esa-sceva/satcom-chunk-collection huggingface dataset:
# Open-Pit Bench Blasting Fragmentation Prediction Based on Stacking Integrated Strategy
## 2 Ensemble Machine Learning (ML) Models
### Bagging and Boosting  
The bagging algorithm, also known as 'bootstrap aggregating', was proposed by [PERSON] in 1996 [32]. The random forest algorithm, also proposed by [PERSON], is based on the bagging concept, combining multiple single decision trees. The principle of the bagging algorithm involves randomly sampling multiple subsets from the original datasetwith replacement, ensuring that the size of each subset is the same as the original dataset. Each subset is used to independently train a learner, and during prediction, the bagging algorithm combines the predictions of all learners to derive the final prediction result. Bagging can significantly reduce model variance and improve generalization performance, but it is sensitive to noisy data and prone to consistency issues.  
The boosting algorithm, on the other hand, combines weak learners into a strong learner by chaining them together. It enhances the accuracy of the algorithm and is more generalizable [33]. Boosting trains its base learners using the initial training set and, based on their performance, constructs the next base learner. If the previous round's prediction was incorrect, higher weights are assigned to the misclassified instances in the next round of prediction. Ultimately, the outputs of the weighted combination of multiple learners are used to derive the final result. Compared to bagging, boosting is more effective in reducing bias and improving training accuracy, thus addressing the problem of underfitting. However, both bagging and boosting are essentially ensemble algorithms of homogeneous learners, and their schematic diagrams are shown in Figure 1.
# Resource-Efficient FPGA Architecture for Real-Time RFI Mitigation in Interferometric Radiometers
## 2 Algorithm Description
### PMS Blanking  
The PMS Blanking subblock (Figure 5) performs the final mitigation of the detected RFI from the input PMS signals, using the masks provided by the RFI Detection subblock, and the Truncated STFT signals provided by the Observable Generation block, to provide the X- and Y-polarization averaged (and mitigated) PMS signals. The output of this block includes the Gamma parameters used to scale the final mitigated signal.  
Figure 4: RFI Detection architecture showing parallel computation of statistical and polarimetric parameters in time and frequency domains. The detection logic combines multiple metrics to generate blanking masks for RFI mitigation. Configurable or external inputs, and debug outputs, are shown with red arrows.  
The mitigation of the power measurements follows a pulse blanking approach (mitigation in the time domain), but instead of using just the instantaneous power value to infer the presence of RFI if it is above a given value (typically several times the standard deviation of the power itself, assuming it is RFI-free), the blanking mask is calculated directly from the temporal moments of the polarimetric kurtosis (\(b_{x}[m]\) and \(b_{y}[m]\)). The ratios, \(\gamma_{x}\) and \(\gamma_{y}\), are calculated for each receiver \(r\), representing the ratio between the power of the bins after mitigation and before, as:  
\[\gamma_{x,r} =\frac{\sum_{m=0}^{M-1}\sum_{k=0}^{K-1}|X_{r}^{mit}[m,k]|^{2}}{ \sum_{m=0}^{M-1}\sum_{k=0}^{K-1}|X_{r}[m,k]|^{2}}, \tag{8}\] \[\gamma_{y,r} =\frac{\sum_{m=0}^{M-1}\sum_{k=0}^{K-1}|Y_{r}^{mit}[m,k]|^{2}}{ \sum_{m=0}^{M-1}\sum_{k=0}^{K-1}|Y_{r}[m,k]|^{2}}, \tag{9}\]  
where \(X_{r}^{mit}[m,k]\) and \(Y_{r}^{mit}[m,k]\) are the different mitigated time and frequency observables for the X and Y polarizations, whereas \(X_{r}[m,k]\) and \(Y_{r}[m,k]\) are unmitigated. These ratios are used to compensate for the bias introduced by the RFI signal into the PMS measurements. Note that, ideally, output PMS signal should be obtained directly from the mitigated STFT if the input signal had multiple quantization bits. After the mitigation of the corrupted PMS, the sum of PMS samples that are not discarded has to be normalized by the gamma factor, in order to estimate properly the power in each channel/polarization, as:  
\[P_{x,r}^{mit} =\sum_{b_{x}^{time}[m]=1}P_{x,r}[m]\cdot\gamma_{x,r}, \tag{10}\] \[P_{y,r}^{mit} =\sum_{b_{y}^{time}[m]=1}P_{y,r}[m]\cdot\gamma_{y,r}, \tag{11}\]  
where \(P_{x,r}^{mit}\), \(P_{y,r}^{mit}\) are the mitigated powers for the X and Y polarizations and receiver \(r\), \(P_{x,r}\), \(P_{y,r}\) are the unmitigated powers, and the \(b_{x}^{time}[m]=1\) and \(b_{x}^{time}[m]=1\) sums iterate over the blanking masks, where they are equal to 1.
## 3 Methods
### The AQM-PROBA Algorithm
#### 3.1.3 Training Sample Selection  
Burned area classification mainly relies on two variables: (1) Post-fire NIR reflectance; and (2) Difference between pre-fire (T1) and postfire (T2) NIR reflectance. VIIRS active fires were used to extract NIR reflectance from PROBA-V pixels to act as training samples to the One-class Support Vector Machine (OC-SVM) classifier. A total of 323,259 VIIRS active fires (for both PROBA-V tiles) were used to extract NIR reflectance information from pixels (one active fire per pixel). As active fires may be detected by the sensor even when only a very small fraction of the pixel area has burned, its reflective spectral signature may not display clear evidence of the burning event. Hence, those pixels are inadequate training samples and have to be discarded. To exclude these samples, the set of pixels containing an active fire detected during the respective compositing period were segmented into three classes, using the Jenks natural breaks method [57]. Natural break segmentation of potential  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Path/Row** & **Initial Date** & **Final Date** \\ \hline
1 & 218/072 & 16 September 2015 & 2 October 2015 \\
2 & 219/068 & 23 September 2015 & 9 October 2015 \\
3 & 219/070 & 7 September 2015 & 9 October 2015 \\
4 & 219/071 & 23 September 2015 & 9 October 2015 \\
5 & 219/072 & 23 September 2015 & 9 October 2015 \\
6 & 220/066 & 30 September 2015 & 19 October 2015 \\
7 & 220/067 & 30 September 2015 & 19 October 2015 \\
8 & 220/068 & 29 August 2015 & 14 September 2015 \\
9 & 221/067 & 20 August 2015 & 5 September 2015 \\
10 & 221/070 & 5 September 2015 & 21 September 2015 \\
11 & 221/071 & 5 September 2015 & 21 September 2015 \\
12 & 222/067 & 27 August 2015 & 12 September 2015 \\
13 & 222/068 & 27 August 2015 & 12 September 2015 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Landsat 8 path/rows and dates used to elaborate reference fire perimeters.  
training data was performed on post-fire NIR reflectance values (T2) and on the pre-fire minus post-fire NIR difference values (T1-T2). To be included in the training sample, candidate pixels must belong simultaneously to: (1) the lowest class of post-fire NIR reflectance and (2) to one of the two highest classes of NIR difference values. Of 323,259 pixels, 93,643 (circa 29%) were selected as training samples for both PROVA-V tiles. Figure 3 shows the cumulative density functions for post-fire NIR reflectance values (Figure 3a) and NIR difference values (Figure 3b), and the thresholds for including pixels in the training sample.
## 3 Experiment and Result Analysis
### Experimental Results
#### 3.3.1 Comparison Experiments
The Paint-CUT proposed in this paper uses the SA to construct the SA-ResBlock so that the model better captures the internal structural features and effectively preserves the texture details of landscape photos. At the same time, perceptual loss and edge loss are added to learn style features and the modeling of landscape paintings. And then, it can generate landscape paintings of a better and higher quality. Therefore, the generated landscape paintings of Paint-CUT have the highest IS and lowest FID, which is consistent with the qualitative analysis. The comparison results indicate that the proposed Paint-CUT generates better results, which can retain the content of original landscape photos and recover the style of target landscape paintings. Similarly, the IS and FID of the comparison experiments on samples of A Thousand Li of Rivers and Mountains and the ChipPhi dataset are shown in Tables 3 and 4, respectively. It can be seen that the proposed Paint-CUT has the highest IS and lowest FID. In conclusion, the proposed Paint-CUT introduces the SA to construct the SA-ResBlock and adds \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **IS**\(\uparrow\) & **FID**\(\downarrow\) \\ \hline MUNIT & 1.154 & 245.977 \\ \hline NICE-GAN & 1.546 & 209.554 \\ \hline U-GAT-IT & 1.649 & 198.231 \\ \hline CycleGAN & 1.688 & 195.785 \\ \hline CUT & 1.784 & 191.346 \\ \hline Ours & 1.950 & 153.791 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of various models in Figure 9. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **IS**\(\uparrow\) & **FID**\(\downarrow\) \\ \hline U-GAT-IT & 1.576 & 234.586 \\ \hline CycleGAN & 1.590 & 230.647 \\ \hline CUT & 1.743 & 227.163 \\ \hline Ours & 2.469 & 225.458 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._perceptual loss and edge loss to generate landscape paintings. On the samples of Dwelling in the Fuchun Mountains, samples of A Thousand Li of Rivers and Mountains, and the ChipPhi dataset, our model has the highest IS and lowest FID compared to other models, which is consistent with the qualitative analysis. And it indicates that the constructed Paint-CUT has a stronger ability of feature learning and can generate landscape paintings with a reasonable layout, clear modeling, and a similar style to target landscape paintings.
## 5 Conclusions  
In this paper, we introduced a novel two-tier caching approach and exploited dual-model data transmission through mmWave and \(\mu\)Wave bands between the UE and serving SBS. Additionally, D2D communications through an mmWave link are incorporated, enabling the UE to obtain the desired content through mmWave-based D2D communications. Furthermore, an RL approach is utilized in the proposed caching scheme to optimize two-tier caching decisions, while taking into account evolving network conditions, cache capacity constraints, and diverse users demands. In an iterative manner, the caching policy is optimized by selecting actions that can improve the rewards and minimize delays. The obtained results demonstrate that our proposed approach substantially minimized delays compared with the benchmark approaches.  
In future work, we aim to utilize mobile unmanned aerial vehicles (UAVs) as cache-enabled aerial base-stations to facilitate high-speed data transmission via LoS links to UE, ensuring seamless connectivity to handoff UE and mitigating communication disruptions.  
Figure 6: Impact of increasing SBS cache capacity on network delay.  
Figure 7: Impact of increasing the amount of content on backhaul consumption, when \(\gamma=1\), \(N=50\), \(c_{i}=50\), and \(c_{n}=10\).  
**Author Contributions:** Conceptualization and methodology, M.S. and M.A.; Supervision, M.A.; software, M.S., M.A. and A.I.; writing, M.S., M.A. and T.C.C.; review and editing, T.C.C., M.B.R. and A.A.-H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Multimedia University research grant MMUI/230013.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 5 Maintenance Decision
### Predictive Maintenance
#### 5.3.2 Rcm  
RCM is a method developed by the aviation industry to ensure asset availability and reliability and gradually applied to other fields [303]. The framework of RCM includes corrective, preventive, and predictive maintenance methods. Although RCM is successfully implemented in wind turbines [304], power distribution systems [305; 306], transformer [307], and aircraft indicators [308], it is less used in merchant ships due to resource and cost constraints [309]. Several classifications such as Lloyd's Register, DNV GL, and Bureau Veritas have proposed to apply the RCM method to the merchant's vessel, and some provide consulting and analysis services. ABS has developed and published the RCM merchant shipping comprehensive guidance note [14]. A holistic maintenance strategy based on RCM principles is utilized to increase the operational reliability of ships [310]. RCM analyses are used to provide a process to optimize maintenance tasks and achieve optimal reliability for vessels [311]. Amendments to the RCM method were proposed for the first time for assessing maintenance needs and reliability challenges on unmanned cargo ships [312].  
Despite its applicability and the potential benefits of RCM, merchant vessels often have relatively unique designs, even sister ships which can still have considerable differences in MSAE. Therefore, in most cases, the RCM successful application for one ship could not be directly used for another ship, which hindered the adoption of the RCM method onboard the ships.
## 3 Fault Diagnosis Experiment and Analysis
### Experimental Analysis of Anti-Noise Ability  
In previous experiments, the TLViT + IFI method performed slightly better than TLViT + CWT method under small sample training and variable load conditions, but the gap between the two is not obvious. The anti-noise capability of the proposed method was further tested. This experiment was only to verify the noise resistance of the model, so only the samples in dataset A were used for training, and white Gaussian noise with different signal-to-noise ratios (SNRs) was added to the original data corresponding to dataset A,  
Figure 8: The diagnosis accuracy of each method under variable load condition.  
and then the data added with noise was converted into images, respectively, as the data of the test set. The SNR is defined as follows  
\[SNR=10\times\lg\left(\frac{P_{raw}}{P_{noise}}\right), \tag{11}\]  
where \(P_{raw}\) is the power of the original signal and \(P_{noise}\) is the power of the noise. From this formula, it can be seen that the SNR is inversely proportional to \(P_{noise}\). The smaller the SNR is, the more serious the pollution in the original signal is.  
In order to verify that the IFI used in this paper has better noise resistance, only the TLViT model was used in the noise resistance comparison experiment. Figure 9 shows the results of TLViT model training on different image datasets and then testing under different SNR. It can be seen from the test results that images containing only one kind of image information have poor noise resistance. Under the influence of noise with a SNR of \(-\)4 dB, the TLViT model is difficult to diagnose fault images containing only one kind of image information. The diagnosis accuracy of TLViT + CWT, TLViT + GASF, and TLViT + GADF are 29.60%, 46.27%, and 43.66%, respectively, and almost all lost the fault diagnosis capability. However, the TL_VIT + IFI proposed in this article still has a diagnosis accuracy of 69.10%, and the diagnosis accuracy is as high as 99.22% when the SNR is 8 dB. It can be seen that the proposed image information fusion method greatly improves the anti-noise capability of the fault diagnosis model, because the fusion of multiple image information makes IFI have richer features and is not susceptible to noise.
# Birds Eye View Look-Up Table Estimation with Semantic Segmentation
## 5 Experiments
### Qualitative Evaluation  
Since the coordinates of the original image corresponding to each coordinate of the BEV can be estimated using the LUT data obtained from the end of the network, the BEV  
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline
**No** & **Scale** & **Parallel Path** & **Pose Regressor** & **Processing Time (s)** & **Seg Loss** & **LUT Loss** & **Pose Loss** \\ \hline v1 & Single & 2 pixel shuffles & X & 0.18 & 0.65 & 1.59 & - \\ v2 & Single & Pixel shuffle and convolution transposed & X & 0.20 & 0.58 & 0.09 & - \\ \hline v3 & Multi & Pixel shuffle and convolution transposed & X & 0.74 & 0.22 & 0.04 & - \\ v4 & Multi & Pixel shuffle and convolution transposed & O & 0.81 & 0.14 & 0.01 & 0.14 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Differences between the versions of the network (v1–v4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**From** & **To** & \begin{tabular}{c} **Processing Time** \\ **Change (To/From)** \\ \end{tabular} & \
\begin{tabular}{c} **Seg Loss** \\ **Change (To/From)** \\ \end{tabular} &
\begin{tabular}{c} **LUT Loss** \\ **Change (To/From)** \\ \end{tabular} \\ \hline v1 & v2 & 1.11 & 0.89 & 0.06 \\ v2 & v3 & 3.70 & 0.38 & 0.44 \\ v3 & v4 & 1.09 & 0.64 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Metrics of gradual change for the network.  
As we progressed from v1 to v4, the aliasing decreased. Particularly, if we compare v3 (without a pose regressor) and v4 (with a pose regressor), we can see that the concept of the overall pose is added, and the distant region is converted relatively well.
# XOR Binary Gravitational Search Algorithm with Repository: Industry 4.0 Applications
## 4 Theoretical Analysis of the Proposed XOR Binary Gravitational Algorithm with Repository  
### General Analysis of the Proposed XOR BGSA  
The proposed XOR BGSA is investigated statistically to show that it is more compliant with the expectations of acceleration and velocity vectors of BGSA. Since the parameter \(X_{k}(t),\ k=1,\ldots,N\) can only accept binary values of _zero_ and _one_, the result of XOR operation \(X_{j}(t)\oplus X_{i}(t)\) may fall in each of the following categories.  
1. If \(X_{i}^{k}(t)=X_{j}^{k}(t)=0\) or \(X_{i}^{k}(t)=X_{j}^{k}(t)=1\), in these cases \(X_{i}^{k}\left(t\right)\oplus X_{j}^{k}\left(t\right)=-1\). It is expected from the acceleration term that since the corresponding bit in the best particle is the same as the bit in the current particle, the probability of change reduces. This expectation is fulfilled using (13).
2. In the case \(X_{j}^{k}(t)=1,\ \ X_{i}^{k}(t)=0\), \(X_{j}^{k}(t)\oplus X_{i}^{k}(t)=1\), the acceleration term introduces a positive value which contributes positively to the probability of change and increases its value.
3. In the case when \(X_{j}^{k}(t)=0,\ \ X_{i}^{k}(t)=1,\ X_{j}^{k}(t)\oplus X_{i}^{k}(t)=1\). Similar to the previous case, since the corresponding bit in the better particle is different than that of the current particle, a positive acceleration term is obtained which results in higher probability of change. This is an expected behaviour from the system.  
According to this analysis, the issues mentioned earlier for BGSA do not exist in the proposed XOR BGSA.  
Figure 1: Flowchart of the proposed XOR binary gravitational search algorithm (BGSA).
# Imaging Diagnostics of Inside of a Building Wall Using Millimeter-Wave Reflectometer
## 2 Reflectometric Measurement
### Principle Verification  
The initial experiment was performed in order to identify the existence of the above interferometry effect. The transmitting and receiving antennas were installed on a tile surface (Figure 1b) and connected to port 1 and port 2 of a vector network analyzer (VNA), respectively. The reflected wave from the tile surface was evaluated using the \(S_{21}\) parameter of the VNA. Probe-type waveguide antennas (WR-19:40-60 GHz) and/or substrate antennas (TSA-23T58G-01 (product of EM-WISE)) were used as a transmitter and a receiver. The antennas' angle was 30 degrees, and the distance between the antenna tip and the tile was approximately 5 mm. Considering the effective lengths of the above antennas, this distance is located near the far-field condition. In Figure 2, the measurements are plotted as a function of the millimeter-wave frequency. The solid line in Figure 2 is an example of a numerical calculation of Equation (2). Min-max normalization was performed to make the graph comparison easier. Here, the parameters of the tile thickness \(l=7.3\) mm, the incident angle \(\theta=10^{\circ}\) and the refractive index \(N=2.19\) were assigned. A good agreement between the experimental results and the numerical calculation was obtained. In a real subject, the interferometry effect is not as simple as explained in the concept of Figure 1a, since the subject has a multilayer structure, as shown in Figure 1b. The dielectric constant (relative permittivity) of each layer is said to be as follows: tile 5-6, organic adhesive 4-6, concrete 6-8. Therefore, the reflection between the tile and the air is much bigger than the reflection between the tile and the organic adhesive, and between the organic adhesive and the concrete. The measured reflection model is rather close to two-layer reflection between the tile and the air. The result of Figure 2 shows that the two-layer reflection effect can be utilized in the present study.
# A Communication-Efficient Secure Routing Protocol for IoT Networks +
## 4 Proposed Method
### Route Selection at the Source Node  
When many nodes exist, the source node \(\mathsf{S}\) may receive multiple RREPs by various routes. The source node must select the route for later communication. In this case, the source selects the packet with the highest sequence number for the routing packet generated by the destination itself. Here, even when the relay node generates the RREP, it is the sequence number in the packet generated by the destination node itself in the past that is referred to, not the sequence number in the RREP generated by the relay node.
## 3 Results and Discussion
### Three-Point Bending Tests  
The composite parameters obtained from three-point bending tests are summarized in Table 1. All the samples were sufficiently ductile, no brittle fracture has been observed at relatively high loads, therefore the flexural stress was calculated at a given deflection of \(S=6\) mm (see Figure 4).  
As can be seen from Table 1, only the samples filled with graphene demonstrated slightly higher flexural stress and somewhat rising flexural elastic modulus, while other composites retained their properties at the level of pure polystyrene. Apparently, the filler concentrations used were not sufficient for providing noticeable changes in the flexural mechanical properties of composites. Most likely, graphene platelets, located in the direction of the melt flow, were capable of exerting a reinforcing effect in block samples loaded in the cross-sectional plane orthogonally to the longitudinal axis of the sample.  
The data obtained in tensile and flexural tests correspond to those reported by other authors. In particular, in experiments by [PERSON] et al. [48] introduction of as-produced  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**PS Pure**} & \multicolumn{3}{c}{**PS + C60**} & \multicolumn{3}{c}{**PS + CNT**} & \multicolumn{3}{c}{**PS + Graphene**} \\ \cline{3-10}  & & **0.1\%** & **0.5\%** & **5\%** & **  
multiwall carbon nanotubes in polystyrene matrix resulted in a weak increase in the elastic properties of the matrix. [PERSON] et al. [49] observed only a modest increase in the flexural modulus of polystyrene with the addition of annealed SWNT at concentrations up to 2 wt%. Significant enhancement of mechanical properties of PS-based composites containing graphene at concentrations up to 5 wt% was reported by a number of research groups, see [50] and references therein. At the same time mechanical properties of PS-based nanocomposites with carbon nanofillers were shown to exhibit significantly different parameters depending upon fabrication method and procedure, functionalization of filler particles, utilization of a masterbatch, etc., see [51].