esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas
## 5 Proposed Future Security Mechanisms
### Blockchain
#### 5.1.3 Blockchain Uses in 6G  
6G will bring many opportunities for the integration of blockchain technology. There are many different areas within 6G that can benefit from the use of blockchain technology. In a survey of opportunities and challenges of blockchain application in 6G, many security-related cases are presented, which are listed below [119].  
* Intelligent Resource Management;
* Elevated Security Features;
* Industrial Applications;
* Smart Healthcare;
* Decentralized 6G Communications ;
* Authentication, Availability, Integrity.  
The authors share how blockchain technology can be a candidate to improve these aspects due to its major fundamental properties and add further trust to a network. If implemented correctly, most apparent properties include but are not limited to the following: decentralization, transparency with anonymity, provenance and non-repudiation of transactions, immutability and tamper-proof of distributed ledgers, and elimination of single points of failure.  
Another proposed area for integration into 6G is the use of blockchain technologies to ensure the authenticity of training data from edge devices used by machine learning models. Data are usually stored and processed on centralized servers, owned or rented by network operators, where the source cannot always be guaranteed to be authentic. One method could be to encrypt the data, but the authors in [120] claim it can be more efficient and verifiable if the data-sharing technique is decentralized without the use of a third party entity or intermediary. Further studies show that blockchain can also be used in federated learning architectures where local edge device learning model updates are exchanged and verified [121]. All training results from the edge devices are put through a validation process, meaning there is no need to specifically identify and isolate untrustworthy devices in the federation.
## 2 Materials and Methods
### Classifying Algorithms Implementation  
To split the dataset for training and testing, we utilized the library \"from sklearn.model_selection import train_test_split\" and activated the \"train_test_split\" function. The data were divided into 70% (226 rows) for training and 30% (98 rows) for testing, ensuring a robust evaluation of the model's performance [38; 39]. A random seed of 42 was applied to maintain consistency and reproducibility across code execution [43]. Five ML classifiers were implemented using the following libraries: (1) \"sklearn.neighbors import KNeighborsClassifier\" for the K-Nearest Neighbors (KNN) model; (2) \"from sklearn.ensemble import GradientBoostingClassifier\" for the Gradient Boosting Classifier (XGBoost); (3) \"from sklearn.svm import SVC\" for the Support Vector Machine (SVM) algorithm; (4) \"from sklearn.ensemble import RandomForestClassifier\" for the Random Forest Classifier (RF); and (5) \"from sklearn.tree import DecisionTreeClassifier\" for the Decision Tree Classifier (DT) [37; 38; 44; 45].  
To evaluate model performance, the library \"from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\" was utilized, activating functions to calculate accuracy, precision, recall, and F1-score [46; 47]. These metrics provided a comprehensive understanding of the classifiers' effectiveness. Finally, the algorithms' assumptions and applications were summarized as follows: (1) KNN is a distance-based algorithm evaluating the similarity between data points; (2) XGBoost is an ensemble learning method combining weak predictors for enhanced accuracy; (3) SVM is a classification technique utilizing hyperplanes for optimal decision boundaries; (4) RF is a tree-based ensemble method focusing on reducing overfitting by averaging predictions; (5) DT is a hierarchical structure used for straightforward classification tasks.  
Model evaluation focused on the accuracy, precision, recall, and F1-score metrics to ensure the robustness and generalizability of the classifiers. To enhance model robustness, future analyses can incorporate k-fold cross-validation techniques, which will provide a more comprehensive assessment of model performance by minimizing potential biases from a single train-test split.
# Multi-Scale CNN-Transformer Dual Network for Hyperspectral Compressive Snapshot Reconstruction
## 2 Related Work
### Transformer Reconstruction Method  
The Transformer is widely used in natural language processing tasks [34]. Through long-range correlation matching and interaction between image feature tokens, the Transformer has also been introduced into computer vision tasks and brought effective performance improvement, including image classification [16; 17; 19; 35], object detection [21; 22; 23] and segmentation [36; 37; 38]. The Transformer also shows good performance in low-level visual tasks [4; 39; 40; 41]. [PERSON] et al. [22] utilized a standard Transformer to construct the backbone model [34] for various image restoration tasks. [PERSON] et al. [40] used the Swin Transformer to build a residual network, and achieved state-of-the-art results in image restoration tasks. [PERSON] et al. [25] proposed a Transformer-based HSI reconstruction model named MST. It treats each spectral band as a token and computes self-attention in the spectral dimension, improving the reconstruction quality effectively. However, hyperspectral images exhibit coupled spatial-spectral correlations, and the MST model is insufficient at representing spatial correlations, which hinders the reconstruction of small-scale spatial structures.
# Study of a Small Robot for Mine Hole Detection
## 3 Mechanical Structure Design of Small Robots for Mine Holes  
Figure 4 shows the overall structure of the robot. The small robot, designed to operate within mine holes, must maintain compact body dimensions in both diameter and length. It also requires significant climbing and adaptive diameter capabilities, as well as an adjustable speed. Based on the design concept of adaptability, the overall structure of the mine hole-inspection small robot is divided into a power system, an auxiliary system, and a detection system. The power system furnishes the driving force propelling the small robot's movement; the auxiliary system manages the positive pressure and the robot's movement status, while also interconnecting various modules to ensure operational stability; and the detection system is tasked with capturing images and gathering environmental parameters within the mine hole.
# Kalman Filter-Based Differential Privacy Federated Learning Method
## 3 Proposed Method
### Method Details
#### 3.2.2 Kalman Noise Reduction Parameter Aggregation  
The TN sends the training result to the RN after adding noise using Equation (3), and the model parameters of node TN\({}_{c}\) are noted as \(g_{c}\); then, the RN receives the set of model parameters \(G=\{g^{\prime},g^{\prime},\cdots,g^{\prime}_{c}\}\).  
The model parameters obtained by the RN contain noise, which will decrease the accuracy of the model. In order to reduce the impact of differential privacy on the model and improve the accuracy, it is necessary to revise the model parameters sent by the TN. In this paper, we use Kalman filtering to revise all model parameters obtained by the RN to improve the accuracy of the model under the situation where differential privacy noise has been added to the global model:  
\[g=Kalman\big{(}g^{\prime}\big{)}\quad g^{\prime}\in G \tag{6}\]  
The details will be described in Section 3.2.3. The global model parameters of the \(t\)th round are obtained after aggregating all the model parameters revised by the Kalman filter; then, the global model is updated.  
\[w_{t+1}=w_{t}-\frac{\lambda}{C}\sum_{c=1}^{C}g_{t,c} \tag{7}\]  
The KDP-FL algorithm is shown in Algorithm 2.  
```
0:\(D_{i}\); \(w_{0}\);
0:\(w\)
1:for\(t=1\)to\(T\)do
2:for\(c=1\)to\(C\) in paralleldo
3: TN\({}_{c}\)do: \(g^{\prime}_{t,c}\leftarrow\) DP-SGD\((D_{i},w,M)\)
4: RN do: \(g_{t,c}\leftarrow\) Kalman\(\left(g^{\prime}_{t,c}\right)\)
5:endfor
6:\(w_{t+1}\leftarrow w_{t}+\frac{\lambda}{c}\sum_{c\in C}g_{t,c}\)
7:endfor
```  
**Algorithm 2** KDP-FedAvg
# Natural Frequency Response of FG-CNT Coupled Curved Beams in Thermal Conditions
## 7 Conclusions
* In all boundary conditions, except C-F/C-F, variations in the stiffness of the interfacial springs do not considerably influence the first dimensionless frequency of the system. * As the volume fraction of CNTs in the bottom beam rises from zero to 0.12, the system frequencies increase across all boundary conditions. However, they remain constant for volume fractions ranging from 0.12 to 0.28. * Elevating the temperature at the upper beam leads to an augmented frequency response of the system. Conversely, raising the temperature at the lower beam diminishes its frequency response. These theoretical studies on the natural frequency response of FG-CNT coupled curved beams in thermal conditions have practical implications for optimizing structural design, material selection, and performance in aerospace, automotive, and related applications. The insights gained also contribute to vibration control, energy harvesting, and structural health monitoring. Conceptualization, F.T. and R.D. ; Methodology, A.R.M., M.A.G., F.T. and R.D. ; Validation, M.A.G., F.T. and R.D. ; Formal analysis, A.R.M., M.A.G., F.T. and R.D. ; Investigation, A.R.M. and R.D. ; Data curation, F.T. and R.D. ; Writing--original draft, A.R.M. and M.A.G. ; Writing--review & editing, F.T. and R.D. ; Supervision, F.T. and R.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data presented in this study are available in article. The authors declare no conflict of interest.
## 3 Discussion
### Theoretical Prediction of Solubility of Wild Thyme Compounds in Green Solvents
#### 3.2.3 Effect of HVED on Phenolic Composition of the Extracts  
Results of the performed UPLC-MS/MS analysis from wild thyme extracts have shown (Table 4) that the main compounds in wild thyme extracts were in the following order, according to their average content in all extracts: rosmarinic acid, oleanolic acid, luteolin, apigenin, diosmetin, hydroxytyrosol, quercetin, carnosol, camphor, thymol, \(p\)-cymene, and carvacrol. Rosmarinic acid was found to be the main phenolic compound found in wild thyme extracts, which was already confirmed by [PERSON] et al. [5]. Rosmarinic acid was found in higher concentrations in HVED extracts compared to CE and it was higher with higher concentrations of ethanol, up to 50% in extracts. Chromatograms of representative extracts obtained by CE (Figure S1) and HVED (Figure S2) present the main components found by UPLC-MS/MS and their differences. The most pharmacological importance of rosmarinic acid is its antioxidant and anti-diabetic properties [34]. For that reason, wild thyme extracts are a valuable source for functional food production. Apigenin, carnosol, luteolin, oleanolic acid, quercetin, rosmarinic acid and camphor were found to be statistically significant higher with higher ethanol content. Also, luteolin and cymene significantly depended on treatment type. The sum of all BACs measured by UPLC-MS/MS was compared with results of TPC and correlation of 0.384 (Table S1) was found between UPLC-MS/MS results and TPC, which is the highest correlation compared to DPPH and FRAP.  
Experimental results have confirmed theoretical results obtained by HSPs and COSMO-RS software. Apigenin, luteolin and rosmarinic acid were confirmed to be better soluble with higher ethanol content of the solvent, which was also indicated by the theoretical results (Tables 1 and 2). Although theoretical results showed better solubility of these compounds in ethanol, compared to water, still poor solubility was expected in both solvents calculated by HSPs (Table 1). Low probability of solubility (red color, Table 2) was also expected by COSMO-RS prediction in water. However, for apigenin, luteolin and rosmarinic acid, a very high probability (100%) was predicted for solution in ethanol. Therefore, it can be concluded that experimental results confirmed theoretical results regarding higher solubility of wild thyme compounds in ethanol compared to water. On the other hand, better results are given with COSMO-RS software than HSPs since high concentrations of apigenin, luteolin and especially rosmarinic acid were found after extraction with HVED.
# Modelling, Analysis and Performance of a Low Inertia AC-DC Microgrid
## 4 Simulation Results
### Effect of Network System Parameters  
In this section, the effect of changing the system parameters is studied. The first parameter is the DC microgrid loading. The DC microgrid loading consists of two types of loads: constant power load, which is deducted from the constant power source that represents the renewable energy source, and the other type is the resistive load.  
Figure 16 shows the frequency response at different DC resistive loading levels 0.25, 0.5 and 1 MW. The constant power source is also varied to keep the power transfer between the DC microgrid and the AC microgrid at 0.5 MW for all cases. The system shows a more stable response and better inertia support when the loading level increased on the DC side. Figure 16 illustrates that the frequency response is enhanced by increasing the DC loading. The reason for this improved performance is the decrease in DC voltage during the contingency by the effect of the virtual inertia loop that also affects the power absorbed by the DC loads which leads to more available power to be transferred to the AC microgrid producing more inertial support and better frequency response, as shown in Figure 17. Figure 18 shows the DC bus voltage at the same loading condition when transferring the constant power from the DC to the AC microgrid. In Figure 19, the effect of changing the synchronous generator inertia on DC bus voltage is depicted under a 3% change in the dynamic load. As expected, the increase in the generator inertia leads to a more stable response in the DC bus voltage as it reduces the burden on the stored energy at the DC link for inertia support. Figure 20 shows the frequency response which is further improved at higher generator inertia.  
Figure 16: Frequency Response at different DC-microgrid loadings.  
Figure 15: Frequency response comparison when dynamic loads increased by 3%.  
Figure 19: DC bus voltage at different generator inertia.  
Figure 17: Power transfer from DC to AC grid at different DC-microgrid loadings.  
Figure 18: DC bus voltage at different DC-microgrid loadings.
# Exploring Deep Neural Networks in Simulating Human Vision through Five Optical Illusions
## 2 Materials and Methods
### Optical Illusions
#### 2.2.1 Stimuli and Procedures  
The experiment was conducted in a dark room using an HP monitor (HP P244 23.8-inch, refresh rate: 60 Hz, resolution: 1920 \(\times\) 1080). The subjects' head positions were stabilized using a chin rest (Tobii Pro AB), ensuring the same viewing angle and distance for all participants, 65 cm from the screen. Before the formal experiment, all subjects underwent practice and testing, and were instructed to observe and adjust based on visual perception only. The experiment was conducted using Matlab(vR2023a) and the Psychtoolbox [28] (v3.0.19).  
Stimuli for color assimilation, the Hermann grid illusion, the Muller-Lyer illusion, the Zollner illusion, and the Poggendorff illusion were presented in sequence, divided into five groups with 5-min breaks in between. The experiment was controlled using ASD keys, as well as the space and enter keys. Specific operations were as follows:  
* **Color assimilation**: Each round displayed a group of colored squares and corresponding three depths with black, gray, and white striped backgrounds, with WASD displayed as labels on the screen (Figure 2A). Subjects were asked to observe the squares labeled ASD (corresponding to depths 1 to 3) and the original color on the right, and rank them from the highest to the lowest similarity. The pressed space to enter the next group.
* **Hermann grid illusion**: 100 stimuli images were randomly displayed (Figure 2B), with ASD corresponding to \"Clear\", \"Weak\", \"None\". The subjects chose based on their perception of flickering dots. They pressed the chosen option to proceed to the next round.
* **Muller-Lyer illusion**: Lines 200 to 380 pixels (20 increments) were randomly displayed at the top of the screen. Below them, lines with inward or outward arrows of random lengths were shown (Figure 2C). Subjects observed the lines and adjusted the length of the arrowed lines using WASD; AD for larger adjustments (\(\pm\)10 pixels) and WS for smaller adjustments (\(\pm\)1 pixels). They pressed space to proceed.
* **Zollner illusion**: 35 stimuli were randomly displayed in the middle of the screen (Figure 2D). In each round, a line parallel to (and at the same angle as) the line above the rectangle was shown below it, randomly positioned along the bottom of the rectangle. Subjects adjusted the lower line's position until it visually aligned with the upper line. AD is for larger adjustments, WS is for smaller. They pressed space to proceed.
* **Poggendorff illusion**: 54 stimuli randomly appeared at the top of the screen, with two parallel, horizontal lines below them (Figure 2E). Subjects adjusted the angle of the lower lines based on the perceived angle of the line in the upper stimulus. They pressed ENTER to switch the control between lines. AD is for larger adjustments (\(\pm\)0.1 deg), WS is for smaller (\(\pm\)0.02 deg). Adjustments were optional; they pressed space to proceed.
# Haze Removal Based on Refined Transmission Map for Aerial Image Matching
## 2 Physical-Based Image Dehazing and Matching
### Dark Channel Prior  
In general, the method of modeling the haze in one image is expressed by Equation (1), which defines the haze image, original image, atmospheric light, and transmission.  
\[I(x)=J(x)t(x)\ +\ A(1-t(x)) \tag{1}\]  
where \(I(x)\) is an image containing haze, and \(J(x)\) is the brightness value of the original image without haze.\(A\) is the atmospheric light and \(t(x)\) is the transmission, which shows the degree to which the unseen light reaches the camera.  
The DCP [40] is based on the observation in dehazed outdoor images of a few pixels whose intensity values in at least one color channel are very low in non-sky patches, namely, red (R), green, (G), or blue (B); that is, the patch is composed of very low values with minimum intensity and the dark channel is defined in Equation (2).  
\[J^{dark}(x)=\underset{c\in\{r,g,b\}}{min}\left(\underset{y\in\Omega(x)}{min} \left(J^{c}(y)\right)\right) \tag{2}\]  
where \(J^{C}\) is the color channel of \(J\), and \(\Omega(x)\) is a local patch centered at \(x\). Based on observation, with the exception of the sky region, the intensity of \(J^{dark}\) is low and tends to be zero if \(J\) is a haze-free outdoor image. Thus, \(J^{dark}\) is the dark channel of \(J\), and the above statistical observation is the DCP. To remove haze in the image, the top 0.1% of the brightest pixels were obtained for the estimated atmospheric light in the dark channel, and the chosen pixels were haze-opaque. In the next step, the coarse estimated transmission is as follows:  
\[\widetilde{t}(x)=1-\omega\underset{c\in\{r,g,b\}}{min}\left(\underset{y\in \Omega(x)}{min}\left(\frac{J^{c}(y)}{\widetilde{A^{c}}}\right)\right) \tag{3}\]  
where \(\widetilde{A^{c}}\) is the color channel of estimated atmospheric light, and \(\omega\) is chosen as (0\(<\)\(\omega\)\(<\)1) [40] to prevent the full elimination of haze because it causes the haze to result in an unnatural image with the loss of depth perception. We fixed the value of \(\omega\) to 0.95 for all subsequent experimental results in this study. The estimated transmission map \(\widetilde{t}\) is refined by applying a soft-matting method. Finally, the estimated scene radiance is expressed as:  
\[\widetilde{J}(x)=\frac{J(x)-\widetilde{A}}{max(\widetilde{t}(x),t_{0})}+ \widetilde{A} \tag{4}\]  
where \(t_{0}\) is the lower limit of the refined medium transmission and\(\widetilde{A}\) is the estimated atmospheric light. When applying the steps of the DCP method, the haze removal results can be obtained. Algorithm 1 provides the procedure for each step, as follows, to estimate the scene radiance \(\widetilde{J}\).
## 6 From Single-Point to Multi-Point Equalization
### Quasi-Anechoic Approach  
An approach that is complementary to the low-frequency techniques introduced in the previous sub-section is the quasi-anechoic approach of [172]. At mid- and high- frequencies, the timbre perception and localization is dominated by the direct sound. Thus, in [172], a quasi-anechoic loudspeaker response is obtained as a gated version (up to the first reflection) of the RIR and is used to design the equalizer in two steps. First, a mixed phase equalizer is derived from the quasi-anechoic RIR, computing the inverse filter with a least-squares approach. The quasi-anechoic loudspeaker response has a short length and the delay introduced by the equalizer is too short to produce pre-ringing artifacts. Then, a minimum-phase equalizer is used to correct the remaining part of the room response (i.e., the magnitude spectrum modifications caused by reverberation).  
In [173], the quasi-anechoic approach is combined with the prototype approach described in Section 6.3. In particular, a novel prototype function is derived from the combination of quasi-anechoic impulse responses with the impulse responses recorded in the real environment to be equalized. The approach is used to equalize the direct sound only in the mid-high-frequency range, while applying full equalization in the modal frequency range. The approach is motivated again by the fact that at mid and high frequencies the timbre perception and localization is dominated by the direct sound. Thus, the measurable but mostly inaudible magnitude deviations due to reflections should not be equalized [174]. In [173], several experiments were conducted in order to validate the proposed approach, reporting objective measurements and subjective listening tests in comparison with approaches of the state-of-the-art. In this context, Figures 8 report the results of the equalization procedure. In particular, Figure 8a shows four impulse responses acquired in a real room--the prototype function and the equalizer obtained with the multi-point approach of [173] and the single-point equalizer derived as an inverse filter of the smoothed frequency response of IR1. Figure 8b shows the effect of the equalization procedure on the IRs applying the multi-point approach, while Figure 8c shows the effect of the single-point equalizer. It is evident that the performance of the single-point equalizer is very good only for IR1, while the multi-point equalizer exhibits flatter frequency responses compared to those obtained with the single-point approach.  
Figure 8: _Cont._