esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study Results  
### Identifying ICT Needs of Rural Community  
Community representative Ms. [PERSON]: \"In the past, education and information agencies also taught senior citizens how to use the computer, but perhaps the materials were too hard, the courses moved too fast, or they were just plain boring, so elders were often unable to understand and apply what was taught. This made them even more afraid of computers and the internet\".  
The most important parts of the service process of PunCar Action (Figure 2) include the two to three visits to the registering community to understand the requirements and arrange courses after the requirements have been evaluated, including the selection of suitable lecturers. During the tours of ICT teaching activities, each lecture was recorded on a blog, and after class, lecturers and volunteers provided either face to face or online assistance to students, and adjusted the content of the next class if necessary. For example, the course on how to use voice and video over IP communication tools, although originally designed using Skype, could be adjusted to teach the installation and operation of Google\" Hangouts if the students' grandchildren were more accustomed to this tool.  
Director [PERSON] of the PunCar south Taiwan center: \"After driving PunCar on a tour of over two hundred courses, I found that 'communication' was an intangible but immense cost. Yet, it was the only way to provide quality courses. If the framework of digital divide does not demand or allow for communication, then pursuing 'what to learn' and 'how to learn' in ICT learning will be deemed unnecessary.\" \"...We had many discussions with community members and students to identify the community's real ICT needs. That is the major difference between PunCar Action and the more common top-down and unitary digital curriculum.\"  
Figure 2: ICT teaching process of PunCar Action.
## Results
### Pathway Enrichment Analysis of DEGs Using KEGG  
KEGG pathway enrichment analysis of the five DEG libraries was performed. All annotated genes were mapped to terms in the KEGG database to search for significantly enriched genes involved in metabolic or signal transduction pathways. A total of 3384 DEGs were mapped to 262 KEGG pathways, including the terpenoid backbone biosynthesis pathways, the ubiquinone and other terpenoid-quinone biosynthesis pathways, the carotenoid biosynthesis pathways, the steroid biosynthesis pathways, the prenyltransferases pathways, the diterpenoid biosynthesis pathways, the limonene and pinene degradation pathways and the geraniol degradation pathways. The 447 DEGs identified in the 60 DAF vs. 75 DAF contrast group were assigned to 120 KEGG pathways. Furthermore, the 1138 DEGs identified in 60 DAF vs. 90 DAF contrast group were mapped to 181 KEGG pathways, the 1320 DEGs identified in the 60 DAF vs. 105 DAF contrast group were mapped to 186 KEGG pathways, and the 1092 DEGs identified in the 60 DAF vs. 135 DAF contrast group were mapped to 180 KEGG pathways. From all those pathways, the terpenoid backbone biosynthesis pathway was selected for further analysis. A total of 26 DEGs were identified in this pathway (Table 2), 14 of which were also identified in the GO analysis. Almost all DEGs (24 of 26) were down-regulated, and only two DEGs (g 8148 and g 56393) were up-regulated (geranylgeranyl diphosphate synthase, type III and geranylgeranyl reductase). In addition, g 4005, g 21893, g 52640, g 5372, g 39745 and g 58660 were up-regulated in the 75 DAF vs. 60 DAF contrast group, g 60046, g 5264, and g 7256 were up-regulated in the 90 DAF vs. 60 DAF contrast group, and g 60046 were up-regulated in the 105 DAF vs. 60 DAF contrast group. Furthermore, g 54221 was up-regulated in all contrast groups except for the 105 DAF vs. 60 DAF contrast group. Figure S2 represents the locations of DEGS involved in the terpenoid backbone biosynthesis pathway. Red or green represents gene was up-regulated or down-regulated genes during the fruit development, respectively. Figure 4: Functional categorization of the differentially expressed genes during development of _L. cubeba_ fruit. (**A**)–(**D**) shows the functional categorization of the differentially expressed genes between the 75 DAF, 90 DAF, 105 DAF, 135 DAF and 60 DAF (as control) developmental stages.
# A Distributed File-Based Storage System for Improving High Availability of Space Weather Data
## 4 Implementation
### Monitoring Web Interface  
A web interface was implemented for data and node status monitoring in the system. This simple index page was built with the Bootstrap 3.3 framework consisting of HTML, CSS, and JS. JQuery was also used to read and retrieve data from daily log files, as well as for automatic refreshing of the page every 20 s. For data transfer, NodeJS version 10.15.3 was implemented as the HTTP server. NodeJS handled data transfer when JQuery extracted the data from the files and communicated it to the HTML page. Each node had several log files according to its instruments. Therefore, separate JQuery functions were used for reading and extracting the data from those files; one function was responsible only for one file. In Figure 10, we could see that this monitoring was displayed on only one page of the index that contained information on the data that had been successfully stored in the IPFS network and distributed to other nodes. In addition, the status of each node was also displayed to determine the current state of the nodes.  
The log files automatically updated every minute. For data monitoring, logs were sent from all sites to the central server. Meanwhile, for node status monitoring, status information came from files generated by the central server after finishing pings. Figure 10 presents data monitoring per node and displays the status of nodes on the bottom. Nodes 2 and 3 succeeded in showing their data because their state was online, contrary to Node 4, which had no data information due to its offline status.  
Figure 9: The Merkle link of folder Jan from the SN-4000 instrument.
# The Role of Buoyancy Induced Instability in Transpirational Cooling Applications
###### Abstract
However, transriprational cooling has additional complexities which are not accounted for by the hydrodynamic stability analysis in these works. These include the highly non-linear density variation with temperature, i.e., a nonlinear Boussinesq approximation [22; 23]. Due to the often extreme temperature difference between the inlet and exit of the porous media, extremely large density gradients can occur. These density gradients arise from heating cryogenic fuels like methane or hydrogen to a supercritical state. Real-gas compressibility effects can be significant at these extreme temperatures and pressures. Thus, the objective of this work is to include the effects of a non-linear cubic density dependence on temperature in a base state configuration with a vertical through-flow to quantify and understand the instability mechanisms occurring in the porous medium. Three cases are considered in this work, each with an additional layer of complexity: (1) A traditional HRL configuration, (2) a HRL configuration with a non-linear density variation in temperature, and (3) adding a base vertical through-flow to Case 2. The third case is most representative of transriprational cooling. The non-dimensionalization introduced in this study to account for the drag term results in a Grashof-type scaling rather than the typical Rayleigh-type scaling encounter in HRL (or Wooding) style problems. The resulting equations are solved using a power series method, Chebyshev method, and finite difference method. The three cases of increasing complexity elucidate how the various effects of a non-linear density and a varying vertical through-flow affect the stability in transriprational cooling systems. This work is divided into five sections. First, the mathematical framework used to study transriprational cooling is described. Second, linear stability analysis is performed and detailed. Next, an overview of the various solution techniques are outlined. Finally, results are given followed by a discussion and conclusions.
## 8 Experimental Results
### Emergency Case study in Vietnam
emergency to be identified using connected data from humans and DOE sensors is shown in Figure 7. The shortest path finding 'Dijkstra algorithm' is used to find the shortest route from the location of the service vehicle from the current location to the location of the emergency. In the event of there being multiple available service vehicles, the STC algorithm will find the distance between service vehicle(s) and the optimal service vehicle can respond to the emergency while other service vehicles will not respond. Prioritisation of service response vehicles is achieved by identifying the shortest available path(s) (route(s)) derived using connected data from humans and sensors as shown in Figure 8.  
Figure 8: The route of service vehicles was appeared on the Google map. Figure 7: Priority service vehicles moved as they appear on the Google map.
## 5 Conclusions  
In this paper, the application of a smart station survey for creating control network points was presented. As we know from the United States Patent (No. 5,233,357 and Date: 3 August 1993) Smart Station set offered by Leica, results may be obtained using measurements in a terrestrial surveying system and in satellite navigation system separately or in mixed-use (hybrid) systems [49].  
The calculated differences of the coordinates between the reference coordinates from the model static GNSS occupation, the results of post-processing from single receiver static surveys as well as from RTK/RTN surveys performed using a GNSS receiver, and, finally, multi-variant surveys performed using smart station equipment were considered.  
The results for determining coordinates of control points using the multi-variant survey method (tacheometry and real-time GNSS methods) stated that the highest differences of coordinates were no more than 0.02 m in the horizontal plane (uncertainty D\({}_{XY}\)) and no more than 0.04 m in the vertical plane (uncertainty D\({}_{Z}\)).  
After using real-time GNSS occupation, the differences of the coordinates were no more than 0.03 m in the horizontal plane (D\({}_{XY}\)) and no more than 0.06 m in the vertical plane (D\({}_{\rm Z}\)). Certainly, the high accuracy of the kinematic measurement using SRS correction is influenced by the fact that the used single reference station was located a short distance from the research area (less than 2 km). The network measurement with MAC correction has an interpolation algorithm, which provides greater possibilities and independence from the need to \"trust\" to VRS correction [44].  
With the use of multi-variant surveys realized using Smart Station angular-linear and RTK/RTN measurements, a weaker solution was obtained on the basis of the corrections from the virtual reference station (VRS correction). The most measurement results close to the reference coordinates were obtained for the multi-variant surveys with corrections from a single reference station (SRS).  
Nevertheless, the research results have shown that the use of Smart Station equipment is very hopeful for measuring urban environments, establishing control network points, and for other purposes (in areas with no full GNSS signals, e.g., near trees and buildings). This is in accordance with the results presented in the research of other authors [50].  
This research was funded by the Department of Geodesy, University of Warmia and Mazury in Olsztyn, Poland, statutory research no. 29.610.001-110.  
Not applicable.  
Research data is available in the author's archive.  
Thanks to the Head Office of Geodesy and Cartography in Poland for main source of the provided data and GNSS real-time corrections, and for Colleagues and Students cooperating in measurement works.  
The author declares no conflict of interest.
# Adversarial Attack for Deep Steganography Based on Surrogate Training and Knowledge Diffusion
## 3 Adversarial Attack Method for Deep Steganography
### Attack on the Container Image
#### 3.2.2 Adversarial Attack to Modify the Secret Image  
The current active steganalysis method [22; 23; 24] for DS can only realize removal attacks. It cannot change the image \(S^{\prime}\) to any image specified by the attacker by slightly modifying the container image \(C^{\prime}\). Given a container image \(C^{\prime}\) and an arbitrary target image \(T\) specified by the attacker, the targeted attack can be described as generating a small additive perturbation \(\delta(\delta<\epsilon)\) so that the image \(S^{\prime\prime}\) revealed by the receiver approximates the target image \(T\) (Equation (4)).  
\[\min_{\delta\leq\epsilon}d\big{(}T,F\big{(}C^{\prime\prime}\big{)}\big{)} \tag{4}\]  
\(F\) is the decoder, \(F(C^{\prime\prime})=S^{\prime\prime}\). From the perspective of attack results, the process of targeted attack is equivalent to the superposition of removal and re-steganography. Recalling the process of DS, encoding a secret image into a container image requires an encoder. The object of a targeted attack is the decoder \(F\), and the attacker needs to guess how to encode any specified secret image only based on the decoder \(F\). Therefore, targeted attacks are more difficult than untargeted attacks. The objective function \(L^{t}\) of such an attack is as follows:  
Unlike Algorithm 1, which solves \(\delta\) iteratively under \(L^{\infty}\), Algorithm 2 directly optimizes \(C^{\prime\prime}\). The objective function of Algorithm 2 is defined as follows for untargeted attack \(L_{ut}\) (Equation (5)) and targeted attack \(L_{t}\) (Equation (6)), respectively:  
\[L_{ut}=mse\big{(}C^{\prime\prime},C^{\prime}\big{)}-\beta_{1}mse\big{(}F\big{(} C^{\prime\prime},C^{\prime}\big{)}\big{)} \tag{5}\]  
\[L_{t}=mse\big{(}C^{\prime\prime},C^{\prime}\big{)}-\beta_{2}\big{(}C^{\prime \prime},T\big{)}+\beta_{3}mse\big{(}F\big{(}C^{\prime\prime}\big{)},T\big{)} \tag{6}\]  
where \(C^{\prime\prime}=\tanh(\omega)\); \(\omega\) is a matrix to be optimized; \(mse(C^{\prime\prime},C^{\prime})\) is a penalty to constrain the distance between \(C^{\prime\prime}\) and \(C^{\prime}\); and \(\beta_{s}\) are hyperparameters used to make a trade-off between the invisibility of the attack and the quality of the decoded image. Algorithms 1 and 2 are adapted from PGD [40] and CW [41], respectively. The main difference lies in the objective functions.  
\[L^{t}=-mse\big{(}F\big{(}C^{\prime\prime}\big{)},T\big{)} \tag{7}\]
## 4 Monitoring and Measurement Results and Analysis of the Bifurcated Tunnel Construction Process
### Monitoring Results for the Two-Lane Section
#### 4.2.2 Force Characteristics of the Surrounding Rock Support Structure  
During the monitoring phase, pressure gauges are placed between the initial support and the secondary lining to measure the pressure data of the K1+870 section's initial support and secondary lining in the two-lane tunnel. The pressure-time curves of the five monitoring points are shown in Figure 20. Since the filling concrete is used to form the profile of the two-lane tunnel above the climbing section, the peak pressure between the initial support and the secondary lining is located above the crown, reaching 0.58 MPa. The stabilizing time is about 7 days.
## 3 Dynamic Response under the Moving Train Loads  
### Numerical Model and Parameters  
In order to further study the effect of the adjacent tunnel on the dynamic response of pile-soil composite foundations under moving train loads, two numerical models of an ordinary pile-soil composite foundation and a pile-soil composite foundation with a proximity tunnel are established according to the project case, as shown in Figure 8. The size of the two numerical calculation models is the same, with a length, width, and height of 160, 60, and 80 m, respectively. The mechanical parameters of the soil and structure are given in Table 2, respectively. The concrete structures mainly considered in the numerical simulation include shield segments, piles, and the piers and caps of the bridge, of which the outer diameter and the thickness of shield segments are 6.0 m and 0.3 m, respectively. The diameter and the length of the pile are 1.25 m and 46 m, respectively. The size of the cushion cap is 12.2 m \(\times\) 6.8 m and the width of bridge is 12.2 m. The distance between two cushion caps is 32 m. The same as for the numerical simulation in Section 2.5, the  
Figure 7: Peak acceleration at different locations of the tunnel lining (A7\(\sim\)A10).  
elastic constitutive model is adopted in the numerical calculation model and the viscous boundary conditions are used to restore the infinite state of the real foundation.  
Before dynamic load analysis, the eigenvalue of the model should be calculated first. In Midas gts, the eigenvalue analysis can be directly carried out by establishing the elastic boundary generated by the ground spring element. The periods of the main vibration of the two numerical models in Figure 8 are shown in Table 3. In this paper, the length of the integration step is 0.005 s, which is less than \(1/100\) of the minimum natural vibration period, and the influence of calculation error can be ignored [27]. The results in Table 3 are input into the software as the main mode periods of the model, and the damping ratio is taken as 0.05 to solve the dynamic response of the numerical models.
# A Neural Network-Based Partial Fingerprint Image Identification Method for Crime Scenes
## 4 Experimental Analysis
### Experiments on NIST Dataset  
To demonstrate the generalization ability of APFI, the identification accuracy of the proposed method and several methods (PFILD, RPFR, PFRA, ALFR, and FERM) introduced in Section 4.4 are compared on the NIST-SD4 dataset. The experimental results are shown in Figure 6. The CMC curves show that the deep-learning-based identification methods (PFRA, ALFR, APFI, FERM) perform significantly better than the minutiae-based identification methods (PFILD, RPFR) on the NIST-SD4 dataset. This proves that the deep learning-based method has better fingerprint feature extraction ability.  
Figure 6: Identification accuracy of different algorithms on NIST-SD4 dataset. (**a**) shows the identification performance of different methods on fingerprint images with an effective area of 100%. (**b**) shows the identification performance of different methods on fingerprint images with an effective area of 75%. (**c**) shows the identification performance of different methods on fingerprint images with an effective area of 50%. (**d**) shows the identification performance of different methods on fingerprint images with an effective area of 25%.  
Experiments on the NIST-4 dataset show that APFI has a higher identification rate than other methods. As the effective area of fingerprint images decreases, the advantages of APFI are more obvious. For fingerprint images with an effective area of 50%, the rank-1 identification rate of APFI is 2.5% higher than other methods, and the rank-20 identification rate of APFI is 1.7% higher than other methods. For fingerprint images with an effective area of 25%, the rank-1 identification rate of APFI is 2.9% higher than other methods, and the rank-20 identification rate of APFI is 3.1% higher than other methods. This evidences that the proposed identification model has stronger generalization ability and robustness.
## 3 FEM Model
### Bond Modelling  
The bond between reinforcement and concrete is modeled according to [32], considering splitting failure and good bond conditions. The differences in the bond laws between the two un-corroded specimens are considered by the different confinement effects given by the stirrups. The expansion of corrosion oxides and the cracking of the concrete cover have a deterioration effect on the bond. Several experimental studies and analytical formulations are available in the literature for the evaluation of the bond between concrete and corroded bars [4; 37; 38; 39]. In this paper, the law proposed by [40], based on regression analyses of experimental data is adopted for the bond degradation as a function of the corrosion penetration. The model accounts for the amount of transverse reinforcement and predicts the residual bond strength, in relationship to either corrosion penetration or surface crack width. These analyses refer to medium-low corrosion levels, i.e., corrosion penetration up to 0.5 mm. In fact, when the corrosion increases, the bond is reduced until the collaboration between steel and concrete is almost negligible. For this reason, beyond a penetration value equal to 1 mm, a perfect plastic frictional bond behaviour with a bond strength value equal to 0.1 MPa [41; 42] is considered. In Figure 11 an example of the bond law reduction assumed for the bars of corroded specimens is presented. As regards the unloading/reloading behaviour, the model unloads with a linear stiffness, that is equal to the initial stiffness, until the opposite residual stress value is reached. This stress value is kept until the back-bone curve in the opposite direction can be picked up [30].  
Figure 10: Assumptions for corroded bars (modelling strategies 3 and 4) compared with original measurement, Specim B30. (**a**) Bar 1-2, (**b**) Bar 2-3, (**c**) Bar 3-4, (**d**) Bar 4-1.
## 3 Development of Prototype Models of the Smart Photovoltaic System Blind
### Discussion  
This study developed three prototype models of the SPSB to be installed on the building facade. It sought to improve the technical performance of the SPSB (i.e., the amount of electricity generation) in the development process of the three prototype models, starting from the first prototype model. In the most recently developed prototype model of the SPSB, the third prototype model, the following technologies were applied according to the three properties (i.e., PV panel, tracking system, and monitoring system): (i) a-Si PV panel; (ii) four tracker type (i.e., fixed type, VSAT, HSAT, and AADAT) and a tracking method (i.e., direct tracking method); and (iii) monitoring system (i.e., electricity generation and environment conditions).  
The experimental studies were conducted using the third prototype model of the SPSB. Figure 7 shows the electricity generation of the third prototype model by tracker type (i.e., fixed type, VSAT, HSAT, and AADAT) (6 August 2015). First, the electricity generation of the third prototype model shows the inverted U-shape (with the highest value at 14:00 (0.80-1.02 W) and a lower value at 7:00 (0.01-0.03 W) and at 18:00 (0.04-0.09 W)). Additionally, the temperature and humidity show the inverted U-shape (highest value at 14:00 (34.2 \({}^{\circ}\)C) and the lowest value at 7:00 (25.1 \({}^{\circ}\)C)) and the U-shape (lowest value at 14:00 (45%) and the highest value at 7:00 (76%)), respectively. This is because the solar radiation in South Korea is highest at 14:00 (2.89 MJ/m\({}^{2}\)) [55]. Second, the dual-axis tracker (i.e., AADAT) can improve the electricity generation of the third prototype model than other methods (single-axis tracker (i.e., HSAT and VSAT) and fixed type). Additionally, compared with the electricity generation of the third prototype model using VSAT, the electricity generation of the third prototype model using HSAT is improved. The VSAT applied to SPSB has a limitation for tracking the east-west motion of the sun. This is because SPSB installed in the window has a limitation on the depth.  
Meanwhile, with the use of the third prototype model of the SPSB, the following experimental studies can be carried out. First, an evaluation on the electricity generation by tracking system  
Figure 6: Graphical user interface of the monitoring system in the third prototype model.  
(i.e., the tracker type and tracking method) according to the location of the SPSB installation can be performed. Second, in the maintenance phase, the optimal electricity generation strategy for maximum electricity output can be established using monitoring data such as electricity generation (i.e., current, voltage, and power) and environmental conditions (temperature, humidity, and solar radiation).