esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
There is an increasing ability to monitor irrigation performance from space with available open-source datasets. It is important that the effectiveness of these datasets be understood based on irrigation scheme characteristics and spatial resolution of the dataset itself. As the methodology for all datasets is the same, the resolution is a unique varying factor to compare applicability in irrigation performance assessment. The following is a brief summary and discussion of this study, presented here to understand and characterize the application of WaPOR datasets with 30 m, 100 m, and 250 m spatial resolutions in irrigation performance assessment for schemes with varying plot sizes of small (<2 ha) to medium (>10 ha) plots.  
The following conclusions can be drawn from this study on comparison of varying remote-sensing-based resolution datasets in irrigation performance assessment:  
* Spatial resolutions of 250 m, 100 m, and 30 m are suitable for inter-annual and inter-scheme assessments for adequacy, equity, and CWP, regardless of plot size.
* Spatial resolutions of 250 m and 100 m should not be used for inter-plot comparison for adequacy, equity, or CWP on plots <2 ha. The 30 m resolution may also be too coarse, and Sentinel-2 application should be considered.
* Spatial resolutions of 250 m and 100 m show general spatiotemporal trends for adequacy, equity, and CWP within a scheme, but not the full extent of plot-to-plot variation for all plot sizes tested.  
It is suggested that these conclusions can be applied generally to irrigation areas that have similar plot sizes. However, further investigation into the resolution requirements to suitably undertake irrigation performance assessment in small plots, i.e., \(\leq\)2 ha, is needed.  
Conceptualization, M.B. and P.K.; methodology, H.N., M.B.; validation, M.B.; formal analysis, M.B. and C.M.M.; resources, C.M.M.; data curation, M.B.; writing--original draft preparation, M.B.; writing--review and editing, C.M.M., H.N., P.K., and Y.Z.; visualization, M.B.; supervision, C.M.M. and Y.Z.; funding acquisition, C.M.M. All authors have read and agreed to the published version of the manuscript.  
This work was partly funded by the United Nations Food and Agricultural Organization (FAO) as part of the Water Productivity Open Access Portal (WaPOR) project, in which we undertook the validation efforts. The WaPOR validation efforts were undertaken under contract number 2016/HQR/NRL-CPA3020260 (FRAME project).  
We thank all of the authors in which we sourced data for validation. We would like to thank the FAO, who co-funded the operational methodology for an open-access database to monitor land and water productivity (WaPOR portal), and the FRAME consortium, in particular eLEAF and VITO, as the primary producers of the WaPOR datasets. We gratefully acknowledge [PERSON] for providing the eddy covariance data.  
The authors declare no conflict of interest.
## 1 Introduction
[25] coupled a PAM instrument and a spectrometer with a portable gas exchange system to enable simultaneous measurements of gas exchange alongside PAM parameters (i.e., active chlorophyll fluorescence) from the adaxial side of the leaf and spectrally resolved chlorophyll fluorescence (i.e., passive chlorophyll fluorescence) from the abaxial side. Notably, both methods employ the transparent chamber of a photosynthesis system, external light sources as incident light, and an independently operated PAM fluorometer. However, the integration of multiple instruments may introduce potential uncertainties arising from variability among the instruments. To minimize such errors, [PERSON] et al. [26] employed the Multiphase FlashTM Fluorometer (LI-COR Inc., Lincoln, NE, USA), an integrated system combining a gas exchange measurement setup, PAM fluorometer, and incident light source, as a replacement for the transparent chamber. By modifying the bottom chamber plate of the Multiphase FlashTM Fluorometer, they enabled the measurement of passively induced chlorophyll fluorescence spectra from the abaxial side of the leaf, in addition to conducting gas exchange and PAM parameter measurements. While these studies successfully achieved simultaneous observations of actively and passively induced chlorophyll fluorescence along with gas exchange,they were limited to chlorophyll fluorescence measurements from a single side of the leaf, which hinders the estimation of fluorescence at the photosystem scale [27; 28]. In this study, we introduce a system capable of simultaneously measuring broadband chlorophyll fluorescence spectra from both sides of the leaf, along with actively induced chlorophyll fluorescence, and leaf-level gas exchange. The design, calibration, and measurement protocols of the system are detailed, followed by the construction of a chlorophyll fluorescence-based \(q_{\text{L}}\) estimation model using experimental data collected from multiple species representing diverse plant functional types (PFTs).
## 3 The ARCOMEM Approach
### Interoperability, Reusability and Provenance  
The web is very dynamic and constantly changing, so it is unlikely that any dataset harvested by a web observatory could ever be re-collected from scratch and end up the same. That being said, it is important that any given dataset has associated provenance that describes the exact strategy that was used to create it. In ARCOMEM, this is achieved by storing the crawl specification and system configuration in the knowledge base along with information about exactly what was harvested and when.  
ARCOMEM allows data to be exported in two forms that can be used together. The raw resource data can be exported as standard ISO 28500 WARC (Web Archive) files [35] and can be used with standard tools for handling WARC files, such as the Wayback Machine (which allows the dataset to be visually reconstructed and explored). The results of the processing and analysis can be exported directly in RDF. Whilst the ARCOMEM system uses its own ontology for describing the analysis, for interoperability, the ontology is provided with mappings to several other standard ontologies.
## 2 Materials and Methods
### Western Blot and Sp1 Transcription Factor Assay  
When HUVECs were grown (approximately 70% confluent), cells were treated with 1, 10, and 100 uM 4 HR for 2, 8, or 24 h; control cells were treated with 0.1% dimethyl sulfoxide in culture medium. TUDCA, a known ER stress inhibitor, was purchased from Sigma-Aldrich (CAT#: T0266, St. Lois, MO, USA). TUDCA inhibits ER stress with an EC50 value of 100 uM [25]. Accordingly, 100 uM TUDCA was applied. Cultured cells were harvested with protein lysis buffer (PRO-PREP(tm), iNtRON Biotechnology INC, Sungnam, Korea) and underwent Western blotting for TGF-$1, VEGF-A, VEGF-C, Sp1, p-Sp1, GRP78, PERK, p-PERK, eIF2, p-elfF2, ATF4, ATF64, Ac-lys, H3, and Ac-H3. Withramycin is an Sp1 inhibitor and was purchased from Sigma-Aldrich (CAT#: M6891). Mithramycin inhibits Sp1 with an EC50 value of 25 uM. Additional Western blot analyses for SIRT1, SIRT3, and SIRT6 were also carried out. Antibodies against TGF-$1, VEGF-A, VEGF-C, Sp1, p-Sp1, GRP78, PERK, p-PERK, eIF2, p-elf2, ATF4, ATF64, Ac-lys, H3, and Ac-H3 were purchased from Santa Cruz Biotechnology (Santa Cruz, CA, USA). Antibodies against SIRTs were purchased from Abcam (Cambridge, UK). Quantification of the proteins was performed as described previously [16,17]. For the assessment of apoptosis by 4 HR administration, a cytochrome c apoptosis antibody cocktail (CAT: ab110415, Abcam) was used. The final concentration of the antibody cocktail was 3.6 ug/mL. The cell collection was separated into mitochondrial and cytoplasmic fractions. Each fraction underwent Western blot analysis using an antibody cocktail.  
Sp1 activation detection was performed using an Sp1 transcription factor assay kit (CAT: ab207226, Abcam). To detect GC box-bound Sp1, an oligonucleotide with an Sp1 consensus binding site coated plate was used. To assess the effect of 4 HR administration on Sp1 activation levels, 1, 10, and 100 uM 4 HR were applied, and nuclear extracts were collected after 2, 8, and 24 h. The subsequent procedure was in accordance with the manufacturer's protocol. Briefly, samples were added to appropriate wells and the plate was incubated for 1 h at room temperature. Primary antibody was then added to the wells, and the plate was incubated again for 1 h at room temperature. After the washing process, HRP-conjugated secondary antibody was applied, and the plate was incubated for 1 h at room temperature. After another washing process, a developing solution was added, and we waited until the medium color turned dark blue. Then, a stop solution was added, and the absorbance was measured at 450 nm.
# EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer
## 3 BAT Algorithm
### Inspiration of BAT  
The bat optimization algorithm was first proposed by [PERSON] in 2010 [54]. [PERSON] proposed the bat algorithm based on microbats' characteristics of echolocation, used for hunting prey, and movement in darkness. A microbat produces extremely loud sound pulses and hears their echo from the objects and prey around it. Based on the echo sound, bats can define the type of prey and the strategy for hunting it. The bats usually produce 10 to 20 pulses every second when they are searching for prey. Once they are close to the prey, the number of pulses increases to 200 pulses every second and the sound pulse becomes quieter.  
In nature, microbats have a collective hunting behavior, where they fly together with determined velocity to hunt prey simultaneously by using the echolocation characteristic, as shown in Figure 1. The figure presents the movement of microbats based on the echolocation mechanism, where the microbats update their locations and move toward prey based on the echo sound and the location of other microbats, determined by interacting with each other. Furthermore, the microbats' velocity becomes higher and the pulses lower when the microbats are close to the prey location.  
In the BAT algorithm, each microbat is considered as a solution in the population, the position of the prey is the optimal solution for a particular optimization problem, the location is presented in a vector \(x\) with a range between \(f_{min}\) and \(f_{max}\), the velocity of microbats is presented in a vector \(v\), and the wavelength is \(\lambda\) with pulse rate \(r\in[0,1]\) and loudness range \(A\), where the largest is presented as \(A_{o}\) and the lowest is \(A_{min}\).
# Influence of Sea Level Anomaly on Underwater Gravity Gradient Measurements
## 5 Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Side Length of** **Integration Area (m)**} & \multicolumn{2}{c}{**Vertical Gravity Gradient at 30 m below Mean Sea Level (E)**} \\ \cline{2-4}  & **Position A** & **Position B** & **Position C** \\ \hline
64 & 5.430 & \(-\)2.200 & 2.281 \\
**68** & **5.503** & \(-\)**2.226** & **2.310** \\
72 & 5.547 & \(-\)2.239 & 2.327 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
Figure 11: Variation of the disturbing gravity gradient with time at the location of the maximum annual range. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Depths** & **Statistics** & **Max** & **Min** & **Mean** & **STD** & **Range** \\ \hline \multirow{3}{*}{10 m} & \(\Gamma_{xx}\) & \(-1.941\) & \(-12.428\) & \(-7.678\) & 2.401 & 10.486 \\  & \(\Gamma_{yy}\) & \(-1.942\) & \(-12.428\) & \(-7.679\) & 2.401 & 10.487 \\  & \(\Gamma_{zz}\) & \(24.856\) & \(3.883\) & \(15.357\) & 4.803 & 20.973 \\ \hline \multirow{3}{*}{30 m} & \(\Gamma_{xx}\) & \(-0.637\) & \(-4.166\) & \(-2.591\) & 0.807 & 3.529 \\  & \(\Gamma_{yy}\) & \(-0.637\) & \(-4.167\) & \(-2.591\) & 0.807 & 3.530 \\  & \(\Gamma_{zz}\) & \(8.333\) & \(1.274\) & \(5.182\) & 1.614 & 7.059 \\ \hline \multirow{3}{*}{50 m} & \(\Gamma_{xx}\) & \(-0.378\) & \(-2.481\) & \(-1.545\) & 0.481 & 2.104 \\  & \(\Gamma_{yy}\) & \(-0.378\) & \(-2.484\) & \(-1.547\) & 0.482 & 2.106 \\ \cline{1-1}  & \(\Gamma_{zz}\) & \(4.966\) & \(0.756\) & \(3.092\) & 0.963 & 4.210 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistics of the annual range of the disturbing gravity gradient at different depths below the mean sea level (unit: E). Figure 10: Annual range of the disturbing gravity gradient at 10 m below the mean sea level (\(\Gamma_{xx}\) and \(\Gamma_{yy}\) are both negative, and the absolute values shown in (**a,b**). \(\Gamma_{zz}\) is positive in (**c**). (unit: E).
The above results are consistent with the theoretical analysis in Sections 3 and 4, which is also consistent with the phenomenon when the realistic traffic system becomes unstable. **Simulation 2. ** To investigate the propagation and evolution characteristics of disturbance after a long time span, the propagation process of disturbance in the system is simulated with the same scenario as Simulation 1, as shown in Figure 6. According to the results of Simulation 1, the headway between any two successive vehicles is employed as the index instead of the velocity. In Figure 6, we can obtain the system state with the FVD, BLVD, SLV, and SLVRV models, respectively, after \(t=14,800\) s. As shown in Figure 6a, the disturbance exerted at the initial time cannot be suppressed by the system with the FVD model and grows with its propagation, which will eventually cause the system to become unstable. As shown in Figure 6b, the disturbance cannot be aborded by the system with the BLVD model, but it can be suppressed by the system within a certain scale. As shown in Figure 6c, the system with the SLV model can suppress and then abort the disturbance and has returned to a stable state. As shown in Figure 6d, the system also can suppress and abort the disturbance and has returned to a stable state. Although the effect of the system with the SLV model and the SLVRV model, respectively, is to suppress the disturbance exerted as the same scale at the initial time when \(t=14,800\) s, combined with the results of Simulation 1, one can see that there is a certain difference in the stabilization performance of the SLV model and the SLVRV model. On the one hand, it takes less time for the system with the SLVRV model than the SLV model to suppress and abort the disturbance. On the other hand, the fluctuation of vehicles' motion state during the process of suppressing disturbance in the system with the SLV model is more intense than that with the SLVRV model.
# A Comparative Study on the Laser Welding of Ti6 Al4V Alloy Sheets in Flat and Horizontal Positions
## 3 Results
### Porosity in Welds  
Figure 6 shows the X-ray radiographs of four laser butt welds produced with the two different welding positions and the two different sets of welding parameters.  
For welding parameters with higher heat input (Figure 6a, heat input 275 J/mm), several isolated pores can be found along the centerline of the flat weld. By contrast, extensive chain porosity is detected above the centerline of the corresponding horizontal weld.  
For welding parameters with lower heat input (Figure 6b, 125 J/mm), a limited amount of porosity can be seen in the flat weld. In the horizontal weld, fine scale chain porosity is detected. The pores are very close to each other and become indistinguishable individually, and they distribute in the upper half part of the weld.  
For a quantitative characterization, the cumulative lengths of porosity over a 100 mm weld length were measured and are presented in Figure 7. It is confirmed that the porosity contents for the flat weld are lower than those in the horizontal welds. For both flat and horizontal welding positions, using higher laser power and correspondingly higher welding speed (2.5 kW, 20 mm/s) can also help to reduce the porosity content.  
Figure 6: X-ray radiographs of butt welds made under flat and horizontal welding positions with two different sets of welding parameters. (**a**) Flat welding (**left**) versus horizontal welding (**right**) for 2.2 kW and 8 mm/s; (**b**) Flat welding (**left**) versus horizontal welding (**right**) for 2.5 kW and 20 mm/s.  
Figure 7: Cumulative lengths of porosity in welds with different welding conditions.
# Evolution towards Smart and Software-Defined Internet of Things
## 6 State-of-the-Art IoT Simulators
### iFogSim  
iFogSim is another full stack simulator but, unlike DWPSim, it offers support for other enabling technologies and protocols as well, such as fog computing and Cloudsim Toolkit. This simulator is based on Cloudsim Toolkit [89]. iFogSim offers facilitation in the evaluation of end-to-end latency, network congestion, power usage, operational expenses, and QoS.
# Analytical Solution and Experimental Validation of the Electromagnetic Field in an IPT System
## 5 Simulation Verification  
In this section, MATLAB is used to provide the numerical results of analytical solutions and the simulation results are obtained through use of the finite element software COMSOL. The analytical solution is coincident with the simulation results, which shows the accuracy of analytical solution deduced in Section 3.
# A Fault Analysis Method Based on Association Rule Mining for Distribution Terminal Unit
## 2 DTU Fault Analysis
### Factors Leading to DTU Fault  
Many factors may give rise to DTU faults, which are summarized in Table 2.  
There are mainly 10 factors that can lead to DTU fault. Among external factors, humidity means that the moist environment causes fault of devices, effects of primary-side equipment refer to the malfunction of primary-side devices (e.g., breakers and transformers) [35], and mis-operation refers to engineers' failed operation of control devices. In terms of internal factors, operating state refers to the hardware and software operating state as well as the upgrading state of DTU devices, long service time means that device reaches or exceeds its lifespan, and family defect means that products of certain manufacturers may have certain types of faults.
## 2 Materials and Methods
### Experimental Setup
#### 2.2.3 Analysis of Cell Viability by MTT Test  
The MTT test (3-dimethylthiazole-2,5-diphenyltetrazolzolium bromide) is a colorimetric test used to estimate the number of cells with mitochondrial activity and, thus, cell viability. This assay is based on a metabolic indicator; the soluble salt tetrazolium is reduced in the mitochondrion of viable cells by active dehydrogenase enzymes to a blue-purple crystal that is insoluble in water (formazan). The dissolved crystals are quantified by a colorimetric method at a wavelength of 570 nm (dye absorption) with background correction at 690 nm. At various time points, the culture medium was removed, and 200 \(\upmu\)L of a solution containing MTT (Merck, Darmstadt, Germany) (5 mg/mL in medium without phenol red) and 1.8 mL of the medium were added to the monolayer cells. The plates were then incubated at 37 \({}^{\circ}\)C for 4 h. After the removal of the supernatant, the blue-violet formazan crystals were dissolved by adding 2 mL of solvent (4% HCl in isopropanol) and were quantified at 570 nm and 690 nm using a spectrophotometer (Secoman, Anthelie light, version 3.8, Contardi, Italy). The results are expressed as a percentage of viability compared to the control cultures. The procedure followed that used by [PERSON] et al. (2022) [12].
# Advancements in Gaze Coordinate Prediction Using Deep Learning: A Novel Ensemble Loss Approach
## 3 Materials and Methods  
In this paper, we propose structural changes to improve the gaze prediction performance and generalization function of the model used to predict gaze position in the existing appearance-based deep learning models. In the past, the predicted values from the final fully connected (FC) layer were used as the gaze position result (in 2D, this would be (x, y) coordinates; in 3D, it would be the gaze angle vector). However, in this paper, an additional loss block is designed on top of the FC layer results to predict the 2D gaze coordinates on a PC screen. First, as in the conventional approach, a CNN-based feature extractor is used to extract feature maps from appearance data such as face images, eye images, coordinates of face and eye images from input picture, head poses, and face grid images. These feature maps are then used to predict 40 bins for both the x and y coordinates based on the resolution of the monitor. For gaze tracking, a combination of euclidean distance loss for gaze coordinates and cross-entropy loss for the bins corresponding to the coordinates is applied. This results in improved generalization capabilities and prediction accuracy for various gaze tracking feature extractors that have been previously utilized.
# Governance and Dynamic Efficiency with Network Structure in the Brazilian Natural Gas Utilities
## 2 Materials and Methods  
Data Envelopment Analysis (DEA) [15] is a good option for understanding the efficiency of piped gas distribution companies, as DEA can measure it at the unit level, referred to as Decision Making Units (DMUs) [16]. DEA is a programming method that assesses comparative efficiencies by defining a linear efficiency frontier among distributors, determining the relative efficiencies of all others within a single group [17].  
Both in the international and in the national literature on the empirical analysis of the piped gas sector, it is common to use DEA for efficiency analysis. However, no study applying the Dynamic DEA with Network Structure: A Slacks-Based Model (DNSBM), developed by [PERSON] and [PERSON] [18], to the piped gas sector is known. The main advantage of this model is the ability to evaluate overall efficiency over the analysis period, as well as the dynamic change in efficiency by period and division, through a network structure.  
When measuring efficiency using the DNSBM model and analyzing the relationship between efficiency, environmental variables, and corporate governance, we characterize this as a quantitative approach, as it can assess the influence on other variables [19]. The study population consists of companies engaged in the distribution of piped natural gas in Brazil, identified through the Brazilian Association of Piped Gas Distribution Companies (ABEGAS--Associacao Brasileira das Empresas Distribuidoras de Gas Canalizado), according to which 25 distributors control the country's distribution network [3], as shown in Figure 1. Of these, 4 distributors were excluded due to data limitations and because they are operating in a pre-operational stage, meaning there was no information available at the time of data collection. From the sample included in our study, 3 companies are publicly traded, and 18 are considered public companies (state-owned with a minimal percentage of shares held by private entities).  
For the variable selection process, both national and international articles that analyzed efficiency through DEA in the gas sector were considered [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30]. The selection of variables also considered the feasibility of data acquisition, its relevance within the gas distribution segment, and, especially, its potential contribution to the study's objectives based on the previous literature contained in Appendix A. The annual data required for the DNSBM model were obtained for the period from 2014 to 2019 through mandatory accounting publications by the companies on their institutional websites; transparency portals of the federative units; reports published by the ANP; statistics published by the Brazilian Institute of Geography and Statistics (IBGE-Instituto Brasileiro de Geografia e Estatistica); and information collected from the Brazilian Association of Gas Distribution Companies (ABEGAS).