esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Model Development  
### Boundary Value Problem  
In the computational domain, a three-dimensional FR-NWT assumes that the fluid is potential flow, which is inviscid, irrotational, and incompressible. In this case, the governing equation is the Laplace equation (Equation (1)) using the velocity potential and continuity equation.  
\[r^{-2}F=0 \tag{1}\]  
The Laplace equation can be transformed to the boundary-integral equation using the Green second identity. The boundary-integral equation can be written as follows:  
\[aF_{i}=\iint_{W}\left(G_{ij}\frac{\mathbf{f}\mathbf{f}_{j}}{\mathbf{f}_{l}n} \quad F_{j}\frac{\mathbf{f}G_{ij}}{\mathbf{f}_{l}n}\right)\,ds \tag{2}\]  
where \(F_{i}\), \(a\), and \(G_{ij}\) are the total velocity potential, solid angle, and Green function, respectively. The solid angle is 0.5 on the boundaries of the computational domain. Equation (3) shows the simple Rankine source as the Green function used in this study, and the image method is used to express the flat seabed and reduce the computational demands (Equation (4)).  
\[G=\frac{1}{4p}\bigg{(}\frac{1}{R_{1}}+\frac{1}{R_{2}}\bigg{)} \tag{3}\]  
\[\begin{array}{c}R_{1}=\sqrt{\left(x\quad x_{0}\right)^{2}+\left(y\quad y_{0 }\right)^{2}+\left(z\quad z_{0}\right)^{2}}\\ R_{2}=\sqrt{\left(x\quad x_{0}\right)^{2}+\left(y\quad y_{0}\right)^{2}+ \left(z+2+2\right)^{2}}\end{array} \tag{4}\]  
where \(\left(x,y,\,z\right)\) and \(\left(x_{0},\,y_{0},\,z_{0}\right)\) are the coordinates of the field and source point, respectively: and \(h\) is the water depth. The total velocity potential can be divided into the incident wave potential (\(F_{l}\)), radiation potential (\(F_{R}\)), and diffraction potential (\(F_{D}\)), as expressed in Equation (5). Incident wave potential can be calculated using Equation (6). Additionally, the radiation potential was decomposed conventionally as six degrees of freedom-of-motion mode components.  
\[F=F_{l}+F_{R}+F_{D} \tag{5}\]  
\[F_{l}= i\frac{gH}{2w}\frac{\cosh k(z+h)}{\cosh kh}\,\,e^{ikx} \tag{6}\] \[F_{R}=iw\sum_{j=1}^{6}z_{j}f_{j} \tag{7}\]where \(w\), \(k\), and \(H\) are the wave frequency, wave number in the finite water depth, and wave height, respectively, and \(z_{j}\) denotes the complex amplitudes of the body motion in its six degrees of freedom. To obtain radiated wave potential and diffracted wave potential, the radiation problem and the diffraction problem must be solved, respectively. The radiation problem aims to analyze oscillating bodies in calm water. The diffraction problem evaluates the wave excitation forces acting on the stationary body in waves. Figure 1 presents an overview of the computational domain of 3D FR-NWT, where \(S_{B}\), \(S_{I\!S}\), \(S_{B\!R}\), and \(S_{w}\) are the body boundary surface, free-surface boundary surface, bottom boundary surface, and sidewall boundary surface, respectively.
## 3 Industrial Applications
### Mining  
Mining projects are often operated in remote and high-risk locations. Thus, when equipment components fail, supply chains are compromised due to access limitation, downtime, and delays in production due to lead-times in obtaining replacement parts. In such circumstances, metal AM may be very suitable, particularly for the manufacturing of on-demand spare parts ([PERSON] et al., 2014). The literature review shows that the mining industry has been slow in adopting metal AM technologies. However, some research projects are exploring applications relating to the production of mining components. For example, Aurora Labs, an Australian metal 3D printer manufacturer, has collaborated with Fortescue Metals Group (FMG), an Australian mining company, to develop on-demand replacement parts for remote mining projects. Through rapid replacement of components, the mining sector can realise up to a 50% downtime cost reduction ([PERSON] et al., 2014). Sandvik, a Swedish equipment and tool manufacturer, is also working on a metal AM project. This company has invested approximately AU $30 million (US $25 million) to promote the use of Metal AM in the mining sector ([PERSON] et al., 2014).  
The goal of this project is applying fine metal powders in the production of mining components such as mining gears. In addition, there is a joint project towards adoption of metal AM supported by the IMCRC (Innovative Manufacturing Cooperative Research Center), involving a partnership between Downer's Mineral Technologies and the University of Technology Sydney Rapido center. The aim of this project is to research solutions that will advance methods of production of mineral separation equipment ([PERSON] et al., 2014). It can be seen that that AM has the potential to reshape mining supply chains through a number of features as listed below ([PERSON] et al., 2014; [PERSON] et al., 2014).  
* On-demand and on-site production of components;
* Customisable and replicable bespoke components;
* Leaner and greener production to reduce waste, while ensuring quality.
# Autonomous Environment Generator for UAV-Based Simulation
## 3 Experimental Setup
### Backend (NodeJs)  
The backend acts as a server to receive and manage remote connections. The backend for this project was built with NodeJS, an open-source Javascript runtime environment. NodeJS was chosen due to its non-blocking single-thread ability, allowing many server requests simultaneously [13]. Our project especially relies on quick data transfer, and NodeJS is known to be one of, if not the fastest, backends for processing requests [14]. NodeJS also supports TypeScript, allowing for a consistent front and backend language for our project.  
This particular use case manages user authentication, restricting platform access from users outside trusted organizations. The backend additionally receives the satellite image and its latitude and longitude from the front end. The image and its respective coordinates are then stored in a MySQL database for later retrieval, if necessary.
# Retinex-Based Relighting for Night Photography
## 2 Retinex Theory  
### Overview  
Land introduced the Retinex theory in 1964 to model how the HVS perceives a scene [1]. The HVS does not perceive absolute brightness but rather relative brightness. The fundamental observation is the insensitivity of human visual perception to a slowly varying  
Figure 1: Overview of the proposed method.  
illumination in a Mondrian-like scene. Recent reviews are presented in [31; 32]. However, the goal has since shifted toward reflectance-illumination decomposition, which is picked up from the Retinex model.  
There are various approaches to the decomposition problem of illumination and reflectance: threshold-based (PDE) Retinex [33; 34], reset-based (random walk) Retinex [35], center-surround Retinex [3; 21; 36; 37], and variational Retinex [31; 38; 39], and more recently, convolutional neural networks used for Retinex [40; 41]. In another context, this separation is called intrinsic image decomposition [42; 43; 44; 45], where the intrinsic image is the reflectance image in the Retinex.  
The Retinex model involves the idea that the captured image \(\mathbf{f}\) is the product of an illumination map \(\mathbf{L}\) and a reflectance map \(\mathbf{R}\) of the object [46]. It is defined as follows:  
\[\mathbf{f}=\mathbf{L}\odot\mathbf{R}, \tag{1}\]  
where the operator \(\odot\) indicates the Hadamard product. The reflectance \(\mathbf{R}\) is the intrinsic feature of the observed scene, which is not affected by the illumination component \(\mathbf{L}\) as the extrinsic property. We can measure \(\mathbf{f}\) but not directly capture \(\mathbf{L}\) and \(\mathbf{R}\). All symbols used in this paper are reviewed in Appendix A.
# A Method for Underwater Acoustic Target Recognition Based on the Delay-Doppler Joint Feature
## 1 Introduction
[PERSON] et al. [20] established an extreme learning machine to recognize the signal by using a deep neural network to extract characteristics of radiated noise, and the identification accuracy was 93.04%. [PERSON] et al. [21] extracted the modified empirical mode decomposition and gamma tone frequency cepstrum coefficient of the signal, and fused the two signal features into new features. The deep neural network's structure is optimized using the Gaussian mixture model, and the target is recognized by the optimized deep neural network. With the iterative progress of computer technology, artificial intelligence technology represented by deep learning will be more widely used to recognize and classify underwater target radiation noise. Using neural networks to identify underwater acoustic targets can significantly reduce labor costs, and it is an area for significant future development in this field. The traditional neural network model has the disadvantage of complex model structure, which can not be well used in underwater acoustic target recognition. In this work, to thoroughly examine the target's characteristics from two angles, we first propose a feature extraction method that utilizes the Delay-Doppler and Time-Frequency domains. Then, this research introduces a target recognition network specifically tailored for the proposed feature extraction approach. Our structure is more straightforward than traditional neural network models, with no overfitting or underfitting problems. The innovative contributions of this study are outlined below:  
1. This work suggests a joint feature identification method that is based on the Delay-Doppler and Time-Frequency domains. The purpose of this method is to analyze ship-radiated noise from a new perspective of signal processing and provide a new basis for underwater acoustic target recognition. The algorithm extracts signal characteristics that include a broader range of target information. 2. This research presents a target recognition model that uses joint features in conjunction with a convolutional neural network (TF-DD-CNN). This model streamlines the model architecture and enhances the efficacy of model training. The experimental findings demonstrate that its recognition accuracy surpasses that of the conventional neural network model.
## Materials and Methods  
### Characterisation of the Soil and Soil Solution  
The acidic soil used was a granitic Eutric Cambisol collected from the top 20 cm of the headland for a long-term natural pasture at Mitra farm, Evora University, Alentejo, Portugal (38\({}^{\circ}\)32\({}^{\prime}\) N; 08\({}^{\circ}\)00\({}^{\prime}\) W). The air-dried and sieved (2 mm) soil with a pH of 5.6 (soil/water = 1:2.5 (\(w/v\))) contained 23 mg P/kg (Egner-Rhiem), 0.4 mg N-NO\({}_{3}\)/kg, 22.6 mg Mn/kg (DTPA-diethylenetriaminepentaacetic acid), 9 g SOM (soil organic matter-chronic acid wet oxidation)/kg, a cation exchange capacity (CEC) of 3.0 centimoles of charge per kilogram (cmol(+)/kg), and a base saturation of 37% [28; 29; 36].  
Increased levels of bioavailable toxic Mn were obtained after supplementation of 0, 45.2, or 90.4 mg MnCl\({}_{2}\) per kg of soil, which corresponded to zero, two-, and fourfold the Mn levels for the acidic soil, respectively, and were left to rest for a week [28].  
The soil solution was extracted according to [33] with the modifications described in [28]. Briefly, the aqueous solution was isolated from around 40 g of acidic soil surrounding the wheat root system through centrifugation at 2500\(\times\)\(g\), for 60 min, at 4 \({}^{\circ}\)C, in 50 mL tubes fitted with a 0.45 \(\upmu\)m polyethersulfone filter. Volume of the extracted soil solution was recorded, and the soil solution was kept at \(-\)20 \({}^{\circ}\)C.
## 3 Case Study
### Design Requirements and the Definition of Design Space and Non-Design Space  
Requirements of the design problem [63] are summarized in Table 1, both as design requirements (DR) and topological optimization requirements (OR).  
The maximum envelope of the DS is the volume inside the statue, without taking into account the head, which is considered bulk (during the restoration process, the ancient filling has been removed except for the head filling, judged able to be safely maintained). DS is the volume where the inner frame can be placed. The statue itself represents part of the non-design space where loads act (the weight of central body, wings, and arms) and where interfaces between the inner frame and bronze are possible. Non-design space has been modeled starting from a 3D acquisition of the outer surface of the statue, made by structured light. Non-design space also includes the connection interface with the ground as prescribed by DR6 (\(\rightarrow\)OR6.1) and a central volume preliminary assumed as a circular beam to accomplish DR4 and 5 (\(\rightarrow\)OR4.1 and 5.1). The central beam's orientation is constrained by the smallest section, highlighted in Figure 4a as S1, and by the necessity of correct alignment during assembly, since it will be the first element to be inserted during the mounting procedure. Its position can be defined using a pocket already present in the inner filling of the head (the only ancient filling maintained) and other reference contacts at the opposite side (e.g., at Sa or S1 of Figure 4a).  
Figure 3: Case study—ancient bronze statue ([PERSON]): (**a**) The Vittoria Alata di Brescia (© Foto StudioRapuzzi Brescia); (**b**) Application of loads in their respective COGs.  
These reasonings, deduced as target values of DR4, DR5, and DR8, define the functional role of the central beam as part of the non-design space (NDS) and fix its position and section length. Solidity, as derived in OR6.2, asks for constrained sections; that means that they must move together without relative rotation in respect to the inner frame along the horizontal plane. Figure 4a shows where these sections may be localized due to the accessibility for assembly (Sa, Sb, and Sc of Figure 4a).  
Minimum condition suggests highlighting three points for Sa and Sc that may be contact points with the statue. Sb has been introduced as a contrast point with the statue with respect to contact at the leg. BCs at Sa are defined on the central beam, to simulate the connection with the base. In these points, boundary constraints (BCs) are defined to lock relative motion among DS and NDS as shown in Figure 4b. BCs are also selected to reproduce the support scheme on the left leg and right shoulder. This means locking the DOF in z direction at the center of the thigh and shoulder, as depicted by P1 and P2 in Figure 4b.
# Fault Detection of Stator Inter-Turn Short-Circuit in PMSM on Stator Current and Vibration Signal
## 5 Conclusions  
In this work, a detection method based on stator current and vibration signal has been presented to stator inter-turn short circuit fault of PMSM. The increased harmonic components in the current signal and vibration signal can be characterized to detect the fault of PMSM. These two fault features are considered in this paper. At the same time, the improved wavelet packet analysis is used to get the fault features, which can analyze the signal in time-frequency domain and has the advantage of eliminating spectrum aliasing. The validity of this method is verified by experiments on the fault diagnosis platform of motor.  
In the future, artificial intelligence and deep learning will be used to diagnose motor faults, such as the convolutional neural network (CNN). This does not require much prior knowledge and can extract the fault characteristics that are more accurate.  
Conceptualization, H.L. and Y.C.; methodology, H.L. and Y.C.; software, S.L.; validation, H.L. and S.L.; formal analysis, H.L. and S.L.; investigation, H.L.; resources, H.L.; data curation, H.L. writing--original draft preparation, H.L.; writing--review and editing, C.W.; visualization, S.L.; supervision, Y.C. and C.W.; and project administration, Y.C.; funding acquisition, Y.C.  
This research was funded by the National key R & D Plan Program (2018 YFB0106100), the National Natural Science Foundation (51607027), the Sichuan Science and Technology support Program (2016 GZ0395, 2017 GZ0395, and 2017 GZ0394), and the Central University basic Research Business funds (ZYGX2016)140 and ZYGX2016)146).  
Figure 11: Reconstruction signals of the vibration of the fault motor.  
Figure 12: Spectrum diagrams of the vibration signal: (**a**) spectrum diagram of the normal motor; and (**b**) spectrum diagram of the fault motor.
###### Abstract  
Most of the available hyperspectral image (HSI) visualization methods can be considered as data-oriented approaches. These approaches are based on global data, so it is difficult to optimize display of a specific object. Compared to data-oriented approaches, object-oriented visualization approaches show more pertinence and would be more practical. In this paper, an object-oriented hyperspectral color visualization approach with controllable separation is proposed. Using supervised information, the proposed method based on manifold dimensionality reduction methods can simultaneously display global data information, interclass information, and in-class information, and the balance between the above information can be adjusted by the separation factor. Output images are visualized after considering the results of dimensionality reduction and separability. Five kinds of manifold algorithms and four HSI data were used to verify the feasibility of the proposed approach. Experiments showed that the visualization results by this approach could make full use of supervised information. In subjective evaluations, t-distributed stochastic neighbor embedding (T-SNE), Laplacian eigenmaps (LE), and isometric feature mapping (ISOMAP) demonstrated a sharper detailed pixel display effect within individual classes in the output images. In addition, T-SNE and LE showed clarity of information (optimum index factor, OIF), good correlation (\(\rho\)), and improved pixel separability (8) in objective evaluation results. For Indian Pines data, T-SNE achieved the best results in regard to both OIF and \(\delta\), which were 0.4608 and 23.83, respectively. However, compared with other methods, the average computing time of this method was also the longest (1521.48 s).  
Keywords:hyperspectral image; visualization; object-oriented approach; manifold methods +
Footnote †: journal: Applied Physics Letters
## 5 Link layer Optimization
### Send Operation  
The send operation have been divided in 3 phases. Let's consider a node \(A\) aiming to send to a node \(B\).  
* The neighbour (\(B\)) is identified. If a \(A\) has a successful send to \(B\) within the last 3 ms, \(A\) starts sending immediately. Otherwise \(A\) calculates its next listening slot and sleeps until it happens. If previous sends to \(B\) have failed (_i.e._, NACK\(>\)0), \(A\) does not select the next listening slot in order to send the packet, but one slot randomly chosen within the following 3 slots. Broadcasts and sends to unknown nodes are started immediately.
* Within the listening slot of \(B\), \(A\) starts sending for a duration \(D\) where \(D=NACK*window\). Broadcasts and sends to unknown nodes are scheduled to send for the whole frame. If \(A\) receives an ACK or the sending time runs out, whichever comes first, the sending is stopped. If an ACK is received, \(A\) updates the neighbour's table information.
* After the send has been completed a node waits further 3 ms to see if any other send request has the same destination. If no other send request arrive the radio is turned off.
## 5 Numerical Results
### Analysis of Results
The superior computational efficiency of ATS-NLMS is evident in Figure 7, where it demonstrates significantly lower normalized running times compared to FSS-NLMS and VSS-NLMS. Figure 5: Traffic prediction curve obtained with different model. Furthermore, ATS-NLMS boasts another significant advantage compared to FSS-NLMS and VSS-NLMS. As shown in Figure 8, the box plots of the prediction errors for the three prediction methods are presented. ATS-NLMS and VSS-NLMS exhibit similar predictive performance, which is significantly better than that of FSS-NLMS, with only a few simulations demonstrating the opposite. Neglecting these isolated simulation points is acceptable because the focus is on the overall effectiveness of the methods. ATS-NLMS stands out with a lower average sampling rate, as depicted in Figures 7 and 8. These figures demonstrate that while FSS-NLMS and VSS-NLMS achieve a time step size of 420 s using the identical simulation configuration, ATS-NLMS surpasses them with a time step size of 371 s. Consequently, ATS-NLMS attains the lowest sampling rate among the three methods, leading to a substantial reduction in sampling overheads. Figure 8: Box plot of three algorithms for prediction errors. Figure 6: Traffic prediction errors obtained with different model. Figure 7: Box plot of three algorithms for normalized running time.
# Practical Comparison Between the CI/CD Platforms Azure DevOps and GitHub
## 9 Results
Azure DevOps follows a pay-per-user and per-service model, allowing organizations to pay only for the features they use. This makes it cost-effective for enterprises that need scalable DevOps solutions tailored to specific business units. However, it can become more expensive for organizations that require extensive CI/CD execution and testing infrastructure, particularly for teams running thousands of concurrent pipeline jobs. GitHub's tiered subscription model is often more budget-friendly for small teams and open-source projects, offering free public repositories, built-in CI/CD minutes, and collaborative tools at no additional cost. However, enterprise features such as GitHub Advanced Security and enterprise-wide policy enforcement require higher-tier subscriptions, which can increase costs for large organizations that require premium security and governance features. The industry adoption of Azure DevOps and GitHub further reinforces their respective strengths. Enterprises that require end-to-end software lifecycle management, strict governance policies, and integration with legacy Microsoft development tools tend to prefer Azure DevOps for its enterprise-oriented capabilities. Cloud-native companies, startups, and open-source communities are drawn to GitHub due to its Git-centric workflows, community collaboration features, and cloud-native CI/CD automation capabilities. The decision to adopt either platform should be based on organizational priorities, such as whether the team values enterprise governance and compliance (Azure DevOps) or agility, flexibility, and community-driven development (GitHub).
## Abbreviations  
* Ace & Acenaphthene
* Acy & Acenaphthylene
* Ant & Anthracene
* B[a]A & Benz[a]anthracene
* B[a]P & Benzo[a]pyrene
* B[b]F & Benzo[b]fluoranthene
* B[ghi]P & Benzo[g.h.i]perylene
* B[k]F & Benzo[k]fluoranthene
* BDL & Below Detection Limit Chr & Chrysene
* CPAHs & Concentration of PAHs
* CQV & Risk Standard Value of PAHs
* CQV(MPCs) & The Normative Value of the Maximum Concentrations of PAHs Giving the Maximum Environmental Risk
* CQV(NCs) & The Normative Value of the Minimum Concentration of PAHs Generating Negligible Risk
* D[a.h]A & Dibenz[a.h]anthracene
* dMRM & Dynamic Multiple Reaction Monitoring
* Fl & Fluorene
* Flt & Fluoranthene
* GC-MS/MS & Gas Chromatography-Tandem Mass Spectrometry
* HMW PAHs & High-Molecular-Weight Polycyclic Aromatic Hydrocarbons
* I[cd]P & Indeno[1.2.3-cd]pyrene
* LOD & Limit of Detection
* LOQ & Limit of Quantification
* MEQ & Mutagenic Equivalent
* MPCs & Maximum Permissible Concentration
* NA & Not Available
* NCs & Negligible/Minimum Concentration
* ND & Not Detected
* Nph & Naphthalene