esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Curing Time/Shear Strength of Non-Conductive Adhesives Using a Neural Network Model
## 2 Materials and Methods  
### Non-Conductive Adhesives Formulation  
In this study, 4 types of resins, 3 types of hardeners, 8 types of catalysts, and a coupling agent were selected. Epoxy resins employed in this study were diglycidyl ether of bisphenol A (DGEBA), diglycidyl ether of bisphenol F (DGEBF), diglycidyl ether of bisphenol F with Novolac (Novolac 160), and 3,4-Epoxycyclohexylmethyl-3\({}^{\prime}\),4\({}^{\prime}\)-epoxycyclohexane carboxylate. Hardeners employed were Methylhexahydrophthalic anhydride (MHHPA), tetrahydrophthalic anhydride (THPA), and adipic dihydrozide (ADH). Catalysts employed were novacure (micro-encapsulated imidazole type latent catalyst), imidazole, 2-methylimidazole (2 MZ, powder imidazole type catalyst), 2- ethyl-4-methylimidazole (2E4 MZ), 1-Cyanoethyl-2-ethyl-4-methylimidazole (2E4 MZ-CN), 2,4-diamino-6-[2\({}^{\prime}\)-methyl imidazolyl-(1\({}^{\prime}\))]-ethyl-s-triazine (2 MZ-A), dicyandiamide (DICY), and triphenylphosphine. A coupling agent of gamma-methacryloxypropyltrimethoxysilane was used. The chemical properties of NCA ingredients are summarized in Table 1.  
Hardeners of MHHPA and THPA were formulated at 1:0.8 of epoxy to hardener equivalent ratio, and ADH hardener was formulated at 1:1 of epoxy to hardener equivalent ratio. Based on the contents of resin and hardener, catalysts were formulated from 5 phr (per hundred resin) to 30 phr, and lastly, 0.55 phr of coupling agent was added.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ingredient** & **Chemical Name** & **Abbreviation** & **Chemical Structure** & **Molar Mass** & **Melting Point** \\  & Diglycidyl ether of & & **(g/mol)** & **(\({}^{\circ}\)C)** \\  & bisphenol A & DGEBA & & & \\ \cline{2-6}  & Diglycidyl ether of & & & & \\ \cline{2-6}  & Diglycidyl ether of & & & & \\ \cline{2-6} Resin & Diglycidyl ether of & & & & \\ \cline{2-6}  & Diglycidyl ether of & & & & \\ \cline{2-6}  & bisphenol F with & & & & \\ \cline{2-6}  & Novolac & & & & \\ \cline{2-6}  & 3,4- & & & & \\ \cline{2-6}  & Epoxycyclohexanemethyl & & & & \\ \cline{2-6}  & 3,4-epoxycyclohex- & & & & \\ \cline{2-6}  & anecarboxylate & & & & \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{} & Methylhexahydrophthalic & MHHPA & & & \\ \cline{2-6}  & anhydride & & & & \\ \cline{2-6}  & Tetrahydrophthalic & & & & \\ \cline{2-6}  & anhydride & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical properties of NCA.
## 2 Materials and Methods
### Study Variables  
#### 2.6.1. Satiety  
Satiety was assessed using the visual analogue scale (VAS), a previously validated questionnaire [23,24] that was applied before, immediately after, and at the time of a standardized breakfast [25]. The satiety test was performed at four different times during the intervention period (initial visit, 30 days, 60 days and 90 days). To perform this test, participants were asked to arrive at the laboratory (in cases where attendance in person was not possible, a video call was used) in the morning after a 12 h overnight fast, and to refrain from alcohol consumption and intense physical activity for 24 h prior to the testing session. The standardized breakfast had an energy content of 616 kcal (2577.34) for men and 477 kcal (1995.77 kJ) for women [3]. For men, breakfast consisted of 200 mL of semi-skimmed milk or unsweetened soy drink, 100 g of whole grain bread with 6 g of oil, 50 g of chicken or turkey breast, 20 g of walnuts, and 200 g of apple. Macronutrient content comprised 82 g of carbohydrates, 19 g of fat, and 29 g of protein. For women, breakfast consisted of 200 mL of semi-skimmed milk, 60 g of whole grain bread with 3 g of oil, 40 g of chicken or turkey breast, 15 g of walnuts, and 200 g of apple. Macronutrient content was 65 g of carbohydrates, 15 g of fat, and 23 g of protein. All participants were instructed to consume the entire meal within 30 min. Before, immediately after, and 60 min after the standardized breakfast, participants were asked to record their appetite sensations using the visual analogue scale (VAS), evaluating their \"desire to eat\", \"hunger\", \"fullness\", and \"prospective food consumption\". Participants were asked to indicate on a scale from 0 to 10 how they felt at the time they answered these questions, for example, \"How strong is your desire to eat?\" (very weak--very strong). Subjects were asked to mark the line corresponding to their feelings at each moment. Figure 3 summarizes the VAS questions and the possible answers and ratings.  
Figure 2: Variables evaluated in each of the visits.
## 5 Evaluation Scheme and Metrics Used  
For the evaluation of the baseline detector and subsequent line polygonization, we used two different metrics. First, we evaluated individual foreground pixels in the document images. A foreground pixel was counted as a TP if it was contained in a prediction text polygon and a GT text polygon, an FP if it was only contained in the prediction, and likewise, an FN if it was only contained in the GT. Using the TP, FP, and FN, we calculated the per-pixel text precision, recall, and F1-score.  
In order to evaluate the accuracy of the generated text polygons for the OCR step later in the pipeline, individual instances of detected page elements (e.g., text lines and dropped capitals) were evaluated. Therefore, we used the following metric: Each GT page element was matched to its best-fitting predicted page element using a maximal bipartite matching using the intersection over union (IoU) measure on the foreground pixels of the predicted and ground truth polygons as a distance function. The polygon was counted as detected if it matched with a GT polygon with an IoU score of 0.9 or higher. For small elements (elements that are not wider than 10% of the page's width), an IoU threshold of 0.75 was used. A detected polygon was counted to be of the correct type (\"correct\") if its class label matched the label in the ground truth.  
Based on the correctly classified polygons, we calculated the precision (prec) and recall (rec) for each class, counting correctly predicted elements as true positives, undetected ground truth elements as false negatives, and other predicted lines of that class as false positives.  
In addition, we evaluated if the line endings of the detected elements matched a GT polygon. The line endings were counted as correct if the left and right side of a detected polygon and its best-fitting ground truth polygon did not differ by more than a given threshold (1%) of the page's width (disregarding white space on either side), which is approximately half the width of a character for the 1% threshold. Therefore, line ending accuracy describes the fraction of detected polygons with correct line endings among all detected GT elements of a given class using a certain threshold.  
We argue that this measure is more representative for the evaluation of detected text elements on a page than other commonly used measures, such as the FePA [10] or the mIoU metric [11], as even small errors at the beginning or ending of a detected line polygon can negatively affect the subsequent OCR step and lead to incorrectly recognized or missing characters. The fact that many lines end with a hyphen or punctuation strengthens the negative effect, as such characters are small and, therefore, contribute even less than regular characters to the overall score in the metrics such as the mIoU or per-pixel accuracy.
## 2 Detection Algorithms
### FFT-Based Detection  
A classical approach is to apply Discrete Fourier Transform (DFT) to convert the signal to the frequency domain:  
\[Y[k]=\sum_{n=0}^{N-1}y[n]e^{-j2\pi kn/N},\quad k=0,\ldots,N-1. \tag{2}\]  
The detection decision is based on identifying the frequency bin with the highest energy:  
\[\hat{k}=\arg\max_{k}|Y[k]|,\quad\text{Declare detection if }|\hat{k}-k_{0}|\leq\delta, \tag{3}\]  
where \(k_{0}=\lfloor f_{0}N\rfloor\) and \(\delta\) make up a tolerance margin (typically one FFT bin).  
In Equation (3), the index \(\hat{k}\) corresponds to the detected peak location in the FFT spectrum, which directly maps to the target's beat frequency and thus to its range. The frequency resolution of the FFT is given by \(\Delta f=f_{s}/N\), where \(f_{s}\) is the sampling frequency and \(N\) is the FFT size. This frequency resolution determines the radar's range resolution \(\Delta R\), which is defined as  
\[\Delta R=\frac{c}{2B}, \tag{4}\]  
where \(c\) is the speed of light and \(B\) is the chirp bandwidth. The minimum detectable range difference between two targets is therefore constrained by this resolution. Accordingly, the tolerance margin \(\delta\) in Equation (3) should be chosen relative to the number of FFT bins per range resolution cell. For example, if zero-padding is applied to increase spectral resolution, the value of \(\delta\) should still reflect the true range resolution determined by \(B\), and not just the FFT bin width. This ensures that the peak detection criterion \(\left|\hat{k}-k_{0}\right|\leq\delta\) aligns with physical radar limits in resolving two nearby targets.
## 2 Research Area and Data Sources
### Data Sources Used in This Study  
Table 1 lists the sources for data used in this study, and all data are freely accessible. The digital elevation model (DEM) has a spatial resolution of 30 m \(\times\) 30 m that is used to generate elevation, slope, aspect, profile curvature, and topographic wetness index (TWI). Distance to faults, distance to roads, and distance to rivers are produced through the buffer analysis function of ArcGIS. The lithology and faults are obtained by digitizing the hard copy of 1:500,000 geological-map. The average annual rainfall is obtained by spatial interpolation based on cumulative annual average rainfall (1981-2010) downloaded from the National Meteorological Science Data Center of China. Continuous causative factors (e.g., elevation, slope, profile curvature, annual average rainfall) are converted to discrete groups using discretization. This study applies a complete landslide inventory [46] (Figure 2) to train and validate the proposed LSM method. The inventory was produced based on the combination of visual interpretation and field investigation using pre- and post-earthquake multi-source imagery from different platforms (e.g., very high-resolution satellite images, aerial photos, and field surveys). This landslide inventory records detailed landslide information, such as distribution, size, and volume of landslide. To reduce the influence of asynchrony between landslide inventory and causative factors, this study selected DEM, geological data, annual average rainfall, soil, and landform as close as possible to the occurrence time of the Wenchuan earthquake, and accordingly updated land use, road and river networks using very high satellite images acquired from 10 October 2007 to 1 April 2008.
# An Improved Hilbert-Huang Transform for Vibration-Based Damage Detection of Utility Timber Poles
## 5 Results and Discussion
### Improved HHT
#### 5.3.2 Selection of Decomposition Level  
In order to select the decomposition levels, it is suitable to select more decomposition levels for reducing the noise interference and extract desirable signal features. However, part of the important local singularities will be lost in the process. Since sampling frequency of 100 kHz gives a signal bandwidth of 50 kHz according to Nyquist sampling theorem, level 5 decomposition level has been selected so that the dominant frequency components between (0 and 1500 Hz) range can fall into node (5,0) of WPT tree. After that, this [5,0] coefficient is reconstructed for all the 12 accelerometers. Frequency band of (5,0) coefficient (0-1562.5 Hz) is considered as the dominant frequency band for our study (based on Figure 8b). An example of reconstructed signal is illustrated in Figure 14. Finally, the reconstructed signals are further decomposed using EMD.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Mother** & **A0L1** & **A0L2** & **A0R1** & **A0R2** & **A90L1** & **A90L2** & **A90C1** & **A90C2** & **A180L1** & **A180L2** & **A180R1** & **A180R2** \\ \cline{2-11}
**Wavelet** & \(R(s)\) & \(R(s)\) & \(R(s)\) & \(R(s)\) & \(R(s)\) & \(R(s)\) & \(R(s)\) & \(R(s)\) & \(R(s)\) & \(R(s)\) & \(R(s)\) \\ \hline db1 & 0.1581 & 0.1523 & 0.1612 & 0.1575 & 0.1588 & 0.1559 & 0.1480 & 0.1412 & 0.1571 & 0.1483 & 0.1516 & 0.1622 \\ db4 & 0.1577 & 0.1542 & 0.1605 & 0.1577 & 0.1583 & 0.1567 & 0.1509 & 0.1404 & 0.1610 & 0.149
# Techno-Economic Potential of Plasma-Based CO\({}_{2}\) Splitting in Power-to-Liquid Plants
## 2 State of the Art
### Plasma-Based CO\({}_{2}\) Splitting--A Short Review
The energetic consideration was carried out via the specific energy consumption, which amounted to 90 MJ/mol at the maximum conversion. Converted into the previously defined efficiency of CO\({}_{2}\) splitting, this resulted in \(\eta_{\rm CO_{2}}=0.31\%\). At \(SEI=6.2\times 10^{7}\) J/kg\({}_{\rm CO_{2}}\), a conversion of 40% was obtained with a specific energy consumption of 30 MJ/mol (\(\eta_{CO_{2}}=0.94\%\)). Under the mixture of CH\({}_{4}\) (ratio 1:1 molar), conversions of \(\chi_{CO_{2}}\approx 85\%\) were obtained with a specific energy consumption of 80 MJ/mol. In the economic analysis, capital costs of \(CAPEX=\rm EUR~{}188,143\) were estimated for a 144 kW pilot plant, based on reactor costs and the associated power supply, which corresponds to specific costs of EUR 1.300/kW for this size. As part of the PlasmaFuel joint project, a gliding arc reactor was developed, and its results were published in two papers by [PERSON] et al. [40; 41]. The GA discharge plasma  
Figure 2: Experimental conversion rates \(\chi_{CO_{2}}\) and energy efficiencies \(\eta_{CO2}\) of plasma technologies for CO\({}_{2}\) conversion [Renninger2021] [27]: gliding arc discharge (GA) performed by [PERSON] et al. [[PERSON]] [28], [PERSON] et al. [[PERSON]] [29] and [PERSON] et al. [[PERSON]] [27]. Microwave plasma (MW) was performed by [PERSON] et al. [[PERSON]] [30] and [PERSON] et al. [[PERSON]] [31]. Dielectric barrier discharge (DBD) was performed by [PERSON] et al. [[PERSON]] [32], [PERSON] et al. [[PERSON]] [33], and [PERSON] et al. [[PERSON]] [34]. Radio frequency discharge (RF) was performed by [PERSON] et al. [[PERSON]] [35]. Atmospheric glow discharge plasma (APGD) was performed by [PERSON] et al. [[PERSON]] [36] and [PERSON] et al. [Raja2020] [37]. reactor achieved a conversion of CO\({}_{2}\) of 27% at an energy efficiency of 42% at ambient pressure. The good performance was attributed to an efficient sweeping of the gas by discharge due to the magnetic field. The reactor setup used a magnetic field to force the plasma into a large disc-like volume and lead the working gas with a laminar gas flow through. The highest performance was achieved at \(P_{el}=160\) W and \(\dot{V}_{in}=1.25\) SLM, which corresponded to \(3.89\times 10^{6}\) J/kg\({}_{\text{CO}_{2}}\).
# Comparison of Traffic Flow Models with Real Traffic Data Based on a Quantitative Assessment
## 2 Theoretical Background  
### Modeling Equilibrium Traffic Flow Relationships  
Fundamental relationship models are divided into single-regime and multi-regime models, depending on how traffic conditions are described. Single-regime models describe the relationship using a single mathematical function for an entire range of traffic conditions. Multi-regime models use two or more mathematical functions for this purpose, each representing different conditions of traffic.  
An important feature of speed-flow-density relationships is the presence of characteristic, boundary values of the particular relations (boundary points, extremes, and points of inflection):  
* Boundary points: free flow speed (\(\mathrm{v}_{\mathrm{sw}}\)) occurs theoretically when volume and density approach zero (\(\mathrm{k}\to 0\), \(\mathrm{q}\to 0\)); maximal density (\(\mathrm{k}_{\mathrm{max}}\)) occurs when flow and speed approach zero (\(\mathrm{q}\to 0\), \(\mathrm{v}\to 0\));
* Extremes and points of inflection of the function: maximal flow (\(\mathrm{q}_{\mathrm{max}}\)) and the corresponding optimal speed (\(\mathrm{v}_{\mathrm{opt}}\)) and optimal density (\(\mathrm{k}_{\mathrm{opt}}\)).  
The values of these parameters define the particular traffic regimes: free-flow conditions when density increases, flow rises from zero until the maximal value is reached (\(\mathrm{q}\to\mathrm{q}_{\mathrm{max}}\)), and speed decreases from the maximal value (\(\mathrm{v}_{\mathrm{sw}}\)) until the optimal value is reached (\(\mathrm{v}\to\mathrm{v}_{\mathrm{opt}}\)); congested conditions when density keeps increasing until it reaches the maximal value (\(\mathrm{k}\to\mathrm{k}_{\mathrm{max}}\)), causing speed and flow to decrease to zero (\(\mathrm{v}\to 0\), \(\mathrm{q}\to 0\)). The characteristics helps to identify the boundary values of the fundamental relationship (further referred to as BC1 and BC2): (a) at zero density, the speed is equal to the free flow speed, (b) at maximum density (jam density), the speed is zero. These conditions are referred to as static properties of traffic flow [2].
# Modified Index Policies for Multi-Armed Bandits with Network-like Markovian Dependencies
## 3 Preliminaries
### Markov Decision Processes in Bandit Problems  
In MABs, a Markov Decision Process (MDP) provides a framework for decision-making where transitions between states are determined not only by the current state but also by the action taken by the decision-maker. Each action in an MDP results in a reward and a transition to the next state where each arm pull can be viewed as an action within a potentially networked system of state dependencies.  
In a typical MAB problem with Markovian rewards, each arm represents an independent Markov process. The player's objective is to maximize cumulative rewards over a sequence of arm pulls. The decision of which arm to pull involves evaluating the current state of each arm and estimating potential rewards based on state transition probabilities, akin to navigating networked dependencies where each choice impacts future outcomes in interconnected states.
## 5 Discussion
Furthermore, hydrological models would also benefit from the integration of continuous and updated information about the real-time situation, having the opportunity to check the quality of their setup as well as improving the quality of their outputs. Finally, the integration of multi-sensor datasets will generally increase the probability of clear sky acquisitions, reducing the impact of clouds, which are the main limitations of optical satellite observations, especially for this kind of application. It is worth mentioning that cumulated flooded maps of the event, allowed by high temporal resolution weather satellites, could help for a better delineation of the involved area, enabling an assessment and relative updating of the flooded risk map. Basilicata rivers are representative of small hydrological basins, with small-size cross-sections (<100 m); therefore, the achieved results indicate that data acquired by medium-resolution optical sensors, if adequately analyzed, can also be profitable for flood detection monitoring of watersheds. In the near future, other test cases should be studied to further confirm the quality of the results achieved here by investigating a different scenario from the one considered here. For example, flood events of a smaller size (in terms of both channel width and floodplain extension) than the ones analyzed here should be investigated to better understand the sensitivity limit of the proposed approach when applied to data at 375 m of spatial resolution. Events occurring in urban areas should also be analyzed to confirm the feasibility of the methodology presented here. Furthermore, it is worth mentioning that while the proposed indices are almost \"mandatory\" for MODIS data, considering the spatial resolution constraints, VIIRS has other imagery bands, such as the I3 (SWIR: 1.58-1.64 m), which will enable other combinations currently under investigation.
# Thermal, Multispectral, and RGB Vision Systems Analysis for Victim Detection in SAR Robotics
## 2 Related Works  
### Context and Historical Evolution  
The utilization of visible information captured by cameras is pivotal across various domains of robotics and other scientific disciplines for the analysis of processes, states, and decision-making. Presently, in search and rescue robotics, a substantial portion of the utilized information originates from the visible spectrum of light (RGB images) and a segment from the infrared spectrum (thermal images). However, the multispectral spectrum has been relatively underexplored in this realm of robotics, particularly in identifying victims during search and rescue missions. Figure 1 shows three distinct spectral ranges of light, specifically RGB [400 nm-700 nm], thermal infrared [8 \(\upmu\)m-14 \(\upmu\)m], and multispectral (Red Edge [690 nm-730 nm] and Near-Infrared [NIR] [750 nm-2.5 \(\upmu\)m]), as described previously. Notably, the human-perceptible visible light spectrum (RGB range) is considerably narrower than the infrared or ultraviolet spectra, which are percepti  
Figure 1: Ranges of light in the visible and non-visible spectrum. Source: Authors. such as snakes or insects. Due to its widespread application in agriculture, the multispectral range is particularly distinctive in victim detection. The selection of these spectral ranges is informed by the anticipated directions of future research, as delineated in contemporary studies. It aligns with the specifications of commercially available portable data acquisition instruments (images) compatible with robot payloads less than 10 kg. A comprehensive examination of the research works over the past two decades is necessary for scientific research on RGB, Thermal, and Multispectral sensors within search and rescue operations. A literature search was conducted using the online database Google Scholar to generate a chronological graph depicting the evolution of publications (Figure 2). The graph identifies three types of histograms: yellow, grey, and blue. These histograms were generated as follows: the search criteria for each type of sensor defined by colour were the respective phrases 'Multispectral sensors in search and rescue', 'Thermal sensors in search and rescue', and 'RGB sensors in search and rescue'. An annual search was performed