esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Modeling Methodology
#### 2.4.2 Model Fitness  
QRF is an extension of RF, which can estimate uncertainty and predictions [48; 49]. Similar to RF, the predicted results of RF are unbiased, and QRF is less affected by outliers and more robust when there are a large number of environmental covariates [48; 49]. Thus, QRF also shows better performance in mapping soil information in different regions of the world [50]. When searching relationships between predictive variables and covariates, QRF randomly generates multiple regression or classification trees. All trees are trained based on randomly selecting calibration datasets with the bootstrap method (sampling with replacement). In addition, the random selected covariate subset determines each split node of a tree. For regression, the final predictive result is the average of predictions of each tree with the following equation:  
\[Pa=\frac{\sum_{i=1}^{n}(P_{i})}{n} \tag{4}\]  
where \(n\) is the number of CART and \(P_{i}\) and \(P_{a}\) are the prediction of single CART and the average prediction of all CART, respectively.  
When fitting the RF model, the new calibration is generated from the whole dataset using the bootstrapping method, and approximately one thirdof the unselected samples are used as an independent validation for unbiased evaluation of the model performance. The OOB samples can be used to estimate the model accuracy. The increased mean square error (%IncMSE) of each variable including or eliminating tree models in OOB samples can be used to explain the importance of covariates [51]. Different from RF, QRF can derive the probability distribution of predictions based on weighted samples, which can be used to calculate the confidence interval (\(CI\)) with assigned quantiles.  
There are three major parameters in QRF, i.e., mtry, ntree, and nodesize. Based on the standard of minimal RMSE in 10-fold cross-validation (CV), mtry, ntree, and nodesize were defined as 9 (tuning range: 1-20), 1200 (tuning range: 500-1500), and 5 (tuning range: 1-20), respectively. To obtain relatively stable results, we repeated the QRF model with 10-fold CV 50 times in the all dataset, and then confirmed the hyperparameter that is most applicable to the model and calculated the average predictions and \(CI\) at the 90% level (i.e., 5% and 95% quantiles).
# A Review of the Role of Critical Parameters in the Design and Operation of Biogas Production Plants
## 3 Process Parameters Involved in a Biogas Production Plant
### Reactor
#### 3.2.1 Configuration
In this configuration, the first phase involves semi-anaerobic hydrolysis of feedstock at low hydraulic retention time and the removal of un-degraded materials to the secondary reactor for acidogenisis. From the secondary reactor, output liquids and solids are transferred to a tertiary reactor where methane-rich biogas is produced. Likewise, in a two-stage system, a part, or all of the effluent from the tertiary reactor can be recirculated back to the digester depending on the targeted HRT and process efficiency [159]. According to the study by [PERSON] et al. [159], using a three-stage reactor, COD removal efficiency of over 95% was achieved for food waste AD. In a more recent study [7], an 11-23% increase in methane yield for a three-stage codigestion of food waste and horse manure was reported. In addition to the configurations mention above, AD systems have undergone several modifications over the last few decades to address the retention efficiency of slow-growing methanogens. With the development of the UASB reactor, where a dense sludge bed allows methanogenic sludge to maintain within the reactor [160], the improvement in retention efficiency was found to be significant. So far, numerous UASB reactor types are being used for treating waste and wastewater effluents of various characteristics. Among these, the internal circulation (IC) reactor was observed to exhibit high efficiencies in terms of considerable feasibility, robust resistance to outside accidents, high organic loading rate, and low investment cost [161]. Membrane bioreactors (MBR) are another technique quite successfully able to retain active biomass in the system. By employing a membrane, both cells and inhibitory components within the bioreactor can be separated and as such, an efficient biological process can be achieved [162]. Available membrane reactors and their application in the context of AD can be learned from a comprehensive review authored by [PERSON] and [PERSON] [162]. Figure 4 illustrates two examples of novel reactor configurations that are used in both lab- and industrial-scale applications. Apart from bioreactor design, the developments in biosensor technologies and molecular biology techniques can enable understanding complex microbiological processes and potential disturbances much earlier than the conventional AD processes that do not involve any sensors for that matter. Reviews of the current progress in sensor technologies for controlling AD is documented excellently by [PERSON] et al. [163].
# Some Aspects of the Modelling of Dried Red Beets Rehydration Process
## 4 Conclusions  
The Page model and modified Page model can be accepted as the most suitable for the description of mass gain (adjusted R\({}^{2}\) = 0.9671-0.9964, RMSE = 0.0179-0.0528) and volume increase (adjusted R\({}^{2}\) = 0.9414-0.9947, RMSE = 0.0210-0.0877), whereas the Peleg model can be treated as the most appropriate for characterizing the dry matter loss (adjusted R\({}^{2}\) = 0.9368-0.9944, RMSE = 0.0135-0.0362), and the modified Page model can be accepted as the most appropriate for the moisture content increase (adjusted R\({}^{2}\) = 0.9625-0.9928, RMSE = 0.0267-0.0603) during the rehydration process for dried red beets. The constants of the investigated models depended (although to a different degree) on the parameters of drying and rehydration processes. It can be concluded from the results of statistical analyses on the modelling of mass, volume, dry matter, and moisture content changes during the rehydration of dried red beets (at model constants expressed as functions of drying and rehydration conditions) that the following models can be recommended as the most acceptable: (1) the modified Page model for mass gain (adjusted R\({}^{2}\) = 0.9628-0.9964, RMSE = 0.0236-0.0897) and for volume increase (adjusted R\({}^{2}\) = 0.9314-0.9945, RMSE = 0.0213-0.0972), (2) the Peleg model for dry matter loss (adjusted R\({}^{2}\) = 0.9348-0.9945, RMSE = 0.0161-0.0610), and (3) the Henderson-Pabis model for moisture content increase (adjusted R\({}^{2}\) = 0.9368-0.9920, RMSE = 0.0350-0.1062). The ANN (MLP 4-4-2) adopted to characterize both indices RI and WAC together can be accepted as the most appropriate for the prediction of these indices (R\({}^{2}\) = 0.9327-0.9348, RMSE = 0.0529-0.0549). A sensitivity analysis of artificial neural networks (using the backward stepwise method) indicated that the characteristics dimension of red beets had the greatest impact on RI and WAC.  
Conceptualization, K.G. and A.K.; methodology, A.K. and K.G.; experiments, K.K.; software, K.G.; visualization, K.G. and M.O.; formal analysis, A.K., M.O. and K.G.; writing--original draft preparation, A.K.; writing--review and editing, K.G.; funding acquisition, K.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 4 Discussion  
The proposed multimodal joint representation learning method integrated with a residual neural network demonstrates superior performance in rotating machinery health state recognition compared to traditional methods. The model effectively addresses the challenges of multimodal signal fusion, achieving a recognition accuracy of 99.64% on the gearbox dataset--outperforming single-modality baselines such as image (94.39%), sound (96.96%), and vibration (97.91%). These results underscore the critical role of multimodal fusion in enhancing classification accuracy.  
The incorporation of residual connections further mitigates the vanishing gradient issue in deep networks, enabling the model to maintain high accuracy while reducing computational complexity and parameter count. Despite its effectiveness under controlled experimental conditions, the model may encounter limitations in complex environments characterized by variable and multiple operating conditions.  
Future research will focus on the following: (1) extending the model to variable and multi-condition scenarios to enhance robustness and generalizability; (2) adopting transfer learning or self-supervised learning to address performance degradation due to insufficient labeled data; and (3) introducing lightweight architectural designs to meet the real-time and resource constraints of edge computing and other practical applications.
# Study of the Properties of Human Milk Fat Substitutes Using DSC and GC Methods
## 3 Results
### The Quality Assessment of Studied Fats  
During the development of the SL, it is not only important to optimize the FA composition and distribution in TAG but also to provide acceptable oxidative stability of SL. There are many methods used for monitoring the autoxidation of lipids. One of the most popular techniques used to evaluate the oxidative stability of fats and oils is DSC [31, 32]. Basic PDSC parameter used to evaluate the resistance of the fat being analyzed to its oxidative decomposition is induction time. Fats with a shorter induction time are less stable than those for which the induction time obtained at identical temperature is longer [33].  
The results of PDSC measurements are shown in Table 3. The PDSC tests for SL performed at constant temperature of 120 \({}^{\circ}\)C showed that their induction times (27.2-53.0 min) were reduced compared with the starting fats (70.9 min for L and 72.0 for RO). The determinant of primary oxidation products in fat is a peroxide value (PV). PV of L, RO, and SL are presented in Table 3. PV of blends after enzymatic modification amounted from 4.1 to 5.7 mmol O\({}_{2}\)/kg of fat. Therefore, this modification causes an increase in PV compared to raw materials (2.7 mmol O\({}_{2}\)/kg for L and 3.4 for RO). Results obtained also show that PV for mixture interestrified for long time (24 h) was lower than for those interestrified for short time (4 h). The determinant of free FAs contents in fat is an acid value (AV). The results of AV for mixtures after interestrification, for L and RO, are given also in Table 3. It has been found that after interestrification the AV increased. AV of blends esterified in the presence of Lipozyme RM IM amounted from 5.4 to 11.0 mg KOH/g of fat. The FFA content significantly depends on the reaction time. The fats obtained after 8 h of interestrification are characterized by the highest content of free fatty acids, while after 24 h of modification, the content of fatty acids was the lowest.
# UAV Application for Typhoon Damage Assessment in Construction Sites
## 4 Case Study and Process Design
### Number Identification  
Three-dimensional modeling and related fundamental data analysis were carried out by acquiring the photo data of the construction site by UAV. However, the digital modeling function of UAVs cannot meet the requirement of disaster prevention and reduction work in a typhoon. Therefore, it is necessary to use photos for another aspect of analysis, such as using the function \"volume measurement\" in the pix4d to measure the earthwork volumes in the photos. If the calculated value exceeded the safety value in the specification, the construction teams were arranged to transfer the earthwork as soon as possible. Because  
Figure 9: Application of volume measurement.  
The stability of large machinery, especially construction machinery such as tower cranes and scaffolds, affects the safety of construction sites. The time left for construction programs to carry out disaster prevention work is minimal when a typhoon strikes. Therefore, we can use the mobility of UAVs to identify dangerous areas at some structural joints. Safety inspection can be completed in a short time, and reinforcement warnings can be made for weak positions. Therefore, for these safety risks that do not conform to the relevant safety standards, management personnel can reduce risk and loss by accurately carrying out targeted disaster prevention and prevention.
# Analysis of Maize Sowing Periods and Cycle Phases Using Sentinel 1&2 Data Synergy
## 2 Materials and Methods
### Satellite Data
#### 2.2.3 Sentinel-2  
The Sentinel-2 constellation provides optical data at the 10 m \(\times\) 10 m spatial resolution every 5 days (Figure 2c). Many indices for terrestrial monitoring can be derived from the optical information. In this work, the NDVI (Normalized Difference Vegetation Index) was used for the monitoring of vegetation changes in terms of optical and infrared signals. The NDVI index is defined as [38]:  
\[NDVI_{i}=\frac{NIR_{i}-RED_{i}}{NIR_{i}+RED_{i}} \tag{4}\]  
where \(NIR\) and \(RED\) are the Near InfraRed and the \(RED\) reflectance respectively, for each \(i\)-th maize field.  
The Theia-Land web service provides 10 m \(\times\) 10 m cloud free NDVI data, correcting the atmospheric effect from Sentinel-2 measurements [39]. The NDVI time series werecalculated by averaging the pixels corresponding to each maize field (classified according to the procedure described in Section 2.3) and interpolating the mean values for each Sentinel-2 acquisition [23; 40]: a linear interpolation was performed to retrieve NDVI grids for the days between two consecutive sets of data, since NDVI changes are mainly driven by the phenological growth of plants.
## 3 The Main Sources of Bacterial Stress in the Intestinal Microbiota
### Stress Resulting from Microbial Interactions  
3.7.1 Stress by Bacterial Antimicrobial Peptides: Microcins, Lantibiotics, Colicins  
Microins are low-molecular-weight antibiotic peptides. They were distinguished in 1976 from colicins, which are higher-molecular-weight antibacterial proteins that are much less stable in the intestinal tract [71]. Microcins are ribosomally synthesized and post-translationally modified peptides (RiPPs), which are mostly produced by Enterobacteriaceae and act on members of this family of microorganisms. They have various mechanisms of action, such as producing pores in the cytoplasmic membrane (McCv, MccE492, and MccL); inhibiting the aspartyl-tRNA involved in protein synthesis (MccC), inhibiting the topoisomerase GyrB, producing double DNA breaks (MccB17); blocking the secondary RNA polymerase channel, impairing transcription and acting on cytochromes inhibiting cellular respiration (MccJ25); or altering the function of the cellular proton channel (MccH47, and possibly MccM and MccL), or the ATP synthase (MccH47). Microcin stress is followed by immunity/resistance mechanisms, including acetyltransferases (MccC), production of immunity proteins (Class IIb microcins), enhanced efflux pumps, and inhibition of DNA gyrase supercoiling activity (MccB17). There is evidence that microcins strongly influence microbial interactions in the gut [13]. The equivalent of microcins in Enterobacteriaceae are lantibiotics in Bacillus, as well as ribosomally produced and modified post-translational peptides [72]. Lantibiotics (lanthionine- and methyllanthionine-containing peptides) can produce holes in the bacterial membrane and eventually interfere with cell wall synthesis [73]. Resistance/protection from lantibiotics is mediated by the production of \"immunity proteins\", specialized ABC-transport proteins, modifications in membrane composition, lantibiotic-lytic proteins, spore formation, and immune mimicry [74]. Colicins are much larger and less stable polypeptides in the intestinal environment, and they are produced and active in Enterobacteriaceae. Their mechanism of antibacterial action includes membrane pore formation, degradation of nucleic activity (DNase, 16S rRNAse, and tRNAse activities), and altering peptidoglycan synthesis. Resistance to colicins involves receptors and translocation mutants (Tol pathway mutants), alteration of outer membrane proteins, including _ompF_, _exbB_, and _tonB_ mutations, and enterochelin hyperproduction [75].
## 3 Results and Analysis
### Spatial Variability of Soil Nutrients
#### 3.3.2 Spatial Autocorrelation Analysis of Soil Nutrients  
The global Moran's I index is adept at gauging the overall convergence intensity of regional variables. The strength of spatial autocorrelation can be inferred from its absolute value (refer to Table 4). Our findings highlight that every soil nutrient in Fengtai County exhibits a strong and significant spatial positive correlation (Moran's I > 0, P < 0.01, Z > 2.58). Ranked by their global Moran's I values, the nutrients are as follows: Olsen-P > alkali-hydrolyzed nitrogen > pH > total N > slowly released K > organic matter > available K. The most pronounced spatial clustering is reflected by the global Moran's I value of 0.884 for available phosphorus. In contrast, quickly available potassium and organic matter, having relatively low global Moran's I indices, show a more dispersed spatial distribution.
## 5 Conclusions  
The presented methodology has demonstrated the suitability and the advantage of using LiDAR data for monitoring aquatic reed beds. The available information obtained from ALS allowed an accurate interpretation and mapping of vegetation structure. Points of the vegetation crown, upper and lower canopy, and ground all contributed to an improved characterisation of aquatic reed beds. The developed decision tree algorithm allowed for an automated classification of reed beds and of vegetation structure (extent, height, and density). With an overall accuracy of 96% and a Kappa coefficient of 0.91, aquatic reed beds could even be subclassified into sparse and dense stocks. Moreover, LiDAR-derived rasters and vector data allowed for the delimitation of the actual shoreline, aquatic reed front, and of sparse aquatic reed beds. ALS revealed several advantages compared to onsite-field monitoring methodology, including the absence of habitat and biological disturbance. The vertical accuracy of the derived DSMs similarly showed a high level of agreement in derived heights of flat surfaces (RMSE = 0.1 m) and an adequate agreement of aquatic reed heights with evenly distributed errors (RMSE = 0.8 m).  
**Acknowledgments:** As part of the larger project \"Bayerns Stillgewasser im Klimawandel - Einfluss und Anpassung\" (TLK01U-6627), this study was carried out as a partial project \"Klimawandel beeintrachtigt Schillgesetabe hyseren - Erfassung mittels modemer Monitoringmethoden\" funded by the Bavarian State Ministry of the Environment and Consumer Protection. In addition, we would like to thank [PERSON], [PERSON] and the staff of the Research Group of Photogrammetry and Remote Sensing of the Department of Geodesy and Geoinformation of the Vienna University of Technology, Austria, for their support with technical advice and the development of OPALS software. AHM GmbH for their support and accurate pre-processing. We also thank [PERSON], [PERSON], and [PERSON] for their help with the fieldwork, and [PERSON] and [PERSON] for their support in English editing.  
**Author Contributions:** The first project idea for this study was conceived by [PERSON] and developed further during discussion with [PERSON], [PERSON] and [PERSON]. [PERSON] and [PERSON] coordinated the data collection. [PERSON] developed the methodology for processing and analyzing the data. The manuscript was drafted by [PERSON] with continuous input and revision by [PERSON] and [PERSON]. All authors read and approved the final version of the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Local Mechanical Properties  
Hardness, reduced elastic modulus and creep of NiTi instruments were measured with a fully calibrated NanoTest instrument (Micro Materials, Wrexham, UK). Standard load-controlled nanoindentation tests were performed with a three-sided diamond Berkovich indenter at a normal force of 50 mN in order to assess hardness and elastic modulus values.  
Figure 1: Experimental setup for cyclic fatigue testing (**a**) overall design: 1. Artificial root canal, 2. Infrared thermometer, 3. Thermostatic bath, 4. Handpiece with endodontic instrument, 5. Mobile device for precise insertion of the instrument and (**b**) detail of the artificial root canal: 1. Diameter in the area of tool tip, 2. Radius of curvature, 3. Angle of curvature.  
In addition, nanoindentation creep experiments were performed at a load of 200 mN to explore the rheological response of the instruments. The indenter was calibrated using a standard fused silica reference sample. The nanoindentation took place with a time course of 10 s loading and 10 s unloading; a dwell period of 10 s was used for the standard test and 120 s for the creep test.  
Nanoindentation tests were performed on transversal cross-sections. Vertically oriented NiTi instruments were fixed in the epoxy resin Dentacryl (Spofadental, Jicin, Czech Republic) and machined through grinding and fine polishing. Two transversal cross-sections were prepared in two steps, first 2 mm and then 6.4 mm from the apex of the instrument. The distance of 2 mm from the apex was chosen so that there was a sufficient area for indentation measurements, and the distance of 6.4 mm was chosen as it corresponds to a typical location of a fracture of instruments (the break point of the test tips) during cyclic fatigue tests. The indentation matrix of 4 \(\times\) 5 indents was located in the central part of both cross-sections, while spacing between indents was 30 \(\upmu\)m. The nanoindentation curves were evaluated by the standard Oliver-Pharr method [38] for determination of hardness and reduced elastic modulus values. Indentation measurements were performed on three instruments for each manufacturer. Creep measurements were done on one arbitrarily chosen instrument for each manufacturer.  
Microscopic observation of the instrument cross-sections and surfaces was carried out using a laser scanning confocal microscope (OLS LEXT 5000, Olympus, Tokyo, Japan) at different magnifications. Dimensional analyses in both 2D and 3D were used both for measuring the area of cross-sections and for non-contact roughness measurement. The latter was measured on the side (outer) wall of the endodontic files. Linear roughness was obtained from ten lines drawn in the field of view acquired using \(\times\)100 objective. A low pass filter (cut-off) of \(\lambda_{\text{c}}=100\)\(\upmu\)m had to be used to remove surface waviness from the roughness data.