esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Results Analysis  
To evaluate the performance of the improved ORB-SLAM3 algorithm, experiments were conducted on a high-performance system equipped with an Intel Core i9-14900 HX CPU (Lenovo, Beijing, China) (24 cores, 32 threads, with a maximum turbo frequency of 5.8 GHz), 32 GB of DDR5-5600 MHz RAM (dual-channel, 2 \(\times\) 16 GB), and an NVIDIA GeForce RTX 4060 Laptop GPU (Santa Clara, CA, USA). The system ran on Ubuntu 18.04, and the SLAM program was developed using C++ ([[https://visualstudio.microsoft.com](https://visualstudio.microsoft.com)]([https://visualstudio.microsoft.com](https://visualstudio.microsoft.com)) (accessed on 21 June 2024)). To ensure fairness and reproducibility, all experiments were  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \begin{tabular}{c} **Threshold L** \\ **(Pixels)** \\ \end{tabular} & **ATE RMSE (m)** & \
\begin{tabular}{c} **Static Features** \\ **Retained (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Dynamic Points** \\ **Removed (\%)** \\ \end{tabular} \\ \hline
0.5 & 0.0187 & 62.3\% & 95.2\% \\
1.0 & 0.0159 & 78.5\% & 89.7\% \\
1.5 & 0.0134 & 87.6\% & 85.4\% \\
2.0 & 0.0142 & 91.2\% & 79.1\% \\
2.5 & 0.0151 & 94.8\% & 72.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensitivity Analysis of Threshold L on Walking_xyz Sequence.  
\begin{table}
\begin{tabular}{c c} \hline \hline \
\begin{tabular}{c} Input: \\ **Potential dynamic object boxes \(\mathbf{B_{p}}\),** Motion threshold L \\ \end{tabular} \\ \hline Begin &
\begin{tabular}{c} Initialize \(\mathbf{D}\leftarrow\varnothing,\mathbf{S}\leftarrow\varnothing\) \\ **for each point p in F\({}_{t}\) do** \\ **if p \(\in\mathbf{B_{p}}\) or \(\mathbf{p}\in\mathbf{B_{d}}\) then** \\ **\(\mathbf{D}\leftarrow\mathbf{D}\cup\{\mathbf{p}\}\)** \\ else \\ **\(\mathbf{S}\leftarrow\mathbf{S}\cup\{\mathbf{p}\}\)** \\ **for each point \(\mathbf{p}\) in S do** \\ \(\textbf{Compute}(u,v)=\textbf{LK}(\mathbf{p})\) \\ If m\(\times\)L then \\ **\(\mathbf{D}\leftarrow\mathbf{D}\cup\{\mathbf{p}\}\)** \\ **\(\mathbf{S}\leftarrow\mathbf{S}\smallsetminus\{\mathbf{p}\}\)** \\ Return S \\ End \\ \end{tabular}
\end{table}
Table 2: Dynamic Feature Point Removal Algorithm.  
performed under consistent conditions, maintaining the same hardware platform, software environment, and parameter configurations across both the proposed algorithm and the comparison methods.
## 5 Conclusions  
The present study allowed to characterize the influence of post-harvest withering of Corvina grapes on the aroma profile of wines. The aromatic contribution given by the withering on-vine or in a traditional withering warehouse (fruttaio) was also been evaluated.  
Withering resulted in a lower content in fermentation-derived volatiles such as esters, higher alcohols, and acids, as well as grape-related compounds such as C6 alcohols, and certain norisoprenoids like \(\beta\)-damascenone. Terpenes showed different behaviors according to the compound. Linalool, the major terpene found in the sample wines analyzed, and _cis_-linalool oxide were negatively influenced by the withering process while \(\beta\)-citronellol and 1,4-cineole showed a different trend and it was found in higher concentrations in withered samples. The same trend was observed for ethyl acetate, ethyl vanillate, benzyl alcohol and vanillin  
The aroma profile of wines obtained by whitering in fruttaio was characterized by higher concentrations of esters such as ethyl acetate compared to on-vine withering. Wines withed in fruttaio were also distinguished by higher concentrations of \(\beta\)-citronellol and 3-oxo-\(\alpha\)-ionol, while on-vine withering showed higher content of \(\beta\)-damascenone. The withering process as well as the technique employed also influenced the behavior of compounds during aging, showing different variation.  
Overall, the results of the present study indicate that on-vine withering with blocked xylem is an interesting alternative to conventional fruttaio withering for the production of wines where a mild withering is requested. Although on-vine withering can only be carried out in years where climatic conditions are suitable, the possibility to explore this kind of withering technique is of interest to reduce the workload of fruttaio facilities and the energy cost associated with their functioning, reducing the environmental impact of the winemaking process.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments. [PERSON] and [PERSON] produced samples and data analysis. [PERSON], [PERSON], and [PERSON] performed all the experimental work and data analysis, [PERSON] and [PERSON] interpreted the data and drafted the manuscript. All authors reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Masi Agricola winery.  
**Acknowledgments:** We warmly thank Masi Agricola, for providing wine samples used in this research, and sharing grape data and information.  
**Conflicts of Interest:** [PERSON], [PERSON], [PERSON] and [PERSON] declare no conflict of interest. [PERSON], [PERSON] and [PERSON] work at Masi Agricola, funder of the project. They contributed to design of the study and sample collection.
# An Active Path-Associated Cache Scheme for Mobile Scenes
## 4 Cooperative Caching Mechanism
### Caching Mechanism Based on Node Cache Value
#### 4.2.1 Node Cache Value  
The purpose of designing edge caching is to place high-value content on the most important nodes. How to judge the value of content? Many researchers have designed algorithms for cache placement based on content popularity [43; 44; 45], but in mobile scenarios, users move frequently, which will trigger active caching to frequently pull content to the cache area. In traditional caching scenarios, we can intuitively understand that the content that needs to be cached the most is often the content with high popularity over a period of time. In our scenario, user movement introduces the dynamics of content popularity. In this case, we propose a new evaluation metric called content freshness. The introduction of content freshness can assess the importance of content over time. We add the current timestamp to the actively cached data packet, and the freshness of the current data packet is equal to the timestamp minus the oldest timestamp in the freshness list of the cache node. The freshness of the content is compared independently for the content list of each node. The freshness value represents the current timestamp of the content minus the oldest timestamp of the node. In addition, we maintain the popularity of the least popular content in the content list of the cache node. The pop of the cache node is equal to the popularity of the content of the cache node divided by the popularity of the least popular content.  
Similarly, how to judge the importance of nodes? We know that the delay that affects the content of the user's request is related to the number of route hops from the current user to the cache node. In addition, the size of the remaining cache space of the cache node also determines whether the content needs to be stored. We know that frequent content replacement will increase the network delay. Therefore, the importance of the node is related to the remaining space of the cache and the number of hops from the user to the cache node.  
Based on the above factors, we propose calculating the \(Cache_{value}\) of a node as follows:  
\[\begin{split} Cache_{value}=&\omega_{1}*N(Hop)+ \omega_{2}*N(Freshness_{value})+\\ &\omega_{3}*N(Pop)+\omega_{4}*N(Cache_{size})\end{split} \tag{7}\]  
The \(N\) in Formula (7) represents standardization, and the value of each parameter is usually not an order of magnitude. To balance this deviation, we use the min-max method to standardize each value [46]. In addition, the larger the cache, the more worthy the node is to be cached. Therefore, when calculating hop, we calculate the hops from the cache node to the content source. In our scenario, the content source can be regarded as the source AP.
# Estimation of Seismically-Induced Crest Settlement of Earth Core Rockfill Dams
## 1 Introduction  
The settlement at the dam crest induced by seismic excitation is an important seismic design parameter for earth dams and is routinely used to evaluate the seismically-induced damage level of dams. Extensive data were collected and summarized to estimate the settlement characteristics of earth dams [1]. The dams considered were earth core rockfill dams (ECRDs), concrete-faced rockfill dams (CFRDs), hydraulic fill dams, and earthfill dams. An empirical formula was proposed that correlated the free-field outcrop peak ground acceleration (_PGA_) and moment magnitude (\(M\)) with the surface settlement. However, because a wide range of dams was used, a significant level of scatter was inevitable. Additionally, the strength of the correlation of various ground motion parameters other than _PGA_ to potentially enhance the correlation was not investigated.  
Recently, a series of numerical simulations was performed to evaluate the dynamic response of earth dams. [PERSON] and [PERSON] [2] performed a series of finite element limit analyses to evaluate the seismic stability of earth core rockfill dams (ECRDs). However, the crest settlement was not calculated. [PERSON] et al. [3] performed numerical simulations to calculate the settlement of concrete-faced rockfill dams (CFRD) and to derive fragility curves. [PERSON] et al. [4] performed dynamic analyses to investigate the stress induced in the concrete slab of CFRDs. However, neither of the numerical models werevalidated against recordings. The literature review demonstrated that a study using a validated or verified numerical model to calculate the dam settlement has not yet been performed.  
The aim of this paper was to develop empirical equations to predict the crest settlement of earth core rockfill dams (ECRDs). The predictive equation could potentially be used for damage assessment of ECRDs. A two-dimensional (2D) nonlinear model validated against centrifuge tests was used to perform a series of dynamic analyses. Multivariate regression analyses were performed to correlate the calculated crest settlements with a number of ground motion parameters.
## 3 Results  
### Biochemical Properties of BG during the Aging Process  
Table 1 contains the biochemical properties of fresh garlic and BG during the aging process. The pH of the fresh garlic extract was 6.19, whereas that of the aging garlic on the 5 th and 35 th day was 5.67 and 3.52; the pH thus decreased as the aging progressed. The water content of the fresh garlic and aging garlic samples was 77.50% and 30.78%, and it decreased as the aging progressed. HPLC was employed to measure the allicin content of the fresh garlic and aging garlic extracts. The allicin content of the fresh garlic was 2.688 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, whereas that of the aging garlic decreased as the aging progressed. The allicin content of the garlic aged for 5, 10, 15, and 20 days was 1.72, 0.56, 0.22, and 0.01 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, respectively. Between the 25 th and 35 th days of aging, allicin was undetectable. Regarding the total phenol content, the fresh garlic contained 5.87 mg GAE/mL, whereas the aging garlic contained 0.98 and 16.96 mg GAE/mL on the 5 th and 35 th days, respectively, indicating that the total phenol content increased as the aging progressed.  
The free amino acid content of the garlic samples aged for different durations were measured, and the results are presented in Table 2. The total free amino acid content of fresh garlic was 1843.12 mg/100 g, and it decreased as the aging progressed. On the 5 th and 10 th days and between the 15 th and 35 th days, the total amino acid content was 2415.20, 1817.05, and 505.66-82.01 mg/100 g, respectively. Regarding the individual amino acid contents, the results varied. Arginine, asparagine, aspartic, cystine, and glutamic acid were found to be most prevalent in the garlic aged for 5 days, whereas the alanine and methionine contents were highest in the garlic aged for 10 days. Glycine content was highest when aging was performed for 15 days. Histidine, isoleucine, leucine, lysine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, and valine were most prevalent in the fresh garlic. Cystine was not identified in fresh garlic, whereas 61.98 and 19.79 mg/100 gof cystine was identified in the garlic aged for 5 and 10 days, respectively, but not in the samples with other aging durations. The fresh garlic contained 15.96 mg/100 g tryptophan, but tryptophan was not detected in the other samples. Loss of tryptophan by aging is rapid with an undetectable level at 5 days. For amino acids, the cut-off for drastic decline might be 10 days (15 days exhibits a much greater decline than 10 days). If the industrial goal is to preserve specific amino acids, the data in Table 2 could be an informative reference.
## 2 Methods
### Geometric Standardization with PCA  
Drone-captured 3D point clouds contain spatial coordinates (XYZ) and RGB color information, providing comprehensive geometric and spectral data. However, for edge detection of rock discontinuities, the high-dimensional complexity of 3D point clouds introduces challenges such as noise, redundancy, and illumination sensitivity. Therefore, point cloud preprocessing is essential to enhance edge pixel features and establish the foundation for accurate discontinuity edge identification.  
Figure 1: The technical structure of rock structure surface identification and parameter calculation.  
This paper first applies geometric standardization to normalize the spatial distribution of point cloud, ensuring consistent input dimensions to simplify matrix operations and memory management. Additionally, large-scale point clouds contain millions of points, and the time complexity of subsequent operations increases quadratically with point count. Thus, voxel-based downsampling [19] is employed to reduce computational load while preserving structural features. To avoid geometric distortion during downsampling, a centroid-based interpolation method is adopted, which retains critical geometric and color information.  
To enhance dominant color variations in point cloud RGB channels while suppressing noise, we employ PCA [20; 21] for dimensionality reduction. PCA is applied to the RGB color space of the point cloud to identify the direction of maximum variance in color distribution. By projecting the RGB data onto the principal component via variance-maximizing reconstruction, the algorithm retains dominant color patterns (e.g., salient edges or homogeneous regions) while isolating zero-mean noise. This step amplifies color contrasts critical for discontinuity detection and reduces redundancy across channels.
# Traffic-Aware Optimization of Task Offloading and Content
## 5 Experimental Evaluation
### Comparative Experiments
#### 5.2.3 Comparison with Changing of Input Data Size  
Figure 5(a) clearly depicts that TOCC outperforms LOCAL, TFO, and F_NSGA-III in achieving the lowest average execution time when the input data size of a single task is increased by 20%, showing a flat upward trend. This is because the input data affect the amount of tasks in the edge cache and the offloading of computational tasks. TOCC improved by 29% over LOCAL, 28% over TFO, and 1.2% over F_NSGA-III. In Figure 5(b), the total energy consumption of TOCC is also the lowest as the number of tasks increases. In terms of total energy consumption, TOCC exceeds LOCAL by 78%, is 93% higher than TFO, and is 94% higher than F_NSGA-III.
## 7 Performance Evaluation and Results
### Results and Discussion  
Our proposed distributed machine learning system effectively enhances traffic flow prediction. Our proposed system achieved notable results of traffic flow prediction for future traffic forecasting especially on predicting abnormal traffic flow. In this study, we evaluated 4 complex segments and each complex segment has 4 segments and is located between two exits. Each segment covers half a mile of distance, so each CS consists of 2 miles. Normal travel time of half a mile is 28 s if a vehicle is traveling at 65 Mph based on Equation (7). So normal travel time for one CS is 112 s or almost 2 min.  
\[Normal\ Travel\ Time=(D_{c}/S)\times 3600 \tag{7}\]where \(Dc\) is the length of a segment, which is 0.5 mile. S represents the speed limit, which is 65 Mph. 3600 is the total number of seconds in one hour.  
The effective performance of the DMLRF model in predicting both high and low values can be attributed to robust data preprocessing, segment-based modeling, optimized hyperparameters using Randomized Search Cross-Validation (RSCV), and the use of Stacking ensemble learning. These factors collectively contribute to the model's ability to accurately capture the unique traffic patterns across different segments.
# Neural Graph Similarity Computation with Contrastive Learning
## 3 Methodology
### Graph Similarity Prediction Network  
As shown in the middle of the figure, the graph similarity prediction network is composed of three graph convolutional layers. After each convolution, an attention-based pooling strategy is used to generate the graph-level embeddings. Finally, the graph embeddings from all layers are concatenated and forwarded to a multilayer perceptron (MLP) to generate the graph similarity scores, which are compared with the ground-truth values to produce the MSE loss. Next, we elaborate on these components.
## 3 Illumination Systems' Performance Comparisons
### Output Radiation and Energetic Transmission
Note that although the VCSEL radiation output could be optimized to maximize energy density at the object plane, the spatial intensity distribution was more uniform when the peak energy density was moved away from the object plane. The logarithmic scale on the energy density axis of Figure 5 shows a linearly decreasing relationship with distance for the LED illumination, for all measurement points except the one taken right at the emitter. The energy density at the emitter is computed as the total radiation energy measured at the LED emitter in relation to the emitter area, i.e., 12.5 mm\({}^{2}\). It should be emphasized that the condenser lens is located between the first two measurement points. The monotonic decreasing trend of the LED profile is expected because the energy density is a function of the illuminated area, which grows with distance. The results highlight the high energy losses as a function of distance with LED lighting, with more than a factor two decrease in energy density as distance doubles. More complex optical configurations to optimally collect and transmit the LED light output have shown improvements, but these are more likely to be effective with a particular set0 up, rather than a general imaging system configuration like the one presented in this document. Note that the illumination intensity distributions were fairly smooth across the field of view for both illumination systems, but that LED was clearly superior with respect to uniformity. With a relative dispersion below 8% across the image for the LED illumination, it represents a substantial advantage compared to the near 25-% dispersion measured with the VCSEL illumination. Intensity dispersion, or fall-off in the present case, is detrimental to the system's dynamic range and signal-to-noise ratio. In this context, it appears that while VCSEL illumination is adapted to small fields of view, it may compromise illumination distribution, and image quality over larger fields.
## 4 Dynamic Simulations  
A three-CA test system was used for the dynamic simulations, where CA\({}_{1}\)-CA\({}_{2}\) and CA\({}_{2}\)-CA\({}_{3}\) were connected by physical tie-lines, whereas CA\({}_{1}\)-CA\({}_{3}\) were not connected with a tie-line. Moreover, all three CAs were connected with virtual tie-lines due to the INP and the CBRR. A Matlab/SIMULINK model was used, where the dynamic simulations were performed with a 50 ms step size.
## 2 Results  
Segregation Distortion Analysis and Distortion Threshold Estimation for VRS-F\({}_{2}\) Population  
Genotype frequency plots for markers in the linkage map showed deviation from an expected 1:2:1 ratio for many markers associated with chromosomes 5, 7, 11, and 15 (Figures 1 and S1). The AA genotype was more frequent and BB genotype less frequent than the expected 1:2:1 Mendelian ratio (Figure 1). While segregation distortion typically was in the middle of the chromosome, distortion for chromosome 15 was at the end (Figure 2, Table S1). This confirmed the presence of some chromosomal regions with natural segregation distortion patterns that needed to be preserved in the map. Analysis of a series of \(p\)-values indicated \(>\) 1210 markers would be removed at the adjusted \(p\)-value 0.05 (Figure S2). To conserve markers representative of the natural genetic character of the VRS-F\({}_{2}\) population, the threshold (adjusted \(p\)-value) was set at \(<\)10-21 for this F\({}_{2}\) population and pairwise marker linkage analysis was conducted. No marker ordering errors were identified in this final map (Figure 3). A total of 677 non-informative markers and 232 distorted markers were identified and removed during map curation and the final map contained 2519 markers (Table 1).  
The overall genome coverage of mapped markers (96.3%) and collinearity (99.9%) relative to the _V. vinifera_ PN40024 12X V2 genome indicated high genetic map quality (Tables 1 and 2, Figure S3). All chromosomes had \(>\)93% genome coverage except for chromosome 15 with 61.5% coverage (Table S1). Eighty-nine markers were tested for chromosome 15, of which 41 were non-informative and 14 were distorted resulting in only 34 markers for this chromosome. The resultant VRS-F\({}_{2}\) GBS-rhAmpSeq integrated map had a total length of 1131.3 cM and an average distance of 0.5 cM between markers (Table 1 and Figure S4).  
Figure 1: Genotype frequency plots for chromosomes 5, 7, 11, and 15. Horizontal lines represent expected genotype count 128 (for AA (red) and BB (blue), dotted) and 256 (for AB (green), dotdash) under 1:2:1 Mendelian ratio.  
Figure 3: Pair-wise recombination fractions and LOD of the VRS-F\({}_{2}\) genetic map. Vertical and horizontal lines indicate the borders of the linkage groups. The estimated recombination fractions (\(r\)) between markers are in the upper left and the LOD are in the lower right of each linkage group rectangle. High correlation between markers indicates marker linkage (yellow, low \(\hat{r}\) or high LOD) and blue (high \(\hat{r}\) or low LOD) represents low correlation values indicating unlinked markers.  
Figure 2: Marker segregation distortion at four separate chi-square adjusted \(p\)-value threshold levels for chromosome 5, 7, 11, and 15. The lines represent negative log scale adjusted \(p\)-value < \(5\times 10^{-2}\) (dot), \(1\times 10^{-5}\) (dot-dash), \(1\times 10^{-10}\) (dash), and \(1\times 10^{-21}\) (solid). The rhAmpSeq (red) and GBS (black) markers show distorted marker type at each threshold level. Markers above \(-\)log(\(1\times 10^{-100}\)) are not in the figures.