esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimating Double Cropping Plantations in the Brazilian Cerrado through PlanetScope Monthly Mosaics
## 3 Results
### Random Forest Classification  
Figure 8 shows the final map of RF classification, after smoothing the classification result by using a \(4\times 4\) window size filter. The dominant LULC class was the double cropped area (37%), followed by cultivated pasture (22%) and sugarcane (21%). The majority of the municipality (80%) is dominated by crop, cattle meat, sugar, and biofuel production, while native vegetation occupies only 18% of the municipality, contrasting with the average portrait of the Cerrado biome, which is roughly occupied by 50% of agriculture and 50% of native vegetation.  
Figure 8: Land use and land cover (LULC) map of the municipality of Goiatuba, Goiás State, Brazil, showing the dominant presence of double cropping areas.  
Figure 7: Rank of 10 most important attributes for Random Forest classification. See Figure 7 for identification of textural attribute short names. NDVI = normalized difference vegetation index; GRNDI = green–red normalized difference index; EVI = enhanced vegetation index.
## 1 Introduction
Alternative two-plane TIE-based phase-retrieval approaches [27; 35] would have a distinct disadvantage for this application. They would result in doubling the dose received by the sample, as it requires two images per projection. In addition, the precise alignment of the two images collected at different propagation distances, required by this method, would likely be creating another problem as it is not a simple task. Breast cancer is one of the two leading causes of cancer-related deaths in the world. The success of breast cancer treatment relies on early detection [36]. Currently, the primary screening method for breast cancer detection is mammography, which produces a 2D image. However, a fundamental limitation in mammography results from the superposition of breast tissue layers within 2D projection images, which potentially obscures fine details and produces errors in diagnosis [32]. On the other hand, 3D imaging techniques such as breast CT are not yet well-established in medical practice due to the issues related to the relatively high radiation doses required. The objective of the current study is thus to explore novel methods of imaging that minimize the dose to the patient while simultaneously enhancing image quality in a way that can be applied in clinical practice. The focus of the current paper is on the current state and progress of propagation-based PCT imaging at the IMBL, which has been found to be highly efficient for low-density samples such as breast and other soft tissues [5; 37]. Phase-contrast imaging has been shown to significantly increase contrast in soft tissue under suitable experimental conditions [38]. Biomedical samples are often very sensitive to radiation, which means that the X-ray doses must be carefully controlled. Another significant benefit of the TIE-Hom phase-retrieval algorithm is that it improves the signal-to-noise ratio (SNR) [34; 39; 40], enabling the radiation dose delivered to soft tissue to be reduced while still producing high-quality images.
## 1 Introduction
[28] studied the heat transfer and pressure drop characteristics of refrigerant HFO-1234 ze (Z) during condensation through experimental methods and compared it with HFC-236 fa, HFC-134a, HC-600a, and HFO-12334 ze (E). The results indicated that at the same mass flow rate, the heat transfer coefficients of HFO-1234 ze (Z) are higher than those of all refrigerants currently used in heat pumps, and its frictional pressure drop is similar to that of HC-600a. [PERSON] et al. [29] studied the system performance of different low-GWP refrigerants for high-temperature heat pumps. The results indicated that although pentane and R365 mfc have the best system COP, the flammability of pentane and the high price of R365 mfc are important factors hindering their future implementation. [PERSON] et al. [30] presented a series of theoretical simulation results using low-GWP refrigerants for high-temperature heat pumps and developed a steady-state thermodynamic model for a single-stage high-temperature heat pump with an internal heat exchanger (IHX). The results indicated that the proposed model is highly reliable and that HCFO-1233 zd (E) and HFO-1336 mzz (Z) are the most likely substitutes for HFC-245 fa and HFC-365 mfc, respectively. In summary, although a considerable amount of research has been conducted to improve the overall performance of high-temperature heat pumps from the perspectives of refrigerant and system structure, all of them focus on individual types of high-temperature heat pumps, and there are few reports comparing the two types of high-temperature heat pumps. Therefore, to fill the current research gap, this paper proposes the research concept of comparing and evaluating the performance and economy of different types of high-temperature heat pumps. This not only promotes energy conservation, carbon reduction, and the development of heat pump technology but also provides guidance for users to choose which heat pump is most suitable. This paper first builds two test benches and a simulation model for two types of high-temperature heat pump. After verifying the accuracy of the model, the optimal new refrigerants suitable for transcritical heat pumps and subcritical heat pumps are determined. Finally, the performance and economy of the two types of high-temperature heat pump are compared through improvements at the refrigerant level, and the impact of key factors on system performance is analyzed through experimental methods.
# Thermal and Energy-Efficiency Assessment of Hybrid CLT-glass Facade Elements
## 4 Testing Program
### Instruments  
The external climate parameters (wind speed, wind direction, precipitation, temperature, and humidity) in the Live-Lab context were measured by means of a meteorological station (Figure 6). This was combined with a central control station designed by SITEL Ltd. (Ljubljana, Slovenia, www.sitel.si) to support the continuous monitoring of climate conditions and the acquisition of relevant data. As a specialty of the Live-Lab facility, a heating source was also considered for the cavity between the two glass panels. As the hygrothermal properties in the cavity were set as a control parameter for continuous measurement, the heater was set to automatically switch on at the first sign of the dew point for glass.  
In the post-processing stage, based on the collected measurement data, the impact of this kind of heating source was assessed in terms of the sensitivity of the thermal transmission of the 3D facility as a whole. This was also assessed in terms of its impact on the energy consumption to maintain a constant indoor temperature for the facility. To optimize the energy consumption of the Live-Lab prototype, solar panels were installed on the roof to act as an additional energy source. Electricity for heating or cooling the facility was thus supplied from both the electricity grid and from the solar panels. The setup instruments included an air conditioner and a fan to reproduce a reliable configuration for a residential building.  
Figure 6: Instruments for the field experimental measurements (schematic cross-section of the Live-Lab mock-up building). RH, relative humidity.
## 6 Discussion  
In order to fully assess the performance of the MISAO-ICEEMDAN-WLSSVM model, it was compared with three existing models, TVF-EMD-ELM (M1), WPD-LFABS-SDS-GRU (M2) and SSA-VMD-Informer (M3).  
Table 9 describes the development process of the existing hybrid models. Table 10 compares the mean values of the error results of the proposed model with the existing  
Figure 11: Comparison of forecast results without weather classification.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Metrics**} & \\ \cline{2-4}  & **MAPE/\%** & **MAE/MW** & **RMSE/MW** \\ \hline TCN & 11.3537 & 1.9486 & 0.7029 \\ GRU & 10.7660 & 1.6261 & 0.5929 \\ SVM & 9.8237 & 1.6269 & 0.6050 \\ WLSSVM & 7.4394 & 1.3569 & 0.5363 \\ SAO-WLSSVM & 5.8246 & 1.0328 & 0.4139 \\ MISAO-WLSSVM & 4.4907 & 0.8707 & 0.3643 \\ MISAO-ICEEMDAN-WLSSVM & 3.3567 & 0.5309 & 0.2287 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance Indicators Comparison of Prediction Models without Weather Classification.  
models for the three weather types under the dataset of this paper. The bold values represent the optimal forecasts among all the model's predictions. As shown in Table 10, the MAE of the proposed model is reduced by 36.25%, 14.82%, and 25.23% compared with M1, M2, and M3, respectively. The MAE of M1 is 1.7254, and its TVF-EMD method is more complicated in parameter selection and has the problem of manual selection, which may be needed to be able to achieve the best decomposition effect for different PV data. The ELM model, when dealing with complex data, finds it difficult to fully explore the deep features, resulting in limited prediction accuracy. The MAE of M3 is 1.4710, and there may be modal aliasing problems during the decomposition process, affecting the feature extraction accuracy. The MAE of M2 is 1.2912, and the wavelet packet transform is time-consuming in selecting the appropriate wavelet basis function and ineffective in dealing with the boundary data, which negatively affects the prediction results. In addition, the combination of multiple algorithms and strategies increases the model complexity and may affect the computational efficiency.  
On the other hand, the proposed model adopts the ICEEMDAN method of adaptive decomposition, which avoids complex parameter selection. The similar day model is constructed by K-means clustering, which improves the data quality. The multi-strategy improved MISAO optimizes the model parameters, combining the multivariate prediction considering meteorological factors and the optimal hyperparameter search of WLSSVM, significantly improving the prediction accuracy and adaptability.  
The comparative analysis proves that the MISAO-ICEEMDAN-WLSSVM model proposed in this paper performs better in ultra-short-term PV power prediction.
## 3 Results
### Determination of Total Phenolics and Flavonoids  
The results obtained in the phenolic and flavonoid determination tests confirmed those found in the TLC. Phenolic compounds were evidenced in all extracts, with leaf and branch extracts showing the highest levels (51.76 \(\pm\) 0.16 mg GAE g\({}^{-1}\) and 48.45 \(\pm\) 0.50 mg GAE g\({}^{-1}\), respectively). The residue and seed extracts showed lower values compared to the other samples, however, these contents are above 24 milligrams of gallic acid equivalents  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Plant Parts from All Areas**} & \multicolumn{5}{c}{**TLC**} \\ \cline{2-6}  & **Terpenes** & **Condensed** & **Hydrolysable** & **Flavonoids** & **Coumarin** \\  & & **Tannins** & **Tannins** & **(1,2-Benzopyrone)** \\ \hline Leaves & + & + & + & + & \(-\) \\ Branches & + & + & + & + & \(-\) \\ Residues & + & + & + & + & \(+\) \\ Seeds & \(-\) & \(-\) & \(-\) & \(-\) & \(+\) \\ \hline \hline \end{tabular} Note: (+) presence of the compounds class; (\(-\)) absence of the class of the compound.  
\end{table}
Table 2: Chemical analysis by thin-layer chromatography (TLC) of ethanolic extracts from different parts of _Dipteryx punctata_ from agroforestry systems in Mojui dos Campos, Eastern Amazon, Brazil.  
Figure 3: Phytochemical characterization of _Dipteryx punctata_ extracts. TLC plates eluted in ethyl acetate: hexane (70:30) and developed with 1% potassium hydroxide. (**A**) L1, L2, L3, L4, and L5 = leaf extracts from areas 1, 2, 3, 4, and 5; (**B**) B1, B2, B3, B4, and B5 = branch extracts from areas 1, 2, 3, 4, and 5; (**C**) R1, R2, R3, R4, and R5 = residue extracts from areas 1, 2, 3, 4, and 5; (**D**) S1, S2, S3, S4, and S5 = seed extracts from areas 1, 2, 3, 4, and 5. Standard: CR = coumarin (1,2-benzopyrone). Source: [PERSON], B.C.M.d.  
per gram of sample (Table 3). About the class of flavonoids, the highest content was obtained in the extract of the leaves, 115.40 \(\pm\) 3.41 mg RE g\({}^{-1}\). For the extracts of branches and residues, the values found were 69.71 \(\pm\) 0.51 mg RE g\({}^{-1}\) and 71.86 \(\pm\) 3.79 mg RE g\({}^{-1}\), respectively; the presence of this class was not detected in seed extracts (Table 3).
# Flexible Simulation Platform for Generating Realistic Waveforms with Voltage Notches
## 4 Analysis of Key Parameter Dependencies, Limitations, and Impacts
### Impact of Key Parameters on Voltage Notch Waveforms
#### 4.2.3 Scenario 2: Rectifier and Linear Loads  
This scenario examines the effect of aggregated linear loads on voltage notch waveforms while using a six-pulse rectifier with snubbers enabled.  
* Simulations vary \(S_{ll}\) from 5 kVA to 170 kVA in ten steps, with \(S_{ll,max}\) ensuring a _PF_ above 0.85 and a voltage drop less than 5% at the PCC.
- Fixed values include \(S_{sc}\) = 5 MVA, \(S_{r}\) = 25 kVA, \(\alpha\) = 30\({}^{\circ}\), \(X_{com}\) = 37.3 m\(\Omega\), \(R_{D}\) = 10.45 \(\Omega\), \(R_{sn}\) = 49.58 \(\Omega\), \(C_{sn}\) = 0.70 uF (optimal RC-snubber), and \(PF_{ll}\) = 0.9.
- Voltage waveforms (Figure 10, top) show that increasing \(S_{ll}\) smooths the voltage notch transitions and reduces oscillations. \(d_{com}\) slightly decreases, and the fundamental voltage drops linearly.
- Line currents of the rectifier (Figure 10, middle) demonstrate that with increasing \(S_{ll}\), commutation is slightly delayed. The line currents become more sinusoidal, reflecting improved commutation and increased linear load proportion.
- Total line currents of LV installation (Figure 10, bottom) show improved sinusoidal quality due to the increased linear load and smoother commutations.  
The simulations demonstrate that adding linear loads helps dampen voltage notches and smooth the transition segments, enhancing overall waveform quality while maintaining technical constraints.  
Figure 9: Impact of (**a**) RC-snubber and (**b**) \(R_{sn}\) on voltage notch waveforms (**top**) and input line currents (**bottom**).
# Secure Delivery Scheme of Common Data Model for Decentralized Cloud Platforms
## 3 Results
### Analysis of SC-CDM  
In the cloud CDM model presented in this study, in order to increase security, CDM data are divided, and a multi-channel method is used. In the cloud CDM model, the multi-channel method is used by dividing each CDM datum into two pieces of data and storing each piece in IPFS, to receive two IPFS hashes.  
When a CDM data owner transmits CDM data, each original CDM datum is divided into two pieces of data, and the divided data are encrypted by using a symmetric key and a random vector. Then, the encrypted segmented data are added to the IPFS network and received in the form of an IPFS hash. Finally, the symmetric key, random vectors, and IPFS hashes are all grouped into a single structure and sent to the requester of the CDM data, along with the signature, through the blockchain. After verifying the owner of the CDM data with a signature, the requestor retrieves the file divided by the IPFS hash of the received structure. After that, the encrypted file is decrypted by using the symmetric key and random vectors to obtain the divided CDM data. The requestor can normally receive the actual CDM data by recombining the CDM data divided by the same rule as the owner to obtain the original CDM data. Even if the requestor receives the IPFS file through the IPFS hash, the data undergo a process of decryption once more, so the information is not exposed except for users authorized by the CDM data owner. Figure 11 shows the overall process for building a secure CDM in SC-Blockchain. As shown Figure 11, H\({}_{1}\) and H\({}_{2}\) represent hash values of CDM\({}_{1}\) and CDM\({}_{2}\). CDM  
Figure 10: The block consists of a set of transactions in SC-Blockchain.  
and CDM\({}_{2}\) are encrypted by the CDM Separate Block Encryption/Decryption Algorithm described in Section 2.2.4.  
In the IPFS network, an unchanging file is stored or downloaded, using an encryption hash function that returns a fixed value for the received data, and the user who will share CDM data is identified and signed through the encryption and decryption process, using the CDM partitioning algorithm. You can prove it through. Therefore, user access control is possible, and CDM data can be safely shared based on trust, because blockchain is used.
## 3 Deep-Learning-Based Address Verification Technique
### Evaluation Metrics  
The evaluation metrics are utilized to measure and summarize the quality of the trained classifier when tested with the unobserved data [44]. Generally, the overall accuracy is the most commonly used metric for classification models, but the only accuracy is not enough to assess the model for some reasons [45]. It requires other metrics to check and verify collectively to assure that the model works properly.  
Principally, true positives (TP) and true negatives (TN) are described as outcomes of the positive class and negative class, respectively, correctly classified by the model. Meanwhile, false positive (FP) and false negative (FN) are the outcomes of positive and negative classes classified incorrectly. Accordingly, the overall accuracy is calculated by Equation (1).  
\[\text{Accuracy}= \frac{(\text{TP}+\text{TN})}{(\text{TP}+\text{FP}+\text{TN}+ \text{FN})} \tag{1}\]  
The precision, recall, and F1 score are calculated by Equations (2)-(4)  
\[\text{Precision}= \frac{\text{TP}}{(\text{TP}+\text{FP})} \tag{2}\]  
\[\text{Recall}=\frac{\text{TP}}{(\text{TP}+\text{FN})} \tag{3}\]  
\[\text{F1}-\text{Score}=\frac{2\ \times\ (\text{Precision}\ \times\ \text{Recall})}{(\text{ Precision}+\text{Recall})} \tag{4}\]  
The additional significant metric is the area under the curve (AUC) of the ROC (Receiver Operating Characteristic) curve. The ROC curve provides a visual implement for checking the ability of the prediction model to correctly classify positive cases and negative cases that were incorrectly classified [45; 46].  
Figure 5: End-to-end address verification process.
# Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery
## 2 Relevant Work
### Remote-Sensing Image Scene Classification  
Many academics have worked in recent years on using various strategies to increase the accuracy of natural scene classification. Traditional classification approaches, such as supervised and unsupervised classification, are frequently employed. To properly classify remote-sensing images, supervised classification techniques such as maximum likelihood classification and support vector machines (SVMs) require labeled training sets. Because it can handle big datasets and complicated feature spaces with high accuracy, SVM has been proved to be a useful approach for the natural scene categorization of remote-sensing images [9].  
Natural scene classification of remotely sensed images has been widely studied in recent years to improve classification accuracy using traditional machine-learning techniques. Among these methods, deep-learning techniques, especially CNNs with transfer learning, have shown promising results for the natural scene classification of remote-sensing images. Attention mechanisms and feature-fusion methods have also shown significant improvements in classification performance.
## 4 Review of Existing Methods to Track Bio-Fraction of Co-Processed Fuels
### Isotope Ratio, Optical and Inline Techniques  
Another method to determine biogenic carbon in fuel blending is based on the measurement of the ratio of 12C and 13C isotopes in the blend for pyrolysis-derived bio-oil by means of simpler equipment than AMS [8]. It has the potential to be lower cost than AMS and faster analysis time than LSC. The amount of 13C depends mainly on organic source and geographical origin. The measurement of the 13C isotope can be carried out by an Isotope Ratio Mass Spectrometer (EA-IRMS or IRMS), or Nuclear Magnetic Resonance spectroscopy (NMR) [28; 29]. IRMS can be used for biofel characterization and can provide information based on the ratio of stable isotopes of carbon (13C/12C). However, C3 and C4 plants shows differences in the ratio of stable carbon isotopes they contain. As a result, C3 plants show 613C ranges that largely overlaps with that of petroleum fossil sources, while for C4 plants the range of values is clearly separated from fossil sources [40; 41]. This makes this method difficult to apply to certain blends where the bio component originates from C3 plants.  
[PERSON] et al. [8] also discuss optical and laser techniques for the detection of carbon isotopes. They note these methods have shown some issues, especially when high precision and low limits of detection are required, although it also appears quite some degree of research is being carried out to improve accuracy. Finally, given that other refinery online analytical tools are already in place, they propose that online biogenic carbon measurement could be a practical route to analyzing the bio-carbon fraction in products from co-processing. However, they clarify that current refinery control and instrumentation systems lack the means to measure renewable carbon contents accurately, reliably, and quickly.
## 3 Resolutional Analysis Results
### HMS System Resolution Analysis Based on RDG Extraction  
Based on the proposed method, the simulation results of the RDG extraction in the case of the reference SAR image are shown in Figure 7. The results contain a 3-D view of RDG and the contour plot of the reference image. Accordingly, a similar procedure is done on the de-speckled SAR images in Figure 3. All the RDG extraction and comparison results with the reference image are shown in Figure 8. It should be noted that all the RDG simulations were carried out under the system parameters of Table 3. According to Figure 8, the maximum magnitude difference of the RDG between  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Carrier frequency & 14 GHz \\ Repetition frequency & 5 KHz \\ Sampling frequency & 180 MHz \\ Pulse width & 0.8 usec \\ Bandwidth & 140 MHz \\ Chirp scale factor & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: SAR system parameters.  
the reference and de-speckled images is about 3.5 dB, which was achieved by the Local sigma filter application. However, in the case of comparison between the other resulting RDGs, the Local sigma, the Local adaptive median, and the Gamma filters have the highest RDG differences with the reference image. As a result, application of the aforementioned spatial de-speckling filters will have destructive effects on the homogeneity of the reconstructed SAR texture. As can be deduced from Figure 8d,e, the high-frequency multiplicative adverse behavior of the HMS while dealing with the raw data is presented as instantaneous changes of the magnitude in the resulting RDG. According to Figure 8, application of a proper spatial de-speckling filter like the Lee sigma with the lowest deviation in the RDG difference obtains the best performance on the phase history of the raw data, which is a sign for better homogeneity of the texture within the reconstructed image. According to Figure 9, in the case of compensated HMS or the noise-free condition, the resulting RDG has less instantaneous changes and it varies smoothly. It also shows that the HMS has really adverse effects on the RDG, which will cause raw data inconsistency. In total, not only does the HMS degrade the system resolution, but also the proper implementation of the spatial de-speckling filter is very important for information retrieval. In other words, the HMS effects on the RDG is presented in the form of instantaneous variations in the magnitude of the RDG or raw data inconsistency. Hence, a complete understanding of the image structure and the texture characteristics for the spatial de-speckling filter application might be helpful for reducing the adverse effects of the HMS on the raw data inconsistency or texture homogeneity.  
Figure 8: _Cont._  
Figure 7: Resulting RDG of the reference image (**a**) RDG 3-D plot; (**b**) RDG contour plot.