esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods: Finite Element Modelling
### Tyre-Pavement Contact Model  
#### 3.3.1 Contact and Boundary Settings  
The stiffness of the simplified pavement model used in this study was considerably larger than that of the radial type model; consequently, the simplified pavement model could be regarded as a rigid body. The contact constraint of the tyre-pavement system was accordingly set to reflect the finite slip contact between rigid and deformed bodies in ABAQUS 2021. The tyre-pavement contact type was set to face-to-face contact, with the pavement set as the \"master surface\" and the tyre tread set as the \"slave surface\". For the contact property setting, the normal property was set as \"hard contact\", and the tangential property was set as the Coulomb friction model.  
In ABAQUS 2021, the pavement was set to be stationary by constraints, and the travel speed of a tyre was simulated by setting the linear and angular velocities at the reference points of the rim. The tyre load was set in the negative direction of the \(Z\)-axis at the rim reference point. The 3D contact finite element model of the tyre and simplified pavement is shown in Figure 10.  
Figure 9: Three-dimensional mesh model of simplified pavement. (a) Aggregate grain size 7 mm. (b) Aggregate grain size 9 mm. (c) Aggregate grain size 13 mm.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Aggregate Grain Size** & \(V_{1}\) (mm\({}^{3}\)) & \(V_{2}\) (mm\({}^{3}\)) & \(A\) (mm\({}^{2}\)) & _MTD_ (mm) \\ \hline
7 mm & 120,000 & 55,417 & 40,000 & 1.61 \\
9 mm & 160,000 & 77,040 & 40,000 & 2.07 \\
13 mm & 200,000 & 85,412 & 40,000 & 2.86 \\ \hline \end{tabular}
\end{table}
Table 3: Pavement mean texture depth (_MTD_) with aggregate grain sizes of 7, 9, and 13 mm.
# The Application of Deep Learning and Image Processing Technology in Laser Positioning
## 1 Introduction  
As science and technology change quickly, the size of parts decreases continuously, the combination of miniature parts requires higher precision than general parts, and the production time for each part is fixed in mass production. If the solder at the junction fails to absorb adequate energy due to laser radiation offset, the weld may fail, and if the high-energy region of the laser irradiates other positions of the part, the part may be rejected directly. Therefore, it is necessary to search for and position the highest energy region of the laser.  
Many researches use different hand-craft features to represent the visual contents for images and try to seek appropriate similarity measurement to make the similarity of low-level features to be closer to the similarity of the high-level concepts. The deep convolutional learning method often improves its ability for feature extraction and its efficiency for similarity measurement [1]. The feature training classifier of a sample image was extracted using a neural network before matching [2], the position of the laser spot in the input image was found [3, 4], the center spot was searched for, and the laser spot position was moved to overlap the image center of a CCD (Charge-coupled Device) camera to complete positioning. However, when a large range of images was taken, images of incomplete spots often appeared. That still lead to precise positioning of the spot re-imaging process, but it took a lot oftime. So the purpose of this study was to combine machine vision with a deep learning algorithm to build a positioning system that could align the laser's center spot automatically and quickly.  
Traditional template matching method has many defects, for example, every time the template is changed, continuous rematching and weight updating are required, wasting much time, and when the matched image has an area similar to the template image but not the target area, there may be false matching. However, it must be considered that imaging systems use different databases, different imaging systems, and different parameters. A high-resolution image provides precise and easy position detection, but in real-time systems, they lead to high computational cost [5]. A hierarchical matching can be used to solve the problem of recognition accuracy [6]. However, none of the existing classification schemes encompasses all the possible features due to the vastness of the variability in feature geometry and topology [7]. The purpose of this study was to use deep learning to enhance feature recognition and shorten the time needed to obtain a result, so as to remedy the defects.
## 4 Aggregate Analysis
### Mode Share  
The commuting mode choice is a function of transportation supply, land use, and socioeconomic characteristics. Table 2 shows the mode share for the two years and its changes by area. For all HBW trips, the share of rail riders increased by 466.1%, which was likely attributed to the large extension of the light rail system during the period. The bus mode share saw a 63.3% increase. Driving was the only mode whose share decreased, and apparently shifted to other modes. There was a 77.8% increase in the mode share of passenger from a low base of 2.8%. The observed trends may suggest a potential increase in carpooling for commuting, but further investigation is required to confirm this hypothesis. The mode share of biking increased by 345.5%, which was likely attributed in part to the improvement in bike facilities throughout the region. Compared to the increase in biking, the increase in walking was low at 48.4%.  
For the CBD commuters, the share of driving saw a larger decrease to 36.5%, along with a 24.39% decrease in the passenger share. This might result from the rise in parking costs during the period. The mode share of rail increased by 362.18%, to 19.1%. However, there was only a 5.55% increase in the bus mode share. It seems that commuters prefer the rail to the bus. In 2011, the combined mode share of rail and bus was more than 44%,higher than the mode share of driving. The Portland case seems to show that investment in transit can reduce private vehicle dependence and that transit can be the dominant mode, at least for commuting trips to/from the CBD.  
For the non-CBD commuters, the changes in mode share were as drastic, even though from a low base for most modes: the rail share increased from 0.5% to 2.7%, while the bus share increased by 127.4%, along with a 15.4% decrease in the driving share and a 103.3% increase in the passenger share.
## 4 Conclusions and Future Work  
In this study, we propose a new approach for fall detection in an outdoor environment. There are two main contributions that our study makes. First, we developed a new fall detection algorithm that works accurately, even with a single camera, in an outdoor environment. Second, our method shows a new approach that combines 2D and 3D features of a depth image generated by a supervised learning method from a single RGB image to significantly improve fall detection in an outdoor environment. Our approach is a promising development, because we can use 3D information without using a special camera, such as the Kinect sensor, a depth sensor, or a multiple camera system. In addition, our method performs human detection using the particle swarm optimization method to locate the area of minimum variance on the aforementioned depth image. Consequently, this method solves the initialization problem mainly encountered in previous tracking studies.  
Furthermore, it can be said that the rapid changes in the background and considerable variation in illumination of an outdoor environment makes it is difficult to use the depth camera to perform segmentation smoothly. However, in this study, we successfully performed fall detection studies in various outdoor environments due to the fact that we do not use the background reference image for segmentation; one of the only requirements of this study is a depth image taken with a normal RGB camera. Moreover, in this study we experimented with several illumination changes to establish the accuracy of the algorithm. Through various experiments, we showed that the proposed method robustly detects falls in complex environments and that the tracking algorithm used in this study works accurately even when the illumination changes, suggesting the possibility of using it in surveillance systems of outdoor environments. For future work, we plan to carry out fall detection research in an outdoor environment comprising of more than one person, multiple cars, and many other objects that are intricately intertwined. The final research goal is to construct a social safety net based on surveillance systems to detect multiple people and multiple falls that simultaneously occur within a single scene.  
The first author (M.K.) developed the fall detection algorithms and lead the entire research and the second author (S.K.) implemented depth extraction. The corresponding authors (M.K. and K.K.) guided the research direction and verified the research results. All authors made substantial contributions in the writing and revision of the paper.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2015R1D1A3A01020539 and 2015R1D1A1A01060715) and supported under the framework of International Cooperation Program managed by National Research Foundation of Korea (NRF-2017K1A3A1A21013755).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Development and Optimization of GBR Model  
A Gradient Boosting Regressor (GBR) model was used to predict SMC due to its ability to capture the nonlinear interactions between studied features. GBR considers the form of ML algorithms that produce prediction models by integrating multiple weak learners, mainly decision trees, in a sequential manner. The procedure begins with an initial model, and the succeeding models are trained to optimize the previous models' errors, which effectively minimizes the loss function utilizing gradient descent approaches. This allowed us to develop a robust model with the ability to process complex patterns in data and enhance accuracy [39; 40].  
The model was trained for the two soil textures (Ioam and silt loam) separately to increase soil-specific prediction; the models were developed using the scikit-learn library; and the models were optimized using GridSearchCV for optimizing hyperparameters, including n_estimators, learning_rate, max_depth, and min_sample_split. A 5-fold cross-validation was utilized for performance validation to reduce overfitting and enhance robustness of the models. The dataset was split into 70% training and 30% testing sets. Feature importance analysis was carried out to determine the most important features that impact the SMC prediction in each soil texture. All modeling, analysis, and visualization were performed using Python libraries such as pandas, numpy, scikit-learn, seaborn, and matplotlib. The methodology used was developed to ensure the model's robustness, explainability, and applicability.
## 2 Conventional Channel Estimation Methods
### Pilot-Based Channel Estimation Method  
In wireless communications, the pilot-based method is the most used channel estimation method, with its process illustrated in Figure 3. The central idea of this method is inserting known pilots into the transmitted signal according to specific rules. After that, the corresponding received signal is extracted at pilot positions at the receiver. Following this, the CSI at pilot positions is computed by combining them with the known transmitted pilots. Finally, various interpolation algorithms are employed to estimate the CSI at non-pilot positions (data positions) [22; 23; 24; 25]. Among them, the LS algorithm [26; 27], the minimum mean square error (MMSE) algorithm [28; 29], and the linear MMSE (LMMSE) algorithm [30; 31] are the most classical pilot-based channel estimation methods. While we will introduce these pilot-based estimation methods using the OFDM system as an example, it is essential to note that these methods are not limited to OFDM systems.
# Secure Aviation Control through a Streamlined ADS-B Perception System
## 5 Analysis and Discussion
### Model Estimation and Optimization  
We split the dataset into training and testing datasets using ten different folds and k-fold cross-validation. The k-fold cross-validation technique helps split the dataset into multiple subsets of training and testing, which helps verify a model's performance by choosing different sets at each iteration to serve as training and testing datasets, which helps with utilizing the whole dataset, shuffling the data and making sure to remove any bias. K is the parameter that indicates the number of folds. Figure 3 displays the method of k-cross validation [36].  
We also performed dimensionality reduction, which is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation has meaningful properties of the originating dataset, ideally close to its  
Figure 3: K-cross validation technique [37].  
intrinsic dimension; because working with data in a high-dimensional space is usually undesirable and computationally hard, lowering the dimensionality of data could boost the performance of a model.  
We used principal component analysis (PCA), which is considered the main linear technique for reducing dimensionality. PCA performs a linear mapping of the data to a lower-dimensional space in such a way that the variance of the data in the low-dimensional representation is maximized.  
In principle, this will boost the effectiveness of our model and lower the needed features. PCA was applied, where 16 principal components were identified. Unfortunately, in our case, it caused a loss of information and problems with data interpretation since it uses linear combinations of original variables; this led to a drop in accuracy from 99.41% to 93.01%. Likewise, F1-score also dropped from 99.37% to 92.15%. Lastly, MCC also dropped from 0.988 to 0.888. The model performed better by utilizing all features of the ADS-MI-2022 dataset with no linear combinations or dimensionality reductions. So, we continued testing our model by utilizing all of the features of the ADS-MI-2022 dataset.
## 2 Materials and Methods
### Steps for Conducting LCCA
#### 2.5.1 Input Data for LCCA Calculation Procedure  
The input data for this study were categorized into two main groups: project specifics and material specifics. The project-specific data included parameters such as traffic volume or annual average daily traffic (AADT) and highway classification, lane width, and length of HFST application. Material-specific inputs focused on aggregate types, binder compositions, and performance characteristics measured during testing. Figure 5 highlights a section of the automated Excel spreadsheet, displaying input data for project specifics and material details based on the up-to-date price in May 2024 within the United States [9]. Key data elements included the following:  
* Aggregate Type and Gradation: Four different aggregates (CB, Rhy, Meramec, and Flint) were tested with three different gradations (original HFST size, coarse size, and medium size). Aggregate costs were measured in dollars per ton, while shipping costs were calculated in dollars per mile, taking into account the transportation distance from the aggregate source in miles. The number of tons per load and the rate at which aggregates were applied, measured in tons per square yard (ton/yd2), were also factored in.
* Binder Types: Binders included PG and emulsions and various blended asphalt-based binders. Modified binders with SBS polymer modifiers were also included. For binders, costs were expressed in dollars per gallon, along with shipping costs per gallon. The binder application rate, measured in gallons per square yard (gallon/yd2), was another key parameter. Additional construction-related costs, including labor,  
Figure 5: Project specifics and material-specific input data.  
equipment, and other expenses, were assessed in terms of dollars per square yard (yd2). The cost of removing HFST was also included in the analysis, quantified in dollars per square yard.
* Performance Prediction Models: The adopted models, recognized as the most reliable in the literature, converted the frictional performance measurements of each sample--using BPN, DFT, and MPD--at different stages (pre-polish, post-polish, and under traffic simulation) into predicted skid numbers (SNs). The predicted SN values were subsequently compared with the recommended terminal SN values, as defined by the rehabilitation matrix (Table 3), to estimate future maintenance and rehabilitation needs. This analysis utilized the present value approach to facilitate the comparison of alternatives. Three prediction models considered for this study are as follows: 2.5.2 and 2.5.3 and 2.5.4 are the models.
## 2 HGF Model
### Evaluation Metrics
Therefore, for the quantitative evaluation, considering the need for the quantification of surface elements at a later stage, the water vapor absorption bands, and the number of bands of hyperspectral satellite images, the whole spectral range was divided into three band intervals (band 1: 390-730 nm, band 2: 730-1400 nm, and band 3: 1400-2260 nm). Furthermore, the soil, vegetation, and water bodies were evaluated separately using the spectral angle index, facilitating the selection of an optimal algorithm for the quantitative analysis of surface elements at different band intervals.
# Proteomics Analysis of _Zygosaccharomyces mellis_ in Response to Sugar Stress
###### Abstract  
The high-osmotic-pressure environment of honey is not suitable for the survival of microorganisms, except for osmotic-tolerant fungal and bacterial spores. In this study, shotgun metagenomic sequencing technology was used to identify yeast species present in honey samples. As a result, _Zygosaccharomyces_ spp. yeast, including _Zygosaccharomyces_roxxii, _Z. mellis_ and _Z. siamensis_, were isolated. The intracellular trehalose and glycerin concentrations of yeast, as well as the antioxidant-related CAT, SOD and POD enzyme activities, increased under a high-glucose environment (60%, \(w/v\)). To learn more about the osmotic resistance of _Z. mellis_, iTRAQ-based proteomic technology was used to investigate the related molecular mechanism at the protein level, yielding 522 differentially expressed proteins, of which 303 (58.05%) were upregulated and 219 (41.95%) were downregulated. The iTRAQ data showed that the proteins involved in the pathway of the cell membrane and cell-wall synthesis, as well as those related to trehalose and glycerin degradation, were all downregulated, while the proteins in the respiratory chain and TCA cycle were upregulated. In addition, formate dehydrogenase 1 (FDH1), which is involved in NADH generation, displayed a great difference in response to a high-sugar environment. Furthermore, the engineered _Saccharomyces cerevisiae_ strains BY471\(\triangle\)scFDH1 with a knocked-out _FDH1_ gene were constructed using the CRISPR/Cas9 method. In addition, the _FDH1_ from _Z. mellis_ was expressed in BY4741\(\triangle\)scFDH1 to construct the mutant strain BY4717 zmFDH1. The CAT, SOD and POD enzyme activities, as well as the content of trehalose, glycerin, ATP and NADH, were decreased in BY4741\(\triangle\)scFDH1. However, those were all increased in BY4717 zmFDH1. This study revealed that _Z. mellis_ could increase the contents of trehalose and glycerin and promote energy metabolism to improve hypertonic tolerance. In addition, FDH1 had a significant effect on yeast hypertonic tolerance.  
y 10 June 2022  
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## 3 Results and Discussion
### Structural Elucidation
#### 3.2.5 Structural Elucidation of DBP11  
The 1D and 2D NMR data indicate the opening of the B ring via the hydrolysis of the bond C-7/N-6. Indeed, in the HMBC spectrum, carbonyl C-7 at 176.01 ppm was correlated with the signals at 2.49 and 1.54 ppm, which were identified as protons H-29 and H-30 of the \(n\)-butyl side chain, while carbonyl C-9 at 180.05 ppm was correlated with the signals at 2.12 and 1.96 ppm, which were identified as protons H-3 and H-5 of the cyclopentane group. MS-TOF analysis showed a molecular ion peak at \(m/z\) 495.88 [M + H]\({}^{+}\) in addition to the signals at \(m/z\) 494.13, 496.15, and 498.13, corresponding to the molecular formula C\({}_{25}\)H\({}_{29}\)Cl\({}_{3}\)N\({}_{2}\)O\({}_{2}\) and to the presence of three chlorine atoms. Indeed, in the HMBC spectrum, proton H-22 at 8.24 ppm was correlated with the carbons at 134.75 and 141.89 ppm, which were identified as carbons C-20 and C-18, respectively, and with carbon C-24 at 97.40 ppm, which, evidently, was obtained from the degradation of the tetrazole ring. The plausible mechanism behind the formation of DBP11 from irbesartan is shown in Figure 4, and the NMR assignments of DBP11 are shown in Table S5.  
Figure 3: Plausible mechanism for the formation of DBP1, DBP5–DBP7, DBP10, and DBP12–DBP16.
## 3 Materials and Methods
### Volatile Compound Analyses  
The analysis of volatile organic compounds from sample headspaces was performed using solid-phase microextraction (SPME) and gas chromatography coupled with mass spectrometry (GC-MS). Volatile compounds were extracted from headspaces using DVB/CAR/PDMS SPME fibre (divinylbenzene/carboxen/polydimethylsiloxane, 1 cm length, 50/30 \(\upmu\)m thickness; Supelco, Bellefonte, PA, USA). Before analysis, the fibre was conditioned by heating in a GC injection port at 270 \({}^{\circ}\)C, and approximately 5 g of the homogenised sample was placed in a 20 mL vial, closed with a silicone-Teflon sealing cap, and heated to 37 \({}^{\circ}\)C for 1 h in order to stabilise the concentrations of volatile compounds in the vial. Then, the SPME fibre was introduced into the sample headspace for a period of 40 min. Thereafter, the fibre was quickly transferred to the GC injection port (splitless mode, temperature of 270 \({}^{\circ}\)C) to desorb volatiles into the GC system. Chromatographic separation was performed using an Agilent 6890 GC system (Agilent Technologies, Palo Alto, CA, USA) coupled with an Agilent 5795 quadruple MS instrument (Agilent Technologies). A DB-5 MS column (30 m, 0.25 mm, 0.25 \(\upmu\)m, 5% diphenyl-95%-polydimethylsiloxane, Agilent Technologies, Palo Alto, CA, USA) was used with helium as the carrier gas at a flow rate of 0.9 mL/min. The GC oven was programmed as follows: an initial temperature of 38 \({}^{\circ}\)C was maintained for 10 min, increased to 200 \({}^{\circ}\)C at a rate of 4 \({}^{\circ}\)C/min, maintained for 2 min, and then raised to 250 \({}^{\circ}\)C at a rate of 20 \({}^{\circ}\)C/min, and the final temperature (250 \({}^{\circ}\)C) was maintained for 7 min. The mass spectrometer was programmed as follows: the temperatures of the ion source and mass analyser were 230 and 150 \({}^{\circ}\)C, respectively, and mass spectra were recorded in electron ionisation (EI) mode with an ionisation energy of 70 eV and an ion scanning range of 33-350 \(m/z\) (amu). The obtained spectra were subsequently analysed using the Mass Selective Detector ChemStation program (D.02.00.237, Agilent Technologies, Palo Alto, CA, USA). Individual peak identification was based on the comparison of the mass spectra with references in the mass spectra databases of NIST 08 (US National Institute of Standards and Technology) and Wiley 8 th Ed. Compound mass spectra targets were confirmed by comparing linear retention indices (LRIs) calculated relative to C6-C20 alkanes with an LRI database built in the NIST.08 mass spectra library. In order to use LRI method, a C8-C20 alkanes mixture (Fluka, Buchs, Switzerland) and pure standards of hexane and heptane (Sigma Aldrich, St. Louis, MI, USA) were analysed. Quantity of each compound was determined as a peak percentage area relative to total area of all peaks on the chromatogram (relative content (%)) [54].