esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of the Battery Pack Heat Dissipation Structure of a Battery-Type Loader
## 3 Numerical Simulations and Experiments  
### Simulation Model and Boundary Conditions  
#### 3.1.1 Simulation Model  
To carry out the simulation and analysis, division of the battery pack model into a grid was necessary. In hydrodynamics, the types of grids used can be divided into structured and unstructured grids. A structured grid is not suitable for complex structures, while an unstructured grid can automatically select the topology according to the structural characteristics of the model, which is more efficient. Therefore, we adopted an unstructured grid to partition the battery pack model.  
The ICEM software was used to grid the battery pack structure. As the battery pack and the air inlet and outlet are rectangular structures, an unstructured grid dominated by a hexagonal grid was used for partitioning. At the end of the delineation, the number of grid sections reached 1,538,146; the results of the grid delineation are shown in Figure 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline
**Battery Type** & LiFePO\({}_{4}\) \\
**Nominal voltage** & 3.2 V \\
**Operating voltage range** & 2.5-3.65 V \\
**Rated capacity** & 277 Ah \\
**Single cell size** & \(72\times 174\times 200\) mm \\
**Maximum sustained charge/discharge rate** & 1 C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Battery parameters.  
Figure 1: Battery pack heat dissipation structure: (**a**) battery pack location (**b**) battery pack internal structure.  
After griding, a quality check was performed on the divided grid. As shown in Figure 3 the determinant and aspect ratio of the grid were checked. The grid determinant check is shown in Figure 3 below.  
Generally speaking, most solvers can accept a value of 0.3 or more for the grid determinant. As seen in the Figure 3, the determinant was 0.36594 at minimum and 1 at maximum, such that the grid satisfied the determinant requirements.  
The aspect ratio check for the grid is shown in Figure 3, although different grid cells require different calculation methods; for solvers in general, the aspect ratio should be greater than 0.2, and an aspect ratio equal to 1 is best. As shown above Figure 3, the minimum aspect ratio was 0.371313 and the maximum was 0.99978, so the grid quality was generally better.  
From the above analysis, the quality of the grid divided in this way was relatively good and was considered to meet the simulation requirements.
# Digital Twin Framework for Road Infrastructure Management
## 2 Methodology  
This study employed a methodology that involved thoroughly reviewing and analyzing the existing literature to address the research questions. The main methodological flow used in this paper consists of four key steps, as illustrated in Figure 1.  
* Step 1: This step entails identifying and reviewing appropriate papers for the study. We searched relevant scholarly databases and other online sources, selecting papers that applied DTs in the road infrastructure domain using a layered approach, whether conceptual or implemented/validated. It is important to note that not all reviewed papers utilized a layered approach when applying DTs; those that did not were excluded from this study. We aimed to explore four domains related to road infrastructure to establish a comprehensive framework for DTs, including the following: (a) the road itself, focusing on pavement, network management, transportation planning, and civil infrastructure management (totaling 8 papers); (b) road bridge maintenance and engineering (totaling 6 papers); (c) road tunnel management (totaling 5 papers); and (d) other civil infrastructures and urban DTs (totaling 7 papers). The final domain, \"d\", is included in addition to domains \"a\", \"b\", and \"c\" in order to ensure and enhance inclusivity and extend the framework to network- and city-level management.
* Step 2: In this step, we thoroughly reviewed and analyzed the selected papers with respect to their application areas and purposes, as well as the DT layers/structures, their functional components/elements, and the relationships between these layers.
* Step 3: This step involves the derivation and generalization of DT layers based on the findings from Steps 1 and 2, using selected criteria to establish a comprehensive DT framework that facilitates connected DTs at the network-to-city level.
* Step 4: In this step, we propose a DT framework or reference model/architecture for road infrastructure management, consisting of five layers. The framework outlines the functional elements and classifications for each DT layer and describes the flow between layers. The comprehensive framework proposal is followed by a comparison with a prominent and widely referenced manufacturing DT framework, the ISO 23247-2, to enhance the understanding of this work. Additionally, we present an implementation scenario for the proposed framework to demonstrate its applicability in the field.  
Overall, steps 1 and 2 focus on exploring RQs 1 and 2, while steps 3 and 4 address RQs 3 and 4, respectively.
# Elevation and Mass Changes of the Southern Patagonia Icefield Derived from TanDEM-X and SRTM Data
## 2 Materials and Methods
### Glacier Outline Mapping  
Different techniques for an automated glacier mapping with optical satellite imagery have been successfully applied in various glaciated regions [29; 30; 31]. Since optical sensors are restricted by cloud-cover and glacier mapping requires a low coverage of seasonal snow, the choice of suitable satellite scenes at the end of the ablation period is limited. Hence, several Landsat-5 TM satellite scenes from the period 1 March 2000-7 May 2001 served as basis for a semi-automatic mapping covering the entire SPI (see Supplementary Materials Table S1). Each scene was checked for its orthorectification quality. Ratio images were calculated for bands TM3/TM5 dividing the raw digital numbers (DN) and were converted to a binary mask using different threshold values. Subsequently, the resulting raster was vectorized to generate glacier polygons. To obtain the most suitable threshold value, we compared the glacier polygons to a false color composite of channels 5-4-3 (mid-infrared, near infrared, red). Rationing the red and mid-infrared channel provided the advantage that glaciers situated in cast shadows could be better delineated than by using a ratio of near-infrared and mid-infrared (TM4/TM5) [32]. Waterbodies that were mistakenly classified as glacier and snow were excluded superimposing water bodies from the HydroLAKES database [33] that precludes lakes >0.1 km\({}^{2}\). A DEM derived mask was used to exclude the sea surface in fjords, discarding glaciers <0.25 km\({}^{2}\). In the next step, some manual correction was still required, especially regarding the separation between seasonal or perennial snow and small glaciers. We used a temporal consistency test to identify and erase annual snowfields. For this we reviewed all available Landsat scenes since the beginning of the Landsat missions (acquisition date summer and autumn) and compared them to the inventory. The arrangement of scenes with different acquisition dates permitted the manual correction for debris-covered glaciers, as some glaciological structures i.e., flow patterns, novel water discharge structures, expanding melting ponds and collapse structures were visible. As a final step, the glacier outlines were subdivided into glacier catchments by extracting the ridgelines and glacier basins using the gap-filled SRTM-DEM. In order to maintain compatibility to the Randolph Glacier Inventory Version 5 [17] a spatial join with its attribute was performed to preserve names and index numbers according to the inventory (cf. Figure 2, titles of plots c-h).
# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 2 Related Work
### Classification in Medical Image Processing  
Deep learning techniques have been widely utilized in medical image classification in the context of pulmonary medical images [10]. The application of deep learning in medical image processing has been particularly significant during the COVID-19 pandemic, with research focusing on utilizing deep learning for processing medical images related to the coronavirus outbreak [11]. Deep learning models have shown promise in medical science, particularly in the field of medical image processing for the diagnosis and prognosis of life-threatening ailments such as lung cancer [12]. Furthermore, the integration of deep learning tools with Internet of Health Things (IoHT) devices has been proposed, to develop a computational intelligence framework for online medical image recognition, including lung nodule images for malignant classification [13]. Deep learning approaches, specifically convolutional neural networks, have been compared for the classification of lung biopsy images, highlighting the automation of biopsy image analysis for cancer diagnosis [14]. A machine learning approach has been employed to diagnose lung and colon cancer, using a deep learning-based classification framework, achieving high accuracy in identifying cancer tissues [15]. Radiomics, which involves extracting features from medical images for automated classification, has been explored in the context of lung cancer screening, emphasizing the challenge of building a cross-validated model based on radiomic signatures [16]. Distant domain transfer learning has been proposed as a method to effectively handle distribution shifts in medical imaging tasks, achieving high classification accuracy compared to existing algorithms [17]. Machine learning and image processing techniques have been utilized for accurate lung cancer classification and prediction, incorporating noise reduction, feature extraction, and analysis of damaged regions in lung images. Moreover, a novel self-supervised pre-training pipeline, Multi-task Self-supervised Continual Learning (MUSCLE), has been developed for multiple medical imaging tasks using X-ray images from various body parts, including lungs [18]. This approach addresses challenges such as data heterogeneity, overfitting, and catastrophic forgetting in multi-task learning scenarios. Overall, the literature highlights the significant role of deep learning and machine learning techniques in medical lung image processing for classification, diagnosis, and prognosis of lung-related ailments.
# QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques
## 4 Methodology
### Ensemble Techniques
#### 4.1.3 Gradient Boosting Tree  
The GBT is also an ensemble technique that uses a boosting method to improve a decision tree (DT), with the concept of combining weak models into a single strong consensus model. In GBT, the task of each tree is to reduce the error of the previous tree rather than develop a new optimized tree. The final model aggregates the outcomes of the previous step, thereby obtaining the notion of a stronger learner [25].
## 3 Results and Discussion
Figure 6: The RF performance for asymmetric SPD where L\({}_{2}\) = 1.2 \(\upmu\)m = 2 L\({}_{1}\): (**a,b**) are the oscillation current waves for the single-2 DEG-channel SPD and triple-2 DEG-channel SPD respectively; (**c,d**) are the spectrums for the single-2 DEG-channel SPD and triple-2 DEG-channel SPD respectively. Based on the research above, we reach the conclusion that in the asymmetric SPD, when L\({}_{2}=n\)L\({}_{1}\), the total number of the harmonic waves generated is equal to \(n\). Highest output characteristics are received at the N\({}_{\text{th}}\) harmonic component and its frequency is mainly determined by the L\({}_{1}\).
## 4 Conclusions  
The analysis of electronic parameters revealed that cyanidin, delphinidin, and petunidin have LUMO energies lower than the conduction band edge of the TiO\({}_{2}\) electrode, making them suitable candidates for electron injection into the semiconductor's conduction band. In terms of absorption properties, delphinidin and petunidin exhibited the highest excitation energies and oscillator strengths in both gas and ethanol phases, suggesting their potential for efficient light absorption.  
The photovoltaic properties results demonstrated that petunidin and delphinidin possess superior attributes. They exhibited the highest LHE values, indicating their ability to efficiently capture and convert incoming light into excited states. Peonidin showed a higher \(\Delta G^{injected}\) value, making it suitable for improving electron injection and charge transfer processes, thereby enhancing the short-circuit current (\(J_{SC}\)) in DSSCs. Pelargonidin displayed excellent electron regeneration efficiency, indicating its capability to effectively regenerate electrons and support a steady current flow.  
In terms of open-circuit voltage (\(V_{OC}\)), petunidin and delphinidin exhibited the highest values, suggesting their potential to generate greater voltage outputs in DSSCs. Additionally, petunidin demonstrated a shorter excited-state lifetime, indicating a more stable excited state and efficient energy transmission. Furthermore, pelargonidin displayed the smallest energy gap and strong electron coupling, making it a favorable sensitizer for enhanced light absorption and rapid electron injection into the semiconductor's conduction band.  
In summary, these findings indicate that anthocyanin dyes, specifically petunidin, delphinidin, and pelargonidin, show potential as promising candidates for enhancing DSSC applications. Each dye exhibits a unique property that surpasses the others when evaluated individually. Consequently, a comprehensive understanding of these subtle properties, as explored in this study, can aid in selecting the most suitable dye for fabricating DSSCs, thereby contributing to enhanced device performance and energy conversion efficiency.  
P.G.: conceptualization, methodology, software, simulation, writing, original draft preparation, investigation, visualization; Y.B.: software, writing, review and editing; M.S.K.: conceptualization, writing, reviewing and editing; Y.-F.C.C.: writing--reviewing and editing; M.R.R.K.: supervision, software, writing, reviewing and editing, funding acquisition; R.T.: conceptualization, supervision, methodology, writing, reviewing and editing, funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Universiti Brunei Daussalam (GrantNox UBD/RSCH/19/FICBF(b)2022/017).  
All data used to support the findings of this study are included in the article.  
We, the authors, would like to thank the Government of Brunei Daussalam, the Universiti Brunei Daussalam, and the Wayamba University of Sri Lanka for their continuous support.  
The authors declare no conflicts of interest.
###### Abstract  
The present study aimed to ascertain how the volatile compounds changed throughout various processing steps when producing a smoke-flavored sea bass (_Lateolabrax maculatus_). The volatile compounds in different production steps were characterized by headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS). A total of 85 compounds were identified, and 25 compounds that may be considered as potential key compounds were screened by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). Results indicated that aldehydes were the major volatile compounds throughout the processing. The characteristic volatile compound in fresh samples was hexanol, and curing was an effective method to remove the fishy flavor. The concentration of volatile compounds was significantly higher in dried, smoked, and heated samples than in fresh and salted samples. Aldehydes accumulated because of the drying process, especially heptanal and hexanal. Smoke flavoring was an important stage in imparting smoked flavor, where phenols, furans and ketones were enriched, and heating leads to the breakdown of aldehydes and alcohols. This study will provide a theoretical basis for improving the quality of smoke-flavored sea bass products in the future.  
smoke-flavored sea bass; volatile compounds; HS-SPME-GC-MS; HS-GC-IMS; smoke flavoring H.G-GC-IMS. _Foods_**2022**, 11, 2614.
## 3 Presentation and Discussion of the Results  
### Ultrasonic Pulse Velocity Test  
Figure 15 illustrates the graph with the correlation obtained between the ultrasonic pulse velocity (V) and the average SCC compressive strength (f\({}_{\text{cm}}\)), as well as the corresponding exponential curve (continuous curve) used to fit the results with a correlation coefficient of about 0.97. Figure 16 compares the obtained correlation for SCC from Figure 15 to that obtained by [PERSON] and [PERSON] [4; 9] for  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Series** & **Age (days)** & **P (kN)** & **S\({}_{\text{d}}\) (kN)** & **C\({}_{\text{v}}\) (\%)** \\ \hline P1 & 1 & 26.81 & 1.50 & 5.58 \\ P2 & 2 & 34.51 & 1.50 & 4.34 \\ P3 & 3 & 35.69 & 1.13 & 3.15 \\ P7 & 7 & 43.20 & 1.29 & 2.98 \\ P14 & 14 & 47.74 & 3.23 & 6.76 \\ P28 & 28 & 53.07 & 4.27 & 8.04 \\ P94 & 94 & 60.96 & 2.25 & 3.69 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Pull-out test results.  
Figure 14: Pull-out test: (**a**) and (**b**) concrete cone trunk fragments.  
NVC, with identical materials but with maximum coarse aggregate size of 25 mm. From Figure 16, it can be stated that a slight difference exists between the correlations. The small observed deviations are probably due to the differences of properties of the elastic medium because of the different mortar/coarse aggregate ratios. However, the results seem to show that for NVC the ultrasonic pulse velocity test loses sensitivity for the estimate of the concrete compressive strength for speeds above 4.6 km/s, while for SCC the same is observed to occur for higher speeds, from about 4.8 km/s. By analyzing the results presented in Table 4, it can be observed that S\({}_{\text{d}}\) varied from 0.011 to 0.063 km/s with the average being 0.029 km/s. These results are similar to those obtained in NVC of compressive strength up to 82 MPa [4; 9], the correlation of which is shown in Figure 16, namely S\({}_{\text{d}}\) shows a variation from 0.006 to 0.086 km/s and a mean value of 0.028 km/s. Likewise, for SCC, the C\({}_{\text{v}}\) varied from 0.2 to 1.3% with the average being 0.6% (Table 4). These results are similar to those for NVC [4; 9], where C\({}_{\text{v}}\) shows a variation between 0.1 to 1.9% with the average being 0.6%. In a previous analysis on repeatability undertaken by [PERSON] and [PERSON] [10], bringing together NVC and SCC results, no evidence was found to conclude about the statistical parameter (S\({}_{\text{d}}\) or C\({}_{\text{v}}\)) that better represents the repeatability of ultrasonic pulse velocity test.
## 4 Description of the Proposed Topology Construction
### Basic Structure of the Proposed Logical Topology  
The features of the basic structure of the proposed logical topology are listed below.  
1. All the deployed sensor nodes in the target field take part in the logical topology construction process.
2. The proposed logical topology consists of multiple chains. Hence, the topology is called multiple chain oriented topology. These chains are called lower-level chains.
3. All the chains of the proposed topology are simple chains, rather than complex chains. A simple chain is defined as a chain where each member node of the chain has, at the most, two neighbouring nodes. On the other hand, a member node may have more than two neighbouring nodes in a complex chain. Figure 3 shows an example of both simple chain and complex chain. Note that in Figure 3 the member node \(C_{2}\) has four neighbouring nodes--\(C_{1}\), \(C_{3}\), \(C_{4}\), and \(C_{5}\).
4. In a lower-level chain, the distances between any two successive nodes are called links. Thus, a chain that consists of \(n\) number of sensor nodes has \((n-1)\) links. The sum of these \((n-1)\) links is the length of that chain.
5. The length of each chain of the proposed topology is similar. As it is assumed that the sensor nodes are deployed randomly in the target field, constructing multiple chains having exactly the same length may not always be possible. However, the proposed logical topology creates chains of similar lengths to avoid uneven energy consumptions by chains of dissimilar lengths.
6. For each chain, a member node of the chain is elected as the leader of the chain. These leaders are called lower-level leaders.  
7. The lower-level leaders construct a higher-level chain. Similarly, a member node of the higher-level chain is elected as the leader of the chain. This leader is called the higher-level leader.  
A sample architecture model of the proposed logical topology is depicted in Figure 4. This figure shows the logical topology using two hierarchical layers.
## 4 Conclusions  
In this study, composite-modified asphalt composed of silane coupling agent pretreated recycled GF from WTB and SBS-modified asphalt was investigated in terms of modification effects and mechanism. The following conclusions can be drawn from this research:  
1. The recycled GF particles from WTB are of good heat- and humidity-resistance performance and in regular asphalt performance tests the rotational viscosity of SBS-modified binders evidently increases with the GF-WTB addition dosage under certain SBS content. In the high-temperature rheology performance DSR test, GF-WTB/SBS composite-modified binders presented better resistance to deformation, anti-rutting ability, and temperature-insensitivity.
2. In the low-temperature BBR test, for 4 wt% SBS content GF-WTB/SBS composite-modified binders under \(-24\)\({}^{\circ}\)C, low-temperature crack resistance evaluation index k-value increased by 15.7% from 0 to 4 wt% GF-WTB dosage. Addition of GF-WTB can elevate the low-temperature crack resistance of SBS-modified asphalt.
3. In comprehensively considering the high-temperature PG, high-temperature sensitivity, k-value increasing rate, and GF-WTB dosage, 4 wt% SBS content with 2 wt% GF-WTB as a secondary modifier is the optimal combination.
4. FTIR, SEM, and EDS microscopic tests analysis shows that the GF-WTB successfully grafted by UP152 coupling agent is of thicker and rougher surface and a vinyl functional group was introduced, improving the adhesion of the GF-WTB to the SBS-modified asphalt. Silane coupling agent modification pretreatment for GF-WTB is one important and indispensable step for GF-WTB/SBS composite-modified binder preparation.
5. AFM observation found that SBS and GF-WTB exist in GF-WTB/SBS composite-modified asphalt in their own specific structural forms and the two modifiers have good compatibility. This compound form can better absorb asphalt light components, improve the elasticity and toughness of asphalt, and then improve high- and low-temperature properties of asphalt.  
The good asphalt properties will transmit to the pavement performance in each respective asphalt mixture. GF-WTB/SBS composite-modified asphalt anti-aging performance and its mixture pavement performance will be further researched.  
Conceptualization, Y.N. and Q.L.; methodology, Z.X.; software, Q.L. and S.Z.; validation, X.H.; formal analysis, Q.L. and Z.X.; investigation, S.Z. and X.H.; resources, Y.N.; data curation, Y.N.; writing--original draft preparation, Q.L., Z.X. and S.Z; writing--review and editing, Y.N., Q.L. and X.H.; visualization, Z.X.; supervision, Y.N.; project administration, Y.N.; funding acquisition, Y.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Hunan Provincial Natural Science Foundation of China, grant number 2022 JJ30259.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding authors upon reasonable request.  
The authors declare no conflict of interest.