esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Analysis and Modeling  
### Problem Description and Assumptions  
Agile satellites possess prominent capabilities for earth observation, but observation resources are still relatively scarce compared to the increasing observation requirements. The agile satellite scheduling problem (AEOSSP) is a comprehensive planning and scheduling problem, whose purpose is to develop an optimal observation plan for a satellite with the maximum observation profit under the current set of tasks to be observed. To study the ability to solve the AEOSSP by the DRL-HA model, when establishing the corresponding mathematical model, the following assumptions should be made:It is necessary to decompose the complex tasks such as stereo imaging tasks and regional target tasks into point target tasks for processing because, in this way, the tasks considered in this paper are all point target tasks, which can be completed within the visible time window (VTW). Since the AEOSSP is a time-dependent problem, the VTW for each point target is significant for analysis and modeling.
2. This paper aims at the single-orbit AEOSSP and neglects the charging process in the sun area and the data download process. These assumptions were made because they are not the conditions of concern for the present research. These excessive conditions are unnecessary and bring dispensable costs.
## 2 Materials and Methods
### Determination of Energy Demand and Energy Systems Contribution  
The contributions of the Oxidative, Glycolytic, and ATP-PCr energy systems were estimated using several parameters: body weight, VO2, BLA, and the fast component of excess post-exercise oxygen consumption (EPOC). To calculate the ATP-PCr system's contribution, we employed the fast component of EPOC kinetics (EPOC_fast), analysed using OriginPro 8.0 software (OriginLab Corp., Northampton, MA, USA). A mono-exponential model was applied to the EPOC kinetics to estimate the ATP-PCr contribution. EPOC_fast is linked with PCr resynthesis because replenishing PCr stores, depleted during high-intensity exercise, is an oxygen-dependent process. This replenishment occurs rapidly post-exercise and is a significant part of the immediate recovery phase, reflected in the increased oxygen consumption during the fast component of EPOC. This system's total contribution was calculated by combining the fast component of EPOC following the final sprint with the sum of the VO2-time integral during rest intervals. PCr_breaks estimated PCr repayment during rest intervals [27; 28]. In the evaluation of the glycolytic system, the methodology was predicated on the premise that an accumulation of 1 mmol-L-1 in blood lactate levels (BLa) was indicative of a 3 mL O2 consumption per kilogram of body mass [29]. The oxidative system's contribution was determined by subtracting resting VO2 from exercise VO2[30; 31; 32]. Total energy expenditure (TEE) was then calculated as the cumulative energy derived from the Oxidative, Glycolytic, and ATP-PCr systems [33]. Oxygen demand (L) from these three energy systems was converted into energy equivalents, based on the assumption that each litre of O2 is equivalent to 20.92 kJ [34].
# Innovative Acoustic-Hydraulic Method for High-Performance Fine Liquid Atomization
## 4 Results and Discussion  
### Experimental Results: Droplet Sizes, Critical Spraying Mode  
The geometric parameters of the nozzle used in the experiment were as follows:  
* Outlet area \(S_{1}=0.38\) mm\({}^{2}\);
* Area of the cavitation zone \(S_{2}=50\) mm\({}^{2}\);
* Cross-sectional area of the tube \(S_{3}=78.5\) mm\({}^{2}\);
* Height of the cavitation zone \(L=1.5\) cm.  
The frequency of ultrasonic radiation was \(\omega=22.5\) kHz, the intensity varied in the range from 30 to 100 W/cm\({}^{2}\), and the hydraulic pressure \(p\) was from 200 kPa to 900 kPa.  
At a fixed intensity, the following picture was observed with increasing pressure. Up to a certain pressure \(p_{\sigma}\), the resulting droplets were relatively large in size (hundreds of micrometers), then the droplet size decreased significantly and remained approximately constant with further increases in pressure (Table 1). This spraying mode corresponds to cavitation flow and was established at pressure \(p_{\sigma}\) and intensity \(I_{cr}\). The critical values of the intensity \(I_{cr}\) depending on the pressure are given in Table 2. With increasing intensity, the critical pressure \(p_{\sigma}\) of the transition to the cavitation mode becomes higher, and vice versa, with increasing pressure, a higher intensity of the transition to the cavitation mode is required.  
It should be noted that in the absence of ultrasound, the characteristic droplet size is of the order of 500 \(\upmu\)m and higher in a given pressure range.  
The productivity (volume flow rate) of the liquid increased with pressure in all experiments, however, when passing through the critical point (\(p_{cr}\), \(I_{cr}\)), the nature of the growth changed (Figure 4). In any case, spraying without the use of ultrasound occurred at a slightly higher speed than with ultrasound (curve \"w/o US\" in Figure 4).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \(p\)/\(p_{a}\) & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline \(I_{cr}\), W/cm\({}^{2}\) & 70 & 190 & 146 & 126 & 111 & 71 & 44 & 39 & 42 \\  & 80 & 217 & 139 & 122 & 98 & 73 & 77 & 35 & 28 \\ \hline \end{tabular}
\end{table}
Table 1: Sautter mean diameter of liquid droplets \(D_{32}\) (\(\upmu\)m) depending on pressure and ultrasound intensity.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \(p\)/\(p_{a}\) & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline \(I_{cr}\), W/cm\({}^{2}\) & 26 & 31 & 49 & 55 & 82 & 86 & 104 & 112 \\ \hline \end{tabular}
\end{table}
Table 2: Critical pressure depending on ultrasound intensity.  
Apparently, the decrease in flow rate is associated with an increase in the hydraulic resistance of the nozzle, due to the large volume of cavitation bubbles in the mode of developed cavitation.
## 2 Methodology
### Metrics for Assessing Predictive Accuracy  
The statistical evaluation metrics for the different prediction models are summarized in Table 2, including root mean square error, mean absolute error, mean absolute percentage error, and the coefficient of determination. RMSE reflects the average magnitude of prediction errors and reveals higher priority to larger errors, increasing its vulnerability to extreme values. In contrast, MAE computes the mean of the absolute errors between predicted and observed values, operating as a straightforward indicator of prediction accuracy. MAPE represents the average percentage error relative to the actual values, offering a normalized measure of predictive accuracy in percentage terms. Finally, R\({}^{2}\) quantifies the degree of fit between the predicted values and actual observations; a value closer to 1 indicates higher predictive accuracy.
## 3 Experimental Results
### Analysis of Radiation Noise Characteristic
#### 3.2.3 Frequency Domain Characteristics of the Radiate Noise  
In order to measure the radiation noise characteristics of the pump under realistic operation conditions, the circulation system is adjusted to engine resistance characteristics, which means the model pump works at \(Q_{t}\) under 6750 r/min. Another two tests under 5875 and 5000 r/min rotational speed without changing the system resistance characteristics are also processed. Signals from microphone 3, located directly above the pipeline, are used to carry out frequency characteristics analysis. The cutoff frequency of the analysis is 6400 Hz. \(f_{0}\) represents the fundamental frequency, which corresponds to the shaft rotation speed. As can be seen from Figure 11, the energy of the noise of all three rotational speed  
Figure 10: Half directivity characteristics curves of radiated noise at different operating conditions: (a) vertical direction; (b) skew 45 \({}^{\circ}\)direction.  
is mainly concentrated in the low-frequency region. At frequencies higher than 20\(f_{0}\), the pressure level amplitudes decay rapidly. The noise spectral component shows obvious discreteness; its frequency is the shaft rotation frequency and its multiples. The SPL amplitude is the highest at the blade passing frequency (six times the shaft rotation frequency) because of the rotor-stator interaction influence. The discrete characteristics of the dimensionless frequency spectrum at three pump rotational speed demonstrate the same, so the next numerical simulation can only focus on the situation of 6750 r/min.
# Secret Image Sharing Revisited: Forbidden Type, Support Type, and Their Two Approaches
## 4 Discussion: Other Details of the Design
### Mixed Use of the Two Approaches  
In this paper, by introducing two distinct approaches when sharing, we can confuse hackers as follows. We may divide the given secret image into several regions; then apply the polynomial approach to some regions and the linear-equations approach to others. Figure 3 illustrates one of many examples. In general, there are a large number of possible ways to partition an image into regions. This is because either of the two approaches can be chosen for each region, and region-specific parameters (for either the polynomial or linear-equations approach) can be used. This makes hacking much more difficult. For example, if we partition the image into \(100\) blocks, then, even if the blocks are of uniform size, and even if the hackers know that we have used \(100\) blocks of the same size, there are still \(2^{100}=10^{30}\) possible choices that the hackers have to sieve through before they can arrive at the correct local sharing parameters. This difficulty is compounded if other shapes, such as triangular blocks, or irregular shapes are used.
## 2 Related Work
### Stereo-Based Methods  
The performance of 3D object detection algorithms based on stereo cameras [4; 5; 6; 24; 25; 26; 27; 28; 29] has also been greatly improved recently. For example, Stereo R-CNN [5] utilizes the dual RPN structure to fuse the stereo features and construct the stereo geometric relationship for 3D object detection and refines the 3D bounding box by the pixel-by-pixel matching of the left and right images. Object-Centric [6] only performs stereo matching on the object of interest in the stereo images, solving the problem of a pixel imbalance between far and near objects. DSGN [4] performs 3D object detection by constructing a derivable 3D geometry volume, thus narrowing the gap between image-based methods and LiDAR-based methods. Pseudo-lidar++ [25] utilizes sparse lidar to correct the depth of the pseudo-lidar point cloud generated based on stereo images, thereby improving the accuracy of 3D object detection without increasing the cost significantly. PLUMENet [24] constructs a pseudo-lidar feature volume directly in 3D space for both the depth estimation and 3D object detection. The stereo cameras can provide more abundant information than the monocular camera and include the depth information required for positioning, but the installation and setting requirements of stereo cameras are relatively high. In comparison, our monocular camera-based 3D object detection is more flexible and less expensive.
# UAV Support for Mission Critical Services
## 3 UAV Characteristics, Classifications and Commercial Deployments Status
### UAVs as Transportation Means
The autonomy level impacts the requirements concerning the Quality of Service (QoS) of the communication links [27]. A highly reliable, low delay C2 link is necessary for direct control. When the flight plan is prepared a priori and the UAV flight is defined by Global Positioning System (GPS) waypoints, the communication with the UAV is not so critical as in the previous case. Yet another option is the 'follow me' approach, in which the UAV is following a moving object. In such a case, embedded UAV intelligence with image recognition capability is needed. The mass-market deployment of UAV-based services requires airspace traffic regulations to avoid UAV collisions and provide flight safety. The most challenging issues are risk management and contingency planning. Currently, activities concerning these issues are conducted as a part of European Union (EU) research projects, by National Aeronautics and Space Administration (NASA) and by national organizations responsible for air traffic control. The heart of a commercial UAV system is the Unmanned Aircraft Systems Traffic Management (UTM) [34] that is responsible for UAV flight admission and the UAV airspace monitoring. The use of UTM is necessary for the large-scale deployment of UAV-based services. In [35], issues related to 5G support to UAV large-scale services (mostly the communication between UAVs, GCS and UTM) have been described.
## 5 Conclusions  
This paper introduces an intelligent measurement system that integrates the pyramid L-K optical flow method and the Split Comparison (SC) model for gas-liquid two-phase flow rate measurement. Utilizing a camera positioned outside a vertical pipeline to capture flow videos, the pyramid L-K optical flow method gathers information about the gas-liquid two-phase flow. A median value screening method effectively eliminates noise from corner points, stabilizing the optical flow results. By coupling optical flow calculations, the obtained results serve as feature variables inputted into a machine learning model, enabling the combination of the pyramid L-K optical flow method and the machine learning model for gas-liquid two-phase flow rate measurement. To resolve the challenge of model generalization difficulty arising from significant flow rate distribution differences in the dataset, the model undergoes optimization via a Split Comparison (SC) approach. Results indicate that the SC model demonstrates notable improvements, with \(R^{2}\) increasing from 0.90 to 0.97, RMSE decreasing from 1.32 m\({}^{3}\)/h to 0.74 m\({}^{3}\)/h (representing 4.63% of the full range), MAE decreasing from 0.92 m\({}^{3}\)/h to 0.52 m\({}^{3}\)/h, and MAPE decreasing from 17.2% to 8.0%. This approach enables the convenient and reliable measurement of gas-liquid two-phase flow rates. It can handle complex flow conditions, obviates the need for cumbersome sensor installation and maintenance, enhances measurement robustness and convenience, reduces measurement costs, and offers a novel perspective for gas-liquid two-phase flow rate measurements.  
Conceptualization, [PERSON]; methodology, [PERSON]; software, [PERSON]; validation, [PERSON]; formal analysis, [PERSON]; investigation, Y.X.; resources, Y.X.; data curation, J.W. and Y.X.; writing--original, [PERSON]; writing--review and editing, D.X.; visualization, Z.H. and Y.X.; supervision, Z.H.; project administration, Z.H.; funding acquisition, Z.H. All authors have read and agreed to the published version of the manuscript. This research was funded by the Science and Technology Department of Zhejiang Province, grant number 2022C01244.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflicts of interest.
# 2D Phase-Based RFID Localization for On-Site Landslide Monitoring
## 3 Theoretical Model
### Geometrical Localization Sensitivity
This matrix can be expressed in the following way, depending on \(K^{-1}\) as well as the hypothetical covariance of the phase measurement matrix \(C_{\phi}\):  
\[C_{r}=(K^{-1})^{\top}C_{\phi}K^{-1}=U\Sigma^{2}U^{\top} \tag{9}\]  
Extracting the eigenvalues and eigenvectors of \(C_{r}\) allows for a completely analytical determination of the localization spot properties (especially the direction of highest error) for a given antenna-tag geometry, as shown in Figure 2. With a phase error of 0.04 rad, and at the given tag position, we expect a localization random error of about 1 cm. Note that in the model, any relative increase in phase error will result in the same relative increase in localization error, as the measurement operator is linear. The calculation presented above can be extended to a three-antenna system for a 3D localization problem, following Equations (1) to (9) with \(K\) a \(3\times 3\) matrix. In the case where \(N_{a}>3\), the system from Equation (5) is overdetermined, and a least-squares solution has to be found [51; 52]. Using the pseudo-inverse of \(K\), Equation (6) then gives:  
\[\delta r=(K^{\top}K)^{-1}K^{\top}\delta\phi \tag{10}\]  
This new system can be solved and the eigenvectors computed by considering the transformation matrix \((K^{\top}K)^{-1}K^{\top}\). Figure 2: Localization error shape at the position of tag A (see Figure 1) compared with the RFID position estimation during a stable period in the Harmalière (November to December 2020). The green point distribution is computed through the \(K^{-1}\) transformation (see Equation (6)), using a Gaussian phase distribution with a standard deviation of 0.04 rad. The eigenvectors of the green distribution (red lines) are scaled up to encompass 97% of the data. The black points correspond to the RFID-phase localization results. The antenna positions are set as in the real experiment (see Figure 1).
# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 5 Experiments
### Quantitative Analysis  
The quantitative results on the LOL and LOL-v2 datasets are summarized in Table 1. The experimental results demonstrate that the SKF framework achieves significant performance improvements over the baseline LLFlow network across most metrics. Specifically, LLFlow-SKF attains PSNR values of 30.804 dB on the LOL dataset and 29.194 dB on the LOL-v2 dataset, establishing new state-of-the-art (SOTA) benchmarks. The SSIM values also show consistent enhancements, with average increases of 0.003 (LOL) and 0.002 (LOL-v2), indicating improved luminance recovery and structural preservation. Notably, the substantial reduction in LPIPS scores further validates that the semantic priors introduced by SKF align better with human perceptual intuition. However, a slight degradation in LRC PSNR is observed on the LOL-v2 dataset, suggesting potential trade-offs in low-resolution robustness. These results collectively underscore the effectiveness of SKF in enhancing both objective metrics and visual quality while highlighting areas for future refinement.  
The improvements translate directly into practical value across critical application domains such as nighttime surveillance, autonomous driving, and medical imaging. For  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**LOL Dataset**} & \multicolumn{4}{c}{**LOL-v2 Dataset**} \\ \cline{2-9}  & **PSNR** & **SSIM** & **LPIPS** & **LRC PSNR** & **PSNR** & **SSIM** & **LPIPS** & **LRC PSNR** \\ \hline LLFlow & 27.476 & 0.942 & 0.078 & 6.502 & 27.524 & 0.91 & 0.111 & 7.787 \\ \hline LLFlow-SKF & 30.804 & 0.945 & 0.064 & 6.503 & 29.194 & 0.912 & 0.093 & 7.755 \\ \hline KinD++ & 18.970 & 0.804 & 0.175 & 5.605 & 19.087 & 0.817 & 0.180 & 6.372 \\ \hline Zero-DCE & 14.861 & 0.562 & 0.335 & 5.103 & 18.059 & 0.580 & 0.313 & 5.843 \\ \hline EnlightenGAN & 17.483 & 0.652 & 0.322 & 5.406 & 18.640 & 0.677 & 0.309 & 6.193 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of different models on LOL and LOL_v2 datasets.  
Figure 7: The test results of LLFlowSKF and LLFlow networks in the LOL dataset, where \(I_{\text{out}}\)-LLFlow represents the test result of LOL in the LLFlow network; \(I_{\text{out}}\)-LLFlow_SKF represents the test result of LOL in the LLFlow-SKF network.  
instance, in nighttime surveillance systems, the higher PSNR and lower LPIPS values correspond to enhanced image detail clarity and more accurate object recognition capabilities, which are essential for security monitoring. In the autonomous driving domain, our model's superior preservation of structural information in low-light scenarios has decisive significance for obstacle detection and path planning during nighttime navigation, directly impacting driving safety.
###### Abstract  
Background and objectives: One of the greatest challenges facing the healthcare of the aging population is frailty. There is growing scientific evidence that gait assessment using wearable sensors could be used for prefraulty and frailty screening. The purpose of this study was to examine the ability of a wearable sensor-based assessment of gait to discriminate between frailty levels (robust, prefrail, and frail). Materials and methods: 133 participants (\(\geq\)60 years) were recruited and frailty was assessed using the Fried criteria. Gait was assessed using wireless inertial sensors attached by straps on the thighs, shins, and feet. Between-group differences in frailty were assessed using analysis of variance. Associations between frailty and gait parameters were assessed using multinomial logistic models with frailty as the dependent variable. We used receiver operating characteristic (ROC) curves to calculate the area under the curve (AUC) to estimate the predictive validity of each parameter. The cut-off values were calculated based on the Youden index. Results: frailty was identified in 37 (28%) participants, prefrailty in 66 (50%), and no Fried criteria were found in 30 (23%) participants. Gait speed, stance phase time, swing phase time, stride time, double support time, and cadence were able to discriminate frailty from robust, and prefrail from robust. Stride time (AUC = 0.915), stance phase (AUC = 0.923), and cadence (AUC = 0.930) were the most sensitive parameters to separate frail or prefrail from robust. Other gait parameters, such as double support, had poor sensitivity. We determined the value of stride time (1.19 s), stance phase time (0.68 s), and cadence (101 steps/min) to identify individuals with prefrailty or frailty with sufficient sensitivity and specificity. Conclusions: The results of our study show that gait analysis using wearable sensors could discriminate between frailty levels. We were able to identify several gait indicators apart from gait speed that distinguish frail or prefrail from robust with sufficient sensitivity and specificity. If improved and adapted for everyday use, gait assessment technologies could contribute to frailty screening and monitoring.  
Keywords:frailty; wearable sensors; gait parameters; accelerometer; aging +
Footnote †: journal: Applied Sciences
## 5 Discussion
### Limitations of the Proposed Method  
Due to the absence of a conventional dataset that incorporates the CSI data obtained from settings with substantial interference, we could not evaluate the suggested model's efficacy in such an environment. However, we aim to investigate this aspect in our future research.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Classifier** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{2}{*}{CSI-HAR} & CNN-GRU-AttNet using CSI data without the PCA denosing method & 98.19\% & 98.27\% & 98.17\% & 98.17\% \\ \cline{2-6}  & CNN-GRU-AttNet using denoised CSI data with the PCA denosing method & 99.62\% & 99.61\% & 99.61\% & 99.61\% \\ \hline \multirow{2}{*}{StanWiFi} & CNN-GRU-AttNet using CSI data without the PCA denosing method & 97.66\% & 97.13\% & 97.00\% & 97.04\% \\ \cline{2-6}  & CNN-GRU-AttNet using denoised CSI data with the PCA denosing method & 98.66\% & 98.43\% & 97.88\% & 98.14\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison between the proposed model using CSI data before and after denoising by the PCA denoising method.