esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling Shearing and Bending Behavior of Kiwifruit Branches Using the Discrete Element Method
## 2 Materials and Methods
### Mechanical Property Test of Kivifruit Branch  
A 10 kN electronic universal testing machine (Figure 2), located in the biomechanics lab of the College of Mechanical and Electronic Engineering, Northwest A&F University (CMT6104, Meister Industrial System Co., Ltd., Shenzhen, China), was used to conduct the shearing test and three-point bending test. The elastic modulus and shear strength of the kiwifruit branches were determined using these measurements. Each test was replicated 20 times.  
For the shearing test, a shearing fixture was utilized as shown in Figure 2. The branch was positioned across the apertures of the stationary and moving blades prior to each test. The moving blade then descended to shear the branch at a loading rate of 20 mm/min. The test was terminated once the branch broke.  
Figure 1: Branch samples used for mechanical property tests: (**a**) The cross-sectional image of kiwifruit branch and (**b**) branch samples for experiment.  
Figure 2: Kiwifruit branch mechanical property test.  
A force-displacement curve can be obtained from each test. Peak shear force can be easily recognized from the force-displacement curve. The shear strength \(\tau\) was calculated according to Equations (1) and (2) [24]:  
\[\tau=\frac{F_{\text{s}}^{\text{max}}}{2A} \tag{1}\]  
\[A=\frac{\pi}{4}(D^{2}-d^{2}) \tag{2}\]  
where \(F^{\text{max}}\) is the peak shear force (N), \(A\) is the cross-sectional area of the branch (mm\({}^{2}\)), \(D\) is the external diameter (mm), and \(d\) is the inner diameter of the branch (mm).  
For the three-point bending test (Figure 2), the two ends of the branch were placed on a steel support. A cylindrical indenter was then used to apply load at a displacement rate of 20 mm/min. The elastic modulus Eb was determined using Equation (3) [24]:  
\[E_{\text{b}}=\frac{F_{\text{b}}L^{3}}{48 SI} \tag{3}\]  
where \(F_{\text{b}}\) is the load of the cylinder indenter (N), \(L\) is the distance between the two supports (70 mm), \(S\) is the deflection (mm), and \(I\) is the inertia moment of the branch.  
Since the cross-sectional geometry of the kiwifruit branch is approximately annular, the moment of inertia was calculated using Equation (4):  
\[I=\frac{\pi}{64}(D^{4}-d^{4}) \tag{4}\]
## 3 Test Result  
### Influence of the Slurry Concentration on the Friction Characteristics of the Interface  
The normal load has no effect on the variation trend of the shear stress (\"\(\tau\)\")-shear displacement (\"\(s\)\") curve of the interface and the friction coefficient of the interface, but only for the value of the shear stress of the interface. Therefore, taking the normal load of 100 kPa as an example, the variation trend of the shear stress-shear displacement curve of the pipe-soil interface under the normal load was analyzed (to facilitate the analysis of the friction characteristics of the interface, the pipe-soil interface without slurry was recorded as \"interface I\", and the pipe-soil interface with slurry was recorded as \"interface II\"), and the curve is shown in Figure 5. The following test results are shown in Figure 5.  
The peak value and stable value of the \"\(\tau\)-\(s\)\" curve of \"interface I\" were greater than those of the \"interface II\" curve. This shows that the slurry reduced the shear stress on the  
Figure 5: Friction characteristics of the pipe-soil interface with different slurry concentrations. (a) “\(\tau\)-\(s\)” curve of the pipe-soil interface without slurry and with slurry with a concentration range of 10–20%. (b) “\(\tau\)-\(s\)” curve of the pipe-soil interface with slurry with a concentration range of 20–40%.  
interface between the pipe and soil, which means that the slurry reduced the friction when the pipe wall slid relative to the sand, and the slurry had a lubrication effect.  
The \"\(\tau\)-\(s\)\" curve of \"interface I\" had an obvious peak value. When the shear displacement continued to increase, the shear stress of the interface gradually declined and tended to a constant value, and the curve showed obvious strain softening characteristics. This occurred as the compacted sand particles were closely arranged, and the sand particles underwent physical changes, such as tumbling and dislocation, during the shear process, resulting in relative rolling between the particles. With the continuous shear, the particle positions were rearranged.  
The \"\(\tau\)-\(s\)\" curve of \"interface II\" also showed that was a strain softening type. This was because the slurry is thixotropic, which under the action of shear, caused the viscosity and static shear stress of the slurry to decrease while after standing, the viscosity and static shear stress increased. Under the action of slurry thixotropy, when the pipe wall and sand slid relatively, the shear stress on the interface rose rapidly and reached the peak value. With the further development of shear, the viscosity and static shear stress of the slurry under shear declined. At this time, the shear stress decreased from the peak value and tended to a stable value [21].
## 2 Literature Survey
### Motivation  
Multicast applications must fulfill a variety of requirements including bandwidth, delay, throughput, and packet loss rate. These include QoS issues regarding how to allocate constrained resources to maximize the user experience. Previous solutions to this problem might have involved constructing a multicast tree to achieve the desirable aim, but these approaches did not optimally address requirements and satisfy the needs of individual subscribers in different groups. Delay is the most important QoS metric and it is especially sensitive in a wireless communication environment. Thus, if wireless sensor networks efficiently distributes resources, wireless users should be able to access multimedia content. Subscribers in the same group may utilize the same backhaul but may have different channel conditions depending on how far or the number of hops to source node. Thus, their experiences as users may vary drastically. It is therefore important to fairly allocate resources to each connection in a multicast tree according to the end-to-end delay. According to our research, in order to achieve both routing and load balancing in the context of a non-splittable flow. The approximation algorithms proposed select the fairest possible routing path in the most optimal manner. The next would be max-min fairness to maximize the total throughput while maintaining optimal fairness.
## 3 Results and Discussion  
### Effect of Occupants on RT  
Previous studies [35; 36; 43; 46] have clearly shown that occupants can provide additional sound absorption to a space, thus reducing its reverberation time; however, this contribution depends strongly on the primitive acoustic conditions of the space, such as the initial amount of sound absorption. To investigate the influence of occupants on the reverberation time inside the automotive cabin, the reverberation times in 250-4000 Hz octave bands under different occupancy modes for eight speaker-listener position configurations were first analyzed. This study employed the \(T_{30}\) as an indicator of reverberation time, which is twice the duration for the decay curves was from 5 dB to 35 dB below the steady state (or initial) level [64], as shown in Figure 2.  
Note that, aside from for the 250-500 Hz octave bands, adding occupants leads to a reduction in the reverberation time value because of the additional sound absorption provided by the occupants (see Figure 2). These trends are mostly consistent with the findings of previous studies [35; 38; 46; 49]. It is worth mentioning that adding occupants produces larger decreases in reverberation time values in the 2 and 4 kHz bands than in the other bands (see Figure 2); this is due to the larger increase in sound absorption provided by occupants in these octave bands [35; 38; 44]. Furthermore, the reverberation time values in the 250 and 500 Hz octave bands were increased after adding occupants in some cases. Moreover, the reverberation time values in the 250 Hz band were higher than in the higher octave bands for the automotive cabin. These should correspond to the low-frequency modal behavior and the modal reverberation [8]. In practice, increasing the low-frequency reverberation time has been confirmed to be adverse to speech intelligibility [65].  
In general, the percentage changes in the reverberation time value after occupants are added are mostly larger than the just-noticeable differences (JND, i.e., 5%) in the reverberation time [64] (aside from some cases in the 250 and 500 Hz octave bands). However, this does not indicate that the addition of occupants will lead to perceptible changes in reverberation because the reverberation time values are already very small (less than 0.15 s). In addition, the variations in reverberation time after adding occupants are much smaller than the results of the existing literature, such as in church [43], theater [34], and classrooms [35]. These confirm what was seen in refs. [35; 49; 50], that is, the addition of occupants to a more absorptive space leads to smaller incremental changes in the reverberation time.
# Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities
## 3 State of the Art of Practice and Research
### Current Practice and Research
#### 3.1.3 Future Logistics--Delivery Services and Load Transportation  
For efficiently delivering packages or parcels, UAV delivery is a highly promising application of UAVs. The potential of UAVs that can offer low-cost and faster delivery options will revolutionize how goods are delivered around the world. In disaster relief scenarios or for delivery of pharmaceutical supplies to remote locations, the use of UAVs can be valuable in cutting the cost and time involved in non-standard delivery networks [71]. However, the technology is still in its early stages of testing and is only available in a very specific region.  
UAV delivery has a number of advantages over the conventional means of delivery. UAV delivery is much faster due to the fact that the UAVs are often unaffected by road infrastructure and traffic conditions. If permitted, UAVs travel via an optimal aerial path from origin to destination. It is also cost-effective in terms of delivery time and being environmentally friendly. UAV delivery produces significantly lesser carbon footprints as compared to the standard road-based delivery methods, like package delivery with trucks [74]. In the United States, Amazon Prime Air is a futuristic delivery service that aims to offer packages to customers in under half an hour via UAVs. Following the lead of Amazon Prime developing a fleet of UAVs for small parcel deliveries, several businesses like DHL, Google, Mercedes-Benz, and UPS also continue to improve delivery technology while trying to comply with different regulations to use UAVs in the urban logistics sector [13]. Domino's Pizza has also begun testing UAVs to deliver the order to the customers.  
Along with the delivery services, many other researchers have also been working on efficient load transportation strategies employing UAVs. The performance of the UAV flight time is directly associated with the load they are carrying. In future transportation systems, the load may include passengers loads in addition to a large number of packages. This will be another new paradigm of UAV usage.  
With the emerging investigation, several dimensions and limitations of UAV load transportation have been unfolded. One of the critical issues of UAV load transportation is the trajectory tracking problem. To solve this problem, [PERSON] and [PERSON] proposed a nonlinear control strategy for transporting a suspended load [75]. Another challenge is navigating UAVs by avoiding obstacles along the way to ensure successful delivery. [PERSON] et al. investigated the importance of the inclusion of obstacle avoidance techniques in navigation UAVs in the forestry environment [76]. Along with many other applications, the use of multiple UAVs in load transportation is also explored. [PERSON] and [PERSON] discussed the application of multi-agent load transportation agriculture spraying [77].
# Applying Deep Learning to Medical Imaging: A Review
## 1 Introduction
### DL Techniques  
Several DL techniques have been applied to medical imaging [47; 48; 49; 50; 51; 52], with convolutional neural networks (CNNs) being the most prevalent [53]. CNNs are particularly suited for image analysis tasks due to their ability to capture local spatial patterns and automatically learn hierarchical representations from input images [54]. Other DL techniques that have been applied to medical imaging include recurrent neural networks (RNNs), which are well-suited for handling sequential data, and generative adversarial networks (GANs), which can generate new samples from learned data distributions [55]. In assessing the performance of our DL models in medical image diagnosis, several evaluation metrics are commonly employed, including Receiver Operating Characteristic (ROC) curves and confusion matrices, among other techniques [1; 2; 3]. The ROC curve is a graphical plot that illustrates the diagnostic ability of our DL models as its discrimination threshold is varied. It presents the trade-off between sensitivity (or True Positive Rate) and specificity (1-False Positive Rate), providing a measure of how well our models distinguish between classes. The Area Under the ROC Curve (AUC) is also considered, which provides a single metric to compare model performance. On the other hand, confusion matrices provide a summary of prediction results on a classification problem. The number of correct and incorrect predictions is counted and broken down by each class. This offers a more granular view of the model performance, including metrics such as precision, recall, and F1-score, which are crucial when dealing with imbalanced classes.
## 2 Materials and Methods
### Permutation Test for Z-DNA Overlap Analysis  
Random genomic coordinates were generated for each of the 22 human chromosomes based on the hg38 assembly. For each chromosome, 1000 sets of random coordinates were produced using a uniform distribution, with telomeric and centromeric regions excluded from the sampling. These random sets were generated to match the length distribution of predicted Z-DNA regions (minimum length \(\geq\) 10 bp). Predicted Z-DNA regions were derived from prior computational analysis using the Z-DNA-BERT model. Enhancer coordinates were obtained from the SEdb 2.0 database for six solid tumor cell lines (HeLa, HCT116, MDA-MB-231, MCF7, BT-549, and LNCaP). Three categories of enhancer elements were analyzed: SE, SE-E, and TE. For each chromosome and cell line, the predicted Z-DNA coordinates were intersected with the enhancer coordinates as well as with each of the 1000 sets of random coordinates. A \(p\)-value was computed as the fraction of iterations in which the number of overlaps between random Z-DNA coordinates and enhancer coordinates equaled or exceeded the observed number of overlaps using the actual predicted Z-DNA regions. These \(p\)-values were then used to assess the enrichment of Z-DNA predictions within each enhancer category.
## 2 Research on UAV Path-Planning Problem
### Polynomial Interpolation Fitting Curves  
During the planning process, a relatively smooth flight trajectory can be generated by adding control points including the start point \(W(0)=(x_{0},y_{0},z_{0})\) and the end point \(W(m+1)=(x_{m+1},\,y_{m+1},\,z_{m+1})\), with the intermediate point \(W(q)=\big{(}x_{q},y_{q},z_{q}\big{)}\)\((q=1,2,\,\dots,\,m)\), and the total number of control points is \(N=m+2\).  
Assign the parameters by equally spacing the control points on \(\tau\in[0,\,1]\):  
\[\tau_{i}=\frac{i}{N-1},\,i=0,\,1,\,2,\dots,N-1 \tag{1}\]  
Obtain the corresponding control points:  
\[W_{(\tau_{i})}=W(i),\,\forall i\in\{0,1,2,\dots,N-1\} \tag{2}\]  
Then \(N-1\) interpolating polynomials are constructed independently for each coordinate component:  
\[\begin{bmatrix}W\big{(}q\big{)}_{(\tau)}x\\ W\big{(}q\big{)}_{(\tau)y}\\ W\big{(}q\big{)}_{(\tau)z}\end{bmatrix}=\sum_{q=0}^{N-1}\begin{bmatrix}a_{q}\\ \beta_{q}\\ \gamma_{q}\end{bmatrix}\tau^{q} \tag{3}\]  
where the coefficients \(a_{q},\beta_{q},\gamma_{q}\) can be determined by solving the Vandermonde system of equations:  
\[\begin{bmatrix}1&\tau_{0}&\tau_{0}^{2}&\dots&\tau_{0}^{N-1}\\ 1&\tau_{1}&\tau_{1}^{2}&\dots&\tau_{1}^{N-1}\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ 1&\tau_{N-1}&\tau_{N-1}^{2}&\dots&\tau_{N-1}^{N-1}\end{bmatrix}\begin{bmatrix} \alpha_{0}\\ \alpha_{1}\\ \vdots\\ \alpha_{N-1}\end{bmatrix}=\begin{bmatrix}x_{0}\\ x_{1}\\ \vdots\\ x_{N-1}\end{bmatrix} \tag{4}\]  
The coefficients of the \(y\) and \(z\) components can be derived identically. Then, \(k\) points are sampled uniformly in the range \(\tau\in[0,\,1]\):  
\[\widetilde{\tau}_{j}=\frac{j}{k-1},\,j=0,1,\dots,k-1 \tag{5}\]  
\(k\) points to generate continuous paths:  
\[W_{(\widetilde{\tau}_{j})}=\left(\sum_{\mu=0}^{N-1}\alpha_{\mu}\widetilde{ \tau}_{j}^{\mu},\,\sum_{\mu=0}^{N-1}\beta_{\mu}\widetilde{\tau}_{j}^{\mu},\, \sum_{\mu=0}^{N-1}\gamma_{\mu}\widetilde{\tau}_{j}^{\mu}\right) \tag{6}\]  
The set of path nodes \(\widetilde{W}\) for the final curve is:  
\[\widetilde{W}=\left\{W_{(\widetilde{\tau}_{j})}\mid j=0,\,1,\dots,k-1\right\} \tag{7}\]  
In this paper, the number of intermediate points \(m=3\), control points \(N=5\), and the number of polynomials is 4. Retaining the display of 5 control points, a smooth curve using polynomial interpolation can be obtained, as shown in Figure 1.
###### Abstract  
Aim: The objective of this research has been to apply a specific simulation to a patient to assess the biomechanical consequences of rotating an upper canine tooth through different attachment-alignen configurations and to predict the most efficient design using a three-dimensional finite element model of a full maxillary arch of a specific patient. Materials and methods: This was obtained by combining Cone-Beam Computed Tomography (CBCT) with the aim of reconstructing tooth roots and bone tissues, and Surface Structured-Light Scanning for creating digital tooth crown models from the patient's impressions. This model was imported into the finite element solver (Ansys(r) [17]). Three different attachment-alignen combinations were created through the exploitation of computer-aided design (CAD) procedures, i.e., without attachments, with a couple of attachments and with an attachment and a pressure point. For each simulation, the resulting force-moment (MF) system applied by the aligner to the target tooth, as well as the tooth displacement and rotation, was computed using a workstation based on Intel Xeon CPU E3-1245 [EMAIL_ADDRESS] GHz and 16 GB RAM. Simulations reported that by adding the pressure point and the attachment to the standard aligner the amount of Moment z (Mz) delivered to the tooth increased almost two times. Results and conclusions: The maximum tooth displacement (0.85 mm) was obtained with the attachment and pressure point aligner, while the lowest (0.058 mm) was obtained with use of a couple of attachments. Both the attachment and the pressure point have the potential to enhance the appliance's effectiveness. Particularly, the pressure point showed a higher influence on the load absolute value. The method applied in the present study should be used to retrieve the best design configuration for each patient and specific tooth movement.  
finite element analysis; aligners; malocclusion; orthodontic movement; digital workflow; three-dimensional tooth movement; three-dimensional diagnosis and analysis 2020  
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# A Thermopile Device with Subwavelength Structure by CMOS-MEMS Technology
## 3 Simulation Results and Discussion  
To explore the effect of the SWSs for the CMOS thermal sensor, the rectangular hole and circular hole structural parameters of the SWS CMOS compatible thermopile are analyzed by the FDTD method and compared with the one without SWS. Figure 2 shows the schematic diagram of lightwave propagation through a CMOS compatible thermopile with SWS simulated by the FDTD method. Here, \(n_{0}\) and \(n_{s}\) are the refractive indices of the incident medium and SiO\({}_{2}\), respectively. In this study, we take \(n_{0}=1\) for air and \(n_{s}=1.42\) for SiO\({}_{2}\) FDTD simulation, absorbing boundary conditions are required to truncate the computational domain without reflection. A perfect matched layer (PML) is a useful method to decrease this error induced form the boundary situation [17; 18], so we use the PML boundary to be the simulated area. To explore the effect of incident infrared radiation in the active area, the absorption, transmittance, and reflectance detectors are used. Figure 3 shows the schematic top-view of the rectangular-hole and circular-hole patterns for the SWS, respectively. For the rectangular-hole type (RHT) cases, the length is taken as 15 \(\upmu\)m, the width is considered as 3, 3.5 and 4 \(\upmu\)m, and the spacing is the same as the width. For the cylindrical-hole type (CHT) cases, the diameter is considered as 3, 3.5 and 4 \(\upmu\)m and the spacing is the same as the diameter.  
Figure 4 shows the simulation results of the normalized absorption difference with the target temperature for the thermopiles without the SWS, with the RHT SWS, and with the CHT SWS, respectively. The normalization for the data to be analyzed in the following is applied while the maximum of absorption curve is adopted as the 100%. One can see that the maximum is the result of the case of RHT thermopile with the SWS width 3.5 \(\upmu\)m at target temperature 75 \({}^{\circ}\)C and those results with SWS are obviously better than those without SWS in Figure 4. For the CHT cases, the case of the diameter of 3 \(\upmu\)m is higher than the other cases. The results of the RHT thermopile and CHT thermopile are higher than those without the SWS.  
Figure 1: Schematic diagram of heat conduction for the CMOS thermal sensor.  
Figure 2: Schematic diagram of light-wave propagation through a CMOS compatible thermopile with subwavelength structure (SWS) simulated by the FDTD method.