esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Performance Metrics  
Four widely used evaluation metrics--accuracy, precision, recall, and F1-score--were employed to assess the classification performance of the proposed model. These metrics provide a comprehensive evaluation of the model's effectiveness in distinguishing between healthy and damaged maize plants, as well as in determining the severity levels of pest-induced damage.  
Accuracy measures the proportion of correctly classified instances relative to the total number of samples:  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{9}\]  
where:  
- \(TP\) (True Positives) represents correctly classified infected samples,  
- \(TN\) (True Negatives) denotes correctly classified healthy samples,  
- \(FP\) (False Positives) corresponds to healthy samples incorrectly classified as infected,  
- \(FN\) (False Negatives) denotes infected samples misclassified as healthy.  
Accuracy provides a general measure of model performance; however, in imbalanced datasets, it may not always be the most reliable indicator.  
Precision evaluates the fraction of true positive predictions among all instances classified as positive:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{10}\]  
A high precision value indicates fewer false positives, making it particularly important in scenarios where misclassifying healthy leaves as infected could lead to unnecessary interventions.  
Also known as sensitivity, recall measures the proportion of actual positive cases that were correctly classified:  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{11}\]  
A high recall value is essential in agricultural pest detection, where missing an infected plant could result in widespread crop damage if left untreated.  
F1-score provides a balanced measure of precision and recall, making it useful in cases where there is an uneven class distribution:  
\[F1\text{-}score=\frac{2\times\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{12}\]  
This metric is particularly beneficial for datasets where both false positives and false negatives have significant consequences.  
To enhance the robustness and generalizability of the model, five-fold stratified cross-validation was applied during performance assessment. Unlike standard k-fold cross-validation, stratified cross-validation ensures that each fold maintains the same class distribution as the original dataset, preventing any class imbalance from skewing the results.  
Additionally, since the dataset was imbalanced, Precision-Recall (PR) curves were also examined to provide deeper insight into the trade-off between precision and recall across different decision thresholds.  
By incorporating these comprehensive evaluation metrics, this study ensures that the proposed model effectively detects and classifies pest-induced damage in maize plants while minimizing misclassification errors.
## 4 Discussion
### Effects of Monitoring Station Density  
As the IMERG product is area-averaged and gauge observation is point time series, the representativeness of these two datasets are essentially different. This directly compared the gauge observation with the grid data in which the station is located. For a grid with more than one station, the precipitation metrics are calculated for each station, and then the averaged values for all the stations in this grid are used as the grid observation. Therefore, the IMERG product is certainly closer to the gauge observation for the grid with more meteorological stations. The evaluation results in this study showed IMERG performs differently over different regions, possibly due to the density of monitoring stations.  
Past works have illustrated that the station density is the major factor in the validation of grid precipitation product, especially in complex terrain areas [76]. To illustrate this point, the average number of meteorological stations within each 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) grid was calculated. It demonstrated that most of the grids have only one station, and few of them have two. The gauge observations in the grid with more stations are preferable to mitigate the inherently different between the grid product and point time series. It was shown that Europe (152 stations in 144 grids) and North Asia (1786 stations in 1436 grids) had the lowest and highest station densities, respectively. This situation could potentially hinder the evaluation of IMERG products in Europe as a whole. Therefore, the analysis outcomes would seem more reliable in the Chinese domain, where more stations are available to use for validation. Satellite precipitation products still have large uncertainty in estimating precipitation over areas with sparsely distributed stations [15].
###### Abstract  
The objective of this study is to investigate the effect of contouring the shoe insole on calcaneal pressure and heel pain in calcaneal spur patients. Calcaneal pressure was measured using three force sensors from 13 patients including three males and 10 females. These patients have plantar heel pain due to calcaneal spurs, and we examined five customized contour insole foot areas (0-100%). Sensors were attached at the central heel (CH), lateral heel (LH) and medial heel (MH) of the foot. The pain was measured using an algometer and evaluated by the pain minimum compressive pressure (PMCP). In this study, it was observed that the calcaneal pressure decreased with increasing insole foot area. In addition, increasing the insole foot area from 25% to 50% can reduce the calcaneal pressure approximately 17.4% at the LH and 30.9% at the MH, which are smaller than the PMCP, while at the MH, pressure reduced 6.9%, which is greater than the PMCP. Therefore, to reduce pain, one can use 50% insole foot area, even though at MH it is still 19.3% greater than the PMCP. Excellent pain relief was observed when using 100% insole foot area, as the pressures in those three areas are lower than the PMCPs, but it is not recommended because it requires large production costs.  
calcaneal spur; pain minimum compressive pressure; contour of shoe insole; insole foot area +
Footnote †: journal: Applied Soft. 2019, 9, 495; doi:10.3390/app9030495
## 4 Results and Discussion
### Yearly Trend of SUHI Footprint and Magnitude
The activities and transportation in larger urban areas, such as BG and CM, are expected to generate greater amounts of anthropogenic heat release, contributing to the rise of the daytime a0 values for BG and CM with respect to the smaller cities. The lack of correlation for Ar61% and a0 with the urban sprawl over the years, except for the slight increase of BG and CM daytime footprints, can be ascribed to different mechanisms underlying the SUHI phenomenon [46]. A possible reason is linked to the outskirt sprawl. Considering the LULC Landsat maps and overlapping the different yearly scenes of Figure 2, it emerges that the new outskirt areas have a lower density of built-up pixels than the city core, especially for the small cities. To better understand how the growth or modifications of a city affect the SUHI patterns, different factors at large and fine spatial scales should be considered. Some works found that urbanization warmed the LST on an annual mean scale, regardless of urban area size [47,48]. The size influence on SUHI depends on urban compactness: stretched or disperse cities are preferable for urban heat island mitigation [6]. Outskirts and suburban areas, if not densely urbanized, contain wide rural patches containing soil moisture, thus helping reduce the warming rate of the suburban zones [49]. Therefore, the results in Figures 11 and 12 confirm the background climate influence on the heating  
Figure 12: UR, KK, and UT yearly trend (2003–2016) of the urban area computed from LULC Landsat maps (black line, upper panel), daytime Ar61% (red line, upper panel), nighttime Ar61% (blue line, upper panel), daytime a0 (green line, lower panel), and nighttime a0 (magenta line, lower panel). trend over the years and suggest that the weak SUHI-urban growth relationship is ascribed to a lack of compactness in the urban sprawl, especially for the smaller cities investigated. It is interesting to highlight other specific drivers of SUHI arising in the urban expansion and modification. Urbanization causes land cover changes, but the analysis of the urban heat island pattern should consider the heterogeneity of the new urban surfaces (materials and uses) and associated LST dynamics, as reported for Bangkok in Bangkok (2018). Land cover changes from rural to urban affect the daytime and nighttime SUHI in different ways, depending on the rural category (grassland, cropland, shrub, forest) [50]. In the study cases, the rural surroundings exhibit a marked land cover variability with evident seasonal surface thermal changes. Factors related to the urban texture at district level that affect the SUHI phenomenon--driving the heat trapping and absorption--rely on surface albedo, material, and color; on building layout, slope, and height; and on greenery cover [51; 52; 53; 54]. At the same time, the urban texture can intensify the warming effect by reducing the wind circulation [55]. These factors suggest how the relationship SUHI-urban growth can show different trends and correlations, depending on the city specificity.
# FLATSIM: The ForM@Ter LArge-Scale Multi-Temporal Sentinel-1 InterferoMetry Service
## 3 FLATSIM Architecture
### Architecture of the CNES HPC Infrastructure  
The CNES HPC infrastructure is a shared facility at the disposal of CNES projects and partners. It is composed of more than 480 batch servers for a total of 12,288 cores (725 Tflops) and a 8.2 PB general parallel file system (GPFS) with 300 TB burst buffer offering a bandwidth up to 100 GB/s. This large computing capacity should enable FLATSIM to meet its production objectives. The integration of NSBAS should also be optimized to the CNES HPC, to avoid FLATSIM deteriorating the level of service for all the other users of this computing center.  
FLATSIM also relies on various services offered by CNES computing division, such as different types of data storage, database management system (DBMS) hosting services, and website hosting mutualized services.
## 3 Experiment Results  
The architecture of the measurement setup is illustrated in Figure 7, comprising two horn antennas operating in the 2-18 GHz frequency range connected to a Vector Network Analyzer (VNA) and aligned with the center of the metasurface. Additionally, a PEC plane with the same dimensions as the chessboard metasurface is fabricated to serve as a reference. Using the aforementioned system, the transmission coefficients of the PEC and chessboard metasurface are measured separately. By subtracting one from the other, the RCS reduction can be obtained.
# Design, Modeling, and Analysis of Piezoelectric-Actuated Device for Blood Sampling
## 5 Conclusions  
The work aimed to design a wearable device for blood sampling, consisting of seven micropumps to be used seven times for blood sampling. Blood diagnostic requires a definite amount of blood. A micropump should be capable of providing the same. Towards achieving higher deflection, a new design of four quarter piezoelectric bimorph (FQPB) actuator was analyzed. FQPB showed better performance in terms of deflection, effect of reduction in actuation voltage, and frequency. Numerical simulations were carried out on the micropump with the help of ANSYS software. For further improvement in operational parameters, in terms of energy requirement and time of actuation, the micropump was improved to have an extended microneedle length of 0.25 and 0.5 mm inside the chamber, along with the specific directional flow, resulting in enhanced flow. The flow rate of 1.256 \(\mu\)L/s was obtained and compared with the flow rate requirement of blood diagnostic devices to realize the device as a product. The following conclusions were drawn from the present research.  
1. The design may be implemented as a wearable device. Considering advancements in the manufacturing of microfluidic systems, manufacturability should not be a problem.
2. The design may be modified to accommodate more sensors for testing other diseases.
3. The FQPB-based micropump with extended microneedle length inside the pump chamber brings the benefit of flow channelization towards the biosensor location, resulting in a reduction in applied voltage (2.5 V) and reduced operating frequency (22 kHz).
4. The simulation results showed that 1.256 \(\mu\)L of the sample was collected at the biosensor in 1 s, which is more than the required volume.
5. The designed device will be easy to use and may allow multiple samplings with a single replacement.
6. The blood was collected in a closed chamber, thereby giving higher accuracy in comparison to the finger-stick, as well as preventing environmental contamination.  
R.K.H.: Conceptualization, Methodology, Software, Writing Original Draft, Visualization, Investigation, and Original draft preparation. V.K.G.: Conceptualization, Methodology, Supervision, Software, Validation, Visualization, and Validation of research methods. T.S.: Conceptualization, Methodology, Supervision, Writing, Software, Reviewing, and Editing. I.A.P.: Review, Editing, and Supervision. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the grant from the Ministry of Science and Higher Education of Russia supported by Southern Federal University, grant No. VnGr-07/2020-04-IM.  
Figure 6: Streamlines plot with the 0.50-mm extended microneedle.
## 4 Empirical Study and Result
### Analysis of Merging Behavior in Different Work Zones Layouts
#### 4.3.1 Merging Location  
The merging location distribution at Site A3 is identical to that of Site B4, but the average distance in the tail of A3 is about 70 m closer to the taper. Though no significant proportion of vehicles makes an early lane change like Scenario 1, the majority of the merging location distribution lies in the range from 50 m to 150 m, as shown in Figure 8. The presence of the solid white line does not advance the overall lane change position. At Site A4, although the sample size is small, most vehicles tend to merge early, and the trend is the most apparent among all the sites. To generalize analysis results, we choose Site B4 as the representative of Scenario 1 because Site B4 has the largest sample size among all areas in the basic scenario and is within a similar traffic volume range to Site A3.  
The merging location distribution of Site A3 is notably similar to that of Site B4, with the primary difference being that vehicles at Site A3 tend to merge approximately 70 m closer to the taper. This slight variation can be attributed to the differences in traffic conditions or road layout. Interestingly, despite the presence of a solid white line at Site A4, which typically limits early lane changes, a large proportion of vehicles still tend to merge early. This suggests that, even with road markings discouraging lane changes, drivers in low-density traffic prefer to merge as soon as they perceive a safe opportunity. The distribution pattern from 50 m to 150 m observed across several sites reinforces this trend. To ensure representativeness, we chose Site B4 as a key reference for Scenario 1 due to its  
Figure 8: Merging location distribution of Scenario 2 and Scenario 3. Vehicles at Sites A3 and B4 tend to merge earlier, while at Site A4, merging occurs closer to the taper, likely due to the smaller sample size and traffic volume.  
large sample size and similar traffic volume to Site A3, allowing for more robust analysis across comparable conditions.
###### Abstract  
Topology optimization is a powerful numerical tool in the synthesis of lightweight structures and compliant mechanisms. Compliant mechanisms present challenges for topology optimization, as they typically exhibit large displacements and rotations. Path-generation mechanisms are a class of mechanisms that are designed to follow an exact path. The characteristics of compliant mechanisms therefore exclude the validity of linear finite-element analysis to ensure the proper modeling of deformation and stresses. As stresses can exceed the limit when neglected, stress constraints are needed in the synthesis of compliant mechanisms. Both nonlinear finite-element analysis as well as the consideration of stress constraints significantly increase computational cost of topology optimization. Multiresolution topology optimization, which employs different levels of discretization for the finite-element analysis and the representation of the material distribution, allows an important reduction of computational effort. A multiresolution topology optimization methodology is proposed integrating stress constraints based on nonlinear finite-element analysis for path-generation mechanisms. Two objective formulations are used to motivate and validate this methodology: maximum-displacement mechanisms and path-generation mechanisms. The formulation of the stress constraints and their sensitivities within nonlinear finite-element analysis and multiresolution topology optimization are explained. We introduce two academic benchmark examples to demonstrate the results for each of the objective formulations. To show the practical, large-scale application of this method, results for the compliant mechanism structure of a droop-nose morphing wing concept are shown.  
topology optimization; compliant mechanisms; nonlinear finite-element analysis; path generation; stress constraints; multiresolution; sensitivity analysis +
Footnote †: journal: Applied Sciences  
1
## 3 Results
### The Impact of Drought on GPP, SIF, and VIs  
To assess the impact of drought indicators (SPEI, SM, PPT, and LST) on VIs (GPP, SIF, SIF\({}_{\text{yield}}\), and NDVI), we employed Pearson to examine the relationships of VIs with drought indicators. As shown in Table 3, GPP, SIF, SIF\({}_{\text{yield}}\), and NDVI exhibited the highest correlations with PPT, with correlation coefficients (**r**) of 0.91, 0.91, 0.82, and 0.85, respectively. There were significant correlations between GPP, SIF, SIF\({}_{\text{yield}}\), NDVI, and PPT. Except for SIF\({}_{\text{yield}}\), GPP, SIF, and NDVI also demonstrated strong correlations with SM, with r of 0.46, 0.41, and 0.53, respectively. However, apart from GPP, there was no significant correlation between SIF, SIF\({}_{\text{yield}}\), NDVI, and SPEI. These findings are generally consistent with our expectations. The significant correlations between VIs (GPP, SIF, SIF\({}_{\text{yield}}\), and NDVI) and PPT, LST, and SM can be attributed to the fact that during drought periods, reduced precipitation leads to soil moisture deficit. SM directly supports water consumption in the plant growth process, and drought events are often accompanied by increased temperatures. On the other hand, the relatively low correlation between VIs (GPP, SIF, SIF\({}_{\text{yield}}\), and NDVI) and SPEI is because meteorological drought indices do not have a direct relationship with vegetation physiology.  
Figure 7: (**a–c**) The relationships between the measured values of monthly solar-induced fluorescence (SIF), SIF\({}_{\text{yield}}\), NDVI, and the measured values of gross primary productivity (GPP) in the Xilingol grassland. (**d–f**) The relationships between the monthly anomalies of SIF, SIFyield, and NDVI and the monthly anomalies of GPP. Each point represents a monthly average value for the entire study area. There are 12 (months/year) \(\times\)9 (years) = 108 points from 2007 to 2015.
## 4 Description of the System  
The objective of the proposed system is to facilitate the use of public transit by highway for people with special needs in non-urban areas, such as rural areas. Therefore, the system is capable of operating in geographic locations that lack basic infrastructure, such as electricity and communications. The system utilizes resources that are commonly available in highway public transit infrastructure, in which the resources available in fleet vehicles serve a primary role. Using these resources and applying principles of Ubiquitous Computing, the system provides intelligent environments in different contexts of the transit network (vehicles, stops, and stations) that facilitate access and the safe use of transportation for people with special needs. These smart environments furnish a series of services that naturally and autonomously interact with the user, _i.e._, they adapt to their needs and transparently operate in the transport infrastructure, affecting existing devices and services in the transportation network as little as possible.  
The system is designed to be deployed in the context of intercity public transportation; more specifically, in the key components of a transit network (stations and stops) and in vehicles (see Figure 1). Stations are basic components of the system architecture because they are the points where vehicles, using Wi-Fi communication infrastructure, update their data and on-board applications. These data represent the transit network (routes, stops and time control points) and the vehicle's planned operations (start of line service and stop schedules), with spatial references (geographic coordinates). The data are used to provide services. In addition, data describing all relevant events that occur during vehicle operation are also downloaded. Since the stops are the physical locations at which passengers embark and disembark, the system considers them to be elements that contain static information (geographic coordinates, lines that pass through the stop, scheduled stop times, equipment and furniture available at the stop, and information on the degree of accessibility for travelers with special needs) and dynamic information (next bus to stop, estimated arrival time and operating bus lines, and issues that affect the use of the stop, _i.e._, stop out of service or stop moved to another point on the network). With regard to the vehicles, they are a key element of the proposed system because they enable services to be deployed anywhere in the network using their infrastructure. The status of each vehicle is represented at all times by its geographical position, the speed at which it is moving, its present operation (in service, line service, last stop it passed through, next stop, travelers on board and their planned journeys, _etc._), and the technical condition of its equipment. Using this information and the on-board equipment, the various services are deployed at all the places through which it passes (route information systems, smart payment systems, surveillance systems in vehicles and stops, _etc._).