esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
After peptide synthesis on the resin, the Boc group and side-chain protecting group were removed by TFA. The objective peptide was cleaved from the resin with 0.5 mol/L NaOH solution. The HPLC analysis of the peptide obtained by solid-phase synthesis in water afforded a single peak for Leu-enkephalin, shown in Figure 3. The presence of only one peak in the chromatogram of the crude peptide suggested the absence of diastereomeric compounds. Thus, there is no significant racemization during the MW assisted coupling reaction. The overall yield calculated from the amino group content of the used resin was 64%. Figure 2: Wet-milling in planetary ball mill **(a)** Photo image of unprocessed Boc-Phe-OH; **(b)** Photo image of water-dispersible Boc-Phe-OH nanoparticles. Difficult sequences peptides are problematic to synthesize using a conventional method due to the aggregation of protected peptide chain on the resin. MW irradiation is useful in breaking up the aggregation of a peptide chain. To further evaluate this aqueous MW-assisted synthesis method, we demonstrated the synthesis of hydrophobic peptide, Val-Ala-Val-Ala-Gly-OH, which is a well-known aggregate sequence [36]. Using a combination of MW irradiation and water-dispersible nanoparticles,  
Figure 4: **(a)** Analytical HPLC profile; and **(b)** mass spectra of Val-Ala-Val-Ala-Gly-OH (VAVAG) obtained by MW assisted solid phase synthesis in water using nanoparticles. Elution was carried out for over 40 min at a flow rate of 1 mL/min with a linear gradient from 95:5 to 55:45 mixture of 0.05% aqueous TFA and 0.05% TFA in acetonitrile. Column: 5C18-Ar-II (2.4 \(\times\) 250 mm). Figure 3: **(a)** Analytical HPLC profile; and **(b)** mass spectra of Leu-enkephalin (YGGFL) obtained by MW assisted solid phase synthesis in water using nanoparticles. Elution was carried out for over 40 min at a flow rate of 1 mL/min with a linear gradient from 9:1 to 5:5 mixture of 0.05% aqueous trifluoroacetic acid (TFA) and 0.05% TFA in acetonitrile. Column: 5C18-Ar-II (2.4 \(\times\) 250 mm). we synthesized peptide on the HMBA-PEG grafted resin according to the same aqueous based protocol (Table 2). Boc-Val-OH nanoparticles and Boc-Ala-OH solution were serially coupled onto H-Gly-HMBA-PEG grafted resin. Figure 4 shows the HPLC analytical profile of Val-Ala-Val-Ala-Gly-OH synthesized in water by this method. The peptide was obtained in high purity.
# Communication Architecture for Grid Integration of Cyber Physical Wind Energy Systems
## 2 Cyber Physical Wind Energy System
### Communication Network Layer  
The communication infrastructure provides the connection between WF elements, and it is divided into two parts: the communication network inside the wind turbine (turbine area network, TAN) and between wind turbines, and the control center (farm area network, FAN). Inside a wind turbine are different communication protocols, such as the field bus, industrial ethernet protocols, and control area network (CAN). The architecture of the WF communication network is switch-based, consisting of ethernet switches and communication links in every wind turbine. The network configuration is based on point-to-point communication and a local area network (LAN). In order to link the WF network  
Figure 1: Proposed architecture of the cyber physical wind energy system. SCADA: supervisory control and data acquisition; CCTV: closed-circuit television; VOIP: voice over internet protocol; IED: intelligent electronic device; WTG: wind turbine generator; WTC: wind turbine controller; HV: high voltage; Met. Mast: meteorological mast.  
with a remote control center, different wide area network (WAN) technologies, either wired or wireless, could be configured, such as optical fiber cables, microwaves, and satellites. Each WF has a dedicated connection to a local control center for real-time monitoring and control. However, one control center can remotely manage and control one or more WFs.
## 1 Introduction  
Since decades, with the advent of earth observation satellites, remote sensing technology has been widely utilized to produce images of the Earth surface with different spatial resolutions. Frequently the analysis of these maps with visual inspection or statistical tools is not enough for quantitative monitoring and comparisons, and different approaches are required.  
Several remote sensing images of geophysical variables could be conveniently represented by a suited fitting surface, able to parameterize and quantify the spatial distribution of the mapped quantity. In particular, satellite images mapping the land surface temperature (LST) over urban areas or the associated urban heat island (UHI) could be successfully fitted by a two-dimensional (2D) Gaussian distribution [1]. The SUHI intensity describes the difference in temperature between the urban locations and the surrounding rural background, and the SUHI effects have been analyzed for several decades, starting from Oke studies [2,3]. The monitoring of spatial and temporal parameters related to SUHI maps may allow the detection of health risk areas [4] and suggests modifications to the urban planning in order reduce the heat effects [5].  
If a SUHI image is centered over an urban area, where the LST is higher with respect to the adjacent rural area, the heat island pattern can be spatially described by a Gaussian surface. With a fast and robust implementation of the fitting technique, such images can be parameterized in order to better understand the intensity, shape and orientation of the SUHI and also to perform quantitative comparisons among different images in time and space [6].  
Different fitting methods can be adopted to generate Gaussian surfaces [7]: in this work the proposed approach is computationally quick and effective, without the occurrence of instability or convergence failure, failure that sometimes other fitting methods could encounter [7]. This is achieved by means of an initial guess parameter estimation performed by a multiple linear regression image-based, before the iterative nonlinear fitting, ensuring stable results.  
The Gaussian fit was executed in Matlab (The MathWorks, Release 2010a).  
A detailed description of the different steps of the fitting procedure is provided, and the results of the parameterization and the algorithm performance are shown both for low-resolution and high resolution images. The processing of the SUHI maps computed from data of two satellite sensors of different spatial resolution (MODIS and Landsat Thematic Mapper, with 1 km and 30 m pixel size, respectively) will also point out the impact of the image detail definition on the fitting representation.
# Research on the Influence Mechanism of Foam Slurry Wall Formation
## 3 Numerical Simulation Results Analysis  
For each scheme in Table 4, the number of bentonite (and foam) particles infiltrating the soil was plotted over time. The results indicate that under different conditions, the infiltration time curves exhibit two types.  
Type One: The number of infiltrating particles increases rapidly at first, then quickly decreases due to the resistance of the soil, and finally, the number of infiltrating particles fluctuates slightly and stabilizes at a relatively constant rate. For example, in Scheme 22 (infiltration of pure bentonite slurry in L6 soil, Table A1), the calculation results of the scheme show that the number of bentonite particles fluctuates around 70 at the end of the calculation. Specifically, the maximum number of particles is 76 and the minimum is 60. At this time, the bentonite has almost filled the entire soil layer, and only particles aggregate in local areas, but there is still a large gap from forming a complete mud film, as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Influence Factors** & **Parameter Studied** & **Range/Values Selected** & **Number of Cases** \\ \hline \multirow{4}{*}{Soil Properties} & \multicolumn{3}{c}{Particle size range: 0.5–2 mm;} \\  & Particle size & maximum particle size, \(d_{max}\): & 11 \\  & & 0.5/1/2 mm & \\  & Gradation b & 0.6–0.99 & 5 \\  & Gradation m & 2–10 & 4 \\  & Swelling ratio & 2.5–10\% & 3 \\  & Foam concentration & 0–30\% & 3 \\  & Infiltration pressure gradient & 1000/2000/3000/4000 & 4 \\ \hline \end{tabular}
\end{table}
Table 4: Classification of influence factors and parameter settings for the numerical modeling of foam slurry.  
Figure 3: Absence of stable mud cake formation after slurry infiltration (Scenario 22).  
Type Two: Bentonite and foam particles infiltrate in large quantities initially, with the number of infiltrating particles quickly reaching a peak. Subsequently, bentonite and foam particles begin to block large pore channels, causing the number of infiltrating particles to decrease rapidly and form an initial mud cake. Finally, bentonite and foam particles form agglomerates on the soil surface, blocking most of the pores, and the number of infiltrating particles gradually approaches zero, forming a stable mud cake. This type of infiltration is illustrated in Scheme 6 (swelling ratio: 5%; foam concentration: 20%, Table A1), as shown in Figure 4.  
The physical implications of these two infiltration types are significant for practical engineering. Type One represents ineffective membrane formation, where particle accumulation is insufficient to fully block the pores, leading to continued slurry infiltration and potential instability in excavation projects. In contrast, Type Two demonstrates effective clogging and stable mud cake formation, which is crucial in applications such as tunneling and pile excavation. In these cases, rapid and complete membrane formation can minimize slurry loss, enhance wall support, and improve construction efficiency.
# Part II--Aroma Profile of Twenty White Grapevine Varieties: A Chemotaxonomic Marker Approach
## 1 Introduction
Despite the difficulty of using volatile organic compounds as a chemotaxonomic tool is well-known, as they have complex biosynthesis and transformation mechanisms behind this, it is an interesting and plausible hypothesis thinking about a relationship between the volatile composition of different genetically related varieties, as it has recently been tested by [PERSON] et al. [26]. Along the same lines, other studies have been recently carried out, in this case with the VOC profiling of different apple cultivars [22; 27] and with pomergnate varieties [28]. Traditionally, grapevine varieties have been classified into different genetic groups using molecular markers [29; 30]. Furthermore, some statistical tools for structural analysis have made it possible to create genetic-geographical relationships between certain grapevine varieties [29]. This have been developed by [PERSON] et al. [31; 32; 33; 34; 35] with varieties included in the grapevine germplasm bank established in EVEGA. This classification has been used in previous research works [36; 37; 38; 39], in which a detailed phytochemical description was carried out. The phenolic, anthocyanin and non-anthocyanin profiles and the aromatic profile of red grapevine varieties were tested to be used as chemotaxonomic tools to discriminate among varieties. It was also analyzed whether those VOC profiles could cluster varieties by their genetic-geographic group, given the promising results obtained with grapevines by [PERSON] et al. [26] or with pomergnate by [PERSON] et al. [28]. This study is therefore part of a broader characterization research, focusing in this case on the white grapevine varieties grown in the germplasm bank. The aromatic profile, including free, as well as glycosidically bound compounds, were evaluated, given that grape's aromas and flavors could not only be attributed to their direct volatile compounds, but also to the effect of their aromatic precursor molecules, which will play different roles in translation into the varietal aroma of the wine, depending on the elaboration procedures, to become odorous compounds [40]. In this paper, the study of the volatile compounds present in twenty white grape varieties was performed in order to achieve the following objectives: (1) to assess if their aromatic profile allows a clear differentiation among varieties, (2) to evaluate if the aromatic profile could be used as a chemotaxonomic tool, testing how it groups the varieties in accordance to their aromatic similarities and if those clusters maintain any relationship with their genetic-geographic grouping and (3) try to bring out new varietal aromatic profiles from traditional varieties that trigger the diversification of winemaking in the study region in support of the grapevine biodiversity in this geographical area.
# An Effective Grouping Method for Privacy-Preserving Bike Sharing Data Publishing
## 4 Experimental Analysis
### Data Utility Comparison
#### 4.2.1 Data Utility  
For publishing the microdata table, privacy preservation is an important issue, and we have to consider the data utility as well. Data loss metric shows how much data utility remains in the published bike sharing dataset. There are many methods to calculate the data loss in the published dataset. The distortion ratio describes a process to calculate the data loss in the published bike sharing transaction table [27].  
To calculate the distortion ratio, we have to consider that every attribute of the bike sharing dataset associated with a generalized taxonomy [27]. Figures 1 and 4 present the taxonomy for attributes Gender and Birth Year, respectively. If the value of a tuple does not generalize, the distortion of that value is 0, and if it is generalized, the distortion is defined by the height of the taxonomy tree. For instance, the Gender Male/Female is not generalized, and it resides in the leaf node, so the height indicates 0, and thus the distortion equals to 0. If it is generalized one level up in the taxonomy tree, the distortion equals \(1/H\). Here, \(H\) indicates the height of the taxonomy tree. Let \(d_{j,k}\) be the distortion of the value of attribute \(A_{j}\) of tuple \(t_{k}\). The distortion of the whole published table corresponds to the sum of the distortions of all values in the generalized dataset. Thus, the distortion is denoted as:  
\[\sum_{j=1,k=1}^{n,m}d_{j,k}.\]  
The distortion ratio is \(D_{R}=\frac{D_{P}}{D_{C}}\), where \(D_{R}\) denotes the distortion ratio, \(D_{P}\) means the distortion of the datasets in the published table, and \(D_{G}\) signifies the distortion of the fully generalized (i.e., all values generalized to the root of the taxonomy trees) table. Data utility is calculated by subtracting distortion ratio \(D_{R}\) from 100% [27]:  
\[Data\ utility=(100-D_{R})\%.\]  
Figure 5 shows the experimental results of the data utility based on information loss from the published datasets. In the original dataset, the data utility is 100% because it has 0 distortions. By contrast, our proposed method and _k-anonymity_[13] have 25% and 60% distortion, respectively. Therefore, we can say that the Grouping method has more data utility than the generalization approach for the _k-anonymity_ method.  
Figure 4: A taxonomy for attribute Birth Year.
## 3 RUL Prediction Method Based on Hierarchical Clustering and Transformer-GRU
### Comprehensive Index Screening  
Not all extracted features are sensitive to the degradation process of rolling bearings. Therefore, feature selection is required. Since the degradation process of a bearing evolves over time and is irreversible, an effective degradation feature should exhibit strong temporal correlation and monotonicity. Monotonicity and correlation [10] are used to construct a comprehensive index (F) for feature selection, eliminating those features that are insensitive to the degradation process and creating a sensitive feature set. A higher F value indicates that the corresponding feature is more sensitive to bearing degradation. The calculations of monotonicity, correlation, and the composite index are as follows:  
Monotonicity:  
\[F_{mon}=\left|\frac{Num\;of\;df>0}{T-1}-\frac{Num\;of\;df<0}{T-1}\right| \tag{14}\]  
where \(df\) represents the differential between adjacent values in the feature curve, \(T\) denotes the length of the feature data series, and \(Num\;of\) denotes the count function, which calculates the number of elements satisfying a given condition.  
Correlation:  
\[F_{cor}=\frac{\left|\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)\left(y_{i}- \overline{y}\right)\right|}{\sqrt{\sum_{i=1}^{n}\left(x_{i}-\overline{x} \right)^{2}\sum_{i=1}^{n}\left(y_{i}-\overline{y}\right)^{2}}} \tag{15}\]  
where \(x_{i}\) and \(y_{i}\) represent the i-th elements of the feature vector and the RUL label, respectively, \(\overline{x}\) and \(\overline{y}\) represent the mean values of the feature vector and the RUL labels, respectively, and \(n\) denotes the sequence length.  
Aggregative indicator:  
\[F=w_{1}F_{mon}(x)+w_{2}F_{cor}(x) \tag{16}\]  
\[\sum_{k=1}^{2}w_{k}=1,w_{k}>0 \tag{17}\]  
where \(x\) denotes the feature vector, and \(w_{k}\) represents the weight corresponding to each evaluation metric. According to reference [10], \(w_{1}\) is set to 0.3 and \(w_{2}\) to 0.7. F denotes the comprehensive score of the feature.
## 1 Introduction
The analysis of fish schools to measure the attenuation coefficient with the number of individual fish has not been performed yet. Furthermore, the attenuation of live Japanese horse mackerel has not been measured at the mid-frequency band. A recent trend in fish finders and sonar systems is to lower the frequency for detecting targets at a longer distance. The naval sonar system has mainly used mid-frequency bands until today. Omnidirectional sonar or multi-beam echo-sounder for the fishery is also being developed at a lower frequency. It is necessary to study fish attenuation to improve the accuracy of various fishery acoustic equipment and its signal analysis, especially at mid-frequency bands. This study aims to measure the sound attenuation by dense fish schools of Japanese horse mackerel at a mid-frequency range (3-7 kHz) to understand the sound attenuation characteristics of fish schools. The sound attenuations with the various densities of fish were measured through in situ net cages. The attenuation coefficients in the experiments were analyzed and compared with those in the previous studies, and new regression models were suggested for normalized extinction cross-sections of the wet weight (\(W\)) and the total length (\(L\)) of fish. These results are expected to be helpful in developing the scientific echo-sounder and increasing the accuracy of estimating biomass and abundance of fish species for the fishery. They will also serve as a basis to improve the accuracy of the target detection for naval sonar operations and analysis.
# On-Orbit Absolute Radiometric Calibration and Validation of ZY3-02 Satellite Multispectral Sensor
## 2 Materials
### Calibration Site and Targets
#### 2.3.2 The Gray-Scale Permanent Artificial Targets  
The Baotou site is currently maintained and operated by the Aerospace Information Research Institute, Chinese Academy of Science. A series of targets and infrastructure has been built in the Baotou site to provide effective support for satellite sensor on-orbit calibration and performance evaluation, and remote-sensing product validation. Figure 3 shows the gray-scale permanent artificial targets. The gray-scale permanent artificial targets are composed of two white, one gray, and one black uniform gravel squares, each of which covers an area of 48 m \(\times\) 48 m, which can be used for radiometric calibration and validation of high-resolution optical sensors. The white, gray, and black targets with known spectral reflectance (56%, 18%, and 7%) have a fairly flat spectral reflectance [16].
## 4 Numerical Model
### Numerical Discretization  
Four-node plane elements and eight-node hexahedral elements have been respectively chosen for the two-dimensional and three-dimensional analyses of the local models, with triangular and tetrahedral elements respectively employed in the transition area near the interface, in the Global coarse model and reference model, when conformal meshes have been built. Figure 8 shows the discretization of Global, Local, and Reference models, with the interface for the Local Model 2 placed at y = 0.9.  
Figure 8: Three–dimensional Global, Local, and reference numerical models (the local model is “cut” at y = 0.9).  
The numbers of elements considered for the Reference, Global, and Local model illustrated in Figure 8 are:  
* 20000 (Reference Model)
* 3700 (Global Model)
* 16300 (Local Model)  
The minimum element edge length is 0.06 mm for all the local and reference models considered in the present work and 0.1 mm for the Global models.
## 3 Results
### Spatial Characteristics of NPP and NEP  
#### 3.2.1 Spatial Characteristics of NPP  
Using the basic input data sets and the optimized CASA model, the grassland NPP in the HKH region from 2001 to 2018 was estimated and its distribution characteristics were analyzed (Figure 3a). Figure 3a shows that the NPP showed an increasing trend along the direction of Hindu Kush Himalayas from west to east, and the gradient distribution of NPP gradually but very obviously increased from northwest to southeast in the Qinghai-Tibet Plateau of China. The average grassland NPP throughout the HKH region was  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Period** & **NEP (gC m\({}^{-2}\).yr\({}^{-1}\))** & **References** \\ \hline
**2001–2018** & **49.35–82.8** & **Our study** \\
2009 & 156 & [52] \\
2001 & 55.26 & [53] \\
2003–2004 & 58.5–75.5 & [54] \\
2008–2009 & 47.1–79.7 & [25] \\
2013–2015 & 152.89–197.69 & [55] \\
2015–2016 & 105.3–258.9 & [10] \\
2009–2013 & 126.06–190.75 & [56] \\
2002–2004 & 78.5–192.5 & [4] \\
2010 & 156.4 & [57] \\
2008 & 140.01 & [58] \\
2013–2017 & 53–284 & [24] \\
2009–2012 & 118.49–195.83 & [23] \\
2001–2015 & 90–170 & [48] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Published studies of grassland ecosystem carbon sources/sinks on the Qinghai–Tibetan Plateau compared with our study.  
Figure 2: Comparison of the estimated net primary production (NPP) with observed data from flux sites.  
294 \(\pm\) 216 gC\(\cdot\)m\({}^{-2}\) during 2001 to 2018. In the western part of the HKH region, namely Afghanistan's Hindu Kush Mountain and the western part of the Tibetan Plateau, NPP was less than 200 gC\(\cdot\)m\({}^{-2}\), accounting for 46.3% of the total grassland area. Areas with NPP greater than 600 gC\(\cdot\)m\({}^{-2}\) were mainly distributed in the eastern part of the Qinghai-Tibet Plateau, accounting for 12.4%. Judging from the difference in NPP between 2018 and 2001 (Figure 3b), the NPP of grassland in 2018 was higher than that in 2001, and most regions had a growing trend. Among them, regions with a growth magnitude of 0-100 gC\(\cdot\)m\({}^{-2}\) accounted for the highest proportion (59.8%), and the proportion of regions with NPP growth magnitude greater than 100 gC\(\cdot\)m\({}^{-2}\) was 9.1%. The growth trend showed that the hydrothermal conditions in 2018 were better than those in 2001, the photosynthesis of grassland was strong, and productivity was high.