esa-sceva/satcom-chunk-collection huggingface dataset:
# Event-Based Path-Planning and Path-Following
## 3 Two-Level Control System
### Event-Based Path-Planning
#### 3.3.1 Discrete-Event System
Because it is not possible to implement continuous checking of event-triggering conditions, we cannot prevent situations when \(R_{\text{max}}^{i}<R_{\text{min}}\). When such situations happen, the AUV must activate a special emergency mode in which it slows down the forward speed and starts turning in one of two possible directions, which is selected based on the last state of the DES. For example, if the last state of the DES is mDOL, which corresponds to avoiding an obstacle on the left, the AUV should turn in the left direction. Once \(R_{\text{max}}^{i}>R_{\text{min}}\) is valid for all \(i\), the AUV returns to the last normal operating mode with planning a new path if necessary. The emergency mode is not desirable, and the AUV should avoid it. The use of this mode can be restricted and, in many cases, eliminated, by temporarily relaxing the requirements for safe distance \(D_{s}\) when planning a path to bypass an obstacle, or increasing the frequency of checking the triggering condition for composite events requiring the calculation of \(R_{\text{max}}^{i}\), or increasing the value of parameter \(\triangle R\) included in some event-triggering conditions. The AUV should act in a similar way when it is too close to an obstacle. In this article, we do not investigate the implementation of the emergency mode and apply the above mechanisms to exclude its use.
# Inorganic Salt Hydrate for Thermal Energy Storage
## 1 Introduction  
With the growing consumption of fossil fuels and increasing emission of carbon dioxide, thermal energy storage (TES) has received a great deal of attention. Latent heat storage and sensible heat storage are two main forms of TES. Using phase change materials (PCMs) for latent heat storage, which can storage and release energy by melting and solidification, is becoming an effective way to solve the contradiction of supply and demand of energy, such as peak difference of power load and gap of solar energy [1; 2]. High storage density, stable performance, wide phase change temperature range and reasonable price make PCMs play a significant role in energy conserving areas.  
In the field of phase change energy storage technology, it is a fundamental step to develop high latent heat, stable, and cost-effective materials. Different types of PCMs have been studied after petroleum crisis in 1970s, including solid-solid PCMs (polyalcohols and other polymers), solid-gas PCMs and solid-liquid PCMs (salt and salt hydrates, clathrate hydrates, paraffins, fatty acids and their mixtures, alloys and ect) [3; 4]. Among these, salt hydrates, which account for a large proportion in inorganic PCMs, have always been attracted attention owing to their reasonable price, wide sources, good thermal conductivity and high volumetric energy storage density. However, some defects, such as leakage, causticity, supercooling and phase separation impose restrictions on their practical applications [5; 6; 7]. In order to reduce such adverse effects, form-stable PCMs are studied recent years by incorporating PCMs into porous materials or through the method of microencapsulation to prevent leakage and phase separation [8; 9; 10]. Moreover, different kinds of nucleating agents have also been developed to reduce supercooling degree.  
It is a promising direction to apply low-cost inorganic PCMs to energy conservation areas such as electric peak-shaving, industrial waste heat utilization, aerospace field, air conditioning cold storageand so on. This paper reviews the research progress of salt hydrates for TES including properties, improvements as well as their specific characteristics, which are suitable for practical application in different fields in recent years. Research trends and potentials of these PCMs are also prospected.
## 3 Results and Discussion
### Functionalization of Aliphatic Amine
#### 3.1.3 Effect of Reaction Temperature on Amine Density  
The amine functionalization was carried out at 60 to 80 \({}^{\circ}\)C to study the effect on the AD. The effect of temperature was also applied to the EDA, DETA, TETA, and TEPA. The results attained exhibited in Table 3. The AD increases marginally as the reaction temperature increase from 60 to 80 \({}^{\circ}\)C for EDA, DETA, TETA, and TEPA aliphatic amine. The EDA achieve the AD of 3.85 mmol/g\({}^{\text{-ad}}\) at 60 \({}^{\circ}\)C and increases to 3.93 mmol/g\({}^{\text{-ad}}\) at 80 \({}^{\circ}\)C. A similar trend was observed with DETA, TETA, and TEPA as well. The thermal energy accelerates the reaction at a high temperature above 60 \({}^{\circ}\)C, which leads to an increased migration rate of the amine group onto the epoxy group of GMA [26]. In contrast, AD gradually reduces as the chains of the aliphatic amine grow longer from EDA to TEPA at the same course temperature. At reaction temperature of 60 \({}^{\circ}\)C, AD of 3.85, 3.31, 2.97, and 2.49 mmol.g\({}^{\text{-ad}}\) were attained for functionalization with EDA, DETA, TETA, and TEPA aliphatic amine, respectively. This agrees well with the previous observation at different experimental conditions. In addition, since the introduced amine groups were polymerized, no significant decomposition was observed in the high-temperature reaction. Conclusively, the bulky structure of aliphatic amine restrains efficiency of the functionalization predominantly regardless of any of the changes in experimental conditions. Thus, the higher AD could be achieved by using the smallest and simplest aliphatic amine, EDA.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**100\%**} & \multicolumn{2}{c}{**150\%**} & \multicolumn{2}{c}{**200\%**} & \multicolumn{2}{c}{**250\%**} \\ \hline
**Aliphatic Amines** & **Dg (\%)** & **AD (mmol/g\({}^{\text{-ad}}\))** & **Dg (\%)** & **AD (mmol/g\({}^{\text{-ad}}\))** & **Dg (\%)** & **AD (mmol/g\({}^{\text{-ad}}\))** & **Dg (\%)** & **AD (mmol/g\({}^{\text{-ad}}\))** \\ \hline
**EDA** & & 3.35 & & 2.36 & & 2.34 & & 1.64 \\ \hline
**DETA** & & 3.54 & 151.46 & 3.03 & 205.58 & 2.53 & 251.32 & 1.92 \\ \hline
**TETA** & & 4.01 & & 3.43 & & 2.70 & & 2.00 \\ \hline
**TETPA** & & 4.20 & & 3.64 & & 3.09 & & 2.26 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effect of different grafting yield of GMA-g-NWF on amine density at 60 \({}^{\circ}\)C for 2 h, 70% amine: 30% isopropanol.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **60 \({}^{\circ}\)C** & **70 \({}^{\circ}\)C** & **80 \({}^{\circ}\)C** \\ \cline{2-4}
**Aliphatic Amines** & **AD (mmol/g\({}^{\text{-ad}}\))** & **AD (mmol/g\({}^{\text{-ad}}\))** & **AD (mmol/g\({}^{\text{-ad}}\))** \\ \hline
**EDA** & 3.85 & 3.90 & 3.93 \\ \hline
**DETA** & 3.31 & 3.50 & 3.52 \\ \hline
**TETA** & 2.97 & 3.25 & 3.28 \\ \hline
**TETA** & 2.49 & 2.65 & 2.71 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of different reaction temperatures on amine density at for 2 h, 70% amine: 30% isopropanol, \(D_{\text{g}}=\) 105.93%.
# Recent Progress on Vegetation Remote Sensing Using Spaceborne GNSS-Reflectometry
## 4 Model Simulations and Discussion  
As for the spaceborne GNSS-R vegetation study, the existing research is mostly based on observational data to establish the sensitivity analysis. It is generally in the research stage of qualitative analysis and lacks the discovery of the physical mechanism behind it. Since GNSS-R is a bistatic radar with separate transceivers, the existing microwave scattering models for monostatic radars or radiometers are no longer applicable.  
Therefore, it is necessary to improve the traditional backscattering and emissivity models to obtain a microwave scattering mechanism model suitable for GNSS-R. According to the existing problems in GNSS-R vegetation remote sensing, this part will combine the previous developed microwave vegetation scattering models to analyze its scattering characteristics and discuss the future development trends [51; 52; 53; 54]. Some basic information of the employed models is provided in Section 4.1. While more detailed information of the model is given in the literature [54], it is worth mentioning that although there are many theoretical simulators as presented in Section 2.4, fewer works give detailed comparisons of vegetation parameters (such as vegetation density, water content, etc.) on the bistatic scattering properties at various polarizations. While the bistatic scattering properties provide the basic information for parameters retrieval, they need to pay more attention to simulations; therefore, we will give more details on the simulations in this section. We will also give a brief introduction to the model we used in GNSS-R.
# Numerical Simulation Research on Plane Alignment Parameters of Desert Highway
## 1 Introduction
The embankment flow-field of different curve elements were numerically simulated, and the influence of the angle and curve elements on airflow velocity was systematically studied. In the present work, after comparing and verifying the sand accumulation situation on the highway, the design points of the plane alignment parameters are summarized, which provides a reference for the selection of highway routes in desert areas.
# Fault Feature Extraction of Parallel-Axis Gearbox Based on IDBO-VMD and t-SNE
## 6 Experimental Verification
### \(t\)-SNE Dimensionality Reduction Effect  
To validate the dimensionality reduction effect of t-SNE, this study selected the Iris dataset provided by the SKLearn library for dimensionality reduction. The Iris dataset consists of three different species of iris flowers, with 150 samples for each flower type and four features for each sample. The PCA, LLE, and t-SNE algorithms were used for dimensionality reduction, and the visualization results of each method are shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**State** & **IMF\({}_{1}\)** & **IMF\({}_{2}\)** & **IMF\({}_{3}\)** & **IMF\({}_{4}\)** & **IMF\({}_{5}\)** & **IMF\({}_{6}\)** \\ \hline Normal & 3.25 & 4.29 & 3.82 & 4.20 & 3.49 & 3.89 \\ Breakage & 2.44 & 2.95 & 3.20 & 2.89 & 4.26 & \\ Fitting & 3.15 & 3.44 & 3.58 & 4.41 & & \\ Cracking & 2.66 & 4.13 & 3.25 & 4.84 & 5.22 & \\ Wear & 2.89 & 13.95 & 15.22 & 8.79 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Kurtosis values of five signal IMF components.  
Figure 8: Gear surface crack fault signal spectrum.  
From Figure 9, it is evident that, through dimensionality reduction of the Iris flower dataset, PCA exhibits a suboptimal intraclass clustering effect. LLE, while preserving the inherent manifold structure, fails to separate different types effectively, resulting in poor intraclass clustering. Conversely, after dimensionality reduction using t-SNE, there is noticeable intraclass cohesion and interclass separation, rendering it the most visually effective. Although t-SNE does not exhibit a significantly superior separation of different types compared to PCA, it notably outperforms PCA in terms of intraclass cohesion. Therefore, t-SNE was chosen for dimensionality reduction of the fault dataset.
## 2 Materials and Methods
### Equilibrium Swelling and Rubber Gel Content  
To determine the swelling degree, a small rubber piece (150-200 mg) was swollen for 24 h at RT in toluene. The swelling degree was calculated according to Equation (2), where the weight of the swollen rubber piece (\(w_{\text{a}}\)) was related to its initial weight (\(w_{\text{b}}\)). Subsequently, the swollen sample was dried for 24 h at 50 \({}^{\circ}\)C, which gave the dried weight (\(w_{\text{d}}\)) of the compounds. For the calculation of the gel content, the composition of the vulcanizate was divided into initial rubber weight (\(w_{\text{r}}\)), the weight of insoluble (\(w_{\text{insoluble}}\)) including fillers, resin, and coagents, and weight of solubes (\(w_{\text{solubles}}\)) including process oil and 40% peroxide.  
The rubber gel content was calculated according to Equation (3)  
\[\text{Swelling degree}\ (\%)=\left(\frac{w_{\text{a}}}{w_{\text{b}}}-1 \right)\times 100 \tag{2}\]  
\[\text{Rubber gel content}\ (\%)=\frac{w_{d}-w_{insolubles}}{w_{r}}\times 100 \tag{3}\]  
The cross-link density (\(M_{\text{c}}\)) was calculated according to a modified Flory-Rehner relationship [26; 27]. This relationship includes the consideration of the volume fraction of the rubber in the swollen gel (\(v_{r}\)), which is calculated according to Equation (4), where \(\alpha\) refers to the weight loss of rubber compounds during swelling, \(\rho_{s}\) to the density of toluene,and \(\rho_{r}\) to the density of EPDM rubber. The weight fraction of the elastomer network, comprising EPDM, coagent, and peroxide, is indicated by \(\phi\).  
\[v_{r}=\frac{w_{b}\times\Phi\frac{1-\alpha}{\rho_{r}}}{w_{b}\times\phi\frac{1- \alpha}{\rho_{r}}+\frac{w_{a}-w_{d}}{\rho_{s}}} \tag{4}\]  
The calculated value for \(v_{r}\) is further used to determine \(M_{c}\) of the rubber compounds according to Equation (5). \(n_{w}\) refers to the network chain density, \(f\) to the functionality of cross-links (which is set to 4), \(V_{s}\) the molar volume of the solvent, and \(\chi\) the Flory-Huggins interaction parameter between rubber and solvent.  
\[M_{c}\Big{(}\frac{g}{mol}\Big{)}=\frac{1}{n_{sw}}=-\frac{\rho_{r}V_{s}\Big{(}1 -\frac{2}{f}\Big{)}\Big{(}v_{r}^{\frac{2}{f}}-0.5v_{r}\Big{)}\left(1.3-0.4v_{r }\right)}{\ln(1-v_{r})+v_{r}+\chi v_{r}^{2}} \tag{5}\]  
The obtained results were collected from three specimens and given as a mean value.
## 4 Experiment
### Data Preparation
#### 4.1.2 Application of Image Augmentation Techniques  
Image augmentation parameters have a significant impact on the overall image quality, which in turn affects model performance. In this study, these parameters were selected through an iterative trial-and-error approach aimed at producing realistic, yet varied augmented images. The final parameter settings, along with justifications for each choice, are provided in Table 1. Geometric transformations--including scaling, rotation, perspective shifting, translation, and shearing--can push portions of an image beyond its original boundaries, creating voids or overlaps. Since deep learning typically demands input images of uniform dimensions [33], any empty regions were filled with a white pixel value (255), ensuring that all augmented images retained consistent sizing. Figure 14 presents examples of original images alongside their augmented counterparts.  
According to Table 1, the original London training set contained 740 images. After applying seven augmentation techniques, that figure grew to 5180 augmented images, reaching 5920 when combined with the non-augmented set. To illustrate the impact of these augmented datasets, the study compared the model accuracy achieved with only the original training data versus the augmented data.  
Figure 13: Examples of unusable images.
## 5 Implementation and Experiments
### Assessment through Personalized Content
#### 5.2.3 Experiment of Personalizing Coding Exercises for Domains of Interests  
This experiment generates coding exercises using 'inheritance' for a student in the eCommerce domain and for another student in the finance domain. The coding exercise for the student in eCommerce is shown in Figure 15.  
As shown in the exercise problem, the three classes, _Product_, _ElectroicsProducts_, and _ClothingProduct_ are entities in the eCommerce domain. In addition, the methods of each class are the common operations, such as _display_product_info()_, commonly used in the eCommerce domain. Hence, users in the eCommerce domain should feel comfortable understanding the exercise.  
Now, the coding exercise generated for the student in the finance domain is shown in Figure 16.  
Figure 14: Generated example for a student in finance.  
The generated coding exercise is about managing banking accounts, including a savings account and a checking account. And, the methods of each class, such as deposit() and withdraw(), are common operations in banks. Hence, this exercise problem is intuitive and easy to understand for users in the finance domain.  
Through the two contents of coding exercises generated for different domains, we can clearly observe that the GPT content for generated coding exercises is well personalized for each domain of interest.
## 3 Results
### Characterization Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **HR** & **SDNN** & **RMSSD** & **N20** & **N50** & **LF** & **HF** & **LHR** \\ \hline Manual & 0.652 & 0.044 *\(\downarrow\) & 0.042 *\(\downarrow\) & 0.075 & 0.039 *\(\downarrow\) & 0.064 & 0.138 & 0.112 \\ Auto & 0.886 & 0.020 *\(\downarrow\) & 0.019 *\(\downarrow\) & 0.892 & 0.014 *\(\downarrow\) & 0.147 & 0.212 & 0.591 \\ \hline \hline \end{tabular}  
* * \(p\) < 0.05, \(\downarrow\) indicates a negative correlation.  
\end{table} 
Table 4: The significant results of the ECG indicator. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Channel**} & \multicolumn{2}{c}{**Theta**} & \multicolumn{2}{c}{**Alpha**} & \multicolumn{2}{c}{**Low Beta**} & \multicolumn{2}{c}{**Theta**} \\ \cline{2-9}  & **Manual** & **Auto** & **Manual** & **Auto** & **Manual** & **Auto** & **Manual** & **Auto** \\ \hline Oz & 0.048 *\(\uparrow\) & 0.517 & 0.974 & 0.894 & 0.701 & 0.862 & 0.013 *\(\uparrow\) & 0.574 \\ O1 & 0.031 *\(\uparrow\) & 0.421 & 0.706 & 0.556 & 0.229 & 0.393 & 0.004 **\(\uparrow\)** & 0.756 \\ O2 & 0.078 & 0.219 & 0.681 & 0.704 & 0.673 & 0.894 & 0.102 & 0.442 \\ \hline \hline \end{tabular}  
* * \(p\) < 0.05, ** \(p\) < 0.01, \(\uparrow\) indicates a positive correlation, \(\downarrow\) indicates a negative correlation.  
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Head Heading** & **Head Pitch** & **Eyelid Opening** & **Pupil Meter** \\ \hline Manual & 0.006 **\(\uparrow\)** & 0.810 & 0.232 & 0.141 \\ Auto & 0.413 & 0.914 & 0.125 & 0.014 *\(\uparrow\) \\ \hline \hline \end{tabular}  
* \(p\) < 0.05, ** \(p\) < 0.01, \(\uparrow\) indicates a positive correlation.  
\end{table} 
Table 3: The significant results of the eye movement indicator. Most ECG indicators that represented driver vigilance showed consistency across both driving modes. SDNN, RMSSD, and N50 consistently represent driver vigilance across both manual and automated driving modes.
# Replacement of Cobalt in Lithium-Rich Layered Oxides by n-Doping: A DFT Study
## 1 Introduction
The accumulation of these point defects leads to structural distortions upon cycling [26; 27], and to a monotonic decay in the electrode performance [28; 29]. A possible way to mitigate the structural rearrangements in LRLO is the incorporation in the transition metal blend of redox inactive metals, such as Al, Zr, Ti [30; 31; 32], or the substitution Li\({}^{+}\) with other alkali cations, e.g., K and Na [33]. Aside from these research trends, the search of innovative LRLO must tackle the challenge of the reduction of the cobalt content. In fact, cobalt removal in positive electrode materials for batteries has been identified by the EU and DOE [34; 35] as a key-goal to improve the environmental benignity of these energy storage device. However, any alteration of the metal blend in the transition metal layer has inevitable effects on the electronic and crystallographic structure of the LRLO, as well as on its thermodynamic stability, thus affecting the resulting battery performance. Here, we tackle the challenge to investigate the impact of the removal of cobalt on the electronic, crystallographic, and thermodynamic properties of a Li-rich layered oxide material with general formula of Li\({}_{1.2}\)Ni\({}_{0.2-x/2}\)Mn\({}_{0.6-x/2}\)Co\({}_{x}\)O\({}_{2}\) by density functional theory (DFT). We adopted a partially randomized 5 \(\times\) 2 \(\times\) 5/3 supercell, built from the hR12 lattice, to explicitly mimic (a) the full atomic disorder on the TM layers, and (b) the occurrence of a stacking fault along the hexagonal c-axis to break the local hR12 lattice symmetry [16]. Our aim is to draw a comprehensive picture of the impact of the p-doping, indirectly induced by the substitution of cobalt with manganese and nickel, on a realistic LRLO lattice, without relevant constraints on symmetries. First principles modeling studies on LRLOs have been reported by [PERSON] et al. [36] and [PERSON] et al. [37], tackling the role of oxygen vacancies and dopant elements on the electronic structure and crystal stability of these partially disordered oxides. In both cases, authors described the transition metal layer using regular alternated motifs among Li/Mn/Co/Ni atomic species to mimic the metal blend. Here, we adopt a different strategy, by applyingthe special quasi-random structure (SQS) approach to mimic through a cluster expansion formalism a fully disordered occupancy of the atomic sites in the transition metal layer. Our method allows a realistic representation of disorder without pre-determined structural motifs or postulated clustering of similar atomic species. To our knowledge, this is the first attempt to apply SQS to DFT calculations on LRLOs.
###### Abstract
These models can extract medical concepts, suggest differential diagnoses, and produce natural-language rationales for clinical decisions. However, integrating LLMs into latency-sensitive CDSS workflows introduces new technical challenges: high computational costs and validation of medical reasoning. In parallel with AI advancements, the growing scale and complexity of clinical data have necessitated the use of big data infrastructure. Modern CDSS platforms must ingest, index, and retrieve data at scale across modalities--from live-streamed vital signs to historical lab results. Technologies such as distributed file systems, parallel processing engines (e.g., Spark [12]), and information retrieval frameworks have become integral in managing these volumes. The proposed architecture leverages such technologies for efficient data fusion and fast access to relevant patient or cohort records, which are both key to low-latency triage. This paper introduces a modular, triage-focused CDSS architecture that integrates LLMs, FL, and big data concepts in a privacy-preserving and adaptable framework. The architecture comprises multiple layers, including structured and unstructured data ingestion, federated model training, LLM-based reasoning, Decision Support Engines (DSE), and clinician-facing interfaces. The system is designed to scale across hospital networks, adapt to local practice variations, and comply with FAIR (Findable, Accessible, Interoperable, Reusable) data principles. We present a two-part evaluation to validate the performance of the system. First, we benchmarked our fine-tuned LLMs with a curated set of United States Medical Licensing Examination (USMLE) [13] questions to assess general clinical-reasoning capabilities. Second, we constructed an infectious disease cohort from the MIMIC-III dataset and applied the CDSS pipeline to both large-scale and expert-reviewed cases to assess real-world triage functionality. From an initial pool of 6 111 de-identified emergency department (ED) vis its whose diagnoses lay within ICD-9 [14] codes 001-139 or the severe-sepsis extensions 995.91/995.92, we retained 132 encounters after applying the full preprocessing pipeline (hash-based de-identification, triage-note normalization, vital-sign cleaning, and completeness checks). These 132 records--each with a time-stamped triage note--form the labeled infectious disease cohort used for downstream modeling and expert review. The remainder of the paper is organized as follows: Section 2 (Materials and Methods) describes the proposed architecture, data sources, and core system components. Section 3 (Results) reports the dual evaluation--USMLE-style benchmarking and the Infectious Disease case study conducted on a curated MIMIC-III cohort. Section 4 (Discussion) analyzes the strengths of the system, limitations, and future directions. Finally, Section 5 (Conclusions) summarizes the main contributions and outlines real-world applicability.