esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Cavity Geometry on the Fracture Strength of Dental Restorations: Finite Element Study
## 2 Materials and Methods  
### Geometrical Models  
The three-dimensional geometry of a human first premolar was created using modeling software (Solidworks(r) 2019, Dassault Systemes SOLIDWORKS Corp., Waltham, MA, USA). For this, the anatomical lines referring to each tooth component were constructed, and those corresponding faces were obtained following the anatomical conditions. Afterward, a solid geometry of a first maxillary premolar (tooth 24) with each tooth component was created. The tooth fixation system was simulated through box geometry, comprising a cortical bone layer with 2 mm of thickness over a layer of trabecular bone with 15 mm of thickness. The selected teeth had approximately the following crown dimensions: 10.35 mm buccolingual length, 7.1 mm mesiodistal width and 7.0 mm cervico-oclus height. Figure 1 shows the final geometry of the assembly. The mesio-ocluso-distal (MOD) cavity preparations were developed on the solid model using Boolean operations and the parameters presented in Figure 1. The dimensions of the inlay and onlay restorations followed the suggestions available in the literature [23].  
In the onlay cavity preparation, the functional cusps were reduced and included in the ceramic restoration (Figure 1d). Moreover, a resin cement layer (RelyXTM Ultimate Adhesive Resin Cement, Technical Data Sheet, 3M ESPE, 3M Deutschland GmbH, Neuss, Germany) with 113 m of thickness was created for both preparations to cover the entire cavity surfaces. Figure 1c shows the resin cement geometry of the onlay cavity preparation. Additionally, the cement geometry was also modified to include a special \"bed\" geometry, of which the dimensions are presented in Figure 2, allowing the inclusion of cohesive zone models (CZM) to perform strength prediction of the adhesive joint [24]. The orange area presented in Figure 2a was subtracted from the resin cement geometry while the gray one was subtracted from the dentin geometry, but each one had a thickness of 50 m. Figure 2b shows a blue surface mesh that represents the cohesive zone. The loading body's geometry was created assuming glue contact between the food bolus and the tooth surface during the closing phase of the chewing cycle [25,26].  
Figure 1: The MOD cavity preparations. All dimensions are in millimeters. (**a**) Buccolingual dimensions of the inlay restoration. (**b**) Buccolingual dimensions of the onlay restoration. (**c**) Mesiodistal dimensions of both restorations. (**d**) Buccolingual view of the assemblage between the onlay ceramic restoration (yellow body) and the resin cement (green volume).
## 5 Discussion  
Previous research has documented firstly the FD method as an approximated solution for DPS. However, these works have either been short-term works or have not focused on using more accurate approximated numerical methods. In this paper, we extended our knowledge to use MOL for a vibrating string to avoid FD method limitations. Secondly, the effectiveness of ILC and \(H_{\infty}\)ILC has been tested for DPS and LPS, respectively. However, these studies have been implemented under distributed control technique and without consideration of using \(H_{\infty}\)ILC for DPS. Thus, in this paper we designed an \(H_{\infty}\)ILBC-based MOL scheme for reducing the vibrations of certain and uncertain vibrating string systems. One found that the MOL technique was successful in obtaining the DPS in the form of the state-space form instead of the PDE system. Also, the \(H_{\infty}\)ILBC-based MOL scheme was successful  
Figure 14: Deflection of \(H_{\infty}\)ILBC under different values of \(\overline{F}\), \(\overline{D}\), and \((H+\Delta H)\).  
Figure 15: \(H_{\infty}\)ILBC inputs.  
in reducing the vibrations of the certain and uncertain vibrating string effectively compared to PD control and model-based control. Also, the proposed scheme could handle the maximum parameters uncertainties besides maximum iteration-varying boundary/distributed disturbances during a specific period. These findings confirmed that the MOL was a superior choice for DPS instead of the FD method, because the resulted state-space system was less complex to control than the PDE system, and also to overcome the FD limitations. Finally, comparing \(H_{\infty}\)ILBC with ILC, the former had robust stability and less expense, especially for DPS. Therefore, the proposed scheme was a superior choice for string-type structures. This work, therefore, indicated that benefits gained from an \(H_{\infty}\)ILBC-based MOL scheme may address the problem of control and dynamic of DPS under iteration-varying uncertainties/external disturbances. Most remarkably, the proposed work has been established by integrating the \(H_{\infty}\) technique with ILBC for both an uncertain string system and iteration-varying external disorders. This result affords compelling indication for using the proposed scheme for complex DPS and sensitive flexible mechanical systems. The future study should include a considerable follow-up work to evaluate the effectiveness of MOL for another uncertain DPS, and then apply another boundary control strategy to enhance the response of the uncertain DPS under external disturbances.
# Digital Twin-Driven Stability Optimization Framework for Large Underground Caverns
## 4 Conclusions
It highlighted the framework's ability to dynamically update virtual models with real-time data, ensuring accurate predictions and timely interventions during construction and operational phases. The implications of this study are substantial for the field of underground engineering. The framework offers a pathway toward safer, more efficient, and data-driven construction practices by providing a scalable and adaptable solution. It bridges the gap among design, construction, and operational phases, enabling a holistic approach to stability management in complex underground environments. One of the key strengths of this study lies in its comprehensive methodology, which integrates geological assessments, virtual modeling, real-time monitoring, and optimization strategies into a unified framework. Although the study has successfully demonstrated its effectiveness in real-world applications, it has certain limitations in terms of computational demands for processing large-scale geotechnical and sensor data in real time, requiring high-performance computing resources that may not be readily available in all project settings. Furthermore, the case study presented is limited to a specific geological and construction context, and further validation across diverse environments is necessary to generalize the findings. This significantly contributes to the body of knowledge by demonstrating the potential of DT, BIM, and IFC integration in underground construction. The framework's ability to provide real-time feedback and optimize rock support systems represents a notable advancement in the field. For future research, it is recommended to explore ways to enhance the framework's automation and predictive analytic capabilities. Additionally, further studies should focus on applying this methodology to a wider range of underground environments, including those with varying geological conditions and construction methods. Conceptualization, A.S., W.A.T. and K.M.M. ; Methodology, A.S.; Software, A.S.; Validation, A.S. and M.U.Z. ; Formal analysis, A.S. and M.U.Z. ; Investigation, A.S.; Resources, W.A.T. ; Data curation, A.S. and K.M.M. ; Writing--original draft, A.S. and K.M.M. ; Writing--review & editing, A.S. and W.A.T. ; Visualization, M.U.Z. ; Supervision, W.A.T. ; Project administration, K.M.M. ; Funding acquisition, W.A.T. and M.U.Z. All authors contributed equally to manuscript preparation. All authors have read and agreed to the published version of the manuscript. This work was funded by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia. Grant No. KFU251479. The authors declare that they have no conflicts of interest.
## 2 Material and Methods
### Recurrent Neural Networks  
RNNs are a special variant of artificial neural networks (ANNs) that have the ability to deal with sequential data and, when trained, retain knowledge of the past to model its effect on the present and on the future [22]. In RNN, data are assumed to involve sequences. As opposed to classical ANNs, where the data sampling times are independent, RNNs are an implementation of the \"memory\" concept, where the states of the previous information related to the inputs are used to generate the next output of the sequence. In classical feed-forward neural networks, the information is fed in a forward manner and processed through hidden layers to finally reach the output layer. However, in RNNs, the network cells remember their states and provide them as feedback information.  
In an RNN, information is transferred through a loop. Therefore, the decision made by the RNN is based on both the current input and recently received past information [29]. Despite the interesting features of RNN, this DL technique has many limitations, including the short memory effect and vanishing gradient problem due to backward propagation. In fact, when training an RNN algorithm, the vanishing/exploding gradient problems must be faced. The main cause of these two problems is that it is too difficult to keep the data for a long time. In the case of long sequences, the training algorithm may fail at updating the RNN weights. Thus, these weights may converge to zero values or may explode when going toward high values. In conclusion, it can be noted that short memory and vanishing gradients are two linked problems of RNNs. Solutions to those problems are provided by introducing GRU and LSTM structures.
# Reporting Key Features in Cold-Adapted Bacteria
## 4 Envelopes and Cold Adaptation
The expression of a single copy of them, as well as genes related to peptidoglycan biosynthesis, was increased under low temperature conditions evidencing microbial-mediated calcium carbonate precipitation at subzero temperatures [60]. Another surprising feature of _P. halocyophilus_ at sub-zero temperatures is that fatty-acid saturation increases with decreasing temperature [13]. It has been reported that _P. halocyophilus_ employs an alternate mechanism for preserving membrane fluidity and that the fatty-acid desaturases are present but inactive at these temperatures [14]. Exopolysaccharideaccharides (EPS), which comprise a substantial component of the extracellular polymers surrounding bacterial cells, are also considered an important cellular function to cope with cold and ice [40; 61]. _Pseudoalteromonas_ sp., isolated from Arctic marine environments, produce a highly complex EPS with mannose as a main component, interestingly _P. haloplanktis_ and other strains isolated from Antarctic environments also presented mannose as an important component of EPS [62; 63; 64]. These findings suggest that EPS production with specific characteristics is shared among cold-adapted bacteria. In addition, extracellular polymeric substances were related to the improvement of the freeze-thaw survival ratio in bacteria belonging to the _Winogradskyella_, _Colwellia_ and _Shewanella_ genera isolated from the Antarctic sponge [65], further indicating a cryoprotective role for these compounds. Present evidence indicates the importance of the maintenance of membrane fluidity that appears as a common feature in cold-adapted bacteria but also highlights the relevance of several envelope components to cope with cold.
# SPEEDY Quantum Circuit for Grover's Algorithm
## 4 Evaluation
### Security Strength Analysis for SPEEDY
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Cipher**} & \multirow{2}{*}{\(r\)} & \multicolumn{2}{c}{**Gates**} & \multirow{2}{*}{**Total Gates**} & \multirow{2}{*}{**Total Depth**} & \multirow{2}{*}{**Cost**} & \multirow{2}{*}{**Security**} \\ \cline{3-3} \cline{6-7}  & & \(T\) & \(Clifford\) & & & & \\ \hline \multirow{6}{*}{SPEEDY\(r\)-192} & \multirow{6}{*}{7} & \(1.48\times 2^{115}\) & \(1.16\times 2^{113}\) & \(1.77\times 2^{115}\) & \(1.53\times 2^{106}\) & \(1.38\times 2^{222}\) & Level 1 \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline \multirow{6}{*}{LEA-128/128[27]} & \multirow{6}{*}{-} & \(1.13\times 2^{21}\) & \(1.92\times 2^{21}\) & \(1.195\times 2^{21}\) & \(1.247\times 2^{27}\) & \(1.491\times 2^{189}\) & Not achieved \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline \multirow{6}{*}{CHAM-64/128[27]} & \multirow{6}{*}{-} & \(1.05\times 2^{21}\) & \(1.04\times 2^{20}\) & \(1.23\times 2^{21}\) & \(1.003\times 2^{26}\) & \(1.234\times 2^{157}\) & Not achieved \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & & \\  & & & & & & & \\ \hline \multirow{6}{*}{HPIGT-64/128[27]} & \multirow{6}{*}{-} & \(1.13\times 2^{21}\) & \(1.34\times 2^{24}\) & \(1.566\times 2^{216}\) & \(1.264\times 2^{146}\) & \(1.98\times 2^{287}\) & Level 3 \\ \cline{1-1}  & & & & & & & \\ \cline{1-1}  & & & & & & & \\ \hline \multirow{6}{*}{PIPIG-64/128[8]} & \multirow{6}{*}{-} & \(1.05\times 2^{21}\) & \(1.04\times 2^{20}\) & \(1.384\times 2^{20}\) & \(1.901\times 2^{25}\) & \(1.316\times 2^{158}\) & Not achieved \\ \cline{1-1}  & & & & & & & \\ \cline{1-1}  & & & & & & & \\ \cline{1-1}  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cost estimation for [PERSON]’s algorithm. On the other hand, looking at the LEA, CHAM, and PIPO ciphers evaluated by [8; 27] in Table 2, security strength was not achieved with a 64-bit key, but was achieved with a 256-bit key length. HIGHT ciphers that only work with 64-bit keys do not achieve security strength. For the LEA cipher, LEA-128/128 (using 128-bit key) did not achieve the security strength, but LEA-128/192 (using 192-bit key) and LEA-128/256 (using 256-bit key) achieved level 1 and level 3, respectively. In the case of the CHAM cipher, CHAM-64/128 and CHAM-128/128 (using 128-bit key) did not achieve the security strength, but CHAM-64/256 (using 256-bit key) achieved level 3. In the case of the PIPO cipher, PIPO-64/128 (using 128-bit key) did not achieve the security strength, but PIPO-64/256 (using 256-bit key) achieved level 3. From the above results, it was confirmed that it is difficult to increase the quantum security strength by increasing the number of rounds and the block length, but it can be increased through the key length. Therefore, in order to strengthen the security in quantum computers, it is necessary to consider measures to increase the number of iterations exponentially by increasing the key length.
## 5 Simulation Results
### Performance of RDUR Scheme
The number of second-stage successful user is small and that of candidate UR is large. Correspondingly, the coverage efficiency becomes small. This also explains that why the coverage efficiency decreases with the increase of \(M_{i}\). To investigate the performance of RDUR further, the capacity efficiency is simulated in Figure 8. Capacity efficiency was to describe the contribution of a single UR to multicast capacity. It equalled to the ratio of the second-stage D2 MD capacity (The second-stage D2 MD capacity is calculated by the same method as [10].) to the total number of URs. From the simulation result, the capacity efficiency after RDUR was larger than that after the original scheme of COM-NOMA and the other two schemes. After RDUR, even though the quantity of URs was smaller than that in COM-NOMA, multicast capacity of the second stage is the sum capacity of each D2 MD group, which employs UR as sender. When small ERSR was used to select URs, the distance between senders and USUs in each D2 MD group decreased. In the influence of smaller loss path than original scheme, the transmission rate of D2 MD is higher. The total capacity of the second stage will not decrease too much. At the same time, the number of UR was smaller than the original scheme. Thus, the capacity efficiency after RDUR scheme enhances in comparision to the original scheme. With the increase of \(M_{i}\), the density of usersbecame large. Via the same UR selection scheme, more URs taking part in cooperation impaired the capacity efficiency. Therefore, capacity efficiency decreased with \(M_{i}\). Even though there was little difference in coverage efficiency between RDUR scheme and the try-best scheme under \(C_{1}=0.7,0.8\), the capacity efficiency performance of RDUR was better. It is because that USU can receive data from multiple URs who are within his/her ERSR. In the try-best scheme, USU only receives data from the USU nearest to him (her). The multicast capacity of the second stage after RDUR scheme is larger than that after try-best scheme. Figure 6: The probability of \(E_{t}\) in different annular areas (AA), when \(M_{i}=130\). Figure 7: Coverage efficiency versus the number of users in MG, in different \(C_{1}\).
## 2 Results and Discussion
### Elemental Analysis  
The total amount of nitrogen (mmol\({}_{\text{N}}\) g\({}^{-1}\)) was measured by CHNS/O analysis to quantify the amino-functional groups presenting in the WMS-NH\({}_{2}\) and NR/WMS-NH\({}_{2}\). The pure silica WMS and NR/WMS exhibited no significant nitrogen content, confirming the successful removal of the DDA template (Table 2). The amine concentration of the NR/WMS-NH\({}_{2}\) series increased with the aminosilane loading level. The NR/WMS-0.15 prepared with 15 mol% APS loading possessed the highest amine content (1.84 mmol\({}_{\text{N}}\) g\({}^{-1}\)). In addition, increasing the APS/TEOS molar ratio of the synthesis mixture from 5% to 15% resulted in an increased yield of the aminopropyl group from 53% to 83%. Compared to the theoretical amine concentration, the degree of surface functionalization with APS was estimated to be 96% and 80% for WMS-0.10 and NR/WMS-0.10, respectively. The smaller functionalization level observed for the NR/WMS-NH\({}_{2}\) nanocomposite suggested that some rubber fraction hindered the accessibility of APS to the silicate species.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{5}{c}{**Si Species Distribution (\%)**} & \multirow{2}{*}{\(\Sigma\)T\({}^{\text{m}}\)/\(\Sigma\)(T\({}^{\text{m}}\) + Q\({}^{\text{m}}\)) (\%)} \\ \cline{2-2} \cline{5-7}  & **T\({}^{2}\)** & **T\({}^{3}\)** & **Q\({}^{2}\)** & **Q\({}^{3}\)** & **Q\({}^{4}\)** & **\(\Sigma\)T\({}^{\text{m}}\)/\(\Sigma\)(T\({}^{\text{m}}\) + Q\({}^{\text{m}}\)) (\%)** \\ \hline WMS & – & – & 11.89 & 68.79 & 19.32 & – \\ WMS-0.10 & 4.78 & 12.75 & 8.54 & 49.38 & 24.55 & 17.53 \\ NR/WMS & – & – & 17.56 & 70.43 & 12.01 & – \\ NR/WMS-0.10 & 3.12 & 11.97 & 8.93 & 52.31 & 23.67 & 15.09 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relative intensities of the T\({}^{\text{m}}\) and Q\({}^{\text{m}}\) species obtained from the deconvolution of \({}^{29}\)Si CP/MAS NMR spectra.  
Figure 2: \({}^{29}\)Si MAS NMR spectra of (**a**) WMS, (**b**) WMS-0.10, (**c**) NR/WMS, and (**d**) NR/WMS-0.05.
# The Application of Fuzzy Theory in the Evaluation of Visual Images of Smartphone Rear Cameras
## 3 Results and Discussions
### The Results of Fuzzy Operation
The above results can also be summarized as follows:  
From Figure 7, it can be seen that different smartphone rear cameras have apparently different visual qualities in the categories of \"fashionable and technical,\" and \"superior and valuable,\" while in the categories of \"premium and technical\" and \"superior and valuable,\" the distinctions are limited. As shown in Table 10, sample S14 received the highest grade in the evaluation for the category \"simple and pure\"; sample S47 received high grades in both the \"harmonious and ordered\" and \"simple and pure\" categories; sample S74 received great grades in each evaluation, achieving its best grades in \"premium and technical\" and \"superior and valuable\"; sample S95 received good grades except for \"superior and valuable,\" and its highest grade appeared in the evaluation of \"innovative and fashionable.\"  
Images evaluation grades, after further processing with a radar map (see Figure 8) and a comprehensive comparison, led to the four clusters of the entire visual images evaluation: (a) similar images appeared in S14 and S99, and they both acquired high evaluation grades in \"superior and valuable\"; (b) for S27, S39 and S49, the linear radius was within the innermost ring, indicating that the visual images were close on the whole, and low grades were received for each images evaluation; (c) S33, S47, S74 and S95 possessed similar visual images on the whole, with relatively higher grades for each evaluation, and an even distribution; (d) S50 and S58 had similar linear radii devoid of extreme highs or extreme lows, denoting an interim level in each visual image evaluation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Samples** & **Innovative and** & **Harmonious** & **Premium and** & **Superior and** & **Simple and** \\  & **Fashionable** & **and Ordered** & **Technical** & **Valuable** & **Pure** \\ \hline S14 & 0.3976 & 0.5387 & 0.4621 & 0.4034 & 0.6859 \\ S27 & 0.4794 & 0.4146 & 0.4742 & 0.4973 & 0.3384 \\ S33 & 0.5154 & 0.5625 & 0.5617 & 0.5388 & 0.5000 \\ S39 & 0.3071 & 0.5790 & 0.4603 & 0.3628 & 0.4929 \\ S47 & 0.4919 & 0.6436 & 0.5468 & 0.5278 & 0.6849 \\ S49 & 0.4317 & 0.3248 & 0.3731 & 0.4155 & 0.3037 \\ S50 & 0.4821 & 0.5382 & 0.5048 & 0.4722 & 0.5856 \\ S58 & 0.5278 & 0.4750 & 0.4681 & 0.5246 & 0.4925 \\ S74 & 0.5391 & 0.6283 & 0.6196 & 0.6496 & 0.5625 \\ S95 & 0.6341 & 0.5246 & 0.5626 & 0.5792 & 0.4647 \\ S99 & 0.3869 & 0.5135 & 0.4478 & 0.4134 & 0.6090 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The total utility values of 11 samples of smartphone rear cameras.
# Multivariate Time Series Data Prediction Based on ATT-LSTM Network
## 3 ATT-LSTM Model
### Attention Mechanism  
The attention mechanism has been widely used in various fields of deep learning in recent years. It is a resource allocation scheme that is the primary means to solve information overload [28]. The attention mechanism is similar to the artificial neural network and originated from the human behavior mechanism. The attention mechanism is derived from human vision and draws on the human visual attention mechanism. The core idea is to select more critical information to the current task goal from a lot of information [29]. The attention mechanism also has a variety of classification methods, which can be divided into spatial attention and temporal attention according to space and time; it can be divided into soft attention and complex attention according to the nature of work, as shown in Figure 3.  
Figure 3: Attention mechanism classification.  
The essence of the attention mechanism has two aspects: (1) Determine which part of the data the model needs to focus on from the input data and assign different weights; (2) Assign limited information processing resources according to the importance weight vector [30;31].It is shown in Figure 4.  
The attention mechanism imagines the constituent elements in the data as a series of <Key, Value> data pairs. The attention mechanism is to perform a weighted summation of the value of the elements in the data, where key and query are used to calculate the weight coefficients of the corresponding value, as shown in the following formula:  
\[Attention(Query,Source)=\sum_{i=1}^{L_{x}}similarity(Query,Key_{i})*Value_{i}. \tag{10}\]  
Among them, \(L_{x}\) represents the length of the data source. Attention mechanisms are widely used in natural language processing, classification, recommendation systems, and extensive data analysis. The analysis and prediction of multivariate time series data have also been further developed because of the emergence of the attention mechanism.