esa-sceva/satcom-chunk-collection huggingface dataset:
# A State Recognition Approach for Complex Equipment Based on a Fuzzy Probabilistic Neural Network
## 3 The Proposed Approach  
### Construction of the Multi-Level State Space  
To train the initial PNN, a fuzzy rule base founded on the existing expert knowledge system is utilized. Based on deep analysis of the complex equipment, a multi-level segmentation is operated to obtain the state space for the complex equipment.  
Generally, a complex equipment is composed of several units: \(U_{1}\), \(U_{2}\), \(U_{3}\),..., \(U_{n}\), and each unit can be described by a number of sub-units, which can be depicted through a series of state variables. Moreover, several features might be extracted from each variable. The multi-level description for complex equipment can be shown in Figure 1.  
For an arbitrary state variable \(i\), descripted by \(P\), it can be described by feature 1 (_f_1 for short), feature 2 (_f_2_),..., feature \(l\) (_f_1), and can be expressed as follows:  
Rule 1: IF_f_1 is NB, and \(f\)2 is NB,..., and \(f\)1 is NB, THEN \(P\) is \(p\)1, Rule 2: IF_f_1 is NB, and \(f\)2 is NB,..., and \(f\)1 is NM, THEN \(P\) is \(p\)2, Rule 3: IF_f_1 is NB, and \(f\)2 is NB,..., and \(f\)1 is NS, THEN \(P\) is \(p\)3,......  
assuming that \(f\)1 could be classed into \(a_{1}\) cases,\(f\)2 into \(a_{2}\) cases,..., and \(f\)1_ into \(a_{l}\) situations. Obviously, there exists \(a_{1}\)\(\cdot\)\(a_{2}\)\(\cdot\)\(\cdot\)\(a_{l}\) fuzzy rules for \(P\), similar to the other variables. Likewise, \(b_{1}\)\(\cdot\)\(b_{2}\)\(\cdot\)\(\cdot\)\(b_{k}\) fuzzy rules  
calculated. Input layer nodes and hidden layer nodes run the length of input eigenvectors. Relations of neural node between type \(i\) and type \(j\) in the hidden layer can be expressed as follows:  
\[\Phi_{ij}(x)=\frac{1}{(2\pi)^{1/2}\sigma^{d}}e^{-\frac{(x-\
u_{ij})(x-\
u_{ij}) ^{T}}{\sigma^{2}}} \tag{2}\]  
where \(i=1,2,3,\ldots\)\(M\), \(M\) is the total number of classes in the training sample, \(d\) is dimension of the training sample, \(x_{ij}\) is \(j\)-th center of the \(x\)-th sample, and \(\sigma\) is smoothing factor.  
In the summation layer, the output is the weighted average of the same type of neuron nodes from the hidden layer. The weighted average can be calculated as follows:  
\[\
u_{i}=\sum_{j=1}^{L}\Phi_{ij}/L \tag{3}\]  
where \(\
u_{i}\) is output of \(i\)-th type, \(L\) stands for the number of \(i\)-th type neuron node, and the amount of summation layer neuron is equal to \(M\). After calculating each neuron node in the summation layer, we select the maximum probability as the result node and transport it into the output layer. The result can be expressed as follows:  
\[\beta=\arg\max(\
u_{i}) \tag{4}\]  
In competitive neurons constituting the output layer, the number of neuron nodes is equal to that in the summation layer. Threshold detection is operated at last, resulting in the output layer with maximum _a posteriori_ estimation of 1, and the others are 0.  
For the PNN training process, the inputs and outputs of state variables could be expressed by matrix \(X_{i}\) and vector \(\beta_{i}\) based on the fuzzy rule base.
## 3 Results and Discussion
### Optimization of Air Inlet Spacing  
The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a method of ranking according to the proximity between a limited number of evaluation objects and the idealized target. It is also a common and effective method for multi-objective decision analyses [55; 56]. The TOPSIS method determines the optimal object with the shortest distance to a positive ideal solution and the longest distance to a negative ideal solution [57]. Thus, it was used to calculate the optimal spacing of the air inlets with SV, as shown in Figure 16. The details on the procedure for TOPSIS were enclosed in Supplementary Materials.  
Figure 16: Flow chart of TOPSIS optimization method.  
Figure 15: Diagram of ADPI, LMAA, and EUC in the plane z = 1.1 m with different air inlet spacings.  
The final calculations are presented in Table 7. The weight factors for ADPI, LMAA, and EUC were 0.21, 0.40, and 0.39, respectively. The best performance was achieved with an air inlet spacing of 800 mm, and the worst performance was achieved with an air inlet spacing of 500 mm. Based on the calculation results, compared with a spacing of 500 mm, a spacing of 800 mm increased ADPI by 5.7%, decreased mean age of air by 7.2%, and increased EUC by 7.4%.
# Data-Driven Damage Classification Using Guided Waves in Pipe Structures
## 5 Training and Validation of the Neural Network
### The Number Effect of Hidden Layers  
The sizes of the input, hidden, and output layers were set to 100, 20, and 5, respectively. The softmax function was selected as the classifier in the output layer, which is expressed  
Figure 5: Typical reflected signals of the five types of damage: (**a**) echoes of no-damage; (**b**) echoes of corrosion; (**c**) echoes of dent; (**d**) echoes of groove; and (**e**) echoes of pipe end.  
in Equation (5). The activation function in the hidden layers is an all hyperbolic tangent sigmoid function tansig, i.e., tanh, which solved the zero-centered output problem in the sigmoid function. The expression and geometry image of tanh can be seen in Equation (6) and Figure 6, respectively. To investigate the effect of the hidden layers number, neural networks containing one and two hidden layers (called multi-layered perceptron, as shown in Figure 7) were built, respectively. The results are shown in Table 2.  
\[\hat{y}_{i}=\frac{e^{a_{i}}}{\sum_{l=1}^{n}e^{a_{i}}},\ i\in[1,n] \tag{5}\]  
where \(a_{i}\) denotes the value before the softmax classifier in the neural network, \(n=5\) means the category number, and \(\hat{y}_{i}\) is the prediction probability belonging to the category \(i\), in which the functional relation \(\sum_{i=1}^{n}\hat{y}_{i}=1\) must be met.  
\[\tanh(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}}\text{tanh}(x)=\frac{e^{x}-e^{-x}}{e ^{x}+e^{-x}} \tag{6}\]  
From Table 2, it can be seen that the number of hidden layers severely affects the recognition accuracy, and a neural network with two hidden layers is more effective for processing guided-wave signals obtained from the pipe.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hidden Layer Numbers** & **Iterations Epochs** & **Test Accuracy** \\ \hline
1 & 20 & 88.5\% \\
2 & 13 & 93.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the number effect of hidden layers.  
Figure 6: Hyperbolic tangent function tanh.  
Figure 7: The neural network framework of MLP containing two hidden layers.
# Enhanced Joint and Separable Reversible Data Hiding in Encrypted Images with High Payload
## 4 The Proposed Separable Data Hiding System  
In the joint data hiding system, data extraction and image restoration are inseparable as they are extremely related. If someone has the data-hiding key but not the encryption key, he cannot extract the embedded information bits from the embedded encrypted image.  
In this section, we present a separable reversible data hiding system in encrypted images, where data extraction and image restoration are separable. There are four phases: image encryption, data hiding, data extraction, and image restoration is demonstrated in Figure 6. In the proposed scheme, the original image is encrypted using an encryption key and the additional data are embedded into the encrypted image with the aid of a data-hiding key. With an encrypted image containing additional data, if the receiver has only the data-hiding key, he can extract the additional data though he does not know the image content. When, he has only the encryption key, he can decrypt the received data and obtain a filtered decrypted image similar to the original one, but cannot extract the embedded additional data. If the receiver has both the data-hiding key and the encryption key, he can extract the additional data and restore the original image without any error. In the proposed system, to calculate the fluctuation, we have exploited the real value of pixels and the absolute difference between adjacent pixels in place of an estimated value and prediction error, respectively. Herein, data extraction must be carried out before image decryption.
# Horizontal Loading Performance of Offshore Wind Turbine Pile Foundation Based on DPP-BOTDA
## 1 Introduction
The spatial resolution of DPP-BOTDA is 20 cm and the sampling rate of data points is 5 cm. Distributed optical fiber sensors are arranged at an angle of 90\({}^{\circ}\) along the perimeter of the pile. Firstly, the strain values of the pile under different loading conditions are obtained. Comparing the relationship between the load and the maximum strain value, the horizontal bearing capacity of the pile can reach 900 kN, which satisfies the standard of the horizontal-load capacity of offshore wind turbines. Secondly, the calculation method of distributed displacement of steel pipe piles based on distributed strain data is analyzed. According to this method, the distribution displacement data are obtained. The displacement data are compared with the measured data using the dial indicators, and the mean absolute percentage error (MAPE) value is only 0.03548. Finally, the practicality of the high spatial resolution DPP-BOTDA technology in the field of offshore pile foundation is verified.
### Approach, Scope and Goal  
Within the agenda of the _IndustRiES_ study in 2019 [10], the authors of this contribution aimed to address the relevant aspects mentioned in the #_mission2030_[1] in a comprehensive approach. In particular, the impact of the transition towards renewable energy supply in the industrial sector has been investigated in terms of the requirements for the future infrastructure (e.g., generation and grid capacity). Besides the focus on infrastructure, this work differentiates from the former mentioned projects and reports by combining energy carrier potentials and requirements without the assumption of comprehensive electrification. Rather, the focus is laid on evaluating the regional demand for required energy carriers and the respective quantities in various scenarios, considering the availability of renewable energy sources, the required process temperatures, and the currently used heat transfer media (steam, direct heating, etc.).  
This study describes perspectives on how renewable energies can cover the total demand of the Austrian industry. Furthermore, it is investigated how the industrial sector can eliminate energy-related emissions. In this context, the following questions are of central importance:  
* Which energy carriers are presently used?
* How can currently-used fossil energy carriers be substituted?
* Which energy carriers are needed for this substitution, and in what quantities, for a successful transition?
* What are the future infrastructure requirements considering spatial and temporal conditions?
# Improvement of Network Flow Using Multi-Commodity Flow Problem
## 3 Preliminary Experiment  
Table 1 shows the multi-commodity flow problem formulated as a linear programming problem when there are \(1\) and \(k\) commodities. The point set is \(V\), the edge set is \(E\), the directed graph consisting of \(V\) and \(E\) is \(G=(V,E)\), the non-negative capacity function defined on the edges is \(u:E\to R^{+}\), any edge is \(e\in E\), the starting point is \(s\in V\) and the end point is \(t\in V\), the flow on each edge is \(x:E\to R^{+}\).  
The above equation maximizes the total flow of all commodities. Here, it is expected that some cases will occur where the flow of one commodity is large and that of another commodity is small. We confirm this hypothesis by conducting experiments using the GNU Linear Programming Kit (GLPK) [17].  
The version of GLPK is 5.0, and the solution method used for the linear programming problem is the simplex method [5].  
Figure 1 shows the topology used in the experiment. There are two commodities in total, with commodity \(1\) flowing from node \(1\) to node \(4\), and commodity \(2\) flowing from node \(2\) to node \(4\). The maximum flow of this graph is \(28\) when the maximum flow problem is calculated only for commodity \(1\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **1 Commodity** & **\(k\) Commodities** \\ \hline Objective Function (Max) & \(\sum_{e\in\delta^{+}(s)}x_{e}-\sum_{e\in\delta^{-}(s)}x_{e}\) & \(\sum_{n=1}^{k}\Bigl{(}\sum_{e\in\delta^{+}(s^{n})}x_{e}-\sum_{e\in\delta^{-}( s^{n})}x_{e}\Bigr{)}\) \\ \hline Capacity Constraint & \(0\leq x_{e}\leq u_{e}\) & \(0\leq x_{e}^{n}\), \(\sum_{n=1}^{k}x_{e}^{n}\leq u_{e}\) \\ \hline Flow Conservation Law & \(\sum_{e\in\delta^{+}(v)}x_{e}-\sum_{e\in\delta^{-}(v)}x_{e}=0\) & \(\sum_{e\in\delta^{+}(v)}x_{e}^{n}-\sum_{e\in\delta^{-}(v)}x_{e}^{n}=0\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Formulation of the multi-commodity flow problem.  
Figure 2 shows the output of GLPK and its graphing as a result of a preliminary experiment.  
The first argument of \(x\) in Figure 2a represents the starting point, the second argument represents the end point, and the third argument represents the commodity. The Activity column is the amount of each flow. The \(totalflow\) in the last line is the total flow of each commodity, and the result of 28 was obtained as the maximum flow for the entire graph. However, commodity 1 inhibited the flow of commodity 2, resulting in a significant bias. As a commodity with a small flow is considered a single path, load balancing by multiple paths cannot be achieved. In addition, if the result of zero flow for one commodity is obtained, routing for that commodity cannot be performed because there is no path.  
Figure 1: Pre-experiment topology.  
Figure 2: Result of preliminary experiment. (**a**) Result of GLPK execution. (**b**) Graph of the result of GLPK executions.
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 2 Micromegas Fabrication Techniques
### Bulk Micromegas  
The first alternative fabrication technology was developed in 2006 and is called \"Bulk Micromegas\" [13]. Most detectors are currently manufactured according to this technology. The basic principle is to embed a metallic woven mesh on a Printed Circuit Board (PCB), which will bear the detector anode. A specific procedure encapsulates the mesh inside insulating spacers (pillars) that are attached to the anode. The mesh, the pillars, and the anode form one single entity.  
Figure 3 shows the four main steps in the production of a bulk Micromegas. The base component for these detectors is a printed circuit board with a printed conductive anode pattern. In the first step, the PCB is laminated with an insulating photo-image-able polyimide material, such as Pyralux(r) (Pyralux(r) is a trademark owned by affiliates of DuPont de Nemours, Inc. Wilmington, NC, USA). The thickness of this layer defines the amplification region of the detector. As the polyimide layer thickness is 64 \(\upmu\)m, two layers are used to define an amplification region of 128 \(\upmu\)m, which is the typical amplification gap used in most applications.  
In the second step, the mesh is placed on the top of the polyimide layer. An additional layer of the same polyimide material is laminated on top, encapsulating the mesh. In the last step, the polyimide layers around the mesh are removed by a photolithographic process. A black mask with a hole pattern is used in this process. The mask is illuminated by UV light, and the polyimide under the holes becomes resistant to acid. The surrounding polyimide is washed away by acid and only the cylindrical pillars and a supporting frame around the active zone remain.  
This process allows the production of robust large area mosaic detectors with negligible dead space or double stage Micromegas. The fixed mesh on each pillar allows non-planar detector designs. The bulk method is the most reliable and standard manufacturing method. Robust and spark resistant detectors can be produced. The meshes can stand mechanical stress during assembly and several sparks before being damaged. The detectors can be segmented and locally repaired after defects.  
The main drawback of this technology arises from the type of the mesh: the woven structure causes variances larger than 10% in the amplification gap length. The mesh itself is quite thick (\(\sim\)30 \(\upmu\)m), imposing the need for a larger amplification gap in order to achieve  
Figure 3: The main production steps of a bulk Micromegas. The mesh is laminated between polyimide layers. The material between the mesh and the anode is removed with a lithographic process, and only the supporting pillar and frame structures remain.  
sufficient field uniformity and gain. This shows as a result some degradation of the energy resolution of the detector (\(\sim\)16%). The energy resolution can be partially restored by using an electroformed mesh, with some cost, however, to the robustness.
# Daily Flood Monitoring Based on Spaceborne GNSS-R Data: A Case Study on Henan, China
## 2 Materials and Methods
### Cygnss Data
#### 2.1.2 CYGNSS SR  
The configuration function of the CYGNSS and GPS constellation can be regarded as the L-band bistatic radar. The CYGNSS receiver receives the forward-scattered circularly polarized signal from the Earth's surface transmitted by the GPS satellite, and obtains the characteristics of and information on the surface below (ocean or land). The inland water bodies are also part of the land information obtained from the bistatic radar. Thus, we first obtain the surface reflectivity (SR) through the bistatic radar equation [29] to further complete flood monitoring.  
The main product parameter of CYGNSS is a delay-Doppler map, referred to as DDM [30]. DDM consists of two parts: coherent and incoherent components [31]. Assuming that CYGNSS land observations are mainly specular reflection points, that is, the surface is relatively smooth, and the reflected signals are coherent and come from the area defined by the first Fresnel zone, the coherent component of DDM can be expressed as [32; 33]:  
\[\mathrm{P_{RL}^{coh}}=\frac{\mathrm{P_{r}^{t}G^{t}}}{4\pi(\mathrm{R_{ts}}+ \mathrm{R_{sr}})^{2}}\frac{\mathrm{G}^{t}\lambda^{2}}{4\pi}\Gamma_{RL} \tag{1}\]  
And Table 1 shows the details of the equation.  
What we actually obtain is surface reflectivity and thus all terms in the Equation (1) are converted into dB form, as in Equation (2) [24]:  
\[\Gamma_{RL}[\mathrm{dB}]=10\mathrm{log}\mathrm{P_{RL}^{coh}}-10\log\mathrm{P_ {r}^{t}}-10\log\mathrm{G^{t}}-10\log\mathrm{G^{t}}+20\log(\mathrm{R_{ts}}+ \mathrm{R_{sr}})+20\log 4\pi-20\log\lambda \tag{2}\]  
Table 2 is the CYGNSS dataset parameters used in Equation (2).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variables** & **Implication** \\ \hline \(\mathrm{P_{r}^{t}}\) & transmitted RHCP power \\ \(\mathrm{G^{t}}\) & gain of the transmitting antenna \\ \(\mathrm{G^{r}}\) & gain of the receiving antenna \\ \(\mathrm{R_{ts}}\) & distance between the transmitter and the specular point \\ \(\mathrm{R_{sr}}\) & distance between the specular reflection point and the receiver \\ \(\lambda\) & GPS wavelength \\ \(\Gamma_{RL}\) & surface reflectivity \\ \hline \hline \end{tabular}
\end{table}
Table 1: The implication of each variable in Equation (1).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variables** & **CYGNSS Parameters** \\ \hline \(\mathrm{P_{r}^{t}G^{t}}\) & gps\_eirp \\ \(\mathrm{G^{r}}\) & sp\_rx\_gain \\ \(\mathrm{R_{ts}}\) & tx\_to\_sp\_range \\ \(\mathrm{R_{sr}}\) & rx\_to\_sp\_range \\ \hline \hline \end{tabular}
\end{table}
Table 2: The variables in Equation (2) and the corresponding CYGNSS dataset parameters.
## 3 Study Area and Data Sources
### Available Datasets
#### 3.2.3 CR  
As shown in Figure 4, to meet the deformation-monitoring needs of the Jinchuan mining area and verify the accuracy of time-series InSAR results, nine 1 m triangular CRs were installed in the mining area. On 3 March 2025, researchers went to the Jinchuan mining area in Jinchang to adjust the pitch and azimuth angles of seven CRs. Based on the orbital parameters of the Sentinel-1A satellite (descending orbit, Path 33, Frame 463), the azimuth angle was set to around 13\({}^{\circ}\) from true north, and the pitch angle was set to about 20\({}^{\circ}\). The specific locations of the CRs were recorded with positioning equipment and are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**ID** & **Latitude (\({}^{\circ}\))** & **Longitude (\({}^{\circ}\))** & **Height (m)** & **Mode** \\ \hline CR1 & 38.482 & 102.171 & 1558.54 & \\ CR2 & 38.479 & 102.170 & 1563.60 & \\ CR3 & 38.485 & 102.166 & 1559.92 & \\ CR4 & 38.479 & 102.162 & 1616.83 & \\ CR5 & 38.477 & 102.179 & 1629.61 & Descending \\ CR8 & 38.464 & 102.166 & 1651.77 & \\ CR9 & 38.481 & 102.171 & 1551.08 & \\ CR10 & 38.481 & 102.166 & 1594.23 & \\ CR11 & 38.470 & 102.172 & 1608.33 & \\ \hline \end{tabular}
\end{table}
Table 3: Information on CR deployment locations.  
Figure 3: (**a**) Strip-mode SAR image with amplitude details taken by “Fucheng-1” over the Jinchuan mining area on 22 January 2025. (**b**) Strip-mode SAR image with amplitude details taken by “Shenqi” over the Jinchuan mining area on 21 November 2024. (**c**) TOPS-mode SAR image with amplitude details taken by Sentinel-1A over the Jinchuan mining area on 7 March 2025.
# Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network
## 3 Measurement Process
### Thermal Environment Model Based on an RVFLNN for a Metro Station
#### 3.3.2 Input Normalization  
The input data is normalized using the following equation:  
\[x^{\prime}=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{9}\]  
Figure 6: Final structures of traditional RVFL.  
where \(x^{\prime}\) denotes normalized data; \(x\) is the filtered data; \(x_{min}\) is the minimum value of \(x\); and \(x_{max}\) is the maximum value of \(x\).  
The purpose of normalization is to allow input variables with different physical dimensions to be used together. In the meantime, the sigmoid function is used as a transfer function in the RVFLNN. The normalization can prevent neuron output saturation caused by excessive absolute values in the net input.
## 2 Study Area and Data Sources
### Landslide Conditioning Factors  
Due to the variety of landslide conditioning factors (LCFs) across different places, there is no clarity on the most essential factors. Hence, we followed these principles in the selection of LCFs.  
1. Inclusion of Multiple Factors  
We included a wide range of LCFs, covering topographic (altitude, slope, aspect, etc.), hydrological (distance from streams and TWI), geological (lithology and fault density), land cover (NDVI), and human activity factors (distance from roads). Prior research has supported these factors. All data sources for the selected factors are listed in Table 1.  
2. Data Accessibility  
Although rainfall is a crucial triggering factor for landslides, it was excluded from this study, as it represents a temporal trigger rather than a permanent conditioning factor. This decision focuses on stable LCFs that influence landslide susceptibility, ensuring the model's reliability and consistency.  
3. Multicollinearity Assessment  
The variance inflation factor (VIF) was used to assess the multiple linearity concerns. The VIF for a factor j is calculated by using the following formula:  
\[VIF_{j}=1/\left(1-R_{j}^{2}\right) \tag{1}\]where \(R_{j}^{2}\) is the coefficient of determination of factor j regressed on the other factors. A VIF value greater than 10 indicates potential multicollinearity among the factors [29]. Factors with a VIF greater than 10 were excluded to ensure model stability. The final selection of the 12 LCFs is shown in Figure 2. These factors were rasterized and resampled into uniform 30 m \(\times\) 30 m layers. After that, the LCF values for both landslide and non-landslide samples were obtained via the \"Extract Multi Values to samples\" tool in ESRI ArcGIS. For categorical factors, such as lithology, soil types, distance from roads, and distance from rivers, categorical encoding was applied. Other continuous factors were retained in their original form.