esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
The Spatiotemporal Heterogeneity Rankings of the 0.01\({}^{\circ}\)\(\times\) 0.01\({}^{\circ}\) Grids  
The spatiotemporal heterogeneity of each 0.01\({}^{\circ}\)\(\times\) 0.01\({}^{\circ}\) grid cell containing an in situ measurement site within the Naqu, Maqu, and Ngari networks was quantified by the synthesis season scores of CV and NIE weighting summation, i.e., the \(Score_{picked}\) defined in Equation (6). This score was further utilized to determine the spatial representativeness of each in situ measurement site over the 0.01\({}^{\circ}\)\(\times\) 0.01\({}^{\circ}\) grid cell. The boxplot with scatters in Figure 3 indicates the heterogeneity distributions of all 0.01\({}^{\circ}\)\(\times\) 0.01\({}^{\circ}\) grid cells containing in situ measurement sites. Such grid cells were classified into three groups, with low (score \(\leq\) 0.4), moderate (0.4 < score \(\leq\) 0.7), and high (0.7 < score \(\leq\) 1) heterogeneity.  
After removing null values, the sample size in Figure 3 was determined by the number of corresponding in situ measurement sites with available seasonal heterogeneity scores during each unfrozen/frozen period. In Naqu, many grid cells had low heterogeneity scores, ranging from 0.12 to 0.40, with the number of low-heterogeneity grids accounting for 79% (42/53), 70% (38/54), 58% (32/55), 64% (35/55), and 53% (28/53) in the five temporal periods. The corresponding percentages of low-heterogeneity pixels in Maqu were 53% (8/15), 53% (8/15), 42% (6/14), 73% (8/11), and 50% (7/14), while for Ngari they were 50% (6/12), 0 (0/12), 17% (2/12), 0 (0/10), and 0 (0/12). Therefore, Figure 3 demonstrates that,  
Figure 3: The heterogeneity distributions of all grid cells containing in situ measurement sites within the Naqu, Maqu, and Ngari networks. Green dots indicate low heterogeneity (score \(\leq\) 0.4), blue dots indicate moderate heterogeneity (0.4 < score \(\leq\) 0.7), and red dots indicate high heterogeneity (0.7 < score \(\leq\) 1).  
on average, Naqu contains less spatial heterogeneity than Maqu, and Maqu contains less than Ngari.  
By excluding in situ measurement sites without data from April to September in 2016, the remaining grids with in situ measurement sites within the same network were sorted by their seasonal heterogeneity scores. Only the last few lowest-scoring grids in each network--i.e., eight from Naqu, five from Maqu, and five from Ngari--are listed in Table 2. Each grid is named after the corresponding in situ node name. Table 2 shows that, among the lowest-scoring in situ measurement sites, the score values still vary between in situ measurement sites and periods. However, some grids maintained relatively stable low spatiotemporal heterogeneity (e.g., P3, MS3494, and MS3518 in Naqu, NST03 in Maqu, and SQ06 in Ngari). In situ SM datasets from these in situ measurement sites were then selected for fine-RES SM validation.
# Performance Analysis of Ground Target Detection Utilizing Beidou Satellite Reflected Signals
## 4 Experimental Results
### Results of the First Experiment
Through the probability density analysis of the phase difference information and the calculation of CNR information, the feasibility of using phase difference information to detect ground targets was verified. This also  
Figure 13: Results of the BeiDou IGSO satellite (PRN24) reflected signal: (**a**) phase difference information (in ms); (**b**) phase difference information (in s). verifies the existence of the reflected signal variation described in Formulas (13) and (14). However, there were some problems in the validation experiment, and thus it could not fully explain the target detection using the BeiDou reflection signals.
###### Abstract  
The variation in the background temperature field in aquatic environments plays a crucial role in the detection of thermal signatures of maritime moving targets. To elucidate the influence of various meteorological and hydrological parameters on the background temperature field of water bodies, this study employs the COARE 3.0 model to analyze the relationship between the net heat flux at the air-water interface and the characteristics of the cool skin layer. By examining the diurnal fluctuations of environmental parameters, the diurnal variation patterns of the cool skin layer properties are investigated. A dynamic temperature field testing platform was established in an outdoor pool to measure air-water volume variables and validate the accuracy of the water temperature field calculation model. The findings indicate that the cool skin phenomenon is indeed present in natural aquatic environments. The properties of the cool skin layer are predominantly affected by factors such as wind speed, the specific humidity gradient between the near-surface and high-altitude regions, and the temperature gradient between these regions. The temperature of the cool skin layer is typically a few tenths of K lower than that of the subsurface water, with a thickness generally ranging from 2 to 5 mm.  
COARE 3.0; cool skin; water temperature field; diurnal variation of water +
Footnote †: journal: Applied sciences
# A 4.1 GHz-9.2 GHz Programmable Frequency Divider for Ka Band PLL Frequency Synthesizer
###### Abstract  
High speed divider is highly desired in the millimeter wave (mmW) frequency synthesizer design. A high operating frequency, low power consumption 90-nm CMOS programmable pulse swallow multi-modulus-divider is presented in this paper. High speed true-single-phase-clock D-flip-flop (TSPC DFF) is used in the counter in order to obtain a high operating frequency. It can operate at a frequency range from 4.1 GHz to 9.2 GHz, with a division ratio of 101-164. It has a power efficiency of 3.1 GHz/mW, and it can be used to provide a high quality reference frequency in the mmW phase-locked loop.  
CMOS; high speed; counter; prescaler; TSPC; multi-modulus-divider; phase-locked loop +
Footnote †: journal: Electronics Electronics
# An Overview on Atomization and Its Drug Delivery and Biomedical Applications
## 5 Mathematical Formulations  
To begin with, the most straightforward description of atomization is the quasi-static state equilibrium of a suspended drop. As explained in Section 1.1, when the gravitational pull overpowers the surface tension of the liquid, the liquid is dragged away from the barrier as a drop. By associating surface tension (\(\sigma\)) and gravitational force (g), we can calculate the mass of the drop (m\({}_{\text{D}}\)) discharged from the orifice with a diameter d\({}_{0}\)[24].  
\[\text{m}_{\text{D}}=(\pi\text{d}_{\text{o}}\sigma)/\text{g} \tag{1}\]  
From Equation (1), the diameter of the drop (D) can be calculated and it is identified as  
\[\text{D}= \left\{6\text{d}_{\text{o}}\sigma/\rho_{\text{L}}\text{g}\right\}^{1/3} \tag{2}\]  
where \(\rho_{\text{L}}\) is the density of the liquid.  
Based on Equation (2), when the diameter of the orifice is reduced, the diameter of the drop generated is also reduced [24]. Another equation derived for calculating the diameter of the drop generated from a liquid film where both gravity and flow rate of the liquid comes in play is  
\[\mathrm{D}=3.3\{\sigma/\rho_{\mathrm{L}}\mathrm{g}\}^{0.5} \tag{3}\]  
It is observed that Equation (3) is suitable when the flow rate of the liquid is low, which gives a larger drop size but this is unprofessional for experimental applications as we prefer fine droplets, where the effect of gravitational force will be negligible for the drops ranging in size from 1-300 \(\upmu\)m [24]. When the diameter of the orifice is minimized to 1 \(\upmu\)m, the diameter of the drop speculated will be one tenth as sizable [1].  
When the aerodynamic force comes into play during atomization that is the liquid flowing out of the orifice when it comes in contact with the neighbouring air, there will be a pressure variation that causes changes in the pressure dispersal surrounding a drop. This can lead to either an equilibrium state linking the aerodynamic force and the interior impact (from surface tension and density of the liquid) or additional distortion of the drop into droplets [24]. At equilibrium, the internal pressure (p\({}_{\mathrm{I}}\)) of the drop is considered as a sum of the aerodynamic pressure (p\({}_{\mathrm{A}}\)) and pressure applied by the surface tension of the liquid (p\({}_{\mathrm{\sigma}}\)).  
\[\mathrm{p}_{\mathrm{I}}=\]  
P\({}_{\mathrm{A}}\)+P\({}_{\mathrm{\sigma}}\) = constant  
For a globular-shaped drop p\({}_{\mathrm{\sigma}}\) is given by,  
\[\mathrm{p}_{\mathrm{\sigma}}=4\sigma/\mathrm{D} \tag{5}\]  
From the above equations it is clear that, if the pressure exerted by the surrounding air can be counteracted by the pressure applied by surface tension then the drop will remain stable without further deformation. When this equilibrium is disturbed that is the pressure of the air is higher than the pressure exerted by the surface tension of the liquid, the drop will be further disrupted into smaller drops [24].
## 4 Experimental Results and Analysis
Finally, we analyze three equalization methods that directly equalize the original collected signals without data enhancement processing. In the 230 GHz/2.1 m channel environment, the BER after Volterra equalization is 0.01104, the BER after traditional LSTM equalization is 0.0022, the BER after one-stage equalization of our method is 0.0013, and the BER after two-stage equalization is 0.0002. The problem reflected by such a BER is that only for the signals we actually collected, the two equalizers except for our method can show a relatively good effect, but the two-stage time-domain equalization method can still reduce the BER obtained by these two methods by one order of magnitude in the end. Combined with the signal equalization results after our data enhancement processing, we believe that our method can obtain slightly better results than the other two equalizers when the degree of channel nonlinearity is generally affected. When facing more complex nonlinearity and low SNR, our method can significantly lead the other two equalizers. Of course, this is the  
Figure 6: Experimental results over a 230 GHz, 2.1 m LoS THz channel. (**a,c**) Time-domain waveform and ideal constellation of the transmitted signal. (**b,d**) Received signal and its constellation after passing through the THz channel, showing significant distortion. result obtained by sacrificing complexity. In the next section, we will discuss the impact of complexity on our method and the possibility of actual deployment. In summary, the proposed two-stage time-domain equalization method mitigates signal distortion progressively, thereby improving transmission quality and reducing BER. This provides a feasible solution for signal equalization in THz communication systems, particularly in scenarios involving severe nonlinearity and signal attenuation. The proposed strategy demonstrates strong potential for future high-speed wireless communication systems. Figure 7: Experimental results over a 230 GHz, 2.1 m LoS THz channel. (**a**,**d**) Equalization result obtained by applying a conventional deep learning model directly to the raw signal, showing limited performance under the current setting. (**b**,**e**) Output after the first stage of the proposed two-stage time-domain equalization method. (**c**,**f**) Final equalized signal, demonstrating effective compensation for channel impairments and substantial restoration of the constellation integrity.
## 2 Materials and Methods
### Measurement of NO Production on LPS-Stimulated RAW 264.7 Cells  
NO production was measured in cultured RAW 264.7 macrophage cells. These cells were maintained in DMEM, supplemented with 10% FBS, 100 U/mL penicillin, and 100 ug/mL streptomycin, at 37 \({}^{\circ}\)C in a 5% CO\({}_{2}\) incubator. The cells were seeded into 96-well plates at a density of \(2\times 10^{5}\) cells/mL, then incubated at 37 \({}^{\circ}\)C for 24 h. The cells were pretreated for 2 h with various concentrations of compounds **1**-**3** and **5**-**8** (20, 30, and 40 \(\upmu\)M) and compound **4** (10, 20, and 40 \(\upmu\)M). Then, the cells were stimulated for 18 h in the medium with 1 ug/mL of LPS. The nitrite concentration in the media was measured by the Griess reaction. Standard sodium nitrite solutions were prepared and a 100 \(\upmu\)L portion of Griess reagent {0.1% \(N\)-(1-naphthyl)ethylenediamine dihydrochloride in H\({}_{2}\)O and 1% sulfanilamide in 5% H\({}_{3}\)PO\({}_{4}\)} was mixed with 100 \(\upmu\)L of each supernatant. The absorbance of each solution was measured at 540 nm using a spectrophotometer (Bio-Rad, Hercules, CA, USA). The nitrite concentration was determined by comparison with a sodium nitrite standard curve. The percentage inhibition was calculated by the equation [1 - (NO level of test samples/NO level of vehicle-treated control)] \(\times\) 100. The 50% inhibitory concentration (IC\({}_{50}\)) was calculated from a nonlinear regression analysis (% inhibition versus concentration). L-NMMA was used as a positive control.
# Influence of Hyperparameters in Deep Learning Models for Coffee Rust Detection
## 6 Conclusions  
When realizing image classification solutions using models that learn from data, there are two main approaches: data-centric and model-centric. In the state-of-the-art of coffee leaf rust recognition, many solutions have been focused on data pre-processing, for example, by including patches from the raw images, deleting the background, and cropping the image, among others. However, little has been discussed about the impact of hyperparameters, and solutions have used values that are not necessarily the most appropriate for this specific problem (for example SGD as optimizer). For this reason, it is necessary to have exhaustive studies of the impact of hyperparameters that allow selecting the best ones for coffee leaf rust recognition using transfer learning. According to the results of the more than 800 trained models, the type of backbone used is the most important hyperparameter, since the variations between the models can reach 70% in terms of accuracy. In second place is the optimizer, with variations of up to 20% between the three optimizers used. Finally, the learning rate is the third most important hyperparameter, with variations of 10% in some cases, although its impact depends largely on the optimizer and the pre-trained network used as the backbone. Other types of hyperparameters, such as the number of neurons in the FC layer before classification and the depth of the model, had less impact with respect to the other three parameters evaluated. It is recommended to use 256 neurons due to the computational cost and shallower models with respect to the base models obtained by transfer learning.  
The presented methodology concludes with different suitable models based on different architectures, including practical considerations to be applied to coffee leaf rust classification problems on a diverse dataset. In consideration, and although model architectures are not defined as hyperparameters in the literature, they could be handled as one of them.
# Design, Deployment and Evolution of Heterogeneous Smart Public Lighting Systems
## 5 Network Architectures and Transmission Technologies  
The fundamental issue that needs to be addressed when designing a smart lighting infrastructure is how to establish a connection between each streetlight and the Control Center. Given the static nature of lighting infrastructures, both wired and wireless networks can be adopted. In the following we discuss both solutions, considering the related network architectures and communication technologies, having in mind the constraint that the Control Center must have the possibility to manage each light fixture individually.
# Anisotropic Effective Elastic Properties for Multi-Dimensional Fractured Models
## 5 Conclusions  
Numerical methods must be used to determine the effective elasticity of genuine fractures. We modify [PERSON]'s numeric FEM for real fractures. For 2D simple fracture models with different fracture densities, the 2D technique is compared to theoretical models ([PERSON]'s model and Eshelby-Cheng model). The results closely match the theoretical predictions at low fracture densities. The simulation method provides a physical prediction at high fracture densities that controls the overall elastic properties of rocks. When applied to a sample of natural fractures extracted from an outcrop at different frequencies, the stress interactions between cracks become highly complex. The stiffness matrix of \(M_{11}\) shows a trend as the frequency increases. With increasing frequency, the stress concentration spreads quickly to cover the network's high fracture density, intensifying the interactions and becoming intense. The effective elastic properties of the resulting real fractured model can be altered to understand fracture interactions and dynamic stress disturbances. The 2D finite-element numerical method is extended to 3D fractured media using the 3D stress-strain relationship defined by the six oscillatory relaxation simulation tests. A least-squares approach can be used to solve the relevant equations for unknown stiffness tensors. The 3D fractured media are compared to effective medium theories in terms of their elastic responses to different fracture densities. The fracture density is found to have an obvious effect on the anisotropic effective properties, and the proposed method gives a physical prediction for high fracture densities. This work has significance calculating the effective elastic properties of multi-dimensional fracture models and fracture interaction mechanisms. Conceptualization, S.J. and L.F.; methodology, [PERSON]; software, S.J. ; formal analysis, S.J. ; investigation, S.J. and Y.C. ; resources, S.J. and L.F.; data curation, S.J. ; writing--original draft preparation, S.J. ; writing--review and editing, S.J. ; visualization, S.J. ; supervision, L.F.; funding acquisition, L.F. All authors have read and agreed to the published version of the manuscript. This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA14010303) and the National Natural Science Foundation of China (Grant Nos. 41821002). Not applicable. The data are available from the corresponding and the first author. Thanks to the teachers and students of the FLY Team at the China University of Petroleum (East China). [PERSON] wants to particularly thank the patience, care, and support from [PERSON]. Thanks to the anonymous reviewers for their constructive comments, and thanks to the editors for their hard work. The authors declare no conflict of interest.
# GNSS-Assisted Visual Dynamic Localization Method in Unknown Environments
## 4 Dataset Validation
### Data Processing and Analysis
#### 4.1.4 Subsequent Camera Poses  
For the subsequent frames, after tracking and local optimization, the corresponding camera poses were sequentially obtained in terms of position and attitude in the motion capture coordinate system, as Figure 9 shows. The red meter sign indicates the GNSS initial-aided positioning for the subsequent frame position, the blue circle indicates the GNSS continuous-assisted positioning for the subsequent frame position, and the green asterisk indicates the real pose data.  
As Figure 9 shows, at the beginning stage, the GNSS initial-aided positioning trajectory, the GNSS continuous-assisted trajectory, and the true trajectory were nearly the same, which proved the effectiveness of the proposed method. However, with the passage of time, the GNSS initial-aided positioning trajectory appeared to drift obviously compared with the GNSS continuous-assisted trajectory. This was because for the GNSS initial auxiliary trajectory, the global position constraint was only effective at the beginning, and the overall accuracy degradation problem was subsequently manifested due to the accumulation of errors in the visual odometer itself.
## 3 Numerical Analysis Results and Discussion under Static Loading
### Vertical Displacements at the Top of the Wall Adutment
The deformation increases in every placement of backfill lifts and are then collated until the end of the abutment construction. Then, the FHR facing in the GRS-IB models was constructed after the GRS wall abutment had been completed. Therefore, the facing deformation during the construction affects greatly the vertical displacement behavior of the GRS-IB models, in addition to the RSF and backfill soil deformation. However, in the IB models, the FHR facing was constructed before the backfill soil lifts were placed. Hence, the vertical displacements obtained by the IB models are less affected by the facing deformation but more by the RSF and backfill soil deformation. In addition, the results show that the maximum _dy_ in the GRS-IB models are located within the first quarter of the reinforced zone or within 0.38 \(H\) from the wall facing, while in the IB models, the maximum _dy_ are located near the first one-third of the backfill soil zone or 0.62 \(H\) from the wall facing. Next, we discuss the GRS-IB versus GRS-IBS models. It is comparable that the GRS-IB (Case 1A-C) and GRS-IBS (Case 2A-C) models exhibited almost similar _dy_ behavior at the top of the wall abutment because these models have geosynthetic reinforcements, although with few reasonable distinctions. The results show that the maximum _dy_ in the GRS-IB models are located within 1.8 m to 2.4 m from the wall facing, while the maximum _dy_ in the GRS-IBS models are located at 1.8 m from the wall facing. Both the GRS-IB and GRS-IBS models showed the maximum _dy_ within the first quarter (0.25 \(L\)) to about one-third (0.34 \(L\)) of the reinforcement length (\(L\)). Hence, one of the few distinctions in the _dy_ behavior of the GRS-IB and GRS-IBS models can be observed from 0.5 m to 2.5 m from the wall facing. This is because for the GRS-IBS model, the bridge loadings are applied directly on top of the bridge seat zone (0.75 m to 2.25 m from the wall facing), which are 2.2-5.6 times greater than the roadway loadings. Another distinction in the _dy_ behavior can be observed at the wall facing. The GRS-IB models exhibited larger _dy_ at the wall facing because the bridge loadings were supported by the 0.90 m-thick FHR facing, whereas the GRS-IBS models had 0.50 m-width block facing that did not carry any external vertical loads. Lastly, it can be observed that after 3.0 m from the wall facing, the GRS-IBS models showed lesser _dy_ than those of the GRS-IB models. In this case, the _dy_ behavior was influenced by the backfill soil deformation during the construction. The GRS-IB models have greater backfill zone deformation than those of the GRS-IBS models because the GRS-IB models have weaker wall facing, thus exhibiting greater wall facing deformation.