esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results of Systematic Literature Review
### Evaluation and Validation Metrics in Studies  
As mentioned above, regression and classification tasks have different evaluation metrics. In most of the classification studies, although similar metrics were used, variations were also observed. This diversity may have arisen from the balanced/imbalanced nature of the datasets or the characteristics of tasks such as binary classification, multi-classification, etc. Similar to model selection, even though more than one evaluation metric was considered in the studies, rates were determined based on the main evaluation criteria mentioned by the authors of these studies. The researchers considered accuracy, recall (sensitivity), precision, F1 score, and ROC-AUC score with the rates of 31% (33/109), 24% (26/109), 19% (21/109), 17% (18/109), and 7% (8/109), respectively, in classification studies.  
Figure 4: The aims of student performance prediction studies in percentages for educational expectations.  
Since each datum represents an independent output in regression studies, there is no balanced/imbalanced problem. Different evaluation metrics give researchers an insight into the ability of the models in terms of the different points that they consider. The most considered evaluation metrics in regression studies were RMSE with 33% (22/67) and MAE with 27% (18/67). Interestingly, the \(R^{2}\) score, which is frequently used in regression studies and determines the model's general regression ability, was one of the least used metrics in the student performance prediction studies (7%--5/67). Figure 6 presents the percentages of the evaluation metrics in the classification and regression studies.  
Since training is performed in a similar way in regression and classification studies, no distinction was considered when analyzing validation metrics. Although k-fold cross-validation was implemented more frequently in the studies, the hold-out method, which does not include data mining applications, was still used frequently, despite the advantages stated above. While 30% (54/176) of the studies validated their results using k-fold cross-validation, 26% (46/176) used the hold-out method. The studies that applied data mining/training data selection and studies for which the validation strategies could not be determined both had a frequency of 22% (38/176). Figure 7 shows the percentages of the validation strategies considered in the studies.  
Figure 5: The use of machine learning models in student performance prediction studies: (**a**) classification and (**b**) regression.  
Figure 6: The frequency of evaluation metrics considered in the student performance prediction studies: (**a**) classification and (**b**) regression.  
Figure 7: Validation strategies in the student performance prediction studies.
# Detection of Denial of Service Attack in Cloud Based Kubernetes Using eBPF
## 3 Architecture of the Proposed Solution
### Generalized Overview of the Proposed Solution  
An eBPF is a robust tool for examining and modifying the Linux kernel in real time. Small, in-kernel programs known as \"eBPF programs\" can be attached to a variety of locations along the kernel's execution path, including system calls, kprobes (dynamic function probes), and tracepoints (static function probes). These applications can be used to gather information, filter events, and even change how the kernel behaves. In the context of detecting a DDoS attack in Kubernetes, eBPF can be used to attach a program to a network interface to monitor incoming traffic and identify patterns that may indicate a DDoS attack. The proposed solution contains the information from all the VMs working on the cloud, i.e., the cloud contains different nodes, which form the cluster of Kubernetes. In our experiment, we have one master node and multiple workers or child nodes working on different networking IP addresses. We have launched separate VMs on each node with different OS interfaces. The effectiveness and low overhead of eBPF for DDoS detection in Kubernetes are two of its main advantages. eBPF programs can process data quickly without affecting system performance because they run directly in the kernel. This is crucial when using Kubernetes since the enormous number of containers and microservices it supports might produce high network traffic. Overall, using eBPF for DDoS detection in Kubernetes can offer a quick and effective solution to watch incoming traffic and spot potential attacks, assisting in the defense of the cluster and preserving service availability.
## 3 Application Examples
### Growing Hairy Roots of Hyoscyamus niger and Sesamum indium  
To compare the yield of hairy roots in the bioreactor and in shaken flask cultures, hairy roots originating from three accession of each _Hyoscyamus niger_ and _Sesamum indium_ were cultivated in bioreactors and in shaking flasks. The cultures were initiated by 100 mg of hairy roots cut into 1 cm-long pieces. Culture flasks of 300 mL volume contained 50 mL MS medium supplemented with 2.5% sucrose. The cultures were grown on a shaker at 90 rpm in the dark at 25 \({}^{\circ}\)C and in bioreactors operated in bubble mode with an air flow of 670 mL/min (Figure 6).  
After 30 days, hairy roots were harvested by filtration, blotted dry with sterilized filter paper, frozen at -20 \({}^{\circ}\)C overnight and lyophilized for 72 h. Then the dry weight of hairy roots was determined (Figure 7).  
Figure 4: Rose plant (_Rosa canina_ L.) growing in sterile hydroponics in 0.5\(\times\) Murashige–Skoog medium supplemented with 2% sucrose in a bioreactor operated in airlift mode.  
The biomass of hairy roots varied among accessions but we have not investigated these differences statistically because our purpose was to compare the performance of the bioreactor with that of flask cultures. In _Sesamun indicum_, the biomass of hairy roots grown in bioreactors was larger than or similar to the biomass harvested from shaking flasks. In _Hyoscyamus niger_, more biomass was harvested from bioreactors than from flasks in all three plant accessions studied (Figure 7). We assume that better supply of oxygen in the bioreactor was the cause of these differences.
## 5 Latency Mitigation Strategies
### Perception Latency Approaches
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference** & **Domain** & **Teleoperation Entity** & **Algorithm** \\ \hline
[36] & CAV teleoperation & Visual, predictive display for latency & Geometric model, homographic transformation \\ \hline
[52] & CAV teleoperation & Predictive display & Predicted vehicle state, curvature model on steering angle \\ \hline
[46] & UGV robot teleoperation & Predictive display & Integrate Pix2 Pix conditional GAN with optical flow \\ \hline
[39] & Robotic teleoperation & Predictive display and control & Kinematic model for augmented predictive display (for state and trajectory) and autonomy of high-level control commands \\ \hline
[86] & Tele-driving vehicle & Visual and control & Model predictive control (MPC)—kinematic bicycle model and potential fields, augmented reality, predictive display \\ \hline
[61] & Telerobotic surgeries during military operations & Visual and control & Virtual representation with object recognition (Mask RCNN semantic segmentation, Kalman filter state estimation) and alpha-blended layout. High-level surgical actions using history from LSTM \\ \hline
[63] & Vehicle teleoperation & Predictive display & Vehicle position and perspective-predictive image transformation \\ \hline
[56] & UGV teleoperation & Predictive display & Image transformation and state estimator for feedforward and feedback functions to estimate the vehicle position \\ \hline
[35] & Vehicle teleoperation & Predictive display & Image transformation, perspective projection, and Smith predictor \\ \hline
[57] & Road vehicle teleoperation & Predictive display & Grid-based distribution, TV-L1 optical flow, semi-global matching (SGM) \\ \hline
[134] & Video streaming & Video rate control during latency & Markov decision process, greedy approach, and stochastic gradient descent (SGD) \\ \hline
[8] & Robotic teleoperation & Visual, control, and other entities & Review enhancement techniques \\ \hline
[15] & Robotic and CAV teleoperation & Visual and control & Review some mitigation techniques \\ \hline
[130] & Robotic telesurgery & Predictive display and control position & Pix2 Pix conditional generative adversarial network (cGAN) for predicting tool position during surgery \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._
# Secure Cloud Infrastructure: A Survey on Issues, Current Solutions, and Open Challenges
## 4 Security Issues in Cloud Computing Infrastructure
### Data-Level Issues
#### 4.1.6 Data Integrity Issues  
Integrity issues arise in such an environment due to data being stored remotely and at multiple locations [35]. Such a distributed environment evokes special attention to the integrity when storing and retrieving the data in multiple places, where many errors could occur, such as manipulating, losing, or corrupting these data fragments. Although these issues could occur due to system internal errors, hackers can inflict such damages as well. The system should be so secure that only the legitimate user can access and/or modify the data. In a cloud-based environment, data integrity must be maintained correctly to prevent data loss. In general, data integrity could be avoided by applying several security measures like hashing, salting, timestamping, and digital signatures. The repeatedly used data integrity protection methods provide information about changes in data and checksums to verify the integrity of the data [36].
## 4 Ensemble Learning IDS for IoV: Research Trends and Gaps
### Evaluation Metrics Used for IoV IDS  
Each ensemble learning model was trained on the selected features from the datasets. The models were evaluated using a range of performance metrics to ensure a comprehensive assessment, as demonstrated in Table 8. Accuracy and Area Under Receiver Operating Characteristic Curve (AUC-ROC) are commonly utilized metrics to evaluate general system performance as well as the trade-offs between false positives and false negatives. Authors in [74] assessed centralized IDS systems using these criteria and reported rather high detection accuracy.  
According to [PERSON] and [PERSON] [69], precision and recall are crucial in circumstances with unbalanced datasets to guarantee the identification of negative actions while reducing false positives and hence lower false positives.  
The study in [75] stressed recall for recognizing zero-day attacks in edge contexts, while the research in [61] focused on specificity to lower false positives in federated learning-based IDSs. Especially in real-time applications, researchers in [77] and others argue that the F1-score offers a fair assessment of performance. By means of these approaches, researchers can enhance ensemble learning-based IDSs, particularly designed to meet the many and dynamic needs of IoV. These criteria should be included in hybrid evaluation systems in future studies to properly manage accuracy, scalability, and flexibility in evolving IoV contexts.
# Aerodynamic Enhancement of Vertical-Axis Wind Turbines Using Plain and Serrated Gurney Flaps
## 3 Results and Discussion
### Effect of GFs on an NACA0021 Isolated Aerofoil
#### 3.1.1 Aerodynamic Forces and Separation Control
Separated flow is reattached to the airfoil's surface, resulting in the formation of a laminar separation bubble (LSB). In Figure 10, the width of the dashed box represents the length of a laminar separation bubble. The different color means the different LSB caused by a clean NACA0021 (baseline), NACA0021 with PGF (PGF), and NACA0021 with SGF (SGF). Figure 10b displays a similar behavior of surface pressure distribution. After flow separation on the suction surface, the separation bubble experiences minimal energy exchange with its surroundings, resulting in relatively constant suction surface pressure coefficients. This gives rise to a pronounced \"plateau\" feature. Subsequently, a point of inflection arises in the pressure coefficient, marking the shift from laminar to turbulent flow. The provided graph distinctly illustrates that flap addition successfully delays flow separation on the airfoil's suction surface, concurrently shifts the transition point towards the airfoil's leading edge, and shortens the separation bubble length. It is noteworthy that at equivalent flap heights, the SGF exhibits a smaller separation bubble length than the PGF, which is demonstrated by different color columns. The incorporation of flaps amplifies the pressure gradient along the airfoil's surface, consequently facilitating lift force generation.
## 2 Materials and Methods  
### Preparation and Characterization of Ferrocenes  
Ferrocenes **1a**, **1b**, **2a**, **2b**, **3**, **4**, and **5** were prepared under argon atmosphere using standard Schlenk techniques, as was previously published [23]. Ferrocene **1c** was prepared from **1b** (155 mg, 244 umol) and AgBF\({}_{4}\) (149 mg, 764 umol) (Scheme 1). The substances were dried under vacuum for 1 h. The vacuum was replaced with argon and dry acetone (10 mL) was added, which caused an immediate color change from yellow to dark green. The mixture was then stirred for 2 days with light exclusion and then filtered. The filtrate was evaporated to dryness to obtain **1c** as a green solid substance with yield 200 mg (99%). M.p. 82 \({}^{\circ}\)C. ATR (Si); cm\({}^{-1}\): 3151 (sh, vw), 3115 (w), 2961 (w), 2932 (sh, vw), 2870 (vw), 1614 (vw), 1518 (w), 1462 (m), 1417 (m), 1367 (w), 1284 (w), 1226 (vw), 1054 (vs), 1035 (sh, s), 857 (m), 839 (sh, w), 764 (vw), 578 (w), 520 (m), 416 (w). Elemental analysis for **1c**--C\({}_{36}\)H\({}_{46}\)B\({}_{3}\)F\({}_{12}\)FeN\({}_{2}\) calculated C, 52.53; H, 5.63; N, 3.40%, found C, 51.41; H, 5.49; N, 3.35%. Its infrared spectrum (ATR) is characterized by the presence of a very strong and broad band at 1054 cm\({}^{-1}\), which could be assigned to stretching B-F vibration of the [BF\({}_{4}\)]- anion.  
Ferrocene derivatives **1a**, **1b**, **2a**, **2b**, **3**, **4**, and **5** were characterized by elemental analysis, melting point, nuclear magnetic resonance (NMR), electrospray ionization mass spectrometry (ESI-MS), and X-ray diffraction analysis, as reported previously [23; 24].
## 6 Conclusions and Future Work  
To solve the problems of high false negatives, long time consumption, and poor interpretability of spacecraft telemetry data anomaly detection methods based on statistical model, an anomaly detection method based on parametric causality and Double-Criteria DSPOT (DCDSPOT) is proposed in this paper. Normalized Effective Transfer Entropy (NETE) is proposed to remove errors and noise in the calculation of transfer entropy so as to improve the MESS algorithm. An experiment on two datasets with known causality verifies that the F1-score of causality inference using Improved MESS is significantly improved. DCDSPOT is proposed for thresholds setting of target parameters to be detected and the Weighted Source Parameter (WSP), then two criteria are formulated to determine the anomaly of the target parameter. Furthermore, to mitigate the time-consumption problem of DCDSPOT, we use PWM instead of MLE to estimate the parameters of GPD to set the thresholds. An experiment on a real satellite shows that our method has better performance on recall, F1-score, and running time.  
Causality is common in the parameters of spacecraft systems, and it also plays an important role in anomaly detection and fault diagnosis of spacecrafts. In future research work, the authors will further study and improve the causal inference algorithm of telemetry parameters, the method of fault root cause diagnosis, and especially the combination of causality and advanced deep learning methods (such as LSTM, Graph Neural Networks, Transformer, etc.), so as to develop methods to detect different types of anomalies and better monitor the health status of the spacecraft and effectively maintain the system.  
Conceptualization, G.J.; methodology, Z.Z. and S.C.; software, C.X.; validation, Z.Z.; investigation, Z.Z.; resources, S.C. and L.Z.; data curation, Z.Z.; writing--review and editing, C.X.; visualization, Z.Z.; supervision, G.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Estimation Method**} & \multicolumn{4}{c}{**Parameter: B**} & \multicolumn{4}{c}{**Parameter: D**} \\ \cline{2-9}  & **Pre (\%)** & **Rec (\%)** & **F1** & **T (s)** & **Pre (\%)** & **Rec (\%)** & **F1** & **T (s)** \\ \hline PWM & 86.93 & 89.14 & 0.8802 & 62 & 83.55 & 88.8 & 0.861 & 59 \\ MLE(Grimshaw) & 87.91 & 89.02 & 0.8846 & 259 & 82.82 & 87.89 & 0.8528 & 265 \\ \hline \multicolumn{9}{c}{**Parameter: Q**} & \multicolumn{4}{c}{**Parameter: T**} \\ \cline{2-9}  & **Pre (\%)** & **Rec (\%)** & **F1** & **T (s)** & **Pre (\%)** & **Rec (\%)** & **F1** & **T (s)** \\ \hline PWM & 82.14 & 91.8 & 0.8671 & 74 & 95.76 & 86.69 & 0.9100 & 73 \\ MLE(Grimshaw) & 81.84 & 91.77 & 0.8652 & 308 & 95.97 & 87.12 & 0.9133 & 318 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Anomaly detection performance of PWM-based DCDSPOT and MLE-based DCDSPOT.
## 2 Related Work  
### Federated Recommender System  
The proliferation of privacy protection laws and regulations, coupled with heightened awareness regarding privacy [2], has spurred further research into the application of FL in recommender systems. As a result, numerous FRSs have emerged.  
FCF [11] is the pioneering work that introduced the first FRS utilizing FL with collaborative filtering models. FedMF [12] introduces the first federated matrix factorization (MF) [13] algorithm based on non-negative matrix factorization [14], though with limitations in terms of privacy and heterogeneity. Several studies endeavor to address the performance gap between FRSs and centralized recommender systems. FedGNN [15] is a GNN-based FRS that trains locally, aggregates gradients applying differential privacy, and expands user-item graphs for high-order interactions. As a federated contrastive learning method, FedCL [16] aims for privacy-preserving recommendation, exploiting high-quality negatives via local differential privacy and clustering for effective model training.  
Some FRSs prioritize safeguarding users' item interaction history. FedRec [17] introduces a hybrid filling strategy, wherein each client randomly samples uninteractive items and assigns virtual ratings, augmenting true ratings in the calculation of item feature gradients, preventing the server from discerning specific interactions between clients and items.  
DFedRec [18] is a decentralized recommendation model using a privacy-aware graph, employing fake entries for privacy and reducing communication by sharing parameters with neighbors, ensuring accuracy and privacy.  
Focused on reducing the training costs associated with FRSs, FedFast [19] accelerates the training process by diverse client sampling and an active aggregation method that propagates the updated model to other clients, thereby reducing communication costs and ensuring rapid convergence. [PERSON] et al. [20] proposed a federated discrete optimization model to learn the binary codes of item embeddings, achieving the dual objectives of efficiency and privacy. HFCCD [21] employs the Count Sketch technique to compress item feature vectors to reduce the communication overhead and offer a privacy protection capability. PrivFR [22] uses shared hash embedding to efficiently represent items, conserving resources, addressing the out-of-vocabulary issue, and reducing storage and communication overheads in FRSs. The FedMAvg algorithm [23], which integrates FedAvg [24] with the alternating minimization approach, focuses on MF models and facilitates partial client communication to reduce communication costs. Cali3F [25] is a calibrated fast and fair FRS. By integrating neural networks, adopting a clustering-based method and new sampling and update strategies, it balances fairness and convergence speed.
## 2 Theory and Experimental Instrument
### Preliminary Configuration and Results
However, the random error of the preliminary experiment, as shown in Figure 2c, far exceeded this calculated value. Speckle noise may have been the main reason for poor SNRs. It can be seen from Figure 2b that the random error of path length measurements was 2 m and the drift over a long time was 1 m. The drift of the CO2 concentration corresponding to the path length drift was less than 0.27 ppm. In the first trial, the detected CO2 mixing ratio strongly fluctuated around the expected 410 ppm ambient concentration, with minimum and maximum values of 300 and 600 ppm, respectively. Therefore, it was believed that unknown sources of systematic errors caused large fluctuations in the CO2 concentration, indicating the necessity for a system calibration. \begin{table}
\begin{tabular}{c c} \hline \hline  & **Receiver** \\ \hline Receiver diameter & 150 mm \\ Field of view & 1 mrad \\ Detector & InGaAs-PIN \\ Transmitting and receiving axis & Coaxial \\ Distinguish ON/OFF & FFT \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 2: (**a**) The carrier-to-noise ratio (CNRs) of the receiving and monitoring signals for both the on and off wavelengths; (**b**) the path length; and (**c**) the CO2 concentration in Shanghai city for one day during the first trial.
# Efficient Tensor Sensing for RF Tomographic Imaging on GPUs
## 6 Results and Analysis
### Error Rate and Running Time of IKEA Model Data  
This experiment evaluated the error rate of the CPU and GPU tensor sensing under different iterations for \(\mathcal{X}\) of size \(60\times 60\times 15\) with rank six. The sampling rate was set to 50%. Figure 5(b) shows  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**n** & **40** & **60** & **80** & **100** & **120** \\ \hline CPU tensor sensing time (s) & 3.07 & 13.65 & 50.43 & 118.84 & 251.54 \\ Unoptimized GPU tensor sensing time (s) & 9.50 & 11.40 & 12.15 & 20.70 & 25.74 \\ Optimized GPU tensor sensing time (s) & 0.44 & 0.63 & 0.98 & 2.01 & 2.97 \\ Speedups-unoptimized & 0.32 & 1.20 & 4.15 & 5.74 & 9.77 \\ Speedups-optimized & 6.98 & 21.67 & 51.46 & 59.12 & 84.70 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Running time under varying tensor sizes (\(n\times n\times 6\)).  
Figure 5: (a) The running time of the CPU tensor sensing and GPU tensor sensing and (b) the speedups of the unoptimized and optimized GPU tensor sensing.  
the recovery results with 10 iterations. The running time of the CPU tensor sensing at five iterations was 14.91 s on average, while the running time of the GPU tensor sensing was 0.97 s on average; thus, the speedup is 15.37\(\times\). As shown in Figure 7, under increased iterations from 1-30, the RSEs dropped significantly. More importantly, the CPU tensor sensing and GPU implementation achieved almost the same RSEs at all iterations (the two curves in Figure 7 overlap with each other), which means that they achieved similar error rate performance in tensor sensing.