esa-sceva/satcom-chunk-collection huggingface dataset:
# Aerodynamic Modeling and Performance Analysis of Variable-Speed Coaxial Helicopter
## 2 Establishment of an Aerodynamic Model  
### Rotor Aerodynamic Model  
#### 2.1.1 Definition of the Coordinate System  
Due to the opposite rotation of the coaxial rotors, the rotor and hub coordinate systems of the lower rotor are assumed to be based on the right-hand system, whereas the rotor and hub coordinate systems of the upper rotor are assumed based on the left-hand system.  
Therefore, the lower rotor is designated with the coordinate system I, and the upper rotor is designated with coordinate system II. The rotor coordinate system is a transitional coordinate system, which is convenient to calculate the rotor aerodynamic force. The origin of the hub coordinate system is defined at the center of the hub. The origin of the fuselage coordinate system is located at the center of gravity of the fuselage and is in the right-hand system.  
The rotor aerodynamic force calculated by the blade element method is in the rotor coordinate system and needs to be converted into the hub coordinate system. Taking the following rotor as an example, the conversion relationship is:  
\[\begin{bmatrix}X_{H1}\\ Y_{H1}\\ Z_{H1}\end{bmatrix}=\begin{bmatrix}-H_{1}\\ Y_{1}\\ -T_{1}\end{bmatrix} \tag{1}\] \[\begin{bmatrix}L_{H1}\\ M_{H1}\\ N_{H1}\end{bmatrix}=\begin{bmatrix}-L_{1}\\ M_{1}\\ -N_{1}\end{bmatrix}\]  
From the equation, \(H_{1}\) is the backward force of the lower rotor, \(Y_{1}\) is the lateral force of the lower rotor, \(T_{1}\) is the pull force of the lower rotor, \(L_{1}\) is the roll moment of the lower rotor, \(M_{1}\) is the pitch moment of the lower rotor, \(N_{1}\) is the yaw moment of the lower rotor, \(X_{H1}\), \(Y_{H1}\) and \(Z_{H1}\) are the three-dimensional forces of the lower rotor in the hub coordinate system, and \(L_{H1}\), \(M_{H1}\) and \(N_{H1}\) are the three-dimensional moments of the lower rotor around the \(X\), \(Y\) and \(Z\) axes in the hub coordinate system. The expressions of the forces and moments of the upper rotor are the same as those of the lower rotor, and only the footmark needs to be changed.  
The balanced equation below is established in the body coordinate system, so it will involve the mutual transformation of the left- and right-hand systems. The transformation relationship is as follows:  
\[\begin{bmatrix}X_{H2}\\ Y_{H2}\\ Z_{H2}\\ Z_{H2}\end{bmatrix}_{LEFT}\Rightarrow\begin{bmatrix}X_{H2}\\ -Y_{H2}\\ Z_{H2}\end{bmatrix}_{RIGHT} \tag{2}\] \[\begin{bmatrix}L_{H2}\\ M_{H2}\\ N_{H2}\end{bmatrix}_{LEFT}\Rightarrow\begin{bmatrix}-L_{H2}\\ M_{H2}\\ -N_{H2}\end{bmatrix}_{RIGHT}\]  
\(X_{H2}\), \(Y_{H2}\), \(Z_{H2}\) are the three-dimensional forces of the upper rotor in the hub coordinate system, and \(L_{H2}\), \(M_{H2}\) and \(N_{H2}\) are the three-dimensional moments of the upper rotor around the \(X\), \(Y\) and \(Z\) axes in the hub coordinate system. For the left- and right-hand systems, the \(x\)-axis points to the forward flight direction.
# Processing of Single-Walled Carbon Nanotubes with Femtosecond Laser Pulses
## Materials and Methods
### Laser Irradiation  
A laser micromachining workstation from FemtoLAB (Workshop of Photonics) having 6 W PHAROS laser and a harmonic generator (Light Conversion, Lithuania) was employed to irradiate the samples according to the desired settings. The system is equipped with a high accuracy linear positioning stage, high performance galvanometer scanners, and versatile micromachining software SCA (Workshop of Photonics, Lithuania). The samples were irradiated with two wavelengths, an infrared (IR) 1028 nm (1 st harmonic), and a green 514 nm (2 nd harmonic) with a time duration close to 200 fs and a frequency of 100 kHz.  
The 1 st harmonic (IR) experiments were performed with an average laser power ranging from 0.05 to 80 mW. An aspheric lens with a focal length of 15 mm. For this wavelength, the plane of  
Figure 1: Schematic illustrating the process for preparation of single wall carbon nanotube (SWCNT) thin film.  
laser irradiation ranged from the actual focus plane of the top surface of the SWCNT ropes up to 200 \(\upmu\)m away from the SWCNTs. The 2 nd harmonic tests were performed with two types of lenses, a 15 mm aspheric lens and a Zeiss A-Plan 10X dry objective, while for the first harmonic experiments only a 15 mm lens was used. The laser power, which ranged from 0.68 to 40 mW, was used in these experiments. The transitional speed of the stage was varied from 0.03 mm/s up to 0.1 mm/s along the line of laser irradiation for both laser wavelengths used. Experiments were conducted by identifying the plane of focus of the samples with the aid of a machine vision setup included in the femtosecond laser system. Then, according to the experimental settings, a single pass was performed with laser irradiation for each setting for the sample. The SWCNTs processed by femtosecond laser irradiation were analyzed by scanning electron microscopy.  
Further analysis of the experimental settings was performed to find the optical properties of the samples for joining. For the current experiments, the spot size (D) of the laser beam was calculated using the following equation, as described in the literature [33]:  
\[D=1.22\ \frac{\lambda}{N.A}\]  
where \(\lambda\) is the laser wavelength and \(N.A\) is the numerical aperture values of the objective lens. Thereafter, the average fluence was estimated by employing the following equation [33]:  
\[F_{ave}=\ \frac{E*F}{D*v}\]  
where \(E\) is the energy of single pulse, \(F\) is the frequency of laser pulses, i.e., repetition rate, and \(v\) is the laser scan speed.
## 3 Results
### Effect of Rock-Bolt Length
#### 3.1.3 Effect of Stresses  
In-Situ Stresses  
At low depths, the in situ stress state represents a minor concern in the stability of the rock mass surrounding an underground excavation. Presently, tunnels are constructed at great depths [46; 47; 48] where the stresses are the main factors that determine the behavior of the tunnel. In empirical classification systems, the rock-bolt design that uses Q-system is preferable over the RMR and RMi systems, owing to the stress reduction factor for the category of \"competent rock having rock-stress problem\". When the magnitude of maximum boundary stress in the vicinity of the opening (\(\sigma_{\max}\)) is  
Figure 6: Effect of water–cement ratio on the pull-out strength of fully grouted rock bolt.  
Figure 7: Effect of the grout’s settling time on pull-out strength of rock bolt in (**a**)moderate rock and (**b**) weathered rock.  
close to the uniaxial compressive strength of the rock mass (\(\sigma_{\rm cm}\)), the rock is overstressed [49]. If the rock is weak and soft, it is frequently observed that either the face plate of the bolt is highly loaded or the thread of the bolt is pulled until it fails [21]. In the case of hard rocks, the rock spalls and sheds pieces like onion skins behind the bolt plate, leaving a short section of the bolt protruding out of the rock surface [22]. In such high-stress environments, the conventional CMC does not adapt well. Energy-absorbing bolts that are characterized by their high load-carrying capacity and large deformation capacity are suitable for such a position. The newly devised energy-absorbing rock bolt called D-bolt (Figure 2c) is a fully grouted rock bolt; it is the most effective energy-absorbing bolt when compared to existing non-grouted energy-absorbing rock bolts [23].
# Remote-Controlled Method with Force and Visual Assists Based on Time to Collision for Mobile Robot
## 2 Modeling
### Mobile Robot  
In this study, the global coordinate system \(\Sigma_{GL}\) and local coordinate system \(\Sigma_{t,C}\) are defined. The origin of the local coordinate system is set at the center of the mobile robot. This center point is defined as the central point on the shaft between the wheels of the mobile robot. The origin of \(\Sigma_{GL}\) is set as the initial position in \(\Sigma_{t,C}\). The direction of the X-axis is defined as the translational velocity direction of the mobile robot when the angular velocity is 0 rad/s. The direction of the Y-axis is defined as the vertical left of the X-axis. As shown in Figure 3, \(\left({}^{GL}x,{}^{GL}y\right)\), and \({}^{GL}\theta\) are the mobile robot positions and orientations, respectively. In this study, \({}^{GL}\bigcirc\) represents the values of the global coordinate system. The superscript is not utilized as the values are in the local coordinate system. The values of each velocity are expressed in a red color. \(D_{r}\) and \(D_{w}\) are the half width of the mobile robot and the diameter of each wheel. Each wheel velocity is generated through calculating the pseudo-differential by using values for each wheel angle measured by the encoder. In this  
Figure 1: System configuration.  
Figure 2: Control device.  
article, it is assumed that the mobile robot is not sliding. The right side and left side wheel velocities, \(v_{R}\) and \(v_{L}\), are calculated as follows:  
\[v_{R} = \frac{D_{w}}{2}\cdot\theta_{r} \tag{1}\] \[v_{L} = \frac{D_{w}}{2}\cdot\theta_{l} \tag{2}\]  
where \(\theta_{r}\) and \(\theta_{l}\) are right side and left side wheel angles. The translational and angular velocities of the mobile robot are calculated considering with the width of the mobile robot.  
\[v = \frac{v_{R}+v_{L}}{2} \tag{3}\] \[\omega = \frac{v_{R}-v_{L}}{2D_{r}} \tag{4}\]  
\(v\) and \(\omega\) are the translational and angular velocities of the mobile robot, respectively. The mobile robot position (\({}^{GL}x\), \({}^{GL}y\)) and orientation \({}^{GL}\theta\) after \(t\) s are as follows:  
\[{}^{GL}x = \int_{0}^{t}v\cdot\cos{}^{GL}\theta\;dt \tag{5}\] \[{}^{GL}y = \int_{0}^{t}v\cdot\sin{}^{GL}\theta\;dt\] (6) \[{}^{GL}\theta = \int_{0}^{t}\omega\;dt \tag{7}\]
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 5 Simulation Study
### Results and Analysis
#### 5.2.2 Network Performance Comparison in Scenario II  
SMTrust shows even better results for Scenario II under Rank and Blackhole attacks due to the inclusion of mobility-based metrics in trust evaluation, as in Figure 7, thus indicating that the performance of SMTrustOF is much improved compared with MRHOF with mobile sender nodes in the network. Since the focus of DCTM is for mobile sender nodes in the network, the SMTrust performance is compared with DCTM under Rank and Blackhole attacks. For Scenario II, SMTrust is compared with DCTM and MRHOF under attacks. It can be observed from Figure 7 that the packet loss rate of SMTrust is 17.9% under Blackhole attack, which is much lower as compared to DCTM (21.5%). Similarly, under Rank attack, SMTrust shows an average packet loss rate of 10.7%, which is much lower than DCTM (19.2%). Similarly, the average power consumption of SMTrust shows only a negligible difference as compared to DCTM and MRHOF under attacks. Hence, overall SMTrust shows a better performance in Scenario II, comprising mobile sender nodes, under Blackhole and Rank attacks.
# The Power System and Microgrid Protection--A Review
## 2 DG and Microgrid Challenges
### Blinding Zones  
Current-based relays (overcurrent (OC) and earth fault (EF)) are the main protection devices in conventional distribution networks. The blinding zone is an area, where the fault cannot be detected by current-based relays. The short circuit current seen by the feeder OC relay is reduced due to the contribution of DG when the distributed energy resources are placed between the fault point and the OC relay. Figure 4 shows the possible blinding zones in the distribution network in the presence of a DG. More possible blinding zones may occur by a DG that is embedded between the feeding substation and the fault point. As shown in the figure, the blinding points occur when the total current is divided among different feeding sources. The short-circuit current at each fault point includes the feeder current and the DG current as shown in Equation (1) [30; 39]:  
\[I_{Sc}=I_{fault,Fender}+I_{fault,DG} \tag{1}\]  
Of course, the type, location, and capacity of the DG are important for distinguishing the blinding points. Various distribution sources have different short circuit levels, which vary depending on whether the DG is inverter-based (photovoltaic source) or synchronous generator (CHP generators) and operates in grid-connected or islanded modes [46].  
In other words, an OC relay cannot pick-up in blind zones. Figure 5 illustrates the operation and blinding zones by the characteristic curve of the OC relay (pick-up current was 400A [30]). Many researchers have tried to improve the performance of OC relays in blind zones. The change in settings is the conventional method to protect the distribution network in blind zones. Reference [30] investigated the recloser effect on covering the blinding zones and improving the reliability in a real distribution network. A recloser relay in a suitable location can also detect high impedance faults.  
Figure 4: Possible blinding zones in a typical distribution network.  
The high penetration of DG may result in underreaching or overreaching problems of distance relays in distribution systems. A distance relay is less sensitive to changes in power system topology comparing to an OC relay [27]. The distance relay setting has a definite time for any fault in the transmission line or a distribution feeder. It may not operate according to the zone setting in the presence of a DG. The measured impedance of the line changes with high penetration of the DG.
## 2 Materials and Methods  
### Literature Selection  
To ensure the representativeness and feasibility of the research findings, this study selected the Web of Science (WOS) Core Collection as the source of the literature data. Based on existing research experience and standardized terminology, the search strategy was formulated as follows: TS = (\"unmanned aerial vehicle\" OR \"unmanned aircraft vehicle\"OR UAV OR drone* OR \"unmanned aircraft*\" OR \"unmanned helicopter*\" OR \"unmanned aerial system*\" OR \"unmanned aircraft system*\" OR \"pilotless aircraft*\" OR \"pilotless helicopter*\" OR UAS) AND TS = (\"remote sensing\"). This search query incorporates commonly used UAV-related terms and the theme of remote sensing to ensure comprehensive coverage of the relevant literature. The SCI-EXPANDED database focuses on the natural sciences, while the SSCI focuses on the social sciences. The citation indices selected include the Science Citation Index Expanded (SCI-EXPANDED, 1900-present) and the Social Sciences Citation Index (SSCI, 1956-present). The search was conducted up to 31 December 2024, yielding a total of 7118 relevant publications. The document types were limited to peer-reviewed journal articles and reviews (Article, Review), excluding editorial materials, meeting abstracts, book chapters, corrections, and news items. Through a combination of manual screening and automated filtering, irrelevant and duplicate records were removed. The final dataset included 4985 UAV Remote Sensing (UAV-RS) publications, spanning multiple disciplinary domains and research directions, thus ensuring both representativeness and temporal relevance of the data for scientometric analysis (Figure 1). Our search strategy did not manually restrict the starting year. The Science Citation Index Expanded (SCI-EXPANDED) covers publications dating back to 1900, but this does not represent the actual starting point of our analysis. Instead, our study was based on the default coverage range of the database to ensure that no potentially relevant early studies were overlooked.
## 1 Introduction
Figure 1 illustrates example CT spine images, corresponding ground truth segmentation masks, and simulated noisy labels. The annotation process may not output masks at a gold-standard level, but may rather generate labeled features that exhibit erosion or dilation of ideal contours, along with various elastic transformations. We refer to these alterations as noisy labels and conjecture their potential detrimental influence on the model's performance. In actual clinical settings, these deviations from the gold standard are likely to be less dramatic. Figure 1: The example images of CT spine images and corresponding segmentation ground truth and noisy labels generated by erosion, dilation, and elastic transform. By incorporating denoising techniques into the segmentation network, it becomes possible to develop noise-robust networks capable of effectively segmenting medical images in the presence of unreliable annotations. Consequently, this could lead to improved segmentation accuracy and enhanced clinical applicability, contributing to advancements in medical image analysis and diagnosis. Further investigation of ViT-based architectures, combined with sophisticated denoising techniques, may yield transformative outcomes in medical image segmentation, overcoming the limitations posed by noisy labels and facilitating more accurate and reliable diagnostic tools. The detailed noise-robust ViT-modified encoder-CNN decoder network proposed in this paper, named NR-UNet, is introduced in Section 2. The demonstration code is accessible at [[https://github.com/ziyangwang007/VIT4](https://github.com/ziyangwang007/VIT4) UNet]([https://github.com/ziyangwang007/VIT4](https://github.com/ziyangwang007/VIT4) UNet). There are four categories of contributions to this work:  
1. Inspired by the previous success of CNN and ViT, a ViT-based modified encoder and CNN-based decoder UNet-style segmentation network is proposed. 2. To simulate real clinical scenarios, noise is manually added to the ground truth to create noisy labels, constructing a CT spine segmentation dataset with noisy labels for evaluation purposes. 3. A simple and efficient adaptive denoising learning (ADL) is proposed for segmentation network to achieve a noise-robust segmentation framework. 4. The proposed segmentation network with ADL attains competitive performance against other baseline methods across various evaluation metrics under the same data conditions with both accurate and noisy label sets.
# The Holistic Perspective of the INCISIVE Project--Artificial Intelligence in Screening Mammography
## 5 Description of AI Toolbox/Breast Cancer Tools  
### Federated Learning--Data Exploitation  
As privacy is a key factor in the healthcare environment, most of the time, hospitals and other entities that store medical data face difficulties or even do not want to share their data with researchers. For this reason, research on fields such as breast cancer imaging analysis can be hard to perform, even though there are an increasing number of open datasets. However, federated learning [47] offers a way of training machine learning models without the need of sharing data.
# Efficient LDPC Encoder Design for IoT-Type Devices
## 6 Numerical Results
### Error-Correction Performance  
In order to show the coding gain that can be achieved with the QC-LDPC codes employed in comparison with typical classic block codes, we performed Monte Carlo simulations for the QC-LDPC codes, as well as for their RS and BCH counterparts. RS codes with rate \(R=0.5\), BCH and QC-LDPC codes with different block lengths were used for this comparison. A QPSK modulator and an AWGN channel model were used in the communication system models. The LDPC codes were decoded with a belief propagation (BP) decoder, the BCH codes were decoded with the hard-decision Berlekamp algorithm, and the RS codes were decoded using the Berlekamp-Massey decoding algorithm.  
Figure 10 presents the results of these experiments in the form of the bit error rate curve versus the effective signal-to-noise ratio \((E_{b}/N_{0})\) in an AWGN channel for three selected cases: (1024, 512), (512, 256), and (512, 320) LDPC codes and their counterparts, as shown in Table 2. An additional coding gain of about 2 dB for the QC-LDPC codes is visible in all of the presented cases. This result represents the amount of transmission power that can be saved when using iteratively decoded QC-LDPC codes instead of standard block codes.  
Figure 10: Error correction performance of the experimental codes.  
Figure 9: Throughput of the encoders vs. code parameters.
## 2 Background
### Investigation of Genetic Response to Pressure in B. subtilis through RNAseq Technique  
RNAseq is a technique that can be used to investigate the quantity and sequences of mRNA in a sample at a particular condition using next-generation sequencing technology. Through this technique, all mRNA sequences in one cell state can be analysed as a transcriptome, to measure the extent to which the genes are expressed.  
RNAseq was used to investigate the genetic response to pressure in _B. subtilis_. _B. subtilis_ cells were grown in 2% (\(w/v\)) LB agarose hydrogel at 37 \({}^{\circ}\)C as a 3D culture. When the growth reached the exponential stage, the agarose hydrogels were placed in a customised pressure vessel and treated with either 200 kPa or 400 kPa for 30 min at 37 \({}^{\circ}\)C. After the pressure treatment, the cells were immediately frozen in liquid nitrogen and the mRNA was extracted and prepared for RNAseq using TRIzol reagent and a Qiagen RNeasy Plant Mini Kit (QRMP). An RNA analysis was then performed using a Bioanalyser with the HS analysis kit. The _B. subtilis_ grown in 3D culture without pressure treatment was used as a control sample and the experiments were performed in four biological replicates and four technical replicates spanning each of the conditions to achieve significant results. RNA samples were sent for sequencing using Illumina HiSeq with 5-10 million reads per sample. In the control, the mix was also added to samples to allow for the creation of a standard baseline measurement of RNA both within this experiment and across other experiments performed using various samples and platforms.  
The RNA sequencing data generated from the sequencing were normalised using the ERCC (External RNA Controls Consortium) RNA spike-in controls and fold change (FC) calculated in comparison to the 0 kPa pressure control. Overall, there were 28 and 88 genes that displayed a significant increase in expression with a cut-off of FC \(\geq\) 2 for 200 kPa and 400 kPa, respectively. Of these, 18 were common to both conditions suggesting a partially shared response to the increased pressure environment.  
To demonstrate this system, a practical bio-geotechnical numerical model of a pressure-responsive MICP system is proposed. This two-dimensional model accounts for varying geotechnical conditions, anisotropic soil permeability, bacteria cell distribution and bacteria genetic response to predict the distribution of calcium carbonate precipitation and the associated reduction in permeability. Cementation scenarios are presented for different soil conditions resulting from the real genetic responses of selected bacteria which were tested in the lab. In addition, to demonstrate to design potential of this system, the cementation response from an ideal hypothetical engineered bacterium is also presented.