esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A freezing method is usually employed in the construction of metro links. Unconfined compressive strength (UCS) is a pivotal mechanical parameter in freezing design. Due to the limitations of indoor experiments and the complexity of influencing factors, the applicability of empirical strength formulas is poor. This study predicts the strength of frozen soil with different particle size distributions based on the highly integrated XGBoost algorithm. Compared with other empirical formula methods, the accuracy is high. Through the analysis of Pearson's correlation coefficient results, further analysis is needed on the nonlinear correlation between the temperature, the strain rate, and the unconfined compressive strength of frozen soil. The results indicated a strong negative correlation between temperature and unconfined compressive strength; the strength initially increased at a faster rate, slowed down during the intermediate phase, and again increased at a faster rate toward the end. There was a positive correlation between the strain rate and the unconfined compressive strength, with the strength exhibiting varying sensitivities to different sizes of strain rates. When the strain rate was relatively small, the strength increased slightly; as the strain rate increased, the strength increased more significantly. Different soils showed similar trends, but differences in the particle size distribution resulted in variations in the final strength. This study can provide a scientific basis for predicting the strength of soil bodies in the freeze-thaw construction of subway connection tunnels.  
freezing method construction; unconfined compressive strength; machine learning; temperature; strain rate +
Footnote †: journal: Applied sciences
## 3 Experiments
### Experiments on AIRS Dataset
#### 3.4.2 U-Net on AIRS  
As shown in Table 3, as expected, a random initialization strategy was able to reduce the FLOPs more strongly than any other initialization strategy, whereas the drop in the Val. Acc. was still negligible. Chaotic initialization showed an almost similar performance with a drop of 0.06% in accuracy and an 81.46% reduction in the FLOPs, which is 2.17% less than with a random initialization strategy. LeCun initialization showed a similar performance to that of He initialization with a little less reduction of the FLOPs. When compressing U-Net with a chaotic initialization strategy, the FLOPs were reduced by 81.46%, which is slightly less than with a random initialization strategy and an equal accuracy drop. Furthermore, using Xavier initialization, our approach reduced the FLOPs by 64.18% and the number of parameters by 73.20% with even a 0.04% improvement in the accuracy. However, the reduction in the FLOPs was 20.8% less than with a random initialization strategy.  
Based on the performance of TeranusNet and U-Net on a large segmentation dataset, we can say that our pruning approach can achieve promising results under all initialization strategies and can obtain a compact network model.
# Geometric Accuracy Evaluation Method for Subway Stations Based on 3D Laser Scanning
## 4 Case Study
### Construction Site Data Collection and Preprocessing  
According to the technical requirements of the specification in [58], we used a Trimble TX8 scanner to perform 3D laser scanning of the Hongqi Road construction site. A checkerboard target with height flush was used during the measurement. A measuring station was set up every 25-30 m, making a total of 85 stations. The scanner has a scanning speed of 1 million points per second and takes approximately 3 min per scan. A total station was also used to conduct control measurements and establish a survey control network. The point cloud was automatically registered using Trimble RealWork v11.2 software, and all points containing x, y, and z coordinate information were exported as input data. Figure 9 shows a panoramic view of the subway station construction site and the acquisition process.  
Figure 8: Location of Hongqi Road station.  
The 3D scene of the subway station after point cloud registration is shown in Figure 10a. The measured data include a total of \(1.10\times 10^{8}\) points, and the average density of the point cloud is 5316 pts/m\({}^{2}\), which includes richly detailed characteristics and clearly shows the internal components. The data show that the density of the point cloud data of the subway station is high enough to accurately represent the geometric information of each facade, slab, and structural column in the subway station. Figure 10b shows the BIM model of the subway station. The BIM is created in the design stage, and the interior of the model includes some typical structures, i.e., facades, slabs (top and bottom slabs), and structural columns.  
Figure 9: (a) Subway station construction site; (b) data collection process.
# Utilizing Trusted Lightweight Ciphers To Support Electronic-Commerce Transaction Cryptography
## 4 Proposed Protocol Methodology for E-Commerce
### Buying and Selling Requests and Invoices  
We implement encryption operations based on the AES algorithm for a set of encryption requests, and there are forms of requests that buy and sell requests and invoices. Figure 8 shows types of request forms. The request information is encrypted, whether it is a request to buy or sell or pay bills, based on the request procedure. Requests forms used in this research are real forms for e-commerce applications, which are forms taken from free databases [38] on the Internet and do not contain personal information or network. In order to complete the encryption process and decryption for the purchase requests to sell and invoice properly, we added some personal information and unreal request data for virtual users. We encrypt a request received from the customer or buyer based on the AES method and then sent it to the TAS. Here, information is encrypted and difficult to hack (if the encryption is strong with high key randomness) by legitimate network parties, and personal information is protected for both the client and the server.
# A Comprehensive Survey on Knowledge-Defined Networking
## 6 Data Plane
### Transmission Media
#### 6.3.3 Wireless Media  
Data transmission through electromagnetic waves without the need for physical wires is referred to as wireless transmission. Data can be conveyed via electromagnetic waves with a range of frequency ranges in various kinds of network configurations. For instance, Wi-Fi uses radio waves having a frequency between 2.4 GHz and 5 GHz to provide wireless internet access [435]. Frequency ranges from 700 MHz to 50 GHz are utilized in cellular networks to link mobile devices to the infrastructure [436]. Wireless media enable user equipment mobility without being constrained by wires, and, because of their great flexibility and scalability, they are more ideal for deployment in KDNs. Cellular networks are less suited to high-speed data transfer or the transfer of a huge amount of data since they have less capacity than copper lines. Furthermore, compared to single-mode optical fibers, wireless media might have a shorter communication range, inferior security due to the ease with which attackers can intercept communications, and significant electromagnetic interference from other devices [437].  
Table 8 summarizes the comparison among the main transmission media used in knowledge-defined networks.
# Querying and Extracting Timeline Information from Road Traffic Sensor Data
## 5 Timeline Query Index and Analytical Query Processing
### TQ-index Construction
#### 5.2.1 Location Index Construction  
The first phase is to construct a graph structure in \(LocationIndex\)\(LI\) to support various types of road networks. The \(LocationIndex\)\(LI\) maintains a road network as a spatial part of the collected traffic sensor data. In our QET system, \(LI\) consists of a HASH INDEX and a ROAD NETWORK GRAPH. For each road link, we create a vertex into ROAD NETWORK GRAPH of \(LI\). Then, we insert a new  
Figure 10: Road network.  
element to HASH INDEX with _lid_ as the key including a pointer to the vertex in ROAD NETWORK GRAPH. Each vertex of ROAD NETWORK GRAPH has two pointers that connect to the next vertex and the previous vertex, which represent a link of a graph structure. By accessing each element in HASH INDEX and following the pointers of vertex in ROAD NETWORK GRAPH, we can quickly identify adjacent road segments. Algorithm 1 outlines the steps in constructing _LocationIndex LI_.  
```
Input: TO-index \(tqi\), Node List \(N\), Road Link List \(L\) Output:
1 procedureLocationIndexConstruction(\(tqi\), \(N\), \(L\)) // Initialize Node-Link relationship index (H) \(H\leftarrow\) new Hash(key:string, value:\(<\) InLinks List, OutLinks List \(>\));
2 foreach nid in \(N\)do
3\(H.\)Add(\(mid\), \(<\) new List(\(),new List()\) \(>\));
4 endforeach// Update Node-Link relationship index
5 foreach link in \(L\)do
6\(n_{from}\gets H.\)Get(\(link.n_{from}\));
7\(n_{from}.\)OutLinksAdd(\(link.\)lid);
8\(n_{to}\gets H.\)Get(\(link.n_{to}\));
9\(n_{to}.\)InLinksAdd(\(link.\)lid);
10
11 endforeach// Construct LI based on Node-Link relationship index
12 foreach link in \(L\)do
13\(l_{from}\gets tqi.\)LocationIndex.GetOrInsert(\(link.\)lid);
14\(n\gets H.\)Get(\(link.n_{to}\));
15 foreachlid\(l_{to}\) in \(n.\)OutLinksdo
16\(l_{to}\)\(l_{to}\)\(l_{to}\)LocationIndex.GetOrInsert(\(lid_{to}\));
17\(l_{from}.\)AddNext(\(l_{to}\));
18
19 endforeach
20 endforeach
```  
**Algorithm 1**Location Index Construction  
**Example 6**.: _Figure 11 illustrates how LI is constructed from a road network in the top-left part of the figure. The road network consists of five nodes \(n_{1},n_{2},n_{3},n_{4}\), and \(n_{5}\), and four links such as \(L_{1},L_{2},L_{3}\), and \(L_{4}\). As explained in the previous subsection, we used the modified road network as shown in the bottom-left part of the figure. The node of the QET graph structure represents four road segments \(L_{1},L_{2},L_{3}\), and \(L_{4}\), and there are four elements in HASH INDEX. Element \(L_{1}\) points to the vertex \(L_{1}\) in ROAD NETWORK GRAPH. The vertex \(L_{1}\) has a next pointer that connects to \(L_{2}\), and vertex \(L_{2}\) has the previous pointer connected to vertex \(L_{1}\). A next pointer is represented as a solid black circle, whereas a previous pointer is represented as an empty circle. Because a road link could be connected to more than one road link, it might have multiple points, such as \(L_{1}\)._  
Figure 11: Location index.
## 3 Experiment Results and Analysis
### Result and Analysis
#### 3.2.2 Performance Comparison of Different Feature Extraction Networks  
Table 3 showed the experimental results of different networks for different visibility ranges. For the reason that ResNet_50 and DenseNet networks were more sensitive to image attenuation and could extract valid features from images at different levels, it could increase the network's extraction rate of valid features and thus could be more sensitive to acquiring valid features for visibility estimation regression. ResNet_50 network was recommended for image feature extraction, especially in the low visibility range, as it was more stable. ResNet_50 network had greater stability and robustness in other ranges. This was due to the fact that more low-visibility image samples were included in the image database, and low-visibility images contain more sensitive image structure and detail.  
Static objects observed in different visibility ranges can be located by performing the gray level averaging for images with different visibility ranges. While gray averaging can filter out the moving objects and objects outside the visibility range will not be appeared on image photo. We can detect observable objects for different visibility ranges by using the grey averaging and the threshold segmentation method [32]. In order to facilitates the grey-averaging preprocessing steps, images could be sorted for different visibility ranges and subregions are extracted for the training of SVR models. Estimated visibilities obtained by multiple SVR models are fused together to provide more accurate piecewise approximation for the mapping surface between image features' values and visibilities. PSO method can reduce the computation load of the algorithm by optimizing the dimension of feature vectors.
## 4 Experimental Setup  
### Regression Prediction Models  
Through the analysis of the partial autocorrelation plot (see Figure 3), the number of lags to use during the training and testing of the regression models was determined [44].  
The _Python_ library, _Skyorecast_[45], was used to forecast the dependent multiple time series. It is important to specify which regressor will be used to forecast the time series and define the regressor hyperparameters. The library can use any regressor included in the _sklearn_ library [46].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Operation** \\ \hline active\_power & Sum \\ current & Sum \\ voltage & Average \\ temp & Average \\ pressure & Average \\ humidity & Average \\ \hline \hline \end{tabular}
\end{table}
Table 4: Operations performed for data grouping. Source: own elaboration.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Date** & **kWh** & **Current** & **Voltage** & **Temp** & **Pressure** & **Humidity** \\ \hline
6 November 2022 & 7.36 & 4004.94 & 124.03 & 20.65 & 1012.70 & 30.34 \\
7 November 2022 & 7.13 & 3914.56 & 123.03 & 19.48 & 1015.37 & 44.95 \\
8 November 2022 & 6.95 & 3840.22 & 123.67 & 18.58 & 1018.25 & 60.72 \\ \(\vdots\) & & & & & & \\
9 August 2023 & 8.86 & 4781.84 & 121.09 & 28.05 & 1009.57 & 37.37 \\
10 August 2023 & 9.07 & 4873.58 & 120.91 & 28.49 & 1008.54 & 35.75 \\
11 August 2023 & 9.16 & 4881.39 & 120.60 & 27.30 & 1010.51 & 39.64 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Transformed data sample. Source: own elaboration.  
The regressors tested were Ridge [47], Lasso [48], random forest regressor [49], XGBoost Regressor [50], and AdaBoost Regressor [51]. An optimization of the main hyperparameter, displayed in Table 6, was conducted to improve the results of each regressor.  
By combining the scopes of _grid search_[52] and backtesting [53], the predictive models were analyzed using _nested cross-validation_[54]. This approach not only seeks the best combination of hyperparameters for one or several regressors, but also evaluates the model's performance across different epochs or periods in the time series.
# Dynamic Behaviour of an Automotive Dual Clutch Transmission during Gear Shift Maneuvers
## 8 Conclusions  
A model for the dynamic study of the DCT transmissions has been presented that allows the gear rattle phenomenon, particularly felt in the DCT systems due to the high number of non-loaded gear wheels, to be highlighted. The developed model allows NVH problems of the gear boxes that, in the automotive sector, begin to be particularly felt problems, to be analysed and reduced. These algorithms should be combined with those normally adopted to define the gear shift criteria, aiming to optimise driveability and fuel consumption. The developed model considers many factors, such as the phases of engagement and disengagement of the clutch in the gear shift, the synchronisation of the gears, as well as the lubrication between the gear teeth, under some typical simplifying assumptions. The numerical results, obtained adopting this algorithm, have demonstrated that DCT transmission systems are characterised by a strong rattling behaviour, which becomes particularly critical during the rapid phases of the clutch actuations and during the gear pre-synchronisations. The simulations were conducted for different time interval of the engagement and disengagement phases of the two clutches, as well as for the preselection of the gear ratios. The algorithm also allows methods of reducing rattle phenomena to be highlighted. The simulations have shown that an adequate accompaniment of the various manoeuvres and transients allows the annoying vibro-acoustic disturbances of the transmission to be reduced. For example, a positive effect was achieved by anticipating the delicate actuation of the synchronisation to the next gear immediately after disengaging the clutch of the previous gear. To confirm the validity of the results obtained in the time domain, the numerical investigation was supplemented by a wavelet analysis. The theoretical model adopted for the analysis can guide the automotive designers of driveline towards better design strategies, with regard to the many vibro-acoustic issues.  
Conceptualisation, R.B. and E.R.; methodology, E.R. and S.P.; software, E.R.; validation, R.B. and E.R.; formal analysis, R.B. and E.R.; investigation, E.R. and S.P.; writing--original draft preparation, R.B., E.R. and S.P.; writing--review and editing, R.B., E.R. and S.P.; supervision, R.B., E.R. and S.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors thank [PERSON] who has contributed since 2005 to the research on \"gear rattle\", particularly in the brilliant idea of considering the oil \"squeeze effect\" between the gear teeth. He also contributed to this paper. His premature death was an irreparable loss to the authors. He will always be fondly remembered.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 15: Time-Frequency Wavelet Transform: 4 th gear.
## 4 Research Methodology
We will define the total air flow rates by integrating the ascending velocity function along the length of the perforated shelf and taking the constant perforation degree:  
\[V=B_{dev}\int\limits_{0}^{L_{sh}}W_{hol}dX=\varphi\cdot\psi\cdot B_{dev}\int \limits_{0}^{L_{sh}}\sqrt{\frac{\Delta p_{i}-zX}{\rho_{gas}}}dX. \tag{10}\]  
Substitution of the subintegral expression by a function \(y=\Delta p_{i}-zX\), where \(dy=-zdX\) enables to obtain the integration result within (0, \(L_{sh}\)):  
\[V_{sh}=\frac{-B_{dev}\cdot\varphi\cdot\psi}{z\cdot\sqrt{\rho_{gas}}}\int \limits_{0}^{L_{sh}}ydy=\frac{2\cdot B_{dev}\cdot\varphi\cdot\psi}{3\cdot z \cdot\sqrt{\rho_{gas}}}\cdot\left(\sqrt{\Delta p_{i}}^{3}-\sqrt{\left(\Delta p _{i}-z\cdot L_{sh}\right)^{3}}\right). \tag{11}\]  
The share of the flow through the outloading space is added to the balance equation of air flow rate along the cross-section of the shelf unit:  
\[ \begin{array}{l}W_{gas}\cdot L_{dev}\cdot B_{dev}=V_{sh}+V_{cl}=\\ =\frac{2\cdot\varphi\cdot\psi}{3\cdot z\cdot\sqrt{\rho_{gas}}}\cdot B_{dev} \cdot\left(\sqrt{\Delta p_{i}}^{3}-\sqrt{\left(\Delta p_{i}-z\cdot L_{sh} \right)^{3}}\right)+\varphi\cdot L_{cl}\cdot B_{dev}\cdot\sqrt{\frac{\Delta p _{i}-z\cdot L_{sh}}{\rho_{gas}}}\end{array}  \tag{12}\]  
where \(W_{gas}\cdot L_{dev}\cdot B_{dev}\)--total air flow rate in the device. The analysis of Equation (11) allows to calculate the gas flow velocity above the perforated shelf with the free cross-sectional area \(f_{cs}=\psi\cdot L_{sh}\cdot B_{dev}\):  
\[W_{cr1}\cdot\psi\cdot L_{sh}\cdot B_{dev}=\frac{2\cdot\varphi\cdot\psi}{3 \cdot z\cdot\sqrt{\rho_{gas}}}\cdot B_{dev}\cdot\left(\sqrt{\Delta p_{i}}^{ 3}-\sqrt{\left(\Delta p_{i}-z\cdot L_{sh}\right)^{3}}\right). \tag{13}\]  
This ratio defines the pressure loss \(\Delta p_{i}\) when installed in the workspace of one shelf device. It is possible to vary the constructive parameters of the shelf, namely its length and perforation degree. It is also possible to obtain profiles of the vertical velocities of the gas flow in the space above the shelf and the outloading space according to the algorithm below. Comparing the total flow rates in the perforation holes to the flow rates calculated through the total area of the shelf (\(W_{hol}\cdot\psi\cdot f_{sh}=\overline{W_{hol}}\cdot f_{sh}\)), one can find the average velocity of the gas flow over the perforation holes in the vertical direction, taking into account Equation (6):  
\[W_{gas}=\overline{W_{hol}}=\psi\cdot W_{hol}=\psi\cdot\varphi\cdot\sqrt{\frac {\Delta p_{i}-zX}{\rho_{gas}}}. \tag{14}\]  
An analysis of this expression showed, first, that the local velocity of the gas flow above the holes in the vertical direction decreases linearly depending on the shelf perforation degree \(\psi\) and nonlinearly in the \(X\) direction along the shelf length to the outloading space (Figure 3).
## 4 Methods
### Constructing a Heterogeneous Graph  
Although adding more types of nodes and edges can enhance the expressive power of a heterogeneous graph, an excessive number of node and edge types can significantly increase computational complexity and resource demands, potentially introducing information redundancy and noise, which could affect model performance. On the other hand, having too few types of nodes and edges may lead to insufficient information representation, making it difficult to capture the complex relationships between user behaviors and POIs, thereby reducing prediction accuracy. Therefore, when constructing the heterogeneous graph, it is necessary to strike a balance between maintaining the complexity of the graph and ensuring computational efficiency.  
In this study, a user-POI heterogeneous graph is constructed based on users' historical check-in records and social relationships in LBSN. This graph consists of user nodes, POI  
Figure 1: GEVEHAN architecture diagram, including five components: (1) construct a heterogeneous graph with two types of nodes and three types of edges based on LBSN user check-in trajectory information; (2) learn POI embeddings using the Skip-Gram model, and user embeddings using Lite-GRU, to initialize the nodes; (3) use the VAE module for dimensionality reduction and denoising; (4) apply edge-level attention, multi-head attention mechanism, and residual connections for feature aggregation and update; (5) perform friend link prediction using cosine similarity. The relationships are as follows: a, b, and c are friends; a, t, c, and t are not friends.  
nodes, and three types of edges: friend edges, check-in edges, and co-class edges. The initial features of each type of relationship edge are as follows:  
**Friend edges:** connect two users with a friendship relationship, and the feature of the edge is the distance between the approximate activity centers of these two users.  
**Check-in edges:** connect a user to a POI they have visited, and the feature of the edge is the frequency of the user's check-ins during different time periods, and the distance between the user and the POI.  
**Co-class edges:** connect POIs with the same category, and the feature of the edge is whether the category labels of these two POIs are the same, along with the geographical distance between them.