esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Orthomosaics and Vegetation Indices  
The images collected in flight were processed into orthomosaic forms, which consist of building a single image from the composition of overlapping photos [32]. The orthomosaics were processed using WebODM (version 1.9.15) [34], an open-source drone mapping software. After the generation of the orthomosaics, georeferencing corrections were performed using QGIS (version 3.20.10) [35] via the LF Tools plugin. This tool performs georeferencing adjustment through control points on the ground, which were manually determined in the image by selecting known points that remained stationary during the collections, such as roofs and water tanks. Additionally, an affine polynomial function of degree one was adopted for the correction, and the nearest neighbor method was used for interpolation. Thus, after georeferencing adjustment, our orthomosaics were manipulated in R software (version 4.1.3) [36] with the aid of the raster [37] and FIELDimageR [38] packages. The orthomosaics underwent four approaches, Agrofor-Complete; Soil-Removed; Tree-Removed, and Tree + Soil-Removed, as described in Table 2.  
The removal of the components, soil, and tree was intended to improve the influence of each component on the correlation between the generated vegetation indexes and the parameters to be estimated. No crop removal occurred due to greater difficulty in differentiating the grasses. For soil removal, threshold values of the VARI vegetation index were used, using a cutoff value of \(-0.16\). For the removal of the trees, a cut mask was manually created. Due to the easier identification, the orthomosaic from the beginning of the experiment (Figure 3) was used as a reference, when the crops had not yet been  
\begin{table}
\begin{tabular}{c c} \hline \hline
**File Type** & **Description** \\ \hline Agrofor-Complete & Original file containing all components of the study area (soil, crops, trees) \\ Soil-Removed & File with solo component removed \\ Tree-Removed & File with tree component removed \\ Tree + Soil-Removed & File with soil and tree components removed \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of the files generated from each orthomosaic.  
Figure 3: **(a)** UAV’s trajectory performed during the flight. **(b)** UAV platform (DJI Phantom 3) and sensor (CMOS camera, Tokyo Electron Ltd., Tokyo, Japan) used in this study.  
implemented. Subsequently, 11 RGB (Red, Green, Blue)-based vegetation indexes (Table 3) were calculated for each of the generated files (Table 2) on all collection dates.
## 3 DCS Scheme Families
### Hierarchical Voronoi Graph Based Routing  
A _Hierarchical Voronoi Graph Based Routing (HVGR)_[33], the Voronoi graph is used to construct and maintain a virtual hierarchy of sensor nodes. The Voronoi approach is a self-organizing hierarchical graph that is constructed without the help of GPS or other geo-location devices. The network is divided into different levels of regions based on landmarks. A landmark selection algorithm is used to select at most \(m_{i}\) (\(i=1\), 2,....) landmarks in an \((i-1)^{th}\) level region. Then the network is divided into first level sub-regions based on first level landmarks. Each node in the first level sub-regions broadcasts a _landmark_ packet to the entire network. By receiving this packet every node estimates its distance from all first level sub-region _landmarks._ Each node selects one _landmark_ as its representative. Then each first level sub-region is again divided into second level sub-regions with second level _landmarks_. Each sensor node again chooses the closest _landmark_ as its second representative. This process continues until the last level sub-regions are small enough so that each node knows all other nodes in its sub-region as shown in Figure 4.  
For landmark selection the authors use the optimized random landmark selection algorithm [34]. According to this algorithm, a random node, say '\(L_{I}\)', starts the landmark selection process becoming the master landmark of first level landmark nodes. It then selects \(m_{I}-I\) nodes for \(m_{I}\) first level networks. Each first level landmark then again acts as master and continues the selection process in its zone to select a second level master node for the second level of the network. A master landmark node stops the landmark selection process once it finds that all sensor nodes in its region are within its communication range. Figure 4 shows an example of the basic routing algorithm that is used to route packets from the source (\(S\)) to the destination (\(D\)). The first level landmark for the destination is \(L_{I}\). The packet first moves hop by hop toward \(L_{I}\) until it reaches the edge of first level region, \(R_{I}\). Now, nodes in \(R_{I}\) know the second level landmark and the packet now moves hop by hop toward the second level landmark, \(L_{2}\). Again once it reaches the node \(B\) of region 2 in Figure 4, the packet starts moving toward \(L_{3}\). Finally, the packet reaches the lowest level sub region whose entry node '\(C\)' knows the path of the destination and forwards the packet to the destination. The interesting part of this algorithm is it never overwhelms the landmark, as the packets never go to the landmark, rather the packets move toward the landmark until the packets reach any node within the region.
## 3 Results and Discussion
### R/S Analysis

Table 2: Hurst exponent by zone for the four experimental tests. Figure 4: \(R/S\) statistic vs. time scale \(N_{S}\). Considering the four experimental conditions, Figure 6 shows the comparison between the COD measurements with Hd\({}_{1}\), where it is observed that the Hd\({}_{1}\) dynamic exhibits the same trend observed with the COD consumption, which confirms that the time scale of Zone 1 corresponds to the hydrolysis stage in the anaerobic treatment of whey. Figure 5: Scale-temporal profile of the Hurst exponent considering a 6 h moving window. Figure 6: Correlations between Hd\({}_{1}\) and COD consumption measurements. Figure 7 shows the comparison between the total VFA measurements produced with the \(\mathrm{Hd}_{3}\) dynamic profile, finding that both profiles exhibit the same behavior, suggesting that the time scale of Zone 3 corresponds to the acidogenic-acetogenic stage. \(\mathrm{Hd}_{2}\) profile shows behavior that cannot be correlated with any of the process variables. Moreover, due to the operating conditions evaluated, there is no significant methane production, so the methanogenic stage is not considered as an active stage of the process. Then, Hurst exponents dynamic could be used as qualitative indexes for monitoring the COD and VFA for different operating conditions.
# Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories
## 4 Conclusions  
Irrigation management is an important issue in tomato cultivation, especially in plant factories. Accurate and timely tomato leaf water status assessment is essential for determining appropriate irrigation management. The aim of this study is to establish an approach to obtain tomato leaf water status in real time by using HSI technology. The results demonstrate the strong potential of using HSI technology for tomato leaf water status monitoring in plant factories, the application of MC assessment showed higher model performance compared with WC, and the best model for MC assessment was observed with NDVI-RAW in this study. In a subsequent study, other formulas will be used to achieve a higher prediction accuracy in tomato leaf water status assessment. To ensure the accuracy of that assessment, the optimal combination with the fewest wavelength bands will be selected. Accordingly, the applicability of the model for assessing different plants under various environmental conditions will be enhanced by directly using a portable hyperspectral camera. This approach is expected to reduce equipment costs and improve equipment stability in the development of visualization equipment in the future.  
Conceptualization, T.Z., A.N., Y.I. and H.U.; methodology, T.Z., A.N., Y.I. and H.U.; software, T.Z.; validation, T. Z. and A.N.; formal analysis, T.Z.; investigation, T.Z., A.N., Y.I. and H.U.; resources, T.Z., A.N., Y.I. and H.U.; data curation, T.Z., A.N., Y.I. and H.U.; writing--original draft preparation, T.Z., and A.N.; writing--review and editing, T.Z., and A.N.; visualization, T.Z., A.N., Y.I. and H.U.; supervision, T.Z., A.N., Y.I. and H.U.; project administration, A.N.; funding acquisition, T.Z., A.N., Y.I. and H.U. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding. This work were supported by Cabinet Office, Government of Japan, Cross-ministerial Strategic Innovation Promotion Program (SIP), and MAFF with the funding number is '16932971'.  
**Acknowledgments:** We would like to thank [PERSON] and [PERSON] (NARO) for support in the implementation of this project.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
###### Abstract  
The quality of positioning services based on Global Navigation Satellite Systems (GNSS) is improving at a fast pace, driven by the strict requirements of a plethora of new applications on accuracy, precision and reliability of the services. Nevertheless, ionospheric errors still bound the achievable performance and better mitigation techniques must be devised. In particular, the harmful effect due to non-uniform distribution of the electron density that causes amplitude and phase variation of the GNSS signal, usually named as scintillation effects. For many high-accuracy applications, this is a threat to accuracy and reliability, and the presence of scintillation effect needs to be constantly monitored. To this purpose, traditional receivers employ closed-loop tracking architectures. In this paper, we investigate an alternative architecture and a related metric based on the statistical processing of the received signal, after a code-wipe off and a noise reduction phase. The new metric is based on the analysis of the statistical features of the conditioned signal, and it brings the same information of the \(S_{4}\) index, normally estimated by means of closed-loop receivers. This new metric can be obtained at a higher rate as well as in the case of strong scintillations when a closed-loop receiver would fail the tracking of the GNSS signals.  
GNSS; ionospheric scintillations; open loop 2019
## 3 Literature Review and Related Work
### Fingerprinting-Based Positioning Implementing Fingerprint Images Processed Using CNNs
The way to collect and arrange the fingerprints in these two works can be interpreted as the use of a 2D sensor array with a number of sensors equivalent to the number of time steps in [153] and with two sensors in [154]. However, if there is spatial side information in 2D, it is not preserved in relation to the displacements in 2D; that is, the 2D side information does not remain associated with the fingerprints collected. Thus, the fingerprints only have a spatial association with the contiguous ones in 1D as a result of collecting these by moving the positioning device along a path. Therefore, the adjacent side information in these two works is interpreted as 1D side information. Finally, it is worth mentioning that we identified two articles considering the spatial information in 2D of the access points generating the fingerprints. These articles were labeled as not containing side information in the table because from the perspective of our work, we are trying to identify the use of spatial side information for the fingerprints on the device side. In [139], the fingerprints are arranged into a matrix consistent with the topology of the access points, preserving the spatial relationship of the sources generating the fingerprints. The spatial relationship described in [139] differs from the spatial side information on the device side that we consider in this article. In [160], a matrix of fingerprints is composed in which the rows represent floors. The elements in a row hold the RSS values of a relative projection of the position of the access points on that floor. Thus, the columns in the matrix associate the access points in the same vertical projection. To summarize our survey of this category, we identified the use of 2D side information in [120] through the use of a 32-element planar antenna array and in [133] through the sampling of a 2D portion of the grid that covers the whole positioning area. We could not find any work that proposed the collection of fingerprints in 2D and the implementation of a 2D CNN as a pattern-matching method, as we propose in our work.
# Anti-Recompression Video Watermarking Algorithm Based on H.264/AVC
## 4 Experimental Results and Analysis
### Complexity Experiment  
Considering the computational complexity of the algorithm in this paper, the time required for encoding and decoding before and after embedding the watermark was measured. The experimental results comparing the encoding and decoding time of the original video and the encoding and decoding time of the video with watermark are shown in Table 3. It can be seen that before and after embedding the watermark, the time difference of encoding is no more than 5.262 s, and the time difference of decoding is no more than 5.093 s, and the complexity is low.
# Wavelet Packet Decomposition to Characterize Injection Molding Tool Damage +
## 2 Experimental Section  
Acoustic emission signals were captured during the production cycle of standard test specimens, designed for shrinkage evaluation of various plastic materials. We used an injection molding tool (TECOS, Celje, Slovenia) with one cavity for production of standard D2 ISO test specimens with square dimensions of 60 \(\times\) 60 \(\times\) 2 mm\({}^{3}\), which can be used for a variety of tests according to ISO 294-3. The injection molding tool is made of OCR12 VN steel. A polypropylene material isofil H40 C2 F NAT manufactured by Sirmax (Cittadella, Italy), mainly used in the automotive industry, was employed. A Vallen-Systeme GmbH acoustic emission measurement system AMSY-5 (Vallen-Systeme GmbH, Icking, Germany) was used to capture and analyze the AE signals at 5 MHz sampling frequency. Two piezoelectric AE sensors V5150-M with a measuring range between 100 and 450 kHz and resonance at 150 kHz were mounted on both sides of the tool via waveguides, as shown in Figure 1. These sensors are widely used in AE testing and monitoring and are low cost. The sensor also covers frequencies characteristic for AE signals generated during plastic deformation and fatigue [19]. We used an 18-kHz high pass filter (Vallen-Systeme GmbH, Icking, Germany). AE waveguides protect the sensors against the high surface temperature of injection molds during the production cycle, possible fumes exhaled out of the mold, and damage in case operator intervention is needed to help remove the specimen. The length of the waveguide is determined based on experience to protect sensors against the aforementioned hazards. PZT sensors were connected to the AMSY-5 measurement system via two AEP4 preamplifiers (Vallen-Systeme GmbH, Icking, Germany) with a fixed gain of 40 dB. The amplitude threshold was set at 40 dB. Experiments were conducted on a hydraulic injection molding machine KM 80 CX-SP 380 (KraussMaffei Group, Munich, Germany), for a broad spectrum of injection molding process parameters.
## 3 Methods and Results
### Scaffold Stiffness Test  
The scaffold, crafted from low-stiffness materials with compliance, can achieve rigidity upon inflation, with its stiffness being contingent on pressure. Figure 15a illustrates the setup for testing scaffold stiffness. A rigid plate was affixed to a linear stage, and the inflatable scaffold was secured onto the plate using double-sided tape. Another rigid plate, pressing against a force/torque transducer (Nano 17, ATI Automation, Apex, NC, USA), was adhered to the scaffold. The force/torque transducer, connected to a 16-bit NI USB-6259 DAQ (National Instruments, Austin, TX, USA), was mounted on the moving platform of the linear stage. To pressurize the scaffold, an air pump (OF302-4 B, JUN-AIR, Inc., Racine, WI, USA) was employed, with a pressure regulator (PRE1-U08, AirCom, Ratingen, Germany) adjusting the pressure. Initially, the transducer made contact with the scaffold, marking the starting point where the compression distance is 0 mm. Subsequently, the force transducer compressed the scaffold by 10 mm before retracting to the starting point. This process was repeated four times, with the mean of these repetitions utilized for analysis. Figure 15b shows the correlation between the applied forces and the compression distances at 50 kPa and 100 kPa pressures within the scaffold, respectively. These results illustrate the scaffold's variable stiffness capability. The burst pressure for both the prismatic scaffold and the triangular support is approximately 270 kPa.
## 3 Variable Selection and Methodology
### Data Sources  
Urban built-up land is derived from the land remote sensing data platform of the Resources and Environmental Science and Data Center of China Academy of Sciences with 30 m \(\times\) 30 m land use type grid data. According to the classification of land use status of the China Academy of Sciences, the land use types are divided into cultivated land, woodland, grassland, water area, urban and rural land, industrial and mining land, residential land, and unused land. This study extracts the urban living land and industrial and mining land as the urban built-up land.  
Additionally, original socioeconomic datasets are compiled from the Henan, Hebei, Shandong, Shanxi, and Anhui Statistical Yearbooks (2004-2021), the China City Statistical Yearbook (2004-2021), and the China Urban Construction Statistical Yearbook published by the National Bureau of Statistics of China for 2004-2021. The study area is the Central Plains Urban Agglomeration, which has made an essential contribution to the urbanization process of the central region. This study area contains prefecture-level cities of Henan Province, Hebei Province, Shandong Province, Shanxi Province, and Anhui Province, covering 289,000 km\({}^{2}\). The research area contains 30 prefecture-level municipal districts, including Zhengzhou, Kaifeng, Luoyang, Pingdingshan, Anyang, Hebi, Xinxiang, Jiaozuo, Puyang, Xuchang, Luohe, Sanmencia, Nanyang, Shangqiu, Xinyang, Zhoukou, Zhumadian, Jiyuan, Xingtai, Handan, Liaocheng, Heze, Huaibei, Bengbu, Suzhou, Bozhou, Fuyang, Changzhi, Jincheng, and Yuncheng. Figure 1 presents the location of the research area. Notably, due to the large number of indicators involved, the missing data were interpolated, and the samples with serious data missing were eliminated during the preparation stage of the study.  
Figure 1: Location of the research area.
# The Effect of Clamping Force on the Wear Behavior of a Steam Generator Tube
## 4 Results and Discussion
### Wear Coefficient  
The ratio of wear rate to work rate is the wear coefficient \(K\). However, the normal force at contact position is not easy to obtain in the experiment since it is affected by the clamping force and the excitation force. We extract normal force and sliding distance in the finite element model to attain the wear coefficient \(K\) of the tube in our experiment. The premise is that finite element results can approximate experimental results.  
Therefore, we compared acceleration at the top of the pole with that in the simulation, as shown in Figure 13. It can be seen that the two are highly compatible.  
The wear rate corresponding to the work rate is shown in Figure 14. Wear coefficient \(K\) of the tube can be obtained as 3.9 \(\times\) 10\({}^{-16}\) Pa\({}^{-1}\) via linear fitting.  
However, the method for calculating the wear coefficient with the work rate obtained by the finite element analysis is relatively simple. There are still some details that have not been considered in this method, which still needs to be improved.  
Figure 13: Comparison of acceleration at excitation position.  
\begin{table}
\begin{tabular}{c c c c} \hline \(\mathbf{F_{\mathrm{c}}}\) (N) & \(\mathbf{l_{\mathrm{max}}}\) (\(\mathbf{\mu}\)m) & **Percentage of \(\mathbf{t}\)** & \(\mathbf{V_{\mathrm{total}}}\) (\(\mathbf{\mu}\)m\({}^{3}\))** \\ \hline
53 & 23.70 & 2.35\% & 7.20894 \(\times\) 10\({}^{7}\) \\
80 & 20.10 & 1.99\% & 3.86827 \(\times\) 10\({}^{7}\) \\
126 & 16.34 & 1.62\% & 1.38893 \(\times\) 10\({}^{7}\) \\ \hline \end{tabular} \(F_{\mathrm{c}}\): clamping force; \(h_{\mathrm{max}}\): maximum wear depth; \(t\): thickness of the tube.  
\end{table}
Table 3: Maximum wear depth and wear volume under different clamping forces.
## 3 Applications of L-Band SAR Data in Forestry  
L-band SAR data have been used in forestry. Most of the data come from ALOS satellites with different polarization modes. The main applications deal with estimations of forest canopy height, moisture and forest stocks.
## 6 Conclusions
### Limitations and Future Research  
Despite its significant findings, this study had several limitations. First, the scope of SNSs was too broad. The commonality between various SNSs is that they are used for information exchange and networking. However, while SNSs such as Twitter and Facebook share certain features in terms of communication and information sharing, they are considerably different when it comes to how articles and photos are posted and how the services are used. Therefore, the fatigue levels and life disorders of SNS users may differ depending on how specific SNSs enable them to fulfill their aims for using SNSs. In future studies, researchers should investigate users' fatigue and living disordersby classifying SNS characteristics. Second, this study did not distinguish between the two types of SNSs: Open and closed SNSs. Therefore, our findings have limited generalizability. Future studies should investigate SNS users' emotional experiences by dividing them into groups (open and closed SNSs) to increase the validity of their findings. Finally, we only included SNS characteristics (irrelevant information overload and open reachability) and user characteristics (engagement and maintaining self-reputation) that induce SNS fatigue as factors in the study model; we did not investigate social and cultural factors. Given that SNSs function as communication methods employed socially and in specific cultures, future studies should investigate various additional factors that affect SNS fatigue.  
**Author Contributions:** Formal analysis, S.K. and M.J.C.; methodology, S.K. and H.P.; visualization, H.P.; writing--original draft, S.K. and H.P.; writing--review and editing, S.K. and H.P.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflicts of interest.