esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Result
### Duplication, Synthesis, and Evolutionary Analysis of _RrcCaCA_ Gene Family  
Duplication events within the _RrcCaCA_ gene family were analyzed and visualized using MCScanX software v 1.098696 (Figure 4a). Among the 22 _RrcCaCA_ genes, 4 were categorized as tandem duplications, 3 as whole-genome duplications (WGD) or segmental duplications, 9 as proximal duplications, and 5 as dispersed duplications.  
The evolutionary dynamics of the _RrcCaCA_ gene family were further elucidated through the identification of homologous genes selected from _A. thaliana_, _Rosa chinensis_, _Rosa vichuraiana_, and _Rosa rugosa_ (Figure 4b). Six pairs of homologous gene pairs were identified between _R. roxburghii_ and _A. thaliana_, eight between _R. roxburghii_ and _R. wichuraiana_, and ten between _R. roxburghii_ and _R. rugosa_, as well as between _R. roxburghii_ and _R. chinensis_. The higher number of homologous pairs between _R. roxburghii_ and other _Rosa_ species, particularly _R. chinensis_, likely reflects their close phylogenetic relationships.  
Figure 3: Structural motifs of _RrcCaCA_ genes. (**a**) MEME motifs; (**b**) conserved functional domains; (**c**) intron and exon structures.  
Figure 4: Collinearity analysis of _CaCA_ genes. (**a**) Collinearity analysis of _RrcCaCA_ genes within _R. rxburghii_ genome; (**b**) inter-genomic collinearity analysis among _A. thaliana_, _R. chinensis_, _R. wichuraiana_, and _R. rugosa_. Black and blue lines represent collinearity of _CaCA_ genes.
## 4 Analysis of Crack Characteristics in Tunnel Lining Images
### Best Gray Value for Crack Extraction  
According to the calculation model of the crack width, it can be known that for the 0.1-2.0 mm fine cracks, the difference between the crack gray value and the gray value of the background is mainly used to identify the crack width. The larger the difference is, the better the identification of the crack. In order to reduce the difficulty of crack width extraction, the background and crack gray values under different background gray values are compared to determine the best gray value. Generally, the smaller the crack width, the more difficult it is to identify, so as long as the minimum crack width is studied, the best background gray value can be determined.  
As shown in Figure 11, the background gray values of the 0.1 mm cracks shown with the gray values of 110, 120, 130, 140, 150, 160, 170 are extracted, and the relationship between the background grayscale and the grayscale difference is obtained.  
Figure 11: Relationship between background grayscale and grayscale difference.  
Figure 10: The relationship between the crack width and the number of pixels occupied.  
With the background gray value increase, the crack grayscale difference shows a trend of first increasing and then decreasing. When the background gray value reaches 140, it has the most significant difference, which is the best background gray value.
## 3 Results and Discussion  
### Effect of HP-\(\beta\)-CD Concentration (\(C_{\text{CD}}\))  
The initial step in the process development was to identify the most suitable \(C_{\text{CD}}\) which would provide the highest total polyphenol extraction yield (Y\({}_{\text{TP}}\)). Thus, \(C_{\text{CD}}\) was tested over a range of 0 (deionized water) to 8 mM (Figure 1). Compared to deionized water (0 mM), a solution with \(C_{\text{CD}}\) of 0.1 mM provided by 14.5% higher Y\({}_{\text{TP}}\). This fact indicated that the presence of HP-\(\beta\)-CD fostered polyphenol extraction to a significant extent. By raising \(C_{\text{CD}}\) levels from 0.1 to 0.25 mM, the Y\({}_{\text{TP}}\) increased from 9.76 \(\pm\) 0.20 to 9.84 \(\pm\) 0.87 mg CGAE g\({}^{-1}\) dm. This difference fell within the limits of statistical error and it was non-significant (\(p\) > 0.05). However, switching \(C_{\text{CD}}\) from 0.25 to 0.5 mM, an increase in Y\({}_{\text{TP}}\) by 19.1% was recorded, whereas a further \(C_{\text{CD}}\) increase up to 8 mM afforded higher Y\({}_{\text{TP}}\) by only 2%. Therefore, a \(C_{\text{CD}}\) of 0.5 mM was chosen as the most appropriate to carry out subsequent process optimization.  
Figure 1: Effect of HP-\(\beta\)-CD concentration (\(C_{\text{CD}}\)) on total polyphenol yield (Y\({}_{\text{TP}}\)). Extractions were carried out at ambient temperature (29 \(\pm\) 2 \({}^{\circ}\)C), at a liquid-to-solid ratio of 30 mL g\({}^{-1}\).
## 2 Background and International Context
### International Context
#### 2.2.2 Sentinels Validation Teams  
In addition to the inter-agency working groups, ESA is contributing to dedicated Cal/Val activities in the frame of the Sentinels Validation Teams, whose objectives are to gather validation experts to contribute to the quality assessment of the Sentinels mission core products. The following two groups are particularly relevant for the scope of this paper:  
* The Sentinel-2 Validation Team (S2 VT) [34] is responsible for coordinating and overseeing the quantitative validation of satellite data products derived from the Sentinel-2 mission. S2 VT brings together, on an annual basis, leading Cal/Val experts addressing all S2 validation requirements, encompassing a large range of domains from radiometric and geometric calibration to land and water products validation. The recommendations gathered during a series of annual meetings are used to further reshape and improve Cal/Val methodologies, as well as easing the exchange of Cal/Val data among the various scientific teams.
* The Sentinel-3 Validation Team (S3 VT) [35] is jointly convened by the ESA and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) to promote and coordinate the involvement of the international community in the domain of Cal/Val activities for the Sentinel-3 mission. The S3 VT is divided in different sub-groups focusing on Land, Ocean Colour, Sea Surface Temperature, Altimetry and Atmosphere. The S3 VT meets on a regular basis to review the status of the Cal/Val activities and the quality of the S3 core products, as well as to support the exchanges of ideas and collaboration among the various members and to discuss potential product evolutions.  
As part of the Sentinels mission and in order to ensure the highest quality of products, the Mission Performance Centers (MPC, Center to be replaced by Cluster for the next generation MPC from 2022 onwards) are established to control the quality of all generated products. Expert Support Laboratories (ESLs) are involved in calibration and validation activities and provide specific assessments of the products (e.g., analysis of trends, ad hoc analysis of anomalies, etc.). Furthermore, the Quality Working Groups (QWGs) are chaired by the ESA and co-chaired with EUMETSAT for S3 L1 to define and evolve the operational baseline algorithm and contribute to the geophysical validation of the operational products.
# Development and Evaluation of Doppler Ultrasound Training Phantom for Human Vessel Simulation
## 3 Results
### The Color Doppler Results  
#### 3.2.1 Color Box  
Color Doppler acquires blood flow information within the color box, by setting the color box. The direction of blood flow in color Doppler is determined by the color map. In Figure 4a,b, the color map is displayed on the left side. The color map indicates the directions of blood flow. The color at the top (red) represents a blood flow toward the transducer. On the other hand, the color at the bottom (blue) represents a blood flow away from the transducer.  
Figure 4: Color Doppler imaging of the color box steer. (**a**) The color box is steered to the left, (**b**) the color box is steered to the right. The red arrows represent the direction of blood flow, while the yellow arrows represent the direction of the ultrasound beam.  
Figure 3: The direction of blood flow and the ultrasound beam. (**a**) A representation of the direction of blood flow, and the ultrasound beam direction, within the phantom. (**b**) An image formed via visualizing the information in panel (**a**), using B-mode imaging. (**c**) A B-mode image of the vessel-mimicking phantom. (**d**) A color Doppler image of the vessel-mimicking phantom.  
The blood flow information varies depending on the steer setting of the color box. In Figure 4a,b, the red arrows represent the direction of blood flow, while the yellow arrows represent the direction of the ultrasound beam. Figure 4a shows an image with the color box steered to the left. In this image, the blood flow appears in blue, indicating that the blood flow is moving away from the ultrasound beam [38; 39]. In contrast, Figure 4b shows an image with the color box steered to the right. In this image, the blood flow appears in red, indicating that the blood flow is moving toward the direction of the ultrasound beam [38; 39]. This experiment demonstrates that the blood flow information appears differently in Figure 4a,b.
###### Abstract  
The incorporation of carbon nanotubes (CNTs) in the process of chemical stabilization of soft soil is only possible when they are dispersed adequately in the medium. The maximum compressive strength (\(\mathrm{q_{\,max}}\)) and the secant undrained Young's modulus (\(\mathrm{E_{u\,50}}\)) are usually used to characterize the behavior of soil stabilized with Portland cement. In the present study, soft soil was additivated with a CNT dispersion prepared in a surfactant solution. This information was then used to produce a model based on an experimental design strategy, which allowed us to relate \(\mathrm{q_{\,max}}\) and \(\mathrm{E_{u\,50}}\) with the CNT concentration and the surfactant hydrodynamic diameter and concentration. The Partial Least Squares (PLS) regression method was selected to perform the regression, given the significant collinearity among the input variables. The results obtained lead us to conclude that the CNT concentration is the most important factor and has a positive impact on the responses (\(\mathrm{q_{\,max}}\) and \(\mathrm{E_{u\,50}}\)). The surfactant concentration and hydrodynamic diameter have a negative impact on the responses, but, curiously, when combined, the impact becomes positive. It means that these variables depend on each other. The results obtained show that it is possible to produce a statistical model for these parameters with good correlation coefficient (\(\mathrm{R^{2}}\)).  
carbon nanotubes; chemical soil stabilization; partial least squares; hydrodynamic diameter; surfactant concentration +
Footnote †: journal: Applied Sciences
# Adaptive Tolerance Dehazing Algorithm Based on Dark Channel Prior
## 4 Comparison and Analysis of Experiential Results
### Qualitative Comparison of Real-World Images  
Figure 7 shows the recovery effect of the improved algorithm and the other four representative dehazing algorithms on outdoor blurred images. The transmission map during the various dehazing algorithms is shown in the first row of Figure 7. Figure 7a depicts the hazy images to be dehazed. Figure 7b-e shows the results of [PERSON] [7], [PERSON] [13], He [8] and the result of the introduced fixed tolerance algorithm. As shown in Figure 7b, most of the haze is removed in [PERSON]'s results, and the details of the scenes and objects are well restored. However, the result is clearly affected by over enhancement, making the entire image much darker than what it should be. In contrast, the results of [PERSON]'s image are much better visually (see Figure 7c). However, in the first two pictures, the sky area still shows significant distortion. This is because the method of [PERSON]'s transmission rate estimation is based on the dark channel prior, and the accuracy of the estimation largely depends on the validity of the dark channel prior. Unfortunately, the dark channel prior is ineffective when the scene brightness is similar to the atmospheric light. As shown in Figure 7d,e, the distortion of the sky region is improved. However, as shown in the sixth line and its enlarged image, the restored images of the two algorithms have lost detail in the region of dense haze. Compared with the results of these four algorithms, our results are not over-saturated and better preserve the details of the image. As shown in Figure 7f, the sky area in the picture is clear, and the details of the mountains and leaves are well preserved.  
Figure 7: Qualitative comparison of different dehazing algorithms for outdoor images. (**a**) The hazy images. (**b**) [PERSON]’s results. (**c**) [PERSON]’s results. (**d**) [PERSON]’s results. (**e**) Results of used fixed tolerance. (**f**) Our results.
## 2 System Design
### Data Collection and Preprocessing  
In our previous work [33], a pulse signal acquisition system was designed to collect the wrist pulse waves. MEMS sensor is employed to transforms the pressure signal into a voltage signal, and an array composed of 12 such sensors outputs 12 differential signals. Operational amplifiers magnify the signals, which are polled by a multiplexer (MUX). Then the MUX's output signal is sampled by an analog-digital converter (ADC), transformed into a digital signal, transmitted to PC, and stored into the disk. The sampling frequency for each channel signal is 218 Hz, much higher than the pulse signal.  
After collecting the subjects' original pulse waves, we need to preprocess them and standardize the data. The complete data preprocessing flow is shown in Figure 3. To classify and label the waveforms more accurately, we design digital filters to obtain a clearer signal. First, we roughly select a two-minute segment from the raw pulse signal, then filter the selected segment by a low-pass filter (LPF, the cut-off frequency is 40 Hz) to remove the power-line interference. Second, to remove the baseline wander, we use a high-pass filter (HPF, the cut-off frequency is 0.2 Hz) to filter the output of LPF. Then we select a certain length of the segment from the prior result to keep the length of all data consistent.  
Figure 2: The system design flow includes data collection and preprocessing, algorithm design, and hardware design and optimization.  
Third, the corresponding segment of the original pulse wave is normalized, labeled manually, and stored in the dataset. Eventually, the dataset, whose size is 2829, includes 1106 Ping pulse, 853 Hua pulse, 376 Xian pulse, and 494 Se pulse.  
Figure 3: _Cont._
# Application of Near-Infrared Spectroscopy for Monitoring and/or Control of Composting Processes
###### Abstract  
The implementation of a suitable and ecologically friendly solid waste management plan is accepted as an essential need. Given that organic matter constitutes the majority of solid waste, composing has gained popularity as an alternative way of organic refuse recycling. Compost quality is defined by its stability and maturity, both of which must be assessed by measuring a large number of physical-chemical parameters, microbiological variables, and enzymatic activities. These procedures are complex and time-consuming, making it difficult to assess compost quality correctly. Spectroscopy methods could be used as an efficient alternative. In this work, general information about composing processes and near-infrared spectroscopy  
Considering the importance of spectroscopy methods in composting process analysis, in this work, the application of NIR spectroscopy for the monitoring of composting processes has been revised. The motivation for this research was a lack of review papers presenting information on composting control and monitoring using NIR spectroscopy. This review includes some general information on composting technologies and some general information on NIR spectroscopy, followed by a comparison and discussion of specific examples of using NIR spectroscopy for composting process monitoring and for compost quality assessment. Furthermore, current challenges and future perspectives regarding composting and online measurements are also discussed.
## 2 Micro-Vibration Measurement System  
Figure 2 shows the schematic diagram of the triaxial micro-vibration real-time measurement system. In view of the characteristics of low amplitude of micro-vibration and the main energy being concentrated in the low-frequency band, three uniaxial acceleration sensors are selected to form a triaxial sensor module.  
Due to the low amplitude of the micro-vibration signal, the peak-to-peak value of the acceleration is generally around 200 \(\upmu\)g. The triaxial micro-vibration real-time measurement system includes a signal conditioning module composed of a filter circuit, an active amplifier circuit and relay components. The digital circuit used FPGA to complete the three-channel AD conversion control. It achieved synchronous acquisition of each channel and improved the real-time performance of the measurement system. The measurement system can accurately measure the micro-vibration signal within the frequency range of 0.5-200 Hz.  
The measurement signal is interfered with by the noise introduced by amplifying the circuit and external power supply. Figure 3 shows the noise randomly distributed over the measurement band. In order to solve the problem that the original acceleration signal is disturbed by noise, this paper proposed a micro-vibration signal denoising algorithm of spectral morphology fitting based on variational mode decomposition and applied it to the micro-vibration measurement system. We can complete the accurate measurement and grade evaluation of the micro-vibration signal.
# Optical High-Speed Rolling Mark Detection Using Object Detection and Levenshtein Distance
## 2 Rolling Marks  
For Vignole rails of contemporary design weighing 46 kg/m or above, the DIN EN 13674-1 standard provides comprehensive guidelines for both conventional and high-speed railway lines. Within this standard, specific requirements are outlined for the textual composition of rolling marks, as depicted in Figure 2. These specifications encompass the following aspects:  
1. Rolling mill;
2. Steel grade;
3. Rolling year;
4. Rail profile.  
Each rolling mark is carefully rolled into the rail web at intervals no greater than 4 m, ensuring that the marks are prominently raised within the range of 0.6 mm to 1.3 mm.  
Figure 1: Profile of a _Vignole_ rail.  
Figure 2: Illustration of a rolling mark highlighting information about (1) the rolling mill, (2) the steel grade, (3) the rolling year and (4) the rail profile.  
The characters comprising the rolling mark exhibit a consistent height falling between 20 mm and 25 mm. These specifications have been in effect since November 2002, serving as a standardized framework for rolling mark implementation. Figure 2 visually portrays a representative rolling mark adhering to the aforementioned structural guidelines. In this specific example, the encoded information contained within the rolling mark was as follows:  
1. The rail was rolled in the Donawitz rolling mill (Donawitz Steelworks is a steel mill located in Donawitz near Leoben in Styria/Austria; it is particularly well known for the first application of the Linz-Donawitz process (LD) for steel production).
2. The quality of the steel corresponds to grade R 350 HT.
3. The rolling year was 2012.
4. It was a rail of profile 54E2.  
Rails manufactured prior to 2002 often deviate from the standard convention described earlier, particularly with regard to the placement of the steel grade indication at the right end of the rolling mark. Additionally, these rolling marks typically include the specification of the month of production and utilize an older profile designation, such as the usage of \"UIC 54E\" instead of the \"54E2\" designation.  
The accurate and automated localization of these specific markings holds significant importance for rail infrastructure operators, as it allows for targeted insights into the rail infrastructure. This capability facilitates the effective planning of maintenance actions according to the age of the rails. Furthermore, it contributes to enhanced safety by enabling the identification of older rails that require replacement in a precise and targeted manner.
# Deep Learning-Based Human Action Recognition with Key-Frames Sampling Using Ranking Methods
## 5 Discussion  
The widespread applications of modern technology such as human-machine or human-object interaction are significantly increasing. In this article, we propose a new approach for human action recognition using 3D-CNN with the raw depth dataset. Several methods have introduced human action recognition using depth sequences, in which they tried to encode the entire sequence into a spatial format called dynamic image. Some methods also focused on the extraction of the skeleton and point cloud information from depth video. These methods are completely dependent on the effective encoding, skeleton, or point cloud extraction to correctly recognize the human action. Generally, it is very difficult or sometimes impossible to maintain all the temporal information during the encoding process. Very few methods used raw depth sequence to avoid human interference to generate dynamic image, skeleton, or point cloud information. Even though they used raw depth sequence, the recognition performance was much lower.  
We analyze the dataset and find that each sequence has a different number of frames. When a sequence contains a large number of frames, most of the frames remain the same, which means there are no spatial and temporal changes. Only a few numbers of frames include action information that changes in the spatial and temporal direction. As a result, we propose a novel ranking-based approach for human action recognition using 3D-CNN with raw depth sequences. We use SSIM and CCM ranking metrics to rank the whole sequence and select \(k\)-ranked frames that contain more spatial and temporal information. Then, we train a 3D-CNN model with the \(k\)-ranked frames to recognize specific actions. We do not need to encode the sequence and extract dynamic images for further classification. We consider different levels of ranking and investigate the recognition performance for better understanding. After a certain level of \(k\), the recognition performance does not improve further because we must copy the same frame multiple times in most of the sequences.
# Practical Performance Assessment of Water Vapor Monitoring Using BDS PPP-B2b Service
## 4 Results and Discussion
### Verification of Near Real-Time (NRT) Processing Mode Through Comparison with Post-Processing Mode
Figure 8: Time series of PWV monitored from NRT processing model using GPS-only (blue), BDS-only (red), and GPS and BDS (green) satellite combinations based on CAS (**a**,**c**,**e**) and B2b ephemeris (**b**,**d**,**f**) during the entire research period. The black line in each subfigure represents reference PWV retrieved from the ERA5 reanalysis. It should be noted that among the CAS results, PWV monitored with GPS-only observation performs the best, followed by that from the GPS and BDS combination, and the BDS-only performs the worst. In contrast, for the results of B2b, PWV monitored with GPS-only performed the worst, followed by that of BDS-only, and the GPS and BDS combination performed the best. This might be because that the production of the B2b ephemeris mainly relies on some domestic stations in China and about 30 global stations [44], which leads to the relatively low accuracy for GPS satellites. Therefore, the accuracy is relatively lower when using GPS-only observations for PWV monitoring based on the B2b ephemeris. However, when the BDS satellites were introduced to form dual-system observations, the estimation of ZTD and the subsequent PWV monitoring were improved, as is reported in previous research [8,39]. Based on the above analysis, the water vapor monitoring based on the BDS PPP-B2b service can generally meet the demand for NRT monitoring, and it is expected to be promoted and contribute to the extreme weather forecasting in the near future.