esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Solution for the Problem of Point Location in Arbitrary Planar Domains
## 5 NRA Crossing in Parametric Paths
### NRA Crossings and Their Sense for Linear Segments
In summary, if a path does not intersect the NRA (-\(\infty\)), the calculation of the line integral is simple (actually, not necessary in practice) because there are no discontinuities in the process and, in the end, all terms of the closed path will cancel altogether; only the intersections of the path with the NRA will affect contour evaluation or the windingnumber. Also, notice that horizontal segments (\(y_{1}=y_{2}\)) never intersect NRA; they may even lie over it but never actually cross it, so their contribution to the \(I_{C}\) is null, as can be seen in the segment that contains the \(a_{2}\) point in Figure 9.  
The major novelties in this approach for polygonal regions are that the existence of NRA crossing can be determined by detecting the value of a single parameter \(t\) and the sense of crossing can be determined by a simple comparison operation.
## 3 Data Acquisition and Pre-Processing
### Image Acquisition  
A dataset of fruit and vegetable images was obtained using the prototype laboratory setup based on considering the real-world supermarket environment. The prototype design was considered carefully to maintain the integrity among the images obtained with both vision sensors used. This integrity is important in order to use the obtained dataset for transfer learning of the CNN for classification, and maintaining classification effectiveness among the images of multiple sensors. The images of 15 different classes of fruit and vegetables were obtained where each class consists of 1000 images. The images were cropped to a maximum size of 3000 \(\times\) 3000 pixels for both sensors, the initial resolution of obtained images is presented in Table 2 for both sensors. This image size was selected by considering the variations in sizes of fruit or vegetables used for building the dataset. The images were further ordered in a unified nomenclature along with the weight of individual fruit and vegetables saved in a separate repository. A description on the nomenclature and average weight of each class is presented in Table 3. Uniform ordering was achieved with the help of the nomenclature to integrate the weights and images in the dataset and to make the dataset consistent for future applications. Examples of obtained images are shown in Figure 3.
# Multi-Frequency Target Detection Techniques for DVB-T Based Passive Radar Sensors
## 4 Theoretical Detection Performance of Multi-Frequency Approaches
With all the considered detection schemes, the actual false alarm rate coincides with the nominal value only for uncorrelated disturbance contributions (notice that this is always the case for the best single channel). In contrast, at the worst DVB-T channel, the actual false alarm rate rapidly increases, as the correlation coefficient exceeds 0.5 as the resulting secondary data correlation yields an underestimation of the detection threshold. However, the results of this simulation show that the MF approaches allow a partial mitigation of this effect since significant deviations from the nominal false alarm rate are observed only for high values of the correlation coefficient. In this case the centralized and the decentralized scheme with \(L=3\) yield comparable results. In Figure 4b we consider the case of a non-homogenous region appearing in the secondary data in two out of the four DVB-T channels. Specifically, we assume that this region contains additive complex Gaussian noise with a power level that deviates from the homogeneous background level by \(\Delta\) dB. This region contaminates 10 out of the 30 available secondary data and appears at different pixels at the two affected channels. As is apparent the false alarm rate control is guaranteed only when \(\Delta=0\) dB (i.e., for homogeneous secondary data) when DVB-T channel are employed that are affected by such non-homogeneity. In fact, the actual false alarm probability rapidly increases/decreases as \(\Delta\) deviates from 0 dB. However, again, the MF schemes allow to mitigate the effect of the non-homogeneity by providing a more stable false alarm rate. In particular, in this case, the decentralized approach shows slightly better performance with respect to the centralized MF scheme. The comparative analysis above gave some insights into the properties of the proposed MF approaches for specific case studies. However, for a full validation of the effectiveness and of the relative performance of the different MF techniques, the application against experimental data sets is strictly required. Basically, many phenomena can occur with live data that cannot be easily simulated or even modeled but that are typical of the considered applications.
## 3 Results
### Model Accuracy and Fractional Vegetation Coverage
The MAPE of FAPAR\({}_{\text{LAI}}\) were 14.0%, 83.4%, and 164.4%, respectively, and the FAPAR\({}_{\text{FVC}}\) were 14.0%, 65.4%, and 97.9%, respectively, which were reduced by 0.2%, 21.5%, and 67.9%, respectively, under high, medium, and low FVC conditions; the MPE of FAPAR\({}_{\text{LAI}}\) were 7.6%, 78.4%, and 158.2%, respectively, and the FAPAR\({}_{\text{FVC}}\) were \(-\)4.1%, 33.1%, and 25.4%, respectively, which were reduced by 45.9%, 79.1%, and 83.9% in terms of quantity, respectively (Table 4). For the FAPAR\({}_{\text{MOD}}\) overestimated biome types, the average FVC was 60.7 \(\pm\) 24.4%, whereas for the biome types underestimated at FAPAR\({}_{\text{MOD}}\), the FVC was 72.3 \(\pm\) 17.9% (Table 5).
# Regional Seismic Damage Simulation of Corroded RC Frame Structures: A Case Study of Shenzhen City
## 6 Case Studies of Shenzhen City
### Results of Simulation  
To compare the simulation results for cases with and without corrosion considered, regional seismic damage simulations for the area in uncorroded state and in corrosion scenario I (Figure 13a) are performed using the M7.5 earthquake scenario (Figure 15b). The simulated distributions of building seismic damages are illustrated in Figure 16. The results in Figure 16a,b indicate that only slight differences of building seismic damages can be found in the red-framed areas. This is because in corrosion scenario I, only a few buildings located in the red-framed areas are corroded. These buildings are very close to the coastline and are susceptible to airborne chloride-induced corrosion (Figure 13a). When corrosion is considered for the case in Figure 16b, these coastal buildings exhibit more severe damage as compared with those in Figure 16a.  
To compare the two corrosion scenarios (Figure 13), regional seismic damage simulations are conducted using the M6.5 earthquake scenario and the results are shown in Figure 17. The seismic damage results of buildings in corrosion scenario II (Figure 17b) are more severe than those in corrosion scenario I (Figure 17a). This is because in corrosion scenario II, some old buildings are assumed to be built using sea sand as construction material and severe chloride-induced corrosion can be observed for these buildings (Figure 13b), which results in more severe damage as compared with those in corrosion scenario I (Figure 17a).  
From the above simulations, corrosion of reinforcements has a relatively large impact on the seismic performance of buildings. The seismic damage simulation results of buildings in corrosion scenario I indicate that airborne diffusion-induced corrosion primarily affects RC buildings near the coastline. The simulation results of buildings in corrosion scenario II show that irregular use of sea sand in RC structures can accelerate the corrosion process, and therefore buildings far from the coastline can also suffer from corrosion-induced seismic performance deterioration.  
Figure 16: Distributions of building seismic damage results (M7.5). (**a**) No corrosion; (**b**) Corrosion scenario I
# Workload Assessment of Operators: Correlation Between NASA-TLX and Pupillary Responses
###### Abstract  
Operators in high-stress environments often face significant cognitive demands that can impair their performance, underscoring the need for comprehensive workload assessment. This study aims to study the relationship between subjective self-reported measures, the NASA task load index (NASA-TLX), objective bio-signal measures, and pupillary responses. The participants engaged in either a visual tracking task or a laparoscopic visuomotor task while their eye movements were recorded using a Tobii Pro Nano eye tracker (Tobii Technology Inc., Stockholm, Sweden). Immediately after completing the tasks, participants provided NASA-TLX scores to assess their perceived workload. The study tested three hypotheses: first, whether increased pupil dilation correlates with higher NASA-TLX scores; second, whether task type affects workload; and third, whether task repetition influences workload. The results showed a moderate positive correlation between pupil size and NASA-TLX scores (r = 0.513, \(p<\) 0.001). The laparoscopic surgery task, which requires visuomotor coordination, resulted in significantly higher NASA-TLX scores (t = -6.23, \(p<\) 0.001), larger original pupil sizes (t = -22.57, \(p<\) 0.001), and more adjusted pupil sizes (t = -22.57, \(p<\) 0.001) than the purely visual task. Additionally, task repetition led to a significant reduction in the NASA-TLX scores (t = 2.86, \(p=\) 0.005), the original mean pupil size (t = 5.50, \(p<\) 0.001), and the adjusted pupil size (t = 6.34, \(p<\) 0.001). In conclusion, the study confirms a positive correlation between NASA-TLX scores and pupillary responses. Task type and repetition were found to influence workload and pupillary responses. The findings demonstrate the value of using both subjective and objective measures for workload assessments.  
workload assessment; NASA-TLX; pupillary responses; visual-tracking task; laparoscopic task +
Footnote †: journal: Applied sciences  
1
# Batch and Column Adsorption of Phosphorus by Modified Montmorillonite
###### Abstract  
Phosphorus pollutants are a crucial component of water eutrophication. In this study, montmorillonite modified by Keggin Al\({}_{13}\) and hexadecyltrimethyl ammonium (Al\({}_{13}\)-O-MMt) was used as an adsorbent to remove phosphorus from solutions and thus simulate the practice of a field trial, such as in wastewater. The ammonium molybdate spectrophotometric method was used to determine the concentrations of phosphorus in samples. In the batch experiment, phosphorus was adsorbed by original montmorillonite (MMt) and Al\({}_{13}\)-O-MMt at various pH values (6-9) to identify the effect of pH during the adsorption process. The batch adsorption results demonstrate that Al\({}_{13}\)-O-MMt can adsorb up to 93% of phosphorus at pH = 8. Six graduated amounts (0.01-0.25 g) of montmorillonite were tested at three different temperatures to determine the most suitable temperature and the minimum dosage of Al\({}_{13}\)-O-MMt needed for the adsorption of 200 mg/L phosphorus in a 30 mL solution, which was 0.1 g at 25 \({}^{\circ}\)C. Therefore, the adsorption capacity of Al\({}_{13}\)-O-MMt was found to be 60 mg/g. Subsequently, a column experiment was conducted. The results showed that the optimized dosage of Al\({}_{13}\)-O-MMt was 6.667 g for phosphorus adsorption with a concentration of 200 mg/L in 2000 mL solution, and the breakthrough time was 4794.67 min.  
olumn adsorption; modified montmorillonite; phosphorus; batch adsorption +
Footnote †: journal: Applied Sciences
## 4 Algorithms and implementations  
There are several ways of computing the decomposition of a graph into atoms. The simplest is to implement Decomposition Step 3.1. We will use Algorithm MCS-M [9], a search algorithm which computes a minimal triangulation \(H=(V,E+F)\) of the input graph \(G=(V,E)\), represented by a set \(F\) of fill edges, as well as an ordering \(\alpha\) of the vertices. MCS-M numbers the vertices from \(n\) to 1, and the ordering \(\alpha\) obtained is a meo of the input graph \(G\) and a peo of the minimal triangulation \(H\) which is computed [14]. Once this ordering and this triangulation are computed, we will use them in a second algorithm, (Algorithm Atoms), which will generate the atoms by scanning the vertices from 1 to \(n\). We will start by giving the general MCS-M algorithm, then an expanded version, which we call MCS-M+, as will be explained afterwards, and finally give the algorithm which yields the atoms. \(G^{\prime}\) will be the transitory subgraph of \(G\) induced by the set of still unnumbered vertices. ```
Algorithm MCS-M input : An undirected graph \(G=(V,E)\). output : A minimal elimination ordering \(\alpha\) on \(V\) and a minimal triangulation \(H=(V,E+F)\) of \(G\). init:\(F\leftarrow\emptyset\); \(G^{\prime}\gets G\); Initialize the labels of all vertices as \(0\) ; for\(i=\)n downto 1 do  Choose a vertex \(x\) of \(G^{\prime}\) of maximal label ; \(Y\gets N_{G^{\prime}}(x)\) ; foreach vertex \(y\) of \(G^{\prime}\) not belonging to \(N_{G^{\prime}}[x]\)do ifthere is a path from \(x\) to \(y\) in \(G^{\prime}\) such that every internal vertex on the path has a label strictly smaller than \(label(y)\)then \(Y\gets Y+\{y\}\); foreach\(y\) in \(Y\)do \(F\gets F+\{xy\}\); \(label(y)\gets label(y)+1\) ; \(\alpha(i)\gets x\) ; Remove \(x\) from \(G^{\prime}\) ; \(H\leftarrow(V,E+F)\);
```  
We will now expand this algorithm in two ways. First, we will implement \"if there is a path from \(x\) to \(y\) in \(G^{\prime}\) such that every internal vertex on the path has a label strictly smaller than \(label(y)\)\". We do this in a similar way as in the implementation of LEX M given in [13] by a single search in \(G^{\prime}\). For each label value \(j\), set \(reach(j)\) contains the reached vertices having label \(j\), as well as the vertices having a label strictly smaller than \(j\) reached from a vertex having label \(j\). Second, we will compute the set \(X\) of vertices that generate the minimal separators of the triangulation \(H\), as described in [5]. The idea behind this is that as long as the labels of the chosen vertices keep getting larger, we are inside a clique of \(H\); when suddenly the label of the chosen vertex \(x\) stops getting larger, \(x\) generates a minimal separator of \(H\). Each such vertex \(x\) is added to set \(X\). **Algorithm MCS-M+**  
```
input : An undirected graph \(G=(V,E)\). output : A minimal elimination ordering \(\alpha\) on \(V\), a minimal triangulation \(H=(V,E+F)\) of \(G\),  and the set \(X\) of vertices which generate a minimal separator of \(H\).
## 5 Experimental Results and Discussion
### AR Rendering
In both devices, higher frame rates were achieved for \"POTTERY\" model compared to the fixed CPU array storages. This result indicates the increased efficiency of GPU memory types in attributes transfer, like texture coordinates and relevant bindings. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \begin{tabular}{c} **Timing** \\ **Metrics** \\ \end{tabular} & \multicolumn{2}{c}{**POTTERY**} & \multicolumn{2}{c}{**STATUE**} \\ \hline  & & **Time (s)** & **Speed Up (\%)** & **Time (s)** & **Speed Up (\%)** \\ \hline \multirow{2}{*}{**Low-end**} & \begin{tabular}{c} Instancing \\ _glDrawElements_ \\ \end{tabular} & 29.41 & 278.44 & 1.926 & 318.62 \\  & \
\begin{tabular}{c} Instancing \\ _glDrawElements_ \\ \end{tabular} & 20.15 & 20.15 & 0.027 & 215.32 \\ \cline{1-1}  &
\begin{tabular}{c} _glDrawElements_ \\ \end{tabular} & 44.36 & 220.15 & 0.080 & 215.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Total time of drawing ten meshes on a simple 3D scene with the standard and instancing rendering methods along with the proportional speed up percentage achieved with instancing. Figure 11: (**a**) Number of vertices being drawn per frame and achieved framerate on FPS; (**b**) FPS for ten models applying _glDrawElements_ and Instancing methods at the low-end and mid-range devices. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rendering Performance** & \multicolumn{2}{c}{**POTTERY**} & \multicolumn{2}{c}{**STATUE**} \\ \cline{2-5}
**on Low-End** & **FPS** & **Time (s)** & **FPS** & **Time (s)** \\ \hline Index Rendering + Arrays & 21 & 82.46 & 12 & 156.23 \\ Index Rendering + VBOs & 39 & 21.68 & 29 & 49.60 \\ Instancing + Arrays & 32 & 28.87 & 19 & 49.15 \\ Instancing + VBOs & 43 & 18.52 & 35 & 22.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Average frame rate and total rendering time of ten meshes of POTTERY and STATUE using a combination of rendering techniques and buffer storage on the low-end device.
# SEM Studies Assessing the Efficacy of Laser Treatment for Primary Teetis: A Systematic Review
## 3 Results
### General Characteristics of the Included Studies  
This systematic review includes studies evaluating the effectiveness of laser treatment of primary teeth in which researchers analyzed data using SEM. Thirteen articles [2; 13; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26] assessed the influence of the Er:YAG laser, six [27; 28; 29; 30; 31; 15; 32; 33; 34; 35; 36] assessed the Nr:YAG laser. Two of the articles [22; 23] analyzed SEM images of both enamel and dentin, twelve [2; 13; 16; 17; 20; 21; 24; 26; 27; 30; 35; 36] analyzed dentin images only, and ten [15; 18; 19; 25; 28; 29; 31; 32; 33; 34] solely images of the enamel surface. Among other assessed parameters were shear bond strength in six articles [17; 21; 23; 24; 25; 36] and microleakage in three articles [13; 27; 31]. Two articles each examined hybrid layer thickness [27; 30], remineralization of enamel [28; 33], and thermal effects on the pulp [22; 29]. One article each assessed demineralization of enamel [15], bacterial adhesion [19], and microhardness [34] (see Table 1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Type of Laser**} \\ \hline
**Er:YAG** & **Er,Cr:YSGG** & **Nd:YAG** \\ \hline [PERSON] et al. [2] & & \\ [PERSON] et al. [13] & & \\ [PERSON] et al. [16] & & \\ [PERSON] et al. [17] & & \\ [PERSON] et al. [18] & & \\ [PERSON] et al. [19] & & \\ [PERSON] et al. [20] & & \\ [PERSON] et al. [21] & & \\ [PERSON] et al. [22] & & \\ [PERSON] et al. [23] & & \\ [PERSON] et al. [24] & & \\ [PERSON] et al. [25] & & \\ [PERSON] et al. [26] & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: General characteristics of the included studies.
# Enhancing Cache Robustness in Information-Centric Networks: Per-Face Popularity Approaches
###### Abstract
In this paper, we present variations of cache replacement techniques as part of larger Per-Face Popularity (PFP) schemes, which seek to normalize the contributions from each face (for our purposes, simply an interface) on a content storage router. This normalization prevents interest message contributions received on any one face from having more effect on what ends up being cached than the contributions on any other face. The three primary algorithms that we have developed as variants of PFP are the original PFP scheme, PFP-DA, and PFP-\(\beta\). The original PFP scheme involves a list of content object request frequencies for each face. This algorithm is effective but not very scalable, as these lists are unbounded. Also, our original scheme can be affected by so-called natural cache pollution, wherein a content object may have been very popular at one time, thus having many requests, but has fallen out of being truly popular but remains on the list due to the incredible frequency achieved once upon a time. To remedy the scalability issues and lack of recency, we developed a variation of PFP with dynamic aging added to the content frequency lists and limited the size of these lists to double the size of the actual cache. This resulted in our second scheme, PFP-DA (PFP with dynamic aging). This scheme was used for most of the tests in this work. Finally, we developed a third scheme, PFP-\(\beta\) (PFP with Beta Parameterization), wherein the PFP-DA scheme remained, but the contributions themselves were scaled using a \(\beta\) parameter. We tested different values for \(\beta\) to determine the effects and made many interesting discoveries as a result, including some scenarios with 0% cache pollution and higher hit rates than were experienced with PFP-DA itself, which already outperformed LFU-DA and LRU. Our experiments demonstrate that PFP-based schemes are not only resistant to the effects of cache pollution attacks but also suggest that PFP-based schemes are better at representing the overall popularity of content objects across multiple faces. Therefore, the schemes work well even when there are no active attacks occurring. In terms of a more general contribution to the state of the art, our schemes employ a novel approach to cache replacement.
# Coarse-Fine Combined Bridge Crack Detection Based on Deep Learning
## 2 Methodology
### Model Effectiveness Experiment
#### 2.4.1 Model Training  
Training results of the ODNMs  
In order to determine the optimal network model for coarse-fine combined object detection, Faster R-CNN, SSD, YOLOV4 and YOLOV5(x) were selected to conduct a comparative experiment for object detection of crack images, and the experimental results are shown in Table 1.  
The object detection of transverse cracks, vertical cracks, diagonal cracks and network cracks of the bridge was completed through experiments. The results showed the confidence of the Faster R-CNN detection was more than 95%, and the crack information of the detection results was more sensitive and relatively complete. When SSD was used to detect bridge cracks, the confidence of the detection results was more than 80%, and the crack information of the detection results was more sensitive and complete. When YOLOV4 was used to detect bridge cracks, the confidence of the detection results was over 20%. The detection effect of bridge cracks was unstable, and the accuracy and sensitivity of the detection results were low. YOLOV5(x) was used to detect bridge cracks, and the confidence of the detection results was over 30%. The crack detection information was sensitive and the cracks were relatively complete, but the confidence of the detection results was low.  
Training results of the SSNMs  
To determine the optimal network model for fine segmentation, U-Net, DeepLabV3+ and PSPNet were each selected to perform semantic segmentation experiments on bridge cracks, and the experimental results are shown in Table 2.  
The semantic segmentation of the transverse, vertical, diagonal and network cracks of the bridge was completed through experiments. The experimental results showed that U-Net was the most sensitive to crack details and could accurately exclude the incompletely hollowed crack segment caused by the presence of sand and gravel debris in the crack (see the transverse crack in Table 2). The U-Net crack segmentation was relatively optimal compared to other models and can meet the requirement for optimal crack segmentation. PSPNet had higher detection and recognition of horizontal, vertical and oblique cracks and  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Original Image**} & \multicolumn{4}{c}{**ODNMs**} \\ \cline{2-5}  & **Faster R-CNN** & **SSD** & **YOLOV4** & **YOLOV5(x)** \\ \hline  & & & & \\ \end{tabular}
\end{table}
Table 1: Experimental results of the ODNMs.  
better segmentation but a higher false alarm rate for mesh cracks and poorer segmentation. The segmentation effect of DeepLabV3+ for the four types of cracks was between U-Net and PSPNet, and the crack segmentation shape was relatively complete, which meant it could basically meet the requirements of crack segmentation.