esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Analytical Techniques
#### 2.3.3 Raman Spectroscopy  
Raman spectroscopy is a vibrational spectroscopic analytical technique that operates on a principle based on an inelastic scattering of monochromatic light which creates a change in energy to study the vibrational and rotational modes of the excited molecules of a sample [26; 27]. This change in energy in a sample during Raman spectroscopy measurement is considered a characteristic of the vibrational modes of the molecule in the material. The Raman spectrum thus acquired from a sample is regarded as the fingerprint of the individual component present in the sample.  
The Raman spectrum obtained from a sample has some features, such as peak intensity, peak or shift (band) position, and full width at half maximum (FWHM), which provide information concerning the components in the material measured with Raman spectroscopy. For example, the Raman shift (band) position illustrates the material's phase or stoichiometric content, while the peak intensity (_I_) or area shows the sample's phase component concentration. The use of Raman spectroscopy as a characterization technique has also gained application in various fields, such as medicine, steelmaking for slag studies [16], and other research sectors.  
A TimeGated(r) Raman spectrometer (TG532 M1) supplied by TimeGated Instruments Ltd., Oulu, Finland was used in this study. Some of the device's features include a spectrometer equipped with a pulsed laser of 532 nm, with a pulse width of 150 picosecond (ps) and a fiber-coupled frequency range from 40 to 100 kHz. The instrument setup also had a probe head made of 200 \(\upmu\)m collection fibre with a spot size of 1 mm, a laser with a spectral width of less than 0.1 nm, a complementary metal oxide semiconductor (CMOS) single-photon avalanche diode (SPAD) CMOS-SPAD array detector, a Photonics RPB532 w/105 \(\upmu\)m excitation fibre, a fibre-coupled spectrograph, and delay electronics.  
The Raman spectroscopy measurements were made at ambient conditions with a wavenumbers range of 100-1100 cm\({}^{-1}\), a spectra acquisition time of 1-3 min, and a resolution of 10 cm\({}^{-1}\). Each sample was measured five times with the Raman spectroscopy, with mixing done (for homogeneity) on the same after each measurement. A rotating sample holder stage to obtain an average spectrum for each batch of the measured sample was used. These steps were considered to ensure good repeatability and to reduce the effect of sample inhomogeneity. The Raman data used for this study was obtained from the averaged signal of five measurements per sample. The Raman spectra obtained from each sample were relatively stable; therefore, the variability of the Raman signal in the Raman data within each sample measured was found insignificant. The pre-processing software package provided by TimeGated Instrument treated the signal with the background subtraction. The time-gated Raman spectroscopy (TimeGated(r)) used for this study was designed for effective fluorescence suppression compared to conventional Raman spectroscopy.
# Modified Joint Source-Channel Trellises for Transmitting JPEG Images over Wireless Networks
## 3 Construction of the Combined Source-Channel Trellis
### Tier 2: Modified Nonlinear Block Code Decoder Trellis
In such a state sequence \(S^{*}\), the prior probability of decoding a codeword is usually equally distributed or unknown; therefore, the state sequence can be described by the Equation (6):  
\[\begin{split} S^{*}=&\underset{S\in T}{\text{ argmax}}P(S|y)\\ =& arg\underset{S\in T}{\text{max}}\sum_{k=1}^{L} \Big{(}\sum_{i=1}^{2n-1}ln\big{(}P(y_{k,i}|s_{k-1},s_{k})\big{)}\Big{)}=& arg\underset{S\in T}{\text{max}}\sum_{k=1}^{L}ln\ P(y_{k}|s_{k-1},s_{k})= arg\underset{S\in T}{\text{min}}\prod_{k=1}^{L}d(y_{k},w_{k})\\ &\text{where }d(y_{k},w_{k})=|y_{k}-w_{k}|^{2}\text{ is the $k$th arc metric between states $s_{k-1}$ and $s_{k}$ in the trellis. }\\ &\sum_{i=1}^{2n-1}ln\big{(}P(y_{k,i}|s_{k-1},s_{k})\big{)}\text{ is the maximum likelihood of the algorithm, which can be expressed in binary symmetric channel form as  
\[ \begin{split} arg\underset{S\in T}{\text{max}}\sum_{k=1}^{L} \sum_{i=1}^{2n-1}ln\big{(}P(y_{k,i}|s_{k-1},s_{k})\big{)}=& arg\underset{S\in T}{\text{max}}\sum_{k=1}^{L}d(y_{k},w_{k}) ln\ P_{e}+[2^{m}-1-d(y_{k},w_{k})]ln(1-P_{e})=\\ & arg\underset{S\in T}{\text{min}}\sum_{k=1}^{L}d(y_{k},w_{k}) ln\frac{P_{e}}{1-P_{e}}+C_{1}\end{split}  \tag{7}\]  
where the metric \(d(y_{k},w_{k})\) is distance measurement and the \(P_{e}\) is the probability of transmission error. By using Equation
# Effect of Sowing Dates on Fatty Acids and Phytosterols Patterns of _Carthamus tinctorius_ L.
## 4 Discussion  
Adaptations of cultural practices, such as delaying the sowing date, are largely used by farmers to manage environmental conditions to reach optimal yields of oil crops. Particularly, delaying the sowing is used to avoid many diseases [11; 12] because weather conditions are modified, i.e., the rain is less abundant and the crop growth cycle could be significantly reduced. In this experiment, the weather conditions recorded during the growing season were characterized by a lower rainfall amount in 2007 (30% less) during grain formation and filling (June to September) compared to the half century data (Figure 1). In addition, a reduction of 37% of rain under the late sowing compared to the conventional one was observed. Mean temperatures were similar over the growing season. Therefore, experimental conditions could be considered as a moderate water stress which affected lipids production.
## 3 Modelling Results
### The Shield Tunneling Considering Heating Effect of Shield Machine  
According to the preliminary simulation results of the above freezing scheme, each part of the frozen soil curtain fully reached the design thickness on the 14 th day, so shield jacking is carried out from the 14 th day. Taking the aforementioned reference line and section as the characteristic line and plane, the development of a frozen soil curtain, considering the thermal disturbance of the shield machine, is observed and analyzed.  
Figure 10 is the temperature cloud of Section 1. It can be seen from Figure 10 that the frozen soil curtain melts as the shield machine continues to tunnel. The bottom and side of the cup-shaped frozen soil curtain melted from the 18 th day, the side of the cup-shaped frozen soil curtain melted more obviously by approximately 19.5 days, and the outer ring inside the cup-shaped freezing pipes arrangement was part of the basic melt.  
To more specifically analyze the melting situation of the frozen soil curtain at the bottom part of the cup-shaped freezing curtain and judge whether the thickness of the curtain after melting can maintain the requirements of shield construction, the temperature distribution curve on the aforementioned characteristic line is taken for analysis.  
The temperature distribution of the characteristic lines (line 3, line 4) at the bottom of the cup-shaped frozen soil curtain is shown in Figure 11. The bottom thickness of line 3 and line 4, which were 3.09 m and 3.23 m, still meet the design requirements on the 19 th day. The bottom thickness of the cup-shaped frozen soil curtain was 2.65 m and 2.69 m, which does not meet the design requirements of 2.8 m after 19.5 days.  
Figure 9: Temperature cloud map in Section 2.  
The temperature distribution curve of the cup-shaped frozen soil curtain is shown in Figure 12. After 19.5 days, the thickest part of the frozen soil curtain is 0.9 m, and the thinnest part is only 0.6 m, which is far less than the design requirement of 1.2 m.  
It can be seen from the diagram that as the top of the shield machine dissipates heat, the cooling capacity of the original freezing scheme is not enough to offset the heat generated by the continuous shield tunneling, the frozen soil melts, and the bottom and side part of the cup-shaped frozen soil curtain cannot maintain the design thickness, which is a high risk. It is necessary to take some construction measures to ensure the thickness of the frozen soil curtain.
# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
## 4 Definitions and Problem Statement
### VEPO-S2S Model
#### 4.3.3 Loss Function  
The VEPO adopts the root mean square error (RMSE) and the Kullback-Leibler Divergence (KLD) (as mentioned in Equation (9)) as the loss function. The objective of this work is to utilize the first \(m\) points denoted by \(X_{k}=(x_{1}^{k},x_{2}^{k},\ldots,x_{m}^{k})\) to predict the subsequent \(n\) track points, where \(m\) and \(n\) are the hyperparameters. The prediction sequence and target sequence are represented by \(Y_{k}=(y_{m+1}^{k},y_{m+2}^{k},\ldots,y_{m+n}^{k})\), and \(\widehat{Y_{k}}=(\widehat{y_{m+1}^{k},y_{m+2}^{k},\ldots,\widehat{y_{m+n}^{k}}})\), respectively. The primary aim is to minimize the loss function during training, ensuring greater accuracy in predicting the last \(n\) trajectory points. The expression for the loss function is shown in the following Equation (12).  
\[\mathcal{L}=\sqrt{\frac{1}{n}\sum_{k=1}^{n}(Y_{k}-\widehat{Y_{k}})^{2}}+D_{ \text{KL}} \tag{12}\]
## 4 Conclusions  
Currently, there is no dependable foundation for constructing grouting treatment plans for multi-seam goafs. This study adopted a numerical simulation method to design models of multi-coal seam goafs with different spacing conditions. By changing the grouting range and opportunity, the influence of coal seam spacing, grouting range, and grouting opportunity on the subsidence reduction effect of multi-coal seam goafs was studied. The main conclusions are as follows:  
1. With constant overburden (OB), the subsidence reduction ratio of the subgrade center increases linearly as the interburden (IB) decreases (\(1\leq\text{OB}/\text{IB}\leq 2\)), then increases exponentially (\(2<\text{OB}/\text{IB}\)), and eventually becomes stable.
2. When the treatment is performed according to the half-width of the subgrade, the width of the subgrade and the range of a trapezoid, the surface residual subsidence tends to the inclined 'W'-shape of the open cutting. The surface residual subsidence has a symmetrical 'W'-shape when full-range grouting is adopted. When the treatment width is selected as the width of the subgrade or half-width of the subgrade, the improvement effect on uneven subsidence of the subgrade is poor. When the treatment width is selected as the trapezoidal range and full-range grouting, the uneven subsidence of the subgrade is significantly improved.
3. For the grouting treatment of goaf areas with multi-coal seam goafs where mining has been stopped for a longer time, the central subsidence reduction rate of the subgrade is smaller, and there is an exponential relationship between the central subsidence reduction rate of the subgrade and the grouting opportunity. With the extension of grouting opportunity and the decrease in OB/IB, the subsidence reduction ratio at the center of the subgrade decreases in an exponential curve.  
In the simulation process of this article, the results of large-scale mining cases under real overburden were not verified, which requires further improvement based on the simulation in this article.  
Conceptualization, H.W. and Y.Q.; methodology, Y.Q. and Y.G.; software, H.W. and Y.G.; validation, Y.Q. and N.X.; formal analysis, Y.G. and Y.Q.; investigation, H.W. and Y.Q.; resources, Y.Q. and N.X.; data curation, Y.G. and Y.Q.; writing--original draft preparation, Y.G. and Y.Q.; writing--review and editing, Y.Q. and N.X.; visualization, H.W. and Y.G.; supervision, Y.Q. and N.X.; project administration, Y.Q.; funding acquisition, N.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (NSFC) under grant no. 42230709 with the title \"Mechanism and prevention methods of tunnel disaster induced by long-term deformation of rock strata in mining-induced subsidence zone\".  
Not applicable.  
The authors thank the National Natural Science Foundation of China (NSFC) under Grant No. 42230709.  
The authors declare no conflict of interest.
## 7 Results and Discussion  
In this study, a total of 60 PAUT simulation S-scan flaw images were used for the testing, of which 29 images for acceptable flaws and 31 images for not acceptable flaws were randomly selected. The simulation S-scans contained 1 mm, 2 mm, 4 mm, and 6 mm lengths of flaws. The 1 mm and 2 mm flaws were annotated as acceptable and the 4 mm and 6 mm flaws were annotated as not acceptable (NA).
## 2 Materials and Methods
### Experimental Setup and Procedure  
The experimental setup of the measurement system used in this work is illustrated in Figure 1B. The specimen was immersed in buffer salt solution (0.145 M, pH 7.3) composed of sodium phosphate monobasic, sodium phosphate dibasic and sodium chloride in a polymethyl methacrylate (PMMA) container [25; 26]. The fluid pressure generated by an air pressure controller drove the buffer solution to flow through the micropores of the specimen from bottom to top. To ensure the solution was flowing through the micropores of the specimen only, a rubber ring of thickness 0.3 mm was placed between the specimen and the shoulder of the container, and its circular edge was clamped on the inner shoulder with a PMMA thread ring. When the solution driven by the fluid pressure flows through a specimen, the streaming potential induced was measured by two platinum/iridium electrodes. The potential signals measured were input into a bioamplifier (BMA-931, CWE Inc.,Ardmore, Pennsylvania, USA) via an ultra-high-input impedance (over \(10^{12}\)\(\Omega\)) head stage (Super Z, CWE Inc., Ardmore, Pennsylvania, USA). Due to the ultra-high-input impedance of the head stage, there is almost no electric current flowing through the solution above the specimen into the left electrode and also there was almost no electric current flowing through the solution below the specimen into the right electrode, which meant that the electric potentials in the solutions above and below the specimen were evenly distributed. Then the installation of the two electrodes was relatively easy; they were placed vertically with each front end of 3 mm being immersed in the solution.  
The amplified voltage signals and the pressure signals from the air pressure controller were recorded by a computer. The maximum duration of data acquisition of the system was 120 s and measurement range was \(-2\) to 8 mV. The streaming potentials measured were weak signals of microvolt amplitude, so easily affected by an ambient electromagnetic field; therefore, a double shield technique was employed. The specimen, electrodes and the head stage were placed in the double shield with their common ends connecting to the inner shield and the outer being ground.
# An Image Processing Method for Measuring the Surface Area of Rapeseed Pods
## 2 Materials and Methods
### Measurement Method and Technical Roadmap  
A correction model to measure the surface area of a single pod using the image processing method needed to be established before the batch measurements of multiple pod surface areas. Several pods of different sizes were collected from different cultivars and plants and sent to a dimension measurement and Data Services Company for single pod measurement using a high-precision 3-D laser scanning system (referred to as the \"3-D method\" from this point on). The measured value was then treated as the \"actual surface area\" (referred to as the \"3-D area\"). A portion of the pods sampled above were used as modeling samples, and the single pods were measured using the image processing method. The correlation between the measurement results of the image processing method and the corresponding 3-D area was analyzed, enabling the establishment of the correction model. The remaining sampled pods were used as test samples, for which the image processing method was also used to measure the surface area of single pods. The corrected area was obtained by correcting the measurement results of the image processing method based on the correction model. The accuracy was then tested by analyzing the relative error and RMSE of the corrected area and the corresponding 3-D area. Moreover, the single-pod measurement of the test samples was performed using the Clark formula, Leng formula, flattening scanning, and quasi-cylinder side area methods. Their accuracy was assessed by analyzing their relative error, RMSE, and the corresponding 3-D areas. We then compared the measurement accuracy of the image processing method and the other four methods; this enabled us to determine whether the image processing method had a relative advantage. In addition, the measurement efficiency of the image processing method and the other four methods was tested by the batch measurement of multiple pod surface areas using the image processing method. The results of measurements were corrected using the correction model, and the rationality of the measurement results was also analyzed. A technical flowchart of this study is shown in Figure 2.
## 5 Discussion
Furthermore, it seems in many cases, especially in the results on the Paderborn dataset, that the classification task is fairly easy for the models to perform. Therefore, for future research, we intend to apply transfer learning by training the models on experiment data and test their performance on real-world data to assess the generalizability of the trained models in other contexts. However, in this particular situation, the properties of the data should be similar, e.g., data derived from vibration sensors or motor current signals. Further, we aim to investigate the performance of the adaptive models in a context with variable sequence lengths. In addition, we plan to research the application of attention mechanisms in time series classification tasks as described in [62; 63] and implement these mechanisms in fault detection and condition monitoring tasks. Conceptualization, J.v.d.H. and M.A. ; methodology, J.v.d.H. ; software, J.v.d.H. ; validation, J.v.d.H. ; formal analysis, J.v.d.H. ; investigation, J.v.d.H. ; resources, J.v.d.H. ; data curation, J.v.d.H. ; writing--original draft preparation, J.v.d.H. ; writing--review and editing, J.v.d.H., S.B. and M.A. ; visualization, J.v.d.H. and S.B. ; supervision, M.A. ; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript. This work has been funded by the Interreg North-West Europe program (Interreg NWE), project Di-Plast--Digital Circular Economy for the Plastics Industry (NWE729). In this research, the following two publicly available datasets were used; the Case Western Reserve University Bearing data ([[https://csegroups.case.edu/bearingdatacenter/pages/download-data-file](https://csegroups.case.edu/bearingdatacenter/pages/download-data-file)]([https://csegroups.case.edu/bearingdatacenter/pages/download-data-file](https://csegroups.case.edu/bearingdatacenter/pages/download-data-file))), accessed on 14 January 2020 and the Paderborn University Bearing data ([[https://mb.uni-paderborn.de/en/kat/main-research/datacenter/bearing-datacenter/data-sets-and-download](https://mb.uni-paderborn.de/en/kat/main-research/datacenter/bearing-datacenter/data-sets-and-download)]([https://mb.uni-paderborn.de/en/kat/main-research/datacenter/bearing-datacenter/data-sets-and-download](https://mb.uni-paderborn.de/en/kat/main-research/datacenter/bearing-datacenter/data-sets-and-download))), accessed on 20 May 2021. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
A-WCNN Adaptive Wide-Kernel CNN Ada-WMCNN Adaptive Wide-Kernel Multichannel CNN CBM Condition-based Maintenance CNN Convolutional Neural Network CWRU Case Western Reserve University K-NN K-Nearest Neighbor
## 2 Materials and Methods
### Data
#### 2.1.2 Himawari-8 AHI/ AOD Data  
Himawari-8 is a second-generation geostationary meteorological satellite of Japan Meteorological Agency (JMA), which was successfully launched in October 2014 and officially put into operation in July 2015. Himawari-8 carries an AHI sensor with 16 observation channels, which includes 3 visible channels, 3 near-infrared channels, and 10 infrared channels, of which the resolution of visible channels can reach 0.5 km, and the resolution of near-infrared and infrared channels can reach 1-2 km. This study used the AOD by the dark target algorithm retrieval with a spatial resolution of 1 km and a temporal resolution of 1 h; please refer to the literature for the detailed retrieval algorithm [41].
# Prediction of Ground Surface Settlements Induced by EPB Shield Tunneling in Water-Rich Soft Strata
## Case Study in Changzhou  
### Description of the Tunnel Project  
Figure 8 shows a case history in Changzhou where the new twin shield-driven tunnels were excavated. The construction project links South Street Station and Cultural PalaceStation. Figure 8a shows a plan view of the studied section. Two EPB shield machines with a diameter of 6.34 m were employed for construction from South Street Station to Cultural Palace Station. The total length of the tunnel is 656.2 m, and the right tunnel was first constructed. A geological profile is presented in Figure 8b, revealing that the tunnels are mostly located in clay sublayer (\(\copyright\)3) with a buried depth ranging from 17.7 to 28.2 m.  
After the right shield machine was launched from the South Street Station, the ground surface deformations were regularly monitored. Two representative monitoring cases, namely case A and case B, were selected to validate the proposed prediction method on the short-term ground deformation. As shown in Figure 8a, case A was approximately 150-200 rings distance from the South Street Station, where a total of 11 sections were set in this area, and the distance between every two sections was 6.0 m. Case B was approximately 210-350 rings distance from the South Street Station, with a total of seven sections set in this area. A total of seven monitoring points, i.e., SD1-SD7 from left to right, were arranged in each section at different distances (8 m, 3 m, 0 m, 3 m, 7 m, 12 m, and 24 m) from the tunnel centerline.