esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Principal Component Analysis  
The calibrated model was run for 42 months and provided groundwater head data in the sampled wells on a monthly basis. Hence, a total of 42 temporal observations from 195 observation wells were available to inform the further prioritization of observation wells in the study area. PCA was run on all 195 observation wells and the 42 monthly temporal observations. The wells were considered as variable (columns) and temporal head values were considered as observations (rows). Since the PCA identifies axes which record the highest variation in the data, these axes are called principal components and are orthogonal to each other. The first component records the maximum variance of the data and the second component records the maximum variance in the remaining data and so forth. The total number of principal components can be equal to the number of variables. The intention of applying PCA in this study was to identify the locations of monitoring wells that were most representative of the overall groundwater head variance, thereby minimizing the required number of monitoring wells. Varimax rotation was then applied to further reduce the representing wells. Varimax rotation additionally cleaned the principal components. This maximized the sum of the variances of the square loadings and preserved the invariance of the variables. The application of varimax rotation further optimized the principle components and this became evident in the results. Indeed, prior to undertaking varimax rotation, a total of 148 wells were optimal in the first two components, and these two components explained 98.489% of the variance. After varimax rotation, the first two components explained the same amount of variance but with 135 optimal wells. In this study, only wells that had a factor loading matrix score equal to or greater than 0.90 were prioritized in the rotated loading factor matrix. The threshold of 0.90 was selected because we had to remove the correlation in the data. This threshold only retained those wells where the groundwater head fluctuations were high [3].
## 4 Test and Analysis Methods  
### Orthogonal Array Test Strategy (OATS) Method  
Orthogonal array test strategy (OATS) enables the test designer to maximize the coverage of the test cases devised for relatively small input domains. OATS is a systematic statistical way to test pair-wise interactions. The testing strategy can be used to reduce the number of test combinations and provide maximum coverage with a minimum number of test cases [17; 18; 19]. Some researchers, such as [PERSON], [PERSON], et.al, used OATS to prepare rubber or nano-modified asphalt binder and analyze properties affected by the influence factors, including shearing time, preparing temperature and shearing rate. According to OATS analysis results, the optimal scheme of preparation could be determined [20; 21]. As a result, in this study, OATS was also applied as a method to determine the optimum preparation process for THFS modified asphalt binder. There were three influence factors (shearing temperature, shearing time and shearing rate) and each of them had three levels. A detailed arrangement of the experiment is shown in Table 4, and the total number of experimental trials was 9 runs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Runs** & **A: Shearing Temp./\({}^{o}\)C** & **B: Shearing Time/min** & **C: Shearing Rate/rpm** & **Combinations** \\ \hline
1 & 150 & 30 & 2000 & A\({}_{1}\)B\({}_{1}\)C\({}_{1}\) \\
2 & 150 & 45 & 4000 & A\({}_{1}\)B\({}_{2}\)C\({}_{2}\) \\
3 & 150 & 60 & 6000 & A\({}_{1}\)B\({}_{3}\)C\({}_{3}\) \\
4 & 160 & 30 & 4000 & A\({}_{2}\)B\({}_{1}\)C\({}_{2}\) \\
5 & 160 & 45 & 6000 & A\({}_{2}\)B\({}_{2}\)C\({}_{3}\) \\
6 & 160 & 60 & 2000 & A\({}_{2}\)B\({}_{3}\)C\({}_{1}\) \\
7 & 170 & 30 & 6000 & A\({}_{2}\)B\({}_{1}\)C\({}_{3}\) \\
8 & 170 & 45 & 2000 & A\({}_{3}\)B\({}_{2}\)C\({}_{1}\) \\
9 & 170 & 60 & 4000 & A\({}_{3}\)B\({}_{3}\)C\({}_{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: OATS Design.  
Figure 1: Soxhlet Extractor. Note: 1: condenser tube; 2: extraction tube; 3: siphon tube; 4: connection tube; 5: extraction flask.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Items** & **Specific Gravity/(g/cm\({}^{3}\))** & **Softening Point/\({}^{o}\)C** & **25\({}^{o}\)C Penetration/0.1 mm** \\ \hline Test Results & 1.12 & 140 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical Properties of tetrahydrofuran soluble fraction (THFS).
## 2 Materials and Methods  
### Study Area  
The Marrakech-Safi (MS) region is geographically diverse and plays a key economic role in agriculture [43]. It includes the prefecture of Marrakech as its capital and seven provincial subdivisions: Al Haouz, Chichaoua, El Kelaa des Sraghna, Essaouira, Rehama, Safi, and Youssoufia [43]. The landscape varies, featuring the Rehama massif, with moderately high mountains forming hills, Bahira Plain, and Gantour Plateau, characterized by moderate relief and flat topography. The Jebilet chain consists of low-lying mountains between the Haouz and Bahira Plains. The other notable features include hills, Abda Plain, Haouz Plain, the rugged Mouissates area, the Sahel's dune belts, and the southern flanks of Mouissates. Additionally, Essaouira- Chichaoua Basin acts as a transition zone between the interior plains and the High Atlas, encompassing a rugged plateau, the ancient High Atlas massif, and Western High Atlas (Figure 1) [43].  
The region predominantly experiences an arid to semi-arid climate, with a sub-humid zone in the High Atlas influenced by the Atlantic Ocean and high altitudes [43]. Temperatures vary widely, with summer highs around 37.7 \({}^{\circ}\)C, winter lows near 4.9 \({}^{\circ}\)C, and an average of about 18 \({}^{\circ}\)C across 80% of the region [44]. Rainfall is minimal and irregular, ranging from 190 mm in the plains to 800 mm in mountainous areas, with snowfall occurring at elevations above 2500 m in the High Atlas. The region receives less than 300 mm of rain annually [44]. Farmers rely heavily on rain-fed agriculture (Bour), cultivating cereals such as barley, soft wheat, and durum wheat, which cover 80% of cultivated land [43]. In  
Figure 1: (**A**) Morocco; (**B**) provinces of the MS region; (**C**) study area.  
contrast, they irrigate only 20% of the agricultural land. Despite this smaller area, irrigated crops are more productive than rain-fed ones [43].
## 5 Critical Analysis of Results and the Sustainability Approach
\begin{table}
\begin{tabular}{c c c} \hline \hline
**EU Total Carbon Budget from** & **EU Transport Carbon Budget 2010–2100** & **EU Diesel and Gasoline Road** \\
**2010–2100 in Ecologic Study [28]** & **in Fraunhofer Study [29], Includes** & **Transport Carbon Emissions in 2000** \\
**Aviation, Shipping and Navigation** & **and 2018 Respectively,** \\ \hline
83–116 GtCO\({}_{2}\)e & 10.2–12.1 GtCO\({}_{2}\)e & 0.23 and 0.24 GtCO\({}_{2}\)e \\ \hline Carbon emissions in BAU trajectory: excluding aviation, shipping and navigation & & \\
0.23 GtCO\({}_{2}\)e and 0.24 GtCO\({}_{2}\)e per year if diesel and gasoline BAU emissions continue for 11 years, 31 years and 81 years from 2019 to 2100 & \\ \hline
2019–2030 & 2019–2050 & 2019–2100 \\ \hline
2.53–2.64 GtCO\({}_{2}\)e & 7.13–7.44 GtCO\({}_{2}\)e & 18.63–19.44 GtCO\({}_{2}\)e \\ \hline EU transport carbon budget in a BAU scenario of 0.23 or 0.24 GtCO\({}_{2}\)e per year could be completely consumed in 2060s. Road transport emissions could consume 16–22% of EU total carbon budget in 2100 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: BAU road transport emissions and the EU carbon budget for 1.5 \({}^{\circ}\)C and 2 \({}^{\circ}\)C scenario.
## 2 Materials and Methods
### Spectral Data Post-Processing  
#### 2.4.1 Unsupervised Band Ratio Classification of Fe(III) Iron and Pixel Distribution Maps  
Previous studies have positively identified the absorption feature for endmembers such as Fe(III) iron, with wavelengths at 400-900 nm, and identified satellites (ASTER, LANDSAT, Sentinel-2) with bands that are operational within this region of the electromagnetic spectrum [17; 18; 45]. The ASD Halo handheld spectrometer Fe\({}^{3+}i\) scalar feature covers 500 nm to 742 nm; UAV Texo Nano-Hyperspec bands cover 665 nm and 560 nm; PS2.SD (multispectral imaging) MSI has bands 2 and 3, 566 nm and 666 nm, respectively; Sentinel-2 MSI bands 4 and 3 cover 560 nm to 665 nm. These interoperable bands were used in band combinations to create Fe(III) abundance maps using band parameter combinations and methodologies as set out by [14; 17; 46]; for example, dividing the red and green band to cover both spectral peaks in Fe\({}^{3+}\) in the red spectrum, and absorption in the green spectrum. Past research also shows that the spectral pattern of secondary iron oxides shows strong absorption in the blue, green, and NIR bands, and strong reflection in the red bands [17; 47; 48; 49]. For PS2.SD, this was achieved by dividing bands 3/bands 2 (for Fe\({}^{3+}\)), performed through the use of ArcGIS raster functions.  
Extracted Fe(III) iron values from the band ratio combination maps for all instruments were imported as CSV databases into IBM SPSS Statistics 27, alongside the Halo handheld spectrometer results collected on 30 July. Datasets that were not normally distributed (such as the Halo handheld spectrometer Fe\({}^{3+}i\) dataset) underwent Box-Cox power transformation to identify the corresponding Lambda values. Reciprocal power transformations were recommended and performed. The normality of the transformed data was confirmed by checking for normal distribution via plotting probability plots and histograms in SPSS and performing a Kolmogorov-Smirnov test [50] for normality. All Fe(III) iron reflectance scatterplots were fitted with linear regression models to test how well air- and space-borne observations predict Halo handheld spectrometer ground-based observations. To test the strength of the relationship between aerial and ground Fe (III) iron spectral reflectance, both the correlation coefficients and coefficient of determinations were calculated. These permitted testing of the hypothesis that, as the air- and space-borne Fe(III) iron reflectance values increase, so do the aerial Fe(III) iron reflectance values. When datasets were flagged for containing extreme outliers during the regression analysis due to saturated or defective pixels, a bivariate outlier analysis was performed to help identify them for further scrutiny.
# Enhancing Channelized Feature Interpretability Using Deep Learning Predictive Modeling
## 1 Introduction
### Overview of Neural Network-Based Technology  
Early neural network approaches in seismic pattern recognition [9] suggested multiple blending of morphologic and curvature attributes to delineate the geomorphology of different features, and highlighted Sobel filter applications in enhancing discontinuity edges, resulting in focused images of specific geological interests. The concept of training features with multiple neural network architectures using gradient descent has been introduced by [10] to reduce the cost function. Under a deep learning platform, fewer hand-engineered data were required with a Convolutional Neural Network (ConvNet or CNN) for training and classifying images or patterns in multiple arrays [11]. End-to-end automated network architecture to predict salt bodies was developed by combining SegNet and a statistical approach [12]. The proposed method solely relied on interactive salt body labeling tools and a limited number of training data. Regardless, accurate prediction on salt bodies in slices, inlines, and crossline sections were observed in the blind dataset, proving faster and quantitatively adding value in the latest automated structural interpretation [13]. The concept of CNN has been shifted by [14] into classified seismic attributes to interpret salt boundaries and compared results with the conventional interpretation. Insufficient labeled geophysical data for supervised classification challenges has been addressed by [15] leading to the possibility of overtraining for small-scale labeled datasets and unstable predictions for unlabeled datasets. Convolutional Neural Network (CNN) and VGG-16 provided solid accuracies and fast transfer learning interpreting different seismic facies patterns to identify salt bodies in F3 Field, The Netherlands as demonstrated by [16].
# SSKM_DP: Differential Privacy Data Publishing Method via SFLA-Kohonen Network
## 7 Conclusions  
In this paper, the balance between data utility and privacy protection of multi-sensitive attribute data was studied, and a differential privacy data publishing method based on the SFLA-Kohonen network was proposed. Our proposed model noisily processes the dataset so that it satisfies the differential privacy of privacy protection, which inevitably affects the availability of data, but as the privacy budget is set, with the better the data availability, there is a corresponding decrease in security. A common approach to differential privacy data publishing adds noise to each piece of data, introducing excessive noise and reducing availability. Most previous clustering algorithms need to be improved to achieve better clustering results. For example, k-means clustering, while effective, is limited by the need to artificially set initial k-values. Although the DBSCAN algorithm does not need to set the  
Figure 7: SSE trends of the four algorithms for Adult and NLTCS datasets.  
number of clusters and it is highly robust, it is not suitable for high-dimensional data. To solve this problem, we introduced the SFLA algorithm to improve the Kohonen network, obtain the insensitive attributes associated with the sensitive attributes through MIC, and add the noise required to satisfy differential privacy to ensure that the data privacy was not leaked. We theoretically proved that the SSKM_DP algorithm improves the availability of published data while satisfying the differential privacy. Finally, the experimental results on real data proved that the performance of the SSKM_DP algorithm is significantly better than other similar methods. Under the premise of meeting the same privacy requirements, the availability of the data to be published by the SSKM_DP algorithm was better. With different sensitivity degrees of attributes, adding noise is not the same. Directly adding the same size of noise is bound to lead to part of the release of data privacy protection as insufficient. Part of the release of data privacy protection is excessive, resulting in the waste of privacy resources and the lack of data information, reducing the utility of the problem of data. In the next research work, we must not only design a more reasonable privacy budget allocation strategy and further improve privacy protection capabilities and data utility, but we must also consider the future in the distributed environment and the security and usability of the algorithm in this paper.  
Methodology, J.H.; Validation, Z.C.; Formal analysis, X.Z.; Investigation, Q.W.; Resources, N.Z.; Writing--original draft, Z.C.; Visualization, J.L. All authors have read and agreed to the published version of the manuscript.  
This paper was supported in part by Applied Basic Research Project of Liaoning Province under Grant 2022 JH2/101300280, Scientific Research Fund Project of Education Department of Liaoning Province under Grant LJKZ0625.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Multi-Level Interactions between TA Systems  
As cited above, TA systems are abundant in bacterial chromosomes [7, 40]. Thus, homologous and non-homologous systems co-exist within a bacterial genome (chromosomes and mobile genetic elements (MGEs)) or within the same replicon (either chromosomes, either plasmids). This raises the question of the interaction between homologous and non-homologous systems and how it can impact TA systems' evolution and activity.  
In this section, we will describe different types of interactions between systems belonging to the same replicon (either on chromosomes or on plasmids) or interactions between chromosomally and MGE-encoded systems. Different levels of interactions can be recognized. Direct interactions between toxin and antitoxin proteins belonging to homologous or non-homologous systems can occur. This can affect TA systems at the level of toxicity or at the level of expression, since in type II systems, antioxins and/or antitoxin-toxin complexes are able to act as transcriptional repressors. In addition, given that most of type II toxins are endoribonucleases, their activity can impact TA system expression at the post-transcriptional level. We will discuss the implications of these various interactions from an evolutionary perspective. Note that this section will mainly concern type II systems.
# A Review of Vertical Shaft Technology and Application in Soft Soil for Urban Underground Space
###### Abstract  
With the ongoing urbanization and densification of cities worldwide, the planning and utilization of urban underground space (UUS) have become crucial for developing urban underground infrastructure. Given the limited construction space within dense urban areas and the influence of declining groundwater levels, technologies such as open caissions and various vertical shaft methods have been introduced for UUS development. However, the dissemination of these technologies remains fragmented across different domains, lacking systematic summarization. A comprehensive, up-to-date overview of open caisson and vertical shaft technologies is essential for their effective application. In the manuscript, a systematic analysis of vertical shaft technologies, specifically focusing on their use in soft ground conditions, is conducted. The analysis is based on an extensive literature review and case study evaluation. It addresses the unique challenges posed by high compressibility, low bearing capacity, and groundwater sensitivity. Conventional shaft technologies and mechanized systems, including open caissions, drilled shafts, and the novel pressed-in ultra-deep assembled shafts (PIAUS), are evaluated systematically. Key aspects such as design principles, construction techniques, and stability in soft soils are discussed. The limitations of conventional methods in soft UUS are highlighted, while the advantages of advanced mechanized systems--such as rapid construction, reduced environmental impact, and improved safety--are emphasized. A detailed comparison of case studies demonstrates that PIAUS construction technology is particularly efficient in urban areas with confined spaces, dense building conditions, and ground conditions up to 200 MPa, with shaft diameters up to 12.8 m and depths of 115.2 m. Additionally, its suitability for rapid construction in soft and medium ground conditions is supported by undrained excavation with parallel excavation and liner sinking techniques. The PIAUS technology shows considerable potential for future projects, including shield construction shafts, ventilation shafts for tunnels, underground parking garages, and stormwater storage wells. This manuscript also highlights emerging mechanized methods in underground space development, their advantages, limitations, and areas for future research and improvement.  
vertical shaft technology; mechanized systems; design theory; underground space; pressed-in assembled shaft; prefabricated lining structure +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Analysis of Multiple Input Features
#### 2.2.2 Separability of Electromagnetic Power and 3 Traditional Features  
Based on the analysis above, four types of input features [\(\delta\),\(\omega\),\(V\),\(P_{e}\)] can be used to train the SVM transient stability prediction model. Figure 6 shows the simulation curves of the 4 types of features, extracted from typical stable and unstable samples of the IEEE 39-bus system.  
It can be seen from Figure 6 that the transiently stable and unstable samples of the \(P_{e}\) curves are significantly different, similar to the \(\delta\), \(\omega\) and \(V\) features in Figure 6a-c. Therefore, it is feasible to use \(P_{e}\) as a novel input feature.
## 6 Experiment and Result
### Monitoring Service
#### 6.3.2 Kafka Cluster Services
The results obtained indicate that the Kafka cluster can support the flow of data that can come from the EMQ cluster. Additionally, the Kafka cluster provides a mechanism to handle the back pressure exerted by the EMQ cluster. Kafka's back pressure mechanism is based on the use of reliable storage for the persistence of messages. Messages can be stored in the Kafka cluster until the consumer can retrieve them. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Apache JMeter**} \\ \cline{2-10}
**Number** & **Zookeepers, \# of Kafka** & **Numbers of Threads** & **Ramp-Up Period** & **Duration of Experiment** & **Avg. ** & **Avg. ** & **Min. ** & **Max. ** & **Percentage of Requests** \\  & **Brokers** & **(\#Users)** & **Ramp-Up Period** & **Duration of Experiment** & **Throughput** & **Time** & **Time** & **Time** & **with the Errors (\%)** \\ \hline
1 & (1, 1) & 18,000 & 1 & 00:00:24 & 932.45 & 491 & 2 & 3814 & 0.00 \\
2 & (1, 1) & 27,000 & 1 & 00:00:45 & 749.27 & 901 & 2 & 9192 & 0.00 \\  
It must be taken into consideration that all the components of the messaging subsystem compete for the resources of the node pool. The throughput of one component affects the others within the node pool. Thus, horizontal or vertical scaling techniques may be required. In general, the performance analysis of system components is important to define the capabilities of a K8s cluster or K8s cluster node pools to meet the system requirements.