esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Point Learning with Uncertainty Quantification to Generate Margin Lines on Prepared Teeth
## 3 Methods
### Deep Learning Model  
We based our model on the AdaPoinTr [44] architecture, which proved to be performing well in set-to-set point completion (Figure 3). The model takes as input a point cloud of the die with 10,000 points. It first uses the farthest point sampling technique (FPS) to obtain the center points of the input. FPS facilitates the grouping of the input point cloud into a smaller set of features to then be converted into a series of vectors and used by the transformer architecture. FPS provides a structured output of points while preserving the shape features in a computationally efficient manner making it suitable for real-time application [34]. A deep graph convolutional neural network (DGCNN) is then applied, which downsamples the input point cloud further by extracting the features of center points. This is necessary to reduce the complexity of the model. Afterwards we inject the N features in the encoder block, to obtain the feature vectors \(V=V_{1}\),\(V_{2}\),...\(V_{N}\) as an output. The output is injected to the adaptive query generator, which consists of a multilayer perceptron (MLP) making queries. Then, another MLP selects \(M\) queries from the previous output. As a result we obtain \(Q=Q_{1}\),\(Q_{2}\),...\(Q_{M}\). Those queries are injected in the first geometry-aware transformer block (self-attention block) from the decoder. Then, the output of the first block is injected with feature vector into the second block of the decoder (cross-attention block). The results provided by the decoder consist of point proxies. To provide a complete prediction, a Folding Net is then used to rebuild the point cloud from the point proxies and output both a coarse and a fine predictions (Figure 3).  
Figure 2: Extracting ground truth margin lines. A crown bottom is first extracted from a crown designed by a dental technician. The internal edge of crown bottom lower horizontal thickness coincides with the margin line on the dental preparation. The internal points are extracted, projected on the die, and augmented to represent the margin line.
## 5 Discussion
### Personal Device Usage and BYOD  
Prior work [42] has identified several directions for future shadow IT research, including user attitudes to shadow IT strengths and weaknesses or Generation Z's specific attitudes and behaviors. The governing of shadow IT or, more generally, the use of personal IT space is unlikely to be optimal for both organizations and users without some sort of dialogue.  
In our survey, we illustrate that work experience students have an increased awareness of related IT policies (\(p<0.001\)). Nonetheless, there are other fundamental difficulties. None of the other three topic areas of our survey have as large a share of participants that cannot identify any relevant IT policy to comply with (82% of work experience and 95% of study only students). A better dialogue would require the transparent introduction of expected guidelines by the organization for all users.  
Downloading apps to mobile devices issued by organizations or downloading software applications to desktops or other computing devices issued by the university or company is handled differently by work experience students. The existence of a pre-approved list is associated with work experience students being more aware of security risks.  
We also asked questions about smartphone usage (both personal and organization-owned) and the associated security measures, as these devices are frequently used for organizational purposes. Endpoint security was confirmed to be installed on a higher portion of company-issued devices.  
We also studied those students who either have personal smartphones or work-related smartphones. We identified that a higher proportion of students with work experience (55%) confirmed using endpoint security compared to the lower portion (41%) of study-only students.  
The use of personal cloud services and the associated cyber-security and privacy risks should be a concern for both corporate entities and teaching institutions. Although the use of unsanctioned cloud services is less prevalent in the group of work experience students, we found that 34% of them are still using personal clouds for company files, but the actual nature and volume of the uploaded company data are unknown and were not part of our survey.  
We recommend that responsible IT-security and cyber-security managers should take steps to analyze, at least by exploratory means, the nature of the uploaded company data and update the requirements in a shadow IT or BYOD/BYOS policy. More importantly, we suggest that organizations should take the first step with the formulation and communication of their general expectations in relation to shadow IT. Without this introduction, the much-needed dialogue with users is unlikely to start.
## 4 Diagnosis and Control Methods
### Fuzzy Logic Control Method  
The fuzzy logic control method is an advanced computational approach used to make decisions and draw conclusions in situations in which the boundaries between different states or conditions are not well defined. Rooted in the principles of fuzzy logic, this method is particularly effective when dealing with complex and uncertain systems in which traditional binary logic might fall short [34].  
Unlike classical logic, which relies on crisp definitions of true or false, fuzzy logic allows for degrees of truth to be expressed. It accommodates the inherent imprecision and uncertainty present in many real-world scenarios, making it well suited for diagnostic applications across various fields such as engineering, medicine, finance, and more [35; 36]. The working scheme for fuzzy logic is illustrated in Figure 10.  
The fuzzy logic algorithm operates through several key stages:  
1. Identification of linguistic variables: Initial parameters are established to construct descriptions for input and output variables. For example, the variable \"speed\" is defined with specific values, such as \"slow\" or \"fast\".
2. Establishment of fuzzy sets: Each linguistic variable and its corresponding value are defined by a fuzzy set, characterized by a membership function. These functions, which can take various forms like triangular or Gaussian, enable the flexibility of fuzzy logic, eliminating the need for precise mathematical models.  
Figure 10: General scheme of the fuzzy logic algorithm.  
* 3. Fuzzification: Input variables are matched with their respective membership functions to generate a fuzzy output, determining the degree of membership of the input variable within a specific fuzzy set.
* 4. Formulation of fuzzy rule base: Fuzzy rules are defined that dictate the algorithm's actions based on combinations of input variables. These rules, based on linguistic variables and sets, employ logical operations like AND/OR to organize them into relevant categories.
* 5. Defuzzification: The inverse process of fuzzification, converting the degree of membership of the output parameter based on fuzzy rules into a numerical value. This numerical value guides subsequent control actions.  
The fuzzy logic control method excels in scenarios in which uncertainty and imprecision are prevalent, such as medical diagnosis, fault detection in complex systems, risk assessment, and decision-making in dynamic environments.
## 3 Experimental Results and Discussion
### Plastic Rotation Angle  
The inelastic rotation angle, \(\gamma_{\mathrm{P}}\), is evaluated by removing the contributions of elastic response from the link rotation angle. While the link rotation included components attributed to the elastic deformation of the vertical link and frame beam, those components were removed to obtain the inelastic rotation angle. Table 5 shows that the plastic rotation angles of all the specimens are between 0.08 and 0.16 except for Specim L1, which satisfies the limit requirement of AISC 341-10 for the  
Figure 11: Skeleton curve of loading displacement of each test specimen.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{\begin{tabular}{c} **Initial Stiffness** \\ **KN/mm** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Yield Load** \\ **\(f_{y}\)/kN** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Yield** \\ **Displacement** \\ \(\Delta_{\mathrm{P}}\)**/mm** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Peak Load** \\ **\(f_{u}\)/kN** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Limit** \\ **Displacement** \\ \(\Delta_{\mathrm{P}}\)**/mm** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Plastic Rotation** \\ **Angle \(\gamma_{\mathrm{P}}\)**/rad** \\ \end{tabular} } \\ \hline L1 & 9.0 & 93.3 & 12.4 & 218.8 & 32.8 & 0.07 \\ L2 & 16.1 & 155.3 & 10.8 & 314.4 & 32.2 & 0.08 \\ L3 & 16.2 & 190.0 & 14.0 & 276.0 & 35.5 & 0.10 \\ L4 & 15.9 & 192.3 & 15.6 & 319.4 & 53.7 & 0.15 \\ L5 & 19.0 & 286.8 & 17.9 & 420.0 & 47.5 & 0.14 \\ L6 & 26.7 & 288.5 & 13.4 & 441.2 & 31.3 & 0.09 \\ L7 & 19.1 & 288.4 & 19.2 & 462.6 & 57.7 & 0.16 \\ L8 & 26.4 & 194.4 & 11.1 & 515.0 & 45.3 & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mechanical properties of the specimens.  
plastic rotation angles of link beams of eccentrically braced steel frames exceeding 0.08 rad. It indicates that the length ratio is the main factor affecting the plastic rotation angles. The plastic rotation angles of L1, L2, and L6 are smaller than those of other specimens. The welding access hole reduces the influence of residual stress at the welds, which avoids the stress concentration and makes the links have a better plastic deformation ability. The plastic angle of L4 is 50% larger than that of L3 and the plastic angle of L7 is 14% larger than that of L5 under a different stiffener arrangement, which indicates that the link with smaller stiffener spacing has a better shear deformation capacity. In summary, the length ratio, welding access holes, and stiffener spacing are the important factors affecting the plastic deformation capacity of links.
# Industrial Image Anomaly Detection via Self-Supervised Learning with Feature Enhancement Assistance
## 2 Related Work
### Self-Supervised Learning Based Approach  
The scarcity of anomaly samples limits the application of supervised learning in anomaly detection. Thus, some researchers have tried to use self-supervision [8; 9; 10]. Self-supervised learning is often used as a proxy task that learns features from unlabeled data, thereby generalizing to different downstream tasks. In anomaly detection tasks, a self-supervised task is often used to fine-tune a pre-trained CNN feature extractor to reduce the domain bias between natural image classification scenarios and industrial anomaly detection scenarios. Commonly used self-supervised enhancement strategies include rotation [33], deletion [34], color transformation [35], and mixing [8]. MixUp [8] takes two images and their labels, superposing them according to their weights to generate a new dataset and corresponding labels. Based on this, techniques such as [36; 37] were developed. MixMatch [9] performs self-supervised learning by augmenting labeled and unlabeled data multiple times. ReConPatch [38] leverages the similarity between patch-level features, employing pairwise similarity and contextual similarity as pseudo-labels for self-supervised learning. The literature [39] achieved self-supervised learning by employing a compact loss to normalize features into a unified distribution prior to reconstructing the feature map.  
There are also methods that use the generation of simulated anomalies for self-supervised training. CutPaste [10] performs self-supervised training by cutting a rectangular block from an image and pasting it into the original image. SimpleNet [40] performs discriminative training by adding random Gaussian noise to the feature space. P2 RW [41] proposes a method to create pseudo-anomaly samples using pixel-point random walk, which realizes more diversified random data generation through random pixel walking.
## 5 Open Issues and Challenges
### Power Consumption  
In order to ensure a long lifespan for the sensor nodes, energy efficiency or power consumption is one of the main goals in any WBASN application. The power capacities of sensor nodes are limited due to the small size of their batteries, which cannot be replaced or recharged. Moreover, in interference scenarios, the power consumption of a WBASN increases because of contention to access the channel, retransmission, and the idle listening channel. Some algorithms consider the minimization power in a WBASN as a main objective. However, a good tradeoff between performance and power consumption is needed to ensure that the interference mitigation schemes work effectively.
# 5G New Radio Open Radio Access Network Implementation in Brazil: Review and Cost Assessment
## 2 Background
### Open RAN Architecture
#### 2.2.7 Deployment Flexibility  
The different RAN architectures also offer varying levels of deployment flexibility, which can impact overall cost and time to market.  
The centralized nature of c-RAN and v-RAN allows for a more streamlined deployment, as the majority of the processing and management functions are handled in the cloud or central data centers.  
In comparison, the distributed nature of d-RAN requires the deployment of dedicated hardware at each cell site, which can be more time-consuming and complex.  
The Open RAN approach, with its emphasis on open interfaces and the potential for a more diverse ecosystem of vendors, may provide greater flexibility in deployment, as the network can be built and scaled using a mix of hardware and software components from different suppliers.
## 2 Materials and Methods
### Deployment View  
That viewpoint depicted in Figure 4 provides a reference architecture with an overview of the system's software components and their interaction within the system's hardware nodes. The physical layout of the system (servers, databases, etc.) showcases the communication correlation between them. For a serious game in educational institutions with smart educational services, that view can include sensor nodes for external BCIs in the end users. BCIs enable neural input recognition, allowing for hands-free interaction with the virtual environment. The system also integrates an AI-driven XR-based educational platform called the Agricultural Smart Game (A.S.G.), which facilitates real-time monitoring, NPC-assisted learning, and immersive interactions. The A.S.G. Cloud Services component includes AI-powered NPCs, 3D visualization tools, and virtual navigation, enhancing the educational experience. Additionally, IoT sensors such as motion-tracking and eye-tracking devices collect user engagement data to personalize learning. Data can later be transmitted to edge computing devices, which preprocess them before being transferred to central, secure servers for further analysis and storage. This layout ensures an efficient and safe data processing performance, facilitating management and personalization of education tailored to the needs of the end users.  
Figure 3: Decomposition view. Adapted from [54].  
Similar deployment architectures (combining cloud-based services with on-premise or edge devices) have been used in smart-farming and IoT-focused projects. However, this work advances the approach by incorporating XR interactions and AI-enabled NPC educators within a single deployment model, showcasing how real-time data, edge computing, and immersive learning can coexist in one integrated framework. This tight coupling of AI-driven XR education with sensor feedback and user engagement metrics is relatively unexplored in the earlier literature, particularly for viticulture and woody crop management.  
Figure 4: Deployment view. Adapted from [54].
# Research on Dynamic Searchable Encryption Method Based on Bloom Filter
## 4 Proposed Construction
### Keyword Storage Scheme  
For keyword storage, this paper designs an Authenticator Bloom Filter (ABF). As shown in Figure 2, the Bloom filter has been modified to add a counting module, and the authenticator is designed to support dynamic updates.  
In the ABF structure, for each keyword, the Data Wyner hashes it into the Bloom filter through k unbiased hash functions. For the problem that there may be multiple keywords corresponding to one location, this article adds the counter A[]. Each time A keyword is computed and mapped to the Bloom filter, the count is increased by one for each position A[i]. This means that there is a keyword mapped in the i-th position.  
Figures 3 and 4 show the process of adding and deleting data for the ABF. Add data as shown in Figure 3. Hash keyword A and map it to bits 1, 3, 5, and 8 in the Bloom filter. Since bits 1, 5, and 8 are already mapped with keywords, only one is added to the counter. On the third bit, not only a one is added to the counter, but also a one is placed on the corresponding bit of the filter. The deletion process is shown in Figure 4. After keyword B is hashed, it is mapped to the first, fifth, sixth, and eighth bits. First, the corresponding counters are reduced by one, and it is found that the eighth counter is reduced to 0. This means that there are no more keywords mapped to this location, so place 0 in the Bloom filter.  
Figure 2: Authenticator Bloom Filter.
## 4 the Proposed Secure Multicast Routing Protocol: DLSMR
### The Proposed Deep Learning Design  
As shown in Figure 1, a wormhole attack occurs when an attacker creates a tunnel between two malicious nodes in the network, allowing them to capture packets at one end and replay them at the other end instantly. A wormhole creates the illusion of a shorter and more efficient route between the two malicious nodes. To solve this problem, we develop a novel deep learning framework to predict the secure next node while establishing the route from the source to multiple destinations in FANETs.  
Each node in the network can determine which node in the network is the most secure for multiple destinations through a deep learning framework. In this subsection, we design the deep learning framework to capture the relation between network parameters and system performances as shown in Figure 6. The main objective of this work is to predict the secure next node in the proposed DLSMR protocol. In this work, we utilize multivariate regression, which is more challenging than single regression. A deep learning model includes two phases, the training phase and the testing phase.  
In the training phase, the input parameters consist of the node identifier (NID), the distance between two nodes (Dist) which are denoted in Equation (7), the destination sequence (\(\text{D}_{\text{seq}}\)), the number of hops (Hop), and the remaining energy of the node (E). According to these input parameters, the model predicts the secure node ID, the unsecured node ID, the secure status, and the unsecured status as the output.  
In a training iteration, as shown in Figure 6, an error is obtained by comparing the deep learning output with the target and the simulation result obtain four outputs. Then, the error is minimized by updating the weights and biases on the neurons using back-propagation, which continues until the iteration is satisfied. The trainable deep learning framework is tested using a new input variable to predict the secure and unsecured next nodes as shown in Figure 6. In our design, we use a feed-forward neural network with \(1\times 5\) dimensional input layers, \(L\) hidden layers, and \(1\times 4\) dimensional output layers to obtain the two kinds of ID and status of the node as shown in Figure 7.  
Therefore, the proposed deep learning design can reduce complexity and also can predict the secure next node in real-time based on the information of its node as the input parameter. The layer structure used in the deep learning design to improve system performance is shown in Table 6.  
Figure 6: Comparison of packet loss ratio and number of packet losses as a function of node speed.
# An Interoperable Component-Based Architecture for Data-Driven IoT System
## 5 Experiment Results and Discussions
### MPPT Implementation Experiment
Furthermore, we can see that our system is able to track the maximum power point and maintain the operating parameters at the MPP. Figure 9a,b shows the tracking results of both solar intensity settings. Observing the tracking results, the x-axis scale, which is 0.5 s per box in all figures, on the left-side shows that the system accurately tracked the MPP within 2 s and maintained the MPP continuously. This proved that the proposed system is able to perform data-driven feedback successfully and able to deliver the performance that exactly the same as existing offline MPPT controllers. This also indicates that the proposed systems response speed is high enough to support time-sensitive system. In addition, the transmissions of data to the _InfluxDB_ and the data visualization through _Grafana_ is also successfully showcased in Figure 10a,b. Overall, the end-to-end functions of a data-driven IoT architecture are shown through the conducted experiments.
# Soil Classification from Piezoone Penetration Test Using Fuzzy Clustering and Neuro-Fuzzy Theory
## 4 Verification of Suggested Neuro-Fuzzy Model
### Jeonnam Dojang Port Site  
This site is located on the southwestern coast of Korea on the sea. The top of the seabed was covered by silty sand up to G.L-1.8 m and layered by clay up to the depth of G.L-9.8 m as shown in Figure 13. A mixture of gravel and sand was distributed below clay. A casing tube was initially driven to G.L-0.5 m, and a piezocone test was carried out up to G.L-10 m. Soil classification results according to USCS from undisturbed samples in the clay layer were mainly \"CH\" or \"CL\". Water contents ranged from 29.59% to 65.27%, liquid limits ranged from 36.1% to 76.2%, and plastic limits ranged from 20.8% to 30.8%. [PERSON] and [PERSON]'s method misclassified top silty sand mainly as clay because it did not consider negative pore pressure, as explained previously. Furthermore, [PERSON]'s method misclassified the upper part of \"CH\" or \"CL\" by USCS as silt. However, the neuro-fuzzy model classified clay and silty sand except at about 1 m thickness, but this may have occurred during the ground investigation considering the 1 m interval of SPT. The two charts proposed by [PERSON] et al. [3] failed to correctly detect silty sand. Moreover, the \(R_{f}\) chart and \(B_{q}\) chart gave different soil types in silty sand, which could confuse engineers.