esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing the Honeycomb Spoke Structure of a Non-Pneumatic Wheel to Reduce Rolling Resistance
## 1 Introduction
found that thin-walled structures and porous materials can exhibit more efficient and effective energy absorption performance by introducing gradient properties and that structures and materials with functionally graded properties exhibit better crashworthiness [21]. A new in-plane gradient honeycomb was proposed and investigated by [PERSON] et al. The results showed that the compressive strength and energy-absorbing capacity of the positive gradient honeycomb structure were significantly enhanced compared to the non-gradient one [22]. It can be seen that the gradient honeycomb structure does have better overall performance than the normal honeycomb structure. This structure can also be applied in the design of the spoke structure of NPT, which can also make NPT have good performance. [PERSON] et al. designed a gradient inverse tetrachiral structure, applied it to NPT, and used the finite element method to study its mechanical properties under compressive load. The results showed that the gradient inverse tetrachiral structure and the NPT based on this structure have some similarity in the deformation mode [23]. The above studies show the good prospect of the gradient structure in the design of NPT spokes. Moreover, at present, NPT mainly adopts spokes with a uniform thickness, and the research mainly focuses on static performance analyses. There are relatively few studies on dynamic performance and rolling resistance, which also proves the novelty of the research in this paper. For this reason, this paper revolves around the rolling resistance of a gradient honeycomb non-pneumatic tyre, which is structured as shown below. In Section 2, a finite element model is developed, and the modelling approach is tested through experiments. In Section 3, an experimental design is used to analyze the sensitivity of different parameters of the spoke structure to the effect of rolling resistance so as to identify the significantly affected components. The optimized design of the significantly affected components is carried out to minimize the rolling resistance and maintain the stiffness performance.
## 3 Results and Discussion
### Dose Response  
Figure 4a shows the TL dose response of windshield glass for doses in the range 1 Gy to 10 Gy while Figure 4b shows the overall TL response, from 1 Gy to 100 Gy, inclusive of the lower dose values included in Figure 4a. Highly desirable for a good dosimeter is that it should exhibit excellent linearity of response over a wide range of dose. In Fig. 4 the TL response of each of the glass samples has been normalized to unit mass, each dose point representing the mean of three individual measurements. Good linearity is observed within the two dose regimes, 1 Gy to 10 Gy and 10 Gy to 100 Gy, pointing to the strong potential for use of windshield glass as a retrospective dosimetric base-material. The variations are associated with inhomogeneity in batch samples [23; 24]. The Honda windshield provides the greatest response, TL yields being respectively 1.08 x and 1.93 x that of the Toyota and Proton windshields. With a regression coefficient (R\({}^{2}\)) of 0.97, the Honda windshield also offers high dose linearity, the other two vehicle brands showing R\({}^{2}\) values of greater than 0.76 when taken across the entire dose range, 1 Gy to 100 Gy. Clearly much superior R\({}^{2}\) values will be obtained when evaluations are made within the restrictive dose range 10 Gy to 100 Gy. As seen from Figure 4a,b, the TL intensity for Honda and Toyota car brand samples displayed a significant difference at 1 Gy and started to diminish further increasing the doses, indicating the possibility of these samples to yield pronounced TL difference in much lower doses. Hence, there is a need for further study on this matter. In more limited study of the TL response of car window glass rather than windshield glass, [25] gave beta doses of 1-10 Gy, showing similarly well-behaved dependency with dose, although in the absence of sampling procedure, brand of car, etc.  
Figure 3: TL sensitivity of glass samples, for doses from 1 to 100 Gy, delivered by \({}^{60}\)Co.
## 3 Results  
### Meteorological Conditions  
Figure 3 illustrates the thermopluviometric graph for the districts of Beja, Evora and Portalegre. The total precipitation (b) in this cycle of pasture production (July 2020 to June 2021) was 500 mm for the district of Beja, 628 mm for the district of Evora and 840 mm for the district of Portalegre. Just based on this climatic data, the climatic variability between districts where the farms of this study are located, is already evident. The differences in the amount of total rainfall were 128 mm, between Beja and Evora, and 240 mm, between Evora and Portalegre, which influences pasture production and quality. The average temperature (a) between July 2020 and June 2021 was 17.2 \({}^{\circ}\)C for the district of Beja, 16.9 \({}^{\circ}\)C for the district of Evora and 16.4 \({}^{\circ}\)C for Portalegre. These data also show that average temperature during the vegetative cycle of the pasture is higher at the farms located further south. As can be seen in Figure 3a,b, the distribution of both precipitation and temperature are irregular throughout the year: some months show large amounts of precipitation and others only residual values. The spring of 2021 was particularly dry, which may have influenced pasture quality and production. In March 2021 the precipitation values were 24.4 mm, 19.6 mm and 14.3 mm for the districts of Beja, Evora and Portalegre, respectively, and in April they were 40.6 mm, 64.2 mm and 92.5 mm for Beja, Evora and Portalegre, respectively. However, we should note the high values of precipitation in February, when temperatures are already beginning to increase.  
The Alentejo region, where the study fields are located, is affected by the Mediterranean climate, with characteristic hot and dry summers and mild and humid winters, although there is a very pronounced irregularity of precipitation. Peak production and pasture quality occurs in spring, when the soil moisture content (SMC) is above 10% [8] and temperatures rise, but not excessively. In the Alentejo region, in dryland pastures, as temperatures increase throughout the spring and, consequently, the available SMC decreases, the pasture begins to dry out and its nutritional value gradually decreases. Therefore, this study took place only in late March and early April, as the pasture was at its peak production.
# Advances in the Neural Network Quantization: A Comprehensive Review
## 3 Quantization Techniques
### The Accuracy Loss Evaluation of the Quantized Models  
The quantized model needs to be tested to ensure that the loss of accuracy is within an acceptable range. Some researchers have designed several quantization accuracy loss assessment criteria from the perspective of statistical data analysis to improve the performance of quantization models. Qualcomm [57] designed the Signal to Quantization Noise Ratio (SQNR) to measure the quantization accuracy of different quantization bit-widths. [PERSON] [58] proposed a method to determine the quantization coefficients using the quantization mean square error as a metric and proposed a method to update the statistical parameters for small networks with serious performance loss. However, the above accuracy loss assessment criteria evaluate the loss from the perspective of data statistics, resulting in a deviation from the actual application data and a lack of practicability in obtaining the accuracy loss of the task. [PERSON] [59] constructs a quantization accuracy predictor based on the highly flexible once for all network [60], which encodes the model structure and quantization strategy to directly predict the accuracy of the quantization model. However, in this method, collecting the quantization dataset takes 16,000 GPU hours, which is quite expensive and time-consuming, and the quantization predictor can only predict neural network models of a predetermined structure.  
Existing studies on the quantization of neural network models have less systematically analyzed the mechanism of computational accuracy loss generated by the quantization process, making it difficult to effectively measure the accuracy loss of different neural network models and different computational tasks after quantization. Therefore, how to accurately measure the accuracy loss of quantized models and how to provide a judgement basis for the quantization method of neural network models are challenges that still need to be solved.
# Adaptive Equalization for Dispersion Mitigation in Multi-Channel Optical Communication Networks
### Optical Compensation  
CD is caused due to the fact that \(\Delta\lambda\
eq 0\) during the transmission. PMD effects are mainly caused by disorders in vector orientation of the propagating light. Appropriate techniques are required to minimize these effects. Dispersion compensation fibers (DCFs) are used to overcome these effects in order to increase the system reliability. The inverse dispersion of conventional dispersion compensation fiber (CDCF) has high impact for optical C and L band [7] windows, to significantly turn down the dispersion losses. The approaches studied thus far in the literature for optical dispersion compensation are described as follows.
# A Drone-Enabled Approach for Gas Leak Detection Using Optical Flow Analysis +
## 3 Methodology
### Frame Buffering for Batch Processing  
The introduced gas leak detection algorithm processes all the frames in an image stream to provide the expected results, which means it cannot be used as a real-time process deployed in an aerial platform. As shown in Figure 2, to address this issue, a frame buffering technique is employed to provide real-time continuous detection and visually-enhancement of gas emanations. In this approach, a fixed-size buffer is defined that collects  
Figure 2: The abstract process flow of the proposed technique.  
all the acquired thermal image frames and passes them as a frame batch to the remaining steps when its size reaches a predefined threshold. In this way, the system is expected to provide continuous results. In addition to real-time capabilities, this approach makes the proposed algorithm more dynamic and more resistant to large drone's movements as it brings the focus more on recent frames. In Algorithm 1, the proposed steps to process the online thermal image stream for gas leak detection and visually-enhancement of gas emanation are explained.  
```
Data: \(H_{camera}\longleftarrow\) handle to the thermal camera \(FS\longleftarrow\) the size of frame buffer begin  define the frame buffer as \(B_{frame}\) with the size \(FS\) whilethe camera handle is alive do \(f_{current}\longleftarrow\) collect one frame from \(H_{camera}\) if\(f_{current}\) is invalid or does not have defined size then  skip the frame and go to the beginning of the loop  end if  add \(f_{current}\) to \(B_{frame}\) /* Check if the size of the frame buffer reached the defined threshold */ if\(length(B_{frame})\geq FS\)then /* Apply the stabilization method to the buffered frames */ \(B_{stabilized}\longleftarrow\) stabilize the frames /* Apply the proposed algorithm on stabilized algorithms */ \(P^{M},R_{HS},R_{HI}\longleftarrow\) apply Algorithm 2 using \(B_{stabilized}\)  visualize the results \(P^{M},R_{HS},R_{HI}\) /* Clear all the frames from the buffer and prepare it for next round */ clear all frames in \(B_{frame}\)  end if  end while
```  
**Algorithm 1**The proposed steps for drone-enabled gas leak detection and visually-enhancement of gas emanation.
## 3 Experimental Set-up  
### Electrical Treeing Test  
The schematic diagram of the experimental setup for conducting studies on electrical treeing is shown in Figure 3. The experiments were conducted to observe the growth of the electrical tree in 1, 3, and 5 wt % filler loading of directly mixed nanocomposites (DMNC), silane-treated nanocomposites (STNC), and plasma-treated nanocomposites (PTNC). The DMNC, STNC, and PTNC specimens were subjected to a fixed voltage of 10 \(\pm\) 0.6 kVrms at 50 Hz. The samples were placed inside an acrylic cell containing silicone oil to prevent surface flashover. All tests were carried out at room temperature of 30 \(\pm\) 4 \({}^{\circ}\)C and 50% \(\pm\) 10% relative humidity. A set of ten samples for each condition were tested to consider the reproducibility of the electrical tree characteristics.  
Tree inception and propagation were continuously observed via a setup that consisted of an SZX16 Olympus research stereomicroscope (Olympus, Singapore), a DP 26 Olympus CCD camera (Olympus, Singapore), and a computer; the microscope and the CCD camera were interfaced to the computer  
Figure 3: Experimental setup for electrical tree growth monitoring.  
for tree images capturing. The effect of the nanofiller on the tree breakdown of the nanocomposites were studied by measuring the tree and breakdown time. A comparative study was carried out by comparing the existing treatment method and the plasma-treated nanofiller filled with SiR to compare the tree-suppression efficiency and effectiveness between DMNC, STNC, and PTNC.
# Validation of a Wind Tunnel Propeller Dynamometer for Group 2 Unmanned Aircraft
## 3 Results
### Manufacturer-Published Propeller Data and BEMT Results  
Figure 11, is a plot of BEMT results including thrust coefficient and propeller efficiency versus the advance ratio for an 18 \(\times\) 12E APC propeller. The plot includes results from blade element momentum theory (BEMT) and APC published data. The published APC data are obtained analytically according to the APC database website, and no further information is provided regarding data methodology. The BEMT results cover a range of propeller speeds from 1500 to 5500 RPM, whereas the APC data range is from 1000 to 6000 RPM. Each line on the plot represents a different RPM. The plot indicates that thrust coefficient decreases as advance ratio increases. Initially, propeller efficiency increases as advance ratio increases, then rapidly decreases for advance ratios greater than 0.63. APC results extend to a maximum advance ratio greater than 0.7, but BEMT results in this study are less than 0.7. Increasing RPM results in both higher thrust coefficient and propeller efficiency. The BEMT propeller efficiency peaks are lower than those from APC and occur at lower advance ratios. Thrust coefficient results from BEMT are lower than those from APC; however, they are similar in slope.  
Figure 11: BEMT and APC \(C_{T}\), \(\eta_{P}\) of a 18 \(\times\) 12 APC propeller. Each line represents a constant RPM increasing from bottom to top of the plot.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Pitch**} \\ \hline Diameter & 10 & 12 & 14 \\ \hline
18 & x & x & x \\ \hline
16 & & x & \\ \hline
14 & & x & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Propeller test matrix to study effects of diameter and pitch.  
Figure 12 shows experimental results from UIUC for a 14 \(\times\) 12E APC propeller compared to those from the BEMT code. The plot for BEMT and UIUC experimental results includes thrust coefficient and propeller efficiency as a function of advance ratio at 3500 RPM [27]. The results are in close agreement in terms of propeller efficiency up to an advance ratio of 0.6. The BEMT code under-predicts thrust coefficient by as much as 15% for advance ratios below 0.3. In general, the BEMT results are more reliable than the APC published performance in the previous figure. Though the BEMT results are only reliable for validating performance over advance ratios of 0.3 to 0.6, they capture the general trends beyond that range, including the slope of the thrust coefficient for advance ratios between 0.6 and 0.8 and the rapid drop in propeller efficiency at an advance ratio of about 0.8. The other main take-away is that the BEMT code used in this study is indeed properly applied, acknowledging that the analytical model is not expected to capture complicated viscous flow effects at high and low advance ratios where the blade experiences very low and high relative angles of attack. Discrepancies may also possibly result from experimental uncertainty and airfoil aerodynamic data that does not capture three-dimensional flow effects.
## 3 The Proposed Method
### Generation of the First Constrained Polygonal Space
#### 3.3.4 Occupancy of Obstacles at Distinct Regions of the CPS  
Besides the other complexity parameters described earlier, another important parameter that can lead to genuine performance concerns while pathfinding is the obstacles' occupancy at distinct regions of the CPS. If obstacles in large numbers are clustered at one location (e.g., obstacles' placement in the CPS is uneven), then the path quality likely degrades. The obstacle occupancy at one place introduces cycles/sharp-turns in a \(P\) because the \(P\) revolves around boundaries of many obstacles before approaching \(t\). To calculate occupancy \(\Pi_{o}\) of obstacles, we partition \(S_{1}^{3}\) into \(n\) sub-spaces \(\{s_{1},s_{2},s_{3},\ldots,s_{n}\}\) and find the obstacles occupancy in each subspace \(s_{i}\). The obstacles' occupancy in a \(s_{i}\) subspace can be determined by taking the ratio of the obstacles' occupancy \(\xi_{obstacles}\) in the \(s_{i}\) divided by overall obstacles' occupancy in the CPS. To calculate occupancy, the \(S_{1}^{3}\) is partitioned in five equal-size sub-spaces. The occupancy \(\Pi_{o_{i}}\) of the \(s_{i}\) can be mathematically expressed as  
\[\Pi_{o_{i}}=\frac{s_{obstacles}^{i}}{\xi_{obstacles}} \tag{14}\]  
where \(\xi_{obstacles}\) denotes occupancy of all obstacles in total from the CPS as given in Equation (2) and \(s_{obstacles}^{i}\) represent the \(ith\) subspace's obstacles occupancy, and its value can be computed using Equation (15):  
\[s_{obstacles}^{i}=\sum_{i=1}^{O^{\prime}}\omega\mathcal{O}_{i}^{\prime} \tag{15}\]  
where \(\omega\mathcal{O}_{i}^{\prime}\) represents an obstacle's volume, and \(O^{\prime}\) denotes all obstacles count in the CPS. After computing the occupancy of five sub-spaces, we determine the overall occupancy \(\Pi_{o}\) of the \(S_{1}^{3}\) using the following equation:  
\[\Pi_{o}=max\{\Pi_{o_{1}},\Pi_{o_{2}},\Pi_{o_{3}},\ldots,\Pi_{o_{n}}\} \tag{16}\]  
where \(\Pi_{o}\) is the obstacles' occupancy in the \(S_{1}^{3}\). The rationale to choose maximum values is to effectively deal with the worst cases. The occupancy analysis assists with finding the smooth paths by giving considerable attention to the regions of high occupancy.
# Model-Based Control and Model-Free Control Techniques for Autonomous Vehicles: A Technical Survey
## 2 Control of Autonomous Vehicle  
The key technologies for autonomous vehicles are structured into three distinct layers, as illustrated in Figure 1 [8], with each layer fulfilling a crucial role.  
The first layer, known as perception and localization, involves the interpretation and understanding of the surrounding environment. It encompasses the utilization of sensor data from various sources to detect and identify objects, analyze their positions and movements, and accurately determine the vehicle's own location in relation to its surroundings.  
The second layer, referred to as planning and maneuver decision making, focuses on generating optimal paths and making informed decisions regarding the vehicle's movements. This involves advanced algorithms and techniques that consider factors such as traffic rules, road conditions, and potential obstacles. Path-planning algorithms determine the safest and most efficient routes, while maneuver decision-making algorithms select appropriate actions, such as lane changes, overtaking, or merging, to navigate the vehicle through its intended trajectory.  
The third layer, known as decision making, involves higher-level cognitive processes that integrate information from perception, planning, and other sources to make intelligent decisions. This includes evaluating potential risks, predicting the behavior of other vehicles and pedestrians, and selecting the most suitable course of action in complex driving scenarios. Decision-making algorithms ensure that the autonomous vehicle can respond effectively to changing situations and prioritize safety at all times.  
The function of longitudinal control is to regulate the vehicle's speed and maintain a safe distance from the preceding vehicle by using acceleration or braking actions. On the other hand, the primary objective of lateral control is to minimize the vehicle's lateral displacement and heading error. This is achieved by precisely adjusting the steering wheel angle, enabling the vehicle to follow a desired trajectory. Both longitudinal and lateral control play essential roles in the foundation of an autonomous vehicle's control system.
# Experimental Study on Horizontal Pipeline Transportation Characteristics of Coarse Particle
## 2 Methodology
### Flow Morphology of Coarse Particle Group in Pipes  
Based on the varying flow states of coarse particles within a pipeline [11; 18], the flow patterns of coarse particle groups are typically classified into five distinct categories.  
1. Sedimentary Bed Type: In this type, particles accumulate statically at the bottom of the pipeline, forming a sedimentary bed, as illustrated in Figure 1.
2. Sedimentary Bed Type with Jumping/Rolling Particles: Here, some particles detach from the sedimentary bed and move forward by rolling or jumping, as depicted in Figure 2.
3. Nudging Movement: In this category, all particles participate in the two-phase flow movement, with the solid particles' motion dominated by nudging. This type is further divided into the following: Intermittent Nudging (sand dune): as shown in Figure 3. Continuous Nudging (sliding bed): as shown in Figure 4.
4. Non-Homogeneous Flow Suspension Movement: In this state, all solid particles are suspended and moving, but the concentration distribution of particles within the group is non-uniform. The fluid is treated as a non-Newtonian fluid, as illustrated in Figure 5.
5. Pseudo-Homogeneous Flow Suspension Movement: Here, all particles are suspended and moving, with a relatively uniform concentration distribution. The two-phase
# A Fast Calibration Method for Sensors of Atmospheric Detection System
## 3 System Control Method
### VPSSD Method
The temperature stability criterion at the current moment can be expressed as  
\[\big{|}T^{*}[t-(j-1)\Delta t]-T^{\prime}[t-(j-1)\Delta t]\big{|}\leq\max\big{\{} \Delta T_{ADC},\Delta T_{exp}\big{\}},j\in[1,q], \tag{16}\]  
where \(\Delta T_{exp}\) is the expected temperature calibration accuracy set according to actual needs. The core idea of the temperature stability criterion is to select the \(\Delta T_{ADC}\) and \(\Delta T_{exp}\) criteria according to the state to judge the stability of the \(T^{\prime}\). The workflow is shown in Algorithm 2. ```
1:Temperature setting signal \(T^{*}(t)\), system temperature response \(T^{\prime}(t)\), temperature error range related to the ADC \(\Delta T_{ADC}\), the expected temperature calibration accuracy \(\Delta T_{exp}\), calibration points \(q\). 2:Temperature stability criterion for \(T^{\prime}(t)\), a value of 0 is considered stable, 1 is considered unstable. 3:\(counter=0\), flag = 1
4:for all\(i=q,q+1,...,t/\Delta t\)do
5:\(counter=counter+1\)
6:for all\(j=i-q+1,i-q+2,...,i\)do
7:if\(\big{|}T^{*}\Big{[}t-\big{(}j-1\big{)}\Delta t\big{]}-T^{\prime}\Big{[}t- \big{(}j-1\big{)}\Delta t\big{]}\big{|}\leq\max\big{\{}\Delta T_{ADC},\Delta T _{exp}\big{\}}\)
8:\(flag=0\)
9:break
10:endif
11:endfor
12:endfor
13:endfor
14:returnflag
```  
**Algorithm 2** VPSSD Method