esa-sceva/satcom-chunk-collection huggingface dataset:
# Validation Analysis of Drought Monitoring Based on FY-4 Satellite
## 3 Results
### Analysis of Trend
#### 3.2.2 Trend of Drought Area  
The trends of proportion in drought areas on TVDI, MCI, and SHRI during the drought process are shown in Figure 7. In the Huanghuai region (Figure 7A) before 15 May 2022, the area of light drought on TVDI increased, and the rest level of drought rapidly decreased. Then, heavy drought intensified, and the area of light and medium drought decreased. After 4 June, the region affected by medium and heavy drought shrank, and light drought exhibited an upward trend, indicating relief from the drought. Yunnan Province (Figure 7B) was dominated by light drought before 22 January 2021, and the area of medium and heavy drought decreased. After 22 January, medium and heavy drought gradually increased, and light drought decreased. At the turning point of February 6 th, the proportion of medium and heavy drought areas decreased rapidly. It showed a sharp decrease in the area of the drought before 12 January 2021 in Guangdong Province (Figure 7C). The area of heavy drought increased, while light and medium drought continued to decrease. Finally, the area of heavy drought disappeared after 6 February. The trend of drought areas on TVDI is consistent with the results on MCI and SHRI.
## 4 Modeling and Results
### Datasets Used for Modeling
#### 4.1.3 Dataset Generalization  
Utilizing both the CASIA-HWDB and Kuzushiji-Kanji datasets provided distinct advantages. CASIA-HWDB served as an excellent source for pretraining, leveraging its extensive and balanced dataset to develop the initial weights, especially beneficial for transfer learning. Kuzushiji Kanji, though smaller and imbalanced, offered authentic Japanese kanji samples, essential for fine-tuning and evaluating the model's performancein the target task. The main differences between the CASIA-HWDB and Kuzushiji-Kanji datasets are represented below (in Table 3 and Figure 6).  
Utilizing both datasets, the training process harnessed the robustness of CASIA-HWDB and the specificity of Kuzushiji Kanji, enhancing the model's accuracy in recognizing handwritten kanji.
## 2 Literature Review  
### Studies Related to the USA  
Several studies have been conducted on the relationship between economic instrument policies and RE development in the USA. In fact, the economic instruments include three main policies named direct investments, financial incentives, and market-based instruments. For example, [PERSON] et al. [10] studied the benefits and impact of one category of financial incentives, called Renewable Portfolio Standards (RPS). The study showed that RPS-based new RE generation represented 2.4% of the nationwide electricity generation and an almost 59 Million tons (Mt) CO\({}_{2}\) equivalent reduction in greenhouse gas emissions in 2013. The study also stated the additional benefits of this policy. It was noted that the adopted RPS supported nearly 200,000 jobs in the country and reduced the wholesale electricity and natural gas prices, saving consumers a combined $1.3-4.9 billion. Additionally, [PERSON] [11] examined the impact of economic instrument policies on the wind energy generation capacity across the USA from 1994 to 2012. The study focused on determining the influence of green power purchase (market-based category) agreements and RPS on the wind generation capacity. The author indicated that the average effect of RPS across the USA was insignificant, while green power purchase (GPP) showed a positive and statistically significant effect on the wind generation capacity across the states. Furthermore, the study showed that, for each additional GPP program, there is an approximately 7 MW of additional wind energy capacity across the USA. The authors of [PERSON] et al. [12] reviewed the application of FIT policies in six USA states, including California, Michigan, Illinois, Minnesota, Rhode Island, and Hawaii. The study focused on several criteria such as generation cost, adopting different technologies and incentives based on different technologies. The authors revealed that FIT in the USA presented a dramatic shift in the policy landscape and could signal the beginning of a new trend of more aggressive RE policies at state and national levels.
## 3 System Architecture and Modeling
### Appliance Dynamics Modeling and Setup
#### 3.3.2 Thermal Load Appliances Modeling  
For the purpose of heating the indoor environment, the house is considered to be equipped with a water source heat pump through floor heating pipes [49]. For hot water requirements, building is connected to a solar water heater system. The dynamics of both the systems is influenced by the solar irradiance and the ambient temperature.  
Heat pump: A reduced order model of a residential building equipped with a heat-pump is developed in Reference [49; 50] as follows using the heat balance equations:  
\[\begin{split} C_{p,r}\dot{T}_{r}&=(UA)_{fr}(T_{f}- T_{r})-(UA)_{ra}(T_{r}-T_{a})+(1-p)\phi_{s}\\ C_{p,f}\dot{T}_{f}&=(UA)_{wf}(T_{w}-T_{f})-(UA)_{fr} (T_{f}-T_{r})+p\phi_{s}\\ C_{p,w}\dot{T}_{w}&=\eta W_{c}-(UA)_{wf}(T_{W}-T_{ f}).\end{split} \tag{2}\]  
The system of Equation (2) is discretized with appropriate sample time using zero-order-hold model and written into state-space representation as in Equation (1). The first sub-equation in Equation (2) represent the thermal dynamics of the indoor environment of the building which depends upon the floor temperature \(T_{f}\), ambient temperature \(T_{a}\) and solar gains \(\phi_{s}\). The second sub-equation captures the heat transfer between floor and the underfloor heating system. The last sub-equation relates the temperature of the floor \(T_{f}\) to the work done \(W\) by the compressor.  
Subsequently, for heat pump state-space model, we get \(\mathbf{x}=\begin{bmatrix}T_{r}&T_{f}&T_{w}\end{bmatrix}^{T}\), \(\mathbf{u}=W_{c}\), \(\mathbf{d}=\begin{bmatrix}T_{a}&\phi_{s}\end{bmatrix}^{T}\), and \(\mathbf{y}=T_{r}\).  
Solar water tank: The heat dynamics of the solar water tank is described using simple first-order differential equations as follows [51]  
\[C_{t}\dot{T}_{t}=\eta_{h}P_{h}+\phi_{s}-Q_{c}-(UA)_{t}(T_{t}-T_{i}) \tag{3}\]  
Equation (3) captures the thermal dynamics of the tank as a function of inlet water temperature \(T_{i}\), power consumption of the heating element \(P_{h}\), and solar gains \(\phi_{s}\).  
After discretization and rewriting Equation (3) in state-space representation, we get \(\mathbf{x}=\mathbf{y}=T_{t}\), \(\mathbf{u}=P_{h}\) and \(\mathbf{d}=\begin{bmatrix}Q_{c}&\phi_{s}&T_{i}\end{bmatrix}^{T}\).
# Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches
## 4 Results and Discussion
### Model Performance
Although all machine-learning-based models produced high PODs, the MLL models did not correctly identify the icing/non-icing cases shown in Figures 5 and 6. In summary, the COMS RF model and the Himawari-8 RF model resulted in the best performance in both the quantitative and qualitative analyses. Figure 4: Accuracy assessment results of each icing detection model. (POD = Probability of detection; POFD = Probability of false detection; OA = Overall accuracy; TSS = True skill statistics; E = Standard error). Figure 5: Maps of potential icing areas produced by the six algorithms for the COMS and Himawari-8 images collected on 24 April 2016 01:00 UTC. The 11 μm channel Tb image ranging from 228 K to 280 K is used as the background image. Areas with no cloud or a Tb of 11 μm \(>\) 270 K were masked out and appear black in the figures.
# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 2 Materials and Methods
### Methods for Measuring Plant Height and Validation  
#### 2.7.1 Measurement of Plant Height in Corn Populations  
In this study, the approximate single-plant segmentation of the maize population point cloud was achieved, as described in previous sections. The calculation of maize single-plant height from the segmented point cloud only required batch extraction using the AABB (axis-aligned bounding box) enclosing box algorithm. The batch extraction height algorithm was implemented in VS2017 using C++ to realize the code functionality. The process flowchart for the height extraction is shown in Figure 5.
# Thermodynamics and Magnetism of SmFe\({}_{12}\) Compound Doped with Co and Ni: An Ab Initio Study
###### Abstract  
Ni-doped Sm(Fe\({}_{1-x}\)Co\({}_{x}\))\({}_{12}\) alloys are investigated for their magnetic properties. The Sm(Fe,Co)\({}_{11}M_{1}\) compound (\(M\) acts as a stabilizer) with the smallest (7.7 at.%) rare-earth-metal content has been recognized as a possible contender for highly efficient permanent magnets thanks to its significant anisotropy field and Curie temperature. The early transition metals (Ti-Mn) as well as Al, Si, and Ga stabilize the SmFe\({}_{12}\) compound but significantly decrease its saturation magnetization. To keep the saturation magnetization in the range of 1.4-1.6 T, we suggest replacing a certain amount of Fe and Co in the Sm(Fe\({}_{1-x}\)Co\({}_{x}\))\({}_{12}\) alloys with Ni. Ni plays the role of a thermodynamic stabilizer, and contrary to the above-listed elements, has the spin moment aligned parallel to the spin moment of the SmFe\({}_{12}\) compound, thereby boosting its saturation magnetization without affecting the anisotropy field or Curie temperature.  
density-functional theory; rare earths elements; permanent magnets; saturated magnetization; anisotropy field; curie temperature +
Footnote †: journal: Applied sciences
## 1 Introduction
Despite growing research interest in glycol-based coolants for BEVs, investigations have predominantly focused on thermal performance and system integration, offering valuable insights into optimizing battery thermal management systems, although without addressing the electrochemical behavior of coolants under fault conditions [14; 15]. To the best of our knowledge, no previous work has evaluated hydrogen suppression in water-glycol-based coolants. This study provides an electrochemical assessment of hydrogen evolution in glycol-based coolants used in BEVs, focusing on the influence of copper corrosion inhibitors and coolant electrical conductivity. Unlike studies that address hydrogen evolution in other environments, this work evaluates HER suppression under high overpotential conditions in complex, commercially relevant coolant formulations. Two HER mitigation strategies are considered: (1) to modify the coolant composition to include HER inhibitors, and (2) to reduce the coolant's electrical conductivity to inhibit the magnitude of the reaction. One possible approach to the first strategy is to refine the coolant's corrosion inhibition package, since it commonly includes copper organic corrosion inhibitors such as azide-based compounds including Toyltriazole (TTZ), Benzotriazole (BTZ), and Sodium Mercaptobenz-zothiazole (MBT-Na). These corrosion inhibitors are classified as mixed-type corrosion inhibitors, i.e., they are expected to reduce the cathodic corrosion reactions, including HER [16]. Therefore, optimizing the coolant corrosion inhibition package can potentially lower the hydrogen formation, improving the overall safety of the system. In this context, this paper aims to unravel the contribution of Cu corrosion inhibitors and the coolant's electrical conductivity on hydrogen formation in Water-glycol coolants. HER was measured through Linear sweep voltammetry (LSV), an electrochemical technique commonly used to assess HER in electrocatalysis and battery applications [17; 18; 19]. Since HER is a cathodic reaction, a linear potential ramp was applied in the cathodic direction, and the system's current response was measured, revealing details of HER kinetics and allowing the comparative evaluation across different systems. The investigation was divided into three parts to decouple the influence of the Cu corrosion inhibitor concentration, Cu corrosion inhibitor type, and the coolant electrical conductivity on HER.
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 4 Fault-Types Classification (FC)
### Artificial Neural Network (ANN) Based FC  
Artificial neural networks (ANNs) are part of learning algorithms and non-linear statistical models family with the aim to emulate behaviors of linked neurons within biological neural systems. Various ANN algorithms have been developed for various applications including fault-type classification in TLs.
# Development of Compact and Robust Physical System for Strontium Optical Lattice Clock
## 4 Environmental Testing
### Magnetic Shielding  
Magnetic field fluctuations pose a significant challenge to optical clocks operating in mobile platforms, e.g., satellites, space stations, planes, or trucks. Notably, as satellites or space stations orbit the Earth, the surrounding geomagnetic field undergoes changes owing to their motion. The magnitude of these variations can exceed 80 \(\upmu\)T, significantly impacting the normal operation of onboard optical clocks [38].  
Theoretical analysis regarding the impact of geomagnetic field changes on strontium optical clocks indicates that, to prevent disruption to lattice loading, the amplitude of magnetic field changes must be less than 10 \(\upmu\)T. Additionally, for a space optical clock targeting stability and a frequency uncertainty of \(5\times 10^{-17}\), magnetic field fluctuations between adjacent interrogation cycles of the clock should be less than 3 nT/s. To comprehensively understand the impact of magnetic field variations on strontium optical clocks, we introduced equivalent external magnetic field changes and observed in experiments how such fluctuations led to the instability of atoms loaded into the optical lattice [39].  
Hence, optical clocks that frequently operate outside laboratories must feature the capability for magnetic field compensation or shielding. In comparison to magnetic field compensation, shielding against various environmental challenges might be a preferable option. As described in the manuscript's second section, we installed a magnetic shielding box outside the MOT chamber. Upon testing, this shield attenuated the magnetic field by approximately 20 dB at its center. This implies that the maximum fluctuation of the magnetic field in the space station orbit can be reduced to 0.6 \(\upmu\)T. Consequently, the maximum rate of change would be 0.22 nT/s, which is significantly lower than the theoretical values of 10 \(\upmu\)T and 3 nT/s.  
Finally, we experimentally evaluated the effectiveness of the magnetic shielding box in mitigating external magnetic field fluctuations. According to an investigation report by the International Geomagnetic Reference Field (IGRF), the magnetic field intensity in the space station's orbit is comparable to that on Earth's surface. Therefore, to simplify the experimental conditions, we opted to simulate the space station's magnetic field changes by rotating the physical system on the ground. The experimental results, depicted in Figure 7, exhibit slight changes in the number and images of lattice atoms recorded by a CCD camera after rotating the physical system by 180\({}^{\circ}\) on the ground. This demonstrates that the magnetic shielding box enables the physical system to withstand extremely challenging magnetic field environments, thus opening up broader possibilities for optical clock applications.  
Figure 7: Images of lattice atoms before and after magnetic field alteration in the presence of the magnetic shielding box.
## 5 Experiment III: Listening Experience Rating
### Results  
Panels 1-5 of Figure 7 present the average subjective ratings for listening experience with the six SBR mixing levels in the five audio scenes. As anticipated, listeners tended to rate LP and OPT similarly in all five scenes; LP and OPT also led to the best listening experiences in almost every scene. \(-\)9-dB and \(\text{SBR}^{\prime}_{min}\) were mostly not rated favourably by listeners. Intriguingly, while listeners also rated 9-dB higher than 0-dB in the background sounds which are relatively steady (i.e., the 'Football commentary' and 'Public speech' scenes) or which introduces informational masking (the 'News broadcasting' scene), an opposite rating pattern was observed in the background sounds prone to fluctuation (the 'Radio drama' and 'Radio poetry' scenes). Across all the five scenes, as shown in the _overall_ panel of Figure 7 LP and OPT were the only two systems which were rated as leading to 'Good' listening experiences to listeners in terms of both speech intelligibility and background sound effect.  
Significant main effects of scene [\(F(4,638)=27.469,p<0.001,\eta^{2}=0.258\)] and system [\(F(5,638)=99.424,p<0.001,\eta^{2}=0.543\)] were further revealed by two-way within-subject ANOVA. The bi-factor interaction was also significant [\(F(20,638)=26.199,p<0.001,\eta^{2}=0.354\)], supporting the notion that audio content affects the preferred SBRs. Table 4 displays the results of post-hoc pairwise comparisons with Bonferroni correction. It confirms that LP and OPT were rated equally in all scenes [all \(p>0.05\)]. In both the 'Football commentary' and 'Public speech' scenes, 9-dB provided similar listening experiences to listeners as OPT did [all \(p>0.05\)], despite a 7.2-dB and 6-dB difference between the two systems in the two scenes respectively. On the other hand, there was no significant difference in listening experience when the 'Radio drama' and 'Radio poetry' scenes were produced using 0-dB or OPT (\(-6.9\) and 1.3 dB in the two aforementioned scenes, respectively) [all \(p>0.05\)]. Nevertheless, scenes with OPT led to better listening experiences than with other mixing levels [all \(p<0.01\)], except for \(-9\)-dB in 'Radio drama' scene [\(p=0.89\)]. When comparing LP to other systems (apart from OPT) in all the scenes, the results are consistent to OPT, except for 'Football commentary' where LP was rated same as 0-dB. Overall, listeners rated both LP and OPT as the best over all other four systems [all \(p<0.001\)].  
Figure 7: Subjective rating for listening experience with various mix levels (shown above each data point in dB) in the five audio scenes (panel 1–5). The ‘_overall_’ panel shows the rating over all the scenes. Error bar indicates 95% confidence interval of the mean.  
\begin{table}  
\end{table}
Table 4: Significance level of post-hoc pairwise comparison with Bonferroni correction between subjective ratings on SRRs shown in Figure 7. Codes \"\", \"\" and \"\" are indicated \(p\) value \(<0.05\), \(<0.01\) and \(<0.001\), respectively. \(p\) values are supplied for insignificant comparisons.
## 3 Testing Results Analysis  
### Analysis of the Whole Process of Rockburst Experiment  
According to the measured stress-time curve (Figure 4), the images of the main failure point (Figure 5), and the variation characteristics of the rockburst evolution phenomenon with time, the experimental process image of slab buckling rockburst is divided into four stages: I (initial geostress), II (the initiation, propagation, and penetration of the shear crack), III (the formation of the slab structure), and IV (rockburst failure), as follows:  
1. I: The surrounding rock is in a quiet period and without failure (Figure 5a,b);
2. II: The prominent feature of this stage is the initiation, propagation, and penetration of the shear crack. When \(\sigma_{\mathrm{v}}\) = 52 MPa, the shear crack parallel to the tunnel axis appears on the inner wall of the right haunch (Figure 5c). With the further increase of vertical stress, the shear crack begins to appear in the inner wall of the left haunch near the arch footing (Figure 5d);
3. III: The prominent feature of this stage is the formation of the slabbing structure. When \(\sigma_{\mathrm{v}}\) = 76.9 MPa, tension cracks parallel to the free face appear in the left spandrel, arch footing, and right haunch, and the slabbing structure at the right haunch is initially formed (Figure 5e). With the expansion and penetration of tension and shear cracks, the slabbing failure area at the right haunch is further expanded, and the slabbing structure at the left haunch is initially formed (Figure 5f).
4. IV: When \(\sigma_{\mathrm{v}}\) = 73.7 MPa, the elastic strain energy stored inside the specimen exceeds the ultimate energy storage capacity of the specimen; the shear crack and the fracture of the rock plate occur on the left arch footing and spandrel, and the first ejection of the rock sheet occurs on the right haunch (Figure 5g). When \(\sigma_{\mathrm{v}}\) = 70.2 MPa, the second ejection occurs on both sides of the rock plate (Figure 5h). From the above analysis, it can be seen that both rockbursts form \"pan-shaped\" rockburst pits, which are consistent with the on-site rockburst situation, proving the rationality of this experiment. The formation of slab buckling rockburst experiences the process of shear failure, tensile failure domination, and shear failure. Slab buckling rockburst has the characteristic of attenuation in the process of rockburst. The two rockbursts respectively reflect the characteristics of a small area of rock mass, long ejection distance, and fast ejection speed in the first rockburst disaster and a large area of rock mass, short ejection distance, and slow ejection speed in the second rockburst disaster.