esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Validation of the Developed Model  
As the configuration was made up of three sub-plants, the model of each one of them was validated separately. At first, the LS-2 parabolic collector model was validated and found to be accurate enough, as it is shown in Table 4. The mean deviation of the collector thermal efficiency and thermal losses per collector aperture, between the proposed model and the experimental work of [PERSON] [45], was calculated at 0.29%, and 1.64%, respectively. Additionally, the ORC module model was compared to the data from the experimental analysis of [PERSON] et al. [46], and the results are presented in Table 5. More specifically, the mean deviation of the electricity production rate and thermal efficiency was found at 0.62% and 0.52%, respectively. These values were considered low and acceptable.  
Figure 2: Flow chart of the solution process.  
The electrolyzer model was also tested through experimental data, which were provided from the study by [PERSON] et al. [47]. The model was considered to be valid enough as the mean cell potential deviation was calculated at 2.88%. Finally, the analytical results are given in Table 6 and Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Examined Cases** & **Present Model** & **Reference [47]** & **Deviation** \\ \hline
**Current Density** & **Cell Potential** & **Cell Potential** & **Cell Potential** \\ \hline
**(A/m2)** & **(V\({}_{\text{cell}}\))** & **(V\({}_{\text{cell}}\))** & **(\%)** \\ \hline
185.35 & 1.78 & 1.70 & 4.96 \\
394.18 & 1.83 & 1.76 & 4.09 \\
590.80 & 1.85 & 1.79 & 3.54 \\
793.62 & 1.87 & 1.81 & 3.70 \\
990.27 & 1.89 & 1.83 & 3.22 \\
1494.27 & 1.91 & 1.86 & 2.77 \\
1998.28 & 1.93 & 1.89 & 2.29 \\
3000.23 & 1.96 & 1.92 & 2.19 \\
3996.03 & 1.99 & 1.96 & 1.52 \\
5004.11 & 2.01 & 1.99 & 0.56 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Validation of the PEM water electrolyzer with experimental data from the literature.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Examined Cases**} & \multicolumn{3}{c}{**Present Model**} & **Reference [45]** & **Deviation** \\ \hline
**G\({}_{\text{bn}}\)** & **u\({}_{\text{air}}\)** & **V\({}_{\text{fluid}}\)** & **T\({}_{\text{amb}}\)** & **T\({}_{\text{col,in}}\)** & **t\({}_{\text{th,col}}\)** & **q\({}_{\text{loss,th}}\)** & **t\(
## 4 Numerical Results
For this purpose, during the development, it was necessary to compare various pre-processing operations to enhance the quality of the computational grid while reducing its generation time using _snappyHexMesh_. T-WorkFlow fully manages the interaction with OpenFOAM, automates post-processing, and provides real-time outputs to quickly identify the most effective configuration in evenly distributing the flow across the radiating ducts. The paper also provides a practical example by applying the software to two different radiator configurations and includes a comparative analysis of their hydraulic performance. In particular, it was successfully demonstrated that a possible geometrical simplification in the tank-channel junction can lead to important computational savings without significantly affecting the accuracy of the numerical results. In future work, several improvements could be implemented in T-WorkFlow, some simple and immediate and others more complex, to increase the impact of the tool on the design practice of the company. The potential new features include but are not limited to (i) allowing users to select different levels of mesh refinement, with the tool automatically adjusting the snappyHexMeshDict; (ii) automatically scheduling multiple simulations for a parametric study involving various geometries or operating conditions; (iii) applying the existing workflow to simulate other components, such as intercoolers. More ambitious goals involve integrating the T-WorkFlow into a complete shape optimization process by utilizing open-source tools like Dakota [32], as shown in [33]. Lastly, a particularly intriguing possibility lies in integrating machine learning methods to support the numerical simulation practice for ground vehicles, as explored, for example, in [34]. Conceptualization, F.M., M.V., E.B., A.B. and A.C.; Methodology, F.M., M.V., E.B., A.B. and A.C.; Software, F.M., M.V., E.B., A.B. and A.C.; Validation, F.M., M.V., E.B., A.B. and A.C.; Writing--original draft, F.M., M.V., E.B., A.B. and A.C.; Writing--review & editing, F.M., M.V., E.B., A.B. and A.C.; Supervision, A.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors. Authors [PERSON] and [PERSON] were employed by the company Tatus Racing S.p.a. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Review and Evaluation of Solar Irradiance Forecasting Methods
### Image-Based Methods  
As introduced in the preceding section, the numerical prediction models have the potential to provide forecasts by simulated clouds at high spatial and temporal resolutions. However, the forecast performance during the first 12-24 h is influenced significantly by the cloud analysis accuracy and initial thermodynamic conditions. Due to non-linear cloud processes and their associated effects on radiation, the numerical models tend to underestimate low clouds (typically stratus clouds and coastal regions) and, therefore, lose accuracy in the solar irradiance forecasting at the surface [168].  
Accurately predicting the extent, motion, formation, dissipation, and transmittance of constantly changing clouds is a complex and unrealistic task for solar forecasting applications on medium time scales. Cloud condition imagery, captured through satellites, imagers, or all-sky cameras, offers excellent data for short-term forecasting or nowcasting due to its high spatial and temporal resolution. This type of imagery is particularly useful for improving the control of the significant impact that clouds have on the solar irradiance reaching the Earth's surface. When compared to numerical models or time-series forecasting, cloud condition imagery is a superior source of data for accurately predicting the immediate effects of clouds on solar irradiance. Solar irradiance forecasting models based on image processing commonly utilize two techniques: the clear sky index and cloud motion vectors. The application of motion vector fields directly on the cloud cover coefficient allows for the accurate prediction of solar irradiance forecasts from images. By combining these two methods, solar irradiance forecasting models can more accurately predict changes in solar irradiance caused by cloud movements.  
The following sections will present a review of research that utilizes sky imagery, regardless of whether the traditional method mentioned above is followed. The studies will be logically differentiated between those that use satellites as an image source and those that employ cameras to take images from the surface.
## 3 Results
### DEM Construction
Figure 3: (**a**) Filtered interferogram (February 21, 2015) after flat Earth phase removal showing topographic features on the glacier surface. Owing to the high sensitivity of the height ambiguity, the remarkably detailed fringe pattern on the glacier surface and the very short wavelength of the fringes in the bedrock regions near the glacier were detected. (**b**) Differential interferometric phase scaled from \(-\pi\) to \(\pi\) subtracted from the simulated topographic phase of the Greenland Ice Mapping Project (GIMP) DEM. (**c**) Interferometric coherence ranges from 0 to 1 showing a very high coherence glacier surface, except for the regions marked by red circles. This might have resulted from relatively faster glacier movement or a difference in glacial surface roughness. The GIMP DEM did not have sufficient vertical accuracy to discriminate the detailed characteristics of the glacier surface. We noticed that the retreat of the glacier terminus could be detected by comparing the time-series of the DEMs. Two major calving events that occurred during August 2010 and July 2012, and the operational TanDEM-X acquisition used for the selected global TanDEM-X DEM were captured from December 14, 2010 to April 6, 2014. Thus, the global TanDEM-X DEM included a part of the history of the calving event during July 2012. The large crack can be clearly seen at the glacier terminus in the global TanDEM-X DEM image, as shown in Figure 4c. Topographic changes such as the crack from these two calving events cannot be detected in the GIMP DEM (Figure 4d). Figure 4: (**a**) Constructed DEM scaled from 0 to 450 m, excluding the bedrock regions that were not of interest in this study. The topographic information was successfully extracted using the pursuit monostatic TanDEM-X Science Phase mode. To impose the advantage of a large perpendicular baseline in a low slope area, the constructed DEMs of (**b**) the TanDEM-X Science Phase mode, (**c**) the global TanDEM-X DEM, and (**d**) the GIMP DEM, scaled from 15 to 100 m, are shown. Note that the derived DEM, thanks to the large perpendicular baseline configuration in this study, shows much clearer topographic change along the glacier surface.
# Evaluating Soil Bacteria for the Development of New Biopreparations with Agricultural Applications
## 3 Results
### Biochemical Characterization of Soil Bacteria
#### 3.2.3 Extracellular Enzyme Activities of Isolated Bacteria Strains in Submerged Cultures  
To assess the biodegradation potential of the 10 selected isolates that exhibited the highest and most diverse range of enzymatic activities in on-plate biochemical assays, submerged cultures were established in media containing maize, winter barley, and triticale residues as the primary carbon sources. After a 5-day incubation period, the supernatants were analysed for cellulolytic, amylolytic, pectinolytic, and xylanolytic activities. Given the pH-dependent nature of enzyme activity, assays were conducted at pH 5.5 and 7.0 (Figure 3). We have selected a model acidic pH of 5.5 and neutral pH 7.0 as a standard in comparative studies.  
The experimental results indicate that strains WK-5, WK-9, WK-14, WK-15, and WK-20 have the highest hydrolytic potential for the degradation of all three types of post-harvest residues. These strains exhibited the most significant amylolytic, cellulolytic, and xylanolytic activities in their supernatants. Interestingly, despite exhibiting broad-spectrum hydrolytic activities in on-plate tests, strain WK-13 showed relatively low potential for the degradation of post-harvest residues in submerged cultures in comparison to strains WK-14 and WK-15.  
Statistical analysis of the results revealed that the hydrolytic activities of most of the tested strains, if exhibited, were significantly substrate-dependent (\(p\)-value < 0.05). Differences were found to be dominant for isolate WK-5 with cellulolytic (pH 5.5; \(p\)-value = 0.023; pH 7.0 \(p\)-value = 3.33 \(\times\) 10\({}^{-7}\)) and amylolytic (pH 5.5; \(p\)-value = 1.32 \(\times\) 10\({}^{-4}\); pH 7.0 \(p\)-value = 1.7 \(\times\) 10\({}^{-5}\)) activities, as well as WK-14 and WK-15 for all measured activities (\(p\)-value from 6.18 \(\times\) 10\({}^{-7}\) to 0.043) indicating statistically significant difference between samples. Exceptions were observed in the case of cellulolytic activity for WK-14 in pH 5.5 (\(p\)-value = 0.244) and WK-15 in pH 7.0 (\(p\)-value = 0.089). Interestingly, the cellulolytic activity of isolate WK-20 did not seem to depend on the type of post-harvest residue used as a primary source of carbon (pH 5.5, \(p\)-value = 0.087; pH 7.0, \(p\)-value = 0.666), despite other activities varying significantly (\(p\)-value from 4.59 \(\times\) 10\({}^{-5}\) to 5.83 \(\times\) 10\({}^{-3}\)). This effect may be due to the specific biodegradation potential of Bacillaceae strains [38; 39]. It could also be related to the complex structures of the natural maize, winter barley, and  
Figure 2: On-plate hydrolytic activities (as enzymatic activity index, EAI) of WK strains after 7 days at respective optimal growth temperatures (Table S4). None of the isolates showed xylanolytic or ligninolytic activities.
# Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
###### Abstract  
In recent years, the application prospect of urban logistics unmanned aerial vehicles has attracted extensive attention. The high-density operation of UAVs requires autonomous separation maintenance capability. To achieve autonomous separation maintenance, it is necessary to conduct autonomous track prediction and formulate the required separation accordingly. Based on the target level of safety requirements for UAV operation, aiming at the autonomous separation maintenance ability of UAVs and considering the accuracy of track prediction, a method to calculate the required separation between UAVs is proposed. This study consists of two parts. Firstly, based on historical data, the position prediction error of the flight track is investigated. Using a machine learning model, a two-stage track prediction method, which involves classification followed by prediction, is proposed for urban logistics UAV track data. Subsequently, based on the track prediction error distribution, by designing a gas model and a position error probability model, a separation-formulating model for urban logistics UAVs in free flight is proposed in which UAV maneuverability is considered. By applying this model, the required separation is formulated for UAVs. When the required separation is set to 48.5 m, the overall collision risk meets the TLS requirements. The research provides a feasible method for establishing autonomous separation for urban logistics UAVs.  
urban logistics unmanned aerial vehicle (UAV); flight separation; track prediction; collision risk; conflict detection and resolution (CDR); target level of safety (TLS)
# FPGA Implementation of a BPSK 1D-CNN Demodulator
## 5 Conclusions  
This paper presented an FPGA implementation of a 1D-CNN demodulator for BPSK. Two 1D-CNNs were contained in the 1D-CNN demodulator, to detect types and moments of the phase shift. A decision module was employed to synthesize results of the 1D-CNNs, and then the information carried by the modulation signal was obtained. A time-delay network for convolutional calculation and a structure for piecewise approximation for the activation function were adopted, improving the efficiency of resource utilization and operation speed. Universal and diversity training data were generated, strengthening the adaptability to the real channel condition. Complexity, power consumption, and latency of the 1D-CNN demodulator and a coherent demodulator were  
Figure 16: The test results in different channel conditions.  
Figure 15: Performance of the 1D-CNN demodulator and coherent demodulator in the offset channel.  
compared. The result shows that the 1D-CNN demodulator had acceptable complexity, power consumption, and outstanding latency. The performance of the 1D-CNN demodulator was tested in different channels. In complicated channels where high offset was introduced, the 1D-CNN demodulator showed better adaptability to frequency offset. In an AWGN channel, the demodulation loss of the 1D-CNN demodulator could be kept almost within 2 dB. Owing to the good performance in the AWGN channel, the proposed 1D-CNN demodulator can meet most of the requirements in wireless communication. However, deficiencies of the 1D-CNN demodulator cannot be ignored; its complexity may be further reduced, and its adaptability to other nonideal factors like multipath effects should be considered.  
This research is supported by National Natural Science Foundation of China (No. 51505358) and the Fundamental Research Funds for the Central Universities of China (No. JB160409).  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed analysis tools; and [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Application of Corrected Methods for High-Resolution XRF Core Scanning Elements in Lake Sediments
## 2 Description of the Calibration Methods  
For conventional XRF techniques, calibration involves converting XRF-scan intensities to element fractions; the calibration is achieved using the following equation [22; 23]:  
\[W_{ij}=K_{j}\cdot I_{ij}\cdot M_{ij}\cdot S_{i} \tag{1}\]  
where \(W_{ij}\) represents the fraction of element \(j\) in specimen \(I\), \(I_{ij}\) represents the net intensity of element \(j\) in specimen \(I\), \(K_{j}\) represents the sensitivity or detection efficiency of the measurement device, \(M_{ij}\) refers to matrix effects which scatter, absorb or enhance the intensity of element \(j\) by other elements in specimen \(I\), and \(S_{i}\) represents specimen effects relative to the sediment properties. In conventional XRF measurements, detection efficiency \(K_{j}\) and specimen effects \(S_{i}\) are fixed, and the element fraction \(W_{ij}\) is estimated from its intensity \(I_{ij}\) with a correction factor given by matrix effects (\(M_{ij}\)) [10]. However, the specimen effects \(S_{i}\) are not constant due to inhomogeneous sediment properties (grain-size distribution and water content) within sediment cores. Two types of calibrations have been developed to eliminate such specimen effects. One is the normalized calibration, which scales the XRF-scan intensities to the scale of conventional-fractions and then normalizes to percentages (%) prior to subsequent calibration, including the NMS calibration [9] and the NPS calibration [7]. The other is the log-ratio calibration included in LRCE [10] and the improved MLC calibrations [21] whereby the ratio of XRF-scan intensities and their log transformations are used during the calibration.
## 2 Basic Concepts
### Fractionated and Non-Fractionated Kinematic Chains  
All KCs fall into one of two categories: fractionated or non-fractionated KCs. If a KC can be separated into two or more independent KCs at a link or joint, it is called a fractionated kinematic chain (FKC); otherwise, it is called a non-fractionated kinematic chain (non-FKC). Thus, separation at a link is called link fractionation, and the link is called a cut-link or a cut-vertex in the graph. Similarly, separation at a joint is called joint fractionation, and the joint is called a cut-joint or a cut-edge in the graph. For example, the KC in Figure 1a is a non-FKC because it cannot be separated into several independent KCs. Conversely, since the KC in Figure 1c can be separated into two independent KCs at link 5, it is an FKC with link 5 as its cut-link.
# A Comparative Study of Ensemble Models for Predicting Road Traffic Congestion
## 4 Empirical Framework
### Experimental Settings  
This work aims to develop a predictive model and to compute the misclassification cost of the developed predictive model. In this study, four traditional ML methods, three ensemble methods, one missing-data method, four performance evaluation metrics, and misclassification cost have been used on an RTC real-world dataset. The experiments were conducted using SPyDER from Anaconda Python Distributor. The study aims to combine three ensemble methods with weak RF, DT, SVM, and LR traditional ML methods to observe the different outcomes of the combination in identifying traffic states. Hyperparameter tuning will be encompassed to obtain various results, as presented in Table 2. Figure 1 shows the step-by-step process flow depicting the overall conducted experiments. The main procedures from the experimental process are data preprocessing, statistical analysis, modelling of the different methods, and model performance evaluation.  
Figure 1: Road traffic congestion experimental process.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Method/Model** & **Hyperparameter Settings** & **References** \\ \hline \multicolumn{4}{c}{Missing Data Method} \\ \hline
1 & Listwise Deletion (LD) & - & [45; 46] \\ \hline \multicolumn{4}{c}{Classifiers} \\ \hline
1 & Random Forest & n\_estimators = 100, criterion = “entropy”, random\_state = 42, max\_depth = 2 & [33; 34] \\ \hline
2 & Decision Trees & random\_state = 42, max\_features = “auto’, criterion = “entropy”, & [35; 36] \\ \hline
3 & Support Vector Machine & kernel = “poly”, degree = 3, C = 1, random\_state = 0 & [37; 38] \\ \hline
4 & Logistic Regression & Default settings & [39; 40] \\ \hline \multicolumn{4}{c}{Ensemble Models} \\ \hline
1 & Bagging & n\_estimators = 1; base\_estimator = RF & \\ \hline
2 & Bagging & n\_estimators = 1; base\_estimator = DT & [42; 43] \\ \hline
3 & Bagging & n\_estimators = 1; base\_estimator = SVM & \\ \hline
4 & AdaBoost & random\_state = 0, algorithm = “SAMME”, base\_estimator = RF, & \\  & n\_estimators = 1, learning\_rate = 5.0 & \\ \hline
5 & AdaBoost & random\_state = 0, algorithm = “SAMME”, base\_estimator = DT, & \\  & n\_estimators = 1, learning\_rate = 5.0 & \\ \hline
6 & AdaBoost & random\_state = 0, algorithm = “SAMME”, base\_estimator = SVM, & \\  & n\_estimators = 1, learning\_rate = 5.0 & \\ \hline
7 & Stacking & Final estimator: LR\_Classifiers/Estimators: RF, DT \& SVM & [41; 44; 50] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental methods for the study.  
Different experiment settings are given in Table 2, containing the summary of the different methods and hyperparameter settings. These methods were chosen due to their popularity, characteristics, and frequent citation from the literature.