esa-sceva/satcom-chunk-collection huggingface dataset:
# Anchor Design of a Ring Joint Based on Reliability in a Precast Shear Wall Structure
## 4 Bearing Capacity Model
### Basic Bearing Capacity Model  
Drawing on existing research, a ring rebar can be symmetrically separated, and the bearing capacity could be calculated according to the calculation method for bent rebar. The schematic diagram for the bent rebar is shown in Figure 12. The bond stress, \(\tau_{\mathrm{u}}\), can be calculated with Equation (1), and the bearing capacity can then be calculated [15].  
Figure 11: Effect of the number of ring units on the strain results.  
Figure 10: Stress analysis of the down horizontal rebar in the specimen. (**a**) Yielding of the horizontal rebar; (**b**) Partial display; (**c**) Stress distribution.  
\[\tau_{\rm u}=\left[\left(0.82\ +\ 0.9\frac{d}{l_{\rm v}}\right)\left(1.6\ +\ 0.7\frac{c}{d}\right)+12.5\frac{d}{l_{\rm v}}\sqrt{\frac{l_{\rm h}}{l_{\rm v}}} \right)f_{\rm t} \tag{1}\]  
Equation (1) is a stiffness equation expressed in the form of strength, where the first term represents the bonding effect of the straight anchor segment, \(l_{\rm v}\), and the second term represents the effect of the rear anchor segment, \(l_{\rm h}\), on the bonding performance of the straight anchor segment. When \(l_{\rm h}\geq 7d\), the displacement-load curves continues to rise after the bending point and anchoring is safe and reliable.  
The ability of the horizontal rebar to bear the shear force is called the dowel action. At the splicing interface of the prefabricated structure, the horizontal rebar serves to resist and transfer the axial load of the ring rebar, and the dowel action is the only transmission mechanism for the shear force [23; 24]. Research has shown that the crack pattern of the horizontal rebar is mainly manifested by the combined failure modes of the generated shear forces and tensile forces. Accurately predicting the bearing capacity is of great significance for ensuring the safety of the joints. [PERSON] and [PERSON] [25; 26] investigated the role of the horizontal rebar and proposed a mathematical model considering the effect of damage that could accurately predict the bearing capacity. The model is applied under the condition of no splicing interface, as shown in Equations (2)-(5); the mechanical diagram was shown in Figure 13.  
\[D_{\rm u}=\ 1.3\ d_{\rm h}^{2}\left(f_{\rm c}\cdot f_{\rm y}\right)^{ \frac{1}{2}} \tag{2}\]  
\[D_{\rm damage}=\ 3\ \times\ 10^{-6}X^{\ 2}+0.0212X \tag{3}\]  
\[X=\ d_{\rm h}^{2}\left(f_{\rm c}\cdot f_{\rm y}\right)^{\frac{1}{2}} \tag{4}\]  
\[D_{\rm c}=\ D_{\rm u}-D_{\rm damage} \tag{5}\]  
In the bearing capacity model, \(D_{\rm u}\) was the basic calculation model, and \(D_{\rm damage}\) was the attenuation of bearing capacity of the horizontal rebar caused by local comprehensive damage of concrete. X was the material factor. Finally, \(D_{\rm c}\), the modified model was obtained.  
Figure 12: Schematic of bent rebar.  
Figure 13: Mechanical diagram of dowel action.
# An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach
## 2 Related Research
### Predicting Customer Churn and Data Mining Techniques  
Customer satisfaction and customer churn have been identified as two factors that contribute to industry success and are therefore the hot topics researched in various industries, such as telecommunication [22], medicine [23] and tourism [24]. A more recent solution states that customer churn prediction requires customer behavior analysis [25]. Churn management, identified as the process of keeping existing customers [26], has always had a vital role in CRM in the telecommunication industry [6; 27].  
Due to the lack of the studies that used social media mining as variable input to the churning prediction model, we did not find research close in content to our current research. Therefore, we will discuss here the researches that developed a churn prediction model based on historical data or other parameters, using different techniques, as shown in Table 1. The existing churn prediction studies have been critically evaluated to find the gaps that would allow to answer the RQ. The review included a full-text assessment of the articles (Table 1). The table briefly compares the aim, used data set, algorithm(s), results and future work for the reviewed studies.  
In the following, these related research in Table 1 are analysed in more details. The only other study to use of tweet sentiment analysis for business [28] found that there is a relationship between the sentiment of tweet feeds related to Telcom's broadband internet service and the customer churn rate. They applied the long short-term memory model (LSTM) for sentiment analysis. Their results showed that churn prediction can be improved by monitoring the negative sentiment about 1.47% Mean average percentage error (MAPE). However, their study did use the social media mining for developing churn prediction model. Related research mainly uses company-provided data for churn prediction. Whilst this is a useful source, this is not always available.  
Therefore, there is a lack of studies that use social media mining as a variable input for the customer churn prediction model to our knowledge. This proves that a knowledge gap exists in demonstrating how social mining can predict customer churn in various industries. By analysing the studies, a significant finding was that the reviewed literature investigating churn prediction models showed that social mining is a powerful tool for assessing customer satisfaction and predicting customer churn.
###### Abstract  
A cognitive radio ad hoc network (CRAHN) is a mobile network that can communicate without any form of centralized infrastructure. The nodes can learn about the environment and make routing decisions. Furthermore, distributed computing, spectrum mobility, and the Internet of Things have created large data sets, which require more spectrum for data transmission. Unfortunately, the spectrum is a scarce resource that underutilized by licensed users, while unlicensed users are overcrowding the free spectrum. The CRAHNs technology has emerged as a promising solution to the underutilization of the spectrum. The focus of this study is to improve the effectiveness and energy consumption of routing in order to address the routing problem of CRAHNs through the implementation of the optimized cuckoo search algorithm. In CRAHNs, the node and spectrum mobility cause some frequent link breakages within the network, which degrades the performance of the routing protocols. This requires a routing solution to this routing problem. The proposed scheme was implemented in NS2 installed in Linux operating system, with a cognitive radio cognitive network (CRCN) patch. From the experimental results, we observed that the proposed OCS-AODV scheme outperformed CS-DSDV and ACO-AODV schemes. It obtained at least 3.87% packet delivery ratio and 2.56% and lower packets lost. The scheme enabled the mobile nodes to adjust accordingly to minimize energy consumption. If not busy, they switch to an idle state to save battery power.  
c 2021 11 June 2021
# Corrosion and Scaling in Geothermal Heat Exchangers
## 3 Environmentally Induced Degradation in Geothermal Plants  
Geothermal Fluid Composition  
Conventional geothermal fluids exhibit significant potential for corrosion due to the presence of various species or moeities. These species include hydrogen or hydronium ions (resulting in low pH levels), carbon dioxide (CO\({}_{2}\)), hydrogen sulphide (H\({}_{2}\)S), ammonia (NH\({}_{3}\)), chloride ions (Cl\({}^{-}\)), and sulfate ions (SO\({}_{4}\)\({}^{2-}\)), to name a few. Geothermal fluids generally maintain a low oxygen content due to the nature of their source underground, which is largely anoxic. The precise composition of geothermal fluids is location-dependent, often varying significantly due to the local geology. While rough estimates can be made based on geological factors such as sedimentary or volcanic origins, the exact composition is typically determined via drilling. Chloride concentrations tend to be lower in basaltic rock reservoirs [17] and higher in sedimentary rock formations [18]. Volcanic zones tend to promote the acidification of geothermal fluids, which may lead to enrichment in metals like iron, aluminum, calcium, magnesium, sodium, manganese, uranium, and thorium through the corrosion and dissolution of surrounding rocks [19].  
The degradation of geothermal systems and the selection of materials depend heavily on thermodynamic parameters (temperature, pressure, phase, etc.) and fluid composition. To facilitate the selection process, efforts have been made to classify geothermal brines based on their composition, thermodynamic parameters, and phase. Common classification indices include total key species (TKS) or total dissolved solids (TDS), which sums the concentrations or combines the content of relevant chemical species in parts per million (ppm). [PERSON] et al. [20] proposed a geothermal brine classification, building upon the work of [PERSON] et al. [21] and [PERSON] et al. [22], as outlined in Table 1. This classification considers the TKS index, salinity, acidity, and the phase of geothermal fluids.
## 4 ATT-BiLSTM Anomaly Data Processing
### ATT-BiLSTM Model Setup and Training
Number of Iterations: The selection of the number of iterations is contingent upon factors such as the size of the training set, the complexity of the model, and the constraints imposed by computational resources. An excess of iterations may result in overfitting, whereas an insufficient number of iterations may hinder the model's learning process. Through a series of experiments and adjustments, the optimal number of iterations was determined to be three. 2. Batch Size: The selection of batch size is constrained by computational resources. A larger batch size may necessitate more memory but can enhance the training speed. A smaller batch size may yield more precise gradient estimation but may result in a noisier training process. Following numerous experiments and adjustments, the batch size is set to 128. 3. Loss Curve: The loss curve is a graphical representation of the loss function, the objective of which is to minimize the squared difference between the predicted value and the actual value. This enables the model to better fit the training data. During the training process, the model parameters are adjusted through the back propagation algorithm according to the loss function. This process enables the gradual improvement of the model's prediction accuracy and ultimately leads to the generation of the model's loss curve. It is evident that the model's fit to the training data is optimized when the loss value is minimal. As illustrated in Figure 7, the loss curve demonstrates the relationship between the number of training batches (horizontal axis) and the model's loss (vertical axis). At the onset of the training process, the loss value is typically substantial (approximately 1.2), and as the training progresses, the loss value undergoes a rapid decrease, ultimately converging at approximately 0.02. This convergence signifies that the model has effectively learned the pattern of the data, exhibits a good fit, and demonstrates a rapid enhancement in performance on the training set. Furthermore, the model loss stabilizes at approximately 0.02, suggesting that there is an absence of overfitting. Furthermore, the loss function exhibits a rapid decrease, with a low fluctuation rate that remains within the range of 0.01. This observation suggests that the model has been set at an appropriate learning rate.
# Impact of Atmospheric Pressure Microwave Plasma Treatment on Quality of Selected Spices
###### Abstract  
Current industry needs are related to higher awareness of modern consumers. These consumers are looking for products in which properties such as bioactive compounds are preserved as much as possible. Plasma treatment is one of the most promising nonthermal technologies that can decontaminate food and keep its original properties. Therefore, the aim of this work was to examine the usefulness of atmospheric pressure argon microwave plasma on decontamination of black pepper seeds, allspice berries and juniper berries. The samples were irradiated by plasma for 15-60 s and their physicochemical (dry matter content, water activity, color, total phenolic content, antioxidant activity, piperine content in black pepper seeds) and microbial (bacteria and molds count) quality was evaluated afterwards. Results demonstrated that plasma irradiation for 15 s was sufficient for partial inactivation of _A. niger_ but less effective against the Gram-positive bacterium _B. subtilis_, regardless of the raw material. At the same time, plasma treatment reduced water activity, which can positively affect further storage of spices. Properly selected plasma parameters may also enhance extractability of phenolics or piperine (from black pepper seeds) and improve antioxidant activity with not very great, but visible, color changes.  
cold plasma; spices; juniper; allspices; pepper; decontamination; quality Article
# FFESSD: An Accurate and Efficient Single-Shot Detector for Target Detection
## 2 Related Work  
In recent years, various methods using CNN have been widely used in target detection, and many successful cases have been achieved in computer vision tasks such as image classification [14; 17; 18; 19], target detection [5; 6; 7; 8; 9; 10; 11; 12; 13], semantic segmentation [20; 21], and instance segmentation [22; 23; 24; 25]. Among them, target detection is a basic research which has been widely used in various fields, and the academics have proposed various strategies to improve the performance of target detection. In the earlier works based on CNN, such as R-CNN [5], Fast R-CNN [6], Faster R-CNN [7], and SPP-NET [8], there was a tremendous improvement in performance compared with the traditional target detection techniques. Specifically, these approaches usually use the separate algorithms such as Selective Searches [26] and Edge Boxes [27], to generate a set of region proposals as samples, and then classify the samples based on a CNN, so these methods are also called the two-stage detectors. Thus, although these methods improve the detection accuracy, they are too slow to meet the requirements of real-time detection.  
In order to solve the problem of slow detection speed, the first single-stage detector is proposed. Named YOLO (You Only Look Once), it divides input images into multiple grids and performs localization and classification for multiple targets on each grid [9]. However, YOLO only uses the top layer of the feature maps to detect targets of different sizes and there is a lack of low-level high-resolution information, which results in somewhat inaccurate detection of small targets.  
To maintain real-time speeds with a higher precision accuracy, [PERSON] et al. [10] proposed the SSD which not only uses the top layer of the feature maps, but also uses low-level feature maps with high-resolution information to detect small targets. Recently, various methods have attempted to improve the accuracy of SSD, especially for small targets. Deconvolutional Single Shot Detector(DSSD) [11] could obtain higher accuracy by applying deconvolution layers to the feature pyramid and using ResNet-101 [17] instead of VGG16 [14]. At the expense of speed, Rainbow-SSD (R-SSD) [15] proposed a method to make explicit the relationship between different layers by using pooling and deconvolution and it achieved higher accuracy than SSD.  
According to the above analysis, we propose a new single-shot network model to achieve the fusion of contextual information of each layer. In addition, we designed the shallow feature enhancement module (SFE) and deep feature enhancement module (DFE) to enhance the extraction of semantic information of network features. The proposed FFESSD can maintain a high computational efficiency without sacrificing precision.
# Research on Replacing Numerical Simulation of Mooring System with Machine Learning Methods
## 3 Machine Learning Method
### Model Evaluation Criteria  
The experiment mainly uses two criteria to measure the effectiveness of the trained model, namely \(R^{2}\) score and relative error. \(R^{2}\) score is also called the coefficient of determination \(R^{2}\), which is used to determine the fitness of the model as follows:  
\[R^{2}=1-\frac{\sum_{i=1}^{n_{samples}}(y_{i}-\hat{y_{i}}
# A Point Crack Source Location Method without Velocity Information in Anisotropic Plates
## 4 Results
### Robustness on TOA Thresholds  
In this section, we provide the experimental results of the numerical AE data to confirm the SDMM method's results for the TOA thresholds. Numerical AE data were obtained by solving the elastic equations. Detailed descriptions can be found in Section 3.2.  
The estimated positions with the SDMM applied are shown in Figure 7a-d. In the figure, the red dot is the source location, and the blue cross (x) indicates the predicted crack location. As can be seen in the figure, the estimated location, marked by a blue cross (x), was always near the crack location, denoted by a red dot. For quantitative analysis, the relative error (distance error for the diameter of a circle with the same area as the experimental area) was calculated as in Equation (9). The relative error due to the TOA value can be seen in Figure 7e, and it can be seen that the relative error was always less than 10% regardless of the TOA value.
## 1 Introduction
### Related Work
The comparison is based on the type of cryptographic algorithms used, the type of cryptographic technique and the evaluation and verification methods. We do not include the complexity of the protocols listed in the table, since these analyses are not always given by the authors of the protocols, and often, it is not feasible to perform a fair analysis of the protocol complexity only based on the presentation given in the papers. As the table shows, and according to our knowledge, none of the existing revocation and key renewal protocols were verified using an automatic formal verification tool. In addition, most of the results in the state of the art are obtained through simulations or complexity estimation when evaluating the cost of the cryptographic scheme. Most of the existing schemes do not specify the cryptographic algorithms of encryption/decryption, leaving the choice of these algorithms to the application. We provide automatic formal verification and real testbed implementation to further support our proposal. We used standard algorithms and used both symmetric and asymmetric cryptography.
## 2 Study Area
Based on the existing data of the two hydrological stations, the missing sediment transport data are supplemented according to the relationship between sediment transport rate and flow rate [40]. Monitoring data (see Figure 2) from the Xinqiman Hydrological Station and Yingbaza Station on the main stem of the Tarim River (1993-2024) indicate that annual runoff and sediment discharge at both stations have exhibited fluctuating upward trends and are significantly positively correlated. At the Xinqiman Hydrological Station, annual runoff and sediment transport fluctuations intensified after 2010, reaching their peak in 2022 (runoff of 8.562 \(\times\) 10\({}^{9}\) m\({}^{3}\) and sediment transport of 3.7074 \(\times\) 10\({}^{7}\) tons), with the lowest values recorded in 2009 (8.37 \(\times\) 10\({}^{8}\) m\({}^{3}\) and 3.624 \(\times\) 10\({}^{6}\) tons). At the Yingbaza Hydrological Station, fluctuations in annual runoff and sediment transport were even more pronounced, with a peak in 2017 (5.947 \(\times\) 10\({}^{9}\) m\({}^{3}\) and 2.60258 \(\times\) 10\({}^{7}\) tons) and the lowest values in 2009 (1.83 \(\times\) 10\({}^{8}\) m\({}^{3}\) and 2.232 \(\times\) 10\({}^{5}\) tons). Both annual runoff and annual sediment transport exhibited a positive correlation.
# PSDFH: A Phase-Space-Based Depth from Hologram Extraction Method
## 3 Proposed Methodology
### Step 2: Extraction of Phase Space Masks  
In this step, a binary mask representing the support of the hologram phase space representation is computed through binarization. To do so, the computed phase space representation \(L\) is fed to a _convolution neural network_ (CNN) denoted by \(G\) based on the U-Net architecture [13] to binarize the phase space representation and estimate phase space masks, with one \(M\times N\) binary mask per color channel. The network is supervised using ground truth masks computed from the scene point cloud \(\mathcal{P}\) which produces the hologram \(\mathcal{H}\) using the analytical formula proposed in [14]. The function \(\Omega\) mapping the 2D point cloud \(\mathcal{P}\) onto phase space can be defined as  
\[\Omega(\mathcal{P})=\bigcup_{n_{1}=0}^{N-1}\bigcup_{p=(y_{1},y_{2})\in \mathcal{P}}\Lambda_{p}(n_{1}) \tag{9}\]  
where  
\[\Lambda_{p}(n_{1})=\bigg{\  
\[\min_{\theta}\mathcal{L}=BCE(\hat{M}_{b},M_{b}),\quad M_{b},\hat{M}_{b}\in\mathbb{R }^{N\times M} \tag{14}\]  
where \((\mathbb{R}e\{L\},\mathbb{I}m\{L\})\) is the concatenation of the real and imaginary parts of the hologram phase space representation \(L\) defined in Equation (7), \(\theta\) are the parameters of the network, and \(BCE\) is the binary cross-entropy loss.  
The use of a CNN model was preferred to thresholding the values of the matrix \(L\) for two main reasons. First, the threshold value varies from one hologram to another; it is therefore necessary to compute an optimal threshold value for each hologram. Second, the resulting binary mask from thresholding is coarse and requires an additional processing step using filters with sizes adapted to the level of degradation. This is an additional operation that requires significant computation time and often results in a degradation of the initial outline of the object. Using a CNN model ensures fast and accurate phase space segmentation without the need for manual editing or an additional post-processing step.
# Deep Learning for Neuromuscular Control of Vocal Source for Voice Production
## 2 Materials and Methods
### LeTalker
#### 2.2.4 Fiber Stress in the Vocal Fold Layers  
The spring constants in the three-mass model can be augmented to account for anisotropic tension in the fibers present in the vocal fold layers. The body-cover representation of the vocal folds was divided into three layers (muocosa, vocal ligament, and thyroarytenoid muscle) to compute the fiber stress. The equations to compute the fiber stress in the three vocal fold layers were detailed in [31]. The passive stress in the three vocal fold layers was computed using the following equation  
\[\sigma_{p}=\left\{\begin{aligned} 0&\quad\quad for \;\epsilon<\epsilon_{1},\\ -\frac{\sigma_{0}}{\epsilon_{1}}(\epsilon-\epsilon_{1})& \quad\quad for\;\epsilon_{1}\leq\epsilon\leq\epsilon_{2},\\ -\frac{\sigma_{0}}{\epsilon_{1}}(\epsilon-\epsilon_{1})+\sigma_{ 0}\Big{(}e^{C(\epsilon-\epsilon_{2})}-C(\epsilon-\epsilon_{2})-1\Big{)}& \quad\quad for\;\epsilon>\epsilon_{2}.\end{aligned}\right. \tag{4}\]  
The mucosa and ligament layers have only passive stress, whereas the TA muscle has both active and passive stress. Therefore, the fiber stress, \(\sigma_{mus}\) in the TA muscle layer was given as  
\[\sigma_{mus}=a_{TA}\sigma_{amb}\text{max}\Big{(}0,1-b(\epsilon-\epsilon_{m})^ {2}\Big{)}+\sigma_{p} \tag{5}\]  
where \(\epsilon\) is the vocal fold strain, \(\epsilon_{1}\) is the strain where the linear portion of the passive stress goes to zero, \(\epsilon_{2}\) is the strain where the exponential part begins, \(\sigma_{0}\) is the stress when \(\epsilon=0\), \(\sigma_{2}\) is a scale factor for the exponential portion, \(a_{TA}\) is the thyroarytenoid muscle activation level between 0 and 1, \(\sigma_{amb}=105\) kPa is the maximum active stress in the TA muscle,\(e_{m}=0.4\) is the optimum sarcomere strain, and \(b=1.07\) is the empirically determined constant for each muscle. Table 1 provides the parameter values used in the current study to compute the fiber stress in the three vocal fold layers. Except where suggested, the parameters in Table 1 do not have any units.