esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Image Recognition Methods to Determine Land Use Classes
## 3 Materials and Methods
### Satellite Data Pre-Processing  
Pre-processing is a crucial stage in preparing satellite data for classification. Several operations were applied to enhance the quality of the images:  
* Image Merging: Sentinel-2 bands were combined into a single multi-band image for comprehensive spectral analysis [6; 47; 48; 49].
* Background Cleaning: NoData pixels and irrelevant background noise were removed using a thresholding approach [50; 51].
* Compression: The lossless DEFLATE type compression method was applied to pre-processed photos to save memory resources [52; 53; 54].
* Cloud Removal and Interpolation: A cloud detection algorithm utilizing the Scene Classification Layer (SCL) was employed to mask clouded areas. Missing data were reconstructed through interpolation techniques, ensuring continuity in classification [55; 56; 57; 58].
* Spectral Index Calculation: Three spectral indices--Normalized Difference Tillage Index (NDTI), Normalized Difference Vegetation Index Red-Edge (NDVIre), and Modified Normalized Difference Water Index (MNDWI)--were computed to enhance feature differentiation [59; 60; 61; 62; 63; 64; 65].
* Spatial Index calculation formulas (see Equations (1)-(3)) are as follows: \[NDTI=\frac{Band11-Band12}{Band11+Band12},\] (1) where \(NDTI\) is calculated by dividing the difference between \(Band11\) and \(Band12\) by the sum of \(Band11\) and \(Band12\). \[NDVIre=\frac{Band5-Band4}{Band5+Band4},\] (2) where \(NDVIre\) is the division of the difference between \(Band5\) and \(Band4\) by the sum of \(Band5\) and \(Band4\). \[MNDWI=\frac{Band3-Band11}{Band3+Band11},\] (3) where \(MNDWI\) is the division of the difference between \(Band3\) and \(Band11\) by the sum of \(Band3\) and \(Band11\).  
Figure 3: Detailed representation of the satellite data download process. Dotted line—message flow; X—exclusive gateway; +—parallel gateway.  
These pre-processing steps ensured that the final dataset was clean and optimized for classification, leading to improved model performance. Detailed pre-processing operational steps are presented in Figure 4.
# Nonlinear Adaptive Robust Control of the Electro-Hydraulic Servo System
## 1 Introduction
Existing adaptive robust control schemes cannot solve the problem with the unknown nonlinear parameters. A special Lyapunov function was developed in [29] to convert unknown nonlinear parameters to unknown linear types. However, this control scheme doesn't consider the effect of parameter estimation errors, which may result in system unstability. In this paper, a nonlinear adaptive robust control for the valve-controlled hydraulic system is developed based on adaptive robust control and discrete disturbance estimation. First of all, the dynamics of hydraulic system are investigated. The uncertainties considered in this paper include unknown linear or the nonlinear parameters. Meanwhile, a nonlinear adaptive robust control law is deduced based on a special Lyapunov function, which not only guarantees the boundedness of state variables and control signal but also compensates for the effect of parameter uncertainties, external disturbance, and errors in parametric estimates. Finally, the proposed control scheme is evaluated by some computer simulations in various working conditions to verify its dynamic performance and robustness. The paper is organized as follows:. In Section 2, problem formulation and the detailed nonlinear model of the hydraulic system are presented. In Section 3, the proposed nonlinear adaptive robust control with a discrete disturbance estimator is described. In Section 4, computer simulations are executed for evaluating the performance and robustness of the proposed control scheme, and the results are discussed. The conclusions are presented in Section 5.
## 3 Background Knowledge  
### Domain Name System (DNS)  
The Domain Name System (DNS) [14] is the Internet's phone book. When a user queries the IP address of a domain name such as 'google.com' or 'facebook.com', DNS resolves the domain name and responds with the IP address of the website. The DNS resolution process is illustrated in Figure 1. The user sends a query to the local DNS resolver. If the local DNS resolver does not find the corresponding IP address in its cache memory, it forwards the DNS query iteratively or recursively through a root server, a Top-Level Domain (TLD) Server, and Authoritative Name Servers until the desired domain name resources are received. The DNS response is forwarded to the application through the Local DNS server. The application can access the web server after it obtains the DNS response.  
From the local network to the public network, the data are transmitted in plain text over TCP or UDP in port 53 [14], which is extremely vulnerable to cyber attack. Attackers exploit this process to perform attacks such as DNS amplification [1], DNS hijacking [2], DNS cache poisoning [3], and DNS tunneling [4].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **Method** & **Drawback** \\ \hline [PERSON] [15] & Supervised Machine Learning technique & Require a huge number of labeled data \\ \hline [PERSON] [16] & Supervised Machine Learning technique & Require a huge number of labeled data \\ \hline [PERSON] [17] & Supervised Machine Learning technique & Only classifies the DoH traffic and traditional DNS traffic \\ \hline [PERSON] [33] & Application Fingerprinting & Not flexible and require to build an application fingerprinting database \\ \hline \hline \end{tabular}
\end{table}
Table 1: The drawbacks of previous DoH detection techniques.
## Materials and Methods
### Leaf-Level Chlorophyll Fluorescence  
To understand seasonal changes in SiF values derived from the optical system, leaf-level fluorescence measurements were carried out on needles from three Scots pine trees located near the IFOV of the optical system. These measurements were made using a system of field fluorometers (MONITORING-PAM, Heinz Walz GmbH, Effeltrich, Germany) [61; 62]. Fluorometers were installed in the top canopy and South-facing shoots across three different trees. Every 15-30 min, a saturating pulse was applied to each sampled branch and actual and maximal fluorescence yield from light-exposed leaves (\(F_{t}\) and \(F^{\prime}{}_{m}\), respectively), PPFD at sample level (\(PPFD_{SL}\), in \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)) and leaf temperature (\({}^{\circ}\)C) were recorded (see Reference [61] for more details). Quantum yield (i.e., operating efficiency) of photosystem II (PSII) photochemistry (\(\Delta F/F_{m}{}^{\prime}\)), an indicator of the acclimation of photosynthetic reactions to environmental conditions, was calculated for each measurement as:  
\[\frac{\Delta F}{F_{m}^{\prime}}=\Phi\text{PSII}=\frac{(F_{m}^{\prime}-F_{t}) }{F_{m}{}^{\prime}} \tag{2}\]  
Values of \(\Delta F/F_{m}{}^{\prime}\) from all three fluorometers were averaged together for each measurement time. To match averaged \(\Delta F/F_{m}{}^{\prime}\) values with the times of the SiF observations, we applied a simple weighted average interpolation method:  
\[\Phi\text{PSII}_{t}=\Phi\text{PSII}_{t-1}\;w_{t-1}+\;\Phi\text{PSII}_{t+1}\;w _{t+1} \tag{3}\]  
where \(\Phi\text{PSII}_{t}\) is the quantum yield of photochemistry at time \(t\) of a given SiF observation, \(\Phi\text{PSII}_{t-1}\) and \(\Phi\text{PSII}_{t+1}\) are \(\Phi\text{PSII}\) values at the closest point in time before and after time \(t\), respectively, and \(w_{t-1}\) and \(w_{t+1}\) are the normalized distances from time \(t\) and calculated as:  
\[w_{t-1}=1-\frac{(t-t_{t-1})}{(t_{t+1}-t_{t-1})}\;;\;w_{t+1}=1-\frac{(t_{t+1}-t )}{(t_{t+1}-t_{t-1})} \tag{4}\]  
where \(t-1\) and \(t+1\) are the closest observations in time before and after time \(t\), respectively. Finally, time series of \(\Phi\text{PSII}_{t}\) were visually inspected and outliers removed. \(\Phi\text{PSII}_{t}\) will be referred to as \(\Delta F/F_{m}{}^{\prime}\) in the remainder of the paper.
###### Abstract  
The multi-scale, high-resolution and accurate structural modeling of rocks is a powerful means to reveal the complex failure mechanisms of rocks and evaluate rock engineering safety. Due to the non-uniformity and opacity of rocks, describing their internal microstructure, mesostructure and macro joints accurately, and how to model their progressive fracture process, is a significant challenge. This paper aims to build a numerical method that can take into account real spatial structures of rocks and be applied to the study of crack propagation and failure in different scales of rocks. By combining the failure process analysis (RFPA) simulator with digital image processing technology, large-scale finite element models of multi-scale rocks, considering microstructure, mesostructure, and macro joints, were created to study mechanical and fracture behaviors on a cloud computing platform. The Windows-Linux interactive method was used for digital image processing and parallel computing. The simulation results show that the combination of a parallel RFPA solver and digital image modeling technology can achieve high-resolution structural modeling and high-efficiency calculation. In microscopic simulations, the influence of shale fractures and mineral spatial distribution on the fracture formation process can be revealed. In the mesostructure simulation, it can be seen that the spatial distribution of minerals has an impact on the splitting mode of the Brazilian splitting model. In the simulation of a joined rock mass, the progressive failure process can be effectively simulated. According to the results, it seems that the finite element parallel computing simulation method based on digital images can simulate the multi-scale failure process of brittle materials from micro to macro scales. Primarily, efficient parallel computing based on a cloud platform allows for the multi-scale, high-resolution and realistic modeling and analysis of rock materials.  
multi-scale; digital images; parallel computation; 3D numerical modeling; mechanical and fracture properties +
Footnote †: journal: Applied Sciences
## 1 Introduction
Hence, radiation damage studies in single-point data collection considering multiple scenarios will provide useful information on efficient data collection strategies. To understand the radiation damage caused by MX experiments, radiation damage to crystals during single-point data collection where the crystals were larger than the beam size of microfocusing X-rays was investigated. The statistics of the collected data and quality of each image were comparatively analyzed. Based on the crystal structure, changes in the temperature factors and specific radiation damage to the disulfide bonds of thaumatin were investigated. In addition, strategies for using data with minimal radiation damage were discussed. The results of this study broaden our knowledge of radiation damage and provide opportunities for improving data processing.
## 6 Results  
### Performance Evaluation of the LSTM Model  
Figure 3 provides a comprehensive visualization of the model's performance. The training loss starts at approximately 109.33 and rapidly decreases, stabilizing around 30.22. The validation loss follows a similar pattern, starting at 47.22 and converging near 32.98, indicating good generalization with minimal overfitting. Similarly, the MAE shows a consistent downward trend, where the training MAE starts at 7.67 and reduces to approximately 3.58, while the validation MAE stabilizes at 3.52. To assess the predictive accuracy, a scatter plot comparing actual vs. predicted trip durations is provided. Most points align closely with the reference diagonal (\(y=x\)), confirming the model's strong predictive performance, though some variance is observed for longer trips.  
In practical terms, an MAE below 4 min indicates that the model predictions are sufficiently accurate for supporting routing decisions in urban delivery contexts, where small time deviations are often tolerable.  
Figure 3: (**a**) Training and validation loss (MSE) over 50 epochs, demonstrating rapid convergence followed by stabilization. (**b**) Training and validation MAE over 50 epochs, showing consistent error reduction. (**c**) Comparison of actual vs. predicted trip durations, where most points align closely with the reference diagonal, confirming high prediction accuracy.  
Evaluation Summary  
The final trained model achieved the following performance metrics:  
* Training Loss (MSE): 30.22;
* Validation Loss (MSE): 32.98;
* Training MAE: 3.58;
* Validation MAE: 3.52.  
The final training performance metrics--specifically MAE and RMSE--are essential not only for validating the accuracy of the LSTM model but also for ensuring that the subsequent route optimization process is grounded in realistic travel time estimates. A low MAE (3.52 min) indicates that, on average, the predicted travel time for a given route leg is very close to the actual expected time. In the context of sales route optimization, this level of error is acceptable for supporting operational decisions such as delivery scheduling or dispatch planning.  
MSE, on the other hand, highlights the presence and impact of outliers or occasional high-error predictions. Its relatively low value in our model confirms that significant mispredictions are rare, which is critical for maintaining confidence in the routing system's reliability. Together, these metrics validate the LSTM model as an effective component within the hybrid algorithm, allowing it to dynamically adapt to traffic conditions while maintaining accurate travel time forecasting essential for route quality evaluation.
###### Abstract  
The measurement of selected norepinephrine metabolites, such as 3,4-dihydroxypropylglycol (DHPG), 3-methoxy-4-hydroxyphenylethylglycol (MHPG), and vanaillymandelic acid (VMA), in biological matrices--including urine--is of great clinical importance for the diagnosis and monitoring of diseases. This fact has forced researchers to evaluate new analytical methodologies for their isolation and preconcentration from biological samples. In this study, the three most popular extraction techniques--liquid-liquid extraction (LLE), solid-phase extraction (SPE), and a new 3D-printed system for dispersive solid-phase extraction (3D-DSPE)--were investigated. Micellar electrokinetic chromatography (MEKC) with a diode array detector (DAD) at 200 nm wavelength was applied to the separation of analytes, allowing for the assessment of the extraction efficiency (R) and enrichment factor (EF) for the tested extraction types. The separation buffer (BGE) consisted of 5 mM sodium tetraborate decahydrate, 50 mM SDS, 15% (\(\
u\)/\(\
u\)) MeOH, 150 mM boric acid, and 1 mM of 1-hexyl-3-methylimidazolium chloride (the apparent pH of the BGE equaled 7.3). The EF for each extraction procedure was calculated with respect to standard mixtures of the analytes at the same concentration levels. The 3D-DSPE procedure, using DVB sorbent and acetone as the desorption solvent, proved to be the most effective approach for the simultaneous extraction and determination of the chosen compounds, achieving over 3-fold signal amplification for DHPG and MHPG and over 2-fold for VMA. Moreover, all extraction protocols used for the selected norepinephrine metabolites were estimated and discussed. It was also confirmed that the 3D-DSPE-MEKC approach could be considered an effective tool for sample pretreatment and separation of chosen endogenous analytes in urine samples.  
catecholamines; DHPG; 3D-printed dispersive solid-phase extraction; MHPG; micellar electrokinetic chromatography; liquid-liquid extraction; solid-phase extraction; VMA +
Footnote †: journal: Molecular Sciences
# The Analysis of WJ Distribution as an Extended Gaussian Function: Case Study
## 3 Results and Discussion
### Method II  
#### 3.2.1 Development of the Method  
This method proceeds in the way of expanding the double exponential part \(e^{-\beta(x-v)}\) of Equation (2) around the point \(x=v\) by the Taylor formula, i.e.,  
\[e^{-\beta(x-v)}=\sum_{n=0}^{\infty}\frac{(-1)^{n}\beta^{n}(x-v)^{n}}{n!},\]  
converting the equation to  
\[f(x,\alpha,\beta,v)=\frac{\beta}{\Gamma(\alpha/\beta)}e^{-\alpha(x-v)-\sum_{n= 0}^{\infty}\frac{(-1)^{n}\beta^{n}(x-v)^{n}}{n!}}. \tag{16}\]  
Provided that \(\beta(x-v)\) is sufficiently small, the terms after \((x-v)^{2}\) in the sum may be cut off, and only the first three terms are retained. Thus, the Taylor series is reduced to  
\[\sum_{n=0}^{\infty}\frac{(-1)^{n}\beta^{n}(x-v)^{n}}{n!}=1-\beta(x-v)+\frac{ \beta^{2}(x-v)^{2}}{2}+O[\beta^{3}(x-v)^{3}].\]  
Subsequently, we reformulate the exponential part of Equation (16) after the truncation in a Gaussian form with a square term about \(x\) and come to the deduction  
\[f_{\rm II}(x,\alpha,\beta,\
u)=\frac{\beta e^{\frac{\alpha^{2}-2\alpha\beta- \beta^{2}}{2\beta^{2}}}}{\Gamma(\alpha/\beta)}e^{-\frac{\beta^{2}(x-v)+\frac{ \alpha}{\beta^{2}}-\frac{1}{\beta})^{2}}{2}} \tag{17}\]  
Analogous to Method I, we compare Equation (17) with the Gaussian distribution function (1a), which results in two choices of estimating the parameter \(\alpha\), one labeled by \(\partial_{\rm III}\) from the exponential part as  
\[\partial_{\rm III}=\frac{1}{\beta} \tag{18}\]  
and the other \(\partial_{\rm II2}\) from the pre-exponential factor as  
\[\partial_{\rm II2}=\frac{\Gamma(\alpha/\beta)}{\sqrt{2\pi}\beta e^{\frac{ \alpha^{2}-2\alpha\beta-\beta^{2}}{2\beta^{2}}}} \tag{19}\]Evidently, the expression for an estimation of the mean \(\hat{\mu}_{\Pi}\) reads  
\[\hat{\mu}_{\Pi}=\
u-\frac{\alpha}{\beta^{2}}+\frac{1}{\beta} \tag{20}\]
## 2 Structure Design and Model Analysis of Straw Guide
### Analysis of the Mathematical Model of Material Movement
#### 2.3.2 Motion Modeling  
In the threshing process, the threshing drum rotates around the axis at the angular speed \(\omega\), and the distance between point O and the axis of rotation is \(R\). On the one hand, the straw particle at point O rotates along the axis driven by the threshing drum with traction velocity \(V_{e}\), and on the other hand, the relative sliding speed between the straw particle and the straw guide board is \(V_{r}\), and the absolute velocity \(V_{a}\) of the straw particle can be obtained by velocity triangle synthesis, as shown in Figure 4a. According to some scholars [20; 21], the relationship between the average velocity of the straw movement and the circumferential velocity of the threshing drum is a coefficient \(\lambda\) (the measured \(\lambda\) is 1/3-1/4). Assuming that the rotating speed of the drum is \(V_{o}\) at the circumference and the average speed of straw at the straw guide board relative to the drum (traction speed) is \(V_{e}\), we obtain  
\[\begin{cases}V_{o}=\omega R\\ V_{e}=\lambda V_{o}\end{cases} \tag{1}\]  
Figure 3: Structure and distribution of the straw guide device.  
The velocity diagram is further refined, as shown in Figure 4b. Assuming that the friction coefficient between the straw and straw guide board is \(\mu\) and that the direction of the absolute velocity of the straw particle and the normal direction of the straw guide are at an angle \(\Phi\), the friction angle, we obtain \(\mu=\tan(\Phi)\). \(\alpha\) is the spiral rising angle of the straw guide board. Using the velocity decomposition and trigonometric variation and considering the correlation between the correction coefficient \(\underline{\gamma}\) (\(\underline{\gamma}\) is related to the crop characteristics, structural clearance, etc., and according to the experiment, \(0<\underline{\gamma}<1\)[21]), the following relationship can be obtained:  
\[\left\{\begin{array}{l}V_{a}=\lambda\underline{\gamma}\omega R\frac{sin \alpha}{cos\Phi}\\ V_{r}=\lambda\underline{\gamma}\omega R(cos\alpha-\mu sin\alpha)\\ V_{z}=\lambda\underline{\gamma}\omega Rsin^{2}\alpha(cot\alpha-\mu)\\ \end{array}\right. \tag{2}\]  
In the above model, the straw particle velocity \(V_{z}\) is along the axial direction of the roller. To ensure normal straw transportation, \(V_{z}>0\), and \(cot(\alpha)-\mu>0\). Furthermore,  
\[A+\Phi<90^{\circ} \tag{3}\]
## 4 Performance Evaluation
### Simulation Results and Discussion
#### 4.2.3 Performance Analysis of Outage Probability in SISO-NOMA  
The performance analysis of outage probability in the SISO-NOMA scheme is carried out using a two-user scenario with fixed PA coefficient pairs as (\(\alpha_{1}=0.70\) & \(\alpha_{2}=0.30\)) and (\(\alpha_{1}=0.80\) & \(\alpha_{2}=0.20\)) and path loss exponent \(\eta=4\). To plot the outage probabilities,  
Figure 4: BER comparison between SISO-NOMA and OMA with \(\eta\) = 2 and 4, and fixed PA coefficient pairs as (\(\alpha_{1}=0.70\), \(\alpha_{2}=0.20\) and \(\alpha_{3}=0.10\)) and (\(\alpha_{1}=0.76\), \(\alpha_{2}=0.16\) and \(\alpha_{3}=0.08\)). (**a**) BER comparison between SISO-NOMA and OMA with \(\eta\) as 2. (**b**) BER comparison between SISO-NOMA and OMA with \(\eta\) as 4.  
Figure 5: Achievable sum rate comparison between SISO-NOMA and OMA.  
let us consider the target rates for Users 1 and 2 as 1.5 bps/Hz and 2.5 bps/Hz. These considered target rates are now compared with the achievable sum rate calculated in Equations (15) and (16). If the calculated achievable sum rates drop below the respective considered target rates of Users 1 and 2, the individual counters will be incremented. Hence, the outage probability is plotted as a function of power transmitted for both Users 1 and 2.  
From Figure 6, it can be observed that User 1, with 70%, has more outage probability than User 1, with 80%. On the other hand, User 2, with 30%, has a lower outage probability than User 2, with 20%. Therefore, it can be concluded that the outage probability depends on the power percentage allocated to the particular user. Thus, in this case, User 1, with 80%, and User 2, with 30%, have shown less outage probability than User 1, with 70%, and User 2, with 20%, respectively.
## 3 Results  
In total, 61 compounds in Trial 1 and 65 compounds in Trial 2, appearing in at least 60% of the samples (i.e., in 18 or more out of the 30 samples in each trial) were tentatively identified (Supplementary Table S1). The compounds that showed significant differences in the medians in Kruskal-Wallis test and the compounds constituting the minimal discriminatory set are marked in Table 1 (for Trial 1) and Table 2 (for Trial 2). Four compounds in each trial were not identified. \begin{table}
\begin{tabular}{c c c c c}  & **C 1** & **SP 1** & **HI 1** & **Statistical Analysis** & \(p\)**-Level** \\ \hline Alcohols & & & & \\ \hline
2-methoxy-ethanol & 9.36 (13.17) & 9.50 (5.57) & 8.45 (3.60) & \\
1-Pentanol & 4.48 (9.87) & 6.15 (5.33) & 7.86 (4.12) & \\
1-Pentanol & 2.13 (3.17) & 2.54 (1.95) & 0.11 (0.11) & KW; LDA & 0.026 \\
1-Hexanol & 1.26 (8.15) & 1.40 (2.59) & 0.71 (0.23) & KW; LDA & 0.030 \\
1-Heptanol & 0.87 (0.92) & 0.73 (0.31) & 0.32 (0.08) & KW; LDA & 0.027 \\
1-Octen-3 ol & 21.3 (21.6) & 31.4 (-0.10) & 13.2 (1.77) & KW; LDA & 0.034 \\
2-Ethyl-2-hexanol & 4.02 (13.1) & 2.17 (0.86) & 1.57 (0.85) & \\
4-Ethyl-cyclohexanol & 0.00 (0.53) & 0.00 (0.00) & 0.00 (0.00) & \\
2-Ethyl-1-hexanol & 4.07 (0.76) & 5.26 (3.16) & 4.41 (3.15) & \\
2-Ethyl-1-decanol & 1.11 (1.11) & 0.77 (0.40) & 0.51 (0.21) & \\
2-Octen-1-ol & 0.75 (0.77) & 0.88 (0.47) & 0.34 (0.24) & \\
1-Octanol & 4.02 (4.25) & 3.19 (0.88) & 2.91 (0.77) & \\ Benzenenenhanol, \(\alpha\), \(\alpha\)-dimethyl & 0.61 (0.78) & 0.46 (0.34) & 0.56 (0.51) & \\ (Z)-2-Nonen-1-ol & 1.14 (0.10) & 0.79 (0.38) & 0.54 (0.20) & \\
1-Nonanol & 0.21 (0.25) & 0.25 (0.07) & 0.26 (0.16) & \\ \end{tabular}
\end{table} 
Table 1: The composition of the volatile profile of chicken breast meat fed with three different diets in Trial 1 ((Control (C) vs. Spirulina (SP) vs. _Hermatia illucens_ (HI)) (results expressed as ng/g).