esa-sceva/satcom-chunk-collection huggingface dataset:
# Deterministic Coresets for \(k\)-Means of Big Sparse Data +
## 7 Comparison to Existing Approaches
### On the Small/Medium Datasets  
we evaluate both the offline computation and the streaming data model. For offline computation we used the datasets above to produce coresets of size \(100\leq m\leq 1500\), then computed \(k\)-means for \(k=10,15,20,25\) till convergence. For the streaming data model, we divided each dataset into small subsets and computed coresets via the merge-and-reduce technique to construct a coreset tree as in Figure 1. Here, the coresets are smaller, of size \(10\leq m\leq 500\), and the values for \(k\)-means are the same.  
We computed the sum of squared distances to the original (full) set of points, from each resulting set of \(k\) centers that was computed from the coreset. These sets of centers are denoted by \(C_{1},C_{2}\) and \(C_{3}\) for uniform, non uniform sampling and our coreset respectively. The \"ground truth\" or \"optimal solution\" \(C^{k}\) was computed using \(k\)-means on the entire dataset until convergence. The empirical estimated error \(\varepsilon\) was then defined to be \(\varepsilon=C_{t}/C_{k}-1\) for coreset number \(t=1,2,3\). Note that, since Lloyd's \(k\)-means is a heuristic, its performance on the reduced data might be better, i.e., \(\varepsilon<0\).  
These experiments were run on a single common laptop machine with 2.2 GHz quad-core Intel Core i7 CPU and 16 GB of 1600 MHz DDR3L RAM with 256 GB PCIe-based onboard SSD.
## 4 Future Research Trends for ML-Based IRS-Assisted Wireless Communication
### Channel State Characterization  
Different models have been proposed to categorize the IRS-assisted communication, such as an independent diffusive scatter-based model, physics model, impedance network-based model, and tile and code-book-based framework [108]. Machine-learning-based CE is now undergoing research, and different techniques have been used to estimate channel modeling [109]. However, many proposed optimization techniques are computationally complex. New optimization techniques can be a scope for future research.
# LTE: Lightweight Transformer Encoder for Orbit Prediction
## 2 Materials and Methods
### Dataset  
The Korea Aerospace Research Institute (KARI) [24], established in 1989, is South Korea's national aerospace research institute. It was founded to explore, develop, and disseminate new aviation and aerospace science and technology. Among its projects is the Korea Multi-Purpose Satellite (KOMPSAT) series, which was designed to observe the surface of South Korea in high resolution to enhance national security and public services. In this study, we utilized a dataset of orbit data from the KOMPSAT-3 (K3), KOMPSAT-3A (K3A), and KOMPSAT-5 (K5) which consists of approximately 4.8 million data points collected every minute from January 2016 to December 2018.  
Measuring the position and velocity of a satellite directly from the ground is difficult. In addition, interferences arise unexpectedly due to various dynamical forces such as Earth's gravitational potential, the gravitational pull from the sun or moon, and solar radiation pressure. To address these challenges, the TLE set is used to predict the orbit of moving satellites. The TLE set is in a standard format for representing the orbital parameters of Earth-orbiting satellites, encoding information in two lines that describe an object's orbit through 12 specific elements. These include the Satellite number, International designator, epoch, First derivative of mean motion, BSTAR drag term, Eccentricity, Inclination, Right Ascension of the Ascending Node (RAAN), Argument of perigee, Mean anomaly, Mean motion, and Revolution number. Among these, we used the six orbital elements most relevant to satellite position and velocity for model training. The preprocessing steps for extracting these six orbital elements from the TLE data are detailed in Section 2.2.  
To understand the characteristics of these six orbital elements, we displayed the elements from the K3 satellite dataset within the data points from 300,000 to 302,000, as shown in Figure 2. In the case of RAAN, a different range of data points (300,000 to 1,700,000) was provided since it has a long-period repetitive pattern compared to the other elements. Each element exhibits a unique period and wave shape, and a repetitive pattern is observed within each element individually. However, the data show subtle deviations in fluctuation even within these repetitive patterns, and the errors caused by these subtle deviations in fluctuation can lead to major satellite orbit changes.
# A Novel Global Key-Value Storage System Based on Kinetic Drives +
## 5 Design and Architecture
### Data Recovery  
All data are stored in three drives from three different regions. If one of these three drives fails, the system can replace the bad drive and recover the data from two other drives. The metadata server just finds the closer drive, initiates a P2P data transfer to recover data, updates its mapping table, and notifies two other metadata servers in different regions.  
For example, in Figure 7, if drive D3 in Region 4 fails, the metadata server MS4 figures out that either drive D21 in Region 2 or drive D18 in Region 5 is the closer one to drive D3. Then, MS4 finds an empty drive (e.g., D137) in its region and initiates a P2P data transfer operation from drive D21 or D18 to drive D137. Finally, MS4 replaces drive D3's IP address with D137's in its mapping table and notifies metadata servers MS2 and MS5 to update their mapping tables to reflect the change as well.  
Following a similar idea of local backup discussed in Section 5.4, practically, for each region, data can be quickly recovered from drives within the same region, if some drives are reserved as local backups for the _original data_. However, in this paper, we just show that our design is also feasible for data recovery between different regions, especially if inter-region data recovery is necessary.
## 3 Materials and Methods
### RoI-Loss Function  
CRTD employs a unified Region of Interest (ROI) loss scheme to compute the entire network jointly. The first supervisory function of TCR utilizes the Mean Absolute Error (MAE), or L1 loss function, to assess the differences between corresponding pixels in the image. Although this loss function is relatively straightforward, numerous studies indicate that image restoration tasks using L1 loss outperform those using L2 loss in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Consequently, this paper adopts L1 loss, as outlined in the following formula:  
\[L_{TCR}=\frac{1}{n}\sum_{i=1}^{n}|y_{i}^{gt}-STD\Big{(}y_{i}^{pre}\Big{)}| \tag{4}\]  
where \(y_{i}^{gt}\) represents the ground truth for the \(i\)-th pixel, \(y_{i}^{pre}\) denotes the predicted value for the \(i\)-th pixel, and \(n\) is the total number of pixels in the image.  
It is important to note that enhancing features across the entire image may introduce unnecessary noise. Therefore, it is essential to establish a supervision mechanism for TCR that allows it to focus more on the target region. Considering this, this paper feeds back the detection results from STD to TCR as an ROI mask. When calculating the L1 loss, different weights are assigned based on whether a pixel is within the target region. Pixels located within the ROI mask have a higher weight, while those outside it have a lower weight. The modified loss function is given by the following:  
\[L_{TCR}=\frac{1}{n}\Big{(}\alpha\sum|y_{i}^{pt}-TCR\Big{(}y_{i}^{pre}\Big{)}| +\beta\sum|y_{j}^{pt}-TCR\Big{(}y_{j}^{pre}\Big{)}|\Big{)} \tag{5}\]  
where in this paper, \(\alpha=0.8\) and \(\beta=0.2\). Here, \(y_{i}\) indicates that the pixel is within the ROI mask, while \(y_{j}\) indicates that the pixel is outside the mask. Subsequently, CRTD will be  
Figure 6: Details regarding Compact Inverted Block (CCIB): low-cost depth-wise and point-wise convolutions for efficient small target detection.  
trained using this unified loss scheme. Experimental results validate the effectiveness of this approach.  
To achieve unified training of hierarchical supervised networks, we introduce a unified RoILoss scheme. We calculate the IoU loss between the TCR and STD detection boxes. The formula for the unified RoI loss is as follows:  
\[L_{\text{RoI}}=1-\text{IoU}(\text{BBox}_{\text{TCR}},\text{BBox}_{\text{STD}}) \tag{6}\]  
Finally, we sum \(L_{\text{TCR}},L_{\text{STD}}\), and \(L_{\text{RoI}}\) to obtain the total loss of the model, with each component weighted differently. This approach enables end-to-end training of the model.  
\[L_{\text{all}}=\lambda_{1}\cdot L_{\text{TCR}}+\lambda_{2}\cdot L_{\text{STD} }+\lambda_{3}\cdot L_{\text{RoI}} \tag{7}\]  
In our experiments, the weight factors \(\lambda_{1},\lambda_{2}\), and \(\lambda_{3}\) are set to 1, 1, and 0.5, respectively.
## 3 Materials and Methods
### Research Methodology
#### 3.2.2 Methodological Framework
CNN's hyper-parameters are properly fine-tuned, and the final model is defined. **Step 5: Testing**  
After its training and validation, the best CNN model was developed and tested on unknown data using the testing dataset. CNN's performance was computed through robust evaluation metrics such as accuracy, loss, AUC, and ROC curve. **Step 6: Explainability through Grad-CAM**  
Since CNNs are not inherently explainable and transparent, post-hoc explainability methods have been employed to inspect the outputs of their layers and visualize them to increase comprehensiveness [35,36]. Towards explainability in our approach, we selected Grad-CAM (gradient-weighted class activation mapping) [37] to interpret the predictions of our CNN model through the production of heatmaps. More specifically, heatmaps highlight regions that indicate a positive impact on the corresponding predicted output of the fully-trained model. To acquire these critical regions, we utilize the extracted gradients/weights of the last convolutional layer of the defined model, which is expected to extract the most important, deep, and abstract features that endorse the final decision [38]. Grad-CAM uses the convolutional layers as they include the spatial information of high-level features of the generated patterns produced during the training process. This is because this spatial information is lost after the flatten layer and the fully connected layers. Therefore, the last convolutional layer's gradients could help locate the regions that indicate the predicted output [39]. **Step 7: Inference phase**  
First, our pre-trained classification model is fed with a testing image, preferably an unknown instance of the training dataset, thus producing the extracted output, which the model for the respective image will predict. Next, we calculate the weights of the feature maps produced by the last convolutional layer of the model. Then, we apply GAP (global average pooling) to obtain the alpha values of the weights. Afterward, a heatmap is generated by computing the weighted sum of the acquired feature maps. The heatmap highlights the critical regions that correspond to the predicted output. The produced heatmap is resized to match the dimension of the testing image. It is worth mentioning that the negative values are discarded so that only the pixel values that positively impact the produced heatmap are kept. Moreover, for the overlay functionality, the heatmap is placed on top of the testing image to ensure interpretability with respect to the results [40]. Grad-CAM constitutes a trustworthy explainability algorithm that achieves excellent results, and thus, it can be applied in a wide variety of CNN architectures and pre-trained networks [37].
# Exploring the Potential of Microextraction in the Survey of Food Fruits and Vegetable Safety
## 2 Extraction Approaches Used to Assess Food Safety
### Microextraction Approaches
#### 2.2.2 Liquid-Based Microextraction Techniques  
Conventional LLE techniques present a few limitations, including emulsion formation, long preparation time, and the use of high volumes of toxic organic solvents. Moreover, the evaporation of the solvent is inevitable, as well as sample reconstitution. To overcome these issues, miniaturized liquid-phase extraction (\(\mu\)LPE) techniques that use microliters of organic solvent to accomplish the selective isolation, preconcentration of the analytes, and clean-up of the sample were developed [12]. These innovations related to the extraction and pre-concentration of analytes have resulted in the development of emergent approaches such as liquid-liquid microextraction (LLME) and dispersive liquid-liquid microextraction (DLLME) [109; 110].  
DLLME and its modifications offer several advantages; namely, a high pre-concentration factor for the target analytes, small sample volumes, low consumption of solvents, a high extraction efficiency, a low cost, simplicity, and a high enrichment factor [12; 109]. These techniques can also be combined with almost every analytical measurement technique [109]. In ionic liquid-based DLLME (IL-DLLME), ILs are used as an alternative to hazardous solvents due to their thermal stability, reusability, high reaction efficiency, and ability to dissolve both organic and inorganic compounds [109]. Due to the cation's fine structure and the anion's identity, it is possible to modify the ILs' properties according to the analytical purpose. Nevertheless, their high cost and toxicity remain the main disadvantages [109]. In ultrasound-assisted DLLME (UA-DLLME), ultrasound is applied to disperse the extraction solvent in the sample, which avoids the reduction of the analyte's partition coefficient between water and the extracting solvent [12]. Single-drop microextraction (SDME), in which the extraction solvent is limited to a single drop, is related to DLLME and allows a great preconcentration factor. The drop is controlled by a microsyringe that loads and withdraws the drop from the solution [109].
## 4 Results and Discussion
### RUL Prediction Results  
RUL prediction is based on the predicted starting point set in the SOH estimation. The left side of Figure 8 shows the real value and estimated value. The real value of RUL is obtained by calculating the cycles needed to reach EOL from the current cycle count. The red circle on the right side of Figure 8 shows the absolute error between the real and estimated values. From Figure 8, the estimated and real values of all four batteries closely match, with a maximum AE of only 2. This observation shows the high accuracy and adaptability of the proposed RUL prediction method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Battery** & **Methods** & **MAE** & **RMSE** \\ \hline \multirow{4}{*}{B0005} & SVR & 0.0339 & 0.0411 \\  & LSE & 0.0177 & 0.0186 \\  & Our Method & 0.0042 & 0.0051 \\ \hline \multirow{4}{*}{B0006} & SVR & 0.0628 & 0.0770 \\  & LSE & 0.0521 & 0.0597 \\  & Our Method & 0.0188 & 0.0193 \\ \hline \multirow{4}{*}{Cell1} & SVR & 0.0236 & 0.0246 \\  & LSE & 0.0142 & 0.0155 \\  & Our Method & 0.0022 & 0.0027 \\ \hline \multirow{4}{*}{Cell2} & SVR & 0.0226 & 0.0235 \\  & LSE & 0.0143 & 0.0153 \\ \cline{1-1}  & Our Method & 0.0036 & 0.0045 \\ \hline \hline \end{tabular}
\end{table}
Table 4: MAE and RMSE of comparison for different model predictions.  
Figure 8: _Cont._  
Notably, the prediction accuracy of health features improves with more cycles, resulting in a decrease in the corresponding RUL estimation error. The overall errors are small in this paper, mainly because the multiscale prediction model can decompose time series signals into components at different frequencies and use the appropriate prediction model for targeted prediction. This method can effectively capture the feature details of the data and can well predict the trend of health features, thus greatly improving the precision and robustness of the RUL prediction method.  
Table 5 shows the MAE and RMSE calculation results. Wherein the battery B0005 starts from cycle 101 until it reaches EOL, and the multistep SOH prediction is performed for 25 cycles to obtain the RUL prediction results, with a prediction error of 0.3600 for MAE and 0.6000 for RMSE. Compared to the B0005 battery, the error metrics of the other three batteries are slightly larger, which is mainly due to the poor results caused by the smaller amount of data. However, the MAE of all batteries is below 0.8 and the highest RMSE value is 1.1094, suggesting that the RUL prediction method proposed in this paper can achieve reliable estimation despite the limited data available.
## 4 Results and Discussions
Similarly, based on Equation (13), the ideal power conversion efficiency variation between 2 \(\upmu\)m and 1.31 \(\upmu\)m wavelengths is \(r_{ni(2~{}\upmu m/1.31\upmu m)}=\left(\lambda_{2~{}\upmu m}^{2}\right)/ \left(\lambda_{1.31~{}\upmu m}^{2}\right)=2.33\). Experimentally, the generated microwave carrier power of \(P_{F2~{}\upmu m}\) and \(P_{F1.31~{}\upmu m}\) can be obtained at 2 \(\upmu\)m and 1.31 \(\upmu\)m wavelengths by using the setup mentioned above under the same optical signal peak power of \(-7.7\) dBm. So that, the measured power conversion efficiency variation \(r_{\text{nm(2~{}\upmu m/1.31~{}\upmu m)}}=(P_{F2~{}\upmu m}/\eta_{\text{2~{} \upmu m}}^{2})/(P_{F1.31~{}\upmu m}/\eta_{\text{1.31~{}\upmu m}}^{2})\) can be sorted out as Figure 6a considering the external quantum efficiency ratio of \((\eta_{\text{2~{}\upmu m}}/\eta_{\text{1.31~{}\upmu m}})=1.146\). It is shown that all of the measured values are close to the theoretical estimation (see Figure 6a) and that the opto-electric power conversion efficiency increesment of \(10 log_{10}\Big{[}(P_{F2~{}\upmu m}/\eta_{\text{2~{}\upmu m}}^{2})/(P_{F1.31~{} \upmu m}/\eta_{\text{1.31~{}\upmu m}}^{2})\Big{]}\) can be correspondingly obtained (see Figure 6b) with the average value of \(\sim\)2.98 dB. From Figures 5 and 6, it can be seen that a higher opto-electrical power conversion efficiency was obtained at the 2 \(\upmu\)m wavelengths compared with at the 1.55 \(\upmu\)m and 1.31 \(\upmu\)m wavelengths under the same optical signal power. This aligns with the theoretical analysis of the larger photoresponsivity that can be obtained at longer input wavelengths \((R=(\eta e\lambda)/(hc))\).
## 3 Results
### Verification of VMFDI
#### 3.1.2. Drought Monitoring Capability of VMFDI for Ecosystems  
To compare drought monitoring accuracy, the correlations between the VMFDI, SPEI (SPEI01, SPEI03, and SPE112), GRACE-DSI, and various indicators of water, heat, and growth conditions (PRE, SM, VPD, ET, LST, NDVI, SIF, GPP, WUE, and LAI) were analyzed (Figure 6). The VMFDI showed strong positive correlations with these drought indices, as well as with PRE (R = 0.71, \(p\) < 0.05), SM (R = 0.63, \(p\) < 0.05), ET (R = 0.62, \(p\) < 0.05), NDVI (R = 0.65, \(p\) < 0.05), SIF (R = 0.65, \(p\) < 0.05), GPP (R = 0.56, \(p\) < 0.05), and the LAI (R = 0.61, \(p\) < 0.05) (Figure 6a). These results indicate that the VMFDI effectively monitors meteorological and hydrological drought, as well as ecosystem drought, especially in agroecosystems. Compared to the SPEI and GRACE-DSI, the VMFDI exhibited stronger correlations with the NDVI, GPP, and LAI, indicating its superior sensitivity for monitoring ecosystem drought. Although the correlation of the VMFDI with VPD, LST, and WUE was weak across the full 2003-2020 period, significant negative correlations emerged in the monthly data (Figure 6a,b1-b12). The atmosphere can hold only a certain amount of water vapor at a certain temperature. As the LST increases, the upper limit for atmospheric water vapor rises, which in turn increases VPD, lowering the VMFDI and indicating drier conditions. A high VPD limits vegetation transpiration and restricts vegetation photosynthesis by closing vegetation's stomata, thereby affecting carbon sequestration and evapotranspiration [65]. The relationship between WUE and the VMFDI depends on the rate of change between carbon sequestration and water loss. The VMFDI was positively correlated with growth status indices (GPP and LAI) from April to September but negatively correlated with these indices from October to March (Figure 6b1-b12). During low-VPD months, VPD was positively correlated with GPP and the LAI, but during high-VPD months, this relationship became negative, which is consistent with previous studies [65]. This shift underscores the importance of VPD thresholds in determining VPD's effect on vegetation productivity, thus explaining the observed seasonal variation in the VMFDI's correlation with GPP and the LAI.  
Figure 6: Correlation coefficients between the VMFDI and other indices in the Yellow River Basin (a) based on all monthly data and (b1–b12) for each month of data in the range of 2003–2020.
# Federated Learning-Based Security Attack Detection for Multi-Controller Software-Defined Networks
## 6 Conclusions  
In an SDN-based environment, the data plane is often confronted with severe threats due to the increased traffic flow from the attackers. For the secure SDN model, it is essential to identify various attacks across the massive data flows with the awareness of the behavior of the SDN nodes in the network structure. This paper proposed a FedSec security model to provide security against multiple MC-SDN attacks. The FedSec mechanism secures the MC-SDN nodes against malicious data flow entries. In the FedSec, the hybrid heuristic algorithm MA-HS optimally reduced the computational complexity and improved the intrusion detection accuracy by utilizing representative features. Moreover, FedSec has customized the NSL-KDD dataset with additional features and attacks. Moreover, the proposed FedSec has enhanced the distinguishing ability of normal and malicious traffic in SDN by potential features-based graph construction, particularly adjacency matrix for the GCNN. Thus, the proposed approach detected malicious traffic during the flow table updating in the data plane by the spatial-temporal correlation combination of the GCNN-GRU model. Finally, the evaluation results have reached a 98.01% recall or detection rate for the proposed FedSec while testing the enriched NSL-KDD dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **SDN Security** \\ **Models** \\ \end{tabular} } & \multicolumn{3}{c}{**Enriched NSL-KDD**} & \multicolumn{3}{c}{**UNSW\_NB15**} & \multicolumn{3}{c}{**InSDN**} \\ \cline{2-10}  & **FAR** & **Error** & **Latency** & **FAR** & **Error** & **Latency** & **FAR** & **Error** & **Latency** \\  & **(\%)** & **Rate (\%)** & **(\%)** & **Rate (\%)** & **(ms)** & **(\%)** & **Rate (\%)** & **(ms)** \\ \hline Hybrid DL [19] & 2.04 & 2.81 & 27 & 8.87 & 14.01 & 25 & 4.44 & 5.03 & 31 \\ \hline Graph-based [21] & 1.99 & 2.73 & 32 & 9.04 & 11.73 & 34 & 4.78 & 5.77 & 40 \\ \hline Federated-based [24] & 2.05 & 2.67 & 28 & 12.06 & 17.31 & 26 & 3.07 & 4.17 & 34 \\ \hline Proposed & 1.75 & 1.98 & 25 & 5.34 & 7.32 & 24 & 2.54 & 3.12 & 28 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparative SDN Security Models on Three Different Datasets.  
**Author Contributions:** Conceptualization, A.A.; Methodology, A.A.; Software, A.A.; Validation, A.A.; Resources, A.A.; Writing--original draft, A.A.; Supervision, I.K. and Supervision, S.M.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.