esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Discussions
### The Influence of Particle Size Distribution
Figure 24: The subsurface particle displacements at 5000 simulation steps. Figure 25: The subsurface particle displacements at 400,000 simulation steps. to 400,000 steps, the density of force chains in the continuous gradation soil significantly decreases compared to the initial stage. This loosening becomes more pronounced, indicating that particle loss initiates from the central region at the lower part, causing the upper soil layers to lose strength. For discontinuous gradation particles, as shown in Figure 27. Force chain density decreases, and values reduce above the leakage point due to particle loss. Nevertheless, the overall skeleton force chain network remains intact, and though force chain strength is lower than at the beginning of the simulations, it is still noticeably higher compared to continuous gradation particles. Force chains align with the direction of applied pressure, and stronger force chains lead to less particle sliding, as they collectively deform to resist external forces. The size and gradation of soil particles significantly influence their movement towards a leakage pipe under high groundwater levels. Several factors contribute to this phenomenon:  
(1) Erosion differences: Finer soil particles, due to their smaller radius, are more susceptible to erosion in saturated conditions, leading to more pronounced movement in water. Consequently, finer soil particles are washed into the pipe more frequently. (2) Percolation pathways: Soils with different particle gradations form distinct percolation pathways. Coarser particles create larger pores, facilitating easier water flow. The skeleton effect between larger particles provides greater resistance to deformation, although smaller particles may be lost through these larger pores. On the other hand, soils with finer particles create numerous small percolation channels. As smaller particles erode more easily, these channels gradually widen, accelerating the erosion process. (3) Chain reaction in continuous gradation: Soils with continuous gradation have particle sizes distributed continuously. Smaller particles fill the voids between larger particles, leading to interlocking and embedding effects. However, when saturated and subjected to hydraulic forces, erosion of smaller particles weakens the interlocking effect between medium-sized and larger particles, making it more susceptible to erosion and failure.
# Impact of Die Radius in a Streamlined Die during Wire Drawing
## 3 Validation of Numerical Model  
Prior to analyzing the results of the numerical simulation, the accuracy of the numerical model used in this study was validated by comparing the numerically simulated results with the measured ones in the TWIP steel wire during the conventional wire drawing process.  
Figure 5a compares the drawing force of the TWIP steel wire based on experiments and numerical simulations. The drawing force obtained by the numerical simulation is in good agreement with the experimental value. Figure 5b compares the measured and numerically simulated center temperatures of the wire. As expected, the center temperature increased along the drawing direction. The center temperature obtained by numerical simulation was also in good agreement with the experimental steady-state center temperature. However, it was found that the measured center temperature was slightly lower than the simulated temperature. This inconsistency is related to the applied \(m\) and \(\beta\) values used in this study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Thermal Conductivity** & **Heat Capacity** \\ \hline TWIP steel & 9.84 [35] & 3.6 [32] \\ Die tip & 70 [34] & 3.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermal properties of wire and die used in this simulation.  
Figure 4: True stress–strain curve of the hot-rolled TWIP steel.  
Figure 3: Schematic explanation of die geometry used in this study.  
Meanwhile, the present results also need to be confirmed by some experimental data with streamline die because the validation of the model was only conducted using the conventional die. However, it is known that if the model is verified under one simple condition, it can be used in other similar situations. In this respect, the strain, stress, temperature, and drawing force of the wire drawn with streamlined dies can be predicted using this model because the drawing condition using the conventional die belongs to one of the streamlined die conditions. Overall, from an engineering application point of view, it can be concluded that the proposed FE analysis for the wire drawing process is suitable for evaluating the characteristics of strain, stress, temperature, damage value, and drawing force with the die shape.
###### Abstract  
Dual-polarization division multiplexing (DPDM) is considered to be a potential method to boost the secure key rate (SKR) of the continuous-variable quantum key distribution (CV-QKD) system. In this article, we propose a pilot alternately assisted local local oscillator (LLO) CV-QKD scheme based on multi-dimensional multiplexing, where time division multiplexing and frequency division multiplexing are combined with dual-polarization multiplexing techniques to dramatically isolate the quantum signal from the pilot tone. We establish a general excess noise model for the LLO CV-QKD system to analyze the influence mechanism of various disturbances (e.g., time-domain diffusion, frequency-domain modulation residual, and polarization perturbation) on the key parameters, such as the channel transmittance and excess noise. Specifically, the photon leakage noise from the reference path to the quantum path and that between quantum signals with two different polarization paths are simultaneously analyzed in the dual-polarization LLO CV-QKD scheme for the first time. Furthermore, a series of simulations are established to verify the performance of the proposed scheme. The results show that the maximal isolation degree achieves 84.0 dB-90.4 dB, and the crosstalk between pilot tones and quantum signals can be suppressed to a very small range. By optimizing the system parameters (e.g., modulation variance and repetition frequency), the SKR with 12.801 Mbps@25 km is achieved under the infinite polarization extinction ratio (PER) and 30 dB residual ratio of the frequency modulation in the nanosecond-level pulse width. Moreover, the performance of the proposed DPDM CV-QKD scheme under relatively harsh conditions is simulated; the results show that the SKR with 1.02 Mbps@25 km is achieved under a relatively low PER of 17 dB with the nanosecond-level pulse width and 20 dB residual ratio of the frequency modulation. Our work lays an important theoretical foundation for the practical DPDM LLO CV-QKD system.  
local local oscillator; continuous variable; quantum key distribution; dual-polarization division multiplexing +
Footnote †: journal: Applied  
Article  
A Novel Continuous-Variable Quantum Key Distribution Scheme Based on Multi-Dimensional Multiplexing Technology 22 January 2024
# Targeted Universal Adversarial Examples for Remote Sensing
## 4 Experiments
### Experimental Settings and Implementation Details  
In comparison with our method, we selected several popular adversarial attacks: FGSM [9], I-FGSM [28], C&W [23], and PGD [11] to conduct the targeted adversarial attacks for scene classifications on UCM, AID, and EuroSAT, respectively. For every dataset, we evaluated the above attacks on several different well-trained victim models with various architectures: AlexNet [29], ResNet18 [30], DenseNet121 [31], and RegNetX-400 MF [32]. Following the setting in [21], we set the number of total iterations to 5 for iterative attacks, such as I-FGSM, C&W, and PGD. The magnitude of perturbations was set to \(l_{\infty}(e)=15\) for images in the range [0, 255]. The implementations of FGSM, I-FGSM, C&W, and PGD were from [33] ([[https://github.com/Harry24k/adversarial-attacks-pytorch](https://github.com/Harry24k/adversarial-attacks-pytorch)]([https://github.com/Harry24k/adversarial-attacks-pytorch](https://github.com/Harry24k/adversarial-attacks-pytorch)) (accessed on 25 September 2022)).  
For our two universal attacks in the following experiments, the number of iterations was set to \(I=500\). We used statistical gradient descent (SGD) with momentum to optimize the perturbations. The target classes and source classes were randomly sampled.  
All our experiments were implemented by PyTorch [34] and ran on a single NVIDIA GPU A100.
## 1 Introduction
In turn, DRL accomplishes the point-to-point task between each pair of nodes so that agents can obtain a smoother path [29]. Sampling path planning methods does not need a reward function in complex environment and has a higher success rate than does the DRL, it can be used to provide a successful experience reference for DRL, and DRL can use these experiences to exert exploration ability and finally complete the learning of the path planning strategy. [PERSON] [30] combined TD3 with PRM, this can decompose the path into multiple local paths, improve development efficiency. [PERSON] [21] proposed traditional path planners with DRL to obtain the path in Cartesian space. [PERSON] [31] decomposed complex problems into multiple subproblems and explored maze paths using dynamic programming. [PERSON] et al. [32] regarded PRM and RRT as global path planning methods, respectively, and searched for intermediate path points of DRL in indoor navigation. The waypoint selection of some fusion methods is influenced by traditional path planning. To address the above issues, the contributions are outlined as follows:  
* In order to improve the ability of DRL algorithm to balance exploration and development, an improved TD3 algorithm was designed and evaluated. * Aiming at the problems existing in robot path planning, a path planning method is proposed that combines the exploration abilities of sampling-based RRT* and the experience replay mechanism of DRL algorithm. * The simulation environment of path optimization based on DRL-RRT* is built. The remaining sections of this paper are organized as follows: Section 2 presents the implementation principle of the proposed CDTD3 algorithm. Section 3 provides a detailed description of the DRL-RRT* path planning algorithm. Section 4 reports the path planning verification in a simulation environment. Section 5 outlines the experimental verification of the manipulator path planning using CDTD3-RRT*. Section 6 concludes the paper.
# Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm
## 3 Results
### Results of the Classification after SMOTE  
The AUC scenarios were also compared: one without any resampling technique and one with a resampling strategy applied. However, after the SMOTE resampling strategy, pruning DT due to overfitting, and setting the k value for k-NN, NB had an improved AUC of about 0.86%. AdaBoost remained unchanged, while a decrease was noted in the two-layer ensemble, DT, and k-NN, as shown in Figure 10.  
Figure 10: Comparison of the Area Under the Curve (ROC) for the models after SMOTE.  
Figure 9: Comparison of the Area Under the Curve (ROC) for the models before SMOTE.  
Overall, among all the base models, the recall value was improved in AdaBoost and the proposed two-layer ensemble when applying SMOTE, while a decrease was noted in DT, k-NN, and NB, as shown in Table 4. The precision of the model after applying SMOTE was reduced on DT, k-NN, NB, AdaBoost, and the two-layer ensemble model. Based on the F1 score, a noticeable increase was noted in the two-layer ensemble model and AdaBoost, while the same was reduced in NB, DT, and k-NN. NB and AdaBoost achieved the highest accuracies of 81% and 79%, respectively, followed by DT at 77%, while k-NN achieved the lowest accuracy of 72% among the base models after SMOTE, as shown in Table 4. Looking at overall accuracy performance, the two-layer ensemble model achieved 85% accuracy.
# A Methodology to Evaluate Standards and Platforms within Cyber Threat Intelligence
## 4 Standards Evaluation Results
### Standards Selected for Analysis
#### 4.1.5 Open Indicator of Compromise  
OpenIOC is a framework that offers a standardized, structured and machine-readable format used to record, define and share information encompassed in the context of cyber attacks and incidents [54]. This format is written in XML, with a relatively light and small design. Its architecture is composed of more than 500 specific types of data incorporated to represent indicators.
## 2 Related Work  
### Classification of PUFs  
Recent works have defined various classifications for PUFs that are based on how a PUF's intrinsic variations are exploited for the response bit generation. The most commonly used classes divide into bi-stable or delay-based PUFs [14; 15; 16; 17; 18; 19; 20; 21]. Other works also target the class of analog-based PUF designs [22; 23; 24; 25; 26]. A further classification relies on the complexity of the challenge-response mechanism, more particularly on the number of challenge-response pairs (CRPs). PUFs that exhibit a small CRP space are called weak PUFs, often used in a controlled PUF configuration to extract secret keys. PUFs providing a large CRP space are referred to as strong PUFs.
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 2 Cooperative HSTN
### Coordinated HSTN
For increased bandwidth demands, 5G backhaul networks operate in millimeter wave frequencies. In such a scenario, rain attenuation becomes a significant fading mechanism. The authors of [51] evaluated the end-to-end performance of an AF-based cooperative-diversity HSTN for backhauling applications operating at millimeter frequencies. An elegant stochastic channel model was employed in order to generate a rain attenuation time-series spatially correlated for the multiple links of the system. However, cooperative strategies in the relay and various reception techniques were not discussed. In [52], the exact symbol-error probability and diversity analysis of a hybrid satellite-terrestrial cooperative network was investigated, focusing on the mobile relays over independent but not identically distributed fading channels. Mobile relays can be both moving vehicles and handheld cell phones. A selective DF scheme was implemented to avoid relaying of erroneous signals to the users. A moment-generating function (MGF) approach was used get the closed-form expressions for SEP. The authors also provided evidence of an achievable diversity order of \(N+1\) for \(N\) number of relays. The work in [53] presented the performance analysis for a selective DF cooperative HSTN over time-selective fading links with multiple-input-multiple-output (MIMO) and space-time block coding for multiple relays. The satellite-relay links experienced non-identical time-selective shadowed Rician fading, and the relay-destination terrestrial links were assumed to be non-identical time-selective generalized Nakagami faded. Closed-form expressions were derived for the per-frame ASER and the asymptotic SER floor. A list of major contributions to HSTN coordinated networks is provided in Table 3.
# A Double Rate Localization Algorithm with One Anchor for Multi-Hop Underwater Acoustic Networks
## 4 Performance Analysis and Simulation Results
### The Localization Performance
#### 4.2.4 Test4: Communication Cost  
In this test, we compare the communication cost during the localization procedure between the proposed DRL algorithm and other localization approaches.  
Communication cost is the transmission data size during the localization procedure which is measured in bits. Nodes in UANs have limited power and communication cost is one of the important part of power consumption in each node. Therefore, communication cost is an essential index of the localization algorithm for UANs.  
The sending data packet usually has two parts, a packet-header and packet-body. In the DRL algorithm, the packet-header involves the transmission modes, time stamps, node's ID, etc., and the packet-body is the transmission data.  
We design the packet-header as in Figure 11.  
M nodes are randomly deployed in the network.  
LM/HM is the transmission mode, 1 bit;  
ID\({}_{s}\) is the ID of sending node, \(\log_{2}M\) bits;  
ID\({}_{r}\) is the ID of receiving node, \(\log_{2}M\) bits;  
Time is the time stamp, 2 bytes;  
Type is the type of sending data. There are two types of data in DRL, 2 bits;  
1. \"Hello\" packet: The \"Hello\" packet triggers the localization procedure and help nodes to measure the distance between each other.
2. Range information packet: Range information packet includes all the distance information of nodes in the network. Length is the length of the packet body in the sending data. The length of the packet body is analyzed below. As described above, there are two types of data in DRL and each of them has different length.
2. \"Hello\" packet: Because \"Hello\" packet only triers the localization procedure and has no data to transmit, the packet length of \"Hello\" packet is 0;
3. Range information packet: Range information packet includes all the distance information of any node between each other, thus the packet length is M byte; \[Communication\ Cost=(3M-2)log_{2}M+39M-20\ (bits)\] (12) Increase the number of nodes from 10 to 30. In Figure 12, we show the communication cost of different algorithms.  
The result shows that the communication cost of all algorithms increases as the number of nodes grows. However, from Figure 12 we can see that the proposed DRL algorithm has a lower communication cost than the other three approaches, especially the MDS-MAP algorithm, which means that DRL can perform better in the low band-width underwater environment.  
Figure 11: The packet-header.
# Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review
## 4 Results and Discussions
### Classification Algorithms
#### 4.3.3 Classification Accuracy
DT and SMA methods are characterized by a similar median classification accuracy of 85.8% and 86.4%, respectively (Figure 9b). Figure 9: Overall accuracies reported in RS-based BA classification studies based on: (**a**) the year of publication, (**b**) the classification method, (**c**) the satellite sensor, and (**d**) the reference data. Figure 9c with the box and whisker plots shows the median overall accuracies of nine different sensors. It is observed that, except for RADARSAT images, the median classification accuracy is generally above 85%, with a range from 76.8% to 98.8%. IKONOS demonstrates the highest accuracy, at 95.9% median overall accuracy of BA classification, followed by Landsat TM (92.5%), SPOT (91.2%), MODIS (90.9%), and Landsat ETM+ (90.2%). Figure 9d shows that among the reference data used for validation, the median overall classification accuracy in BA studies is highest for the ground truth data and combined data, at about 90.5%. A similar median classification accuracy of 88% is also observed for ancillary data and high resolution images. The advances in RS-based forest BA mapping methods described in the reviewed papers also leads to a great variety of overall classification accuracies. In the case of approaches using input from several factors (e.g., combination of sensors, ancillary and ground truth data), this might have the potential to improve the accuracy and interpretation of classification results. In turn, the accurate maps of BA might provide a more reliable interpretation of forest structure relative to the modelling of post-fire patterns and recovery processes.
## 3 CGR-NER Architecture
### Semantic Encoding Layer  
BiLSTM is suitable for modeling sequence problems. It not only effectively solves the problem of gradient disappearance and explosion when traditional recurrent neural networks deal with long-distance dependency information, but also captures more dependency features from the forward and backward direction of the sequence and outputs hidden feature vectors with both forward and backward sequence dependency information. CGR-NER uses BiLSTM to encode the glyph feature sequence and fusion representation sequence and extract their contextual semantics and global dependency features.  
For the fusion representation sequence \(U_{fusion}=\{u_{1},u_{2},\ldots,u_{n}\}\) output from the fusion layer, the process of BiLSTM to calculate the corresponding hidden state of each representation is as follows:  
\[\overrightarrow{h_{i}}=\overrightarrow{LSTM}(u_{i},\overrightarrow{h_{i-1}}) \tag{17}\]  
\[\overleftarrow{h_{i}}=\overleftarrow{LSTM}(u_{i},\overleftarrow{h_{i-1}}) \tag{18}\]  
\[h_{i}=[\overrightarrow{h_{i}}:\overleftarrow{h_{i}}] \tag{19}\]  
where \(\overrightarrow{h_{i}}\) and \(\overleftarrow{h_{i}}\) denote the hidden state of the forward and backward for \(u_{i}\). The final output hidden state sequence is \(H=\{h_{1},h_{2},\ldots,h_{n}\}\).  
Figure 3: The architecture of the fusion layer consists of two crossmodal Transformer encoders and a gate mechanism. Each crossmodal Transformer encoder is stacked by multiple crossmodal attention blocks.
## 5 Simulation
### CASE 1 (Observers Performance Comparison)
#### 5.1.2 Various Observers Comparison  
To demonstrate the effectiveness of the introduction of the saturation function into SMO, a simulation similar with last part is conducted under FASTESO with an applied disturbance under \(a=5\) shown in Figure 10. Meanwhile the 2 nd-order linear ESO (ESO2) and STESO are also invited for comparison. The parameters of the mentioned observers are listed. FASTESO:\(\alpha_{1}=diag(0.4,0.4,0.5)\); \(\alpha_{2}=diag(0.7,0.7,0.9)\); \(e=0.0001\); \(k_{e}=0.05\). ESO2:\(k_{1}=diag(4.5,4.5,6.5)\); \(k_{2}=diag(22,22,28)\); \(k_{3}=diag(5,5,8)\); STESO:\(\alpha_{1}=diag(0.2,0.2,0.3)\); \(\alpha_{2}=diag(0.3,0.3,0.4)\); BAC: \(K_{b1}=diag(5,5,8)\); \(K_{b2}=diag(3,3,4)\).  
From the response results in Figure 10, we could find that FASTESO has a perfect performance on estimation even at the special point with a sudden change. Although the curve under ESO2 could usually follow the actual value, it performs an overestimation near the peak. As for STESO, it could follow the fast variation of disturbance; however, it has a terrible chattering problem which is also the main reason for the introduction of saturation function in this work. To summarize, the FASTESO performs well at all range of disturbance (\(a=5,10,20\)). Meanwhile, it follows better than other traditional observers under varied disturbance.