esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Existing Bond Models for Externally Bonded SRP Composites
## 2 Bond Performance of External Strengthening
The models analyzed in the article were sorted according to the first classification and summarized in Table 1. Some of these theoretical models have been developed and included in technical guidelines for the design of externally bonded FRP composites. Their examples were also included in the comparisons presented in this manuscript. The design procedures used therein are described in Table 2. \begin{table}
\begin{tabular}{c c} \hline
**Reference** & **Relationships** \\ \hline [PERSON] [14] & \(F_{ub}=b_{f}L_{b}(6.13-\ln(L_{b}))\) \\ [PERSON] and [PERSON] [15] & \(F_{ub}=b_{f}L_{b}\big{(}5.88(0.1L_{b})^{-0.669}\big{)}\) \\ [PERSON] et al. [16] & \(F_{ub}=\big{(}110.2\cdot 10^{-9}E_{f}f_{f}\big{)}b_{f}L_{e}\) \\  & \(L_{e}=\ell^{6.13-0.58\ln 0.001E_{f}f_{f}}\) \\ [PERSON] [17] & \(F_{ub}=\sqrt{\frac{2E_{f}f_{f}G_{f}}{1+4\alpha_{f}}}b_{f}\) \\ [PERSON] [17] & \(\alpha_{T}=\frac{E_{f}f_{f}}{E_{e}L_{e}}\), \(G_{f}=0.14f_{c}^{\prime}\) (\(G_{f}=0.65\) if \(f_{om}>46,2\)) \\  & \(F_{ub}=\left\{\begin{array}{c}0.78\cdot k_{b}f\sqrt{0.408\ell_{cm}E_{f}f_{f}} \text{ if }L_{b}\geq L_{e}\\ 0.78\cdot k_{b}b_{f}\sqrt{0.408\ell_{cm}E_{f}f_{f}}\frac{L_{b}}{L}\big{(}2- \frac{L_{b}}{L_{e}}\big{)}\text{ if }L_{b}<L_{e}\\ \end{array}\right.\) \\ Nidermeier [18] & \(k_{b}=\sqrt{\frac{1.125\big{(}2-\frac{b_{f}}{L}\big{)}}{1+\frac{b_{f}}{L} \infty}}\geq 1\), \(L_{e}=\sqrt{\frac{E_{f}f_{e}}{4f_{em}}}\) \\ F_{ub}=\left\{\begin{array}{c}0.64\cdot k_{b}b_{f}\sqrt{E_{f}f_{f}\ell_{cm} }\text{ if }L_{b}\geq L_{e}\\ 0.64\cdot k_{b}b_{f}\sqrt{E_{f}f_{f}\ell_{cm}}\frac{L_{b}}{L}\big{(}2-\frac{L_{ b}}{L_{e}}\big{)}\text{ if }L_{b}<L_{e}\\ k_{b}=\sqrt{\frac{1.125\big{(}2-\frac{b_{f}}{L}\big{)}}{1+\frac{b_{f}}{L} \infty}}\geq 1\text{, }L_{e}=\sqrt{\frac{E_{f}f_{e}}{2f_{em}}}\end{array}\right.\) \\ \hline \end{tabular}
\end{table} 
Table 1: Predictive models for the FRP-concrete bond. The following technical standards were considered: British TR55/2000 [33], international _fib_ Bulletin 14/2001 [34], Japan JCI 2003 [35], Swiss Code SIA 166/2004 [36], Italian standard CNR-DT200R1/2013 [37], and the newest international _fib_ Bulletin 90/2019 [38].
## 3 Results  
Samples of relatively healthy mucosa and tissue samples of nasal cavity polyps and paranasal sinuses have been studied. The comparison of substances revealed in the spectra of thermal decomposition products of relatively healthy tissue samples and polypous tissue samples some characteristic substances that have intense absorption lines in the operating ranges of the used spectrometers and appear in the gas mixture during thermal decomposition of the samples in a sufficiently large amount. The spectral data for the absorption lines of all the studied substances are available on-line in the databases [15; 16]. One of these substances is acetaldehyde. It has a whole series of lines (over 900) with an integral intensity greater than \(10^{-6}\) nm\({}^{2}\) MHz in the range of 118-178 GHz [15]. Since the thermal decomposition products are a mixture of substances in the gas phase, the concentrations of the substances that form it are quite low. Therefore, the most intense absorption lines of a particular substance, including acetaldehyde, are observed in the spectrum. An example of recording a spectrum part containing lines of acetaldehyde appearing during thermal decomposition of both relatively healthy mucosa (No. 2) and polyp tissue (No. 6) is shown in Figure 3.  
We have performed a comparative analysis of the data and their schematic interpretation as line diagrams. An example of the chemical composition of the products of thermal decomposition of a relatively healthy nasal mucosa for three samples (Samples 1-3) is shown in Figure 4.  
We can see in the above diagrams that some metabolites are repeated and can be regarded as markers creating the so-called metabolic profile of a healthy tissue of the nasal mucosa and paranasal sinuses. These are, first of all, acetaldehyde, as well as methyl mercaptan, azide, butyronitrile, benzonitrile, ethylene sulfide, and carbonyl sulfide. The spectroscopic study of the pathological process, in which substances differ significantly in concentration from those present in relatively healthy tissue, or absent in it are identified, makes it possible to conclude that there are decay products that characterize a particular pathology. An example of a substance that does not appear in all samples of relatively healthy tissue (only in one sample out of three, shown in Figure 4), is acetonitrile. However, acetonitrile is present in all the considered samples of polyp tissue. An example of recording a spectrum section containing acetonitrile (CH\({}_{3}\)CN) lines for a polyp tissue sample (No. 6) is shown in Figure 5.  
Figure 4: Chemical composition of thermal decomposition products of a relatively healthy nasal mucosa. Figure 3: Example of recording a spectrum section containing acetaldehyde (CH\({}_{3}\)CHO) lines for Sample No. 2 (blue line) and Sample No.6 (red line) with experimentally measured central frequencies f\({}_{\text{lexp}}\) = 134.9087 GHz (cf.
## 6 Results
### Scenario with Fewer Servers and Fewer Fog Nodes
#### 6.2.2 Latency Results  
**Low average latency:** The average latency of 66.58 ms remains a positive value within the system's context, as it is within a reasonable range to ensure a smooth user experience. This level of latency suggests that the system responds efficiently under heavy load conditions and that response times are satisfactory for most users.  
**Excellent minimum latency:** The minimum value of 1.41 ms is a clear indicator that the system can respond almost instantaneously under ideal conditions. This is a sign that the system is optimized to operate with low latency when conditions are favorable, providing users with a high-quality experience in terms of response times.  
**Reduced variability during high traffic:** Although there is a higher standard deviation compared to the previous scenario, the moments of highest latency are associated with traffic peaks that the system manages without reaching critical limits. This demonstrates that the system can handle high demand moments without drastically affecting the user experience.  
Resource Usage Optimization  
**Adaptability to a reduced infrastructure:** Despite the reduction in the number of servers and fog nodes, the system continues to maintain its stability and operability. This is a positive point because it indicates that the system is scalable and adaptable to different infrastructure configurations, ensuring good performance even with fewer available resources.  
**Cost reduction with reduced infrastructure:** Operating with six servers instead of eight, and two fog nodes instead of four, significantly reduces operational costs. Less hardware implies lower energy consumption, less maintenance need, and lower costs associated with server management. Despite this reduction, the system is still capable of delivering good response times and high availability, which is a notable gain from an efficiency and cost perspective.
## 2 Materials and Methods
### Surgical Procedure, Osteotomy and Distractor Application  
Four nude rats (RNU rat, Charles River Laboratories, Sulzfeld, Germany) with an average weight of 178 g were operated on according to the protocol approved by the Veterinary Office Basel, Switzerland (ethical approval code 2295). The animals were anesthetized by inhalation of a mixture of isoflurane (1.5% to 3.0%) and oxygen with a flow rate of 0.01 L/s. This was followed by the intra-peritoneal injection of fentanyl/fluanisone (Hypnorm, Janssen Pharmaceutical, Beerse, Belgium), using a volume of 0.3 to 0.6 mL/kg, and diazepm with a mass of 2.5 mg/kg. After shaving and disinfection, the mandible was exposed. Bilateral osteotomy of the mandibular angle was performed in the retromolar area, using a piezoelectric instrument. After osteotomy, the locked extraoral distractors made of polyetheretherketone (PEEK), Ti6 Al7 Nb (TAN) and 316L stainless steel with a pitch of 0.5 mm per full turn (RatDis Mandible, RISystem AG, Davos, Switzerland) were applied and fixed to the mandible with two pins made of TAN and one standard length (RatExFix Mounting pin 0.85 mm, RISystem AG, Davos, Switzerland) on each side of the osteotomy, as illustrated in Figure 2.  
In Rat 1 and Rat 2, one million MSCs were injected at each distraction gap after resuspending the cells in a volume of 50 \(\mu\)L fibrinogen mixed with thrombin. The control animals Rat 3 and Rat 4 were only treated with fibrin glue, containing the same composition of fibrinogen and thrombin as for Rat 1 and Rat 2 (Tissucol, Baxter, Vienna, Austria). The skin was closed with 5-0 polypropylene sutures (Surgipro, Syneture, Norfolk, VA, USA). Postoperatively, 0.01 to 0.05 mg/kg buprenorphine was administered every twelve hours for a period of maximal 72 h.  
After a latency period of five days, the mandibles were distracted according to a regular or rapid distraction protocol until a distraction gap of 6 mm was reached. The regular distraction protocol applied to Rat 1 and Rat 3 included an initial lengthening of 1.0 mm at Day 1, followed by a distraction speed of 0.5 mm per day. In the rapid distraction protocol, applied to Rat 2 and Rat 4, the distraction speed was set to 1.5 mm, started on Day 1.  
After the consolidation period (regular distraction: 72 days, rapid distraction: 66 days), the animals were sacrificed by inhaling carbon dioxide. The mandibles were dissected from the heads and fixed in 4% phosphate-buffered formalin (pH 7.4).
Other advantages, such as the use of in-office printers for guide manufacturing [11], a clinically acceptable accuracy of dental implant insertion [5; 12; 13], and better drill cooling for avoiding bone necrosis due to the special built-in window, increased the popularity of sleeve-incorporated resin surgical guides [14]. The accuracy of the reproduction of the planned implant position is mandatory for a successful treatment and the avoidance of complications. In spite of the high degree of predictability of the surgical guides reported in the literature, some complications, such as guide fracture, could occur [15; 16]. Few studies are focused on analyzing the mechanical behavior of full resin surgical guides and stress distribution during implant surgery to improving their design and strength [17; 18], while they are custom-created for each individual patient. Finite Element Analysis (FEA) is a numerical method for stress and deformation analysis of a given and complex geometry, simulated under various physical conditions, commonly used nowadays in medical and dental fields. For analysis, the shape is discretized into many small elements, so-called finite elements, coupled at the corners through nodes. Each element is a tetrahedron, with its sides having different lengths. In a complex structure, each face, side, and node are shared with those of an adjacent element. For each individual element, its mechanical behavior can be described as the function of the displacement of the nodes. When certain loading conditions and specific restraints are applied to the whole 3D model/structure, and the specific characteristics of the material are provided, the behavior of this model can be obtained, and the stress and strain can be computed on the desired parts of the shape. The accuracy of the results is determined by the total number of elements used for the model (also called discretization of the model in finite elements), as well as by the type of the network mesh (linear or parabolic) [19; 20]. Each setting (3D model complexity, finite element type, discretization level, structure loading mode, FEA computation accuracy) influences the analysis results and their interpretation. The objective of the present study is to develop surgical guides without metal sleeves (with incorporated sleeves) and to explore and simulate through FEA the use of such a dentally supported guide for dental implant insertion. Additionally, the behavior of the guide's 3D model is evaluated by simulation for design validation and improvement proposals. The present study is structured in two parts. Firstly, the manufacturing workflow of surgical guides with sleeves incorporated (without metal sleeves/sleeveless) is discussed. In the second part, a FEA and methodology are applied to a surgical guide for simulating the implant bed preparation.
## 2 Data and Methodology
### Occurrence Probability Determination Method  
[PERSON] et al. [42] analyzed the meteorological data detected when drifting snow occurred near the Alps and found that the main meteorological feature was that snow occurred when the wind speed reached a certain range, and this model was also applied by [PERSON] et al. [43]. Based on this discovery and previous studies, the drifting snow in this paper is defined as periods with precipitating snow with a wind speed in the range of 3-8 m/s [19]. In order to ensure that the precipitation is solid, the temperature is limited to less than 0 \({}^{\circ}\)C. Therefore, we calculated the probability by the percentage in the number of times that met the three conditions during the simulation period.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Meteorological Element** & **Station** & **R** & **MBE** & **RMSE** & **Comparison** \\ \hline \multirow{2}{*}{Temperature} & Kangding & 0.82 & 2.64 & 4.06 & \multirow{2}{*}{0.60–0.80} \\  & Litang & 0.90 & 1.43 & 3.21 & \\ \hline \multirow{2}{*}{Wind speed} & Kangding & 0.54 & 0.52 & 2.26 & \multirow{2}{*}{0.51–0.56} \\  & Litang & 0.63 & \(-\)1.69 & 2.37 & \\ \hline \multirow{2}{*}{Rainfall} & Kangding & 0.54 & 1.32 & 1.80 & \multirow{2}{*}{0.50–0.60} \\  & Litang & 0.58 & 1.21 & 1.52 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation results of temperature, wind speed, and rainfall.  
Figure 7: _Cont._  
Figure 8: Comparison of daily rainfall in Litang.  
Figure 7: The results of the WRF fitting process and a comparison between the simulated and observed values. (**a**) The fitted line of temperature in Kangding; (**b**) the variation in temperature in Kangding; (**c**) the fitted line of temperature in Litang; (**d**) the variation in temperature in Litang; (**e**) the fitted line of wind speed in Kangding; (**f**) the variation in wind speed in Kangding; (**g**) the fitted line of wind speed in Litang; (**h**) the variation in wind speed in Litang.
## 3 Fusion Precomputed AES Encryption Algorithm
### Optimized AES Encryption Process  
In the normal encryption/decryption process, the plaintext or ciphertext is used as input, and the ciphertext or plaintext output is obtained after the AES encryption or decryption operation, as shown in the left side of Figure 4. The precomputation optimization in this paper, on the other hand, calculates the data required in the AES encryption process in advance and performs some preprocessing steps to reduce the computation in the actual encryption process. This optimization scheme may increase the time overhead in the initialization phase, but it can significantly reduce the time overhead in the actual encryption process as shown in the right side of Figure 4.  
In the precomputation optimization process of Figure 4, the S-boxes, inverse S-boxes and the Mixcolumn matrix are first generated and then stored in the data structure. These precomputed results can be reused through table lookup operations to improve the efficiency of encryption or decryption operations, and the overhead generated by AES encryption is reduced.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
63 & C6 & 23 & 49 & 8A & 98 & FF & 16 & 05 & F7 & 3A & EA & 06 & FF & B0 & 0E \\
# Quantitative Analysis of Forest Water COD Value Based on UV-vis and FLU Spectral Information Fusion
## 4 Results and Analysis
### Analysis and Comparison of Modeling Results
It is worth noting that after data fusion at the decision level, the predictive accuracy and stability achieve an excellent performance during the fitting process. The determination coefficient \(R^{2}\) between the predicted value and the real value can reach up to 0.99. At the same time, the RMSE of the model is reduced significantly. For example, the RMSE obtained from the TOPSIS voting mechanism is reduced to 0.4582, which is  
Figure 4: Fitting results between predicted values and actual values. (**a**) SPA and (**b**) CARS algorithm for feature-level data fusion. 41.9% lower than the corresponding feature-level data fusion model. After decision-level data fusion, we find that there is little difference between the predictive models of the two chosen voting mechanisms due to the significant improvement in accuracy. For better presentation, we list the predictive results of all models in Table 2. Compared with the single-spectrum model, the accuracy of the model has been greatly improved after data fusion. During the feature selection process, the accuracy of the CARS algorithm is better than that of the SPA algorithm. Considering the accuracy and stability of the model, the decision-level data fusion model based on entropy weight TOPSIS achieves the best prediction performance, making it more valuable for predicting forest surface waters with relatively low COD values. To better demonstrate the results of our model, we make a comparison with the representative results mentioned in the introduction, including the UV-vis and FLU single-spectrum models. Combined with the CNN model, [PERSON]'s group realized the optimization of the COD prediction model (\(R^{2}=0.9991\), RMSE = 3.8745) using UV-vis spectra [7]. Compared with them, the RMSE value of our model is reduced by 88.17%. [PERSON]'s group used feature-level data fusion to detect COD in FLU spectra at excitation wavelengths of 265, 290, and 305 nm, which significantly improved the accuracy of detection (\(R^{2}=0.9982\), RMSE = 0.5342) [8]. Compared with their single-FLU-spectrum model, the RMSE value of our multispectral model is reduced by 14.2%. The introduction of multispectral data further improves the anti-interference ability of the model. Considering the differences in the COD concentration range of actual water bodies, the water samples in this paper mainly come from forest water bodies with low COD concentrations. To further improve the generalization ability of the model, more test samples and various environmental factors can be added for comprehensive consideration.
# Assessment of Pohang Earthquake-Induced Liquefaction at Youngil-Man Port Using the UBCSAND2 Model
## 2 Methodology
### UBCSAND2 Constitutive Model
#### 2.1.1 Elastic Response  
The elastic response is assumed to be isotropic and composed from shear modulus \(G^{e}\) and bulk modulus \(B^{e}\). It is also a function of mean effective stress as follows:  
\[B^{e} = k_{B}^{e}\cdotp P_{a}\left(\frac{\sigma^{\prime}{}_{m}}{P_{a}} \right)^{me} \tag{1}\] \[G^{e} = k_{G}^{e}\cdotp P_{a}\left(\frac{\sigma^{\prime}{}_{m}}{P_{a}} \right)^{me} \tag{2}\]  
where \(k^{e}{}_{B}\) and \(k^{e}{}_{G}\) are the elastic bulk and shear modulus number, respectively. \(ne\) and \(me\) are the stress exponent for \(B^{e}\) and \(G^{e}\) (approximately 0.5 for both), respectively. \(P_{a}\) is the atmospheric pressure (approximately 100 kPa). \(\sigma^{\prime}{}_{m}\) is the mean effective stress.
# Sports Risk Prediction Model Based on Automatic Encoder and Convolutional Neural Network
## 2 Overview of Relevant Theories
### Introduction to Relevant Algorithms and Technologies
#### 2.2.3 Automatic Encoder  
Auto encoder (AE), a classical deep learning framework, is an unsupervised learning method, and the self-encoder usually consists of two parts: an encoder and a decoder [18]. The encoder can be considered a function of compressing high-dimensional data into a low-dimensional potential space, and the decoder can be considered a function of reducing data from the potential space into the original high-dimensional space. The structure of a self-encoder can be customized according to the actual problem, but generally consists of the following components:  
1. Input layer  
The input layer of a self-encoder is usually a vector of the same dimension as the original data; e.g., for a self-encoder used on image data, the dimension of the input layer is usually equal to the number of image pixels.
2. Encoder  
An encoder usually consists of a string of neural network layers that are used to compress the input data from a high-dimensional space into a low-dimensional potential space. The final encoder output is a vector as a representation in the latent space.
3. Decoder  
A decoder usually also consists of a string of neural network layers for mapping the vector in the latent space back to the original high-dimensional space to achieve a mapping to a low-dimensional representation.
4. Loss calculation  
The self-encoder usually requires minimizing the reconstruction error, so it is necessary to define a loss function to measure the error between the encoder and decoder, which generally uses the mean square error (MSE) as the loss function to optimize the self-encoder model by the back propagation algorithm.
5. Potential spatial representation  
The potential space representation of the self-encoder can be used for tasks such as data dimensionality reduction, data visualization, and data compression. Similarly, the latent space representation can also be used for interpolation or interpolation operations to generate new data or to explore the features of the data distribution in that space.
## 3 Simulation Results
### LSTM Simulation Results  
In this section, we delve into the simulation results obtained from LSTM networks applied to OFDM systems. The simulation settings are carefully chosen to reflect real-world OFDM scenarios, ensuring a comprehensive evaluation of the LSTM model's performance across different channel conditions.  
Our OFDM system configuration consists of 64 sub-carriers and a cyclic prefix length of 16, operating at a carrier frequency of 2.6 GHz with QPSK modulation. To emulate realistic wireless environments, both Rayleigh fading and realistic channels are considered, providing a robust assessment of the LSTM model's effectiveness.  
Within the LSTM model architecture, the sigmoid function serves as the activation gate, while the 'tanh' function acts as the state activation function. Training of the LSTM model is conducted using the 'Adam' solver, with the parameters set to train for 15 epochs. To mitigate gradient explosion, a gradient threshold of 1 is enforced, ensuring stable training dynamics.  
Figure 14 illustrates the training progress of the LSTM model, generated over 10,000 frames, across SNR values ranging from 0 to 15 dB. A stratified split allocates 75% of the data for training, while the remaining portion is reserved for validation. The performance evaluation is conducted on a separate test set, specifically evaluated at 5 dB SNR under the Rayleigh fading channel.  
These meticulously selected parameters ensure that the LSTM model captures the intricacies and complexities inherent in real-world OFDM systems. By training and evaluating the model across diverse channel conditions, particularly in Rayleigh fading environments, we can gain valuable insights into its adaptability and robustness in practical deployment scenarios.  
Figure 15 shows the BER performance that has been observed for the OFDM signal for the QPSK-modulated signals in the Rayleigh fading channel.  
Figure 14: Training progress of LSTM method for 10,000 frames.  
As can be seen from Figure 15, the comparison between the BER performances of the LSTM method and MMSE traditional approach is carried out. The results show that the LSTM model is closely aligning and slightly outperforms the traditional approach across various SNR values.