esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
### Two-Stage Detection  
Two-stage object detection usually generates a region of interest (RoI), and then classifies and regresses on the generated RoI. The most typical detectors of this type are the R-CNN series. R-CNN, as a pioneering work, uses selective search to obtain region proposals, converts these region proposals into fixed sizes, and inputs them into a CNN. Then, an SVM is used to classify the regions, and linear regression loss is used to regress the bounding boxes. Fast R-CNN [5] uses CNN to extract images to get feature maps;then, it generates region proposals on the feature maps instead of the original images, and classifies and locates them through the full connection layer, thus reducing a lot of repetitive computational complexity and significantly improving the detection speed. Faster R-CNN uses an RPN to generate an RoI, which greatly accelerates the speed of the region proposal. Mask R-CNN [6] adds a mask branch to detect objects or segment them. Cascade R-CNN [7] uses an RPN for the proposal and trains multiple cascaded detectors to work with different IoU thresholds, thus improving the performance. R-FCN [8] replaced the full connection layer in Faster R-CNN with a convolution layer and used position-sensitive score maps to enhance detection performance.  
Since Faster R-CNN gained great success in the field of general object detection, the same trend appeared in pedestrian detection. A series of algorithms based on two-stage detection were proposed one after another. The Faster R-CNN framework became the most popular framework in the field of pedestrian detection, and a lot of research was based on it. Among them, RPN+BF [9] was the first to generate RoI by RPN proposed in Faster R-CNN and classified by boosted forest. It also pointed out many problems existing in Faster R-CNN. For example, the Faster R-CNN dose not perform well in pedestrian detection, especially for small objects, due to the small size of the final feature map and poor branching effect of classification. However, it also pointed out that RPN has a good effect as a network of proposal. Then, in order to solve this problem, MS-CNN [10] designed detectors with different scales for different output layers and made more use of multi-scale feature maps.  
In the field of pedestrian detection, the more advanced Reploss [11] designed a new loss function to separate bounding boxes to deal with the dense crowd with occlusion.
# Design and Evaluation of Dynamic Topology for Mega Constellation Networks
## 4 Modeling System Approach
### Call Model Based on Global Population Density  
In the model utilized in this paper, low-earth orbit satellite networks that provide communication services are analyzed as loss systems. When a new service request is made, there will be no idle channels in the entire satellite communication network, and a window will inevitably be available to provide service. The service system does not need to wait. If there is no available window for service, the request will be blocked and recorded as a failed call.  
The global population data as of 2020 is about 7.970 billion, the per capita broadband demand is about 10 Mbps, and the total transmission rate demand is about 796.96 Tbps. Figure 3 shows the global population density by provincial administrative divisions, with population deciles ranging from 5 to 45 per square kilometer. The more economically developed continents are mainly North America, Asia, and Europe. The level of population density and economic development varies from region to region, and this characteristic will undoubtedly affect the division of communication business volume.  
The global population distribution shows that most people are concentrated in the lower latitudes of the northern hemisphere, accounting for 80% of the total global population. Therefore, it is possible to predict the business volume demand in each location from the global population density. It is observed that the regions are divided according to the latitude and longitude of the site where the application service request is sent, mainly subdivided into South America, Australia, the Eurasian plate, etc. Eurasia has the highest demand for call services, accounting for over 40% of the total, followed by North America, accounting for 30% of the total, and each of the remaining segments accounts for 10%.
## 4 Performance Evaluation
### Tail Pulling Detection Performance
#### 4.4.2 Results  
After collecting the data from the participants, we selected a threshold that resulted in high detection performance: 595 (ADC value), about 2.9 [V]. A precision and recall analysis was performed using the selected threshold and the data collected from the 14 participants. The performance of the gesture detection resulted in Precision = 0.84, Recall = 0.93, and F-measure = 0.88.
## 3 Results and Discussion  
### Calibration and Model Performance  
The known parameters and calibrated results are presented in Table 1. Overall, the calibration was good for both CS-3 and CS-4 for November 2018 to December 2020. The NSE and RSQ for daily flow for CS-3 were 0.50 and 0.50, respectively, while that for field CS-4 were 0.49 and 0.53, respectively. Similarly, the difference in observed and simulated cumulative flow for field CS-3 was \(-5.70\%\), and that of field CS-4 was \(1.86\%\). The simulated cumulative flow was underpredicted (PBIAS = \(-5.70\%\) for calibration and PBIAS = \(-28\%\) for validation) for CS-3, while the flow was slightly overpredicted (PBIAS = \(1.86\%\) for calibration and \(-25\%\) for validation) for CS-4. High PBIAS values for these fields could be because of low total outflow and inefficient simulation of peaks at the validation period. The calibrated model was validated from January 2021 to December 2021. NSE and RSQ for field CS-3 for validation were 0.58 and 0.62, respectively, that of field CS-4 was 0.46 and 0.52, respectively. Though the peaks were not well-simulated, water balance, correlation,and closeness were good enough to represent the observed data for the overall study period (Figure 3).
# Multi-Agent-Supported Tracking Based on Hidden Markov Model with Weighted Entropy
## 4 Experiment
### Setting
#### 4.1.3 Implementation Details  
All experiments were conducted on a server equipped with a single NVIDIA V100 GPU. And the experimental results were validated through five independent trials. For object detection, we adopted YOLOX-X [22] with a COCO-pretrained model [23] as the initialized weights for its optimized balance between detection speed and accuracy. The training schedule was similar to the configuration of ByteTrack [24], with 80 epochs. A threshold of 0.6 was set for the confidence of detection to filter low-confidence detections while preserving recall performance. The parameters optimized by CMA-ES (covariance matrix adaptation evolution strategy, an evolution strategy optimization method that can accommodate non-convex optimization problems) of the HMM-based Kalman filter are listed below:  
\[\mathbf{H}=\begin{bmatrix}1\\ 0\end{bmatrix},\mathbf{Q}=\begin{bmatrix}24.8^{2}&24.8&0\\ 24.8&0&11.8^{2}\end{bmatrix},\mathbf{R}=\begin{bmatrix}46.2\end{bmatrix}, \mathbf{P}_{t=0}=\begin{bmatrix}77.5&0\\ 0&67\end{bmatrix} \tag{28}\]
## 3 Measurement Results
### Measurement Results Using a Real Application  
For a resistive/capacitive-sensing-mode test using a real application, a flexible bend sensor, which senses a bending force via a change of resistance and a capacitive \(z\)-axis accelerometer, were used. Each sensor specification is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**Overall Performance of the Proposed Multimodal Readout Circuit**} \\ \hline \multicolumn{6}{c}{**Technology**} & \multicolumn{6}{c}{**1P6M 0.18-\(\mu\)m CMOS Process**} \\ \hline \multicolumn{6}{c}{**Supply Voltage**} & \multicolumn{6}{c}{**1.8 V**} \\ \hline \multicolumn{6}{c}{**Multimodal Readout Circuit**} & \multicolumn{6}{c}{**Bias Block**} \\ \hline Signal type & Voltage & Resistance & Current & Capacitance & Power & 483.7 \(\mu\)W (sim) \\ \cline{3-6} Input range & 70 mV & 0.4–400 \(\Omega\) & 1.65 \(\mu\)A & \(\pm\)2 pF & \multicolumn{2}{c}{**Low-Pass Filter**} \\ \cline{3-6} Total power & 2.784 mW & 2.552 mW & 2.8 mW & \multicolumn{2}{c}{} & \multicolumn{2}{c}{Bandwidth} & 1 kHz (sim) \\ \cline{3-6} Input noise & 5.25 \(\mu\)V\({}_{\text{rms}}\) & 2.9 m\(\Omega_{\text{rms}}\) & 742 pA\({}_{\text{rms}}\) & 0.313 aF\({}_{\text{rms}}\) & \multicolumn{2}{c}{**PGA**} \\ \hline \(\text{P}_{\text{chop}}\) & \multicolumn{6}{c}{1 MHz (chopping frequency)} & Gain & 13.9–44 dB \\ \(\text{P}_{\text{cds}}\) & \multicolumn{6}{c}{125 kHz (CDS frequency)} & Power & 608 \(\mu\)W (sim) \\ \hline \multicolumn{6}{c}{**Multi-Path Operational Amplifier**} & \multicolumn{6}{c}{**Clock Generator**} \\ \hline Power & \multicolumn{6}{c}{10.09 \(\mu\)W (sim)} \\ UGBW & \multicolumn{6}{c}{4.81 MHz} & Power & 138 \(\mu\)W (sim) \\ \cline{3-6} Open loop gain & \multicolumn{6}{c}{98 dB} \\ \hline \multicolumn{6}{c}{**12-bit SAR ADC**} \\ \hline Power & \multicolumn{6}{c}{802.98 \(\mu\)W (sim)} \\ Sampling frequency & \multicolumn{6}{c}{1 MHz} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance summary. CMOS: complementary metal-oxide semiconductor, UGBW: unit gain bandwidth.  
Figure 15: The measured offset voltage of the capacitive-sensing mode after adjustment.  
In Figure 18, in capacitive-sensing mode, the parasitic capacitance was reduced by using an 8-bit capacitive DAC inside the chip after considering the input parasitic capacitance inside the sensor. Also, the gain was adjusted to 4 mV/fF. The amplitude of the output of the readout was measured in the range of 400-450 mV, which was a variation of about from 0.1 pF to 0.12 pF. Considering the specification of the z-axis accelerometer sensor, the acceleration was about 0.8 g to 0.96 g.  
The performance summary and comparison with references is shown in Table 3.
# Expansive Soil Stabilization with Time, Cement, and Silica Fume
## 5 Conclusions  
The objective of this study was to stabilize an expansive soil with lime, cement and silica fume, to reduce the free swelling and swell pressure. The methodology adopted in this paper presents a practical method of utilizing cement, lime and silica fume as grout to swell soil in order to enhance its properties. Thus, the suggested method of applying the stabilizer through grouting can be considered as a new method.  
Three percentages of lime, cement and silica fume (5, 7, 9%) were used to stabilize the expansive soil. The work was divided into two stages: the first used a consolidation test to record the free swell and swell pressure for the natural and stabilized soils; in the second part, the grouting technique was utilized as a process that can be used in the field to maintain the improvement in the bearing capacity. From the test results, the following can be concluded.  
1. From the laboratory swelling test, the free swelling and swelling pressure are increased rapidly for untreated soil.
2. The soil treated with lime, cement and silica fume had a reduction in both free swell and swelling pressure of around 65 and 76%, respectively, as compared with untreated soil.
3. According to grouting models, when a footing is supported by soil that has been grouted with lime, the bearing capacity of the soil increases from 66 to 85% when compared to untreated soil for soil that has been treated with 5 and 9% lime, respectively.
4. The soil grouted with cement increased the bearing capacity of footings by around 60 to 75% for the soil treated with 5 and 9% cement, respectively, as compared with untreated soil.
5. The soil grouted with silica fume increased the bearing capacity of footings by around 64 to 82% for the soil treated with 5 and 9% silica fume, respectively, as compared with untreated soil.
## 3 The Proposed Algorithm
### Computational Complexity Analysis  
The computational complexity of the proposed ego-motion estimation algorithm is primarily influenced by the genetic algorithm (GA) optimization process, which iteratively evaluates multiple candidate solutions. In order to provide a clear understanding of the algorithm's performance, we analyze its complexity in terms of the number of motion candidates (\(N\)) and the number of iterations (\(T\)) required for convergence.  
* Genetic Algorithm Iterations: The genetic algorithm begins with an initial population of \(N\) candidate solutions, where each candidate represents a potential ego-motion trajectory. In each iteration, the algorithm evaluates the fitness of each candidate based on image similarity between consecutive frames. The evaluation of each candidate involves calculating the similarity function, which is computationally intensive and requires the processing of image patches. Given that each candidate is evaluated in parallel using CUDA, the time complexity to evaluate each candidate is \(O(1)\) with respect to the number of candidates, due to parallelism. The total complexity for one iteration of the genetic algorithm is \(O(N)\), where \(N\) is the number of candidates. The algorithm iterates \(T\) times to arrive at an optimal solution, where \(T\) represents the number of iterations required for convergence. Therefore, the overall time complexity of the genetic algorithm is \(O(N\cdot T)\).
* CUDA Optimization: Parallel execution on the GPU significantly reduces computational time by allowing the simultaneous evaluation of all candidates. CUDA's parallel processing capabilities enable the algorithm to handle large populations efficiently. The GPU handles \(N\) evaluations simultaneously, with minimal overhead. As a result, the computational bottleneck is mainly determined by the memory bandwidth and the efficiency of the kernel functions. The complexity of memory management and data transfer between the CPU and GPU also contributes to overall performance. By optimizing the memory hierarchy and ensuring that data is transferred efficiently between the CPU and GPU, the algorithm minimizes latency and maximizes throughput.
* Memory Usage: The memory complexity is dominated by the storage of candidate solutions and image patches. Each candidate requires storage for its motion parameters and the associated image patch, leading to a memory requirement of \(O(N)\). In addition, intermediate results and image data must be stored during the evaluation process. However, by utilizing memory optimization techniques, such as memory pooling and shared memory on the GPU, we can minimize memory overhead and ensure that the algorithm operates efficiently even with large populations.  
In summary, the proposed algorithm has an overall time complexity of \(O(N\cdot T)\) and memory complexity of \(O(N)\), where \(N\) is the number of candidates and \(T\) is the number of iterations. Through efficient parallel processing with CUDA, the algorithm is able to achieve real-time performance in autonomous vehicle systems while maintaining high accuracy in ego-motion estimation.
# A Secure and Efficient Multi-Factor Authentication Algorithm for Mobile Money Applications
###### Abstract
Besides, none of those studies mentioned above implemented combining PIN, OTP, and biometric fingerprint for mobile money authentication where SHA-256 is used to secure the PIN and OTP, FIDO to secure biometric fingerprint, and Fernet encryption to secure a QR code and the records in the databases. This study develops a secure and efficient multi-factor authentication algorithm for mobile money applications. Prototypes known as Genuine mobile money (G-MoMo) applications were developed based on the proposed algorithm to achieve the security principles of confidentiality, integrity, availability, authentication, privacy, and non-repudiation. The essential contributions of our proposed algorithm are as follows:  
* We propose a secure and efficient multi-factor authentication algorithm for mobile money applications where PIN, OTP, and biometric fingerprints authenticate users. It also authorizes mobile money withdrawal by scanning the fingerprints of the mobile money users and the secure QR code of the mobile money agent that contains the unique mobile money agent code. * We present the different ways of securing user authentication credentials such as PIN and OTP using SHA-256, biometric fingerprint by FIDO services that use the public-key cryptography technique (RSA), and a QR code and records in the databases by Fernet encryption. * We design and implement the proposed secure and efficient multi-factor authentication algorithm for mobile money applications by developing G-MoMo application prototypes to prove that the algorithm is feasible and provides a higher degree of security. * We present a comparative security and performance analysis of our proposed algorithm. The relevant related work on mobile money authentication schemes, problem statements, and technologies is discussed in Section 2. Section 3 covers the proposed algorithm and system architecture, while Section 4 presents the system implementation. Security analysis is discussed in Section 5, performance analysis is discussed in Section 6, comparison of the proposed algorithm with other related works takes place in Section 7, and finally, Section 8 covers the conclusion and recommendation.
# High-Temperature Rheological Properties of Asphalt Mortar Modified with Spent FCC Catalysts
## 1 Introduction
[PERSON] [32] analyzed the rheological properties of FCC catalysts and asphalt binders by examining their interaction. The study revealed that the addition of S-FCC catalysts to asphalt mastic increased the rutting factor, decreased the fatigue coefficient, improved high-temperature performance, and reduced low-temperature crack susceptibility. However, the existing study solely analyzed the effect of the powder-to-binder ratio on the high-temperature rheology of asphalt mortar, without addressing the impact of the substitution level of S-FCC-Cs for mineral powder on high-temperature rheology. In this study, five S-FCC-Cs with different surface areas and hydrophilic coefficients were used as fillers to prepare five different asphalt mortars. The high-temperature rheological properties of these five asphalt mortars were investigated using a dynamic shear rheometer (DSR). The results showed that the dosage, surface area, and hydrophilic coefficients of S-FCC-Cs play key roles in the high-temperature rheological properties of asphalt mortar. The asphalt mortar based on S-FCC-C (JX) with a surface area and hydrophilic coefficient of 105 m\({}^{2}\)/g and 1.016, respectively, exhibited the best rutting resistance and resilience performances among the five S-FCC-C asphalt mortars. To the best of our knowledge, this is the first study to investigate the high-temperature rheological properties of S-FCC-C asphalt mortar based upon both the temperature scan test and the multiple stress creep recovery (MSCR) test.
# Data Analysis of the TK-1G Sounding Rocket Installed with a Satellite Navigation System
## 3 The Data Validity Rate in the Detection  
The data validity rate in the detection is defined by the following formula: (the number of valid data packet/the number of receivable data packet) \(\times\) 100%. It is employed to evaluate the reliability of the data acquisition system.  
The data sampling frequency of the rocket sonde is 1 Hz. The rocket sonde reached the trajectory vertex at 6:32:21 a.m. and fell to the height of 20 km at 7:05:02 a.m. The duration of total detection lasted 1961s with 1961 data packets to be collected. After eliminating the error code spot and measurement-missing spot, the mean data validity rate in Station 1 was 89.65% and amounted to 95.7% after the supplement data from Station 2 and 3. The data validity rates at different heights of measurement are displayed in Table 1.  
As shown in Table 1, the data validity rate of this test met the required criteria for a successful test (\(\geq\)85%) in _General Technical Requirements for the New Rocket Radiosonde_, and the quality of data garnered by the satellite navigation system is better than that garnered by radar system. If more receiving stations were put into use to provide supplementary data, such validity rate should be further improved.
# Automatic Detection Method of Sewer Pipe Defects Using Deep Learning Techniques
## 2 Literature Review
### Sewer Defect Detection Based on Computer Vision Techniques  
In order to overcome the drawbacks of manual inspection, automatic inspection methods based on traditional computer vision technology are gradually developed.  
[PERSON] et al. [7] used wavelet transform and co-occurrence matrix to extract texture features of sewer defects and used support vector machine (SVM) to classify defects. Experimenting on 291 images containing defects, an accuracy of 60% was obtained. However, this method classifies based on the texture details of the defects, which is easily affected by the pipe background. When the sewer background is complex or the defect texture features are not obvious, the detection effect is poor. [PERSON] et al. [8] used threshold segmentation to segment the region of interest containing defects from the sewer images. Following this, the extracted HOG features are classified using SVM classifier. This method can be used for the detection of root defects with an accuracy of 91.2%, but it cannot be applied to the detection of other defects. [PERSON] et al. [9] detected crack defects by morphological segmentation method, detected deposition defects based on the Gabor filter, and detected pipeline deformation by ellipse fitting algorithm. Although a variety of defects can be detected, the accuracy is low with a maximum of 74%.  
It can be seen that the traditional computer vision methods mainly rely on manually designed features for defect identification. However, the sewer environment is complex and diverse, and there are many types of sewer defects with different morphological characteristics. It is difficult for traditional methods to detect multiple defects at the same time, and the accuracy rate is also affected. Since manual intervention is still required, it cannot meet the needs of automatic detection of sewer defects.
## 2 VSWT-PMSG and LC Filter Model
### LC Filter Model  
Switching frequency of the converter involves high order harmonics that can disturb sensitive equipment in the grid system [7]. In order to reduce the current harmonics, the LC filter is considered in this paper. To avoid the resonance, a passive damping resistance should be allocated in series with a filter capacitor on the filter. Therefore, in designing the converter controller system, a passive damping resistance should be considered in the model system plant.  
Equivalent circuit of single-phase LC filter including transformer impedance is shown in Figure 4. The LC filter is composed of an inverter side inductance (\(L_{i}\)) and its parasitic resistance (\(R_{i}\)), a filter capacitor (\(C_{f}\)), and a damping resistance (\(R_{d}\)). Transformer impedance consists of leakage inductance (\(L_{g}\)) and resistance (\(R_{g}\)). \(V_{i}\) and \(I_{i}\) are voltage and current on the converter side of the LC filter. \(V_{g}\) and \(I_{g}\) are voltage and current on the grid side of the transformer. It should be noted that \(V_{cf}\) is a voltage on the filter capacitor (\(C_{f}\)).  
Differential equations of the grid connected LC filter in d-q rotating reference frame can be written as Equations (14) to (19).  
\[L_{i}\frac{dI_{id}}{dt} =V_{id}-V_{cfd}-(R_{i}+R_{d})I_{id}+L_{i}\omega I_{iq}+R_{d}I_{gd} \tag{14}\] \[L_{i}\frac{dI_{iq}}{dt} =V_{iq}-V_{cfq}-(R_{i}+R_{d})I_{iq}-L_{i}\omega I_{id}+R_{d}I_{gq}\] (15) \[L_{g}\frac{dI_{gd}}{dt} =V_{cfd}-V_{gd}-\big{(}R_{g}+R_{d}\big{)}I_{gd}+L_{g}\omega I_{gq} +R_{d}I_{id}\] (16) \[L_{g}\frac{dI_{gq}}{dt} =V_{cfq}-V_{gq}-\big{(}R_{g}+R_{d}\big{)}I_{gq}-L_{g}\omega I_{gd} +R_{d}I_{iq}\] (17) \[C_{f}\frac{dV_{cfd}}{dt} =I_{id}-I_{gd}+\omega C_{f}V_{cfq}\] (18) \[C_{f}\frac{dV_{cfq}}{dt} =I_{iq}-I_{gq}-\omega C_{f}V_{cfd} \tag{19}\]  
The dynamic block diagram of the grid connected LC filter in the d-q rotating reference frame can be derived as shown in Figure 5.  
Figure 4: Single phase LC filter equivalent circuit.