esa-sceva/satcom-chunk-collection huggingface dataset:
# A Spatio-Temporal Encoding Neural Network for Semantic Segmentation of Satellite Image Time Series
## 3 Methodology
### STENN Architecture
#### 3.2.1 Spatial Encoding with Dilated Convolution  
Dilated Convolution: Dilated convolutions were introduced for image segmentation tasks, and they ensure that model parameters and feature map resolution remain unchanged while effectively controlling the receptive field of convolution to capture multi-scale information. Compared to the common approach of using pooling layers and convolution layers to increase the receptive field, dilated convolutions avoid the irreversible loss of spatial precision that occurs when feature maps are first reduced and then enlarged. Additionally, by setting different dilation rates and using dense connections, it is possible to capture multi-scale features.  
Dense Connection: Dense connections, pioneered by DensNet [22], are a crucial strategy for information exchange between network layers. This strategy primarily involves passing the output of each layer directly to all subsequent layers, fully utilizing the feature maps of each layer and reducing feature redundancy.  
Spatial encoding is carried out by the dilated convolution encoder \(\varepsilon\), which consists of N layers \((1,\dots,n,\dots,N)\). Each layer is composed of a series of dilated convolutions with increasing dilation rates, rectified linear unit (ReLU) activation, and group normalization. As there is no down-sampling operation employed, the feature map size remains consistent for each layer, facilitating dense connections between the dilated convolution layers, as shown in Figure 6.  
Figure 5: STENN consists of both a spatial encoding and a temporal encoding. The spatial encoding abandons down-sampling, ensuring consistent feature map sizes, and simultaneously enhances feature map utilization through dense connections, enabling the extraction of rich multi-scale features.  
For each image in SITS, the feature map \(f^{t}_{n-1},t\in[1,T]\) serves as the input to the encoder \(\varepsilon^{n},n\in[1,N]\). The output feature map \(f^{t}_{n}\) is obtained by concatenating the input feature map \(f^{t}_{n-1}\) and the output feature map \(f^{t}_{n}\) along the channel dimension, and it becomes the input for the subsequent encoder \(\varepsilon^{n+1}\). Its shape is \(C_{n}\times H\times W\), where \(C_{n}=C_{n-1}+C^{\prime}_{n}\). Finally, the feature maps from each image are stacked together to create the feature map time series \(f_{n}\) for that layer, with a shape of \(T\times C_{n}\times H\times W\), as represented in the following formula.  
\[f^{\prime}_{n}=concat[\varepsilon^{n}(f^{t}_{n-1})]^{T}_{t=0},n\in[1,N] \tag{1}\]  
\[f_{n}=concat[f_{n-1},f^{t}_{n}],n\in[1,N-1] \tag{2}\]  
where \(f_{0}=X\). For each layer of the encoder, the concatenated feature map of the input and output from the previous layer is used as the input. Additionally, the feature maps for each layer have the same size as the original image.
## 5 Working Operations of the Proposed Architecture and Discussion
### Challenge, Limitations, and Issues Emerged in the Current System
#### 5.1.4 HEC Standardization and Compliances Limitations  
The HEC regulatory authority plays an important role to manage the standards of degree attestation and verification including the secrecy of candidate certificate credentials and authenticity, checking system quality and maintainability, safety, data protectability, sensibility, resource effectiveness, transfer and exchange records between the universities and federal education authorities. These regulatory authorities look after the complete mechanism of retrieval, preserve, share, transfer, store, and exchange degree, attestation, and verification related data records, and more crucial to provide transparency, scalability, cross-chaining, and security [43; 44]. In this way, the degree traceability limitations and issues can be solved with more efficiency, and they can be reorganized to deliver a better educational solution for HEC. Another challenging aspect is to cope with the manual credential records feeding to the distributed system and the requirement of system and legislation in blockchain networks. Until now, blockchain technology is still not decisive on the laws and rules regarding the predefined system of HEC.
## 4 Multiple Ant Colony-Nondominated Sorting Genetic Algorithm II  
Multi-UAV aided wireless power and information transfer is a multi-objective optimization problem, and there are two difficulties: firstly, how to allocate a large number of SDs to different UAVs in an optimal order, and secondly, how to determine the hovering time of the UAV so that the SDs can receive a certain amount of energy and ensure that the time delay of information collection is as short as possible.  
We notice that in problem \(P1\), the energy efficiency of UAV and the average time delay of information transfer are optimized simultaneously. Moreover, the moving time and hovering time are independent, while the former only depends on the trajectory. Therefore, the first step is to minimize the moving time of several UAVs. Substantially, this is a multi-traveling salesman problem, which can be reduced to a traveling salesman problem. Obviously, it is an NP-complete (nondeterministic polynomial-complete) problem, which is difficult to solve by classical optimization methods. It can be known from literature [47] that ant colony algorithm simulates the foraging behavior of ants in nature and is a heuristic algorithm to find the optimal path. Compared with the heuristic algorithms such as particle swarm optimization algorithm and simulated annealing algorithm, ant colony algorithm is more suitable to solve the path planning problem. Professor [PERSON] proposed NSGA II (nondominated sorting genetic algorithm II) in literature [48], where the fast nondominated sorting with elite strategy and the crowded distance principle are employed to strengthen the diversity and uniformity of a noninferior solution set, improving the defects of the NSGA algorithm, such as slow searching speed and easy to fall into the local optimal. For this reason, the MAC-NSGA II (multi-ant colony-nondominated sorting genetic algorithm II) is proposed. In trajectory planning, the total flight trajectory of multiple UAVs is minimized. Moreover, the longest path among them is minimized by using the min-max framework to balance the flight load. On this basis, the Pareto sets of the hovering time and energy utilization of the UAVs are obtained. Therefore, MAC-NSGA II refers to a combination of a multiple ant colony algorithm and NSGA II. The state transition and pheromone update strategies in the algorithm are explained below, and NSGA II will not be detailed. The algorithm pseudocode is given in Algorithm 1.
# A Novel Method for Tool Identification and Wear Condition Assessment Based on Multi-Sensor Data
## 2 System Framework and Platform Construction
### Platform Construction  
Tool state detection methods are divided into direct and indirect methods. The direct method can identify the blade appearance, surface quality or geometric shape changes, and it can generally only be carried out when not cutting. It has two obvious disadvantages: first, is the requirement of shutdown detection and occupied production hours; secondly, it is impossible to detect the tool in the process of sudden damage, so that its application is limited. The indirect measurement method is to use the effect of the state of tool wear or about to be damaged on different working parameters to measure the parameters reflecting the degree of various effects of tool wear and damage, which can be detected during cutting without affecting the cutting process [21]. Therefore, referring to the wear data of milling cutters from PHM society, this paper proposes to use an indirect method to build a multi-sensor platform by collecting three-axis force data, three-axis vibration data, and acoustic emission signals.  
Since the tool must be detected quickly and the acquisition frequency of the sensor is required to be high, the sensor adopts three-axis force sensor 1303M-160, three-axis vibration sensor CT100 SLC and acoustic emission sensor ae144 sa40-BNC. These three sensors are used to collect the force, vibration frequency, and acoustic emission signal of the tool, which are respectively installed on the workbench and spindle of the machining center. All the signals sent by the sensors are amplified and filtered, then fed back through the rate adjustment, and finally entered into the computer for data acquisition. In addition, the acoustic emission signals of tool breakage are mostly in the frequency range 80-400 KHZ, and the signal acquisition frequency range of AE sensor AE144 SA40-BNC is 50 to 500 KHZ. Since the data acquisition speed of the general upper computer is slow, in order to meet the higher sampling frequency, this paper uses NI-PCI-MIO-16E-1 data acquisition card with the maximum sampling rate of 1.25 MHZ to send data. The signal from the motor current sensor of the spindle can be input into the computer without further processing.
# MAOA: A Swift and Effective Optimization Algorithm for Linear Antenna Array Design
## 4 Results and Discussion
### Antenna Position Optimization
#### 4.1.4 Design 4: Side Lobe Average Power Minimization Along with Null Placement of a 20-Element LAA
\[\text{fitness function}=\text{w}_{1}*\sum_{i}\frac{1}{\Delta\theta_{i}} \int_{\theta_{i}}^{\Theta_{\text{ui}}}|\text{AF}(\theta)|^{2}\text{d}\theta+ \text{w}_{2}*\left(\text{FNBW}_{\text{Computed}}-\text{FNBW}(\text{I}_{\text{n }}=1)\right) \tag{31}\]  
Table 7 summarizes the location of the various elements of the antenna array obtained by applying the ALO, DA, sooty term optimization (STO), and MAOA optimization algorithms, along with the FNBW offered by different algorithms. The array pattern is showcased in Figure 10, whose outcome reveals that MAOA reduces the SLL of the antenna array more efficiently and offers a comparatively constant FNBW of 11.60 degrees. Figure 11 depicts the convergence plot of different optimization algorithms for attaining the position of antenna elements, and the result shows that the proposed MAOA has a good rate of convergence in comparison to other optimization algorithms. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{8}{c}{**Optimized Element Positions (\(\times\lambda\))**} & \multirow{2}{*}{
\begin{tabular}{c} **FNBW** \\ **(Degree)** \\ \end{tabular} } \\ \cline{2-11}  & **I** & **II** & **III** & **IV** & **V** & **VI** & **VII** & **VIII** & **IX** & **X** \\ \hline ULA & 0.2500 & 0.7500 & 1.2500 & 1.7500 & 2.2500 & 2.7500 & 3.2500 & 3.7500 & 4.2500 & 4.7500 & 11.40 \\ \hline ALO & 0.2454 & 0.7488 & 1.2374 & 1.7366 & 2.2092 & 2.6932 & 3.1621 & 3.6545 & 4.1762 & 4.7500 & 11.80 \\ \hline DA & 0.2500 & 0.7500 & 1.2500 & 1.7500 & 2.2209 & 2.7500 & 3.2500 & 3.7500 & 4.2500 & 4.7500 & 11.60 \\ \hline STO & 0.2500 & 0.7450 & 1.2300 & 1.7343 & 2.2134 & 2.6992 & 3.1569 & 3.6774 & 4.1904 & 4.7500 & 11.60 \\ \hline MAOA & 0.2330 & 0.7370 & 1.2366 & 1.7454 & 2.2084 & 2.6829 & 3.1693 & 3.6633 & 4.1764 & 4.7500 & 11.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Optimized positions for positive half elements and FNBW of design 5. Figure 10: Array pattern of a 20-element LAA for design 5.
## 5 Results
### Analysis of the FSIR Approximations  
The purpose of this section is to quantitatively analyze the error of the proposed model caused by the m-order approximation used in FSIR. The multilook echoes obtained from the proposed model are analyzed to examine the effect of different antenna mis-pointing angles and varying numbers of terms \(m\). The validation involves comparing the model (13) with the numerical calculation (21). The approximation error of the proposed model is evaluated using the normalized quadratic error (NQE), which is defined as follows:  
\[\text{NQE}(s,r)=\sqrt{\frac{\sum_{k=1}^{K}\left(s_{k}-r_{k}\right)^{2}}{\sum_{ k=1}^{K}{r_{k}}^{2}}}. \tag{41}\]  
The approximation of the FSIR is affected by the mis-pointing angle of the antenna and the number of terms \(m\). Considering the use of normalized waveforms in the re-tracking algorithm, it is necessary to explain the relationship between the maximum error of the echoes and the number of terms \(m\). NQE is used to describe the error of the maximum value of the echo \(\text{NQE}_{\text{max}}=\sqrt{\left(s_{\text{max}}-r_{\text{max}}\right)^{2}/r _{\text{max}}^{2}}\).  
Figure 9 shows the results of the NQE of the semi-analytical model as it changes with \(\psi_{ac}\). It can be seen that NQE increases as the mis-pointing angle \(\psi_{ac}\) becomes larger. When considering \(\psi_{ac}\leq 18^{\circ}\) (\(\psi_{ac}<\theta_{\text{3 dB}}/2\)), \(m=4\) is adequate to achieve the required level of error.  
For the case in which \(m=4\), the global NQE matrix of the antenna mis-pointing angle is shown in Figure 10. Note that the NQE of a noisy echo may increase in real application, which requires \(m\) to take a larger value in order to reduce the error.  
Figure 9: Overallerror versus \(m\) for different \(\psi_{ac}\), showing the global NQE (continuous line) and NQE of echo maximum (crossed line) for \(\psi_{ac}=6^{\text{o}}\) (in red), \(\psi_{ac}=12^{\text{o}}\) (in green), and \(\psi_{ac}=18^{\text{o}}\) (in blue).
###### Abstract  
Radio frequency interference (RFI) is a major issue in accurate remote sensing by a synthetic aperture radar (SAR) system, which poses a great hindrance to raw data collection, image formation, and subsequent interpretation process. This paper provides a comprehensive study of the RFI mitigation techniques applicable for an SAR system. From the view of spectrum allocation, possible terrestrial and spaceborne RFI sources to SAR system and their geometry are analyzed. Typical signal models for various RFI types are provided, together with many illustrative examples from real measured data. Then, advanced signal processing techniques for removing RFI are reviewed. Advantages and drawbacks of each approach are discussed in terms of their applicability. Discussion on the future trends are provided from the perspective of cognitive, integrated, and adaptive. This review serves as a reference for future work on the implementation of the most suitable RFI mitigation scheme for an air-borne or space-borne SAR system.  
microwave remote sensing; synthetic aperture radar; radio frequency interference; frequency allocation and management; interference mitigation 2920191010390/rs11202438  
1  
Mitigation of radio frequency interference in Synthetic Aperture Radar Data: Current Status and Future Trends  
[PERSON], [PERSON], [PERSON], J.  
in low-frequency bands like P and L band [3]. Figure 1 illustrates observed RFI cases from different frequency bands spreading over the global area [4]. The circles with different colors corresponding to the location of RFI sources observed in various frequency bands. It is shown that RFI at low band like L-band is abundant and spread worldwide. The RFI situation differs in geographical areas, in which the well-developed area with dense population is much more severe. For example, the situations in East Asia and Europe are much worse than regions like Canada and Australia. It is worth noting that this map does not report all occurrences of existing interference due to the limited number of monitoring sensors. In addition, RFI may still be present in other bands, even if it is not reported here.
## 4 Conclusions  
The regeneration of spent or exhausted activated carbon (AC) generally refers to the desorption and/or decomposition of adsorbed contaminants without damaging the original carbon structure, thereby restoring its adsorption capacity for reuse. In this study, spent cylindrical GAC from an electronics manufacturing facility was thermally regenerated using two approaches: simple pyrolysis and CO\({}_{2}\)-enhanced pyrolysis-activation. Based on the pore properties of spent GAC and its regenerated products, gasification of a carbon matrix carried out above 800 \({}^{\circ}\)C using CO\({}_{2}\) was able to open and create more pores, thereby improving the pore properties as compared to those of spent GAC and its N\({}_{2}\)-pyrolyzed regenerated products. The maximal pore properties (BET surface area of 723.23 m\({}^{2}\)/g and total pore volume of 0.327 cm\({}^{3}\)/g) of the CO\({}_{2}\)-activated GAC products were produced at 900 \({}^{\circ}\)C and a 30 min holding time. Approximately 70% of the pore properties can be restored based on the investigated pyrolysis-activation regeneration conditions. SEM images confirmed enhanced surface porosity, while EDS analyses showed an increase in carbon content and a reduction in surface oxygen after regeneration.  
Conceptualization, W.-T.T. and W.-S.C.; methodology, Y.-C.Y.; formal analysis, Y.-C.Y.; data curation, C.-H.T.; writing--original draft preparation, W.-T.T.; writing--review and editing, W.-T.T.; supervision, W.-S.C. and W.-T.T. All authors have read and agreed to the published version of the manuscript. This study was supported by funding from the Ministry of Science and Technology, Taiwan (MOST 111-2622-E-020-009).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
Sincere appreciation was expressed to acknowledge the National Pingtung University of Science and Technology for their assistance in the scanning electron microscopy (SEM) and the energy-dispersive X-ray spectroscopy (EDS) analyses.  
The authors declare no conflicts of interest.
# Fusion Information Multi-View Classification Method for Remote Sensing Cloud Detection
## 3 Proposed Method
### Cloud Detection Model Training and Application Process  
Combined with the above contents, the cloud detection model method can be summarized. The specific process is shown in Figure 2. Notably, privileged information does not appear in the application phase.  
In the model training phase, the following steps are taken: (1) The super pixels come from simple linear iterative cluster (SLIC) method [30], which is used for super pixel division in the original image dataset to construct the dataset with super pixel blocks as the classification objects; (2) three different feature extraction methods are used to extract the feature of super pixels one by one, and the feature vectors of view A, view B, and view P are composed; (3) FIMV-SVM classifier is trained by using the extracted numerical feature dataset and labels.  
```
0:\(S=\left\{x_{i}^{A},x_{i}^{B},x_{i}^{P},y_{i}\right\}_{y=1}^{l}=\left\{\left(x_{i}^ {A};1\right),\left(x_{i}^{B};1\right),\left(x_{i}^{P},1\right)\right\}_{i=1}^{l}, y_{i}\in\left\{\right.+1,-1\right\}\)
0: Decision functions: \(f=sign(\frac{1}{2}{w_{A}^{*}}^{\top}\phi_{A}(x^{A})+\frac{1}{2}{w_{B}^{*}}^{ \top}\theta_{B}(x^{B}))\)
1: Grid method generates parameter sets: \(paraSet=\left\{C_{A},C_{B},C_{P},\gamma,\gamma_{P}\right\}_{i=1}^{n}\).
2:for each \(i\in[1,n]\)do
3: Set parameters \(C_{A},C_{B},C_{P},C,\gamma,\gamma_{P}=paraSet[i,:]\).
4: Set kernels function of view A, view B and view P: \(K_{A}(x_{i}^{A},x_{j}^{A}),K_{B}(x_{i}^{B},x_{j}^{B}),K_{P}(x_{i}^{P},x_{j}^{ P})\).
5: Create and solve quadratic programming problem and Solving quadratic programming and retaining optimal parameters \(\alpha_{i}^{A*},\alpha_{i}^{B*},\beta_{i}^{A*},\beta_{i}^{B*},\lambda_{i}^{A*},\lambda_{i}^{B*},\mu_{i}^{A*},\mu_{i}^{B*}\).
6: Get the optimal weight \(w_{A}^{*}\) and \(w_{B}^{*}\) by substituting formula: \[w_{A}^{*}=\sum_{i=1}^{l}(\alpha_{i}^{A*}y_{i}-\lambda_{i}^{B*}y_{i})\phi_{A}( x_{i}^{A}),w_{B}^{*}=\sum_{i=1}^{l}(\alpha_{i}^{B*}y_{i}-\lambda_{i}^{A*}y_{i}) \phi_{B}(x_{i}^{B}).\]
7: Decision function is solved by parameters \(w_{A}\) and \(w_{B}\).
8: The accuracy of statistical validation set was calculated as \(ACC[i]\), \(W_{A}[i]=w_{A}\), \(W_{B}[i]=w_{B}\).
9:endfor
10: The final \(w_{A}^{*},w_{B}^{*}\): \[w_{A}^{*}=W_{A}[find(acc==max(ACC))],w_{B}^{*}=W_{B}[find(acc==max(ACC))].\]
```  
**Algorithm 1** QP Algorithm for FIMV-SVM  
Figure 2: FIMV-SVM cloud detection model training and application process framework.  
In the model application phase, the following steps are taken: (1) The image to be processed is divided by SLIC super pixel segmentation method, and then the super pixel block is resized; (2) the features of view A and view B are extracted from the super pixel set one by one to form their feature vectors; (3) super pixel classification object view A, view B, two view feature vectors, are input through the FIMV-SVM decision function to obtain the corresponding classification results; (4) the final segmentation cloud mask result is formed by combining the super pixel classification results.
# Re-Understanding the Sedimentary Environment of the Wufeng-Longmaxi Shales in the Sichuan Basin
## 2 Evidence That Marine Source Rocks Developed in a Shallow-Water Restricted Environment
### Association with Shallow-Water-Environment Strata  
In many regions, black shales co-develop with strata characteristic of shallow-water environments, such as stone coal, gypsum salt layers, and bentonite. This co-development suggests a continuous phase sequence rather than formations in entirely distinct environments. Furthermore, these rock layers exhibit sedimentary structures typical of shallow-water environments.  
The black shales at the base of the Longmaxi Formation within the Silurian section of Luomuru are intercalated with a layer of stone coal that contains graptolite [44]. In the Early Silurian Dagipiping Formation and Meiziya Formation, located in the ZiyangHaoping Coal Mine and Dazhuyuan Mining Area in Southern Shaanxi, the black shales also contain stone coal and graptolite, indicating a brackish water environment that represents a transitional phase between marine and terrestrial settings [45]. Additionally, the shales from the Turtlegate Formation of the Upper Ordovician-Lower Silurian System in Southern Ankang contains coal with a total organic matter content of 66.3% [46].  
The Middle Triassic Leikoupo Formation exhibits a coexistence of shales and gypsum salt in the central Sichuan area, representing the final layer of the marine carbonate sequence in this region [47]. In the Late Jurassic to Early Cretaceous, the North Quigtang Basin developed as a relatively closed, expansive bay that opened to the northwest, characterized by the presence of both oil shale and gypsum salt layers [48]. During the Silurian period in North America, extensive reefs and gypsum deposits were evident in the sedimentary areas adjacent to the distribution of graptolite shales [49]. Furthermore, the middle and lower Cambrian source rocks (type I kerogen) in the western section of the Bachu uplift within the Tarim Basin are associated with evaporative lagoon phases that exhibit high abundance and coexistence with gypsum salt [31].  
In addition, there exists a significant number of sedimentary structures in organic-rich shales within shallow-water environments, including bioturbation, ripple bedding, erosion surfaces, and normal grain sequence laminae [50; 51; 52]. The depositional environment of the Devonian Marcellus-Dunkirk black organic-rich shales in the Appalachian Basin is primarily shallow water [53]. Similarly, the Cambrian Yuertus Formation shales in the northwestern part of the Tarim Basin represent a shallow-water overlapping sedimentary phase [54].