esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Viral Investigations
#### 2.5.1 Negative Staining Electron Microscopy (nsEM)  
All the samples taken in 2020 and 2021 (Table 3) were used for the nsEM analyses. The extracts were prepared and treated using the method commonly applied to honeybees [58]. Each specimen was placed in 2.4 mL 0.001 M potassium buffer (BP), pH 6.7, containing 0.2% sodium diethylditiocarbamate (Dieca) to prevent melanization and then mechanically homogenized (Ultraturrax--Ika Werk, Staufen, Germany). The extracts were emulsified with 0.3 mL chloroform and 0.3 mL diethyl ether and then cleared by low-speed centrifugation at 4500\(\times\)\(g\) for 30 min. The supernatants were separated and again centrifuged at 9500\(\times\)\(g\) for 30 min. Next, 100 \(\upmu\)L of each supernatant was ultracentrifuged with an Airfuge (Beckman, Indianapolis, IN, USA) operating at 21 psi 82,000\(\times\)\(g\) for 15 min, which was fitted with an A100 rotor that could hold six 175 \(\upmu\)L test tubes and was equipped with specific plastic adapters that would enable the direct pelleting of virions onto 3 mm carbon-coated Formvar copper grids. The grids were negatively stained with 2% sodium phosphotungstate (NaPT) at pH 6.8 for 90 s and observed with a FEI Tecnai G2 Biotwin transmission electron microscope (FEI Company, Hillsboro, OR, USA) operating at 85 kV at 16,500-43,000 magnifications. Morphological differences in viral particles (shape, size, presence of structural elements, symmetry, etc.) were recorded and described. The identification of DWV and ABPV species was performed by means of an IEM method using anti-DWV (produced in IZSLER) and an anti-ABPV polyclonal serum produced in rabbits, respectively [56; 59; 60]. Compared to the method described above, an aliquot (50 \(\upmu\)L) of the test sample was incubated with an equal amount of serum for 1 h at 37 \({}^{\circ}\)C under gentle agitation before ultracentrifugation. On observation, a positive reaction was expressed by the formation of immunoaggregates, i.e., dense clusters consisting of numerous antibody-coated particles. These EM methods allow for a semi-quantitative evaluation of viral particles. As the observation support (grid) is divided into meshes, the results are expressed by referring to them as follows: 1+ = 1-5 particles in the observed grid as a whole; 2+ = 1-5 particles in each of the observed grid meshes; 3+ = 20-50 particles in the grid as a whole; 4+ = 20-50 particles for each of the observed grid meshes; and 5+ = more than 50 particles for each of the observed grid meshes. The semi-quantitative IEM results consider the number and size of the immunoaggregates on the grid, as follows: 1+ = 1-5 small aggregates (2-10 particles) in the grid as a whole; 2+ = 1-5 small aggregates (2-10 particles) for each of the observed grid meshes; 3+ = 5-20 variable-sized aggregates in the grid as a whole; 4+ = 5-20 larger aggregates 10-50 particles) for each of the observed grid meshes; and 5+ = many aggregates (more than 20) or larger aggregates (more than 50 particles) for each of the observed grid meshes.
# Research on Evaluation Methods of Black Soil Farmland Productivity Based on Field Block Scale
## 2 Materials and Methods  
### Data Sources and Data Preprocessing  
#### 2.1.1 Description of the Experimental Site  
The data in this paper were obtained from Keshan Farm in Qiqihar City, Heilongjiang Province, China, which is located in the territory of Nehe and Keshan County, at 48\({}^{\circ}\)12\({}^{\prime}\)-48\({}^{\circ}\)23\({}^{\prime}\) N, 125\({}^{\circ}\)8\({}^{\prime}\)-125\({}^{\circ}\)37\({}^{\prime}\) E. Its land area is about 351 square kilometers (km\({}^{2}\)), and the cultivated area is about 272 km\({}^{2}\). The farmland's landscape is primarily characterized by rolling hills and undulating terrains. It is located on the western foothills of the Lesser Khingan Mountains and in the northeastern part of the Songnen Plain. The slopes generally range from 1 to 5 degrees, with an average slope of 3 degrees. The length of slopes is mostly between 500 and 100 m. The average altitude is 315 m. The annual average temperature is 1.3 \({}^{\circ}\)C. The main climatic characteristics in spring are windy with sparse rainfall, while summer is primarily characterized by concentrated and heavy rainfall. Winter temperatures are cold, with a lowest temperature of \(-37.6\)\({}^{\circ}\)C, and snowfall is the primary form of precipitation. The soil of Keshan Farm is mainly black calcareous soil, belonging to the typical black soil region of Northeast China.  
In October 2022, the research team selected Sample Plot A (48\({}^{\circ}\)16\({}^{\prime}\)31.7\({}^{\prime\prime}\) N, 125\({}^{\circ}\)25\({}^{\prime}\)10.4\({}^{\prime\prime}\) E) in Keshan Farm, which had been reclaimed for 26 years. In this area, 21 field sampling points were obtained. Sample Plot B (48\({}^{\circ}\)16\({}^{\prime}\)37.7\({}^{\prime\prime}\) N, 125\({}^{\circ}\)25\({}^{\prime}\)4.6\({}^{\prime\prime}\) E), reclaimed for 39 years, yielded 15 field sampling points. Sample Plot C (48\({}^{\circ}\)17\({}^{\prime}\)0.0\({}^{\prime\prime}\) N, 125\({}^{\circ}\)24\({}^{\prime}\)6.4\({}^{\prime\prime}\) E), reclaimed for 59 years, provided 45 field sampling points. Lastly, Sample Plot D (48\({}^{\circ}\)21\({}^{\prime}\)12.5\({}^{\prime\prime}\) N, 125\({}^{\circ}\)33\({}^{\prime}\)5.3\({}^{\prime\prime}\) E), reclaimed for 90 years, contributed 33 sampling points. In total, 114 soil physicochemical data points and corresponding yield data were collected. The sampling methods were identical across all four plots, and a schematic diagram of the sampling process in Sample Plot A is shown in Figure 1.
# A New Algorithm Model Based on Extended Kalman Filter for Predicting Inter-Well Connectivity
## 3 Construction of Numerical Model  
### Project Background  
Qinhuangdao Oilfield 32-6, located in Bohai Bay, is a large, thick oil field with complex fluvial features, whose main reservoirs are the lower part of the Neoproterozoic Minghuazhen Formation and the upper part of the Guantao Formation. The oilfield is characterized by high porosity (32% on average) and high permeability (2-8474 mD) as well as medium to high crude oil viscosity (22-260 mPa-s).  
The distribution of oil layers and well location in the Qinhuangdao Oilfield 32-6 is shown in Figure 2. Wells in Qinhuangdao Oilfield 32-6 are being produced with fixed BHP adopting stimulation measures, such as leakage plugging, which has greatly improved the output. Brine saturated with the active agent was injected into the formation to displace oil, and the injection pressure, according to the monitoring results, fluctuated significantly. Currently, the field faces problems of increased dynamic inhomogeneity and ineffective water circulation caused by long-term development, especially after the main sand body enters the high water content stage.
# The Role of Smart Meters in Enabling Real-Time Energy Services for Households: The Italian Case
## 7 Assessment of the Available Architectures
### Network Services Category  
As for use cases regarding the support to the electric network stability, the customer could modify his/her scheduling according to signals coming from the grid. This implies the need for a different channel for forwarding information to the customer or to an. Requirements for these use cases are similar to the ones for market services, with more stringent requirements regarding latency. Hence, Architecture 1 is not suitable for this family of use cases, unless it is extended into Architecture 4.  
Architecture 2 can let all the IHDs receive metering data, network signals, and, eventually, dedicated commands coming from an authorized third party, thanks to the WAN. As for the other scenarios, short-range wireless technologies such as BLE or Wi-Fi can be used for reaching the HAN with Architecture 2. For deep-indoor SM installations, PLC-C is a valid option, with some regards to use case N1, which requires higher speed, which might not be available over PLC.  
Architecture 3 allows a service provider to supply the customer with customized requests based on the network status simply through a smartphone or a dedicated dashboard connected to the public network. This third party could also feed connected IHDs with scheduled activities. To achieve this, special agreements between DSOs and service providers are necessary to allow forwarding grid information. Technologies used with Architecture 3 can be wireless WANs over licensed spectrum, such as NB-IoT, or over unlicensed spectrum, such as LoRa or wM-Bus. Use case N1 could be challenging for all the solutions since the latency required for all the communications paths through the wireless network can be very long.  
Finally, Architecture 4 can be adopted for extending a simple Architecture 1 thanks to the usage of an IHD-GW, able to collect metering data and network signals for making decisions.  
It is worth noting that if network signals are sent simultaneously to different customers served by the same secondary substation, a shared communications medium (e.g., PLC-C) used by SMs and IHDs might not have enough capacity for fast notification.  
Figure 7: Example of Scheduling & Control services with Architecture 4: an IHD, acting as a gateway (IHD-GW), collects metering data from the SM using PLC-C and other information from a WAN with cellular or IoT-dedicated technologies. Finally, it calculates and/or forwards set points to the smart appliances and statistical data to the customer’s devices.
## 2 Materials and Methods  
### Study Site, Experimental Design and Treatments  
Bovine serum albumin (BSA; Fraction V, 96-98%), tannic acid, triethanolamine, and acetonitrile were purchased from Sigma-Aldrich (St. Louis, MO, USA). Urea was purchased from Promega (Madison, WI, USA). A sodium hydroxide standard solution (0.1 M) was purchased from Yakuri Pure Chemicals (Kyoto, Japan). All the analytical aroma standards were purchased from Sigma-Aldrich. All other reagents used were of analytical grade. Shine Muscat grapes were cultivated in vineyards in Ansumg, Korea, in 2020. The experiment was a randomized block design with three treatments and three replicates. Each block consisted of five vines that were spaced at a fixed distance of 5 \(\times\) 2.7 m. The grape berries were treated with a combination of gibberellic acid and thidiazuron (25 and 2 ppm, respectively) twice, at 2 and 14 days after full bloom. Berry thinning was performed by removing approximately half the berries. The grapes were harvested at 6, 8, 10, 12, 14, 16, 18, 20, and 22 weeks after full bloom (WAFB), from July to October 2020. The harvested grape samples were grouped according to the WAFB. Grape berries from twenty clusters collected from different vines were analyzed immediately after harvest. Ten clusters were subjected to instrumental analyses and the other ten clusters were used for sensory analysis.
# A Proposal of Printed Table Digitization Algorithm with Image Processing
## 3 Adopted Technologies
### OpenCV  
_OpenCV (Open Source Computer Vision Library)_ is the open-source library of programming functions that are mainly aimed at real-time computer vision [8]. In the algorithm implementation, _opencv-python version 4.6.0.66_ is adopted to call _OpenCV_ functions from the _Python_ program. The following four tasks are completed by _OpenCV_ functions in this algorithm:  
1. Recognize every intersection of the horizontal line and the vertical line to find the location of the cells in the table.
2. Divide the table into a collection of rectangles using the intersections.
3. Arrange the rectangles from left to right and top to bottom by recognizing the boundary of the horizontal and vertical lines.  
Recognized rectangles sometimes are too large or too small. The rectangle will have multiple cells if it is too large, and it will have none if it is too small. In order to prevent both of these situations, we restrict the rectangle's size range.
## 4 Receiver Implementation Approaches  
There are two implementation approaches for a GPS receiver that are explained in the following sections.
## 5 Results  
The SBAS-InSAR processing strategy yielded 458 interferograms. The topographic phase in the interferogram was filtered by external DEM. The interferogram was filtered using the Goldstein algorithm to reduce the coherence noise, and the corresponding coherence coefficient diagram was obtained. Using the high-coherence points in the obtained diagram, the phase unwrapping was realized through the minimum cost flow (MCF) algorithm, and the images with a poor unwrapping effect were removed, thus obtaining the filtered interferogram. In Figure 9, one out of 458 interferograms is shown. Two active areas are clearly identified in this interferogram, represented by closed circular shaped fringes. The location of these active areas (subsidence basins) corresponds to the location of coal mining areas, suggesting a clear relationship between the mining activity and measured deformation.  
Figure 8: Spatial baseline distribution and temporal baseline distribution: (**a**) spatial baseline distribution; (**b**) temporal baseline distribution.  
Figure 7: Technical flow of SBAS-InSAR.
## 3 Proposed System Model
### Selection and Application Methods of Machine Learning Algorithms
#### 3.3.2 Naive Bayes Algorithm  
Naive Bayes is a fast, efficient classification method based on Bayes' Theorem, assuming feature independence. It calculates the conditional probability of each feature belonging to a specific class and combines these probabilities to make decisions. There are three types: Gaussian Naive Bayes for continuous data (normal distribution), Multinomial Naive Bayes for discrete data (e.g., text or word frequencies), and Bernoulli Naive Bayes for binary  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Class 1** & **Class 2** & **Class 3** & **Class 4** & **Class 5** & **Total** \\ \hline Sound sensor value (dB) & 1418 & 1788 & 1504 & 2166 & 1438 & 8314 \\ \hline Light sensor value (lux) & 1245 & 1697 & 1660 & 2296 & 1741 & 8639 \\ \hline Temperature sensor value (\({}^{\circ}\)C) & 1054 & 1469 & 1465 & 1662 & 1404 & 7054 \\ \hline Humidity sensor value (\%) & 1054 & 1469 & 1465 & 1662 & 1404 & 7054 \\ \hline CO\({}_{2}\) sensor value (ppm) & 1502 & 1755 & 1730 & 2534 & 1713 & 9234 \\ \hline eCO\({}_{2}\) sensor value (ppm) & 1502 & 1755 & 1730 & 2534 & 1713 & 9234 \\ \hline TVOC sensor value (ppb) & 1502 & 1755 & 1730 & 2534 & 1713 & 9234 \\ \hline Crowd sensor value (Count) & 1942 & 2175 & 1697 & 2974 & 1546 & 10,334 \\ \hline \end{tabular}
\end{table}
Table 2: Distribution of sensor data by class.  
data. It is widely used in text classification, spam detection, sentiment analysis, medical diagnosis, and cybersecurity. However, feature dependencies and unobserved feature values can reduce its effectiveness, with techniques like Laplace smoothing addressing these issues [56].
## 2 Experimental Overview
### Measurement Scheme  
The primary objective of this test is to measure the slip at the steel-concrete interface and the strain in the studs of the four push-out specimens. The main physical quantities measured include load-slip curves and stud strain. The specific measurement methods are outlined as follows:  
1. Load-Slip: Four displacement meters were placed at the same height (bottom of the steel beam) on each specimen to record displacement, with load data obtained in real-time through the control system.
2. Stud Strain: For specimen Z-40, 40 strain gauges were placed on the studs, positioned at the upper ends in the loading direction, where shear deformation is maximized, as illustrated in Figure 5a. For specimen Z-32, one strain gauge was placed on each row of studs in each slot, resulting in a total of 64 strain gauges, as shown in Figure 5b. For specimen Z-16, one strain gauge was placed on each row outside the slots, with a total of 16 strain gauges arranged in 4 rows, as shown in Figure 5c. For specimen S-13, one strain gauge was placed on each external stud in each row, resulting in a total of 24 strain gauges, arranged in 4 rows, as shown in Figure 5d.  
Figure 4: Test loading device. (**a**) Schematic diagram; (**b**) Physical drawing.  
Figure 5: Cont.
# Overview of an Experimental Program for Development of Yield Surfaces Tracing Method
## 5 Conclusions  
This publication presents a complete overview of an experimental program that aims to develop a yield surface tracing method for metallic materials. The objective of the method is to determine the shapes of the initial yield surfaces, as well as shapes of subsequent yield surfaces to obtain information on the evolution of the directional distortional hardening process. The precise observation of directional distortional hardening is essential for calibration of phenomenological plasticity models. These models provide vital predictions of the behavior of structures under complicated and intensive loading. Despite some imperfections discussed in the paper, the method proved to effectively capture yield surfaces in an axial stress--shear stress space induced by combined tensile/compressive and torsional loading of thin-walled tubular specimens. The most important remaining issue with this methodology is the optimization of the procedure for the evaluation of the actual elastic stiffness in the measured stress-strain data that will result in significantly increased precision of the overall method.  
Investigation, J.S.; methodology, S.P. and C.C.; software, R.M.; supervision, J.P.; validation, S.P.; visualization, R.M.; writing--original draft, J.S.; writing--review and editing, C.C. and H.F. All authors have read and agreed to the published version of the manuscript. This work was supported by (1) Ministry of youth, education and sports under Grant LTAUSA18199 Advanced Phenomenological Models of Plasticity and Phase Transformations in Modern Engineering Materials, by (2) Czech Science Foundation under Grant No. GA19-03282S, by (3) the European Regional Development Fund under Grant No. CZ.02.1.01/0.0/0.0/15_003/0000493 (Centre of Excellence for Nonlinear Dynamic Behaviour of Advanced Materials in Engineering), and by (4) US Army Research Laboratory and the US Army Research Office under Grant No. W911 NF-19-1-0040.  
**Data Availability Statement:** Data are contained within the article--see Appendix A.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks
###### Abstract  
This study aims to develop a robust and efficient system to identify ties and ballasts in motion using a variety of non-contact sensors mounted on a robotic rail cart. The sensors include distance LiDAR sensors and inductive proximity sensors for ferrous materials to collect data while traversing railroad tracks. Many existing tie/ballast health monitoring devices cannot be mounted on Hyrail vehicles for in-motion inspection due to their inability to filter out unwanted targets (i.e., ties or ballasts). The system studied here addresses that limitation by exploring several approaches based on distance LiDAR sensors. The first approach is based on calculating the running standard deviation of the measured distance from LiDAR sensors to tie or ballast surfaces. The second approach uses machine learning (ML) methods that combine two primary algorithms (Logistic Regression and Decision Tree) and three preprocessing methods (six models in total). The results indicate that the optimal configuration for non-contact, in-motion differentiation of ties and ballasts is integrating two distance LiDAR sensors with a Decision Tree model. This configuration provides rapid, accurate, and robust tie/ballast differentiation. The study also facilitates further sensor and inspection research and development in railroad track maintenance.  
automated railroad track inspection; non-contact; in motion; LiDAR; machine learning +
Footnote †: journal: Applied
## 3 Copper  
### Cycloaddition Reactions  
[PERSON] and co-workers have earlier developed a class of ferrocenes, substituted with _tert_-butyl-sulfide and different phosphines, to be used as ligands in metal-catalyzed asymmetric transformations [14]. One of these, the so called Fesulphos ligand, was subsequently equipped with two different handles to enable attachment to Wang resin as well as Merrifield resin and applied in the copper-catalyzed 1,3-dipolar cycloaddition of azomethine ylides (Scheme 2) [15]. Copper coordination to the polymer-bound ligands was found to be slower than the corresponding preparation in solution, requiring one hour rather than a few seconds. However, the polymer-supported catalyst prepared from ligand **2** then performed very well in the cycloaddition reaction of various mines with _N_-phenyl-maleimide and methyl fumarate, giving the cyclized products in high yield and excellent enantioselectivity. Somewhat surprisingly, the use of Fesulphos ligand **3**, attached to a Wang resin via a spacer, afforded the same high stereoselectivity, but markedly lower yields. Ligand **2** was also applied in the palladium-catalyzed asymmetric allylic substitution with good results.  
**Scheme 2.** Application of polymer-bound Fesulphos ligands in the copper-catalyzed 1,3-dipolar cycloaddition of an imine to _N_-phenylmaleimide.  
The click reaction, _i.e._ the copper-catalyzed azide-alkyne cycloaddition (also called CuAAC), to form triazole products, is an extensively used method for creating a link between two molecules due to the simple reaction conditions and the high yields obtained [16]. [PERSON] and [PERSON] have developed a Cu(I)-stabilizing tristriazole ligand, prepared via the click reaction, that in turn functions as a catalyst for CuAAC-type reactions [17]. To minimize copper contamination of the product, the ligand was connected to a NovaSyn(r) TG amino resin (a polystyrene-polyethylene glycol support) and evaluated in the click reaction of phenyl acetylene and benzyl azide (Scheme 3) [18]. Copper(I) was preloaded onto the resin-tethered ligand via washing with a solution of a copper salt. The click reaction itself gave essentially quantitative yield using a wide range of solvents. Leaching of copper was minimal, and the polymer-bound ligand could be recycled up to ten times with only a small decrease in efficiency.  
**Scheme 3.** A polymer-supported copper-tris(triazoleyl) complex as a catalyst for the cycloaddition of an azide to an alkyne.
# Bearing Fault Diagnosis Based on Vibration Envelope Spectral Characteristics
## 4 Experimental Results and Analysis
### Analysis of Experimental Results
#### 4.3.4 Analysis of Algorithmic Complexity of Fault Diagnosis Methods  
The accuracy and algorithmic complexity of our experimental method were compared with literature published in the past 5 years that utilized the CWRU bearing database for fault recognition, as shown in Table 3. Due to the significantly higher algorithmic complexity of deep learning compared to machine learning, the algorithmic complexity of fault diagnosis methods based on deep learning is not further detailed here. Moreover, for facilitating algorithm complexity comparison, the sample size is uniformly set to N.  
Table 3 shows that the accuracy of the diagnostic method proposed in this paper is similar to the accuracy of other algorithms. The algorithm complexity is close to reference [3] and much lower than other methods. Although the algorithm complexity in reference [3] is similar to the method proposed in this paper, it is limited to a three-class fault diagnosis for a healthy state, inner race fault, and outer race fault.  
The results above indicate that the diagnostic method proposed in this paper has a high classification accuracy, significantly reduces algorithm complexity, improves classification efficiency, and is more suitable for industrial bearing fault diagnosis and maintenance.