esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Analysis of Settlement of Composite Foundation of CFG Piles  
### Theoretical Analysis  
The deformation of a composite foundation consisting of CFG piles (as vertical reinforcements in the soil) and soil can be divided into two parts: deformation in the reinforcement zone, \(S_{1}\), and deformation in the underlying layer, \(S_{2}\). For the reinforcement zone, the vertical reinforcement (i.e., the CFG pile) and the natural soil are regarded as a unified whole, and the compression modulus of the composite foundation is used to calculate the deformation. For the underlying layer, the compression modulus of the natural foundation is used to calculate deformation. Based on the work in Reference [28], the final deformation of the CFG pile composite foundation, \(S\), can be calculated using the following equation:  
\[S=\psi_{\text{sp}}\left[\sum_{i=1}^{n_{1}}\frac{p_{0}}{\xi E_{\text{si}}}\left( z_{i}\overline{x}_{i}-z_{i-1}\overline{x}_{i-1}\right)+\sum_{i=n_{1}+1}^{n_{2}} \frac{p_{0}}{E_{\text{si}}}\left(z_{i}\overline{x}_{i}-z_{i-1}\overline{x}_{i -1}\right)\right], \tag{1}\]  
where \(\psi_{\text{sp}}\) is the empirical coefficient of the calculation of settlement, which is generally between 0.2 and 1.0; \(n_{1}\) is the number of layers of soil in the strengthened area; \(n_{2}\) is the total number of layers of soil in the range of the calculated depth of the settlement; \(p_{0}\) is the additional pressure at the bottom of the basement (kPa); \(\xi\) is the increasing coefficient of compression modulus of the composite soil layer, which is the ratio of characteristic values of the bearing capacity of the composite foundation to that of the natural foundation; \(E_{\text{si}}\) is the compression modulus of the \(i\)th soil layer under the basement (MPa); \(z_{i}\) and \(z_{i-1}\) are the distances between the bottom of the basement and that of the \(i\)th soil layer and the \((i-1)\)th soil layer (m), respectively; and \(\overline{x}_{i}\) and \(\overline{x}_{i-1}\) are the average additional stress coefficients between the point of calculation on the basement bottom and the bottom surface of the \(i\)th soil layer and the \((i-1)\)th soil layer, respectively.  
The above calculation of soil stress is based on the theory of isotropic homogeneous linear deformation. The nonlinear characteristics of soil, the influence of the stress path on strain in the soil, and the rebound-recompression deformation are not considered in this theory. Moreover, the effect of 3D space is not considered in this theory, because of which, the influence of large loads in the adjacent area on the stress-strain characteristics of soil is not reflected. Moreover, the synergetic interaction between the foundation structure under the action of the upper load and the soil cannot be considered in this theoretical calculation.
## 3 Research Methodology and Model Development
### Design Framework and DLA Development
#### 3.4.3 DLA Modeling  
The DLA concept was developed through the Python-VISSIM interface to enable dynamic lane direction changes in response to varying traffic conditions. This process  
Figure 11: The structure of the developed DLA model.  
incorporated parameter tuning, stochastic variability, and iterative scenario testing to refine model behavior and validate simulation outputs.  
To assess DLA performance under high-demand conditions, the morning peak hour was selected as the primary use case. Emphasis was placed on addressing the imbalance in lane allocation: the corridor currently provides one lane for west-to-east traffic and two lanes for the opposite direction despite significantly increased eastbound volumes during the morning rush period. This asymmetry results in operational inefficiencies under the static lane arrangement.  
Accordingly, three simulation scenarios were introduced: a calibrated baseline without DLA (Scenario 1), an active DLA configuration during the morning peak (Scenario 2), and a transitional setup (Scenario 3) capturing the effects of real-time directional switching between morning and afternoon flow patterns.  
In the DLA-enabled scenarios, the central lane dynamically accommodates eastbound traffic when predefined congestion thresholds are exceeded. This logic (Figure 12) also incorporates a temporary closure period during lane reversal to ensure vehicle clearance and maintain operational safety. This dynamic configuration highlights the adaptability of the DLA system in managing directional traffic imbalances and improving overall corridor performance.  
Figure 12: The logic of DLA modeling according to traffic conditions.
# On the Potential of the RST-FLARE Algorithm for Gas Flaring Characterization from Space
## 3 Results
### Detection of Flaring Sites  
By applying the methodological step described in Section 2.3.1 the flaring sites active in each of the years between the 2000-2016 period in the Niger Delta basin were first identified (black line in Figure 5), discriminating also between onshore and offshore sites (green and light blue bars, respectively).  
Besides the total number, also the spatial distribution of the onshore and offshore detected flaring locations among the seven states of the region has been assessed (Table 1).  
Looking at these values, Rivers is the state with the greatest number of flaring sites both in the terrestrial and marine environment, followed by Delta and Bayelsa.  
The spatial distribution of the detected flaring sites, together with their frequency of occurrence, is reported in a map, shown in Figure 6.  
The map, showing a total of 314 flaring locations (derived from the multitemporal aggregation), of which 68% (215) onshore and 32% (99) offshore, provides also information about the activity duration in order to have a general overview of the areas mostly perturbed by gas flaring. In particular, sites have been depicted with different colors on the basis of how long (in terms of years) they were detected as active within the whole investigated temporal period. By observing Figure 6, the most of  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**State**} & \multicolumn{3}{c}{**N. Detected Flaring Sites**} \\ \cline{2-4}  & **Onshore** & **Offshore** & **Total** \\ \hline
1. Edo & 19 & 0 & 19 \\
2. Delta & 56 & 23 & 79 \\
3. Bayelsa & 54 & 15 & 69 \\
4. Imo & 12 & 0 & 12 \\
5. Rivers & 70 & 34 & 104 \\
6. Aiba & 1 & 0 & 1 \\
7. Akwa Ibom & 3 & 27 & 30 \\
**Total** & **215** & **99** & **314** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of flaring sites in the Niger Delta states between onshore and offshore.  
Figure 5: Temporal trend of total flaring sites detected applying RST-FLARE (black line) in the Niger Delta region, for each year from 2000 to 2016, as sum of onshore (green bars) and offshore (light blue bars) sites.  
Figure 6: Spatial distribution of flaring sites detected by RST-FLARE in the years 2000–2016, differently colored according to their occurrence frequency.  
sites were active in between two and four years (41%, orange dots), followed by the ones active in one year only (25%, yellow dots) and operating in five or at most eight years (25%, green dots). Only a 9% of sites has been identified as active for over 9 years (violet dots).
## 3 Field Monitoring  
### Monitoring Scheme  
In the field, the pit was mainly monitored by instruments and supplemented by visual observation. A master survey section was set up each 20 m along the edge of the foundation pit, which had a total of 34 master survey sections. The top and body of the pile in the foundation pit were fitted with measurement points in each main section. The TOPCOM 7001 total station, Leica DNA03 precision level, and CX-08A inclinometer were used to monitor the horizontal displacement of the top of the pile, the vertical displacement of the top of the pile, and the horizontal displacement of the body of the pile. The above monitoring instruments are highly accurate and can measure up to 0.01 mm of the deformation, which can play a good role in measuring the deformation of the foundation pit. All anchor cables in each main survey section were equipped with force gauges to monitor the axial force of the anchor cables. Two rows of measurement points were arranged at the surface of each main survey section at the vertical pit edge, with each row of four measurement points spaced at 0.5 m, 3 m, 5 m and 10 m, to measure surface settlement using Leica DNA03 precision levels. At the edge of the foundation pit, monitoring posts were placed at 20 m intervals (in line with the main survey sections). While the top of the bored piles was being filled in each main survey section, prefabricated metal markers were placed and the horizontal displacement of the pile top was measured using a total station and the polar coordinate method. At the same time, a tilt pipe hooked directly to the bored pile's reinforcing cage was used to monitor the pile's horizontal displacement. Before the foundation pit was excavated, initial values were measured for each project. Monitoring occurred once every three days when the excavation depth was small. The frequency of monitoring increased as the depth of the excavation increased. Monitoring once or twice each day was required when the excavation depth surpassed 15 m. Figure 3 shows the location of each measurement point, and Figure 4 shows the arrangement of a typical monitoring section. In the figure, ZQT, DBC, M, ZQC, and ZQS represent the horizontal displacement at various depths, ground settlement, anchor cable internal force, vertical displacement of the pile top and horizontal displacement of the pile top, respectively.
# Intelligent Environment-Adaptive GNSS/INS Integrated Positioning with Factor Graph Optimization
## 3 Adaptive GNSS Uncertainty Estimation by CNN  
This section will mainly discuss the selected features in deep learning, the settings of the proposed CNN structures, and the mechanisms used to tune the GNSS solution uncertainty adaptively. This uncertainty requires adjustment of the GNSS solution noise covariance (\(\Sigma_{GNSS}\)) in Equation (17). The purpose of adaptation is to help improve the positioning accuracy of GNSS/INS integration compared with the methods with fixed solution uncertainty. In each second during the selected path, the truth 2D positioningerror is obtained by comparing between the GNSS receiver's solution and the ground truth position.
## 2 Methods
### Data Preprocessing  
In this study, the data preprocessing period mainly comprises resizing and denoising. The real-world wafer bin map dataset contains WBMs of slightly different sizes. To ensure consistent input dimensions for subsequent modeling, the original WBMs are resized using nearest-neighbor interpolation. In this study, all wafer maps are resized to 64 \(\times\) 64.  
The WBM defect pattern can be seen as a superposition of two independent components: random noises (particle correlation) and system clustering (process correlation). Random noises are generally problems with the clean room environment and tend to rise and fall with the overall cleanliness of the clean room. Reducing random noises requires long-term, incremental improvements or expensive equipment overhauls, but systematic defects can be easily eliminated through corrective efforts. Conversely, system defects caused by assignable causes are often attributed to processing equipment and human error. Therefore, it is necessary to remove random noise dies before conducting defect pattern analysis.  
To reduce random dies, we applied the median filtering algorithm adapted to WBM denoising. The median filtering algorithm effectively removes various types of noise, including salt-and-pepper and Gaussian noise. It is also able to preserve image edges and details better than other types of smoothing filters. The algorithm works by smoothing the image using the median value of the pixels in a sliding window. Specifically, a window of size \(N\times N\) is centered around each pixel in the image. The pixel values in the window are sorted in ascending order, and the median value is then assigned to the central pixel. In this study, we set \(N\) equal to three.
## 3 Results
### Data Balancing: Application of Enhanced Sampling Methods
#### 3.3.2 Under-Sampling Results  
Using the ENN under-sampling algorithm to filter out confusing samples from the original dataset does not control the balance of sample quantities for each class. Therefore, the dataset processed by ENN remains unbalanced, with little change in the relative quantities and proportions of samples for each class compared to the original dataset (Figure 7g,j). The ENN algorithm can remove redundant majority class samples and construct a more lightweight and reliable well logging dataset without losing critical information. However, the under-sampling algorithm does not increase the quantity of minority class samples, resulting in limited improvement in the predictive performance for relevant lithologies.  
In summary, combining the sample filtering capability of the ENN under-sampling algorithm with the data synthesis function of over-sampling algorithms can maximize the enrichment of lithological information content in well logging data. This approach maintains adaptability and flexibility in handling actual well logging datasets, ultimately leading to the training of optimal intelligent discrimination models.
## 2 State-of-the-Art and Related Topics
### Traffic Information Collection  
As traffic information is the base data of ITS, how to collect traffic information efficiently is an important area of research. Loop detectors are pressure, magnetic, and other sensors buried underground to detect if there are vehicles passing over them. They are widely deployed in urban areas to count the number of vehicles passing through fixed points of roads. Compared to loop detector technology, machine-vision-based traffic monitoring is a state-of-the-art approach with the advantages of easy maintenance, real-time visualization, and high flexibility [14]. With properly installed cameras, traffic information such as speed, volume, and even traffic accidents can be detected. However, it is expensive to establish these systems as well as to maintain a huge number of fixed devices on the road side, and they can only gather the traffic information of fixed points.  
With the equipment of global position system (GPS) receivers on vehicles or mobile phones, vehicles can detect their own real-time location and speed. An alternative traffic information collection approach has been proposed to estimate the traffic state by checking the location and speed of some vehicles running on the road [15; 16]. These probe vehicles are known as floating vehicles, and are normally buses and taxis. Due to the low cost of GPS receivers, the overall cost of the floating vehicles system is low. The shortcoming of floating vehicles is that their distribution in the urban traffic network is not even in space and time, which means that they may not be able to provide complete traffic states of the whole road network.  
Vehicular Ad hoc Networks (VANET) [17; 18; 19] are an emerging technology developed for traffic security and data transformation [20]. In recent years, many research studies have used VANET to collect traffic information. The first type of system uses VANET to only estimate the traffic density of the road by detecting the number of vehicles in the VANET communication range [21]. The other type of system assumes that each vehicle is also equipped with a GPS receiver so more detailed traffic information can be collected [22; 23]. As GPS receivers are more commonly equipped when compared with VANET and GPS can provide more information, more research has focused on the approach with GPS. With the communication capability of VANET, the collected traffic information can be easily shared and used by other ITS applications such as traffic prediction, route planning, and so on.  
After traffic information is collected, it can be used as an input to other ITS applications as base data. Since VANET can obtain the traffic information of the whole network without infrastructure and can be easily integrated with other ITS systems, it was used to collect traffic information in our paper. Section 3.3 discussed our traffic information collection method via VANET in detail.
# On the Integration of VLBI Observations to GENESIS into Global VGOS Operations
###### Abstract  
The upcoming European Space Agency (ESA) satellite mission GENESIS is an Earth-orbiting satellite carrying instruments of all four space geodetic techniques. The onboard transmitter for Very Long Baseline Interferometry (VLBI) will allow the observation of the satellite with VLBI radio telescopes. The objective of this study is to investigate the integration of VLBI observations of GENESIS into the operations of the VLBI Global Observing System (VGOS). Based on both current and foreseeable modern VGOS antenna networks, we consider the realistic observability of both geodetic radio sources and GENESIS. We conduct a comprehensive scheduling and perform extensive simulations of the VLBI observations. We assume that observations of GENESIS are scheduled within regular, geodetic experiments. The integration of GENESIS as an additional source in the scheduling results in a minimal degradation in the geodetic parameter estimation of station positions and dUT1 of less than 0.09 mm and 0.06 us, respectively. The results suggest to schedule scans of GENESIS at intervals of about 5 min to limit the decrease in the number of observations of geodetic sources to less than 5% with respect to schedules containing only geodetic radio sources. The schedules for 24 h experiments comprise about 150 to 200 scans and 1000 to 5000 observations of GENESIS, depending on the size of the utilized network. The frame ice accuracy between the VLBI and GENESIS frames is assessed in the form of station positions, which are solely estimated from observations of GENESIS. Multiple 24 h experiments are simulated over 52 weeks with assumed session cadences of two to three experiments per week. By stacking the normal equations from three months of experiments, we obtain station position estimates with a precision of less than 10 mm. After 12 months, the repeatabilites are reduced to less than 5 mm.  
space geodetic technique; VLBI; GENESIS; terrestrial reference frame; scheduling; simulations +
Footnote †: journal: Remote Sensing  
0
Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
## 4 Conclusions  
In summary, this study provides the comprehensive determination of the key odorants of six CSSs. A total of 55 aroma-active compounds were positively identified by comparing their MS data, RIs and odor characteristics with those of standard compounds, and their FD factors were measured using GC-O, coupled with AEDA. Twenty-seven volatile compounds with OAVs \(\geq 1\) were furtherly screened as key odorants contributing to the characteristic aroma profile of six CSSs by means of quantitative analyses combined with the calculation of OAVs. The results show that the key odorants in CSS were the same as those in GSS. Further research should focus on how to quantitate the odorants with higher FD factors and without responses to MS detection, as well as performing aroma reconstitution experiments and omission tests to further confirm the results and investigating if there are differences between the nutrients of CSS and GSS.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Compound**} & \multirow{2}{*}{
\begin{tabular}{c} **DOT** \\ **(\#/L)** \\ \end{tabular} } & \multicolumn{6}{c}{**OAV ** \\ \cline{3-8}  & & & **Chinese CSS Samples** & & **Japanese CSS Samples** \\ \cline{3-8}  & & & **C1** & **C2** & **C3** & **J1** & **J2** & **J3** \\ \hline
24 &
\begin{tabular}{c} methional \\ 3 \\ 2-methylbutanal \\ 1.5 1 \\ \end{tabular} & 0.43  & 6166 & 359 & 332 & 3202 & 3867 & 11165 \\
3 & 3-methylbutanal & 0.50 1  & 3479 & 379 & 1570 & 2512 & 2468 & 21142 \\
3 & 2-methylbutanal & 1.5 1  & 2363 & 58  
**Author Contributions:** Conceptualization, J.H., Y.L. and B.S.; methodology, J.H., H.C. and Y.L.; investigation, J.H. and H.C.; data curation, J.H., H.C. and Z.Z.; validation, J.H., H.C. and B.L.; supervision, H.C. and B.S.; project administration, H.C., Y.L. and B.S.; writing--original draft preparation, Y.L.; writing--review and editing, H.C. and Y.L.; software, Z.Z. and B.L.; funding acquisition, H.C., Y.L. and B.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was financially supported by National Key Research & Development Program of China (No. 2017 YFD0400501 and 2018 YFD04006).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data shown in this study are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Using Machine Learning and Candlestick Patterns to Predict the Outcomes of American Football Games
## 2 Related Work
### Candlestick Charts and Machine Learning  
Candlestick charting can be traced back to the Japanese rice trades and financial instruments from centuries ago. It is a method for visualising data in different ways to predict recent stock price fluctuations and provide insights into market psychology. Japanese candlestick theory has become one of the most widely used technical analysis techniques for making investment decisions based on empirical finances. Future trends in financial time series are considered to be predictable by identifying specific candlestick patterns.  
Some scholars have proposed ways to describe these known patterns with ordered fuzzy numbers ([PERSON] and [PERSON], 2018) or formal specifications ([PERSON] and [PERSON], 2018) to provide recognition using machine learning. Some scholars have characterised financial time series with candlestick charts and then used ANN, fuzzy logic, genetic algorithms, decision trees and various hybrid approaches to provide predictions about the market trend for investment decision use ([PERSON] and [PERSON], 2018, 2018). Given that candlestick charts incorporating machine learning have been beneficial in the application of finance, some studies in other fields have initiated applying them as a tool for analysis and prediction. They have been applied in studies on predicting teens' stress level change on a micro-blog platform ([PERSON] and [PERSON], 2018) and in sports metrics ([PERSON] and [PERSON], 2018) to forecast game outcomes.  
However, the application of candlestick charts in other fields is slightly beyond the applicability of the original financial time series. They need to be adjusted and modified to accommodate specific characteristics of the field. Whether candlestick charts exhibit certain patterns and corresponding behaviours as in the financial field and whether they are equally suitable for pattern recognition using machine learning to provide predictions are research areas to be examined.
## 4 Performance and Simulation of 2D Maximum Energy Search Approach
### Performance Analysis and Processing Parameter Influence Evaluation
Thus, the coherence loss function seems to be more tolerant with the residual Doppler rate than with residual Doppler frequency, which is shown in Figure 10.  
Figure 10: Comparison of coherent loss between residual frequency and residual frequency rate. Figure 9: Comparisons of carrier acquisition probability for different integration approaches. According to the above analysis, if the Doppler rate could be directly estimated and removed from the raw signal samples, the coherent integration time could be expanded, and a better frequency estimation performance may be acquired.