esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Validation of the Swarm Control Algorithm  
The effectiveness of the algorithm was verified by comparing it with the swarm driving data of two small USVs in the real sea. The USV utilized for verification purposes is depicted in Figure 11.  
The verification was conducted in two ways.  
(1) A mathematical model of the USV swarm was designed, incorporating behavioral control rules for the swarm and analyzing the resemblance between the actual driving path of the USV swarm at sea and the path generated by the simulator, and the accuracy of the model was subsequently verified.  
(2) To validate the effectiveness of the designed ocean current learning algorithm and the ANN-PID control algorithm, the position error was determined by comparing the movement path of the USV swarm using the disturbance learning algorithm with the results obtained by applying the ANN-PID control algorithm to the simulator.  
Figure 12 shows the swarm control movement path of two small USVs following the leading-following formation technique in the actual sea.  
Figure 10: ANN-PID control algorithm using learning ocean current model.  
The position, movement speed, and direction of each USV were used in a simulation vessel design based on driving data during real sea operation, where the data are presented in Table 3.  
Figure 11: Structure of leader and follower USV.  
Figure 12: Movement trajectory in the actual sea.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Index** & **Value** \\ \hline Moving speed & 2 knot \\ Sampling time & 1 (s) \\ Update rate & 0.5 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 3: Behavior of swarm robots as measured.
## 3 Experimental Result Analysis  
### Strength and Deformation Characteristics  
Figure 1 shows the strength and deformation characteristics. The mechanical behavior of rock samples shows significant stage evolution characteristics under different strain rates. According to the morphological characteristics of the stress-strain curve, it is divided into three typical stages: the compaction stage, the elastic stage, and the plastic stage. During the compaction stage, the primary defects inside the rock sample close, and the curve presents an upward concave shape. After entering the elastic stage, the rock sample undergoes elastic deformation, and the curve is approximately a straight line. As the strain increases to the yield strain, the rock sample enters the plastic stage, and secondary cracks initiate in the stress concentration area and show unstable propagation characteristics. When the strain reaches the peak strain, the rock sample undergoes brittle failure, the stress shows a cliff-like decline characteristic, and the residual strength approaches zero, as shown in Figure 1a. In addition, the strain rate has a significant influence on the mechanical behavior of rock samples. By comparing and analyzing the evolution laws of mechanical characteristic parameters of rock samples under different strain rates, both the peak stress and the elastic modulus show a monotonically increasing trend with the increase in the strain rate, but their growth rates show an obvious decreasing characteristic. Especially at low strain rates, the sensitivity of these two mechanical characteristic parameters to the strain rate is the most significant. For example, when the loading rate increased from 1 mm/min to 3 mm/min, the growth ratios of the peak stress and the elastic modulus were the largest, increasing by 71.1% and 147.4%, respectively. On the contrary, the peak strain shows a monotonically decreasing law with the increase in the strain rate, presenting an evolution trend opposite to the peak stress and the elastic modulus, as shown in Figure 1b. This reverse correlation among parameters reveals the influence mechanism of the strain rate on the deformation capacity and bearing performance of rocks.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Rock** & **Loading Rate** & **Ultrasonic** & **Weight** & **Height** & **Diameter** \\
**Samples** & **(mm/min)** & **Wave (km/s)** & **(g)** & **(mm)** & **(mm)** \\ \hline A1-5 & & 3.04 & 505.24 & 100.51 & 49.96 \\ A2-5 & 5 & 3.02 & 504.76 & 99.75 & 49.87 \\ A3-5 & & 2.97 & 504.18 & 100.48 & 49.97 \\ \hline A1-7 & & 3.00 & 504.23 & 99.83 & 49.94 \\ A2-7 & 7 & 3.04 & 505.58 & 100.52 & 49.86 \\ A3-7 & & 3.02 & 504.61 & 100.46 & 49.93 \\ \hline \end{tabular}
\end{table}
Table 1: Cont.
# Risk Assessment of Landfalling Tropical Cyclones in China Based on Hazard Risk Theory
###### Abstract  
As a frequent hazard, tropical cyclones have a great impact on the social and economic development of China, which is close to the origin of tropical cyclones in the western Pacific Ocean. The primary objective of this study was to construct a comprehensive risk assessment model for tropical cyclone hazards based on natural influencing factors, informing recommendations for hazard prevention and mitigation in affected regions. This research focused on tropical cyclones that made landfall in mainland China and Hainan from 1949 to 2023, utilizing hazard risk theory and classical extreme value theory. The wind speed and rainfall data during the peak cyclone periods (June to October) from 1997 to 2021 gathered from various meteorological stations, as well as altitude and vegetation cover data, were examined. Hierarchical analysis and ArcGIS spatial analysis methods were employed to study the characteristics of the spatiotemporal distribution of landfalling tropical cyclones and the comprehensive risk of tropical cyclone hazards, and the regions of China were delineated according to these methods. The results showed that, during the period from 1949 to 2023, the overall number of landfalling tropical cyclones decreased in a fluctuating manner, while the intensity of the cyclones increased. Furthermore, severe typhoons tended to occur more frequently in the summer than autumn with time, intensifying the challenge to resist short-term hazards. Moreover, the hazard-causing factors in areas affected by tropical cyclones displayed an increasing trend from north to south and from west to east. In detail, the regions sensitive to natural hazards were primarily located in the central part of Liaoning province, Tianjin, central and eastern Hebei province, Shandong province, eastern Henan province, central and northern Anhui province, Jiangsu province, and Shanghai, which are characterized by flat terrain and relatively low vegetation cover. Overall, the comprehensive risk of tropical cyclone hazards showed a geographical distribution that decreases from south to north and from east to west, with coastal cities in provinces such as Hainan, Guangdong, Guangxi, Fujian, and Zhejiang--including Haikou, Zhanjiang, Xiamen, Beihai, and Taizhou--exhibiting the highest levels of risk.  
tropical cyclone hazard assessment; comprehensive evaluation; geographic information system (GIS); risk assessment
## 3 Integrity Monitoring Method of Space-Ground Integration
### Simulation Results and Sensitivity Analysis
#### 3.4.2 Simulation of GNSS Integrity Service Performance Under LEO Constellation Enhancement
In reality, LEO satellites have significantly lower development costs, ground testing requirements, on-orbit validation processes, and production costs than GNSS satellites; thus, the fault probability of LEO satellites tends to be higher than that of GNSS satellites. Comparing Figures 13-15 indicates that in this scenario, employing LEO satellites as navigation satellites provides more integrity performance improvement than employing them as space monitoring stations. Specifically, employing LEO satellites as navigation satellites leads to a CAT-I global coverage rate of 99%, i.e., reaching near-complete global coverage for CAT-I operations. Figures 15-18 illustrate the protection level world maps under different LEO satellite fault probabilities and LEO constellation fault probabilities. The results in these figures indicate that as the fault probabilities of LEO satellites and constellations increase, both the VPL and HPL significantly rise, leading to a marked decrease in the CAT-I global coverage rate. As shown in Figure 18, when the fault probabilities of LEO satellites and constellations are relatively high, it would be better to employ LEO satellites as space monitoring stations than using them as navigation satellites. Finally, Figures 19-21 compare the global average VPL, global average HPL, and CAT-I global coverage rate across different scenarios. The results indicate that when the fault probabilities of LEO satellites and constellations are both below \(10^{-4}\), employing LEO satellites as navigation satellites provides more integrity performance improvement than using them as space monitoring stations. However, as the fault probabilities of LEO satellites and constellations further increase, employing LEO satellites as space monitoring stations produces better integrity performance. Figure 19: Global average vertical protection level comparison. Figure 18: Protection level world map with BDS-3, GPS, and LEO satellites, while LEO satellites act as navigation satellites. (LEO satellite fault probability is \(10^{-3}\); LEO constellation fault probability is \(10^{-2}\)).
## 5 Conclusions  
This paper aims to investigate the measurement of pupillary distance and height and proposes a measurement scheme based on an integrated regression tree and BlendMask algorithm. In our proposed method, we first utilize an open dataset to create labels and train a facial keypoint detection model. After obtaining the coordinates of the key points in the eye region, we calculate the position information of the pupils. Next, we create a dataset for glasses recognition, train a BlendMask detection model, identify the glasses lens, and obtain the lowest point information of the lens. Finally, we calculate the pupillary distance and height using the pupil coordinates and the lowest point coordinates of the lens. The experimental results demonstrate that our proposed method accurately calculates pupillary distance and height and is suitable for various scenarios. Using the values measured by an auto-refractometer as a standard, the relative error of pupillary distance calculated by our model in this paper is below 2%, and the accuracy is higher than that of measuring with a ruler. The results are highly consistent with the measurements obtained by an auto-refractometer. Furthermore, we have deployed the program on mobile devices, making it convenient to measure pupillary distance and height anytime and anywhere. Although this framework can be deployed on various platforms, the program is relatively complex, and certain human factors can affect the measurement results, such as the face not being parallel to the camera or the head having a deflection angle when taking photos, which can cause data deviation. Therefore, in the future, we will focus on simplifying the program framework and optimizing the program to eliminate interference from human factors.  
Conceptualization, H.X.; Methodology, Z.Z. and H.X.; Software, Z.Z. and D.L.; Validation, D.L.; Investigation, Z.Z. and D.L.; Resources, C.L.; Writing--original draft, Z.Z.; Writing--review & editing, H.X. and C.L.; Supervision, C.L. All authors have read and agreed to the published version of the manuscript.  
Postgraduate Scientific Research Capability Enhancement Program Project Funding 2023.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Ruler Measurement** & **AU Measurement** & **Our Measurement Method** \\ \hline Accuracy & Average & Excellent & Excellent \\ Cost & Very low & High & Low \\ Portability & Portable & Non-portable & Portable \\ Operation & Complex & Easy & Easy \\ \multirow{4}{*}{Others} & The operation process is complex, & It requires professional personnel & It can be embedded into various devices \\  & requires manual measurement, and & and equipment, but the process is & equipped with cameras, requiring no \\ \cline{1-1}  & takes a long time. & relatively fast. & specialized operators and providing quick \\ \cline{1-1}  & & & measurement results. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the advantages and disadvantages of three methods.  
Data Availability Statement: Our image dataset mainly comes from the 300W dataset and the CAS-PEAL-R1 dataset.
# Efficient Implementation of PRESENT and GIFT on Quantum Computers
## 2 Related Work
### GIFT Block Cipher  
The GIFT block cipher is a symmetric key cryptography using the Substitution Permutation Network (SPN) method. There are GIFT-64/128 (64-bit block and 128-bit key) and GIFT-128/128 (128-bit block and 128-bit key). In the GIFT block cipher, each round performs four steps: Sbox, Permutation, AddRoundKey and Constant XOR. The encryption operation of GIFT block cipher is described in Figure 2.
## 6 Model Interpretation Results
### Local Explanations  
Local explanations explain the reasoning of the model prediction based on a distinct input. Given an arbitrary input value for a receiver point, the local explanation declares the contribution of each input feature's value to the generated prediction. Figure 7 depicts EBM local explanations by means of exemplary input-output from the test dataset. EBM illustrates how much each input feature's value contributes to the predicted single PL in the positive (orange bar) or negative term (blue bar) beyond the regression's intercept term (\(\beta_{0}\)) (gray bar), which is described in Equation (11). In this case, the value of the angle (+108.75 degrees), from which the signals traverse towards the receiver points, plays a significant role in generating PL prediction by contributing \(-10.055\) dB to the predicted PL value. However, the contribution of the angle is offset by the feature elevation of the receiver point (63 m), having a +13.429 dB effect on signal weakening, as well as the feature distance from the transmitter (720 m), having a +2.658 dB contribution on PL. The altitude of the receiver (57 m) is shown to have a negative effect equal to \(-2.890\) dB on PL prediction. The interactive incorporation of distance between transmitter and receiver and the corresponding transmitting angle is shown to have a \(-1.850\) dB effect on signal attenuation. Further, the presented horizontal bars in Figure 7 can be read accordingly.  
Figure 7: EBM Local explanation by means of an exemplary input-output from the study’s test set (gray bar: intercept, orange bar: positive contributing input value, blue bar: negative contributing input value).
## 3 Results
### Expression Stability Analysis of the Candidate Reference Genes
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Stability**} & \multicolumn{2}{c}{**geNorm**} & \multicolumn{2}{c}{**NormFinder**} & \multicolumn{2}{c}{**BestKeeper**} \\ \cline{2-7}  & **Reference** & **M** & **Reference** & **SV** & **Reference** & **SD** \\  & **Genes** & & **Genes** & & **Genes** & \\ \hline
1 & _HIS_ & 0.533 & _HIS_ & 0.086 & _CYP_ & 0.126 \\
2 & _EF-1a_ & 0.535 & _PGK_ & 0.174 & _AQP_ & 0.134 \\
3 & _CYP_ & 0.543 & _CYP_ & 0.175 & _EF-1a_ & 0.135 \\
4 & _UBQ_ & 0.598 & _EF-1a_ & 0.182 & _HIS_ & 0.146 \\
5 & _PGK_ & 0.611 & _AQP_ & 0.211 & _UBQ_ & 0.153 \\
6 & _TUB_ & 0.615 & _TUB_ & 0.233 & _TUB_ & 0.166 \\
7 & _AQP_ & 0.620 & _UBQ_ & 0.280 & _RPL13_ & 0.180 \\
8 & _RPL13_ & 0.717 & _RPL13_ & 0.420 & _PGK_ & 0.204 \\
9 & _GAPDH_ & 1.466 & _GAPDH_ & 0.994 & _GAPDH_ & 0.217 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Stability rank of candidate reference gene expression by geNorm, NormFinder, and BestKeeper analysis. Figure 3: The pairwise variation values (\(\rm{Vn/Vn}+1\), \(\rm{V}\)) of candidate reference genes. (SV = 0.126) and _GAPDH_ (SV = 0.217) ranked first and last in terms of stability, respectively (Table 2). All three programs showed that _GAPDH_ was the most unstable gene. In order to determine the comprehensive stability ranking of the nine candidate reference genes, we integrated the results from the three programs based on GM value of ranking. GM value indicated that _HIS_ was the most suitable reference gene, followed by _CYP_, _PGK_, _UBQ_, _AQP_, _EF-1a_, _TUB_, _RPL13_, and _GAPDH_ (Table 3).
## 6 Simulation Experiments and Performance Analysis
### Results Analyses
This suggests that applications employing IEEABR techniques minimize their energy consumption values and optimize energy efficiency. Optimizing energy consumption for big data collection in LS-WSNs is one of the critical targets of this study, which proposes an SI IoV-based technique for data collection for SC waste management applications. It can be seen that this objective can be clearly achieved by employing SI-based techniques exploiting IEEABR strategies. Figure 19 shows the performance in terms of latency. The latency in this context refers to the time required for a DCV to traverse the OPs/DCPs for data gathering and transferal of the collected data to the BS. However, the time spent by the CHs on data gathering within their clusters is negligible. This is because data transmission speed within clusters is faster than that between CHs and DCVs, so the data gathering latency is mostly affected by the trajectory length. Hence, in determining the latency, there is a need to consider the total number of location points or DCPs. As shown in Figure 19, IEEABR outperformed the other protocols on this metric by achieving the lowest latency compared to the other models, even at a maximal node density, followed by Bee-Sensor-C, EBAR, and FF. The reason for the high latency of FF with a potential high energy consumption may be its redundant message retransmissions of data and frequent message collisions in a dense network, as well as flooding of the network, resulting in network congestion. However, waste management scenarios may not involve latency as a major issue since the vehicles are exploited opportunistically for data collection within some degree of delay tolerance. In general, SI-based protocols are preferrable for real-time applications such as environmental monitoring applications, including detection of waste disposal violations [70], target tracking scenarios (event-driven), and surveillance operations [2; 71; 72; 73]. This is because such protocols have potential for a high data delivery rate per unit of energy consumption compared to their classical counterparts and therefore generate fewer overheads and optimize energy consumption. The performance of the SI-based routing protocols shows that the proposed system exploiting an SI IoV-based method for data collection for SC waste management is efficient, especially for LS-WSN scenarios.
# Study About the Performance of Ascon in Arduino Devices
## 5 Performance Analysis of the Ascon Implementation
### Measures in the Encryption/Decryption Operations with Ascon
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variant**} & \multicolumn{6}{c}{**Number of Cycles and Encryptions in the Encryption Process. Arduino Nano ESP32**} \\ \cline{2-7}  & **\# Cyc. (1 s)** & **\# Enc. (1 s)** & **\# Cyc. (5 s)** & **\# Enc. (5 s)** & **\# Cyc. (10 s)** & **\# Enc. (10 s)** \\ \hline Ascon-128 & 240,016,080 & 10,785 & 1,200,012,240 & 53,925 & 2,400,014,160 & 107,857 \\ Average & 22,524 & — & 22,523 & — & 22,523 & — \\ Ascon-128a & 240,003,120 & 11,328 & 1,200,005,760 & 56,643 & 2,400,048,000 & 113,287 \\ Average & 21,486 & — & 21,487 & — & 21,487 & — \\ Ascon-80 pq & 240,021,360 & 10,453 & 1,200,013,680 & 52,264 & 2,400,010,800 & 104,528 \\ Average & 22,961 & — & 22,960 & — & 22,960 & — \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Number of cycles (Cyc.) and encryptions (Enc.) in the encryption operation in Arduino Nano ESP32 for the three versions of Ascon. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variant**} & \multicolumn{6}{c}{**Number of Cycles and Decryptions in the Decryption Process. Arduino Nano ESP32**} \\ \cline{2-7}  & **\# Cyc. (1 s)** & **\# Dec. (1 s)** & **\# Cyc. (5 s)** & **\# Dec. (5 s)** & **\# Cyc. (10 s)** & **\# Dec. (10 s)** \\ \hline Ascon-128 & 240,018,720 & 10,656 & 1,200,006,960 & 53,279 & 2,400,009,600 & 106,558 \\ Average & 2,252,410,656 & — & 22,523 & — & 22,523 & — \\ Ascon-128a & 240,021,120 & 11,170 & 1,200,019,920 & 55,846 & 2,400,018,720 & 111,691 \\ Average & 21,488 & — & 21,488 & — & 21,488 & — \\ Ascon-80 pq & 240,003,840 & 10,328 & 1,200,008,880 & 51,644 & 2,400,016,080 & 103,289 \\ Average & 23,238 & — & 23,236 & — & 23,235 & — \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Number of cycles (Cyc.) and decryptions (Dec.) in the decryption operation in Arduino Nano ESP32 for the three versions of Ascon. Note that the third columns in Tables 5-12 show the number of encryption and decryption operations that each one of the four devices considered is capable of executing in one second. These values provide information on the time complexity of all devices. In fact, the order of the devices in relation to their efficiency, from best to worst, is the following: Arduino Nano ESP32, Aduino DUE, Arduino Nano Every, and Aruino Mega2560, as shown in Table 13.  
In addition, to make a comparison between the number of cycles and the number of encryption and decryption operations for the encryption and decryption processes, respectively, for the Arduino devices and the PC used with Linux, Tables 14 and 15 show the same values as in the previous tables but for the Linux case. As expected, the number of encryptions and decryptions per second on a PC like the one we used is much higher than on the most efficient Arduino under consideration (Nano ESP32). In fact, it can be said that this number varies between 27 and 28 times more.
## 2 Traditional Canny Algorithm and Its Improvement  
### Traditional Canny Algorithm  
#### 2.1.1 Algorithm Principle  
The Canny algorithm is a classical edge detection algorithm [13], the core idea of which is to accurately locate the edge of the image through multi-stage processing, while suppressing noise. The algorithm consists of the following four steps:  
1. Gaussian filtering: Firstly, the original image is smoothed by Gaussian to eliminate high-frequency noise and avoid interference in subsequent gradient calculation.
2. Gradient calculation and direction extraction: The Sobel operator (two small matrices) is used to calculate the gradient of the image in the horizontal (x) and vertical (y) directions, respectively. Based on the gradients in the x and y directions, calculate the gradient intensity (how sharp the edge is) and the direction (whether the edge is facing left/right/up/down) for each pixel. The gradient amplitude reflects the edge strength, and the direction indicates the potential direction of the edge.
3. Non-maximum suppression: The amplitude is locally compared in the gradient direction, only the pixels with the largest amplitude are retained, and the edge width is refined to the single pixel level to eliminate redundant responses.
4. Double threshold detection and edge connection: Candidate edges are screened by high and low thresholds: strong edges are determined by high thresholds, weak edgesare retained by low thresholds, and weak edges are connected to strong edges in combination with connectivity analysis to ensure the continuity and integrity of edges.