esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Botnets: Incentives, Evolution, Detection and Current Trends
## 6 Detection and Mitigation
### Detection Mechanisms--Techniques
#### 6.1.1 Neural Network Detection Mechanisms
The authors of \"Real-time botnet detection using non-negative tucker decomposition\" [104], propose a method for detecting group activities from extracted features in darknet traffic using tensor factorisation. While this method requires too high computational costs to run in real time, they propose implementing a two-step algorithm in order to achieve fast, memory-efficient factorisation. More nontraditional methods like [96] seek to identify botnets through the usage of power consumption as the parameter for their CNN model. In [29] a similar lightweight solution is also mentioned for use in small memory capacity devices with low-power processors, since these are not able to have reliable anti-malware systems. It is based on the use of NeuroMesh, which is a combination of neural and mesh detection networks used to secure the devices. It can detect and delete malware and implements IP-based blacklist and whitelist access control to provide secure channel for IoT devices via the Bitcoin communication protocol.
###### Abstract  
The analysis of liquid-fueled molten-salt reactors (LFMSRs) during steady state, operational transients and accident scenarios requires addressing unique reactor multi-physics challenges with coupling between thermal hydraulics, neutronics, inventory control and species distribution phenomena. This work utilizes the General Nuclear Field Operation and Manipulation (GeN-Foam) code to perform coupled thermal-hydraulics and neutronics calculations of an LFMSR design. A framework is proposed as part of this study to perform modeling, design optimization, and uncertainty quantification. The framework aims to establish a protocol for the studies and analyses of LFMSR which can later be expanded to other advanced reactor concepts too. The Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) statistical analysis tool was successfully coupled with GeN-Foam to perform uncertainty quantification studies. The uncertainties were propagated through the input design parameters, and the output uncertainties were characterized using statistical analysis and Spearman rank correlation coefficients. Three analyses are performed (namely, scalar, functional, and three-dimensional analyses) to understand the impact of input uncertainty propagation on temperature and velocity predictions. Preliminary three-dimensional reactor analysis showed that the thermal expansion coefficient, heat transfer coefficient, and specific heat of the fuel salt are the crucial input parameters that influence the temperature and velocity predictions inside the LFMSR system.  
uncertainty quantification; sensitivity analysis; CFD; GeN-Foam; multi-physics +
Footnote †: journal: Applied sciences
# Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model
## 3 Methods  
This paper uses the quantum model architecture in series with TensorFlow's LSTM in the QCNN section. The CNN, LSTM, and quantum models are all built using TensorFlow and Tensorflow quantum in Python in the model training submodule. TensorFlow Quantum (TFQ) is a framework that integrates Cirq with TensorFlow. TensorFlow is an upper-layer framework for wrapping machine learning or deep learning algorithms. Cirq is closer to quantum computing device operation, including a variety of common logic gates, circuit design, and support for different structures of quantum devices (Google's Xmon quantum device is mesh qubits). In essence, the TFQ model aims to embed quantum circuits into ordinary deep learning models and let quantum circuits encode the information of classical deep learning; Figure 1 is a high-level abstract overview of the computational steps involved in the end-to-end pipeline [34] and includes a hybrid quantum-classical discriminant model for reasoning and training quantum data in TFQ.  
As shown in Figure 1, information in the classical world can be encoded by formulating some quantum circuit operations and \"disentangling\" the input qubits [35]. This section will first introduce the data transformation used for the quantum model, and then describe how the transformed quantum data is fed into a quantum-inspired neural network. Subsequently, the overall construction of the model will be presented, followed by a discussion of the advantages of our model over traditional numerical models and conventional neural networks.  
Figure 1: A high-level abstract overview of the computational steps involved in the end-to-end pipeline for inference.
## 1 Introduction
To overcome these challenges, this study proposes a hybrid framework that combines a second-order RC ECM with a multi-head-attention bidirectional LSTM (MHA-BiLSTM) network. Unlike traditional approaches that rely on experimental data, ECM is used to simulate long-term charge-discharge cycles, generating synthetic data that significantly reduce the need for costly experimental data collection. This approach not only lowers the data acquisition barrier, but also ensures high fidelity simulation results. Furthermore, this model predicts both the voltage and SOC sequences 30 steps ahead, addressing the practical need for multi-step forecasting in BMS applications. The incremental charge and voltage change between consecutive time steps are introduced as additional features. The battery's parameters are processed by a multi-head attention (MHA) mechanism, which dynamically weighs the importance of different features and captures cross-parameter dependencies. This design allows the model to better comprehend how battery states evolve, in terms of the incremental charge and voltage changes, across consecutive time steps under varying load conditions. By integrating physical modeling with advanced deep learning techniques, this work provides a scalable and efficient solution for the battery state prediction. The proposed framework not only improves the prediction accuracy, but also broadens the applicability of data-driven methods by reducing the dependency on experimental data. This paper is organized as follows: Section 2 details the methodology, including the hybrid framework combining a second-order RC equivalent circuit model with a MHA-BiLSTM neural network. Section 3 presents the results and discussions, covering ECM validation, parameter identification, and the comparative analysis of prediction models. Section 4 concludes this study and provides an outlook of future research.
# Monocular Depth Estimation Based on Dilated Convolutions and Feature Fusion
## 3 Method Design and Implementation
### Depth Perception  
The module concerning depth perception represents the ultimate component within the network architecture discussed in this paper. It plays a primary role in establishing the mapping relationship between the feature maps and pixel-level depth categories. The specific relationship is illustrated in Figure 7.  
Most monocular image depth estimation algorithms that rely on deep learning transform the depth estimation problem into a regression problem. These methodologies treat depth information in images as a continuous variable and employ appropriate regression models to forecast the depth values of pixels, thereby producing the ultimate depth estimation outcomes. By examining experimental findings from reputable studies in the contemporary domain of monocular depth estimation, this paper suggests that the task of depth estimation becomes more challenging when aiming to predict continuous depth values. This paper simplifies the depth estimation issue by transforming the depth regression problem into a depth classification problem, considering the limited sensitivity of the human eye to subtle changes in depth information. By discretizing continuous depth values, distinct depth intervals are assigned to specific classes, thereby creating an association between continuous depth values and depth categories. Ideally, the depth intervals corresponding to different depth categories should be equal. Given that variations in depth should be more acceptable in depth intervals with greater depth values, we employ the magnitude of the depth values as the criterion for segmenting the depth interval ranges. The specific mapping model is illustrated in Figure 8.  
Figure 6: Multi-receptive field feature fusion.  
Figure 7: Relationship between feature maps and depth categories.  
In Figure 8, we segment the continuous depth values ranging from 0 to 25 m into six depth intervals, each representing a distinct depth category. By modifying the span of each depth interval based on the depth values' magnitude, a greater tolerance for depth discrepancies is attained in intervals characterized by larger depth values in contrast to those with smaller depth values. It should be noted that Figure 8 is a schematic diagram of the mapping relationship. The actual number of depth intervals and the values of the depth range are determined by different datasets as detailed in the experimental section. The formula illustrating the mapping relationship between continuous depth values and depth categories is presented in Formulas (1) and (2):  
\[c=K\times\sqrt{\frac{(d\times(\beta-\alpha)-\alpha)}{(\beta-\alpha)}} \tag{1}\]  
\[d=\alpha+(\beta-\alpha)\ \times(\frac{c}{K})^{2} \tag{2}\]  
Here, \(K\) denotes the number of depth categories, \(d\) represents the continuous depth value, \(c\) indicates the depth category, \(\alpha\) stands for the starting depth of the continuous depth values, and \(\beta\) represents the ending depth of the continuous depth values.
## 3 Verifications
### Computational Results  
The simplified rock slope is simulated numerically in Section 3.1 in the discrete element software 3 DEC, and the physical and mechanical parameters are assigned in Table 2. The slope is initially unstable, and the initial stress state is mainly caused by geological structure and gravity. In addition, topography, rock mechanical properties, water, and temperature will also affect the initial stress state. As the number of iteration steps gradually increased, the rock blocks on the jointed rock slope began to slide, and some rock blocks began to slide out of the boundary of the model. When the number of iteration steps reaches 14,000, a large number of rock blocks have slipped and accumulated, and the rock blocks slide along the weak interlayer, as shown in Figure 6. Figure 6a shows the overall sliding situation of the simplified jointed rock slope when the number of iteration steps reaches 14,000. With the gradual increase in the number of iterations, the rock blocks on the simplified jointed rock slope will continue to slide, and result in greater displacement. Figure 6b is a diagram of a certain surface when the simplified slope is iteratively calculated to the 14,000 th step. Figure 6b shows that the rock blocks slide along the joint surface and the weak interlayer.  
The discrete element software 3 DEC is used to obtain the positions and volumes of all rock blocks every 1000 steps until 50,000 steps. Then, data cleaning is performed on the acquired data files; the energy under gravity is calculated from the initial state to any state. Then, the Spearman correlation coefficient based on energy is calculated, and the relationship curve between the number of iteration steps and the Spearman correlation coefficient is drawn. To verify the correctness of the proposed method, the result of the proposed method is compared with the displacement-based method. The results obtained using two different methods are shown in Figure 7. We can see that the suggested number of iteration steps in the displacement-based method is 12,000, while the suggested number of iteration steps in the proposed method is 14,000. The inflection points of the two methods are almost the same, and the results are basically consistent. In addition, from Figure 7, it is thought that when the calculation reaches approximately the 14,000 th step, the iterative calculation process could be terminated.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Type Unit** & \(\rho\) **(kg/m\({}^{3}\))** & **BULK (GPa)** & **\(G\) (GPa)** & **\(C\) (MPa)** & **\(\varphi\) (\({}^{\circ}\))** & **JKN (GPa/m)** & **JKS (GPa/m)** \\ \hline Rockmass & 2300 & 3.33 & 2.0 & 0.13 & 32 & 1.5 & 0.35 \\ Weak interlayer & 1800 & 0.33 & 0.2 & 0.01 & 19 & 1.2 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical parameters of rockmass and weak interlayer.  
Figure 6: The \(Z\)-direction displacement when iterating to the 14,000 th step. (**a**) Overview, (**b**) cross-section.
# Study on the Common Rail Type Injector Nozzle Design Based on the Nozzle Flow Model
## 2 Methodology  
### Details of Injector  
Figure 1 shows a schematic diagram and SEM (Scanning Electron Microscope) image of an injector nozzle. The injector (solenoid driven, maximum fuel injection pressure of 160 MPa) to be modified in this study has a Mini-SAC nozzle (conical sac hole with conical tip). The base injector nozzle has \(\varnothing=0.14\) (mm) and 8 holes, and the injection angle, which is the angle between the injected fuel spray, is \(152^{\circ}\) (see Figure 1a). The diameter of the nozzle hole along the nozzle length is constant. In Figure 1a, the fuel in the injector is sealed through line contact of the inner nozzle surface and needle seat, and the minimum pass-through flow area of the seat (\(A_{th}\)) is the cross-sectional area, which has been calculated as the area corresponding to the normal distance from the needle seat during needle behaviour at the line contact point. The nozzle flow model is based on the idea that the flow in the nozzle is modelled as a quasi-steady, zero-dimensional flow, and the flow can be divided and analysed for cavitating flow and non-cavitating flow, as shown in Figure 1b [11; 12]. In general, the nozzle flow model is useful for determining boundary conditions for the spray analysis and designing the injection flow rate in the nozzle; it is possible to predict these conditions based on experimental data. In this study, the proposed nozzle flow model was used to design the nozzle concept for different flow regimes and nozzle geometries without experimental data. However, more accurate injection rate characteristics require full modeling and analysis, including flow passages, hydraulic valves, and actuator in the injector body. Specifications for the nozzle and needle and material properties are summarized in Tables 1 and 2, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Unit** & **Value** \\ \hline Number of hole (\(N_{h}\)) & - & 8 \\ Diameter of hole (\(D\)) & mm & 0.124 \\ Length of hole (\(L_{n}\)) & mm & 0.756 \\ Diameter of SAC (\(D_{s}\)) & mm & 0.44 \\ Diameter of needle seat (\(D_{st}\)) & mm & 0.94 \\ Seat angle (\(\theta\)) & \(\circ\) & 60 \\ Injection angle (\(\alpha\)) & \(\circ\) & 152 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the base injector nozzle.
## 1 Introduction  
The topology of distribution equipment and its influence on power distribution systems (PDSs) were analyzed. There are three methods for single-phase to three-phase power conversion: static phase converter, rotary phase converter, and electronic phase converter. However, the static and rotary methods are not suitable for use in PDS and not in customer facilities; therefore, the electronic phase converter topology is proposed. The electronic conversion method ultimately relies on power semiconductors. Power semiconductors include diodes, IGBTs, and MOSFETs. In this paper, MOSFETs are selected due to their suitability for low-voltage distribution systems (LVDSs) and their capability to operate at high switching frequencies. The power electronic equipment proposed in the paper is called a Single-to-Three-Phase Converter (STPC). The proposed topology comprises an input converter, a DC-link, and an output inverter. The Korea Electric Power Corporation (KEPCO) stipulates that the three-phase line-to-line voltage should remain within \(380\pm 38\) V. Therefore, the output voltage of the STPC must also output a voltage that conforms to the reference voltage. To achieve this, the STPC monitors both the output voltage and the DC link voltage to regulate the PWM signal effectively.
# Denial-of-Service Attacks on Permissioned Blockchains: A Practical Study
## 4 Methodology
### Threat Model for HLF
#### 4.1.5 Threats to Consensus Protocols  
The Raft consensus protocol is a widely used algorithm for achieving agreement on shared states in distributed systems. Raft simplifies distributed agreement using a leader-follower model [31]: a single leader node proposes transaction ordering, while followers replicate and acknowledge entries. Once a quorum approves, transactions are committed network-wide. This approach simplifies the consensus process and ensures consistency across the network. Raft's leader election mechanism ensures continued operation even if the leader fails. Followers periodically send heartbeats to the leader. If a heartbeat is missed, an election is triggered. Randomized time-outs and a term-based system prevent simultaneous elections and ensure a single leader is chosen. The leader manages transaction orders and communicates with followers. While Raft tolerates node failures (e.g., crashes), it remains vulnerable to Byzantine faults (e.g., malicious leaders or colluding followers), misconfiguration, and network partitioning [59]. For instance, if an attacker manipulates Raft's heartbeat intervals, they can artificially slow the consensus or force repeated leader elections, degrading the throughput. Misconfigurations, such as incorrect time-outs, can destabilize the consensus process, leading to delays in block finalization, failed consensus, and potential network partitioning. Properly configuring Raft is crucial for the stability and performance of the HLF network.
# A Review of Blast Loading in the Urban Environment
## 4 Experimental Methods and Instrumentation
### Small-Scale Blast Waves
#### 4.3.6 Detonation Transmission Tubing  
[PERSON] et al. [163] explored the use of detonation transmission tubing (DTT) to study and visualise micro-blast waves. The tubing used, termed a Nonel tube, consists of a small-diameter tube, which is internally coated with a thin layer of explosive compound. This equates to an explosive loading of roughly 18 mg per meter of tube length. To initiate detonation, a spark is generated at the distal end of the tube. A schlieren setup was used to capture high-speed video of the tests. CFD was also used in a comparative basis. Both the high-speed video and the CFD displayed a visually uniform circular blast front from the detonation of the DTT. However, the pressure data suggested a non-uniform strength of the blast wave was prevalent. The blast front that continued along the same trajectory as the axis of the tubing displayed significantly higher pressures than aspects of the blast wave directed radially to the tube (similar to findings from long aspect-ratio charges [20]).This effect was attributed to the _\"directed momentum of the jet of combustion products along the tube axis\"_[163]. If this non-uniformity can be accounted for, then DTT may serve as a viable approach to shock generation for small-scale blast experiments.
# An Operational Image-Based Digital Twin for Large-Scale Structures
## 4 3D Geometry Reconstruction
The comparison between the CAD model and the reconstructed model, based on LiDAR scans, is shown in Figure 9. Figure 9a shows, again, the differences in distances between the CAD model and the reconstructed 3D model with the use of colors superimposed on the CAD model. The probabilities for the distance differences are shown in Figure 9b, together with the corresponding color distribution. The distance differences have values from \(-2.8\) to \(4.1\) m. The histogram has a relatively low standard deviation of \(0.57\) m and is centered on a mean value that is very close to zero. Therefore, the error associated with the reconstructed model, compared to the CAD model, was, in general, relatively low. Some parts of the ladders on the platform were not reconstructed, due to the LiDAR beam blockade. This, together with the small holes in the reconstruction, increased the width of the histogram. The quality of the model reconstruction based on RGB images is better than the reconstruction based on LiDAR scans, which is evident from a comparison of the histograms in Figures 7b and 9b. The ladders on the platform were better captured in the image-based reconstruction, because the platform itself blocks for the LiDAR beams. Accordingly, placing the LiDAR  
Figure 8: The reconstructed 3D model based on six static LiDAR scans. The ladders on the platform are not perfectly captured, due toa blockade of the scanning beams from the platform itself. A LiDAR scanner aboard a drone should not have this problem. Information that possibly leads to the identification of the TP owner is removed from the images.
# Guided Spatial Transformers for Facial Expression Recognition
## Appendix C Analysis of Errors
### Errors Due to Facial Detector  
Both AffectNet and FER-2013, are two datasets commonly used in the literature for Facial Emotion Recognition. However, both contain some challenging images that may affect the recognition rate.  
One of the first tests to detect complex images is to understand the outputs of the facial detector. In our case, the MTCNN detector fails in 1.6% of the images for AffectNet and 13.57% for the FER-2013 dataset. Some samples of the misdetected faces appear in Figures 15 and 16. We have arranged the type of errors into 4 groups that may cause the miss-detections: the presence of occlusions, extreme poses or rotations, poor illumination conditions, and too much zoom. On the row of 'Others', we have included samples in which there are no faces, there are cartoons or the faces contain some peculiarities as tattoos. This last group is a minority but it is still important to understand the difficulties of the task and why the facial detector fails.  
Some of these misdetections may cause errors in the emotion recognizer too. Analyzing the matches between the errors of the 'STN with landmarks--soft masks' strategy and the misdetected faces by MTCNN, we see that in AffectNet from the 1.6% of the lost faces, the 42.10% are incorrectly classified by the model. For FER-2013, this error rate represents the 26.14% of the 13.57% miss-detections.  
To decrease the errors due to wrong images on the datasets, we could try to detect and remove the images without faces, as some examples under the 'Other' row. Detecting them and training the models without them probably would improve our results since these images introduce noise during the learning.  
For the occlusions, illumination, etc. we could test other facial detectors trained on these conditions or create a new model to learn emotions on these conditions. For this case, we should use another dataset with more images that represent these situations.  
Figure 15: Samples of miss-detected faces on AffectNet by the STN with landmarks—soft masks model.