esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Comparison between SSO and Lower Inclination Orbit  
As defined previously, the inclination of a satellite refers to the angle that the satellite's orbital plane has with the Earth's Equator. This angle can be tuned depending on the desired latitude coverage of a satellite orbit.  
In polar orbits (with inclination between 96 and 98 deg) the satellite passes over the Earth's poles as it orbits the earth, providing a wide-ranging view at all latitudes of the Earth. Polar sun synchronous orbits are well-suited for applications that require a global coverage of the Earth, such as weather monitoring. Additionally, despite seasonal variations, the angle between the orbital plane and the sun (Beta angle) is kept near constant throughout the year. Due to this stability, scientists may compare photos taken during the same season across several years without having to worry too much about the appearance of change due to significant variations in lighting and shadows [30].  
In contrast, satellites with lower inclination (less than 90 deg) orbits spend their time within a confined range of equatorial bands and therefore have a narrower coverage region with more persistent contacts within that band. Additionally, the target area will be passed over by the satellite in a lower inclination orbit at various times throughout the day while the satellite in SSO will pass at the same time range daily because it is oriented constantly with respect to the sun illumination [1]. Lower inclination orbits are well-suited for missions that require frequent passes and longer coverage time on certain regions of the Earth such as communication, and remote sensing. The rationale behind a lower inclination orbit is that satellites in these orbits cover more frequently certain coverage regional bands allowing for more frequent imaging opportunities of the areas of interest (higher temporal resolution). Missions that call for a specialized perspective of a certain location, such as a single continent or a specified region will benefit from this. The satellite can deliver more frequent and dependable data for these applications by keeping near to a confined latitude range.  
The capabilities and performance of a satellite can be impacted by a number of significant changes between polar SSO and lower inclination orbits. The amount of time the satellite spends over a certain spot on the surface of the Earth is one of the key distinctions. The satellite will repeatedly cross a specific point while in an orbit with a lower inclination, delivering regular updates and information. However, the satellite will pass over a certain place less frequently in an orbit with a high inclination, producing fewer frequent updates. Furthermore, satellite constellations in lower inclination orbits allow total number of satellites and mission costs to be reduced by focusing on confined latitude ranges. These constellations can be designed to ensure ideal revisit times only in the geographical regions where it matters the most [31].  
Figure 4: Inclination angles as a function of earth’s rotation [28].
## 2 Computational Fluid Dynamics (CFD) Simulations and Flow Model Test (FMT) Experiments  
### Description of the SCR-deNO\({}_{x}\) Facility  
The schematic geometry of the SCR-deNO\({}_{x}\) facility for a 1000 MW coal-fired power plant is shown in Figure 2. This power plant is built by Guangdong Baolihua New Energy Stock Co., Ltd. and is located in Hudong Town, Lufeng City, Guangdong Province of China (22\({}^{\circ}\)49\({}^{\prime}\)56\({}^{\prime\prime}\) N, 115\({}^{\circ}\)59\({}^{\prime}\)10\({}^{\prime\prime}\) E). The baffles geometry is optimized to regulate the flow field and obtain a homogeneous distribution of gas velocity at the entrance of the catalysts.  
The SCR-deNO\({}_{x}\) facility shown in Figure 2 is the classic \(\pi\) type. The height of the facility is about 33,000 mm while the inlet size is about 10,000 mm \(\times\) 19,000 mm. The sectional area of the catalyst layer is about 194 m\({}^{2}\). The flue gas leaving the economizer passes the dust hoppers and enters the sloped duct. Then, it goes through the AIG and hybrid grid in the vertical duct. After passing the wedge-shaped turning and the straightener, it finally enters the two catalyst layers. At the 100% boiler maximum continuous rating (BMCR) operational condition, the total flow rate of the flue gas is 979.60 kg/s with a temperature of 648 K. The mass flow rate of the ash particles is 12.54 kg/s. The characteristics of the ash particles including the diameter distribution are presented by the Instrumental Analysis Center of Shanghai Jiao Tong University, which is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline
**Density** & **Particle Diameter \(\mathbf{\Phi}\)** & **Mass Fraction** \\ \hline \multirow{4}{*}{1.0 g/cm\({}^{3}\)} & \(>\)0.15 mm & 10.97\% \\  & \(8.8\times 10^{-2}\) mm \(<\mathbf{\Phi}\leq\) 0.15 mm & 8.07\% \\ \cline{1-1}  & \(6.3\times 10^{-2}\) mm \(<\mathbf{\Phi}\leq\) 8.8 \(\times 10^{-2}\) mm & 2.92\% \\ \cline{1-1}  & \(\leq\)6.3 \(\times 10^{-2}\) mm & 78.04\% \\ \hline \end{tabular}
\end{table}
Table 1: The characteristics of the ash particles collected from the SCR-deNO\({}_{x}\) facility.  
Figure 2: The schematic geometry of the SCR-deNO\({}_{x}\) facility for a 1000 MW coal-fired power plant.
## 3 Results and Discussion  
### CPB Failure Process  
Brazilian split tensile tests were carried out on the specimens prepared in accordance with Table 4. As shown in Figure 3b, all specimens were broken vertically along the diameter direction and fracture failure occurred.  
Figure 4 shows the tensile stress-strain curves of RSCPB and CCPB. From the stress-strain curves, the following can be found:  
1. The peak tensile stress (\(\sigma_{t}\)) of RSCPB is generally higher than that of CCPB. However, the peak tensile strain (\(\epsilon\)) of most RSCPB specimens is lower. From the stress-strain relationship, both CCPB and RSCPB exhibit obvious brittle failure characteristics and basically satisfy the tensile loading failure process of brittle materials, as shown in Figure 5. The stress-strain relationship passes through four phases during the process from loading to failure: OA-linear elastic phase; AB-yielding phase; BC-failure phase, characterized by stress drop; CD post-failure phase. However, some of the specimens showed no residual strength and even fractured directly after the tensile force exceeded the ultimate stress, and the stress dropped to 0 MPa.
2. In the pre-peak phase, RSCPB specimens have a greater elastic modulus. Although relatively small critical deformation limits the deformation energy that the RSCPB can withstand and inhibits its toughness development, the RSCPB specimens possess enhanced toughness performance under the same strength or deformation, which is important for preventing mine stratigraphic displacement and maintaining ground pressure stability. Therefore, RSCPB has a promising application prospect.
3. In the post-peak phase, stress drops occurred in all CPB specimens when the stress exceeded the peak tensile strength, indicating that CPB fractured under a tensile stress load, and CPB failed instantaneously and completely. CCPB showed almost no residual strength. A part of RSCPB specimens showed a slow stress drop and retained residual strength, which indicates that the RS fibers play a positive role in connecting the damaged parts and improving their residual strength during the tensile damage of the specimens.
## 3 Results
### Fabrication Techniques for Nanosensors
#### 3.5.1 Top-Down Approaches  
Top-down fabrication techniques involve the reduction or modification of larger structures to create nanoscale features (Table 1). These techniques offer precise control over the sensor dimensions, shape, and arrangement.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technique** & **Description** & **Advantages** & **Disadvantages** & **Reference** \\ \hline \multirow{4}{*}{Lithography-Based Fabrication} & Involves using a mask or & & \\  & template to selectively & Offers high precision and & Time-consuming and \\  & expose a photosensitive & allows the creation of & requires specialized \\  & material, followed by & complex sensor & equipment. Limitations in \\  & subsequent processing & geometries and patterns. & scalability and \\  & steps to create desired & geometries and patterns. & cost-effectiveness for \\  & nanoscale features. & & large-scale production. \\ \hline \multirow{4}{*}{Chemical Etching Methods} & Selectively removes & Offers flexibility in & \\  & material from a substrate, & crating various sensor & Control over the \\  & using chemical reactions, & structures on planar & selectivity of the etching \\  & resulting in the formation & substrates and allows for & process and uniformity of \\  & of nanoscale features. & the formation of & etching can be \\  & of nanoscale features. & nanochannels, nanogaps, & challenging. \\ \cline{1-1} \cline{2-4}  & & or nanostructures. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Top-down approaches in nanosensor fabrication.  
Figure 4: Quantum dot-based sensors in bridge maintenance.
# Research on Output Voltage Stability of Non-Contact Excitation Motor
## 5 Simulated Analysis
### Comparison of Output Voltage Stability under Variable Load for Two Consecutive Times  
To enable the system to meet more complex conditions, the load was changed at 0.001 s and 0.003 s, respectively. The differences in stable output voltage and output current between the traditional sliding mode control and the improved sliding mode control are observed.  
According to the simulation results of the output voltage in Figure 11, both algorithms need a certain amount of time to stabilize the output voltage when the load is changed twice in a row, and the output voltage has a certain amount of overshoot. Specifically, the red line represents the result of the output voltage when the load changes twice in a row  
Figure 10: Simulation plot of the output voltage under a single load change.  
Figure 9: Simulation plot of the output voltage under controlled load variation.  
under traditional sliding mode control. After the first load change, the output voltage has a sudden change, which takes 0.001 s to stabilize, and the output voltage overshoot is 20 V. On the second load change, it takes 0.0005 s to stabilize. The output voltage overshoot is 25 V. The blue line shows the result of the output voltage under improved sliding mode control. At the first load change, it takes 0.0005 s for the output voltage to stabilize, which is two times faster than the traditional sliding mode control, and the voltage overshoot is 15 V. The second load change takes 0.00025 s to stabilize, and the voltage overshoot is 20 V, which is smaller than the traditional sliding mode control. Therefore, the improved sliding mode control has a shorter adjustment time and an overshoot when the load changes continuously. The volume is smaller, and the efficiency is higher.
## 2 Results and Discussion
### Antioxidant Activity  
The antioxidant activity assay was estimated using an ABTS free radical scavenging activity assay (2,2\({}^{\prime}\)-azinobis(3-ethylbenzothazoline-6-sulfonic acid) [42]. Inhibition free radical ABTS in percent (I %) was calculated as per the equation:  
\[\mathrm{I}\%=(\mathrm{A_{blank}}-\mathrm{A_{sample}})/(\mathrm{A_{blank}}) \times 100\]  
where \(\mathrm{A_{blank}}\) is the absorbance of the control reaction (containing all reagents except the test compound), and \(\mathrm{A_{sample}}\) is the absorbance of the test sample.  
Figure 2: The electron density, the HOMO and LUMO frontier orbitals of 4-antipyrinylthiazole derivatives **9**, **10**, **12**, and **14**.  
The antioxidant activities of the newly synthesized 4-antipyrinylthiazole scaffolds **2**, **4**, **5**, **6**, **7**, **9**, **10**, **12**, and **14** were evaluated using the ABTS Radical Cation Decolorization Assay [43]. The results, shown in Table 2, indicated that compounds **10b**, **10c**, and **7a** have excellent antioxidant properties compared to the reference of the test (L-Ascorbic acid, 88.88%). The derivatives of 2-(4-antipyrinylthiazolylamino)-5-substituitedbenzylidene-thiazol-4(5\(H\))-one substituted with electron-withdrawing groups at the benzylidene moiety exhibited the best antioxidant activity. The percentage inhibition of scaffolds **10b** and **10c** substituted with nitro and bromide groups at the benzylidene moiety are 85.74% and 83.51%, respectively.  
The replacement of the chlorine atom of the 2-chloroacetamido-thiazole compound **6** (percentage inhibition of 73.33%) by the benzothiazolyl moiety to afford the corresponding antipyrinyl-thiazole scaffold **7a** enhanced the antioxidant activity with percentage inhibition 78.14%.
# Human Appropriation of Net Primary Production: From a Planet to a Pixel
## 4 Results
### HANPP at a Pixel Microscale: Lyon County, IA  
The USDA-NASS Cropland Data Layer (2021) provides a means to apply big data techniques to study HANPP at a pixel scale. By combining these data with annual mean  
Figure 3: **(a)** HANPP (harvest) per U.S. county in 2012 (kilotonnes). **(b)** HANPP (harvest) density per U.S. county in 2012 (gCm\({}^{2}\)yr\({}^{-1}\)).  
yield data for counties from USDA Quick Stats (2018) [28], a pixel-level measurement of HANPP (harvest) can be derived, such as for corn grain (Figure 4). Applying these data for all crops grown in a specific county and assigning HANPP estimates through livestock grazing to pixels in the grassland/pasture category allows for a pixel-level analysis of HANPP (harvest) for a U.S. county, such as the Corn belt county of Lyons county, Iowa.  
HANPP (harvest) density in gCm\({}^{-2}\)yr\({}^{-1}\) of corn grain in Lyon County is 855 gCm\({}^{-2}\)yr\({}^{-1}\), representing a mean county yield in 2012 of 10 tonnes per hectare (162 bushels per acre). With a lower yield of 3 tonnes per hectare (52 bushels/acre), HANPP (harvest) from soybeans is 341 gCm\({}^{-2}\)yr\({}^{-1}\) (Figure 5b). Alfalfa is grown on steeper slopes and less fertile soils. A perennial crop that is harvested multiple times per season, it has a high HANPP of 644 gCm\({}^{-2}\)yr\({}^{-1}\) (Figure 5c). The highest slopes and poorest soils are assigned to livestock grazing, where HANPP (harvest) is a lower 41 gCm\({}^{-2}\)yr\({}^{-1}\) (Figure 5d).  
Figure 5e captures the overall map of HANPP (harvest) for Lyon County, IA in 2012 at the 30 m pixel scale. This map shows the variable intensity at which humans appropriate net primary production at the finest scale that is currently possible using widely available geospatial data. At this scale, definitional questions emerge pertaining to the minimum scale at which HANPP is definable. Is it a crop field, a pasture, a timbering tract--all of which range upwards from 900 m\({}^{2}\)--or is it an individual plant at a scale of meters? We have thus titled this paper \"HANPP: From a _planet_ to a _pixel_\", capturing the full range of scales at which HANPP is currently meaningfully measurable.  
Figure 4: HANPP (harvest) density for corn grain (gCm\({}^{2}\)yr\({}^{-1}\)) at 30m pixel scale for the contiguous US in 2012.
## 3 Procedure for Estimation of Optimal Analysis Parameters  
A procedure illustrated in Figure 5 is proposed to analyze the effect of thermal analysis parameters on the thermal analysis results and to estimate the optimal thermal analysis parameters for the thermal analysis model to predict the characteristics of actual builds more accurately by using reference data such as melt pool size from test builds. In the first step of this procedure, the ranges of some uncertain thermal analysis parameters are determined considering existing studies and physical conditions as described in Section 2. In the next step, thermal analysis is performed for the analysis cases constructed by design of experiments (DOE) such as Box-Behnken design. The thermal analysis results obtained from the previous step are used to construct a regression model including quadratic terms, and the sensitivity of analysis parameters to the analysis results is evaluated by using this regression model. According to the sensitivity of analysis parameters, dominant analysis parameters are selected for the next step. Finally, for given reference data such as the melt pool size obtained from analysis or experiments for the LPBF process, the optimal values of the selected dominant analysis parameters that can produce the closest thermal analysis results to the reference data are estimated by minimizing the differences between the reference data and the output data from the regression model using efficient optimization techniques such as the particle swarm optimization (PSO) algorithm or the genetic algorithm. By applying the optimal analysis parameters obtained from the optimization step, a thermal analysis model that produces more accurate and reliable analysis results can be constructed. The techniques used in the proposed procedure are briefly described below.  
Figure 4: Interaction of laser and powder bed in the SLM process.
## 2 Materials and Methods
### Core Insights of Qsa  
1. **Hybrid:** QSA merges quantum-like \"tunneling\" and classical \"thermal\" updates with local search and partial structure mixing (superposition).
2. **Exploration-Exploitation Balance:**_Thermal_ and _tunneling_ encourage exploration, especially in early iterations. _Pull-to-best_ and _local search_ intensify exploitation in later iterations.
3. **Population Diversity:** Maintained by _collision_ forces. Without collision, the population might cluster too quickly around a single solution.
4. **Adaptability:** By modifying the discrete mapping \(g\), superposition rules, and local search, QSA can be adapted to various combinatorial problems.  
The **QSA** can be viewed as a meta-heuristic framework that embeds discrete solutions in a continuous space \(\mathbb{R}^{d}\); simulates \"thermal\" motions and \"quantum\" leaps to escape local minima; employs collision for diversity and superposition for knowledge sharing; uses local search to refine solutions and a greedy acceptance scheme to ensure progress.  
This structure allows QSA to handle large-scale or complex discrete optimization problems by systematically combining random exploration with best-solution guidance and local neighborhood improvements. Each formula described above contributes to one of the key pillars of the approach, ensuring a deep balance between exploration (via random or quantum moves) and exploitation (through local search and best-solution guidance) throughout the iterative process.
## 3 Results
### Moisture and Freeze-Thaw Susceptibility  
The results confirmed the trend of a decrease in the indirect tensile strength ratio (ITSR) value after the addition of fibers. When testing the freeze-thaw susceptibility, the decrease was even more significant in most cases. Figure 8 displays the mixtures without recycling agent. The reference mixture showed the best ITSR values, followed by the mixture with alkali-treated fibers (0.2% J300 NaOH). Both showed a slight increase in ITSR after the freeze cycle.  
Furthermore, all mixtures with 20 mm fibers showed an increase in dry indirect tensile strength. After the freeze-thaw and water immersion, the strength of mixtures with 20 mm fibers did not drop below the level of the reference mixture, which compensates for the lower value of the ITSR. The lowest indirect tensile strength was achieved by the mixture with 10 mm fibers, followed by the 0.3% J300 mixture with the highest fiber content.  
The mixture J300 6% SR achieved the lowest ITSR values of all the tested mixtures, as shown in Figure 9. Treatment of fibers with NaOH led to a significant increase in dry indirect tensile strength, in the case of the \"SR\" recycling agent, and basic content of 6%. However, an increase in the recycling agent content, up to 9%, led to the opposite effect. On the other hand, ITSR values rose above the 70% mark. Surprisingly, the mixture with the recycling agent \"SR\" without fibers did not demonstrate any increase in any monitored parameter compared to the reference mixture. In contrast, the application of recycling agent \"RF\" without fibers resulted in the highest ITSR values and a decrease in indirect tensile strength (ITS). This dry value was again increased by the addition of fibers. Likewise, there was a drop in ITS values after the freeze cycle and water immersion. The difference between applying the recycling agent to the fibers and to the RA was small, but the 6%RF_RA mixture achieved slightly better results. The mixture with alkali-treated fibers achieved high ITS after water immersion. However, subjecting the mixture to a freeze cycle resulted in a drop to the lowest level of the \"RF\" mixes. A more pronounced reduction in ITSR values can be attributed to the lower content of fresh binder. However, the addition of recycling agent to fiber blends did not lead to the expected reduction in susceptibility. This phenomenon should be further investigated in future research. However, according to CSN 736121:2019, AC 16 base mix types do not have set ITSR limits. However, for use in practice, it is good for each mixture to have the highest possible values. The asphalt base layer is not often replaced during roadway reconstructions [39]. Once damage/cracks develop in the layer, degradation and the need to replace all bound layers could potentially occur more rapidly.  
Figure 8: Indirect tensile strength (left vertical axis) and ITSR according to EN 12697-12 and modified AASHTO T283 protocols. Mixtures without the recycling agents.
## 4 Challenges and Future Research Directions
### Challenges for Energy Optimization Algorithms, Energy Flow, and Traffic Flow Algorithms  
It must be noted that 6G wireless communication for smart grid applications must meet specific requirements, including ultra-low latency, high reliability, data rates, edge computing, security, energy efficiency, and adaptable QoS. Challenges include interoperability, scalability, regulatory compliance, and resilience. Balancing these requirements and overcoming challenges is crucial for the successful integration of 6G into smart grid energy optimization and traffic flow algorithms.