esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Cloud Computing Analysis: A CCE-Based HTTP-GET Log Dataset
## 4 Dataset Design Principles and Significance
### The Significance of Datasets  
In the field of IDSs, a variety of datasets have been utilized. These IDSs can be classified into several categories: ML-based, DL-based, data-mining, and rule-based systems.  
For instance, DL-based and ML-based IDSs depend on network traffic data [60], represented by a set of traffic features, to run their detection models. As such, the datasets used in the learning or training and testing stages of IDSs must be validated and efficient. The significance of having datasets for CCE requests can be outlined as follows:  
* To illustrate attack behavior: IDSs must comprehend the behavior of illegitimate requests. This aids in the identification of the features and characteristics that differentiate these requests from legitimate ones.
* To ensure experimental repeatability: Repeating experiments by using the same dataset is necessary to enhance a specific detection system. This enables the production of accurate, valid, reliable, and consistent results.
* To validate new IDSs: CCEs are susceptible to various attacks, prompting the creation of new detection solutions. Each unique IDS, however, must be validated and tested against a dataset to assess its reliability.
* To compare new IDSs with existing ones: evaluating the efficiency of an IDS and comparing it to other IDSs by using datasets is essential to achieve significant improvements.
* To effectively tune parameters and configurations: The performance of most IDSs is influenced by specific configurations and/or parameters. Conducting various experiments, such as cross-validations, to determine the optimal parameter values is crucial.
* To determine the most important feature sets: The validity of the selected feature sets positively impacts the detection accuracy. Multiple tests using datasets are required to select the best set of features to represent normal and abnormal requests.  
Evaluating IDSs in the context of cloud computing environments is crucial due to the rising prevalence of cloud-based services and their unique security challenges. Cloud environments are complex and dynamic and involve multitenancy, virtualization, and shared responsibilities. The existing evaluation approaches are inadequate because they often lack cloud-specific testing scenarios and features and may not scale effectively in dynamic cloud settings. Additionally, IDSs may be blind to the hypervisor layer, limiting their ability to detect attacks targeting virtualized infrastructure. To address these shortcomings, there is a need for a dataset collected from real-world implementations in CCEs that contains CCE-specific features from multiple sources, incorporates cloud-specific testing scenarios, and considers specific attacks. These efforts will aid in developing effective IDSs tailored to cloud computing environments.
### Research Gaps and Motivation  
Existing works have applied various algorithms for voice disorder detection [12; 13; 14; 15; 16; 17; 18]. However, further research and exploration are necessary to address the following limitations in existing works.  
1. Some existing works [12; 16; 17] did not apply cross-validation during their performance evaluation of the algorithms. The first concern is that one may pick up a biased training dataset to train the detection model, which takes advantages of this bias by yielding high accuracy. Secondly, not all the data were evaluated, which may affect the fine tuning of the model and the robustness of its applicability in real-world scenarios.
2. There is room for improvement in the accuracy, sensitivity, and specificity of voice disorder detection models [13; 14; 15; 16; 18]. Particularly in smart healthcare applications, the performance of the machine learning model has high expectations, as such applications are related to the health status of humans.
3. Current works [12; 13; 14; 15; 16; 17; 18] have formulated the voice disorder detection problem as binary detection that only outputs a healthy or pathological result. It is desirable for machine learning algorithms to suggest the actual type of voice disorder to minimize screening time by medical professions.
4. Current works [12; 13; 14; 15; 16; 17; 18] have not considered the issues of imbalanced datasets in both SVR and VOICED. This may alter the gap between the sensitivity and specificity of the detection model.  
To solve these limitations, this paper adopts the following measures.  
1. A 10-fold cross validation is adopted for the performance evaluation of the voice disorder detection algorithm.
2. The proposed algorithm incorporates a generative adversarial network and fuzzy c-means clustering to improve the performance of the detection model.
3. Voice disorder classification is formulated as a multi-class detection problem, thereby allowing the actual type of voice disorder to be suggested.
4. A generative adversarial network is proposed to generate new training data, which will reduce the influence of imbalanced datasets and improve the performance of the voice disorder detection model.
## 2 Materials and Methods
### Standardized Z-Values  
To enable the application of inter-variable correlation, the patient cohort generator algorithm standardizes all sampled values into Z-scores. This step ensures that all variables are on a common scale, with a mean of zero and a standard deviation of one, which is a necessary condition for applying Cholesky decomposition to a correlation matrix.  
The algorithm first uses the generated uniform random number \(R_{i,j}\sim U(0,1)\) to sample a value from the probability distribution for each parameter. The sampling is performed using the inverse cumulative distribution function (CDF):  
\[X_{i,j}=F_{i}^{-1}\big{(}R_{i,j}\big{)}\]  
where \(F_{i}^{-1}\) is the inverse CDF for the distribution assigned to parameter \(i\), and \(X_{i,j}\) is the raw sampled value for patient \(j\).  
Each sampled value is then transformed into a standardized Z-score using the user-defined mean \(\mu_{i}\) and standard deviation \(\sigma_{i}\):  
\[Z_{i,j}=\frac{X_{i,j}-\mu_{i}}{\sigma_{i}}\]  
This transformation produces a matrix of standardized values \(Z_{i,j}\) that can be used in correlation adjustments. Standardization allows the algorithm to impose the specified correlation structure consistently across variables, regardless of their original scales. The pa rameter definitions and the distribution-specific details required for inverse CDF sampling are provided in Appendix B.
## 2 Modeling Methodology
### Approaches to Capture Soil Behavior during Liquefaction
#### 2.1.1 Total Stress Analysis  
In the event of an earthquake, the seismic wave propagated from the bedrock reaches upper soil particles through the soil layer, as shown in Figure 1. In this case, liquefaction occurs when the underground cyclic shear stress caused by the earthquake exceeds the magnitude of the cyclic shear stress required to cause liquefaction. The zone of liquefaction is determined by comparing the magnitudes of the generated cyclic shear stress and shear strength according to the depth, as shown in Figure 2 ([PERSON], 1971 [18]). Liquefaction is similar to the strain-softening behavior that occurs when sandy soil is subjected to shear stress under undrained conditions. This is the concept whereby the undrained strength gradually decreases to a certain state where the strain exceeds a maximum value; this strength value is referred to as the residual strength of the soil. Using this concept, it is possible to analyze the deformation of the soil-pile system caused by liquefaction through dynamic numerical analysis. This analysis is conducted by evaluating the zone of liquefaction caused by the seismic load, calculating the residual strength after liquefaction, and inputting the strength parameter corresponding to the residual strength into the zone of liquefaction. Liquefaction analysis by this process is referred to as total stress analysis.  
Figure 1: Earthquake wave transmission.
## 3 Results  
### Preliminary Experiment in a Building  
To confirm the importance of LOS connections between communication devices, a preliminary experiment was conducted in a building at the Samcheok Campus of Kangwon National University in South Korea prior to its application in the mine. The developed technology was applied to a 3D model (Figure 4a) based on a 2D design. Figure 4b shows the results of the LOS analysis from the observation point at the end of the corridor with a communication distance of 100 m; here, blue is used to represent closer points, whereas red indicates farther points. Evidently, a protrusion can be observed in the central part of the corridor, where the exit is located. This signifies that the LOS is not connected. Figure 4c,d show the central part of the corridor and a view from the observation point, respectively.  
Figure 4: Results of preliminary experiments within the building. (**a**) Three-dimensional model, (**b**) LOS analysis result, (**c**) the central part of the corridor, and (**d**) view from the end of the corridor.  
Figure 3: Concept of device location selection considering a pre-installed device (point O) and communicable areas. Point B has a wider communicable area than point A.  
In this study, ZigBee-based wireless communication tests were performed using Digi International XBee 3 PRO. ZigBee is a low-power wireless communication protocol for local areas, where one node can be connected to 255 devices, thus allowing expansion up to 65,000 devices [25]. In addition, it has the advantage of low power consumption. Therefore, it is suitable for constructing WSNs in underground mines. Figure 5a shows a coordinator device connected to a laptop and portable router device. The communication strength between two devices can be expressed as a received signal strength indicator (RSSI) value. RSSI is the power measurement of the received wireless signal and is expressed as a negative dBm value. A higher RSSI represents better signal strength, and a value of approximately -70 dBm or higher implies a good communication environment. Stronger signals have signal strength closer to 0 dBm. In the preliminary experiment, the coordinator device was fixed at the end of the corridor, and the change in RSSI was recorded while moving the router device away from the coordinator. Figure 5b shows the change in the RSSI value as the router device moves away from the coordinator device. The horizontal axis represents time; however, as the router moves, it also represents the distance. In Figure 5b, the RSSI decreases rapidly in the section when the router device is located on the protrusion (Figure 4c) in the center of the corridor. In reality, electromagnetic waves do not propagate only linearly owing to refraction and reflection; nonetheless, the communication strength dropped sharply in areas where the LOS was not connected owing to obstacles.
# Automatic Vertical Parking Reference Trajectory Based on Improved Immune Shark Smell Optimization
## 4 Experimental Verification  
### Description of the AVP Experiment Scenes  
The experimental vehicles in this work were equipped with four ranging radars on the rear bumper, four on the side of the front and rear bumpers, and four on the front bumper, for a total of twelve ranging radars. During the AVP process, these 12 ranging radars can realize the close-range blind area monitoring function, which can be used to detect the front and rear obstacles and identify the position of each side line and corner point of the garage. AVM (Around View Monitor) is an important subsystem of the image transmission and processing system. It collects the data from the surrounding environment through four image sensor cameras (one each installed in the front, rear, left, and right directions) and transmits the image to the around view monitor controller through LVDS (HD). Combined with the image transmission processing system, the 12 ranging radars can realize the recognition function of the side line position data of the garage position and the actual obstacle data in the automatic reversing parking system. The information of the garage side lines, the corner point between the garage side line and the side line, the current area of the experimental vehicle, and the size and position of the sudden obstacle can be clearly determined and transmitted to the upper computer.  
In China, standard vertical parking garages are widely used in automatic vertical parking. For the standard vertical parking garage, the near side and far side lines of the garage were equal in length, at 5 m, the bottom line length of the garage was 2.5 m, and the width of the marker line was 0.1 m. For Dalian Shell Museum, the experimental site selected in this work, the vertical parking garages are standard. Two AVP experiment scenes were established and used for verification. For the first AVP experiment, the No.139 garage of Dalian Shell Museum in Dalian Xinghai Square was used. The experimental vehicle was a Toyota Leiling Shuangqing 185T Sportline (2020 model), with a length of 4720 mm, a width of 1860 mm, and a height of 1435 mm. The horizontal and vertical distances between the right corner point at the bottom of the starting area of the experimental vehicle and the near corner point of the garage top were 2 m and 2.1 m, respectively. For the second AVP experiment, the No.156 garage of the Dalian Shell Museum was used. The experimental vehicle was a Toyota Corolla 1.2T S-CVT GL Pioneer Version (2019 model),with a length of 4635 mm, a width of 1780 mm, and a height of 1455 mm. The horizontal and vertical distances between the right corner point at the bottom of the starting area of the experimental vehicle and the near corner point of the garage top were 2 m and 1.8 m, respectively.
## 3 Results
### Step 3--Habit Assessment Analysis with VHR Imagery
#### 3.2.3 Skylark  
This bird can nest in silage fields (grazed pasture) if the field is not grazed between early April and the end of May. Figure 14 presents an example of monitoring such habitat suitable for Skylark nesting in four grass/silage fields at intensive study Site 1, by using VHR satellite images in mid-May and again in late-June 2018 and species sighting recorded in the same year. The fields are clearly uncut in May 2018, thus maintaining this habitat for Skylark nesting and rearing, and they are showing as cut (for silage) at the end of June 2018, after this important period for the Skylark. It is clear that the detail available from the VHR imagery is sufficient to assess the status of these fields (cut/uncut), and therefore, with appropriate overflies and image collection frequencies, VHR can be used readily to monitor the grass management in support of Skylark nesting habitat. We believe that the Skylark seen in 2018 was successfully maintained by the agricultural practices in place at that time.  
Figure 13: Small Blue butterfly–Step 3 VHR habitat assessment, intensive study Site 2, visual interpretation, supervised image classification with quantification.  
Figure 15 shows a grass field at intensive study Site 2 in 2017 and 2020. This location is a suitable nesting habitat for Skylark (skylark recorded there in 2017). The VHR imagery (Map 1 Figure 15) shows the field with full grass cover in early-July 2017, indicating that it had not been cut during the Skylark breeding season. Furthermore, the grass margins alongside hedgerows (lighter green (Map 1) and lighter grey (Map 2) Figure 15) are clearly present with a width of \(\sim\)10 m in both 2017 and 2020. These two practices, grass cutting time and field margins, plus the presence of hedgerows and neighbouring woodland (supporting insect and seed diversity), play a crucial role for the Skylark by providing a good physical location, source of food, and materials for nesting.
# Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction
## 4 Proposed Algorithm
### UAV Dynamic Deployment Algorithm
\[L_{\text{total}}(\theta)=L(\theta)-c_{1}L_{\text{value}}(\theta_{v})+c_{2}S( \pi_{\theta}) \tag{46}\]  
The specific pseudo-code of the algorithm is as Algorithm 2:  
```
1:Initialize the strategy network \(\pi\) and the value network \(V\);
2:Initialize the experience cache pool \(D\);
3:Initialize the optimized LSTM model to predict vessel trajectories;
4:Initialize the communication service coverage optimization algorithm PPO;
5:for each episode do
6: Initialize the environment state \(s\), set the initial state to contain information about the satellite, the UAV, and the ground base station, as well as information about the relative position of the UAV and the target vessel;
7:for each step do
8: Adopting the current policy \(\pi\), action \(a\) is selected according to state \(s\);
9: Performing action \(a\), the environment returns a new state \(s^{\prime}\) and reward \(r\);
10: Store the experience (\(s\), \(a\), \(r\), \(s^{\prime}\), relative positions of ships and UAVs, number of covered ships, time delay) in \(D\);
11: Update status \(s\)\(\leftarrow\)\(s^{\prime}\);
12:endfor
13: Update the policy network \(\pi\) and the value network \(V\) using the data in the cache pool \(D\);
14: Sample a batch of data from \(D\);
15: Calculate the advantage function \(A^{\pi_{\text{old}}}\) for each action;
16: Updating the strategy network to maximize expected reward and entropy and reduce action bias;
17: Update the value network to minimize the value function error;
18: Empty cache pool \(D\);
19: Predicting the next position of the target vessel using the LSTM model
20: The predicted locations are fed into the PPO algorithm to optimize the communication service coverage;
21: Update the policy network \(\pi\) to consider the optimization results of the PPO algorithm;
22:endfor
```  
**Algorithm 2** Reinforcement learning model for maritime communication service coverage using RUDD algorithm
## 3 Results and Discussion
### Energy Dispersive Spectroscopy (EDS) Analysis  
Figure 3B shows the energy dispersive spectroscopy (EDS) analysis, which reveals the elemental composition of the prepared nanoparticles. These EDS values are useful in reflecting the nuclear content on the surface regions of the FeNP. The EDS profile shows intense peak signals of iron with a K\(\alpha\) peak at 6.400 keV, a K\(\beta\) peak at 7.059 keV, and an L\(\alpha\) at 0.75. Other signals observed include that of chlorine, oxygen, carbon, and copper. EDS quantification gives atomic percentages of 34.64% C, 39% O, 15.33% Fe, 4.13% Na, 3.48% S, 1.27% Cu, 0.48% Cl, and 0.13% P. Copper signal is due to the copper grid onto which the sample was placed during the analysis. The existence of elemental iron and oxygen demonstrating that the nanoparticles are essentially present in oxide form [25]. Furthermore, the presence of C and O peaks are related to polyphenols or any other C and O containing a compound in Acacia _nilotica_ pods extract [10]. The S peak is an indication of the presence of a sulfate group from the FeSO\({}_{4}\) precursor used. The appearance of K is related to the complex of plant extract. It was reported that potassium plays an important role in enzyme activity. In addition, it neutralizes organic anions within the plant extract and stabilizes pH between seven and eight, which is optimized for enzyme activity [8]. Very similar results were reported for Fe nanoparticles prepared with other leaf extracts [11; 18].  
Figure 3: _Cont._
# Switched Observer Design for a Class of Non-Linear Systems
## 2 System and Observer Description
### LPV Formulation  
Notice from the DMVT, there exists an \(\tilde{x}\in Co(x,\hat{x})\) such that:  
\[f(x)-f(\hat{x})=\left(\sum_{i=1}^{i=n_{f}}\sum_{j=1}^{j=n_{x}}e_{n_{f}}(i)e_{n_ {x}}(j)^{T}\frac{\partial f_{i}}{\partial x_{j}}(\tilde{x})\right)(x-\hat{x}).\]Hence, using the notations  
\[\begin{split} h_{ij}(t)&=\frac{\partial f_{i}}{ \partial x_{j}}(\bar{x}(t))\\ h(t)&=(h_{11}(t),...,h_{n_{f}n_{x}}(t))\end{split} \tag{6}\]  
we may define  
\[A(h(t))=A+\sum_{i,j=1}^{n_{f},n_{x}}h_{ij}(t)B_{f}e_{n_{f}}(i)e_{n_{x}}(j)^{T} \tag{7}\]  
which casts the observer error dynamics in (5) as follows:  
\[\dot{x}(t)=(A(h(t))+L_{\sigma(t)}C)\bar{x}(t)+B_{d}d(t). \tag{8}\]  
Indeed, this representation allows us to have a more appropriate and flexible handling of the non-linearities in consideration while still making use of all of its time-varying properties according to the so called LPV formulation [31]. In light of this, and using the results presented in Lemma 2 for locally Lipschitz non-linear functions, the functions \(h_{ij}\) in (7) are bounded according to  
\[\overline{h}_{ij}<h_{ij}(t)<\underline{h}_{ij},\ \ \forall t\geq 0\]  
suggesting that the parameter \(h(t)\) evolves in a bounded domain \(\mathcal{H}\), of which its \(n_{p}\leq 2^{n_{x}n_{f}}\) vertices are defined by:  
\[\mathcal{V}_{\mathcal{H}}=\Big{\{}(h_{11},...,h_{n_{f}n_{x}}):h_{ij}\in\Big{\{} \underline{h}_{ij},\overline{h}_{ij}\Big{\}}\Big{\}}. \tag{9}\]  
At this stage, we may even describe the estimation error dynamics in (8) as an uncertain-switched polytopic linear system  
\[\dot{x}(t)=(A(\alpha(h(t))+L_{\sigma(t)}C)\bar{x}(t)+B_{d}d(t) \tag{10}\]  
with  
\[A(\alpha(h(t)))=\sum_{j=1}^{n_{p}}\alpha_{j}(t)A_{j} \tag{11}\]  
and the matrices \(A_{j},\ j=1,...,n_{p}\) are constructed by taking all elements of the set \(\mathcal{V}_{\mathcal{H}}\), defined in (9) and  
\[\begin{split}&\alpha(t)\in\Lambda=\\ &\Bigg{\{}[\alpha_{1},...,\alpha_{n_{p}}]^{T}\in\mathbb{R}^{n_{p} }:\sum_{j=1}^{n_{p}}\alpha_{j}=1,\ \alpha_{j}\geq 0\Bigg{\}}.\end{split} \tag{12}\]  
This description is the foundation of the switching design approach put forward in this work, which aims to improve the estimation performance in addition to reducing the conservatism of the stabilizing conditions, particularly in cases when the range of the time variant parameters is large [32].
# A Reputation Model of OSM Contributor Based on Semantic Similarity of Ontology Concepts
## 3 Methods
### Contributor's Initial Reputation  
The contributor's initial reputation is the reputation before they participate in the contributions. Different types of users provide data of different quality. For example, the data quality provided by professional contributors is higher than that provided by novice or unskilled contributors [38]. Therefore, the method adopted in this paper is to divide the contributors in the region into different types and determine a different initial reputation for different types of contributors so as to obtain more accurate evaluation results of contributor's reputation. In existing research, [PERSON] et al. grouped volunteers according to three indicators: activity, contribution duration, and contribution cycle change [39]. [PERSON] et al. proposed a method based on the combination of PCA and K-means to classify contributors into four categories; [38] however, classification of contributor is implemented based on various contributor's characteristics. Due to the different types and dimensions of contributors, the unified normalization method will lead to the problem that the classification characteristics are not obvious.  
In response to the above problems, [PERSON] et al. proposed the use of the improved WPCA-based feature dimension reduction and classification method to cluster contributors with similar features into four types, namely novice, unskilled contributors, major contributors, and professional contributors [37]. On the basis of the above study, this paper determines different initial reputation according to data quality provided by three types of contributors. According to the current research on OSM user's reputation, such as [PERSON] and [PERSON] [32; 40], most of them set a fixed value to the initial value of user's reputation. The research of [PERSON] et al. shows that the reliable dataset in OSM is provided mainly by three-quarters of contributors [41]. Therefore, according to this statistic, this paper classifies contributors into three categories [37]: novice and unskilled contributors, major contributors, and professional contributors. In addition, this paper set 0.75, 0.9, and 1 to the initial reputation values of the three categories of users, respectively.