esa-sceva/satcom-chunk-collection huggingface dataset:
# Cybersecurity Knowledge Extraction Using XAI
## 2 Materials and Methods
### Interpretable Machine Learning Methods
#### 2.4.1 Shapley Values  
Shapley values are a concept borrowed from the literature of cooperative game theory, and they were originally used to fairly attribute a player's contribution to the end result of a game. An assumption is made that a cooperative game exists in which a set of players works together to achieve some value. Shapley values capture the marginal contribution of each player to the end result. The method can be used to interpret the machine learning prediction by assuming that each feature value of the instance is a player in a game where the prediction is the payout.  
Mathematically, the Shapley value is the average contribution of a feature value to the prediction in all feasible coalitions. The Shapley value for feature \(j\) is calculated as follows (see Equation (1)):  
\[\phi_{j}=\sum_{S\subseteq\overline{N}\setminus\{j\}}\frac{|S|!(|N|-|S|-1)!}{|N |!}(f_{x}(S\bigcup\{j\})-f_{x}(S)), \tag{1}\]  
where  
\(N=\) set of all features  
\(S=\) a subset of the features used in the model  
\(f_{x}(S)=\) a function that gives a prediction for any subset of features  
Simply put, the Shapley value for a feature is calculated as follows:  
1. Create the set of all possible feature combinations \(S\) (called coalitions).
2. Calculate the average model prediction.
3. For each coalition, calculate the difference between the model's prediction without feature \(j\) and the average prediction.
4. For each coalition, calculate the difference between the model's prediction with feature \(j\) and the average prediction.
5. For each coalition, calculate how much the feature \(j\) changed the model's prediction from the average (the difference between the values calculated in steps 4 and 3)--this is the marginal contribution of a feature \(j\).
6. The Shapley value is the average of all the values calculated in step 5 (i.e., the average of features' \(j\) marginal contributions).  
It is important to note that when constructing all possible coalitions, the input features that are not observed in that specific coalition are not removed (because this changes the input dimension), but are instead replaced with random samples from the training dataset. The Shapley value calculation is very time consuming. For a set of \(n\) features, there are \(2^{n}\) coalitions (subsets) that should be analyzed in order to compute the Shapley values.
# Electrocoagulation Based Chromium Removal Efficiency Classification Using Logistic Regression
## 1 Introduction  
Chromium is a widely used heavy metal in the surface treatment and tamneries industries due to its physical properties such as strength, hardness, and resistance to corrosion and oxidation capabilities. Consequently, large quantities of this metallic waste are produced every year and released in the environment. Chromium can exist in several chemical forms with degrees of oxidation ranging from 0 to VI but only the trivalent and hexavalent forms are the most stable in the environment [1]. Chromium (III) is necessary for the body but in limited quantities. The maximum allowable limit for total chromium in drinking water is from 50 ug/L to 100 ug/L [2]. It is involved in various biochemical reactions of carbohydrates and lipid metabolism. Excessive consumption of chromium (III) can also cause health problems as well as metabolic disorders in the case of diabetes. However, the hexavalent form of chromium: chromium (VI) is the most dangerous, even in small quantities. It has varioushealth consequences, including allergic phenomena, skin rashes, stomach ulcers, and carcinogenic effects [3].  
In view of the above, it is important to treat all this waste before getting rid of it. There are several techniques that are used such as chemical treatments by oxidation or physic-chemical treatments such as coagulation flocculation, membrane filtration, and ion exchange. Electrochemical methods such as electrocoagulation (EC) and electro-dialysis are used as well. Environmental issues about the chemical and biological contaminations of water have become a major concern for society and the paper [4] lists the advantages and disadvantages of techniques used for wastewater treatment. The majority of these treatments remain expensive. According to these constraints, we have proposed a statistical method which makes it possible to predict the experimentation efficiency class given the parameters that influence most the chromium removal using electrocoagulation. This helps to optimize the purification process without losing time and energy.
## 3 Experimental Results
### Results Comparison
Within 200 MHz BW, the proposed co-design microwave amplifier-phase shifter exhibits a maximum phase shift range of 185.62\({}^{\circ}\) with a phase deviation error of \(\pm\)4.3\({}^{\circ}\) while the conventional reflection-type phase shifter cascaded with a conventional microwave amplifier provides a maximum phase shift range of 195.35\({}^{\circ}\) with a phase deviation error of \(\pm\)6.14\({}^{\circ}\). This result indicates that the proposed co-design microwave amplifier-phase shifter provides a lower phase deviation error than a conventional reflection-type phase shifter cascaded with a conventional microwave amplifier. If a phase deviation error of \(\pm\)6.14\({}^{\circ}\) within the operating BW is used, the proposed co-design microwave amplifier-phase shifter provides a higher phase shift range and a wider BW than the conventional reflection-type phase shifter cascaded with a conventional microwave amplifier. Besides this, the proposed co-design microwave amplifier with the reflection-type phase shifter input matching network also achieves an approximate size reduction of 38.5%. These improved performances and compact sizes demonstrate the advantages of the co-design method, rather than cascading individual circuits.
## 5 Experimental Validation
### Experimental Results  
The supra-system generator has been implemented in MATLAB R2014 platform for evaluating its feasibility, the supra-system generator is applied to a large set of experimental current samples, obtained through the test bed described in the previous section, testing healthy and faulty IMs under different conditions (supply conditions, load conditions).  
The samples database generated in this research comprises 726 phase current samples, with 369 healthy rotor samples and 357 faulty rotor samples. During the sampling process the frequency and load torque has been changed to obtain also transient regime samples. Table 2 summarizes the conditions under which the tests were conducted.  
The supra-system developed in this work has explored and optimized a search space given by all the possible combinations of the following diagnostic systems options:  
* two HOTA implementations (with STFT or SFFT).
* two SVM expert system variants (with Polynomial or RBF Kernel).
* two ANN expert system variants (one or two hidden layers)  
By an optimized search in this search space, applying the techniques introduced in Section 4.1, the global optimum result (the best fault diagnostic system) is obtained, that is, the optimized expert system for IMs fault diagnosis which has the highest diagnosis hit rate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Regime** & \multicolumn{1}{c}{**Supply**} & **Load** & **Tests** \\ \hline \multirow{3}{*}{Steady} & DOL & 50 Hz & Constant & 12 \\ \cline{2-4}  & VSD & 25 Hz & Constant & 24 \\ \cline{2-4}  & VSD & 50 Hz & Constant & 24 \\ \hline \multirow{6}{*}{Transient} & DOL & 50 Hz & Pulse & 8 \\  & & & Ramps & 11 \\ \cline{2-4}  & & 25 Hz & Pulse & 15 \\ \cline{2-4}  & & & Ramps & 19 \\ \cline{2-4}  & VSD & 50 Hz & Pulse & 15 \\ \cline{2-4}  & & & Ramps & 19 \\ \cline{2-4}  & & & Constant & 60 \\ \cline{2-4}  & & Ramps & Pulse & 33 \\ \cline{2-4}  & & & Ramps & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the tests performed for the experimental validation.  
Figure 12: Top rotor with an artificially rotor broken bar fault and bottom rotor in healthy conditions.  
Time Required by the Supra-System to Generate Locals and Global Optimized IMs Fault Diagnostic Systems  
In this experimental test, 286044 SVM expert systems and 252000 ANN expert systems have been analyzed in the process of finding the final optimum fault diagnostic system by the supra-system. The total time used for training (Table 3) and for classification of the IM condition (Table 4) shows that the fastest approach is to use the SFFT technique for implementing the HOTA method.
# Quality of Service Based NOMA Group D2D Communications
## 4 Numerical Results and Discussion
### Impact of d on Outage Probability  
The impact of varying distance between probe DR and test GT on the outage performance is investigated in Figure 3. It can be observed that varying \(d\) has a larger impact on the average achievable outage probability at lower SNR value of 5 dB. This is intuitively plausible because increasing transmission power results in improved SINR at the receiver and hence better outage performance.
# Radio Signal Modulation Recognition Method Based on Deep Learning Model Pruning
## 4 The Proposed Approach
We pruned the convolution layer that needs a convolution operation and realized the data reconstruction by adding an LSTM layer. This has the advantage of reducing the amount of calculation required for convolution operation and of saving more data features for the LSTM layer operation.
## 4 Discussion
### Applicability of NPP as a Carbon Sequestration Capacity Indicator  
NPP is an important indicator for evaluating ecosystem productivity and carbon absorption capacity, and its trend can reflect fluctuations in ecosystem functions. The indicator directly characterizes the ability of plant communities to fix atmospheric carbon dioxide through photosynthesis as carbon sinks [37]. However, NPP only reflects the carbon fixation capacity of vegetation layers and fails to cover key carbon cycling processes such as soil carbon pool changes, litter decomposition, and soil respiration [38]. In addition, gross primary productivity (GPP) can also quantify the total amount of photosynthetic products or organic carbon fixed by plants through photosynthesis per unit time [39]. However, in comparison, NPP more directly reflects the actual carbon storage capacity of ecosystems, their sensitivity to environmental changes, and the practicality of data acquisition.  
Carbon sequestration capacity involves not only the accumulation of fixed carbon but also the re-release of carbon. Some ecosystems with high NPP in the short term, such as farmland, may have lower actual carbon sequestration benefits than expected due to high respiration and decomposition rates [40]. In addition, the NPP of different vegetation types may be differentially affected by climate change. For example, the NPP of alpine grassland systems exhibits higher sensitivity to short-term climate fluctuations, while forest ecosystems have stronger stability and adaptability [41]. Therefore, relying solely on NPP to evaluate carbon sequestration capacity may lead to bias. This study suggests that in future research on carbon sequestration capacity, measurement methods for NPP, soil carbon pool, and other carbon cycling processes should be further integrated to comprehensively evaluate the carbon sequestration capacity of ecosystems [42].
## 2 Materials and Methods
### Stability and Activity Measurements  
To measure the polydispersity index, particle size and zeta potential values, a Zeta-sizer (Brookhaven Instruments 90 Plus Particle Size Analyzer, Holtsville, NY, USA) was used. The Smoluchowski model was selected for zeta potential measurements using an SR-542 probe (Brookhaven Instruments 90 Plus Particle Size Analyzer, Holtsville, NY, USA). The Zeta-sizer device automatically provided the particle size, polydispersity, mobility and zeta potential values for the samples. The particle size, zeta potential, and mobility data shown in the table represent averages obtained from 10 measurements, all generated automatically by the device. To evaluate the significance of pH changes and time changes in simulated body fluids at the Zeta-sizer measurements of the samples, the standard deviation and uncertainty values based on Zeta-sizer measurements of the samples were calculated.  
Estimated standard uncertainty (U) was calculated by the ratio of estimated standard deviation (SD) to the square root of number of measurements (n) [24], as shown in Equation (1).  
\[\text{U}=\frac{\text{SD}}{\sqrt{\text{n}}} \tag{1}\]  
In this research, the (n) value represented the number of pH values and time points, which is equal to 12. Standard deviation values and estimated standard uncertainty values of samples were calculated and appended to the last rows of tables in the Zeta-sizer analysis part.  
A Sentek P14/BNC electrode (Sentek, Braintree, UK) was utilized for pH measurements. All analyses were conducted at 37 \({}^{\circ}\)C degrees to mimic body temperature.  
The activity of samples was determined by using a Shimadzu UVmini-1240 model UV/VIS spectrophotometer instrument (Kyoto City, Japan) with Hellma 100-QS quartz cuvette (Mullheim im Markgraflerland, Germany). Analyses were conducted in a wavelength range between 600 nm and 190 nm with 0.5 nm increments between each scan. The baseline correction was conducted against ultra-pure water, PBS (phosphate buffer saline) solution, 0.9% isotonic sodium chloride solution or 5% dextrose solution depending on the type of sample. Samples were prepared as homogenous, liquid solutions. Results were saved in the form of absorbance-wavelength (nm). All samples were analyzed at 37 \({}^{\circ}\)C to mimic the body temperature.
# Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
## 2 Separation Formulation Method
### Separation Formulation Based on Position Error
#### 2.2.2 Air Traffic Service Capability  
Air traffic service capability includes communication, navigation, and surveillance capability, which need to be considered when setting separation. Communication capability covers the methods of communication between controllers and pilots and the availability, reliability, and capacity of communication systems. Navigation capability considers required navigation performance, navigation precision, and timing accuracy. Surveillance capability depends on the surveillance system used, such as automatic dependent surveillance systems, which need to consider update rates, display precision, and sensor accuracy.  
This paper focuses on urban logistics UAVs, analyzing the impact of positioning errors on collision risk, referring to track prediction errors rather than the traditional study of navigation and location-update-frequency-induced positioning errors.
# DARI-Mark: Deep Learning and Attention Network for Robust Image Watermarking
## 3 Proposed Framework
### Extraction Network  
The extraction network is the inverse process of the embedding network, as shown in Figure 5.  
The detailed process is as follows:  
1. A \(3\times 3\) Conv: change the attacked image from \(M\times N\times 1\) to \(M\times N\times 64\).
2. Attention network: The tensor with 64 channels is fed into the attention network to obtain the attention feature map. The attention feature map is used to provide the watermark feature extraction network with areas to focus on and to suppress.
3. DCT: This is the same as step (1) of the embedding network. Because the watermark information is embedded in the frequency domain, the image features are first changed from the spatial domain to the frequency domain.
4. Watermark feature extraction: The input is the DCT transformed tensor, and the output is the extracted watermark. The convolutional network parameters setting are basically consistent with the embedded network. Each Conv includes convolution, batch normalization, and ReLU activation functions. Since the final output is a watermark image with the channel number of 1, the number of convolution kernels in the last layer is set to 1. As with the watermark embedding, the \(2\times 2\) convolutional layers are also designed as residual structures, and since there are only three layers of \(2\times 2\) convolution, two residual structures are designed, corresponding to two and three convolutional layers, respectively.
## 1 Introduction
Effective traffic classification, however, requires efficient feature selection (FS). FS helps identify the most informative features, thereby reducing the amount of data that needs to be stored and processed, simplifying the model and making it more efficient [17]. FS becomes even more critical in the context of resource-constrained devices, such as those commonly found in IoT networks. IoT devices often have limited computational power, memory, and energy resources. Thus, it is essential to minimize the computational overhead to ensure real-time processing and extend the operational lifespan of these devices [18]. Moreover, traditional ML models are computationally intensive, often requiring substantial processing power and memory. Hence, efficient FS and dimensionality reduction not only improve the selection of the most discriminative features but also enable the deployment of more lightweight ML models on devices with limited capabilities without compromising performance or resource availability [18; 19]. The contributions of this paper are summarized as follows:  
* We propose Statistical Moments Difference Thresholding (SMDT), a feature selection method that leverages four statistical central moments (mean, standard deviation, skewness, and kurtosis) to select the most significant features. To our knowledge, this is the first approach to leverage these specific statistical moments for FS, particularly in the context of IoT network security. The aim is to reduce feature dimensionality while maintaining a decent detection accuracy, particularly in resource-constrained environments. * We validate the proposed FS method on the MQTset [20] dataset and evaluate the performance of seven ML models in detecting malicious traffic using the selected features, demonstrating its practicality and effectiveness. * We compare the effectiveness of binary and multiclass classification approaches, providing a comprehensive analysis of each method's advantages and limitations. * We analyze the trade-offs between model accuracy, computational cost, and feature dimensionality, highlighting the balance between performance and efficiency. The rest of this paper is structured as follows. Section 2 briefly reviews related work. Section 3 discusses the dataset utilized in our experiment, the preprocessing and exploratory data analysis methods used, and feature selection for malicious traffic classification, focusing on the method and criteria employed in our study to identify the most significant features. Section 4 presents the experiments conducted and the results obtained. Section 5 discusses these results and their implications, limitations, and future work. Finally, Section 6 provides concluding remarks.
## 3 System Overview
### ROI Management
#### 3.2.1 ROI Classification  
For frames that are determined as I-frames, detected UAV information, achieved using an object detection algorithm, is employed to classify ROI during ROI management operation. The proposed system adopts YOLOv7 for the UAV detection algorithm. YOLOv7 is a real-time object detector based on a well-known deep-learning architecture called CNN.  
By processing an image through CNN, the algorithm detects objects. YOLO achieves high speeds and high efficiency by integrating detection tasks--typically performed in multiple stages--into one. YOLO detects objects in real time. This speed makes it suitable for applications requiring real-time processing, such as video analysis, autonomous driving, and real-time surveillance. The details of YOLO can be found in [28; 29]. YOLO shows the detected object, in this case, a UAV, on the video frame (or image) by drawing a bounding box on the image. Figure 2a shows an example of the result. Each bounding box indicates that the detection algorithm determines the region to contain an object. This region in the bounding box is set as ROI in the proposed system. Even though the adopted UAV detection algorithm is an efficient algorithm that claims to support real-time detection, it would only mean anything if such a live stream could be sent to others. By performing UAV detection on only I-frames, the proposed system significantly decreases the overhead of UAV detection. For other frames, the target UAV is tracked by applying optical flow and moving the ROI according to the target's movement. Every time an I-frame is detected, the ROI is set as the bounding box by YOLO. This prevents the ROI from drifting due to accumulated errors.
# Improving DFT-Based Image Watermarking Using Particle Swarm Optimization Algorithm
## 5 Experimental Results and Discussion
### Watermark Robustness
#### 5.2.3 Geometric Distortions and Combined Attacks  
Considering 1000 color images extracted from the COCO dataset [30], and a binary watermark of \(L=32\) bits, in Table 9 we summarize the average BCR obtained by PSO-DFT and conventional DFT watermarking, respectively, against geometric manipulations and combined attacks composed by JPEG compression with quality factor 70+ geometric distortions. Italics in Table 9 indicate that the detection has failed. For the sake of brevity, in this test we included aggressive tolerances assuming that the minor tolerances are detected in a successful manner. From Table 9, we show that the optimized 2D DFT-based watermarking by PSO algorithm outperforms to the conventional 2D DFT image watermarking in terms of watermark robustness against attacks that involve image resizing by scaling factor 0.6, as well as random cropping by 35% combined with re-scaling and JPEG compression with QF = 70. To the rest of geometric manipulations, the PSO-DFT watermarking keeping the robustness obtained by the conventional 2D DFT watermarking, obtaining average BCR values over the threshold value \(Th=0.78\). This behavior is due to the fact that the watermark embedding in the DFT domain has a certain number of robust properties with respect to rotation, scaling and translation (RST) invariance. The robustness against general affine transformation is supported by our resynchronization method previously reported in the literature. For more details of the resynchronization technique, interested readers can refer to [16].