esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Validation
### Ellipse-Based Trochoidal Toolpath  
For the slots with given dimensions, the ellipse-based trochoidal toolpaths can be generated for different toolpath parameters by applying Equation (2). Figure 10 shows the toolpaths for four different compression ratios of 100%, 75%, 50%, and 25%. The cutter radius is 5 mm. The trochoidal step is 0.6 mm. Then, the semi-major axis of the fundamental ellipse can be determined by Equation (4) to be 15 mm. The four corresponding semi-minor axes of the fundamental ellipse are 15, 11.25, 7.5, and 3.75 mm. A compression ratio of 100% signifies a non-compressed state. It means that the toolpath is a true trochoidal toolpath. Both the engage and retract distances of the four trochoidal toolpaths are set to 10 mm. The path lengths for the four different trochoidal toolpaths during a single milling cycle are 94.25, 82.89, 72.67, and 64.34 mm, respectively. The entire number of milling cycles for the 4 different trochoidal toolpaths are 83, 77, 71, and 64, respectively. Then, the total length for the four trochoidal toolpaths can be calculated to be 7842.8, 6402.5, 5179.6, and 4137.8 mm, respectively. Compared with the case of a compression ratio of 100%, the total toolpath lengths for the compression ratios of 75%, 50%, and 25% are reduced by 18.4%, 34.0%, and 47.2%, respectively. It can be seen that the lower the compression ratio, the smaller the number of milling cycles, the shorter the total toolpath length, and the higher the machining efficiency. In other words, the roughing efficiency of the trochoidal milling strategy can be further improved by compressing the toolpath in step direction. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cutter Type** & **Cutter Material** & \begin{tabular}{c} **Number of** \\ **Flutes** \\ \end{tabular} & \begin{tabular}{c} **Diameter** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Helix Angle** \\ **(\({}^{\circ}\))** \\ \end{tabular} & \begin{tabular}{c} **Flute Length** \\ **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Total Length** \\ **(mm)** \\ \end{tabular} \\ \hline Flat-end mill &
\begin{tabular}{c} 3 \(\mu\)m TiAlN-coated \\ carbide \\ \end{tabular} & 4 & \(10\) & 38 & 25 & 75 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detailed specifications of the cutter. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Workpiece** & \multicolumn{3}{c}{**Geometrical Dimensions (mm)**} & \multirow{2}{*}{ \begin{tabular}{c} **Density** \\ **(kg/m\({}^{3}\))** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Elastic** \\ **Modulus** \\ **(GPa)** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **Yield** \\ **Strength** \\ **(MPa)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Poisson’s** \\ **Ratio** \\ \end{tabular}
# Transmission Line Fault Classification of Multi-Dataset Using CatBoost Classifier
## 3 Methodology
### The Use of CatBoost in Fault Classification  
The CatBoost classifier algorithm is used as a machine language tool to train datasets for fault classification to improve its performance, ease of use, and automatic handling of categorical features over other machine language techniques (e.g., the PCA, SVM and ANN). It also requires no explicit pre-processing of data to convert all fault data categories into numbers. A team of engineers from Yandex proposed the model in 2017 [35]. Gradient boosting is a good machine language tool for solving heterogeneous, noisy data and complex variables. It uses binary decision trees as base predictors, and it has the robust characteristics of reducing hyperparameter tuning, and lowering the chances of data overfitting. It combines a gradient boosting decision tree (GBDT) with categorical features, focuses on categorical variables, and deals with gradient bias and prediction shift problems [36]. It helps to improve the robustness of the algorithms by putting all sample datasets into the algorithm for training. When transforming the characteristics of each sample, the target value of the model was calculated before the sample, and the weight and priority were subsequently added. Assuming a data sample size  
\[D=\{(X_{j},Y_{i})\};j=1,...m, \tag{1}\]  
where \(X_{j}=(x_{j}^{1},x_{j}^{2},...x_{j}^{n})\) is a vector of \(n\) features and response feature \(Y_{i}\in R\), which are binary (1 or 0), and a sample \((X_{j},Y_{i})\) identically and independently distributed by an unknown distribution \(P(.,.)\). The aim is to train a function \(H:R^{n}\to R\) that minimises the expected loss given in equation (2)  
\[L(H)=EL(y,H(x)), \tag{2}\]  
where \(L(.,.)\) is a smooth loss function and \((X,y)\) is a sample of test data drawn from the training data \(D\)[36].  
The CatBoost also helps improve the algorithm's robustness by putting all sample datasets into the algorithm for training. When transforming the characteristics of each  
Figure 7: Single-phase to ground fault (a-g).  
sample, the target value of the model is calculated before the sample, and subsequent weight and priority are added. The CatBoost classifier requires minimal data preparation, and it also handles missing values for numerical variables and non-encoded categorical variables. The classification accuracy is used as a criterion to assess the result of fault classification.
# Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
## 2 Types of Common LPBF Part Defects
### Microstructure Defects  
Microstructure defects are mainly porosities that negatively alter the homogeneity and strength of the part. Porosities can be categorized into three major types: lack-of-fusion (LOF), gas, and keyhole porosities. Each porosity type is associated with specific process parameters and material properties, which result in deep metallurgical problems and poor mechanical performance. Figure 2 presents SEM imagery of the three major types of microstructural defects.  
LOF porosities result when the level of laser energy is low and the scanning speed is high, typically occurring at the melt track interface. LOF porosities are usually linked to the balling effect, in which there is not enough time for the powder to melt [15]. Irregular, broken, and dendrite powder particles reduce powder flowability, facilitating the formation of voids [16]. Gas porosities usually form due to trapped gas within the metallic particles or are generated from chemical reactions due to printing parameters, chamber gas conditions, and contaminants, including oxidized powder, impurities, and moisture content. Gas porosities form during the LPBF process when the gas pressure surpasses the total pressure (atmospheric, hydrostatic, and capillary). Like LOF pores, an insufficient level of laser beam energy at a high beam velocity provides less time for complete metallic particle melting, allowing for gas retention in the solidifying melt pool [17]. Keyhole porosities, or depression defects, occur when excessive laser beam power is used. An elevated power density creates thicker and overheated melt pools, which cause low-melting-temperature elements to vaporize. The generated gas destabilizes the melt pool, increases the gas pressure which pushes the melted liquid away, and forms deep porosities [18].
## 3 Integrity Monitoring Method of Space-Ground Integration
### Simulation Results and Sensitivity Analysis  
Multiple sets of simulations were carried out to demonstrate and compare the performance of the proposed L-SBAS schemes, i.e., LEO satellites acting as space monitoring stations and LEO satellites acting as navigation satellites. The configuration of the LEO constellation is given first. Based on this configuration, the integrity performance of the two schemes is evaluated by considering global users. Finally, sensitivity analyses are conducted to reveal the impact of LEO satellite/constellation fault probabilities on the user-end integrity performance.  
Figure 7: LEO satellites augment navigation integrity by broadcasting navigation signals.
## 3 Digital Beamforming General Considerations
### PAU-RAD Antenna Array Design Criteria  
The PAU-RAD antenna array was defined after a comprehensive study to ensure that the resulting beams would be suitable for radiometric applications [23]. The main requirements related with the antenna are:  
1. Beamwidth, \(\Delta\theta_{\text{-3 dB}}<25\) \({}^{\circ}\),
2. Main Beam Efficiency (MBE) \(>\)0.94;
3. minimum mutual coupling between the array elements
4. cross-pol coupling \(<\)-25 dB, and
5. DBF creates the beams in the vertical axis only, with an incidence angle up to \(\pm 20\) \({}^{\circ}\) from the array boresight in 5 \({}^{\circ}\)steps to achieve an angular scan range from 25 \({}^{\circ}\)to 65 \({}^{\circ}\)when the antenna is tilted 45 \({}^{\circ}\)(Figure 1).  
A 4 \(\times\)4 antenna array with triangular illumination in both directions fulfills these requirements. The orientation of these elementary antennas over the ground plane (Figure 4) takes the antennas feeders into account. For this reason, the elementary antennas have been rotated within adjacent elements to reduce the mutual coupling and the cross-polar component [23, 25], and the spacing between array's elements is 0.63\(\lambda\) (Figure 4).  
The system requirements specify that it has to be able to steer the beam over the vertical axis direction, so that the antennas are analogically combined in rows (horizontal axis) and the DBF is created in columns (vertical axis).  
From here, each antenna is analogically combined with the other three that compose a row (Figure 5) using a RF combiner network with the weights 1:2:2:1 (triangular illumination). Each combiner has its own receiver that down-converts the signal. Finally, the DBF is performed inside the FPGA.  
Figure 4: Polarimetric antenna array for the DBF. Using patch rotated antennas and with a distance between elements of 0.63\(\lambda\).  
Figure 5: Sketch of the PAU-RAD array antenna configuration. Although the system has digital beamforming capabilities, it has four analog network combiners (with triangular illumination, 1:2:2:1) which compose the horizontal beam and one digital part which performs the digital beam steering on the vertical axis.  
The above specifications have been set taking into account that PAU-RAD is a ground-based instrument. In this case, the array beamwidth and the beam overlapping do not compromise the geophysical parameters retrieval. In case the system was conceived to be an airborne or spaceborne instrument, some of these parameters have to be improved; for example, the beamwidth would have to be reduced to have an acceptable spatial resolution and non-overlapped beams, which could be achieved with an array larger than 4 \(\times\)4.
## 3 Results and Discussion
### Multivariate Analysis  
For best results, all species to unit variance was standardized using the package VEGAN in the R package for showing a more balanced ordination plot, because only abundant specific As species such as As(V) and AsB with high variances were sufficiently explained. Additionally, the points or labels for marine organisms and As species were represented using the standard ordination plot command, and biplot arrows for six arsenicals and clustering groups in PCA were used to elucidate the correlation between As and seaweeds or marine animals.  
The correlation results obtained from the PCA revealed associations between six As compounds (As[V], As[III], MMA, DMA, AsB, and AsC) and seaweeds with different types of marine organisms (Figure 3), while the multivariate analysis was performed in many ways such that there was no direct relationship between them and heavy metals (Cd, Pb, and As). Six groups classified on the basis of the levels of the six arsenicals were additionally confirmed by biplot of PCA: Group A, associated with _Sargassum_ spp. in brown seaweeds (with exclusively higher inorganic As than others); Group B, associated with _Pyropia_ sp., _Enteromorpha_ sp., _Undaria_ sp., and _Saccharina_ sp. in the other seaweeds (with no detection or low values for six As compounds); Group C, associated with crustaceans (with high AsB than others); Group D, associated with cephalopod (with relatively higher AsB than others); Group E, associated with fish (with relatively higher AsB than others); Group F, associated with shellfish (with high MMA and DMA). In this analysis, we checked again that inorganic As had a decisive effect on clustering only _Sargassum_ seaweeds and this group was a food safety threat because of highly toxic inorganic As. [PERSON] and [PERSON] [54] suggested that the variations in the levels of inorganic As were due to specific seaweeds like the family _Sargassaceae_ rather than environmental factors or the origin of the samples.  
Principal component 1 (PC1; 36.20% of the variance) was clearly associated with major inorganic As and organic As, where, when divided into two types, the group comprising _Sargassum_ spp. contained As(V) and As(III) and the other groups contained AsB. PC2 (23.25% of the variance) was slightly associated with minor components such as MMA, DMA, and AsC, which differentiated seaweeds and cephalopods from other marine animals like crustaceans, fish, and shellfish.
## 4 Results and Discussions
### Experimental Results and Comparison
#### 4.3.4 Statistical Analysis  
The significance difference between two methods can be measured by the statistical analysis. [PERSON]'s rank sum test is a reliable and robust statistical analysis tool and is widely used in metaheuristic methods. In this paper, the [PERSON]'s rank sum test at a 0.05 confidence level is used to identify the significance difference between SOS and other compared methods on the same case. The test results are tabulated in Table 11. The symbol \"+\" denotes that SOS is statistically better than its competitor. The results demonstrate that SOS significantly outperforms every method on every case (\(p<0.05\)), indicating the better performance of SOS from another perspective.
## 4 Microservice-Based Architecture
The Custom Metrics Adapter retrieves monitoring data, which combine utilization, cost, and performance metrics, and forwards them to the Vertical Pod Autoscaler (VPA), which is responsible for updating the resources of a pod. Based on the custom metrics, VPA computes the pod's resources. The actual implementation of the scaling decision is performed through deployment, which includes all the parameters of the running pod. The most important attribute of the deployment is the resource limits, which are responsible for vertical pod scaling. Regarding resource scaling, two controlling parameters, namely _Request_ and _Limits_, configure the allocated CPU and memory to the pod. _Request_ (either CPU or memory) defines the minimum amount of resources that containers of the pod need. In parallel, based on this information, K8s decides on which node to place the pod. _Limit_ (either CPU or memory) defines the maximum amount of resources that a specific container can consume. Although the above parameters define the resource range that containers consume, there is still a risk that VPA recommends resources greater than those available in the cluster. To overcome this limitation, _LimitRange_ policy allows us to constrain the pod's resource allocation to a maximum value, usually the total available resources. Regarding data integrity, the most crucial element in CoC, K8s provides a secure storage mechanism through persistent volumes. A _PersistentVolume_ (PV) is a storage block in the cluster provided statically or dynamically. The PV subsystem provides an API for users and administrators that configures how storage is provided and consumed. Contrary to volumes, PVs are independent from any pod that uses them. Users and pods are allowed to consume PVs through _PersistentVolumeClaims_ (PVCs), which request specific size and access modes (e.g., ReadWriteOnce, ReadOnlyMany, ReadWriteMany, or ReadWriteOncePod). This mechanism allows for the creation of bindings between PVs and PVCs with specific user access privileges in a secure way that protects from data loss. In the proposed architecture, the User Group Policy component provides access to a specific group of users by configuring a PVC. Through this claim, a pod hosting a forensic application is bound to a PV, where digital evidence is stored. In such a way, the case handlers have direct data access and parallel processing is feasible. Last but not least, we must refer to the agile application's lifecycle support of the proposed architecture. K8s is an open-source platform with wide adoption and support by the academic and industrial community. This ensures the long-term deployment of digital forensics applications following the microservice concepts. Furthermore, considering that public cloud infrastructure is not an applicable option for public regulatory authorities, like competition commissions, open-source resource orchestration tools like Kubernetes is a viable alternative for maintaining private cloud infrastructure.
# Fact-Checking Reasoning System for Fake Review Detection Using Answer Set Programming
## 8 Discussion  
The problem of fake review detection is complicated since fake reviews are linked to at least two sources: the review content and the reviewers' behavior. Commonly, the content is analyzed and classified by adopting natural language processing techniques. However, determining the behavior of a fake reviewer is a complex problem. Various spatial-temporal patterns of behavior can be modeled as part of an optimization problem and because of the complexity, these problems are time-consuming and require a lot of computational resources to be solved. ASP is an exciting and powerful tool to address such problems, mainly offering a declarative way to specify the problem and properties of the desired solution(s). ASP inherits the advantage of rule-based approaches, (a) flexibility which means that adding new rules or updating the system requires only a few changes compared to other paradigms, (b) it does not require a vast amount of training data, (c) rule-based techniques are considered a white box; thus, it provides traceability and transparency for critical decisions that demand a higher degree of explanation usually provided by machine learning approaches, (d) same rules can be used for another data type, like tweets or Facebook posts, for fact-checking purposes, and (e) clear and understandable specification for users. Regarding threshold values used in ASP approaches, they can be calculated regularly and automatically based on newly inserted data. Additionally, adding new threshold values can help in restricting and limiting the spamming behavior.  
Table 1 shows a list of different reviews, representing examples of labeled reviews by the ASP solver, using the proposed review-centric KB. The table consists of the review content in the first column, following the review rating, sentiment score using the trained model, the Levenshtein/Cosine review similarity score, the triggered rules, and the actual label has given review. The last two reviews show an example of two reviews posted shortly after each other, thus resulting in the second review being labeled as a contradicted review, according to the given input parameters for the ASP solver.  
Figure 6: Contradicted review example.  
Figure 7 shows a confusion matrix of 50 movie reviews, obtained from the Rotten Tomatoes dataset, including their sentiment score from 1 to 5, that were processed by the website to analyze the accuracy of the sentiment analysis. The matrix shows that the prediction of the sentiment analysis is correct for most of the reviews, while only few reviews had a difference higher than 1, as, for example, the one review that had a true sentiment score of 1 and the system predicted a score of 3. The highest accuracy was for reviews with the sentiment score of 4.
# Network Security Node-Edge Scoring System Using Attack Graph Based on Vulnerability Correlation
## 3 Vulnerability Correlation and Attack Graph-Based Node-Edge Scoring System
### Access Location  
The access location is one of the important factors to determine the vulnerability of hosts and servers, and it can be divided into external and internal access. External access refers to direct access to the host or server from the external network, without going through a host or server in another internal network, and an attacker can perform various attacks through the external network. Representative attack methods include DDos, hijacking, drive-by, password, and phishing attacks. Conversely, internal access means that the host or server can only be accessed through another internal host or server. As such, external access can only be exposed to a larger variety of attacks than internal access, so there are much more vulnerabilities in external access.  
The access location can be largely divided into external and internal, and in detail, it can be divided into network, adjacent network, local, and physical. \"Network\" means that \"the node (host, server, etc.) can be accessed from all external networks\"; \"adjacent network\" means \"the node can only be accessed from the adjacent external network\"; \"local\" means \"the node can be accessed only from the internal network\"; and \"physical\" means \"the node can only be accessed by a physical method\". The access location was defined based on the attack vector (access vector) provided by the CVSS, and it was proposed to be applicable according to the CVSS version as presented in Table 1.  
We calculated the edge weights based on the association between the two nodes; the access location weights defined in Table 1 are defined for the two nodes and the edge weights in Table 2 are defined according to Equation (4).
# Combining Semantic and Structural Features for Reasoning on Patent Knowledge Graphs
## 3 Experiment Result and Analysis
### Experimental Comparison and Analysis
#### 3.2.1 Comparative Experiments with Baseline Models

Table 4: Comparison of SS-DGA and Baseline Models on Dataset 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods of Comparison**} & \multicolumn{3}{c}{**Evaluation Indicators**} \\ \cline{2-4}  & **MR** & **MRR** & **Hits@n** \\ \hline ComplEx & 631 & 0.311 & 0.255 \\ \hline RotatE & **142** & 0.322 & 0.351 \\ \hline MLMLM & 451 & 0.287 & 0.275 \\ \hline DensE & 158 & 0.321 & 0.371 \\ \hline SS-DGA & 173 & **0.362** & **0.541** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of SS-DGA and Baseline Models on Dataset 2.  
structural information. Thus, the effectiveness of the proposed model in the reasoning process is demonstrated. As shown in Table 4 and Figure 8, in Dataset 3, the SS-DGA model achieves only the second-best results in the MR metric. However, it achieves the best results in both the MRR and Hits@3 metrics. Specifically, the MRR metric shows improvements of 29%, 75%, 187%, and 4%, respectively. The Hits@3 metric shows a maximum improvement of nearly 39% compared to the MLMLM model. A high MRR indicates that the model is very adept at quickly ranking the correct triples at the top, which directly impacts the efficiency with which users can find information. A high Hits@3 means that the probability of the correct answers appearing within the top three rankings has significantly increased, further demonstrating the model's superiority in predictive accuracy. The experimental results validate the model's ability to discern the importance of different entities and relationships, providing more refined responses for complex queries, and effectively reasoning about triplet information. Particularly, it excels in rapidly and accurately identifying the correct triplets. Through the comparative experiments conducted on datasets of varying complexity, the SS-DGA model has been validated as being capable of thoroughly mining structural information. The use of the self-attention mechanism effectively assists the model in knowledge graph reasoning. Figure 6: Boxplot of comparative experimental results between ss-dga and baseline models on Dataset 1. Figure 7: Boxplot of comparative experimental results between SS-DGA and baseline models on Dataset 2.