esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methodology
### Sentinel-1A Water Index  
Water bodies create smooth and homogenous surfaces and, thereby, the return of radar pulses will be weak and the radar imagery over the area will be black or very dark in contrast with the surrounding non-water areas that produce a stronger return and brighter color [39]. Optical water indexes, such as MNDWI, have the opposite characteristics. In MNDWI imagery, water bodies appear as brighter colors and non-water areas appear as darker colors. Thus, there is a negative correlation between S1A imagery and MNDWI imagery. The S1A water index (SWI) was built based on the linear regression equation between S1A (including VH and VV polarization) and MNDWI. Simultaneously, SWI has a characteristic similar to MNDWI computed from OLI based on the flowing equation [30]:  
\[MNDWI=\left(\rho_{green}-\rho_{swir}\right)/\left(\rho_{green}+\rho_{swir}\right) \tag{1}\]  
where \(\rho_{green}\) represents the reflectance of the green band (B3 of OLI) and \(\rho_{swir}\) represents the reflectance of the shortwave infrared band (B6 of OLI).  
To match S1A pixels with MNDWI imagery pixels, the spatial resolution of S1A was resampled to 30 m (because the spatial resolution of MNDWI imagery is 30 m) using the nearest-neighbor method. Then, the MNDWI imagery and S1A (with spatial resolution of 30 m) were stacked using the stacking layer function in ENVI (version 4.8) software. Thus, a pixel on the stacked imagery had three attributes (e.g., VH (the abbreviation for the backscattering coefficient in VH polarization and VV has the same meaning), VV, and MNDWI values) and then a regression analysis was performed.  
A total of 44,284 pairs of data were selected evenly and randomly from the stacked imagery to build the SWI. OLI and S1A were acquired on the same day (27 September 2016), which helped reduce the error from changes in water surface areas for different imaging dates when the SWI was built. These data cover the water body, grassland, forestland, farmland, bare land, and built-up land, with 42.31% being the water body. MNDWI and S1A were defined as dependent variables and independent variables, respectively. The SWI was built using the stepwise regression analysis method based on MNDWI, and six independent variables (VH, VV, VH \(\times\) VV, VH\({}^{2}\), VV\({}^{2}\), and VH \(-\) VV) were derived from S1A.
## 2 Materials and Methods
### Small Punch Test Procedure  
After grinding and polishing the three samples per condition described above, the diameter, thickness, and roughness were measured to ensure that the testing standard ASTM E3205 [29] was satisfied. For the diameter, two measurements were taken 90\({}^{\circ}\) apart and the thickness was measured at four positions around the perimeter at 90\({}^{\circ}\) intervals and in the middle.  
Figure 4: (**a**) Scheme of the DED-LB deposition strategy for the wrought base material. Cuts made for the metallographic analysis and for the SPT sample extraction are indicated (all indicated dimensions are in millimetres). (**b**) Details of the positions of the extracted cylinders for the SPTs, where (1) is 100% filler material, (2) is 100% wrought base material, (3) is 50% filler and 50% base material, and (4) is 20% filler and 80% base material. (**c**) Filler–base joint position in the SPT for situations (3) and (4).  
To perform the SPT, each specimen was positioned in the receiving die located centrally below the punch and clamped with a clamping die. For this purpose, four M5 screws were used, where a torque of 10 Nm was applied to each. Finally, the ball and the punch were inserted into the clamping die centered on the top face of the specimen.  
In total, 12 SPT samples were tested with a universal testing machine (Zwick All Round Z100, Zwick, Ulm, Germany), coupling a load cell of 5 kN and a custom designed SPT tool like the one shown in Figure 5. The specimens were tested until failure at a constant displacement rate of 0.5 mm/min. The characteristic geometrical dimensions of the SPT tool are described in [30] and briefly summarized here: the diameter of the receiving die was 32 mm, the diameter of the receiving die was 4 mm, the punch diameter was 2.5 mm, the corner radius of the receiving die was 0.2 mm, and a ball of 2.5 mm diameter with a hardness of 55 HRC was employed to force the central portion of the specimen through the hole in the receiving die.  
During the test, the force and the displacement were measured, obtaining for each specimen a characteristic force-displacement (\(F\)-\(v\)) curve. From these curves, the elasto-plastic transition force, \(F_{e}\), the maximum force, \(F_{m}\), and the displacement corresponding to the maximum force, \(u_{m}\), were obtained.  
Finally, calculations were performed to estimate the yield strength (\(R_{p0.2}\)) and tensile strength (\(R_{m}\)), both in MPa, from the characteristic values of the SPTs and the initial thickness. The formulas proposed in ASTM E3205 are  
\[R_{p0.2}=\beta_{Rp0.2}\cdot\frac{F_{e}}{h_{0}^{2}}\, \tag{2}\]  
\[R_{m}=\beta_{Rm}\cdot\frac{F_{m}}{h_{0}\cdot u_{m}}\, \tag{3}\]  
where the parameter \(\beta\) is a material constant obtained by correlating tensile and SPTs on metallic materials.
## 2 Data and Methods
### Analysis Methods  
#### 2.3.1 Kernel Density Analysis  
Kernel density estimation is a non-parametric statistical method used to estimate the probability density function of a random variable. Kernel density analysis is commonly employed to study the spatial distribution patterns of point data, revealing the clustering characteristics and density variations of these points. The method involves placing a kernel function around each point data and then superimposing all the kernel functions to obtain the density distribution across the entire area. The formula is as follows:  
\[\hat{f}(x)=\frac{1}{nh}\sum_{i=1}^{n}K\bigg{(}\frac{x-X_{i}}{h}\bigg{)} \tag{1}\]  
In the formula, \(\hat{f}(x)\) represents the kernel density estimate at location \(x\), which in this study refers to a site, \(n\) is the number of point data, i.e., the number of archaeological sites, and h is the bandwidth. In this study, the bandwidths used were 80 km for the Neolithic period, 120 km for the Shang to Spring and Autumn periods, and 60 km for the Warring States period. \(K\) is the kernel function, and \(X_{i}\) is the location of the point data sorted by \(i\), which means the location of the site sorted by \(i\).
Distribution of Diatom Resting Stages in Sediment near Artificial Reefs Deployed in the Dysphotic Zone: Exploration of New Artificial Reef Function  
[PERSON]  
1 Civil Engineering Research Institute for Cold Region, Public Works Research Institute, Sapporo 062-8602, Japan 1  
[PERSON]  
1 Civil Engineering Research Institute for Cold Region, Public Works Research Institute, Sapporo 062-8602, Japan 1  
[PERSON]  
2 Seed Bank Co., Ltd., Kyoto 606-8267, Japan 2  
[PERSON]  
2 Seed Bank Co., Ltd., Kyoto 606-8267, Japan 2  
[PERSON]  
1 Civil Engineering Research Institute for Cold Region, Public Works Research Institute, Sapporo 062-8602, Japan 1  
Footnote 14: [[https://www.md](https://www.md)]([https://www.md](https://www.md))phytoplankton in a water column was pointed out by previous studies after the deployment of ARs [9; 10]. In this way, AR deployment may play a role in enhancing the primary production of the overlying water column, subsequently contributing to the augmentation of fishery productivity through food chains.  
Among the primary producers in the ocean, diatoms (Bacillariophyceaeae) are the main organisms and are estimated to contribute as much as 20% to the total primary production [11; 12]. Many diatom species have been shown to form dormant cells called resting stages (resting spores and/or resting cells) to survive under unfavorable environmental conditions [13; 14]. The formation of resting stages is induced when they are exposed to adverse conditions, such as nutrient depletion [15]. Resting stages settle down on the seafloor and will germinate/rejuvenate when the environmental conditions are favorable for their growth by being resuspended in a water column by a disturbing event such as vertical mixing and/or upwelling [16; 17]. Therefore, diatom resting stages can serve as a seed population for a future diatom bloom [18]. If AR deployment increases pelagic diatom populations in the water column, the subsequent formation of diatom resting stages around AR is likely to be enhanced simultaneously.  
One of the beneficial physical characteristics of ARs is a wake region generated behind an AR on the downstream side of the prevailing flow, where counterflows and eddies occur [19]. The flows and eddies allow phytoplankton and biologists in the water column to remain there and accumulate around the structure [19; 20]. It is thus likely that an artificial reef also traps settling diatom resting stages and accumulates them in the vicinity. Through the above-mentioned processes, deployment of an AR may not only enhance primary production in the sympatric water column but also potentially boost primary production by supplying diatom resting stages. To our best knowledge, however, no studies have ever investigated the distribution of diatom resting stages around ARs.  
In the present study, we investigated the concentration and composition of resting stages and vegetative cells of diatoms by direct microscopic observation and a culturing method (MPN method) of the sediment around ARs and a non-reef station at a depth of about 90 m in the dysphotic zone to reveal the accumulation effect of ARs.
## 3 Experiment and Analyses
### Static Experiment with Zero-Baseline
#### 3.1.2 Fault Exclusion
Considering Strategies 1 and 2 comprehensively, Strategy 2 is better from the perspective of a fixed rate. However, considering the precision of the positioning results, Strategy 1 is better. No matter which strategy is adopted, it will be more accurate and reliable than before. In practical applications, both strategies are acceptable for users according to the results (the precision can reach less than 1 cm) and the positioning results are more reliable than before. The availability can be also improved. However, for dynamic positioning (or real-time navigation), Strategy 2 is more suitable than Strategy 1, because Strategy 2 can ensure that a correct positioning result is obtained in each epoch. The continuity and integrity of dynamic positioning will be guaranteed. From the perspective of redundant observations and final positioning precision, the operating state of PL5 (or faulty pseudolite) should be monitored in real-time. Once it returns to normal, the stations should continue to receive its signal, which will make the constellation configuration better. The number of redundant observations can also be added. The following experiment is a dynamic one and the most reasonable strategy will be determined according to the real-time state of faulty pseudolite.
###### Abstract  
In wireless sensor networks, system architectures and applications are designed to consider both resource constraints and scalability, because such networks are composed of numerous sensor nodes with various sensors and actuators, small memories, low-power microprocessors, radio modules, and batteries. Clustering routing protocols based on data aggregation schemes aimed at minimizing packet numbers have been proposed to meet these requirements. In clustering routing protocols, the cluster head plays an important role. The cluster head collects data from its member nodes and aggregates the collected data. To improve reliability and reduce recovery latency, we propose a checkpointing scheme for the cluster head. In the proposed scheme, backup nodes monitor and checkpoint the current state of the cluster head periodically. We also derive the checkpointing interval that maximizes reliability while using the same amount of energy consumed by clustering routing protocols that operate without checkpointing. Experimental comparisons with existing non-checkpointing schemes show that our scheme reduces both energy consumption and recovery latency.  
**Keywords:** checkpointing; wireless sensor networks; clustering routing protocols
## 4 Measurement/Calibration of \(R_{s}\)
### In Situ Calibration  
#### 4.2.1 Passive Flow Monitors  
Passive flow monitors (PFMs) is based on plaster dissolution that has been successfully used to assess water movement (flow and turbulence) in a field experiment [117]. At a given salinity and temperature, the loss of a specific plaster material, such as calcium sulfate, over a given surface area could be described by an empirical calibration exponent that linked the loss of plaster with the velocity and/or turbulence of the water.  
As a cheap, simple, and reproducible in situ external calibration technique, PFMs have been successfully used to calibrate the \(R_{\mathrm{s}}\) of chemicals, including phosphate and polar or nonpolar herbicides and pesticides in the phosphate sampler, chemcatcher, POCIS, and SPMD [117; 118; 119; 120; 121]. This calibration explained the change in the \(R_{\mathrm{s}}\) with water flow velocity and ionic strength by chemical-involved and sampler-specified empirical equations, and thus the TWA concentration of chemicals could be estimated more accurately.
# Experimental Study of Biogas-Hydrogen Mixtures Combustion in Conventional Natural Gas Systems
## 2 Materials and Methods
### Fuel and Oxidant Properties
#### 2.3.2 Heating Value  
The main properties of gaseous fuels include the Wobbe Index, the heating value and the interchangeability [37]. The heating value is defined as the amount of heat released in the combustion reaction per unit of mass or volume and is closely related to fuel kinetics and adiabatic flame temperature. The heating value of a gas mixture is easily approximated when the molar fraction of each component is known. According to the bibliographical data, Figure 3 compares the heating value of standard biogas, natural gas (Table 1) and the three different biogas under study (BG50, BG60 and BG70), enriched with different percentages of hydrogen [37].  
The heating value of standard biogas and natural gas (Table 1) remains constant in Figure 3. The heating value of the blends increases almost linearly when biogas is enriched with hydrogen from 5% to 20%. Previous experimental researches agree with both the increasing trend and other combustion parameters, such as the increase in radical H+ in the combustion reactions and the laminar burning velocity [35; 43]. Depending on the CO\({}_{2}\) content, the quantity of needed hydrogen to improve the heating value varies. According to Figure 3 and theoretical calculations, while rich biogas needs a 15% of H\({}_{2}\) to reach the heating value of natural gas, poor biogas needs more than 25% of H\({}_{2}\).
# A Decentralized, Flat-Structured Control System for Chiller Plants +
## 3 Decentralized Control Algorithm
### Device Self-Organizing Optimization Control Algorithm  
The adjustment process of the device self-organizing optimization control algorithm is an iterative process in which multiple devices negotiate with each other. The basic principle of optimal control is that all equipment takes the highest point of efficiency as the starting point of adjustment, and determines the direction of adjustment according to the direction in which the initial working point of the equipment deviates from the highest efficiency point, and at the same time, transmits the expected efficiency \(\varepsilon\) and demand deviation \(\Delta\) to the neighbor nodes. According to the received efficiency expectation and demand deviation, the node combines its current efficiency expectation, outputs new efficiency expectation \(\varepsilon_{out}\) and demand deviation \(\Delta_{out}\), and transmits it to the neighbor nodes. When the transmitted demand deviation is less than the set calculation precision \(\delta\), the calculation converges. Then the nodes output and execute the new control results. The flow chart of the device self-organizing optimization control is shown in Figure 5.  
For different devices, \(\varepsilon\) and \(\Delta\) need to be defined separately. The chiller efficiency expectation \(\varepsilon\) can be defined as the ratio of the current COP to the highest COP, and the deviation \(\Delta\) of the distribution amount is the deviation between the current assumed cooling load and the total system cooling load demand.  
The pump efficiency expectation \(\varepsilon\) can be defined as the ratio of the current efficiency to the highest efficiency, and the deviation \(\Delta\) of the distribution amount is the deviation of the currently provided flow from the total system flow demand.  
In actual engineering, in order to avoid uneven water distribution, the lower limit of the flow rate of the cooling tower is usually set to avoid uneven flow of the flow due to the flow rate of the cooling tower. Therefore, the number optimization problem of the cooling tower can be simplified as follows, the cooling towers are turned on as much as possible under the given total flow rate of the condenser water, and the flow rate assigned to each cooling tower is not lower than the minimum flow rate set by each. Therefore, the efficiency expectation \(\varepsilon\) can be defined as the ratio of the actual condenser water flow rate \(G\) of the cooling tower to the minimum condenser water flow rate \(G_{\min}\) set by the cooling tower, i.e., \(\varepsilon=G/G_{\min}\), and the deviation \(\Delta\) of the distribution amount is the difference between the total flow taken by the current cooling tower and the total flow of the system condenser water. Moredetailed description of the device self-organizing optimization control algorithm can be referred to article [28].
# Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link
## 2 Observatory Measurements
### Geomagnetic Instruments  
The measurement of geomagnetic parameters requires considerable experience from both a scientific and technical point of view because the resulting magnitude to be retrieved is a vector. The coordinate systems used in geomagnetism are classified according to its geographic reference.  
The main sensors and system of the geomagnetic station are as follows:  
1. _8D/8I Vector Magnetometer._ A \(\delta\)D/\(\delta\)I variometer is mounted in the ASJI to automatically measure (once per minute) the variations of the magnetic field (see Figure 3a). It consists of two pairs of Helmholtz coils positioned perpendicularly and a proton magnetometer located at the center. More details are given in [15]. The proton magnetometer measures the polarization of the coils to acquire the declination and inclination variations and, the value of the total magnetic field intensity is measured when the coils are not polarized [16].
2. _D/I Fluxgate Theodolite._ The huts of the ASJI also contain a declination and inclination (D/I) fluxgate theodolite (see Figure 3b) which is used to manually measure the inclination and declination angles of the magnetic field vector in absolute values. This instrument consists of a fluxgate magnetometer bar mounted on a non-magnetic theodolite telescope, more details in [15]. The uncertainties of this sensor are taken into consideration in [17].
3. _Three-axis Fluxgate Magnetometer._ A three-axis fluxgate magnetometer was added during the 2008 expedition (see Figure 3c). It measures the magnetic field variations automatically sampling an analogue output at both 1 and 0.1 Hz using an Analog-to-Digital Converter (ADC).
4. _Control System._ The electronic system controlling the automatic instruments is also housed indoor in the ASJI. Once the sensors data is processed, the definitive data set is sent to the World Data Centers to become available for the scientific community. The real-time access to the data is provided by a satellite link to the INTERMAGNET. However, a safe skywave link designed by La Salle and the OE is used as a backup. The sensor data can be sent autonomously by the skywave link becoming the channel itself another interesting sensor of the ionosphere.
## 2 Materials and Methods
### Uncertainty Characterization
#### 2.2.2 Selection of Outputs  
Numerous potential output parameters were available for selection; however, the interdependence between different outputs, as well as their reliance on shared input parameters, necessitated a discerning approach. Consequently, outputs were chosen that provide insight on power plant performance as parameters and parameters of intrinsic importance to the power plant and its operation but that are not, difficult or only indirectly measurable. So the selected output parameters are divided in three groups: plant performance indicators, steam quality indicators, and furnace operation indicators. The selection of the output parameters can be found in Table 2. The first trio of parameters, namely gross power, gross efficiency, and CHP efficiency, is an obvious choice. These output parameters provide a good picture of the impact of the retrofit on the power plant performance and are indispensable for estimating both the financial implications as well as the generation of electricity and heat after the conversion. The next two parameters are the pressure and flow of the steam leaving the boiler and entering the high-pressure steam turbine. These two output parameters provide better insights into the quality of high-pressure steam and the potential capacity loss of the boiler and steam turbine due to changes in fuel type and different LHV due to fuel source modifications. The last three output parameters are adiabatic flame temperature, furnace exit gas temperature (FEGT) and effective radiation temperature. These parameters are important because they are difficult or impossible to measure [42]; nevertheless, these parameters significantly affect combustion dynamics and kinetics within the boiler, especially in the case of a fuel retrofit. These parameters influence factors such as soot formation and the distribution of heat exchange mechanisms, more precisely, the interaction between radiation and convection. The soot formation will initiate slagging and fouling issues which are very undesirable because coal and biomass fired power plants are very sensitive to it [43; 44].