esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results and Analysis
### Cracking Stress Analysis  
Figure 6a shows the crack formation threshold \(\sigma_{cr}\) and its axial displacement profile for fiber shotcrete beams with different thickness ratios during the loading of the four-point bending test. Figure 6b shows the error bar chart of the parallel experiment \(\sigma_{cr}\) and its axial displacement under different working conditions.  
Figure 6 reveals a general trend of an increasing crack formation threshold \(\sigma_{cr}\) under different thickness ratios, followed by a subsequent decrease. The crack formation threshold increases from 6.255 MPa for the thickness ratio \(\lambda_{h}=0\) to 6.837 MPa for the thickness ratio \(\lambda_{h}=2\) and then decreases to 5.565 MPa for the thickness ratio \(\lambda_{h}=5\). The axial displacement shows a gradually increasing trend, and the deflection increases from 0.15 mm for the thickness ratio \(\lambda_{h}=0\) to 0.31 mm for the thickness ratio \(\lambda_{h}=5\). The deformation coordination ability from thickness ratios \(\lambda_{h}=0-2\) is better, and with the increase in the thickness ratios, the crack formation threshold increases and the deflection is slow. After that, with an increase in the thickness ratio, the crack formation threshold increases, and deflection is slow. Thereafter, with the increase in the thickness ratio, the load decreases while the deflection increases, and the deformation coordination ability becomes worse gradually. The main reason is that the existence of interfaces affects the height distribution of the compression and tension layers of the fiber-sprayed concrete beam. The heights of the compressive and tensile layers of the structure are basically the same under to thickness ratios \(\lambda_{h}=0-2\), which can fully utilize the load-carrying capacity of the beam. With the increase in the thickness ratio, the thickness of the compression layer of the fiber-sprayed concrete beam increases gradually, and the thickness of the tensile layer becomes thinner, which cannot play a good role in the structural load-bearing capacity.  
Figure 6: Crack formation threshold and axial displacement. (**a**) Crack formation threshold and axial displacement. (**b**) The error bar chart.
# Autonomous Target Tracking of UAV Using High-Speed Visual Feedback
## 6 Experimental Results
### Position Hold  
Before the experiment for dynamic target tracking, we tested the performance of direct visual servoing in a position holding experiment. The experimental configuration is shown in Figure 11. The setpoint position \({}^{0}x\) was set to \([0.6,0.1,0.1]^{T}\) [m], and the setpoint yaw angle \({}^{o}\psi_{sp}\) was set to 0 [deg]. Note that they were set relative to the original point on the target.  
Figure 12 shows the result of an autonomous position hold flight with a target recognition rate of about **350 Hz**. The experimental results show that the positioning errors were \([30.2,63.2,19.0]\) [mm] with standard deviations \([0.64,1.4,0.43]\) [mm] in the x, y, and z directions, respectively. The angular tracking mean error was \(2.3291^{\circ}\) with a standard deviation \(0.1689^{\circ}\) in the yaw direction.  
Figure 10: Flowchart of direct vision-based position controller.
## 5 Results and Discussion
### Performance Evaluation against MCW and ME  
For these MCL-based detectors, the simulation was run using 2000 particles. No re-localization process was implemented and the localization was performed in feature-less corridor map as shown in Figure 5. The performance of the two detectors against our proposed method without and with relocalization is depicted in Figures 6 and 7, respectively.  
It is shown clearly on Figure 6 where the black pixels indicate \(\eta\leq 0\) that under no relocalization policy, MCW and ME perform very poorly. The huge improvement of these two methods when the test is changed to employ relocalization (as shown in Figure 7) indicates that the two methods depend very heavily on the success of the relocalization process. The relatively high dependency is caused by the fact that when relocalization fails (or for the extreme case, none is implemented) the weight of particles exhibit similar low values as in the kidnapping case, inducing detections when no kidnapping event occurs. On the other hand, QS-2 DDTW is independent towards the localization process, thus it can maintain a high performance in both tests (with and without relocalization).  
Figure 5: A corridor map used in the Gazebo (**a**) and the instance of a kidnapping event (**b**).  
Figure 6: Performance results of MCW (**top**), ME (**middle**), and proposed QS-2 DDTW (**bottom**) without relocalization. Black pixels indicate \(\eta\leq 0\).
# Spectrum-Based Logistic Regression Modeling for the Sea Bottom Soil Categorization
## 5 Discussion  
Application of the logistic regression approach to a wide range of applied tasks is part of the mainstream of geophysical research, especially in marine geophysics [45; 46; 47; 48; 49; 50; 51; 52]; for example, this approach is used for the study of changes in soil properties [45], ocean processes [46], etc. However, such an approach is rarely used for soil classification of the sea bottom. The results above show that the two sea bottom types can be successfully and quantitatively characterized by applying logistic regression models to either the backscatter time series of a frequency-modulated signal, or to the spectrum of that backscatter.  
The achieved classification accurate\" (ov'r the test sets) is 90% for the time series and 94% for the spectra. The improved results when using spectral data may be due to the option to clean noise with frequencies outside the bandwidth of the reflected signal. The models were trained using a relatively small dataset, which suggests that even higher accuracy may be achievable with larger datasets.  
It is evident from the results of this study that applying machine learning algorithms has the potential to enhance sonar-based soil classification accuracies compared to manual extraction and classification criteria [43].  
The model achieved comparable accuracy to [43], with a training set that is approximately 60% of the required training set. This might be important when collecting data in actual survey activities wherein the survey vessel is less stationary. Hence, fewer data are collected over each area and the corresponding soil type, making the method presented here more advantageous for soil classification based on data collected during standard hydrographic surveys.  
As for feature selection for machine learning algorithms, it is assumed that a frequency-modulated signal's reflected spectrum (and corresponding time series) is affected by reflector characteristics such as grain size, relief, voids, stratification, etc. Hence, using these data as the feature is superior to using integral components such as peak pressure, total intensity, angular intensity, etc., as were used by previous works performed on sonar data [35; 40; 41; 42]. Applying a relatively simple logistic regression model on spectral data achieved superior accuracy to neural network methods used to integrate data (intensity, angular intensity, elevation, etc.) in the case of sonar data [35; 40; 41; 42]. Based on these findings, it is assumed that combining spectral analysis with machine learning and using the spectral series as features can enhance the performance of machine learning algorithms for sonar base soil classification.  
Figure 3: Sensitivity and accuracy vs. size of the training set: (**a**) sensitivity for rock detection, (**b**) sensitivity for sand detection, and (**c**) accuracy.
# Analysis of Maize Sowing Periods and Cycle Phases Using Sentinel 1&2 Data Synergy
## 2 Materials and Methods
### Satellite Data
#### 2.2.4 Soil Moisture Satellite Product  
The surface soil moisture (SM) was estimated by [PERSON] et al. [41] from a synergy between the Sentinel-1 SAR and Sentinel-2 optical data and using neural network techniques. The S\({}^{2}\)MP product (Sentinel-1/Sentinel-2 Moisture Product) provides gridded data of soil water content [Volwater/Volsoil] over agricultural areas with 6-days frequency and a spatial resolution of 10 m \(\times\) 10 m (Figure 2d). The S\({}^{2}\)MP data used in this work were downloaded from the open access service of Thisme Data Service [42]. The S\({}^{2}\)MP operational data refer to the upper soil layer (3-5 cm) and are derived both from the ascending and descending orbits of Sentinel-1, passing at the zenith at around 5:30 p.m. (ascending) and 5:30 a.m. (descending) respectively. The SM information derived from the descending orbit was not used, to avoid the effect of morning dew, in agreement with [PERSON] et al. [43]. The backscattering coefficient used for the S\({}^{2}\)MP estimation was obtained combining the contributions from vegetation and soil. The accuracy of the S\({}^{2}\)MP estimation was estimated to be about 6% for dry soils and up to 6.9% for very wet soils [41] and the RMSE was 1.5% higher for NDVI increasing from 0 to 0.75. The S\({}^{2}\)MP product can be applied for NDVI lower than 0.75 (for denser vegetation covers, the backscattering signal appears to be too attenuated to provide good SM estimations).
# Novel Janus Fibrous Membranes with Enhanced Directional Water Vapor Transmission
## 3 Results and Discussion
### Chemical Characterization and Wettability of As-Prepared Membranes
For the hydrophobic PU-8G fibrous membrane, there is no obvious change because of non-adsorption feature of hydrophobic membrane. Once water droplet contacted on the hydrophobic surface, it stays on the surface instead of spreading or getting absorbed. The slight change in force with the increased testing time was caused by the capillary absorption of the hydrophobic surface which keeps the water droplet in Wenzel state or  
Figure 4: (**a**) Fourier transform infrared spectrometry (FTIR) spectrum of the PU, CA, Janus fibrous membranes, the solvent DMF, THF, acetone. (**b**) Water contact angles of PU-6G, PU-8G, PU-10G, and PU-12G. (**c**) Water contact angles of CA-6G, CA-8G, CA-10G, and CA-12G. (**d**) Schematic diagrams of the two testing types of the force between tiny water droplet and the surface of the membrane. (**e**) Testing type 1: Water droplet (5 \(\upmu\)L) was brought to contact the membrane and then pulled off. It enabled to probe the interaction between water droplet and membrane. (**f**) Testing type 2: Water droplet (5 \(\upmu\)L) was brought to contact the membrane without pulling off. The real-time force change was recorded. the water evaporation. Compared to the hydrophobic PU-8G, the hydrophilic CA-8G exhibits obvious change in the time-force curve, which results from the water droplet adsorption and spreading that caused a pull straight downward. The water vapor transmission capability of the Janus membrane was not only affected by the internal porous structure of the membrane, but also by the wettability on both sides of the membrane. The wettability in this work was mainly governed by the surface roughness. Surface roughness enhances the surface wettability, that is surface roughness makes hydrophobic surface more hydrophobic and hydrophilic surface more hydrophilic. Therefore, in order to explain the influence of surface roughness on wettability, water contact angles of PU and CA membranes prepared by spin-coating (relative smooth surface) and electrospinningning (rough surface) were measured respectively (Figure 5). It can be seen that the wettability improves to some extent, such as PU membrane becomes more hydrophobic and CA membrane becomes more hydrophilic.
# Sensitivity Estimation for Differentially Private Query Processing
## 6 Discussion  
These experimental findings demonstrate that our methods successfully address the fundamental efficiency-accuracy trade-off in differential privacy for join queries. The superior efficiency of Sampling-SE stems from its adaptive sampling strategy that focuses computational resources on high-impact residual queries, while the accuracy advantage of Sketch-SE originates from its ability to capture join attribute correlations through AGMS sketches. Notably, the maintained accuracy of Sampling-SE (compared to RS) and the improved accuracy of Sketch-SE (over ES) suggest that our approaches achieve better privacy-utility trade-offs without compromising their respective efficiency baselines. These results have important practical implications. For instance, Sampling-SE is suitable for skewed data due to minimizing sample waste on minor groups, and Sketch-SE enables real-time streaming protection through constant-time operation, which offers system designers flexible accuracy-latency trade-offs. The sampling-based and sketch-based sensitivity estimation methods proposed in this study significantly enhance the efficiency and accuracy of differential privacy protection for complex join queries. This breakthrough enables critical real-world applications, such as real-time anomaly detection in social networks, to achieve near non-private query utility while maintaining rigorous individual privacy protection with sub-second response times. By addressing the fundamental limitations of existing approaches, our work overcomes the key barriers to deploying large-scale privacy-preserving systems in practice. However, there are still some limitations that need to be considered. First, the sampling-based approach requires the careful tuning of sampling rates for optimal performance across different data distributions, which may increase implementation complexity. Second, the performance of our sketch-based approach may be impacted by data dimensionality and the number of join attributes, where high-dimensional join operations can amplify approximation errors due to the curse of dimensionality in sketch compression, and more join attributes require larger sketch sizes to maintain target accuracy levels. This paper establishes an effective framework for privacy-preserving query processing with immediate practical applications, while the limitations discussed provide promising directions for our future research.
## 2 Theoretical Solutions
### Derivation of Analytical Solutions
#### 2.2.1 Case 1: Current Flows in and out of the Same Boundary  
The configuration when a current is injected on and exits the same surface is illustrated in Figure 2a. Constant \(D\) becomes 0 by the boundary condition of Equation (12) at y = 0.  
\[0=\frac{\partial u}{\partial y}\bigg{|}_{y=0}\ \Rightarrow 0=Y^{\prime}(0)=-C \lambda\sin(\lambda 0)+D\lambda\cos(\lambda 0)\Rightarrow\ \text{D}=0 \tag{12}\]  
The boundary condition at \(y=b\) derives \(\lambda\) using the following equation:  
\[0=\frac{\partial u}{\partial y}\bigg{|}_{y=b}\ \Rightarrow 0=Y^{\prime}(b)=-C \lambda\sin(\lambda b)\Rightarrow\lambda=\lambda_{n}=\frac{n\pi}{b} \tag{13}\]  
Figure 2: Illustration of the boundary conditions (**a**) where current flows in and out of the same surface (Case 1); (**b**) where current flows in from one boundary and out from the opposite boundary (Case 2).  
Constant \(B_{n}\) becomes 0 by the boundary condition of Equation (14) at \(y=0\).  
\[0=\left.\frac{\partial u}{\partial x}\right|_{x=0}\ \Rightarrow\ 0= X^{\prime}(0)=A_{n}\lambda_{n}\text{sinh}(\lambda_{n}0)+B_{n} \lambda_{n}\cosh(\lambda_{n}0)\Rightarrow\ \text{B}_{n}=0 \tag{14}\]  
Therefore, only \(A_{n}\) remains, and \(u(x,y)\) can be expressed as follows:  
\[u(x,\ y)=X(x)\cdot Y(y)=\sum\limits_{n=1}^{\infty}\Bigl{[}A_{n}\cdot\cosh \Bigl{(}\frac{n\pi Tx}{b}\Bigr{)}\cdot\cos\Bigl{(}\frac{n\pi y}{b}\Bigr{)} \Bigr{]} \tag{15}\]  
Now, the final boundary condition at \(x=a\) is applied, and \(A_{n}\) can be obtained using a Fourier transform as follows:  
\[\begin{array}{c}f(y)=\frac{\partial u}{\partial x}\Bigr{|}_{x=a}=\sum \limits_{n=1}^{\infty}\bigl{[}A_{n}\bigl{(}\frac{n\pi}{b}\bigr{)}\cdot\sinh \bigl{(}\frac{n\pi a}{b}\bigr{)}\cdot\cos\bigl{(}\frac{n\pi y}{b}\bigr{)} \bigr{]}\\ A_{n}\bigl{(}\frac{n\pi}{b}\bigr{)}\cdot\sinh\bigl{(}\frac{n\pi a}{b}\bigr{)} =\frac{2}{b}\int_{0}^{b}f(y)\cdot\cos\bigl{(}\frac{n\pi y}{b}\bigr{)}dy\end{array} \tag{16}\]  
The final solution is as follows:  
\[\begin{array}{c}u(x,\ y)=\sum\limits_{n=1}^{\infty}\bigl{[}A_{n}\cdot\cosh \bigl{(}\frac{n\pi x}{b}\bigr{)}\cdot\cos\bigl{(}\frac{n\pi y}{b}\bigr{)} \bigr{]}\\ \text{where, }A_{n}=\frac{2}{n\pi\sinh\bigl{(}\frac{n\pi x}{b}\bigr{)}}\int_{0}^{b}f (y)\cdot\cos\bigl{(}\frac{n\pi y}{b}\bigr{)}dy\end{array} \tag{17}\]
## 3 Results
### NEP Simulations under All Sky Conditions during 2013-2016
#### 3.3.2. NEP Simulations Using EMGPP and Re (DDR) Models  
Secondly, the hourly NEPs were computed using EMGPP and Re (DDR) (with a modified factor 1.1). Generally, the ratios of the mean calculated to the measured Re in 2013, 2014, 2015, 2016, and 2013-2016 were 1.45, 1.89, 2.78, 3.12, and 2.25 for using the 3-factor EMNEP, and 1.54, 1.78, 2.60, 2.94, and 2.15 for using the 2-factor EMNEP. Similar ratios of the mean daily, monthly, and annual sums were also obtained. Thus, the NEP values were overestimated by a factor of 2.3 (ranged from 1.45 to 3.12).
## 5 Mathematical Formulation, Assumptions and Notation
### Assumptions  
In addition to the assumptions described within the problem description, the inventory replenishment problem is modelled in accordance with the following assumptions:  
* Lead time (\(LT\)) includes the time necessary for the delivery of goods from the manufacturer to the distributor. Delivery time includes time periods from ordering to receiving the goods.
* Shortages are allowed but not backlogged, i.e., stock-out situations should be minimized.
* The initial inventory level is known.
* Order quantity depends on the observed instance: \(\bigcirc\) Fixed, i.e., the manufacturer defines the fixed order quantity per each product, but periods between orders are not fixed. \(\bigcirc\) Lot-for-lot, i.e., cumulative monthly sales forecast for three months, in a moving horizon situation, after the lead time period is expired, but periods between orders are not fixed.
* The sales forecast is known and forecasted for two years.
# Feature Selection from Lyme Disease Patient Survey Using Machine Learning
## 4 Discussion
### Comparison with Companion Study  
In a companion study performed in parallel to this work [22], the authors use more traditional statistical methods and investigative analysis on the MyLymeData dataset. The results of our classification and regression models agree with many of its findings. Specifically, this study found that the features most indicative of level of treatment response were a participant's use of antibiotics and symptom severity (specifically, fatigue), aligning closely with our analysis of the results from Section 3. This study also found that longer treatment durations are associated with high treatment response, aligning with our ranking of feature Abx_Dur corresponding to duration of antibiotic treatment protocol as one of the top 5 features. Additionally, the study suggests that high treatment response is associated with close medical care provided by clinicians whose practices focus on tick-borne disease, aligning with our ranking of feature Provider_5 concerning the type of healthcare provider treating the patient's Lyme disease within the top 10 features.
## 2 Materials and Methods
### Snap Detection and Analysis  
The snapping shrimp sound waveforms are impulse signals and exhibit broadband frequency characteristics [21; 22; 23; 24; 25; 26]. There are many impulsive models such as Class B and alpha noise to extract impulse signals underwater [27; 28]. However, this study extracted signals in the same way as in Figure 2 to compare with other studies that calculated snap rates similarly. To minimize ship noise from the total 10 min data, the smallest mean power spectrum (mean PS) value between 100 and 1500 Hz was extracted and further processed, bypassing the main frequency band (1.5-20 kHz) of the snapping shrimp sound signal [16; 17; 20; 29].  
\[\mathrm{S}\left(\mathrm{t}\right)=\frac{\sum\epsilon(\mathrm{t})\kappa( \mathrm{t})}{\sqrt{\sum\epsilon(\mathrm{t})^{2}\kappa(\mathrm{t})^{2}}} \tag{1}\]  
Figure 1: (**a**) The location of the hydrophone installation. The Hankyung-myeon area in the western region of Jeju Island (Red mark: 33\({}^{\circ}\)19\({}^{\prime}\)48.9\({}^{\prime\prime}\) N 126\({}^{\circ}\)08\({}^{\prime}\)59.7\({}^{\prime\prime}\) E) and Moseulpo tidal station (Yellow mark: 33\({}^{\circ}\)12\({}^{\prime}\)50.8\({}^{\prime\prime}\) N 126\({}^{\circ}\)15\({}^{\prime}\)04.5\({}^{\prime\prime}\) E); (**b**) Schematic diagram of the installation of a hydrophone (SM3M, Wildlife Acoustics Inc.) at a depth of 10 m from the seabed.  
In the processed signal, as presented in Figure 2b, after normalizing the signals over 1 Pa within a unit time (2 ms: 192 samples) and taking the envelope, the median signal for all measurement periods was defined as the kernel function (\(\kappa\)). The normalized correlation coefficient between the kernel function (\(\kappa\)) and the signal (\(\varepsilon\)) was obtained by normalizing the signals of the unit time length (2 ms: 192 samples) and taking the envelope (upper envelope using a moving Hilbert filter with a length of about 10% (19 samples)) is called score, S (t), as shown in Equation (1). Snapping shrimp signals were identified and counted when a score value over 0.78 and a signal over 120 dB were satisfied. The score threshold of 0.78 is the top 1% of the total score during this period [16; 17; 29].  
\[\textbf{Percent excess snap rate}(\%)=\frac{\text{N}_{\text{s}}-\text{N}_{\text{d}}}{ \text{N}_{\text{d}}}\times 100 \tag{2}\]  
The percent excess snap rate in Equation (2) represents the relative snap rate compared to the daytime. N\({}_{\text{s}}\) is the number of snap rates of the signal, and N\({}_{\text{d}}\) is the number (N\({}_{\text{d}}\): 2167 in 1 st, 1819 in 2 nd [min\({}^{-1}\)]) of the minimum snap rate at daytime between sunrise and sunset.
# Distributed-Satellite-Clusters-Based Spectrum Sensing with Two-Stage Phase Alignment
## 5 Simulation Results
It can be found that the SNR of the DSC system decreases gradually with the decrease in time synchronization accuracy. When the accuracy is at the level of \(10^{-14}\), the impact of synchronization error on satellite cooperation is very small, but at the level of \(10^{-13}\), the performance is only \(66.3\%\) when \(L=2,70.6\%\) when \(L=4\), and \(79.5\%\) when \(L=8\), respectively. The reason is that the time synchronization error will change the initial phase of each satellite, and the weighted value obtained according to the perfect synchronization can not completely eliminate the phase difference, thus reducing the cooperation performance. In addition, the performance of DSC improves with the increase in the number of satellites. This is because the probability of weakening or even offsetting the time synchronization error increases in the process of signal fusion, which improves the robustness of the system to time synchronization. In short, time synchronization is very important to the system performance. Fortunately, the time synchronization accuracy of the current cesium atomic clock can reach the level of \(10^{-15}\), which can meet the needs of SS_DCS. Figure 9: ROC curves of the five different algorithms where \(\gamma=-10\) dB and \(Num=20\).