esa-sceva/satcom-chunk-collection huggingface dataset:
# Vulnerability Assessment of Ubiquitous Cities Using the Analytic Hierarchy Process
## 2 The Components of U-Cities and Hazards
### Information and Communication Technologies (ICTs)  
Following [PERSON], [PERSON] and [PERSON] [31], three main groups of communication technologies have been established, namely, Local Area Network (LAN), Wide Area Network (WAN) and Satellite/Aerial, which covers 12 technologies that are predominately used in functionalities of ubiquitous cities (see Figure 2). Services within a ubiquitous city network rely heavily on these technologies to function effectively [32]. As the networks grow and become a substantial staple in the proceedings of city management the reliability of these networks is imperative as failure can prove catastrophic [33].
## 5 Hardware Design of the Proposed Algorithm
### Hardware Optimization  
During hardware design, the principal aim is to trade between speed, accuracy and used resources. The algorithm of 3D reconstruction uses multiple division operations and square root approximations. The COordinate Rotation DIgital Computer (CORDIC) cores offered by Xilinx to realize the mentioned functions were tested under different configuration. However, this block provides only a rough approximation of the required result especially when a large fractional precision is needed [47]. The square root results were not enough accurate and although the satisfying CORDIC division result, its large size and low execution speed make it less advantageous. Therefore, custom hardware architectures for the cited functions are proposed.  
Using XSG development tool, maximum precision could be obtained by configuring all blocks, between the gateways, to run with a full output type. However, this could lead to an impossible synthesis result. Word-length optimization is a key parameter that permits to reduce the use of FPGA hardware resources while maintaining a satisfactory level of accuracy. In the case of square root approximation, word-length is hard to compute since output format increase by the looped algorithm feedbacks. For that reason, successive simulation reducing iteratively outputs word length is made until achieving the minimum world length while ensuring the same maximum precision.
# An Efficient Distributed Coverage Hole Detection Protocol for Wireless Sensor Networks
## 3 System Model
### Definitions
Similarly, \((x-X_{i})^{2}+(y-Y_{i})^{2}=R_{s}^{2}\) represents the equation of the sensing disc of any sensor, where \((X_{i},Y_{i})\) is the location of a sensor \(i\), \(\forall\)\(i=1\), 2, 3,..., \(n\). As shown in Figure 2, \(\overline{AB}\), \(\overline{BC}\), \(\overline{CD}\) and \(\overline{DA}\) represent the equations of those borderlines. **Definitions 7 (critical intersection points):** The critical intersection points (CIP) of a node are the points of intersection, which satisfy the conditions as follows. Let \(I\) be the set of points of the intersection of sensor \(S_{i}\) with a sensing disc of its sensing neighbor \(S_{j}\), for \(i\
eq j\). The point \(p_{ij}\in I\) is said to be a critical intersection point, if it is not covered by any other sensing neighbor \(S_{k}\) of sensor \(S_{i}\), for \(i\
eq k\), or it lies on the border line of the monitoring region \(R\). For example, as shown in Figure 2,  
Figure 3: Example of the sensing disc and connecting neighbors. the points in red color are the critical intersection points (CIP)due to the intersection of the sensing disc of one sensor with another or the sensing disc of one sensor with the border line of the monitoring region. It is to be noted that throughout the paper, the set of critical intersection points is denoted as \(P\), where \(|P|\leq|I|\). **Definitions 8 (covered points):** Let \(S_{j}\) and \(S_{k}\) be the sensing neighbors of a sensor \(S_{i}\), for \(i\
eq j\
eq k\). \(p_{i}\) and \(p_{j}\) are two points of intersection of the sensor \(S_{i}\) with the sensing disc of its sensing neighbor \(S_{j}\), such that both or any one of the points \(p_{i}\) and \(p_{j}\) are covered by the sensing disc of the sensor \(S_{k}\). Then, the point(s) of intersection that is covered by the sensing disc of sensor \(S_{k}\) is called the covered point (CP) of sensor \(S_{i}\). For example, as shown in Figure 2, \(S_{2}\), \(S_{10}\) and \(S_{11}\) are one-hop sensing neighbors of sensor \(S_{1}\). There are several points of intersection of the sensing disc of \(S_{1}\) with its sensing neighbors. However, only the red points \(p_{1}\), \(p_{10}\), \(p_{15}\) and \(p_{16}\) are the CIP of \(S_{1}\), as they are not covered by any other sensors, whereas the blue points \(p_{11}\), \(p_{12}\) and \(p_{13}\) are covered points (CP) of \(S_{1}\), as shown in Figure 2, since they are covered by its sensing neighbors \(S_{10}\), \(S_{11}\) and itself. It is to be noted that a point of intersection of the sensing disc of one sensor with the sensing disc of another sensor could be either a _CIP_ or a _CP_.
## 2 Model Formulation
### Logistic-\(\beta\) Construction Timing Sub-Model
#### 2.2.2 Parameter Analysis  
The variations in growth curves of different cities are mainly controlled by parameters \(A\), \(B\), and \(K\), as shown in Figure 2. In actual projects, the final scale of the URT network should match the demand for urban development, and the overall mileage scale of the network is affected by the value of parameter \(A\) (Figure 2a).  
The timing and speed of URT construction are affected by parameter \(B\). There are two construction stages, the start-up period and the improvement period, in parameter \(B\). The larger parameter \(B\) is, the longer the start-up period and the slower the construction speed, which results in a longer period of centralized construction and optimization, and vice versa. Parameter \(B\) should take a lower value from the perspective of operation (see Figure 2b).  
The construction period is reflected by parameter \(K\). The larger the parameter \(K\) is, the shorter the construction period and the faster the stage will completed, and vice versa. In engineering practice, the value of parameter \(K\) should be adapted to city development such that it is neither so large that it puts pressure on urban finance nor so small that URT construction lags behind the demand for urban development (Figure 2c).
# Hybrid Early Warning System for Rock-Fall Risks Reduction
## 4 Methodology
### Rock-Fall Prediction Model Development  
The machine learning technique was used to develop a prediction model. For this study, logistic regression was selected because it is helpful in estimating the occurrence or the absence of a consequence dependent on the values of predictor variables. The benefit of logistic regression is that the variables, or any combination of all forms, may be continuous or discrete, and the data do not need a regular distribution [44]. A rock-fall event was used in this analysis as a dependent variable (binary) describing the rock-fall event occurring or not occurring with values between 0 and 1. The logistic regression method yields coefficients  
First, four calibration points, (x\({}_{1}\), y\({}_{1}\)), (x\({}_{2}\), y\({}_{2}\)), (x\({}_{3}\), y\({}_{3}\)), and (x\({}_{4}\), y\({}_{4}\)), were manually placed at the mountain surface. Second, the distorted image was captured, and the calibration points were detected in an image. Third, the transformation matrix was calculated by using the plane homographic method [41]. This method computes the transformation matrix by projecting points from the image plane into four points, (v\({}_{1}\), w\({}_{1}\)), (v\({}_{2}\), w\({}_{2}\)), (v\({}_{3}\), w\({}_{3}\)), and (v\({}_{4}\), w\({}_{4}\)) in world plane, which forms a rectangle plane as illustrated in Figure 5. Finally, the transformation matrix was used to map all points from image coordinates to real-world coordinates. The plane homograph was used to remove the projective distortion from a perspective image, as seen in Figure 5a [47]. The homogeneous transformation matrix was introduced to apply perspective rotation and scaling in the perspective image. Figure 5b shows the image after correction.  
Figure 4: Field of view calibration process steps.  
Figure 3: Detection model development steps.
## 3 Results  
### Preliminary 1: Data Selection  
The following experimental data were recovered between 1 October 2021 and 1 February 2022 inclusive (124 days in total). We consider that 73 days give exploitable results. The other days are either without any EV charging session (34 days) or with measurement errors or communication or hardware breakdowns (17 days). During these field tests, 887 charging sessions were recorded in total, and, altogether, 12.4 MWh were transferred to the EVs. The battery of the EVs were all charged before the departure time forecasted by the users, except in very few cases (mainly short sessions of 1 h or 2 h). In these cases, the users were informed by email or phone that there were issues with their charging sessions.  
In order to estimate the efficiency of the control algorithm, we compared two self-production rates. The first one, SP, is computed from the available power and from the energy consumption measured by the charging stations, as explained in Section 2.7.1. The second rate is computed in the same way, except that we consider the energy consumption as the simulated consumption of all the EVs, under the assumption that none of them are controlled. In other words, we quantify the total energy in the case where the EVs charge at their \(\mathrm{P_{Max}}\) from the start of the charging sessions until their batteries are fully charged. The associated self-production ratio is denoted \"Uncontrolled Self-production ratio\" or \(\mathrm{SP_{u}}\).  
Figure 3 displays a scatter plot with the self-production ratio \(\mathrm{SP}\) on the Y-axis and the uncontrolled self-production ratio \(\mathrm{SP_{u}}\) on the X-axis, computed both on the same days. As the points are clearly above the line \(\mathrm{Y}=\mathrm{X}\), this scatter plot clearly shows that the control algorithm globally increases the self-production ratio. A small (resp. great) difference between \(\mathrm{SP}\) and \(\mathrm{SP_{u}}\) corresponds to a minor (resp. large) increase in the self-production ratio thanks to the control algorithm.  
The days associated with the red dots in Figure 3 are chosen according to their different locations on the plot (on the middle, in the upper right corner, and at top left). Indeed, these red dots represent cases that will be outlined in the following paragraphs:  
* On the middle: low values for both \(\mathrm{SP}\) and \(\mathrm{SP_{u}}\)
* In the upper right corner: high values for both \(\mathrm{SP}\) and \(\mathrm{SP_{u}}\)
* At top left: high value for \(\mathrm{SP}\) and low value for \(\mathrm{SP_{u}}\)
## 3 Effect of Incident Angle of the Light on the Measurement Sensitivity
### Experimental Assessment of Sensitivity  
Figure 3 shows the experimental apparatus for measuring the sensitivity of the optical fiber probe. The experimental apparatus consists of the laser diode (57108-L, Edmund, Barrington, NJ, USA) with a wavelength of 630 nm and a light output of 5 mW, the condenser lens (SPAHL-5, SIGMAKOKI CO., LTD., Tokyo, Japan), the dual-element photodiode (S5870, Hamamatsu Photonics K.K., Shizuoka, Japan) and the XYZ precision stage (P-611.3S, Physik Instrumente (PI) GmbH & Co. KG, Karlsruhe, Germany). The numerical aperture of the condenser lens is 0.13. The closed-loop resolution of the XYZ precision stage is 1 nm. The laser beam was irradiated on the stylus shaft (of 70 \(\upmu\)m diameter) placed at an angle \(\theta\) with respect to the Z-axis, as indicated in Figure 3. The experiment was conducted using the passive vibration isolating table in the chamber controlled to 20 \({}^{\circ}\)C \(\pm\) 0.1 \({}^{\circ}\)C. The light transmitted through the inside of the stylus shaft was detected by the dual-element photodiode. When the stylus shaft was displaced from \(-\)2.5 to 2.5 \(\upmu\)m in the Y-direction by using the precision piezo stage, the output  
Figure 2: Section A-A in Figure 1.  
signal \(I_{\rm X}\) as defined in Equation (1) was recorded. Figure 4 shows the experimentally measured values of \(I_{\rm X}\) for \(\theta=0^{\circ}\), \(10^{\circ}\) and \(20^{\circ}\). The inclination of the curves near the origin in Figure 4 indicates the measurement sensitivity. That is, it is 3.61, 3.80 and 4.04 V/\(\upmu\)m in the range of \(-1\) to 1 \(\upmu\)m in the displacement Y for \(\theta=0^{\circ}\), \(10^{\circ}\) and \(20^{\circ}\), respectively. The sensitivity for \(\theta=10^{\circ}\) and \(20^{\circ}\) is enhanced by 5 and 12%, respectively, compared to \(0^{\circ}\).  
It is evident here that the sensitivity increases with the increase in the incident angle \(\theta\). For \(\theta=0^{\circ}\), the cross-sectional shape of the stylus shaft through which the incident laser beam passed in the XY plane was circular. However, the stylus shaft assumed an elliptical sectional shape for \(\theta\
eq 0\), as shown in Figure 2. The difference between the major and minor axes of the elliptical cross-section increases with the increase in \(\theta\). Therefore, the enhancement of sensitivity may be attributed to the increase in the cross-sectional curvature of the stylus shaft and the increase in the refraction angle.
## 5 Sensor Faults and Outlier Detection Models for IoTs
### Nearest-Neighbour Techniques  
Nearest-neighbour-based technique is the widely used technique to analyse the sensors' data point concerning its nearest neighbours. Basically, the nearest-neighbour technique for sensor fault and outlier detection explicitly relies on the notion of proximity. The nearest-neighbour technique works by relying on the distances between sensor data measurements to differentiate between the abnormal and correct readings. The Local Outlier Factor (LOF) is a prominent nearest-neighbour algorithm [33], which attributes a fault or the outlier score to each sensor reading based on the number of measurements around its k-nearest-neighbours and the number of measurements around the sensor reading. The sensor readings with the higher scores are labelled as anomalies.
## WT1 Immunohistochemical Expression in Human Embryonal/Fetal and Neoplastic Tissues
### WT1 Expression in Melanocytic Lesions  
Several studies have reported the usefulness of WT1 in the differential diagnosis between nevi and melanomas. The first study on WT1 expression in melanocytic lesions dates back to 1997 [110], when it was observed that WT1 in melanoma, in the context of absent or mutated p53, acted as a transcriptional activator, whereas in the presence of wild-type p53, it acted as a repressor. Later, [PERSON] and [PERSON] [111] showed that WT1 (6F-H2) was a complementary marker of malignant melanoma. Indeed, by using immunohistochemistry, they demonstrated cytoplasmic WT1 expression in most invasive primary cutaneous melanomas, including spindle cell and desmoplastic melanomas, and in metastatic melanomas. However, its usefulness was limited by the fact that most Spitz nevi and a minority of dysplastic nevi expressed WT1. In another study, apart from demonstrating cytoplasmic, and more rarely, nuclear WT1 expression in melanoma, it was proven that the silencing of WT1 inhibited melanoma cell proliferation, supporting the role of WT1 cell proliferation in melanoma [21, 112, 113]. In contrast, [PERSON] et al. [114] reported a higher rate of WT1 staining in melanocytic nevi against melanomas, and an increased expression in advanced stages of melanoma progression. In addition, they supported an association of WT1 protein expression with a shorter overall survival. A significant expression of WT1 in desmoplastic (71%), compared with non-desmoplastic melanoma (47%), has also been recently observed. The same study reported that vertical growth phase melanomas exhibited an expression of WT1 more frequently than radial growth phase melanomas (46.5% vs. 16.0%) [115]. Finally, a study on 40 cases of desmoplastic melanomas showed a strong and diffuse cytoplasmic expression of WT1 (6F-H2 antibody) in all cases examined, suggesting the use of WT1 along with S-100p, SOX10, p75 and nestin as an optimal panel for the diagnosis of desmoplastic melanoma [116].
# Local Dependency-Enhanced Graph Convolutional Network for Aspect-Based Sentiment Analysis
## 3 Proposed Approach/ Methodology
### Ldegcn  
#### 3.4.1 Enhanced by Affective Knowledge  
GCNs have the capability to directly operate on graph-structured data, making them widely used for encoding syntactic information [26]. To construct the local dependency-enhanced graph, we first construct the dependency graph for each input sentence over the dependency tree following the approach proposed in [11]. The dependency tree encapsulates lexical relationships and holds vital significance for understanding sentence structure and meaning. By extracting the connections between words from the dependency tree, we can more accurately capture the associations between emotional expressions and context, leading to better analysis and interpretation of the sentiment polarity of sentences. The adjacency matrix, \(D\in R^{n\times n}\), of the sentence is obtained as follows:  
\[D_{i,j}=\begin{cases}1&\text{if $w_{i}$ and $w_{j}$ have dependences}\\ 0&\text{otherwise}\end{cases} \tag{8}\]  
Inspired by [PERSON] et al. [24], we incorporate the sentiment knowledge from SenticNet into the construction of the adjacency matrix to represent the emotional information between words. We utilize the latest version of SenticNet7 ([[http://sentic.net](http://sentic.net)]([http://sentic.net](http://sentic.net)), accessed on 1 January 2023), which employs a novel commonsense-based neural-symbolic AI frameworkfor interpretable sentiment analysis and assigns semantic and emotional values to 400,000 concepts. Table 1 showcases examples of selected words and their corresponding sentiment intensities.  
The calculation of specific sentiment scores is as follows:  
\[I_{i,j}=|Intensity(w_{i})|+|Intensity(w_{i})| \tag{9}\]  
The intensity score, \(Intensity(w_{i})\in[-1,1]\), indicates the emotional intensity score of the word, \(w_{i}\), in the SenticNet sentiment dictionary, with negative values indicating negative emotions, positive values indicating positive emotions and 0 indicating neutrality. The SenticNet sentiment dictionary comprises mappings of words to their corresponding emotional polarity and intensity. By simply matching each emotion word with SenticNet and extracting emotional intensity values from it, we can assign the intensity scores to the emotional words. For example, consider the sentence: \"This product is excellent.\" When we analyze this sentence using SenticNet, the model looks up its emotional intensity score in SenticNet. Consequently, the word \"excellent\" in the sentence would be assigned an emotional intensity score of 0.744, conveying a positive sentiment. The graph convolutional network can extract sentiment dependencies from two dependent nodes, where the sentiment dependency correlation is determined by the sum of the absolute values of their sentiment intensity scores. By considering the strength of sentiment relationships between nodes, the model can understand the sentiment dependencies in the graph and analyze the sentiment associations between different aspects or entities.
## 3 Results and Analysis
### Results and Analysis of Basic Performance Test of Modified Bitumen
#### 3.1.3 Ductility Test  
The results are shown in Figure 3.  
Figure 2: Softening point test results.  
Figure 3 shows that after incorporating the Sasobit modifier, the modified bitumen exhibited brittle breakage at the test temperatures of 5 \({}^{\circ}\)C and 15 \({}^{\circ}\)C; the bitumen ductility was inversely proportional to the amount of modifier incorporated. The test results indicate that the Sasobit modifier deteriorates the low-temperature ductility of the bitumen and harms its low-temperature performance. After mixing with the XT-W3 and Evotherm modifiers, the bitumen ductility increased to some extent at different test temperatures, indicating that these two modifiers improved on the low-temperature ductility of bitumen. Meanwhile, when the test temperature was 15 \({}^{\circ}\)C, according to the amount of Sasobit modifier, the modified bitumen ductility value was first higher than the SBS-modified bitumen. With the increase in the amount of modified bitumen, and lower than the SBS-modified bitumen, this phenomenon indicates that to a certain extent the Sasobit-modified bitumen low-temperature ductility is better than the SBS-modified bitumen and XT-W3-modified and Evotherm-modified bitumen are better than the SBS-modified bitumen in low-temperature ductility.
## 3 Proposed PBFT Architecture and Model
### Roles of Nodes and Network Architecture
#### 3.1.1 CPN (Cluster Primary Node)  
The Cluster Primary Node (CPN) is responsible for cluster management. The role includes managing and supervising groups and hierarchies within the cluster. The CPN initiates the consensus process by broadcasting a consensus message to the GPNs in the topmost layer. It then aggregates the results from each group, and then summarizes the consensus results, and sends them back to the client. CPNs directly participate in the consensus process and play an important role, CPNs must be chosen from nodes that are highly trustworthy. Additionally, the system may consist of multiple CPNs to handle various errors, such as system failures.  
In the proposed PBFT method, GPNs are randomly selected for consensus during the initial layering and grouping stages. Subsequently, groups are made by choosing NNs to participate in PBFT with the selected GPNs. When the consensus operation begins, the CPN broadcasts the consensus messages to the GPNs in its managed cluster's topmost layer (Layer 1). These consensus messages originate from a client's or manager's request, pulling transactions from the transaction pool to create a block message. To explain the roles performed by the CPNs in more detail, it is as follows.  
After broadcasting, the CPN waits for each group to reach a consensus on the messages. Once the CPN receives the consensus results from the GPNs during the Pre-Reply phase, it aggregates these results and relays the overall consensus of the cluster to the client or manager. Throughout the consensus process, the CPN monitors changes in the number of nodes in the cluster and counts the number of consensus operations performed. If there is a change in the number of participating nodes or consensus operations exceed a predetermined threshold, the CPN dynamically re-adjusts the hierarchical structure and grouping. Figure 2 illustrates a blockchain network utilizing the proposed PBFT method. In this figure, black nodes represent CPNs, and each cluster contains one CPN.