esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Thermogravimetric Analysis  
The thermal behavior of clays was studied using TGA (Figures 2 and 3). The thermograms of these clays show multiple endothermic peaks: first, all clays show an endothermic peak at ca. 80-90\({}^{\circ}\) C with weight loss (less than 14%). This peak corresponds to loosely bound water molecules [26]. The second stage loss (of ca. 3%) with an endothermic peak at temperature ca. 230 \({}^{\circ}\)C, seems to be due to the dehydration of the zeolitic water (i.e., water of hydration within cavity) [27; 28]. However, this peak is observed only in the TGA curves of palygorskite-rich clays--namely, C1 and C3 (Figures 2a and 3) [28]. The third stage loss with an endothermic peak at ca. 470 \({}^{\circ}\)C is observed in the TGA curves of all clays and becomes more pronounced in the case of palygorskite- and calcite-rich clays--namely, C4 and C5 clays, respectively; the weight loss is about 15%. This peak may be due to the dehydration of the bound water sheets [26; 27]. However, in the case of C4 palygorskite-rich clay, this percentage weight loss is higher than that theoretical value of 8.53% [28]. According to the chemical formula of palygorskite, this difference may be due to a part of the zeolitic water, not considered as bound water [27; 29]. The phase formed after the dehydration of bound water is called palygorskite anhydride of a chemical formula Si\({}_{\text{S}}\)O\({}_{20}\)Mg\({}_{5}\)(OH)\({}_{2-\text{x}}\). The fourth stage loss with an endothermic peak at around 700 \({}^{\circ}\)C may be due to the calcination of calcite (carbonate decomposition) in the C5 sample. The weight loss linked with this peak is more significant in the case of C5 clay (ca. 10.5%), which contains a high percentage of calcite (Figures 2b and 3). However, this peak can be attributed to dehydroxylation of palygorskite (i.e., forming Si\({}_{\text{S}}\)O\({}_{20}\)Mg\({}_{5}\) phase) present in the C3 and C4 clays [28].  
Figure 3: Differential thermogravimetric analysis (dTGA) curves for in the study clays.  
Figure 2: (**a**) Thermogravimetric analysis (TGA) curves of C1, C2 and C3 clays. (**b**). TGA curves of C4 and C5 clays.
## 4 Analysis and Discussion
### Detecting Delicate Changes  
In the developed workflow, the utilization of OBIA has the advantage in detecting delicate changes and thus can quantify the accurate change amount. One advantage is that the selection of an optimal scale for image segmentation, which has been a difficult task in OBIA, does not have a great influence on the final result. It is because we only focused on extracting changed elements, so the scale parameter can be set to a relatively small value to detect delicate changes without worrying about spatial discontinuity in the classification results. For example, a large amount of solar panels were built in the Yungang Grottoes. By applying our workflow with the scale parameter set to 40 for image segmentation, most of the solar panels can be solely extracted, with the shortest width of a solar panel being only 2 m in the image. In an internal monitoring report of the Yungang Grottoes, the changes in cultural landscapes were manually interpreted based on the aerial photograph in 2013 and the Gaofen-2 image in 2018. It is almost impossible to delineate each solar panel in such a large area; therefore, the whole area covered by solar panels was regarded as a changed patch (Figure 10a). Moreover, the area covered by sparsely distributed solar panels was regarded as an unchanged patch (Figure 10b). The traditional interpretation fails to delineate delicate changes and would lead to an inaccurate estimation of the area value of changed objects. In comparison, our workflow can take the full advantage of VHR images and extract delicate changes more efficiently.  
Figure 8: \(F\)-score of each class and overall classification accuracy (OA) for three study sites.  
Figure 9: \(F\)-score, precision, and recall of the change-detection results based on the proposed workflow for three study sites.
## 3 Methodology and Model
### Three-Dimensional PSA-DV-Hop Algorithm Based on Optimal Subset of Nodes
#### 3.3.3 The 3D Weighted Hyperbolic Algorithm  
Extending the hyperbolic localization algorithm to 3D introduces challenges in estimating unknown node positions, especially when four nodes are nearly coplanar. To address this, the position of the localization unit is corrected using the degree of coplanarity (DCP) [24]. The ratio of the tetrahedral radius can be expressed as follows:  
\[\rho=\frac{216\upsilon^{2}}{\sum_{i=0}^{3}s_{i}\cdot\sqrt{(a+b+c)(a+b-c)(a-b+c) (b+c-a)}} \tag{36}\]  
where \(\rho\) ranges from 0 to 1; as it approaches 0, these four points become increasingly coplanar.  
An effective threshold is set based on the DCP, and nodes that exceed this threshold are excluded from the localization process. The 3D weighted hyperbolic algorithm is then applied to the selected set of nodes. The fitness function of the PSA for the 3D scenario is expressed by Equation (37):  
\[fitness(x,\ y,\ z)=\sum_{i=1}^{M}\biggl{[}\sqrt{(x_{i}-x_{u})^{2}+(y_{i}-y_{u})^{ 2}+(z_{i}-z_{u})^{2}}-d_{int}\biggr{]}^{2} \tag{37}\]  
Due to the increased complexity of solving in a 3D scenario, both the number of iterations and population size must be increased accordingly. The coordinates are then optimized using DCP weights. The 3D IPSA-DV-Hop algorithm is employed to calculate the position coordinates, achieving a notable improvement in localization accuracy with only a slight increase in computational complexity.  
The overall flowchart of the 3D algorithm is presented in Figure 3:
# An Overview of Key Technologies in Physical Layer Security
## 2 Background
### Performance Metrics in PLS  
The secrecy metrics from information-theoretic security are well-established and have laid the foundation of secrecy coding in the physical layer. However, they are challenging to evaluate and measure, especially when the coded sequence has a finite block length [42]. Therefore, different metrics that are much easier to work with have been proposed. In this subsection, we discuss some of these metrics according to how they are used to evaluate performance in PLS.
## 3 Results
### Morphology of High-Temperature Oxidation (HTO) Film
Based on the EDS point scanning analysis results from Table 4, the oxygen content on the surface of the interface intermetallic compound is 4.29 at.% (point 2), and the oxygen content of the substrate is 1.92 at.% (point 4); the oxygen content on the surface of the 304 side is 6.00 at.% (point 5), while the substrate is 1.61 at.% (point 6); the oxygen content on the surface of the 6061 side is 9.82 at.% (point 7) and 7.04 at.% (point 9), while the substrate is 1.69 at.% (point 8). The oxygen content at point 10 is 7.45 at.%, which is similar to that of the sample oxidized at high temperature. Therefore, it can be inferred from the EDS point scanning results that different parts of the 304/6061 dissimilar metal welding-brazing joint treated by HTO can react with oxygen to generate oxides. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Point No.**} & \multicolumn{5}{c}{**at.\%**} & \multirow{2}{*}{**Remarks**} \\ \cline{2-2} \cline{4-7}  & **Al** & & & & & & \\ \hline
1 & 0.39 & 82.26 & 0.40 & 7.26 & 5.47 & spot scan \\
2 & 6.91 & 75.51 & 1.37 & 8.43 & 4.29 & spot scan \\
3 & 75.58 & 11.91 & 5.18 & 1.41 & 5.11 & spot scan \\
4 & 8.79 & 71.06 & 1.7 & 12.05 & 1.92 & spot scan \\
5 & 0.30 & 75.01 & 0.69 & 13.02 & 6.00 & spot scan \\ \hline \hline \end{tabular}
\end{table} 
Table 4: EDS analysis performed at the points shown in Figure 5.  
Figure 4: EDS micrograph analyses of the HTO-treated specimen shown in Figure 3, (**a-c**): the oxygen content on the surfaces of the IMCs, 304 and 6061 respectively (The number in the bottom right corner of the image is the atomic concentration). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Point No.**} & \multicolumn{5}{c}{**at.\%**} & \multirow{2}{*}{**Remarks**} \\ \cline{2-2} \cline{4-7}  & **Al** & & & & & \\ \hline
1 & 0.35 & 67.12 & 0.75 & 18.71 & 5.99 & spot scan \\
2 & 63.35 & 19.5 & 3.56 & 5.73 & 4.41 & spot scan \\
3 & 74.71 & - & 3.11 & - & 8.61 & spot scan \\ IMC & 56.05 & 16.32 & 3.81 & 4.89 & 4.12 & surface scan \\
304 & - & 57.12 & 0.87 & 15.75 & 7.65 & surface scan \\
6061 & 61.52 & 0.61 & 2.71 & 0.31 & 20.51 & surface scan \\ \hline \hline \end{tabular}
\end{table} 
Table 3: EDS analysis in Figure 3.
## 7 Black Start Capabilities through HVDC
### Fast Frequency Support within Safe Limits  
As well as the previous proposal, but without delving into the semiconductors' thermal performance, the authors of [27] aimed to mitigate the effects of low-inertia technologies being added to power grids. This scheme, however, does not contemplate an overload capability of the devices and, on the other hand, enhances the performance of the grid while staying within safety limits.  
The proposed controller delivers extra active power through the VSC when needed. It generates a fundamental frequency waveform through the use of vector control. Consisting of two control loops, this cascaded controller has an external loop which aims to meet the system's performance requirements, while its inner loop is in charge of the protection and stability of the converters.  
Figure 5 represents both loops of the controller and outputs \(i_{dref}\) and \(i_{qref}\) are used to regulate DC voltage (or active power) and AC voltage (or reactive power), respectively. The inner control loop has \(v_{dref}\) and \(v_{qref}\) as outputs, which are modulated voltage references for producing control signals.  
The outer controller has two gains:  
* \(K_{f}\), which is the ratio between the maximum permissible DC voltage variation and the maximum drop expected in the frequency.
* \(K_{V_{d,e}}\), defined as the ratio between the extra active power that can still be delivered (difference between measured and nominal values) and the maximum permissible DC voltage variation.  
Thus, the provision of this service by a converting station will not risk the integrity of the system. Some advantages worth mentioning for this scheme are (a) that power injection rises as frequency falls and the system reaches steady state at the same time as the frequency, and (b) this scheme prevents overloading AC transmission lines during load or abrupt generation variations.
## 1 Introduction
The inspection based on the endurance to the reverse-bias voltage is introduced in the component test (each solar cell). The typical criterion is less than \(-13\) V [37; 38; 39]. However, the current technology inspects solar cells before assembly to the module, and there is no way of examining them in the product test (module level) or detecting the risk after degradation. These can be covered by the proposed method in this work. One of the advantages of this non-contact and non-destructive inspection approach is the test without disconnecting the electrical circuit. If the modules are accessible by technicians on the roofs of houses or a rack of solar power plants, all they need is to approach the probe on the surface of the module. Disconnecting modules from the string for inspection is unnecessary, and onsite checking with keeping the circuit is possible. For rough detection, measured bias voltage at the non-standard solar irradiation level can be scaled and corrected by the low linear proportion. However, regarding the reproducible \begin{table}
\begin{tabular}{c c c} \hline
**Item** & **IR Camera** & **This Method** \\ \hline Visual presentation & Yes & No \\ Indicator & Temperature & Heat \\ Sensing & IR radiation & Electric field \\ Scaling to the worst-case & Not applicable & Applicable \\ Measurement error & \(1\ ^{\circ}\)C & 1\% of the fire risk threshold \\ \hline \end{tabular}
\end{table} 
Table 1: Differences between the new measurement method and the standard IR camera inspection. discrimination of the fire risk, taking a reverse-bias voltage under a given circuit current or controlled solar irradiance will be necessary. Therefore, in this study, we verified a high-accuracy voltage detection method for the PV module and the element cells in the PV module by non-contact potential measurement in two states: the open state and the short-circuited state. To improve the quality, we also discuss a method for detecting degraded cells in PV modules and quantifying the magnitude of the local heat potentially leading to fire using the method based on the _I-V_ characteristics.
# Special Issue on Advances in Maritime Transport: Sustainability, Contamination and New Technologies
###### Abstract  
The need to adopt sustainable solutions in the maritime sector has become more urgent in recent years, driven by stricter MARPOL Annex VI regulations, global decarbonization commitments [1], and a growing focus on the environmental health of our ports and oceans. In this context, this edition aligns with a wave of technological innovation seeking integrated and effective pathways to reduce air pollutants generated by ships, especially nitrogen oxides (NOx), sulfur oxides (SOx), and particulate matter (PM) [2].  
One of the most notable articles in this collection details the implementation of a pioneering hydrogen selective catalytic reduction (H\({}_{2}\)-SCR) technology, which achieves the removal of up to 80% of NOx, 99.8% of SO\({}_{2}\), and 72% of PM under real-world conditions onboard a cruise ship [3]. The innovation here lies in the combination of three subsystems: seawater scrubbers, an oxidative catalytic reactor, and an onboard electrolysis-powered H2-SCR system, offering a robust alternative to the traditional urea-based system, which carries operational and environmental risks [3]. In addition, other featured articles address methodologies to predict vessel capsize risks [4], the optimization of navigation parameters to improve maritime safety [5], and the control of generator synchronization using artificial intelligence. From a logistics and territorial perspective, the analysis of the port hinterland in Spain opens new opportunities to understand the role of ports in the economic and environmental development of the region. And in line with emerging technologies, synthetic generation of maritime traffic for evaluating autonomous vessels presents a truly innovative and flexible approach to validating navigation and control systems [6].  
These contributions are complemented by international scientific literature that validates the technical and environmental feasibility of technologies such as the Pt/MgO-CeO\({}_{2}\) system for NOx conversion [3], sulfidation-resistant catalysts, and metallic oxidizers for particulate removal [7]. Furthermore, advances in computational modeling of marine engine behavior, life-cycle assessment tools, and coherent regulatory proposals contribute to a sustainable transition in the maritime sector [8].  
The authors declare no conflict of interest.  
1  
[FOOTNO
## 2 Problem Description
### Mathematical Formulation  
We define the task set \(I=\{i|i=1,2,\ \cdots,\ M\}\), where \(M\) is the total number of tasks. For task \(i\), the observation profit is \(P_{i}\), the observation duration is \(d_{i}\), and the observation time requested by the customer is \([rs_{i},re_{i}]\). The number of VTWs for task \(i\) is \(nt_{ui}\). The set of VTWs for task \(i\) is \(TW_{i}=\{tw_{ik}|k=1,2,\cdots,nt_{ui}\}\), where \(tw_{ik}=[tw_{ik},tw_{ik}]\). \([ts_{i},te_{i}]\) represents the observation time for task \(i\), where \(te_{i}=ts_{i}+d_{i}\).  
1. Objective function: The objective function of the optimization problem in this study was designed to maximize the total profit of scheduled tasks. We define \(y_{i}=1\) if task \(i\) is successfully scheduled, and \(y_{i}=0\) otherwise. The objective function is defined as follows: \[\text{Maximize}\ f=\sum_{i\in I}y_{i}P_{i}\] (1)
2. Uniqueness constraint: None of the tasks are repetitive in this study. Each task can be observed at most once over the entire execution time horizon. \[\sum_{k=1}^{n_{ni}}\sum_{\tau=tw_{ik}}^{tw_{ik}}y_{i\tau}\tau\leq 1,\ i\in I,\] (2) where \(y_{i\tau}\) is a binary decision variable. \(y_{i\tau}=1\) if the start time of task \(i\) is \(\tau\) and \(y_{i\tau}=0\) otherwise.
3. Observation time request constraint: The observation of task \(i\) should start and be completed within the user's observation time request. \[rs_{i}\leq ts_{i}<te_{i}\leq re_{i},\ i\in I\] (3)4. VTW constraint:  
The observation of task \(i\) should start and be completed within the VTW. If task \(i\) is observed during the \(k\)-th VTW of \(TW_{i}\), then  
\[tws_{ik}\leq ts_{i}<te_{i}\leq twe_{ik},\,i\in I \tag{4}\]  
**Remark 1**.: _We define the intersection of the user's observation time request and the \(\ TW_{i}\) as the initial feasible time interval (FTI) for task \(i\). \(FTI_{i}=\{interval_{i1},interval_{i2},\cdotsinterval_{ik},\cdots\}\), \(interval_{ik}=\{start_{ik},end_{ik}\}\). Figure 1 provides a description of the initial time intervals. The length of an interval is denoted as len_j. If len_j \(\geq d_{i}\), the initial time interval is defined as a feasible time interval._  
5. Attitude transition time constraint:  
When the satellite executes two adjacent tasks, the attitude transition time constraint must be satisfied as follows:  
\[te_{i}+tr_{ij}\leq ts_{j} \tag{5}\]  
The time gap between the two scheduled tasks exceeds the satellite attitude transition time. The transition time \(tr_{ij}\) is the ratio of the satellite angles for observing the two tasks at the start time of the observation and the satellite maneuvering velocity.  
**Remark 2**.: _Figure 2 provides a description of new time intervals. If scheduled task \(j\) overlaps with the feasible time interval of task \(i\), the feasible time interval of task \(i\) is updated according to the duration of task \(j\) and the transition time between the two tasks. If task \(i\) is scheduled, it should be observed in one of the feasible time intervals._  
Figure 1: Description of initial time intervals.  
Figure 2: Description of new time intervals.
## 4 Experimental Analysis
### Feature Selection and Data Splitting  
An essential component of a prediction framework is known as feature selection, and its primary function is to choose the characteristics and terms that are the most pertinent from the terms that have previously been retrieved. A better model's performance can be achieved by the selection of appropriate features. This current research obtained a combined \"terms\" of 15,000,000 entries. This dataset's characteristics can play a role in determining the features that should be selected for a particular dataset. The strategy that is utilized for feature selection is TF-IDF. Both datasets make use of TF-IDF because this research makes use of it, which is why it is adopted. With the use of this method, the significance of a phrase within a dataset can be quantified by giving weight in the form of numerical values. Therefore, the dataset comprises objects with the highest weight.  
The implementation of splitting data is crucial for conducting model validation. To assess the model's performance, the datasets must be partitioned into two categories: training and testing sets. The training set is employed for model construction, whereas the testing set is utilized to confirm the established model. As a result, 80% of the data is allocated for training, while 20% is reserved for testing the model's performance.
# Laboratory Performance Evaluation of Hot-Mix
## 5 Results and Analysis  
### High-Temperature Performance  
The results of UPST are presented in Figure 3. For comparison purposes, the shear strength of HMAs with different asphalt types and numbers of \(N_{\text{des}}\) (at middle gradation) are presented in Figure 2(a), and the shear strength of HMAs with different asphalt types and aggregate gradations (at \(N_{\text{des}}\) = 50) are presented in Figure 2(b).  
As shown in Figure 2(a), with the \(N_{\text{des}}\) increasing by one level (25 numbers), the shear strength of HMA rises by approximately 0.21-0.4 MPa, which corresponds to 26-43% rise. When the binder penetration grade decreased by one level (20#), the shear strength of HMA was improved by about 0.23-0.33 MPa. In addition, the HMAs with asphalt = 30# and \(N_{\text{des}}\) = 30 had almost the same shear strength as those with asphalt = 50# and \(N_{\text{des}}\) = 50 or asphalt = 70# and \(N_{\text{des}}\) = 75. This fact proves that increasing the gyration number (\(N_{\text{des}}\)) contributes to increasing the HMA shear resistance, and the contribution of increasing gyration number for shear resistance enhancement is able to make up for the shear resistance loss caused by increasing asphalt binder grade.  
As shown in Figure 2(b), aggregate gradation has a significant impact on the shear properties of asphalt mixture. The finer the gradation corresponded to the better the shear resistance for the HMA. Moreover, the shear strength of HMA with 30# binder and coarse gradation was equivalent to that of HMA with 50# binder and middle gradation.  
Figure 3: The result of uniaxial penetration shear test.  
The results of UCT were presented in Figure 4. As shown in Figure 3(a), when the \(N_{\text{des}}\) increases by 25 numbers, the compressive strength of HMA rises by approximately 10-23%. With the binder penetration grade decreasing to one lower level (20\(\#\)), the compressive strength of HMA increased by about 15-25%. In addition, there was also no significant difference appeared in compressive resistance between the HMAs with binder = 30\(\#\) and \(N_{\text{des}}\) = 30 and those with binder = 50\(\#\) and \(N_{\text{des}}\) = 50, or binder = 70\(\#\) and \(N_{\text{des}}\) = 75. This further proves the substitutability of these two design parameters for HMA high-temperature properties. As shown in Figure 3(b), the finer gradation presented the better compressive resistance, and compared with fine and middle gradation, coarse gradation appeared to be more sensitive to the high-temperature resistance. In general, the high-temperature properties of HMAs exhibited in UCT were similar to those in UST.  
In addition, three design parameters affected the high-temperature performance of HMAs in different ways. Specifically, the effects of compaction effort (\(N_{\text{des}}\)) and aggregate gradation on HMA' properties were realized through VMA and asphalt content in the design process, while the effects of asphalt type was realized by the viscosity of binder.
## 2 Structure of the Monitoring and Control System
### GUI Interface Based on LabVIEW  
The interface platform was developed based on LabVIEW, which includes four main parts, including communication configuration, monitor, database, and dynamic Fuzzy-PID control. The communication configuration part realizes the configuration of the closed-loop bidirectional data transmission and the distribution of data cache. The received data and the recent variation trend will be displayed as the classified environmental factors in the monitor part. The link between a Structured Query Language (SQL) database and LabVIEW format is used to record the collected environmental information. The dynamic Fuzzy-PID controller is responsible for the whole operations, especially the CO\({}_{2}\) concentration regulation. Besides the real-time CO\({}_{2}\) concentration, the related environmental factors, such as luminance, temperature, and plant growth stage, are used to determine the dynamic Fuzzy-PID parameters for optimizing the regulation performance. In addition, the utilization of libraries in data-sharing realizes the function of remote monitoring and control. Both the browser and a Personal Digital Assistant (PDA) can act as remote terminals. Supported by GSM (core chip is SM900A, SIMCom, Shang Hai, China), the message is decoded as a mobile Attention (AT) instruction set and sent to a mobile for information broadcasting.