esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Analysis for Landscape Changes: A Bibliometric Review
## 2 Methodology  
### Bibliometric Search Engine, Tools, and Software Used for S.A.L.C. Analysis  
To retrieve the scientific literature for this paper, we used the Web of Science database (WoS). Among the most popular bibliographic databases of research papers such as Scopus, WoS, and Google Scholar, we chose WoS because it represents the best compromise among database completeness and cataloguing of higher rank journals [43].  
This research was conducted by using the combination of s.a.l.c. keywords, written between the quotation marks, in order to include at the same time all the four keywords and by looking for them inside the title, abstract, and keywords of the scientific paper. Then, the analysis excluded from the research all the WoS categories including the life science disciplines, such as biotechnology, applied microbiology, immunology, oncology, etc.  
Regarding the time of publication, two temporal ranges were considered.  
The first range considers years from 2001 to 2020, in order to study the general characteristics of scientific literature in the highlighted topic.  
The second range considers the same period, from 2001 to 2020, but is divided into four five-year intervals, in order to investigate the long-term literature trends.  
To achieve this goal, the following tools were used: first, some tools present inside WoS were used to extract some statistics and graphs; second, an open-source Geographic Information System, QGIS (open source software, downloadable at the site [[https://www.qgis.org/it/site/](https://www.qgis.org/it/site/)]([https://www.qgis.org/it/site/](https://www.qgis.org/it/site/)), accessed on 10 October 2021), was used to analyze the geographic distribution of selected works; third, for more specific diagrams, the VOSviewer software was used. It is a free software [44] based on text-mining and it is useful to construct bibliometric maps.  
The bibliometric mapping discipline, also called science mapping, recently appeared in order to look for relationships between documents, keywords, or authors [45; 46], and with the main aim to investigate the structure and the dynamics of a topic [1; 47].  
In the literature, other software programs exist for bibliometric mapping, such as SCImat [48] or CiteSpace [49]. Some software reviews are present in [50; 51]. We chose VOSviewer for its simplicity, flexibility, and clearness of the results.
## 3 Methodology
### Analysis of the Land Conversion Matrix
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Land Use Type** & \
\begin{tabular}{c} **Cultivated** \\ **Land** \\ \end{tabular} & **Forest Land** & **Grassland** & **Water Area** &
\begin{tabular}{c} **Construction** \\ **Land** \\ \end{tabular} & **Unused** \\ \hline Cultivated land & 39.91 & 2.64 & 0.19 & 1.3032 & 21.14 & 0 & 0 \\ Forest land & 2.38 & 610.84 & 7.22 & 2.61 & 18.16 & 0 & 0 \\ Grassland & 0.27 & 15.16 & 146.20 & 0.1062 & 1.28 & 0 & 0 \\ Water area & 1.06 & 2.28 & 0.08 & 37.93 & 0.88 & 0 & 0 \\ Construction land & 3.19 & 8.38 & 9.48 & 3.09 & 183.86 & 0 & 0 \\ Unused land & 0 & 0.0063 & 0.0045 & 0 & 0 & 0.09 & 0 \\ Sea area & 0 & 0.0315 & 0 & 0 & 8.82 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Land use conversion matrix, 2000–2010 (km\({}^{2}\)). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Land Use Type** & \
\begin{tabular}{c} **Cultivated** \\ **Land** \\ \end{tabular} & **Forest Land** & **Grassland** & **Water Area** &
\begin{tabular}{c} **Construction** \\ **Land** \\ \end{tabular} & **Unused** \\ \hline Cultivated land & 62.33 & 0.0054 & 0 & 0 & 6.44 & 0 & 0 \\ Forest land & 0.0045 & 637.68 & 0 & 0 & 7.751 & 0 & 0 \\ Grassland & 0 & 2.98 & 163.79 & 0 & 0 & 0 & 0 \\ Water area & 0 & 0.05 & 0 & 42.42 & 1.27 & 0 & 0 \\ Construction land & 0.0045 & 1.3599 & 0 & 0 & 175.06 & 0 & 0 \\ Unused land & 0 & 0 & 0 & 0 & 0.10 & 0 \\ Sea area & 0 & 0 & 0 & 0 & 17.48 & 0 & 15.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Land use conversion matrix, 1990–2000 (km\({}^{2}\)). project of the Hong Kong Port artificial island of the Hong Kong-Zhuhai-Macao Bridge on the east side of the new airport. In this decade, a total of 9.65 km\({}^{2}\) of construction land was transferred out, mainly cultivated land, forest land, and water area, and the transferred land types were relatively scattered.
## 3 Results and Discussion
### FT Raman Spectra Spectroscopy  
The Raman spectra in different ranges of fruit and leaf bagasse (Figure 3), with respect to xylem cellulose, were from 2800 to 3030 cm\({}^{-1}\) (Figure 3A), and the lignin phloem promoted by the delignification process by organosolv was from 1540 to 1700 cm\({}^{-1}\) (Figure 3B). These signals were previously reported to different vascular cell types (e.g., xylem and phloem) in woody and herbaceous plants by the Raman spectroscopy technique [49] and were used to interpret the xylem and phloem molecular components in the cell plants.  
Figure 2: FTIR spectra of standards (**a–d**) and fruit and leave bagasse fiber (**e**). FB = fruit bagasse fiber before organosolv; FBO = fruit bagasse fiber after organosolv; LB = leaf bagasse fiber before organosolv; LBO = leaf bagasse fiber after organosolv. Standards: (**a**) cellulose, (**b**) hemicellulose, (**c**) lignin and (**d**) pectin.  
Plant cell walls are highly complex structures due to the presence of saccharides, and during organosolv delignification, these undergo changes. In the first range (Figure 3A) of the Raman spectra, it was not possible to visualize significant changes due to the delignification process. For the fruit and leaf bagasse xylem and phloem residuals, Raman analysis was carried out in combination with the DRX methods for use as a predictive model for estimating the cellulose crystallinity for cellulose and amorphous polysaccharides as lignin's presence [50]. The complex multicomponent lignocellulosic samples were described with the functional groups for the FTIR spectroscopy technique.  
The Raman spectroscopy technique is complementary for the interpretation of the changes suffered by the _Bromelia hemisphaerica_ fruit and leaf bagasse after having been subjected to the microwave-assisted organosolv delignification process. Resulting from the microwave-assisted organosolv process, a bagasse with a structure of greater crystallinity in the case of the fruit and with a visually significant reduction in lignin with the final component absent were found.
# Time-Aware and Feature Similarity Self-Attention in Vessel Fuel Consumption Prediction
## 2 Related Work
### Feature Importance  
Addressing significant features has been studied for a long time. One of the methods is feature selection based on criteria. The correlation coefficient and mutual information are used to measure the relationship between features [30]. The features are selected depending on the criteria values and their threshold [31].  
The features can also be addressed during the model training phase. Various word embedding techniques [32] were adopted for the model to learn how to express the features (i.e., words). The authors of [33; 34] focused on weighting features and forced to learn it. They regarded the frequencies or correlation with a target as the feature importance. As feature importance became a factor to weight features, the model could make better feature representations. Weighting features also made both the outstanding model performance and explanation of features feasible.  
In the vessel fuel consumption prediction, there were efforts to handle important features [6; 7]. They attempted to reflect the feature importance by selecting them based on correlation with a target. As the feature selection is just preprocessing to modify data, the model still regards the remaining features equally [33]. Besides, not only the feature selection requires the subjective deduction of the threshold, but also trials and errors to verify it. This indicates the necessity that the model itself regards features differently based on their importance. Researchers have recently shown that attention mechanisms can make the model itself handle important features [35; 36; 37; 38; 39]. However, there are difficulties in the application of the previous methods in the vessel domain. The authors of [37] only focused on replacing the feature selection method using attention modules. In addition, the authors of [39] addressed the importance of feature interaction in the sparse feature space, and the authors of [35; 38] used the estimated feature importance in the unstructured data. Although the authors of [36] proposed self-attention to estimate the feature importance in tabular data, the feature importance was acquired on the instance-level, which did not reflect the feature interaction in the sequence. Those backgrounds motivated us to develop attention modules to handle important features in ship data.
## 5 On-Orbit Mode to Support Intercalibration with Radiometric Traceability
### Using Vicarious Reference Targets for Intercalibration  
The reference satellite can also observe stable targets such as Pseudo Invariant Calibration Sites (PICSs) and Deep Convective Clouds (DCC) over a long period of time, thereby collecting multiple angle observational data. Figure 11 shows the schematic diagram of intercalibration transfer mode using vicarious reference targets. When the target satellite flies over those reference sites or DCC, the co-located observations will be stored to monitor the long-term stability of instrument calibration [29].  
Figure 10: Lunar observation intercalibration transfer mode.  
Figure 9: Simultaneous Nadir Observation (SNO) intercalibration transfer mode (Geostationary earth orbit (GEO)-Low earth orbit (LEO) or LEO-LEO).
## 3 Materials and Methods
### Cluster Analysis  
The RHP estimates obtained using Equation (4) do not account for the effects of magnetic susceptibility and seismic velocity, both of which directly influence RHP, as discussed in Section 3.2.2. To address this limitation, we applied three statistical models and three unsupervised artificial intelligence (AI) models to identify clusters of the most favorable sites for geothermal energy exploration in the study region. These models used magnetic susceptibility, seismic velocity, and geochemical variables as input features for clustering. A list of geochemical variables used in the analysis is provided in Section 3.1. All features were normalized. This is a critical step, as some algorithms, such as KMeans, are sensitive to the scale of the data. Normalization also mitigates the risk of features with larger ranges dominating the clustering process. The target variable is RHP, which does not require standardization.
malicious attacks and inevitable device faults and link failures challenging. Therefore, assessing trust levels becomes paramount given the critical applications supported by these networks, ranging from autonomous vehicles to smart cities [1].  
Robust trust assessment mechanisms enhance the security posture of 6G networks by identifying and mitigating vulnerabilities, as well as safeguarding data confidentiality, integrity, and availability. Furthermore, continuous evaluation of trustworthiness enhances network reliability and resilience, crucial for meeting the stringent requirements of latency-sensitive applications. Trust management in 6G networks necessitates efficient collection, processing, and distribution of evidence while adhering to regulatory frameworks such as the General Data Protection Regulation (GDPR) [5; 6]. Reputation management, utilizing interaction data, aids in determining participants' trustworthiness, with blockchain technology offering enhanced security and reliability [7]. Additionally, trust management systems leverage reputation and trustworthiness to facilitate interactions among network entities, addressing the challenge of information asymmetry. Smart contracts serve as third-tier entities, enforcing policies and managing security, privacy, and resilience violations [1; 8].  
This study provides a comprehensive comparison of trust models using fuzzy algorithms. The key research contributions are:  
* Comprehensive analysis of trust concepts;
* Systematic examination of trust assessment algorithms;
* Evaluation of fuzzy logic approaches.  
This paper is structured as follows: Section 2 delves into the foundational concepts of trust, offering a detailed exploration of how trust is defined and perceived in a network environment. The methodology employed in this research is detailed in Section 3, outlining the systematic approach taken to gather, analyze, and synthesize relevant literature and data. Section 4 presents a thorough examination of the prevalent algorithms for trust assessment, providing insights into various existing methodologies and their applications. Section 5 is dedicated to fuzzy logic approaches to trust management, exploring the application of fuzzy logic algorithms in trust assessment across diverse network environments and offering a rigorous evaluation framework through effective metrics and comparative analysis of methods. The simulation environment used to validate these methods is also discussed. Sections 6 and 7 address open issues and outline future research directions, followed by a conclusion.
## 3 Results
### Individual Adaptation and Machine Learning Classification  
A linear mixed-effects model (LMM) revealed a significant three-way interaction (Group \(\times\) Time \(\times\) Asymmetry Class) for RSI (\(p\) = 0.017), indicating that individuals in the EG with initially high asymmetry demonstrated the greatest performance gains following the intervention.  
To further explore the predictive utility of novel indices, machine learning models were trained to distinguish responders from non-responders based on pre-intervention FER and AII values. Given the absence of naturally occurring non-responders under traditional thresholds (e.g., \(\Delta\)RSI > 0.2), a median-split approach was adopted: participants in the EG whose RSI improvements exceeded the group median were labeled \"Responders\", and those at or below the median as \"Non-Responders\".  
A Random Forest classifier was evaluated using Leave-One-Out Cross-Validation (LOOCV) to ensure stable performance in the small elite sample. Probabilistic predictions were used to generate the receiver operating characteristic (ROC) curve, while classification accuracy was assessed through the confusion matrix.  
The model yielded an area under the curve (AUC) of 0.50, suggesting no significant ability to discriminate between responder groups under this classification framework (Figure 3A). The confusion matrix reflected a near-even split between correct and incorrect predictions, with overall model accuracy only marginally above chance (Figure 3B).  
Although Hypothesis H5 posited that FER and AII would serve as effective predictors of jump adaptation, the current model results suggest that their isolated use may be insufficient for reliable classification in small samples. However, this limitation highlights the need for further exploration of multivariate and ensemble-based predictors in larger cohorts.  
Figure 3: _Cont._The model was evaluated using leave-one-out cross-validation. An AUC of 0.50 indicates no discriminatory ability between responders and non-responders under current data constraints.  
Classification based on pre-intervention FER and AII values. The matrix shows near-equal rates of correct and incorrect predictions, reflecting the model's limited reliability in this sample.
# The Sensitivity Analysis of Parameters in the 1D-2D Coupled Model for Urban Flooding
## 5 Conclusions  
This study employs a 1D-2D coupled waterlogging model for the Honggutan District of Nanchang City to conduct a sensitivity analysis of 12 parameters in the Storm Water Management Model and Telemac, using water volume as the objective function. The results indicate that minimum infiltration rate, Manning's coefficient of permeable surfaces, pipe roughness, and surface roughness have the most significant impact on total surface water-logging volume, while other parameters exhibit varying degrees of sensitivity depending on rainfall patterns.
# An Improved Data-Driven Integral Sliding-Mode Control and Its Automation Application
## 4 Simulation and Experiment
### Experiment 2: Water-Level Control of CFB
Additionally, it is evident from Figures 9 and 10 that the FFDL-ISMC method can still respond faster to the expected signal even under changing operating conditions (at the 500 th second), and the chattering phenomenon is significantly better than the FFDL-SMC method, enabling better monitoring of the specified boiler bubble water level. This supports the effectiveness and excellence of the suggested approach even more. Figure 11 shows the variation curves of the water-supply inputs of the FFDL-MFAC, FFDL-SMC, MFNHTSMC and FFDL-ISMC methods. It is easy to see that the water supply \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **FFDL-MFAC** & **FFDL-SMC** & **FFDL-ISMC** & **MFNHTSMC** \\ \hline Rise time (s) & 9 & 15 & 4 & 4 \\ Adjust time (s) & 75 & 58 & 35 & 35 \\ MAE & \(9.66\times 10^{-2}\) & \(6.27\times 10^{-2}\) & \(3.14\times 10^{-2}\) & \(4.48\times 10^{-2}\) \\ MSE & \(1.15\times 10^{-1}\) & \(6.62\times 10^{-2}\) & \(2.43\times 10^{-2}\) & \(5.37\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Control schemes’ performance indexes. Figure 10: Control-methods system output error curve. of the FFDL-MFAC and FFDL-SMC methods were in the process of large fluctuations, and the peak value is more than 14 m; thus, it is significantly impacted by the disruption and is vulnerable to significant or even minor issues. Additionally, adjusting the feedwater level frequently will put a lot of strain on the system's parts. In contrast, the FFDL-ISMC method maintains the feedwater quantity within a reasonable working range with small variations. In addition, when the working conditions change, the control input of this method can also respond quickly. The convergence of the input and output signals is demonstrated to further improve the application of the MFAC method in adjoint noise-interference systems in this chapter, which also proposes an ISMC method based on a data model for systems with adjoint noise interference. Simultaneously, experiments are conducted in various simulation environments, and the experimental findings demonstrate that, under various simulation conditions, the output error of the control method proposed in this chapter is clearly better than that of the original method, and it has the characteristics of quick responses and slight overshooting. The MFAC and SMC methods largely maintain their quick response times and robustness.
## 3 Application to Synthetic Data  
We apply the method to two synthetic data sets generated from distinct 3D models to demonstrate it. These models were designed to depict real-life features in reservoir environments, such as two simultaneous targets at different depths or a resistive target positioned above a resistive basement. These examples also showcase the use of two types of constraints to regulate the problem: relative equality constraints to ensure smooth solutions and absolute equality constraints applied to specific parameters to generate solutions where those parameters closely align with the imposed values [12].
## 4 Discussion
ECD grain size measurements by EBSD confirmed fine microstructures of ECD of 8.45 \(\upmu\)m and 11.93 \(\upmu\)m for the 80 \(\upmu\)m and 140 \(\upmu\)m hatch distance print sets, respectively. Higher hatch distances tend to generate slower cooling rates compared to smaller hatch distances which influences the grain morphology [46]. The grain size of the 140 \(\upmu\)m hatch distance was, therefore, slightly bigger than the 80 \(\upmu\)m hatch distance print set. As a result of the slow cooling rates of a typical conventional manufacturing process like casting, in the as-cast microstructure, undissolved and coarse NbC particles were detected with EDS/EBSD analysis, which were not detected in the as-built LPBF microstructure. As expected, mechanical properties of as-built (HV\({}_{0.2}\), UTS, YS) and PHT (HV\({}_{0.2}\), UTS, YS, El) samples were generally superior to properties of the as-cast condition. The high microhardness observed in the as-built cubes, which is attributed to both the fine microstructure and residual stress build-up, was slightly reduced after a PHT at 1000 \({}^{\circ}\)C for 1 h mainly because of stress relief. The comparatively higher cooling rate of the 80 \(\upmu\)m print set which accounted for its finer microstructure resulted in the slightly higher microhardness than in the 140 \(\upmu\)m hatch distance print set. Anisotropy in LPBF built samples was severely investigated [39; 47]; this was reflected in the tensile properties which were built in different orientations. The vertical orientation-built tensile specimen showed a higher UTS (739 MPa) and ductility (20 %), but lower YS (531 MPa). The horizontal orientation-built tensile specimen had a higher YS (567 MPa), but lower UTS (731 MPa) and El (16 %). The PHT tensile specimen instead had higher ductility in both orientations (V: El (31%) and H: El (25%)) than the as-built specimen but lower UTS (V--674 MPa, H--665 MPa) and YS (V--674 MPa, H--665 MPa). Due to the steep temperature gradient associated with the LPBF process, grain growth typically takes place in a directional manner usually along the building direction. The resulting microstructure and texture influence the mechanical anisotropy [48]. The disparity in the tensile properties may, therefore, have originated on the angle between the columnar oriented grains which were along the building direction and the tensile axis [49]. Stress relief through post heat treatment at 1000 \({}^{\circ}\)C for 60 min also significantly contributed to the increase in ductility at the expense of both yield and ultimate tensile stress when the as-built samples were heat-treated.
## 2 Related Work
### Studies on the Technology of Sensing Based on Wi-Fi  
The commercial technology of Wi-Fi has penetrated into all aspects of people's lives. The Wi-Fi technology has been applied in multiple research areas, including indoor localization [18; 19], fall detection [20; 21], motion detection [22], human identification [23], and gesture recognition [24; 25]. A Wi-Fi-based indoor 3D positioning system using a multi-signal classification algorithm was used to estimate the angle of arrival for linear propagation of the signal, and later, the calibration data were deployed to estimate the time of flight [26]. In another study, an optimized non-uniform planar array was applied in the world-first array at the receiver side. In the field of gesture recognition, location-independent gesture features only related to the mechanical orientation of the gesture were extracted in [23]. In [21], high recognition accuracy was achieved by extracting the part of the signal that displayed fall by using the short-time Fourier (STFT) algorithm to extract time-frequency features. The study applied a forward selection algorithm to filter out feature data sensitive to environmental changes after integrating them into a Support Vector Machine (SVM) classifier for classification. It was proposed in [27] to combine a motion detection method using RF system and Wi-Fi to achieve human motion detection. Human gait was extracted and combined with CSI and acoustic information of footsteps for human identification [23]. A study by [28] generated effective feature signals to prevent Wi-Fi APs from being used by unauthorized people. In [29], gesture recognition was implemented in multi-user scenarios using multiple Wi-Fi links after filtering out noise. The study designed an outlier detection algorithm that can determine if the gesture is a predefined gesture and can reduce the impact of other irrelevant features on the accuracy by using a dynamic link selection algorithm. Despite its good experimental results, the signal processing process is complex and difficult to implement.