esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Neural Networks in Distribution of the Load in Cluster-Based Web Systems
## 5 Design of Executor Model
### Comparison with Other Algorithms  
The results obtained were compared with the results in the following experiment steps (\(NN1\) and \(NN2\)) and with other popular load balancing algorithms--\(FNRD\) (fuzzy-neural request distribution), \(LL\) (request handling by the server loaded with the least number of requests), and \(RR\) (request handling in circular order). The results of the comparison of mean response time are shown in Figure 14. Table 1 presents the data in tabular form.  
Figure 14: Results of the comparison of mean response time. Compared algorithms: the proposed algorithm (\(NN\)) with the following research steps (\(NN1\), \(NN2\)) and other popular methods of web server load balancing—\(FNRD\), \(LL\), \(RR\).  
Figure 13: Comparison of the results for modified neural networks.  
Another metric by which the algorithms can be compared is the 95 percentile of response time. The results of the comparison of 95 percentile of response time are shown in Figure 15. Table 2 presents the data in tabular form.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number of Clients** & _FNRD_ & _LL_ & _RR_ & _NN1_ & _NN2_ & _NN_ \\ \hline
100 & 0.0200 & 0.0402 & 0.0543 & 0.0529 & 0.0275 & 0.0281 \\
300 & 0.0550 & 0.1231 & 0.1533 & 0.1563 & 0.0742 & 0.0736 \\
500 & 0.1031 & 0.2042 & 0.2520 & 0.2560 & 0.1152 & 0.1165 \\
700 & 0.1426 & 0.2850 & 0.3503 & 0.3465 & 0.1647 & 0.1571 \\
900 & 0.1957 & 0.3650 & 0.4499 & 0.4444 & 0.2017 & 0.1977 \\
1100 & 0.2520 & 0.4438 & 0.5481 & 0.5387 & 0.2415 & 0.2519 \\
1300 & 0.3181 & 0.5274 & 0.6457 & 0.6306 & 0.2842 & 0.2880 \\
1500 & 0.3686 & 0.6077 & 0.7461 & 0.7273 & 0.3365 & 0.3161 \\
1700 & 0.4418 & 0.6889 & 0.8448 & 0.8157 & 0.3896 & 0.3535 \\
1900 & 0.5921 & 0.7701 & 0.9454 & 0.9103 & 0.4317 & 0.3984 \\
2100 & 0.6660 & 0.8602 & 1.0412 & 1.0123 & 0.4896 & 0.4531 \\
2300 & 0.7235 & 0.9399 & 1.1366 & 1.0947 & 0.5403 & 0.4788 \\
2500 & 0.8572 & 1.0199 & 1.2225 & 1.1997 & 0.5620 & 0.5022 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the comparison of mean response time in tabular form.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Clients** & _FNRD_ & _LL_ & _RR_ & _NN1_ & _NN2_ & _NN_ \\ \hline
100 & 0.0437 & 0.1355 & 0.1812 & 0.1746 & 0.0670 & 0.0606 \\
300 & 0.0643 & 0.3567 & 0.4566 & 0.4553 & 0.1730 & 0.1260 \\
500 & 0.1057 & 0.5469 & 0.7352 & 0.6941 & 0.2057 & 0.1975 \\
700 & 0.1638 & 0.8114 & 0.9988 & 0.9174 & 0.3130 & 0.2354 \\
900 & 0.2296 The results presented show that the proposed method provides shorter response time results compared to other popular methods and it is also stable for high loads.
## 4 Experiments
### Ablation Experiment  
A comprehensive ablation experiment was conducted to evaluate the impact of various components of DBGNN. Fully armed DBGNN is used as the baseline, which consists of GN, GELU activation, skip connection, and subgraph sampling. The variants represent the architecture component that has been replaced or removed in the fully armed DBGNN. The variants were trained and tested using the same data as before. The experimental results are shown in Figure 10.  
Fully armed DBGNN demonstrated the highest accuracy, while the accuracy of ACC-Door and AP-Door reached over 91%. Removing the activation function caused a significant drop in prediction performance, especially in wall classification accuracy (ACC-Wall), which decreased by about 6 percentage points, indicating that the activation function has a significant impact on performance. Additionally, GELU was replaced with the commonly used ReLU [42] and the recently introduced SELU [43] to find the optimal activation functions. Both transformations resulted in a decrease in accuracy, confirming the effectiveness of GELU. Removing normalization led to a noticeable performance drop, particularly in AP-Door and ACC-Door. Furthermore, we compared several graph normalization techniques, including node normalization and batch normalization [32]. Batch normalization  
Figure 9: Link prediction results of DBGNN and baseline models.  
performed slightly better than node normalization, but both had lower accuracy than GN, with ACC-Door dropping by 5.55% and 2.36%, respectively, indicating that GN is more suitable for bubble diagram generation tasks. The inclusion of skip connections significantly improved all four metrics. This is because skip connections effectively address the gradient vanishing problem in graph embedding learning, ensuring the efficient transfer of topological information. Subgraph sampling drove the model to achieve optimal performance, increasing ACC-Door by 2.04% and ACC-Wall by 1.33%, as this strategy enhances feature learning for both non-central and central subgraphs, thereby improving the prediction accuracy of local links and ultimately enhancing overall prediction performance. Ablation experiments showed that normalization, activation functions, and skip connections are key factors in improving model performance, and removing any of these would lead to a decrease in accuracy. Subgraph sampling is crucial for perceiving local features in bubble diagrams; it further promotes the learning of complex patterns, thus improving prediction performance.
## 3 Model Validation and Numerical Examples
### Numerical Example  
According to the model comparison, it is found that the modified K&C model is more suitable for the RPC material model in RPC-FST. Therefore, the RPC material model is established by using the modified K&C model. In addition, a group of samples after the Latin hypercube sampling plan is selected as the structural parameters of RPC-FST. This process establishes the basis for the reliability sensitivity analysis of RPC-FST in Section 4. The dynamic response analysis of the RPC-FST column under explosive load is carried out based on FSI theory. The bottom and top of the RPC-FST columns are restrained in the \(x,y\), and \(z\) directions, whereas all the other degrees of freedom can move freely.  
The ALE method can evaluate a blast scenario with more detail, including explosive detonation. Therefore, the velocity of the shock wave and the displacement of RPC-FST are extracted. Figure 5 illustrates the functional mechanism between air and RPC-FST.  
Figure 5: Explosive process analysis of a numerical example: (**a**) four processes of explosion and (**b**) details of each process.  
The evolutions of RPC-FST dynamic response under explosion load can be roughly divided into four phases: process \(A\), process \(B\), process \(C\), and process \(D\), as shown in Figure 5a. Meanwhile, the details of each process are shown in Figure 5b. The results show that the shock wave did not reach the blast surface of RPC-FST at 0.5 ms. The maximum displacement of the RPC-FST occurred at 2.5 ms. In addition, the maximum displacement (process C) in the RPC-FST displacement time history curve is selected to establish the Kriging surrogate model.
## 7 Conclusions  
This study provides a detailed analysis of the rock mass discontinuities for the exposed section of the northern slope of an open-pit mine in Fushun. UAV photogrammetry technology was used to reconstruct a 3D slope model of the exposed slope surface. After denoising the point cloud data, the rock discontinuity information was extracted using KNN (k-nearest neighbor) search and PCA algorithms, and the dominant groupings of the rock discontinuity were discussed. The validation of the results obtained using UAV photogrammetry technology and manual measurement was then carried out, and the further refined analysis of rock mass discontinuities was conducted. The main conclusions are as follows:  
* In total, seven dominant orientation groups from the studied slope surface can be categorized. The rock mass discontinuity J1 (95.19\({}^{\circ}\)\(\angle\) 76.54\({}^{\circ}\)) is main orientation group, which has a point cloud radio of \(\sim\)30%. The dominant groupings calculated by UAV photogrammetry technology are closely aligned with the corresponding results obtained from compass measurements, with an error margin of less than 5\({}^{\circ}\).
* The proportions of small-scale rock mass discontinuities (0-12 cm) within the marlstone (\(\sim\)45%) and brown shale (-48%) layers are significantly higher compared to that in the green mudstone (-15%), indicating a greater degree of fragmentation within the marlstone and brown shale layers.
* The dip angle of rock discontinuity within the marlstone and green mudstone layers shows a gradual increase in proportion with increasing angle. In contrast, the dip angle distribution within the brown shale layer is more uniform, without obvious statistical patterns.
* The presence of numerous small-angle rock discontinuities in the brown shale layer plays a crucial role in the overall slope stability of the open-pit mine in Fushun. The small-angle rock discontinuities may interpenetrate with the discontinuities with large angles, forming a potential slip surface.  
All the authors contributed to this research. [PERSON] and [PERSON] performed the software calculations. [PERSON], [PERSON], and [PERSON] and [PERSON] analyzed the scanned point cloud data. [PERSON] and [PERSON] wrote the paper. [PERSON] and [PERSON] and [PERSON] revised the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China and Natural Science Foundation of Shandong Province, grant numbers 51879135, 42272334 and ZR2022 QD004, ZR2023 QE074.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# A New Model for Emotion-Driven Behavior Extraction from Text
## 5 Methodology
### Encoder  
The encoder's role is to convert tokens into their vector representations. We employ Bidirectional Encoder Representations from Transformers (BERT) [41], a powerful text representation model, for this purpose. The BERT encoder architecture is illustrated in Figure 2(a).  
To use BERT, we first obtain token embeddings similar to other NLP models. BERT utilizes a sentence separator (\([SEP]\)) at the end of each sentence and a classifier token (\([CLS]\)) at the beginning of the first sentence. The representation of \([CLS]\) serves as the representation of the entire sentence.  
Given a document \(D=\{x_{1},x_{2},\dots,x_{d}\}\), the input representation for the document is:  
\[D_{input}=\{[CLS],x_{1},x_{2},\dots,x_{d},[SEP]\} \tag{1}\]  
where \(d\) is the number of tokens in \(D\).  
BERT employs a multi-layer bidirectional transformer encoder, primarily using stacked multi-head attention [42]. An attention function maps a query and a set of key-value pairs to an output. It can be computed as follows:  
\[Attention(Q,K,V)=softmax(\frac{QK^{T}}{\sqrt{d_{k}}})V \tag{2}\]  
where \(Q\), \(K\), and \(V\) are matrices containing queries, keys, and values, respectively.  
Multi-head attention consists of multiple attention heads, enabling the model to attend to different positional information from various representation perspectives. Multi-head attention can be calculated as follows:  
\[MultiHead(Q,K,V)=Concat(head_{1},\cdots,head_{h})W^{O} \tag{3}\]  
\[head_{i}=Attention(QW^{Q}_{i},KW^{K}_{i},VW^{V}_{i}),i\in[1,h] \tag{4}\]  
where \(h\) is the number of heads, and \(QW^{Q}i\), \(KW^{K}i\), and \(VW^{V}i\) are parameter matrices for \(head_{i}\).  
We denote the encoding operation as \(BERT\), and the representation can be obtained as:  
\[Rep=BERT(D_{input}) \tag{5}\]  
Figure 3: Structures of the encoder and decoders in the proposed method: (a) BERT encoder; (b) Bi-LSTM decoder for emotion and behavior extractors; and (c) FC decoder for the emotion–behavior filter.  
where \(Rep=\{r_{[CLS]},r_{1},r_{2},\ldots,r_{d},r_{[SEP]}\}\) represents the token representations in \(D_{input}\), and \(r_{[CLS]}\) serves as the representation of \(D_{input}\) for text classification tasks.
# Wireless Power Transfer Approaches for Medical Implants: A Review
###### Abstract  
Wireless power transmission (WPT) is a critical technology that provides an alternative for wireless power and communication with implantable medical devices (IMDs). This article provides a study concentrating on popular WPT techniques for IMDs including inductive coupling, microwave, ultrasound, and hybrid wireless power transmission (HWPT) systems. Moreover, an overview of the major works is analyzed with a comparison of the symmetric and asymmetric design elements, operating frequency, distance, efficiency, and harvested power. In general, with respect to the operating frequency, it is concluded that the ultrasound-based and inductive-based WPTs have a low operating frequency of less than 50 MHz, whereas the microwave-based WPT works at a higher frequency. Moreover, it can be seen that most of the implanted receiver's dimension is less than 30 mm for all the WPT-based methods. Furthermore, the HWPT system has a larger receiver size compared to the other methods used. In terms of efficiency, the maximum power transfer efficiency is conducted via inductive-based WPT at 95%, compared to the achievable frequencies of 78%, 50%, and 17% for microwave-based, ultrasound-based, and hybrid WPT, respectively. In general, the inductive coupling tactic is mostly employed for transmission of energy to neuro-stimulators, and the ultrasonic method is used for deep-seated implants.  
wireless power transfer; implanted device; inductive link; microwave link; ultrasound link; hybrid link +
Footnote †: journal:
## 4 Results
### Technologies for the Proposed Architecture  
In order to support the functionalities of the proposed architecture, a series of technologies have been identified to be used for the implementation of the system. In order to be able to match the questions asked by the user using the smart learning interface module and implicitly to calculate semantic similarities, Sentence-BERT or similar will be used, with the help of which text transformations can be made into embedding vectors [127]. For the generative AI module, an open-source model such as LlaMA2 will be used [128].  
The system is designed as a module-based web application, using the JavaScript programming language together with Node.js and Express.js. The frontend is built using React.js 19.1.0 [129; 130; 131]. The application modules will communicate with each other via RESTful APIs (currently version v1).
## 3 Nanotechnology in Wound Dressings
### Major Architectures of Nanomaterials  
Nanoemulsions, biphasic dispersions of two nonmiscible liquids, are among the most common types nanomaterials used for wound treatment. They show small droplet size, long shelf life, and simple synthesis processes, but their pharmacokinetics and pharmacodynamics are affected by the type of route of administration [83].  
Nanoparticles generally refer to nanospheres, nanocapsules, solid lipid nanoparticles (SLNs), as well as nanostructures lipid carriers (NLCs). Nanocapsules consist of biopolymers containing a core of lipidic/lipophilic molecules. Their feature is the capability to release their cargo safely and enhance its diffusion into deeper skin layers. However, their efficacy can be compromised by a large amount of substance being directly administered on the skin.  
Nanospheres are usually made of poly(lactic-co-glycolic acid) (PLGA) and chitosan as colloidal systems useful for delivering proteins and peptides involved in wound healing. However, their loading capacity is low, and the release is characterized by a kinetic burst. Protein administration can also be obtained by SLNs using a topical route.  
NCLs exhibit good efficiency, safety, and sustained drug release, but they have a low drug-loading capacity. They are similar in structure to liposomes, but they accommodate synthetic non-ionic surfactants. They exhibit specific target recognition thanks to the expression of surface receptors. This characteristic improves their effectiveness and reduces side effects.  
Metal nanoparticles have antibacterial properties, low penetration into the skin, and transparency after application. However, some physicochemical and biological have to be improved. For these reasons, metal and metal oxides nanoparticles, such as zinc and gold nanoparticles, are preferred. They have toxicity related to their size, so the synthesis process must be strongly controlled [84; 85].  
Polymeric nanoparticles represent architectures where the active molecule is embedded in the polymeric matrix and/or absorbed on its surface. They are used in versatile systems for wound dressings and delivery vehicles [86]. The degradation of polymeric nanoparticles is progressive and occurs in situ, and their products are metabolized by the host, allowing them to avoid additional surgical intervention [18]. Another advantage of using polymeric nanoparticles is regulating the release mechanism of a bioactive molecule from its matrix by choosing the right type of polymer and synthesis method [87].
## 4 Training and Validating/Testing the Dataset
### Intrusion Prevention System (IPS) Deep Learning Approach  
We utilize our proposed DLMIDPSM capability to highlight other IPS evaluations which are further based on training and validation based on our intrusion prevention system (IPS) method, which we achieved through our proposed IDPST for capturing data for analysis. IDPST is effective for intrusion detection and prevention using command operations in Linux, such as iptables. Thus, the data packets can be easily blocked (as observed in our Algorithm 2) by our proposed IDPST through our DLMIDPSM using iptables, based upon the prevention method required for the specific intrusion attacks. The IPS method employs our proposed DLMIDPS model. It prevents all new intrusion attacks, including DDoS, DoS, MITM, Mirai, and Recon, as investigated in this research [53], and based on the analysis of the CICIoT2023 dataset.  
Moreover, our proposed DLMIDPSM drives an input through the classification part to identify the intrusion attack features corresponding to which IPS operation is suitable.  
Figure 14: No regularization model accuracy vs. number of epochs.  
Figure 15: No regularization model accuracy loss vs. number of epochs.  
For example, if DDoS and DoS attacks occur, they reveal the sender's IP address and any intrusion attacker, and all packets from the attacker's IP are either dropped or blocked (as we showed in Algorithm 2 previously). Similarly, any MITM and Recon intrusion attacks can take place. In this case, our proposed DLMIDPSM IPS system notices the connection port number at which the intrusion attack occurs and simultaneously blocks or prevents all of the packets from going through that port number. Nothing happens if the output of the detection part is benign or normal traffic [54; 55].
## 3 Dual Pointer Network Model for Relation Extraction
### Experimental Results
In Table 7, NovelTag [17] is an end-to-end model that extracts entities and their relations based on a novel tagging scheme designed for relation extraction. MultiDecoder [2] is a Seq2 Seq-based model that combines the entity and relation extraction using a decoder with a copy mechanism. GraphRE [23] is a joint model that extracts entities and their relationships using graph convolutional networks (GCN) [24]. As shown in Table 7, the proposed model outperformed all models. It is not reasonable to directly compare the proposed model with these models because it requires gold-labeled entities, while the other models automatically extract entities from sentences. Although direct comparison is unfair, the proposed model exhibited considerably better performance. If we adopt a state-of-the-art named entity tagger based on BERT [25] with F1-scores of 0.9 or more, the proposed model is expected to show F1-scores of 0.662 or more based on simple multiplication. Figure 5 describes the performances according to relation types. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Recall** & **Precision** & **F1-Score** \\ \hline SPTree [6] & 0.54 & 0.57 & 0.56 \\ FCM [19] & 0.49 & 0.72 & 0.58 \\ DYGIE [9] & 0.57 & 0.64 & 0.60 \\ Span-Level [20] & 0.58 & 0.68 & 0.63 \\ HRCNN [21] & - & - & 0.74 \\ Walk-Based [22] & 0.60 & 0.70 & 0.64 \\ Our model & 0.83 & 0.79 & 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison on the ACE-2005 corpus. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Recall** & **Precision** & **F1-Score** \\ \hline NovelTag [17] & 0.414 & 0.615 & 0.495 \\ MultiDecoder [2] & 0.566 & 0.610 & 0.587 \\ GraphRE [23] & 0.600 & 0.639 & 0.619 \\ Our model & 0.820 & 0.749 & 0.783 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparisons on the NYT corpus. Figure 5: Performances per relation type. As shown in the right graph of Figure 5, the proposed model obtained the F1-score of 1.0 for the relation type \"ethnicity\", but it obtained the F1-score of 0.0 for the relation type \"industry\". The imbalance of training data caused these performance differences. For example, the \"industry\" relation did not occur in the NYT training data at all. The cases where the proposed model incorrectly extracted relations were also grouped in Table 8.  
Most incorrect predictions included cases where the decoders incorrectly pointed out subjects or objects, and these incorrect entities lead to incorrect relation names, as shown in the first and third sentences in Table 8. In some cases, the decoder did not point out subjects or objects. As a result, any triples in a sentence were not omitted, as shown in the second sentence.
# GraphDBSCAN: Optimized DBSCAN for Noise-Resistant Community Detection in Graph Clustering
## 3 Proposed Method
### Identification of Optimal DBSCAN Parameters: bestEps and minPts
The vector \(Pts[v_{i},v_{j}]\) represents the number of points contained within this circle while \(Eps[v_{i},v_{j}]\) represents the resulting radius r. To obtain best_Eps and minPts, the following is executed:  
\[best_{E}ps=argmax_{x}\hat{f}_{Eps}(x) \tag{4}\]  
The density function f is used to estimate the probability distribution of these parameters based on the data. While adaptive methods like Gaussian Mixture Models (GMM) can model complex KNN distance distributions for DBSCAN parameter tuning, they are not always well-suited for GraphDBSCAN. Studies evaluating GMM in clustering contexts, such as geochemical anomaly detection, have shown that its dependence on fitting multiple density components can introduce instability, particularly in datasets with skewed or sparse distributions like scale-free networks [28]. In such cases, GMM may overfit minor density variations or produce overly restrictive Eps values, leading to excessive filtering and disruption of community structures. In contrast, our KNN-based approach ensures modularity and structural robustness with a simpler, more scalable design, avoiding the computational complexity of probabilistic models and offering greater reliability across diverse graph topologies.