esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impact of Cloud Versus Local Infrastructure on Automatic IoT-Driven Hydroponic Systems
## 3 Results
### Hardware Comparison Infrastructure Between AFCI and LSI  
The hardware infrastructure includes the same equipment for both AFCI and LSI. The authors proposed using M5 Stack equipment and sensors to monitor hydroponic cultures. The module was programmed for both types of networks using Arduino IDE, utilizing the logical schema from Figure 7. This logical schema highlights a comparative analysis of the distinctive elements between AFCI and LSI. In Figure 7a, it can be observed that the request enabling data transmission to the cloud is made using Message Queuing Telemetry Transport (MQTT) technologies, whereas in the case of LSI, Figure 7b, the request is made via Hypertext Transfer Protocol (HTTP).  
Figure 7a,b show that the hardware structure is not fundamentally altered between the two approaches. The controlling element is the nutrient pump activated by the processing computer. The process computer used in these tests is a Hewlett Packard personal computer (PC), as it has small dimensions, integrated Wi-Fi capability, and connectivity features with other devices. The functions performed by the PC differ between the two configurations. In the case of AFCI, the process computer runs a cron job that checks if a flag is set in the database stored in the cloud to trigger the activation of the pump. In the case of LSI, the  
process computer performs many functions, hosting the database, the ML model, and the cron job that runs the database's flag check to control the pump's activation.  
Figure 8 presents the hardware structure of the monitoring equipment, which includes the DS18B20 temperature sensor, the DHT22 temperature and ambient humidity sensor, the FC-28 electrical conductivity sensor, the SEN18 liquid level sensor, and the M5 Stack monitoring unit. The unit is responsible for data acquisition and transmission to the data server for both methods.  
Figure 8: Hardware configuration for the monitoring system.  
Figure 7: Comparative logical schema of data transmission for: (**a**) AFCI; (**b**) LSI.  
The constituent elements of the AFCI and LSI proposals are identical at the hardware infrastructure level. The difference between the two is exclusively at the software infrastructure level.
## 4 Results and Discussion
### Validation
#### 4.3.1 Validating the Capacity to Identify the Environment  
Figure 10 represents the measurements taken in the sensors embedded in the three manufactured columns (300 samples in all). The clusters of samples are distributed in the graph according to the environment to which the sensor was exposed, except for the 11 samples surrounded by the dotted line. With this model, and following the protocol implemented in the INESSCOM system, it was possible to identify the different concrete conditions: no aggressive agents, carbonation, and chlorides present. The success rate was 96.4%.
## 1 Introduction
Based on controlling systematic errors in the honing process and external disturbances, the honing process and the parameters that characterize the properties of the honing wheel are considered key elements affecting honing quality. Based on the above literature and existing research foundation [13; 14; 15; 16], the honing process parameters mainly include honing speed, honing depth, feed rate, and cross-axis angle, and the main factors characterizing the surface morphology of the honing wheel are abrasive grain size, shape of abrasive grain, effective number of abrasive grain, clearance between abrasive grains, and grinding cutting angle. Some studies showed that the dimensional wear of grinding wheels is the main factor affecting the accuracy of gear machining. Honing of teeth is a generative machining process that requires high dimensional accuracy of honing wheel surfaces. The critical factors affecting the dimensional wear [17; 18] of grinding wheels include abrasive grain shedding, abrasive grain fracturing, and abrasive grain abrasion. The 12 parameters listed above were selected as key factors affecting the quality of honed teeth. At present, research on honing quality is primarily evaluated from the aspects of tooth shape accuracy, tooth surface quality, and honing efficiency. Tooth shape accuracy includes helix error, tooth pitch error, and tooth profile error. Surface roughness affects tooth wear resistance, fit stability, corrosion resistance, etc., which is often used to evaluate surface quality. Compared with other gear finishing processes, the unique surface texture of honing can reduce transmission noise and vibration [19], and the surface residual stresses can significantly increase service life [20]. Therefore, the research on the quality of honed tooth surfaces focuses on three factors: surface roughness, residual stresses, and tooth texture. Honing efficiency is characterized by the ratio of material removal rate to machining time. Existing studies on the quality of honed teeth mostly analyze individual factors without considering a comprehensive evaluation of multiple factors. Based on the above shortcomings, the study introduces a comprehensive evaluation method for multiple factors.
# Vegetation Health in China Is Severely Compromised by Drought, Wet and Heat Stress Events
## 4 Discussion  
### Evaluation of the Effectiveness and Trend Changes of Vegetation Health Assessment Systems  
This study developed a comprehensive vegetation health assessment system to evaluate the health status of vegetation in China. We constructed vegetation health systems using CIM, TOPSIS, and PSR models, and we conducted fitting analysis with LAI, NPP, and SIF. To reveal the robustness and applicability of the models at different time points, this study systematically and comprehensively evaluated the optimal model for constructing a vegetation health system using a combined analysis of \(R^{2}\) and Pearson correlation coefficients (\(r\)). In nonlinear fitting, \(R^{2}\) was used to assess the effectiveness of the vegetation health models. The results indicated that the overall \(R^{2}\) of the PSR model was higher than that of the CIM and TOPSIS models. Specifically, the PSR model better captured the relationships between VHI and LAI, NPP, and SIF, whereas the CIM and TOPSIS models relied more on linear assumptions, potentially overlooking key nonlinear mechanisms in vegetation health responses. Particularly for the SIF indicator, the PSR model achieved an \(R^{2}\) as high as 0.74 (2001). Therefore, the PSR model demonstrates stronger reliability in constructing VHI evaluations. Compared to models CIM and TOPSIS, the PSR model stands out for its comprehensiveness and systematic approach. The CIM often focuses on specific causal relationships, potentially overlooking multidimensional influencing factors, while TOPSIS prioritizes ranking alternatives based on similarity, which, although useful for decision making, fails to fully account for the interactions and feedback across different levels in ecosystems affected by multiple environmental factors. Through its hierarchical analytical structure, the PSR model better captures the complex relationships among various pressure sources, vegetation states, and responses, thereby providing a more accurate and comprehensive assessment of vegetation health. Thus, the vegetation health assessment system based on the PSR model was used for subsequent research.  
The analysis revealed significant trends in VHI. In the Tibetan Plateau and northern arid regions, VHI showed a significant decline with the maximum change trend being \(-0.002\cdot\mathrm{a}^{-1}\). Conversely, the Yunnan-Guizhou Plateau, South China, and the Loess Plateau exhibited an increasing VHI trend. This indicates that increased precipitation has provided favorable conditions for vegetation growth and recovery in these regions [45].
## 3 Results and Discussion
### Analysis of the Surface Temperature Distribution
From the results shown in Figures 3 and 4, it can be concluded that the difference in the maximum temperature reachable at all locations throughout the computational domain is smallest when the zig-zag pattern and 50% overlap ratio are used. This indicates that the most uniform heating outcome can be realized when these conditions are used. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test No.** & **Heating Rate** & **Heating Rate Difference** & **Cooling Rate** & **Cooling Rate Difference** \\  & **(\({}^{\circ}\)C/ms)** & **According to Overlap (\%)** & **(\({}^{\circ}\)C/ms)** & **According to Overlap (\%)** \\ \hline
1 & 227 & 130 & 52 & 150 \\
2 & 521 & 130 & 130 & 150 \\
3 & 227 & 130 & 52 & 150 \\
4 & 521 & 130 & 130 & 150 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Heating and cooling rate comparison (calculated with the first peaks in the graphs in Figure 5). Figure 3: Temperature at the center of the beam spot as a function of time: (**a**) test 1, (**b**) test 2, (**c**) test 3 and (**d**) test 4 (red and blue circle in the figure indicate the end of the 1 st track and the start of the 2 nd track, respectively). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test No.** & **Temperature Change (AT \({}^{\circ}\), \({}^{\circ}\)C)** & **Rate of Change \({}^{\circ}\) (\%)** & **Increase or Decrease** \\ \hline
1 & 74 & 5.5 & Increase \\
2 & 96 & 7.2 & Increase \\
3 & 787 & 58.9 & Decrease \\
4 & 658 & 49.2 & Decrease \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Temperature change induced by the corner zone of the beam path.
# Real-Time Terrain-Following of an Autonomous Quadrotor by Multi-Sensor Fusion and Control
## 5 Conclusions  
This study investigated the position, attitude, velocity and height estimators based on GPS, accelerometers, gyroscopes and Lidar-lite onboard sensors, which were operated on a quadrotor of the Pixhawk open-source autopilot. For the terrain-following of the quadrotor,  
Figure 12: Comparison of the cascade PID height controllers for the quadrotor above a cardboard box on the ground.  
we have presented real-time height control, navigation and obstacle avoidance above outdoor undulant ground. In our system, GNSS and INS information were used to verify the position, velocity and attitude estimates with a constant height control for autonomous flights. The navigation and estimations depended on an available multi-sensor fusion. Apart from the absolute altitude estimation, the terrain-following required relative distance measurements from the Lidar-lite sensor. The proposed control algorithm was tested extensively in MATLAB Simulink simulations and then implemented on the real-world quadrotor vehicle. The results confirmed that the proposed combination of the multi-sensor fusion and cascade PID controller could ensure autonomous navigation, obstacle avoidance and height control, achieving better accuracy, response time and sensitivity. Future developments will fuse trajectory and velocity smoothing that enable the balance between control efficiency and terrain-following criteria.  
Conceptualization, Y.Y. and X.L.; Data curation, Y.H.; Formal analysis, Y.Y.; Funding acquisition, H.Y.; Methodology, Y.Y.; Resources, H.Y., Z.W. and X.L.; Software, T.Z.; Supervision, H.Y. and T.Z.; Validation, Y.H.; Visualization, T.Z. and Z.W.; Writing--original draft, Y.Y., Y.H. and T.Z.; Writing--review and editing, X.L. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the projects of the National Natural Science Foundation of China (Grant Number: 51979041, 61973079 and 61802428) and Zhishan Youth Scholar Program of Southeast University 2242020R40136.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The following are available online at [[https://github.com/yangyuancsi/Altitude-control](https://github.com/yangyuancsi/Altitude-control)]([https://github.com/yangyuancsi/Altitude-control](https://github.com/yangyuancsi/Altitude-control)): PID: code for the PID controllers, Altitude estimation: code for the Kalman filter, Video: video for carton box avoidance experiment. The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
## 6 Stability Analysis of Surrounding Rock Based on Refined Modeling
### Analysis of Stress Evolution
#### 6.4.2 Stress Analysis of KH1 Profile  
As shown in Figure 21, under the combined mining conditions of the two sublevels, an unloading zone is formed, and the maximum tensile stresses in the inter-pillars are 0.87 MPa, 0.86 MPa, and 0.81 MPa, respectively, all below 1 MPa. As the thickness of the isolation pillar in the 220 m Sublevel increases, the maximum tensile stress in the inter-pillars gradually decreases, indicating better stability. This shows that increasing the thickness of the isolation pillars helps improve the stability of the inter-pillars. The pillars, inter-pillars, and isolation pillars are primarily compression zones, with the isolation pillars bearing greater compressive stress. When no isolation pillars are set in Phase II, localized areas of excessive compressive stress occur in the 340 m Sublevel isolation pillar, and excessive compressive stress areas appear at the junction of the bottom of the 100 m Sublevel and the ore body faults. This indicates that the stability in these areas is poor, and plastic deformation is likely to occur. When the isolation pillars are set, and as their thickness increases, the average compressive stress of the isolation pillars between Phase I and Phase II gradually decreases, and stability gradually improves. However, stress concentration zones are still likely to form at the junctions of the isolation pillars and ore body faults, reducing the stability of the isolation pillars and affecting the stability of the surrounding rock near the slopes, pillars, and faults.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Isolation Pillar Thickness (m)** & **Range of Minimum Principal Stress (Pa)** & **Range of Maximum Principal Stress (Pa)** \\ \hline
0 & \(-6.6922\times 10^{7}\)–\(1.4813\times 10^{4}\) & \(-2.3786\times 10^{7}\)–\(1.0002\times 10^{6}\) \\
10 & \(-6.0441\times 10^{7}\)–\(9.3152\times 10^{4}\) & \(-2.1936\times 10^{7}\)–\(9.2852\times 10^{5}\) \\
20 & \(-4.5129\times 10^{7}\)–\(2.6060\times 10^{4}\) & \(-2.1475\times 10^{7}\)–\(8.4438\times 10^{5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Range of maximum and minimum principal stresses for different isolation pillar thicknesses in profile KZ1.  
Figure 21: Stress evolution analysis of KH1 profile. (**a**) Minimum principal stress with 0 m isolation pillar; (**b**) maximum principal stress with 0 m isolation pillar; (**c**) minimum principal stress with 10 m isolation pillar; (**d**) maximum principal stress with 10 m isolation pillar; (**e**) minimum principal stress with 20 m isolation pillar; (**f**) maximum principal stress with 20 m isolation pillar.  
The minimum and maximum principal stress ranges for the KH1 profile were summarized, and the variation pattern is the same as that of the KZ1 profile. The summarized results are shown in Table 7.
# Recognition of Tunnel Fracture Zones in Seismic Waves and Ground-Penetrating Radar Data
## 5 Discussion  
The seismic wave data and ground-penetrating radar data were combined to identify the fracture zones of the tunnel, and they are consistent with the tunnel excavation results, which ensures the feasibility of the method. However, the seismic wave method may not be able to provide sufficiently fine information for smaller-scale fracture zones or fine structures when detecting the fracture zones of the tunnel-surrounding rock. The high water saturation of the rock mass has a certain absorption and attenuation effect on the energy of seismic and electromagnetic waves. This results in a gradual loss of energy during propagation, reducing the resolution and detection capability. At the same time, fluctuations in the water cause the scattering of seismic and electromagnetic waves, which complicates the waveforms. This complicates the interpretation and analysis of fracture zones.
# Impact of Putting on Traffic Safety: A Synthesis of Research Findings
## 3 Results and Discussion
### Description of the Retrieved Articles
#### 3.1.1 Investigation Approaches  
Two main approaches were utilized in the reviewed studies to investigate the relationship between rutting and traffic safety: simulation and field observation approaches. The simulation methods employed included mathematical models [25; 26], finite element models [27; 28], and vehicle dynamics models [29; 30]. Field observation studies typically employed regression models such as negative binomial regression [9; 18; 19; 20], zero-inflated Poisson regression [20], maximum likelihood estimation of a Poisson regression [15], logistic regressions [21], seemingly unrelated regression equations [22], and random-parameters negative binomial regression [23]. However, the use of machine learning algorithms was lacking across the reviewed studies. The field observation studies were conducted in eight countries: Germany, Sweden, Norway, Australia, New Zealand, the USA, Japan, and China.  
Field observation studies vary in their approach to analyzing rutting, either in isolation or as part of a broader investigation. Some studies [8; 9; 11; 19] have focused exclusively on the relationship between accident rates and rut depth or considered only a limited range of contributing factors. This approach introduces methodological limitations that can challenge the validity of the findings, particularly when working with small datasets. Overlooking the influence of other variables while lacking the ability to ensure their uniformity or negligible impact weakens confidence in the derived conclusions. Conversely, a few studies [20; 22] have adopted a more comprehensive approach, examining the impact of rutting alongside other relevant factors such as traffic characteristics, environmental conditions, and additional pavement attributes, including road geometry and skid resistance.
## 3 Results and Discussion
### Material Characterization
#### 3.1.5 Scanning Electron Microscopy (SEM)  
Figure 4 shows the scanning electron microscopy (SEM) micrographs of the Hap series at 500X and 1500X magnifications.  
The sample synthesized using a manually ground calcium precursor (Hap-H1) at 500X magnification (a) reveals monolithic agglomerates of varying sizes and shapes, displaying more uniformity compared to other samples (c, e, and g). At 1500X magnification (b), these monolithic agglomerates are observed to be covered by clusters of smaller particles.  
For the Hap-H2 sample, generated using a calcium precursor processed through high-energy milling, agglomerates of diverse sizes and shapes are observed, with a more pronounced level of heterogeneity than in the Hap-H1 sample (b). At 1500X magnification (d), these agglomerates are observed to be sparsely covered by clusters of smaller particles, further highlighting the unique characteristics of this sample.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID Sample** & **S\({}_{\text{BET}}\) (m\({}^{2}\)/g)** & \
\begin{tabular}{c} **Pore Volume** \\ **BJH (cm\({}^{3}\)/g)** \\ \end{tabular} &
\begin{tabular}{c} **Pore Diameter** \\ **BJH (nm)** \\ \end{tabular} \\ \hline Hap-H1 & 4.51 & 0.05 & 35.16 \\ Hap-H2 & 24.72 & 0.41 & 36.73 \\ Hap-H3 & 41.46 & 0.47 & 31.63 \\ Hap-H3 750 & 128.41 & 0.86 & 20.95 \\ \hline \hline \end{tabular}  
*S\({}_{\text{BET}}\): The specific surface area calculated using the BET method.  
\end{table}
Table 1: Physisorption Isotherms of Hap Samples.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**ID Sample** & **ZP (mV)** \\ \hline Hap-H1 & \(-\)18.6 \\ Hap-H2 & \(-\)19.1 \\ Hap-H3 & \(-\)21.9 \\ Hap-H3 750 & \(-\)22.6 \\ Hap-FCF & \(-\)22.8 \\ \hline \hline \end{tabular}  
\end{table}
Table 2: Average Zeta Potential (ZP) of the Hap Series and Hap-FCF Composite.  
When the calcium precursor was sieved to 8 \(\upmu\)m, the resulting hydroxyapatite sample (Hap-H3) exhibited laminar monoliths of varying sizes and shapes. The agglomerates observed are smaller than the Hap-H1 and Hap-H2 samples (e). The 1500X magnification micrograph provides a closer view of the size heterogeneity of these agglomerates, where smaller particles predominate. Upon subjecting this calcium precursor to a crucial heat treatment at 750 \({}^{\circ}\)C, the resulting sample (Hap-H3 750) exhibits a notable reduction in laminar monolith size, with enhanced disaggregation and size uniformity compared to its counterpart, Hap-H3 (g and h).
###### Abstract  
Research on gold nanoparticles (AuNPs) has often focused on their physical, chemical, and crystalline characteristics. Commercial AuNPs have been applied in the diverse fields of biomedicine, catalysis, photovoltaics, and sensing. In this study, we explored the various activities of AuNPs to widen their applicability. This paper presents a simple and rapid synthesis process of AuNPs with bacteria isolated from a gold mining area. We also investigated the optimization of reaction parameters for AuNP synthesis. The study results revealed that among the isolated strains, _Bifidobacterium lactis_ and _Escherichia coli_ demonstrated the highest capabilities of AuNP synthesis. The optimal pH values for AuNP synthesis by _B. lactis_ (BLAuNPs) and _E. coli_ (ECAuNPs) were 5.0 for 72 h of incubation and 8.0 for 24 h of incubation. The average particle sizes of ECAuNPs and BLAuNPs were 4.2 and 5.6 nm, respectively. Furthermore, these biogenic AuNPs were found to be stable with no aggregation after 3 months of storage. BLAuNPs and ECAuNPs exhibited high levels of antimicrobial, antioxidant, photocatalytic, and antityrosinease activity. Moreover, they were noncytotoxic to skin cells even at 100% melanin inhibitory concentrations. Considering the demonstrated multifunctional activities of AuNPs, BLAuNPs and ECAuNPs have promising potential for commercialization.  
antioxidant activity; CCD-966 SK cells; cytotoxicity; gold nanoparticles; melanin +
Footnote †: journal: Applied Sci. 2021, 11, 3670. [[https://doi.org/10.3390/app11083670](https://doi.org/10.3390/app11083670)]([https://doi.org/10.3390/app11083670](https://doi.org/10.3390/app11083670))
## 2 Materials and Methods
### Machine Learning Methods
#### 2.4.2 Linear Discriminant Analysis (LDA)  
Linear discriminant analysis (LDA) is a supervised learning method that performs both classification and dimensionality reduction [18]. LDA converts the initial D-dimensional feature space into a lower-dimensional subspace (D\({}^{\prime}\), where D \(>\) D\({}^{\prime}\)) for classification tasks. By increasing inter-class variation and decreasing intra-class variance, this projection seeks to improve class separability.
# Fault ride-Through Control Strategy for Variable Speed Pumped Storage Unit with Full-Size Converter
## 4 Fault Ride-Through Control Strategies
### Fault Ride-Through Requirements  
At present, research on fault ride-through requirements is mainly focused on wind turbines. However, there are no relevant requirements for the FSC-VSPSU. The complementary operation of pumped storage and renewable energy is an effective way to address issues such as power fluctuations. In order to maximize support for renewable energy, it is required that the fault ride-through ability of the FSC-VSPSU is superior to that of renewable energy.  
Figure 6 compares the LVRT and HVRT characteristics of the proposed FSC-VSPSU with those of wind turbines. Specifically, Figure 6a illustrates the LVRT characteristics. The FSC-VSPSU should sustain operation for 625 ms at a voltage drop to 15% of the rated voltage at the grid connection point and continue without disconnection if grid voltage recovers to 85% of the rated voltage within 2 s.  
Figure 6b illustrates the HVRT characteristics of the FSC-VSPSU. At voltage increases to 130-135% of the rated voltage at the grid connection point, it should continue operating for 1 s without grid disconnection. At 125%, it should maintain operation for 9 s. At 115% of the rated voltage, the FSC-VSPSU can operate indefinitely.