esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Four-Layer Model: Finite Difference Data
The black arrows are the result of primary wave correct imaging, the solid white arrows are the crosstalk artifacts of surface-related multiple, and the dashed white arrows are the crosstalk artifacts of internal multiple. Figure 9b-d illustrates least-squares full-wavefield migration using the imaging conditions from Equations (12), (13) and (15), respectively. Within the dashed yellow box, only one true horizontal reflector exists; the white arrows represent the multiple crosstalk artifacts. Figure 9a is a least-squares migration of the primary wave, incapable of suppressing the artifacts. Figure 9b-d approximately depicts the three true reflectors, exerting a certain degree of suppression onto the multiples. Upon careful examination of the position indicated by the white arrows within the dashed yellow box, it can be observed that in Figure 9b, the energy of the crosstalk artifacts is weakest at the location pointed to by the white arrows. This indicates that the global normalized imaging condition suppresses the crosstalk artifacts of multiples better than the other two imaging conditions. The reason for this is that the demigration operator relies on the gradient (migration result) and migration velocity in forward modeling, and the deconvolution-type imaging condition boasts a higher resolution than cross-correlation, which proves more advantageous for subsequent data residual processes. Multi-order wavefield normalization introduces new illumination conditions for each single-frequency component in the imaging results. During the first iteration, only the point sources of the shot gather and surface-related multiple illumination effects are considered. From the second iteration onward, virtual source illumination between layers is further incorporated, which in turn enhances the gradient term for each iteration. Figure 9: Least-squares migration results: (**a**) global normalization primaries migration; (**b**) global normalization full-wavefield migration; (**c**) cross-correlation full-wavefield migration; (**d**) deconvolution full-wavefield migration. The black arrow is primary correct imaging, the solid white arrow is surface-related multiple artifacts, the dashed white arrow is internal multiple artifacts, the yellow box indicates the area of interest. Figure 10a,b displays the multi-order wavefield illumination compensation (denominator term of Equation (15)) for the gradient term at the primary frequency of 15 Hz during the first and second iterations, respectively. It is evident that the illumination effect of the second iteration surpasses that of the first, thereby promoting subsequent iteration processes.
## 3 Results
### Use of Diagnostic Algorithms  
Out of all reviewed articles, a significant majority proposed or applied at least one diagnostic algorithm. Only 25 articles across all sources lacked an algorithmic component and were either conceptual or descriptive.  
One of the central aspects investigated in this review is the prevalence of algorithmic methods used in fault detection and diagnosis (FDD) systems across the 185 analyzed articles. The results reveal a high degree of reliance on algorithm-based approaches, with over 85% of the studies employing at least one algorithmic framework for fault  
Figure 1: Article types by source (simplified).  
Figure 2: Number of articles by year.  
identification, classification, or prediction. This highlights the growing trend in research and development toward automation, data processing, and intelligent diagnostics in the energy sector.  
Only a minority of articles--approximately 15% across all sources--relied solely on qualitative analysis, descriptive case studies, or theoretical conceptualization, without applying or validating a concrete algorithm. These were often review articles or early-stage proposals that laid the groundwork for future experimentation, rather than contributing fully realized implementations.  
When disaggregated by source, the prevalence of algorithmic methods becomes even more evident:  
* Elsevier featured the highest concentration of studies with algorithmic content, where over 90% of the articles involved either classical statistical models or modern AI techniques.
* MDPI showed similar commitment to algorithm-driven research, especially in recent years, where supervised machine learning and hybrid frameworks became increasingly prominent.
* Google Scholar also included a wide range of studies with algorithms, although some works were less methodologically structured, possibly reflecting the inclusion of conference proceedings or academic theses.  
The near-ubiquitous presence of algorithms in these articles underscores the paradigm shift from manual diagnosis and heuristic inspection to automated, data-informed decision-making. In power plants, where real-time monitoring and reliability are critical, the role of robust, validated algorithms has become indispensable for ensuring fault resilience and system safety.
# Evaluation of Mechanical Performance of a New Disc Spring-Cable Counter Pressure Shock Absorber
###### Abstract  
Mechanical performance evaluation of a new disc spring-cable counter pressure shock absorber is focused on in this study. The proposed shock absorber is always in a compressive working state with energy dissipation capacity under both tension and compression loadings. The design formulas of its axial bearing capacity, vertical stiffness, deformation energy of the shock absorber were derived, and the corresponding specific design process was provided in detail. Experimental and numerical investigations of the mechanical performance were conducted under static and dynamic loadings. The parameters influencing the laws of the mechanical performance of the shock absorber, including loading frequency, pre-compression deformation and loading amplitude, were investigated. The rationality of the proposed shock absorber was firstly verified through comparative analysis using experimental, numerical and theoretical calculations. The shock absorber with a friction coefficient of 0.005 between disc springs, and a friction coefficient of 0.3 between the disc spring and cover plate has the smallest error between experiment and theory for the flattening force. The bearing capacity of the shock absorber exhibits a linear relationship with the loading displacement in static loading. In dynamic loading, however, the bearing capacity shows a trend of slow growth followed by rapid growth. The energy dissipation capacity of the shock absorber shows an increase with the increase of loading displacement. The minimum equivalent damping ratio of all of the dynamic test cases is 7%, with a maximum up to 15.3%. Under the same loading frequency, the equivalent stiffness and equivalent damping ratio have a linear amplification trend with the increase of pre-compression deformation, and the maximum increase of equivalent stiffness is equal to 41.37%. Under the same loading frequency and pre-compression deformation, the energy consumption capacity can be improved by increasing the loading amplitude.  
disc spring; shock absorber; mechanical performance; energy dissipation; experiment 2023
# The Microstructures of _Tetradesmus obliquus_ Biofilms Formed Under Different Nutrient Supplies
## 2 Materials and Methods  
### Microalgae Biofilm Culture  
The microalgae _T. obliquus_ (FACHB-276) was used in this work. Before biofilm culture, the _T. obliquus_ with an inoculation concentration of OD680 = 0.860 was pre\(-\)cultured in 500 mL flasks containing 100 mL BG 11 culture medium (pH = 7.1) at 25 \(\pm\) 1 \({}^{\circ}\)C. Detailed information on the BG11 culture medium was shown in the supporting information (Supplementary Materials Tables S1 and S2). The culture was maintained under continuous aeration with ambient air and exposed to a light intensity of 100 \(\upmu\)mol photons m\({}^{-2}\) s\({}^{-1}\) for six days in a shaking incubator. After that, the obtained cell suspensions were vacuum filtered onto filter membranes (diameter: 50 mm, pore size: 2 \(\upmu\)m) to form primary biofilms with an inoculum density of 4.0 \(\pm\) 0.1 g m\({}^{-2}\).  
Then, these biofilms were placed into bioreactors (polymethyl methacrylate chamber: 200 \(\times\) 196 \(\times\) 76 mm) to maintain microalgae growth. As shown in Figure 1, these biofilms were put onto a layer of medical gauze, which was attached to a perforated plate. Under the perforated plate, the liquid culture medium was cycled by a peristaltic pump. The culture medium was renewed every three days to ensure that cells received sufficient nutrients. The compressed air enriched with 1% CO\({}_{2}\) (\(v/v\)) was first purified with a filter, then humidified with deionized water, and finally introduced into the bioreactor with a constant flow rate of 0.1 VVM (volume of air per volume of culture per minute). The white light\(-\)emitting diodes (JK\(-\)W300200, J&K Photoelectric Technology, Shanghai, China) with a light intensity of 100 \(\upmu\)mol photons m\({}^{-2}\) s\({}^{-1}\) were fixed at the top of the bioreactor and used as the light source. These bioreactors were placed into an incubator, where the temperature was controlled at 25 \(\pm\) 1 \({}^{\circ}\)C.  
In this work, to explore the microstructures of microalgae biofilms formed under different nutrients, the _T. obliquus_ biofilms were cultured with six kinds of nutrient supplies, i.e., pure BG11 culture medium, BG11 media with an additional 30 mM glycerine (named as BG11*glycerine), BG11 with an additional 30 mM NaHCO\({}_{3}\) (i.e., BG11*NaHCO\({}_{3}\)), BG11 with an additional 15 mM urea (i.e., BG11*urea), BG11 with an additional 15 mM NH\({}_{4}\)Cl (i.e., BG11*NH\({}_{4}\)Cl), and BG11 with an additional 15 mM NaNO\({}_{3}\) (i.e., BG11*NaNO\({}_{3}\)). During the experiment, the _T. obliquus_ cells were biofilm\(-\)cultured in the bioreactors for 7 days under different nutrient supplies. To obtain reliable results, we carried out three runs of experiments for microalgae culture at each nutrient supply.
## 2 Real-Time Reconstruction System of Forest Point Cloud  
A real-time reconstruction system of a point cloud using a single-line lidar based on visual-IMU fusion in a forest is shown in Figure 1. The whole system consists of a monocular camera, IMU, and a single-line lidar. In order to ensure a uniform density distribution of the 3D point cloud map, the camera is placed horizontally to obtain the front image, and the single-line lidar is placed vertically for longitudinal scanning. When the system moves in the forest area, the monocular camera obtains forest image information and the IMU sensor obtains the angular velocity and acceleration. The global odometry information of the system is obtained by the fusion of the two sensors, and the single-line lidar obtains the laser point cloud plane.  
The overall framework of this paper is shown in Figure 2. First, we use a nonlinear optimization fuse method to fuse the visual reprojection error extracted from the monocular camera images and the IMU pre-integration error from the IMU data, which can project the pose estimation matrix \(T_{C}^{W}\) from the monocular camera coordinate system (C) to the global world coordinate system (W). Then, by calculating the pose transformation matrix \(T_{L}^{W}\) of the single-line lidar coordinate system (L) corresponding to (W), the current laser data in the forest can be projected and reconstructed to form a three-dimensional point cloud map. The monocular camera coordinate system (C), the IMU coordinate system (B), and the lidar coordinate system (L) are bound together, and the relative positions of \(T_{B}^{C}\) and \(T_{L}^{B}\) between (C), (B), and (L) can be directly obtained through external parameter calibration. Meanwhile, each sensor is time-synchronized.  
Figure 1: Point cloud real-time reconstruction system with a single-line lidar based on visual–IMU fusion.  
Figure 2: The overall framework of forest point cloud real-time reconstruction method with a single-line lidar based on visual–IMU fusion.
## 1 Introduction  
The COVID-19 pandemic has forced many countries to impose lockdowns and restrictions on their residents to control the surge of this disease [1]. After more than one year of restricted life, bringing high economic and social burdens, COVID-19 vaccines have allowed countries to relax some restrictions and return to a more or less normal life. While most people are eager to do so, the path is uncertain and should be planned with caution. The use of vaccination certificates is not new. Green pass policies have been already used in some countries to restrict the entrance of children without infant immunisation to kindergarten [2, 3]. Others require vaccine passports (e.g., for yellow fever) for foreign visitors [4]. While contentious, many countries have been long considering the implementation of vaccine certificates for different purposes [5]. Among others, South Korea, Austria, Denmark, the UK, New York (voluntarily), Germany, France, and Estonia are already implementing GPPs to allow the relaxation of some COVID-19 restrictions [6, 7, 8, 9, 10]. On 1 July 2021, the European Commission (EU) launched a vaccine passport to facilitate safe free movement within the EU during the COVID-19 pandemic for those who are vaccinated, recovered, or negatively tested [11].  
By early May 2021, Israel had vaccinated about 90% of its residents aged 16+ and was ahead of other countries in terms of vaccination coverage and a return to normal life [12]. Israel was also a pioneer in establishing a vaccine certificate policy, also known as the \"Green Pass Policy\" (GPP), with the main objective, at that point, of allowing safe relaxation of COVID-19 restrictions. The rationale was that the GPP significantly reduced the risk of disease and its transmission, as only those who were vaccinated, recovered, or had a negative test were allowed to return to normal life. It was implemented for about four months, from 21 February to 1 June 2021. In mid-May, the government decided to lift the GPP in view of the high vaccination coverage along with consistent low and declining morbidity and mortality rates. However, on 21 July, Israel gradually reintroduced the GPP as a response to the upsurge from the Delta variant.  
The aims of this paper are to describe the design and implementation of the GPP between February and June 2021 and analyse how it overcame some of the ethical and practical drawbacks mentioned in the literature. We discuss and highlight previously unexplored issues that policymakers should consider when planning a GPP policy as a dynamic public health tool.
# Automatic Crack Classification by Exploiting Statistical Event Descriptors for Deep Learning
## 3 Framework for the Real-Time Classification of Acoustic Emission Data
### Analysis of Acoustic Emission Events Using Feature Extraction
#### 3.2.2 Spectral Entropy  
The SE [30] of a signal is a measure of its spectral power distribution. The SE treats the signal normalized power distribution in the frequency domain as a probability distribution and calculates the Shannon entropy of it. The Shannon entropy, in the AE context, is the spectral entropy of the signal \(s(t)\). This property has already shown to be useful for features extraction in fault detection and diagnosis [79]. The equations for SE arise from the equations for the power spectrum and probability distribution for a signal.  
If the domain of definition for \(s(t)\) is restricted to a set of discrete points \(t_{n}=nT\), where \(n\) is an integer and \(T\) is the sampling period, the signal \(s(t_{n})\) is called a _discrete-time_ signal. For such discrete time-varying signal \(s(t_{n})\), the frequency power spectrum (PS) can be defined as \(PS(f)=\left|S(f)\right|^{2}\), where \(S(f)\) is the discrete Fourier transform of \(s(t_{n})\). The probability distribution function \(PDF(f)\) is then written as:  
\[PDF(f)=\frac{PS(f)}{\sum\limits_{i}PS(i)} \tag{4}\]  
The normalized spectral entropy \(SE\) is a function of frequency and follows as:  
\[SE(f)=-\frac{\sum\limits_{m=1}^{M}PDF(m)\log_{2}(PDF(m))}{\log_{2}M} \tag{5}\]  
where \(M\) is the total number of frequency points derived by the Fourier transform and equal to half the length of the time series \(x(t_{n})\). The denominator \(\log_{2}M\) represents the maximal value for the spectral entropy of white noise, which is uniformly distributed in the frequency domain. A plot of \(SE\) as calculated from the different signals of Figure 4a is shown in Figure 5b. As illustrated, peculiar differences can be observed from the \(SE\) of the signals. These differences will contribute to DL-based classification, although they are not easily classifiable by humans. However, from the viewpoint of power distribution among different frequency components, there is a relationship between \(IF\) and \(SE\). We have noticed a substantial agreement with the mechanics of the events, which precedes the transition from a prevalent damage process to the other one. Such transitory coexistence is well represented by the corresponding decrease in \(SE\), which evidences a distinctive pattern in mixed-mode deformations when compared to tensile and shear damages. [10; 13; 64; 66; 80].
## 4 Materials and Methods
### RNA Sequencing  
After the total RNA was prepared, agarose gel electrophoresis (1.0%) was used to determine the integrity of RNA and DNA contamination. A Nano-Photometer spectrophotometer (Implen, Calabasas, CA, USA) and Agilent 2100 bioanalyzer (Agilent Technologies, San Francisco, CA, USA) were used to determine the purity and the integrity of RNAs, respectively. Subsequently, rRNAs were removed using Ribo-Zero Plus rRNA Depletion Kit (Lot No. 20037135) (Illumina, San Diego, CA, USA) to obtain the mRNA, which were then randomly fragmented into short fragments to construct strand-specific libraries. After the libraries passed the quality inspection, RNA sequencing was performed using the Illumina HiSeq sequencing platform at Novoygene Technology Co., Ltd. (Beijing, China).
## 2 Materials and Methods--Implementation, Integration, and Testing of the IDEASSat Flight Model
### Environmental and Functional Test
#### 2.5.4 Thermal Vacuum Cycling Test (TVCT)  
Per NSPO recommendations, IDEASSat was subjected to thermal vacuum testing following flight model integration. The temperature range selected corresponded to the maximum and minimum predicted temperatures from thermal analysis with an additional margin added [30]. Figure 13 shows the temperature cycle setting of TVC. The temperature was cycled between 0 and 40 \({}^{\circ}\)C during the initial survival cycle, followed by 8 TVCT cycles. The TVCT temperature range was between 5 and 35 \({}^{\circ}\)C, with each cycle lasting a total of 5 h and hot/cold soaks lasting 1 h. The selected hot and cold temperatures were based on the operating temperature range of the battery module, which had the narrowest temperature range of all the subsystems. These temperatures were attained during the TVCT test, with the pressure maintained at below 3.75 \(\times\) 10\({}^{-5}\) Torr. The spacecraft was powered on during the TVCT test. A limited performance test (LPT) was performed at room temperature and at the beginning and end of each hot/cold soak. The LPT consisted of using the OBC to poll the various temperature sensors throughout the spacecraft to establish whether the operating temperature limits were exceeded, while also individually commanding soft resets of the individual spacecraft subsystems to verify that they were functional and would respond to commands.  
Heat exchange for the TVCT was by way of heat conduction from a heat exchange plate located in a vacuum chamber. In order to prevent the solar cells from rupturing, the +/\(-\)X-sides and +Y-side of the spacecraft chassis were used as the thermal interface, as shown in Figure 14. Red copper blocks with lower heat capacity were used as heat distributors to the +/\(-\)X-sides, while stainless steel plates were used to apply pressure on these copper blocks to achieve a uniform heating effect.  
To avoid multi-path interference which occurs when the communication subsystem transmits inside the closed vacuum chamber, no signals were transmitted during TVCT. Before and after TVCT, the weight of the spacecraft was measured to ensure that the spacecraft was compliant with outgassing requirements of less than 1% total mass loss. The communications subsystem was also subject to functional verification.  
Figure 12: Vibration test setup for IDEASSat inside the TestPOD.
###### Abstract  
In this paper, a high-density gas-liquid discharge plasma is obtained combined with nanosecond pulse voltage and a floating electrode. The discharge images, the waveforms of pulse voltage and discharge current, and the optical emission spectra are recorded. Gas temperature and electron density are calculated by the optical emission spectra of N\({}_{2}\) (C\({}^{3}\Pi_{u}\to B^{3}\Pi_{g}\)) and the Stark broadening of H\({}_{\alpha}\), respectively. The emission intensities of N\({}_{2}\) (C\({}^{3}\Pi_{u}\to B^{3}\Pi_{g}\)), N\({}_{2}^{+}\) (B\({}^{2}\Sigma\to X^{2}\Pi\)), OH (A\({}^{2}\Sigma\to X^{2}\Pi\)), O (3p\({}^{5}\)P \(\to\) 3s\({}^{5}\)S\({}^{9}\)), He (3d\({}^{3}\)D \(\to\) 3p\({}^{3}\)P\({}^{2}_{2}\)), gas temperature, and electron density are acquired by optical emission spectra to discuss plasma characteristics varying with spatial distribution, discharge gap, and gas flow rate. The spatial distributions of discharge characteristics, including gas temperature, electron density, and emission intensities of N\({}_{2}\) (C\({}^{3}\Pi_{u}\to B^{3}\Pi_{g}\)), N\({}_{2}^{+}\) (B\({}^{2}\Sigma\to X^{2}\Pi\)), OH (A\({}^{2}\Sigma\to X^{2}\Pi\)), O (3p\({}^{5}\)P \(\to\) 3s\({}^{5}\)S\({}^{6}\)), and He (3d\({}^{3}\)D \(\to\) 3p\({}^{3}\)P\({}^{2}_{2}\)), are presented. It is found that a high-density discharge plasma with the electron density of 2.2 \(\times\) 10\({}^{15}\) cm\({}^{-3}\) and low gas temperature close to room temperature is generated. While setting the discharge gap distance at 10 mm, the discharge area over liquid surface has the largest diameter of 20 mm; under the same conditions, electron density is in the order of 10\({}^{15}\) cm\({}^{-3}\), and gas temperature is approximately 330 K. In addition, the discharge plasma characteristics are not kept consistent in the axial direction, in which the emission intensities of N\({}_{2}^{+}\) (B\({}^{2}\Sigma\to X^{2}\Pi\)), N\({}_{2}\) (C\({}^{3}\Pi_{u}\to B^{3}\Pi_{g}\)), OH (A\({}^{2}\Sigma\to X^{2}\Pi\)), and gas temperature increased near the liquid surface. As the discharge gap is enlarged, the gas temperature increases, whereas the electron density remains almost constant. Moreover, as the gas flow rate was turned up, the electron density increased and the gas temperature was kept constant at 320 K.  
Keywords:gas-liquid discharge; optical emission spectra; electron density; gas temperature +
Footnote †: journal: Applied Sciences
# A Comparative Study of Machine Learning Techniques for Cell Annotation of scRNA-Seq Data
## 2 Materials and Methods
### Dataset Description
The **Pancreatic dataset** profiles gene expression in individual pancreatic cells from human donors, including those with and without type 2 diabetes. It aims to identify pancreatic cell types, discover novel markers, and understand functional alterations in diabetes. The dataset has been sequenced using single-cell RNA sequencing via the CEL-seq technique on platforms like Illumina HiSeq 2500 and NextSeq 500. The dataset can be accessed from gene expression omnibus with accession number GSE81076.