esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Streaming Video with Deep Learning-Based Network Throughput Prediction
## 4 Prediction of Network Throughput
### Playback Algorithms Supported by Prediction
For static traces generated by the 4G system, this value is closer to 10%; for mobile scenarios, the improvement is about 17%. The mobile use case obtains better results on average, but the higher nQoE variation indicates that the refinement is not uniform but depends on network conditions. Throughput in mobile scenarios, especially involving the 5G network, has higher variability (see Table 1); therefore, some parts of the traces may be harder to predict. The poorer performance of the default prediction based on moving average can also contribute to better relative improvement. For Tian, the resulting pattern is similar, but the gain is a few percent lower than in the other two algorithms. The more complex algorithm based on a PID controller and less reliance on direct throughput estimation probably reduces the benefits of the prediction. Comparing the LSTM versions, the algorithms with support of the BdLSTM have slightly better scores for the MSS and ExoPlayer algorithms. In the case of Tian, the results are roughly the same. Figure 12: Performance of MSS algorithm without and with support of traffic prediction. Throughput prediction eliminates some of the video bit rate drops observed at the beginning, end and approximately the 20 th min of the trace. However, as the prediction sometimes overestimates the throughput, the bit rate unnecessarily switches to a higher level at about the 20 th min of the trace.
## 2 Related Work
### Malicious Certificate Feature Extraction  
Extracted certificate features play a very important role in the detection of malicious certificates, and the results of malicious certificate detection algorithms depend heavily on whether the extracted features can describe the data well. Feature extraction of digital certificates is either by manual methods or by using expert rules to extract valid fields. Manual methods of extracting features [11; 12] obtain features directly from certificates, digitizing features with one-hot encoding [13], implementing discrete data vectorization with CLE, or processing data to get features with TF-IDF [14]. To address the shortcomings of manual feature extraction, tools for extracting features through expert rules were designed and implemented. [PERSON] et al. designed and employed the VFE [15] method for certificate verification and feature extraction; the VFE extracts features through four parts: base analysis, criteria checking, certificate chain construction, and certificate chain verification. The extracted features were put into classical machine learning models and ensemble learning models for training. The ultimate result is that the ensemble learning model has better results for malicious certificate detection. [PERSON] et al. [16] designed and implemented a certificate detection system containing a certificate downloader, a feature extractor, a classification actuator, and a decision-making section. The feature extractor uses expert knowledge to extract features in this system. This method is easier and less time-consuming, but blacklist expansion is time-consuming and tedious.
## 2 Proposed Method
### Uniform Robust Feature Point Extraction  
When processing the registration of SAR and optical images, the distribution of feature points has a great influence on the registration result, which obtains a suitable number of  
Figure 1: The workflow of the proposed algorithm.  
evenly distributed feature points and retains stable and highly repeatable feature points in heterogeneous images. It can accurately estimate the geometric transformation model between images and improve the registration accuracy. In order to solve the problem of uneven distribution of feature points in a single image, this section introduces the scale space scale factor and coordinate space blocking strategy, and uses the phase consistency intensity value to filter the feature points, and obtain as much as possible from heterogeneous images with robust feature points of the same name. The steps of the algorithm are as follows:  
1. The upper limit of the total number of feature points is determined by the original image size N.
2. Calculate the phase consistency response intensity map of the original image I\({}_{\text{PC}}\).
3. Calculate the scale parameters of each group of each layer to construct a Gaussian pyramid.
4. Calculate the feature point set of each group of each layer in the scale space I\({}_{\text{ol}}\): (a) Determine the upper limit of the number of feature points in this layer; (b) detect extreme points as initial candidate points of interest; (c) divide the layer of the Gaussian image into regular grid cells and determine the features that each cell needs to retain the number of points in the n_celli; (d) position the coordinates of the candidate points of interest and analyze the principal curvature to eliminate unstable points on the edge; (e) find the I\({}_{\text{pc}}\) according to the coordinates to obtain the PC intensity value of the reserved interest point, and according to the PC intensity, the degree value and the absolute value of the Gaussian difference response are sorted, and the top n_celli are retained as the feature points in the cell; (f) summarize the feature points in all cells to obtain the feature point set P\({}_{\text{ol}}\) of the current layer I\({}_{\text{ol}}\).
5. Summarize the feature points of all scale layers in the scale space to obtain a set P.  
According to the SIFT feature points obtained by the above feature point detection algorithm, because in the process of feature point detection, the number of feature points in the scale space distribution and coordinate space distribution is consciously restricted, and then the stability and saliency of the feature points can be characterized by the index screens of the feature points, and finally obtains a uniform and robust feature point set.
# Challenges of PBFT-Inspired Consensus for Blockchain and Enhancements over Neo dBFT
## 5 Promising dBFT Extensions: Towards dBFT 3.0
### Design Principles  
The dBFT 3.0 proposal intends to improve dBFT 2.0 in the following manner:  
(X.1) allowing multiple \"simultaneous\" block proposals (in same view)  
Figure 12: Complex attack on dBFT 2.0+, where replica \(R_{2}\) is malicious. Byzantine \(R_{2}\) helps \(R_{0}\) commit, and then it assumes new proposal \(\#H^{\prime}\). By unfortunate delays, replicas \(R_{5}\) and \(R_{6}\) are unaware of previous proposal from \(R_{0}\), and delays prevented \(R_{0}\) to participate on next view change, which “elects” byzantine \(R_{2}\). Although proposal \(\#H\) is effectively informed during view change (always at least one non-faulty will inform), there is not enough \(f+1\) confirmations of \(\#H\) to force malicious primary to keep it (then it starts a new one \(\#H^{\prime}\)). Replica \(R_{3}\) dies after response to \(R_{0}\) (this can also be considered a malicious attack under quota \(f=2\)). As long as delays are large enough, replicas \(R_{1},R_{2},R_{4},R_{5},R_{6}\) may form a new block \(\#H^{\prime}\).  
* remove the \"special status\" of a single primary node: by having (X.1), a single faulty replica cannot delay block generation
* community participation: by having (X.1), one can afford letting \"unvoted\" and \"highly staked\" _guest nodes_ to participate on consensus without risking the network safety, eventually contributing and also earning fees
* in-time consensus: exact \(T\) seconds with block finality (on average)  
Some interesting extensions can also be included, such as:  
* community interaction messages from nodes via p2p  
We believe that (X.5) could incentivize the early reporting of failures (or even scheduled ones) via P2P, and instantly pass information to users via integration with social networks, wallets and traditional messaging systems. This can also be used (although in a limited way) to provide new monetization means for consensus nodes, by exploring its visibility and \"status\", to merchandise for its affiliate projects via P2P.
# Multi-Objective Optimization of Three-Stage Turbomachine
## 6 Assessment of FEM and TMM with Optimized Design  
To demonstrate the accuracy of the TMM's results we obtained, a comparison with the FEM was conducted in this section. For this comparison, a 3D model was initially created as shown in Figure 16 over optimized ABC geometry. Utilizing the geometric properties of this 3D model, a 2D axisymmetric model was then generated using Ansys, as depicted in Figure 17. The use of the axisymmetric model was chosen because its mathematical representation is similar to the TMM and it provides the most stable results for rotordynamic modeling in Ansys. The blades on the disks on the rotors were defined as point masses using the MASS21 element. The COMBI214 element type was selected for the bearings. The SOLID272 element type was used for the axisymmetric model. The obtained results demonstrate the high quality and speed of the TMM solutions. The comparison focused on evaluating the convergence of frequency response and natural frequency results with respect to different mesh sizes and solution times.
## 4 Discussion
### Effects of Different Factors on the Capillary Water Absorption of Concrete
#### 4.1.2 Effect of the Fly Ash Content  
Mineral admixtures such as fly ash increase the durability of cement-based materials and influence capillary water absorption characteristics. Figure 6 shows the effect of fly ash content on \(S_{1}\)[4; 19; 4; 5; 4; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51]. The data from the literature were unified and normalized (\(S_{\rm r}\)) to more intuitively analyze the influence of fly ash content and exclude the effect of the W/B; namely, the capillary water absorption coefficient without fly ash was 1. Figure 6 shows that the dosage of fly ash exerts an obvious effect on \(S_{1}\). When the dosage of fly ash did not exceed the critical value, \(S_{1}\) showed a decreasing trend with an increasing dosage of fly ash. The rational function was used for the fitting analysis, and the result is shown in Equation (4) (correlation coefficient R\({}^{2}\) = 0.974). When the fly ash content increased from 0 to 30 and 40%, \(S_{1}\) decreased by approximately 27.8 and 14.2%, respectively. This finding is attributed to the improvement in the microstructure and increase in the density of the pore structure when an appropriate dosage of fly ash was added to cement-based materials, making it greater, which increased the difficulty for water to enter the interior and thus increased the durability. Therefore, in cement-based material production, an appropriate amount of mineral admixtures can be mixed, increasing the permeability and durability of cement-based materials.  
\[S_{\rm r}=\frac{1-0.01546M_{\rm f}}{1+0.00723M_{\rm f}-5.279\times 10^{-4}M_{ \rm f}{}^{2}}, \tag{4}\]  
where \(S_{\rm r}\) is the normalized \(S_{1}\) for different fly ash dosages, and \(M_{\rm f}\) is the fly ash dosage (%).  
Figure 5: Effect of the W/B on \(S_{1}\).
## 4 Experiment Setup and Performance Analysis
### Experiment Results and Analysis in the Ocean Scenarios
#### 4.3.2 Performance Comparison of Diversity Scheme with Different Antenna Placement Strategies  
The fourth experiment mainly analyzes the PER and FER of diversity scheme with different antenna placement scheme at different locations within a radius of 1100 m. Four locations were selected for testing and the transmission performance of the two reception schemes are analyzed. Among them, the distance from each location to the sink node is as follows: Location1-1000 m, Location2-1100 m, Location3-800 m, Location4-900 m. The experimental results are shown in Figure 14.  
It can be seen from Figure 14 that the macrodiversity reception scheme with two clusters can achieve higher reliability transmission (PER \(<\) 10\({}^{-2}\)) compared with single cluster reception scheme within the transmission distance of 1100 m. When the sensor node is located in Location1 and Location4, the paths from the sensor node to the two local convergence nodes are approximately in a straight line, and the received signals are highly correlated. Under the circumstances, the transmission gain of the macrodiversity reception scheme is close to that of single cluster reception scheme. When the sensor node is located in Location3, the paths from the sensor node to the two local convergence nodes are quite different, and the correlation of signals in the two local convergence nodes is also weak. At this location, the FER of single cluster reception scheme using eight antennas scheme at LCN1 and LCN2 are \(9\times 10^{-4}\) and \(2.8\times 10^{-3}\) respectively, while the macrodiversity reception scheme can achieve error-free transmission.  
Figure 13: The relationship between performance of macrodiversity reception scheme using different number of antennas and transmission distance. (**a**) The relationship between PER of macrodiversity reception scheme using different number of antennas and transmission distance. (**b**) The relationship between FER of macrodiversity reception scheme using different number of antennas and transmission distance.
## 5 Bio-Based Solvents  
Bio-based solvents are a group of green solvents that have several advantageous characteristics such as low toxicity and non-flammability, besides being biodegradable and renewable, as they are produced from biomass and agricultural materials [86]. One example is ethanol, which has been used for decades in classical liquid-liquid extraction. Among others, glycerol, 2-methyl tetrahydrofuran (meTHF), ethyl lactate, p-cymene, and terpen are part of the bio-based solvent groups that have attracted interest for applications in separation methodologies due to their characteristics [87]. A very interesting bio-based solvent is d-Limonene. It is derived from citrus peel and, like other bio-solvents, is low-cost and biodegradable and exhibits low toxicity. Its main attraction has been in the substitution of traditional solvents such as acetone, toluene, and chlorinated and fluorinated solvents in several applications. Due to its characteristics as a degreaser, this solvent has been applied to the removal of oils and fats [88]. In addition, d-Limonene may be a substitute for toxic organic solvents in Soxhlet extraction procedures [89].  
Recently, a liquid-liquid dispersive microextraction (DLLME) method applying d-Limonene and B-carotene for the determination of b-cyclodextrintrin (b-CD) was developed [90]. These two mixed bio-solvents show a strong adsorption characteristic. When placed in the presence of b-CD, B-carotene forms a complex that increases the absorbance of the extracted phase, generating an excellent analytical signal for the determination of the target compound. The validation of the method presented an excellent limit of detection (0.00004 mol L\({}^{-1}\)) with a linear range from 0.0004 to 0.006 mol L\({}^{-1}\). The method was applied for the determination of b-CD in water and pharmaceutical samples, obtaining recovery values between 94.2 and 108.0%, confirming the efficiency of the method.  
Despite the advantages, these solvents are still poorly explored as the extraction phase for microextraction techniques, mainly with chromatography techniques. Some drawbacks may have to be overcome, such as the high viscosity that causes poor analyte mass transfer and also the incompatibility with analytical instrumentation [91]. Nevertheless, an alternative would be their combination with other green solvents or their use as modifiers of solid sorbents, as already proposed by [PERSON] et al. (2018) [63].
# Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber) 1
## 4 Security and Cryptanalysis on PQC KEM: How Kyber Was Selected
### Side-Channel Attack on NIST's Third Round
[44] presented a correlation attack on Dilithium, which is the NIST fourth-round finalized digital signature. They used a rawhammer target and an attack on a single flip bit in a secret vector, and successfully recovered 1851 bits out of 3072 bits.
# Computational Test for Conditional Independence
## 4 Results
### Assessing with \(p\)-Value Distribution  
To assess how CCI can analyze CI statements by calculating a distribution of \(p\)-values instead of relying on one \(p\)-value, we generated data from the three simulation scenarios, Multinomial, TrigData, and NonLinearData, where a false CI is the most challenging to test. For each simulation scenario, we generated 20 datasets, each containing 500 observations for testing both the true null of \(M(P(Y=k\mid X^{*},Z_{1},Z_{2}))=M(P(Y=k\mid X,Z_{1},Z_{2}))\), and an erroneous null, \(M(P(Y=k\mid X^{*},Z_{2}))=M(P(Y=k\mid X,Z_{2}))\). For each dataset (i.e., 60 datasets), we estimated 500 \(p\)-values by estimating an empirical null distribution and the corresponding distribution of test statistics.  
In Figure 9, the 500 \(p\)-value points for each test are color-coded. Each QQ plot has 20 lines of color-code dots, one for each dataset. When testing a true null, we obtain the plots in Figure 9a,c,e. There is a close alignment with the theoretical uniform distribution, which suggests good adherence to the null hypothesis. Some degree of variability, as in minor deviations from the diagonal, is acceptable. Kappa scores seem to have somewhat less idiosyncratic variation.  
The QQ plots, depicted in Figure 9b,d,f, show the distribution of \(p\)-values for testing \(Y\perp X|Z_{2}\). The points, especially using Kappa scores as the underlying metric, have a notable deviation compared to their theoretical counterparts. This deviation suggests a systematic tendency towards lower \(p\)-values than those expected under the null hypothesis, indicating a likely violation of the null, the desired outcome. One can note that when assessing the QQ plots, one should evaluate the difference between the points and the line based on vertical distance [28].  
Kappa scores provide better testing performance using QQ plots. With Kappa scores, \(p\)-values seem more stable around the diagonal line when testing a true null and more deviate from it when testing a false null.  
In addition to providing a testing framework in small samples, using QQ plots has some other benefits. QQ plots can reveal patterns that can indicate fundamental errors, for instance, if the \(p\)-values tend to be high, meaning that the points would bend upwards to the left; such a pattern can indicate the non-independence of samples or highly correlated features.  
Figure 8: Means of type I error and power over all simulation scenarios and testing methods. **(a)** Type I error means for all simulation scenarios the CCI test is represented with the blue and green line (log loss and Kappa scores). Logarithmic Y-scale. **(b)** Testing power over all simulation scenarios, which are all increasing with sample size. Note that the Y-scale goes from 0.5 to 1.
## 2 Data and Methods
### Methodology  
#### 2.2.1 Data Aggregation  
Within the framework of the project \"Long-term variability of solar radiation in Greece: effect of air quality and clouds\" all the datasets presented above have been spatially and temporally homogenized. The MODIS/Aqua aerosol, cloud, and water vapor data have been gridded on a monthly basis at a standard 0.125\({}^{\circ}\)\(\times\) 0.125\({}^{\circ}\) grid covering the Eastern Mediterranean for the period 2005-2019. The spatial resolution of the grid was selected as the best compromise between the pixel size (at nadir) of the different core satellite observations used here (from MODIS/Aqua clouds 5 km \(\times\) 5 km to OMI/Aura NO\({}_{2}\) 13 km \(\times\) 24 km). For clouds, taking into account the MODIS/Aqua cloud phase parameter their optical, microphysical and macrophysical properties are calculated separately for liquid (CP: 2 or 4) and ice clouds (CP: 3). While gridding, each aerosol, cloud and water vapor single-pixel observation is weighted by its fractional area within the grid cell following the gridding methodology of the Royal Netherlands Meteorological Institute (KNMI) for the OMI/Aura tropospheric and stratospheric NO\({}_{2}\) data [32]. The weighted pixel values are averaged on a daily basis for each grid cell and finally the daily gridded data are averaged on a monthly basis. All the other datasets were gridded (at the same standard 0.125\({}^{\circ}\)\(\times\) 0.125\({}^{\circ}\) grid) on a monthly climatological basis (12 values per grid cell) using bilinear interpolation.
## 4 Simulation Results and Analysis
### Return Loss
Figure 6 presents the simulated and measured return loss (S\({}_{11}\)) of the standalone 1 \(\times\) 6 linear microstrip patch antenna array without the integrated power divider. The simulated result shows a primary resonance at approximately 28.0 GHz with a minimum return loss of \(-\)13.3 dB, while the measured result exhibits resonance at approximately 28.05 GHz with a minimum return loss of \(-\)12.6 dB. The measured curve closely follows the simulated trend, with a frequency deviation of only 50 MHz (0.18% of the center frequency), indicating a strong correlation between the prototype and the design. Figure 6 compares the simulated and measured reflection coefficients of the 1 \(\times\) 6 array integrated with the power divider. The measured curve shows a resonance at 28.3 GHz with a minimum S11 of \(-\)20.7 dB, while the simulated curve has a resonance at 28.1 GHz with a minimum S11 of \(-\)23.5 dB. The -200 MHz frequency shift and -2.8 dB difference are attributed to fabrication tolerances, connector parasitics, and soldering effects. Despite these variations, the curves show consistent trends, confirming the effectiveness of the integrated power divider and array design. The 3 dB bandwidth of the simulated result spans from -26.9 GHz to \(\sim\)28.9 GHz, giving a bandwidth of \(\sim\)2.0 GHz (7.14%), while the measured bandwidth is slightly narrower, from -27.1 GHz to -28.8 GHz, yielding -1.7 GHz (6.1%). The reduction in measured bandwidth is typical and expected due to practical implementation factors such as connector transitions, soldering effects, and minor substrate property deviations. This comparison quantitatively confirms that the antenna resonates efficiently around 28 GHz, with return loss values better than \(-\)10 dB over the operational range [19]. The minor discrepancies are acceptable within mm-wave fabrication tolerances and validate the integrity of the simulation model.