esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
However, it is important to note that the implementation complexity of the algorithm presents a significant challenge, and the resulting outcomes can be influenced by the specific parameter choices. Paper [26] proposed a BP neural network optimization algorithm based on the GA. The algorithm could effectively deal with the problem of redundant parameters, and the training error is small. It can make multiple robots quickly complete the task allocation. However, the implementation of this algorithm is very complicated. For ease of understanding, the main findings and limitations of the relevant work in this article should be summarized in the following Table 1.  
This paper introduces a task allocation model [27; 28] aimed at maximizing network revenue within Intelligent Unmanned Wireless Sensor Networks (IUWSNs). This model factors in the impact of IUWSNs on routing energy consumption. Additionally, a fresh approach, known as the Chaotic Elite Adaptive Genetic Algorithm (CEAGA), is proposed to optimize the network revenue in IUWSNs. In comparison to other task allocation algorithms, the CEAGA demonstrates swifter convergence and a more robust capability to escape local optima. Furthermore, the CEAGA's crucial parameters can be dynamically adjusted during algorithm operation, thereby enhancing its ability to explore the solution space.
## 1 Introduction
In many cases, spatial aggregation, spectral adjustments, radiometric intercalibration factors or adjustments for the Bidirectional Reflectance Distribution Function (BRDF) are applied in order to reduce differences and to minimize the effect of differences in viewing and illumination geometries. This study focuses on the transition from SPOT VGT (and PROBA-V) to Sentinel-3 SYN VGT. However, in order to conduct this evaluation from a user perspective, we decided to not apply any adjustments or corrections and to evaluate the operational products in the form they are made available to the user. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**SENSOR** & **BLUE [nm]** & **RED [nm]** & **NIR [nm]** & **SWIR [\(\mu\)m]** \\ \hline SPOT4/VGT1 & 459 (43) & 658 (85) & 834 (121) & 1.649 (0.092) \\ \hline SPOT5/VGT2 & 458 (37) & 653 (74) & 838 (109) & 1.635 (0.101) \\ \hline PROBA-V & 464 (47) & 655 (82) & 837 (130) & 1.603 (0.065) \\ \hline \multirow{4}{*}{Sentinel-3/OLCI} & \multirow{4}{*}{Oa03: 442 (10)} & \multicolumn{2}{l}{Oa07: 621 (10)} & \multicolumn{1}{l}{Oa16: 779 (15)} \\  & & \multicolumn{1}{l}{Oa08: 666 (10)} & \multicolumn{1}{l}{Oa17: 866 (20)} & \\ \cline{1-1}  & & \multicolumn{1}{l}{Oa09: 674 (7.5)} & \multicolumn{1}{l}{Oa18: 886 (10)} & \\ \cline{1-1}  & & \multicolumn{1}{l}{Oa10: 682 (7.5)} & \multicolumn{1}{l}{S5: 1.610 (0.065)} & \\ \hline Sentinel-3/SLSTR & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The spectral range (the center wavelength and Full Width at Half Maximum (FWHM) (between parentheses) for SPOT4/VGT1, SPOT5/VGT2 [7], PROBA-V [8] and the relevant Sentinel-3 OLCI (Oa) and SLSTR (S) bands [9]. The goal of this paper is to evaluate how the consecutive improvements in Sentinel-3 SYN processing have (positively) affected the level of consistency of Sentinel-3 SYN VGT V10 products with SPOT VGT Collection 3 and PROBA-V Collection 2 products, and to determine what level of consistency is reached in the SYN VGT products that are currently made available shortly after new Sentinel-3 OLCI and SLSTR acquisitions. Furthermore, we aim to inform users about the quality concerns present in the current SYN VGT data set, particularly when these products are used alongside the SPOT VGT and PROBA-V product archives. To reach these goals, we first compare the combined time series of 10-day NDVI synthesis products from SPOT VGT, PROBA-V and Sentinel-3 SYN VGT with an external NDVI time series derived from the AVHRR onboard the METOP satellites. Secondly, Sentinel-3 SYN VGT V10 products for three distinct periods with different levels of product quality are compared to 5-year climatology products derived from SPOT VGT and PROBA-V, respectively. Analyses are performed on a systematic global spatial subsample.
# Energy Consumption Framework and Analysis of Post-Quantum Key-Generation on Embedded Devices
### Key Generation with Hybrid Encryption  
Whilst this asymmetric method can be used for data encryption, it is typically far less efficient (in time and energy resources) than for communications protected with symmetric encryption [3]. The asymmetric channel's typical main purpose therefore becomes the secure sharing of the symmetric algorithm's key material, with the bulk data then conveyed symmetrically and encrypted with this key. This standard approach to modern cryptography is known as 'hybrid encryption' [4], trading-off the characteristics of each technique for reasons of convenience, efficiency, and confidentiality.  
Asymmetric key generation techniques are a critical part of current hybrid cryptography, with popular methods including RSA [5] and Elliptic Curve Cryptography (ECC) [6; 7]. If these methods were to be fundamentally compromised, then access to the negotiated secret symmetric keys could be revealed, with the consequence of data decryption becoming trivial.
## 2 Related Works
### Image Quality Assessment (IQA)  
IQA is a field of computer vision research that focuses on evaluating image quality by evaluating the degree of loss or degradation caused by various distortions such as blurring, white noise, and compression. This task involves analyzing a given image and determining whether it is of good or bad quality. The IQA algorithm quantifies the perceived visual fidelity of an image by taking a random image as input and producing a quality score as output [28; 29]. There are three types of IQA: full-reference (FR), reduced-reference (RR), and no-reference (NR) [30]. FR-IQA requires clean original images to evaluate image quality and compares the distorted image to the original to provide a quality score. RR-IQA requires some information from the original image and evaluates image quality based on features extracted from both the distorted image and the reference image. NR-IQA does not require any reference to the original image; it evaluates image quality using manually extracted features from distorted images. NR-IQA methods require training, are label-dependent, and are difficult to apply due to the subjective nature of image quality perception. As a result, it may not be possible to generalize NR-IQA models trained on unstable labels to diverse datasets. IQA methods include representative PSNR, SSIM, VIF, MAD, FSIM, GSM, GMSD, and BRISQUE. In addition, algorithms which use machine learning or deep learning, such as blind multiple reference images-based measure (BMPRI), DeepFL-IQA, and DISTS, have been proposed due to the continuous development of artificial intelligence technology [31; 32; 33; 34; 35; 36; 37; 38].
# Deep Learning for EEG-Based Preference Classification in Neuromarketing
## 4 Proposed System for the EEG-based Preference Detection
### Feature Extraction
#### 4.4.1 PSD  
The PSD is one of the most popular feature extraction methods based on the frequency domain analysis in the neuromarketing research. Research studies [11; 37; 39] have demonstrated that the PSD obtained from the EEG signals works well for determining consumer preferences. The PSD method converts the data in the time domain to the frequency domain and vice versa. This conversation is based on the FFT, which calculates the discrete Fourier transform and its inverse.  
We used the PSD technique in this study to divide each EEG signal into four different frequency bands: theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-40 Hz). The Python signal processing toolbox (MNE) was used for PSD calculation, and the average power over the frequency bands was computed to build a feature using the avgpower function in the MNE toolbox.
## 6 Results and Discussions
### Comparison between Our Optimum Results with Others from the Literature  
To demonstrate a clear perception of our optimum outputs obtained from Case 3 (see Table 8) simulated in the CHEMCAD program with a membrane, Table 11 exhibits a clear comparison between our optimal outcomes and different results from the literature that have already been researched and published in terms of technical and economical estimation.  
The optimal outcomes obtained from the current paper have been validated by comparing them with other results found in the literature. The usage of different gas flow, CO\({}_{2}\) concentration, CO\({}_{2}\) permeability, and number of membrane units leads to achieving various membrane executions for the current study compared with other papers' results. In the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Present Work**} & \multicolumn{4}{c}{**Literature Papers**} \\ \cline{2-5}  & **Optimum Scores** & [69] & [70] & [71] \\ \hline Flue gas flux, (kmol/h) & 80,460 & 18,260 & 52,623.6 & 53,520 \\ CO\({}_{2}\) stream before membrane, (kmol/h) & 10,583 & 2355.5 & 7051.5 & 6690 \\ Membrane units’ number & 3 & 2 & 2 & 2 \\ Total membrane area, (\(\times 10^{3}\) m\({}^{2}\)) & 888 & 679 & 643 & 16,200 \\ CO\({}_{2}\) permeability, (GPU) & 3000 & 740 & 370 & 270 \\ CO\({}_{2}\)/N\({}_{2}\) selectivity & 150 & 135 & 200 & 34 \\ CO\({}_{2}\) removal rate, (\%) & 90.7 & 80.3 & 96.5 & 67 \\ CO\({}_{2}\) stream captured, (kmol/h) & 9598.8 & 1884.4 & 6804.7 & 4482.2 \\ Membrane power needed, (MW) & 298.3 & 23.7 & n.a. & 137 \\ CO\({}_{2}\) purity, (\%) & 99.7 & 95.1 & 89.6 & 88 \\ LCOE\({}_{\text{tax}}\), (EUR/kWh) & 0.1338 & n.a. & n.a. & n.a. \\ SPECCA, (MJh\({}_{\text{th}}\)/kg) & 12.45 & n.a. & n.a. & n.a. \\ SEPCCA, (MJ\({}_{\text{el}}\)/kg) & 2.78 & n.a. & n.a. & n.a. \\ CO\({}_{2}\) avoided cost (EUR/t) & 93.83 & n.a. & n.a. & 197 \\ CO\({}_{2}\) captured cost (EUR/t) & 51.82 & 47.87 & 20.40 & n.a. \\ \hline \hline \end{tabular}
\end{table}
Table 11: The comparison of our optimum results with literature concerning various indicators.  
Figure 14: Influence of different factors on LCOE.  
reference paper [69], the CO\({}_{2}\) capture cost is less than ours due to the low carbon dioxide removal rate achieved (80.3%) by the author, which decreased the total power requirement. The author in [70] captured 96.5% CO\({}_{2}\) content with a concentration of almost 90%, the low purity obtained helped to reduce CO\({}_{2}\) capture cost, whereas a higher purity requires more power consumption, see Figure 12. The utilization of high CO\({}_{2}\) permeance in our simulation enhanced the results compared with the author's results in [71], where 197 EUR/tCO\({}_{2}\) of CO\({}_{2}\) avoided the costs gained, which is almost 52% higher than our outcome.
## 2 Materials and Methods
### Workflow for Predicting Cardiovascular Diseases
#### 2.2.1 Encoding Categorical Data  
Databases with categorical features are common in real-world applications and provide relevant information for predictive tasks. A categorical feature is composed of a discrete set of values called categories. In ML, working with categorical features is challenging because most algorithms work adequately with numerical data [25]. To make proper use of the information on these features, a pre-processing stage named encoding is needed, which consists in transforming all the categories of the feature into numerical values. In the literature, several techniques have been proposed to encode categorical data considering target-agnostic or target-based methods [25]. One of the most popular target-agnostic methods is one-hot encoding, in which for each individual arrays of '0'/'1' are created based on the presence/absence of a category in the corresponding feature [40]. Despite its extensive use, prior works that have analyzed categorical encoding techniques have pointed out two main shortcomings of the use of one-hot encoding techniques [41, 42, 43, 40]. First, the dimension of the input space directly increases with the number of categories in the encoded features, thus substantially augmenting the dimensionality [25]. Second, the new features created are characterized to be sparse.  
To overcome the limitations raised by one-hot encoding, a target-based method called target encoding has been proposed by [25]. In this technique, framed within Bayesian encoding techniques, each value of the categorical feature is mapped to a target mean conditional (target's posterior probability) on the value of the variable [25]. Broadly speaking, the target variable's mean for each category is computed and the original category is replaced by this value. The target encoding is obtained as follows:  
\[\mu=\frac{N\times\overline{x}+m\times w}{N+m},\]  
\(\mu\) being the computed mean that will replace categorical data, \(N\) the number of samples, \(\overline{x}\) the estimated mean for each category of a feature taking into account the target, \(w\) the smoothing parameter and \(m\) the overall mean of the target.  
This encoding technique is not affected by the increase of dimensionality, is computationally less demanding than one-hot-encoding, and allows us to work with numerical data [25, 45]. Despite these advantages, the main drawback of this technique is that it can be affected by overfitting since the encoding depends on the target [45]. Furthermore, a bias can be introduced in the ML model when categories with few samples are replaced by values close to the desired target feature since the model over-trusts the target encoded feature and the model is prone to overfitting. To overcome this problem, we applied a regularization-based approach that uses a smoothing parameter \(w\) aiming to shrink the effects toward the global mean [25].  
Figure 2: Proposed workflow using oversampling techniques (SMOTEN, TVAE, CTGAN) and different classifiers designed using ML approaches.
# Geographic Routing Decision Method for Flying Ad Hoc Networks Based on Mobile Prediction
## 5 Discussion  
The extended Kalman filter model is used to predict node positions, with a computational complexity of \(O(n)\), where \(n\)is the number of nodes. The neighbor discovery process requires real-time calculation of neighbor distances and updating of the adjacency table, with a time complexity of \(O(n^{2})\). Although the time complexity of neighbor discovery is relatively high, due to the dynamic adjustment and intermittency of Hello packet transmission, the actual calculation frequency is low, effectively reducing the computational burden. Therefore, although there may be certain computational challenges in large-scale networks, the resource consumption in practical applications is controllable.  
Although MP-QGRD performs well under existing experimental conditions, we also recognize that this approach still has certain limitations. For example, in larger-scale FANET environments, the dynamic Hello packet adjustment strategy and the computational cost of Q-learning models may need further optimization. Future research will focus on exploring the adaptability of MP-QGRD in different network scales and various mobile modes to ensure its superior performance in a wider range of application scenarios.  
Figure 4: Simulation results of changes in the number of nodes: (**a**) packet delivery ratio; (**b**) end-to-end delay; (**c**) energy consumption in communication; (**d**) routing overhead.
# Humans and Automation: Augmenting Security Operation Centers
## 3 Results and Findings
### SOC Challenges Necessating Automation
#### 3.1.1 Lack of Cyber Threat Intelligence  
A core issue was the lack of adequate solutions to convert threat data into threat intelligence. Ref. [26] (p. 02) define threat intelligence as \"evidence-based knowledge, information or data about existing or emerging threats that can be used to prevent or mitigate them\". Additionally, ref. [27] (p. 149) define threat intelligence as \"evidence-based knowledge, including context, mechanisms, indicators, implications and actionable advice, about an existing or emerging menace or hazard to assets that can be used to inform decisions...\". Simply collecting threat information does not assist security analysts in learning about and responding to threats [28]. In the form of actionable intelligence, alert meaning must be derived, the data redundancy removed, and the information must be structured. Given the amount of threat information collected, identifying novel threats amongst this data can be a cumbersome endeavor [29]. Ref. [30] contend that where automation can assist in scripting rules for intrusion detection systems, security analysts are further responsible for identifying the threats to which the rules pertain. Once threat intelligence had been extracted, studies expressed their dissatisfaction with the lack of integrated and automated threat intelligence information-sharing platforms across the industry [31, 32]. These challenges were mentioned in 8 out of 49 (16%) articles analyzed.
## 4 Experiment
### Performance Evaluation
#### 4.2.3 Taillight Pairing  
For evaluating the proposed pairingClassifier, 2312 images captured by the left camera in the raw dataset were used. These images were split into two parts for generating a training set and a test set, with the corresponding numbers of images being 1312 and 1000. The procedure for generating each set is presented as follows:  
* From each image \(I\), a list of detected taillights \(\mathbf{T}\) = {\(T_{1}\), \(T_{2}\),..., \(T_{k}\)} is obtained after processing \(I\) using the proposed taillight detection method.
* All pairs of taillights (\(T_{i}\), \(T_{j}\)) are generated from \(\mathbf{T}\).
* A function pairingFeatureExtraction processes each pair (\(T_{i}\), \(T_{j}\)) to extract its feature vector _pairVec_, while the expected _status_ value is manually set through direct observation.
* A tuple (_pairVec_, _status_) of (\(T_{i}\), \(T_{j}\)) is added to the dataset.
* The procedure keeps running until all taillight pairs from every image have been considered.  
An overview of the above procedure is shown in Figure 27.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Correct Matching** & **False Matching** & **Missed Matching** & **Precision (\%)** & **Recall (\%)** & **F-Score (\%)** \\ \hline
5336 & 196 & 315 & 96.46 & 94.43 & 95.43 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Stereo taillight matching results.  
In order to evaluate the performance of pairingClassifier, we counted the number of detected taillight pairs, _Correct Pairing_ (_True Positive_), the number of missed taillight pairs, _Missed Pairing_ (_False Negative_), and the number of false taillight pairs, _False Pairing_ (_False Positive_). Then, the precision, recall, and f-score rates were determined using Equations (35)-(37).  
From the test set, we obtain the values of _Correct Pairing_, _False Pairing_, and _Missed Pairing_ as 3618, 386, and 196, respectively. The results of the taillight pairing method are shown in Table 6.  
The results indicate that pairingClassifier works well in determining the two taillights that belong to the same vehicle. However, there are many cases in which the false taillight pair is recognized, as shown in a large number of _False Pairing_. The main reason for this is that many taillights from different vehicles approach each other from the camera's perspective and they have similar appearances, which makes it difficult to determine whether the two taillights belong to the same vehicle or not. This problem should be addressed in the future work to better identify the two arbitrary taillights that belong to two different vehicles.
## 3 Results
### GIS Interface  
The automation process was carried out on ArcGIS Pro 3.0 utilizing the model builder tool. This process returns a series of visual outputs at individual functions. The first visual output was two IDW raster surfaces representing individual pressure and flow data points (sensor readings). Following this step, the locations selected to be indicative of leaks, based on numerical criteria, in each IDW were overlaid using the intersect tool and were subsequently intersected with the network layer as well. The third visual output was a capture of the chlorine monitoring points located in the vicinity selected in the previous steps. The chlorine values were further compared with the minimum acceptable limit and the values that appeared to be within the limit were discarded from the map. The final visual output of the automation process then returned a network area showing drops in all three parameters that can indicate the presence of a leak. Figure 6 below illustrates the series of outputs returned during the automation process. These different subfigures indicate the pressure and flow patterns throughout the WDN.  
Figure 5: Comparison between GWR and LOF model results (circle: match; rectangle: no match).  
Figure 6: Series of GIS outputs—**(A)** pressure, (B) flow, (C) buffer, (D) intersect, (E,F) intersected WDN.  
The IDW method was employed to generate density grids presenting the spatial distribution of pressure, flow, and water quality data throughout the study area. Three scenarios were carried out to test the efficiency of the automation model in leak detection. The first days of October, January, and July were selected to test the leak progression in water pipes throughout the seasons autumn, winter, and summer, respectively.
## 3 Results
### Mesh Convergence and Model Validation  
A negligible difference (of less than 2%) in the peak contact pressure was observed from the element size of 5 to 4.5 mm and further to 4.0 mm. According to the aforementioned convergence criteria, the element size of 5 mm (203,482 elements) was considered to be a computationally optimal mesh size for further analysis.  
Using CMI, it was found that the peak contact pressure at the hindfoot with EVA was higher compared with forefoot and midfoot. The trend of predicted pressure distribution from the regions of the foot was similar to the peak plantar pressure measured from the experiment (Figure 3A). Therefore, it was a good agreement of the predicted FE results and experimental peak plantar pressure mapping especially at hindfoot during balanced standing with the increase in contact area with CMI fabricated from EVA (Figure 3B).