esa-sceva/satcom-chunk-collection huggingface dataset:
# Parametric Optimization Study of Novel Winglets for Transonic Aircraft Wings
## 5 Optimization Studies
### Analysis of Variance (ANOVA) of Winglet Design Using the Taguchi Method  
ANOVA analysis is performed to determine the percentage contribution of each design parameter of the winglet design at a confidence level of 95%. Tables 9-11 shows the contribution of each parameter to the dependent parameters, \(\mathrm{C_{L}/C_{D}}\), \(\mathrm{C_{M}}\), and \(\mathrm{(C_{L}/C_{D})/\,\mathrm{1}\,C_{M}\,\mathrm{1}}\). It is observed from Figure 14 that the cant angle is the most contributing parameter with percentages of 53.97, 59.36, and 65.31 for the dependent parameters \(\mathrm{C_{L}/C_{D}}\), \(\mathrm{C_{M}}\), and \(\mathrm{(C_{L}/C_{D})/\,\mathrm{1}\,C_{M}\,\mathrm{1}}\), respectively. The taper ratio is the least influencing parameter for \(\mathrm{C_{M}}\), which has a contribution of 4.21%. In the case of \(\mathrm{C_{L}/C_{D}}\), the sweep angles have a contribution of 6.93%, which is the least, as well as lower than the error contribution. This denotes another possible significant design parameter whose contribution is more than the sweep angle for \(\mathrm{C_{L}/C_{D}}\). It is also observed that the Taguchi method and ANOVA determined the same order of significance. For \(\mathrm{C_{L}/C_{D}}\), the contribution of sweep angle is less than the error percentage, denoting some other parameter or combination of parameters that influences the aerodynamic efficiency more than the sweep angle. Additionally, the effect levels of winglet design parameters of a transonic wing on \(\mathrm{(C_{L}/C_{D})}\) and \(\mathrm{C_{M}}\) are identified using the analysis of variance. For \(\mathrm{C_{M}}\), the contribution of error percentage is less than the other input parameters; i.e., all the input parameters are involved to decide the optimum response of \(\mathrm{C_{M}}\) for the winglet design of the transonic wing, even though the cant angle is the most influential parameter on the transonic wing design, followed by sweep angle, twist angle, and, finally, the taper ratio, which define the optimum response of the \(\mathrm{C_{M}}\) value.  
Similarly, for the response \((C_{L}/C_{D})/\mid C_{M}\mid\), the contribution of error percentage is less than the other input parameters considered for this transonic wing design, which results in the optimum response of \((C_{L}/C_{D})/\mid C_{M}\mid\). From Table 11 and Figure 14, the cant angle is the most dominant parameter in the transonic wing design followed by sweep angle, taper ratio, and twist angle, which determine the optimum dependent response parameter of the \(C_{M}\) value.
## 2 Materials and Methods
### Methodology
#### 2.3.1 Satellite Data: Sentinel-2, Sentinel-1, PlanetScope  
The two European Space Agency (ESA) S2 satellites (A and B) are equipped with a multispectral sensor (MSI) that can detect 13 spectral bands with a spatial resolution of \(10,20\), and 60 m and a temporal resolution of 4 days on average [48; 49]. In GEE, S2 data are provided in the L1C (orthorectified top-of-atmosphere reflectance) and L2A (orthorectified atmospherically corrected surface reflectance) processing levels. Three quality assessment (QA) bands are provided at the L2A level, one of which (QA60) is a bitmask band with cloud mask information.  
The S1 satellites (A and B), also launched by ESA, use a dual-polarization C-band Synthetic Aperture Radar (SAR) sensor with a spatial resolution of 10, 25, or 40 m and a temporal resolution of 6-12 days. S1 Ground Range Detected (GRD) scenes are pre-processed with the Sentinel-1 Toolbox (thermal noise removal, radiometric calibration, terrain correction) to provide the calibrated, ortho-corrected imagery available in the GEE cloud environment. Due to the S1 satellites' operational characteristics, the S1 images are available in the study area with a revisit time of 12 days and only from descending orbits.  
PlanetScope controls the world's largest earth-observation satellite constellation, with over 150 satellites popularly known as \"Doves\". These satellites have tiny dimensions (10 \(\times\) 10 \(\times\) 30 cm and a weight of about 4 kg) and provide daily sun-synchronous images of the entire Earth's surface. The sensors on PL satellites capture imagery with a 3-5-m resolution and four spectral bands (RGB and NIR). PlanetScope data access from GEE is currently a paid service based on Planet's Orders API cloud-storage destinations [50]. To access NICFI PL data, available for the tropical areas, the GEE users must follow a specific procedure and require approval by the NICFI [51]. In GEE, PL composite images are available in the WGS 84/Pseudo-Mercator (EPSG 3857) Coordinate Reference System, in contrast to the S2 and S1 images, which are georeferenced according to WGS84 (EPSG 4326).
## 2 Preliminaries
### Reduced Lists and Rotation Digraphs  
In constructing algorithms and proving W[1]-hardness, we use _reduced lists_ and _rotation digraphs_. These are defined on an instance \(I\) and its stable matching \(M\), and are constructed in the following way: For each man \(m\) and each woman \(w\) below \(M(m)\) in \(m\)'s preference list, delete \(w\) from \(m\)'s list and \(m\) from \(w\)'s list. The resulting preference lists are called the _woman-oriented reduced lists with respect to \(I\) and \(M\)_, denoted \(RL_{I,M}\). (We may simply call them the _reduced lists_ if there is no ambiguity). Note that for each woman \(w\), \(M(w)\) lies at the top of \(w\)'s reduced list since \(M\) is stable. For each woman \(w\), let \(next_{I,M}(w)\) be the man at the second position of \(w\)'s reduced list (if any). The _woman-orientedrotation digraph with respect to \(I\) and \(M\)_ (or simply the _rotation digraph_) is a digraph \(D_{I,M}=(V,A)\) where \(V\) is the set of men and \(A\) includes an arc \((M(w),next_{I,M}(w))\) (_i.e._, a directed edge from \(M(w)\) to \(next_{I,M}(w)\)) for each woman \(w\) such that \(next_{I,M}(w)\) exists. Suppose that there is a directed cycle \(m_{0},m_{1},\ldots,m_{r-1}\) (\(r\geq 2\)) in \(D_{I,M}\), and let \(w_{i}=M(m_{i})\) for all \(i\) (\(0\leq i\leq r-1\)). Then we call an ordered list of pairs \(\rho=(m_{0},w_{0}),(m_{1},w_{1}),\ldots,(m_{r-1},w_{r-1})\) a _rotation_ exposed in \(M\). If we modify \(M\) by removing the pairs of rotation \(\rho\) and adding \((m_{i},w_{i-1\bmod r})\) for each \(i\), then the resulting matching is also stable [2]. In this case we say that the new matching is obtained by _eliminating_ the rotation \(\rho\) from \(M\). Note that by eliminating a rotation, every man in the rotation obtains a better partner, while every woman in the rotation obtains a worse partner. It is also known that \(M\) is the man-optimal stable matching of \(I\) if and only if there is no rotation exposed in \(M\) (or equivalently, \(D_{I,M}\) has no directed cycle) [2].
# A Review on the Erosion Mechanism in Cavitating Jets and Their Industrial Applications
## 4 Industrial Applications of Cavitating Jets
### Bitumen Separation Using Cavitating Jets  
In some applications such as bitumen separation based on cavitation jets [7; 8], it is important to understand the behavior of the cavitation jet in multiphase media.  
Oil sands, also known as tar sands, are unconventional heavy oil deposits. They are composed of very heavy crude oil, water, clay stones, and other minerals. As a part of the separation process, oil sand is mixed with water forming multiphase slurry, then the slurry is pumped through hydrotransport which consists of a system of long pipelines and large slurry pumps in order to breakdown the lumps of oil sands. The hydrotransport line is several kilometers long and the turbulent flow in the pipeline is found to be very effective in breaking down the lumps of oil sands, improving the recovery of bitumen in the main extraction plant. One of the disadvantages of hydrotransport is the high erosion rates since the oil sand is very abrasive. [PERSON] et al. [65; 66] suggested replacing hydrotransport with cavitation jet vessels. It was shown that cavitating jets produced typical cavitation erosion on aluminum plates and had an ability to separate bitumen from oil sand. In their recent study, Bukharin et al. [8] demonstrated that self-resonating nozzle jet presents greater performance as compared to both conventional nozzles and conventional nozzles with flow control in the bitumen-oil separation process.
# Real-Time Detection of Insulator Defects with Channel Pruning and Channel Distillation
## 2 Related Work  
Object detection technology can be broadly categorized into traditional methods and deep learning-based methods [6], with the introduction of AlexNet [7] marking the transition between the two. Currently, mainstream deep learning-based object detection algorithms are classified into two major categories: (1) region proposal-based two-stage object detection algorithms and (2) regression-based end-to-end single-stage object detection algorithms. Typical two-stage object detection algorithms include R-CNN [8], Fast R-CNN [9], and Faster R-CNN [10]. These methods involve generating region proposals and then classifying these proposals, which often results in higher accuracy but at the cost of increased computational complexity. Representative single-stage object detection algorithms include YOLOv3 [11], YOLOv4 [12], YOLOv7 [13], and RetinaNet [14]. The YOLO (You Only Look Once) series, known for its speed and versatility, has become a significant focus of research in recent years. [PERSON] et al. introduced the YOLO algorithm in 2016 [15], framing object detection as a regression problem to enable end-to-end rapid detection. Since its inception, the YOLO series has undergone continuous improvements and upgrades, incorporating effective advancements from other researchers in the field. Network pruning, a viable method for eliminating redundant branches within networks, is divided into two primary types: weight pruning and structured pruning. Weight pruning targets and removes inconsequential connections with minimal weights in trained networks, as noted in [16]. This approach, while effective, requires specialized sparse matrix operation libraries for acceleration, which may not be well suited for hardware implementations. In contrast, structured pruning, as discussed in [17], tends to offer a more balanced trade-off between flexibility and ease of implementation. Recent advancements in this field [18] involve entirely removing channels based on their small incoming weights, rather than just individual weights, followed by fine-tuning the network to restore accuracy. Additionally, methods cited in [19; 20] apply sparsity regularization to various structural levels, including filters, channels, or layers, in convolutional neural networks (CNNs).
## 5 Discussion
It is not possible to propose one, universal form of point cloud representation allowing the above requirements to be met. Therefore, a priori knowledge concerning the positions of the scanner stations is required, allowing for selection of the optimum representation of a point cloud. It is necessary to separately consider two cases, i.e., closely located scanner stations where the same fragments of an object are measured under similar angles (a situation which does not result in a high influence of \"distortion\" in spherical images) and scanner stations from which fragments of an object are measured under different angles and from different distances. When the registration of point clouds acquired from close scanner stations is performed, it is recommended to use raster images in the spherical or the Mercator projections. Selection of a particular projection is directly connected with the position of the scanner station. If the instrument is placed lower, (i.e., closer to the floor) it is reasonable to use raster images in the Mercator projection, though not when the scanner is located closer to the ceiling, since data from fragments of a cloud projected from the ceiling are not lost. The analysis of the distribution of tie points shows that for close scanner stations, the best and most even distribution of control and check points was obtained for raster images in the Mercator projection, for orthoimages and for spherical images, respectively. In the case where point clouds are acquired from stations for which fragments of an analysed object are measured under considerably different angles and from a different distance, it is recommended to use orthoimages. The use of other forms of point cloud representation influences the presence of \"distortion\", which is directly connected with difficulties in explicit identification of tie points when using any algorithm for detecting characteristic points. When RMSE error values are analysed for points detected both on fragments and on entire scanned areas, it may be noticed that the difference between them is twice as high. Therefore, it is recommended to acquire scans at the maximum angular resolution. In the summary, it should be stressed that the proposed method is recommended for historical rooms where it is not possible to place marked points on the object surface. In the case of complex objects, it is possible to process RAW data in such a way that the automatic and more accurate orientation of scans could be obtained. It also allows for selecting the determined cartographic projection that influences the better detection of evenly distributed tie points. Compared to the conventional, target-based approach, the number of detected control and check points is considerably bigger; this influences the registration accuracy. The authors have utilized the proposed method for measurements performed at the Museum of King Jan III's Palace at Wilanow and in the Royal Castle in Warszawa.
# EDC-Net: Edge Detection Capsule Network for 3D Point Clouds
## 3 Proposed Method
### Network Architecture
#### 3.1.6 EdgeCaps  
Our proposed EdgeCaps is the first use of a capsule network architecture for edge detection of point clouds in literature. We set up a similar voting agreement hierarchy to [23] and embedded it into EDC-Net to build a segmentation network. Our model is built upon a dynamic routing between capsules and treats the capsules as a probabilistic model which is capable of inferring visual entities based on the probabilistic vectors of the features. EdgeCaps captures edge points of a given point cloud and exploits this information through a recursive procedure to derive the class memberships (edge, non-edge) for individual points. Our contribution to the capsule network is that we do not apply the primary capsules' activation directly in the routing process; but we apply an attention module to the primary capsules and concatenate them with the features of the previous convolution layer in order to combine the local and global features for a point-wise segmentation process through the EdgeCaps module. Furthermore, this modification allows us to operate on various sizes of point clouds. EdgeCaps returns the capsule activations in two classes as edge and non-edge based on the voting agreement hierarchy of the dynamic routing between the capsules. Moreover, another contribution of this work is that we do not incorporate margin or reconstruction loss; we define a specific loss function in Section 3.2 which focuses merely on the point-wise edge segmentation problem.
# A Low-Power WLAN Communication Scheme for IoT WLAN Devices Using Wake-Up Receivers
## 4 Proposed System for Massive Uplink Scenario
### Wake-Up Frame and Data Transmission for Triggering Uplink Transmission from Multiple Users
#### 4.1.2 Multi-User Transmission Procedure as in IEEE 802.11 ax (Multicast-MAC)  
The repetition of the entire transmission procedure causes a large delay as the number of STAs increases, as the long wake-up delay and contention procedures are repeated. In order to reduce the repetition, we can apply the MU transmission procedure adopted in IEEE 802.11 ax. As depicted in Figure 9, in the MU transmission with wake-up radio, after the wake-up packet for multiple users is transmitted, the data transmission procedure starts with the TF, as in the uplink MU OFMDA procedure of IEEE 802.11 ax. The response of the TF can be a data frame aggregated with the acknowledgment of the TF, as in the unicast-MAC procedure. After the successful reception of the uplink MU OFDMA frame, the AP transmits a multi-STA blockade frame to respond to multiple STAs simultaneously.  
Figure 8: Data transmission procedure to gather the sensed data from sensors operated in MAC layer based on unicast transmission.  
This procedure reduces the delay in the entire transmission procedure simultaneously, but the AP continuously waits for the wake-up delay for each transmission procedure. Because the wake-up delay is longer than other transmission delays, the repetition involved in waiting for a wake-up delay is inefficient.
## 1 Introduction
### Main Contributions  
This paper extends our research on the utilization of spectrum (UoS) [5; 6]. In [5], the proposed study was conducted for the multichannel scenarios considering imperfect sensing and spectrum handoffs. The utilization of spectrum of the SU nodes, opportunistically operating on different PU channels, was investigated under different PU and SU co-existing network topologies. In our previous studies, the UoS was investigated only for the HD radios deployed in time-slotted CRNs. In addition, only a one-dimensional temporal spectral hole-sensing model was considered for the synchronous PU activities. This paper is inspired by the advances in FD techniques, making FD-CRN a potential candidate to complement and sustain the demands of future dense IoT network. The main aspects of the proposed work are as follows:  
1. The realistic small-cell CR-IoT scenario is considered where FD-enabled SU transmitters (FD-SU TXs) with sensing capabilities are distributed randomly across the transmission region of primary network. Such a dense distribution of small-cell FD-SU TXs creates  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(P_{\text{OFF}}\) & Probability that the PU is in \(OFF\) state \\ \(P_{ON}\) & Probability that the PU is in \(ON\) state \\ \(R\) & Mean of \(OFF\) durations \\ \(S\) & Mean of \(ON\) durations \\ \(A\) & Random variable for \(OFF\) duration \\ \(B\) & Random variable for \(ON\) duration \\ \(T\) & Time-slotted frame length \\ \(N\) & Number of sensing samples in each sensing slot \\ \(R1\) & Transmission region \\ \(R2\) & Sensing region \\ \(D1\) & Radius of transmission region \\ \(D2\) & Radius of sensing region \\ \(X\) & SIS factor \\ \(\gamma_{p}\) & Instantaneous sensing signal-to-noise ratio (SNR) \\ \(\gamma_{1}\) & Instantaneous interference-to-noise ratio (INR) \\ \(\gamma_{s}\) & SNR of secondary transmissions \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of adopted notations.  
spectrum-heterogeneous environment with different spectral opportunities. In this regards, we propose a two-dimensional spatial-temporal spectral hole-sensing model for FD-SU TXs deployed in CR-IoT spectrum-heterogeneous environment, and incorporate the traffic variations of PU both in time and space domains.
2. As a performance metric of the spatial-temporal sensing model, we propose and evaluate the probability of finding the non-availability of spatial-temporal spectral holes, and the probability of missing the availability of spatial-temporal spectral holes.
3. Incorporating the proposed sensing model, we present an analytical formulation and evaluation of a UoS scheme for the FD-SU TXs present at different spatial positions. The performance of UoS scheme is investigated in terms of average number of sensing slots used for the successful secondary communication in each time-slotted frame.
# Quantitative Modeling of Near-Field Interactions in Terahertz Near-Field Microscopy
## 2 Methods
### Simulation Settings  
To match the tip parameters in THz s-SNOM, the tip is set as a gold-cone longitudinal section with main length L = 100 \(\upmu\)m and tip radius \(\alpha\) = 100 nm. The physical domain is set as a 30 \(\upmu\)m \(\times\) 200 \(\upmu\)m rectangular range surrounded on all sides by a 20 \(\upmu\)m perfectly matched layer, and the sample thickness is 30 \(\upmu\)m. In order to improve the calculation efficiency, the grid size needs to be distinguished. The maximum mesh size at the small range of the tip is set to 1 nm to facilitate subsequent simulation of the vibration fluctuation of the tip. By setting a transition range, the general grid size of other areas can be transitioned to nano-size to ensure the accuracy of the simulation. Theoretically, the smaller the grid size near the tip, the more accurate the calculation results will be, but the amount of calculation will be greatly increased. For THz s-SNOM, the radius of curvature of the tip is generally above 20 nm, and this model can meet the research accuracy requirements for such systems. The specific meshing setting is shown in Figure 2a.  
In most cases, to acquire the scattering field of the tip sample, the solution is solved in two steps. In the first step, the scattering boundary conditions are established in our 2D model. The top boundary of the model is set as a port, and launched a plane wave towards the surface at an angle of 60\({}^{\circ}\) relative to the vertical. The left and right bounds are specified as periodic boundary conditions to simulate parallel light incident on infinite plane. The perfectly matched layer set does not need to be enabled in the first step. In the second step, the electric field calculated in the first part should be used as the input electric field in the second part when there is a tip-sample scatterer. In this way, the perfectly matched layer can absorb the scattered wave radiated outward and eliminate the reflection caused by setting the boundary. In Figure 2, the scattering simulation model is displayed. The different near-field electric field values are generated by adjusting the distance value of several tip samples to replicate the actual tip shaking. We choose 20 simulating values (the tip-sample distances) between 2-200 nm and obtain the electric field information of each point in our 2D model. Using these data, the Fast Fourier Transform (FFT) is employed to extract the various order signals of tip vibration in order to imitate the near-field signal extracted from the harmonic frequency of tip vibration during the experiment.  
Figure 2: (**a**) Simulation of each area grid division; (**b**) simulation boundary condition setting.