esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Effect of Burial Depth  
To further study the effect of the depth of tunnel burial on the freezing construction of the project, four additional freezing options were selected for the tunnel burial depths of 15 m, 17 m, 21 m, and 23 m, respectively. The vertical displacement of the ground surface frost heave under different burial depth conditions is shown in Figure 21, and the maximum ground surface frost heave displacement is shown in Figure 22. The study's findings indicated that it is decreasing for the frost heave displacement at each location while increasing the burial depth of the tunnel. This suggests that the frost heave displacement of the soil during the freezing-method construction is severely affected by the tunnel's depth.  
The ground surface thawing wettlement displacement for different burial depths is shown in Figure 23, and the maximum ground surface thawing wettlement displacement is shown in Figure 24. As can be seen from the graphs. The greater the depth of burial, the greater the overburden load on the soil, and the smaller the thaw settlement displacement is at each point. The reduction is greater compared to frost heave displacement. This suggests that the tunnel's level of embedment has a significant inhibitory impact on the displacement of soil caused by thaw settlement during freezing-technique construction.  
Figure 21: Freezing heave displacement curve with tunnel buried depth: (**a**) vertical displacement; (**b**) horizontal displacement.  
Figure 22: Maximum frost heave displacement about the tunnel buried depth: (**a**) vertical displacement; (**b**) horizontal displacement.  
When the burial depth of the tunnel increases, the pressure load acting on the frozen wall increases, which inhibits the development of frost heave and thaw settlement movement of the soil. On the other hand, an increase in tunnel burial depth implies an increase in the distance to the surface, leading to a reduction in the displacement transmitted to the surface.
## 3 System Architecture and Key Technologies
### CMMN-Based Heterogeneous Cloud Resource Integration and Scheduling  
Heterogeneous cloud resource integration focuses on data integration and resource management across platforms. Data are stored in private clouds, and applications are deployed on various platforms, creating resource fragmentation and information silos. Therefore, a framework for cloud resource integration and communication is established. An optimized CMMN-based adaptive task scheduling strategy is proposed to improve resource allocation efficiency, balance load, and reduce task completion time.  
Figure 1: System architecture diagram.
## 2 Materials and Methods
### Multi-Task RNN Models with LSTM  
We used three different model architectures: a multi-task LSTM RNN model that can make simultaneous classification of APS types (NS, EAS) types and PA types (SS, SB, TR) and estimation EE (Figure 4a), a multi-task LSTM RNN model that can make simultaneous classification of APS types (NS, EAS, MS) types and PA types (SS, SB, TR) (Figure 4b). Three separate models of the independent LSTM RNN model are developed for classification of APS types (NS, EAS, MS) and PA types (SS, SB, TR) as in Figure 4c and estimation of EE (Figure 4d) [6]. The nodes in RNN networks are connected in a cycle, so that output from one node affects the input to another, causing RNNs to demonstrate dynamic behavior over time [47; 48]. Ordinary RNN suffer from the vanishing gradients and exploding gradients problems. LSTM is a class of RNN that is capable of learning long-term dependencies. Unlike RNN, the LSTM unit is able to handle the problem of vanishing gradients and exploding gradients problems [49; 50]. The RNN models with LSTM used in this study have several layers (Figure 4): an input layer, an LSTM with 40 units, a dropout layer 20%, a fully connected layer with 40 units, a dropout layer 20%, and output layers. For classification tasks, the output layer has softmax as an activation function to predict the probability distribution of target classes [50; 51]. The model parameters are summarized in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Value/Technique** \\ \hline Number of units in the LSTM layer & 40 \\ \hline Dropout in the LSTM layer & 20\% \\ \hline Number of units in the dense layer & 40 \\ \hline Dropout in the dense layer & 20\% \\ \hline Number of units in the softmax layer & 2 or 3 \\ \hline Learning rate & \(10^{-5}\) \\ \hline Optimization algorithm & Adam \\ \hline \(\beta_{1}\) & 0.9 \\ \hline \(\beta_{2}\) & 0.999 \\ \hline \(\varepsilon\) & \(10^{-7}\) \\ \hline Batch size & 1000 or 10,000 approximately equal to \\  & 9\% of the number of training samples \\ \hline Activation function & ReLU \\ \hline Number of epochs & Variable, depending on the target variable and \\  & the size of samples \\ \hline \hline \end{tabular}
\end{table}
Table 4: The value of adjustable parameters used in the LSTM RNN models [6].  
Figure 4: (**a**) Multi-Task LSTM RNN model architecture for simultaneous classification of APS types (NS, EAS) or (NS, EAS, MS) and PA types (SS, SB, TR) and estimation of EE; (**b**) Multi-task LSTM RNN model architecture for simultaneous classification of APS types (NS, EAS, MS) and PA types (SS, SB, TR); (**c**) Independent LSTM RNN model architecture for classification of APS types (NS, EAS, MS) or PA types (SS, SB, TR); (**d**) Independent LSTM RNN model architecture for EE estimation.
# Design of Universal Code Generator for Multi-Constellation Multi-Frequency GNSS Receiver
## 1 Introduction
In this paper, a spread spectrum code generation scheme is proposed, incorporating the IZ4 code and based on LFSR structure. The structural characteristics of IZ4 are leveraged to support the parallel generation of spread spectrum codes in multi-channel scenarios. This paper is structured as follows: the first section introduces the background and research status of compatible code generators, Section 2 discusses spread spectrum code generation schemes for all signals, Section 3 is divided into two parts presenting existing shift register-based schemes and a proposed scheme supporting various shift register structures, Section 4 provides experimental results on the accuracy of generated spread spectrum codes and resource utilization using an FPGA module, and finally, conclusions and future work are presented in the Section 5.
# _photonics_
## 4 Simulation Results
### Feasibility of LEO Satellite-to-Ground QKD in Daytime and Nighttime  
In this subsection, we analyzed the performance of the satellite FSO downlink considering one LEO satellite and one OGS. For the evaluation studies included in this subsection, the receiver's telescope aperture diameter was set to be 0.75 m (if not stated otherwise).
# Detecting Phishing Domains Using Machine Learning
## 5 Model's Flowchart  
Phishing is a concern to many individuals. However, existing methods, such as browser security indicators, cannot detect phishing websites. Due to the limits of current technology, users must evaluate whether a URL is phishing or not on their own. As a result, an automated technique for phishing website identification should be explored for increased cyber safety. This study shows how an implemented feature extraction approach and a prediction model based on a random forest classifier help increase the likelihood that a user will correctly identify a phishing website.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **Before Use Normalization** & **After Use Normalization** \\ \hline \multirow{4}{*}{SVM} & Accuracy: 94.46 & Accuracy: 94.66 \\  & Precision: 93.64 & Precision: 93.9 \\  & Recall: 96.62 & Recall: 96.6 \\  & F1-measure: 95.10 & F1-measure: 95.3 \\ \hline \multirow{4}{*}{ANN} & Accuracy: 95.5 & Accuracy: 96.2 \\  & Precision: 95.6 & Precision: 96 \\  & Recall: 96.3 & Recall: 97.2 \\  & F1-measure: 96.6 \\ \hline \multirow{4}{*}{RF} & Accuracy: 96.86 & Accuracy: 97.3 \\  & Precision: 96.56 & Precision: 96.9 \\  & Recall: 97.84 & Recall: 98.62 \\  & F1-measure: 97.20 & F1-measure: 97.6 \\ \hline \multirow{4}{*}{DT} & Accuracy: 95.4 & Accuracy: 96.3 \\  & Precision: 95.8 & Precision: 96.5 \\ \cline{1-1}  & Recall: 95.9 & Recall: 96.8 \\ \cline{1-1}  & F1-measure: 95.8 & F1-measure: 96.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance results before and after using the normalization technique.  
Each of the developed models, as shown in Figure 7, employs a feature selection technique to increase its accuracy. The data analysis heat map picks those that are most crucial in affecting the forecasted result by filtering the most interesting features out of the original dataset. As a result, irrelevant features have no effect on the model's efficiency or prediction.  
A summary of models' flowchart steps follows:  
1. Read the URL's UCI phishing websites dataset.
2. Check the data features.
3. Check the proposed data types.
4. Clean missing values from the data.
5. Split the data into training and testing sets.
6. Train the model using four machine-learning techniques: RF, SVM, DT, and ANN.
7. Evaluate the model's performance to estimate the accuracy and calculate the accuracy results.
8. Select the best model as the final model.
## 3 Background of the Event
### Meteorological Background
However, Milano Linate is roughly 120 km distant from the studied area, and its profile could be only partly representative. The deep convection located on Ossola Valley at 0600 UTC on 12 August is well depicted in the satellite RGB products (Figure 3f); afterwards, the cloudiness expands towards entire Piedmont, Aosta Valley and Lombardy regions (i.e., North-Western Italy), and it moved towards East during the afternoon. Fair weather conditions characterised the following days. The weather conditions were also deducted with the several webcams in the San Domenico area, and with the global radiation measured at the Alpe Veglia station (B in Figure 1; Table 1). The last meteorological event that caused the activation of the Croso stream debris flow dated back to October 2000, when a flood affected entire North-Western Italy. In this case, the precipitation lasted for four days, from the 11 to the 17 October 2000. The precipitation type was totally different from the 2019 event, in fact, it was characterised by steady rain, with a mean rain-rate of 2-3 mm over ten minutes (roughly 12-20 mm/h), averaged over the five weather stations. The core of this flood was concentrated between Friday 13 th and Monday 16 th October, when a cyclonic area was generated on Great Britain Islands and a pronounced trough elongated from North to the Iberian Peninsula. A high-pressure ridge on Eastern Europe promoted the block of the frontal zone on Western Europe and the Alps. A South-South-West, and subsequently a South-East advection brought warm and moist air on Piedmont region from the Mediterranean Sea. The baroclinicity of this situation enhanced the convergence on the Western Po Valley and the Western Alps, causing the concentration of precipitation from the Alps down to the Piedmont plain. In the following days, a cold-drop was generated from the previous trough and slowly moved towards the French Coast; this situation favoured the stationarity of the frontal zone on Piedmont and the moist advection from the Mediterranean Sea. On Sunday 15 th, a second depression moved towards the North and triggered the formation of thunderstorms on plains, due to the saturated air related to the decreasing of temperature on the region. The event ended in the night of Monday 16 th, when the Azores Anticyclone progressively expanded over Western Europe [39].
## 3 Materials and Method  
### Materials  
#### 3.1.1 Dataset Collection  
In our research, we collected stock price and medical imaging data to support our secure computing model using hash trees and skip attention mechanisms, especially in finance and healthcare. For stock price data, we deployed advanced web scraping to gather real-time information from reputable financial sources, such as global financial news sites and stock exchange announcements, capturing key price metrics (opening, closing, high, and low prices). Our multi-threaded crawler system, designed for scalability and error resilience, efficiently fetched over 2.7 million records from 2010 to 2023, covering thousands of companies, including data from high-volatility periods like financial crises.  
For medical imaging, we obtained brain CT datasets from established databases, yielding high-resolution, annotated images. These datasets, carefully anonymized and ethically sourced, include brain conditions like tumors and hemorrhages, providing essential materials for training models to recognize complex disease patterns. This extensive dataset collection forms the basis for our model's secure and precise applications in critical, data-sensitive fields.
# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
## 3 Problem Modeling  
### Satellite Network Model  
As shown in Figure 1, the satellite network routing attack architecture consists of the low-Earth-orbital satellite (LEO) network and the corresponding ground infrastructure (urban building and satellite receiving stations). Among them, the nodes of the LEO will be attacked by routing, which will pollute the data flow, and then affect the data communication of the entire space-Earth network. The red satellite node in the figure indicates that it has been invaded and become a malicious node. The yellow satellite node indicates a communication interruption or information loss due to a malicious attack by a red satellite node. The remaining blue satellite nodes indicate normal satellite nodes. Because the satellite network will be attacked by malicious nodes during the routing process, this article uses a trust model to strengthen the routing algorithm. In this way, the satellite network route established by the trust model can actively identify and avoid malicious nodes, and can effectively establish a secure route, thus improving the security of the satellite network route.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Literature**} & **Internet** & **Satellite** & **Traditional Trust** & **Intelligent** & **Energy** & **Resource** \\  & **of Things** & **Network** & **Model** & **Trust Model** & **Factor** & **Consumption** \\ \hline
[14] & & \(\surd\) & & \(\surd\) & & & \\
[15] & \(\surd\) & & \(\surd\) & & & & \\
[16] & & \(\surd\) & \(\surd\)
## 2 HST-Net Model
### Head Network with Segmentation Head  
Insulator defect segmentation can be regarded as a semantic segmentation problem. The goal of semantic segmentation is to categorize each pixel within a given image or image sequence into corresponding classes. Since this essentially involves assigning a class to every pixel, it is also considered as a dense prediction problem. For defect segmentation, pixels demand a higher computational volume than the minimal external contour used for defect localization.  
Traditional semantic segmentation algorithms, such as Unet [36] and OCRNet [37], can no longer meet the substations' demand for real-time detection. Therefore, in this study, we introduce an improved real-time segmentation head network capable of accurately segmenting insulator defects in real-time. Given that the objective of insulator defect segmentation is to attain a mask of defects with natural spatial connectivity, most primary detectors can only obtain classification results through box parameters. While secondary detectors can acquire information through feature localization steps such as box, and use Conv layer output masks. This greatly influences the algorithm's real-time performance.  
As shown in Figure 5, the proto modules are used to add an instance segmentation detection head, replacing the original object detection head. The initial YOLOv5 output consists of the class number \(a\) and the corresponding anchor box matrix, on top of which we add a segmentation result of [512; 512; \(k\)]. The Proto module, based on FCN implementation, outputs \(k\) mask masks. The Proto module likes a semantic segmentation model, differing in that it does not set a separate loss value but adds a new mask loss at the end of the entire network.  
In terms of loss function, in addition to the original classification loss and bounding box loss, a mask loss is added to measure the distance between the validation mask and the real mask of the labels. The mask loss function \(\mathrm{L_{mask}}\) is defined as follows:  
\[\mathrm{L_{mask}}=-\frac{1}{n\sum_{i=1}^{N}[\mathrm{y_{i}log_{i}}+(1-\mathrm{ y_{i}})\log(1-\mathrm{x_{i}})]} \tag{6}\]  
where i is the serial number of the pixel of the bearing image. N is the product of the length and width of the bearing image. \(\mathrm{x_{i}}\) is the classification of the ith point predicted by the segmentation head, and \(\mathrm{y_{i}}\) is the label classification of the ith point.  
In the placement of the proto module, the deepest backbone network is selected to obtain the feature information so as to generate the most accurate mask. Although the segmentation accuracy of insulator defects is related to the pixels of the image, considering the need for real-time segmentation, the calculation amount is reduced by upsampling the  
Figure 5: Structure of proto segmentation head.  
size of defects to 0.25. Use the Equation (7) to process the output of two branches through matrix operation and sigmoid function.  
\[\mathrm{M}=\ \sigma\Big{(}\mathrm{PC}^{\mathrm{T}}\Big{)} \tag{7}\]
## 4 Enabling Technologies for FSO Communication
### Cooperative Relaying
#### 4.8.3 Cooperative FSO System Model  
The system model for parallel cooperative FSO communication comprises a source node \(S\), multiple relay nodes \(R_{i}\), and a destination node \(D\). This system features two primary communication links: a direct link from \(S\) to \(D\), enabling \(S\) to send data directly to \(D\), and a relay-assisted link where data are transmitted from \(S\) to \(D\) via relay nodes \(R_{i}\). The relay node index \(i\) ranges from \(1\) to \(M\). The relay node employs DF or AF protocols to assist in the transmission. As illustrated in Figure 12, \(X_{0}\) denotes the modulated optical signal transmitted directly from \(S\) to \(D\), while \(X_{i}\) represents the signal transmitted from \(S\) to \(D\) via \(R_{i}\).  
In the conventional system model, the optical signal transmitted over each link remains identical, a concept referred to as synchronous transmission. This implies that \(X_{0}=X_{1}=X_{2}=\ldots=X_{M}\). Consequently, the received signal after electrical combination in a synchronous transmission system is expressed as [284; 360]  
\[Y_{sc}=\sum_{i=0}^{M}\left(Y_{i}+N_{i}\right), \tag{50}\]  
where \(Y_{i}=\bar{g}|X_{i}|^{2}H_{i}G_{i}\) represents the electrical signal received from either the direct link (for \(i=0\)) or the relay links (for \(i=1,\ldots,M\)); \(G_{i}\) represents the gain for optical-to-electrical  
Figure 12: A typical model of a cooperative FSO communication system.  
conversion; \(N_{i}\) denotes the AWGN, encompassing thermal noise and background light noise; \(|X_{i}|^{2}\) is the average optical power of \(X_{i}\); and \(H_{i}\) denotes the combined channel effect, expressed as  
\[H_{i}\stackrel{{\Delta}}{{=}}h_{t}h_{\mathbf{a}}. \tag{51}\]  
The destination node combines the received signals using techniques such as MRC to enhance the signal quality.
# Principles of Product Design in Developing Countries
## Results
### Technical Factors
#### 3.4.2 Local Connectivity  
Kaza was approximately one day's drive away from the next economic center. Several delays were incurred waiting for suppliers to respond to resource requests. In one instance the requested part (polycarbonate sheet) arrived after two weeks of waiting but was not useable as it was damaged in transport. This showed an importance facet of design, namely, that materials used in the design solution should account for the infrastructure required for them to reach destination as safe transport of delicate or brittle materials is not assured.  
Plans were made ad hoc, and did not extend more than a day or two into the future. Lack of communication infrastructure between villages meant that plans could not always be changed remotely. Several stone masons requisitioned to help with one of the cooker builds waited for four hours at the gate to a satellite village due to unforeseeable delays and there was no way to contact them. No animosity was shown by the stone masons, as the cultural viewpoint appreciates that plans do not always go as expected. Village life is not regimented in terms of minutes and hours. Seasonal changes that are important for harvesting crops or moving stock help dictate workloads. The native vocabulary lacks words to describe relatively short time periods (minutes, hours). Sometimes a supplier would indicate they would reach Kaza four or five days earlier than they did. Delays were inevitable with the poor infrastructure connecting centers and the access road was often blocked with rock falls.  
**Deployment Principle 1.2: Plan for project delays when working in remote contexts by integrating parallel work-streams when possible**  
Parallel work streams are useful to add value to the waiting time and reduce the impact of delays in a project. Deadlines should include weekly or fortnightly allowance periods to account for extraneous factors that cause delays. Acknowledgement that the culture may have developed without the concept of a regimented, precise western system of ordering time is an important consideration. The acknowledgement that local people will not have the same sense of urgency or need for efficient time use as the embedded participant is important to maintain a positive working relationship.  
**Deployment Principle 1.3: Consider the reliability of transporting materials in from external suppliers**Transportation of delicate materials must be considered in the feasibility of their use in a project. Labelling of fragile goods may not be heeded or even provided at all. The infrastructure to reach remote communities is considerably less forgiving than urban infrastructure. This issue can be mitigated in the embodiment design phase by considering transit robustness during material selection.