esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis
#### 2.2.3 Calibration of Readiness Index/ClimAdaptCap Index  
Once the poverty and literacy rates data were subjected to linear interpolation to fill the gap of the missing data and the climate data were normalized. This investigation proceeded to develop an index called \"_ClimAdaptCap Index_\". This index used both climate and adaptive capacity proxy data to determine the readiness of each region to climate change adaptation. This is the first time that this index is being developed and used to determine readiness for climate change adaptation in Africa. The uniqueness of this index is that it integrates both normalized temperature and precipitation data (climate score) and the adaptive capacity component through the proxies of the adaptive capacity (adaptive capacity score), which in this case are poverty and literacy rates. The only weakness of this index is that it uses only two proxies of adaptive capacity, which are poverty and literacy rates. It is, however, true that there are other proxies of adaptive capacity, but in the case of the African continent, these two proxies are very representative of the reality of the African continent. In addition, this index was developed within the frames of the African continent and therefore meant to test the readiness of African countries and regions to climate change adaptation. This index is scored on a scale of \(-1\) to \(1\), and the higher the score, the higher the level of readiness for climate change adaptation. The weighting of all the indicators isequal, and this approach facilitates comparison and integration. The calibrated equation used to compute this index is given as follows:  
\[\mathit{ClimAdaptCapIndex}\ =\ \Bigg{(}(1+Nt)+(1+Nppt)+\frac{\left(10^{2}-Pr\right)}{10^{ 2}}+\frac{\left(Lr\right)}{10^{2}}\Bigg{)}/10 \tag{3}\]  
where \(\mathit{ClimAdaptCapIndex}\) is the readiness Index for climate change adaptation, 1 is a constant that the maximum \(\mathit{ClimAdaptCapIndex}\) is 1, \(Nt\) is the normalized average regional temperature, \(Nppt\) is the normalized average regional precipitation. The first part of the equation denotes the climate score. For the adaptive capacity score, \(Pr\) is the poverty rate, \(Lr\) is the literacy rate, and \(10^{2}\) denotes the fact that these proxies are expressed in percentages.
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 5 Evaluation
### Evaluation of the Attack Effectiveness  
We also implement the saturation attack program and evaluate its effectiveness as a whole process. We implement existing timeout decision algorithms and deploy monitoring programs on the SDN controller.  
In the attack process, the generation rate of malicious flow rules is controlled. We use up to 80 flows to simultaneously generate malicious flow rules during the triggering phase. To comprehensively analysis of the attack's impact on various network performance metrics, we demonstrate the attack's effectiveness in three typical scenarios: (I) direct forwarding, (II) dropping request, and (III) rule eviction.
## 5 Experiments and Results
### Fault Detection and Classification
#### 5.2.5 Results Analysis
The success of ViT and DenseNet in our study proposes these models as the optimal choices for tasks requiring faultless performance in image-based classification. Moreover, the high marks achieved by Xception, ResNet, VGG, and AlexNet reinforce the potential of deep learning models in achieving near-perfect detection rates, albeit with slight distinctions in their precision-recall balance. This analysis not only affirms the value of employing advanced deep learning techniques for PFB inspections, but also highlights the importance of model selection based on specific performance criteria to address the precise needs of the task at hand.
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 2 Structure of Filter Bank Multi-Carrier Systems
### Multirate Filtering  
One basic notion that is adopted in DSP is the concept of sampling rate [26]. Sampling rate involves the conversion of a signal from a given rate to a different rate. It is worth noting that systems that adopt multiple sampling rates in DSP are known as multirate digital signal processing systems. These systems employ the use of multirate filters which are classified into hybrid and polyphase filters. Polyphase filters have demonstrated good performances over the years, therefore proving to be an excellent multirate filter that can be effectively adopted to solve wireless communication problems [26]. Multirate filters find useful applications in digital audio and video processing, speech coding, image compression, adaptive signal processing, scrambling, software define radios and modern DSP systems [26; 27; 28]. Multirate Filtering is based on two filtering techniques: decimation and interpolation [28; 29].  
Interpolation is a sampling rate conversion technique that is adopted to remove the effects of imaging through the use of an interpolation filter while increasing the sampling rate of the signal using an interpolator (upsampler). Upsampling is used to increase the sampling rate of a signal by the insertion of an integral number of samples (zeros) between consecutive samples of input signals. Figure 1 shows a simple diagrammatic representation of an interpolation process that consist of an upsampler/expander of factor \(M\) and an interpolation filter with response \(g[\hat{n},k]\). In this figure, \(y[\hat{n},k]\) represents the higher rate output symbols that are received after upsampling process while \(x[n,k]\) represents the input symbols at time \(n\) and sub-carrier \(k\). The output time index \(\hat{n}\) is at a higher rate in comparison to the input index \(n\). This process of interpolation/signal rate conversion is illustrated in Figure 2, where the sampling rate of Figure 2 is increased in comparison to the sampling rate of Figure 2, by the insertion of zeros between consecutive samples. On the other hand, decimation is a sampling rate conversion technique that is usually adopted at the receiver of a communication system to suppress aliasing by the use of a decimation filter and a downsampler [30]. In addition, they are used to reduce the sampling rate of a signal by a factor \(M\) as shown in Figure 1. Similar to Figure 2, the decimation process is shown in Figure 3.  
Figure 1: Structure of multirate filtering.  
Figure 2: Time domain plot of signal interpolation for \(M\) = 2. (**a**) Before upsampling. (**b**) After upsampling.
## 3 Results
### MLP and 1D-CNN MAE  
Figure 5 shows the MAE prediction values for speech audiometry using PTA data with the MLP and 1D-CNN models. Regarding MAE, the MLP model recorded an average MAE of 7.26, while the 1D-CNN model achieved a slightly lower average MAE of 6.90. This indicates that the 1D-CNN model demonstrated better performance in predicting speech audiometry scores, as it was able to learn essential frequency bands from the PTA data more accurately.  
MAE is a metric used to evaluate the performance of regression models, representing the average absolute difference between the predicted and actual values. MAE is measured by calculating the absolute error between the predicted and actual values for each data point and then averaging them. It is used as an intuitive metric for evaluation. The MAE formula is defined as follows:  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert \tag{7}\]  
where \(y_{i}\) represents the actual values, \(\hat{y}_{i}\) is the predicted values, and \(n\) is the number of data points. In the formula, \(\lvert y_{i}-\hat{y}_{i}\rvert\) is the absolute difference between the predicted and actual values for each data point. The sum of these errors is then divided by the number of data points to calculate the average. MAE indicates how far, on average, the predicted values are from the actual values, with a smaller value indicating that the model's predictions are closer to the actual data.  
The most significant advantage of MAE is its intuitive interpretation. For example, if the MAE is 10, it means that the model's predictions differ from the actual values by an average of 10 units. Additionally, unlike mean squared error (MSE), MAE is less sensitive to outliers, as it calculates the absolute difference rather than squaring the errors. This prevents significant errors from disproportionately affecting the overall evaluation, making MAE more stable when there are outliers in the data.
## 2 Materials and Methods
### Breach Evolution Model
#### 2.2.1 Breach Flow Calculation  
The flow of the breach is influenced by various intricate aspects, including the inflow of water from upstream, the storage capacity of the reservoir, the features of the breach section, and the water depth near the breach. The general dam break model involves computing the variation in reservoir storage capacity during each time interval based on the inflow of water from upstream and the reservoir's capacity curve. The weir formula is employed to compute the discharge of the breach, whereas the Manning formula is utilized to determine the water level of the breach [20,33,34]. The proposed integrated model in this research calculates the water depth and flow velocity by updating the terrain at each time step and solving the two-dimensional shallow water equation. Moreover, the hydraulic parameters, such as breach discharge and water depth, can be obtained, thereby circumventing the inaccuracies resulting from calculation methods such as the average water depth of the reservoir, the weir flow formula, and the Manning formula. This ultimately improves the accuracy of the breach discharge calculation.
# Assessing the Security and Privacy of Baby Monitor Apps
## 4 Evaluation of the Privacy Policies
### Third-Party Collection/Use  
Article 14 of the GDPR requires affected users to be informed about the collection of personal user data through third-party sources i.e., without the user's knowledge [32]. Based on the information provided in the privacy policies, third parties collect personal data in all applications, except the Dormi app. These personal data include cookies and tracking elements, but also more extensive personal information, such as IP address and device ID, further device information, and user online activities. The purpose of the collection ranges between advertisement or marketing, analytics and research, and essential features. The applications with third-party collection list the entities that collect data on their app. The Saby app does not provide any option to object to third-party data collection, while BabyCam and Babyphone Mobile provide choice types such as an opt-in and an opt-out link, or not using part of the service.
## 2 Materials and Methods
### Experimental Design of Response Surface Methodology  
In the study, RSM was used to optimize the conditions by which to obtain fertilizer with the highest encapsulation efficiency and water absorption. The Box-Behnken design was used to investigate the influence of five independent variables, the concentrations of sodium alginate (X\({}_{1}\)), carboxymethyl starch sodium (X\({}_{2}\)), urea (X\({}_{3}\)), CaCl\({}_{2}\) (X\({}_{4}\)) and polydopamine (X\({}_{5}\)), on the two dependent variables of EE and WA. The ranges of independent variables were determined based on previous single-factor experiments. Each test was repeated three times, and the mean values and standard deviations were calculated. The Design Expert statistical software (Version 10.0) was used to statistically evaluate the experimental data. The coding level and actual level of independent variables used in RSM design are shown in Table 1.  
The change of the response value (Y) relative to the independent variable was fit into a response surface model and presented as a second-order polynomial equation:  
\[Y_{m}=\beta_{0}+\sum\beta_{i}X_{i}+\sum_{i\
eq j}\beta_{ij}X_{i}X_{j}+\sum\beta _{ii}X_{i}^{2} \tag{3}\]  
where Y\({}_{m}\) is the dependent variable analyzed (Y\({}_{1}\)--encapsulation efficiency, Y\({}_{2}\)--water absorption); \(\beta_{0}\) is the constant coefficient; \(\beta_{i}\), \(\beta_{ii}\) and \(\beta_{ii}\) are the coefficients of linear, interaction and squared effect, respectively; and X\({}_{i}\) and X\({}_{j}\) are independent variables. The linear term analyzes the influence of one variable on the response at a time, while the interaction term evaluates the influence of two variables at the same time. The squared term is used to study nonlinear response.
# Interference Signal Feature Extraction and Pattern Classification Algorithm Based on Deep Learning
## 3 Interference Monitoring Research
### Interference Monitoring Technology Based on Artificial Feature Extraction  
The general process of interference monitoring technology is shown in Figure 2, including interference signal existence check, interference signal classification and interference parameter estimation. Firstly, the technology detects the interference of the received sampling data to judge whether the interference signal exists [22; 23]. When there are interference signals, the interference is classified and the parameters are estimated. Finally, the results of detection and recognition are output.
## 5 Discussion
### Discovery of Collaborative Effect
Meanwhile, S6_1 has strong correlation relationships with T6_1, T6_4, T6_5, and T6_8, and the collaborative effect is evident. During stage 7, distributed architecture (\"Distributed Cybersecurity Resource Management (T7_1)\") and technologies such as the Internet of Things and Internet of Vehicles (T7_4, T7_6, T7_7) emerged successively. This led to a diversification of the directions of scientific research and technological research and a weakening of short-term collaborative effects. During this period, the dynamic interaction between technology-driven progress and incident response led to the shift in scientific research from basic defense to complex scenarios, forming a coupling-decoupling cycle of phased technology development and scientific exploration. The overall situation exhibits significant fluctuation-differentiated scene-based collaborative features. Stage 8 to Stage 10 (2019-2024): exploration and integration of emerging technologies  
From the perspective of quantity, the number of pairs of the two types of topics decreased from 345 pairs and 21 pairs in stage 8 to 252 pairs and 13 pairs in stage 9. Then, they rose again to 459 pairs and 28 pairs in stage 10, showing an overall collaborative trajectory in a \"U-shaped\" curve. Based on the topic recognition results, the topics during stage 8 and stage 9 showed two trends: The first one is artificial intelligence-driven security collaboration. With the evolution of technology, the application of artificial intelligence technology in the field of cybersecurity became increasingly prevalent, making it a key focus of scientific and technological research (such as S9_2 and T8_1). The second trend is the challenges of emerging technology scenarios. Affected by factors such as the pandemic and cloud computing, the focus of technology research topics began to concentrate on fields such as Internet of Things security (such as T8_3 and T9_4) and cloud storage security (T9_3). Scientific research, however, focused on cybercrime (such as S8_3 and S9_3), blockchain technology (S9_7), and other aspects. During stage 10, the research topics of science and technology continued to be innovative, and the optimization of threat detection and attack-defense game technology became the focus of research. Meanwhile, the security demands in interdisciplinary fields saw an explosion, driving deep integration of science and technology in specific scenarios. Scientific research started to focus on new infrastructure, Internet of Vehicles, medical Internet of Things, and other fields, with a significant increase in the collaborative nature of science and technology.
# Mental Workload Assessment Using Machine Learning Techniques Based on EEG and Eye Tracking Data
## 3 Materials and Methods
### Data Acquisition and Pre-Processing
#### 3.3.1 EEG Data
Techniques like independent components analysis are valuable in uncovering EEG processes that exhibit dynamic characteristics associated with behavioral changes. It is a commonly employed technique to identify and eliminate noise originating from eye and muscle movements [41]. The ICLabel function was used to assess the attributes of the components, and those components that were determined to be ocular, muscular, or originating from another source with a probability exceeding 90% were discarded. Figure 4 illustrates the signal image before and after the filtering process.
# A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks
## 4 Results and Discussion
### Discussion and Comparison  
This study distinguishes itself from previous research by integrating three different feature selection techniques with three classification algorithms. Table 3 provides a comparison between the outcomes of this study and those of related works.  
Table 3 provides a comparison of our proposed intrusion detection system (IDS) with various existing methods, considering factors such as datasets used, feature selection techniques, and achieved accuracy. Our approach, which utilizes the NF-ToN-IoT-V2 dataset along with a combination of wrapper and filter feature selection methods, achieved the highest accuracy of 99.89% using the Random Forest algorithm with the Wrapper method. This performance surpasses that of other studies employing the NF-ToN-IoT-V2 dataset, including those by [PERSON] et al. (2022) [24], [PERSON] et al. (2022) [27], and [PERSON] et al. (2022) [28]. Specifically, [PERSON] et al. [24] reported an accuracy of 95.7% without using any feature selection method, [PERSON] et al. [27] achieved 98% accuracy using a Feature  
In contrast, studies utilizing the AWID dataset, such as those by [PERSON] et al. (2022) ([PERSON] et al., 2022) and [PERSON] et al. (2019) ([PERSON] et al., 2019), also reported high accuracies but slightly lagged behind our method. [PERSON] et al. (2022) achieved an accuracy of 99% by combining Decision Trees, Random Forest, and SVM with a feature selection strategy involving recursive feature elimination and constant removal. [PERSON] et al. (2019), using a custom feature selection method, attained an accuracy of 99.87% with their deep network intrusion detection model. Despite these impressive results, our proposed method demonstrated marginally superior performance, highlighting the effectiveness of our approach in intrusion detection, as illustrated in Figure 8.
# Robust and Accurate Wi-Fi Fingerprint Location Recognition Method Based on Deep Neural Network
## 4 Experience and Analysis
### Experiments of China University of Mining and Technology  
#### 4.2.1 Experimental Setting  
In the UJIIndoorLoc public dataset, the test data are merely simple discrete points, which cannot be used to test the positioning accuracy of user motion. Therefore, a positioning experiment was set up on the fourth floor of the School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, China. This is a typical indoor environment with 800 square meters of the experimental area. We set up 330 fingerprint points in total (1.2 m \(\times\) 1.2 m) and collected 60 Wi-Fi samples at each fingerprint point, for a total of 19,800 \(RSS\) training samples. During the offline stage, we observed 195 APs. The layout of the experimental site is shown in Figure 9.  
We constructed a three-hidden-layer (400-300-200) DNN structure in line with the training data, which possessed 195 and 330 neurons in the input and output layers, respectively. The fingerprint database had 19,800 \(\times\) 195 dimensions. The same smartphone models (Samsung Galaxy S5) were used as test devices to avoid the interference that heterogeneous devices can wreak on positioning results, and the sampling frequency of Wi-Fi was 0.35 Hz. The red line in Figure 9 represents the user's dynamic walking trajectory.
# Less Rare-Earth Electromagnetic Design for a High-Performance Permanent Magnet Motor
## 3 Proposed Magnetization Pattern for Banana-Shaped Magnets with Parallel Magnetization
The analysis is done by exporting the magnetic flux density and magnetic field of all geometric nodes within the magnet regions and comparing them against the BH curve of the magnet. In addition, all the nodes of each magnet were exported for each position in one electrical period. Figure 11 shows the results for both magnetization cases. It can be noticed that all points are on or above the magnet demagnetization curve. Therefore, it is concluded that no magnet is at risk of demagnetization at this temperature level. Figure 10: Baseline motor, no-load flux density distribution: (**a**) parallel magnetization: straight pattern; (**b**) parallel magnetization: tilted magnetization. Figure 9: Tilting the magnet part within parallel magnetization to obtain the titled magnetization pattern. (**a**) Illustration how the real magnet part should be tilted within parallel magnetization; (**b**) View of the desired magnetization pattern in the motor model if magnetized as in (**a**). Finally, it is important to notice from Table 6 that the tilted magnetization pattern with the same tilt angle results in a machine with 7.14% more output torque than the baseline motor with the conventional parallel magnetization (straight magnetization) for the same thermal performance. Therefore, this higher magnetic loading opens the possibility of reducing the magnet weight. So, to satisfy the requirements Table 3 in Section 2, the following section shows the workflow followed to obtain a less rare-earth motor using tilted magnetization.