esa-sceva/satcom-chunk-collection huggingface dataset:
## 3. Experimental Results and Discussion
### Orthogonal Simulation Results and Analysis
#### 3.1.1 Study on Seed Distribution Uniformity  
To investigate the distribution of wheat seeds after passing through the pressurized tube and the influence of the pressurized tube structure on seeding performance, the entrance of the pressurized tube into the distribution head was selected as the seed distribution statistics frame in the EDEM(2020) software (shown as Figure 4).  
This area was divided into four equal parts along the pressurized tube's axial direction, and the number of seeds passing through the same region in the same time period was  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Serial** & **Wave** & **Wave** & **Deepth** & **Wave** & **Pipe Length** & **Pressure** & **Distribution** & **Consistency** \\
**Number** & **K/mm** & **S/mm** & **Number N** & **L/mm** & **Loss/Pa** & **Uniformity** & **Coefficient of** \\  & & & & & & & & **Each Row/\%** \\ \hline
1 & 30 & 4 & 4 & 700 & 208 & 47.22 & 6.79 \\
2 & 50 & 8 & 4 & 900 & 268 & 86.54 & 4.12 \\
3 & 60 & 10 & 4 & 1000 & 276 & 87.01 & 3.51 \\
4 & 40 & 6 & 4 & 800 & 257 & 93.41 & 2.33 \\
5 & 40 & 10 & 8 & 700 & 241 & 88.72 & 3.36 \\
6 & 60 & 8 & 6 & 700 & 227 & 80.00 & 4.38 \\
7 & 50 & 6 & 10 & 700 & 253 & 94.37 & 2.36 \\
8 & 30 & 10 & 10 & 900 & 308 & 97.20 & 1.99 \\
9 & 60 & 4 & 10 & 800 & 288 & 90.20 & 3.28 \\
10 & 30 & 8 & 8 & 800 & 274 & 90.11 & 3.24 \\
11 & 40 & 8 & 10 & 1000 & 360 & 95.20 & 2.18 \\
12 & 40 & 4 & 6 & 900 & 271 & 88.65 & 3.58 \\
13 & 50 & 4 & 8 & 1000 & 342 & 93.57 & 2.99 \\
14 & 50 & 10 & 6 & 800 & 262 & 86.19 & 3.01 \\
15 & 60 & 6 & 8 & 900 & 286 & 84.65 & 5.60 \\
16 & 30 & 6 & 6 & 1000 & 306 & 88.04 & 3.71 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Orthogonal experimental results.  
Figure 4: Seed distribution status statistics. 1. Pressurized tube; 2. outlet; 3. seed-counting frame at the upper part of the pressurized tube.  
counted as \(Q_{i}\) (\(i=1,2,3,4\)). The seed distribution uniformity coefficient was then calculated using the following formula:  
\[k=\left[1-\frac{\sqrt{\frac{\frac{1}{i-1}\sum\left(Q_{i}-\overline{Q}\right)^{2} }{\overline{Q}}}}{\overline{Q}}\right]\times 100 \tag{10}\]  
where \(k\) is the seed distribution uniformity coefficient (%); \(Q_{i}\) is the number of wheat seeds in the \(i\)-the region of the statistics frame (\(i=1\), \(2\), \(3\), \(4\)); and \(\overline{Q}\) is the average number of wheat seeds in the statistics frame.  
Seed quantities at each seeding outlet of the distribution head were counted using seed statistics frames, and the coefficients of variation in discharge consistency for each row were calculated. The results are shown in Table 5.
## 4 Result and Discussion
It should also be acknowledged that the proposed algorithm may encounter tracking failures, particularly when dealing with a fast-moving hand or a heavily occluded hand. Although the integrated SSD algorithm helps improve the tracking rate by reinitializing the system, it does not provide full support to the tracker during tracking. The absence of a motion-handling algorithm in the visual tracker poses challenges in accurately tracking fast-moving hands, where the target object may appear deformed or blurry to the tracker. Furthermore, the proposed algorithm has limitations in detecting and tracking small or distant hands due to the constraints of the SSD algorithm. The FPN \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Trajectory** & **Num. of Frames** & \
\begin{tabular}{c} **Hand Object Include** \\ **Arm Region** \\ \end{tabular} & **Cluttered Background** &
\begin{tabular}{c} **Camera Distance to** \\ **Object (Meter)** \\ \end{tabular} \\ \hline
0 & 244 & Yes & Cluttered & 2 \\
1 & 238 & No & Partially & 1 \\
2 & 237 & Yes & Partially & 1.5 \\
3 & 229 & No & Partially & 1 \\
4 & 210 & No & Partially & 1 \\
5 & 222 & Yes & Partially & 1 \\
6 & 242 & No & Cluttered & 1.5 \\
7 & 221 & Yes & Cluttered & 2 \\
8 & 242 & Yes & Cluttered & 2 \\
9 & 242 & No & Partially & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detailed properties of trajectory-based gestures from IBGHT dataset [34]. Figure 6: Tracking results of proposed algorithm (red) and KCF tracker (green) on deformable hand shape. utilized in the SSD algorithm consists of a bottom-up pathway, top-down pathway, and lateral connections. However, the semantic values in the lowest layer of the bottom-up pathway are relatively low, resulting in poor performance when detecting small or distant hands. Additionally, the proposed system is designed to detect and track only one hand to reduce system complexity. Consequently, it may not be suitable for implementation in systems that require the detection and tracking of multiple hands, such as hand gesture recognition systems where interpreting gestures often requires the presence of a pair of hands. It is essential to consider these limitations when evaluating the applicability of the proposed algorithm in various contexts and to explore potential enhancements or alternative approaches to address these challenges effectively.
# Analysis of Powertrain Loading Dynamic Characteristics and the Effects on Fatigue Damage
## 2 Collection and Analysis of Test Loading Spectra  
### Rotary Telemetry Torque Measurement System  
In order to measure the torque on the engine and the transmission output shafts, a rotary telemetry torque measurement system (model 8180) manufactured by PCB Load & Torque, Inc. (Depew, NY, USA) was used. The measurement system consists of a control unit, rotor electronics, an inductive power module, a transfer winding, and an inductive head, as shown in Figure 1a. The rotor electronics, inductive power module, and transfer winding were mounted on the engine and the transmission output shafts, respectively, as shown in Figure 1b. The signals measured by the strain gauge were wirelessly transmitted through the transfer winding to the inductive head and then collected by the control unit. The acquisition software on the computer analyzed the signals and realized the real-time torque on the engine and the transmission output shafts. Additionally, the shaft rotational speeds were recorded by the rotational speed sensors mounted on the flanges of the shafts.
# HAE: A Hybrid Cryptographic Algorithm for Blockchain Medical Scenario Applications
## 4 Experiments and Analysis
### Experimental Analysis  
In this section, we provide a comprehensive description of the experimental methodology employed for the hybrid cryptographic algorithm, HAE, and elucidate the advantages it holds over other algorithms. For our experiments, we utilized medical data extracted from patient records. These data were encrypted and decrypted using HAE within a Java environment, with a specific focus on protecting the AES key. From a security perspective, both AES and ECC underwent rigorous mathematical analysis. Their resilience against cryptographic attacks was scrutinized to affirm the superior security attributes of HAE. For assessing time efficiency, we selected the ECC, AES+RSA hybrid cryptographic algorithms, and the HAE algorithm, as previously explored by other researchers. We conducted encryption and decryption tests on medical data sets of sizes 1 MB, 5 MB, 10 MB, 15 MB, and 20 MB. By comparing the time taken by each algorithm for these operations, we aimed to highlight the time efficiency advantages of HAE. Regarding resource consumption, we again used the ECC, AES+RSA, and HAE algorithms to encrypt and decrypt medical data sets of sizes 1 MB, 5 MB, 10 MB, 15 MB, and 20 MB. By observing the differences in CPU utilization during the execution of these algorithms, we sought to demonstrate the resource consumption benefits of HAE.
## 3 Results  
### Ultrafine Dental Aerosol Particles Are Generated during Full-Crown Teeth Preparation  
To determine the behaviour of AGP in each simulated condition (SS, standard suction; RDI, rubber dam isolation; RDI + HVE, rubber dam isolation plus high-volume evacuator), a volume frequency distribution in the range 0.1-10 \(\upmu\)m was generated for both vestibular and palatal sides (Figure 2a,b, respectively). Using only standard suction, the curve showed a displacement to the left, in the range below 1 \(\upmu\)m in size, in both vestibular and palatal sides (Figure 2a,b). In contrast, in the RDI group, the curve was displaced to the middle and to the right, in the range between 1-10 \(\upmu\)m, for both sides. For the RDI + HVE group, the location of HVE in the palatal side was consistent with less particle generation whereas in the vestibular side the curve exhibited two peaks: below 1 \(\upmu\)m and over 7 \(\upmu\)m (Figure 2a,b). During full-crown preparation, in the SS group, no PM\({}_{2.5}\) was detected in the vestibular side or in the palatal side, and no significant difference was found in the vestibular side between PM\({}_{0.1}\) and PM\({}_{10}\) (Table 1). In contrast, there was a significant difference between PM\({}_{0.1}\) and PM\({}_{10}\) in the palatal side for the SS group (\(p<0.05\)). Only one of three experiments detected a small percentage of fine particles (PM\({}_{2.5}\)) in both vestibular and palatal sides of the RDI group (Figure 2a,b, Table 1). No significant difference was found in the volume fraction for PM\({}_{0.1}\), PM\({}_{2.5}\) and PM\({}_{10}\) between vestibular and palatal sides (\(p>0.05\)).  
Rubber Dam Isolation and High-Volume Section Reduce the Volume Fraction of Ultrafine Dental Aerosol Particles  
To determine the effectiveness of mitigation strategies to reduce dental aerosol particles, a comparison among SS, RDI and RDI + HVE groups was performed. In comparison to the SS group, a significant reduction of the ultrafine particles (PM\({}_{0.1}\)) was found when the RDI was implemented (\(p<0.05\)) but not for the use of RDI + HVE in the vestibular side (Figure 2a, Table 1). In the same side, a significant reduction of the coarse particles (PM\({}_{10}\)) was achieved with the use of both rubber dam isolation and high-volume suction (\(p<0.05\)) when compared to the SS group (Figure 2a, Table 1). On the other hand, in the palatal side, the ultrafine particles (PM\({}_{0.1}\)) were significantly reduced with the use of RDI alone or combined with HVE (RDI and RDI + HVE groups) (\(p<0.05\)) (Figure 2b, Table 1).
## 3 Results  
### Rotary Control  
#### 3.1.1 Identification of Significant Factors Influencing the Evaluation Parameters  
At least one factor had a significant effect on the evaluation parameters _time on task_ (ToT), _positioning accuracy_ (Acc. ), _presence_ (SUSQ), _workload_ (NASA TLX), _perspicity_ (UEQ\({}_{\text{P}}\)), _efficiency_ (UEQ\({}_{\text{E}}\)), and _dependability_ (UEQ\({}_{\text{P}}\)) of the User Experience Questionnaire (\(p<0.05\)). Table 5 shows a comparison of the factors and evaluation parameters as well as the significance values calculated using variance analysis. If no significance value was entered, this factor had no significant effect (\(p\geq 0.05\)) on the respective evaluation parameter. The factors AC, AG, etc., are interaction effects. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Factor** & **ToT** & **Acc** & **SUSQ** & **NASA TLX** & **UEQ\({}_{\text{P}}\)** & **UEQ\({}_{\text{E}}\)** & **UEQ\({}_{\text{D}}\)** \\ \hline
**A** & \textless{}0.001 & 0.003 & — & \textless{}0.001 & — & \textless{}0.001 & 0.012 \\
**B** & — & — & — & 0.038 & — & \textless{}0.001 & — \\
**C** & \textless{}0.001 & — & — & — & — & — & — \\
**D** & 0.020 & — & — & — & — & — & — \\
**E** & — & — & — & — & — & — & — \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Factors that had a significant effect (\(p<0.05\)) on the evaluation parameters when operating the rotary control. If a factor had a significant influence on an evaluation parameter, the significance value was entered. If the factor has no significant influence, “—” was entered. Figure 4 shows the effect of the factor angular resolution on the evaluation parameters _time on task_ (ToT) and _positioning accuracy_ (Acc.). The figure shows that the participants with an angular resolution of 5\({}^{\circ}\) needed on average 54.4 s less to complete the tasks than the participants with a resolution of 45\({}^{\circ}\). The angular resolution had the opposite effect on positioning accuracy. The test participants with an angular resolution of 5\({}^{\circ}\) made more errors. Table 6 presents the mathematical models used to calculate the evaluation parameters. In addition, the adjusted coefficient of determination (R\({}^{2}\)A\({}_{\text{Adj}}\)) and signal-to-noise ratio (SNR) are presented. The critical Cook's distance was not exceeded in any of the tests. By doubling a factor coefficient, the effect of the level change (e.g., 5\({}^{\circ}\) angular resolution to 45\({}^{\circ}\)) on the respective evaluation parameter could be determined in the respective unit. Figure 4 shows that by changing the angular resolution (A) from the low level (5\({}^{\circ}\)) to the high level (45\({}^{\circ}\)), the test participants needed on average 54.4 s longer to complete the tasks. This difference can also be seen in the equation \"ToT\" in Table 6. By doubling the factor coefficient 27.2, the difference of 54.4 s could be determined. This equation thus also shows that the test participants without haptic feedback (D) took on average 9.6 s longer to complete the tasks. Figure 4: Effect of the factor angular resolution on time on task and positioning accuracy.
## 4 Experimental Results
### Free Vibration Case  
Under this loading condition, a 20 mm initial displacement was imposed on the model, with its free vibration response captured as illustrated in Figure 6. Analytical results demonstrated that the SPPTMD significantly reduced the displacement response of the model under free vibration conditions. The SPPTMD responded rapidly, being activated almost instantaneously with vibration initiation. By 8.5 s into the vibration, a 50% vibration-reduction rate was achieved. At 14.5 s, the SPPTMD-controlled model exhibited a displacement of 1.00 mm with a 71.7% vibration-reduction rate. Furthermore, the damper reduced the model's settling time (defined as the duration required for displacement to decay to 5% of the initial value) from 102 s in the uncontrolled state to 14.5 s, representing an 85.8% vibration-reduction rate. These findings conclusively demonstrate the efficacy of the SPPTMD in suppressing structural free vibrations.
## 1 Introduction  
Understanding unconfined compression parameters (\(q_{u}\) and \(E_{t}\)) is critical in geotechnical engineering. These parameters aid in determining soil cohesion (\(c\)), which is required for calculating cohesive soil bearing capacity. Furthermore, the elasticity modulus (\(E_{t}\)) is important in predicting settlements. To perform the unconfined compression test, a soil sample must be collected from the field, which requires advanced skills and extensive experience. However, performing these tests in a laboratory is known to be both tedious and time-consuming [1]. To overcome these challenges, researchers investigated the relationships between unconfined compression parameters and physical soil properties. This correlation is useful for reducing the number of tests and the overall investigation costs.  
Several studies have been conducted over the last decade to establish relationships between soil's mechanical and physical properties. This approach, aimed at saving time and money during investigations, is based on previous research in soil mechanics and foundation engineering [1; 2]. Preliminary investigations and studies for any project can be conducted using predictive models and correlations in conjunction with relevant information, making the process of obtaining design parameters simpler. Machine learning beats traditional lab tests and basic regression models in unconfined compression testing because of its automation, speed, and cost-effectiveness. Machine learning reduces the need for tedious manual testing by rapidly analyzing soil data and predicting compression parameters, leading to faster results and reduced expenses.  
Expansive soils, which cover large areas of land worldwide, present a significant challenge in the construction of structures and roads on them due to the high cost associated with repairing damage caused by expansive soil shrink-swell cycles, including building and road cracks and collapse, costing billions of dollars annually [3; 4; 5; 6]. [PERSON] and [PERSON] [7] assert that expansive soils may result in greater financial losses than earthquakes or floods. In addressing this challenge, the behavior of expansive soils has been studied in numerous studies by adding different materials to improve their characteristics [8; 9; 10; 11; 12; 13; 14; 15]. Although these studies have shown that soil characteristics can be improved via the addition of materials, many unresolved questions remain. Therefore, the main purpose of this study is to investigate the effects of sand content on the unconfined compression test characteristics of expansive soil. This study evaluates the impact of varied percentages of sand, initial saturation levels, and dry unit weights on expansive soil behavior. Additionally, it aims to build a dataset for predicting unconfined compression test parameters.
# Exploratory Research on Satisfaction Degree in Distance Education
## 5 Results and Discussion
### The Relationship between Students' Distribution and Satisfaction  
#### 5.2.1 Distribution of Living Areas  
Generally speaking, regional environment and economic level also affect the students' acceptance of some novel things, and hence, we surveyed the students from different areas with different economic levels. From the perspective of regional economic level, 1537 people came from economically developed areas, such as Jiangsu, Beijing and Shanghai; 576 people came from economically medium areas such as Sichuan and Hunan; and 528 people came from economically disadvantaged areas such as Gansu and Guizhou. As shown in Table 3, their satisfaction ratios are 0.9562, 0.8813, and 0.8786, respectively. Students living in economically disadvantaged areas tend to experience worse educational environments and commonly have lower expectations for distance education, and hence, they cannot easily accept distance learning. In addition, it can be seen that students from economically developed areas have higher satisfaction with distance learning. Certainly, the influence of living areas may be very small for students' satisfaction, and the living standards of each student are not consistent. Therefore, we will make more detailed considerations for the analysis of the influences of living areas in the future.
# An Overview of Terahertz Imaging with Resonant Tunneling Diodes
## 2 THz Imaging  
The current THz imaging systems fall into two categories: active and passive imaging. Active imaging systems illuminate the target with a THz electromagnetic wave and acquire the amplitude and phase information through reflection or transmission of the signal [6]. This enables the construction of two-dimensional (2D) or three-dimensional (3D) images with high signal to noise ratio (SNR). In contrast, passive THz imaging captures the radiation that is naturally thermally emitted by or scattered from a target [18]. These two modalities have different characteristics such as contrast, resolution, dynamic ranges, and SNR.  
For active THz imaging, there are two different approaches: time domain (TD) and continuous wave (CW). TD THz imaging is based on a femtosecond laser to generate ultrafast optical pulses and stimulate THz radiation (photo-Dember effect) from low bandgap semiconductors such as GaAs, InGaAs, InAs, or GaAsBi [19; 20]. The THz transient normally covers very broad bandwidth extending from around 100 GHz to a few THz [19] and hence THz TD imaging can provide rich spectrum information for spectroscopy analysis. However, the system requires a complex, delicate setup with a large footprint. In contrast, CW systems probe the sample with single frequency or a narrow bandwidth signal. CW imaging affords a compact, simple, and relatively low-cost system [21]. Since it does not require complex optics such as those involved in pulsed THz TD system (THz-TDS), and it does not require a time delay scan, the image information can be processed more quickly [6; 22] which is an important factor for real-time applications. One of the typical pulsed TD and CW imaging systems is shown in Figure 1. By comparison, it can be seen that the CW system significantly reduces the system complexity at the cost of adaptability due to missing spectral information of limited bandwidth [22]. The CW THz system involves many parts such as the source and detector, optical components such as lenses and mirrors to collimate the beam, a signal processing algorithm to rebuild the image, etc. This paper will only focus on the THz source/detector device technology by using the emerging RTD devices.  
Figure 1: (**a**) Schematic diagram of a pulsed TD THz imaging system. (**b**) Schematic diagram of a CW THz imaging system.
## 6 Conclusions  
The accuracy of the surrogate models computed by using the RSM and ANN are compared in predicting the lift-to-drag ratio of a wind turbine airfoil. The surrogate models were constructed with several datasets that can be categorized depending on the type of input variables, the number of input variables, and the method regarding the design of experiment to generate datasets. Then, the optimization to maximize the lift-to-drag ratio of a wind turbine airfoil was implemented with the RSM-computed and ANN-computed surrogate models with G-DLB-8-3 and O-DLB-6-4.  
From the prediction accuracy tests of the surrogate models, the following conclusion are deduced: First, the ANN-computed surrogate model's prediction accuracy is higher when the offered datasets have a high level of complexity, but when it is not, the accuracy of the RSM-computed surrogate models can be higher. Second, the accuracy of the RSM-computed and ANN-computed surrogate models is not highly affected by the two types of design of experiment (FCCD method and discrete m-level based method) when it comes to generating the datasets. Rather, they are more sensitive to the number of datasets. Finally, when a large number of datasets is provided, the prediction accuracy of the ANN-computed surrogate model improves more than the one computed from the RSM. From the optimization results, optimization with the high-accuracy surrogate model finds the better optimal design than the one with a low level of accuracy, which reveals the significance of constructing a surrogate model with a high level of accuracy.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1G1A1007856).  
The author declares no conflict of interest.