esa-sceva/satcom-chunk-collection huggingface dataset:
# Post Quantum Cryptographic Keys Generated with Physical Unclonable Functions
## 5 Experimental Evaluation
### Evaluation of the Effective Key Throughput  
In this section, we evaluate the effective (peak) key throughput. Figure 10 plots the median of each RBC cryptosystem's effective key throughput on a logarithmic scale. The AES256 implementation, aided by AES-NI, runs several orders of magnitude more efficiently than the public key cryptography variants at 2.17 \(\times\) 10\({}^{8}\) keys per second. ECC Secp256r1 performed the second slowest at 4.77 \(\times\) 10\({}^{4}\) keys per second. The post-quantum algorithms largely performed better than ECC with 1.97 \(\times\) 10\({}^{5}\) and 6.83 \(\times\) 10\({}^{5}\) keys per second for CRYSTALS-Dilithium 2 and LightSABER respectively. qTESLA-p-I was the worst performing PQC and overall cryptosystem out of all five at 2.24 \(\times\) 10\({}^{4}\) keys per second.  
To get a better sense of the relativistic scaling, we set ECC Secp256r1's effective key throughput results as the reference point since we are interested in how the PQC algorithms perform when replacing it in future PKI cryptosystems. This is plotted in Figure 11, where now the response variable is displayed in a percentage of the throughout relative to ECC Secp256r1's. Shown here, AES256 is roughly 4550 times more performant than ECC Secp256r1. CRYSTALS-Dilithium 2 is over 4.14 times more efficient than ECC Secp256r1. The most efficient PQC was LightSABER at 14.3 times faster, and the worst overall cryptosystem was qTESLA-p-I at 0.469 times slower.  
From these results, we confirm NIST's position that qTESLA is slower than the algorithms selected in round three. Out of what was tested, this leaves CRYSTALS-Dilithium as the strongest candidate for DSA in a PQC environment. For key encapsulation, our results show that SABER is a strong candidate for its relatively fast key generation. Future testing might consider comparing FALCON against CRYSTALS-Dilithium for DSA, and CRYSTALS-Kyber, NTRU, and Classic McEliece against SABER for KEM.
# Logistics Distribution Vehicle Routing Problem with Time Window under Pallet 3D Loading Constraint
## 5 Example Analysis
### Results and Analysis
#### 5.2.2 Carriage Loading Results  
Figure 10 shows the layout diagram of the pallet in some carriages obtained by the hybrid simulated annealing-genetic algorithm. In this case, each rectangular body is considered a loaded pallet, the number shown is the number of the pallet unloading node, and the left side of the carriage is the direction of the carriage door. The unloading time is different for each node because of the different number of pallets. For example, the distribution route of the first car in the figure is 0-103-6-4-5-26-0. The first delivery is at customer point 15, and it takes 0.083 h to unload one pallet; the second node is at customer point 6, and it takes 0.167 h to unload two pallets; and the third node is 4, and the unloading time is also 0.167 h. Therefore, the unloading time should be calculated according to different conditions for each unloading customer point, and the impact on the service time window should be obtained by combining the distance consumption and docking time.
# A Color Matching Method for Mosaic HY-1 Satellite Images in Antarctica
## 3 Results
### Multi-Image Color Match Results
Figure 7d shows that the distribution value of the brighter region (glacier) in the Wallis result is lower than that in the reference image, so the glacier in the Wallis result is darker than that in the reference image. Figure 7: Histogram comparison before and after color matching: (**a**) reference image histogram, (**b**) original mosaic image histogram, (**c**) histogram of the general histogram specification results, (**d**) Wallis results histogram, and (**e**) histogram of the paper results. Table 4 analyzes the time efficiency of the different methods. The calculation time of the proposed method is comparable to that of the traditional histogram matching method, and the Wallis method has the highest processing efficiency, which may be due to the difference in computing efficiency caused by the Wallis method based on matrix calculation, while the other two methods are based on the mapping relationship of pixel values.
###### Abstract  
Task-oriented dialogue systems (DS) are designed to help users perform daily activities using natural language. Task-oriented DS for English language have demonstrated promising performance outcomes; however, developing such systems to support Arabic remains a challenge. This challenge is mainly due to the lack of Arabic dialogue datasets. This study introduces the first Arabic end-to-end generative model for task-oriented DS (AraConv), which uses the multi-lingual transformer model mT5 with different settings. We also present an Arabic dialogue dataset (Arabic-TOD) and used it to train and test the proposed AraConv model. The results obtained are reasonable compared to those reported in the studies of English and Chinese using the same mono-lingual settings. To avoid problems associated with a small training dataset and to improve the AraConv model's results, we suggest joint-training, in which the model is jointly trained on Arabic dialogue data and data from one or two high-resource languages such as English and Chinese. The findings indicate the AraConv model performed better in the joint-training setting than in the mono-lingual setting. The results obtained from AraConv on the Arabic dialogue dataset provide a baseline for other researchers to build robust end-to-end Arabic task-oriented DS that can engage with complex scenarios.  
task-oriented dialogue systems; Arabic; multi-lingual transformer model; mT5; natural language processing +
Footnote †: journal: Applied  
0000-000-0000-0000-0000-0000-00000-0000-0000-0000-0000-00000-0000-00000  
RQ1: To what extent can mT5, a multi-lingual pre-trained language model, produce satisfactory results for Arabic end-to-end task-oriented DS? RQ2: To what extent can joint-training the mT5 model on Arabic dialogue data and data for one or two high-resource languages (namely, English or English and Chinese) improve the performance of Arabic task-oriented DS?  
To answer these research questions, we conducted several experiments, leading this work to make the following contributions:  
* Development of the first Arabic task-oriented dialogue dataset (Arabic-TOD) with 1500 dialogues. By translating the English BiToD dataset [1], we produced a valuable benchmark for further exploring Arabic task-oriented DS. Furthermore, Arabic-TOD is the first code-switching dialogue dataset for Arabic task-oriented DS.
* Introduction of the first Arabic end-to-end generative model, the AraConv model, short for Arabic Conversation, that achieves both DST and response generation tasks together in an end-to-end setting.  
The paper comprises five sections. The next section explores related works in the area of task-oriented DS for both English and Arabic. The third section demonstrates the methodology used in this research, including the data collection process and the model architecture. Next, we detail our experiments, discussing the tasks and evaluation metrics, experimental setup, and findings. Finally, the fifth section summarizes our work and the significance of the AraConv before considering possible future research directions.
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 4 The Sparse Time-Series Clustering Framework
### Time Complexity  
The overall time complexity of our Sparse Time-Series Clustering framework is \(\Theta(NT\log^{2}T+INk\log^{2}T)\), where the first term corresponds to the time complexity of the Sparse Gaussian Process Regression with \(m=\Theta(\log T)\) inducing points and the second term corresponds to the running time of \(k\)-means with the \(\beta\)-DTW distance when applied to \(N\) sparse time series with \(m=\Theta(\log T)\) inducing points each. As expected, the running time of our framework crucially depends on the number \(m\) of inducing points.  
In our experimental evaluation, we compute a baseline clustering by applying Algorithm 3. The overall time complexity for computing a baseline clustering with \(k\)-means on the original dataset with \(N\) time series of \(T\) data points each is \(\Theta(INkT^{2})\).  
Therefore, the asymptotic running time of our Sparse Time-Series Clustering framework is about \(\Theta(\max\{T^{2}/\log^{2}T,\,lkT/\log^{2}T\})\) times faster than the asymptotic running time of directly applying \(k\)-means to the original dataset. Intuitively, the asymptotic running time of our framework is \(\Omega(T^{2}/\log^{2}T)\) times faster than the standard \(k\)-means because the improved running time for computing DTW from \(\Theta(T^{2})\) in the original data to \(\Theta(\log^{2}T)\) in the sparsified data.
## 6 Analysis of Experimental Results
### Detection Performance Comparison
Comparison [PERSON]'s Mytrhil [8], [PERSON]'s Oyente [9], Smart Dec's Smart Check [3], and [PERSON]'s Slither [4] use formulas to calculate the accuracy of each method as shown in Table 3:  
As shown in Table 3, the table shows the vulnerability detection results of the scheme, Mythril method, Oyente method, Smart Check method and Slither method are proposed in this paper. The experimental results show that the improved Mythril method in this paper has a better effect on detecting smart contract vulnerabilities and a higher accuracy \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Detection Tool**} & \multicolumn{2}{c}{**Total Number of**} & \multirow{2}{*}{**TP**} & \multirow{2}{*}{**FP**} & \multirow{2}{*}{**FN**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**F1\(-\)Score**} \\  & & & & & & & \\ \hline Mythril & 236 & 203 & 20 & 23 & 86.02\% & 90.42\% \\ Text scheme & 236 & 222 & 16 & 4 & 94.07\% & 95.69\% \\ Oyente & 236 & 198 & 26 & 12 & 83.90\% & 91.24\% \\ Slither & 236 & 200 & 23 & 18 & 84.75\% & 90.70\% \\ Smart Check & 236 & 196 & 28 & 14 & 83.05\% & 90.34\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Compare the results with previous methods of the research community. rate. The average accuracy rate has increased from 84.5% before improvement to 94.07% after improvement. Compared with Oyente, another detection method of the symbolic execution method, the effect is significant.
## 3 Finite Element Models
### Test-Simulation Comparison  
Simulation results are compared with experiments at three points (see Figure 6): point (a) at the center of the envelope (Figure 10), point (b) on the lower part of the envelope (Figure 11) and point (c) at the center of the bottom (Figure 12). The measurements on the envelope (vertical configuration shown in Figure 3) were performed with a 100 dB SPL excitation level, while the measurements on the bottom (horizontal configuration shown in Figure 5) were performed with a 110 dB SPL excitation level. The corresponding excitation levels were used to obtain the simulation results. The qualitative comparison of the simulation results with the experiments is excellent at the three locations. Both major structural resonances and secondary acoustic resonances are well correlated with the experimental resonances.  
The PSD levels are also coherent over the whole frequency range ([100-2200 Hz]). The RMS levels corresponding to experiments and simulations are shown in Table 1. The differences are lower than 6 dB. With such good test-simulation correlation, simulation helps to interpret the experimental resonances. Figure 13 highlights the four first resonances in the simulated response at the center of the bottom. In this frequency band [100 Hz, 600 Hz], the numerical modal analysis of the structure, without any acoustic coupling, presents a unique structural mode of the bottom at 407.8 Hz. This mode enables an explanation of the main resonance in Figure 13 at 411 Hz.  
Figure 10: Acceleration PSD at point (a) on the envelope (see Figure 6) with comparison between the experimental data (black) and simulation results (red).  
The frequency shift is due to acoustic coupling. The three other resonances in Figure 13 must be due to acoustic resonances. In order to demonstrate these resonances, a view of the internal acoustic pressure field simulated at those frequencies is presented in Table 2. The three resonances correspond to different acoustic mode shapes. The experimental results thus confirm that acoustic resonances affect the bottom response. This validates the vibroacoustic model with the internal and external air volumes modeled in a BEM subsystem.  
Figure 11: Acceleration PSD at point (b) on the envelope (see Figure 6) with comparison between the experimental data (black) and simulation results (red).  
Figure 12: Acceleration PSD at at point (c) on the bottom (see Figure 6) with comparison between the experimental data (black) and simulation results (red).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Point** & **Experiment** & **FEM–BEM Simulation** & **Difference** \\ \hline (a) & 2.19 m\(\cdot\)s\({}^{2}\) & 1.12 m\(\cdot\)s\({}^{2}\) & \(-\)5.8 dB \\ (b) & 0.96 m\(\cdot\)s\({}^{2}\) & 0.63 m\(\cdot\)s\({}^{2}\) & \(-\)3.6 dB \\ (c) & 6.82 m\(\cdot\)s\({}^{2}\) & 9.06 m\(\cdot\)s\({}^{2}\) & +2.5 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test-simulation comparison of the overall RMS level (in the [100–2200 Hz] range) for the three locations studied.  
Figure 13: Zoom of Figure 12 below 600 Hz.
# An Improved Image Filtering Algorithm for Mixed Noise
## 2 The Filtering Algorithm for Mixed Noise with Salt and Pepper + Gaussian Noise
### The Improved Median Filtering Algorithm
#### 2.1.3 The Selection for Window Size  
The selection of filtering window size will affect the filtering effect. A large window has a strong filtering ability but a weak detail preservation ability; a small window can retain many details of the image, but its filtering performance is bad. Selecting the filter window adaptively according to the size of the noise density can alleviate the contradiction between filter performance and detail preservation, but it also increases the time complexity of the algorithm.  
If the median value in the 3 \(\times\) 3 neighborhood window of a noise point is still noise, the filtering fails. If the neighborhood window is enlarged to 5 \(\times\) 5 and the median value in the window is no longer noise, the filtering is successful after replacing the noise point with the median value. In fact, if a noise point cannot be successfully filtered using a 3 \(\times\) 3 window, it can be put aside and used to process those noise points that can be successfully filtered using a 3 \(\times\) 3 window. Once all noise points that can be filtered by a 3 \(\times\) 3 window have been replaced with their corresponding median values, those noise points can no longer be processed before being filtered. Therefore, the filtering results of these two methods are the same. In our proposed algorithm, the size of the filtering window is selected as 3 \(\times\) 3.
# Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles
## 9 Multidimensional Evaluation
### Average Latency for Delivered Messages
#### 9.2.1 Black Hole Attack  
For the range of results collected, MaxProp consistently took longer to deliver the messages successfully. There was a large difference in speed between the all the algorithms, however it was important to recognise that even though Spray and Wait delivered the messages faster, far fewer messages were received at all as demonstrated in the graphs showing the delivery probabilities.  
Figure 8: The average latency across multiple experiment settings.  
The gradient of the lines suggested that should more and more black holes be introduced, eventually Epidemic, MaxProp and PRoPHET would perform better than Spray and Wait. It was far less affected by black holes. If plotted as a linear trend line, the gradients allowed us to see that Spray and Wait was approximately four times as vulnerable to black holes in terms of latency than Maxprop. Based on the trend lines and by solving the equation: 12.5x + 2671.8 = 3.1x + 4933.9, it can be shown that the crossing point of the algorithms was at the x coordinate of 240.6. This was not possible to extrapolate due to the fact that as the proportion of black holes reaches 100%, no data would be sent through the network so no messages would be delivered.  
The difference in delivery speed was not entirely surprising as the spray phase of the basic algorithms meant that messages were initially spread very quickly throughout a network even if not very efficiently. As MaxProp and PRoPHET took a more calculated approach, they took longer. Current results showed that Maxprop offered a more reliable delivery probability and so was more widely considered as the better performing algorithm.
# Autonomic Network Management and Cross-Layer Optimization in Software Defined Radio Environments
## 2 State of the Art and Background Information
### Architectures Combining ANM with SDN/NFV  
A number of approaches exist in the current literature, as efforts to combine ANM with SDN [2; 6; 26], and the most recent of them also with NFV [25]. However, none of them focuses on D2D communications in CRN environments, as proposed by our approach, combining SDR with ANM-SDN-NFV concepts. A scheme that combined SDN and ANM, which also encompassed some form of virtualization, was proposed in [6], introducing self-attributes into OpenFlow-based SDN [27].  
PolicyCop [26] presented an autonomic QoS policy-enforcement framework based on SDN. It provided an interface for specifying QoS policies, which exploited a SDN controller to enforce them. PolicyCop framework was organized in three planes: data plane, control plane and management plane, which monitors the network to detect policy violations and adapts control plane rules based on network conditions and high-level policies, implementing a feedback loop.  
AUTOFLOW [2] presented an experimentation framework that combined ANM and SDN components, using the GEANT OpenFlow facility. The framework contained three ANM core blocks, including governance, coordination and knowledge, implementing ACLs, including traffic engineering and load (congestion) prediction. The ANM core components were implemented as SDN applications, whereas the complete control of the programmable network was performed by a centralized controller.  
SELFNET [25] proposed an architecture combining ANM, SDN and NFV. SELFNET aimed to enable the automatic deployment of NFV applications in the network infrastructure to facilitate distributed monitoring of the network. To be aligned with the SDN principles, the control plane and the data plane functionalities were separated into different logical elements, implementing a group of horizontally and vertically distributed SDN controllers. SELFNET presented a six-layer architecture, including an orchestration sub-layer responsible for orchestrating the virtual resources and network functions, while an autonomic layer was positioned on top of the other layers.
## 5 Discussion
### The Relationship between Landslide and Influencing Factors  
For the landslide in the TGRA, most of their movements were influenced by the fluctuation of the reservoir water level [38]. Seen from the InSAR results of the Muyubao landslide (Figure 17, March 2016-September 2017), the deformation mainly occurred during the fluctuation period and high-water level period of the reservoir (\(>\)170 m). From March to June 2016, when the reservoir water level dropped from 167 m to 145 m, the landslide deformed by about 70 mm. The relationship between the stability of the Muyubao landslide and the reservoir water level during this monitoring period was similar to that of many other reservoir landslides [15; 39; 40]. Such as the Lorenzo-1 and Rules Viaduct landslides of the Rules Reservoir in Southern Spain [15], the InSAR data shows that the three acceleration periods of both landslides are related to drawdown periods of the water level. Many similar cases are also studied in the TGRA [17; 41]. When the reservoir level drops rapidly, the pore-water pressure within the landslide begins to dissipate, and the dissipation speed lags greatly behind the reservoir drawdown speed. This process will induce hydrodynamic pressures. High hydrodynamic pressure is no more balanced by water lateral confining in the basin, which increases the sliding force of the landslide, thus reducing its stability. From November 2016 to February 2017, when the reservoir was at a water level of higher than 170 m, the Muyubao landslide deformed by 25 mm. Therefore, it was still deforming during the period of high water level. The high water level increased the height of the groundwater level inside the reservoir landslide, which changed the saturation state of the soil and reduced the shear strength. At the same time, the uplift force formed by the reservoir water in the submerged sliding mass reduced the sliding resistance force of the landslide, thereby reducing the stability of the landslide. We also analyzed the relationship between the rainfall and acceleration of movement within the Muyubao landslide. It showed a deceleration in movement related to the rainfall peaks. Due to the large thickness and scale of the Muyubao landslide, it is difficult for rainfall to seep into the slip surface to promote landslide movement. The decorrelation between reservoir landslides and rainfall are also shown in the Lorenzo-1 and Rules Viaduct landslides and other landslides in the TGRA [42]. Based on the long-term GPS monitoring data of the Muyubao landslide, [PERSON] et al. [19] and [PERSON] et al. [20] analyzed the relationship between the landslide deformation and its inducing factors (reservoir water level and rainfall). These papers showed how water level variation and rainfall have influences on landslide displacement patterns, which is consistent with the conclusion of this paper. Moreover, it indicates that the application of InSAR data is reliable for analyzing the deformation mechanism of landslides.  
Figure 17: Displacement, rainfall and reservoir levels of the Muyubao landslide.