esa-sceva/satcom-chunk-collection huggingface dataset:
# The Ground-Based Absolute Radiometric Calibration of the Landsat 9 Operational Land Imager
## 2 Methodology
### On-Site Personnel: The Reflectance-Based Approach
#### 2.3.5 TOA Spectral Radiance Determination  
The final data processing step in the reflectance-based approach is the determination of the TOA quantities of interest using the radiative transfer code [31]. The spatially averaged surface BRF data are used with the atmospheric measurements as input into the radiative transfer code, and the result is the band-averaged TOA spectral radiance and reflectance, which are then compared to the Landsat 9 OLI data products. An example of the TOA spectral radiance determined at one of the SDSU vegetated sites for a Landsat 9 OLI collection on 18 August 2023 is shown in Figure 12. A general summary of the reflectance-based approach processing methodology is shown in Figure 13.  
Figure 11: Surface BRF measurements. (**a**) UArizona personnel operating a portable spectroradiometer; (**b**) NASA GSFC personnel operating the CaTSSITTR; (**c**) SDSU personnel operating a portable spectroradiometer at a vegetated site. [PERSON] photos courtesy of [PERSON].  
Figure 12: TOA spectral radiance at an SDSU vegetated site on 18 August 2023 at 17:11 UTC for the Landsat 9 OLI viewing conditions.
# Cellulose Recovery from Agri-Food Residues by Effective Cavitational Treatments
## 2 Acoustic and Hydrodynamic Cavitation: Highlights  
### Acoustic Cavitation  
The term \"sonochemistry\" is employed to describe the physicochemical processes that occur in solution beyond the energy introduced by ultrasound (US) power [32]. Cavitation phenomena, consisting in the formation, growth and collapse of gaseous microbubbles in a liquid phase (Figure 2) are the consequence of US effects [33]. The powerful local effects (mechanical, chemical and thermal) due to the sudden collapse of those microbubbles and the resulting microjets are the basis of all sonochemical applications.  
Even if the full mechanism has not yet been clarified, it is commonly accepted that, in H\({}_{2}\)O\({}_{2}\), low frequencies (20-80 kHz) better lead to physical effects (microjets, shockwaves, micro-convection, etc.) [34; 35; 36]. On the contrary, high ultrasonic frequencies (150-2000 kHz) mainly cause chemical effects due to the formation of hydroxyl radicals (HO) through local hotspots generated by cavitation. In a broad outline, it is possible to distinguish two large groups of possible US applications in chemistry based either on \"sonophysical\" or \"sonochemical\" effects.  
The proper choice of all sonochemical parameters (US frequency and acoustic power), as well as the geometry of the used reactor, are strictly connected to US potentiality [35].  
For example, in homogeneous systems US are often utilized for their sonochemical effects such as radical formation and bond-breakage to perform chemical reactions. Contrariwise, in heterogeneous systems US are mainly exploited for their enhanced mass transport, erosion and mixing effects.  
According to their features, US have been largely applied on biomasses for a large number of purposes. The application of acoustic cavitation for the recovery of secondary metabolites from vegetal matrixes is well known in the literature [37; 38]. Furthermore, the acknowledgment of US as a green technique for biomass exploitation has been established by more than 5000 recent papers produced only in the last five years, concerning US-assisted extraction (UAE) [39]. Hence, this technology could play a key role to improve lignocellulosic biomass pretreatment, aiming to subsequent enzymatic and acid hydrolysis: sonochemical and mechanoacoustic effects exerted on the matrix could positively affect the final biomass conversion to added-value products [40; 41].
## 2 Materials and Methods
### Toxicity Bioassay: Aged Residue  
Specific concentrations of nicotine (0.1%), pyrethrins (0.05%), rotenone (0.1%) and stemonine (0.5%) were selected for this experiment based on their abilities to achieve \(>\)75% (Abbott-corrected) mortality in the fresh residue toxicity bioassays. Tween 20 solution (0.05%) was used to dilute all plant extracts and served as the untreated control. Each solution was sprayed onto a group of six potted flowering periwinkle plants maintained in a greenhouse with a compressed-CO\({}_{2}\) sprayer (at 240 kPa) fitted with a flat-fan nozzle (TP8004 VS; Teejet Technologies, Springfield, IL, USA) and at an application volume of 935 L/ha. Petri dishes (with a hole drilled in the bottom) and chifon mesh (16 \(\times\) 16 cm) were sprayed at the same time and aged so that, when WFT were introduced into the arenas, all surfaces contacted by the thrips contained residue of the same age. Senescing flowers and unopened flower buds were removed from the plants before treatment. Flower buds were continuously removed throughout the experimental period, so that only treated opened flowers remained. The plants, Petri dishes and chifon mesh were kept in the greenhouse for 1, 3, 5 and 7 d before fully opened flowers were collected and used in the experiment. The greenhouse was maintained on average 33.9 \({}^{\circ}\)C (23.0 \({}^{\circ}\)C to 38.9 \({}^{\circ}\)C), 50% R.H. (36% to 84%), and 35% shade (achieved with shade clothes). Phytotoxicity (percentage of flower or leaf surface area showing discoloration or bleaching) on leaves and flowers of plants treated by all treatments were recorded daily for 3 d after the application.  
The aged residue toxicity bioassay was conducted as a no-choice test. Fully opened, treated flowers were collected and brought back to the laboratory, where they were transferred individually into treated Petri dishes as previously described. Ten adult WFT were introduced onto a flower with residue of one plant extract of a specific residual age and covered with treated chifon mesh. Live WFT on each flower was counted at 24, 48 and 72 h after introduction.  
Proportion of WFT killed in each Petri dish was calculated and corrected with [PERSON]'s formula [15], after which the data were arcsine-transformed and analyzed with two-factor repeated measure ANOVA (with plant extract and residual age as the main factors and observation time as the repeated factor) at \(\alpha\) = 0.05 [14]. When the assumption of Sphericity Test was violated, Huynh-Feldt-Lecoutre adjusted \(p\) values were used to detect significant differences. [PERSON]'s LSD test (\(\alpha\) = 0.05) was used to separate mean percent mortality among concentration treatments within each plant extract and residual age combination [14].
# A Novel Impact Rotary-Linear Motor Based on Decomposed Screw-Type Motion of Piezoelectric Actuator
## 4 Experimental Tests and Discussions
### Prototype Motor
#### 4.2.2 Linear Motion of the Prototype Motor with No Load  
When adjusting the axial prepresure generated by spring to about 11.7 N and adjusting the radial prepresure generated by snap ring to about 21.1 N, the piezoelectric motor realizes linear motion under the saw-shaped driving voltage. Meanwhile, the measured friction torque between the PA and coupler is about 0.0035 N\(\cdot\)m and the measured friction force between coupler and shaft is about 6.3 N in axial direction.  
When the driving voltage is 720 V\({}_{\text{P-p}}\) with a duty ratio of 100%, and the driving frequency is 2 kHz, 4 kHz, and 6 kHz, the output rotary angle and linear displacement of the prototype motor in the two directions are shown in Figure 9. As the motor can realize negative movement when the duty ratio of the driving voltage is 0%, the motor will realize positive movement when the duty ratio of the driving voltage is 100%. The tested maximum rotary angle of the PA is about 1012 \(\upmu\)rad and the maximum linear distance is about 1.01 \(\upmu\)m; the maximum angle and distance are similar to the results in the rotary motion. With the increase of driving frequency, the slip displacement becomes smaller and smaller, and the linear motion becomes more and more smooth.  
Figure 8: The rotary velocities of the prototype motor under different driving frequencies.  
Figure 7: The output motions of the prototype motor under different driving frequencies: (**a**) 200 Hz; (**b**) 500 Hz; (**c**) 1 kHz.  
When driving frequency continues to increase and other conditions remain unchanged, the prototype motor will keep positive linear motion until the driving frequency reaches 11 kHz. By changing the duty ratio, the motor can move in reverse direction. The two directions of the average linear velocity of the prototype motor are tested when the driving frequency ranges from 2 kHz to 11 kHz, and the results are shown in Figure 10. The results show that the prototype motor can realize linear motion from 2 kHz to 11 kHz, and the prototype motor can achieve the maximum linear velocity of about 2.4 mm/s at 11 kHz under the driving voltage of 720 V\({}_{\text{P-p}}\) with a duty ratio of 100%.
## 2 Materials and Methods
### Radiographic Evaluations  
All peri-apical radiographs for the measurement of the marginal bone modification (MBM) were taken with a standardized parallel technique (FONA(tm) Dental Image Plates). The images were then analyzed with a computer software (Image J, National Institute of Health, Bethesda, MA, USA). Before measurement, each radiograph was calibrated by using the implant diameter as a reference to correct any distortion. Measurements were repeated twice by a single operator (MZ). Each measurement was repeated two times at three different time points. Calibration of the operator was performed by measuring MBM (BL and BR) on a sample of 10 radiographs not included in the study. Intra-examiner reliability was computed using Intra-class Correlation Coefficient (ICC = 0.86%). MBM measurements were taken at both mesial and distal crest.  
For each implant, radiographs performed at the time of surgical implant placement (T0), restoration delivery (T1), and after 6 (T2) and 12 (T3) months of prosthetic function were analyzed. The most coronal extension of the crestal bone at the time of the surgical placement was identified as marginal bone level (MBL). The MBL in the BLG group corresponds to the level of the implant neck, being positioned coronal to it in the SCG group. In the assessment of the MBM, bone remodeling (BR) and bone loss (BL) were distinguished. Bone loss was measured as the distance between the initial MBL and first radiographically visible bone-to-implant contact apical to the implant neck, whilst bone remodeling as the distance from the initial MBL to first point of contact coronal to the implant neck. The mean value of the mesial and distal MBM measurements was pooled for each implant. Any apical MBM was expressed as negative number (Figures 12 and 13). MBM was measured at baseline (T0), at crown delivery (T1), and at 6- (T2) and 12-month (T3) follow-up visits (Figures 14 and 15).  
Figure 12: Example of marginal bone level (MBL), marginal bone modification (MBM), bone loss (BL), and bone remodeling (BR) in bone-level and sub-crestal implants.  
Figure 14: Radiographic control at (T0), (T1), (T2), and (T3) months, respectively. Control (BLG) group.  
Figure 13: Graphical representation of marginal bone level (MBL), marginal bone modification (MBM), bone loss (BL), and bone remodeling (BR) in bone-level and sub-crestal implants. Refer to Figures 3, 5, 7 and 10 for the full BLG group workflow, and to Figures 4, 6, 8 and 11 for the full SCG workflow.
## 1 Introduction
Therefore, within this study, a novel system for an operational crop yield forecasting at Nomenclature des Unites Territoriales Statistiques level 2 (NUTS-2) and Local Administrative Units (LAU) is proposed that builds on a fusion of satellite-based vegetation indices, agro-meteorological indicators, and crop phenology approximated by thermal time. The system exploits Copernicus data sets and climate reanalysis available free of charge at a global scale, and thus can be applied at any location. Within this study, the system is utilized for predicting yields of winter wheat, winter rapeseed, and maize in Poland for a period 2000-2019. To build a system, the first specific objective is to calibrate vegetation indices from Sentinel-3, that are used operationally, with MODIS data which extends the data record to the period before 2018. A second specific objective is to verify the applicability of ERA-5 climate reanalysis data to describe agro-meteorological conditions that affect crop development. A third specific objective is to quantify the impact of the length of the time series used to train the forecasting models, which differs depending on the availability of reference crop yield statistics, on the model performance. Finally, the details on numerical implementation of the system are provided.
# Improvement of ID3 Algorithm Based on Simplified Information Entropy and Coordination Degree
## Improved ID3 Algorithm
### Variety Bias Problem and the Solution
#### 3.2.1 The Selection Process of the Optimal Attribution Based on the ID3 Method  
The attribution that has the biggest information gain will be selected as the root node. From Equations (13)-(16), the attribution \"Number\" has the biggest information gain in Table 1. Hence, this attribution will be selected as the root node. Referring to Algorithm 1, the decision tree model is presented in Figure 3:  
\[Gain(D,Number)=Ent(D)-\sum_{i=1}^{V}\frac{p_{i}+n_{i}}{p+n}Ent(D^{V})=\frac{2 pn }{p+n}-\sum_{i=1}^{12}\frac{2p_{i}n_{i}}{p_{i}+n_{i}}=6, \tag{13}\]  
\[Gain(D,Color)=Ent(D)-\sum_{i=1}^{V}\frac{p_{i}+n_{i}}{p+n}Ent(D^{V})=\frac{2 pn }{p+n}-\sum_{i=1}^{2}\frac{2p_{i}n_{i}}{p_{i}+n_{i}}=5.3, \tag{14}\]  
\[Gain(D,Body\,Shape)=Ent(D)-\sum_{i=1}^{V}\frac{p_{i}+n_{i}}{p+n}Ent(D^{V})= \frac{2 pn}{p+n}-\sum_{i=1}^{3}\frac{2p_{i}n_{i}}{p_{i}+n_{i}}=1.5, \tag{15}\]  
\[Gain(D,Hair\,Type)=Ent(D)-\sum_{i=1}^{V}\frac{p_{i}+n_{i}}{p+n}Ent(D^{V})=\frac {2 pn}{p+n}-\sum_{i=1}^{2}\frac{2p_{i}n_{i}}{p_{i}+n_{i}}=0.3. \tag{16}\]  
Figure 3: Decision tree model based on ID3 method.
# Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness
## 1 Introduction  
### _Related Work_  
The proliferation of distributed computing paradigms, such as the Internet of Things (IoT) and edge computing, has resulted in an unprecedented volume of data being generated and stored locally on end devices. The efficient utilization of this distributed data, while ensuring the preservation of privacy, has emerged as a critical challenge in intelligent computing. Federated learning (FL) has been proposed as a privacy-preserving distributedlearning framework, where local clients perform model training without transmitting raw data to a central server. This paradigm has shown promising applications in domains such as healthcare, smart transportation, and industrial IoT [1; 2; 3; 4].  
Despite its practical success, FL remains fundamentally constrained by the inherent heterogeneity of client data--commonly referred to as the non-independent and identically distributed (non-IID) problem. Variations in data distributions, feature representations, and label spaces across clients often result in divergent local model updates, severely impairing the generalization ability of the global model [5; 6; 7; 8; 9].  
Recent efforts to mitigate this issue can be broadly categorized into three methodological streams:  
* Data Preprocessing-Based Approaches: Clustering-based techniques have been employed to group clients with similar data characteristics and assign customized models accordingly. Approaches such as Iterative Federated Class Averaging (IFCA), Federated Semi-Supervised (FedSEM), and Fine-Tuning and Clustering (FPFC) cluster clients based on model weights or task similarities, while Federated Model-agnostic Meta-learning with Optimized Objective Normalization (FedMOON) leverages contrastive learning to align feature spaces [10; 11; 12; 13; 14].
* Model Robustness-Oriented Strategies: These strategies incorporate regularization techniques or meta-learning paradigms for enhanced robustness against distribution shifts during local training. Federated Proximal (FedProx) introduces a proximal term to constrain local updates, while Federated Contrastive (FedCL) and other personalized FL methods optimize local solutions tailored to individual clients [15; 16; 17; 18].
* Framework-Level Enhancements: Instead of exchanging model weights, these methods transmit distilled \"knowledge\" to alleviate divergence under non-IID settings. Notable examples include Federated Model Distillation (FedMD), the Federated Generative Network (FedGEN), and Federated Knowledge Distillation (FedKD), which use public or synthetic datasets to facilitate the distillation of knowledge [19; 20; 21].
## 3 Methodology
### Data Preprocessing and Encoding  
To prepare the quantitative data for statistical analysis, categorical variables (e.g., industry sector, employee size) were transformed using one-hot encoding, allowing conversion into a binary format. For example, a categorical variable representing \"Industry Sector\" with values such as \"Tech\", \"Retail\", and \"Manufacturing\" was decomposed into three separate binary variables, enabling precise statistical modeling.
## 3 Congestion Prediction Model
### Preliminaries
#### 3.1.3 Bureau of Public Roads (BPR) Function  
Travel time functions are used to solve traffic problems when planning urban transportation or calculating increases in traffic over time. Existing research on travel time functions can be divided into empirical and theoretical types. The traditional method, used mainly in the 1960s, uses linear equations, exponential equations, logarithmic equations, and Bureau of Public Roads (BPR) for empirical expressions. [PERSON] and Wordrop models are used for theoretical expressions.  
The most widely-used BPR expression was proposed by the United States Bureau of Public Roads in 1964. It shows changes over the passage of time depending on increases in traffic volume. Its performance changes depending on the increase, and it accurately predicts free flow speed and capacity [31].  
Conical functions [32] were proposed in order to overcome problems related to the rapid growth of the BPR passage delay functions curve. Other several repair function expression was proposed ([PERSON], [PERSON], Traffic Research Corporation, [PERSON], [PERSON], _etc._) [33].
# Advancing Fault Detection in Distribution Networks with a Real-Time Approach Using Robust RVFLN
## 2 Fault Detection Method  
### Feature Construction  
In order to determine the types and locations of faults occurring in a distribution network and to distinguish them from normal system operation, fault data should be prepared. In our study, firstly, 3-phase current and 3-phase voltage data were taken in the single-bus transmission system created in the PSCAD simulation program. From these data, attribute vectors were created, as given in Table 1. These 14 attribute vectors were created by considering different fault types and fault impedance values. Feature vectors U1, U2, U3, and U4 represent the magnitudes of the current and voltage (\(\varphi In(t)\), \(\varphi Vn(t)\)) and their angles (\(\varphi In(t)\), \(\varphi Vn(t)\)) to prevent parameter readjustment in the distribution network. U5 and U7 denote instantaneous changes in the current and its angle. U6 and U8 refer to instantaneous changes in the voltage and its angle. Finally, the remaining 6 feature vectors can be transformed from raw data into discriminative features through signal norms [44]. U9, U10, and U11 are the 1, 2, and infinity norms from the raw voltage data, respectively (\(\ell_{1},\ell_{2}\) and \(\ell_{\infty}-norms\)), which are used to obtain the appropriate signal characteristics. From the raw current data U12, U13, and U14, appropriate signal characteristics are obtained with norms 1, 2, and infinity.  
The feature vectors in Table 1 are carefully selected to capture both the time-domain and frequency-domain characteristics of the electrical signals in the distribution network. These features are designed to enhance the fault classification and localization performance, especially for HIFs, which exhibit subtle signal variations.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **WO-DBN** & **DBN** & **ANN** & **LSTM** \\ \hline \multirow{3}{*}{Vertical displacement} & RMSE & 0.2312 & 0.5697 & 0.6563 & 0.5595 \\  & MAE & 0.1604 & 0.4285 & 0.4852 & 0.3777 \\ \cline{1-1}  & R & 0.9742 & 0.8642 & 0.7585 & 0.8435 \\ \cline{1-1}  & RMSE & 22.0217 & 31.2888 & 38.1835 & 34.5109 \\ \cline{1-1}  & MAE & 12.3726 & 24.7234 & 30.4551 & 28.2891 \\ \cline{1-1}  & R & 0.6825 & 0.5011 & 0.3411 & 0.5145 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of evaluation indexes for four prediction models (WO-DBN, DBN, ANN and LSTM). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & \(\mathbf{n_{1}}\) & \(\mathbf{n_{2}}\) & \(\mathbf{n_{3}}\) & \(\mathbf{n_{4}}\) & \(\mathbf{\eta}\) & \(\mathbf{t_{1}}\) & \(\mathbf{t_{2}}\) \\ \hline Values & 45 & 50 & 30 & 10 & 0.0014 & 15 & 65 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters of the DBN model. Therefore, the performance of the prediction model by WO-DBN is significantly better than those by DBN, ANN and LSTM. The proposed WO-DBN model is a more suitable method for predicting the performance of the utility tunnel structure during the operation period. Moreover, in four prediction models, the performance of ANN is the poorest, whose computed R for the vertical displacement and the horizontal stress is the least. The computed R for the vertical displacement by DBN is larger than that by LSTM, but the computed R for the horizontal stress by DBN is slightly less than that by LSTM. However, with comprehensive consideration of the computing errors, the performance of DBN is superior. Therefore, for the prediction of the performance of the utility tunnel structure during the operation period, the order of the four models is as follows: WO-DBN, DBN, LSTM and ANN. Although the proposed WO-DBN model can well predict the performance of the utility tunnel structure in a soft foundation during the operation period and its performance is much better than that of other models (DBN, ANN and LSTM), as a preliminary study, the amount of used data is not very large, and the considered structure disturbance factors are not very comprehensive. Moreover, in this deep learning model, the WOA is only used to select the hyperparameters of the DBN, and the DBN is still trained by the traditional greedy layer-by-layer learning algorithm. Finally, in this study, the offline data are used in the prediction model, which restricts the real-time prediction. Therefore, the future works can be summarized as follows: (1) a prediction study on the real big data of the measured disturbance of the utility tunnel structure during the operation period, (2) a prediction study considering more structure disturbance factors for the utility tunnel structure during the operation period, (3) the development of a new deep learning model, in which both the hyperparameters and algorithm parameters of DBN are all optimized by the WOA and (4) a prediction study based on the real-time monitoring data to enhance the predictive capabilities of the deep learning model.