esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application Examples for Turbomachinery
### Hybrid RANS/LES Model Menter SST-SAS
#### 4.2.2 Simulation of Film Cooling Based on C3X Cascade
It can be observed that the cooling efficiency decreases rapidly along the centerline and then levels off in downstream regions, showing good quantitative agreement with the experimental results. Figure 15 depicts contour plots of the average vorticity and average strain rate near the film cooling holes on the suction surface of the blade upon extracting flow fields near the film cooling holes. There is a noticeable increase in vorticity and strain rate along both sides beneath the film cooling holes, indicating the formation of kidney-shaped vortices as the jets entrain into the mainstream flow downstream, gradually mixing with the mainstream. In Figure 15a,b, high vorticity and strain rate occur near the leading edge of the jet. Combined with mechanistic analysis, this position corresponds to a stagnation point in the mainstream flow, where a horseshoe vortex forms. The range of existence of the horseshoe vortex coincides with the positions of high vorticity and strain rate near the leading edge in Figure 15. Thess phenomena are consistent with the trend of cooling effectiveness shown in Figure 14.  
In summary, through the application of the fifth-order WENO-ZQ scheme, numerical calculations of film cooling can accurately describe flow field phenomena, and the accuracy of predicting film cooling efficiency is also guaranteed. Figure 13: Film cooling computational domain of C3X cascade.
# Development of Piezoelectric Inertial Rotary Motor for Free-Space Optical Communication Systems
## 4 Experimental Investigation of Motor
These results are critical for space  
Figure 15.5 Angular motion characteristics of the motor under different temperature conditions. applications, particularly for LEO-operating satellites. The wide temperature fluctuations experienced in this orbit can significantly affect motor performance, as shown by the reduction in speed at both high and low temperatures. Finally, a comparison of the key parameters of the developed inertial piezoelectric motor with the most recent state-of-the-art solutions was conducted. The results of the comparison are given in Figure 15.6  
Therefore, as can be found in Table 4, the proposed inertial motor exhibits a compact footprint, a lightweight design, a high angular speed, and moderate torque, making it highly suitable for applications requiring precise and efficient motion control, such as small satellite systems. While other motors excel in specific areas, such as resolution or torque, the presented motor offers a balanced performance optimized for space-oriented tasks.
# A Novel Method Based on Topological Perception Theory for 3D Landmark Building Model Retrieval
## 5 Proposed Method
### Pre-Processing of 3D Landmark Building Models  
The pre-processing of 3D landmark building models is mainly to ensure that the extracted features remain unchanged for translation, scaling, and rotation transformations. The pre-processing of a 3D landmark building model starts with the removal of the flat base plane at the bottom of the model, which not only affects the alignment of the model  
Figure 3: Model categories: top view is non-concave, side view is a hole; top view is non-concave, side view is concave unclosed; top view is non-concave, side view is non-concave; top view is hole, side view is non-concave; top view is concave unclosed, side view is non-concave; top view is separated, side view is separated.  
but also reduces the normalized size of the model. Then, the landmark building model is standardized in terms of position and size by translating it to the origin with its center of mass and scaling it accurately within the unit sphere before projection. Upon investigation, it was found that the majority of landmark building models exhibit the correct upright orientation compared to generic models. Therefore, a few models with incorrect upright direction were individually adjusted to upright separately in this paper. After the upright direction was corrected, the information of the landmark building model's sides was captured by positioning the cameras uniformly on the sides. In 3D model retrieval algorithms, there are two projections that are often used: One method will yield 12 views, and the other yields 20 views [21]. Although in this paper the upright direction was established, given that views containing significant topological features are predominantly found in the top or side views of the model and the topological structure is inherently stable and undergoes only minor changes in neighboring views, having too many views does not make much sense. Consequently, the efficiency of extracting and matching descriptors can be improved by reducing the number of views created for each model. In this paper, one camera was selected to capture the topological structure of the top view, whereas the topological structure of the side view was captured by placing eight virtual cameras around the mesh every 45 degrees. The cameras were pointing towards the center of mass of the model and the side camera system was on a plane that passed through the center of mass of the model and parallel to the ground plane. Due to the symmetric nature, one top view and four side views were obtained, as show in Figure 5. More importantly, the established upright direction facilitated the storage of views in the spatial sequence of the top surface and the four sides. This arrangement ensured the order during feature extraction, thereby assisting in the subsequent fusion of top and side view features.  
Figure 4: Framework of 3D landmark building model retrieval system.  
Figure 5: Five projection views: (**a**) top view and (**b–e**) side views.
## 4 Simulation Analysis
### Experimental Results
#### 4.3.4 Algorithm Robustness Analysis
phenomenon is also an objective manifestation of the impact of orbital perturbation on navigation and positioning; however, in addition to the local jitter caused by the impact of orbital perturbation, the overall navigation and positioning results are within an acceptable range and this impact can be compensated by upgrading the satellite's orbit in the later stage. Figure 12: Navigation and positioning error curve under the combined perturbation of aspherical earth perturbation and atmospheric drag perturbation. (**a**) Error result. (**b**) Trajectory curve. Figure 13: _Cont._  
From the above analyses, it can be seen that the 3-satellite alternate switching algorithm has strong anti-jamming capability and robustness, so it is very suitable for navigation and positioning solutions in complex challenging environments. However, in practical applications, we must also consider the impact of orbital perturbation on the navigation and positioning algorithm and try to suppress the errors caused by the perturbation as much as possible.
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 5 Discussion
### VANET Integration with Other Technologies  
One of the most viable communication standards by the Third Generation Partnership Project for vehicular networking is long-term evolution (LTE). LTE standard offers high throughput and lower latency. Its downlink and uplink data rates are 300 and 75 Mbps, respectively [80]. LTE technology improves cost and performance efficiency due to the simplification in network architecture (fewer network elements) and advanced algorithms for resource utilization [81]. Nowadays, cellular networks such as 3G or LTE have wide communication range and coverage, which are ideal characteristics for vehicular-network scenarios. Combinations of VANET technology with 3G or LTE form a heterogeneous network that is adopted for data collection and dissemination to/from vehicles to manage road traffic. Based on this idea, [82] proposed a solution based on speed, location, direction, destination, and LTE link quality. In [80; 83], LTE was used in VANET scenarios. In [83], a hybrid solution was highlighted that integrates LTE-A (4G LTE-Advanced), which is a less complex and more cost-effective solution compared to other options. Each vehicle could use its LTE-A interface for V2I communications, offering a reduction in mobility signalling overhead. In [80], a hybrid architecture was proposed that combined IEEE 802.11p-based multihop clustering and LTE called VMaSC-LTE. The principal goals in these heterogeneous network proposals are to achieve a high data-packet-delivery ratio and low delay while keeping the usage of the cellular infrastructure. However, they are not focused on routing design because these proposals use infrastructure-based LTE without direct communication among vehicles, so messages pass through the infrastructure.  
The development of Fifth Generation (5G) networks made the internet ubiquitous Internet and the growth of new applications possible. This is also possible in VANET by internet of vehicles (IoV) communication that uses network infrastructure to allow cars to be connected to new radio technologies [84]. It is worth noting that the proposals reviewed in this article have tested by using DSRC framework. However, thanks to LTE device-to-device technology [85] that can be implemented in a decentralized fashion, the performance of all proposals could be significantly improved. LTE D2D has been the perfect candidate to improve communication in VANETs. Since LTE D2D offers improvements in terms of capacity, cost reaction, and spectral efficiency, some car manufacturers are using it to provide applications such as remote monitoring, assisted driving, and infotainment. Thus, extensive simulation tests should be performed for routing proposals under the decentralized LTE device-to-device umbrella. This would help VANET communication meet the 5G requirement in terms of throughput and latency [86] because current performance (under DSRC umbrella) is far from this according to the presented results.
## 5 Conclusions  
Fine dead fuel load (i.e., 1-h fuel load) estimation was conducted using multi-source remote sensing data and machine learning techniques in a Mediterranean landscape of Southern Europe. The RF, SVM, and MLR models were employed to predict 1-h fuel load and to understand which remote sensing variables are most important to determine it. Based on performance metrics, the RF model was the most accurate in estimating 1-h fuel load compared to the traditional MLR model that showed a weaker predictive power. The SVM model showed similar performances to the MLR model. The results showed that the variables providing information on vegetation structure (i.e., Canopy Height Model, Canopy cover) are the most important in fuel estimation, thus underscoring the possibility of using these remote sensing data as a proxy of fine dead fuel load. Therefore, this methodology could be very useful for managers and decision makers, since it would allow the regular estimation of fuel loads, especially the fine dead fuel load. Future research should investigate the importance of climatic variables in fine dead fuel estimation modeling and understand how these affect fire ignition.  
The following are available online at [[https://www.mdpi.com/article/10.3390/rs13091658/s1](https://www.mdpi.com/article/10.3390/rs13091658/s1)]([https://www.mdpi.com/article/10.3390/rs13091658/s1](https://www.mdpi.com/article/10.3390/rs13091658/s1)), Figure S1: Five sub-regions of the Apulia region used as masks to extract radar images., Figure S2: Correlation matrix of independent variables. VH = VH polarization; VV = VV polarization; NDVI = Normalized Difference Vegetation Index; NDWI = Normalized Difference Water Index; CHM = Canopy Height Model; CHM_maxq = 95 th percentile of CHM.  
M.D.: conceptualization, methodology, formal analysis, investigation, writing and revising. M.E.: conceptualization, investigation, writing, and revising. V.G.: conceptualization, investigation, and revising. G.S. ([PERSON]): revising. [PERSON], [PERSON] ([PERSON]): supervising, editing, and revising. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This study has been developed within the F.OR.MA. (Fuel models and fORest roads Maps) project in collaboration with the Civil Protection Department of the Apulia Region (Italy). The authors wish to thank [PERSON] for support in revising the manuscript.  
The authors declare no conflict of interest.
## 2 Data and Methods
### Study Methods
#### 2.2.1 Park Falls Wisconsin--EO-1/ ALI NDVI vs. Landsat NDVI
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **EO-1 ALI** & ** Landsat 5** & ** Landsat 7** \\ \cline{2-4}  & **Wavelength (\(\mu\)m)** & **Wavelength (\(\mu\)m)** \\ \hline Pan 1 & 0.48–0.69 & & 0.52–0.90 \\ Blue\({}^{\prime}\) & 0.43–0.45 & & \\ Blue & 0.45–0.52 & 0.45–0.52 & 0.45–0.52 \\ Green & 0.53–0.61 & 0.52–0.60 & 0.52–0.60 \\ Red & 0.63–0.69 & 0.63–0.69 & 0.63–0.69 \\ NIR & 0.78–0.81 & 0.76–0.90 & 0.77–0.90 \\ NIR\({}^{\prime}\) & 0.85–0.89 & & \\ SWIR\({}^{\prime}\) & 1.20–1.30 & & \\ SWIR 1 & 1.55–1.75 & 1.55–1.75 & 1.55–1.75 \\ TIR & & 10.40–12.50 & 10.40–12.50 \\ SWIR 2 & 2.08–2.35 & 2.08–2.35 & 2.09–2.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Wavelengths ranges per band for each Advanced Land Imager (ALI), Thematic Mapper (TM), and Enhanced Thematic Mapper plus (ETM+) sensor. For the analysis, the NDVI associated with each pixel was differenced (ALI minus Landsat), and those differences were averaged per NLCD land cover class. From visually looking at pixels with large NDVI differences, any delta NDVI between the ALI and Landsat data greater than 0.15 were most likely due to something other than sensor difference. These other differences could be from clouds, a rain event, or a land disturbance in one of the images, and they were removed from the comparison.
# Allocating Small Transporters to Large Jobs
## 7 Experiments
### Performance of the Saturated Algorithms  
We consider the following saturating algorithms:  
* Algorithm (SGJ), assigning transporters to jobs one by one.
* Algorithms (SGR\(i\)) and (SGR\(c\)), iteratively assigning transporters to the job with maximum idle time per period and maximum completion time, respectively.
* Algorithm (SMI\(g\)), the mixed integer program grouping transporters with equal filling time.  
We excluded the simple mixed integer program (sMIP), as it turned out that it is already too slow for our problem instances (see Figure 4).  
Figure 4: Average running time of Algorithm (sMIP) over five randomly generated instances, in seconds. We set up the solver with a one hour time-out limit, such that an instance takes a maximum of 3600 s duration.  
Figure 5 presents the running times of the algorithms. Algorithm \((\mathrm{sMIP}g)\) is reasonably fast for our small problem instances of up to 10 jobs. All greedy heuristics always run in a few milliseconds. We remind here that the complexity of Algorithm \((\mathrm{sG})\) is exponential in the saturating transporter cardinality, which should not exceed six in our test sets.  
Figures 6 and 7 show for every algorithm the total number of infeasible instances and the average number of used transporters, respectively. Since our test sets only contain feasible instances, the optimal algorithm \((\mathrm{sMIP}g)\) has no infeasible instance. Among the greedy heuristics, the heuristic by job \((\mathrm{SG})\) performs better than the heuristics by transporters. This is an intuitive result in saturated scenarios, as \((\mathrm{SG})\)) looks for the tightest assignment for every job, whereas Algorithms \((\mathrm{sGR}i)\) and \((\mathrm{sGR}c)\) assign transporters on the fly, without ensuring that the transporters assigned to a same job fit well together. Algorithm \((\mathrm{sGR}c)\), which minimizes the completion time at every step, performs slightly worse than Algorithm \((\mathrm{sGR}i)\), which minimizes the idle time at every step, for a similar reason: since the objective is to remove the idle time, it is more beneficial to focus on the idle time than the completion time, as the latter also depends on the superfluous total size of the job.  
Figure 5: Distribution of running times of the algorithms, in seconds. The distributions are shown as box plots. The dot shows the average value and the line the median. The box (if it exists) shows the 25% to 75% quantile. If there is no box, then the values in those quantiles coincide with the average. Outliers are shown as diamonds.  
Figure 6: Share of the 100 instances where the respective algorithms do return a feasible, saturated solution.  
We conclude that when looking for a saturated solution, a good recommendation is to use the greedy heuristic by jobs \((\mathrm{SGJ})\).
# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 2 Advancements and Techniques Used for Improving Crop Production
### Research Questions  
_Research Question (RQ1): How does applying the Synthetic Minority Oversampling Technique (SMOTE) influence the performance of classifier techniques in crop recommendation, and what are the observed changes before and after the SMOTE?_  
_Research Question (RQ2): How do accuracy, recall, precision, F1-score, ROC AUC, sensitivity, and specificity change for different classifiers before and after the application of the SMOTE?_  
_Research Question (RQ3): How do the classification performance metrics vary among different classifiers before and after the SMOTE regarding boxplots, AUC-ROC curves, and statistical summaries?_  
_Research Question (RQ4): Does the SMOTE along with classifiers contribute an improvement in the prediction of area-wise suitable crops accurately?_
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 7 Evaluations
### Drone Experimental Configuration and Simulated Environment  
The experimental setup for this study involved a simulated environment in Python to test the FHSS with QRNG-generated sequences under controlled conditions. The simulation environment involved the following configurations and conditions:  
* Drone Configuration: Each drone in the network was represented by an instance of a Python class, which implemented QRNG-based frequency hopping. Each simulated drone had independent QRNG access through shared virtual memory, providing real-time access to randomized frequency values. The drones communicated in a ring topology, where each drone transmitted to two neighboring drones.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Study** & **Technique** & **Anti-jamming Adaptability** & **Complexity** & **Randomness Source** \\ \hline \([2]\) & Seamless UAV multicasting & Medium (limited FHSS) & \(O(N^{2})\) for network transitions & PRNG \\ \([2]\) & Jammer detection in IoT & High (targeted detection) & \(O(N_{10}N)\) & PRNG \\ This Study & QRNG-based FHSS for drones & High (true random FHSS) & \(O(N\log[F])\) for FHSS & QRNG \\ \([20]\) & Secure routing for UAVs & Medium (route-level resilience) & \(O(N^{2})\) for path finding & PRNG \\ \([21]\) & Protocol optimization & Low (limited hopping) & \(O(N)\) & PRNG \\ \hline \hline \end{tabular}
\end{table}
Table 2: Qualitative Comparison with Related Studies.  
Figure 17: Drone 2: Spectral Analysis.  
* Communication Environment: The simulation modeled a wireless communication channel subject to random interference (jamming) at specific frequency bands to evaluate the system's anti-jamming capabilities. Interference at a central frequency (e.g., 5500 Hz) was introduced to observe the adaptability of the FHSS algorithm.
* Topology: A ring topology was chosen for its suitability for real-world drone networks, where each drone maintains a secure line of communication with its immediate neighbors. This configuration provides resilience and redundancy, as data can flow continuously around the ring even if one link fails.  
While tested in a simulated environment, this approach could be adapted to a real-world drone network with ring topology. Each drone would require a lightweight QRNG device or access to a ground-based QRNG for random number generation. The ring configuration would enhance reliability and enable real-time data relay across the network, even under jamming conditions.
## 3 Review of Existing Channel Models for Underground Mines
### Multimode Waveguide Model  
In the environment of underground mines, the multimode model can be used to estimate the signal propagation characteristics for the near and far regions. This model is valid for multiple frequencies, and the operating frequency in this model can be higher than the cut-off frequencies of propagation by increasing communication frequency [26]. The propagation loss analysis concerning the root mean square (RMS) delay spread can be estimated using the multimode waveguide model. However, this model is not friendly for complex environments in underground mines, having multiple branches and being incapable of estimating a mine's roughness parameter [27].
## 3 Results
### Parameter Setting  
In our experiments, 448 12-lead ECG records in the PTB diagnostic ECG database are randomly divided, 70% as the training set and the other 30% as the testing set. To obtain reliable experimental results, we repeat each set of experiments 20 times independently based on a Bi-LSTM network with heartbeat-attention mechanism. And the testing set are also randomly divided for each repeated experiment. Our model was trained on a processor for the Intel Core i5-8400 and a GPU for the Nvidia GeForce GTX 1060 6 GB. Its convergence usually occurs after 1000 batches (batch size is 12) and takes about 5090.1 s.  
To find optimal experimental parameters, we studied the impact of three parameters on experimental performance by control variable method, including the number of heartbeats in a sliding window (Window Size), dimension of hidden unit (Hidden Size), and dropout ratio (keep prob). First, only the Window Size was adjusted, and the values of the Hidden Size and the keep prob were set as 80 and 0.4, respectively. As shown in Figure 5, we can see that the accuracy, sensitivity, and specificity all have similar trends. When the Window Size is set to 24, the experimental performance is optimal, and the accuracy can reach close to 94%.  
With regard to the hyper-parameters of Bi-LSTM networks: Hidden Size and keep prob, we can observe from Figure 6 that the red curve is in a relatively high position. Accordingly, the performance is  
Figure 5: Impact of window size.  
relatively good when the keep prob is 0.3. As the green curve is in a relatively low position, we believe that the network may be over-fitting when the keep prob is 0.5. Because the peak point of the red curve is obtained when Hidden Size is 90, we conclude that the relatively optimal Hidden Size and keep prob are 90 and 0.3, respectively, and the accuracy can reach nearly 95%.