esa-sceva/satcom-chunk-collection huggingface dataset:
# A Text-Generated Method to Joint Extraction of Entities and Relations
## 3 Materials and Methods
### Model Description  
#### 3.2.1 Encoder  
Giving a sentence s = [\(w_{1}\), \(w_{2}\),..., \(w_{n}\)], where \(w_{t}\) represent the \(t\)-th word in the sentence of length \(n\), we first convert the word with one hot encoding into the embedding matrix through the word embedding layer, and get e = [\(x_{1}\), \(x_{2}\),..., \(x_{n}\)], where \(x_{t}\in R^{d}\) represents the embedding vector of \(t\)-th word. The embedding layer randomly initializes the embedding matrix and updates the weight parameters with the training of the model.  
Then, we use LSTM to further encode the sequence. Long Short-Term Memory (LSTM) is a variant of Recurrent Neural Network (RNN) which is widely used in various NLP tasks because it has ability to capture long-term dependencies and solve the problem of gradient vanish in RNN. Specifically, we use bidirectional LSTMs (Bi-LSTMS) which consists of two separate LSTM layers. The forward LSTM layer \(\overrightarrow{h}\) encodes the input sequence from \(x_{1}\) to \(x_{n}\). Similarly, the backward LSTM layer \(\overleftarrow{h}\) will  
Figure 1: The overall structure of our model. The blue block represents the bidirectional LSTMs in the encoder, the red block represents the unidirectional LSTM in the decoder, the green block represents the attention weight distribution, and the yellow block represents the final generation probability distribution. All these above descriptions will be introduced in Section 3.  
encode the input sequence from \(x_{n}\) to \(x_{1}\). We then concatenate \(\overrightarrow{h_{t}}\) and \(\overleftarrow{h_{t}}\) to represent final encoder information of t-th word, denoted as \(h_{t}=\left[\overrightarrow{h_{t}},\overleftarrow{h_{t}}\right]\), in this way, the encoder vector of each step can obtain the semantic information of its context.  
\[\begin{split} f_{t}&=\sigma(W_{f}\cdot[h_{t-1},x_{ t}]+b_{f})\\ i_{t}&=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i})\\ \tilde{C}_{t}&=tanh(W_{c}\cdot[h_{t-1},x_{t}]+b_{c} )\\ C_{t}&=f_{t}*C_{t-1}+i_{t}*\tilde{C}_{t}\\ o_{t}&=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o})\\ h_{t}&=o_{t}*tanh(C_{t})\end{split} \tag{1}\]  
In LSTM, as shown in Equation (1), \(x_{t}\) represents the word vector of the \(t\)-th word, and \(h_{t}\) represents the hidden state vector at the time \(t\). \(W\) and \(b\) represent the weight matrices and bias vectors that can be learned, respectively.
# XTTS-Based Data Augmentation for Profanity Keyword Recognition in Low-Resource Speech Scenarios
## 3 Experimental Results and Comparisons of Various Algorithms
### Performance Evaluation with Mixed Speech for the Proposed-I Algorithm  
The test set in this section consists of a mixture of RHS and synthetic speech data, split into a 7:1:2 ratio for training, validation, and testing, respectively. This was paired with 5-fold cross-validation, where training and validation data accounted for 80%, and the test set comprised 20%, with swapping performed to assess model robustness. The results, presented in Figures 10-12, indicate that the average test accuracy for segmented data reached 88.92%, with the average loss reduced to 1.9873. Following adjustments with the statistical mechanism, the overall average test accuracy further improved to 93.35%. From validation to testing, the statistical mechanism effectively mitigated classification errors due to lexical similarities between categories, significantly enhancing the model's recognition accuracy. These results demonstrate the classification model's high efficiency and robust performance in a mixed-data environment.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Hyperparameters** & **Value** \\ \hline Batch Size & 8 \\ Epochs & 150 \\ Learning Rate Scheduler & CosineAnnealingLR \\ Initial Learning Rate (LR) & \(1\times 10^{-4}\) \\ Loss Function & CrossEntropyLoss \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameter settings for experiments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Ratio** & **Loss** & **Accuracy** & **Improvement** \\ \hline
5:3:2 & 2.319 & 55.35\% & - \\ \hline
6:2:2 & 2.288 & 58.48\% & 3.13\% \\ \hline
7:1:2 & 2.258 & 61.33\% & 5.98\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Dataset ratio of RHS for the Proposed-I algorithm.  
Figure 11: Average loss and accuracy of the Proposed-I model in cross-validation on the mixed speech dataset. The results indicate consistently high accuracy across folds, confirming the model’s stable generalization capability.  
Figure 10: Loss and accuracy curves of the Proposed-I model on the mixed speech dataset. The model exhibits stable convergence and robust performance when trained on real and synthetic data.  
Figure 12: _Cont._
# Location Optimization of Emergency Bell Based on Coverage Analysis for Crime Prevention
## 3 Materials and Methods
### Exploratory Data Analysis and Statistical Analysis
#### 3.3.2 Principal Component Analysis  
In this subsection, prior to conducting K-means analysis, dimensionality reduction was carried out through PCA to obtain three principal components.  
PCA is a statistical technique that reduces high-dimensional correlated data into lower-dimensional uncorrelated data to extract features. Specifically, this method identifies the problem of multicollinearity, which occurs when there is a correlation between independent variables and then aims to reduce dimensionality while minimizing the loss of information and maintaining the high explanatory power of the data.  
In this subsection, PCA was employed to improve the efficiency of cluster analysis. The most important cluster derived from K-means clustering was then examined at the administrative dong level.  
Further, to determine the optimal number of principal components for the analysis, a scree plot was utilized, as shown in Figure 3 and Table 2. The cumulative explained variance of the first three principal components amounted to 0.8602, and beyond this point, the slope of variance thereafter flattened. As a result, it was determined that three principal components were appropriate. In particular, to identify the variables that contribute to each principal component, only those variables with large absolute values of coefficients were selected for analysis.  
Figure 3: Scree plot of eigenvalues for principal component analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Comp. 1** & **Comp. 2** & **Comp. 3** & **Comp. 4** \\ \hline Standard Deviation & 2.7570326 & 1.02277867 & 0.90301824 & 0.80002960 \\ Proportion of Variance & 0.6910208 & 0.09509784 & 0.07413109 & 0.05818612 \\ Cumulative Proportion & 0.6910208 & 0.78611862 & 0.86024971 & 0.91843583 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Importance of components.  
Figure 2: Heat Map (Correlation matrix). Num.CCTV: Number of CCTVs; Num.Bell: Number of emergency bells; Float.pop.D: Transient population during daytime (07:00 a.m. to 05:00 p.m.); Float.pop.N: Transient population during daytime (18:00 a.m. to 06:00 p.m.); Pop.Woman: Population of women; Fac.Build: Number of buildings; Num.Crime: Number of crimes; Request.CCTV: Requests for CCTVs; Area.Building: Building area; and Area.CCTV: Surveillance area of CCTV.  
As shown in Figure 4, the first principal component comprised \"Number of emergency bells (Num.Bell),\" \"CCTV surveillance area (Area.CCTV),\" \"Number of CCTV cameras (Num.CCTV),\" \"Transient population (07:00-17:00) (Float.pop.D),\" and \"Transient population (18:00-06:00) (Float.pop.N).\" The second principal component comprised the variable \"Building area (Area.Building).\" The third principal component comprised the variable \"Requests for CCTV (Request.CCTV),\" and variables that were under zero (Num.Crime, Fac.Build, Population, Pop.Woman) of the Dim2 axis were excluded from the analysis.
# Recurrent Deep Learning for Beam Pattern Synthesis in Optimized Antenna Arrays
This article introduces a novel deep learning-based approach using a long short-term memory neural network for antenna synthesis. Our contribution involves calculating complex excitations in response to ideal input radiation patterns with beam patterns featuring reduced sidelobes and high directivity. The proposed DL methodology is also extended to attainable and fixed radiation patterns. The remainder of this paper is organized as follows: Section 2 addresses linear array optimization and deep neural network modeling, while Section 3 presents the antenna pre-synthesis process, data arrangement, pre-processing, and results obtained with the considered DNNs. Finally, in Section 5, the conclusions of this work are presented.
# Reconditioning of Diamond Coated Tools and Its Impact on Cutting Performance for CFRP Laminates
###### Abstract  
In recent years, CVD diamond-coated tungsten carbide (WC-Co) tools have been widely utilized due to their benefits in the machining of non-ferrous alloys and polymer composite materials, especially carbon-fiber-reinforced plastics (CFRPs). The reconditioning of such coated tools is economically attractive due to their high cost and short tool life. The decoding of the remaining diamond film from the used tools and the subsequent surface preparation by wet chemical pretreatment are essential steps for new CVD diamond film formation. Previously, it was shown that reactive ion beam etching (RIBE) could effectively remove CVD diamond films. However, some degree of WC-Co tool substrate damage is expected due to the high ion energy in RIBE and the chemical activity in wet etching. This study addresses the effects of RIBE decoding and surface pretreatment steps on WC-Co tools with a complex shape in terms of the ion-induced surface damage, geometry alteration, and adhesion of a subsequently re-applied CVD diamond film. Moreover, the cutting performance of the tools subjected to the RIBE decoding and repeated film deposition was studied via CFRP cutting tests. It has been shown that the RIBE decocated and recoated tools had a high level of cutting performance comparable to the new tools.  
cutting tool reconditioning: decoding: CVD diamond film; CFRP; flank wear; delamination Article  
1
## 3 Sesquiterpenol Biosynthesis
### Sesquiterpenol Synthases  
Sesquiterpene synthases catalyze the conversion of farnesyl diphosphate (FPP) into unsaturated hydrocarbon scaffolds, including farnesene and bisabolene, through Mg\({}^{2+}\)-dependent pyrophosphate elimination. By contrast, sesquiterpenol synthases utilize the same FPP substrate to generate oxygenated derivatives via distinct hydroxylation mechanisms [53]. Both enzyme classes operate through a conserved catalytic framework. First, the metal-coordinated cleavage of FPP generates a reactive farnesyl carbocation intermediate. Then, carbocation rearrangement via hydride shifts, cyclization, or ring modifications. Finally, termination is achieved through deprotonation to yield hydrocarbons in sesquiterpene synthases or via hydroxylation to produce sesquiterpenol in sesquiterpenol synthases [57]. Hydroxylation of the sesquiterpenol synthases proceed through two distinct pathways. The direct hydration mechanism employs strategically positioned hydrophilic residues (Thr/Ser) in the active sites of sesquiterpenol synthases to the coordinated water-mediated protonation of the carbocation intermediates, as exemplified in patchoul biosynthesis [58]. Alternatively, oxidative post-modification involves sequential actions of sesquiterpenol synthases and cytochrome P450 monooxygenases (CYP450s),  
Figure 3: Reaction mechanism of STSs. (E,E)-Germacradienyl cations (C1-C10 closure) and (E,E)-humulyl cations (C1–C11 closure) are initiated from FPP. The aryl diphosphate (NPP), isomerized from FPP, can be cyclized into bisabolyl cations (C1–C6 closure) and (Z,E)-germacradienyl cations (C1–C10 closure). Both farnesyl cations and noridyl cations have the potential to directly undergo deprotonation or hydration, resulting in the formation of acyclic sesquiterpenols, such as farnesenol and noralidol.  
where neutral hydrocarbon products from sesquiterpenol synthases undergo subsequent hydroxylation, as observed in santalol biosynthesis [59]. Structurally, both sesquiterpene synthases and sesquiterpenol synthases belong to the terpene synthase (TPS) superfamily. They are characterized by conserved \(\alpha\)-domain architecture and Mg\({}^{2+}\)-binding motifs (DDxXD, NSE/DTE). Sesquietpene synthases have evolved precise active-site topographies that enable stereochemical control over single hydrocarbon products [57]. Phylogenetic evidence supports the emergence of sesquiterpenol synthases from sesquiterpene synthase ancestors through adaptive mutations that introduced hydrophilic residues, facilitating water access [46].
## 2 High Diffraction Efficiency and Damage-Resistance Threshold Gratings
### Multilayer-Dielectric-Film Grating  
[PERSON] pioneered the concept of holographic optics in the 20 th century. With the advancement and maturation of laser technology, the technology of preparing gratings under laser interference conditions (holographic exposure), has been developed. This technology can produce large-area diffraction gratings with good uniformities and diffraction efficiencies, which has promoted the rapid development of grating theory. The limitations in metal-grating performance have driven scientific researchers to constantly innovate to meet more stringent requirements, and multilayer-dielectric-film gratings have been developed one after the other. Dielectric-film gratings are feasible because the inherent absorbance of dielectric materials is approximately zero. The diffraction efficiency is 100%, and the damage-resistance thresholds are many orders of magnitude higher than those of metal gratings. The grating period, duty cycle, groove depth, and residual thickness of the top relief structure without etching are the most important structural characteristics of multilayer-dielectric-film gratings. The diffraction effectiveness of a multilayer-dielectric-film grating is affected by the incident angle and wavelength of the incoming light, as well as the groove depth, duty cycle, groove structure, multilayer-dielectric-film structural parameters, and surface relief-structure refractive index. To develop a diffraction grating with a high diffraction efficiency and anti-laser-damage threshold, it is necessary to use software to optimize the design of the film-stack structure of the multilayer bonding film and structural parameters of the diffraction grating. This design process results in a multilayer-dielectric-film grating with good performance. The development process of the topmost multilayer dielectric film is to continually improve the diffraction efficiency and anti-laser-damage threshold, as well as the preparation procedure [23, 24, 25, 26].
## 5 Numerical Results and Performance Analysis
### Numerical Results
#### 5.2.2 Transport Network Traffic Distribution  
We analyze the average traffic distribution for s-VCC slice, eRTC slice and eMBB slice respectively under the uniform and non-uniform VNU capacity conditions, presented in Figure 7. In Figure 7, all s-VCC slice traffic deploys into access layer network resources, whereas most of the eRTC slice traffic and all eMBB slice traffic deploys into aggregation and core layer network resources. For the  
Figure 5: Transport network latency for latency-sensitive network slices vs node connectivity in access layer network.  
Figure 6: Transport network latency comparison among proposed model, NSDP and RFPA.  
case with non-uniform VNU capacity, Figure 7b reveals the similar result as Figure 7a, whereas the traffic distributions of three type slices move backward, obviously. It can be proved that the s-VCC slice has a higher priority to occupy access layer network resources for ultra-lower latency in our proposed model. The detailed reason is that the shorter transmission distance can effectively reduce the latency, and has been proven in the above simulation results. Meanwhile, the eMBB slice traffic prefers to be deployed into the core layer network which owns powerful capacity and bandwidth to support the high traffic volume of such slices. Furthermore, Figure 7a depicts that the traffic of latency-tolerant NSIs, e.g., eMBB, goes into the aggregation or core layer network for computation offloading of the access layer network when the VNU capacity is limited.  
In order to further evaluate the traffic distribution principle for three type slices, we evaluate the average traffic distribution when the node connectivity changes in the access layer network, as shown in Figure 8. In Figure 8a,b, the traffic ratios of the s-VCC slice and eRTC slice on access layer network increase continuously with increased node connectivity, whereas the ones on aggregation layer network increase first and then decrease. In contrast, the traffic ratio on core layer network continues to decrease to 0, because of the insufficient access layer network resources when the node connectivity is lower. Comparing to the eRTC slice, we can notice that the traffic ratio of the s-VCC slice on aggregation layer network drops preferentially and reaches 0 eventually. The phenomenon can be attributed to the allocation principle: a higher allocation priority is provided to the s-VCC slice for latency reduction, since its latency demand is further stricter. On the contrary, the traffic of eMBB goes into the core layer network observably as shown in Figure 8c. Because of eMBB's massive traffic, the link capacity of access layer network cannot afford the target rate. When the node connectivity is greater than 35%, the traffic ratio on core layer network drops down visibly. The reason is that the access layer network resource is sufficient enough to accommodate more slice traffic, so more aggregation layer network resources are released for eMBB NSIs.  
Figure 7: Transport network traffic distribution when the VNU capacity of DCs is uniform and non-uniform.
## 5 Business Security Operation Mechanism with LiSEA
LiSEA orchestrates a strategic amalgamation of three synergistic mechanisms as follows: the risk model, the business model, and ACLs. This strategic fusion acts as a robust bulwark against both familiar and novel risks, serving as a cogent response to Research Question 2 (RQ.2). In addressing unknown risks, LiSEA's API intervention points discern pertinent business contexts, subsequently underpinning a dynamic evaluation of risk by interfacing with the risk model. This process is further expedited through the constitution of a repository housing a spectrum of well-defined business scenes. This repository augments the evaluation speed of the risk assessment model. Conversely, for known risks, the efficacy of API calls is positively affirmed in congruence with the tenets stipulated within the business model. Complementing this, ACL proves instrumental in delineating forbidden API calls in line with stipulated security policies. This multidimensional assessment, premised upon risk evaluation, business validation, and rule analysis, culminates in a determination of whether an API call merits blocking, as visually explicated in Figure 3.  
Figure 2: Multidimensional security control based on an aspect. Specifically, for a particular business process \(P\), the unknown risk value \(V_{R}(P)\) is measured by the risk model \(F_{R}(P)\)  
\[V_{R}(P)=F_{R}(P) \tag{1}\]  
For unknown risk warning values \(W_{R}(P)\), calculated by setting a suitable threshold value \(T\), the following applies:  
\[W_{R}(P)= \begin{cases}1,&V_{R}(P)\geq T\\ 0,&V_{R}(P)<T\end{cases}  \tag{2}\]  
For known risks, the business model positive determination is similar to a whitelist mechanism, and for the business policy \(P_{B}\), the alert value \(W_{B}(P)\) is calculated as follows:  
\[W_{B}(P)= \begin{cases}0,&P\in P_{B}\\ 1,&\text{otherwise}\end{cases}  \tag{3}\]  
For the ACL policy \(P_{A}\), the alert value \(W_{A}(P)\) is calculated as follows:  
\[W_{A}(P)= \begin{cases}1,&P\in P_{A}\\ 0,&\text{otherwise}\end{cases}  \tag{4}\]  
Finally, for business process \(P\), the final security analysis result \(W(P)\) was obtained by considering \(W_{R}(P)\), \(W_{B}(P)\), and \(W_{A}(P)\) together. \[W(P)=W_{R}(P)\bigcup W_{B}(P)\bigcup W_{A}(P) \tag{5}\]  
If \(W(P)=1\), business process \(P\) is blocked. Otherwise, the execution of \(P\) is permitted.
## 3 Main Findings
### Technological Effects Related to the Absence of Gluten, and Differences in Breadmaking  
As highlighted in the introduction, breadmaking and the quality of bakery products depend on both the quantity and quality of gluten [14; 15; 17]. When wheat flour is kneaded with water, gluten proteins enable the formation of a cohesive viscoelastic dough that is able to hold the gases that are produced during proofing, resulting in high-quality, structured products. Breach (in particular), and other leavened goods, have higher loaf volume and other desirable characteristics [1; 6; 7; 12; 14; 15]. The absence of gluten in dough has a major impact on rheology, and gluten-free doughs are much less cohesive and elastic than wheat doughs [13; 14; 15; 17].  
This unique structure and function of gluten makes the search for an alternative ingredient very challenging [14; 15; 17]. Gluten-free bread often has a poor color, mediocre texture, and other problems related to volume and crumb texture [14; 15; 17]. Although various novel ingredients, such as dairy powders, sorghum, rice, starches, pseudo-cereals, etc. have been tested, in combination with hydrocolloids, the literature reports little success [15]. The absence of gluten often results in excessively fluid doughs, which are closer in viscosity to cake batters, with a lower gas holding capacity than wheat flour doughs [14; 15; 17].  
Although the fundamental breadmaking steps (kneading, proofing, and baking) must be followed, [PERSON] and [PERSON] (2012) [17] summarizes three important differences between gluten-free and wheat flour breadmaking processes. First, gluten-free bread formulations often include ingredients (hydrocolloids, and other sources of starch and protein) that improve dough structure, increase its gas retention capacity, and improve bread volume. Second, more water is usually added, leading to doughs with a consistency that is more similar to batter [14; 15; 17]--with the aim to increase starch hydration and subsequent swelling. Third, gluten-free breads usually require shorter mixing, proofing, and baking times than wheat breads [13; 15; 17].  
Figure 1: Flow charts pertaining to the selection process of papers on ScienceDirect (**a**), PubMed (**b**), and the Web of Science (**c**), summarizing the obtained results of the systematic literature review.