esa-sceva/satcom-chunk-collection huggingface dataset:
# Paraphrase Identification with Lexical, Syntactic and Sentential Encodings
## 3 LSSE Learning Model
### Paraphrase Identification  
Because graph convolution of the R-GCNs model is a special form of Laplacian smoothing, it mixes the features of a node and its neighbors [41, 42]. This smoothing operation makes the features of nodes less distinguishable [32]. Therefore, when the training data is small, it often the case that it does not work well. Adding more layers also does not work well as empirically it has been observed that the best performance is achieved with a 2-layer model [41]. Therefore, as illustrated in the right-hand side of Figure 2, after the hidden state \(\mathbf{h}_{i}\) has been learned, we concatenate the hidden state \(\mathbf{h}_{i}\) with the lexical encoding \(\mathbf{a}_{i}\) to keep the original encoding. We obtain the syntactic encoding with the context information aggregated, i.e., \(\mathbf{a}_{i}^{\prime}=(\mathbf{a}_{i},\mathbf{h}_{i})\). The result by concatenation has a fixed length, i.e., \(2\times d\).  
Figure 4: R-GCNs model [28]: The **left**-hand side is the flow of the model and the **right**-hand side shows Graph Convolution in the R-GCNs.  
The two matrices \(\mathbf{M}_{s_{1}}\in\mathbb{R}^{2d\times m}\) and \(\mathbf{M}_{s_{2}}\in\mathbb{R}^{2d\times n}\) corresponding to each sentence \(S_{1}\in\mathbb{R}^{d\times m}\) and \(S_{2}\in\mathbb{R}^{d\times n}\) are obtained by R-GCNs and passed to the semantic measurement layer which is shown in Figure 5. For each of the two matrices \(\mathbf{M}_{s_{1}}\) and \(\mathbf{M}_{s_{2}}\), we applied the row-based average pooling over them and obtained two vectors, \(\mathbf{u}_{1}\) and \(\mathbf{u}_{2}\in\mathbb{R}^{2d}\), respectively. We then calculate the similarity between these vectors, i.e., for each dimension, we applied \(L_{1}\) distance, and obtain a similarity vector \(\mathbf{sv}\in\mathbb{R}^{2d}\). The \(\mathbf{sv}\) is further concatenated with sentential encoding obtained by BERT, and the result is passed to the fully connected layer \(\mathbf{FC}\). We set the size of the output layer of the \(\mathbf{FC}\) to two. Finally, we apply the softmax function to obtain probabilities of two predicted labels, paraphrase or non-paraphrase, in the output layer. The network is trained with the objective that minimizes the binary cross-entropy loss of the predicted distributions and the actual distributions (one-hot vectors corresponding to the ground labels) by performing Adam optimization algorithm [43].
## 2 Materials and Methods
### EEG Feature Extraction  
Following the pre-processing step, the subsequent stage in the EEG software pipeline is the feature extraction stage. As previously mentioned, the purpose of feature extraction is to extract pertinent information contained within the signals.  
We decided to implement the Power Spectral Density (PSD) analysis because it is a robust extractor largely used for EEG quantitative analysis. Specifically, among the several methods for PSD estimation reported in the literature, [PERSON]'s method was employed in our analysis. Let \(x[n],\ n=0,\cdots,N-1\) be the samples from an EEG epoch. The evaluation of PSD by using [PERSON]'s method consists of the following steps:  
* the EEG epoch is divided into \(N\) sections (possibly overlapped \(O\)) of equal lengths \(M\); \[x[n]=x[n+iO]\qquad i=0,\ldots K-1,\ \text{and}\ n=0,\ldots N-1\] (1)
* a window is applied to each section, and then the periodogram on the windowed sections is calculated. We can define the periodogram in the following way: \[\tilde{S}_{xx}(k)=\frac{1}{N}\left|\sum_{n=0}^{N-1}x(n)e^{-\frac{2\pi\beta nx }{N}}\right|^{2}.\] (2)
* the periodograms are averaged from the \(K\) sections in order to obtain an estimation of the spectral density \[P_{yx}(f)=\frac{1}{K}\sum_{i=0}^{K-1}P_{i}(f).\] (3)  
In this equation, \(P_{yx}\) represents the estimation of the cross power spectral density between two discrete-time signals, \(x\) and \(y\). The Welch method eliminates the trade-off between spectral resolution and variance by allowing overlapping segments. When a high-frequency resolution is required, the recorded data can be divided into a small number (\(N\)) of segments with a length of L. However, in our analysis, we used non-overlapping segments and applied the Hamming window.After PSD calculation on all EEG epochs, we consider the following EEG sub-bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), gamma (30-70 Hz) and calculated the cumulative power for each sub-band. Moreover, we evaluated the total cumulative power for all EEG frequencies.  
The main processing steps of our feature extraction approach can be summarized as:  
* Power Spectral Density (PSD) was estimated through Welch method;
* From PSD matrix output, we selected five frequency sub-bands;
* For each band (delta, eta, alpha, beta, gamma) we computed cumulative power.  
Figure 4 shows an example of cumulative power in beta band for all epochs extracted from an EEG signal.  
We constitute the features vector with the six power cumulative coefficients for all EEG epochs.  
The features vector has the following dimension: the number of cumulative power coefficients (six) times the number of epochs (an average of 105 epochs for each EEG) times the number of channels (19) times the number of subjects (225).
# Single-Shot Temporal Contrast Enhancement Measurement of a Plasma Mirror by a Chirped Pulse
## 2 Method for Spatiotemporally Overlapped Chirped Pulse
As mentioned above, the temporal contrast enhancement of the PM is the ratio between the plasma reflectivity of the PM and cold reflectivity, which is equal to the contrast of the temporal reflectivity profile. Thus, by spectral measurement of the reflected SOCP, the temporal contrast enhancement of the PM can be directly and conveniently acquired with a single-shot measurement. \[R(t)=C*\frac{S_{1}(\lambda)-S_{b}(\lambda)}{S_{0}(\lambda)}, \tag{1}\]  
\[\Delta_{t}=C*\Delta_{\lambda}, \tag{2}\]  
In addition, the temporal measurement resolution could be controlled by adjusting the chirp of the SOCP. The temporal resolution is proportional to the chirp rate. The relationship can be written as Equation (2). The temporal resolution \(\Delta_{t}\) of temporal contrast enhancement is equal to the product of the chirp rate \(C\) of the SOCP and the spectral resolution \(\Delta_{\lambda}\) of the spectrometer. For example, the spectral resolution of the spectrometer in 818.5 nm is about 0.157 nm with a chirp rate of 25 ps/nm. Thus, the temporal resolution is 3.925 ps. To further improve the temporal resolution of this method, one feasible way is to using a higher spectral resolution spectrometer. Moreover, another direct and recommended way is to adjust the chirp rate of the SOCP, usually through grating or a chirped mirror. Obviously, the latter is more attractive for tuning and operation.
# Wafers Orthopathic Surgery with Customized Osteosynthesis and Surgical Guides: A Prospective Study
## 1 Introduction  
Computer-assisted planning and orthognathic surgery (OS) for the treatment of denotofacial deformities has been extensively documented over the last decade [1, 2]. The transformative influence of three-dimensional (3D) imaging, coupled with the rapid evolution of computer-aided design (CAD) and computer-aided manufacturing (CAM) technologies, has marked a significant paradigm shift in the field [3]. Despite these advances, challenges remain, particularly with regard to achieving optimal 3D maxillary positioning using CAD/CAM occlusal surgical splints, as highlighted by multiple studies [4, 5, 6].  
The era of digital treatment planning, augmented by patient-specific guides, has ushered in a new era characterized by improved feasibility, precision, surgical efficiency, and enhanced clinical outcomes in complex orthognathic procedures [1, 7, 8, 9, 10]. Recognizing the limitations of traditional two-dimensional (2D) treatment planning [11], recent innovationshave introduced diverse methodologies for seamlessly translating 3D virtual treatment plans into the intraoperative sphere [2; 8; 12; 13; 14]. However, the absence of a universally accepted standard for implementing various designs of CAD/CAM customized surgical cutting guides and fixation plates has led to inherent variations in surgical protocols and outcomes [15; 16; 17].  
The present study describes and evaluates a new protocol involving CAD/CAM-generated cutting guides and customized titanium plates (CMTPs) for waferless positioning of the maxilla in OS. Notably, this approach enables independent maxillary positioning, eliminating the necessity for an intermediate splint. The study hypothesis is that waferless OS using customized surgical guides (CMSGs) and CMTPs for osteosynthesis is an accurate procedure with potential transformative implications for the field. Through this exploration, this study aims to offer valuable insights into refining surgical techniques and advancing the overall efficacy of orthognathic interventions.
###### Abstract  
Signal acquisition and sensing in underwater systems and applications is typically a challenging issue due to the small signal strength within the background noise. Here, we present a ring vector sensor communication receiver that can significantly improve signal acquisition, by utilizing the underwater acoustic vector field components, compared to the scalar component. The vector sensor receiver is a multichannel receiver that measures particle velocities, which are vector components of the underwater acoustic field, in addition to the scalar field component. According to the combination of our measured experimental data with our signal acquisition performance analysis, the introduced ring vector sensor receiver exhibits higher signal acquisition probabilities for the vector components compared to the scalar component. This can be attributed to certain characteristics of the vector field components. Another advantage of this multichannel receiver is that combining all of its channels can further increase the signal acquisition and packet detection probability in underwater communication systems compared to a single-channel approach.  
signal acquisition; underwater sensing; underwater sensors; vector sensors +
Footnote †: journal: Sensors  
0
Footnote 0: Copyright** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Active Noise Control over Space: A Subspace Method for Performance Analysis
## 2 Problem Formulation
Similar to the primary noise field, inside the region of interest with the radius of \(R_{1}\), the secondary noise field can be truncated by  
\[s(\mathbf{x},k)\approx\sum_{u=0}^{N}\sum_{m=-u}^{u}\gamma_{um}(k)j_{u}(kr)Y_{um}( \phi_{\mathbf{x}},\psi_{\mathbf{x}}). \tag{6}\]  
The ATF in (2) can be parameterized in the wave domain [29] as  
\[G(\mathbf{x}|\mathbf{y}_{t},k)\approx\sum_{u=0}^{N}\sum_{m=-u}^{u}\eta_{um}^{(l)}(k)j_ {u}(kr)Y_{um}(\phi_{\mathbf{x}},\psi_{\mathbf{x}}), \tag{7}\]  
where \(\eta_{um}^{(l)}(k)\) is the ATF in wave domain for each loudspeaker. Substituting (6) and (7) into (2), the secondary sound coefficients \(\gamma_{um}(k)\) can also be represented by  
\[\gamma_{um}(k)=\sum_{l=1}^{L}d_{l}(k)\eta_{um}^{(l)}(k). \tag{8}\]  
In matrix form, the relationship between the secondary source decomposition coefficients and the loudspeaker driving signals is given by  
\[\mathbf{\gamma}(k)=\mathbf{\eta}(k)\mathbf{d}(k), \tag{9}\]  
where  
\[\mathbf{\eta}(k)= \begin{bmatrix}\eta_{00}^{(1)}(k)&\eta_{00}^{(2)}(k)&\cdots&\eta _{00}^{(L)}(k)\\ \eta_{-11}^{(1)}(k)&\eta_{-11}^{(2)}(k)&\cdots&\eta_{-11}^{(L)}(k)\\ \vdots&\vdots&\ddots&\vdots\\ \eta_{NN}^{(1)}(k)&\eta_{NN}^{(2)}(k)&\cdots&\eta_{NN}^{(L)}(k)\end{bmatrix} , \tag{10}\]  
and \(\mathbf{d}(k)=[d_{1}(k),\ldots,d_{L}(k)]^{T}\). We investigate the maximum achievable ANC performance based on the primary noise field coefficients \(\mathbf{\beta}(k)\) and the secondary-path information \(\mathbf{\eta}(k)\) in the wave domain, and derive loudspeaker driving signals using two methods: the wave-domain least squares method and the subspace method.
# A Voxel-Based Watermarking Scheme for Additive Manufacturing
###### Abstract  
Digital and analog contents, generated in additive manufacturing (AM) processes, may be illegally modified, distributed, and reproduced. In this article, we propose a watermarking scheme to enhance the security of AM. Compared with conventional watermarking methods, our algorithm possesses the following advantages. First, it protects geometric models and printed parts as well as G-code programs. Secondly, it embeds watermarks into both polygonal and volumetric models. Thirdly, our method is capable of creating watermarks inside the interiors and on the surfaces of complex models. Fourth, the watermarks may appear in various forms, including character strings, cavities, embossed bumps, and engraved textures. The proposed watermarking method is composed of the following steps. At first, the input geometric model is converted into a distance field. Then, the watermark is inserted into a region of interest by using self-organizing mapping. Finally, the watermarked model is converted into a G-code program by using a specialized slicer. Several robust methods are also developed to authenticate digital models, G-code programs, and physical parts. These methods perform virtual manufacturing, volume rendering, and image processing to extract watermarks from these contents at first. Then, they employ similarity evaluation and visual comparison to verify the extracted signatures. Some experiments had been conducted to validify the proposed watermarking method. The test results, analysis, discussion, and comparisons are also presented in this article.  
watermarking; additive manufacturing; geometric modelling; self-organizing mapping +
Footnote †: journal: Applied  
14 August 2021  
Accepted: 28 September 2021  
Published: 2 October 2021
# AEA-RDCP: An Optimized Real-Time Algorithm for Sea Fog Intensity and Visibility Estimation
## 4 Experiments and Evaluations
### Visibility Estimation  
To validate the accuracy of the AEA-RDCP algorithm in estimating sea fog density, a set of images taken between 6:00 AM and 6:00 PM was prepared. These images, presented in Figure 20, comprise a dataset captured at 10 min intervals. The dataset illustrates the dissipation of dense fog over time, starting with heavy fog that gradually clears as time progresses. The proposed algorithm was applied to this dataset to analyze the changes in the DCP percentage over time.  
Figure 21 visually represents these changes, with the x-axis indicating time and the y-axis showing DCP percentages. The analysis reveals that, as time progresses, fog density decreases and DCP percentage values increase, demonstrating that the algorithm effectively tracks fog density over time. This confirms the proposed algorithm's high reliability in accurately estimating fog density in response to temporal changes.  
\begin{table}
\begin{tabular}{c c c} \hline
**Sea Fog Intensity** & **RDCP** & **AEA-RDCP** \\ \hline DCP percent & 85\% & 100\% \\ Processing time & 11 ms & 9 ms \\ Pixel count & 92,160 & 92,160 \\ State & No-fog & No-fog \\ \hline \end{tabular}
\end{table}
Table 3: Performance comparison of RDCP and AEA-RDCP algorithms.  
Figure 20: An image set showing the changes in sea fog conditions over time.
# Extacorporeal Shockwave Applicator for Spinal Pain and Muscular Contractive: A New Design Approach
## 5 Discussion  
Given the numerical data obtained from the finite element analysis, statistical analysis, and design optimization, we consider that the objective of this study was successfully achieved along with the future research directions in the field of ESWT.  
The experimental plan, suggested by the statistical analysis, was a useful tool in organizing the numerical trials. Moreover, the assembly of all the inspected data was interpreted with ease. The analysis of variation has shown that each input factor has a significant impact on the response. This study pointed out the mathematical models that link the input factors to the response, and to what extent. The mathematical solution can be classified as very good, as the finite element analysis data confirmed the optimization results.  
During the simulations, low stress values were developed in the applicator and no critical areas were detected. This aspect was useful for shape optimization, as large areas of the applicator have a low material use coefficient. A direct link between the tip distance and the soft tissue stress highlights that the conventional applicator has local results while the dual-tip one induces shockwaves that are scattered on larger areas. The dual-tip design proved that it can deliver optimum results in terms of soft tissue stress and shockwave propagation velocity. Lower values of strain energy are consistent with high frequency, low contact pressure, and decreased tip distances, while strain energy and contact pressure were optimal in the case of the dual-tip applicator.  
This study shows the possibility of improving medical equipment by using finite element analysis and statistical data interpretation. It had pointed out how the effects of the shockwave propagate through the soft tissue, by comparing the two applicators. The dual-tip applicator design leads to slower velocities of the shockwaves but they are spread out through a larger area. It can be stated that this new design induces effects with higher propagation lengths.  
From the treatment point of view, the applicator can reduce the overall treatment time, with benefits both for the therapist and for the patient. The fact that the contact pressure has significantly lower values comes with the benefit of a more bearable treatment from the patient's point of view, while, if necessary, this can be increased by raising the air pressure.  
Future directions of research in this field can relate to aspects such as variable tip distance, technology for manufacturing (casting, assembling, five-axis milling), new materials (polymers), new technologies, such as metal printing (selective laser melting (SLM) or direct metal laser sintering (DMLS)), or further optimization of the dual-tip applicator.
## 4 The Future of AM in Industry 4.0
### Future of AM in Applications  
In order to use additive manufacturing and operation of industry 4.0 for equipping the existing manufacturing processes and in the future production, artificial intelligence and innovations in approaches and materials will be inevitable [169]. The most applications of AM processes occur in aerospace and automotive, art industry, medical and even architectural industry.  
The aerospace industry has shown interest in these technologies because it enables them to directly manufacture metallic parts such as from titanium (for aircraft) and the ability to easily manufacture complex and high-performance products with significantly less tooling considerations [170]. Selective Laser Sintering (SLS) and Electron Beam Melting (EBM) are now used in the aircraft and aerospace industry. The principle goal of the automotive and aerospace industries is to manufacture lightweight vehicles or aircrafts. These AM-based technologies are capable of manufacturing lightweight components [171]. The AM has many applications in medical fields. It plays a key role in the production and design of bio-models, surgical support tools, and implants. AM processes are able to fabricate the various scaffolds for the restoration of tissues and they are useful in printing organs, cell-laden biomaterials, biomaterials, and producing cells individually. AM technology has excessive potential for the production of complex geometries of the implant. It should not be forgotten that AM technology helps in reducing costs and producing a medical model in a shorter time [167]. The most complex and complicated designs in the jewelry and art industries could be fabricated using AM technology [172]. AM technology can provide effective tools for the work of jewelers and artists, enabling them to create unique shapes in hours instead of days or weeks.  
In general, there are many advantages for AM processes over traditional manufacturing processes in rapid prototyping, spare parts, complex workpieces, machine tools, rapid manufacturing, and so on. The most important advantages are dropping time and cost in different categories of production [18]. The methods of production will not change by additive manufacturing. However, improving most areas of the industry is inevitable. As mentioned earlier, the cost and speed of productions are predicted. Figure 12 indicates the production savings based on AM technology for metal products for the next five years.
## 2 Materials and Methods
### Data Acquisition and Preprocessing  
#### 2.6.1 Soil MC Acquisition  
The soil MC nodes were placed about one meter away around the tree root [34], and they were buried in the soil with flags on the ground. In this paper, three underground nodes with 10 cm and 20 cm depths were used for demonstration, respectively, and were parted in a circle by 120 degrees around the center of the root.
## 3 Methodology
### Evaluation Metrics
#### 3.9.1 SpBLEU (Sentence-Piece BLEU)  
SpBLEU is crucial for evaluating translation quality at the sentence level, especially in low-resource languages like Twi, where structure and fluency can vary significantly. Unlike traditional BLEU, SpBLEU operates on subword units, making it particularly effective in handling morphologically rich languages. It captures n-gram precision, ensuring that translations maintain lexical similarity while penalizing overly short outputs through a brevity penalty. This helps in assessing how well the model generates fluent and contextually appropriate translations for Twi. This fine-grained evaluation provides insights into the model's strengths and weaknesses, helping to identify areas for improvement in translating complex sentences [33,34]. Formally, the SpBLEU score for a given sentence can be expressed as follows:  
\[\text{SpBLEU}(S)=\text{BP}\ \times\ (\prod_{n=1}^{N}{precision_{n}(s)}^{1/N}) \tag{13}\]  
where \(S\) is the sentence being evaluated, \({precision_{n}(s)}\) is the modified precision for n-grams of order \(n\) in the sentence \(S\). \(N\) is the maximum n-gram order considered (commonly 4). \(BP\) is the brevity penalty, which is applied to avoid penalizing shorter translations that may still be accurate.
## 2 System Requirement
### System Description and Implementation  
A wearable WBAN system should be comfortable to wear. This indicates that the system should be as unobtrusive to the user as possible: it should be light, have a small footprint, and be mechanically flexible so it will not interfere with the user's movements. The goal of this design is a system that meets the IEEE 802.11b/g/n standard requirements. Therefore, the button sensor antenna works at 2.45 GHz and 5.5 GHz WiFi bands. The reflection coefficient (S\({}_{11}\)) should be kept below \(-10\) dB in the band. The radiation efficiency of a minimum of 65% was targeted. Many features, such as operating frequency band, radiation pattern, and impedance matching, should be steady and meet the requirements even when the antenna is placed on different body locations and sizes. To achieve maximum stability, it is necessary to reduce the interaction with the body. The reduction of the coupling to the body tissue not only keeps the antenna performance as if it works in free space, but also reduces the amount of energy absorbed by the wearer's body and keeps the SAR levels down. In any practical scenario, for line-of-sight (LOS) and non-line-of-sight (NLOS), the BSA should be able to connect and interact with the system within a range of 40 m.