MITM should be taken into account before implementing in the real network. For instance, encrypting the messages can reduce the probability of disseminating false information among network entities within VANET. Further, trust-based solutions can also be employed within the network to cater MITM attacks with delaying and tampering abilities. Trust-based environment can enable the legitimate vehicles to identify and evaluate the trustworthiness of the received messages, thus reducing the probability of attackers to launch MITM attacks. Similarly, different plausibility checks in the network can also be helpful to reduce the propagation of compromised messages. These checks can be based on message threshold and detection ranges, message acceptance range, and some mobility controlled criteria including vehicles speed and direction.
# A Two-Stage Registration Strategy for Thermal-Visible Images in Substations
## 2 Proposed Methodology  
The whole registration process of the thermal visible image in the fire alarm system is shown in Figure 1, which includes two stages: (i) initial registration and (ii) second registration. The initial registration process is carried out at the monitoring end, and the spatial coordinates are roughly aligned by preliminarily registering the initial frames of the image pairs collected in one cycle. The monitoring camera used to collect thermally visible image pairs includes infrared sensors and general visible-light cameras. Due to factors such as product design and cost control, the thermal images collected by the equipment are usually low-resolution and have a narrow line of sight, which leads to great differences in multiple dimensions among thermally visible image pairs. Because direct registration is usually suitable for image pairs of the same scale, it is not recommended in this case. Therefore, the rapid initial registration of the image at the monitoring end can not only reduce the impact of differences in image scale, but also reduce the dependence on camera parameter settings and expand the application range of the method.  
The second registration process is running in the background processing system of fire-monitoring. The image pairs first registered at the monitoring end are used as input, and the image pairs are registered more accurately by the improved PIIFD algorithm. The second registration process is the same as the common feature-based registration algorithm process, including feature detection, feature matching, Transform model estimation, and image resampling and transformation.
###### Abstract  
This work quantifies the impact of using sCO\({}_{2}\)-mixtures (s-CO\({}_{2}\)/He, s-CO\({}_{2}\)/Kr, s-CO\({}_{2}\)/H\({}_{2}\)S, s-CO\({}_{2}\)/CH\({}_{4}\), s-CO\({}_{2}\)/C\({}_{2}\)H\({}_{6}\), s-CO\({}_{2}\)/C\({}_{3}\)H\({}_{8}\), s-CO\({}_{2}\)/C\({}_{4}\)H\({}_{8}\), s-CO\({}_{2}\)/C\({}_{4}\)H\({}_{10}\), s-CO\({}_{2}\)/C\({}_{5}\)H\({}_{10}\), s-CO\({}_{2}\)/C\({}_{5}\)H\({}_{12}\) and s-CO\({}_{2}\)/C\({}_{6}\)H\({}_{6}\)) as the working fluid in the supercritical CO\({}_{2}\) recompression Brayton cycle coupled with line-focusing solar power plants (with parabolic trough collectors (PTC) or linear Fresnel (LF)). Design parameters assessed are the solar plant performance at the design point, heat exchange dimensions, solar field aperture area, and cost variations in relation with admixtures mole fraction. The adopted methodology for the plant performance calculation is setting a constant heat recuperator total conductance (UA\({}_{\text{total}}\)). The main conclusion of this work is that the power cycle thermodynamic efficiency improves by about 3-4%, on a scale comparable to increasing the turbine inlet temperature when the cycle utilizes the mentioned sCO\({}_{2}\)-mixtures as the working fluid. On one hand, the substances He, Kr, CH\({}_{4}\), and C\({}_{2}\)H\({}_{6}\) reduce the critical temperature to approximately 273.15 K; in this scenario, the thermal efficiency is improved from 49% to 53% with pure s-CO\({}_{2}\). This solution is very suitable for concentrated solar power plants coupled to s-CO\({}_{2}\) Brayton power cycles (CSP-sCO\({}_{2}\)) with night sky cooling. On the other hand, when adopting an air-cooled heat exchanger (dry-cooling) as the ultimate heat sink, the critical temperatures studied at compressor inlet are from 318.15 K to 333.15 K, for this scenario other substances (C\({}_{3}\)H\({}_{8}\), C\({}_{4}\)H\({}_{8}\), C\({}_{4}\)H\({}_{10}\), C\({}_{5}\)H\({}_{10}\), C\({}_{5}\)H\({}_{12}\) and C\({}_{6}\)H\({}_{6}\)) were analyzed. Thermodynamic results confirmed that the Brayton cycle efficiency also increased by about 3-4%. Since the ambient temperature variation plays an important role in solar power plants with dry-cooling systems, a CIT sensitivity analysis was also conducted, which constitutes the first approach to defining the optimum working fluid mixture for a given operating condition.  
Bryton cycle; fluid mixture; solar plant; supercritical CO\({}_{2}\)
# Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels
## 2 Research Status of Tunnel Lighting System
### Progress of Tunnel Lighting Control System
[PERSON] [25] proposed a replacement plan of TLCS using DALI as the communication interface to flexibly address individual or multiple devices, in addition to redundancy design for the master CPU, power supply and network. However, all of the above research projects on the reliability of TLCS focus mainly on luminance only, with little care afforded to color temperature, and considering the effectiveness and functions of LED, especially of VCT LED lights, and the fact that there have been many research achievements in the functions of color temperature that act on drivers visual performance [26], it is desirable to study the safety and reliability of TLCS lit by VCT LED lights.
## 1 Introduction  
The movements of Korean traditional dance are not merely physical motions but are expressions of cultural context and historical background. These movements have continuously evolved over time and across regions, with each community developing its own unique styles and techniques [1, 2, 3, 4, 5, 6, 7]. Therefore, preserving intangible cultural heritage, such as traditional dance, through digital means requires capturing these complex and layered characteristics.  
With recent advancements in artificial intelligence (AI), research on applying deep learning to dance recognition has gained momentum [8, 9, 10, 11, 12, 13]. Machine learning techniques, such as body detection, provide new methods for analyzing and understanding traditional art forms by creating real-time visual models that capture body movements [14]. Unlike static images, dance involves a flow of various movements over time, which requires precise recognition and tracking of multiple body parts in motion. AI algorithms can analyze these movement patterns to recognize and classify actions and even generate new movements.  
To enable real-time recognition of Korean traditional dance, this study implemented an AI-based system that integrates a structured classification framework and a metadataenhanced dataset reflecting the unique attributes of these movements. A systematic classification framework was first developed through literature review and expert consultation, creating a classification system of 36 fundamental movements that characterize Korean traditional dance. Using this framework, video data of these movements were collected from multiple angles, and a subset of 521 metadata-enhanced samples was constructed to train the AI model.  
For real-time pose estimation, the open-source BlazePose model was employed to generate skeletal key points, which were then processed using a customized Bi-LSTM (bidirectional long short-term memory) architecture to analyze sequential motion data. This integration enabled the prototype AI model to recognize five fundamental Korean traditional dance movements accurately and efficiently.  
This study demonstrates the potential of AI in the systematic analysis and digital preservation of complex dance movements. By combining BlazePose with domain-specific adaptations, such as a tailored classification framework and a metadata-enhanced dataset, the research successfully addresses the challenges of capturing and interpreting the intricate patterns of Korean traditional dance. This approach highlights the role of AI in preserving intangible cultural heritage while offering new possibilities for its reinterpretation and transmission in contemporary digital contexts.
## 3 Hybrid Compensation Scheme
### Adaptive NDI Controller for Inertia Uncertainty
 \tag{31}\]  
where \(P_{3\times 3}\), \({R_{1}}^{-1}{}_{3\times 3}\), \({R_{2}}^{-1}{}_{1\times 1}\) is the positive definite symmetric matrix to be designed, and select \(P\) = diag(15, 2, 0), \(R_{1}\) = diag(0.5, 0.5, 0.5), \(R_{2}\) = 20. When debugging parameters, the larger \(R_{1}\) is, the smaller the system oscillation is, but the slower the response speed is; the larger the \(R_{2}\) is, the faster the system response speed is, but the oscillation will also increase. For (31) to hold, the following conditions should be met:  
\[{A_{m}}^{T}P+PA_{m}<0 \tag{32}\]  
To ensure that (31) is negative definite, the second and third terms are always 0, thence  
\[\dot{\widetilde{\Theta}}_{1}=-R_{1}({B_{m}}{\Theta_{2}}^{*-1}){Pe_{1}}{x_{p}}^{T} \tag{33}\]  
\[\dot{\widetilde{\Theta}}_{2}=-R_{2}({B_{m}}{\Theta_{2}}^{*-1}){Pe_{1}}{u}^{T} \tag{34}\]  
where the adjustable parameter errors are  
\[\left\{ \begin{array}{l}\widetilde{\Theta}_{1}={\Theta_{1}}^{*}-\Theta_{1} \\ \widetilde{\Theta}_{2}={\Theta_{2}}^{*}-\Theta_{2}\end{array} \right. \tag{35}\]  
The adaptive parameter update law is designed as follows:  
\[\dot{\Theta}_{1}=R_{1}({B_{m}}{\Theta_{2}}^{*-1}){Pe_{1}}{x_{p}}^{T} \tag{36}\]  
\[\dot{\Theta}_{2}=R_{2}({B_{m}}{\Theta_{2}}^{*-1}){Pe_{1}}{u}^{T} \tag{37}\]  
\[\Theta_{1}(t)=\int_{0}^{t}{R_{1}({B_{m}}{\Theta_{2}}^{*-1}){Pe_{1}}{x_{p}}^{T} dt}+\Theta_{1}(0) \tag{38}\]  
\[\Theta_{2}(t)=\int_{0}^{t}{R_{2}({B_{m}}{\Theta_{2}}^{*-1}){Pe_{1}}{u}^{T}dt}+ \Theta_{2}(0) \tag{39}\]  
According to the above inequality (32) and the adaptive laws, Theorem 1 can be proved. Ultimately, the general system (24) is stable. \(\square\)  
In summary, when inertia uncertainty occurs, the passive adaptive NDI controller with multi-adaptive laws can directly compensate for the uncertainty and keep the high maneuvering HFV in stable flight.
# Multipath Mitigation for BOC Signals Based on Prompt-Assisted-Offset Correlator
## 4 Performance Evaluation and Results
### Thermal Noise Performance Analysis
The tracking threshold of the PLL usually requires the standard deviation of the carrier phase error to be no more than 15\({}^{\circ}\). Figure 8b demonstrates that the application of the OC technique in the PLL may cause the tracking loop to lose lock. Compared with the OC technique, the PAOC technique can greatly reduce the risk of losing lock.
# A Compact Monopole Wideband Antenna Based on DGS
## 3 Simulation Results and Analysis
### Parametric Analysis on G-Shaped Patch Antenna
#### 3.6.3 Variation of K  
A parametric analysis was conducted on the dimension K to improve impedance matching and bandwidth, as shown in Figure 13c. Adjusting K from 5 mm to 7 mm in 1 mm increments revealed that K significantly affects antenna performance. The simulated S\({}_{11}\) values indicate optimal impedance matching and bandwidth at 6 mm length. The antenna illustrates less optimal bandwidth and impedance match for dimensions above and below 6 mm.
## 2 Related Work
[PERSON] and [PERSON] [38] proposed a clustering algorithm on the basis of interval type-2 FL model, expecting to handle uncertain level decision better than T1 FL model. Based on simulation results, it is concluded that T2 FL model provides better scalability, better lifetime compared to T1 FL, LEACH single hop, and LEACH multi-hop protocol. [PERSON] et al. [39] proposed a fuzzy logic based mechanism that according to the battery level and the ratio of Throughput to Workload defines the sleeping time of sensor devices in an Industrial WSN (IWSN) based on IEEE 802.15.4 protocol. They introduced a Particle Swarm Optimization (PSO) algorithm to obtain the optimal values and parameters of the proposed Fuzzy Logic Controller (FLC), i.e., optimizing the membership functions, by varying their range, to achieve the best results regarding the battery life of sensor nodes. They provided a detailed description of the FLC configuration, a logical analysis of the PSO algorithm for the derivation of best performance conditions values, and simulative assessments, obtained through Matlab simulations. Finally, [PERSON] et al. [40] addressed the problem of partial coverage in WSNs. They presented PCLA (Partial Coverage with Learning Automata), a novel algorithm that leverages the probabilistic framework of Learning Automata (LA) to find a convenient subset of sensor nodes to ensure partial coverage. We remind that an automaton is a machine designed to automatically follow a predetermined sequence of operations or respond to encoded instructions. LA adapt to changes in the Random Environment and this adaptation is the result of the learning process. Their approach [40] uses the smallest number of sensors at any given time, thus extending WSN lifetime. The PCLA algorithm minimizes the number of sensors to activate for covering a desired portion of the region of interest preserving the connectivity among sensors. In particular, PCLA properly schedules sensors into active or sleep state in order to extend the network lifetime. Precisely, each node runs the PCLA algorithm that first creates a backbone by selecting a number of nodes and leveraging the coverage graph of the network. Then, these nodes use their neighbors to meet the network coverage and connectivity requirements. Simulation results [40] showed how PCLA can select sensors efficiently to satisfy partial coverage area requirements, thus guaranteeing good performance in terms of time complexity, working-node ratio (i.e., the number of nodes to activate), scalability, and WSN lifetime. Moreover, compared to the state of the art, PCLA is able to guarantee better performance.
## 2 Results
### Performance of SPAD Prediction Regression Models
#### 2.3.1 Cowpea SPAD Values Prediction  
Table 2 shows the list of extracted CVIs with their corresponding correlation coefficients (r) for cowpea. The correlations of the measured SPAD values and the extracted CVIs were influenced by the quality of the segmented images; for example, poorly segmented images with high pixel misclassification and noise led to lower correlations with the measured SPAD values and vice versa. The CVIs extracted using the MLP segmentation method had high correlation coefficients, with DGCI having the highest (0.948) and EGI having the lowest (0.464) coefficient. This was followed by the RF segmentation method, with the highest correlation (0.850) recorded with the DGCI index and lowest (0.295) recorded with the EGI color vegetation index. The ExG segmentation method had the lowest correlation coefficient (0.228) recorded for the blue CVI whiles its highest correlation coefficient (0.742) was recorded with ExG CVI.  
Figure 4: Comparison of segmentation accuracy rate (Qseg., Sr, Es) for quality assessment of five different segmentation methods; multilayer perceptron (MLP), support vector machines (SVMs), random forest (RF), excess green (ExG), and excess green–red (ExGR) for field-based images. Qseg measures the segmentation consistency on a pixel-by-pixel basis, Sr measures the consistency of plant pixels between image regions, and Es measures the rate of pixel misclassification. These were applied to the five segmentation methods.  
Table 3 shows the results of the regression models for predicting SPAD values for the glasshouse-based images. The MLP-GR model had the highest R\({}^{2}\) value in both the training and testing phases, while the ExG-GR model had the lowest. The R\({}^{2}\) values for both training and testing datasets were close, indicating that the models did not underfit nor overfit the datasets. The RMSE has an inverse trend with the R\({}^{2}\) metric (the lower the RMSE, the better). Figure 5 is a scatterplot of observed and predicted SPAD values from test data which shows that the MLP-GR model with a high R\({}^{2}\) (0.904) had the best prediction performance, followed by the RF-GR model (R\({}^{2}\) = 0.837), while the ExG-GR model had the lowest prediction power (R\({}^{2}\) = 0.701). Overall, all the models performed well, with high prediction power (R\({}^{2}\) > 0.7) in both the training and testing phases.
# Tonic and Phase Amoremetric Monitoring of Dopamine Using Microelectrode Arrays in Rat Striatum
Electrochemical sensors are designed for use in freely moving behavioral recordings where higher resolution is necessary to capture transient DA events. While the chemical selectivity achieved with powerful separation technologies is irrefutable, the spatial-temporal resolution of electrochemical techniques greatly exceeds that of microdialysis probes and, thus, enables sub-second measures with an electrode diameter of 5-200 um [19]. Importantly, smaller sized electrochemical electrodes produce less tissue responsiveness and damage post-implantation in the brain [20; 21]. Although advances in electrochemical approaches have opened up new avenues in the area of in vivo neurotransmitter monitoring including 3D printed carbon electrodes, submicron sized cavity carbon-nanopipette electrodes, sub-millisecond resolution measuring DA over months, and optogenetic stimulation, the most significant shortcoming of these approaches for measures of extracellular DA continues to be the inability to resolve resting neurotransmitter levels [14; 22; 23; 24; 25]. Here, we describe the development and implementation of ceramic-based microelectrode arrays (MEAs) for intracranial monitoring of tonic and phasic DA neurotransmission by: (1) demonstrating the ability of MEAs to measure DA in vitro and in vivo with constant potential amperometry; (2) discussing in vivo testing through proof-of-concept experiments using normal and denervated striatum in anesthetized rats; and (3) introducing new developments for the measurement of brain DA through a conformal MEA design that enable the real-time, simultaneous monitoring of DA from multiple depths in the rat brain.
## 3 Results
### Statistical Analysis Results  
The data obtained from the experiment were statistically analyzed using nonparametric statistical methods since most of the data were not normally distributed. Statistical  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factors** & **NC (_n = 24_)** & **MCI (_n = 24_)** & _p-Value_ \\ \hline Gender (\%) & & 0.093 (\(\chi^{2}\)) \\ Male & 9 (37.5\%) & 3 (12.5\%) & \\ Female & 15 (62.5\%) & 21 (87.5\%) & \\ Age, years & 72.0833 \(\pm\) 5.1068 & 76.6250 \(\pm\) 7.0267 & 0.014 * \\ Education, years & 10.3333 \(\pm\) 3.5098 & 7.0833 \(\pm\) 4.4224 & 0.004 * \\ MoCA score & 26.6250 \(\pm\) 1.81330 & 15.6250 \(\pm\) 7.5746 & 0.000 * \\ \hline \end{tabular} Data, except for gender, is expressed as mean \(\pm\)standard deviation. The factors of the two groups were compared using Mann-Whitney U test (\({}^{\star}\) p \(<\) 0.05), except for gender, which was compared using Chi-Square test (\(\chi^{2}\)). Abbreviations: NC, Normal Cognition; MCI, Mild Cognitive impairment; MoCA, Montreal Cognitive Assessment  
\end{table}
Table 2: Demographic and Characteristics of the NC and MCI subject groups.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Normal Cognition** & & & \\ \hline
**Feature** & **Rest 1** & **Game 1** & **Rest 2** & **Game 2\&3** \\ \hline HR & 71.75 (11.73) & 73.90 (12.50) & 68.60 (11.13) & 69.25 (12.33) \\ RRI & 839.50 (141.25) & 812.25 (135.02) & 876.10 (137.13) & 867.85 (147.73) \\ SDNN & 34.40 (21.85) & 23.15 (13.30) & 27.90 (21.30) & 25.00 (15.50) \\ pNN50 & 0.00 (4.68) & 0.15 (2.10) & 0.40 (7.50) & 0.30 (7.03) \\ RMSSD & 17.15 (15.88) & 13.65 (10.03) & 18.10 (17.60) & 17.35 (14.45) \\ TP & 149.735 (346.08) & 112.925 (170.71) & 162.090 (321.51) & 163.490 (163.93) \\ LF & 34.205 (98.3525) & 37.08 (56.475) & 33.225 (89.9975) & 46.450 (82.78) \\ HF & 43.23 (68.245) & 21.885 (30.415) & 40.05 (105.8) & 37.145 (53.3925) \\ LF/HF & 0.614 (1.6123) & 1.6445 (1.6950) & 0.746 (1.1583) & 1.306 (1.2670) \\ SampEn & 1.5040 (0.52) & 1.5500 (0.57) & 1.6770 (0.57) & 1.6460 (0.52) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Extracted ECG features.  
analysis was used to investigate the relationship between the variables group, session and the ECG features. The scores and reaction times of the subjects were also analyzed to determine if there were significant differences between the two groups.