esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations of the Method  
Vegetation phenology monitoring based on remote sensing data faces major challenges for non-forest types and for high latitude, arid, and humid tropical regions [47]. In this study, we found that snow cover information, such as snow cover periods from MOD13C1 products, is strongly correlated with spring and autumn phenology. Considering snow cover information as a variable in the model improves the accuracy of spring and autumn phenology estimates in high-latitude regions. However, the statistical models proposed in this study for estimating vegetation phenology may have a number of uncertainties for arid and humid tropical regions because only a few FLUXNET sites located in these regions can be used for model calibration. The method proposed in this study only considers temperature as a major climate factor for VPP estimation. On the other hand, the photosynthetic capability has a weak relationship with temperature, and is primarily regulated by radiation, precipitation, and available soil water content in arid and humid tropical regions [76].  
On a global scale, growing seasons for vegetation in different climatic zones are diverse. For example, in the majority of the evergreen forests in humid tropical regions the growing season begins in the preceding year or ends in the following year [56]. Furthermore, some vegetation types in arid regions have multiple and variable growing seasons depending on the variation in temperature in the early months and precipitation in the mid growing season [72]. Considering these specific situations will further improve the model and the method used to decrease uncertainties of VPP estimates in arid and humid tropical regions. The CRO is a special vegetation type and its phenology is largely determined by the cropping pattern of land management. The method proposed in this study has the ability of exacting VPP for CRO with a single cropping pattern but does not allow for the exacting of all start and end dates of growing seasons for CRO with multiple growing seasons. Exacting VPP of CRO with multiple cropping patterns could be the third improvement of the method to increase the accuracy of VPP estimates for CRO.
# Delegation-Based Personal Data Processing Request
## 3 Delegation-Based Personal Data Processing Request Notarization Framework
### Notarization Process  
DS transmits a request for processing personal data through the notarization system. The notarization system presents the notarization upon request and delivers it to the SP via e-mail. The SP, which is the DC, performs appropriate processing according to the request and thereafter submits the processing result to the notarization system. Consequently, the notarization system creates a block containing the processing request, processing result, and notarization content and then adds it to the ledger. Figure 8 shows the notarization process for the processing of personal data complying with the GDPR.  
Algorithm 3 is the algorithm for smart contract implement of request registration in Figure 8.  
Figure 8: Notarization process of the request and response.  
```
INPUT: DS's ID, SP's URL, SP's e-mail, process type, contents of request, timestamp OUTPUT: transaction // make key 1 SET transaction key to the result of CALL make request number with the DS's ID and timestamp RETURNING request number 2 SET request structure with the transaction key, DS's ID, SP's URL, SP's e-mail, process type, contents of request, and timestamp 3 SET JSON formed request to the result of CALL transform to JSON with request structure RETURNING JSON formed request 4 RETURN the result of CALL make transaction with transaction key and JSON formed request RETURNING transaction
```  
**Algorithm 3** Input Request  
Algorithm 4 is the algorithm for the smart contract implement of response registration in Figure 8.  
```
INPUT: request transaction key, [PERSON]'s ID, [PERSON]'s URL, [PERSON]'s e-mail, process type, contents of request, contents of response, timestamp OUTPUT: transaction // check request being 1 SET existing to the result of CALL checks whether it exists with request transaction key RETURNING existing 2 IF existing is false THEN 3 PRINT \"the request does not exist\" RETURN null 5 ENDIF // make key 6 SET transaction key to the result of CALL make response number with [PERSON]'s URL and timestamp RETURNING response number STET response structure with transaction key, [PERSON]'s ID, [PERSON]'s URL, [PERSON]'s e-mail, process type, contents of request, contents of response, timestamp 8 SET JSON formed response
```  
**Algorithm 4** Input Response  
Figure 9 shows the process of inquiring transactions stored in the notarization framework by an authorized participant.
## 3 Results
### Forage Harvest NPP Based on the Feed-Supply Balance  
The biomass harvest from the grasslands, defined as forage harvest NPP (HNPP), was estimated to be in the range of 6.7-10.2 Tg C yr\({}^{-1}\) over the period of 2000 to 2017. The average total HNPP was 8.9 Tg C yr\({}^{-1}\), accounting for about 17.1% of the total grasslands  
Figure 4: Spatial distribution of simulated averaged ANPP (**a**) and its trends (**b**) over the period of 2000 to 2017 in eastern Inner Mongolia.  
Figure 5: Spatial distribution of simulated averaged PNPP (**a**) and its trends (**b**) over the period of 2000 to 2017 in eastern Inner Mongolia.  
ANPP. As shown in Figure 6c, the total grasslands HNPP in Hulunbuir City and Xing'an League presented a clear increasing trend, and the average values were 3.4 Tg C yr\({}^{-1}\) and 2.2 Tg C yr\({}^{-1}\) in these two regions, respectively. However, the trend in Xianan League was not pronounced, and its average value was 3.3 Tg C yr\({}^{-1}\). In most areas of eastern Inner Mongolia, the average annual HNPP was less than 20 g C m\({}^{2}\) yr\({}^{-1}\), and as shown in Figure 6a, the average HNPP increased from west to east.  
Quantitative Assessment of the Impact of Human Activities on Terrestrial NPP in Eastern Inner Mongolia  
The HAI was divided into two parts to better analyze its changing trends. One was the difference between the PNPP and ANPP, defined as \(\mathrm{NPP}_{\mathrm{LUC}}\), which denotes the impact of human-induced land conversions on terrestrial NPP. The other was the HNPP. As shown in Figure 7c, the annual mean HAI changed with fluctuations from 0.47 to 0.75 during 2000-2017, with an average of 0.63, and the HAI exhibited a clear decreasing trend especially in the period of 2007-2015. The \(\mathrm{NPP}_{\mathrm{LUC}}\) of the grasslands was significantly larger than the HNPP, ranging from 80.9 Tg C yr\({}^{-1}\) to 34.8 Tg C yr\({}^{-1}\), while the total HNPP changed from 6.7 Tg C yr\({}^{-1}\) to 10.1 Tg C yr\({}^{-1}\) over the period of 2000-2017. Thus, it is obvious that the changes in HAI was mainly due to the \(\mathrm{NPP}_{\mathrm{LUC}}\) in eastern Inner Mongolia. As shown in Figure 7b, a clear decreasing trend of the \(\mathrm{NPP}_{\mathrm{LUC}}\), which was similar to that of the HAI, was presented and compared with the gradually increasing trends of the HNPP. The multi-year averaged HAI increased from southwest to northeast (Figure 7a). Due to the low ANPP, the HAI in most areas of the southwest was larger than 0.7. Nevertheless, there were many points scattered in the Greater Khingan Mountains with HAI values less than 0, where the ANPP was significantly larger than the PNPP because of the misclassification of the forests as grasslands.  
Figure 6: Spatial distribution of calculated averaged HNPP (**a**), variation of total grasslands ANPP and HNPP (**b**), and variation of total grasslands HNPP in Xilingol League, Hulunbuir City, and Xing’an League (**c**) over the period of 2000 to 2017 in eastern Inner Mongolia.
## 3 Results and Discussion
### Cell Adhesion and Proliferation  
The cell morphologies on the scaffolds were analyzed by SEM and fluorescent staining (Figures 9 and 10). In comparison to the rounded cells in the control group, the cells on the scaffolds with nHAp spread well after cultivating for 1 day and exhibited a fusiform morphology. Besides, the extension degree of the cells gradually enhanced with increasing nHAp content. After 3 days, the cells formed a confluent layer by cytoplasmic extension and attached closely throughout the scaffolds. The MTT assay was used for investigating the cell proliferation behavior after culturing for 1, 2, and 3 days, respectively (Figure 11). The experimental result showed that the cells presented a good proliferation trend on the scaffolds with the culture time extension. Meanwhile, the cells numbers on the scaffolds with nHAp was obviously higher than that of the control group.  
Figure 8: (**a**) Scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) analysis of the precipitate on the CaSO\({}_{4}\)/15 wt % nHAp scaffolds after soaking in SBF for 3 days; (**b**) Fourier-transform infrared (FTIR) spectra of the CaSO\({}_{4}\)/15 wt % nHAp scaffolds soaked in SBF for 3 days.  
The results of cell culture suggested that the scaffolds containing nHAp could promote cell growth and proliferation, showing good biocompatibility. This was due to the fact that nHAp helps to adsorb more protein, such as fibronectin or vitronectin, on the scaffold's surface. The adsorbed protein attracts more cells towards this region and thus enhanced the cell adhesion and proliferation ability.  
Figure 11: Proliferation behaviors of cells cultivated on the scaffolds for 1, 2, and 3 days. Significant difference between the composite scaffolds and pure CaSO4 scaffolds (\({}^{*}p<0.05\), \({}^{**}p<0.01\)).  
Figure 9: SEM images of MG-63 cells cultured on the scaffolds containing (**a**,**f**) 0 wt % (control); (**b**,**g**) 5 wt %; (**c,h**) 10 wt %; (**d,i**) 15 wt %; and (**e,j**) 20 wt % nHAp for (**a–e**) 1 day and (**f–j**) 3 days.  
In addition, nHAp restrained the acidification effect during CaSO\({}_{4}\) degradation and created a slightly alkaline environment, which is beneficial for supporting cell metabolism.
# Numerical Simulation of Vacuum Leak Jet and Jet Noise
## 4 Conclusions  
The realizable k-\(\varepsilon\) model is adopted in this paper to numerically simulate the aerodynamic properties and aeroacoustic characteristics of a leak-induced jet with different external environment, different leak hole diameter and different external thermal condition. Numerical results show that:  
(1) On the space station, the leak gas will expand into outer vacuum space rapidly and form a supersonic jet with a Mach number of 2 to 3, the diameter of the jet potential core being about 25 times the diameter of the leak hole. Although there are large velocity, density, pressure, and temperature gradients, no typical shock cell structure is observed. In contract, conventional terrestrial leak jet under the same pressure difference of 1 atm is still subsonic and no obvious expansion can be seen.  
(2) The jet noise in vacuum environment shows a phenomenon of acoustic cavity reflection. Compared with widely used _Kn_ criterion in vacuum plume dynamics, the \(\lambda\) criterion seems more appropriate to define the acoustic cavity wall. Within the acoustic cavity which is judged by \(\lambda=0.6\) mm, the reflected waves reflect upstream along the acoustic cavity wall, exciting multiple resonance harmonic modes. Interestingly, the \(\lambda\) criterion is universally suitable for different leak hole diameter and external temperature, which may confront in the on-orbit applications. In addition, it is not difficult to find that the source of vacuum leak jet noise is mainly composed of turbulent mixing broadband noise and resonance harmonic noise.  
(3) Although the RANS method's weaker resolution of the fluctuation terms makes it unable to accurately describe the turbulent mixing broadband noise, it still has a high resolution of the jet dynamic properties and the time-averaged characteristics of pressure disturbance propagation. Furthermore, the computation resource requirement is relatively small. Therefore, RANS method can effectively analyze the characteristics of harmonic noise caused by resonance.  
[PERSON] participated in the design of this study. [PERSON] conducted the numerical simulation under the guidance of [PERSON]. [PERSON] provided important background information of this problem. [PERSON] gave important suggestions on the improvement of simulation. [PERSON]-[PERSON] helped the numerical simulation. [PERSON] and [PERSON] conducted the manuscript preparation. The other authors reviewed the manuscript carefully. All authors read and approved the content of the manuscript.  
This research was funded by the Training Program of the Major Research Plan of the National Natural Science Foundation of China grant number 91741107.  
The authors declare no conflict of interest.
# Structure Preserving Convolutional Attention for Image Captioning
## 3 Proposed Method
### Structure Preserving Convolutional Attention
#### 3.2.1 Convolutional Spatial Attention  
This submodule generates \(M_{t}^{S}\) to tell 'where' in the image should be focused on. While grid-based attention has been proven to be effective on image captioning, it does not take into consideration the spatial structure of image regions and treat them as independent vectors. In fact, lack of spatial structure will lead to inaccurate positioning, which affects the quality of the generated captions.  
To enable preservation of the spatial structure of image regions, a convolutional spatial attention is proposed.  
We use a convolution operation instead of the original fully connected layer and use 2D latent state and 2D image features instead of those used before. On the one hand, our convolutional spatial attention preserves the structure of the image. On the other hand, we use convolution operation with \(3\times 3\) kernel size to provide larger receptive field to accurately determine 'where' we should focus at \(t\) step.  
In short, our convolutional spatial attention is computed as:  
\[M_{t}^{S}=\sigma(Conv2(Relu(Conv1(F_{t})))) \tag{11}\]  
where \(M_{t}^{S}\in R^{1\times W\times H}\) is the convolution attention weights, \(Conv1\) and \(Conv2\) are both convolution operation with \(3\times 3\) kernel sizes, \(Relu\) denotes RELU activation function and \(\sigma\) denotes the sigmoid function.
# Finite Element Analysis of Proposed Self-Locking Joint for Modular Steel Structures
## 5 Parametric Study on the Proposed Connection
### Behavior of Proposed Connection (Cp1) under Static Lateral Loading  
The control model of the proposed connection represented by model Cp1 behaved in an elastic manner up to the moment of 192 kNm and inter-story drift ratio of 0.021 radian; at this stage, initial stiffness was 8835 kNm/rad as shown in Figure 6a. Relative movement between adapter and column was noticed that was due to differences in the internal dimensions of column and tolerance of adapter. With further increase in applied loading, the yield began to develop at the root of the ceiling beam, and the slope of the M-0 curve changed. The moment-rotation response increased gradually in the yield development stage. The joint reached its maximum moment capacity of 287 kNm at 0.055 radian drift ratio, while at 0.04 radian, the moment of 274 kNm was recorded, which was 0.96 of maximum moment carried by joint (satisfying requirement of special moment frame, SMF [33]). Joint showed an equilibrium condition until the yielding of ceiling beam achieved. After the yield developed in the ceiling beam, the walls of the adapter came in to contact with respective columns (this was because of the difference in the rotation between the ceiling and floor beams) and produced bearing stress on the walls of adapters; these stresses start to develop at the corners of adapter walls and propagate toward opposite direction as shown in Figure 7b. The bolt joining upper column and adapter, and spring pin also developed stress in shear. It is shown in Figure 7a that the initial stiffness rotation of the proposed joint is falling between the rigid and pinned connection and can be categorized as a semi-rigid connection according to Eurocode 3 (EC3) [32].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Parameters/Variables**} \\ \hline \multirow{2}{*}{**Model ID**} & \multirow{2}{*}{**Load Type**} & **Bolt** & **Coefficient** & \multirow{2}{*}{**Spring/Stud**} & \multirow{2}{*}{**Beam Thickness Ratio (Floor Beam/Ceiling Beam)**} \\  & & **Pretention** & **(kN)** & **(\(\upmu\))** & & **Ratio (Floor Beam/Ceiling Beam)** \\ \hline Cp1 & Static load at beam end & 35.6 & 0.23 & Yes & 8/6.3 \\ Cp2 & Load in tension on upper column & 78.5 & 0.23 & Yes & 8/6.3 \\ Cp3 & Load in tension on upper column & 78.5 & 0.23 & No & 8/6.3 \\ Cp4 & Static load at beam end & 35.6 & 0.23 & Yes & 14/12.5 \\ Cp5 & Static load at beam end & 35.6 & 0.23 & Yes & 10/10 \\ Cp6 & Static load at beam end & 17.18 & 0.23 & Yes & 8/6.3 \\ Cp7 & Static load at beam end & 53.5 & 0.23 & Yes & 8/6.3 \\ Cp8 & Static load at beam end & 35.6 & 0.19 & Yes & 8/6.3 \\ Cp9 & Static load at beam end & 35.6 & 0.27 & Yes & 8/6.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Details of models developed in ABAQUS.
## 1 Introduction
This paper explores promising proposals that use VSC-HVDC links for black starts, showing the diversity of literature and research that has recently showed how this technology can support grids through frequency, voltage support, control [27; 28], transient behavior control [29], power oscillation damping [26; 30], and soft-starting schemes [31]. All of the proposals included in this paper are proof of the huge potential that HVDC technologies have for achieving a superior performance on future power grids.
## 2 Experimental Procedure  
Boron-alloyed steel, Docol(r) PHS CR 2000, with a thickness of 1.2 mm was used as the base metal (BM). The specimens were machined to a dimension of 150 \(\times\) 120 mm (Figure 1), and the oxide scales on the specimen surface were removed using fine sandpaper to prevent dilution of the oxide layer into the weld metal (WM) [13]. The chemical composition of the BM was analyzed using an optical emission spectrometer (Table 1), and it was matched with the range in the technical specifications provided by the steelmaker SSAB AB [14]. Then, the specimens were heat-treated according to the manufacturer's guidelines. The specimens were heated for 5 min in a furnace at a temperature of 950 \({}^{\circ}\)C to complete the austenitic phase transformation and then quenched in a water-cooled die where the temperature was maintained constantly at 25 \({}^{\circ}\)C. The cooling rate in the cooling die was estimated to be 100 K/s.  
A fiber laser system (IPG photonics, Oxford, MA, USA) with a wavelength of 1070 nm with the laser output power set at 2 kW was used. The laser beam was delivered to a laser optics (IPG photonics, Oxford, MA, USA) device with a focal distance of 200 mm through an optical fiber with a diameter of 200 \(\upmu\)m. The laser beam was irradiated perpendicularly onto the specimen placed at the focal position. At the focal point the laser beam had a  
Figure 1: Welding joint preparation.  
beam diameter of 0.27 mm (Figure 2). The welding speed was varied from 6 to 8 m/min: shielding gas was not supplied in this study.  
Mechanical and metallurgical tests were performed on the welded specimens. After laser welding, three tensile-test specimens were machined based on the ISO 4136 standard for each condition (Figure 3) and the static tensile tests were carried out at a head speed of 1 mm/min (Shimadzu, Kyoto, Japan). The welded specimen was polished and etched with 3% nital solution (3 mL HNO\({}_{3}\) and 100 mL ethanol) and the microstructure was observed by optical microscopy (OM, Olympus, Kyoto, Japan) and field-emission scanning electron microscopy (FE-SEM, Thermo Fisher Scientific, Waltham, MA, USA). The micro-Vickers hardness (Matsuzawa, Akita, Japan) was measured on the cross-section with an indent spacing of 0.1 mm along the specimen centerline, according to the ASTM E384-99 standard; the applied load and holding time for hardness measurement were 1.96 N and 10 s, respectively. In addition, nanoindentation hardness tests were carried out in the lower-hardness region between the intercritical HAZ (ICHAZ) and the subcritical HAZ (SCHAZ) with a load of 0.2 N and a holding time of 20 s, according to the ISO 14577-1:2015 standard.
###### Abstract  
A non-destructive evaluation method is proposed to identify the sectional loss of the external tendon of prestressed concrete structures by detecting the change of the magnetic flux in the external tendon exposed to a magnetic field. The method uses a solenoid-shaped device with two coils: a primary coil for producing magnetic field and secondary coil for damage detection, wrapping the external tendon. A current applied to the primary coil in the device causes the magnetic field. Then, the change in the magnetic flux by the damage in the external tendon is detected by the variation of the voltage in the secondary coil in the device as the device moves along the tendon. An alternating current is applied to the primary coil to minimize the effect of the moving speed of the device. As a result, the damaged area can be detected with a low-level energy current. In addition, a wrapping solenoid-shaped device that is easy to disassemble and assemble was developed for in situ inspection. The measured signal from the secondary coil has a sinusoidal form with the same frequency as the applied current to the primary coil, and the peak curve of the measured signal provides enough information to detect the damage. It is shown that the proposed method can quantitatively identify one or multiple damaged-tendon locations as well as damages of at least 2 cm.  
Keywords:total flux leakage; non-destructive evaluation(NDE); Prestressed Concrete (PSC); corrosion of the prestressing tendon; section loss of external tendon +
Footnote †: journal: Applied Sciences
## 1 Introduction
MIMO C-OOK is based on the matched filter proposed in [8] with MIMO technique for high-rate OCC, but the mobility effect was not considered. We presented a rolling-shutter orthogonal frequency-division multiplexing (OFDM) technique for a high data-rate OCC system in [9]. Depending on the camera's rolling-shutter effect, the OFDM waveform could be received based on the LED intensity levels in each image. It reveals the drawback of the mobility effect, which is essential for OCC systems. In this paper, we propose an RS-OFDM, LED detecting method based on deep learning and the YOLOv5 algorithm to achieve high accuracy in real-time. With YOLOv5, we can achieve a low bit error rate with a mobility effect. In addition, a deep learning decoder presents a way to improve OCC performance as compared to the traditional [9] approach. By using deep learning for the start of frame detection, we can reduce the error in the mobile environment. The remainder of this study contains five sections structured as follows. In Section 2, we present technical contributions. In Section 3, we represent the related work of OFDM techniques for OWC systems. Section 4 displays the system architecture of RS-OFDM using deep learning. Section 5 depicts the simulation and implementation results of the RS-OFDM system for the mobility environment. Section 6 concludes this study.