esa-sceva/satcom-chunk-collection huggingface dataset:
### Solving Challenge 2 (C2): Analysis of Sensor Data  
Two types of data analysis are generally recognized: knowledge-driven and data-driven analysis. Knowledge-driven approaches provide analytics by applying human-implemented logic rules on available data. These approaches are based on evidence, guidelines, as well as expert and domain knowledge. Their biggest benefit is that the resulting models are white-box, providing all the needed insights into a decision support system. However, knowledge-driven approaches require intensive, and therefore costly, intervention of experts to construct and maintain them. Moreover, often system experts cannot express all system behavior with logic rules, this limits knowledge-driven techniques in their capabilities to detect unknown events or anomalies, since these can only detect what is modelled. On the other hand, the data-driven approach is based on machine learning techniques, which learn patterns directly from the data. The benefit of these techniques is that they require minimal expert intervention, are very efficient, and adaptable to new situations. However, training these models requires a large amount of data that is representative for all operating conditions to avoid data bias. Moreover, these models are often black-box models, making results difficult to interpret.  
In the context of anomaly detection, knowledge-driven techniques are better at detecting known faults by checking system behavior against the available expert knowledge. However, they cannot detect system failures that experts are not yet aware of, i.e., unknown faults or anomalies [4]. On the contrary, data-driven approaches can detect anomalies that experts have not yet described, but they lack specificity when they cannot be trained on large, labeled datasets. Unfortunately, the unavailability of labeled datasets is a problem in many industry domains.  
When companies have both sufficiently large (unlabeled) datasets and expert knowledge at their disposal, or are prepared to collect them, knowledge- and data-driven techniques can be combined, overcoming the disadvantages of both techniques. When datasets are unlabeled and too large in size to have experts label them, data-driven methods can be used to detect candidate events. It then suffices to have experts validate these candidate events, rather than have them explicitly label all data, saving a lot of effort. In this way, the expert knowledge is also incorporated into the decision making process. As user feedback becomes more detailed, adaptive detection algorithms become more accurate and/or will be able to find new types of events. This process can be facilitated by combining visualization and feedback mechanisms, as discussed next.
## 4 Multiple Objective Motion Optimization Problem
### Preventive Control to Avoid Future Collisions
#### 4.3.2 Way-Blocking Metric  
We propose a way-blocking metric to evaluate this risk. The way-blocking metric is defined by evaluating the proportion of ways around the MASS that are covered by VOs.  
**Definition 3** (Way-blocking metric).: _Let \(C\) be a circle centered at the location of the MASS and with radius \(R\). Let us define the circumference covered by VOs by \(CI(VO)\), then the portion of the circumference of the circle covered by VOs is defined as the way-blocking metric._  
\[B(R,P,VO)=\frac{CI(VO)}{2\pi R} \tag{13}\]  
To reduce the probability of falling into local optima, the MASS can add the consideration of the way-blocking metric to the speed scheduling metric.  
\[A_{t}^{*}=\underset{A_{t}\in\mathbf{A}_{t}}{\operatorname{argmin}}\,\mathcal{ V}(X_{t})=\left\{\begin{array}{c}T_{E}\left(X,\operatorname{Target}\right)\\ +\lambda_{1}\left(\arccos\frac{\left\langle V_{t}+A_{t},V_{t}\right\rangle}{ \left\|V_{t}+A_{t}\right\|V_{t}\right\|}\right)\\ +\lambda_{2}(\left\|V_{t}+A_{t}\right\|-\left\|V_{t}\right\|)^{2}\\ +\lambda_{3}B(R,P,VO,A_{t})\end{array}\right\} \tag{14}\]  
where \(B(R,P,VO,A_{t})\) evaluates the way-blocking metric at the next time interval. Therefore, we can revise the greedy velocity optimization algorithm by replacing the cost function by (14), which is called a greedy-risk algorithm.
# Scaling of Rain Attenuation Models: A Survey
## 3 Existing Scaling Techniques
### Frequency Scaling  
The advantage of rain attenuation would have happened if the transmission of different frequencies had gone through the same storm. It is helpful to determine the amount of rain attenuation that will occur at a given frequency. For example, it is helpful to have experimental scale data available at one frequency to predict the device output at another frequency. The application of frequency scaling can be beneficial in determining predicted attenuation in a dual-frequency use of satellite communication or even in predicting attenuation at different frequencies [8,26].
## 5 Conclusions  
In this paper, we proposed a novel classification method, namely MSCDN, for multi-temporal PolSAR data classification. To solve the problem of the dimension disaster, firstly, we constructed a sparse auto-encoder with non-negativity constraints (NC-SAE) which has an improved sparsity to reduce the data dimension of scattering features extracted from multi-temporal PolSAR images. Meanwhile, the simulated multi-temporal Sentinel-1 data provided by the ESA and the established ground truth map for experimental site were used to evaluate the performances of the proposed methodology. Comparing the performance of classification result, we can see that the OA of MSCDN classifier is approximately 20%higher than that of traditional SVM, but only 1-2% higher than CNN. It seems that the advantage of MSCDN over the CNN classifier is somewhat trivial, but we have to note that this insignificant improvement comes from the accuracy improvement of easily confused crops with very small samples. So the overall improvement is somewhat limited. If the proposed method is applied to classify the easily confused crops with higher proportion, the remarkable improvement of OA performance will be anticipated.  
[PERSON]-[PERSON] proposed the methodology. [PERSON] designed the experiments and performed the programming work. [PERSON] contributed extensively to the manuscript writing and revision. [PERSON]-[PERSON] supervised the study. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 62071350) and the Key R & D projects of Shaanxi Province (2020 GY-162).  
Not applicable.  
The data presented in this study are available in the article.  
We thank the European Space Agency (ESA) for providing the simulated multi-temporal PolSAR data sets and ground truth information under the Proposal of C1F21329: Preparation of Exploiting the S1A data for land, agriculture and forest applications.  
The authors declare no conflict of interest.
# Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study
## 4 Results and Discussion
### Results with GRU
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{\
\begin{tabular}{c} **DL** \\ **Algorithm** \\ \end{tabular} } & \multicolumn{8}{c}{**Parameter Settings**} & \multirow{2}{*}{
\begin{tabular}{c} **ACC** \\ **(\%)** \\ \end{tabular} } \\ \cline{2-11}  & & **Learning** & **Input** & **Hidden** & **Output** & **Number** & **Kernel** & **Dropout** & **Batch** & **Size** & **Epoch** & **(\%)** \\ \hline
[18] & DNN & 0.01 & 30 & (20 10 5) & 2 & - & - & - & - & 200 & 97.50 \\
[28] & CNN & - & 30 & (64 64) & 1 & 64/64 & 12/6 & - & - & 220 & 97.20 \\
[6] & MLP & - & - & (100 100) & - & - & - & - & - & - & 96.65 \\
[64] & DNN.BA 1 & 0.0185 & 30 & (50 30) & 2 & - & - & - & 44 & 155 & 95.76 \\  & DNN.HBA 2 & 0.0462 & 30 & (42 30) & 2 & - & - & - & 101 & 135 & 95.00 \\  & DNN.FA 3 & 0.0053 & 30 & (50 30) & 2 & - & - & - & 37 & 192 & 96.65 \\  & DNN & 0.001 & 30 & (16 42) & 1 & - & - & - & 32 & 500 & 97.29 \\ Our & CNN & 0.005 & 30 & (16) & 1 & 16 & 3 & 0.5 & 32 & 50 & 96.56 \\ study & LSTM & 0.0005 & 30 & (128) & 1 & - & - & 0.5 & 32 & 700 & 97.20 \\  & GRU & 0.001 & 30 & (128 128) & 1 & - & - & 0.5 & 32 & 200 & 96.70 \\ \hline \hline \end{tabular}  
* Parameter settings for DNN using Bat Algorithm. 2 Parameter settings for DNN using Hybrid Bat Algorithm. 3 Parameter settings for DNN using Firefly Algorithm.  
\end{table} 
Table 9: Comparison of parameter settings with other studies using the same dataset. the MLP algorithm in all four metrics: ACC, PR, RC, and F1-Score. In addition, although DNN and CNN accuracies in [18; 28] demonstrated slightly better results than ours, some of the performance metrics were not calculated in these studies. In [18], for instance, AUC, time complexity, and memory constraints were not included in the evaluation process. Even though training time, testing time, and parameter size were measured in [28], other metrics (FPR, FNR, AUC, and memory storage) were not reported. On the contrary, our study provided a complete set of performance metrics for four different DL algorithms. Conventional metrics (FPR, FNR, ACC, PR, RC, F1, and AUC) were used to evaluate the effectiveness of the DL mechanism in detecting phishing websites. In contrast, additional metrics (training time, testing time, parameter size, and memory usage) were utilized to assess the computational complexity of the phishing detection model.
# Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes
## 2 Overview of State-of-the-Art Approaches
### Critical Analysis of Contemporary Approaches Based on Evaluation Criteria
#### 2.4.1 Analysis of Content-Based Approaches (Single Label Classification)  
The state-of-the-art approaches which exploited the content of the documents are shown in Table 1. Researchers of these approaches performed content-based document classification and classified documents into only one class. Different algorithms were used to predict the most relevant class. Similarly, different datasets were used for the classification of documents. For example, the datasets 20-Newsgroups and Reuters were used by many researchers [12; 21; 22; 23; 24; 44]. Similarly, some other datasets have also been used for the document classification [31; 32; 33; 34].  
From Table 1, we can examine that the number of classes vary from dataset to dataset. Different researchers classified documents into a different number of pre-defined classes. By using these datasets and pre-defined number of classes, a variety of state-of-the-art approaches were presented in the last couple of decades and exploited content of the documents to classify documents into single label. These approaches have used different evaluation parameters such as accuracy, precision, recall, and F-measure. These approaches achieved accuracy from 0.4 to 0.95 by exploiting the content of the documents. Similarly, parameter precision achieved from 0.61 to 0.8, recall achieved from 0.55 to 0.76, and parameter F-measure achieved from 0.71 to 0.94 as mentioned in the literature. These values are significantly good because these techniques have exploited the content of documents which contain a huge bag of words (features) for the classification.
## 8 Conclusions  
In this work, we proposed VoLTE call failures in the IMS network prediction model using machine learning. Firstly, we analyzed different call failure use cases obtained from an Egyptian mobile operator. The analysis demonstrated that the failure in any communication protocol leads to SIP failure. Secondly, we study the SIP protocol to extract the features used to classify the calls either as failures or successes. Based on the used dataset, which contains one million SIP CDRs, we built eight different machine learning models using four different classifiers (decision tree, naive Bayes, KNN and SVM) combined with two different feature selection methods (Filter and Wrapper methods). The results demonstrated that the SVM provides the highest prediction accuracy among all the classifiers and the Wrapper selection method is more efficient than the Filter method in terms of the number of the output features and the computational time. Finally, we analyzed the output of the model with a highest prediction accuracy of 97.5% (SVM Classifier combined with Wrapper Method in feature selection) to obtain the call failure root causes; this enables the network operator to reduce the call drops.  
Conceptualization, M.S., S.M.G. and M.S.E.-M.; Methodology, A.B. and M.S.; Software, A.B.; Supervision, M.S., S.M.G. and M.S.E.-M.; Writing--original draft, A.B.; Writing--review & editing, M.S. and S.M.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Research on Phase Stabilization Algorithm of Femtosecond Timing System
## 3 Phase Correction Module Design  
### Overall Design  
The structure of the phase correction scheme is illustrated in Figure 2, which employs a phase correction solution using optical delay devices. The Optical Delay Line (ODL) is responsible for the coarse adjustments of substantial delays on the order of hundreds of picoseconds, whereas the fiber stretcher is employed for the fine-tuning of minor delays, achieving precisions on the scale of tens of femtoseconds, with an accuracy reaching up to \(1\) femtosecond.  
In the system architecture, the EPICS software system (version 3.15.5) is deployed on a personal computer (PC), functioning to acquire real-time phase information of the high-frequency signal via a network optical fiber from a hub (HUB) [18]. Once this information is obtained, the system calculates the necessary control parameters through an automatic control algorithm and achieves precise control of the optical delay line and the fiber stretcher via data lines. The optical delay line is equipped with a standard RS232 communication interface, allowing for straightforward data exchange between the PC and the device. However, the design of the fiber stretcher employs a relatively rare 20-pin digital input parallel interface, which presents unique challenges for data transmission.  
To overcome this challenge and ensure efficient and flexible data transmission, this study introduces the Field Programmable Gate Array (FPGA) technology [19]. The FPGA serves as a core component responsible for interface conversion during the data transmission process. Specifically, the host PC first establishes a communication link with the FPGA via a Universal Asynchronous Receiver-Transmitter (UART) serial connection. Subsequently, the FPGA packages and organizes the data received from the PC.  
After completing the packaging and organization of the data, the FPGA further transmits this information to the fiber stretcher in parallel format. This design not only leverages the FPGA's powerful capabilities in data processing but also effectively resolves the data transmission challenges posed by the fiber stretcher's specialized interface.
## 3 Statistical Analysis
### Daily Values
#### 3.2.3 Daily Radiation Indices
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{\(K_{\rm{T}}\)} & \multicolumn{4}{c}{**I**} & \multicolumn{4}{c}{**II**} & \multicolumn{4}{c}{**III**} & \multicolumn{4}{c}{**IV**} \\ \hline
**Month** & **Mean** & **StDev** & **CV(\%)** & **Min** & **Q1** & **Median Q3** & **Max** & **N** & **Perc(\%)** & **N** & **Perc(\%)** & **N** & **Perc(\%)** \\ \hline
1 & 0.519 & 0.137 & 26.460 & 0.177 & 0.432 & 0.493 & 0.629 & 0.715 & 4 & 12.90 & 13 & 41.94 & 8 & 25.81 & 6 & 19.35 \\
2 & 0.619 & 0.130 & 20.970 & 0.170 & 0.550 & 0.666 & 0.714 & 0.744 & 1 & 3.57 & 6 & 21.43 & 5 & 17.86 & 16 & 57.14 \\
3 & 0.603 & 0.132 & 21.930 & 0.261 & 0.532 & 0.628 & 0.716 & 0.764 & 2 & 6.45 & 8 & 25.81 & 9 & 29.03 & 12 & 38.71 \\
4 & 0.630 & 0.129 & 20.430 & 0.347 & 0.558 & 0.678 & 0.722 & 0.761 & 1 & 3.33 & 5 & 16.67 & 5 & 16.67 & 19 & 63.33 \\
5 & 0.694 & 0.036 & 5.130 & 0.585 & 0.667 & 0.707 & 0.724 & 0.735 & 0 & & 0 & & 3 & 9.68 & 28 & 90.32 \\
6 & 0.681 & 0.064 & 9.360 & 0.510 & 0.680 & 0.703 & 0.718 & 0.744 & 0 & & 4 & 13.33 & 1 & 3.33 & 25 & 83.33 \\
7 & 0.692 & 0.019 & 2.770 & 0.643 & 0.679 & 0.698 & 0.704 & 0.733 & 0 & & & 1 & 3.23 & 30 & 96.77 \\
8 & 0.688 & 0.027 & 3.890 & 0.614 & 0.677 & 0.692 & 0.706 & 0.737 & 0 & & & 3 & 9.68 & 28 & 90.32 \\
9 & 0.662 & 0.027 & 4.080 & 0.566 & 0.659 & 0.667 & 0.679 & 0.699 & 0 & & & 5 & 16.67 & 25 & 83.33 \\
10 & 0.634 & 0.052 & 8.210 & 0.507 & 0.598 & 0.646 & 0.680 & 0.698 & 0 & & 2 & 6.45 & 15 & 48.39 & 14 & 45.16 \\
11 & 0.497 & 0.138 & 27.700 & 0.163 & 0.373 & 0.530 & 0.626 & 0.671 & 4 & 13.33 & 12 & 40.00 & 13 & 43.33 & 1 & 3.33 \\
12 & 0.520 & 0.151 & 29.010 & 0.115 & 0.466 & 0.549 & 0.646 & 0.677 & 5 & 16.13 & 11 & 35.48 & 8 & 25.81 & 7 & 22.58 \\ \hline
**Year** & 0.620 & 0.121 & 19.440 & 0.115 & 0.577 & 0.664 & 0.700 & 0.763 & 17 & 4.66 & 61 & 16.71 & 76 & 20.82 & 211 & 57.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Monthly statistics of KT and the number of days (N) and their percentage according to the classification of days with respect to KT for each month of the year. sunshine duration is 9.2 h with an annual total of 3358 h. The monthly mean daily values range between 5.1 h in January to 12.8 h in July. Figure 28a shows the relationship between the \(K_{D}\) and \(K_{T}\), while Figure 28b shows the relationship between \(K_{D}\) and daily relative sunshine (\(\sigma\)). The cubic equations of the above relationships have the following form:  
\[K_{D}=0.818+1.679*K_{T}-6.423*K_{T}^{2}+3.866*K_{T}^{3}R^{2}=0.871 \tag{44}\]  
\[K_{D}=0.975-1.382*\sigma+1.189*\sigma^{2}-0.704*\sigma^{3}R^{2}=0.905 \tag{45}\]
## 4 Method
### Receiver Operating Characteristic  
The Receiver Operating Characteristic (ROC) curve is a graphical representation used to evaluate the performance of a binary classification system by plotting the true positive rate (TP) against the false positive rate (FP) across varying threshold values [47]. It is a powerful tool for evaluating the performance of prediction models, and visually represents the model's performance, providing valuable insights into its overall predictive accuracy and capability. The FPI values are normalized, and the area under the ROC curve (AUC) is employed to assess the performance of the methods. To validate the results, the predictive accuracy of the models is estimated using key parameters, as detailed in the confusion matrix below. Moreover, the evaluation of the flood identification model involves several key metrics, each providing unique insights into the model's performance. The Accuracy (ACC) measures the overall correctness of the model by calculating the proportion of correctly classified instances to the total instances. The Negative Predictive Value (NPV) quantifies the likelihood that a negative prediction is correct, offering insights into the model's reliability in identifying negative cases. The False Positive Rate (FPR) evaluates the proportion of false positives out of all negative instances, indicating the model's susceptibility to incorrectly classifying negative samples as positive. The True Positive Rate (TPR) indicates the proportion of actual positive cases that are correctly identified using the model. Specificity, also known as the true negative rate, complements FPR by quantifying the model's ability to correctly identify negative cases [48].  
As shown in Table 1, these parameters are used in the following formulas to calculate predictive accuracy and related metrics:  
\[\begin{split} ACC=\frac{TP+TN}{TP+TN+FP+FN}\\ NPV=\frac{TN}{TN+FN}\\ FPR=\frac{FP}{TP+TN}\\ TPR=\frac{TP}{TP+FN}\\ Specificity=\frac{TN}{TN+FP}\\ AUC=\int_{FPR=0}^{FPR-1}TPR(FPR)d(FPR)\end{split} \tag{11}\]  
Higher values of PA and TPR, along with a smaller FPR, indicate improved performance of the FPI index.
## 2 Related Work  
### Fog Computing Architecture  
To overcome the limitations of Cloud computing, various paradigms such as Edge computing and FC have been proposed [5]. The primary concept of Edge computing is to establish a hybrid architecture that integrates peer-to-peer and Cloud servers with mobile terminals [6]. On the other hand, FC is often seen as an extension and evolution of the Cloud toward a more distributed model that gets closer to end users and exploits network nodes for user computing. The new FC paradigm was proposed by Cisco in 2012 [7]. As such, it is defined as \"an intermediate computing architecture between IoT devices and the Cloud, providing computing and storage resources at the Edge of the network\" [8]. The comparison between Cloud, FC, and Edge computing is summarized in Table 1.  
Various architectures of FC have been proposed, and most of them are based on three tiers. The first tier is the Edge tier, which consists of IoT devices equipped with sensors, actuators, and computing capabilities [9]. Edge refers to the processing of data close to the device where it is generated, often at the device itself or in proximity. Additionally, Edge devices can perform initial processing tasks using their onboard processing capabilities, such as data filtering, aggregation, or simple analysis directly on the device itself. Moreover, Edge computing resources like gateways or more powerful IoT devices may have more advanced processing capabilities. These capabilities enable them to run more complex algorithms or perform extensive data processing tasks directly at the Edge of the network. This allows Edge devices to filter, aggregate, or analyze the collected data before transmission to higher tiers for further processing or storage. These data can include environmental parameters (such as humidity, temperature, or motion), device statuses, or user interactions. The second tier is the Fog tier, which acts as an intermediary between the Edge and Cloud tiers. The Fog tier encompasses colonies or domains that contain various powerful node types, including Fog servers managed by an orchestrator. Here, more complex processing tasks can be performed compared to the Edge tier, allowing for deeper analysis of the collected data. Additionally, the Fog tier provides services closer to the end devices than the Cloud tier, reducing latency and bandwidth usage for certain applications. Unlike Edge computing, Cloud computing services like SaaS, IaaS, and PaaS can be extended by FC to the network Edge. Due to these stated features, FC is believed to be an efficient and promising computing paradigm for IoT compared to other associated computing models [6]. Finally, the Cloud tier is the top tier within the FC architecture. This level typically consists of powerful servers and storage systems located in remote data centers. The Cloud tier is in charge of storing and analyzing large volumes of data collected from the Edge and Fog tiers. Figure 1 shows an overview of the FC architecture.