esa-sceva/satcom-chunk-collection huggingface dataset:
# Rough-Terrain Path Planning Based on Deep Reinforcement Learning
## 4 Simulation Experiment
### Training Setup  
The hardware used in this simulation experiment included Intel(r) Xeon(r) CPU, 128 G RAM (Intel Corporation, Santa Clara, CA, USA), and NVIDIA GeForce GTX 1080 Ti (NVIDIA Corporation, Santa Clara, CA, USA). The platform was built based on Gym, and the results are presented in the MATLAB 2016b software environment. In order to verify the performance of the A-DQN algorithm, in this study, the simulation map with a \(5000\,\mathrm{m}\times 5000\,\mathrm{m}\) area and an elevation value of [0, 300] m was sampled as a \(50\times 50\) elevation raster map for the experiments; i.e., the x and y axis range was [0, 50], the \(z\)-axis range was [0, 3], and the width of the grid point was 100 m. A map of the simulation experiment is shown in Figure 4. After several trials with random starting and ending points in different rounds, the A-DQN algorithm hyperparameters were determined, as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Meaning** & **Value** \\ \hline \(\gamma\) & Attenuation factor & 0.99 \\ Learning\_rate & Learning rate & 0.0001 \\ Episodes & Number of training rounds & 8000 \\ Replay\_memory & Memory pool size & 20,000 \\ Max\_step & Maximum number of steps per episodes & 150 \\ Batch\_size & Batch size of memory replay & 64 \\ k\({}_{1}\) & Slope penalty intensity factor & 4 \\ k\({}_{2}\) & Terrain penalty intensity factor & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameters of the A-DQN algorithm.
# _Geronomy_
## 2 Materials and Methods
### Molecular Techniques and Phylogenetic Analyses  
CTAB rapid plant genome extraction kit-DN14 (Aidlab Biotechnologies Co., Ltd., Beijing, China) was used to extract DNA from dried specimens from China and other regions, and 2 xTaq PCR Mix (Aidlab Biotechnologies Co., Ltd., Beijing, China) was used to perform PCR products [30]. The primer pair ITS5 and ITS4 were amplified for the ITS region [32]. The primer pair LR0R and LR7 were for the nuclear r28S region [33]. The primer pair MS1 and MS2 were for the mitochondrial SSU region [32]. The primer pair EF1-983F and EF1-2218R were for Tef1 [34]. The PCR cycling program for ITS, mtSSU, 28S and TEF1 followed previous studies [27]. The Beijing Genomics Institute (Beijing, China) was in charge of purifying and direct sequencing of the PCR products. All sequences used in the phylogeny were downloaded from GenBank (Table 1) with references. The sequence alignment was deposited in TreeBase (submission ID 23826). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Species Name**} & \multirow{2}{*}{**Sample No.**} & \multicolumn{4}{c}{**GenBank Accessions**} & \multirow{2}{*}{**References**} \\ \cline{3-3} \cline{5-6}  & & **ITS** & & **LSU** & **mtSSU** & **TEF1** \\ \hline _Abortiporus biennis_ & TFRI 274 & EU232187 & EU232235 & & [21] \\ _A. biennis_ & EL65-03 & JN649325 & JN649325 & & [21] \\ _Abundisporus roseodibus_ & Dai 12269 & KC415908 & KC415910 & KF051037 & KF181131 & [5] \\ _A. pubertatis_ & Cui 5776 & KC78565 & KC787572 & KF051029 & KF181129 & [5] \\ _A. sclerosestosus_ & MUC41438 & FJ411101 & FJ393868 & & [28] \\ _A. violacous_ & Ryvarcand 10775 & KF0811826 & KF018134 & KF051058 & KF181152 & [5] \\ _Amylocytosis leptoptica_ & KHL 11755 & EU118603 & EU118603 & & [21] \\ _Antrada albida_ & CBS 308.82 & DQ491414 & & [35] \\ _A. albida_ & FP 105979 & EU232272 & EU232272 & & [36] \\ _A. heteromorpha_ & CBS 200.91 & DQ491415 & AY515350 & & [35] \\ _A. macra_ & MUAF 887 & EU340898 & & [23] \\ _Bjerakandera adusta_ & NB6 4983 & A373156 & AB733333 & & [23] \\ _Ciremomyces lindbaliti_ & FBCC 177 & HQ659223 & HQ659223 & & [23] \\ _Climacocystis borealis_ & KIM 13318 & JQ031126 & JQ031126 & & [23] \\ _Coriolopsis capenta_ & LE(BIN)-0677 & AB158316 & AB158316 & & [37] \\ _Donikoporia expansa_ & MUCL 35116 & FJ411104 & FJ393872 & & [28] \\ _Eariella scakrosa_ & PR1209 & JN165009 & JN164793 & & [38] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information of the sequences used in this study.
## 1 Introduction  
In laser science a rapid progress in generating laser pulses with peak intensities exceeding \(10^{22}\,\mathrm{W}/\mathrm{cm}^{2}\) could be witnessed over the past decades [1]. These laser pulses are used for a variety of applications, e.g., laser-driven electron [2; 3] or ion-acceleration [4], the laser-based generation of x-rays [5], high energy physics [6] or laser-based proton radiography [4; 7].  
Most of these applications would strongly benefit or even ultimately require laser pulses with a well known temporal structure and a temporal intensity contrast as high as possible. Unfortunately the amplification of high power laser pulses in reality is always associated with the amplification of unwanted noise or pre- and post-pulses on different intensity- and time-scales [8]. Since these pre- and post-pulses significantly alter the experimental conditions a lot of effort is put into improving the temporal intensity contrast [9; 10]. Commonly used techniques within the laser systems are, e.g., saturable absorbers [11], or OPCPA [12], Double-CPA [13] including XPW [14]. The application of such techniques results in very high contrast laser pulses with temporal intensity contrast ratios of \(10^{12}\) or better [9; 10; 15]. Even those high contrast laser pulses are, however, not sufficient for some experimental applications, e.g., radiation pressure acceleration of ions [16] where a steep leading edge of the laser pulse is required for nm-scale thin targets. Further improvement of the temporal pulse quality is necessary and could be realized after the pulse compression, e.g., with plasma mirrors [17], plasma shutters [18] or by generating the second harmonic [19; 20; 21].  
In this paper we present experimental results of the second harmonic generation (SHG) of 100-TW POLARIS [10] laser pulses centered at 1030 nm wavelength and 150 fs pulse duration. Using a 2 mm thin 160 mm-diameter potassium dihydrogen phosphate (KDP) type-I SHG-crystal we achieved a maximum pulse energy of 3 J with a pulse duration of 120 fs at 515 nm center wavelength. A detailed characterization of the newly generated laser pulses' temporal structure is presented from ms- to fs-timescales covering a dynamic range of more than 20 orders of magnitude for the focussed intensity. Due to the used wavelength separators for 1030 nm and 515 nm and the nonlinearity of the second harmonic generation, the laser pulses' temporal profile for the target interaction is shortened and contrast-enhanced. The results were obtained during a five-month experimental campaign starting at the end of 2014. In this experiment, ultra-thin targets (down to 5 nm) were used to study radiation pressure acceleration of protons with high intensity laser pulses.
###### Abstract  
The aim here was to explore the potential of visible and near-infrared (Vis/NIR) hyperspectral imaging (400-1000 nm) to classify fresh chicken breast fillets into different water-holding capacity (WHC) groups. Initially, the extracted spectra and image textural features, as well as the mixed data of the two, were used to develop partial least square-discriminant analysis (PLS-DA) classification models. Smoothing, a first derivative process, and principle component analysis (PCA) were carried out sequentially on the mean spectra of all samples to deal with baseline offsets and identify outlier data. Six samples located outside the confidence ellipses of 95% confidence level in the score plot were defined as outliers. A PLS-DA model based on the outlier-free spectra provided a correct classification rate (CCR) value of 78% in the prediction set. Then, seven optimal wavelengths selected using a successive projections algorithm (SPA) were used to develop a simplified PLS-DA model that obtained a slightly reduced CCR with a value of 73%. Moreover, the gray-level co-occurrence matrix (GLCM) was implemented on the first principle component image (with 98.13% of variance) of the hyperspectral image to extract textural features (contrast, correlation, energy, and homogeneity). The CCR of the model developed using textural variables was less optimistic with a value of 59%. Compared to results of models based on spectral or textural data individually, the performance of the model based on the mixed data of optimal spectral and textural features was the best with an improved CCR of 86%. The results showed that the spectral and textural data of hyperspectral images together can be integrated in order to measure and classify the WHC of fresh chicken breast fillets.  
chicken breast fillet; water-holding capacity (WHC); gray-level co-occurrence matrix (GLCM); texture; partial least square-discriminant analysis (PLS-DA) +
Footnote †: journal: Applied Sciences
## 3 Mitochondrial Dysfunction in AAI-Induced Kidney Diseases
### Investigation Methods of Mitochondrial Function
#### 3.2.1 Assay of Oxygen Consumption Measurement  
Mitochondrial respiration rates are evaluated by measuring oxygen consumption in the presence of various substrates. Glutamate and malate, which selectively fuel electron transport at Complex I, or succinate combined with an NADH dehydrogenase inhibitor (like rotenone) to target Complex II, are commonly used. ADP-stimulated respiration (State 3) is assessed with ADP present, while ADP-independent respiration (State 4) is measured after ADP depletion. The respiratory control ratio (RCR), calculated as the ratio of State 3 to State 4 oxygen consumption, indicates the efficiency of ATP synthesis coupled to respiration and reflects mitochondrial integrity [12]. Oxygen consumption rate, determined by the decline in dissolved oxygen within the cell culture, serves as a direct measure of mitochondrial respiratory function. Studies have shown that AAI treatment leads to a reduction in respiratory function after 12 h [19]. Moreover, a significant decrease in mtDNA copy number is observed after 24 h of exposure to 50 \(\upmu\)M AAI [2], further highlighting the detrimental impact of AAI on mitochondrial function.
# Advancing Real-Estate Forecasting: A Novel Approach Using Kolmogorov-Arnold Networks
## 1 Introduction
### Scientific Background and the Proposed Approach  
Researchers and industries are actively working on projects related to predicting house prices [6]. Based on a set of input features/factors derived from real-estate datasets they are developing models capable of estimating house prices. Machine learning techniques and deep neural networks (NNs) have been widely employed for this purpose [7].  
The objective of this study is to implement and propose a novel approach using KANs. KANs are based on the Kolmogorov-Arnold theorem, which states that any continuous function of several variables can be represented as a superposition of continuous functions of one variable and a finite number of parameters. Formally, for any continuous function \(f:\mathbb{R}^{n}\rightarrow\mathbb{R}\), there exist continuous functions \(g_{1}\), \(g_{2}\),..., \(g_{k}\): \(\mathbb{R}\rightarrow\mathbb{R}\) and a continuous function \(h\): \(\mathbb{R}^{k}\rightarrow\mathbb{R}\) such that \(f(x_{1},x_{2},\ldots,x_{n})=h(g_{1}(x_{1}),g_{2}(x_{2}),\ldots,g_{k}(x_{k}))\).  
This theorem has several important implications for function approximation and neural network design. It suggests that complex, multi-dimensional functions can be decomposed into simpler, one-dimensional functions, which can be more easily modeled and interpreted. Various generalizations of the Kolmogorov-Arnold theorem have been proposed, extending its applicability to different types of functions and higher dimensions. These generalizations have significant implications for fields such as machine learning, where understanding the structure of data is crucial for developing effective predictive models. Unlike the static, non-learnable activation functions in Multi-Layer Perceptrons (MLPs), KANs incorporate univariate functions that act as both weights and activation functions, adapting as part of the learning process. They use complex learnable functions (called B-Splines) to represent nonlinear input transformations, with relatively fewer parameters than a traditional neural network. By decentralizing activation functions to edges rather than nodes, KANs align with the Kolmogorov-Arnold representation theorem, enhancing model interpretability and performance (Figure 1) [8]. These models are designed to handle complex, nonlinear relationships within the data, capturing intricate patterns that traditional models might miss. KANs excel in representing highly nonlinear functions, making them particularly suited for customer behavior analysis, where interactions between features can be complex and multi-faceted. By leveraging the Kolmogorov-Arnold representation theorem, these networks can approximate any continuous function, providing high flexibility and accuracy in prediction problems [9].
# Impact of Ar/CF4 Mixed Gas Flow Rate on Silicon Etching Using Surface Discharge Plasma
## 4 Discussion
Figure 10 illustrates the discharge power at various Ar/CF4 mixed gas flow rates. The discharge power was calculated as the average power derived from one cycle of the discharge voltage and current waveform during the silicon substrate etching process. Throughout the measurements, the discharge-sustaining voltage was maintained at 9.4 kV. As depicted in Figure 10, the discharge power remained approximately 10 W across all gas flow rates. However, a noticeable trend of increasing discharge power with higher flow rates of Ar/CF4 gas was observed. This increase in discharge power, corresponding with higher Ar/CF4 gas flow rates, suggests a concurrent rise in the quantity of active species such as Ar ions and CFn, which play a critical role in the etching process. Etching via surface discharge relies on a filament-like surface streamer, roughly 200 \(\upmu\)m wide [10]. Beyond the reach of this streamer in the width direction, etching occurs through the diffusion of active species like F, F2, and CFn, generated within the surface discharge plasma. As the Ar/CF4 gas flow rate increases, not only does the generation of these active species conducive to etching rise, but they also diffuse more extensively due to the heightened gas flow rate, thereby bolstering etching in the width direction. Additionally, the effectiveness of removing etching byproducts from the plasma region to exterior regions via high gas flow before they can redeposit improves with increased gas flow rate, thus expediting the silicon etching process. Under these experimental conditions, elevating the gas flow rate enhanced the etching efficiency of Si. However, surpassing a certain threshold in increasing the flow rate of the Ar and CF4 mixed gas to the plasma region could induce plasma instability, potentially diminishing both the etching rate and uniformity. Hence, optimizing the gas flow rate is pivotal in surface discharge plasma etching to guarantee consistent and efficacious outcomes.
# Enhancing Discoverability: A Metadata Framework for Empirical Research in Theses
## 7 Discussion
### Combining DDI and Dublin Core  
A hybrid approach is proposed, integrating selected DDI fields into a Dublin Core-based cataloging system. This strategy leverages the wide adoption and simplicity of Dublin Core while enriching it with targeted DDI elements that provide meaningful insights into the presence of empirical research. Only those DDI fields that add value to the discovery, classification, and retrieval of empirical theses are included, deliberately avoiding unnecessary complexity.  
The goal is not to create a new exhaustive standard, but to enhance systems like _Ellanikos_ with metadata that can enable users to identify whether a thesis includes empirical research and to what extent. This allows users to locate not only the text of a thesis but also, when available, any supporting data or materials.  
In this envisioned system, metadata entry will be carried out either by the students themselves--who authored the work--or by designated administrative personnel. These individuals do not need to be research experts, as the recording form and metadata model are designed to be accessible and manageable for non-specialists.  
Ultimately, this hybrid model provides a practical compromise between detailed documentation and operational feasibility. It strengthens existing library infrastructures without imposing the burden of full empirical data curation on cataloging systems that were never intended for such tasks.
## 4 Discussion
### Markers and Marker Size
For assessing mouth and jaw movements, where a greater distance from the camera to the subject is anticipated, 5 mm markers are proportionally sized to be useful. Relatedly, in assessing leg and ankle movement or head movement, with a camera necessarily farther from the subject, 10 mm provides an effective size with an adequate working distance to visualize the desired body parts in the full viewing frame of the camera without cutoff of the image.
# Mobile Personal Multi-Access Edge Computing Architecture Composed of Individual User Devices
## 2 Proposed Mobile Personal Multi-Access Edge Computing Architecture
### Components to Utilize Mobile Device as MECS  
As shown in Figure 2, the components of the proposed scheme are the service requestor that requests the computing task, access MECS, which serves to generate task segmentation information by analyzing the requested task, and mobile devices, which act as a worker to perform tasks by receiving task segmentation information. In the proposed scheme, the mobile device used as the MECS is named mobile MECS (mMECS). The basic components and roles of the proposed scheme are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Description** \\ \hline Access MECS & Creates the segment information of the task requested from the service requestor. \\ mMECS & Performs task offloading based on the segment information. \\ Service Requester & Serve task offloading requests to the access MECS. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the elements of the proposed scheme.  
Figure 1: The design of the proposed mobile personal Multi-Access Edge Computing (MEC) architecture.  
Figure 2: The components and operation steps of the proposed scheme.  
The procedure for using MECS as the mobile device to provide fast response and continuous service is as follows. First, the service requester sends metadata of the task for requesting the task offloading to access MECS. The access MECS, which received the metadata, analyzes the requested task from the service requestor and generates segment information of the appropriate size. The generated segment information is transmitted to the mMECS, which has registered to be used as an MECS resource. Upon receiving of the segment information, mMECS receives the task segment from the service requestor and performs task offloading. The mMECS, which completed the task offloading of the task segment received from the service requestor, transmits the completed task to the access MECS. Access MECS, which has received the completed task segment from mMECS, reassembles the received task segment based on the segment information created by itself, and transmits the completed task to the service requester.  
In other words, the proposed scheme uses the mobile device as an MECS in addition to the existing MEC architecture, so that it is possible to provide continuous and rapid service delivery even when the number of users increases. In addition, if the requested content is frequently used and highly shareable, the content is stored in the storage of the cloud layer and provided to the mobile user. Then the mobile user can receive the service from the cloud quickly even if the mobile user frequently requests the content. The detailed operational process of dispersion and reassembly of the task requested for computing offloading is described in Section 2.2. Also, the detailed operational procedures of the proposed scheme are described in Section 2.3.
# Evaluation of Forward Models for GNSS Radio Occultation Data Processing and Assimilation
## 2 Experiments  
In this study, two experiments were conducted. The first experiment compared the algorithms together with interpolation methods. Subsequently, the most accurate integral and interpolation method was used in the second experiment, in which the refractivity profiles on levels of widely used analyses--FNL, EC4 Dvar, and ERA5--were calculated to the BA by FMs. Then, the bias of results was evaluated.
# Enhancing Cybersecurity Through Comprehensive Investigation of Data Flow-Based Attack Scenarios
## 3 Proposed Approach
### Research Design  
Our study employs a mixed-method approach, combining qualitative threat modeling with quantitative impact analysis. The research design consists of two main steps: **(1) Threat Intelligence** and **(2) Attack Impact Analysis**. An overview of the proposed approach is shown in Figure 2. We systematically analyze the threat landscape, quantifying the probability of actions from threat lists to attack scenarios.  
In the first step, **Threat Intelligence**, the STRIDE framework and Microsoft Threat Modeling tool are employed to capture security properties by generating a threat list, introducing an impact enumeration, and generating an attack profile. The attack profile, acting as a baseline, identifies potential attack surfaces and an attack scenario for enhanced connectivity. This service is offered as an API, establishing a standardized connection or interface with other software in the pipeline. A detailed explanation of this step is provided in the following Section 3.2.  
In the second step, **Attack Impact Analysis**, we take a closer look at the attack scenarios from the previous step, which entails assessment of the potential risks and vulnerabilities. By considering factors such as the severity, likelihood, and impact of these attacks, it is possible to gain insight into their potential impacts on the system. This assessment helps to prioritize the identified risks, allowing resources to be focused on eliminating the most critical vulnerabilities. Ultimately, this process strengthens the overall security  
Figure 2: Our proposed approach for enhancing cybersecurity through comprehensive investigation of data flow-based attack scenarios.  
posture of the system by supporting the development and implementation of proactive security measures.