esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Although FEA is a research method applied in a planned model with an ideal arrangement, it has the limitation of not reflecting individualized patient conditions such as tooth arrangement, alveolar bone condition, masticatory pressure, or chewing habit. Through continuous FEA in our study, it was confirmed that many parts of tooth movement that could be observed in actual clinical practice were implemented in FEA. Further clinical studies are needed to support this finding. \begin{table}
\begin{tabular}{c c c c c} \hline  & **D1** & **Force** & **M1** & **Rotation** \\  & **(mm)** & **(gram\(\cdot\)f)** & **(gram\(\cdot\)f-mm)** & **(CW, \(-\); CCW, +)** \\ \hline HOMS1 & 3.93 & 300 & 1180.09 & – \\ HOMS2 & 3.38 & 300 & 1014.88 & – \\ HOMS3 & 2.64 & 300 & 790.66 & – \\ LOMS1 & 5.47 & 300 & 1640.33 & – \\ LOMS2 & 4.37 & 300 & 1309.91 & – \\ LOMS3 & 2.36 & 300 & 708.06 & – \\ \hline  & **D2** & **Force** & **M2** & **Rotation** \\  & **(mm)** & **(gram\(\cdot\)f)** & **(gram\(\cdot\)f-mm)** & **(CW, \(-\); CCW, +)** \\ \hline HOMS1 & 10.03 & 300 & 3009.24 & + \\ HOMS2 & 9.20 & 300 & 2761.42 & + \\ HOMS3 & 8.30 & 300 & 2490.00 & + \\ LOMS1 & 2.75 & 300 & 826.07 & + \\ LOMS2 & 2.05 & 300 & 613.65 & + \\ LOMS3 & 1.10 & 300 & 330.43 & + \\ \hline  & & **M1** & **M2** & **Total M** \\  & **(gram\(\cdot\)f-mm)** & **(gram\(\cdot\)f-mm)** & **(gram\(\cdot\)f-mm)** \\ \hline HOMS1 & \(-\)1180.09 & +3009.24 & +1829.15 \\ HOMS2 & \(-\)1014.88 & +2761.42 & +1746.54 \\ HOMS3 & \(-\)790.66 & +2490.00 & +1699.34 \\ LOMS1 & \(-\)1640.33 & +826.07 & \(-\)814.27 \\ LOMS2 & \(-\)1309.91 & +613.65 & \(-\)696.26 \\ LOMS3 & \(-\)708.06 & +330.43 & \(-\)377.63 \\ \hline \end{tabular}
\end{table} 
Table 8: Magnitude and direction of moment at initial time.
# An Improvement of a Mapping Method Based on Ant Colony Algorithm Applied to Smart Cities
## 3 Algorithmic Simulation and Result Analysis
### Simulation Analysis for Special Cases of the Classical Topological VOPD  
VOPD is a classical problem graph in the topological mapping problem. Its communication graph is shown in Figure 5 with 16 cores, compared to 16 neurons in the application scenario of this paper. Different from the previous simulation, the pulse emission frequency of the neurons here is different; that is, the size of the information transmission of the communication core is different.  
Figure 4: Cost of the ant colony algorithm and CNDE mapping varies with the number of iterations.  
Figure 3: Comparison between the traditional and the improved ant colony algorithm.  
The CNDE algorithm is proposed to optimize the total energy consumption of the system, leaving the relatively small part, mainly analyzing the link energy consumption and the router energy consumption.  
\[\mathrm{E}=\mathrm{E}_{\mathrm{L}}+\mathrm{E}_{\mathrm{R}} \tag{14}\]  
The link energy consumption is determined by the distance of all connections in the system and the number of corresponding transmitted bits.  
\[\mathrm{E}_{\mathrm{L}}=\sum\
olimits_{\mathrm{I}}\mathrm{Distance(I)}\times \mathrm{Bits(I)}\times\mathrm{E}_{\mathrm{L/bit}} \tag{15}\]  
The router energy consumption is determined by the amount of information sent and received by the node.  
\[\mathrm{E}_{\mathrm{R}}=\sum\
olimits_{\mathrm{n}}[\mathrm{SendBits(n)}+ \mathrm{RecvBits(n)}]\times\mathrm{E}_{\mathrm{R/bit}} \tag{16}\]  
This problem background is very similar to that examined in this paper, as the only difference relates to the neuron transmission frequency. Therefore, the mapping algorithm based on ant colony algorithm proposed in this paper can be obtained by modifying the cost function.  
\[F(\omega)=\sum\
olimits_{v\in Net}C(v)\times p(v)\times(E_{L/bit}+2\times E_{R /bit}) \tag{17}\]  
Compared with the CNDE algorithm, this paper uses the same problem background and the same iterative jump-out conditions. After performing multiple simulations and averaging, the improvement in energy consumption compared with stochastic mapping is shown in Table 2. The proposed algorithm, AVNSACA, PSO, and CNDE are compared with stochastic mapping. The performance improvement of the proposed algorithm is 56.56%, while the performance improvement of AVNSACA, PSO, and CNDE are 54.03%, 48.82%, and 50.71%, respectively.  
Figure 5: VOPD communication core diagram.  
It is worth mentioning that the application scenario of the algorithm proposed in this paper allows for multiple neurons to be deployed on a single node, whereas only one can be deployed in the VOPD task.
## 3 Proposed ML Method
### Feature Compression  
Feature compression was applied to both the discrete-time STFT- and EA-generated spectral feature sets referenced in Sections 3.1.1 and 3.1.2. For the STFT features, dimensionality reduction from 512 down to 25 spectral features was applied in order to extract the most useful features to train the learning model algorithm. In addition, dimensionality reduction simplifies the complexity of the computations so that more optimal and accurate estimations could be obtained. This was to avoid the well-known phenomena often defined as the _curse of dimensionality_[70; 71]. This term describes the inherent problem caused by the exponential increase in volume associated with adding extra dimensions to a Euclidean space [71].  
Feature reduction was achieved using a filter band approach as described in Equation (4) for both linear and non-linear sized bands. The linear bands, \(L[m,k]\), consisted of equidistant bands applied evenly to 512 spectral features, whereas the non-linear approach, \(O[m,k]\), comprises a one-third octave band filter being applied. The one-third octave band filter places a higher emphasis on the lower end of the frequency spectrum by having smaller bands that increase non-linearly in size as the frequency increases.  
\[Y[m,k]=X[m,f]\times H[f,k] \tag{4}\]  
where the feature vector \(Y[m,k]\) is the result of a matrix multiplication of \(X[m,f]\), the average STFT values of each discrete time-sample, and \(H[f,k]\), a matrix comprised of ones and zeros strategically placed according to the desired filter parameters. The feature vector \(Y[m,k]\) is equal to \(L[m,k]\) when linear filtering is used, and \(Y[m,k]\) is equal to \(O[m,k]\) when one-third octave band filtering is used.  
Two distinct \(H[f,k]\) matrices were chosen and tested, allowing for linear and non-linear banding. The linear band feature reduction technique was achieved by filtering the frequency amplitude spectrum of each independent time-sample into 25 equidistant bands ranging from 0 to 12,800 Hz. The non-linear feature reduction approach uses a one-third octave band filter scale to segment the frequency spectrum into 25 octave bands in the scale 0 to 12,800 Hz.  
For the signal envelope features, \(EA[n]\), three feature compression techniques were explored using a linear and one-third octave band scale. The first involves the time-series values for each of the 25 filter bands being transferred to the frequency domain by means of the Fast Fourier Transform (FFT). The sum of the frequency powers, in decibel scale, is calculated to create feature vectors comprising of 25 features for each of the discrete-time vibration signals, \(\mathcal{F}\Big{\{}EA_{L}\Big{\}}\) and \(\mathcal{F}\Big{\{}EA_{O}\Big{\}}\). The second technique is comprised of summing all the signal envelope time-series points for each of the 25 filter band vectors, \(EA_{L}\) and \(EA_{O}\). The third approach used the signal energy value by calculating the square of the signal envelope values and summing together for each of the 25 filter band, \(EA_{L}^{2}\) and \(EA_{O}^{2}\).
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 5 Performance Analysis
### Computation Overhead for [PERSON] et al. [5]
#### 5.2.3 Computational Cost for Multiple Iterations  
Finally, the total computational cost over \(n\) iterations of the algorithm proposed by [PERSON] et al. [5] is computed as a formula given in Equation (25):  
\[C_{Bonawitz} =C_{Server}+C_{Client} \tag{25}\] \[=C_{VER}+C_{DEC}+C_{AGG}+C_{ENC}+C_{SIG}+999,000\text{ ms}.\]  
However, client-to-client communication, signature generation, and verification are performed only once for \(n\) iterations; hence, the computational cost can be given as formula given in Equation (26):  
\[CCost_{(Bonawitz,n)}=C_{SIG}+C_{VER}+999,000\text{ ms}+n\times(C_{ENC}+C_{DEC}+C_{AGG}). \tag{26}\]  
Therefore, for different values of \(n\), cost can be computed as:  
\[CCost_{(Bonawitz,50)}\] \[=103.78+55.582+999,000+50\times(103.78+55.582+1.612)\] \[=1,007,208.062\text{ ms}=1007.2\text{ s},\] \[CCost_{(Bonawitz,100)}\] \[=217.85+102.37+999,000+100\times(217.85+102.37+1.612)\] \[=1,031,503.42\text{ ms}=1031.5\text{ s},\] \[CCost_{(Bonawitz,150)}\] \[=347.89+145.40+999,000+150\times(347.89+145.40+1.612)\] \[=1,073,728.59\text{ ms}=1073.72\text{ s},\] \[CCost_{(Bonawitz,1000)}\] \[=5210+1060+999,000+1000\times(5210+1060+1.612)\] \[=7,276,882\text{ ms}=7276.88\text{ s}.\]
# A Lightweight and Partitioned CNN Algorithm for Multi-Landslide Detection in Remote Sensing Images
###### Abstract  
Landslide detection is crucial for natural disaster risk management. Deep-learning-based object-detection algorithms have been shown to be effective in landslide studies. However, advanced algorithms currently used for landslide detection require high computational complexity and memory requirements, limiting their practical applicability. In this study, we developed a high-resolution dataset for landslide-prone regions in China by extracting historical landslide remote sensing images from the Google Earth platform. We propose a lightweight LP-YOLO algorithm based on YOLOv5, with a more-lightweight backbone that incorporates our designed PartitionNet and neck equipped with CSPcrossStage. We constructed and added the vertical and horizontal (VH) block to the backbone, which explores and aggregates long-range information with two directions, while consuming a small amount of computational cost. A new feature fusion structure is proposed to boost information flow and enhance the location accuracy. To speed up the model learning process and improve the accuracy, the SCYLLA-IoU (SIoU) bounding box regression loss function was used to replace the complete IoU (CIoU) loss function. The experimental results demonstrated that our proposed model achieved the highest detection performance (53.7% of Precision, 49% of AP50 and 25.5% of AP50.95) with a speed of 74 fps. Compared to the YOLOv5 model, the proposed model achieved 4% improvement for Precision, 2.6% improvement for AP50, and 2.5% for AP50.95, while reducing the model parameters and FLOPs by 38.4% and 53.1%, respectively. The results indicated that the proposed lightweight method provides a technical guidance for achieving reliable and real-time automatic landslide detection and can be used for disaster prevention and mitigation.  
landside detection; remote sensing image; deep learning; LP-YOLO; YOLOv5 +
Footnote †: journal: Applied sciences
###### Abstract
The system is experimentally implemented and validated by successfully landing a commercial quadrotor on the roof of a car moving at speeds of up to 50 km/h. Ref. ([PERSON] et al., 2019) presented a field-tested mini gimbal mechanism to produce an estimation of the target position, which allowed a flying-wing UAV to fly around the target. [PERSON] et al. (2019) presented the architecture of a pan/tilt/roll camera system implemented on the Georgia Tech's UAV research helicopter. Each axis is driven by a servo and optical encoders are utilized to measure the gimbal orientation. [PERSON] et al. (2019) performed flight tests using a SeaScan UAV with a gimballing camera to track ground targets and studied the effect of altitude, camera FOV, and orbit center offsets within the geo-location tracking performance for both stationary and moving targets. In this paper, motivated by the existing challenges, an onboard vision-based system for tracking arbitrary 3D objects moving at unknown velocities is proposed by utilizing a 3-axis gimbal system. A KCF tracker is used to detect and localize the target of interest from images acquired by the gimbaled camera. The 3-axis stabilized gimbal system is driven by brushless direct current motors (BLDCMs) and a magnetic rotary encoder is attached to each axis with a high-resolution output of the angular position. Then, the results of the KCF tracker are put into a proportional-derivative (PD) controller, which aligns the optical axis of the gimbaled camera with the LOS joining the camera and the target. A tracking strategy for multi-rotor UAVs is proposed based on the proportional navigation (PN) method, which makes it possible to keep following the target, although the target position is not known. A low-cost experimental quadrotor is customized for real-time flight tests, which is equipped with a microcontroller using consumer sensors and an onboard computer for image processing. The presented system is experimentally implemented and validated by successfully tracking a commercial quadrotor flying along an unknown path. By analyzing the flight data of both the target and the interceptor, the system is demonstrated to be reliable and cost-effective. The rest of this paper is organized as follows. The vision system and control of the gimbaled camera are described in Section 2. The visual tracking algorithm used to detect and localize the target in image stream is presented in Section 3. Section 4 established the dynamic model of the quadrotor, and the target tracking strategy as well as the control law is presented. Section 5 describes preparation for flight experiments, and the results of the flight tests are given in this part. Finally, Section 6 concludes this paper and presents the future work.
# Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority
## 4 Simulation Experiment  
This simulation experiment utilized NS2 simulation software to simulate the information transmission network. In the simulation software, the LEO satellites constellation in this paper adopted the Iridium constellation, a diagram of which is shown in Figure 4. The constellation structure consists of 66 satellites orbiting in six polar circular orbits, at an altitude of approximately 780 km, to ensure global coverage. Additionally, the constellation composed of three GEO satellites can cover the whole Iridium constellation. The SDN controller was deployed for the GEO satellite constellation. The SDN controllers of the three GEO satellites synchronized their data with each other and provided services for the LEO satellites covered by them. The SDN controller can monitor satellites within the Iridium constellation and schedule multi-DAG tasks received by the LEO satellites (DAG scheduling can be achieved through software programming and then deployed to the SDN controllers on the GEO satellites through the north interface), achieving global optimization. The Iridium communication network can provide reliable communication connections globally, enabling the SDN controller to effectively manage and schedule DAG tasks. This is particularly helpful for scenarios requiring task distribution and data transmission across different geographical regions. The processing speed of edge computing nodes, the workload of subtasks, the power of edge computing nodes, and the transmission power between satellites were set [30] as listed in Table 1.
# Detection of Logos of Moving Vehicles under Complex Lighting Conditions
## 5 Conclusions  
In view of the fact that commonly used object detection frameworks fail to solve the problem of vehicle logo detection and recognition under complex lighting conditions, this study puts forwards a logo detection and recognition method based on image enhancement technology and an improved YOLOF framework. The experimental results from the comparison between the proposed method and the original YOLOF algorithm demonstrate that: (1) The adaptive image enhancement algorithm proposed in this study can make reasonable adjustments to image samples under complex lighting conditions while reducing the rate of missed detection of subsequent detection algorithms; (2) The detection accuracy and speed of the trained detection model are improved by boosting the error loss function of YOLOF and the NMS algorithm; (3) The comparative experimental analysis of the detection performance of the proposed method and the original YOLOF algorithm proves that better detection results can be delivered by decomposing the logo detection under complex lighting conditions into two tasks: image enhancement and logo detection. This approach provides another solution to vehicle logo detection and recognition. In future work, the ablation study will be used as a research direction for vehicle logo detection and unravel the effects of image enhancement, focal loss, Gaussian loss, and NMS on instances of different sizes.  
Conceptualization, Q.Z. and W.G.; methodology, Q.Z.; software, Q.Z.; validation, Q.Z.; formal analysis, Q.Z.; investigation, Q.Z.; writing--original draft preparation, Q.Z.; writing--review and editing, W.G.; visualization, Q.Z.; supervision, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available in the submitted article.  
The authors declare no conflict of interest.
## 5 Experimental Results  
The experimental results presented in this section are grouped in three main blocks. Firstly, the evaluation of the classifiers for the existing datasets is presented. Secondly, the proposed cascade approach is evaluated. Finally, the best classifiers are evaluated in an embedded system.  
Figure 3: Example of a hierarchical dataset to be used by our cascade approach.  
Figure 2: The multi-stage sound classification is composed of two stages, an initial stage performs the broad classification, while a bank of classifiers are used to determine the sound’s category.  
The results presented in this section are:
* **Evaluation of the classifiers for the existing datasets**: The LibROSA Python package [25] is used for the audio feature extraction for each dataset discussed in Section 3.4. The evaluation of each selected classifier (k-NN, ANN, Naive Bayes, Decision Tree and SVM) uses 80% of the dataset samples for the training and the remaining 20% for the validation. The performance of the classifiers is compared.
* **Optimization of the classifiers**: The classifiers are optimized per dataset. This optimization is performed by looping the classifiers' parameters on different data configurations and checking for the set of parameters resulting in the smallest mean error. The evaluation of the classifiers on the hierarchical dataset is also done. This evaluation involves different feature sets in order to identify the impact of the feature selection on the recognition's accuracy and the computational speed.
* **Dataset for the cascade approach**: Existing datasets are combined in a hierarchical dataset to be used by the cascade approach.
* **Evaluation of the cascade approach**: The cascade approach is compared to the traditional solution using an equivalent dataset where all the categories of the hierarchical dataset are different categories in the same dataset.
* **Evaluation on an embedded system**: According to the results, the optimal classifier is embedded on a Raspberry Pi. The performance and computational speed of the classifier are then analysed.
# A Novel Solution for Reconstructing More Details High Dynamic Range Image
## 2 Related Work
### CNN-Based Method  
After 2015, due to the rapid development of neural networks, many scholars used CNN-based methods to reconstruct HDR images [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21]. In 2017, [PERSON] et al. aligned LDR images with optical flow and then used a CNN network to fuse them [10]. In 2018, [PERSON] et al. aligned the camera motion by homography and used CNN to reconstruct HDR images [11]. In 2019, [PERSON] et al. suppressed motion and saturation by proposing an attention mechanism [12]. In 2020, [PERSON] et al. released the constraint of locality receptive field with a nonlocal block to globally merge HDR images [13]. In 2021, [PERSON] et al. proposed generative adversarial networks, named HDR-GAN, to recover missing content [14]. In 2021, [PERSON] et al. generated ghost-free images by a proposed multi-step feature fusion [15]. In 2021, [PERSON] et al. removed ghosts by utilizing the pyramid, cascading, and deformable alignment subnetwork [16]. The method requires a huge number of labeled samples, and we all know that labeled samples are not easy to prepare.
# Enhancing Clay Soil's Geotechnical Properties Utilizing Sintered Gypsum and Glass Powder
## 2 Experiment Program
### Method
#### 2.2.5 Microstructural Tests  
The pozzolanic response between soil additives and soil was studied by microstructural testing, focusing on the interaction between clay soil and sintered gypsum in a water-containing environment. These investigations involved techniques including XRD and SEM. A field-emission scanning electron microscope (QUANTA 400F) was used for SEM testing. Sample pieces, approximately 10 mm in size, were mounted on aluminum stubs. Under the electron beam, a thin layer of gold was placed to reduce the impact of the charges. From 1 K to 10 K, various magnification levels were used to capture SEM pictures. An X-ray powder diffractometer (XRD) equipped with a Bruker AXS D8 Advance Model X-ray Diffractometer was utilized. The device utilized a high-speed PSD: a Vantec-1 detector and a Cu-K\(\alpha\) X-ray source. Scans were conducted in a 2-theta range of 2-90 degrees at a rate of 2 degrees per minute, with step intervals of 0.02 degrees, operating at 40 kV and 30 mA. In order to identify peaks, the International Center for Diffraction Data's (ICDD) database was queried, utilizing Crystal Impact Match Software, Version 3.11.1. XRD analyses were conducted via HighScore (Plus version 3.0.5).