esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Artificial Intelligence Methods for GWL Modelling
### Adaptive Neuro-Fuzzy Inference Systems
These studies demonstrate that ANFIS outperform ANNs, and the spatial distribution data of Inverse-Distance Weighting (IDW) are also reliable. Ref. [34] examined the accuracy of the Improved Simulated Annealing (ISA) algorithm and Least Squares Support Vector Machine (LSSVM) in predicting GWL in Iran. They indicated that the ISA-LSSVM has a higher accuracy. Research conducted by [41] comparing the accuracy of the Neuro-Fuzzy-based group method of data handling (NF-GMDH) improved through Particle Swarm Optimisation (PSO) algorithms with other methods in predicting GWL in the United States shows that NF-GMDH-PSO outperforms other methods and employs innovative modelling approaches. Evaluated studies by [42] in South Korea, assessing the acceptability of the ANFIS and Stochastic Coded Federated Learning (SCFL) methods, demonstrate that the ANFIS method is acceptable. Ref. [40] evaluated the accuracy of ANFIS-FCM (fuzzy c-means) using sym4 mother wavelets in predicting GWL in Iran. They showed that this achieved higher accuracy and benefits from their combination. Evaluated studies by [39], comparing the accuracy of the ANFIS with other methods in predicting GWL in China, demonstrate that ANFIS has higher accuracy than others, such as RBFNN and Grey Self-Memory (GSM). Ref. [43] compared the accuracy of Gaussian Process Regression (GPR) with ANFIS's in predicting multi-step monthly GWL in Sullia Taluk, India. They found that GPR performed better than ANFIS. Ref. [35], by comparing the accuracy of the ANFIS with other methods in predicting monthly GWL fluctuations in Kashan Plain, Iran, demonstrate that the ANFIS method (based on the orthogonal mean shape) has higher accuracy. Evaluated studies by [33] in the Langat Basin, Malaysia, comparing the accuracy of ANFIS with other methods in predicting GWL, show that the ANFIS method has higher accuracy than others. Table S2 (Supplementary Materials) summarises the evaluated articles that used ANFIS methods and draws a general conclusion.
## 1 Introduction
[34] used low-resolution and high-resolution data as the input of the functional regression model to enhance its ability to capture patterns of different time scales. [PERSON] et al. [35] applied the kernel-based ELM (KELM) to combine predictions by a model trained using low-resolution data and by a model trained using high-resolution data. In the aforementioned studies, the introduction of high-resolution data can provide details of wind speed series and improve the accuracy. Therefore, it is possible to combine truncated real-time decomposition and multi-resolution data to mitigate the influence of end effect without losing information significantly. In this paper, we propose multi-resolution real-time decomposition, which is a new scheme of real-time decomposition that combines truncated real-time decomposition and multi-resolution data. Furthermore, we propose a wind speed prediction model with the proposed scheme and GRU neural networks and term the proposed model as SSA-TRTD-MR-GRU. Specifically, the proposed scheme consists of two parts. One is to denoise the original series with the scheme of truncated real-time decomposition. The other is to collect the high-resolution data to compensate for the information loss caused by truncated real-time decomposition. The proposed model is composed of two GRU neural networks and one MLP. In the proposed model, two GRU neural networks are used to extract features from the original-resolution input and the high-resolution input, respectively, and the MLP is used to take extracted features as input and make the prediction. The comparison of the proposed model with relevant research studies is summarized in Table 1. The proposed model is validated with monitoring wind speed data in the site of two long-span cross-sea bridges. The proposed scheme connects truncated real-time decomposition with multi-resolution data, which extend real-time decomposition techniques. Furthermore, the proposed model connects the proposed scheme with deep learning techniques, which can make more accurate predictions. The remainder of this paper is as follows. In Section 2, the theoretical background of the SSA and GRU neural network, schemes of real-time decomposition are introduced, and the details of the proposed scheme and model are given. Section 3 introduces the data description and the parameter setting of SSA. Analyses of the experimental results and the conclusion are provided in Sections 4 and 5, respectively.
## 4 Experiment
### Ablation Experiment  
#### 4.6.1 Impact of Different Components on the Model  
In order to evaluate the impact of each component on the overall performance of the model, we performed a quantitative analysis using ablation experiments. Specifically, we examined three factors: the AFM module, the DNN network, and the RMGAT module. The findings of these experiments are reported in Table 5.  
By conducting experiments using different components and datasets, we discovered that incorporating both DeepAFM and RMGAT into the recommendation system led to improved performance. However, we found that the RMGAT module outperformed the DeepAFM module. In detail, by adding the attention mechanism to the DeepAFM module, we observed an increase in the AUC by 0.9%, 0.6%, 0.8%, and 1.0% across the  
Figure 8: The Recall@K results in the top-K recommendation.  
Figure 7: The Precision@K results in the top-K recommendation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **MovieLens-1M** & **MovieLens-20M** & **BookCrossing** & **Last.FM** \\ \hline MKR (none) & 0.914 & 0.963 & 0.734 & 0.791 \\ AFM\_MKR & 0.919 & 0.967 & 0.737 & 0.782 \\ DNN\_MKR & 0.917 & 0.965 & 0.738 & 0.775 \\ DeepAFM\_MKR & 0.923 & 0.969 & 0.742 & 0.801 \\ MKR\_RMGAT & 0.925 & 0.973 & 0.744 & 0.804 \\
**MDAR** & **0.927** & **0.975** & **0.753** & **0.810** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Impact of different components on AUC.  
four datasets. This highlights the importance of distinguishing different features through the attention network and leveraging FM to extract linear interaction features for accurate target prediction, ultimately enhancing recommendation performance. Additionally, the inclusion of the RMGAT model also yielded favorable results. On the MovieLens-1M dataset, the AUC increased by approximately 1.10%. On the MovieLens-20M dataset, the AUC increased by approximately 1.00%. On the BookCrossing dataset, the AUC increased by approximately 1.00%. Lastly, on the Last.FM dataset, the AUC increased by approximately 1.30%. These findings demonstrate that the designed relation-fused multi-head attention network can extract more detailed feature information during the analysis of users and items, resulting in more accurate prediction outcomes and significantly improving recommendation performance, especially for datasets with high sparsity.
## 3 Results and Discussion  
### Selection of SuSys for Constructing One-Pot Two-Enzyme Systems  
UGT-SuSy system has been widely applied in glycosylation reactions, especially for natural products [16; 26; 27]. Most research focuses on the enzyme engineering of UGTs and SuSys [24; 28]. Few has been done with the collaborative relationship between these two enzymes. As is known, various SuSys had different abilities to synthesize UDPG, which could have a significant impact on the glycosylation reaction [12]. Firstly, four SuSys, that is, _At_SUS1 and _At_SUS3 from _A. thaliana_[29; 30; 31], _St_SUS1 from _Solanum tuberosum_[32; 33], and _Ac_SuSy from _Acidithiobacillus caldus_[13; 34], were co-expressed with UGT76G1, respectively, and the crude extracts prepared from the corresponding strains were used to set up four individual one-pot two-enzyme reactions, which are called At1-76G1, St1-76G1, Ac-76G1, and At3-76G1. All enzymes were successfully expressed, although inclusion still dominated (Figure S1). As is shown in Table 3, At1-76G1 and At3-76G1, including SuSys from the same source (_A. thaliana_), had a high yield of RebA. After 8 h, stevioside was almost converted to RebA by At1-76G1. The RebA yield for the reaction catalyzed by St1-76G1 ranked third (66.8%). Unexpectedly, in the reaction mixture of Ac-76G1 containing prokaryotic _Ac_SuSy that has the highest specific activity (59.2 mU/mg, above 5-fold of _St_SUS1) among the four SuSys, the yield of RebA was even less than 1%. The UGT activity in Ac-76G1 (114.7 mU/mg) was evidently higher than that in St1-76G1 (88.8 mU/mg), suggesting that it was _Ac_SuSy that led to the worse performance of Ac-76G1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Strains Used for the Crude Extract Preparation**} & \multicolumn{2}{c}{**Specific Activity**} & \multicolumn{2}{c}{**RebA**} \\ \cline{2-5}  & **UGT76G1 (mU/mg)** & **SuSy (mU/mg)** & **Concentration (g/L)** & **Yield (\%)** \\ \hline At1-76G1 & \(125.1\pm 0.5\) & \(47.6\pm 0.2\) & \(35.2\pm 1.6\) & \(99.3\) \\ At3-76G1 & \(104.4\pm 4.8\) & \(13.7\pm 0.3\) & \(31.6\pm 2.7\) & \(90.5\) \\ St1-76G1 & \(88.8\pm 9.3\) & \(11.6\pm 0.6\) & \(24.6\pm 0.4\) & \(66.8\) \\ Ac-76G1 & \(114.7\pm 5.7\) & \(59.2\pm 0.4\) & \(0.63\pm 0.04\) & \(0.8\) \\ \hline \hline \end{tabular} Note: The reaction mixture (20 mL), consisting of approximately 5 mg/mL of the crude extract, 30 g/L stevoside, and 90 g/L sucrose, was incubated at pH 7.2 and 30 \({}^{\circ}\)C for 8 h.  
\end{table}
Table 3: Effect of the coupled SuSy and UGT76G1 on RebA synthesis.
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 5 Performance Analysis
### Computation Overhead
#### 5.1.1 Computation Cost for the Client Message (\(C_{Client}\))  
The computational cost for the client message consists of the signing cost (\(C_{\sigma}\)) and encryption cost (\(C_{\mathrm{ENC}}\)); these are combined as a formula given in Equation (13):  
\[C_{Client}=C_{sign}+C_{ENC}. \tag{13}\]  
1. Signing Cost (\(C_{sign}\)): This includes the cost incurred during the computation of \(A_{i}\). The various symbols used for computation and their meanings are given in Table 1. The cost of actual signature generation is \(C_{\sigma}\) given in Equations (14)-(16):  
\[C_{sign}=C_{A_{i}}+C_{sec}+C_{\sigma}, \tag{14}\]  
where  
\[C_{A_{i}}=C_{EXP}+C_{DIV}+C_{MUL}, \tag{15}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Notation** & **Operation** & **Unit Cost (ms)** \\ \hline \(C_{ADD}\) & Addition/Subtraction & 0.001 \\ \(C_{XOR}\) & XOR & 0.002 \\ \(C_{RAND}\) & Random number generation & 0.045 \\ \(C_{HASH}\) & Hash function & 0.067 \\ \(C_{MUL}\) & Multiplication & 0.612 \\ \(C_{EXP}\) & Exponentiation & 1.0 \\ \(C_{DIV}\) & Division & 1.22 \\ \(C_{MOD}\) & Modulus & 1.24 \\ \(C_{BP}\) & Bilinear pairing & 4.51 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Computation cost of operations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & \multicolumn{4}{c}{**Cost (ms)**} \\ \cline{2-5}  & \(n=50\) & \(n=100\) & \(n=150\) & \(n=1000\) \\ \hline \(C_{ENC}\) & 103.78 & 217.85 & 347.89 & 5210 \\ \(C_{DEC}\) & 55.58 & 102.37 & 145.40 & 1060 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Cost of encryption and decryption for different numbers of clients.  
\[C_{\sigma}=C_{BV}+C_{SV}+C_{TV}+C_{HASH}.\] (16) We can calculate the cost of various parameters of signature generation (\(C_{\sigma}\)) as: \(C_{BV}=6C_{EXP}+3C_{BP}+4C_{M}=6\times 1+3\times 4.51+4\times 0.612=21.978\), \(C_{SV}=5C_{ADD}+5C_{M}=5\times.001+5\times 0.612=3.065\), \(C_{TV}=3C_{EXP}+3C_{M}+C_{ADD}=1+3\times 0.612+0.001=2.837\), and \(C_{HASH}=0.067\). From Equation (16), we obtain \(C_{\sigma}=21.978+3.065+2.837+0.067=27.947\) ms.
2. Encryption Cost (\(C_{ENC}\)): This is the cost of encrypting the message we want to send to the server.  
Finally, the total computation cost on the client side \(C_{Client}\) is computed as a formula given in Equation (17):  
\[C_{Client}=C_{\sigma}+C_{ENC}=27.947\text{ ms}+C_{ENC}. \tag{17}\]
# Characteristic Analysis of Bump Foil Gas Bearing Under Multi-Physical Field Coupling
## 2 Theoretical Modeling  
### Elastohydrodynamic Theoretical Model of Bump Foil Air Bearing  
The theoretical model of an air bearing needs to solve the compressible gas Reynolds equation and the gas film thickness equation.  
Under stable conditions, that is, when temperature, viscosity, density, and other parameters do not change with time, the steady-state compressible gas Reynolds equation in the Cartesian coordinate system is expressed as [22]  
\[\frac{\partial}{\partial x}\left(\frac{\rho h^{3}}{\mu}\frac{\partial p}{ \partial x}\right)+\frac{\partial}{\partial y}\left(\frac{\rho h^{3}}{\mu} \frac{\partial p}{\partial y}\right)=6U\frac{d}{dx}(\rho h) \tag{1}\]  
In the field of science and engineering, it is important to use analytical methods to express equations in a dimensionless form. The core reason for this is to transform physical quantities into unitless quantities by introducing characteristic scales (such as characteristic length and velocity). After dimensionless, specific units no longer limit the equation and the form is more concise, which is convenient for solving differential equations and analysis, the dimensionless form (1) is reduced to  
\[\left(\frac{D}{L}\right)^{2}\frac{\partial}{\partial\lambda}\left(\overline{ ph^{3}}\frac{\partial\overline{p}}{\partial\lambda}\right)+\frac{\partial}{ \partial\varphi}\left(\overline{p}h^{3}\frac{\partial\overline{p}}{\partial \varphi}\right)=\Lambda\frac{\partial\left(\overline{p}\overline{h}\right)}{ \partial\varphi}, \tag{2}\]  
The following dimensionless quantities are introduced:  
\[\lambda=\frac{y}{L/2};\overline{p}=\frac{p}{p_{0}};\overline{h}=\frac{h}{c};x =R\cdot\varphi;\Lambda=\frac{6\mu w}{p_{0}}\left(\frac{R}{c}\right)^{2} \tag{3}\]  
where \(c\) represents bearing clearance, \(w\) represents rotor speed, \(\mu\) represents gas dynamic viscosity, \(p_{0}\) represents ambient pressure, \(\Lambda\) represents bearing number, \(L\) represents bearing width, \(D\) represents journal diameter, \(R\) represents journal radius, \(x\) represents peripheral direction, \(y\) represents axial direction, \(p\) represents gas film pressure, \(\varphi\) represents circumferential angle, \(U\) represents circumferential velocity of journal, \(h\) represents air film thickness, and \(\rho\) represents lubrication gas density. The following is the coordinate diagram. The air bearing structure and the coordinate system are shown in Figure 1.  
The structures of the bump foil and the flat foil are shown in Figure 2. The relationship between the thickness of the gas film and the structural deformation of the flat foil and the bump foil is [14]  
\[\overline{h}=1+\varepsilon\bullet\cos\varphi+\alpha\bullet(\overline{p}-1), \tag{4}\]  
\[\alpha=\left(2p_{0}s/CE\right)(t/t_{B})^{3}(1-v^{2}). \tag{5}\]  
Figure 1: Air bearing structure diagram and coordinate system diagram.
## 6 Experimental Results and Discussion
Moreover, the average computation time (excluding training and validation times) to generate the 24 h-ahead forecasts from the trained model is about 10 s using the neural network toolbox of MATLAB software (version R2018b) on a computer with Intel core i5-5200 CPU, 2.20 GHz processor, and 4 GB random access memory (RAM). Thus, the devised method is new and effective for a 24 h-ahead WPP.
###### Abstract  
Guided apicoectomy performed with 3D-printed polymer-based static surgical guides is an emerging trend in endodontic surgery. Static-guided apicoectomy is carried out with either a drill or a trephine. The aim of this in vitro study was to assess the accuracy of osteotomy and apicoectomy performed through a polymer guide, with both drill and trephine, and to compare the accuracy achievable with the two instruments. Six plaster models of a maxilla master model with extracted human maxillary teeth in polymethyl-methacrylate resin were used. The modeled osteectomies were performed in these. The master model was CBCT-scanned, and digital surgical plans were prepared, based on which the surgical guides were printed. The plans contained both drill and trephine apicoectomies. Digital three-dimensional position analysis was performed with dedicated algorithms. A total of 39 drill and 47 trephine osteectomies were analyzed. A statistically significant difference between the two instruments was found only in the global deviation of the distal endpoint, indicating lower deviation for the trephine procedure (1.53 mm vs. 1.31 mm, \(p=0.038\)). Nevertheless, from a clinical perspective, this distinction is inconsequential. The results suggest that, for all practical purposes, the two approaches to apicoectomy allow the same level of accuracy.  
endodontic microsurgery; apicoectomy; 3D printing; static surgical guide Article
# Algorithm Development for the Non-Destructive Testing of Structural Damage
## 6 Conclusions  
This paper first proposed a damage detection algorithm, using the developed ML approaches for concrete and RC beams, and then investigated the performance of microwave imaging on the local detection and visualization of damage in a steel plate, a composite material, and a metal plate. The SVM-based algorithm was developed and compared with traditional SVM, through the response data of various numbers of specimens mounted on SAs, to validate its effectiveness. Accuracy and a t-test were used for this validation. The proposed algorithm outperformed the performance of traditional SVM. It can be concluded that the extraction of subsequent knowledge from the training phase, and its fusion with unsupervised machine learning (k-nearest neighbor), can help to improve the performance of traditional SVM.  
Then, the microwave- and millimeter-wave imaging approach was used as the second approach of this paper. The obtained images had a very low resolution, and in some cases, no information was observable. The images were processed through different proposed image-processing algorithms. The algorithms were based on the Fourier transform, Wavelet transform, Fuzzy c-mean, Gaussian filter, Median filter, and Gradient magnitude. The proposed algorithms made it possible for clear observations of anomalous occurrences in structures to be made. The results prove that the algorithms have the potential to successfully enhance microwave images with poor resolution, and to identify areas of interest, specifically in structural health monitoring.  
The purpose of this study was to minimize the need for manual inspections of civil structures, and it sought to complement edge-based damage detection methods for hazardous scenarios. However, the proposed algorithms of this study can be applied to different imaging applications where the enhancement of quality, and obtaining the area of interest are critical approaches.  
Methodology, A.N.H.; Conceptualization, B.S. and R.L. and M.R.; Supervision, B.S., R.L. and S.K.; Writing-Review & Editing, A.N.H., R.L., M.R. and B.S.; Resources, S.K. This research received no external funding.  
The authors acknowledge the intellectual contribution of the late [PERSON] of the Centre for Infrastructure Engineering on the electromagnetic imaging part of this work.  
The authors declare no conflict of interest
## 8 Query/Data Protection Mechanism
### Stress Test  
For the stress test, we modified the senders to send 100-byte packets with MAC/encryption as quickly as possible, at about 17 packets per second. Note that in practice the senders are unlikely to send their sampled data at such a high rate, due to energy consumption concerns. We wanted to evaluate this scenario solely to see how the system would perform at its hardware limits. Figures 11 and 12 show the throughput and packet loss. These two figures demonstrate that a single receiving mode is able to reliably handle data from up to 4 senders sending at their maximum rate. Beyond this point, the receiving radio is unable to keep up with the incoming packets, resulting in significant packet loss. Additionally, when there are more than 4 senders, the serial connection between mode \(B\) and the PC also becomes a bottleneck, resulting in a higher loss rate observed at the PC than at the receiving mode. To address the radio packet loss issues, we attempted using the PacketLink reliable transmission layer in TinyOS. However, we found that this further lowered throughput as the high loss rate resulted in many retransmissions. Overall, we feel that the MAC layer being used in TinyOS can be improved to more gracefully handle high loads; we plan to further investigate this in the future.  
Figure 11: Aggregate throughput at receiving mode and PC as a function of the number of senders (maximum possible sending rate).  
Figure 10: Packet loss at receiving mode and PC as a function of the number of senders (sending rate = 4 packets/sec).
## 2 Problem Formulation  
We use a WSN with \(N\) spatially-distributed sensor nodes to detect a random Gaussian signal with an unknown variance \(\sigma_{1}^{2}\). Each sensor collects an observation described as follows:  
\[\left\{\begin{aligned} & H_{0}:\;x_{n}\sim\mathcal{N}(0,\sigma_{0}^{2 }),\\ & H_{1}:\;x_{n}\sim\mathcal{N}(0,\sigma_{1}^{2}),\end{aligned} \quad n=1,2,\ldots,N\right. \tag{1}\]  
Specifically, \(x_{n}\) are independent and identically distributed (i.i.d.) Gaussian random variables with zero mean and variance \(\sigma_{i}^{2}\) under hypothesis \(H_{i}\), \(i=0,1\), where it is assumed that the scale parameter \(\sigma_{0}\) is known, whereas \(\sigma_{1}\) is not. For the considered problem, the sensors are employed to monitor a sudden change of the signal power from a normal state with a known \(\sigma_{0}\) to an abnormal state with an unknown \(\sigma_{1}\).  
There are several applications to which our formulation is relevant. One notable example is spectrum sensing (SS), where the secondary user (SU) needs to reliably detect weak licensed user (LU) signals of possibly unknown types over a targeted frequency band in order to opportunistically use under-utilized frequency bands [14; 15]. In distributed SS scenarios, multiple SUs collaborate in detecting the presence/absence of the LU [14]. The received signal at each SU is often modeled as a zero-mean Gaussian random variable with different variances under the null and alternative hypotheses, respectively (e.g., [14; 15; 16]). We assume the sensors are in a small neighborhood, and each of them is exposed to the same phenomenon. For the aforementioned spectral sensing problems, this implies that the collaborating secondary users are relatively close to each other, compared with their distances to the licensed user (e.g., a TV tower). As a result, their received signals have similar power.  
Due to limited bandwidth/power budgets in WSNs, we consider a case where all sensor nodes quantize their local observations \(\{x_{n}\}\) into one-bit binary data \(\{b_{n}\}\) before transmitting them to the FC. The quantized observations \(\{b_{n}\}\) are transmitted to the FC through some noisy wireless links, and the FC forms a global decision. The problem of interest is to design the one-bit quantizer for the sensors and the fusion rule for the FC.