esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Quantitative Analysis of Support Effect of the Tunnel Face  
### Support Optimization System of the Tunnel Face  
Based on the above discrimination method of tunnel face stability, the discrimination of tunnel face stability and the support optimization system were developed by using the appdesigner function in Matlab software. This support optimization system takes the theoretical calculation in Section 2 as the optimization algorithm to realize the initial stability and stability coefficient under pipe roofing support, tunnel face bolting support, tunnel face grouting support and reserved core soil. Then to judge the stability of tunnel face and suitability of supporting measures. The operation interface for solving support stress and stability coefficient is shown in Figure 5. Its main functions include inputting the tunnel geometric parameters and soil parameters and then calculating the stability coefficient of the tunnel face. The advantage of the support optimization system is that the stability coefficient of the tunnel face under the four support measures can be calculated simultaneously, so as to achieve a comprehensive evaluation of tunnel face stability.  
The operation flow chart of the support optimization system is shown in Figure 6. The contribution of different reinforcement measures to tunnel stability is further evaluated by combining them with the geometric parameters [36] of the shallow buried tunnel. Tunnel parameters: the buried depth (\(H\)) is 10 m, the tunnel height (\(D_{0}\)) is 10 m, and the length of unsupported section (\(L_{e}\)) is 1 m. Rock mass parameters: the cohesion (\(c\)), internal friction angle (\(\varphi\)) and porosity (\(n\)) are 7.50 kPa, 28.60\({}^{\circ}\) and 40%, respectively. The density (\(\gamma\)) and lateral stress coefficient (\(\lambda\)) of soil are 18 kN/m\({}^{3}\) and 0.5217, respectively.
## 2 Materials and Methods
### Data Collection and Preprocessing  
We tested our algorithm with the data recorded from 7 healthy volunteers. All participants gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the University of Pisa (0102206/2019).  
We performed two experiments, the first dedicated to the extraction of sleep posture and the second oriented to respiratory activity.  
In the first experiment, we asked the seven subjects to change their body position over the mattress in 4 predefined positions: supine position, lying on the left side, lying on the right side, and prone position. The data related to different positions were manually labeled during the data collection. For each participant, a minimum number of 15 repetitions for each predefined position was collected. During the sample collection, participants were asked to be silent and to maintain the position. In addition, also data about movements (i.e., transitions between static postures) and the absence of the subject over the smart bed were collected and labeled.  
Overall, we gained 2076 samples during tests carried out on the different positions, movements, and absence of subjects. The numbers of samples for each position and condition were 118 \"not on bed\", 179 \"supine position\", 166 \"lying on the left side\", 165 \"lying on the right side\", 178 \"prone position\", and 1270 \"movement\".  
In the second experiment, we collected data while the participants were over the sensing mattress in the supine position. We collected simultaneously the PML signals and a reference breathing rate signal obtained from a nasal cannula equipped with a thermistor. Both signals were acquired with a sampling frequency of 8 Hz. The use of thermistor and nasal cannula is a common method to extract breathing activity in polysomnography, as reported in [30; 31; 32]. During data collection, participants were asked to be silent and steady for at least 3 min. The participants were also requested to voluntarily modulate their respiratory activity.
# Grid Anchor Lane Detection Based on Attribute Correlation
## 1 Introduction
SCNN [13] achieves global feature  
Figure 1: Four difficult scenarios for lane detection: (**a**) scenarios with lane lines affected by lighting interference; (**b**) scenarios involving dashed lane lines; (**c**) false detection results (green lines) for no lane line scenarios in intersections; (**d**) high-curvature lane lines (blue lines) in curved road scenarios. interaction by applying convolutions within the feature layer in different directions. In some anchor-based detection methods, global features are aggregated through fully connected layers; however, this approach neglects the information from local positional features. To effectively integrate global features and local detailed features, this paper improves the feature pyramid network (FPN) [20] by incorporating global information refined using a multi-layer perceptron (MLP) and proposing a novel convolutional reordering upsampling method. Furthermore, in previous studies, the relational information between different anchors on the same lane line has yet to be fully exploited. To address this issue, this paper introduces an attribute correlation loss function. The loss function significantly enhances detection accuracy by incorporating feature association between anchors. The proposed method was evaluated on two widely used benchmark datasets, CU-Lane [13] and Tusimple [21]. Experimental results demonstrate that the proposed model achieves superior detection performance on both datasets. The main contributions of this study can be summarized as follows:  
1. Grid Anchor Design: A novel representation for lane lines, termed \"grid anchor\", was proposed. This design has the same resolution as the feature map and can be directly computed through convolution. Furthermore, it can accurately capture and describe the interrelations between lane lines by incorporating attribute information. 2. FPN Improvements: The FPN was optimized by integrating an MLP to construct a long-range feature association and proposing a convolutional reordering upsampling method. The improved FPN enhances the interaction between global and local features. 3. Attribute Correlation Loss: An attribute correlation loss function was designed to establish feature associations between anchors. This ensures that the loss value is not only dependent on the predictions of individual anchors but also influenced by the predictions of neighboring anchors.
## 3 Simulations for the Model Verification
### Three-Dimensional Model for Verification  
The two-dimensional model's calculation only considers the structure's deformation in the stress plane, ignoring the out-of-plane deformation, finite stiffness constraints of boundaries, and the interaction of walls, beams, and wall columns. The impact of the above factors on the accuracy of the structural calculation needs to be further discussed. Therefore, the rationality and accuracy of the two-dimensional model will be verified through three-dimensional modeling calculations.  
The three-dimensional global model is built by the finite element software Midas gen [31]. The beam element is used to simulate frame beams and wall columns, and the shell element simulates the wall in the model. The structure can deform both in and out of plane, and the interaction of walls, beams, and columns is simulated through common node coupling. The essential parameters of this model are the same as those in Section 2.1. In addition, the impact of envelopes on the main construction of the metro station is ignored. The overall deformation of the three-dimensional simulation and the bending moment of the wall column is shown in Figures 14 and 15, respectively.  
In addition, the main reason that the analytical solution is slightly less than the three-dimensional calculation result is that the limited rotational restriction of side walls at both ends of the frame beam and boundary columns cannot meet the hypothesis of absolute rigid connection. Therefore, the stiffness of the elastic support in the analytical model is slightly larger than that in the actual frame beam, and the corresponding bending moment at the bottom of the column is marginally smaller than that in reality.  
The three-dimensional model that takes into account solid structures can simulate actual projects in a better way. The consistency between the calculation results of the two-dimensional model and the three-dimensional model further validates the basic assumptions of the two-dimensional calculation. The two-dimensional model can be used for efficient calculation during the preliminary structural selection analysis. Using three-dimensional models is more accurate when conducting quantitative structural analysis.
## 2 System Modeling
### DC/DC Buck Converter Model  
The DC/DC buck converter reduces the voltage (input voltage, V\({}_{\text{S}}\)) value it receives from the hydrogen fuel cell to the required voltage (output voltage) value for the motor with the help of an integral (I) controller. The output voltage V\({}_{0}\) is calculated according to the current and voltage value passing over the inductor when the switch in the converter circuit is open or closed. The comparison of the time between the switch on and off is carried out with the duty cycle (DT). By setting the DT between 0 and 1, the on and off times of the switch are adjusted. Depending on this value, the output voltage is obtained using the following equations, depending on the input voltage of the system [46; 47].  
\[\left(\Delta\text{i}_{\text{L}}\right)_{\text{CLOSED}}=\left(\frac{\text{V}_{ \text{S}}-\text{V}_{0}}{\text{L}}\right)\text{DT} \tag{14}\]  
\[\left(\Delta\text{i}_{\text{L}}\right)_{\text{OPEN}}=-\left(\frac{\text{V}_{ 0}}{\text{L}}\right)\left(1-\text{DT}\right) \tag{15}\]  
\[\left(\Delta\text{i}_{\text{L}}\right)_{\text{CLOSED}}+\left(\Delta\text{i}_{ \text{L}}\right)_{\text{OPEN}}=0 \tag{16}\]  
The DC/DC buck converter internal structure consisting of an RLC circuit, and the MOSFET creates the output voltage over the resistor inside by controlling the PWM signal that triggers the MOSFET switching by an integral controller. A positive voltage value from the fuel cell is constantly received by the DC/DC converter input. However, while the AGV follows the determined route, the direction of rotation of the motors changes, and accordingly it is necessary to send positive and negative voltages to the motors. In cases where the positive high voltage at the output of the fuel cell is required, depending on the voltage needed to turn it into negative, the voltage value at the DC/DC converter output is inverted, and its negative value can be obtained. A diagram of the obtained DC/DC converter system is given in detail in Figure 4.  
In addition, 10,000 Hz was determined as the switching frequency (f\({}_{\text{s}}\)) for the DC/DC converter. In line with the information in the literature, this value is good for a low-power system.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value (Unit)** \\ \hline Motor viscous friction (b) & 0.250 (kg\(\cdot\)m\({}^{2}\)/s) \\ Motor constant (\(\mathbf{K_{i}}\)) & 0.002 (Nm/A) \\ Moment of inertia of the rotor (J) & 0.15 (kg\(\cdot\)m\({}^{2}\)) \\ Inductance (\(\mathbf{L_{j}}\)) & 0.12 (H) \\ Resistance (\(\mathbf{R_{i}}\)) & 0.5 (H) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The values of the DC motor’s parameters.  
Figure 4: Block diagram of DC/DC buck converter jth (j = 1, 2, 3, 4).
## 2 Literature Review
[PERSON] and [PERSON] [35] explored the relationship between surface finish and tool wear and found the Pearson correlation coefficient between surface finish and tool condition to be significant to establish a strong correlation. The study used a random forest-based fault estimation model to determine the relation between surface finish and tool condition. [PERSON] and [PERSON] [36], developed a semi-supervised model to predict the surface finish using vibration signals; Fourier transformation was used to transform signals to frequency space, and only the relevant frequency ranges were considered for the study. [PERSON] et al. [15] developed a two-stage system that aimed to determine the type of wear in the first stage and tried to quantify the wear of the insert in a milling cutter. The system used CNN to determine the type of wear, and the wear value was obtained using the relation between image pixel value and actual value and width of the minimum circumscribed rectangle. [PERSON] et al. [37] used surface texture descriptions to determine tool life using the grey level co-occurrence matrix; the images of the resulting surface finish were captured, and the tool wear was measured using a microscope. [PERSON] et al. [25] used a computer vision system to determine the usefulness of milling cutters. The system used a support vector machine methodology to classify the wear patterns. The system identified the state of the tool with about 90 percent accuracy. The studies mentioned above correlate tool condition with the specific quality and design requirements like surface finish. As discussed in Section 1, there are a variety of quality and design requirements that are defined by TCP. These different TCPs form the ultimate definition of TCM; considering this, a TCM that is flexible enough to accommodate different TCPs is the need of the hour. Most of the studies are also limited by the materials and tool geometries they have used, and changing any one of the factors means the findings of the studies cannot be used. For the TCM to be autonomous and independent, it must be capable of working with different materials, tool geometries, and tool coating grades. The requirements of flexibility to work with different TCPs and the ability to generalize the system for different working materials, tool geometries, and tool coating grades, form the basis for the development of the proposed system.
## 4 Experiments Results and Analysis  
### Experimental Data, Multiple Features, and Evaluation Metrics  
#### 4.1.1 Data Collection and Labeling  
This paper selects 200 shipping enterprises in the Yangtze River Delta region, whose business scope involves domestic coastal and middle and lower reaches of Yangtze River general cargo shipping, foreign trade container feeder liner shipping, cargo transportation, domestic ship agency, domestic waterway freight forwarding, etc. as the research object. The 2612 textual data in the field of shipping enterprise credit crawled from the national and local maritime bureaus ([[https://www.msa.gov.cn/](https://www.msa.gov.cn/)]([https://www.msa.gov.cn/](https://www.msa.gov.cn/)), accessed on 1 March 2021), magistrates' websites ([[https://wenshu.court.gov.cn/](https://wenshu.court.gov.cn/)]([https://wenshu.court.gov.cn/](https://wenshu.court.gov.cn/)), accessed on 15 April 2021), official websites of shipping companies, and major news websites are used as the dataset for this experiment.  
The shipping company credit text contains some expressions, spaces, and bulleted items that are not useful to NER and should be deleted. In this paper, we first use regular expressions to complete the work of format conversion and data cleaning, removing meaningless contents such as spaces, emoticons, and bulleted items from the text and converting data from different sources into a uniform format. The commonly used meta characters in regular expressions and their descriptions are shown in Table 1.  
Before formally training the model, the textual data in the shipping enterprise credit domain are first annotated with entity types, and the commonly used annotation methods for the dataset are Beginning, Inside, Outside (BIO); Beginning, Inside, End, Outside, Single (BIOES); and Beginning, Middle, End, Single (BMES) [29]. With BIO, i.e., Beginning, Inside, Outside, the first token of an entity is labeled B-[type], tokens at other locations of the entity are labeled I-[type], and tokens not belonging to any entity are labeled O; With BIOES,i.e., Beginning, Inside, End, Outside, Single, End is used to identify the end of an entity, and Single is used to identify an entity that contains only one token. With BMES, i.e., Beginning, Middle, End, Single, S denotes a single word. This paper uses the BIO [30] format for annotation with the help of the YEDDA [31] tool. The entity types include eight types of entities: company, honor, ship, risk penalty, organization, person, certificate, and intellectual property, and the partial annotation results obtained are shown in Table 2, where B-X denotes the beginning of the entity word, I-X denotes the middle or end of the entity word, and O denotes other words than the entity, i.e., non-entity. The BIO annotation samples are shown in Table 3 below. In this experiment, the shipping enterprise credit dataset is divided into training set, validation set, and test set according to 6:2:2.
###### Abstract  
Beef bones contain a gelatin component that can be further extracted. This extraction process requires the right solvent to produce good yield and quality. Gelatin has multifunctional properties, namely biodegradable, biocompatible and non-toxic. This is because it is a natural ingredient that contains high amino acids. The most dominant amino acid content as a parameter for determining the quality of gelatin is proline, glycine, and hydroxyproline. The purpose of this study was to study the effect of the solvent used as a medium for soaking cow bones to be converted into gelatin. The solvent variations used include NaOH, HCl, H\({}_{2}\)SO\({}_{4}\), CH\({}_{3}\)COOH, and NaHCO\({}_{2}\). The concentration variations are 4, 5, 6, 7, 8 (%). This research method includes the preparation of cow bone samples, fat removal, mineral removal, soaking for 7 days, and extraction. The extraction process was carried out with variations times of 4 h and 6 h. The results of the study showed that the highest yield value was with 5% HCl solvent with 4 h extraction time of 26.5% with 8.6% water content, 0.9% ash content, pH 4.64, and viscosity 3.19 cP (\(p<0.05\)). A good isoelectric point is produced when using an acidic solvent, which is between 5.3-5.8. The cross-linking of gelatin with chitosan, glutaraldehyde, and glucose was successfully carried out with the FTIR absorption indicator at a wavelength of 3200 cm\({}^{-1}\), which indicates the presence of hydrogen bonds, 1022 cm\({}^{-1}\), which indicates the breakdown of aldehyde bonds in glutaraldehyde compounds into C-O bonds. According to the microbial test, when gelatin is combined with chitosan, there will be an increase in the microbial inhibition zone. This shows that the development of gelatin materials is very prospective and promising.  
beef bones; gelatin; solvents; biodegradable; biocompatible; mechanical properties Article
# Void Avoidance Opportunistic Routing Protocol for Underwater Wireless Sensor Networks
## 4 Simulation Results  
In this section, we evaluate and analyze the performance of the EEDOR-VA protocol for UWSNs and compare it with the original EEDOR and DBR protocols through simulation experiments. All results were performed via simulations conducted in MATLAB. We aimed to enhance the reliability of the network through increasing the packet delivery ratio, which is achieved through our novel forwarding set selection by bypassing the void and trapped nodes in the packet forwarding procedure. In addition, we kept the network connectivity while reducing node energy consumption by minimizing the packet retransmissions, which decreases the packet collisions. In this work, two different scenarios were simulated using the network topologies that exist in [15,19]. These two topologies can be classified as networks with a small number of nodes. Since a void area is more likely to exist in networks with a small number of nodes, we choose to evaluate the EEDOR-VA with this type of network. In fact, since EEDOR-VA works well with these network topologies, it is pertinent to state that it also works well when increasing the number of nodes due to the low probability of void areas.  
The metrics used for performance evaluation are elaborated as follows:  
1. Total Energy Consumption (\(E_{total}\)) denotes the total energy consumed in hop count discovery HCREQ and HCREP messages and data packets delivery, including the transmitting, receiving, and idling energy consumption of all nodes in the network. The total energy consumption is a cumulative summation that starts at 0. This can be calculated mathematically as: \[E_{total}=\sum_{i=1}^{rounds}\sum_{j=1}^{n}(E_{init}-E_{resd})\] (10) where _rounds_ is the number of simulation rounds, \(n\) is the number of underwater sensor nodes, \(E_{init}\) is the sensor node initial energy, and \(E_{resd}\) is the sensor node residual energy.
2. Mean Energy consumption per node (\(E_{Mam}\)) is defined as the average of the total energy consumption. Mathematically, \(E_{Mam}\) is presented as: \[E_{Mam}=E_{total}/n\] (11) where \(E_{total}\) is the total energy consumption calculated by Equation (10) and \(n\) is the number of deployed nodes.
3. Total number of transmissions (\(N_{trans}\)) denotes the total number of nodes that forward the data packet starting from the source node to reach one of the sinks on the surface. \(N_{trans}\) can be presented mathematically as follows: \[N_{trans}=\sum_{i=0}^{rounds}FN\] (12) where _rounds_ is the number of simulation rounds and \(FN\) is the number of transmitting nodes in one round.
4. Packet Delivery Ratio (\(PDR\)) is defined as the ratio of the total number of distinctive packets received successfully at any of the sinks (\(P_{success}\)) to the total number of generated packets (\(P_{sent}\)). We calculate \(PDR\) mathematically as: \[PDR=P_{success}/P_{sent}\] (13)
## 2 Model Construction and Algorithm Enhancement
### Validation and Evaluation
#### 2.3.1 Logistic Regression Model  
The logistic regression model [25] is a classification model used when the dependent variable \(y\) is binary, taking values of 0 (non-occurrence) or 1 (occurrence). It assumes a linear relationship between the independent variables \(x_{1},x_{2},\ldots,x_{n}\) and the log-odds of the outcome. The model predicts the probability of the event occurring using the logistic (sigmoid) function [26].  
The formula for the logistic regression model is as follows:  
\[P(y=1)=\frac{1}{1+e^{-(\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2}+\ldots+\beta_{n }x_{n})}} \tag{1}\]  
Here, \(\beta_{0},\beta_{1},\ldots,\beta_{n}\) are the coefficients learned through maximum likelihood estimation (MLE), ensuring predicted probabilities align closely with actual outcomes. Logistic regression is simple, efficient, and interpretable, making it well-suited for binary classification. However, it may struggle with complex or highly nonlinear data patterns.