esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Federated Learning with Byzantine-Robust Client Weighting
## 3 Experimental Evaluation
### Experimental Setup  
#### 3.1.1 The Machine Learning Tasks and Models  
Shakespeare: Next-Character-Prediction Partitioned by Speaker  
Presented in the original _FedAvg_ paper [2] and also as part of the LEAF benchmark [18], the Shakespeare dataset contains 422,615 sentences taken from _The Complete Works of [PERSON]_[19] (freely available public domain texts). The next-character-prediction task with the per-speaker partitioning represents a realistic scenario in the FL domain. Each client trains using an LSTM recurrent model [20] with hyperparameters matching those suggested by [PERSON] et al. [21] for _FedAvg_.  
MNIST: Digit Recognition with Synthetic Client Partitioning  
The MNIST database [22] (available under Creative Commons Attribution-ShareAlike 3.0 license) includes 28\(\times\)28 grayscale labeled images of handwritten digits split into 60,000 training images and 10,000 testing images. We randomly partition the training set among 100 clients. The partition sizes are determined by taking 100 samples from a Lognormal distribution with \(\mu=1.5\), \(\sigma=3.45\) and then interpolating corresponding integers that sum to 60,000. This produces a right-skewed, fat-tailed partition size distribution that emphasizes the importance of correctly weighting aggregation rules and the effects of truncation. Clients train a classifier using a 64-unit perceptron with ReLU activation and 20% dropout, followed by a softmax layer. Following [PERSON] et al. [12], on every communication round, all clients perform a mini-batch SGD with 10% of their examples.  
Note that the the Shakespeare and MNIST synthetic tasks were selected because they are relatively simple, unbalanced tasks: simple, because we want to evaluate a preprocessing phase and avoid tuning of the underlying algorithms we compare; unbalanced, since as can be understood from Theorem 3, when the client sample sizes are spread mostly evenly, ignoring the client sample size altogether is a viable approach. See Figure 2 for the histograms of the partitions.
# Key Bit-Dependent Side-Channel Attacks on Protected Binary Scalar Multiplication +
## 2 Conventional SCAs on Scalar Multiplication
### Sophisticated Power Analysis  
SPA-and DPA-resistant countermeasures can be defeated by sophisticated attacks, such as a template attack (TA) [6; 10; 12] or collision attack (CA) [7; 11; 13]. A TA characterizes power-consumption traces by a multivariate normal distribution to build templates, and matches power-consumption leakage to the templates to find a secret scalar value. A CA is a kind of higher-order DPA and is an attack based on the inter-relationships among intermediate data (i.e., collisions of two intermediate values). So far, no theoretically perfect countermeasures against TAs and CAs have been presented. However, there is a disadvantage, in that they require precise preprocessing, such as decapsulation, localization, and a multiprobe to obtain a power-consumption trace having a high signal-to-noise ratio [6; 7; 11; 13]. Decapsulation in particular requires to physically modify the target devices, and numerous traces are required to build templates.  
To thwart previous attacks, various countermeasures to eliminate patterns of data-dependent conditional branches, statistical characteristic according to intermediate values, or data inter-relationships have been studied. However, no countermeasure has been taken into account for the secure design of the key bit identification phase, although secret scalar bits are directly loaded during that phase. Since the secret scalar bit value is extracted and stored in the variable, the secret scalar can be exposed if the vulnerability is discovered. Thus, in this paper, we verify that this vulnerability is sufficient to find a secret scalar.
###### Abstract  
Wireless communication has become an integral part of modern vehicles. However, securing the information exchanged between interconnected terminals poses a significant challenge. Effective security solutions should be computationally inexpensive, ultra-reliable, and capable of operating in any wireless propagation environment. Physical layer secret key generation has emerged as a promising technique, which leverages the inherent randomness of wireless-channel responses in amplitude and phase to generate high-entropy symmetric shared keys. The sensitivity of the channel-phase responses to the distance between network terminals makes this technique a viable solution for secure vehicular communication, given the dynamic behavior of these terminals. However, the practical implementation of this technique in vehicular communication is hindered by fluctuations in the communication link between line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. This study introduces a key-generation approach that uses a reconfigurable intelligent surface (RIS) to secure message exchange in vehicular communication. The RIS improves the performance of key extraction in scenarios with low signal-to-noise ratios (SNRs) and NLoS conditions. Additionally, it enhances the network's security against denial-of-service (DoS) attacks. In this context, we propose an efficient RIS configuration optimization technique that reinforces the signals received from legitimate users and weakens the signals from potential adversaries. The effectiveness of the proposed scheme is evaluated through practical implementation using a 1-bit RIS with 64 \(\times\) 64 elements and software-defined radios operating within the 5G frequency band. The results demonstrate improved key-extraction performance and increased resistance to DoS attacks. The hardware implementation of the proposed approach further validated its effectiveness in enhancing key-extraction performance in terms of the key generation and mismatch rates, while reducing the effect of the DoS attacks on the network.  
Chebyshev chaotic mapping; denial-of-service attacks; physical-layer security; reconfigurable intelligent surfaces; secret key extraction +
Footnote †: journal: _Sensors_  
_Article_  
RIS-Enabled Secret Key Generation for Secured Vehicular Communication in the Presence of Denial-of-Service Attacks  
[PERSON]  
1 James Watt School of Engineering, University of Glasgow, Glasgow G12 8 QQ, UK; [EMAIL_ADDRESS] (S.T.S.); [EMAIL_ADDRESS] (Q.H.A.); [EMAIL_ADDRESS] (M.A.I.); [EMAIL_ADDRESS] (A.T.)  
22 Department of Communications Engineering, Military Technical College, Cairo 11646, Egypt; [EMAIL_ADDRESS]  
3 Directorate of Research Services, University of New England, Armidale, NSW 2351, Australia; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS] (M.A.S.); [EMAIL_ADDRESS] (S.A.)
## 1 Introduction
Nevertheless, it should be noted that the sensorless schemes in [16; 17] are all firmly tied to the use of a modulator (SVPWM or SPWM) and will be inoperable if the MPC method is applied due to the absence of said modulator. Additionally, incorporating sensorless control methods and a 3L-NPC inverter scheme into a PMSM drive can greatly extend the application scope, improving performance and reliability. To this end, [18] implemented a sensorless control for a 3L-NPC drive, which was based on an extended Kalman filter. The information of both speed and flux was obtained using the extended Kalman filter to take the place of the sampled ones. Then, the prediction model utilized the estimated information, avoiding the measurement noise accordingly. However, the extended Kalman filter was horrifically computation-intensive, which is very demanding for digital implementation. In light of the above analyses, this paper proposes a simple and effective position-estimation scheme reliant on a current observer, which is constructed on a \(d\)-\(q\) frame. Firstly, the voltage vector applied to the inverter is obtained using the current prediction model, and then their \(d\)-\(q\)-axes components are obtained through coordinate transformation. Thereafter, a current observer is constructed to observe the \(d\)-\(q\)-axes currents. On this basis, the position and speed of the machine can be extracted from two PI controllers associated with the tracking errors of \(d\)-\(q\)-axes current. In this condition, the proposed sensorless control can effectively operate the PMSM drive within the full speed domain and is not limited by the types of inverter and method used. Moreover, because this strategy senses fewer parameters and processes fewer calculations, it can be readily implemented into embedded systems. This paper illustrates the study of the proposed sensorless control based on the following parts. In Section 2, the model of the PMSM fed by a three-level NPC inverter is constructed in detail. Then, in Section 3, the proposed sensorless predictive current control is elaborated upon, as well as the current and rotor position observers. Experimental results are presented in Section 4. Finally, a conclusion is given in Section 5.
## 4 Results and Discussion
### Comparison with Multiple Models  
To verify the accuracy of the PFP-XPOI model, this study compared the performance of different models as listed in Tables 2-5. We used the RMSE, mean absolute error (MAE) and R-squared to evaluate different models. The MAE can be expressed as  
\[\text{MAE}=\frac{1}{M}\sum_{m=1}^{M}\lvert y_{m}-\hat{y}_{m}\rvert \tag{12}\]  
where \(M\) is the total number of samples. \(y_{m}\) is the true value, and \(\hat{y}_{m}\) is the predicted value. R-squared can be expressed as  
\[\text{R}-\text{squared}=1-\frac{\sum_{m=1}^{M}(y_{m}-\hat{y}_{m})^{2}}{\sum_{m= 1}^{M}(y_{m}-\overline{y}_{m})^{2}} \tag{13}\]  
Figure 7: The feature importance of different XGBoost models with POI data (**a**) and without POI data (**b**).  
where \(M\) is the total number of samples. \(y_{m}\) is the true value. \(\hat{y}_{m}\) is the predicted value, and \(\overline{y}_{m}\) is the mean of samples.  
Figure 8: Predicted and true values of on-board passenger numbers using three models.  
Figure 9: Predicted and true values of alighting passenger numbers using three models.  
Results reveal that the PFP-XPOI performs best, followed by LSTM and XGBoost. This phenomenon is similar to that obtained by [PERSON] [16]. Because the alighting passenger flow is more stable, the alighting passenger flow prediction model is more accurate than the on-board passenger flow prediction model for both lines.  
The results demonstrate that the PFP-XPOI model performs better in prediction and improves the prediction accuracy due to the addition of new features. The historical average data cannot effectively take the impact of week, POI and other factors into account so the error is relatively large. The error of using XGBoost model individually is similar to that of the historical mean. This also indicates that the direct application of XGBoost model for passenger flow prediction is mainly based on the station index.
## 4 Experiments and Discussion
### Discussion
#### 4.6.2 Comparison and Analysis of Prediction Results of Different Models  
To verify the prediction effectiveness of the models, the following models were built for comparative analysis: LSTM, a combined model of convolutional neural network and gated recurrent unit (CNN-GRU), prediction of the components obtained from the two-stage decomposition using the GA-optimized BP (D-GA-BP), and prediction of the components obtained from the two-stage decomposition using LightGBM (D-LightGBM).  
Figure 11: Prediction errors across models.  
Figure 10: LA different neuron prediction errors.  
In Figure 12a-e, it can be seen that the prediction curve of the D-T-Q (E-L-LA) model proposed in this paper matches the actual traffic flow curve the best, while the prediction curve of the LSTM model without using the second-level decomposition only roughly predicts the trend of the actual traffic flow curve without capturing the features at the inflection point; the CNN-GRU uses spatio-temporal data for prediction, and its prediction effect is better than the prediction effect of CNN-GRU using spatio-temporal data, which is better than that of LSTM. After introducing spatio-temporal features, the model can extract more information, which is more beneficial to the model prediction.  
From Table 6, it can be seen that D-T-Q (E_L_LA) reduces \(E_{MAE}\) by 72.58% and \(E_{RMSE}\) by 72.08% compared with LSTM, \(E_{MAE}\) by 66.91% and \(E_{RMSE}\) by 66.03% compared with CNN-GRU, \(E_{MAE}\) by 49.89%, and \(E_{RMSE}\) by 48.11% compared with D-GA-BP, and \(E_{MAE}\) by D-LightGBM \(E_{MAE}\) reduced by 9.05% and \(E_{RMSE}\) reduced by 11.09%. From the comparison of the prediction effect and error of each model, it can be seen that D-T-Q (E_L_LA) has the smallest prediction error and can effectively reduce the unsteadiness of traffic flow and fully consider the external factors to fully extract the features in the traffic flow by combining models to improve the prediction accuracy of the model. However, the current study only verified the effectiveness of the model on single-step prediction, but for path planning and congestion formation analysis, multi-step prediction of traffic flow is more necessary.  
Figure 12: Prediction effect of each model. (**a**) LSTM; (**b**) CNN-GRU; (**c**) D-GA-BP; (**d**) D-LightGBM; (**e**) D-T-Q (E-L-LA); (**f**) prediction error.
# W-Band 4 th Order Waveguide Filter Based on Double Layer SU8 Microfabrication
## 3 Experimental Results
### Difficulties and Solutions in the Fabricating
#### 3.2.3. Overlay Accuracy of Multilayer Structures  
To reduce the tolerance introduced by the alignment of multilayer SU-8 chips, this paper adopts a two-layer overlay method to reduce the number of SU-8 chips during assembly. Alignment difficulty during overlay is reduced by using a method in which the overlay marks of the first layer are designed to be smaller than those of the second layer. However, the size of the measurement marks on the two layers is the same to facilitate the measurement of the overlay accuracy, as shown in Figure 8a. Figure 8b shows that the engraving accuracy of the two-layer structure in this article is about 10 \(\upmu\)m.
# Knee Osteoarthritis Classification Using 3D CNN and MRI
## 3 Results
### Comparison and Further Evaluation with Testing Set  
The results presented in the above subsections are from the validation set. We set aside a testing set of 100 samples that has not been seen by any model yet. This testing set was balanced between the 5 KL grades. Table 8 shows the performance of our 3D model with MRI data as well as the best 2D CNN model (InceptionResNetV2) for X-ray, against both the validation set and the testing set. We can see that the 3D CNN model with MRI outperformed the 2D CNN model with X-ray significantly for the 5-category and 2-category classifications on both the validation and testing sets. Table 9 further shows a group of different measures to evaluate OA and non-OA classification using the testing set. Our 3D CNN model with MRI achieved a much higher specificity (0.850 vs. 0.650), F1 score (0.831 vs. 0.767), and AUC (area under ROC curve; 0.911 vs 0.867), while the sensitivity is lower than that of the X-ray model (0.817 vs. 0.850). Figure 7 plots the ROC curves for the two models in the OA/non-OA classification.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Act.\textbackslash{}Pred.** & **P0** & **P1** & **P2** & **P3** & **P4** & **Total** & **Acc.** & **Acc. of MRI** \\ \hline A0 & 26 & 3 & 11 & 0 & 0 & 40 & 65.0\% & 70.0\% \\ A1 & 22 & 3 & 14 & 1 & 0 & 40 & 7.5\% & 45.0\% \\ A2 & 7 & 1 & 26 & 5 & 1 & 40 & 65.0\% & 37.5\% \\ A3 & 1 & 1 & 11 & 18 & 9 & 40 & 45.5\% & 60.0\% \\ A4 & 0 & 0 & 0 & 2 & 38 & 40 & 95.0\% & 90.0\% \\ Total & 56 & 8 & 62 & 26 & 48 & 200 & 55.5\% & 60.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix for the 5-category KL classification using InceptionResNetV2 and X-ray.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Act.\textbackslash{}Pred.** & **Non-OA** & **OA** & **Total** & **Acc.** & **Acc. of MRI** \\ \hline Non-OA & 56 & 24 & 80 & 70.0\% & 88.8\% \\ OA & 16 & 104 & 120 & 86.7\% & 85.0\% \\ Total & 72 & 128 & 200 & 80.0\% & 86.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Confusion matrix for the 2-category OA/non-OA classification using InceptionResNetV2 and X-ray.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Validation Set** & \multicolumn{2}{c}{**Testing Set**} \\ \hline  & **5-Category** & **2-Category** & **5-Category** & **2-Category** \\ \hline
2D CNN with X-ray & 55.5\% & 80.0\% & 50.0\% & 77.0\% \\
3D CNN with MRI & 60.5\% & 86.5\% & 54.0\% & 83.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Accuracy for the 5-category and 2-category classifications for the 3D CNN model and 2D CNN model against the validation and testing sets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Sensitivity** & **Specificity** & **F1** & **AUC** \\ \hline
2D CNN with X-ray & 0.850 & 0.650 & 0.767 & 0.867 \\
3D CNN with MRI & 0.817 & 0.850 & 0.831 & 0.911 \\ \hline \hline \end{tabular}
\end{table}
Table 9: More evaluation metrics for the 2-category non-OA/OA classification on the testing set.
# Methodological Framework to Collect, Process, Analyze and Visualize Cyber Threat Intelligence Data
## 5 Proposed Solution and Methodology  
The study presented in [29] proposed a six-step CTI model and a proprietary CTI tool. All functionalities related to the context of threat intelligence and each of the six proposed steps were described, as well as CTI tool implementation details and interconnections. The overall result of that work was a tool that allows threat information collection, preparation and sharing.  
Thus, based on the model proposed at [29] and the state of the art of the CTI works compiled about collection, sharing, visualizing and analyzing threat data, this work proposes an eight-step CTI model, as shown in Figure 3.  
The two additional steps proposed are visualization and analysis. These two steps as well as all the other six steps are intrinsically related since one does not exist without the other and vice versa. Thus, there is no hierarchy between steps, i.e., they are all important for the correct application of our methodological framework.  
Step 1 (Management) is responsible for managing the interaction of users with each functionality that the application offers and the interaction between them. It is also responsible for controlling the flow of data and its access through the management of access permissions. This functionality must be linked to all actions that can be performed by users, such as creating requests to import data, manipulate stored data, share feeds and collections. This is why this step is at the base of our model.  
Step 2 (Indexer) must be supported by a storage structure capable of supporting the amount of data input and output on our tool. Relational databases are the main storage structures used on our tool to support the ingestion of collected data and the feeding of upper steps.  
Step 3 (Collect) is responsible for providing external data gathering and insertions on our tool. It needs to collect data in different formats and standardize them on the same format required in Step 2.  
Figure 3: CTI 8-step proposed model.  
Step 4 (Generate) must provide the normalization of internal data using the same Step 2 and 3 pattern so that ingested data can be converted into Feeds for later use in our tool.  
Step 5 (Search) must provide mechanisms and methods to allow data manipulation and exploration in an effective manner, permitting fast data indexing and full visibility of stored data.  
Step 6 (Share) must allow the sharing of internal data such as feeds, collections and threat campaigns between users, as well as sharing with third-party tools that use STIX language.  
The visualization step (Step 7) introduces the visualization of the CTI data in a temporal format in order to create a threat timeline so that one can have a \"complete picture\" of the threat footprints, IOCs and any useful information that was enriched and shared by other interested parties.  
The analysis step (Step 8), which is intrinsic to the Step 7, implements functionalities allowing the analysis of data and to manipulate and obtain the best information from available threat data.
## 4 Application of Moisture Identification and Algorithm Comparison
### The Signal Variability and Handling Method in Actual Engineering Applications  
During the AE excitation experiment, the loading was exerted homogeneously to guarantee data consistency and avoid the interference caused by collision, friction, and so on. However, the loading in a collapse process will not be produced evenly in real geotechnical engineering situations. Loading is intense, creating an abundance of abnormal data. Although statistical laws are adopted during the clustering analysis in the two-step method, the probabilities of mistaken deletion and reservation are large. The influence of abnormal interference should be considered in any actual structural monitoring engineering. In actual geotechnical engineering applications, the  
The floating threshold method can be used to capture the initial AE signal. Assume that the time-domain AE signal is \(f(t)\), where the maximum values \(f(t_{1})\), \(f(t_{2})\cdots f(t_{n})\) are obtained at \(t_{1}\), \(t_{2}\cdots t_{n}\). Set the capture threshold \(\omega=\frac{1}{n}\sum\limits_{i=1}^{n}f(t_{i})\). The point should be determined as the waveform entrance when the \(\beta\) continuous signal amplitudes are greater than the threshold; the point should be determined as the waveform exit when the \(\beta\) continuous signal amplitudes are less than the threshold. The value of \(\beta\) can be adjusted according to the actual needs.