esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Fundamental Concepts and Background
### Types of Explanations
#### 2.4.2 Post-Hoc Methods  
Post-hoc methods can be divided into global explanations, for example, the model-agnostic method BETA [76] and neural networks specific method GAM [77] or local explanations including model-agnostic approaches such as LIME [78], SHAP [79] and Anchors [80]. These methods provide feature level explanations by learning an interpretable model that attempts to approximate the behavior of the original model. Global explanations can also be provided by these methods by summarizing local explanations, such as with SHAP summary plots [79] or SP-LIME [78]. CLEAR [81] and CERTIFAI [82] are both model-agnostic methods that generate local explanations supported by the provision of counterfactual explanations that show examples of inputs that are generated to be close to the original input but for which the model provides a different outcome. Other common methods used for the explanations of DL models include gradient-based attribution methods [83], such as integrated gradients [84], or DeepLIFT [85]. Deconvolution [86, 87], Class Activation Maps, or CAM [88], and Grad-CAM [89] are techniques to visualize Convolutional Neural Networks. Variations on all these techniques are being developed and apply to different scenarios. Visual explanation techniques are also a means to providing model-agnostic explanations, and a summary of them is presented in the work by [18].  
There are two main ways of evaluating post-hoc methods: mathematically quantifiable metrics and human-centered evaluations [67]. However, there is currently no consensus on how to evaluate how interpretable a model is, how correct an explanation is, or how to benchmark methods against each other [18, 66, 67]. There is some concern around the reliability of post-hoc explanations [63, 90]. There are also concerns that post-hoc methods could expose the original models to adversarial attacks [18] or could lead to the generation of classifiers whose post-hoc explanations could be arbitrarily controlled [63]. Adversarial attacks can \"trick\" the ML algorithm and significantly affect its output with slight changes in the input data. As XAI provides insight into the functionality of the CDSS, it can allow for more effective attacks. Solutions for DL models, SVM models, or even unsupervised ML models have been proposed [18]. Others have deployed explainable techniques such as SHAP to discriminate between normal and adversarial inputs in Deep Neural Networks [91]. Techniques such as a \"goodness checklist, explanation satisfaction scales, elicitation methods for mental models, computational measures for explainer fidelity, explanation trustworthiness and model reliability\" have been suggested as appropriate methods of evaluation [18].
## 2 Motor Parameters and Structure of Initial Scheme  
A 200 kW 56 rpm motor is taken as the research object. The motor structure and parameters of initial scheme are provided in Figure 1 and Table 1. Considering the low speed, the motor adopts an outer rotor structure to meet the large torque [13]. In addition, the surface-mounted permanent magnet (PM) structure is applied, because the surface-mounted permanent magnet structure has less flux leakage and simpler structure than the interior permanent magnet structure [17]. In order to achieve a better cooling effect, the structure of axial spiral water cooling is adopted.
## 3 Y-Branch Coupler
### 3 dB Fabrication Tolerance of the Y-Branch Coupler  
Fabrication errors make the fabricated coupler different from the designed one, leading to reduction of the coupling efficiency, especially for the parameters of the submicrometer scale. 3 dB fabrication tolerance refers to variation range of the parameter when coupling efficiency drops to half of the maximum. In this section, 3 dB fabrication tolerance of the width \(W\), and gap \(G\) are analyzed.  
Firstly, 3 dB fabrication tolerance of the gap \(G\) is analyzed and width \(W\) is 0.10 \(\upmu\)m during the analysis. As shown in Figure 5a, mode matching degree \(\eta_{y1}\) reaches its maximum value of 0.24 when gap \(G\) is 0.86 \(\upmu\)m. Once gap \(G\) decreases to 0.16 \(\upmu\)m or increases to 2.26 \(\upmu\)m, mode matching degree \(\eta_{y1}\) drops to half of the maximum. Therefore, 3 dB fabrication tolerance of the gap \(G\) is \(-\)0.70 \(\upmu\)m\(+\)1.40 \(\upmu\)m.  
Figure 4: Influence of the width \(W\) and gap \(G\) on the mode matching degree \(\eta_{y1}\) when (**a**) TE mode or (**b**) TM mode in the SMF. (**c**) Influence of the length \(L_{b}\) on the transmission efficiency \(\eta_{y2}\).  
Figure 5: **(a)** 3 dB fabrication tolerance of the gap \(G\) when width \(W\) is 0.10 \(\upmu\)m. (**b**) 3 dB fabrication tolerance of the width \(W\) when gap \(G\) is 0.86 \(\upmu\)m.  
Secondly, 3 dB fabrication tolerance of the width \(W\) is analyzed and gap \(G\) is 0.86 \(\upmu\)m. The relationship between the mode matching degree \(\eta_{\mathrm{y1}}\) and width \(W\) is shown in Figure 5b. When width \(W\) is smaller than 0.10 \(\upmu\)m, mode matching degree \(\eta_{\mathrm{y1}}\) is higher than 0.24. When width \(W\) increases from 0.10 \(\upmu\)m to 0.15 \(\upmu\)m, mode matching degree \(\eta_{\mathrm{y1}}\) drops from 0.24 to 0.12. Therefore, 3 dB fabrication tolerance of the width \(W\) is -0.10 \(\upmu\)m-+0.05 \(\upmu\)m.
# Efficient Wireless Monitoring and Control of a Grid-Connected Photovoltaic System
## 3 Description and Design of the Photovoltaic System
### Wireless Conversion Unit (WCU)
#### 3.2.2 DC/AC Converter Control  
The controller responsible for managing the inverter is a PI controller that generates a PWM signal to inject energy into the power grid. In this case, a cascade PI with an inner loop is used to control the inductor current. This inner loop is the fast loop, and it receives the sinusoidal reference current that the inverter should provide, _igrith(t),ref_. In addition, the reference current \(i_{\textit{nf}}\). is synchronized with the grid signal. This inner loop achieves the current error \(e_{i}\). The outer control loop is responsible for controlling the DC/AC converter input voltage, \(V_{o}\), and generating the sinusoidal reference current. In this loop, the input voltage is compared with the reference inverter input voltage, \(V_{o,ref}\), which must be reached, obtaining the voltage error \(e_{V}\). The procedure is presented in Figure 6, where the duty cycle of the inverter, \(\gamma_{\textit{inv}}\), is obtained. This control method has been used because it is effective and simple to implement, requiring a reduced computation time. The PWM frequency is 10 kHz and the PWM resolution is 10 bits. The sampling time of the adaptive backstepping control loop is 0.1 ms, whereas the duty cycle change rate is also 0.1 ms.
## 3 Methodology
### Multi-Camera Cross-Region Trajectory Splicing  
Multi-camera cross-region trajectory splicing is an important step in continuous vehicle trajectory reconstruction. Since most of the visual sensors deployed on the road network today cannot directly capture the ID information of the vehicle, it is difficult to determine the matching relationships between the vehicle trajectories extracted from different visual sensors. An additional module is required to match the trajectories.  
Considering the above issues, a multi-camera cross-region trajectory splicing module is constructed in the framework. In this module, considering that similar trucks may appear on the road simultaneously, reducing the accuracy of trajectory splicing, we built a new model. The model can extract the vehicle's visual and lane features from the videos and calculate the feature similarity of vehicles in different videos to find the same vehicle and splice the trajectory.  
As shown in Figure 4, the visual feature extraction part of the network uses the Reid network proposed by [PERSON] et al. [56]. The model uses the latest network pre-trained on ImageNet as the backbone module, including ResNeXt101, ResNeXt101_32x8l_wsl, and ResNet50_IBN_a. The lane feature extraction part of the network uses a fully connected layer to convert the number vector of the lane where the vehicle is located into a feature vector with the same length as the vehicle's visual feature. We then use the fusion feature of the visual feature and lane feature to calculate the similarity between vehicles.  
We fuse the vehicle's visual features and lane features according to the following Equation (2):  
\[F_{fusion}=F_{vision}+k*F_{lane} \tag{2}\]  
In Equation (2), \(F_{fusion}\) is the fusion feature of the vehicle, \(F_{vision}\) is the visual feature of the vehicle, and \(F_{lane}\) is the lane feature of the vehicle; \(k\) is the scale factor, which is obtained by model training.  
We use the fused features to calculate the cosine similarity between different vehicles and find the vehicle-matching result with the best similarity matching.  
\[dist(A,B)=1-\cos(A,B)=\frac{||A||_{2}*||B||_{2}-AB}{||A||_{2}*||B||_{2}} \tag{3}\]  
Figure 3: Calibration of the coordinate system transformation matrix.  
In Equation (3), \(A\) and \(B\) are the fusion features of two vehicles, and \(dist(A,B)\) is the feature similarity of the two vehicles.  
After obtaining the vehicle-matching results, we splice the trajectories belonging to the same vehicle.
# Multi-Issue Negotiation Protocol with Pre-Domain Narrowing
## 4 Negotiation Protocol with Pre-Domain Narrowing
### Narrowing Issues Using the Pre-Negotiation  
We also propose a method for determining how to narrow the issues before the main negotiation using pre-negotiation. During the pre-negotiation, negotiation is performed for each issue in the negotiation domain space, in a domain where the options are \"subject to the main negotiation\", \"Agent A chooses\", and \"Agent B chooses\". This pre-negotiation domain is shown in Table 3. In the pre-negotiation method, negotiations are performed in the domain using the alternating offers protocol, as usual, and the alternatives of agreements are decided upon.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Issue** & \multicolumn{2}{c}{**Agent**} & \multicolumn{2}{c}{**Determination Method**} \\  & **A** & **B** & **for Agreeing on an Option** \\ \hline Destination & & & Main negotiation \\ Travel expense & \(\times\) & & Agent B chooses \\ Travel method & & & Main negotiation \\ Accommodation & \(\times\) & \(\times\) & Select randomly \\ Meals & & \(\times\) & Agent A chooses \\ \hline \hline \end{tabular}
\end{table}
Table 2: An example of issue-narrowing using the simultaneous submission method (”\(\times\)” represents an unnecessary issue).  
Figure 1: Outline of negotiation domain space pre-narrowing protocol.  
Based on the consensus reached during pre-negotiations, the issues that will not be the main negotiation subjects are decided. For example, consider the case where the following agreement was obtained in the pre-negotiation: _[Travel destination: Main negotiation, Travel expense: Agent B, Travel method: Main negotiation, Accommodation: Agent A, Meals: Agent A]_. In such a case, Agent A chooses the issues \"accommodation\" and \"meals\", whereas Agent B chooses the issue \"travel expense\". The remaining issues, \"travel destination\" and \"travel method\" are the main negotiation subjects. Notably, if no agreement is reached during the pre-negotiation, the narrowing of issues does not occur, and all issues will be negotiated during the main negotiation phase.  
The domain size of the pre-negotiation method is \((a+1)^{n}\), where \(a\) is the number of agents participating in the negotiation, and \(n\) is the number of issues. If the number of options \(k_{i}\) for each issue in the original domain is greater than 3, the pre-negotiation domain would be smaller than the original domain, making pre-negotiation easier. Compared with the simultaneous submission method, where the mediator makes a random decision for issues submitted as unnecessary by both agents, in this method, the selection is always made by either agent. This allows agents to strategize during the narrowing process; for example, an agent could forgo the right to decide on some issues in exchange for the right to decide on an issue it considers relevant. Thus, the pre-negotiation method makes it easier for both agents to reach a win-win agreement.
# Shifting of Meteorological to Hydrological Drought Risk at Regional Scale
## 2 Materials and Methods
### Spatiotemporal Analysis of Drought
#### 2.2.2 Hydrological Drought Assessment  
The Streamflow Drought Index (SDI) was developed by [36] and is widely used to characterize hydrological drought events. SDI computation also includes fitting distributions to runoff data and estimation of PDF and CDF and then transforming into a standardized distribution, yielding the SDI value [13]. Similarly, the positive SDI value represents wet conditions, while the negative value represents drought conditions. Furthermore, SPI thresholds were adopted for hydrological drought analysis.  
\[V_{i,k}=\sum_{j=1}^{3k}Q_{i,j}\;i=1,2,3,\ldots,n\;j=1,2,3,\ldots,12\;and\;k=1,2,3,4 \tag{3}\]  
\[\text{SDI}_{i,k}=\frac{V_{i,k}-\overline{V}_{i,k}}{S_{k}} \tag{4}\]  
where \(V_{i,k}\) the cumulative runoff for reference period \(k\) and the \(i\) is the hydrological year; \(S_{k}\) is the standard deviation and \(\overline{V}_{k}\) is the mean value of runoff.  
The SDI can also be calculated at various time scales, such as one, three, six, and twelve months. For the estimation of SPI-3, SDI-3, SPI-6, SDI-6, SPI-9, SDI-9, SPI-12, and SDI-12, the cumulative sums of precipitation and runoff for 3, 6, 9, and 12-month timescales were used. When calculating SPI-3 and SDI-3, for example, the cumulative sum for October was obtained by adding the following two-month data (November and December) to the October data [20]. SPI-6 and SDI-6 cumulative sums were calculated by adding six months from October to March.  
Furthermore, the Mann-Kendall method [37] was used to estimate the trend in SPI-3, SPI-6, SPI-9, and SPI-12 at a significance level of 5% and similarly for SDI values at different time scales.
# Optimization of Intelligent Maintenance System in Smart Factory Using State Space Search Algorithm
### Literature Review on Available Solutions
#### 1.1.2 Intelligent Maintenance System (IMS)  
An intelligent maintenance system (IMS) utilizes data collected from machines to predict and prevent potential failures. It is designed to provide decision-making support tools that optimize maintenance processes and help avoid equipment failures. Analysis of machine behavior is facilitated by advanced sensors, data collection systems, storage capabilities, data transfer mechanisms, and data analysis tools. It is essential to identify effective, reliable, and cost-efficient maintenance strategies to ensure consistent production while minimizing unplanned downtime [7; 12]. The Center for Intelligent Maintenance Systems (IMS) was established in 2001 as an industrial collaborative research center/National Science Foundation University (I/UCRC). The vision of the center is to help products and systems achieve and maintain near-zero breakdown efficiency and, ultimately, to transform traditional maintenance practices in a fail-and-fix manner into a method of predicting and preventing [13; 14].  
These predictive data are gathered from interconnected devices across various stages of the production process. These data aid decision-making by providing insights into the current state of all systems, facilitating effective planning for the utilization of assets in a smart factory. This concept underpins the intelligent maintenance system (IMS), which employs the Watchdog Agent(tm) system [15] as illustrated in Figure 5(a). The Watchdog Agent(tm)'s diagnostic and predictive capabilities can significantly enhance the system by enabling performance comparisons. This is particularly beneficial because multiple identical machines often operate simultaneously. This peer-to-peer (P2P) paradigm will enhance diagnostic and predictive performance evaluation functions through co-learning among similar machines or production lines, as illustrated in Figure 5(b).  
Figure 6: Intelligent maintenance system format as (**a**) representation of an intelligent maintenance system; (**b**) peer-to-peer functionalities of the Intelligent Watchdog Agent(tm).
## 2 Experimental Program and Data Preparation
### Material Properties  
The SMA samples were fabricated with three main ingredients: crushed stone coarse and fine aggregates, bitumen and stabilizer. Crushed granite aggregates were collected from a local quarry at Phu Man (Ha Noi, Viet Nam). The physical properties of the fine and coarse aggregates are given in Table 1. The bulk specific density of the mineral filler is 2720 g/cm\({}^{3}\). In this work, asphalt cements were selected as 60/70 penetration bitumen (denoted as Bitumen 60/70) and Polymer Modified Bitumen I (denoted as PMB I). They were provided by Vietnam National Petroleum Group (Petrolimex, Ha Noi, Viet Nam). Physical properties of the bitumen samples are shown in Table 2. Cellulose fiber was added to the SMA mixtures as a drain down inhibitor. It was a type of Chinese-grown fiber and the properties are provided in Table 3.  
\begin{table}
\begin{tabular}{l c} \hline
**Properties** & **Value** \\ \hline
**Coarse aggregate** & \\ \hline Los Angeles abrasion (\%) & 16.22 \\ Flat and Elongated (3 to 1) (\%) & 8.80 \\ Water absorption (\%) & 0.53 \\ Bulk specific density (g/cm\({}^{3}\)) & 2.670 \\ Crushed content (one face) (\%) & 100 \\ Crushed content (two faces) (\%) & 100 \\ \hline
**Fine aggregate** & \\ \hline Water absorption (\%) & 0.79 \\ Bulk specific density (g/cm\({}^{3}\)) & 2.667 \\ \hline
**Mineral filler** & \\ \hline Bulk specific density (g/cm\({}^{3}\)) & 2.720 \\ \hline \end{tabular}
\end{table}
Table 1: Physical properties of coarse, fine aggregates and mineral filler.  
\begin{table}
\begin{tabular}{l c c} \hline
**Properties** & **Bitumen 60/70** & **PMB I** \\ \hline Specific gravity at 25 \({}^{\circ}\)C (g/cm\({}^{3}\)) & 1.030 & 1.027 \\ Penetration at 25 \({}^{\circ}\)C (0.1 mm) & 64.5 & 48 \\ Flash point (\({}^{\circ}\)C) & 310 & 248 \\ Softening point (\({}^{\circ}\)C) & 48.1 & 67.5 \\ Ductility at 25 \({}^{\circ}\)C (cm) & \textgreater{}100 & \textgreater{}100 \\ \hline \end{tabular}
\end{table}
Table 2: Physical properties of Bitumen 60/70 and PMB I bitumen.  
\begin{table}
\begin{tabular}{c c} \hline
**Properties** & **Test Value** \\ \hline Cellulose content (\%) & 85\% \\ Length (mm) & \textless{}5 \\ Diameter (\(\upmu\)m) & 46 \\ Density (g/m\({}^{3}\)) & 1.6 \\ pH Value & 6.5 \\ \hline \end{tabular}
\end{table}
Table 3: Properties of cellulose fiber.
## 3 Recent Advances in Different Configurations of STSCs
### Intrinsic-Based STSCs  
The structural stability of intrinsic STSCs on the surface is optically less efficient compared to that of multilayer absorbers [40]. Categories of intrinsic absorption materials include Fe\({}_{3}\)O\({}_{4}\), Al\({}_{2}\)O\({}_{3}\), LaB\({}_{6}\), and V\({}_{2}\)O\({}_{5}\). For ultra-high temperature applications, ceramics such as ZrB\({}_{2}\), SiC, HfB\({}_{2}\), TaC, and ZrC are used as intrinsic STSCs [41; 42]. Transition metals, when combined with oxygen and nitrogen, can enhance the absorption of solar radiation in the visible region of the electromagnetic spectrum due to the interaction of their empty  
Figure 5: Schematics of various solar selective absorber coating designs [31].  
orbitals [43]. These materials that are optically less effective but structurally more stable are commonly found among transition metals (or lanthanides). These materials benefit from the partially occupied orbitals of transition metal ions, which can readily gain or lose electrons. When combined with nitrogen, oxygen, or oxynitrides, these orbitals have energy-level gaps that are well suited for absorbing visible photons [24]. This chemical characteristic makes these materials promising candidates for STSC surfaces. While intrinsic absorbers alone are not ideal for use in STSCs without modifications, they are well suited as components in multilayer and composite surfaces for CSP applications [44]. [PERSON] and [PERSON] [45] reported an exceptional example of the Pyromark 250 proprietary coating, which has been utilized in high-temperature CSP configurations at temperatures ranging from 600 to 1000 \({}^{\circ}\)C. This coating has demonstrated an absorptance of 0.96 and an emittance of 0.80, even under heating and cooling cycles.
## 4 Fluid Dynamic Performance
### Experimental Techniques
#### 4.2.1 Flow Visualization  
In order to perform flow visualization, the flow streak-line photography technique is a method in which the fluid is seeded with suitable tracer particles, and long-exposure photographs are taken in order to characterize the Lagrangian path of a fluid element particle. With this methodology, an analysis of the path line can be done to assess the length of the wake formed behind the prototype. An example of this type of measurement can be found in Figure 4, where an emerging wake appears. The length of this wake can be calculated using image processing analysis.
# Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review
## 2 Defining A Proxy in Remote Sensing  
Indirect estimation using proxies allows researchers to deduce the condition of an element or process based on their relations and links with directly identifiable and observable features in the surrounding/neighboring areas. According to the Oxford Dictionary, a proxy is a figure that can be used to represent the value of a non-directly measurable object in a calculation. In statistics, it is a measurable variable that is used in place of a variable that cannot be measured [35]. Moreover, there are good examples of using proxies in economics, such as per capita Gross Domestic Product (GDP) being a proxy for growth in wealth and potentially quality of life [36].  
There is currently no clear definition for proxy in RS-based studies, though proxies play a critical role in this field. When considering the growing role of proxies in RS, similarities and differences can be found when compared with the traditional use of proxies, particularly in the wider geosciences (including climatology). In all disciplines, proxies have been used as indirect variables to determine or to constrain unobservable or immeasurable variables or as physical variables to go back in time and measure the immeasurable or unobservable parameters because of lack of data from that time. However, passive RS is by definition an indirect form of measurement and an information source that acquires data at a certain time. By that logic, any image derivative or index measure is a proxy. Therefore, the traditional definition of the proxy cannot be used in this field. In this paper, the term proxy is defined as use of observable physical features or directly measurable variables to understand and extract what actually exists on the ground, but what is not directly observable or measurable from RS data. For example, the proportion of built-up and vegetated area has been measured and used as a proxy to determine a settlement type to determine the vulnerability of such settlements to disasters [33]. Proxies can also be separated into two groups based on their use in the RS field:  
1. not-directly observable physical parameters, e.g., use of shadow to determine the height of an element at risk (building) [37; 38],
2. and measures to assess processes and functions, e.g., the building morphology and use to determine functionality of buildings and urban areas to assess the post-disaster recovery processes and vulnerability [39].