esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Dispersion Analysis of the I-Shaped Steel Beam  
### The SAFE Model  
A SAFE model is constructed in the Cartesian coordinate system to calculate the dispersion curves for the I-shaped steel beam. The wave is assumed to propagate along the z axis with a harmonic exponential solution of the form \(e^{-i(kz-\omega t)}\). \(k\) represents the wavenumber, and \(\omega\) denotes the frequency. The displacement of the guided wave can be expressed as follows:  
\[\mathbf{\mu}(x,\ y,\ z,\ t)=\begin{cases}\mu_{x}(x,\ y,\ z,\ t)\\ \mu_{y}(x,\ y,\ z,\ t)\\ \mu_{z}(x,\ y,\ z,\ t)\\ \end{cases}=\begin{cases}\mu_{x}(x,\ y)\\ \mu_{y}(x,\ y)\\ \mu_{z}(x,\ y)\\ \end{cases}e^{-i(kz-\omega t)} \tag{1}\]It can be observed that only the cross section requires meshing. According to the virtual work principle, the motion equation of the guided wave under the external force can be derived as  
\[\left(\mathbf{K}_{1}+ik\mathbf{K}_{2}+k^{2}\mathbf{K}_{3}-\omega^{2}\mathbf{M}\right)\mathbf{U}=f_{ex} \tag{2}\]  
where \(\mathbf{K_{1}}\), \(\mathbf{K_{2}}\), and \(\mathbf{K_{3}}\) are the global stiffness matrix; \(\mathbf{M}\) is the global mass matrix; \(\mathbf{U}\) is the global displacement matrix; and \(f_{\rm ex}\) is the external force. To obtain the relationship between the excitation frequency (\(\omega\)) and the wave number (\(k\)), Equation (2) is rewritten as follows:  
\[\mathbf{A}=\begin{bmatrix}\mathbf{A}-k\mathbf{B}\mathbf{\overline{U}}=\mathbf{F}\\ \mathbf{K_{1}}-\omega^{2}\mathbf{M}&\hat{\mathbf{K}_{2}}\end{bmatrix},\ \mathbf{B}=\begin{bmatrix}\mathbf{K_{1}}- \omega^{2}\mathbf{M}&0\\ 0&-\mathbf{K_{3}}\end{bmatrix} \tag{3}\] \[\overline{\mathbf{U}}=\begin{bmatrix}\mathbf{U}\\ k\mathbf{U}\end{bmatrix},\ \mathbf{F}=\begin{bmatrix}0\\ f_{ex}\end{bmatrix}\]  
Given a specific frequency range, the phase velocity \(c_{p}=\omega/k\) and group velocity \(c_{g}=d\omega/dk\) can be calculated to obtain the dispersion curves. The geometrical and material parameters of the I-shaped steel beam are detailed in Table 1. Figure 1 illustrates the meshes of the cross section, which consists of 1256 triangular elements with a maximum mesh size of 2.5 mm. The phase velocity and group velocity dispersion curves of the I-shaped steel beam are shown in Figure 2, which exhibits severer dispersion than those of a plate.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Young’s Modulus** & **Density** & **Poisson Ratio** & **Width of Flange** & **Height of Web** &
\begin{tabular}{c} **Thickness of** \\ **Flange and Web** \\ \end{tabular} \\ \hline
210 GPa & 7800 kg/m\({}^{3}\) & 0.28 & 126 mm & 290 mm & 8 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: The material and geometrical properties of the I-shaped steel beam.  
Figure 1: Cross section and meshes of the I-shaped steel beam.
# Thermogram Breast Cancer Detection: A Comparative Study of Two Machine Learning Techniques
## 4 The Proposed Method  
As shown in Figure 2 earlier, there are four main stages of a typical breast cancer detection system using digital thermography: (a) image preprocessing including artifact removing, (b) segmentation (extracting Region of Interest (ROI)), (c) feature extraction (i.e. extracting different features representing each sample), and (d) breast tumor detection (i.e. classifying each sample to either normal or abnormal). In the proposed method, these phases are summarized as follows (more details are given in Sections 4.1-4.4):  
* In the image pre-processing stage: Noise reduction was achieved using the combination of homomorphic filtering and spatial domain morphology and image enhancement was accomplished using top-hat transform and adaptive histogram equalization.
* In the segmentation stage, the tumor was segmented using binary masking and K-means clustering to produce the boundary image.
* In the feature extraction stage, two types of features were extracted: geometrical and textural features.
* In the tumor detection stage, MLP and ELM classifiers were used and compared.
## 3 Materials and Methods
### Three-Dimensional Models of the Sculptures
#### 3.1.1 Digitized Sculptures  
Digitized sculptures include five 3D models (S1, S7, S8, S9, S10) of real-world sculptures created through photogrammetric digitization. Photogrammetry is one of the most commonly used methods for digitizing real-world objects and reconstructing their original appearance in virtual environments [41; 42; 43; 44]. By generating a point cloud from a series of photographs, photogrammetry enables the creation of 3D models with high-resolution textures, contributing to their realistic and reliable appearance. As a result, 3D models reconstructed using photogrammetry are typically characterized by precise geometric features and high-quality textures.  
In this study, five sculptures, two abstract (S7, S8) and three figurative (S1, S9, S10), were digitized using a photogrammetric approach, resulting in realistic 3D models. The digitization workflow involved data acquisition using a mobile device for capturing photos and data processing with photogrammetric software. For photo capture, the Motorola Edge 40 Neo mobile device was used, equipped with a 50 MP main camera featuring an f/1.8 aperture, wide lens, 1/1.55\({}^{\prime\prime}\) sensor size, 1.0 \(\upmu\)m pixel size, multi-directional Phase Detection Autofocus (PDAF), and Optical Image Stabilization (OIS). The photographs of the sculptures were taken using the convergent method, which entails moving the camera around the object to cover its entire surface while ensuring an 80% overlap between adjacent photos. The point clouds and textured 3D meshes were then automatically generated using RealityCapture [45], a photogrammetric software, available free of charge for educational institutions through the Epic Games account.  
The created 3D models of the digitized sculptures are distinguished from the digital models due to the realistic appearance of the materials they replicate, such as the clay used for sculptures S1, S7, S9, and S10, and the rusty sheet metal for sculpture S8. Additionally, the geometry was successfully reconstructed, as the three of the digitized sculptures (S1, S9, S10) exhibit figurative art representations that reliably depict recognizable forms of animals and fire as the subjects from the real world. The remaining sculptures, S7 and S8 both feature abstract designs, characterized by simpler and more complex geometries, respectively. It can be observed that, while incorporating both abstract and figurative art representations, the digitized sculptures preserve the traditional form of real-world sculptural art.
# SCCDNet: A Pixel-Level Crack Segmentation Network
## 3 Results and Experimental Evaluations
### Dataset  
Currently, there is no consistent public dataset for pixel-level crack segmentation task, so it is difficult to compare other networks on the same dataset. Most of the previous papers train and test their networks on small datasets established by themselves, which cannot verify the generalization ability of the model. Therefore, we set up a large-scale public dataset to verify the effectiveness of the network. This dataset consists of 7169 images with manually annotated labels with a resolution of \(448\times 448\). Some typical crack images and their labels are shown in Figure 4. It can be seen that the data set contains crack images of different environments, different shooting distances, and different forms, covering the common crack characteristics to the greatest extent. The images in the dataset come from published datasets [9, 30, 40, 41, 42], crack images on the Internet, and images of similar crack objects.  
The dataset is divided into two parts: training set and test set: the training set contains 6164 input images and label images, including 4965 crack images and 1199 no crack images; the test set contains 1005 crack images and label images, including 793 crack images and 212 no crack images. In addition to the various crack images, we add about 20% of the no crack images to the dataset. The purpose is to strengthen the anti-interference ability of the model and reduce the probability of detecting environmental objects as cracks.
## 3 Validation
### Computer Simulation
The FEA test started with the manipulator at its default resting position. The test commenced with the rotation of the winch pulley, which retracted the tendon and pulled the manipulator's lower end. The retracting tendon, in turn, pulled the arm from its tip pulley. The retraction of the tendon continued until the arm completed a full-loop and the arm's tip reached the winch pulley. The number of winch-pulley rotations were determined through the following equation:  
\[N=\frac{L}{2\pi r} \tag{12}\]  
where \(N\) is the number of winch-pulley rotations, \(L\) is the length of the bending segment, representing the arm section below the ACTR, and \(r\) is the radius of the winch-pulley. The ACTR in Figure 8 represents the tendon's second attachment point. To obtain the response of the manipulator's arm for different bending segment lengths, the FEA test was performed for three ACTR locations: 100, 250, and 400 mm. These locations produced bending segments that are 20%, 50%, and 80% of the arm's full length. The different bending segment lengths provided different trajectories of the arm's tip in a planar workspace, as shown in Figure 9a. The tip trajectory depends on the location of the ACTR which determines the length of the bending segment. Accordingly, the variable location of the ACTR produces a planar workspace, as shown in Figure 9b. By applying \(\theta\) values covering the full rotation of the manipulator about its vertical axis, a volumetric workspace emerges, as illustrated by Figure 10. \begin{table}
\begin{tabular}{c c} \hline \hline
**Object** & **Mesh** \\ \hline Arm & 5 mm \\ \hline ACTR & 4 mm \\ \hline Tendon & 3 mm \\ \hline Winch Pulley & 1.5 mm \\ \hline Winch–Tendon Connection & 0.5 mm \\ \hline Tendon—Lower-Binder Connection & 0.4 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mesh resolution for manipulator components and connection points. Figure 9: The results obtained from the simulation: (**a**) Arm tip trajectories for three bending segment lengths. (**b**) Planar workspace.
## 3 Example Verification  
In order to verify the absorption effect of the proposed method under the conditions of various seismic wave fields, the numerical models of the NRB, CALM, and MVDB based on the damping absorbing layer method were established to compare the examples. Additionally, the criteria were unified, with the absorbing region size \(L\) being taken as \(2\lambda_{min}(\lambda_{min}=2\text{m})\). In Equation (11), \(\Delta l\) is determined as the maximum grid size (20 cm) and \(k\) as 1.08. A total of 50 m \(\times\) 50 m homogeneous linear elastic isotropic soil was analyzed, and [PERSON]'s modulus \(E\) is taken as 20 MPa, Poisson's ratio \(v\) = 0.25, density \(\rho\) = 2000 kg/m\({}^{3}\), shear wave velocity \(C_{\text{s}}\) = 200 m/s, longitudinal wave velocity \(C_{p}\) = 346 m/s, which setting up sensors (A-D) in the model. Specifically, A and B are sensors for endogenous seismic wave fields, and others are exogenous seismic wave fields, as shown in Figure 4.  
Figure 4: The uniform soil calculation model. (**a**) Endogenous seismic wave field. (**b**) Exogenous seismic wave field.  
Figure 3: Working mechanism of the MVDB.  
In this study, the model was discretized using four-node plane stress elements, reduced integral (CPS4R) cells based on ABAQUS/explicit dynamics analysis, and hourglass stiffness correction using the artificial damping method (see e.g., [55]), with a torsion control of 0.1 and an hourglass control of 0.45. The cell dimension was \(\Delta x=\Delta y=\Delta l=20\) cm. The seismic source was a sinusoidal function modulated by a Hanning window as follows:  
\[\mathrm{y}=\left[\frac{1}{2}\bigg{(}1-\cos\frac{2\pi ft}{n}\bigg{)}\right]\cdot A \sin(2\pi ft),\ 0\leq t\leq\frac{n}{f} \tag{13}\]  
where the source frequency \(f\) adopts 100 Hz, the number of Hanning periods \(n\) is three, the amplitude scale factor \(A\) is \(1\times 10^{-3}\), the effective duration of source excitation \(t\) is 0.03 s, the calculation time is 0.7 s, and the waveform is shown in Figure 5.
## 2 Instrument operating characteristics  
Before going into some details in the technique of wide swath interferometric altimetry and into the optimization of a proposed space mission to primarily observe ocean mesoscale features, let us recall one main requirement at mission level. Indeed, to observe ocean mesoscale features and assimilate observations in models usefully, it is now agreed that the lower the nadir altimeter instrument range noise, the better mesoscale features will be retrieved, assuming that resolution is provided thanks to multiple orbiting instruments in a well-phased configuration. The optimal range noise level is at the level of some 2 cm, which is a figure that is reached by the well known TOPEX, POSEIDON-1, POSEIDON-2, ENVISAT RA-2 instruments in typical situations characterized by a 2 meter significant waveheight and a 11 dB backscatter coefficient. As far as swath instruments are considered, estimating the measurement noise is a multi-parameter task that will be described in the coming sections. For the purpose of data assimilation in ocean models, it is important to have a good knowledge of the statistics of the error measurements over the entire swath. Previous use of wide-swath simulated data in a high-resolution ocean model have shown that a 4 to 5 cm overall noise level may be adequate to retrieve ocean signals at the same level as would be achieved by a constellation of 3 to 4 nadir instruments (see [4]). Thus, the basic assumption of the paper will be to design an instrument and a complete system to reach such a 4 to 5 cm noise level.
###### Abstract  
This paper discusses using computer-aided diagnosis (CAD) to distinguish between hepatocellular carcinoma (HCC), i.e., the most common type of primary liver malignancy and a leading cause of death in people with cirrhosis worldwide, and liver abscess based on ultrasound image texture features and a support vector machine (SVM) classifier. Among 79 cases of liver diseases including 44 cases of liver cancer and 35 cases of liver abscess, this research extracts 96 features including 52 features of the gray-level co-occurrence matrix (GLCM) and 44 features of the gray-level run-length matrix (GLRLM) from the regions of interest (ROIs) in ultrasound images. Three feature selection models--(i) sequential forward selection (SFS), (ii) sequential backward selection (SBS), and (iii) F-score--are adopted to distinguish the two liver diseases. Finally, the developed system can classify liver cancer and liver abscess by SVM with an accuracy of 88.875%. The proposed methods for CAD can provide diagnostic assistance while distinguishing these two types of liver lesions.  
classification; F-score; gray-level co-occurrence matrix (GLCM); gray-level run-length matrix (GLRLM); hepatocellular carcinoma (HCC); liver cancer; liver abscess; image texture; sequential backward selection (SBS); sequential forward selection (SFS); support vector machine (SVM); ultrasound images +
Footnote †: journal: Applied Sciences
# Enhancing PAPR and Throughput for DFT-s-OFDM System Using FTN and IOTA Filtering
## 3 Proposed IOTA-Based FTN-DFT-s-OFDM Waveform Design  
We consider improving the spectral efficiency and reducing the PAPR on the basis of OFDM. Based on the commonly deployed DFT spreading technique, we further enhance the spectral efficiency with FTN signaling, where FTN acceleration can reduce the transmission time of each symbol. However, due to the introduction of FTN, ISI is unavoidable. Therefore, the design of prototype filter with better focusing capability is studied.  
In Section 3.1, we present the design of the waveform framework. The corresponding receiving method is illustrated in Section 3.2.
## 2 Biosynthesis of \(\beta\)-Ocimene  
\(\beta\)-Ocimene (3,7-dimethyl-l,3,6-octatriene) is a monoterenoid with the chemical formula \(C_{10}H_{16}\). It has two stereoisomers, _cis_- and _trans_-\(\beta\)-ocimene (or (\(Z\))- and (\(E\))-\(\beta\)-ocimene, respectively), which are the _cis_ and _trans_ forms of the central double bond (Figure 1). The _trans_ isomer is more common and more abundantly emitted in floral scents than the _cis_ isomer [1, 9]. It is synthesized from the precursors isopentenyl pyrophosphate (IPP) and dimethylallyl pyrophosphate (DMAPP) via the methyl-erythriol-phosphate (MEP) pathway. Monoterpenes are obtained by the transformation of DMAPP and IPP into geranyl diphosphate (GPP) inside the chloroplasts, and the later synthesis of terpene compounds from GPP by enzymes called terpene synthases (TPS), which are very diversified across the phylogeny of the plant kingdom [10, 12, 13, 14, 15]. \(\beta\)-Ocimene is synthesized by the enzyme (\(E\))-\(\beta\)-ocimene synthase (with the systematic name geranyl-diphosphate diphosphate-lyase ((\(E\))-\(\beta\)-ocimene-forming)), which transforms GPP into _trans_-\(\beta\)-ocimene and diphosphate. (\(E\))-\(\beta\)-Ocimene synthase produces mostly _trans_-\(\beta\)-ocimene (94-97%), but other monoterpenes such as _cis_-\(\beta\)-ocimene or myrcene are produced in low proportions (2-4% and 1-2%, respectively) [16, 17, 18, 19]. _cis_-\(\beta\)-ocimene is thus less common than _trans_-\(\beta\)-ocimene in floral scents; it is produced and emitted in small but detectable amounts only in species where _trans_-\(\beta\)-ocimene is produced in moderately high amounts.
# Performance Analysis of NB-IoT Uplink in Low Earth Orbit Non-Terrestrial Networks +
## 7 Conclusions  
In this study, to improve NB-IoT coverage, we considered the NB-IoT NTN structure, which assumes that the LEO satellite is transparent. We formulated the residual Doppler shift compensation problem in LEO satellite communications. The residual Doppler shift is calculated by realizing the actual STARLINK satellite in 3D. We calculated the residual Doppler shift related to the beam coverage size. We proposed a beam diameter that fits the NB-IoT receiver compensation range. Since the NB-IoT device has a small EIRP, _DMRS_ symbol addition is proposed to improve link performance. The proposed _DMRS_ symbol addition avoids the degradation of link performance even in the presence of Doppler shift. These results are presented through link-level simulation, and then link performance results are analyzed through link budget analysis. A 3D satellite simulation is implemented through real satellite orbits. In this study, we analyzed results such as _P\({}_{OTR}\)_, _T\({}_{S}\)_, _MPST_, and _MPOT_. We observe that just one _DMRS_ symbol addition remarkably saves 72 satellites to  
Figure 17: The maximum persistent outage time (_MPOT_) for different numbers of _DMRS_ symbols per slot. (**a**) one orbit, (**b**) six orbits.  
Figure 18: The per\(-\)day total service time for different numbers of _DMRS_ symbols per slot. (**a**) one orbit, (**b**) six orbits.  
achieve 23.55 h of service time per day. The results can offer an interesting perspective for implementing cost-effective NB-IoT NTN. In future research, we could find ways to increase throughput, such as the HARQ process, which is not currently considered in NB-IoT.  
Conceptualization and methodology, M.-G.K. and H.-S.J.; software, M.-G.K.; validation, formal analysis, and investigation, M.-G.K. and H.-S.J.; resource and data curation, M.-G.K. and H.-S.J.; writing--original draft preparation, M.-G.K. and H.-S.J.; writing--review and editing, M.-G.K. and H.-S.J.; visualization, H.-S.J.; supervision, H.-S.J.; project administration, H.-S.J.; funding acquisition, H.-S.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ICAN (ICT Challenge and Advanced Network of HRD) program (IITP-2022-RS-2022-00156212), supervised by the IITP (Institute of Information & Communications Technology Planning & Evaluation). Not applicable. Not applicable. Not applicable. Not applicable. Not applicable. Not applicable.  
The authors declare no conflict of interest.
## 3 Simulation and Optimization of the Joint
### Contact and Loading Conditions  
ANSYS provides five contact modes: _bonded_, _frictionless_, _no separation_, _rough_, and _frictional_. The _bonded_ contact model completely binds two contact surfaces, thereby preventing both separation and sliding. The _frictionless_ model defines the friction coefficient between the contact surfaces as zero and allows normal separation. The _no separation_ model allows frictionless sliding over a small area. The _rough_ model is similar to the _frictionless_ model, except that contact sliding between contact surfaces is not allowed. The _frictional_ model defines that there will be shear forces based on the friction coefficient between the contact surfaces. The latter three models are nonlinear contact models. The _frictional_ model was selected in the simulation, as shown in Figure 7. In the simulation, the coefficient of friction between the threads of the pin joint and box joint is set to 0.1, which is the actual coefficient of friction between steel and steel.  
After the joints are connected, it is necessary to continue tightening at a certain angle to produce an interference fit, achieve sealing and prevent thread tripping. In the previous study, the interference fit between the threads was achieved by setting the contact offset. In this simulation, the interference fit between the threads was achieved by rotating the pin joint by a certain angle, which is consistent with the actual working conditions. Moreover, the circular cross section of the pin joint is set to the cylinder connection, that is, both the movement and rotation in the x and y directions are prohibited (wherein UX, UY, RX, and RY are constrained). After completing the above settings, the model can be loaded, as shown in Figure 8.  
Figure 8: Loading conditions: (**a**) tensile load and (**b**) torsional load.  
Figure 7: (**a**) Contact settings and (**b**) contact surfaces.
## 4 DRL Based TDIA Transmission Scheduling Algorithm (DRLSA-IA)  
In this paper, we proposed a deep reinforcement learning-based time-domain interference alignment scheduling algorithm (named DRLSA-IA). In DRLSA-IA, nodes learn efficient transmission scheduling in continuous interaction with the environment. Therefore, DRLSA-IA can effectively adapt to the highly dynamic underwater environment. Unlike other algorithms that require enumeration of the state space, DRLSA-IA can obtain the current transmission decision when the observed state is fed to the trained algorithm, so there is no computational restriction.  
Specifically, we first describe the transmission scheduling problem in UWANs as a DRL problem. Furthermore, we present our time-domain interference alignment-based reward mechanism to improve the network throughput. Considering large propagation delays and the partially observed network state of UWANs, we proposed a novel DRL algorithm in DRLSA-IA. Finally, we describe DRLSA-IA in detail.
# A Family of ACO Routing Protocols for Mobile Ad Hoc Networks
## 3 AntOR-Based Protocol
### AntOR Phases
#### 3.2.3 Established Path Maintenance and Exploration of New Routes  
As its name suggests, this phase consists of a proactive process of established route maintenance and exploration of new routes, which updates and expands the available routing information. This allows us to build multiple routes that serve as support for the created initial route in the reactive route setup process. This proactive process contemplates two subprocesses: pheromone diffusion and proactive ant sending.  
This phase of AntOR differs from the similar AntHocNet in the separation of pheromones in the diffusion process, the disjoint ability and the use of the distance metric, differences that directly affect the two subprocesses of this phase.