esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Agriculture Applications Using Deep Learning Technologies: A Survey
## 3 Deep Learning
### Convolutional Neural Network (CNN)  
A CNN is a deep learning algorithm composed of multiple convolutional layers, pooling layers, and fully connected layers [9]. It is a multi-layer neural network based on the animal visual cortex [10]. CNNs are mainly used for image processing and handwritten character recognition. In several computer vision studies, CNNs have been used for image classification, object detection, fragmentation of images, voice recognition, text and video processing, and medical image analysis, among other functions. A CNN architecture typically consists of convolutional, pooling, and fully connected layers [11]. Figure 1 illustrates the architecture of CNNs, and the layers are explained briefly:
## 3 Retrieval
### Setup
#### 3.1.7 Parameter Vector \(\vec{b}\)  
The state vector includes only a small subset of geophysical and instrumental properties that influence a simulated radiance measurement. All these additional properties are assumed to be known and form the parameter vector \(\vec{b}\).  
The observation geometry (particularly, the solar and satellite zenith angles \(\theta_{0}\) and \(\theta\)), Earth/Sun distance, Doppler shifts, ILS, measurement wavelength grid, etc. are used as provided or calculated from data in the satellite L1b orbit files. Atmospheric temperature, pressure, and dry-air sub-column profiles are obtained from ECMWF analysis data. Gaseous absorption cross sections are calculated from NASA's (National Aeronautics and Space Administration) tabulated absorption cross section database ABSCO v4.0 (H\({}_{2}\)O) and v5.0 (O\({}_{2}\) and CO\({}_{2}\)) [32].  
We use a high resolution solar irradiance spectrum (\(F_{0}\)) which we generated by fitting the solar irradiance spectrum of [PERSON] [33] with the high resolution solar transmittance spectrum used by [PERSON] et al. [12], a forth order polynomial, and a Gaussian ILS. The used solar induced chlorophyll fluorescence irradiance spectrum (\(F_{SIF}^{0}\)) has been obtained from the publication of [PERSON] et al. [34] and scaled to 1.0 mW/m\({}^{2}\)/sr/nm at 760 nm. In order to account for OCO-2 measuring one polarization direction only, we divided the solar and the chlorophyll fluorescence irradiance spectrum by a factor of two.  
All RT simulations are performed at a high resolution wavelength grid (not to be confused with the measurement wavelength grid) with a sampling distance of 0.001 nm for the SIF and the \(O_{2}\) fit window and 0.005 nm for both CO\({}_{2}\) fit windows.
## 3 Analysis of Test Results
### SEM Test Results  
The variations in the microscopic structure of rocks directly influence their macroscopic failure modes. To further investigate the alterations in the microscopic structure of sandstone during prolonged soaking, SEM analysis was performed on the samples at different immersion times. In this study, Scanning Electron Microscopy (SEM) was used to examine the microstructure of both sandstone and mudstone samples after different immersion durations. The Apreo 2C Field Emission SEM (Thermo Fisher Scientific, Waltham, MO, USA) in Shenyang, China was employed to capture high-resolution images of the sample surfaces. The specimens were first cut into 5 mm \(\times\) 5 mm blocks and cleaned using an ultrasonic cleaner to remove impurities. After immersion, the samples were dried in a vacuum dryer at 40 \({}^{\circ}\)C to prevent moisture residue. A thin metal coating (gold or carbon) was applied to improve conductivity before imaging. SEM analysis was conducted at an accelerating voltage of 15-20 kV, with a working distance of 8-15 mm and magnifications ranging from 1000\(\times\) to 10,000\(\times\). Images were captured from different regions of the samples to examine pore structures, mineral expansions, and water penetration effects. The images were obtained at magnifications of 500\(\times\), 2000\(\times\), and 10,000\(\times\), as shown in Figure 6. The following are the observed changes in the structure of mineral particles, pores, and cracks in sandstone subjected to prolonged soaking:  
Figure 6: _Cont._  
(1) Dry sandstone (Figure 6a) exhibits a smooth, continuous surface with dense matrix. The clay minerals, including kaolinite and chlorite, are tightly cemented and uniformly distributed among the quartz particles.  
(2) After submerging for 7 days (Figure 6b), the sample surface gradually becomes rough. The pores between quartz particles and clay minerals increase, and microcracks begin to gradually develop among the clay minerals.  
(3) In the SEM images of the sample soaked for 30 days (Figure 6c), the structure of the clay minerals loosens, leading to the formation of penetrative cracks between them. These cracks weaken the connections between particles and allow the cracks to expand and penetrate further.  
(4) After 60 days of soaking (Figure 6d), penetrative cracks are formed on the rock surface. The internal penetrative cracks within the clay minerals are visible, with some clay minerals detaching and forming debris.
# State-of-Charge Prediction Model for Ni-Cd Batteries Considering Temperature and Noise
## 5 Conclusions  
* The advantages and disadvantages of SOC prediction models proposed in the literature were analyzed, and a GRNN model with adaptive adjustment was proposed based on the characteristics of nickel-cadmium batteries. This contribution enriches the research methods for the SOC prediction of nickel-cadmium batteries and provides a new theoretical reference for developing energy management strategies for train battery packs.
* A comparison of the prediction results of the APSO-GRNN and GRNN models showed that APSO can enhance the diversity of mode-layer smoothing factors and improve the accuracy of SOC prediction. It was demonstrated that APSO can filter the time series and adjust the size of each neuron smoothing factor based on the correlation of the time series. Thus, the model prioritizes historical data that has a more significant impact on the current moment.
* Two experimental scenarios were designed to verify the robustness of the APSO-GRNN model against noise interference and temperature changes. The prediction results were compared with those of the GRNN, SVR, and XGBoost models. The experimental results demonstrated that the APSO-GRNN model can maintain a high prediction accuracy, even under changing experimental conditions, temperatures, and noise interference.
* The APSO-GRNN model is suitable for the online prediction of the SOC of nickel-cadmium batteries due to its reduced number of parameters, shorter training time, and stronger real-time performance. It can be deployed in an onboard battery management system (BMS) to provide a theoretical basis for battery energy management strategies. In future research, we plan to broaden the scope of this study by incorporating real-world applications of train Ni-Cd batteries.  
Conceptualization, H.X., C.C. and T.Y.; methodology, H.X. and T.Y.; software, H.X.; validation, H.X. and X.W.; formal analysis, H.X., T.Y. and X.W.; data curation, H.X.; writing--original draft preparation, T.Y., C.C., H.X. and X.W.; supervision, T.Y.; project administration, T.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Hunan Province (2020J)[5757].  
Not applicable.  
Figure 11: Prediction results at different temperatures.
## 4 Discussion  
### Differences between SIF and NDVI  
By analyzing the consistency between the SIF and NDVI (see Section 3.1), we recorded large differences between these indices in the southeastern and southwestern parts of China (Figure 3). High vegetation-cover levels have been recorded in the southeastern and southwestern parts of China [5; 46]. Previous studies have shown that in areas with high vegetation coverage, the NDVI reaches saturation [47; 48], which may lead to poor consistency between the NDVI and the SIF. The results obtained in the consistency comparisons between the SIF and the NDVI in different seasons indicated that the relationship between the SIF and the NDVI was less consistent during summer than during autumn or spring (Figures 3 and 4). [PERSON] et al. [49] found that the photosynthetic intensity of vegetation could have a positive linear relationship with the leaf area. As the leaf area increases from spring to summer and decreases from summer to autumn, with a maximum value in summer [13], the differences between the NDVI and the SIF may have been consistent due to the leaf area changes that occur in spring and autumn [32]. However, smaller changes were recorded in the leaf area of vegetation during summer than in other seasons [49]. When the leaf area remains stable, NDVI changes can be small, while the changes in photosynthesis that occur during summer could provide more information on influential factors, such as solar radiation [50] and drought conditions [28]. Therefore, the SIF had a better performance than the NDVI in monitoring the physiological state of the vegetation during summer.  
In previous investigations, the NDVI generally underestimated the vegetation dynamics at high vegetation-cover levels [51; 52; 53; 54]. Figure 8 shows the relationship obtained between the NDVI and the SIF when applied to forest vegetation areas. In regions where  
Figure 7: Percentage changes in the per-unit grain yield/SIF/NDVI in the 2009–2010 drought year compared with the corresponding 2001–2019 mean annual values.  
the vegetation had lower coverage levels (NDVI values not greater than 0.60), good relationships between the SIF and the NDVI were found; by contrast, in regions where the vegetation had higher coverage levels (NDVI values greater than 0.60), poor relationships between the SIF and the NDVI were found (Figure 8a). The distributions of the SIF and the NDVI also revealed differences between the two indices. The SIF distribution fitted a normal distribution better than the NDVI distribution (Figure 8b). In addition, NDVI-based vegetation monitoring corresponds to the reflectance of leaves to radiation, while SIF-based vegetation monitoring is related to photosynthetic intensity. The reflectance changes that occur in evergreen forests are smaller than those of deciduous forests; therefore, the correlations between NDVI and SIF are smaller in evergreen forest areas than in deciduous forest areas.
## 4 Discussion  
### Factors That Influenced Sentinel-2 Soil Surface Reflectance Spectra  
The lower reflectance obtained by Sentinel-2 was observed in most previous studies [33; 61] and is related to the acquisition conditions: in particular, atmospheric water, soil moisture, and soil roughness. These parameters strongly influence the energy that is reflected and emitted, which reduces reflectance over the entire spectrum [15; 17; 112] compared to the laboratory spectra acquired after samples were air-dried, sieved to 2 mm, and oven-dried for 24 h. The variability in this information resulted from the diversity of soils and differences in acquisition conditions among dates, since Sentinel-2 reflectance depends on soil complexity, soil surface conditions, and acquisition conditions. To retrieve the relevant information in the images, all Sentinel-2 spectra that corresponded to bare soil for each sample were grouped in a single table, which can provide an alternative to the  
Figure 10: Mean performance of the three iterations of model validation using the random forest (RF) and deep neural network (DNN) algorithms when adding laboratory spectral indices incrementally in the order of decreasing correlation with soil organic carbon content: **(a)** The coefficient of determination R\({}^{2}\); **(b)** The root mean square error of validation (RMSEP); **(c)** The ratio of performance to the interquartile distance (RPIQ). Error bars indicate 1 standard deviation. Horizontal red lines indicate performance thresholds (see Section 2.6.4). See Table 2 for definitions of the spectral indices.  
mosaicking approaches developed in previous studies [28; 38; 113]. [PERSON] et al. [38] found that temporal mosaicking approaches increased the predicted area but did not predict SOC content better than single-date models. This is because soil conditions, such as soil moisture, soil roughness, and vegetation significantly influence prediction performance by disturbing soil reflectance [31]. Considering Sentinel-2 images acquired on different dates allows one to assess the ability of models to represent different soil conditions and, thus, increase the number and composition of training sample datasets.
## 4 Realization of IFC-Based 4D Construction Information Model of Prefabricated Buildings
### IFC Standard and Its Extension Mechanism
#### 4.2.5 Realizing Information Extension Based on IFC Standard  
Based on the above steps, the relevant IFC-based 4D construction management information model is extended. This model takes the IfcTask as its mainline and relates to the relevant components, resource information, cost information, schedule network analysis, and delay analysis information. The extended IFC-based information model provides essential technical support to realize the interoperation of construction information, data update, schedule network analysis, delay analysis, and 4D visualization simulation. Figure 6 illustrates the completed schema of the construction information model based on the IFC standard. Specifically, in this figure we can find that the relationship entity IfcRelAssignsToProduct associates IfcTask with the components related to the construction process. The relationship entity IfcRelAssignsToProcess associates IfcTask with the resource information and cost information related to the construction process. The relationship entity IfcRelDefinesByProperties associates IfcTask with the schedule network analysis information and delay analysis information related to the construction process. Therefore, the extended schema to describe the construction management information of prefabricated buildings is completed.
## 3 Simulation Results
### Base Scenario
EVM vs. RB, throughput, and BER are measured for various SJR values to examine the dependencies of the jamming detection metrics on SJR. According to the results shown in Figure 6d, jamming cannot be sensed using the throughput and BER observations when SJR exceeds 10 dB. However, using the EVM vs. RB measurement, jamming signals are successfully detected, even under extreme SJR conditions, such as 20 dB (Figure 6b). To demonstrate the performance of EVM vs. RB under other SJR conditions, the peak value of EVM vs. RB for each SJR is calculated and the results are shown in Figure 6c. It is  
Figure 4: Tone Jammer Case, SJR \(=-5\) dB, Obtained Throughput = %0 (BER = 0.343). (**a**) shows the Rx signal spectrum and (**b**) shows the EVM vs. RB. Figure 5: Tone Jammer Case, SJR \(=10\) dB, Obtained Throughput \(=100\%\) (BER \(=0\)), (**a**) the Rx signal spectrum and (**b**) EVM vs. RB. concluded that jamming signals with an SJR of 25 dB can also be detected using EVM vs. RB. However, beyond 25 dB, EVM vs. RB also becomes unsuccessful in jamming detection. The sensitivity performance of the proposed method for tone jamming is also valid for chirp jamming, as shown in Figure 10. In the following sections, the BER results are not presented alongside the throughput results, because, as shown in the figures, the BER is inversely proportional to the throughput and does not provide any additional information.
# Can IMU Provide an Accurate Vertical Jump Height Estimate?
## 3 Results
Figure 4 shows the discrepancies of the estimated VJ height between an FP and an IMU for the squat jump (SQJ) as the VJ test modality. Figure 5 shows the discrepancies of the estimated VJ height between an FP and an IMU for the counter-movement jump (CMJ) as the VJ test modality. Table 3 summarizes the results of the reliability analysis of the VJ height measurement using an individual IMU placed on the metatarsal part of the foot. High ICC values of 0.888 and 0.872 were determined for the CMJ and SQJ, respectively. The mean coefficient of variation of the results was 4.116 for the CMJ and 5.933 % for the SQJ. Table 3. Test-retest reliability of an inertial measurement unit (IMU) for vertical jump height estimation. Figure 4: Force plate vs. IMU measuring SQJ height agreement (Bland-Altman plot). Figure 5: Force plate vs. IMU measuring CMJ height agreement (Bland-Altman plot). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **IMU Reliability** & \\ \hline  & **CMJ** & **SQJ** \\ \hline IMU\_I (95\% CI) [cm] & 30.46 \(\pm\) 3.7 (28.45; 32.48) & 28.29 \(\pm\) 3.15 (26.58; 30.01) \\ \hline IMU\_II (95\% CI) [cm] & 29.75 \(\pm\) 2.82 (28.22; 31.29) & 26.79 \(\pm\) 3.65 (24.81; 28.77) \\ \hline MCV [\%] & 4.116 & 5.933 \\ \hline ICC (95\% CI) & 0.888 (0.633; 0.966) & 0.872 (0.58; 0.961) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test-retest reliability of an inertial measurement unit (IMU) for vertical jump height estimation.
## 4 Results
### Model Validation and Comparison  
The performance of each model was analyzed using the training dataset. The RF model showed a higher sensitivity value (98.00%) than did the BRT model (79.57%). This result showed that the RF model classified more correctly than the BRT model in the landslide class. The specificity results also indicated that the RF model had higher specificity (100.00%) in the non-landslide class, indicating that the non-landslide pixels were more correctly classified. The specificity value of the BRT model was 76.70%. Because of the lower sensitivity and specificity values of the BRT model, the overall accuracy and kappa index values were lower, with values of 78.16% and 0.561, respectively. In the case of the RF model, the overall accuracy and kappa index were 98.98% and 0.980, respectively.  
In addition, the success rate and the prediction rate were analyzed using the training dataset and the validation dataset, respectively (Figure 3). In the case of success rate, the RF and BRT models had values of 0.999 and 0.887, respectively. The prediction rate curve also showed that the RF model had a higher AUC (0.865) than the BRT model (0.851). Overall, the AUC values of all models were greater than 0.8. These results show that the LSMs constructed in this study have good accuracy in the spatial prediction of landslide susceptibility.
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 7 Case Studies
### Real-World Applications of ML in Smart Buildings  
ML has revolutionized the management and operational efficiency of smart buildings, leading to substantial improvements in energy conservation, system reliability, and occupant comfort. Table 6 lists some real-world applications where ML has been effectively employed in smart buildings [107].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Application** & **Purpose** & **Description** \\ \hline Energy Efficiency & Reduce energy use and cost & ML algorithms optimize building systems to consume less energy while maintaining comfort and functionality. \\ \hline Predictive Maintenance & Prevent equipment failures & ML models analyze historical and real-time data to predict and prevent equipment failures, reducing downtime and maintenance costs. \\ \hline Load Forecasting & Managing energy demand & ML techniques are used to predict the building’s energy load, facilitating more efficient energy management and planning. \\ \hline NILM & Monitor energy usage & NILM uses ML to disaggregate total energy usage data into appliance-specific consumption without individual sensors. \\ \hline HVAC Optimization & Optimize climate control & ML optimizes HVAC operations to improve comfort and energy efficiency based on real-time data and usage patterns. \\ \hline Lighting Control &
\begin{tabular}{c} Automate and \\ optimize lighting \\ \end{tabular} & ML-driven systems adjust lighting based on occupancy and ambient light levels to improve energy savings and comfort. \\ \hline Occupancy Detection & Understand space utilization & ML algorithms analyze sensor data to detect and predict occupancy patterns for better building management and energy use. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Real-world applications of ML in smart buildings.
# Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection
## 3 Results and Discussions
### Correct Predictions per Recording Sample  
Understanding how classification performance varies across different operational speeds and unbalance levels is crucial for assessing the model's effectiveness. Figure 10 illustrates the percentage of correct predictions per data sample across different speed levels for the scenarios with the highest data availability. These values were calculated separately for each unbalance level, providing a detailed comparison of model performances.  
Figure 10: Level-wise comparison of the percentage of correct predictions per data sample for ML and TL models across different unbalance levels in scenarios with 100% data availability. The red line represents the rotational speed corresponding to each data sample.  
For the ML model, correct identification of the balanced class improved as speed increased, aligning with expectations. The Signal-to-Noise Ratio (SNR) decreases at higher speeds, making unbalance patterns more distinct and easier to detect. Additionally, unbalance effects become more pronounced at higher speeds due to increased centrifugal forces, further improving the pattern visibility and model learning. However, level 1 follows the opposite trend, with the accuracy decreasing as speed increases. This effect requires further investigation. Level 2 performs particularly poorly within the 1200-1400 RPM range but significantly improves beyond this threshold, suggesting a possible resonance effect. Levels 3 and 4 achieved near-perfect accuracy, maintaining close to 100% correct predictions across all speed levels.  
For the TL model, balanced cases were consistently well classified across all speeds. Level 1 shows a positive correlation with speed, with accuracy increasing at higher speeds, whereas Level 2 remains relatively stable across different speed levels. Level 3 demonstrates a marked improvement beyond 1400 RPM, whereas Level 4 maintains high accuracy across all speeds with no significant impact from speed variations.  
These findings highlight the strong influence of speed on the classification accuracy of the ML model, where higher speeds improve the detection of both balanced cases and severely unbalance levels. In contrast, the TL model demonstrated greater stability across speeds, with classification accuracy more dependent on unbalance levels than speed variations.