esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Dynamic Reserve and Energy Arbitrage of Energy Storage System
###### Abstract  
Replacing the traditional rotating generators with renewable energy will reduce the grid's inertia and with it the minimum frequency when N-1 contingency occurs triggering an Under-Frequency Load Shedding (UFLS). This study proposes a method for the energy storage system (ESS) to simultaneously provide energy arbitrage, reserve capacity, and assist N-1 contingency, by modifying the restriction formula of economic dispatch (ED) and limiting the SOC range of the ESS. Let the ESS join the Spinning Reserve. Through the PSS\({}^{\text{\#}}\)E iterative ESS charging power required at moments when the frequency of contingency is too low in the ED. Let the ESS act as a N-1 contingency extra frequency reserve. This would prevent UFLS and still maintain the demand. The proposed method is applicable to different types of ESS. The method allows energy storages, originally designed for energy arbitrage, to participate in frequency support and spinning reserve.  
energy storage system; renewable energy; economic dispatch; security constraint; PSS\({}^{\text{\#}}\)E
## 2 Study Area and Data
### Data Preparation
Basic geographic information data originates from the National Catalogue service For Geographic Information ([[http://www.webmap.cn/](http://www.webmap.cn/)]([http://www.webmap.cn/](http://www.webmap.cn/)) (accessed on 19 October 2022)). It includes vector data for administrative boundaries, road networks, railway networks, and water systems within Sichuan Province. The overall temporal reference for this dataset is 2019, using the 2000 National Geodetic Coordinate System and the 1985 National Elevation Datum, expressed in latitude and longitude coordinates. Natural environmental data primarily include elevation, slope data, land cover data, and normalized vegetation index data of Sichuan Province. The elevation data is sourced from the ASTER GDEM V3 product of the Geospatial Data Cloud ([[https://www.gscloud.cn/](https://www.gscloud.cn/)]([https://www.gscloud.cn/](https://www.gscloud.cn/)) (accessed on 23 October 2022)), and the slope data is calculated from elevation data. The land cover data comes from GlobeLand30, which is the first 30 m global land cover data product on the world developed by the National Geomatics Center of China. It mainly has 10 land cover types, such as cropland, forest, grassland, shrubland, wetland, water bodies, tundra, artificial surfaces, bare land, glaciers, and permanent snow and ice. Normalized vegetation index data is calculated using Landsat 8 SR data through the Google Earth Engine platform. Building height data is sourced from the CNBH dataset produced and released by the GC3S team of the School of Life Sciences at Fudan University. This dataset has demonstrated strong correlation between simulated results and actual building observations [48], making it a crucial element in studying urban environments and population distribution. Nighttime light data consists of the annual data products NPP-VIIRS V2.1 from the Earth Observation Group of the National Centers for Environmental Information in the United States, as well as data from the Luojia-01 satellite released by the High-Resolution Earth Observation System Hubei Data and Application Network ([[http://www.hbeos.org.cn](http://www.hbeos.org.cn)]([http://www.hbeos.org.cn](http://www.hbeos.org.cn)) (accessed on 25 October 2022)). Social perception data is acquired through batch extraction of Points of Interest (POI) using the application programming interface (API) provided by Amap (Amap is a mapping, navigation, and location-based service provider). More than 3,340,000 records are obtained. POI data typically includes information such as name, address, longitude, latitude, and category. After data cleaning and filtering, 11 categories of POI data are selected as study objects. The details of POI data used in this study are listed in Table 2.
## 5 Conclusions and Recommendations  
This research evaluated various H\({}_{2}\) production technologies based on cost (USD/kg H\({}_{2}\)), energy consumption (MJ/kg H\({}_{2}\)), technology efficiency (%), and carbon emissions (kg COeq/kg H\({}_{2}\)). Steam-methane reforming (SMR) with and without carbon capture and storage (CCS) served as the benchmark H\({}_{2}\) production technology. Despite its low cost, SMR without CCS has high carbon emissions. Water electrolysis, hindered by high energy demands (kWh/kg H\({}_{2}\)) and high costs (USD/kg H\({}_{2}\)), shows promise, with advancements in renewable energy sources for green electricity production. Waste valorization methods such as biomass gasification or reforming and food waste fermentation offer dual benefits of waste management and sustainable H\({}_{2}\) production, albeit at higher costs at this early stage of development. Emerging technologies like biomethane pyrolysis and nuclear-powered thermochemical cycles also present promising avenues for achieving clean and sustainable H\({}_{2}\) production with minimal environmental impact.  
Future research should focus on enhancing the technological maturity, scalability, and cost-efficiency of promising yet underdeveloped H\({}_{2}\) production technologies, such as biomass waste valorization via gasification (or reforming) and water electrolysis using low-cost green electricity, to achieve low carbon emissions and competitive production costs. Additionally, the impact of governmental policies and incentives on H\({}_{2}\) economics should be rigorously analyzed to provide a comprehensive techno-economic framework for sustainable H\({}_{2}\) production. Emphasizing the environmental impacts of these technologies and conducting comparative life cycle assessments along their supply chains can further elucidate the global benefits of green H\({}_{2}\) initiatives.  
This research received no external funding.  
Not relevant to this study.  
Not applicable.  
The original contributions presented in the research are included in the article; further inquiries can be directed to the corresponding author.  
The author of this research is grateful for the valuable comments and reviews provided by his colleagues from the University of Oxford, University of Cambridge, and Imperial College of London, in the United Kingdom, as well as by Harvard University, in the U.S.  
The author declares no conflicts of interest.
# Identification, Control, and Characterization of Peristaltic Pumps in Hemodialysis Machines
###### Abstract  
Peristaltic pumps represent a fundamental component of hemodialysis machines. They facilitate the transfer of fluids, particularly in the collection and treatment of blood. This study aims to improve pump precision and reliability by reducing steady-state error and optimizing flow consistency, measured in milliliters per minute. A detailed characterization established the relationship between revolutions per minute (RPM) and flow rate (mL/min), with redundant mass and volume measurements supporting accuracy. To model the system's behavior, two non-linear functions and one linear function were compared, with the polynomial model proving the most accurate and revealing the pump's inherently non-linear flow behavior. A proportional-integral (PI) controller was then applied, and optimized through step input and non-linear least squares fitting. A key aspect of this study is a comparative validation against a commercial hemodialysis machine, configured identically with the same blood circuit diameter, tubing brand, and filter, in order to ensure equivalency in conditions. Results showed a maximum flow rate error of 0.5296%, highlighting the integration of control and characterization methods that enhance system precision, dependability, and reproducibility--critical factors for ensuring the safety and effectiveness of hemodialysis's treatments.  
peristaltic pump; hemodialysis machines; non-linear system identification; PI control; flow rate estimation Article
# Effect of Admixtures on Selected Properties of Fly Ash-Based Geopolymer Composites
## 2 Materials  
Silica fly ash from the local heat and power plant was used as a binder material. The material met the requirements of the EN 450-1 standard [26]. The chemical oxide compositions of the fly ash are presented in Table 1. Fly ash was characterized by a loss on ignition of 4.37% (according to EN 450-class A).  
Sand in the 0-2 mm fraction was used as an aggregate. The grain size distribution of the sand is presented in Table 2.  
Three different admixtures (AD) were used: a liquifying admixture based on styloxlyates (AD1), a plasticizing admixture (AD2), and a liquifying admixture based on modified polynaphthalenes (AD3). The characteristics of the ADs used in this study are summarized in Table 3.  
The activator (AC), composed of sodium hydroxide and sodium silicate solutions, was used in this study. The sodium hydroxide solution was prepared at a concentration of 12 M using sodium hydroxide (NaOH) beads of 99% purity and distilled water. The mass of solid NaOH needed to prepare 1 dm\({}^{3}\) of a 12 mol/dm\({}^{3}\) NaOH solution was 480 g. The sodium silicate solution (Na\({}_{2}\)SiO\({}_{3}\)), with a specific gravity of 1.26-1.71 g/cm\({}^{3}\) and a modulus ratio (Ms) equal to 2.5 (where Ms = SiO\({}_{2}\)/Na\({}_{2}\)O, Na\({}_{2}\)O = 12.07%, and SiO\({}_{2}\) = 29.35%), was used. Sodium silicate and sodium hydroxide solutions were mixed with a Na\({}_{2}\)SiO\({}_{3}\)/NaOH ratio of 2.5 to prepare the activator.  
\begin{table}
\begin{tabular}{c c} \hline
**Oxide** & **Fly Ash** \\ \hline SiO\({}_{2}\) & 54.6 \\ Al\({}_{2}\)O\({}_{3}\) & 25.3 \\ Fe\({}_{2}\)O\({}_{3}\) & 4.97 \\ MgO & 1.8 \\ CaO & 2.14 \\ Na\({}_{2}\)O\({}_{eq}\) & 2.68 \\ Na\({}_{2}\)O\({}_{0}\) & 0.84 \\ K\({}_{2}\)O & 2.8 \\ TiO\({}_{2}\) & 1.07 \\ P\({}_{2}\)O\({}_{5}\) & 0.55 \\ SO\({}_{3}\) & 0.37 \\ BaO & 0.15 \\ SrO & 0.07 \\ Mn\({}_{3}\)O\({}_{4}\) & 0.06 \\ \hline \end{tabular}
\end{table}
Table 1: Chemical oxide composition of fly ash (%).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Material** & **\textgreater{}2.0 mm** & **2.0–1.0 mm** & **1.0–0.5 mm** & **0.50–0.25 mm** & **0.250–0.125 mm** & **0.125–0.063 mm** & **0.063–0.00 mm** \\ \hline Sand & 5.8 & 21.6 & 32.7 & 29.6 & 9.0 & 0.9 & 0.3 \\ \hline \end{tabular}
\end{table}
Table 2: Grain-size distribution of sand (%).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Admixture** & **Characteristics** & **Appearance/Color** & **pH** & **Density (g/cm\({}^{3}\))** \\ \hline AD1 & Liquifying admixture based on stabilized polycarboxylates & Green-amber liquid & 5.0 \(\pm\) 1.0 & 1.12 \(\pm\) 0.03 \\ AD2 & Plasticizing admixture activating & & & \\  & additives, i.e., fly ash and blast & & & \\  & furnace slag, for high early strength & & & \\ AD3 & Liquifying admixture based on & & & \\  & modified polynaphthalenes & Brown liquid & 6.0 \(\pm\) 1.0 & 1.20 \(\pm\) 0.03 \\ \hline \end{tabular}
\end{table}
Table 3: Physical and chemical properties of the admixtures.
# Image Deblurring under Impulse Noise via Total Generalized Variation and Non-Convex Shrinkage
## 3 Model Proposal and Solution Method
### Solution Method
addition, the comparison reveals that the image restoration performance of the FTVd algorithm is much worse than that of the other three algorithms; thus, the subsequent experimental results of the FTVd algorithm are not listed in some tables. #### 4.3.2 Numerical Performance Comparison for Image Recovery in the Presence of Average Blur

The image restoration capability of the proposed method for degraded images treated with average blur and impulse noise was investigated, with Table 4 presenting the results of image restoration tests performed on nine degraded test images treated with an average blur kernel \((A,7)\) and two levels (30% vs. 60%) of noise. With regard to the image restoration performance of the algorithms for different images treated with average blur and different levels of impulse noise, the data in Table 4 lead to conclusions similar to those obtained in the case of Gaussian blur and impulse noise. When restoring the nine degraded test images treated with an average blur kernel \((A,7)\) and impulse noise, the proposed method achieved 0.48-4.16 dB higher PSNR values compared with the TGV method. For images with many lines and complex textures, such as the \"Fingerprint\" image of Figure 3a, \"Butterfly\" image of Figure 3b, and \"Baboon\" image of Figure 3c, the proposed method achieved 0.8-2.14 dB higher PSNR values compared with the OGSTV method. However, the OGSTV performed well in restoring smooth images in the presence of low noise levels. #### 4.3.3 Numerical Performance Comparison for Image Recovery in the Presence of Motion Blur

Furthermore, the image restoration capability of the proposed method in the case of motion blur and impulse noise was investigated. Table 5 presents the PSNR results of image restoration tests performed on the degraded \"Butterfly\" image of Figure 3b, \"Baboon\" image of Figure 3c, \"Bird\" image of Figure 3e, and \"Truck\" image of Figure 3g treated with different motion blur kernels and 30-60% noise.
###### Abstract
Therefore, EPBs remain an important component of space weather and continue to be studied vigorously even after many decades since their existence was discovered. Traditional diagnostic methods of EPBs exploit various instruments, including ionogram traces obtained from ionosonde observations [18; 19], coherent and incoherent scatter radars (e.g., [20; 21; 22]), airglow imagers (e.g., [22; 23; 24]), in situ rocket- and satellite-based measurements (e.g., [25; 26; 27; 28; 29; 30]), and GNSS observations (e.g., [31; 32; 33; 34; 35]). Such methods have allowed the assessment of the main mechanisms for generating post-sunet plasma bubbles, including their multiscale temporal variability, such as long-term variability mainly due to solar cycle dependencies and medium-term variability mainly induced by seasonal variations (see reviews by [36; 37; 38]). However, mechanisms for generating the post-midnight irregularities are still unknown and extensively debated. In this context, the CSES mission provides unique sun-synchronous observations to address important questions on post-midnight EPBs variability. The availability of measurements from both the LP and the EFD onboard CSES-01 offers the opportunity for conducting a new systematic study of the statistical properties of post-midnight ionospheric depletions. In this work, we present two new techniques for the automatic detection of post-midnight plasma depletions, one using LP data and the other using EFD data. We validate these techniques by performing a statistical analysis of a 4-year LP nighttime dataset, where we analyze EPBs' climatology in terms of spatial extension, latitudinal and longitudinal occurrence, and seasonal dependence. Moreover, by exploiting Swarm B LP data collected for the same period as the CSES-01 dataset, we test the reliability of the proposed techniques in catching both post-sunet and post-midnight plasma bubbles. This paper is structured as follows: Section 2 provides an overview of the data considered here and detailed descriptions of both the LP and the EFD algorithms developed to automatically detect EPBs. Section 3 discusses the results of the statistical analysis performed on the 4-year LP nighttime dataset. Finally, Section 4 provides a summary and conclusions.
## 2 Sample Preparation and Testing Method
### Model Generation of the SRMs
#### 2.1.3 Generation of Clump and Spatial Location  
When finishing the above steps, the next step is to import the file containing the coordinate points of the polygons generated above into the DEM software. Then, the geometry of the aggregate is then set to be used as the boundary of the clump and used as a template for creating the clump. A clump template is not a model object in itself; it serves as a basis for creating clumps in the model with the same shape as the clump template. The facilities to create clumps from clump templates include the ability to rotate, translate,  
Figure 2: Illustration of aggregate generation.  
and scale the clumps relative to their template. One can simply replicate a clump from a template. Next, the pebbles are automatically calculated via an approximation of the medial surface of the polygons via the Bubble Pack algorithm of [PERSON] [36]. Both convex and concave geometries can be automatically filled via this algorithm in both 2D and 3D. Finally, due to the absolute rigidity of the clumps, all of the clumps will collide freely in the domain until there is no overlap between any two clumps. Then, the rock blocks will redistribute, and the positions of each rock block aggregate such as the coordinates of the vertices and the center of the aggregates will be recorded and output.  
To consider the impact of the aggregates coinciding with the boundaries, in this paper, the border effect is also taken into account. A spatial location algorithm, which is based on the traditional method, is employed.
# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
## 2 Review of Fake News Detection Approaches and Multimodal Learning Methods
### Attention Mechanisms in Multimodal Learning  
Attention mechanisms have revolutionized multimodal learning by enabling models to focus on relevant information from different modalities while suppressing noise inputs [41; 42]. [PERSON] et al. [41] initially introduced attention mechanisms for machine translation tasks, allowing models to dynamically attend to relevant parts of input sequences when generating outputs.  
Cross-modal attention mechanisms enable identification of correspondences and relationships between different data types [43; 44]. [PERSON] et al. [45] proposed visual attention models capable of precisely aligning textual descriptions with image regions. [PERSON] et al. [46] developed bottom-up attention mechanisms that extract visual features through object detection techniques, significantly improving performance on vision-language tasks. [PERSON] et al. [47] designed collaborative attention mechanisms that facilitate bidirectional information flow between modalities, enabling each modality to mutually enhance representational capabilities.  
The emergence of Transformer architectures has further advanced attention mechanism capabilities. [PERSON] et al. [42] proposed self-attention mechanisms capable of modeling long-range dependencies. Building upon this, [PERSON] et al. [48] developed ViLBERT and [PERSON] et al. [49] proposed VisualBERT, achieving breakthrough progress in multimodal understanding tasks. [PERSON] et al. [50] unified multiple vision-language pre-training tasks with UNITER, demonstrating the powerful potential of Transformers in multimodal learning.  
Despite the significant success of attention mechanisms in multimodal learning, their application in fake news detection, particularly integrating user comments as primary modalities, remains limited, providing important opportunities for improving detection accuracy and robustness.
## 3 Influence of Structural Parameters of the Magnetic Circuit on Magnetic Induction Intensity
### Effect of Piston Rod Wing Radius on Magnetic Induction Strength  
While keeping the values of the other parameters of the magnetic circuit structure unchanged, the length of the piston flanks was set in the range of 2 to 10 m, with an interval of 1 mm between the values. The simulation model was established, and the simulation results are shown in Figures 11 and 12.  
In Figures 11 and 12, it is observed that as the flange width increases, the effective length of the damping channel also increases. The region with the highest magnetic induction intensity shifts from the components on both sides of the flange toward the middle of the piston rod. Initially, there is a tendency for the maximum magnetic induction  
Figure 11: Simulation results of piston rod wing radius with different lengths. (**a**) Plot of magnetic induction intensity versus distance; (**b**) plot of average magnetic induction intensity versus distance.  
Figure 12: Plot of magnetic induction intensity clouds corresponding to different lengths of piston rod wing radius.  
intensity to increase, followed by a decrease. Simultaneously, the widening of the wing edge leads to a transition of the magnetic flux density from dense to sparse, resulting in a noticeable drop in the average magnetic induction intensity. The maximum magnetic induction intensity reaches its peak at 4 mm, but there is a risk of magnetic induction oversaturation at this point. Therefore, it is deemed appropriate to select a flange length between 5 and 7 mm.
# A Review of Convolutional Neural Network Applied to Fruit Image Processing
## 5 CNN-Based Approaches for Fruit Quality Control Tasks  
The accurate detection and classification of fruit quality are one of the critical tasks to avoid losing added value in the markets. For this reason, continuous efforts are made to improve the methods of detecting damage, diseases, and the maturity level of the fruit. This quality control is carried out before, during, and after the fruit harvesting. We perform an analysis of the recent use of CNN in this area, understanding by fruit quality control the process of determining internal-external damages in the fruit, degrees of maturity and diseases. Table 2 shows an overview of recent articles where CNNs are adopted for fruit quality control. In a recently published study, [PERSON] et al. [73] took a modified AlexNet model with an 11-layers structure, aiming to identify and detect defects in apples. Furthermore, as a comparison of the classification, they use three known algorithms--backpropagation neural networks (BP), particle swarm optimization (PSO), and support vector machine (SVM). The dataset consists of laser-induced backscatter images (i.e., speckle images of 5472 \(\times\) 3648 pixels), where the acquisition process is carried out with a laser system with a beam expander, a complementary metal-oxide-semiconductor (CMOS) color camera with a zoom lens, and a polarizer. The dataset has a total of 500 apple samples of about a similar size (equatorial diameter 80-100 mm). The proposed CNN model for apple detection achieves a recognition rate of 92.50%, which is higher than other algorithms commonly used, such as BP, SVM, and PSO algorithm. [PERSON] et al. [74] designed and used three models of CNN with 15, 16, and 18 layers, aiming to detect and qualify apparent defects of lemon sour fruit. In total 341 samples of healthy and unhealthy sour lemon were used to take RGB images of \(4320\times 3240\) pixels. The images were preprocessed by removing the background and resizing them. The CNN models were compared against KNN, fuzzy method, artificial neural network, decision tree, and SVM. It extracts the features with local binary patterns (LBP) and histogram oriented gradients (HOG). The results showed that on average, the accuracy of the proposed CNN models was 100%. [PERSON] et al. [75] used an LSL (Light Separation Lab) to capture illumination-separated images of grapevine berries with a dataset of 270 images, for phenotyping the distribution of epicticular waxes (berry bloom). They used a CNN model for image analysis. The validation over six grapevine cultivars