esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, we have proposed a highly efficient SCA-resistant binary field multiplication method and applied it to the GHASH function of GCM on 8-bit AVR MCUs. The proposed \(BF\) multiplication method is efficient and secure against TA and SPA. Our method has adopted a concept of the Dummy XOR technique using a set of garbage registers and reduced the number of garbage registers from \(n\) to one by investigating the security impact of using only one garbage register. Furthermore, with a novel multiplier encoding, our method has achieved block size 8, which is known as the largest block size in the Block-Comb multiplication method on 8-bit AVR MCUs. As a result, our \(BF\) method presents an improved performance by 32.8% (resp. 38.5%) with the multiplier encoding (resp. without multiplier encoding) compared with the previous best work. The proposed \(BF\) multiplication method can be used as underlying \(BF\) multiplication in the GHASH function of GCM and NIST-compliant binary ECC arithmetic. As a case study, we have also presented enhanced GHASH function implementation with the proposed \(BF\) multiplication method and additional optimization techniques, which can improve the performance by over 42% compared with the previous best GHASH function implementation.  
As future works, we will apply the concept of the proposed method on 16-bit and 32-bit embedded MCUs, such as MSP430 and ARM processors. Furthermore, we will apply our proposed \(BF\) multiplication method to NIST-compliant Binary Elliptic Curve Cryptosystems (ECCs).  
S.C.S.: conceptualization, methodology, software implementation, paper writing, and project management, D.K.: SCA analysis and result verification. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1F1A1058494).  
We appreciate the anonymous reviewers for their valuable comments.  
The authors declare no conflict of interest.
# Multi-Target Detection Algorithm for Fusion Images Based on an Attention Mechanism
## 3 Dataset Creation
### Image Collection  
The GAN-based image fusion algorithm used in this paper needs to be trained using a dataset containing a large number of images. The construction of the fusion dataset requires a large number of infrared image and visible-light image pairs. Since the fusion algorithm in this section does not perform image preprocessing operations such as pixel registration on the images, it is required that the infrared images and visible-light images in the dataset have been registered. The fusion dataset in this paper mainly comes from two sources, namely, image pairs obtained from the Internet, and image pairs generated using style transfer technology.
## 6 Real Field Deployment of Systems Based on DAS+PRS
### Data Acquisition and Processing in a Field Deployed System  
When the system runs continuously and the sensed positions are in the order of thousands, recording all the acoustic traces along the full fiber length is not possible due to processing times and communication throughput restrictions. Therefore, a careful selection of the positions which will actually be evaluated in search of possible threats must be carried out so that the actual processing in the PRS side will only be done for these selected traces.  
This selection may use a threshold-based strategy from the energy measurements of the vibrations along the fiber, as presented in [25]. In this work, when the energy of the vibrations occurring in any point of the fiber optic cable is above a predefined threshold, an acoustic trace is recorded to indicate a possible suspicious activity occurring at that point, so that the trace is further processed by the PRS.  
The problem with the threshold-based approach is that the energy profile along the full fiber length will heavily depend on the different activities that are present at each position. For example, in agricultural areas, the energy profile will be low, but in sections passing under or near heavy traffic roads or industrial areas, the sensed energy values will be higher. Also, the energy profile will significantly vary along the day (consider for example the expected traffic and industrial activity level variations between day and night).  
In addition to this activity and time-related dependence, different fiber locations posses different sensitivity, which increases the complexity of the threshold estimation procedure.  
To approach the calculation of these location-dependent thresholds, recordings that spread wide temporal ranges must be carried out, so that an accurate average energy profile behavior can be estimated. Ideally, these recordings should also be dependent on time and date (for example, we could have different energy profiles for week days, weekends, holidays, etc., and for day and night times).  
To provide an example, in [25] 5 min of background noise were recorded for every sensed fiber location along the surveilled zone, and the energy profile values corresponding to the measurements were used to define the detection thresholds to be used in the real system deployed in field.
## 3 Experimental Results
### Experimental Results Obtained with the CFNN  
In the adopted CFNN, four fusion methods (GMP fusion, GAP fusion, CGMP fusion, and CGAP fusion) are used to perform malware image classification. This network contains two convolutional layers and two pooling layers, and an FNN is used in it to replace a fully connected network. Therefore, the CFNN has a small architecture, a small number of parameters, and a high operation speed. Table 2 presents the learning parameter settings of the CFNN, and Table 3 presents the network architecture parameter settings of the CFNN. The test loops of TensorFlow and Keras were used for deep learning in this study. The parameters of the CFNN were the same in the experiments conducted using the four  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Virus Species** & **Number of Images** \\ \hline
1 & Adialer.C & 122 \\ \hline
2 & Agent.FYI & 116 \\ \hline
3 & Allaple.A & 2949 \\ \hline
4 & Allaple.L & 1591 \\ \hline
5 & Alueron.gen\(\lx@sectionsign\) & 198 \\ \hline
6 & Autorun.K & 106 \\ \hline
7 & C2 LOP.gen\(\lx@sectionsign\)g & 200 \\ \hline
8 & C2 LOP.P & 146 \\ \hline
9 & Dialplatform.B & 177 \\ \hline
10 & Dontovo.A & 162 \\ \hline
11 & Fakerean & 381 \\ \hline
12 & Instantaccess & 431 \\ \hline
13 & Lolyda.AA1 & 213 \\ \hline
14 & Lolyda.AA2 & 184 \\ \hline
15 & Lolyda.AA3 & 123 \\ \hline
16 & Lolyda.AT & 159 \\ \hline
17 & Malex.gen\(\lx@sectionsign\)J & 136 \\ \hline
18 & Obfuscator.AD & 142 \\ \hline
19 & Rbotfgen & 158 \\ \hline
20 & Skintrim.N & 80 \\ \hline
21 & Swizzor.gen!E & 128 \\ \hline
22 & Swizzor.gen!I & 132 \\ \hline
23 & VB.AT & 408 \\ \hline
24 & Wintrim.BX & 97 \\ \hline
25 & Yuner.A & 800 \\ \hline \multicolumn{3}{c}{Total number of images} & 9339 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Numbers of images of various types of viruses adopted in this study [33].  
fusion methods. The adopted dataset contained 9339 malware images, 80% of which were randomly used as the training set, and the remaining 20% of the images were used as the test set. In the first convolution and pooling layers, the filter size was 32, the size of the convolution kernel was 3 \(\times\) 3, and the stride was 2. In the second convolution and pooling layers, the filter size was 64, the size of the convolution kernel was 3 \(\times\) 3, and the stride was 2. The feature fusion layer was used to reduce the dimensions of the feature layer. Finally, the FNN was used for classification.  
Accuracy was used as the evaluation index of the proposed network. This parameter is determined as follows:  
\[\text{Accuracy}=\frac{(\text{TP}+\text{TN})}{(\text{TP}+\text{FN})+(\text{FP} +\text{TN})} \tag{5}\]  
where TP, FP, TN, and FN represent the numbers of true positives, false positives, true negatives, and false negatives, respectively. Table 4 presents the accuracy achieved with the four fusion methods. The accuracy values achieved by the CFNN with the GMP, GAP, CGMP, and CGAP fusion methods were 96.68%, 97.86%, 98.13%, and 97.64%, respectively. The experimental results indicate that the proposed CFNN achieved the highest accuracy with the CGAP fusion method.
## 1 Introduction  
With the development of satellite space missions, satellite health is facing severe challenges due to the drastic changes of thermal environment and internal thermal load. Due to the change of satellite orbit or transfer [1] or the different working modes of satellite components, time-varying thermal load will be caused. In addition, due to the highly integrated package of electronic equipment [2; 3], the use of high heat flux density components [4] and wireless energy transmission, the transient thermal load of the satellite is extremely large. Such as lasers, electronic chips, and advanced propulsion devices, are expected to involve high heat fluxes (above 100 W/cm\({}^{2}\)) [5; 6]. Time variations and extremely high thermal loads can affect satellite health and even lead to satellite system failures [7; 8]. Therefore, the rapid and effective dynamic health assessment of satellites is of great significance.  
According to data from insurance analysis, from 25% to 28% [9; 10] of satellite failures on orbit, are related to the electrical power system. The main failure mode of electronic equipment is thermal failure, and its failure rate increases exponentially with increasing temperature [11]. The traditional researches on the influence of temperature on the failure rate of components are based on empirical models, such as the exponential model of Arrhenius [12]. Therefore, there is a lack of accurate quantitative relationship between temperature and failure rate.  
The past research on satellite health is to calculate the lifetime and reliability of satellites through reliability analysis. Traditional reliability analysis is based on probabilistic statistical analysis of large amounts of in-orbit data. Therefore, to study the satellite fault caused by thermal failure requires necessary data to determine various parameters of the model. Unfortunately, limited empirical data and statistical analysis of satellite reliability exist in the technical literature [13]. Many methodologies such as failure mode effect analysis (FMEA) and the fault tree analysis (FTA) are used in the reliability analysis [14; 15]. However, this model has some limitations in reliability analysis. It is not easy for these models to conduct further quantitative analysis automatically due to the lack of effective means of mathematical expression [16]. In addition, the traditional reliability analysis model only considers failure death or survival and lacks analysis methods for sub-health.  
In order to analyze the satellite thermal health response under time-varying and extreme thermal loads, an intelligent evaluation model based on fuzzy logic is proposed. In addition, this paper presents an evaluation index that can represent satellite sub-health state. Fuzzy reasoning is used to solve the problem of missing models and non-quantization which are caused by temperature. The continuous process simulation and discrete event simulation of thermal load change complete efficient collaborative computation by thermal health assessment algorithm. Finally, the effects of typical thermal loads on satellite health are analyzed.
## 2 Materials and Methods
### Sensitivity Analysis  
To determine how changing the parameters of compression artifacts removal methods affects the final image quality, two methods were implemented. The difference in \(SSIM\) and \(PSNR\) between the analyzed method and compressed images were used as image quality metrics. First, the Partial Rank Correlation Coefficient (PRCC), which measures nonlinear and monotonic relationships between parameters and image quality metrics, was calculated. PRCC uses the Spearman correlation after removing linear dependencies between parameters. Second, the impact of parameters on image quality was estimated using the variable importance measure from the multivariate adaptive regression spline (MARS) model implemented in the caret R package [35]. Specifically, the changes in the generalized cross-validation estimate of error for each predictor are tracked, and reductions in the statistic when each predictor's feature is added to the model are accumulated. The variable importance is scaled to sum up to 100%.
# An Automated Framework for Streamlined CFD-Based Design and Optimization of Fixed-Wing UAV Wings
## 3 Framework Breakdown  
The proposed framework automates the CFD workflow for UAV wings, integrating geometry generation, meshing, and simulation execution into a Python-based pipeline, as illustrated in Figure 4. It eliminates manual intervention, promotes consistency across a large design space, and significantly accelerates the aerodynamic evaluation process for both individual case studies and optimization-driven analyses.  
The three modules are as follows:  
* Geometry Modeling: Utilizes OpenVSP's Python API [33] for generating 3D UAV wing models. Implemented using an object-oriented programming (OOP) approach, the geometry module employs a _Wing_ class that encapsulates design parameters, flight conditions, and methods for geometry generation and blending.
* Meshing: Utilizes BETA CAE's ANSA pre-processor [34] to transform geometries into solver-ready grids, adhering to stringent quality standards for subsonic flows.
* Simulation Execution: Employs ANSYS Fluent via the appropriate API--PyFluent (v0.21) [35], a module for automating simulation setup, execution, and result post-processing.  
The framework relies on XML files for data exchange between modules, allowing for consistent information flow. As presented in Section 5, high-performance computing (HPC) with the GPU-native Fluent solver significantly enhances computational efficiency, reducing processing times and enabling rapid CFD analysis execution.  
An important comment must be made regarding the turbulence modeling approach, since it affects two latter key modules (meshing and solution). Several main approaches are encountered in the CFD literature, balancing accuracy and computational cost. Direct Numerical Simulation (DNS) resolves all turbulence scales but is computationally impractical for most applications, due to its high computational cost. Large Eddy Simulation (LES) resolves larger turbulent structures while modeling smaller eddies, requiring significant resources, especially for optimization purposes when the number of required solutions is in the hundreds or thousands, depending on the optimization study [17]. However, Reynolds-Averaged Navier-Stokes (RANS) models offer computational efficiency suitable for steady-state aerodynamic analyses, making them the industry standard [36,37].  
In this study, the RANS approach with [PERSON]'s k-omega SST [38] turbulence modeling is adopted, validated through prior work against both in-house test flight data and assessed against experimental results [39]. While this validation confirms its suitability within the design space intended, it is important to note that the model has not been explicitly validated for every possible wing configuration that may arise from the sampling of this space. However, its extensive use in similar studies further supports its applicability to this framework [40]. This choice is made to align with the computational tools available, specifically the GPU-native solvers in the utilized version of ANSYS Fluent 2023R2 [41,42].
## 6 Conclusions  
We presented a modified ECM that took hysteresis and temperature effect into account for the first time. The proposed model aimed to improve the voltage tracking accuracy of the existing ECM whose applicability is often limited to steady current profiles and certain temperatures. With the offline hysteresis-SOC-T table correcting the OCV and the rest of the model parameters were estimated online, the improved model has the adaptability to ambient temperature rather than origin model. Then the improved model was applied to three model-based recursive estimators including EKF, UKF, and STF for SOC estimation. The procedures to execute each step for parameter identification and the algorithms were explained in detail. Lastly, the model and the estimators were validated by experimental test with commercial 18650 type Li(Ni\({}_{1/3}\)Co\({}_{1/3}\)Mn\({}_{1/3}\))O\({}_{2}\) cells. The validation test consisted of several high dynamic FUDS microccycles, rest period, and a discharge process. This test was carried out under different temperatures ranging from 0 \({}^{\circ}\)C to 50 \({}^{\circ}\)C. The MAE and RMSE of the proposed model were smaller than the ordinary model in voltage estimation. In the same vein, the performances of EKF, UKF, and STF were also evaluated by MAE and RMSE. The STF algorithm outperformed UKF and EKF in estimation accuracy, robustness, and convergence behavior, thereby demonstrating a better solution for BMS application.  
[PERSON] designed the experiments and wrote the paper; [PERSON] and [PERSON] dominated and supervised the project; [PERSON] help to review and edit the paper.  
This work was funded by the National Natural Science Foundation of China, grant number 51775393; the platform construction of Hubei province renewable energy intelligent vehicle, grant number 2016 BEC116; the 111 Project, grant number B17034 and Research and Development of Products Test Cycle for New Energy Vehicles in China and the Fundamental Research Funds for the Central Universities, grant number WUT:2017-IVA-034.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **MAE (\%)** & **RMSE (\%)** \\ \hline EKF & 2.262 & 2.385 \\ UKF & 2.222 & 2.305 \\ STF & 2.186 & 2.280 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The MAE and RMSE of three algorithms when with voltage measurement offset.  
Figure 16: The estimated SOC error of the algorithms with \(-5\) mV voltage offset at 30 \({}^{\circ}\)C.
## 2 Attacks in Underwater Acoustic Networks  
As mentioned previously, the opening and sharing of the underwater acoustic channel make UANs vulnerable to various threats and attacks from MNs. Three common threats and attacks in UANs are as follows [13; 14; 15; 16].  
1. A HELLO packet flooding attack occurs when an MN sends HELLO packets with higher power than that from a good node, which causes the nodes receiving the HELLO packet to consider the MN as their neighbor. In the subsequent data forwarding, the nodes that have received the HELLO packets from the MN may choose the MN as the best next-hop node and send the data packets to the MN. However, the transmission power of the normal nodes is less than that of the MN, which means that the data packets are unable to reach either the sink node or the MN.
2. Another HELLO packet flooding attack is considered in this paper. It occurs when an MN frequently floods HELLO packets in the network, which interferes heavily with the normal communication in UANs, intensifies the packet re-transmission, increases the network energy consumption, and reduces the packet delivery rate.
3. In a selective forwarding attack, the MN masquerades as a normal node in the network and drops packets with a certain probability, resulting in packet loss. So, selective forwarding attacks exacerbate packet re-transmissions, reduce the packet delivery rate, and consume much energy, which is unfavorable for energy-limited UANs.  
The above attacks severely waste the resources of the network, such as bandwidth, energy, and channel utilization. Therefore, a fuzzy comprehensive evaluation decision model is introduced to establish a trust evaluation mechanism for underwater nodes and identify the MNs in the network to mitigate the threats caused by the above attacks and ensure reliable routing.
# A Review on Methods and Systems for Remote Collaboration
## 5 Communication Methods and Cues  
In face-to-face collaboration, for both collaborators involved in exchanging information, not only what the person is saying but also where they are looking and what their hands and body gestures are showing is important.  
[PERSON] [95] showed that for remote collaboration, video communication is better than having only audio. For a helper-worker pair involved in a bicycle repair task, [PERSON] compared the task performance in the following scenarios: one where the communication was done using visual information, and another where the communication was audio-only. The conclusions showed that a shared visual space was essential for the collaborative task.  
In addition, in order to establish common ground, users need to use communication cues such as speech and no-speech audio, gaze, facial expression, and hand and body gestures. Many of these cues can be captured using different video telecommunication systems, such as Skype, Google Hangouts, or Zoom. However, in some cases, the remote user needs to point to a specific object or draw specific instructions; here, pointing gestures and representational gestures (annotations) are important [96]. [PERSON] and [PERSON] demonstrated how remote gestures influence the structure of collaborative discourse and how their use can also influence the temporal nature of the grounding process [97].  
In [98], [PERSON] and [PERSON] performed a study on three different gestures' formats. The research compared the performance of unmediated hands only, hands and sketch, and digital sketch only.  
We divide the following section into two subsections. In the first, we present studies on pointers and annotations, and in the second, we look at behavior detection.
## 3 Circuit Parameter Design  
A. Transformer turn ratio \(n\)  
To simplify the analysis, the voltage across the primary windings of the transformer is idealized as a square-wave voltage source with the amplitude swinging between \(V_{Cbus}\)-\(V_{Cb1}\) and \(-V_{Cb1}\), as shown in Figure 4. With the turn ratio of the primary winding to the secondary winding being \(n\):1, the peak-to-peak value across the transformer secondary winding is \(V_{Cbus}/n\). Referring to Figure 4, it is known that the average value of the voltage across the primary winding is zero.  
\[(V_{cbus}-V_{Cb1})(1-D)T-V_{Cb1}DT=0, \tag{9}\]  
where \(D\) and \(T\) are the duty ratio and the switching period of the active switch \(S_{2}\). From (9), the following can be obtained:  
\[V_{Cb1}=(1-D)V_{cbus}, \tag{10}\]At steady-state operation, the average voltage of the inductor is zero, i.e., the average value of the rectified voltage of the secondary winding is equal to the output voltage.  
\[V_{CD}=\frac{(V_{cbus}-V_{cb1})(1-D)+V_{cb1}D}{n}=V_{o}, \tag{11}\]  
Substituting Equation (10) into Equation (11), the transformer turn ratio can be expressed as  
\[n=\frac{2D(1-D)V_{cbus}}{V_{o}}, \tag{12}\]  
B. Output inductor \(L_{o}\)  
The selection of the output filter inductance \(L_{o}\) is related to its current ripple. During the conducting interval of \(S_{2}\), the current variation of \(L_{o}\) can be expressed as  
\[\Delta i_{Lo}=\frac{DT((1-D)V_{Chus}/n-V_{o})}{L_{o}}, \tag{13}\]  
By rearranging Equation (13), the inductance of the output inductor can be expressed as:  
\[L_{o}=\frac{DT((1-D)V_{Chus}/n-V_{o})}{\Delta i_{Lo}}, \tag{14}\]  
C. PFC Inductor \(L_{PFC}\)  
In order to pursue the high power factor, the boost-type PFC converter should be operated at DCM. The inductance of the PFC inductor is inversely proportional to the output power and can be expressed as follows [21].  
\[L_{PFC}=\frac{\eta D^{2}{V_{m}}^{2}}{2P_{o}f_{s}}\cdot y, \tag{15}\]  
where \(P_{o}\) is the output power; \(f_{s}\) is the switching frequency; \(\eta\) is the circuit energy-conversion efficiency, and \(y\) is expressed as Equation (16).  
\[y=\frac{k^{3}}{\sqrt{k^{2}-1}}\Big{(}1+\frac{2}{\pi}\sin^{-1}\frac{1}{k}\Big{)} -k^{2}-\frac{2}{\pi}\cdot k\Big{(}k\equiv\frac{V_{Chus}}{V_{m}}\Big{)}, \tag{16}\]