esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Pulsed Electric Field on Oil Extraction Rate and
###### Abstract  
Peony seed oil, known for its high nutritional value and low production yield, has become a crucial component in high-quality health products. Consequently, enhancing the extraction efficiency of peony seed oil has become an industry objective. Pulse electric field (PEF) technology, as a non-thermal extraction method, has shown promising advancements in improving plant oil yield by enhancing cell permeability. In this study, we designed a static parallel plate PEF treatment unit to process peony seed particles. By manipulating pulse voltage parameters, we investigated the effects of particle size and PEF strength on the oil yield. We also analyzed and evaluated tocophero1 in the oil before and after treatment. The results demonstrated that PEF significantly increased the oil yield. Both treated and control groups exhibited gradually increased oil yields with decreasing particle size until reaching saturation at a certain particle size. Increasing voltage frequency did not have a significant impact on the oil yield; however, increasing voltage amplitude resulted in an optimal point for maximum oil yield. Analysis of oil composition indicated that PEF appropriately increased tocophero1 content. These findings provide a foundation for further optimization of PEF parameters to assist in extracting peony seed oil and facilitate its industrial application.  
pulsed electric field; peony seed; oil yield; irreversible electroporation;tocopherol
# A Review of Ghost Imaging via Sparsity Constraints
## 1 Introduction  
With the concept of photon introduced by [PERSON] in 1905, the statistical description of light wave is absolutely necessary for characterizing the fluctuation of light sources. Classical and quantum optical coherence theory were established by [PERSON] in 1954 [1; 2; 3; 4] and [PERSON] in 1960 [5; 6], respectively, to interpret the statistical properties of light fields. From the viewpoint of optical coherence theories, conventional imaging methods are based on the first-order correlation of light fields, and the attempt to extract spatial information of an object from high-order correlation of light fields can be traced back to the famous HBT experiment, which was conducted by [PERSON] and [PERSON] in 1956 [7; 8] based on the second-order autocorrelation of light fields in time domain. In 1995, [PERSON]'s group first demonstrated a novel imaging modality, called GI [9], by correlating an output of a single-pixel (bucket) photodetector, which collected light transmitted through or reflected from an object, with an output from a high spatial-resolution scanning photodetector or photodetector array whose illumination has not interacted with the object. The physics of GI has been thoroughly discussed in the past decades [10; 11; 12; 13; 14; 15; 16; 17]. Theories and experiments have showed that both quantum two-photon interference and classical intensity-fluctuation correlations could be used for GI. In this review, for the sake of the consistency of the discussion, we focused on the GI researches performed by our group, which is mainly based on the classical coherence theory.  
The remainder of the paper is organized as follows. In Section 2, we introduce the theoretical framework of GISC with an emphasis on that the ensemble average required by GI could hardly be satisfied in real applications. In Section 3, we present a brief review of application researches of GISC conducted in Shanghai Institute of Optics & Fine Mechanics (SIOM), including two imaging applications in real space, GISC LiDAR [18; 19] illuminated by pseudo-thermal light and GISC spectral camera [20] illuminated by natural light, and two imaging applications in Fourier space, X-ray Fourier-transform GISC [21] and lensless Wiener-Khinchin telescope based on high-order spatial autocorrelation of thermal light [22]. Section 4 is devoted to a discussion of the prospect of GISC.
# An Adaptive INS-Aided PLL Tracking Method for GNSS Receivers in Harsh Environments
## 8 Conclusions  
In order to improve the adaptability of GNSS carrier tracking for harsh environments with high dynamics and signal attenuation, an adaptive INS-aided PLL tracking method was proposed. Based on the theoretical analysis of the INS-aided PLL tracking error in the discrete-time domain, both the noise bandwidth and the integration time of INS-aided PLL can be adjusted adaptively under the minimum tracking error criterion. Software simulation results demonstrate that the adaptive INS-aided PLL tracking method can achieve the theoretical minimum tracking error for the harsh environment by choosing the optimal bandwidth and integration time. Hardware simulation results show that the adaptive tracking method can effectively improve the PLL tracking ability and GNSS/INS integrated navigation performance. For harsh environments, the tracking sensitivity is increased by 3 to 5 dB, velocity error is decreased by 36% to 50% and position error is decreased by 6% to 24% when compared with the INS-aided PLL method of adjusting the noise bandwidth. Finally, the adaptive method is evaluated in a field test in an urban environment with low-quality MEMS inertial sensors. The measurements obtained are in accordance with the simulation results and demonstrate the tracking performance of the method. Therefore, the adaptive INS-aided PLL tracking method can offer a superior performance for the GNSS receiver under harsh environment conditions.  
Figure 28: Position solutions during the field test.  
Figure 27: The output of PD (**left**) and the corresponding PLI (**right**) for SV6.  
**Acknowledgments:** This work is supported by the National Natural Science Foundation of China (Grant No. 61101077), the National Basic Research Program of China (Grant No. 2011 CB707004) and the National Key Technology R&D Program (Grant No. 2011 BAH24B02). The authors would like to thank the anonymous reviewers for their valuable comments.  
**Author Contributions:** [PERSON] proposed the adaptive tracking method, determined the whole research frame, and supervised the writing process. [PERSON] and [PERSON] performed the main research and the simulation, and wrote the original manuscript. [PERSON] designed and developed the software and hardware simulation systems, and reviewed the manuscript and provided revisions. [PERSON] offered advice for the simulation.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Results
### Aeromagnetic & Airborne Gravity Interpretation
#### 5.1.2 Airborne Gravity Interpretation
However, the estimated depth might be shallower from the predicted depth considering interbedded sediments in Central Luconia that include thick limestone layers with higher velocity reaching 3500 m/s to 4000 m/s. Figure 9: (**A**) Anomaly map of FTG_Txz domain; (**B**) Interpretation of structural trend on FTG_Txz data; (**C**) Rose plot diagram of interpreted structural trends for FTG_Txz showing NNW-SSE orientation. (**D**) Anomaly map of FTG_Tyz domain; (**E**) Interpretation of structural trend on FTG_Txz data; (**F**) Rose plot diagram of interpreted structural trends for FTG_Tyz showing NW-SE to E-W orientations. The contact lineament map of 5 to 20 km wavelength (Figure 10A) is categorized as short wavelength, presenting structures at depth approximately from 2.5 to <10 km. Strikes that appears on short wavelength data give dominant NE-SW to N-S orientation (Figure 10B,C). The intermediate wavelengths CLP map (Figure 10D) captures data at depths approximately 10 to <25 km, exhibits the prominent broad NW-SE trend with auxiliary trends oriented at NE-SW to E-W (Figure 10E,F). Interpretation of lineaments on the long wavelength of CLP map (Figure 10H) exhibit NW-SE to NNW-SSE trends. The estimated depth of data captured on long wavelength is approximately between 25 to 55 km. A consistent magnitude of NE-SW trend has been interpreted on all CLP data (pointed with red arrows on all rose plot on Figure 10), regardless the wavelengths focus. This must be treated with cautious since it might represent the artefact from the edge of survey area. Contrarily to the other datasets, the Contact Lineament Processed analysis appears to record diverse structural trends that highly depend on the wavelength ranges.
# Analysis of the Influence of Segmented Rollers on Slab Bulge Deformation
## 6 Discussion  
Slab bulge deformation is an important factor that affects the quality of the slab. Most researchers have focused on the deformation mechanism of the slab and rarely explored an effective way to control slab bulge deformation. Also, rigid casting rollers were most commonly used in the slab bulging models in order to simplify the calculations of slab bulge deformation. Although this simplification could greatly reduce the model's calculation time, the influence of roller deformation on slab deformation was neglected. In this paper, the influence of segmented rollers on slab bulge deformation was explored, and the structural parameters of segmented rollers were optimized to effectively control the bulge deformation of the casting slab. Also, elastic casting rollers were introduced to replace the traditional rigid casting rollers in the bulging model for simulation analysis to obtain more ideal results. The results showed that elastic casting rollers were more suitable for analyzing slab bulge deformation than rigid casting rollers. Then, by comparing the slab bulge deformation under the segmented and solid rollers separately, it was found that the stiffness of the segmented rollers was greater than that of the solid rollers, so that the bulge deformation of the slab could be effectively controlled. Finally, the roller spacing and diameter of the segmented rollers were further studied. It was found that reducing the segmented roller spacing and increasing the roller diameter could effectively control the bulge deformation of the slab. The research results in this paper could provide an effective theoretical basis for controlling slab bulge deformation in actual production.
# Antibacterialarial Agents Used in Modifications of Dental Resin Composites: A Systematic Review
## 4 Discussion
### Modification of RBC Compositions
#### 4.2.2 Other Additives
The study showed that the Sr-PBG-enriched composite material inhibited the growth of _Streptococcus mutans_, and the release of remineralizing Ca and P ions increased with increasing Sr-PBG concentration in the material. The addition of this modifier was found to not affect the mechanical properties (microhardness, tensile strength, and elastic modulus) of the composite, even after thermocycling. [PERSON] et al. [58] used a low-shrinkage resin consisting of a 20 wt% mixture of the anti-shrinkage expansive monomer 3,9-diethyl-3,9-dimethyl-1,5,7,11-tetraoxyipro[5,5] undecane and the epoxy resin monomer bisphenol diallo. They also included diglycoldyl ether and the addition of antimicrobial microcapsules--nanoparticle-modified MCs with a nano-antibacterial inorganic filler (NIF) containing a quaternary ammonium salt--at concentrations of 0, 2.5, 5, 7.5, 10 wt%. The microcapsules can self- regenerate. This dental resin's mechanical properties, polymerization shrinkage, antimicrobial activity, self-healing ability, and cytotoxicity were investigated. No negative effect on mechanical properties was shown. It was found that the volumetric polymerization shrinkage of the composite containing 20 wt% EU and 7.5 wt% microcapsule resin was reduced by 30.12% compared to the control group. Good self-healing performance and high antimicrobial activity were also demonstrated. Still, studies of the filled composites in simulated oral environments are needed. [PERSON] et al. [59] used silver sodium zirconium hydrogen phosphate as a matrix filler. They determined the efficacy against _Enterococcus faecalis_. The study showed that bacterial colonies of the _Enterococcus faecalis_ strain were reduced. A similar phenomenon of bacterial colony reduction was achieved at concentrations ranging from 7% to 13%, and it was also found that at a concentration of 1% and in the control group, the number of colonies increased [59]. Despite the promising antimicrobial properties, further studies, such as durability tests and cytotoxicity tests, are needed. Analysis is necessary to reliably assess the clinical suitability of the material. [PERSON] et al. modified a composite resin restorative material (SDR flow, Dentsply Sirona, Charlotte, NC, USA) with essential oils. These composites were evaluated only for antimicrobial and antifungal activity. It was shown that thymre and cinnamon oils showed the best antimicrobial properties against _Streptococcus mutans_ and _Lactobacillus acidophilus_the bacteria responsible for caries formation. Additionally, all tested oils showed antifungal properties against _Candida albicans_[60].
# Speed Bump and Pothole Detection Using Deep Neural Network with Images Captured through ZED Camera
## 2 Materials and Methods
### Basics of Deep Learning in Images  
Deep learning is a set of machine learning algorithms that models high-level abstractions on large amounts of data using multiple iterative nonlinear transformations of data expressed in matrix or tensor form. A typical architecture in this scheme for image understanding is a convolutional neural network (CNN) that is organized in layers. Usually, the first layers perform convolution operations and the last one has a fully connected neural network.  
Linear filtering of an image is accomplished through an operation called convolution indicated by \(*\) and also expressed as (4), (5) and (6) for color images. This is an operator in the neighborhood of each pixel, where the output is the weighted sum of neighboring input pixels. The weight matrix is called the convolution kernel or filter. For example, Figure 4 illustrates how a pixel resulting from the convolution is calculated when filtering it with a \(3\times 3\) kernel in position (2, 4).  
When an output pixel at the edge of an image is computed, a portion of the convolution falls outside the image edge, as illustrated in Figure 5. The padding of pixels outside the image to complete the processing of the image convolution is simply called \"Padding\". If set to \"same\", MATLAB calculates the padding value at training time so that the output is the same size as the input when stride is equal to 1.  
On the other hand, stride operation controls the jumps of the filter on the input image in pixels. If the stride is set to 1, the filter moves 1 pixel at a time, and if the stride is 2, the filter moves 2 pixels at a time. The higher the stride value, the smaller the size of the resulting output image, as shown in Figure 6.  
Figure 3: The three types of images to classify.  
The pooling operation reduces resolution of the feature map by reducing its height and width, while retaining features of the map required for classification. This is called down-sampling. Figure 7 shows an example of this process.  
Figure 4: The yellow area of the image is convolved (*) with the kernel (matrix) on the right and its result is 575.  
Figure 5: In \"same\" padding the border pixels are copied out in order to complete the convolution operation. In order to allow the center of the kernel to be applied to the edges of the image.  
Figure 6: The colors indicate the different types of strides that are made during the convolution. In stride = 1 we have two jumps while for stride = 2 we only have one jump.  
For several decades in image processing, convolution has been used to filter images, segment them, improve their contrast, and even classify them. However, this processing is a very data-heavy task, which is why stride and pooling operations are used to subsample data; however, these processes waste the high quality of the current images.
# Parallelizing the Computation of Grid Resistance to Measure the Strength of Skyline Tuples
## 1 Introduction
We focus here on an indicator, called the grid resistance [20], that measures how robust a skyline tuple is to slight perturbations of its attribute values. Quantizing tuple values (e.g., in a grid) affects dominance, since, as the quantization step size grows, more values tend to collapse, causing new dominance relationships to occur. The computation of this indicator may involve, in turn, several rounds of computation of the skyline or of the dominance relationships. In this respect, the parallelization techniques that have been developed for the computation of skylines may prove especially useful for the indicators too. In this work, we describe the main parallelization opportunities for the computation of the grid resistance of skyline tuples and provide an experimental evaluation that analyzes the impact of such techniques in several practical scenarios. The grid resistance indicator was only proposed very recently [20], and no prior algorithmic study exists that has detailed the steps necessary to compute the result, particularly in a parallelized setting. The solution presented here goes beyond the sketchy sequential pattern given in [20] and, building on consolidated research on data partitioning in skyline settings, offers the first attempt to parallelize the computation of grid resistance. In fact, to the best of our knowledge, this is also the first attempt to adapt the parallelization strategies used for skylines to compute other notions.
# Study of Motion Control and a Virtual Reality System for Autonomous Underwater Vehicles
## 3 Design of AUV Motion Control Algorithm
### Convergence Analysis  
**Hypothesis 1.**  
1. _There is a constant such that for any_ \(i=1,2,\cdots,n,k=1,2,\cdots m\)_, there is:_ \[\|a_{0}^{k}\|\leq C_{0}\,\|a_{i}^{k}\|\leq C_{0},\|b_{i}^{k}\|\leq C_{0},\] (43)
2. _There is a set_ \(D_{0}\) _for_ \(\left\{W^{k}\right\}_{k=0}^{\infty}\subset D_{0}\) _, and a set_ \(H=\left\{W\in D_{0}:\frac{\partial E(W)}{\partial W}=0\right\}\)_, that contains only a finite number of points._ **Theorem 1.**_Let the error function E(W) be defined by Equation (31). Starting from the initial point, {\(w_{k}\)}, is the weight sequence of the network is obtained by Equation (40). If hypothesis (i) is satisfied, then \(E\left(W^{k+1}\right)\leq E\left(W^{k}\right),\ k=0,1,2,\ldots;\lim\limits_{k \rightarrow\infty}\frac{\partial E\left(W^{k}\right)}{\partial W}=0\)._
2. _If hypothesis (ii) is also true, then there is a point \(W^{*}\) that makes_
3. \(\lim\limits_{k\rightarrow\infty}W^{k}=W^{*}\)_._  
Conclusion (i) in Theorem 1 shows that the E(W) decreases monotonically during training. Conclusion (ii) shows the weak convergence of the weight sequence {\(w_{k}\)}, i.e., starting from any point \(W_{0}\) in the weight space and updating the weight according to Equation (40), the weight sequence, {\(w_{k}\)}, obtained satisfies Equation (40). Conclusion (iii) shows that the number of stable points on the error function surface is limited, and thus the weight sequence must converge to a local minimum. Hypothesis 1 indicates that the conclusion of the theorem requires an a priori condition, i.e., the bounded nature of the weight sequence in the training process. This assumption is also necessary for the convergence analysis of the gradient training algorithm of an ordinary neural network.
# Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks
###### Abstract  
Artificial intelligence in the mining industry is key to improving safety, optimizing resources, and ensuring sustainable operations in complex environments. The main objective of this research is to develop an autonomous mobile station equipped with artificial vision and artificial intelligence to identify and track equipment, people, and animals in critical areas of mining operations, issuing real-time alerts to reduce occupational risks and improve operational control. The research is applied with an experimental approach, designed to validate the effectiveness of the proposed system in real open-pit mining environments. The proposed methodology consisted of five stages: (i) Selection of data collection equipment, (ii) Definition of the positioning scheme, (iii) Incorporation of the communication system, (iv) Data processing and transformation, and (v) Equipment identification and tracking. The results showed an average accuracy of 98% in the validation and 95% in the test, achieving perfect performance (100%) in key categories such as excavators and drills, highlighting the potential of this technology to transform mining towards safer and more efficient standards.  
artificial intelligence; artificial vision; mining industry; autonomous mobile station 2025  
6  
G.; Herrera-Vidal, G.; Coronado-Hernandez, J.R.
### Contributions and Novelties  
This study addresses this critical gap by providing the first comprehensive investigation of in-context learning for Tarifit machine translation. We systematically evaluate the impact of various ICL components: shot selection strategies, prompt formulation techniques, and linguistic context integration across three language pairs (Tarifit-Arabic, Tarifit-French, Tarifit-English) that reflect the multilingual environment of Tarifit speakers. Through controlled experiments using state-of-the-art LLMs (GPT-4, Claude-3.5, PaLM-2) and a carefully curated dataset of 1000 sentences, we establish baseline performance metrics and identify optimal ICL configurations for this morphologically complex, low-resource language.  
Our contributions include: (1) the first systematic evaluation of ICL for Tarifit translation, establishing baseline benchmarks across multiple language pairs; (2) comprehensive analysis of shot selection strategies and their impact on translation quality; (3) identification of optimal ICL configurations for morphologically rich, low-resource languages; and (4) practical recommendations for developing effective few-shot translation systems for endangered Berber languages. These findings advance our understanding of ICL capabilities for low-resource MT and provide a methodological framework for similar investigations in other underrepresented language families.  
This study establishes baseline performance metrics for Tarifit ICL translation while acknowledging important methodological constraints. Our evaluation focuses on Latin script representation and unidirectional translation (Tarifit as source), with a dataset of 1000 sentences that, while substantial for low-resource language research, remains modest for comprehensive linguistic coverage. These limitations, detailed in Section 6, define the scope of our findings and highlight critical directions for future work in Berber language processing.  
This study addresses three key research questions: **RQ1:** What is the optimal number of demonstration examples (shot count) for Tarifit ICL translation across different target languages? **RQ2:** How do different shot selection strategies (random, similarity-based, diversity-based) impact translation quality for morphologically complex low-resource languages? **RQ3:** To what extent does linguistic proximity between Tarifit and target languages (Arabic, French, English) influence ICL translation performance?