esa-sceva/satcom-chunk-collection huggingface dataset:
# Ontology-Based Production Simulation with OntologySim
## 5 Design Principles for the OntologySim  
The OntologySim concept and the structure of the developed ontology presented below is intended to enable mapping diverse production systems, such as line production, workshop production, and matrix production, efficiently, in a modular way. The basic requirement for the OntologySim development is the ability to save the simulation state at any time, without data loss, and the ability to restart the simulation using the saved ontology. From this requirement follows that all entities have to be mapped in this ontology, achieving Ontology Integration level (OIL) 5. Thus, not only machines and transporters are stored in the ontology, but also set-up processes, defects and services, their statistics, the production plan for all products, and relevant future and past events. The storage of any entities in the ontology enables a high degree of flexibility since, on the one hand, relationships from zero to n are possible, and, on the other hand, a high degree of detail can be generated. Furthermore, the information and relationships within the ontology are dynamically adapted. Each executed event or simulation step executes changes to the ontology. Because past events are also stored in the ontology, it is possible to recreate past states from the current state of the ontology. This past information is also seamlessly available to enable machine learning-based production control, for instance, with reinforcement learning [55, 58]. The concept and functioning of the ontology are exemplified by the entities Machine, Event, and Product type in the following.  
Figure 8: Classification of simulation according to VDI3363 [29].
# Second-Harmonic Generation of Blue Light in GaN Waveguides
## 3 Results and Discussion
Dashed black lines represent the dispersions of the extraordinary refractive indices of bulk GaN, given in Equation (4) [20] and sapphire [26]. The red circle indicates the modal-dispersion phase matching (MDPM) interaction, which is responsible for the measured SHG signal. (**b**) Overlapping of the electric field distributions, \(\psi_{p}^{\lambda}\), for two possible waveguide interactions responsible for the measured SHG signal. The waveguide modes at the pump wave are presented with blue lines, modes at the SHG wavelength with green lines, while squares of pump modes \(\left[\psi_{p}^{\lambda}\right]^{2}\) are presented with black dotted lines. shows the largest overlap. However, the excitation of the first pump mode was less efficient, therefore, this SH peak was not observed. A small additional peak in Figure 4a could be attributed to this interaction. The total efficiency of SHG can be estimated as the following: Taking the large waveguide aspect ratio (1.1 \(\upmu\)m to 10 \(\upmu\)m), losses by reflection at the front surface, and coupling to the unwanted waveguide modes into account, approximately 4% of a 1 mW input beam is coupled into the pump mode for conversion. Inside the waveguide, propagation losses were also observed, mainly due to the scattering from the waveguide sidewalls as shown in Figure 3a. However, these were negligible in comparison with the coupling losses. By using Equation (2) it was estimated that nearly 100 nW of blue light was generated.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### High-Resolution AGB Maps from Field Measurements and LiDAR Data
#### 3.3.5 LiDAR-Derived Aboveground Biomass and Uncertainty for Californian Forests, 2005-2014  
[PERSON] et al. [83] produced an aboveground biomass map of Californian forests between 2005 and 2014 using FIA and airborne LiDAR data to explore the uncertainty of LiDAR remote-sensing-based biomass estimation by performing error propagation analysis at both tree and plot level. This study is among the first examples of AGB estimation and uncertainty analysis based on an individual tree detection method [84]. The average uncertainty of estimated biomass was 153% at the tree level and 214% at the plot level. Errors originating from the generalized allometric equation contributed most to total AGB uncertainty.
## 2 Materials and Methods
### Data Sources
#### 2.2.2 Eddy Covariance Data  
Eddy covariance systems, also known as flux towers, are among the most effective instruments for measuring carbon exchange between the atmosphere and various ecosystems. These systems provide direct measurements of ecosystem-level GPP and NEP [25; 26].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sites** & **Latitude** & **Longitude** & **Elevation** & **Plant** & **Annual** & **Precipitation** & **Provines** \\  & (\({}^{\circ}\), **N**) & (\({}^{\circ}\), **E**) & **(m)** & **Type** & **Temperature** (\({}^{\circ}\)C) & **(mm) & **Properties** \\ \hline NMG & 43.33 & 116.40 & 1200 & C3 grass & 0.9 & 338 & Inner \\  & 42.05 & 116.28 & 1317 & C3 grass & 2.0 & 318 & Mongolia \\  & 37.61 & 101.32 & 3202 & C3 grass & \(-1.1\) & 490 & Qinghai \\  & 30.50 & 91.07 & 4332 & C3 grass & 1.3 & 450 & Tibet \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._In this study, all 10 representative forest and grassland ecosystems were equipped with flux towers. We obtained flux observation data from these 10 study sites for the years 2007 and 2008, including daily GPP and NEP measurements. The 2007 data were used for parameter optimization, while the 2008 data were utilized to validate the model's simulation accuracy. These flux data were sourced from the China Flux Observation Network (Chinaflux, [[http://www.chinaflux.org/](http://www.chinaflux.org/)]([http://www.chinaflux.org/](http://www.chinaflux.org/)), accessed on 23 August 2024) [27] and the global Fluxnet 2015 dataset ([[https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)]([https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)), accessed on 13 November 2022) [28].
## 2 Literature Overview
### Maintenance Management Information Systems  
Since a huge amount of maintenance data needs to be analysed quickly and efficiently to make better decisions [20; 21], MMISs became necessary systems to improve the overall maintenance process. The literature highlights the growth in this field in recent years. Different positive and negative features have been discussed in many studies. Indeed, the scientific literature on this topic can be divided into different clusters related to the specific issues addressed regarding MMISs. Some studies focused on describing and/or improving the common features of an MMIS, since it can be considered as a set of functions that process data to develop indicators supporting maintenance activities. In addition, according to the literature, the main use of MMIS appears to be as a storehouse of maintenance information and data. Companies consume a significant amount of management and supervisory time compiling, interpreting and analysing the data captured within an MMIS. Based on these issues, the authors considered most of the existing off-the-shelf software packages, especially the Computerized Maintenance Management Systems (CMMS) and Enterprise Resource Planning (ERP) systems, as systems greedy for data input that seldom provide any output in terms of decision support. Despite the relevant benefits associated with MMIS as a support tool in maintenance management, different factors affect its implementation. Several surveys showed the existence of various barriers to MMIS implementation [3; 4]. The most frequently indicated reasons are the maturity level of the maintenance department, high implementation costs, lack of knowledge of the system, unskilled and scarce workforces, and the difficulties in changing organisational culture [4]. Thus, if, on one hand, an MMIS is declared as a suitable tool to enhance maintenance activities, on the other hand, it yields low success due to the readiness level of the smaller industrial contexts that are still unaware of the key role played by the maintenance function.  
SMEs should be ready to adopt an MMIS. This requires a change in the recognition of the maintenance function as a key tool, not only for saving money by reducing the frequency of failures, but also for improving the availability of the plant (as equipment reliability increases) and the quality of the products being manufactured [22]. Furthermore, this trend highlights the importance of introducing an appropriate MMIS that reflects the readiness level of the specific industrial context. This satisfaction is dictated not by the features of the system, but by the culture of the maintenance department. One of the main issues in the development of an MMIS is to understand the maturity stage of the maintenance function.
# Deep Visual Waterline Detection for Inland Marine Unmanned Surface Vehicles
## 4 Experiments and Evaluation
### Comparison to Other Alternative Approaches
#### 4.4.3 Quantitative Assessment on Stability  
To evaluate the stability of the waterline detection approach on video data, especially at the moments when environmental factors (e.g., weather or illumination conditions) cause variations in a visual surveillance scenario, we further conduct related experimental comparisons between the edge detection approach and ours. Here, we take foggy conditions bringing about illumination variations as an example, and test their impacts on the stabilities of the two approaches during the procedure that time-sequence images captured by USV are successively dealt with. Correspondingly, we sample 150 image frames from  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics** & \multicolumn{4}{c}{**Environmental Interference Factors**} \\ \cline{2-5} \(\mathbf{\lambda}=\mathbf{10}\) & \(\mathbf{Rail}\) & \(\mathbf{Ripple}\) & \(\mathbf{Shadow}\) & \(\mathbf{Fog}\) \\ \hline _precision_ (\%) & 11.5/96.5 & 29.2/95.4 & 21.2/96.1 & 93.3/90.6 \\ _recall_ (\%) & 97.6/98.2 & 95.2/97.1 & 83.7/98.6 & 41.9/84.5 \\ _FP (pixels)_ & \(\sim\)54 K/40 & \(\sim\)32K/36 & \(\sim\)33K/38 & 16/43 \\ _irrelevance_ & \(-\)1.119/0.092 & \(-\)1.413/0.068 & \(-\)1.216/0.073 & 0.059/0.081 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantitative comparisons of assessed metrics corresponding to the results by edge detection method (left of /) and ours (right of /) under different noises.  
eight hours of video captured by our USV, which cover varied foggy conditions in the same monitoring scenario. Then, based on the precision-recall metrics and FP-irrelevance metrics associated with each of these samples that are achieved, respectively, in terms of Equations (3)-(6), we calculate the stability over the previous four metrics according to Equation (7), as shown in Table 6.  
In the experiment about stability assessment, to achieve more impartial effect, we practically employ the classic Canny edge detector with necessary image preprocessing as an evaluated edge detection method to compare with ours. From Table 6, we can see that the measurements of our approach regarding stability over our concerned metrics are closer to zero than the Canny edge detector with image preprocessing, except for the metric FP due to more irrelevant pixels caused by our approach. It indicates that our measuring results over these samples with respect to most of our concerned metrics, e.g., _precision_, _recall_ and _irrelevance_, are more convergent to normal distribution. Therefore, our approach has more stability on the corresponding metrics against environmental variations.
## 3 Results
### Binding Free Energy between the (-)-Spirobrassinin and PSBD1  
The MM/GBSA scores revealed that the binding free energy, \(\Delta\)G\({}_{\text{bind}}\), between (-)-Spirobrassinin and PSBD1 was determined to be \(-9.09\) kcal/mol. In terms of energy contributions, the binding was primarily facilitated by van der Waals forces, including hydrogen bonding and hydrophobic interactions, as well as electrostatic interactions such as \(\pi\)-\(\pi\) stacking interactions. The van der Waals energy contribution, \(\Delta\)E\({}_{\text{vdw}}\), was calculated to be \(-9.71\) kcal/mol, while the electrostatic energy contribution, \(\Delta\)E\({}_{\text{ele}}\), was \(-2.09\) kcal/mol. Conversely, the solvent free energy, \(\Delta\)G\({}_{\text{solv}}\), was positive at \(2.7\) kcal/mol, comprising both the polar solvent free energy, \(\Delta\)EGB, and nonpolar solvent free energy, \(\Delta\)E\({}_{\text{surf}}\) (Figure 7).  
Figure 6: Analysis of amino acid contacts. (**A**), (-)-Spirobrassinin interaction with amino acids in PSBD1 and (**B**), the types and quantities of interactions between (-)-Spirobrassinin and PSBD1.  
Figure 7: Statistical analysis of the binding free energy between the (-)-Spirobrassinin and the PSBD1; each bar graph was the mean \(\pm\) standard error. The binding free energies are predictions from MM/GBSA calculations.
## 2 Related work
### Overview of IEEE 802.15.4g
#### 2.1.3 Sun-Ofdm  
SUN-OFDM allows to provide high data rates and a long range, while dealing with multi-path fading and external interference problems. Unlike FSK and OQPSK, OFDM has not been widely used for low-power wireless communications, due to the stringent processing, memory, and energy consumption requirements.  
SUN-OFDM may be used in different frequency bands (both sub-GHz and 2.4 GHz) and provides data rates from 50 kbps to 800 kbps. The sub-carrier spacing is constant and equal to 10416-2/3 Hz, while the symbol rate is equal to 8-1/3 ksymbol/s (a symbol period of 120 \(\upmu\)s). Four different options are defined, each one with a different number of active tones. For each option, a set of MCS (Modulation and Coding Schemes), numbered from 0 to 6, may be used. If a device supports a given option, it has to support all BPSK and QPSK modulations and all MCS for that option. The support for QAM (Quadrature Amplitude Modulation) is optional.  
The MCS determines the scheme used to modulate the sub-carriers (BPSK, QPSK, or 16-QAM), whether frequency repetition is applied (i.e., \(4\times,2\times\), or no frequency repetition), and the FEC coding rate (i.e., 1/2 or 3/4). Thus, the effective data rate depends on the MCS used. For example, for Option 2, which uses 52 active tones, the effective data rate may vary from 50 kbps, when using BPSK, a coding rate of 1/2, and 4\(\times\) frequency repetition, to 800 kbps, when using QAM, with a coding rate of 1/2, and no frequency repetition.  
The PPDU of SUN-OFDM is composed by a SHR, a PHR, and the payload. The SHR has a preamble, and the PHR contains the information about the configuration used to transmit the payload. Both SHR and PHR are transmitted using the lowest supported MSC for the option being used.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 3 Methodology
### Zigbee Measurement Descriptions
#### 3.3.1 XBee Transmitter Module  
One of the XBee modules was configured in order to establish transparent communication in sender mode with the other node. In this way, this module only presented the function of sending characters every certain period of time. In addition, it should be noted that both the characters and the sending speed are configured by the Pycom module,  
Figure 5: Measurement configuration with Zigbee technology.  
which is connected by serial communication. Finally, it is worth mentioning that a program similar to the one used with LoRa was implemented in the Pycom module. In this way, this microcontroller allows the user to choose the sending speed, as well as to send, by serial communication to its local XBee module, known characters every certain period of time. In this study, the sending speed was the same as with the LoRa technology, 2 data/s.
###### Abstract  
This study conducted a detailed comparative analysis of various machine learning models to enhance wind energy forecasts, including linear regression, decision tree, random forest, gradient boosting machine, XGBoost, LightGBM, and CatBoost. Furthermore, it developed an end-to-end MLOps pipeline leveraging SCADA data from a wind turbine in Turkiye. This research not only compared models using the RMSE metric for selection and optimization but also explored in detail the impact of integrating machine learning with MLOps on the precision of energy production forecasts. It investigated the suitability and efficiency of ML models in predicting wind energy with MLOps integration. The study explored ways to improve LightGBM algorithm performance through hyperparameter tuning and Docker utilization. It also highlighted challenges in speeding up MLOps development and deployment processes. Model performance was assessed using the RMSE metric, conducting a comparative evaluation across different models. The findings revealed that the RMSE values among the regression models ranged from 460 kW to 192 kW. Focusing on enhancing LightGBM, the research decreased the RMSE value to 190.34 kW. Despite facing technical and operational hurdles, the implementation of MLOps was proven to enhance the speed (latency of 9 ms), reliability (through Docker encapsulation), and scalability (using Docker swarm) of machine learning endeavors.  
machine learning; wind energy; MLOps; RMSE; latency
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 7 The Future of the Spaceborne LiDARs
### Future Concepts
#### 7.2.3 LiDAR Satellite Constalations  
Deployment of a constellation of satellites with LiDAR payloads enables near-continuous, global data collection, allowing for more frequent and timely updates of Earth's surface conditions. An Orlando startup named NUVIEW plans to develop and launch the first constellation of LiDAR-equipped satellites, counting 20 satellites in total, promising 100\(\times\) faster data collection than conventional methods [177].
## 3 Results and Discussion
### Metal Analysis
It has been shown that accumulation of trace elements in marine crabs might arise via saltwater or food [8]. The contribution of each route of uptake is linked to the bioavailability of these minerals. Cu and Zn concentrations in water are fairly low in the Gulf of Gabes region [21; 57] for a Mediterranean-wide comparison, whereas sediments can be moderately to highly enriched in these two elements, as well as other heavy metals, based on the area [58; 59]. As a result, as various previous investigations have shown, a transfer mechanism through sediments can be considered the primary predictor of reported accumulation patterns [60; 61]. As a result, the contamination can be attributed to anthropogenic origins. \begin{table}
\begin{tabular}{l l l l l l l l} \hline
**Species** & **Site** & **Tissues** & **Cu** & **Zn** & **Cd** & **Pb** & **Reference** \\ \hline _Sylla serrata_ & Canada & Digestive Gland & 637 & 208 & 3.1 & - & [59] \\ _Pseudoarcirus gigas_ & Australia & Digestive Gland & 52 & 108 & 2.2 & - & [62] \\ _Menippener scenario_ & South Carolina-United States & Digestive Gland & 308 & 46 & 4.6 & - & [63] \\ \hline _Uclickedatus_ & Brazil & Digestive Gland & 6.6 & 8.89 & 0.16 & - & [64] \\ \hline _Xenogradusussteduintants_ & Taiwan & Whole tissue & 53–290 & 119–610 & 0.49–1.29 & 1.83–2.64 & [65] \\ _Ericheir sinensis_ & Poland & Whole tissue & 3.17–21.9 & 1.15–26.6 & 0.016–0.025 & 0.29–0.61 & [60] \\ \hline _Carcinus maneus_ & Bizerte Lagoon, Tunisia & Gills & 5.12–28.98 & 5.39–13.78 & 1.08–1.99 & 5.82–8.75 & [13] \\ \hline \multirow{4}{*}{_Carcinus maneus_} & Bizerte Lagoon, Tunisia & \multirow{4}{*}{Gills} & \multirow{4}{*}{376.5} & \multirow{4}{*}{83.4} & \multirow{4}{*}{0.38} & \multirow{4}{*}{15.57} \\ \cline{1-1} \cline{5-5}  & Monastir Coast, Tunisia & & & & & \\ \cline{1-1} \cline{5-5}  & Sfax Coast, Tunisia & & & & & \\ \cline{1-1} \cline{5-5}  & Bizerte Lagoon, Tunisia & & & & & \\ \cline{1-1} \cline{5-5}  & Monastir Coast, Tunisia & & & & & \\ \cline{1-1} \cline{5-5}  & Monastir Coast, Tunisia & & & & & \\ \cline{1-1} \cline{5-5}  & Monastir Coast, Tunisia & & & & & \\ \cline{1-1} \cline{5-5}  & Sfax Coast, Tunisia & & & & & \\ \cline{1-1} \cline{5-5}  & Sfax Coast, Tunisia & & & & & \\ \cline{1-1} \cline{5-5}  & Muscle & 206.45 & 590.04 & 0.21 & 0.4 & \\ \cline{1-1} \cline{5-5}  & Gunad & 584.85 & 36.46 & 0.23 & 0.14 & [6] \\ \cline{1-1} \cline{5-5}  & Exoskeleton & 65.78 & 11.4 & 0.14 & 0.06 & \\ \hline \end{tabular}
\end{table} 
Table 3: Average contents of Cu, Zn, Cd, and Pb (mg/kg dw) in crab tissues from the literature.
# Low-Altitude Sensing Model: Analysis Leveraging ISAC in Real-World Environments
## 4 Performance Verification and Analysis
### Networked Sensing Validation
The CDF curves are obtained by statistically analyzing the SNR and positioning accuracy across the entire test route, as shown in Figure 19. Taking the CDF 95% statistical value, the overall horizontal and vertical positioning accuracies along the flight route were determined, as shown in Table 9. The previously derived equation showed that the theoretical minimum sensing SNR was 13 dB. The statistical results showed that, regardless of the flight height, over 99.9% of the points along the entire flight route met this requirement. Therefore, it can be concluded that no sensing blind spots existed within the test area. Compared to the single base station test results, the overall average horizontal and vertical positioning accuracies in the networked environment were relatively worse. This was primarily due to the increased complexity of the networked environment, where operations such as trajectory fusion and target deduplication among multiple stations introduced additional sources of errors. However, the overall positioning accuracy still met the sensing performance requirement of less than 10 m, and the vertical accuracy was generally better than the horizontal accuracy, which aligned with the theoretical expectations. In summary, the multi-BS cellular-like topology sensing model demonstrated significant advantages in practical tests. It effectively addressed the issue of single base station sensing blind spots and accurately replicated the actual sensing capabilities in real-world scenarios. This provides reliable theoretical references and practical insights for the future planning and construction of ISAC base stations. Figure 19: Multi-BS sensing test CDF. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Height** & **Horizontal Positioning Accuracy** & **Vertical Positioning Accuracy** \\
**(m)** & **at CDF 95\% (m)** & **at CDF 95\% (m)** \\ \hline
100 m & 8.49 & 4.51 \\
300 m & 8.72 & 4.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Multi-BS sensing test result.