esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Experimentation of Tensegrity Jumping Robots
## 1 Introduction
Jumping robots have significant application potential in the field of scientific exploration and survey. In planetary exploration, its unique jumping ability enables it to easily cope with the complex terrain of planetary surfaces, thus realizing efficient movement. In addition, they have high autonomy and adaptability, can work under extreme environmental conditions, and synergize with other exploration equipment to form a planetary exploration network [34]. In the field of exploration, field exploration work is often faced with complex and variable terrain conditions. Traditional exploration equipment is limited to moving in these terrains, while small tensile jumping robots, with their unique jumping ability, can easily cross obstacles and realize efficient movement. These robots can carry a variety of sensors and exploration equipment for surface and subsurface data acquisition and analysis. By jumping and moving, the robots are able to cover a wider area, collect more data, and improve the efficiency and accuracy of exploration [35]. In this article, we propose a modular tensegrity-based jumping robot, which can achieve a stable jumping process. Based on that, dynamic models of the jumping robot are established, which include dynamic modeling of the simultaneous jumping process and step-up release process of the robot. Afterward, the effect of structural parameters on the jumping performance of the robot is analyzed in detail. In particular, we find that releasing each module of the robot sequentially is better than releasing modules of the robot simultaneously in terms of jumping height and distance. Finally, simulation and experimentation are conducted to demonstrate the jumping performance of the robot proposed in this article. The organization of this article is as follows. In Section 2, we cover the design of the jumping robot based on tensegrity structure, which includes the structure design of the jumping robot and the jumping mechanism of the robot. In Section 3, we delve into the dynamic-based performance analysis of the jumping robot. Section 4 presents the results of the experimental study. Finally, in Section 5, we draw our conclusions.
## 3 Results
### Comparison of Turbulent Kinetic Energy
#### 3.3.3 Comparison of Turbulent Kinetic Energy at z/b = 1.25  
Figure 17 shows the contour plots of the turbulent kinetic energy at z/b = 1.25. Table 14 presents the locations of the contour line at z/b = 1.25. Judging from the 0.75 contour line in the low-wind-speed area, the x/b-value for the STLKE model was 0.88, which was quite close to the value of 0.87 from the experiment, followed by the results of the EBKE model and RTLKE model, while the values for the AKNKE model and the RKE model were quite different from those from the experiment.  
Figure 16: Contour plots of turbulent kinetic energy at z/b = 0.125.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **AKNKE** & **EBKE** & **RKE** & **STLKE** & **RTLKE** & **Wind Tunnel Experiment** \\ \hline Location of the contour line of 0.5 in & \multirow{2}{*}{1.75} & \multirow{2}{*}{1.50} & \multirow{2}{*}{-} & \multirow{2}{*}{0.55} & \multirow{2}{*}{1.47} & \multirow{2}{*}{0.70} \\ X-direction (low-wind-speed area) & & & & & & \\ \hline Location of the contour line of 1 in & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-0.48} & \multirow{2}{*}{-1.02} \\ Y-direction (high-wind-speed area) & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 13: Location of contour line at z/b = 0.125 (turbulent kinetic energy).  
Based on the comparison of the 1 contour line in the high-wind-speed area between the simulation results and the wind tunnel experiment results, the z/b-value for the STLKE model was \(-0.90\), which was the closest to the value of \(-1.22\) from the experiment, followed by the values for the AKNKE model, the EBKE model, and the RKE model, while the value for the RTLKE model was quite different from that from the experiment.  
Based on the comparison of the turbulent kinetic energies on the vertical and horizontal planes (Tables 15 and 16), the RTLKE model and the STLKE
# Influence of Multiple Openings on Reinforced Concrete Outrigger Walls in a Tall Building
## 2 Design of Reinforced Concrete Outrigger Wall with Multiple Openings
### Verification by Numerical Examples  
The accuracy of the proposed equations for lateral displacement at the top of tall buildings and shear forces developed at the outrigger walls are verified by comparing with the results from linear finite element analysis. MIDAS-Gen [17] was used for the linear finite element analysis because it is widely used in the structural design practice. The outrigger walls and cores were modeled with the quadrilateral plane stress elements with incompatible modes [17].  
Six models were analyzed to verify the proposed equations. Models A1-A3 were the single-outrigger structures. Model A4 and A5 were the two-outrigger structures. Model A6 included the three-outrigger structures. The overall height of the structure is \(H=280\) m, with story height being equal to \(3.5\) m with \(80\) stories. The additional dimensional conditions are shown in Figure 4. The uniform lateral load \(w\) is applied at the center of the core wall. The floor diaphragm was neglected to assign the same boundary conditions as assumed in Equations (6) and (7). Except for the locations, all outriggers have the same dimensions and elastic modulus of \(36.4\) GPa. The lateral displacement at the top of the structure \(\Delta_{top}\) and internal shear forces of the outrigger walls \(V\) from two analysis methods are presented in Table 1. The difference shown in Table 1 is the percentage of the absolute value of the difference between the two results, which is compared to the results from the finite element method. As shown in Table 1, the differences in the top drift and shear forces between two analysis results were less than \(5\%\). A possible explanation for the discrepancy might be that the effective bending stiffness given in Equation (1), in which the clear distance and shear deformation are included, does not represent the actual deformation of the outrigger walls. However, these differences can be regarded as permitted in a structural design practice. Taken together, these results can hold the view that the proposed analytical equations for the outrigger walls that introduce the equivalent bending stiffness are valid.
# A Novel Power-Saving Transmission Scheme for Multiple-Component-Carrier Cellular Systems
## 4 Proposed Scheme  
The scheme proposed and presented herein is composed of five components, namely, the session admission control (SAC), transmission power estimation (TPE), SCC activation/deactivation (SAD), fair bandwidth allocation (FBA), and fair power adjustment (FPA) components.  
The significance of each individual component is described as follows. The SAC component is used to periodically determine whether the system should accept or deny new session requests to maintain the data rates of the users being served. The TPE component is utilized to estimate the total transmission power needed to satisfy the active users that have been granted admission by the SAC.  
The SAD component is employed in order to activate/deactivate the SCCs as necessary in order to meet the needs of the active users as determined by the TPE. The FBA component is used to ascertain which active users should be assigned to which individual sub-channels in order to maintain the fairness indexes of the different types of users at acceptable levels. The FPA component is then utilized to further adjust the transmission power levels for the individual sub-channels scheduled by the FBA in order to enhance the channel efficiency (which is accomplished by employing the water-filling concept) and maintain the required minimum data rates of the users selected by the FBA.  
Moreover, the SAC component is employed in the admission gate, while the TPE, SAD, FBA, and FPA components are employed in the scheduler. A conceptual flow chart of the proposed scheme is presented in Figure 2.  
Notice that the SAC component constitutes the first operation executed at the beginning of every round for the purpose of scheduling session requests. In addition, the average signal-to-noise ratios for those CCs which are activated will be estimated on a periodic basis (_i.e._, during each round) by the individual users, and those estimates will then be sent back to the BS. Given all the conditions stipulated above, a detailed description of the entire proposed scheme is provided in the following subsections.
## 3 Results and Discussion
### Adsorption Studies
Moreover, the adsorption capacity of magnetic nanocellulose not sulfonated was evaluated on the amine with the best performance, \(N\),_N_-disisopropylplethylamine, observing a very low result (\(\cong\)2%). Therefore, it can be concluded that the adsorption activity is due to the sulfonic group presence on the surface on the cellulose nanomaterial. Finally, the calculated loading factors (Q) are listed in the following table (Table 2). Finally, reusability experiments of the composite material were conducted on \(N\),_N_-disisopropylplethylamine. The obtained result demonstrated that after the first use (fresh = 94%) and the first recycle at 92%, the composite material lost its adsorptive properties, dramatically decreasing the absorption activity to 62%. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Analyte** & **Q (mg/g)** & **Analyte** & **Q (mg/g)** \\ \hline Piperidine & 57.8 & 4-methoxybenzylamine & 4.8 \\ \hline \(N\),_N_-disopropylplamine & 76.3 & Aniline & 2.4 \\ \hline
1,5-dimethylhexylamine & 56.0 & _N_-methylaniline & 11.9 \\ \hline
2-ethylhexylamine & 56.4 & 2,4-dimethylaniline & 1.4 \\ \hline Octylamine & 42.7 & \(N\),_N_-diethylaniline & 19.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Loading factors of the magnetic composite.
## 5 Result and Analysis
### Performance Comparison
#### 5.2.2 Performance Comparison on Testing Dataset
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multicolumn{2}{c}{**PH**} & \multicolumn{2}{c}{**10 Minutes**} & \multicolumn{2}{c}{**30 Minutes**} & \multicolumn{2}{c}{**60 Minutes**} \\ \cline{2-13}  & **Time** & **R.H** & **PN** & **UN** & **C.L** & **A.E** & **R.H** & **PN** & **UN** & **C.L** & **A.E** & **R.H** & **PN** & **UN** & **C.L** & **A.E** \\ \hline \multirow{4}{*}{12-03} & 08.00 & **0.8784** & 0.8735 & 0.8336 & 0.8359 & 0.7709 & **0.8653** & 0.8541 & 0.8463 & 0.8296 & 0.7063 & **0.8522** & 0.8293 & 0.7770 & 0.7302 & 0.6978 \\  & 09.00 & **0.9142** & 0.8726 & 0.8496 & 0.8236 & 0.7848 & 0.8280 & **0.8294** & 0.8180 & 0.8357 & 0.7292 & 0.8150 & 0.8293 & 0.7930 & 0.7118 & 0.7263 \\  & 10.00 & 0.8732 & **0.8793** & 0.8231 & 0.8554 & 0.7936 & **0.8688** & 0.8500 & 0.8210 & 0.8286 & 0.7514 & 0.8529 & 0.8489 & 0.8222 & 0.7341 & 0.6954 \\  & 11.00 & **0.9261** & 0.8794 & 0.8344 & 0.8490 & 0.7748 & 0.8439 & **0.8347** & 0.8120 & 0.8361 & 0.7164 & **0.8475** & 0.8375 & 0.8160 & 0.7447 & 0.6882 \\ \hline \multirow{4}{*}{12-04} & 08.00 & **0.8888** & 0.8716 & 0.8279 & 0.8264 & 0.7605 & **0.8550** & 0.8340 & 0.8369 & 0.8250 & 0.7223 & 0.8386 & 0.8407 & 0.8281 & 0.7638 & 0.7041 \\  & 09.00 & **0.8952** & 0.8743 & 0.8430 & 0.8417 & 0.7466 & **0.8667** & 0.8243 & 0.8284 & 0.8212 & 0.7338 & 0.8863 & **0.8400** & 0.8035 & 0.7425 & 0.6938 \\  & 10.00 & **0.9176** & 0.8639 & 0.8120 & 0.8412 & 0.7699 & **0.9070** & 0.8437 & 0.8265 & 0.8296 & 0.7227 & 0.8651 & 0.8400 & 0.8205 & 0.7512 & 0.6908 \\  & 11.00 & **0.9112** & 0.8716 & 0.8763 & 0.8427 & 0.7630 & 0.8321 & **0.8383** & 0.8441 & 0.8282 & 0.7152 & **0.8433** & 0.8317 & 0.7881 & 0.7408 & 0.7162 \\ \hline \multirow{4}{*}{12-05} & 08.00 & **0.9094** & 0.8714 & 0.8461 & 0.8320 & 0.6998 & 0.8263 & **0.8301** & 0.8246 & 0.8344 & 0.8355 & 0.8384 & 0.8194 & 0.8474 & 0.7239 & 0.6908 \\  & 09.00 & **0.9083** & 0.8473 & 0.8439 & 0.8356 & 0.79 Figure 4: Road-wise accuracy for traffic congestion prediction on 3 December 2019. (**a**) Prediction accuracy for prediction horizon of 10 min. (**b**) Prediction accuracy for prediction horizon of 30 min. Figure 5: _Cont._
# Integrated Gradients for Feature Assessment in Point Cloud-Based Data Sets
## 1 Introduction  
With the proliferation of point clouds for processing 3D data and the falling cost of Light Detection and Ranging (LiDAR), cameras, and other sensors that can generate point clouds, it becomes advantageous to combine multiple sensor types to increase the information acquired [1; 2]. This could even consist of synthetically generated data not based on LiDARs but using existing geospatial data sources [3], thus vastly increasing the number of possible input features. Combined with large neural networks based on different approaches [4; 5; 6; 7], it is difficult to evaluate which model fits best or what parameters to choose. This can either be performed manually by training each model with each combination of features, or on the other side of the spectrum using automated machine learning [8]. However, due to the nature of point clouds, the computational resources for both these methods are enormous. Therefore, we propose using the gradients generated while training a model to generate a quantitative evaluation of the impact each feature has on training. This has the added benefit of enabling some insights into how a network works and enable streamlining some hurdles faced when applying or refining machine learning models.  
Significant progress has been made in this area when using data sets composed of sequences of discrete symbols, such as words or subword units used in natural language processing [9]. This allows for handling out-of-vocabulary words and capturing morphological information. Furthermore, it allows the network designer to learn whether a phrase has a positive or negative connotation such as the words \"good\" or \"bad\". Transferringthis sequential approach that relates to nuances and structures of the human language to the unstructured nature of point clouds and their geometric and spatial properties presents unique challenges and is one of the reasons why there has not been as much progress in this area.  
We adapted the integrated gradients algorithm to be run on a large-scale urban point cloud dataset in order to gain greater insight into how a prediction is made and what influence the input features have on the prediction. RandLA-Net [10] was chosen as the network architecture to implement as it has 1/12 th of the parameters compared to KPConv [6] and a similar mean Intersection over Union (mIoU) score. The low number of parameters is essential as the gradients accumulated by each pass scale with the number of parameters. Processing the gradients was implemented for two datasets composed of outdoor scenes, the first being Semantic3D, which is generated using a laser scanner. Moreover, the second is referred to as SVGEO as it was generated photogrammetrically by svGeosolutions GmbH using orthographic pictures taken from a drone and then annotated by Fraunhofer IPM.
## 4 Conclusions  
The current study offers a detailed analysis of engine performance and emissions in a heavy duty diesel engine during cold, intermediate, and hot start operation. The experiments were performed over a custom designed drive cycle, which consisted of eight engine run segments of 120 s each, having abrupt load changes within the segment and an engine stop period of 60 s between each segment. The current study was conducted to highlight the performance inefficiencies during the intermediate start stage, which was observed to be longer than the cold start stage. Listed below are the main conclusions of the current study:  
1. The BSFC, NOx, and HC emissions were primarily influenced by the injection strategy.
2. The IMEP, BMEP, and FMEP were primarily dependent on the engine coolant and oil temperature and only showed improvements in Stage 3, when the oil temperature reared normal operating temperature.
3. The intermediate start stages (Stages 2 and 3) were found to be as critical as the cold start (Stage 1).  
Figure 17: Fuel-to-air equivalence ratio at each load in each segment at **(a)** 1500 and **(b)** 2000 rpm. The blue circle represents 100% load, the red diamond 50% load, the green pentagon 75% load and the pink hexagon 25% load.  
4. The advanced injection strategy during cold start was helpful in generating higher in-cylinder pressure and temperatures and thus achieving a faster warm up of the engine.
5. While switching to a retarded injection strategy aided in keeping the in-cylinder temperatures and pressure within the allowable range of the engine design, it also reduced the NOx emissions and led to increased BSFC and HC emissions.
6. The higher the load, the smaller the normalised FMEP (by IMEP), which indicated higher mechanical efficiency at higher loads and the energy lost at lower load most likely being utilised to heat up the oil during the intermediate warm up stage.
7. A decrease in NOx and an increase in HC emissions with an increase in ISFC at 25% load at 2000 rpm occurred in Stage 2 and onwards, which is understood to have been caused by combustion deterioration at lower loads at 2000 rpm.  
Conceptualization, F.L. and R.J.B.; Formal analysis, F.L. and T.B.; Investigation, F.L., A.Z., P.A., M.J., Z.R. and R.J.B.; Methodology, F.L., A.Z., P.A., S.S., Z.R., R.J.B. and T.B.; Project administration, S.S. and T.B.; Supervision, T.B.; Writing--original draft, F.L.; Writing--review & editing, T.B. All authors have read and agreed to the published version of the manuscript.  
This work was jointly funded by the School of Engineering at Deakin University and the Biofuel Engine Research Facility at Queensland University of Technology.  
The authors would like to express their gratitude to [PERSON], [PERSON] and [PERSON], the laboratory technical staff in the BERF at QUT for their immense support with conducting the experiments. The authors would like to acknowledge and express gratefulness to Caltex for donating the required diesel to run this experimental campaign.  
The authors declare no conflict of interest.
## 3 Analysis Key Components
### Analysis of the Chain Drive Film-Soil Separation Mechanism  
#### 3.2.1 Structure of the Soil Separation Mechanism  
The chain drive film separation mechanism is a key component of the tillage layer film recovery machine, and it mainly consists of active chain rollers, driven chain rollers, carrier rollers, chain, shaking rollers, chain rods and spring teeth. Its structure is shown in Figure 5. The chain and tooth film separation device consist of four 24 A chains and two rows of chain rods forming two sets of parallel chain-and-tooth devices. The ends of the chain rods are mounted on two customized 24 A chains and fixed to the chains by bolts. The spring teeth are mounted on the two adjacent chain rods to achieve the radial limit of the spring teeth around the chain rod axis, and the axial positioning of the spring teeth on the chain rods is achieved by a positioning sleeve.  
The chain drive film and soil separation device are designed to separate the soil and film residue from a 150 mm deep layer of tillage by shaking the chain rods and pulling the times together. The minimum clearance between the chain rods is 55 mm, and the distance between adjacent teeth is 100 mm, with the last row of teeth being installed centrally in relation to the previous row.  
The projectile tooth structure is shown in Figure 6. The teeth are machined from 65 Mn spring steel of \(\varphi\)4 mm, the tip of each tooth is 30 mm long, the distance between adjacent tips is 80 mm and the center distance of the winding circle is 80 mm, which creates a minimum gap of 4 mm and a maximum gap of 13 mm when the teeth are installed on the chain stays with a minimum distance of 55 mm to increase the shaking of the teeth in the unloading area and improve the unloading rate.  
Figure 5: Schematic diagram of the chain drive film soil separation mechanism. 1 driven chain rollers; 2 upper rollers; 3 lower rollers; 4 chains; 5 jiggle rollers; 6 chain rods; 7 pop-up teeth, and 8 active chain rollers.  
Figure 6: Schematic of spring tooth.  
To obtain a better screening capacity for the chain drive membrane-soil separation mechanism, a speed of 2.2 m/s was used for the chain operation [29], and the theoretical speed of the active chain roller was calculated to be 283.75 r/min.
# A Survey on Different Plant Diseases Detection Using Machine Learning Techniques
## 2 Basic Steps in Identification of Diseases from Leaf Images
### Preprocessing  
Preprocessing is one of the most important steps in the identification of plant diseases. Several preprocessing steps exist such as the resizing of the images to fit the model, the removal of noises, color transformation, morphological operations, the segmentation of the disease region, etc.  
Different filtering techniques, such as the Wiener filter, median filter [30] and Gaussian filter [31], are used to remove the noises in the disease-affected image. Different color spaces are used in image processing, such as RGB, HSV, CIEL*a*b* [32], YCbCr. To find the region of interest (ROI)/disease area in the leaf images, different segmentation techniques are used such as color thresholding [33; 34], the Sobel edge detector [35], [PERSON]'s segmentation [36; 37] and K-means clustering [38; 39; 40].