esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Detection of Large-Scale Internet of Things Botnets Based on Graph Partitioning
## 2 Related Work
### ML-Based Approaches  
In the early stages, the detection of botnets primarily relied on machine learning algorithms [19; 20] such as random forest [21], SVM [22], k-means [23], XGBoost [24], and others. In 2007, [PERSON] et al. [25] proposed a mechanism for detecting botnets by monitoring Domain Name System (DNS) traffic. This mechanism detects botnet activity formed by simultaneous DNS query requests from distributed botnet programs. However, this approach requires significant processing time when monitoring large-scale networks. [PERSON] et al. proposed BotHunter (2016), BotSniffer (2017), and BotMiner (2018), which analyze traffic features such as abnormal packet sizes and port numbers from the perspectives of multiple infection detection stages, multi-round communication activities, and longer communication response times to identify botnets. [PERSON] et al. [PERSON] et al. (2019) utilized the Classification of Network Information Flow Analysis (CONIFA) framework to extract a set of network traffic features to capture patterns in C&C channels and malicious traffic. Current machine learning-based botnet detection methods require a substantial amount of domain knowledge and expert feature extraction, such as packet sizes and inter-arrival times. Since 2010, with the gradual development of deep learning ([PERSON] et al., 2017), the latest deep learning algorithms have significantly surpassed traditional machine learning algorithms in terms of data prediction and classification accuracy. Deep learning does not require manual feature extraction but automatically filters and extracts high-dimensional features from the data. Recently, transfer learning ([PERSON] et al., 2017) and ensemble learning ([PERSON] et al., 2017) have been increasingly employed in network data detection. However, both approaches face challenges regarding their generalization capabilities. Existing detection methods, such as extracting features of botnet attack vectors ([PERSON] et al., 2017), flow features ([PERSON] et al., 2017), and utilizing auto-encoders to extract packet features ([PERSON] et al., 2017), combine with convolutional neural networks (CNN), long short-term memory (LSTM), and other methods for botnet detection. Nevertheless, the processing of high-dimensional data is inherently complex, demanding significant computational resources and storage capacities ([PERSON] et al., 2017; [PERSON] et al., 2017). This presents a particular challenge for IoT devices, which are often constrained by limited memory capacities, impeding their ability to store the requisite volume of network traffic data for analysis. To address the above issues, this article proposes the application of graph partitioning techniques as a solution.
## 4 Discussion
### Spatial Heterogeneity: Implications for Parameters Conversion Across Different Scales  
Our study underscores the significant influence of spatial heterogeneity field-scale \(V^{25}_{cmax,0}\). The spatial heterogeneity originates from the diverse elements in paddy fields, including the bare soil, water body, and weeds, as well as rice in different growth states, all of which have distinct photosynthetic properties. Field-scale \(V^{25}_{cmax,0}\), representing a macroscopic feature within the flux footprint climatology, is intricately linked to this spatial heterogeneity. In contrast, leaf-scale \(V^{25}_{cmax,0}\) solely reflects the local feature. This inherent spatial heterogeneity contributes to the observed mismatch between field-scale and leaf-scale \(V^{25}_{cmax,0}\), as depicted in Figure S6 (see Supplementary Material Section S11). Given the global prevalence of spatial heterogeneity, as evidenced in various studies [59], relying solely on the leaf-scale \(V^{25}_{cmax,0}\) model for field-scale simulation introduces uncertainties into \(GPP\) estimation. Our study advocates for the direct use of field-scale parameters to model \(GPP\), acknowledging the complexities introduced by spatial heterogeneity.  
Figure 8: The spatial distribution of the AlexNet-model-based SHAP.
# Zeroes: Robust Derivative-Based Demodulation Method for Optical Camera Communication
## 6 Discussion  
The main limitation of a correct demodulation of OCC images based on rolling shutter lies in the blooming effect. The aim of this work was to implement a new demodulation method, named ZEROES, which relies on the first-order derivative of the extracted signal to accurately detect changes in signal states while removing the blooming effect. The conducted experiments demonstrated the robustness of the ZEROES method by comparing its performance to other demodulation methods while using the same PCA method for ROI detection. The obtained results show that the ZEROES method outperforms other methods in both LOS and NLOS scenarios by achieving a target BER of \(10^{-3}\) in LOS at a distance of approximately 50 cm (when other methods cannot), and by increasing the communication distance in NLOS by 43.5% compared to BPBPLFT. However, the robustness of the ZEROES method against transmission errors results in a reduced effective data rate, receiving 85.5% of the transmitted data with a BER of \(10^{-3}\) at 330 cm, while BPBPLFT achieves 89.9% at a shorter distance of 230 cm. Therefore, the combination of PCA and ROI with the ZEROES  
Figure 19: Bit Error Rate of demodulation methods in NLOS.  
Figure 20: Comparison of median execution time of demodulation methods.  
demodulation method paves the way for the use of OCC in daily life, especially in NLOS scenarios, with execution times being compatible with real-time applications.  
Methodology, M.D.M.; Investigation, M.D.M.; Resources, H.B.; Writing--review and editing, H.B. and A.J.-V.; Supervision, H.B. and A.J.-V. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the VEEPI Regional Project of Nouvelle-Aquitaine under grant AAPR2021A-2020-12026410.  
The source code implementing all the methods employed in this work is available on GitLab at [[https://gitlab.xlim.fr/mdemur01/occ_zeroes](https://gitlab.xlim.fr/mdemur01/occ_zeroes)]([https://gitlab.xlim.fr/mdemur01/occ_zeroes](https://gitlab.xlim.fr/mdemur01/occ_zeroes)).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
3 rd-OPF & Third-Order Polynomial Fitting \\ BER & Bit Error Rate \\ BPBPLFT & Boundary Pixel-Based Piecewise Linear Function \\ CCD & Charge-Coupled Device \\ CMOS & Complementary Metal-Oxide Semiconductor \\ ER & Extinction Ratio \\ HS & HyperSight \\ HSV & Hue saturation value \\ IFG & Inter-Frame Gap \\ IoT & Internet of Things \\ ISO & International Standard Organization \\ LED & Light-Emitting Diode \\ LOS & Line Of Sight \\ NLOS & Non Line Of Sight \\ OCC & Optical Camera Communication \\ OOK & On-Off Keying \\ OWC & Optical Wireless Communication \\ PCA & Principal Component Analysis \\ PWM & Pulse-Width Modulation \\ RF & Radio Frequency \\ RGB & Red Green Blue \\ ROI & Region Of Interest \\ SMD & Surface-Mounted Device \\ V2V & Vehicle-to-Vehicle \\ VLC & Visible Light Communication \\ ZEROES & Zeros Evaluation for Robust and Optimal Edges Selection \\ \end{tabular}
## 1 Introduction
However, the available algorithms for this method either require manual construction steps or are restricted to periodic topologies. The complex task of smoothly solidifying a wide range of different node topologies in lattice frames with irregular topology is either solved manually [17] or with convex hull algorithms [11; 13; 14]. Regarding the possibility to respect different local shape parameters such as strut radii gradients within the lattice structure, the reviewed methods face different limitations. Polygon mesh-based approaches allow for different strut radii in the definition of the initial mesh model. According to [11], the application of subdivision schemes during the smoothing process leads to deformations in these initial radii and cross-section shapes. Therefore, parameters have to be selected carefully or manually and extensive post-processing is required. Furthermore, an exact parametrisation is impossible. In [10], an iso-surface construction method with varying strut radii is discussed that allows for radius gradients along vectors. Consequently, the radii are not separately parametrisable for each strut. In [16], the iso-surface construction method is combined with threshold distribution fields that allow for the local exact radii parametrisations of individual struts. However, the wireframe topology supported by this method is unit cell-based. Surface patch methods offer the possibility of exact strut radii parametrisation, as it is shown in [12]. Nevertheless, the algorithm developed in [12] is also restricted to periodic lattice structures with repeated unit cells. So far, the only known surface patch-based approach for constructing irregular lattices with smooth surfaces requires manual construction steps [17]. Therefore, the objective of this work was to develop an automated surface patch-based algorithm without manual construction steps that is able to construct smooth lattice structures based on given lattice frames and strut radii for as general cases as possible. The method expanded on in this work has been applied by the authors for constructing a planar lattice as an infill for a lightweight structure in [8]. The algorithm is implemented as a macro in the commercial software CATIA V5-R2019; however, it can be used in any computer-aided design software that supports the three basic surface construction methods discussed in Section 2.
# TransPGC: Point Cloud Geometry Compression Based on Transformers
## 3 Methods
### Loss Function  
It is common practice to consider both the bitstream and the distortion simultaneously while optimizing the compression of point clouds, utilizing the Lagrangian loss and weighted binary cross-entropy as a joint loss function. In Equation (9), this optimization strategy balances the bitstream and distortion tradeoff. Here, the bitstream is symbolized by \(R\), the distortion is represented by \(D\), and \(\lambda\) regulates the weight of the BD-rate. As demonstrated in Equation (10), the bitstream is formed from \(t_{i}\) and \(z_{i}\), and its estimation can be stated using the cross-entropy loss function. The two bitstreams are added to create the complete bitstream, which is represented as \(R=R_{\hat{t}}+R_{z}\), with the hyperprior bitstream \(\hat{z}\) acting as auxiliary data.  
\[J_{loss}=R+\lambda D, \tag{9}\]\[\left\{\begin{aligned} R_{I}=\sum_{i}-\log_{2}\left(p_{t_{i}|\psi^{(i)}} \left(\hat{z}_{i}\mid\psi^{(i)}\right)\right)\\ R_{\hat{z}}=\sum_{i}-\log_{2}\left(p_{\hat{z}_{i}|\psi^{(i)}} \left(\hat{z}_{i}\mid\psi^{(i)}\right)\right).\end{aligned}\right. \tag{10}\]  
To evaluate the distortion of point cloud compression, the weighted binary cross-entropy loss function is employed to classify the decoded voxels. This loss function, as depicted in Equation (11), measures the probability value, denoted as \(p_{x}=signoid(\bar{x})\), of voxel occupancy ranging between 0 and 1. Here, \(\bar{x}_{o}\) represents the occupied voxel, \(\bar{x}_{n}\) signifies the unoccupied voxel, and \(\alpha\) is a hyperparameter that controls the relative weight, typically set to 3. Furthermore, \(N_{o}\) and \(N_{n}\) represent the quantities of occupied and unoccupied voxels, respectively.  
\[D_{WBCE}=\frac{1}{N_{o}}\sum^{N_{o}}-\log p_{\bar{x}_{o}}+\alpha\frac{1}{N_{n }}\sum^{N_{n}}-\log(1-p_{\bar{x}_{n}}). \tag{11}\]
## 4 Voting Algorithm
### Voting Procedure
#### 4.3.1 Initiated Revocation Request  
The revocation message of publisher \(E_{i}\) contains the following: revocation event serial number \(CN_{i}\), revocation public key \(Pub_{r}\), or revocation content \(Cont_{r}\) of _publisher_\(E_{r}\), \(E_{i}\)'s public key \(Pub_{i_{c}}\)\(E_{i}\)'s \(UID_{i}\), revocation reason \(M_{i}\), security weight \(SW_{i}\), valid time stamp to vote on revocation message \(T_{i}\), and signature \(Sig_{i}\) of revocation message by \(E_{i}\). The generated revocation information \(M_{ir}\) is as follows:  
\[Sig_{i}=SIG(H(CN_{i},M_{i},UID_{i},\alpha_{i},SW_{i},Pub_{i},Pub_{r}/Cont_{r},T _{i})). \tag{3}\]  
Then, the initial threshold value \(ht_{c}\) is calculated by Formula (4), and \(VW_{i}\) is the voting weight of \(E_{i}\):  
\[ht_{c}=VW_{i}\times\alpha_{i} \tag{4}\]  
\[M_{ir}=(Sig_{i},CN_{i},M_{i},ht_{c},UID_{i},\alpha_{i},SW_{i},Pub_{i},Pub_{r}/ Cont_{r},T_{i}) \tag{5}\]  
\[SW_{new}=SW_{i}+1 \tag{6}\]  
\(SIG\) (\(\bullet\)) is the signature function. \(H\) (\(\bullet\)) is the hash function. After generating message \(M_{ir}\), user \(E_{i}\) still needs to add its \(UID_{i}\), new security weight \(SW_{new}\), and \(\alpha_{i}\) to RVWL. The user \(E_{i}\) publishes the generated revocation information to the local resolution handler through the publish-subscribe mode. All users in the domain who have subscribed to participate in secure voting events can receive this publication information and choose whether to vote.
# Static and Dynamic Response of Micropiles Used for Reinforcing Slopes
## 3 Results
### The Internal Force Response Analysis of the Pile  
#### 3.4.1 The Bending Moment Response of Pile  
Figure 14 shows the bending moment distribution curve of each row of piles in the static model test. As it can be seen from the figure, the distribution of each row of piles present a \"S\" shape, the maximum negative bending moment appears above the sliding surface (the loaded section of the pile), and the maximum positive bending moment appears below the sliding surface (the embedded section of the pile). However, the location is slightly different. The distances from the maximum negative bending moment of piles #2, #6 and #11 to the sliding surface are 0.15, 0.13, and 0.07, and the distance of the maximum positive bending moment from the sliding surface are 0.13, 0.11, 0.10, respectively. It can be seen that the closer to the trailing edge of the landslide is, the closer the position of the maximum positive and negative bending moment of the pile body to the sliding surface is. Therefore, plastic failure is more likely to occur under the same load. In addition, the bending moment of each row of piles increases as the load increases.  
Figure 14: Bending moment distribution of each row of piles (static model test).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Loading**Location** & **0.3 g** & **0.4 g** & **0.6 g** & **0.8 g** \\ \hline Back row pile & 0.466 & 0.453 & 0.436 & 0.390 \\ Middle row pile & 0.370 & 0.394 & 0.400 & 0.244 \\ Front row pile & 0.164 & 0.153 & 0.163 & 0.366 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The earth pressure sharing ratio of each row of piles (shaking table test).  
Figure 15 shows the bending moment distribution curve of each row of piles in the shaking table test. As it can be seen from the figure, similar to the static loading case, the bending moment distribution was also an \"S\" shape. The maximum negative bending moment appeared above the sliding surface (the loaded section of the pile), and the maximum positive bending moment appears below the sliding surface (the embedded section of the pile). However, what is different from the static loading is that the maximum positive bending moments were all approximately 0.033 m away from the sliding surface (here: about one time the pile diameter), and the largest negative bending moments were around 0.15 m away from the sliding surface (here: about four times the pile diameter). This was because the direction of the applied seismic force was completely horizontal and had an elevation amplification effect, which directly caused the point of action of the resultant force acting on the pile to move upward. As a result, the position of the maximum positive bending moment of the pile was closer to the sliding surface, and the location of the maximum negative bending moment was farther away from the sliding surface than in the static test. Similarly, the bending moment of each row of piles increased as the peak acceleration of the input wave increased.
# Rapid Molecular Identification of Scolytinae (_Coleopterara: Curculionidae_)
## 4 Materials and Methods
### DNA Threshold for Molecular Identification Using D2F1/D3R2  
To determine the lowest amount of DNA that can be used for molecular identification of ambrosia and bark beetles using D2F1/D3R2, PCRs were performed using seven progressively smaller quantities of DNA (1.5, 0.3, 0.06, 0.012, 0.0024, 0.00048, and 0.000096 ng), decreasing by a factor of five each time.  
Figure 4: To compare extraction protocols DNA yield was standardized per specimen by dividing yield by body length then multiplied by 2000 μm. Specimen BC2 (extracted using protocol A24) is displayed here to illustrate the standardization method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Gene** & **Primer** & \begin{tabular}{c} **Annealing Tem** \\ **(°C)** \\ \end{tabular} & \
\begin{tabular}{c} **Annealing Time** \\ **(s)** \\ \end{tabular} &
\begin{tabular}{c} **Touch Down** \\ **Temp (°C)/cycle** \\ \end{tabular} & **Number of Cycles** \\ \hline
28S & D2F1 & 58 & 45 & \(-\)0.1 & 33 \\  & D3R2 & & & & \\ \hline COI & LCO1490 & 51 & 45 & \(-\)0.2 & 40 \\  & HCO2198 & & & & \\ \hline EF-1a & eflafor1 & 56 & 45 & \(-\)0.1 & 38 \\  & efarev1 & & & & \\  & ets149 & 51 & 45 & \(-\)0.1 & 40 \\  & efa754 & & & & \\ \hline ITS & ITS2F & 56 & 30 & \(-\)0.1 & 40 \\  & ITS2R & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Thermal cycler programs were optimized for each primer pair. All reactions started at 95 °C for 12 min (Immomix), followed by 94 °C for 1 min in primer-specific annealing conditions, and an extension at 72 °C for 1 min. A final elongation at 72 °C for 5 min was used in all reactions. Primer-specific conditions are shown here.  
For each level, amplification was attempted from DNA of three specimens (_Theoborus_ sp.) and one negative control (water) using the primer pair D2F1/D2R2 (Table S2). Amplification reactions and PCR product visualization was carried out as described above.  
For levels where clear bands were obtained from all samples (1.5 and 0.3 ng), one representative of each was sequenced to serve as a positive control against which to compare the accuracy of the sequences obtained at lower DNA thresholds (and lower concentration PCR product). At the lower concentrations, samples showing bands of any intensity were purified and sequenced to confirm amplification success and the quality of resulting sequences.
# Antipodal Vivaldi Antenna Arrays Fed by Substrate Integrated Waveguide Right-Angled Power Dividers
## 2 Design Process
### Out-of-Phase Two-Way Power Divider  
By using the same technique as in Section 2.1, an SIW TE\({}_{10}\)-to-TE\({}_{20}\) mode transducer is designed. It includes a right-angled SIW two-way power divider with an an over-moded SIW section, and four metallic vias whose locations are optimized for improved match and out-of-phase power division. Figure 4 shows the two-way divider structure and its electric fields. The four metallic vias improve both matching and mode conversion between the single-mode and over-moded SIW sections. This power divider provides the same amplitude (Figure 4) with 180-degree phase difference over a 3 GHz (10%) bandwidth which is presented in Figure 5a. Figure 5b shows the two-way power divider's transmission coefficients with differences between 0.7 dB and 2.5 dB over the frequency range.  
Figure 4: Electric field of two-way H-plane SIW power divider.  
Figure 3: Scattering parameters of the three-way H-plane SIW power divider.  
Figure 2: Electric field of three-way H-plane SIW power divider.  
To present the phase control capability of this kind of power divider, another right-angled two-way divider with 120-degree phase difference is designed and presented in Figure 6a. This six-via design provides a good match and about 120-degree output port phase difference between 26.5 GHz and 27.5 GHz (Figure 6b). The insertion loss on top of the 3 dB power division is negligible in the operating bandwidth as presented in Figure 6c.  
Note that due to the waveguide nature of the SIW technology, the phase differences change slightly with frequency and, therefore, so will the beam directions in the array applications presented in Section 3.  
Figure 5: (**a**) Output port phase difference, and (**b**) power level of two-way H-plane SIW power divider with 180-degree phase difference.  
Figure 6: _Cont._
## 3 Vibration Localization Characteristics and Their Influence Factors
### Eigenvalue Splitting  
#### 3.2.1 Eigenvalues Under Different Mistuning Strengths  
Maintaining the coupling coefficient \(R=0.1\) between the resonant units, the eigenvalues are calculated under the four different mistuning strengths specified in Table 1.  
Figure 4: Mode shapes for different coupling strengths.  
Figure 3: Mode shapes for different mistuning strengths.  
Additionally, for enhanced comparison, the eigenvalues in the tuned state are also computed simultaneously. The results are shown in Figure 5. It can be observed that in the tuned state, the eigenvalues of the resonant units are in close proximity, with multiple repeated eigenvalues present. As the mistuning strength increases, the structure demonstrates eigenvalue splitting phenomena. The resonant frequencies of the units expand to a wide frequency range, which is beneficial for broadening the vibration reduction frequency band of the CMS. But the resonant frequency interval between the resonant units also enlarges, resulting in the ineffective connection of the vibration reduction frequency bands of the resonant units, thereby reducing the vibration reduction performance stability. Therefore, it is necessary to choose an appropriate mistuning strength, which can enable the CMS to achieve good broadband vibration reduction performance while also ensuring the stability of vibration reduction performance.
## 2 Related Works
### Vertex Block Descent  
#### 2.3.1 Key Characteristics of Vertex Block Descent  
The VBD algorithm provides an efficient approach to solving the dynamics of deformable objects in physically based simulations by employing a position-based update mechanism akin to PBD. Unlike traditional methods, VBD incorporates a modified Implicit Euler integration scheme to enhance stability during time integration [35]. Central to VBD is the Block Coordinate Descent approach, which iteratively updates the position of each vertex using a Gauss-Seidel-like process across the mesh of the object, significantly reducing the computational cost. The key characteristics of the VBD algorithm are as follows:  
1. Block Coordinate Descent: This method performs vertex-level parallel computation, independently calculating and updating each node of the mesh to maximize parallelism. In particular, mesh coloring techniques are employed to minimize task handling bottlenecks, thereby improving computational efficiency.
2. Stability: VBD ensures high stability even under large time steps, making it well suited for real-time simulations. This stability is particularly advantageous when computational resources are constrained, enabling robust results without compromising accuracy.
3. Efficiency: By using a localized linear system, VBD achieves relatively low computational cost, thus demonstrating high efficiency in its calculations.
4. Optimization for Large-Scale Simulations: The VBD algorithm is adept at handling common optimization challenges inherent in large-scale simulations. It can efficiently manage complex physical interactions, such as object-to-object collisions and self-collisions, even in scenarios involving a substantial number of nodes.