esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Hen clam (_Mactra chinensis_) is a clam species widely found in the soft sandy bottom of the upper subtidal and intertidal zones of the coastal areas of Russia, Korea, Taiwan and Japan [1]. According to the results of a cytochrome oxidase I (COI) DNA investigation, hen clams living in the Asia-Pacific region can be categorized into three types of species [2]. In Korea, they are abundant in the low and mid-intertidal zones of the west and south coasts, within a depth range of approximately 10 m; around Jeju Island, they are only known to be found off the coast of Gwakjiri, a village in the town of Aewol (Eup).  
The hen clam is the main source of income for the villagers in Gwakjiri, and more than 300 kg of hen clams is produced annually. This shellfish only inhabits the Gwakjiri village, but the catch has recently been decreasing since the average water temperature in the coastal waters of Jeju Island has risen by 1.5 \({}^{\circ}\)C over the past 40 years due to climate change [3]. However, there have been few studies on their habitats.  
Hen clam (_M. chinensis_) is distributed across Korea, Taiwan, and Japan.  
Shellfish species are generally referred to as bivalves, whose fertilized eggs develop through the stages of blastula, gastrula, and trochophore to form facets and shells, eventually becoming D-stage (veliger) larvae. Larvae of both adherent and non-adherent bivalve species develop through the free-floating stage until they either attach themselves to a substrate or settle into the sediment.  
As the free-floating larvae are greatly affected by their surrounding environment, it is essential to monitor their breeding environment and diet. In particular, _M. chinensis_ is a non-adherent bivalve with larvae that undergo a free-floating veliger stage; thus, it is necessary to carefully manage the surrounding environment during their early fertilization and larval stages for the efficient mass production of this clam species.  
The key factors affecting the growth of bivalve larvae include water temperature, salinity, illuminance, and the supply of microalgae. Several studies have been conducted on the factors that affect the growth and survival of bivalve larvae, including the effects of water temperature and salinity [4; 5], diet [6], quality of fertilized eggs [7], amount of feed [8], feeding behavior of larvae [9], and food selectivity [10]. In Korea, some research on the feeding and growth rates during the larval stage of certain bivalves [11] has been reported, and studies on clams [12], sea scallops [13], oysters [14], and silk scallops [15] have been recently conducted.  
Studies have addressed the ecology [16] and reproduction [17] of _M. chinensis_; however, the environmental properties contributing to their survival and growth have not been systematically researched despite their contribution to the local economy. Therefore, we designed this study to investigate the effects of environmental conditions such as water temperature, salinity, and microalgal supply on the growth and survival rate of hen clam (_M. chinensis_) larvae.
# Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach
## 6 Experiments Simulation and Analysis
### PAGWO-IDWA and DWA Comparison Experiment  
To evaluate the impact of incorporating PAGWO-generated global waypoints into DWA, the first experiment was designed with the mobile robot's starting point at (1, 1) and its endpoint at (10, 10). In the simulation environment, the triangle denotes the start point, the circle denotes the goal, the asterisks represent local goal points generated by PAGWO, and the green area indicates the detection range of the DWA algorithm. The experimental results are presented in Figure 9. In Figure 9, the robot is trapped in the local optimum and cannot reach the end point successfully, while in Figure 9, the robot is guided to the endpoint by the global path of PAGWO and successfully arrives at the endpoint. This method effectively reduces the limitation imposed by a single global goal point on path planning, allowing the robot to complete the path planning task successfully.  
To evaluate the effect of the adaptive facing angle on DWA, a second set of experiments was conducted. In these experiments, the mobile robot's starting point was set at (1, 1) and the endpoint at (20, 20). Path planning was performed using both the DWA algorithm and the PAGWO-IDWA algorithm on the same raster map, and the results are shown in Figure 10. In Figure 10, the initial facing angle of DWA is directed toward the endpoint, causing the robot to deviate from the optimal path at the outset and resulting in redundant path segments. Furthermore, after encountering an obstacle, DWA continues to follow the wall because its facing angle remains fixed toward the endpoint, lacking adaptive guidance. In contrast, Figure 10 shows that during the initial stage of path planning with PAGWO-IDWA, the robot's facing angle is oriented toward the PAGWO-generated path point. Under the correct guidance of this path point, the robot follows the optimal path to the endpoint, thereby verifying that the improved algorithm outperforms DWA in path optimization. The data for these experiments are presented in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ENV. Model** & **Index**Algorithm** & **GA** & **SCA** & **PSO** & **GWO** & **PAGWO** \\ \hline \multirow{3}{*}{ENV. 3} & Path Length (m) & 52.68 & 52.57 & 53.56 & 51.30 & 46.82 \\  & Number of turns & 18.50 & 18.27 & 19.57 & 18.40 & 12.03 \\ \cline{1-1}  & Running time (s) & 13.49 & 0.84 & 4.13 & 4.20 & 2.58 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 9: Comparison experiment 1. (a) DWA; (b) PAGWO-IDWA.
# Quantifying Privacy Risks for Continuous Trait Data
## 4 Model and Methodology
### Threat Model  
We consider a scenario where the adversary has access to both genetic data and continuous trait data. The continuous trait data is anonymous, while some shareable genetic profiles have real identifiers, such as information that can be targeted to specific individuals. Examples of genetic databases include OpenSNP [16] and the Personal Genome Project [17]. This information can be taken by the adversary to re-identify personal information from other anonymous continuous trait data [48]. Furthermore, even if a genetic profile is anonymous, the adversary can still link it to its pedigree with surname-genome associations [45], kinships [14], or online social networks [49].  
We assume a threat model under which the correlations between continuous traits and SNPs may be acquired as the background knowledge by the adversary, as shown in Figure 1. First, there are two ways for an adversary to obtain this background knowledge. One approach is to obtain the relationships by analyzing public QTL datasets, such as the cerebrospinal fluid proteome dataset in National Center for Biotechnology Information (NCBI) [48]. The other approach is to rely on existing findings of the statistical relationships between genes and continuous traits. For example, the adversary can obtain prior probabilities, expectations, and standard deviations of RNA-binding proteins (RBP) with disease-related SNPs from academic QTLs websites [50]. Next, the adversary needs to determine whether the individual genomic profile is the member of the continuous trait dataset, and if so, which individual. Finally, the adversary successfully matches continuous trait profile and genotype as the same individual, which leads to privacy breaches.
# Enhancing Safety in Automatic Electric Vehicle Charging: A Novel Collision Classification Approach
###### Abstract  
With the rise of electric vehicles, autonomous driving, and valet parking technologies, considerable research has been dedicated to automatic charging solutions. While the current focus lies on charging robot design and the visual positioning of charging ports, a notable gap exists in addressing safety aspects during the charging plug-in process. This study aims to bridge this gap by proposing a collision classification scheme for robot manipulators in automatic electric vehicle charging scenarios. In situations with minimal visual positioning deviation, robots employ impedance control for effective insertion. Significant deviations may lead to potential collisions with other vehicle parts, demanding discrimination through a global visual system. For moderate deviations, where a robot's end-effector encounters difficulty in insertion, existing methods prove inadequate. To address this, we propose a novel data-driven collision classification method, utilizing vibration signals generated during collisions, integrating the robust light gradient boosting machine (LightGBM) algorithm. This approach effectively discerns the acceptability of collision contacts in scenarios involving moderate deviations. Considering the impact of passing vehicles introducing environmental noise, a noise suppression module is introduced into the proposed collision classification method, leveraging empirical mode decomposition (EMD) to enhance its robustness in noisy charging scenarios. This study significantly contributes to the safety of automatic charging processes, offering a practical and applicable collision classification solution tailored to diverse noisy scenarios and potential contact forms encountered by charging robots. The experimental results affirm the effectiveness of the collision classification method, integrating LightGBM and EMD, and highlight its promising prediction accuracy. These findings offer valuable perspectives to steer future research endeavors in the domain of autonomous charging systems.  
collision classification; plug-in safety; automatic charging; light gradient boosting machine; empirical mode decomposition +
Footnote †: journal: Applied sciences
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 6 Blockchain Potential in V2X Networks
### Integrating Blockchain with Mec and 5G Technology in V2X Networks  
The integration of the blockchain with MEC and 5G cellular technologies enables next-generation vehicular networks by providing secure vehicular network orchestration, intelligent resource management,  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Authors** & **Ref.** & **Description** & **Block Chain** & **MEC** & **Cellular /5G** & **System Characteristics** \\ \hline \multirow{3}{*}{\begin{tabular}{} \end{tabular} } & \multirow{3}{*}{[70]} & Proposed a self-managed & \multirow{3}{*}{[71]} & Private & \multirow{3}{*}{No} & \multirow{3}{*}{No} & \multirow{3}{*}{No} \\  & & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \cline{1-1} \cline{5-6}  & & Proposed a self-managed & \multirow{3}{*}{[70]} & Private & \multirow{3}{*}{No} & \multirow{3}{*}{No} & \multirow{3}{*}{No} \\  & & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \hline \multirow{3}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{3}{*}{[71]} & Proposed a regional & \multirow{3}{*}{No} & \multirow{3}{*}{No} & \multirow{3}{*}{No} & \multirow{3}{*}{
\begin{tabular}{} \end{tabular} } \\  & & & & & & \\ \cline{1-1}  
trust management, and enhanced privacy. The edge computing, i.e., MEC reduces the delay and background congestion by computation offloading [74; 75] and distributed content caching [76].
# Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods
## 2 Materials and Methods  
### Theoretical Foundations  
#### 2.1.1 Selection and Placement of Transmitters  
Using new wireless standards such as IEEE 802.15.4, low-power Bluetooth, and radio-frequency identification (RFID) helps to develop new positioning technologies in indoor environments [7]. Table 1 represents a good comparison of the performance of these three technologies.  
In this research, hardware called iBeacon will be used to receive the signal, which has the ability to send RSSI signals up to 50 m, and these components can be used for up to 5 years without the need to replace the battery. iBeacon was developed in a collaboration between Apple and the Estimate team and is available to researchers for research projects [18].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **WiFi** & **RFID** & **iBeacon** \\ \hline Coverage & 50 m & 10 m & 50 m \\ \hline Cost & high & Low & A little high \\ \hline Power Consumption & high & Low & Low \\ \hline Bandwidth & 1.8 G & 250 kb & 1 M \\ \hline Battery Life & several Days & 1–2 Years & 1–2 Years \\ \hline Positioning Accuracy & 2 m–3 m & 1 m–2 m & 1 m–2 m \\ \hline \hline iBeacons (iBeacon Estimate location beacons), hardware used. & & \\ \end{tabular}
\end{table}
Table 1: Comparison of three different hardware in internal routing.  
iBeacon technology has recently become very popular among researchers, used to position objects and smart devices indoors [19].  
In the research that has been conducted in [8], the same transmitters were used as the generator of Bluetooth signal. (Currently, three of these transmitters have been purchased and will be used during the investigation).
# Chapter Detection in Robotic Milling Using Entropy Features
## 2 Theoretical Basis and Framework for the Chatter Detection System
### Framework for the Chatter Detection System for Robotic Milling  
The framework for the proposed chatter detection system is shown in Figure 1. It consists of three steps:Step 1: Acquiring online data  
Extensive experiments is conducted to collect the vibration signals in robotic milling under variable robot configurations and cutting parameters.  
Step 2: Extracting features  
The ICEEMDAN method is performed on the vibration signals, and a set of IMFs is obtained. The effective IMFs are selected according to the correlation between the original signals and each IMF. Subsequently, the \(wRCMDE\) is extracted from the reconstructed signals using the selected IMFs.  
Step 3: Constructing classification model  
Based on the extracted \(wRCMDE\) features, the classification model can be constructed and trained for chatter detection in robotic milling.
## 6 Conclusions and Future Directions
### Coherent Control  
Over the last decade, quantum and coherent control has emerged as an exciting sub-field of atomic, molecular, and optical physics [69; 70; 71]. It is now understood that efficient control of a quantum system requires laser light with a broad spectrum with frequencies that match as many transitions in the system as possible [72]. Using arbitrary optical waveforms with a spectrum ranging from 200 nm to 10 \(\upmu\)m in wavelength, one can simultaneously control the electronic, vibrational, and rotational coordinates of molecules. This will facilitate the understanding of various couplings between different coordinates of the system. For example, one possibility is to study the ionization of a molecule as a function of the vibrational coordinate. This cannot be done with only the soft X-ray pulses provided by HHG since their spectrum does not have the infrared wavelengths that are needed to efficiently control the slow vibrational coordinate.  
The ability to shape the pulse beyond the slowly-varying envelope approximation will likely extend the ideas of coherent control to a completely new regime. Currently, because of the limited bandwidth of laser sources, coherent control experiments are done in the quasi-steady-state regime, in which the electric field of the laser light cycles back and forth many times before its envelope changes. Sub-cycle pulse shaping allows true manipulation of the electric field itself instead of just the envelope, providing a completely new tool. Up until now, sub-cycle pulses have only been available in the THz region of the spectrum. These pulses have been used for many pioneering experiments in Rydberg atoms in which the Rydberg wave-packets are precisely controlled [73, 74]. Extending sub-cycle pulses to the optical region of the spectrum will extend these experiments to electronic wave-packets with much shorter characteristic time scales. As demonstrated by the recent exciting experiments from the HHG community [75], there remains much to be understood about the ultrafast dynamics of electronic motion and its coupling to other coordinates.  
We thank [PERSON] for his contributions to the early stages of the project. We also thank [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for many helpful discussions. Since 2009, this work has been supported by the National Science Foundation (NSF). We also acknowledge support from the Wisconsin Alumni Research Foundation (WARF).  
[PERSON] supervised the research, designed the experiments, and wrote the text. [PERSON] and [PERSON] performed the experiments, took the data, and edited the text. All authors contributed to the interpretation of the experimental results.  
The authors declare no conflict of interest.
## 2 Material and Methods
### Germination of Barley Grains after ACP  
For assessing the effect of ACP on germination of barley grains, ACP treatment was applied to seeds for 0, 1, 6, 10 min. Each treatment contained three batches of grains, where ACP treatment was applied independently to each batch. Then, the individual grains were transferred to germination pouches (\(17.8\times 16.5\) cm, Mega International, Minneapolis, MN, United States) on light shelves containing 100 mL of deionized water. Five growth pouches, each containing two grains, were used for each treatment (\(n\) = 10) and every one of the batch. Altogether, there were 30 plants per each ACP treatment. Barley plants were grown at room temperature with supplemental lighting (200 \(\mu\)mol m\({}^{-2}\)s\({}^{-1}\) at the top of the growth pouch, SunBlaster T5 HO, 54W, 0900304), maintaining a photoperiod of 16 h/8 h light/dark cycles. After 1 week of germination, the shoot length and number of roots were measured manually. The roots were scanned using an optical scanner STD 4800 and analyzed for root length, surface area, average diameter, and volume, using WinRHIZO software (Regent Instruments Inc., Quebec City, QC, Canada).
# An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization
## 2 Related Work
### Image Denoising Literature Review
Although this method obtained better or comparable performance in computation costs compared with existing SR-based methods, it was still time-consuming due to numerous matrix computations in sparse coding and dictionary learning. After reviewing the above literature, our proposed denoising method effectively improves the robustness and estimation accuracy of the model by introducing a nonconvex \(\gamma\)-norm and TV norm, provides a more accurate rank approximation, and avoids the singular value penalty problem. Moreover, the proposed model not only improves image denoising and enhances image smoothness but also preserves important features of the image while removing image noise.
## 2 Materials and Methods
### Refractive Index Measurements  
For the solvent mixture of 2-butanone in cyclohexane, a refractive index calibration curve was generated using a Fisherbrand(tm) Handheld Digital Brix/Refractive Index Refractometer (Fisher Scientific, Pittsburgh, USA) with a refractive index range of 1.3330 to 1.5177. Each analysis employed approximately 0.50 mL of the liquid standard or the evaporating test mixture. The calibration curve was established using compositional variations ranging from 0 to 100% v/v of 2-butanone and cyclohexane. All measurements were performed in triplicate. Similar measurements were not performed on the isopropanol:n-hexane system due to the nearly identical refractive indices of these two solvents.
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 3 Materials and Methods
### Multispectral Data  
Multispectral satellites acquire images using several different wavelength bands. This technique enables the derivation of different spectral indexes, e.g., Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), allowing the identification of flooded areas (including after the water has already withdrawn) by analyzing the flood's effects on the soil [67]. Considering the spatial resolution, it is possible to distinguish two classes of multispectral satellites: The medium-low resolution (100-500 m) satellites, such as MODIS, Proba-V or Sentinel-3, and the medium-high resolution (10-30 m) satellites, such as Sentinel-2 or the Landsat series.
## 2 Materials and Methods
### Economic Analysis  
The present economic analysis is based on small-scale production, with production below 150 kg/h. Olive oil production is seasonal; therefore, three months per year were considered, with production taking place 24 h a day, seven days a week, totaling 2208 h of production per year. The investment analysis was conducted with a focus on achieving a target return on investment of 20%. Subsequently, the initial cost of the equipment was calculated based on the proposed ROI and factored in the expected rise in gross profit from the implementation of PEF. Estimates for medium- and large-scale productions were obtained based on a production rate of 5 tons/h.  
The economic analysis had three main categories, as described below:  
* _Raw Material Cost:_ The cost of olives was obtained from the General Planning and Political Administration Office (GPP) [18,19]. The cost was calculated by averaging the market values of olives in November and December of 2022 and January and February of 2023.  
Cost of olives: 0.67 EUR/kg* _Final Product Selling Price:_ The market price of extra virgin olive oil was determined based on the market price observed in Portugal in the 32\({}^{\circ}\) week of 2023 [20]. Olive oil sold on this date is mainly produced from olives harvested during the months considered in the raw material cost. Cost of extra virgin olive oil: 6.42 EUR/L
* _Operational Costs:_ Electricity was the only operational cost considered; other costs inherent to the process (e.g., labor, water consumption, packaging) are identical in both processing methods and therefore have no impact on the comparative analysis. The assumed cost per megawatt-hour is the European Union's average industry energy cost in the first semester of 2023, published by the Energy Services Regulatory Authority of Portugal (ERSE) [21]. Energy price: 219.4 EUR/MWh
* \(\Sigma\)Machine Energy Consumptions per cycle: With PEF (malaxation 30 min): 14.20 kWh Without PEF (malaxation 45 min): 15.29 kWh The economic analysis calculations made in this study were carried out by using the following equations: Energy Costs \(=\sum\)Machine Energy Consumptions per cycle \(\times\) Energy Price \(\times\) Number of cycles (2) Annual Production \(=\dfrac{\text{Hours of Production}}{\text{Cycle Time}}\times\text{Production per cycle}\) (3) Revenue \(=\) Annual Production \(\times\) Olive Oil Market Price (4) Gross Profit \(=\) Revenue \(-\) (Eletricity Costs \(+\) Raw Material Costs) (5) ROI \(=\dfrac{\text{Difference in Gross Profit}}{\text{Cost of investment}}\times 100\) (6)