esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Autonomous intelligent transportation systems consistently require effective and secure communication through vehicular networks, enabling autonomous vehicle communication. The reduction of traffic congestion, the alerting of approaching emergency vehicles, and assistance in low visibility traffic are all made possible by effective communication between autonomous vehicles and everything (AV2X). Therefore, a new adaptive AV2X model is proposed in this paper to improve the connectivity of vehicular networks. This proposed model is based on the optimization method and a distributed deep learning model. The presented approach optimizes the inter-vehicle location if required for ensuring effective communication between the autonomous vehicle (AV) and everything (X) using the Lagrange optimization algorithm. Furthermore, the system is evaluated in terms of energy efficiency and achievable data rate based on the optimal inter-vehicle position to show the significance of the proposed approach. To meet the stated goals, the ideal inter-vehicle position is predicted using a distributed deep learning model by learning from mathematically generated data and defined as a restricted optimization problem using the Lagrange optimization technique to improve communication between AV2X under various environmental conditions. To demonstrate the efficiency of the suggested model, the following characteristics are considered: vehicle dispersion, vehicle density, vehicle mobility, and speed. The simulation results show the significance of the proposed model in terms of energy efficiency and achievable data rate compared with other proposed models.  
autonomous vehicles; vehicular communication; vehicle-to-everything (V2X); cooperative communication; energy efficiency; achievable data rate +
Footnote †: journal: Electronics
## 1 Introduction
[19], marine clay, characterized by excessive swelling and shrinkage, was stabilized by mixing it with polyurethane. The polyurethane mixture had 1%, 2%, 3%, 4%, and 5% of the bulk mass of the specimen. [PERSON] et al. [19] inferred that adding polyurethane increased the shear strength of the marine clay. Several studies also used techniques to reduce compacted clay's hydraulic conductivity, including incorporating polymer materials to stabilize the soil matrix and prevent volumetric changes as the soil moves to a drier state. The study of [PERSON] and [PERSON] [21] incorporated a polyurethane polymer into an expansive soil matrix, which reduced the permeability and improved the water-holding capacity of the compacted specimen due to the increase in carbon content. In the study, the compacted clay mixed with polyurethane in a saturated state had an average hydraulic conductivity of 2.9 \(\times\) 10\({}^{-8}\) cm/s, which makes the compacted specimen a suitable landfill liner [21]. Studies on clay stabilization using polymers have demonstrated potential in enhancing soil properties, yet the long-term impacts on unsaturated hydraulic conductivity remain underexplored. Thus, this study contributes to the advancement of sustainable landfill engineering by providing insights into the behavior of polyurethane-clay covers under unsaturated conditions. In addition, the influence of wet-dry cycles on the polyurethane-clay's deterioration, microstructure, and corresponding hydraulic behavior has not been comprehensively addressed in the existing literature. The simplified data acquisition of using the filter paper method enables the establishment of water retention curves during the drying process in several wet-dry cycles, which are essential for modeling the unsaturated hydraulic conductivity. By adopting this method, the study aims to provide a robust and accessible tool for advancing landfill liner and cover evaluations. The insights gained in this study could assist engineers in optimizing cover configurations to minimize leachate leakage, thereby enhancing environmental protection and reducing maintenance costs. The findings offer critical data for predicting the unsaturated hydraulic conductivity of landfill covers, improving the understanding of soil-water dynamics under environmental stresses. This research also supports the development of durable and efficient landfill covers, promoting better waste management practices. Beyond landfill applications, the study holds broader implications for soil stabilization techniques and the engineering of resilient geotechnical materials. These contributions align with global efforts toward sustainable development and environmental protection.
## 1 Introduction
The results demonstrate that, in uniform two-stage configurations, the second-stage pressure drop percentage exceeded the first stage by approximately 20%, exhibiting significant distribution inhomogeneity. Despite significant progress, existing studies lack a comprehensive framework that links sealing efficiency, interstage pressure imbalance, and entropy generation mechanisms in multistage brush seals. This paper establishes a numerical solution model of a four-tooth three-stage brush seal based on a numerical model of fractal geometry and porous media. Based on the verification and comparison of experimental data, the influence of the seal's structural parameters and the sealing condition parameters on the pressure drop imbalance and sealing characteristics of three-stage brush seals is studied, the structural parameters of the combined seal are improved, and the mechanism of pressure drop imbalance of three-stage brush seals is elaborated from the viewpoint of entropy increase. The results of this paper provide a theoretical basis for the design of a three-stage brush seal structure.
# A Novel Breast-Volume Self-Measurement Method with Improved Convenient and Accuracy
## 2 Methodology
### Selection of Measurement Parameters  
When measuring breast volume, it is essential to first select the appropriate measurement parameters. Due to the varying shapes of women's breasts, even breasts of the same volume may differ in shape and position. To ensure accuracy and relevance, this study selected parameters that effectively represent breast shape and position, including length, width, and height.  
Following the Chinese national standards GB/T 16160-1996 Measurement Methods for Body Measurements for Garments [24] and GB/T 5703-2010 Basic Human Body Measurements for Technological Design [25], as well as measurement methods related to bra design from the Beijing Institute of Fashion Technology and Aimer Human Engineering Research Institute, this study identified five key breast measurement parameters. These include length measurements (upper-cup vertical distance, lower-cup arc distance), width measurements (anterior-cup arc distance, lateral-cup arc distance), and height measurement (breast height). These parameters provide essential references for subsequent breast shape fitting and volume calculation.
## 2 RS Systems
### Passive  
#### 2.1.1 Optical  
Optical RS systems mainly record the solar radiance reflected from the Earth's surface at visible (400-700 nm), NIR (720-1300 nm), and SWIR (1300-3000 nm) parts of the electromagnetic spectrum. Optical RS primarily works based on the fact that different objects reflect and absorb the incoming solar light differently at various spectral bands. Therefore, each object has a unique spectral behavior, called spectral reflectance signature, by analyzing of which, different objects can be discriminated [15]. Optical satellites have been employed for various oceanographic applications, such as biogeo-optics, Ocean Color (OC), coastal waters, bathymetry, and sea surface topography [16; 17]. Since the existence of solid microparticles alters the spectral behavior of seawater, scientists can globally monitor these substances, such as phytoplankton, algal bloom, nonalgal particles, and colored dissolved organic matter, using OC measurements [16; 18; 19]. The optical images also reveal oceanic waves, including internal and shallow-water waves [20]. Additionally, optical sensors provide useful images for mapping, monitoring, and managing aquatic vegetation and coral reef ecosystems [21]. Marine pollution, especially Ocean Oil Spill (OOS), can also be detected by optical images [22; 23; 24]. Despite the successful employment of optical RS systems, consideration should be taken into account, as all of the optical RS applications can be hampered by several environmental factors, such as sun glint, cloud cover, and incoherent weather conditions [16].
# Chemicals from Brominated Flame Retardants: Analytical Methods, Occurrence, Transport and Risks
## 2 Analytical Methods for the Detection of BFRs in Environmental and Biological Samples
### Food Contact Articles  
There are limited reports on the analysis of BFRs in food contact articles. However, the analysis of BFRs in food contact articles (FCA) samples starts by first grinding or cutting the sample into small pieces to ensure uniformity. This step is particularly important for larger plastic objects or composite materials. Possible techniques for extraction may include SE, ASE, or PLE. [PERSON] et al. employed ultrasonic assisted extraction (UAE) to extract BFRs from several food contact articles (FCAs) purchased in Spain [47]. The extraction was carried out in 1 mL of methanol-isopropanol (50:50 \(v/v\)) solution and in line cleanup using a PTFE membrane filter prior to DART-HRMS, XFR, and HPLC-MS/MS analysis. In evaluating extraction methods, ultrasonic extraction demonstrated effectiveness for extracting BFRs from polymeric materials, particularly when low levels are expected. This method not only saves solvent compared to the traditional Soxhlet extraction but also enhances efficiency. In their report, they selected the mixture of methanol and isopropanol due to their varying polarities, which makes them suitable for targeting a range of BFRs. The DART-HRMS and XRF were used as screening techniques for BFR identification. The HPLC-MS/MS method gave recoveries from 80 to 120% from LOD (\(\leq\)0.02 ug/mL) and LOQ (\(\leq\)0.05 ug/mL). The BFR concentrations in the polymeric food contact articles were found to range from below the limit of detection (LOD) to 260 ug/kg for TBP, below LOD to 2180 ug/kg for TBBPA, and below LOD to 9340 ug/kg for BDE-209.
# A Systematic Review of the Trends and Advances in IFC Schema Extensions for BIM Interoperability
## 5 Evaluation of IFC Schema Extension Cases
### Analysis of Extension Cases by Product and Process Representations
#### 5.3.2 Detailed Analysis of Extension Approaches in the Infrastructure Domain
Meanwhile, process representations predominantly aimed to illustrate information critical for alignment representation, asset management, and O&M, or devised extensions to enable engineering-centric analyses, such as finite element analysis. A few cases exclusively adopted 'property extension' approaches for process representations, using architectural domain entities as proxies. Furthermore, sectoral differences were marked in extension objectives: bridge sector cases largely focused on process representations, whereas the tunnel sector primarily targeted product representations. This divergence is attributed to the different structures of bridges and tunnels. Bridges allow the utilization of entities from the architectural domain due to their components, such as _IcfSlab_, _IcfColumn_, and _IcfBeam_, while tunnels, with their variety of unique components, necessitate independent representation and definition.
## 3 Results and Discussion  
### Dewatering Efficiency  
The appearances of the final sludge after thermal treatment at different temperatures for 30 min are shown in Figure 1. The sludge dewaterability was significantly improved after low-temperature thermal treatment. When thermally treated below 40 \({}^{\circ}\)C, the final sludge was flocculent, and the flocs were dispersed and irregularly shaped, and almost no change was observed in comparison with raw sludge (Figure 1a). When the temperature was elevated to 60 \({}^{\circ}\)C, the particles aggregated and formed large flocs, resulting in good settleability (Figure 1b). However, the formed flocs were composed mostly of loosely clumped sludge, which was easily broken into fragments when placed under vigorous shaking. When the temperature was increased to 80 \({}^{\circ}\)C, the obtained sludge was a clot of compacted cake, and the residue solution was clear and transparent in appearance (Figure 1c). Large flocs often denote good settleability and dewaterability. Obviously, some factors that related closely with excellent dewaterbility were activated under a higher temperature of 80 \({}^{\circ}\)C. The detailed mechanism of the above interesting phenomena will be revealed in the following text after more investigation.
## 2 Results and Discussion
### Perceptual Interactions
#### 2.2.1 Selection of Aroma Compounds  
Black tea is a fully fermented tea, and its floral aroma plays an important role in the complex aroma [25]. As compared to other teas, black tea has a stronger, sweeter floral aroma. Therefore, this study focused on the effect of aroma perception of key aroma ester (MeSA) with floral compounds.  
This part took the floral compounds as the research object, and common floral compounds in different black teas were used as targets for sensory interaction, specifically including linalool, phenylethyl alcohol, benzyl alcohol, geraniol, nerol, alpha-terpineol, cis-jasmone, \(\beta\)-ionone, \(\beta\)-ocimene, and benzeneacetaldehyde.  
Another target compound is MeSA. Most importantly, because it was regarded as an important key aroma compound in various black teas; secondly, among the esters detected in this study, it had a high concentration (0.28-1.51 mg/kg), also a high odor activity value (OAV > 3) and FD (256-512); and its aroma quality similar to wintergreen oil was also very important. Therefore, MeSA and floral compounds were selected for the perception interaction, in order to achieve the purpose of providing theoretical guidance for most black teas.
# Robust Visual Tracking via Patch Descriptor and Structural Local Sparse Representation
## 3 Patch Descriptor and Structural Local Sparse Representation  
### Target Region Division  
In this work, we normalized the target region into 36 \(\times\) 36 pixels by applying affine transformation and extracting non-overlapped 12 \(\times\) 12 local patches within the target region with 12 pixels as a step length, as shown in Figure 1a. Then, each local patch was further decomposed into several sub-patches again. The sub-patch size was \(4\times 4\) pixels, and the step length was 4 pixels, as shown in Figure 1b,c.
## 5 Discussion  
The results of the laboratory tests carried out confirmed the achievement of the required quality parameters of electricity within the adopted range. Particularly important is the time to switch between on/off-grid modes, which does not exceed 20 ms. Furthermore, the values of phase voltages in the off-grid mode with unbalanced load do not exceed the permissible values (\(\pm\)10% Un).  
During the tests, the problem of limitations related to the DC bus and the battery in off-grid mode when changing the load power became apparent. Power limitation during a step load change results from the instantaneous battery voltage and the maximum discharge current, as well as from the current rise time in the DAB converter. The current rise time is mainly due to the inductance of the transformer and the parasitic inductance of the DC circuit. The transformer ratio was selected for the rated voltage of the battery, i.e., 48 V, and the voltage of the DC circuit (approx. 700 V) and was equal to approximately 14 V/V. In order to minimize the current rise time, it was necessary to limit the number of turns. With a small number of turns of the primary winding, the range of changes in the ratio is relatively large. For the built prototype, the range of step power change at which the DC bus voltage will be restored, depending on the voltage at the battery terminals (40-48 V during discharge), is 6.6-8 kW. Above this value, the PC2 grid converter will switch off. The use of a 48 V battery for higher load power is unjustified, because the increase in current will complicate the power electronics system. Therefore, for higher power it will be  
Figure 30: Off-grid operation, switching on a load with power of 6.5 kW. -- load phase current; -- battery current; -- output AC voltage; -- DC bus voltage. Source: author’s development.  
Figure 29: Off-grid operation, switching on a load with power of 8.5 kW. -- load phase current; -- battery current; -- load voltage; -- DC bus voltage. Source: author’s development.  
necessary to increase the DC bus voltage. This will also allow the use of a simpler PC3.1 converter circuit in the buck-boost topology.
# Graph Convolutional Networks with Bidirectional Attention for Aspect-Based Sentiment Classification
## 4 Experiments
### Results and Analysis
#### 4.4.3 Impact of GCN Layers  
According to the experimental results, we know that the number of GCN layer is an essential parameter affecting the final performance. The number of GCN layers is a very important setting parameter that affects the final performance. In order to study the effect of the number of GCN layers on the final performance, we experimented ATGCN (GloVe) at different GCN layers from 1 to 5. Figure 3 shows the experimental results of accuracy. Figure 4 shows the experimental results of Macro-F1. Basically, we can see from the Figures 3 and 4 that, in most cases, our model will get the best or acceptable results when the number of GCN layers is 2. Specifically, when the number of GCN layers increases, with the increase of the number of GCN layers, the performance indicators of the dataset show a trend of first increasing and then decreasing. With the increase of the number of layers, the training and over fitting of the model become more difficult, which may be the reason for the performance degradation of the model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Twitter**} & \multicolumn{2}{c}{**Lap14**} & \multicolumn{2}{c}{**Rest14**} \\ \cline{2-7}  & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** \\ \hline
**ATGCN w/o pos** & **73.16** & 70.63 & 72.39 & 68.15 & **81.03** & **73.52** \\
**ATGCN w/o Biatt** & 72.80 & 69.59 & 72.07 & 69.25 & 78.83 & 69.01 \\
**ATGCN-Siatt** & 72.91 & 69.73 & **73.68** & **69.93** & 79.37 & 70.95 \\
**ATCNN** & 72.65 & **70.83** & 73.64 & 68.91 & 79.24 & 69.57 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study results.  
From Figures 3 and 4, we can observe that Accuracy and Macro-F1 fluctuate in the same trend on Rest14 and Twitter datasets as the number of GCN layers changes. In the three datasets, Rest14 result is the best, Lap14 is the second, and Twitter is the worst. The reason for this result is caused by the dataset itself. The reason Lap14 has worse results than Rest14 is that the Lap14 has more samples of implicitly expressed sentiment. The more samples of implicit expression of sentiment, the more difficult the task of sentiment classification. As Lap14 has more implicit expressions than Rest14 and Twitter, Lap14 fluctuates less with the number of GCN layers. The Twitter dataset has the worst results because the Twitter dataset samples are more colloquial and the dataset quality is lower.
###### Abstract  
Tropical cyclones can form over open ocean where in situ observations are limited, so forecasters rely on satellite observations to monitor their development and track. We explore the utility of an operational satellite sounding product for tropical forecasting by characterizing the products retrieval skill during research flights. Scientists from both the NOAA-Unique Combined Atmospheric Processing System (NUCAPS) research team and tropical cyclone communities collaborated to target relevant tropical cyclones during the campaign. This effort produced 130 dropsondes that are well-timed with satellite sounder overpasses over three different tropical cyclones and one Saharan Air Layer outbreak. For the combined infrared and microwave retrieval, the NUCAPS temperature has a root mean square error (RMSE) of 1.2 K near the surface (1000-600 mb) and 0.8 K in the mid-troposphere (600-300 mb), which is in line with global product requirements. The water vapor mixing ratio RMSE was 26% near the surface and 46% in the mid-troposphere. NUCAPS microwave-only retrievals can also be useful for cloudy scenes, with surface RMSE values of 4 K (temperature) and 23% (water vapor). Using information content analysis, we estimated that the vertical resolution near the surface was 1.7 km for the temperature retrievals and 2.2 km for the water vapor retrievals in this study. We discuss the feasibility of implementing NUCAPS in an operational forecasting setting, which requires rapid data delivery to forecaster software tools.  
tropical cyclone; satellite sounding; real time; operational forecasting 20221431899. [[https://doi.org/10.3390/rs14133189](https://doi.org/10.3390/rs14133189)]([https://doi.org/10.3390/rs14133189](https://doi.org/10.3390/rs14133189))  
18 May 2022  
27 June 2022  
2 July 2022  
27 June 2022  
27 July 2022  
27 June 2022
# Map-Matching-Based Localization Using Camera and Low-Cost GPS for Lane-Level Accuracy +
###### Abstract  
For self-driving systems or autonomous vehicles (AVs), accurate lane-level localization is a important for performing complex driving maneuvers. Classical GNSS-based methods are usually not accurate enough to have lane-level localization to support the AV's maneuvers. LiDAR-based localization can provide accurate localization. However, the price of LiDARs is still one of the big issues preventing this kind of solution from becoming wide-spread commodity. Therefore, in this work, we propose a low-cost solution for lane-level localization using a vision-based system and a low-cost GPS to achieve high precision lane-level localization. Experiments in real-world and real-time demonstrate that the proposed method achieves good lane-level localization accuracy, outperforming solutions based on only GPS.  
autonomous driving; lane-level localization; lane detection; GNSS; GPS; map-matching +
Footnote †: This paper is an extended version of our paper published in ABC2021 (The First International Workshop on Artificial Intelligence Methods for Smart Cities, Online Event, 4 November 2021).