esa-sceva/satcom-chunk-collection huggingface dataset:
# GNSS Signal Quality in Forest Stands for Off-Road Vehicle Navigation
## 2 Material and Methods
### Characteristics of Localities No. 1 and No. 2  
The forest in locality 1 (WGS 84 coordinates: B = 16\({}^{\circ}\)58\({}^{\prime}\)2,29\({}^{\prime\prime}\), L = 49\({}^{\circ}\)19\({}^{\prime}\)55,68\({}^{\prime\prime}\); 642,930 E, 5,466,240 N, UTM 33N; H = 456 m) consists of the predominant winter oak (Quercus petraea), with mixed white birch (_Betula pendula_) and aspen (_Populus tremula_), which are either mixed or form more independent vegetation parts, surrounded by oak--see Figure 3. The growth is probably around 50 years old. The spatial structure of the stand is diverse, the dispersion of individuals is from individual to cluster. The location is on the top plateau, slightly exposed to the south, almost flat, and the terrain surface is slightly bent in places (possibly anthropically). Geologically, the locality is located on the Drahansky kulm, the soil-forming rock is consolidated sediments, the character is shale and debris. The measurement in this locality took place on 25 August 2020 between 9:00 and 16:00 (UTC+2). The temperature fluctuated between 18.2 \({}^{\circ}\)C in the morning and 21.9 \({}^{\circ}\)C in the afternoon at the end of the measurement; the medium humidity during GNSS measurement was 60%.  
Figure 2: Study areas: 1—deciduous forest, 2—coniferous forest, both in the military training area Brezina (www.mappy.cz).  
The forest in locality 2 (WGS 84 coordinates: B = 17\({}^{\circ}\)00\({}^{\prime}\)6,57\({}^{\prime\prime}\) L = 49\({}^{\circ}\)22\({}^{\prime}\)25,92\({}^{\prime\prime}\); 643,315 E, 5,470,945 N, UTM 33N; H = 392 m) is situated on the small plateau northwest of the village of Podivice in the central part of the military training area (MTA) Brezina. Vegetation consists mostly of conifers, predominantly with Scotts pine (_Pinus silvestris_) and Norway spruce (Picea abies), mixed winter oak (_Quercus petraea_), and places with aspen (_Populus tremula_), and white birch (_Betula pendula_) or beech (_Fagus sylatica_), and larch (_Larix decidiau_)--see Figure 4. The measurement in this locality took place on 26 August and 9 September 2021 between 9:00 and 16:00 (UTC+2). The temperature fluctuated between 16.2 \({}^{\circ}\)C in the morning and 18.9 \({}^{\circ}\)C in the afternoon at the end of the measurement; the medium humidity was 90%.  
Figure 4: Study area 2—mostly coniferous forest.  
Figure 3: Study area 1—deciduous forest.
## 4 Future Directions and Emerging Trends
Blockchain could enable consumer apps where one scans any produce or meat and instantly sees its \"identity card\"--origin farm, journey, certifications, even videos or images from the farm--fostering a strong connection and trust in the food. Such transparency can drive more informed consumer choices and reward producers who invest in quality and sustainability. From a food safety standpoint, if blockchain traceability becomes widespread, outbreaks of foodborne illness could be contained much more rapidly. Health agencies could pinpoint the contaminated batch within minutes and use the blockchain's data to quickly identify all distribution points, ensuring swift recalls. Over time, the rich data collected might allow pro-active prevention, as patterns in the supply chain data could reveal risks before they manifest (for example, noticing that a certain supplier consistently has temperature excursions in shipping and working to fix that). The vision for the future is a \"farm-to-fork\" digital trail for every food item, enhancing accountability at each step. Blockchain, especially in combination with other technologies, is poised to be a backbone for this vision--improving not just efficiency for businesses but also delivering societal benefits in terms of food safety, sustainability, and trust. As these systems mature, they hold the potential to transform consumers' relationship with their food, enabling a level of transparency and assurance that was not possible before.
# A Study on Data Profiling: Focusing on Attribute Value Quality Index
## Materials and Methods
### Data Quality Diagnosis
#### 2.3.3 Data Quality Diagnostic Comparison  
The calculation method of data quality error performs data profiling on all tables of the database and all attributes of each table in order to calculate a data quality error rate (quality error rate) of the database. The attribute extraction method using the geometric mean extracts an attribute with error possibility and performs data profiling targeting the extracted attribute. Table 5 shows a comparison of data quality diagnostic methods when targeting a single table.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Type** & **Criteria Applying Attribute Correction Value** & **Correction Value** \\ \hline All data types & The number of missing values (NA) is more than 1\% & 0.1 \\ Integer or numeric & Outlier Bonferroni \(p\leq 0.00001\) & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Attribute correction value extraction criteria.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Type** & **Weight Applying Criteria** & **Weight** \\ \hline All data types & Missing value (NA) \(>\) 0 & 0.1 \\ Integer or numeric & Near-zero variance (0) & 0.1 \\ Integer or numeric & Standard deviation (SD) \(\geq\) 100 & 0.1 \\ Integer or numeric & Outlier Bonferroni \(p<\) 0.05 & 0.1 \\ Factor & Space \(>\) 0 & 0.1 \\ Date & (Last date – first date) \(>\) (current date – first date) & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Attribute weight extraction criteria. NA—not available.  
As can be seen from Table 5, the extraction method using geometric mean is more scientific and shows better performance [1]. However, attribute extraction using the geometric mean has a disadvantage in that it is not possible to determine the degree of data value quality for each attribute. Thus, it was judged that, if this method was complemented, it could be helpful to perform data profiling more promptly.
# Quantification and Correction of Wave-Induced Turbulence Intensity Bias for a Floating LIDAR System
## 4 Results and Discussion
### Theoretical Impact of Buoy Movements on the FLS Horizontal Velocity Estimates
#### 4.2.4 Effect of Motion Subsampling for an Isolated Beam  
Now, we take into account the timing of the alternative measurements of the rays. We recall here that the approximate \(LOS\) measurement times of the five rays are defined by: \(t_{LOS}=(0.8\ s,0.8\ s,0.8\ s,0.8\ s,1\ s)\). The measurement of a given beam occurs approximately every 4.2 s and for a duration of about 0.8 to 1 s. During a \(LOS\) measurement time, the six degrees of freedom of motion of the buoy shift. The radial wind speed measurement is an average of a large number of measurements subjected to different movements. In addition to this phenomenon, a motion subsampling effect is induced by the incremental evolution of the beam direction, which returns to its original position every 4.2 s. These phenomena apply to rotational (roll, pitch, yaw) and translational (surge, sway, heave) motions and can strongly influence the radial wind speed measurement of one line-of-sight. In order to observe the influence of subsampling on the measured radial wind speed, we consider the ray A described in Section 4.2.2, aligned to the component \(V_{1}\), subjected to a tilting motion \(T\) of amplitude \(a_{tilt}=10^{\circ}\).  
Figure 9 shows an example of subsampling and averaging on the radial wind speed measurement. The radial wind speed is calculated with Equation (24) and is normalized by the value measured in the motionless case \(V_{r0}\).  
For motion periods \(P_{tilt}<8\) s, we see that the period of the measured radial wind speed signal \(V_{r}\) is larger than the period of the tilt motion. The radial wind speed is affected by a subsample of the motion. For motion periods \(8\ s<P_{tilt}<40\) s, harmonics due to the subsampling phenomenon are introduced but the period of the motion is respected by the radial wind speed measurements. These harmonics affect the standard deviation of the measurement and may also change the mean value of the measurement. For the period \(P_{tilt}=40\) s, the period of the motion is sufficiently greater than the period of one rotation of the LIDAR beam for the two signals to follow the same evolution.  
Figure 8: Influence of a sinusoidal yaw motion on the radial wind speed measurement of the beam N as a function of the incident wind components \(V_{1}\) and \(V_{2}\). All the results are plotted as a function of the amplitude of the motion and the time relative to the period of the sinusoid. (**a**) Variation of the \(V_{r_{IN,V_{1}}}\) component. (**b**) Variation of the \(V_{r_{IN,V_{2}}}\) component.
# Dynamic Bending Behaviour of Sandwich Structures for Marine Applications
## 4 Results and Discussion
### seawater Effects on Single Low-Velocity Impact Bending Performance of FRPSS
#### 4.2.2 Energy-Time Impact Properties  
Generally, the kinematics of the modified low-velocity pendulum large-deflection bending (cantilever-type constraints) test vary from that of the drop-weight low-velocity impact experiment primarily due to its boundary conditions. Therefore, lower in-plane membrane stresses are experienced because of the absence of an additional stress-stiffness effect, which augments the damage resistance of the samples. In a drop weight set-up, the energy-time behaviour of composite structures can be idealised as shown in Figure 6, with an initial increase in the energy with time until it reaches the maximum energy level, after which the energy drops and then stabilises [28]. However, in this arrangement, the energy absorption increases until it reaches the maximum, and then it reaches a stable value.  
Figure 7 depicts the energy absorption characteristics of the FRPSS specimen subjected to a single impact before and after seawater exposure. While GSP and GSH specimens absorbed 95% of the impact energy and GSV had an absorption rate of 89%, there was minimal change in absorbed energy between the exposed and unexposed samples.  
This could be indicative of an increase in the damping effects or change in the failure mode from brittle fracture to a more ductile mode occasioned by plasticization and swelling of the matrix as moisture uptake increased. Another possible reason for this phenomenon could be the redistribution of more uniform stresses because of the uptake, which leads to a reduction in the impact forces while absorbing similar energy after seawater exposure. Similar material behaviour of composite materials after environmental exposure was reported in [29; 30]. Therefore, under single impact bending, the energy absorption performance alone is inadequate in understanding the effects of moisture on the damage resistance performance, and further morphology assessment could provide a clear picture of the extent/nature of damage to marine vessels.  
Figure 6: Load–time behaviour for single impact.
## 4 Discussions
### Uncertainties and Limitations  
This study acknowledges potential uncertainties, particularly regarding the use of MODIS GPP products, which are based on the light use efficiency algorithm for large-scale GPP assessment. Although MODIS GPP products are validated and widely applied across various ecosystems, they tend to underestimate GPP in arid cropland and forest ecosystems as well as overlook GPP values in desert ecosystems. These inherent uncertainties remain a concern [54]. Additionally, the use of multiple regression analysis in this study simplified the complex interactions between climate and forest coverage changes, potentially reducing the nonlinear relationships between GPP dynamics and climate-related factors to a linear framework [55]. This simplification could affect the accuracy of quantifying the relative contributions of these factors. Furthermore, changes in vegetation productivity are also influenced by other factors such as topographic and the atmospheric CO\({}_{2}\) concentration. Additionally, other anthropogenic variables and ecological engineering activities besides afforestation, such as grassland restoration and wetland conservation, could also impact the GPP.
## 4 Studies of the Molecular Profiling of EVs as Potential Biomarkers  
EVs carry different classes of molecules, including proteins, lipids, nucleic acids, and other metabolites [9], and their content is affected by different environmental factors [61]. This, along with their ability to act as powerful cell-cell communication mediators, has contributed to their emergence as biomarkers of numerous diseases, including cancer [62]. However, mainly due to its small size, the purification of EVs is still a challenge and different isolation methods are used to obtain EVs efficiently and with high purity [63].  
The techniques widely used for isolating EVs include differential ultracentrifugation (dUC), size exclusion chromatography (SEC), density gradient flotation, immunoprecipitation, and polymer-based precipitation, as well as commercially available kits [64]. dUC is the most extensively used method for isolating EVs, consisting of sequential centrifugations with increasing speed and time, to separate particles depending on their size and density [65]. This method can obtain a high yield of EVs, but it is time-expensive and requires specialized equipment [66]. Furthermore, it could damage the integrity of these vesicles, affecting their profiles of RNA, protein, and other metabolites which are a potential source of biomarkers [67]. Hence, other techniques, such as SEC and density gradient centrifugation are gaining relevance to isolate EVs [66].  
On the other hand, it has been reported that the choice of a method for EVs isolation could impact down-stream analysis of their cargo, either due to the isolation of different EV populations, or the co-isolation of contaminating proteins and other possible matrix contaminants [68; 69]. In addition, various challenges come into play when isolating EVs from different biological sources, such as possible co-purification of chylomicrons and lipoprotein particles with EVs in serum and plasma [70], or the presence of Tamm-Horsfall protein in urine [71], which can interfere in posterior biomarker analysis. Thus, a methodological standardization of EV isolation is still needed, in order to assure the reproducibility of the subsequent analyses, especially for clinical settings [72]. A better understanding of the protein, lipid, and metabolic composition of EVs and the extent to which EVs composition reflects the source cell composition provides a solid basis for further development of diagnostics and therapeutics [9].  
In this review, we summarize the recent advances in bioactive EVs contents focusing on proteins, lipids, and metabolites that could play a significant role as diagnostic markers.
## 2 Materials and Methods
### Data Collect and Analysis Architecture  
Taking the Arduino unit as the data collector, we conducted the measurements for 20 days by sampling data once per hour and by entering the constructed data for training into the database. The Long Short-Term Memory (LSTM) RNN model data training had been established, and the backhaul data based on time parameters had also been created. The Raspberry Pi creates data to shape the pattern. We set up five different tables for the calculation, including temperature, soil pH, nitrogen, phosphorous, potassium and leaf height. We used MQTT to apply the analysis model to another Raspberry Pi node, and the data model was sent to different experimental places and was used to verify the node status interactively, as shown in Figure 2.  
Figure 1: Raspberry Pi and Arduino connected in series from the USB.  
Once the data are retrieved, we proceeded with a quantitative analysis of the data on the Raspberry Pi. The predictive growth trends had been established, and sophisticated depth learning models could be used to provide predictions of growth results. As the Chinese cabbage growing cycle is approximately 30 days, the value of the 20-day data is sufficient to shape and analyze the data, and the value of another node is used for prediction. In Table 1, we have extracted a part of the data.  
The experiments were divided into three sections: training group, audit group and test group. In this study, the data are first collected and then validated. In parallel, training results are subject to model verification. We can see that the validated predictive formation model corresponds to the actual results. The test group then conducted post-test modeling forecasting experiments using control test groups at different locations. Prediction of the corresponding future growth rate and leaf length growth rate used preset factors such as temperature, soil pH, nitrogen, phosphorus and potassium.
# Evaluation of Factors Affecting the MgO-C Refractory Lining Degradation in a Basic Oxygen Furnace
## 3 Results
### Refractory Lining Wear Mechanism in Basic Oxygen Furnace  
#### 3.6.1 Thermodynamic Modeling of Compounds Causing Wear of BOF Refractory Lining  
The average values of the chemical composition of the BOF slag (Table 6), composition of the MgO-C refractory lining in the wear area (Table 7), and composition of the applied gunning refractory material (Table 8) are given below.  
Thermodynamic modeling revealed that the predominant chemical reaction products within the BOF slag originated from the prevalent chemical elements in the converter slag, including Ca, Fe, Si, Mg, and O. The chemical products of these elements are listed in Table 9. Using the chemical compositions provided in Table 8 for the gunning material and specific temperatures, the most probable chemical compounds were modeled (Table 10).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Component** & **CaO** & **Fe\({}_{\text{general}}\)** & **SiO\({}_{\text{2}}\)** & **MgO** & **MnO** & **P\({}_{\text{2}}\)O\({}_{\text{5}}\)** & **Al\({}_{\text{2}}\)O\({}_{\text{3}}\)** & **S** \\ \hline Content \% & 46.12 & 19.13 & 11.37 & 8.27 & 12.75 & 0.94 & 0.99 & 0.06 \\ \hline \end{tabular}
\end{table}
Table 6: Average composition of analyzed BOF slag.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Component** & **MgO** & **CaO** & **P\({}_{\text{2}}\)O\({}_{\text{5}}\)** & **SiO\({}_{\text{2}}\)** & **Al\({}_{\text{2}}\)O\({}_{\text{3}}\)** & **Fe\({}_{\text{2}}\)O\({}_{\text{3}}\)** \\ \hline Content \% & 87 & 7.7 & 2.7 & 1.3 & 0.4 & 0.1 \\ \hline \end{tabular}
\end{table}
Table 7: Average composition of analyzed MgO–C refractory lining.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Name** & **Chemical** & \multicolumn{3}{c}{**\(\Delta\)G kJ\_mol–1**} & **Melting** \\  & **Formula** & **1300**\({}^{\circ}\)**C** & **1700**\({}^{\circ}\)**C** & **Point**\({}^{\circ}\)**C** \\ \hline Merwinite & Ca\({}_{\text{3}}\)MgSi\({}_{\text{2}}\)O\({}_{\text{8}}\) & \(-\)274.424 & \(-\)285.121 & – \\ \hline Akermanite & Ca\({}_{\text{2}}\)MgSi\({}_{\text{2}}\)O\({}_{\text{7}}\) & \(-\)146.355 & \(-\)126.345 & 1454 \\ \hline Diopside & CaMgSi\({}_{\text{2}}\)O\({}_{\text{6}}\) & \(-\)139.683 & \(-\)166.519 & 1391 \\ \hline Monticellite & CaMgSiO\({}_{\text{4}}\) & \(-\)101.513 & \(-\)101.158 & 1503 \\ \hline Wolastonite & CaSiO\({}_{\text{3}}\) & \(-\)90.774 & \(-\)91.510 & 1190 \\ \hline – & Ca\({}_{\text{2}}\)Fe\({}_{\text{2}}\)O\({}_{\text{5}}\) & \(-\)76.285 & \(-\)106.286 & 1477 \\ \hline Cyclowollastonite & CaSiO\({}_{\text{3}}\)(C) & \(-\)66.134 & \(-\)50.025 & 1540 \\ \hline Forsterite & Mg\({}_{\text{2}}\)SiO\({}_{\text{4}}\) & \(-\)58.338 & \(-\)58.020 & – \\ \hline – & CaFe\({}_{\text{2}}\)O\({}_{\text{4}}\) & \(-\)43.149 & \(-\)74.190 & 1240 \\ \hline – & MgSiO\({}_{\text{3}}\) & \(-\)40.568 & \(-\)46.535 & 1577 \\ \hline \end{tabular}
\end{table}
Table 9: List of the most probable products within the input components in BOF slag.
## 4 Experimental Validation
### Case 2
#### 4.2.4 Comparison of Domain Adaptation Algorithms  
The proposed method, which integrates two domain adaptation algorithms, achieved the highest average accuracy and attained the highest accuracy across all the transfer tasks, as shown in Table 15 and Figure 14. Additionally, it was observed that all three domain adaptation methods achieved accuracies exceeding 99% on the JNU dataset. This may be related to the strong feature extraction ability of the backbone network KAResNet. On the relatively simple JNU dataset, a high accuracy can be achieved using various domain adaptation methods.  
In the 2-0 transfer task, we observed a significant decline in the accuracy of all the methods. This was primarily due to the considerable speed difference from 1000 rpm to 600 rpm. Specifically, in Condition 2 (1000 rpm), the bearing speed was higher, which likely introduced more high-frequency noise and complex features in the signal. Conversely, in Condition 0 (600 rpm), these features may have been less prominent or masked. Therefore, in the 2-0 transfer task, the features learned by the model in the source domain (1000 rpm) did not generalize effectively to the target domain (600 rpm), leading to a drop in the accuracy.  
In contrast, the accuracy in the 0-2 transfer task was relatively higher. This might be because at 600 rpm, the signal noise was lower, making the features more distinct and clear, which allowed the model to learn these features more effectively. Additionally, the features between the source domain (600 rpm) and the target domain (1000 rpm) were easier to align, resulting in better performance in the 0-2 transfer task.  
In the 1-0 transfer task, we found that all the methods achieved a 100% accuracy. This is mainly due to the high similarity in distribution between the source domain (800 rpm) and the target domain (600 rpm). The data features and noise distribution levels were similar, and the fault features learned by the model at 800 rpm could adapt well to the 600 rpm condition, enabling the model to generalize effectively in the target domain.
## 2 Methods and Data
### Data Source and Pre-Processing
#### 2.2.8 Ecosystem Model Simulations Used in the TRENDY Project  
Eleven state-of-the-art ecosystem models from the TRENDY project were used to simulate the historical trend of LAI and GPP over global drylands from 1982 to 2018 in this study. These ecosystem models mainly include the following: CABLE-POP, CLM5.0, IBIS, ISAM, ISBA-CTRIP, JULES-ES-1P0, LPI-GUESS, ORCHIDEEE, ORCHIDEEv3, SDGVM, VISIT. Both LAI and GPP were generated by these 11 ecosystem models from the TRENDY project with a spatial resolution of 0.5 degrees and a monthly interval from 1700 to 2019. These models were operated at the four simulation scenarios: S0, S1, S2, and S3. S0: no forced changes; S1: only CO\({}_{2}\) concentration changes; S2: both CO\({}_{2}\) concentration and climate changes; and S3: varying CO\({}_{2}\) concentration, climate, and land cover changes (LCCs) [14]. This study quantifies the contributions of CO\({}_{2}\), climate change, and LCC to the interannual trend of LAI in global drylands from 1982 to 2018 by S1 subtracting S0, S2 subtracting S1, and S3 subtracting S2, respectively.