esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Lithospheric Magnetic Field Model of the Tibetan Plateau  
### Related Parameter Setting  
To achieve the best fit between the selected spherical cap and the modeling region, the pole of the spherical cap is generally set at the center of the region or nearby. The geographic latitude and longitude of the Tibetan Plateau are 26.0\({}^{\circ}N\)\(\sim\) 40.0\({}^{\circ}N\), 73.0\({}^{\circ}E\)\(\sim\) 105.0\({}^{\circ}E\), respectively. The difference of longitude is approximately twice the difference of latitude. Under a certain level of truncation, partition modeling of a small spherical cap can be used to improve the accuracy of the model in the whole region and generate good spatial resolution [51]. Therefore, the Tibetan Plateau was divided  
Figure 4: Z component of the residual field before and after correction of the CHAMP (in 2009) and Swarm (in 2015) satellite data. We used the MF7 model to remove the lithospheric field for this illustration.  
into two subregions, and each subregion was independently modeled. The pole of the spherical cap in Region 1 is located at \((33^{\circ}N,\ 81^{\circ}E)\), and the pole of spherical cap in Region 2 is located at \((33^{\circ}N,\ 97^{\circ}E)\). To suppress the boundary effect, the half aperture of the spherical cap was amplified of \(1^{\circ}\), and \(\theta_{1}=\theta_{2}=10^{\circ}\) was set. The largest overlap between adjacent spherical caps is approximately \(7.8^{\circ}\). Thus, all the observed data are located within the range of the spherical cap.  
The mean altitude of the Tibetan Plateau is greater than 4 km. The height of the lowest orbit of the selected CHAMP satellite data is approximately 250 km. The height of the highest orbit of Swarm satellite data is approximately 510 km. Therefore, the geocentric distances of the lower and upper boundaries of the cone are defined as \(a=Re+\ 240\ km\) and \(b=Re+520\ km\), where \(Re=6371.2\ km\) refers to the earth's mean radius. The related parameters are listed in Table 1.  
In Table 1, \(K_{\max}^{i},K_{\max}^{e}\) represent the maximum truncation degree of the Legendre function in the internal and external source fields, respectively. \(P_{\max}\) denotes the maximum truncation degree of the Mehler function. Comprehensive consideration of the data size in each layer and the magnetic sensors do not easily capture the weak middle- and small-scale lithospheric magnetic field signals with lengths less than 200 km at the CHAMP orbital altitude. Therefore, \(K_{\max}^{i}=15,\ K_{\max}^{e}=10\) are selected to correspond to the minimum wavelengths of magnetic fields in the horizontal direction, which are approximately 200 and 250 km, respectively. The Mehler function mainly provides a stable space continuation in the radial direction to prevent the shock effect of the model in the data gap layer (340-450 km). After testing, \(P_{\max}=\ 5\) is set, and this can balance the data resolution in radials and the fitting in each height layer.
# Handover Management for Drones in Future Mobile Networks--A Survey
## 5 Handover Challenges in Drone Networks
### Handover Decision Algorithm  
The handover decision algorithm is another key challenge in drone networks. Numerous types of handover solutions exist for managing handover decisions, such as distance, RSRP plus distance, RSRP, route information, SINR, loads, mobility speed, cost functions, and machine learning. The algorithms based on RSRP are generally less complex but are also less precise. A vital feature of algorithms is that various standards can be applied for the handover decision-making procedure. This further increase computational complexity  
Figure 7: The antenna beam pattern and antenna sidelobes effect.  
but enhance efficiency and accuracy. Most existing algorithms are for previous generations, which are completely different in terms of specifications compared to the current generation. Drones have distinct characterizations, making existing algorithms inefficient. Further analysis and enhancements are still required.
## 5 Cluster-Based Energy Consumption Optimization Problem
### Cluster Head Selection Optimization Model  
To address the cluster head selection problem, it is important to achieve energy consumption balance of network. We set up a cluster head selection optimization model which depends on the following two relative energy efficiency factors.  
(1) Total residual energy ratio of cluster head candidates  
It is defined as the ratio of total residual energy of all active sensor nodes \(n_{i},i=1,2,...,n\) in the DSN to the total residual energy of cluster head candidates \(CH_{c},c=1,2,...,k\) in current round. In the data transmission phase, the cluster heads receive the data from member nodes and send aggregated data to the BS. The energy consumption of cluster heads is much more than member nodes so it is necessary to guarantee enough residual energy. One of the objective function is defined as follows.  
\[f_{3}=\sum_{i=1}^{n}S\_E\left(n_{i}\right)/\sum_{c=1}^{k}C\_E\left(CH_{c}\right) \tag{13}\]  
where \(S\_E\left(n_{i}\right)\) is the residual energy of sensor node \(n_{i}\), and \(C\_E\left(CH_{c}\right)\) is the residual energy of cluster head candidate \(CH_{c}\) in current round. The objective is to minimize the objective function value.  
(2) Energy consumption balance degree of the cluster head candidates  
It is defined as the energy consumption variance of the cluster head candidates in current round. To improve the lifetime of network, the BS needs to ensure that the energy consumption is evenly distributed among all the cluster heads so it is necessary to guarantee all the cluster heads consume approximate energy. The second objective function is defined as follows.  
\[f_{4}=\sqrt{\sum_{c=1}^{k}\left[CE_{r}\left(CH_{c}\right)-u\right]^{2}} \tag{14}\]  
where \(CE_{r}\left(CH_{c}\right)\) is the energy consumption of a cluster head candidates in current round, and \(u\) is the average energy consumption of all cluster head candidates. The objective is to minimize the objective function value.  
The above two objectives are not conflicting with each other so the cluster head selection fitness function is defined as a linear combination of the two objective functions.  
\[Fitness2=\beta\times f_{3}+\left(1-\beta\right)\times f_{4},0<\beta<1 \tag{15}\]  
where the \(\beta\) is a constant defined by weighing the importance of the sub-objective, and its value is between 0 and 1. Our objective is to minimize the fitness value in order to achieve better cluster head selection optimization effect.  
The cluster head selection optimization problem can be summarized as searching for a cluster head group \(\left(CH_{1},CH_{2},CH_{3},CH_{4},...,CH_{c}\right)\) to minimize the fitness value. When the fitness is minimal, we can effectively guarantee energy consumption balance.
# Electrospinning-Based Carbon Nanofibers for Energy and Sensor Applications
## 2 Electrospinning of Carbon Precursor Polymer
### Polymer Nanofibers for Carbon Nanofibers
#### 2.2.2 Polyvinyl Alcohol-Based Carbon Nanofibers  
Research on polyvinyl alcohol (PVA)-based CNFs began in the 1960s [41]. PVA is a thermoplastic polymer, which is soluble in water, non-toxic, odorless, and environmentally friendly [42]. Although the mechanical stability of PVA is not as good as PAN, it is still better than that of other polymers such as polypropylene [43; 44]. The excellent physical properties of PVA allow it to be made into such a small-sized fiber via electrospinning [45]. The heating process of PVA fibers includes two main stages which are stabilization and carbonization (Figure 3) [46]. Although the tensile strength of PVA fibers is weaker, the calcination temperature is much lower than PAN (Table 1). This allows PVA to be used or combined with other materials to create a composite. In addition, the carbonization of PVA does not release toxic gases such as NH\({}_{3}\) or HCN.
# Two-Dimensional Shape Analysis of Complex Geometry Based on Photogrammetric Models of Iconostases
## 2 State of the Art
### Methods for Cultural Heritage Digitization  
Three-dimensional digitization technologies are particularly relevant for accelerating spatial data collection from existing cultural heritage. Actual techniques for 3D surveying such as photogrammetry and laser scanning still present two methods that have been frequently used in the critical analyses and applications of digitization of cultural masterpieces [19]. Three-dimensional scanning uses laser rays to estimate and directly position data in 3D space. Terrestrial laser scanning has been proved as a suitable method for 3D surveying, applied not only to certain structures but also to whole cities and areas representing assets of cultural interests [20]. The digital documentation of large cultural heritage sites such as historic churches can also be accomplished by using integrated techniques (laser scanning and photogrammetry) [21; 22] or by combining photogrammetry and drone survey [23]. Photogrammetry is based on a passive system of optical recording sensors that delivers 2D images. It represents a measurement technique that uses a series of photographs taken from different angles and positions to generate a 3D model of an object. Over the last ten years, researchers in the field of cultural heritage digitization have agreed that in most cases, photogrammetry presents a complete, reliable, economical, and flexible approach for a faithful and accurate reconstruction of 3D objects [24; 25; 26; 27; 28]. Moreover, the advances of the Structure from Motion (SfM) algorithm have potentiated 3D model generation from their 2D photographs available on web repositories [29]. However, 3D digitization is a complex process and there is no general approach or a default technology for 3D surveying (3D data acquisition) and modeling.  
Many case studies illustrate attempts to develop a methodology for the photogrammetric surveying and modeling of fragile cultural heritage sites [30], addressing the challenging texture and complex shape of the object [31]. In addition, for archiving purposes, cultural heritage digitization usually requires high precision of the 3D reconstruction, specially 1 mm or higher for smaller objects [2; 26]. [PERSON] et al. [32] proposed a photogrammetric workflow for the accurate 3D construction and visualization of museum assets, intended for small-medium museums, that relies on mobile equipment used for data acquisition.  
Nevertheless, the method of 3D digitization depends on an individual type and the complexity of objects of cultural heritage, as well as on the specific application requirements. Once created, the digital replicas obtained using 3D data acquisition methods allow various possibilities to document, manage, and analyze the available information of cultural heritage data inside a virtual environment.
# The Quanta Image Sensor: Every Photon Counts
## 3 Imaging Characteristics
### High Dynamic Range (HDR)  
Since the QIS consists of multiple fields of jot data that may be combined in a cubic ensemble, it is possible to have a different electronic shutter duty cycle or \"speed\" for each field or time slice. Thus some time slices can have high flux capacities allowing capture of brighter portions of scenes without saturation. A similar idea has been used in CMOS image sensors for many years [55] for high dynamic range (HDR) imaging, although it can suffer from imaging artifacts due to relative motion of the scene between captured fields. The higher field readout rate of the QIS will ameliorate some of those artifacts. The Hurter-Driffield response characteristics of the QIS help reduce SNR \"dips\" caused by the fusion of multiple fields of data taken with different shutter speeds [9]. Multi-bit QIS devices can also be operated in HDR mode. For example, consider 2b-QIS output formed from a 16 \(\times\) 16 \(\times\) 16 cubic. In normal readout mode summing all 16 fields, each with 100% shutter duty cycle, the \(DR\) is approximately 98 dB. In an HDR mode, with summing a cubicle where 13 fields are exposed with 100% shutter duty cycle, 1 field at 20% duty cycle, 1 field at 4% duty cycle, and 1 field at 0.8% duty cycle, the dynamic is extended to approximately 135 dB as illustrated in Figure 6. Figure 6 shows the log signal vs. log exposure characteristic of a 1b QIS, 2b QIS and their attendant \(SNR_{H}\) when the 16 fields in the cubicle all have 100% duty cycle and are summed (green and blue respectively). For the 2b QIS, the maximum signal is 3 e\(-\) per jot leading to a maximum sum of the cubicle of 3 \(\times\) 4096 = 12,288. Also shown in the figure are the 2b QIS cubicle sums vs. exposure for the 4 component fields. S1 shows the sum of 13 fields taken with 100% shutter duty cycle. The number of fields should be large in order to capture low light detail in the image with good SNR. The three following fields' cubicle sums S5, S25 and S125 are taken with 20%, 4%, and 0.08% duty cycles respectively--essentially 1/5, 1/25, and 1/125 relative shutter speeds. The sum of all these fields of the cubicle is the 2b HDR signal vs. exposure characteristic (red) along with its attendant \(SNR_{H}\). The latter has one large \(SNR_{H}\) dip from its peak of \(\sim\)37 dB to a plateau of 27 dB in the extended range. While on a log scale, the HDR curve (red) looks similar to the normal readout curve (blue), the inset shows a linear-linear plot of the extended range, showing significant contrast for the HDR response. Different transfer curves can be generated by varying the relative duty cycles and number of fields and this particular set of signal vs. exposure curves are just one example set.  
Figure 6: Log signal vs. log exposure for a 2b QIS operated in normal readout mode (blue) and high dynamic range mode (red), showing extension of the dynamic range by approximately 40 dB. Inset shows linear-scale response curves for the extended exposure range.
## 9 Testing and Validation of Product Variants  
After integration, product variants undergo rigorous testing and validation to ensure they meet functional and performance requirements. Testing focuses on evaluating data quality, model accuracy, pipeline reliability, and overall system performance under the intended operational conditions. For example, an Earth observation satellite's ML variant would be tested for the accuracy of image classification models and the reliability of image preprocessing pipelines.  
_Deployment_  
Validated product variants are deployed to their target environments. Deployment involves preparing and transferring configured pipelines and models to operational systems, such as ground stations or onboard satellite platforms. Deployment pipelines ensure compatibility with the operational context and may include mechanisms for resource optimization and system monitoring.  
5. _Feedback and Continuous Improvement_  
A key aspect of application engineering in ML PLE is the incorporation of feedback from deployed systems. Feedback loops are implemented using automated monitoring pipelines that track performance indicators such as prediction accuracy, data drift, latency, and failure rates. These pipelines trigger alerts or initiate retraining workflows when thresholds are breached. Monitoring tools (e.g., Prometheus, MLflow) can be integrated to visualize and analyze real-time model behavior. Collected feedback is systematically linked to specific core assets, such as the pipeline variant or dataset version in use, enabling traceable refinement of configurations.  
6. _Automation and Scalability_  
Automation plays a critical role in application engineering by enabling efficient configuration, testing, deployment, and monitoring of ML variants. CI/CD pipelines are tailored for ML automate repetitive tasks, such as retraining models or updating pipelines, ensuring consistency across product variants and reducing manual effort. Automation enhances scalability, allowing organizations to handle a growing number of product variants with minimal additional resources.
## 5 Implementation and Results
### Comparison between the Ada-DCNN and Hybrid Ada-DCNN  
The diagnosis performance of Ada-DCNN is superior to the DCNN in Section 5.1. In this part, we investigate the performance of the hybrid Ada-DCNN and compare it with the DCNN and Ada-DCNN in all the 4 accuracy levels (S1-S4). Under each level, the features are extracted by the DCNNs and integrated as the final feature vector for classification.  
We donate [Si, Sj, Sk] as the integration of TFD-Net, ThD-Net, and FD-Net under the \(i^{\text{th}}\), \(j^{\text{th}}\), and \(k^{\text{th}}\) accuracy levels, respectively. The accuracy of hybrid Ada-DCNN and the average values of related Ada-DCNNs and DCNNs for each integration are listed in Table 7.  
According to Table 7, the hybrid Ada-DCNN improves the accuracy by an absolute value of 13.89%, 12.29%, 8.28%, and 7.81% compared to the average of the Ada-DCNNs, and 29.26%, 24.59%, 18.68%, and 12.95% compared to the average of the DCNNs from S1 to S4, respectively. It is concluded the hybrid Ada-DCNN can further improve the diagnosis performance by combining the DCNNs together, which in actuality fuses the information from different types of samples. It is significant as the enhanced performance can be obtained even if the DCNNs do not perform well. It gives a possibility for reducing the work of parameter tuning as a good performance of DCNN is not a must for a hybrid Ada-DCNN. Figure 11 gives the accuracy and difference among them in each level. Additionally, the maximal accuracy of Ada-DCNN at each level is also plotted in the figure. It can be found from the difference that the improvement is decreased with increasing DCNN accuracy, which is in accord with the conclusion in Section 5.1. Also, the accuracy of hybrid Ada-DCNN is closely correlated with the maximum of Ada-DCNNs with a difference of \(\sim\)5% for each level; this gives a method for the performance prediction of hybrid Ada-DCNN.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Accuracy** & **[S1, S1, S1]** & **[S2, S2, S2]** & **[S3, S3, S3]** & **[S4, S4, S4]** \\ \hline Average of DCNNs (\%) & 54.43 & 65.13 & 78.31 & 84.75 \\ Average of Ada-DCNNs (\%) & 69.80 & 77.42 & 88.71 & 89.89 \\ Hybrid Ada-DCNN (\%) & 83.69 & 89.72 & 96.99 & 97.70 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The accuracy of the hybrid Ada-DCNN as well as the average accuracy of related Ada-DCNNs and DCNNs for each integration.  
Figure 10: The detail results of FD-Net in the accuracy level S1: (**a**) The training and validation process of DCNN (55.85%); (**b**) The fusion matrix of the related Ada-DCNN (65.78%).
## 3 Features, Targets and Dataset Generation  
### Ground Motion IMs  
The basic parameters adopted in order to perform a seismic structural damage prediction analysis are the characteristics of the ground motion. By this, the identification of the seismic parameters that affect the dynamic response is of utmost importance. For this purpose, a set of 16 ground motion IMs is calculated. Amplitude parameters such as the maximum absolute values of ground accelerations (a\({}_{\text{g}}\)(t)), velocities (v\({}_{\text{g}}\)(t)), and displacement (d\({}_{\text{g}}\)(t)) signals, which were referenced as PGA, PGV, and PGD [41], respectively, are examined. Additionally, the Arias intensity (I\({}_{\text{A}}\)) [42] and the cumulative absolute velocity (CAV) [43], which are calculated by the integral of the accelerogram time history, are considered.  
Figure 2: The examined Reinforced Concrete frame.  
An inherent feature of signals is the frequency content, which varies dynamically over time in the case of ground motion records. However, it can be quantified using the equivalent frequency PGA/PGV [41] as if it was a sinusoid signal. Another quantity that is related to the frequency content of a ground motion is the potential destructiveness measure after [PERSON] and Saragoni (\(\mathrm{I_{AS}}\)) [44], determined by the zero-crossing number of the acceleration signal (\(\mathrm{u_{o}}\)) per unit of time.  
Various definitions have been given in the past for the strong motion duration of a seismic excitation in order to identify the time interval of the signal in which the vast amount of its total intensity is released. In this work, the strong motion durations defined by [PERSON] and [PERSON] (\(\mathrm{SMD_{TB}}\)) [45] and by [PERSON], [PERSON] and [PERSON] (\(\mathrm{SMD_{ROG}}\)) [46] are assumed. Both of these are based on the time evolution of Arias intensity according to the Husid diagram [47]. Additionally, the bracketed duration as described by [PERSON] (\(\mathrm{SMD_{Bolt}}\)) [48], which is defined by the first and last exceedance of 5 percent of g, is employed.  
Combining the above parameters results in more complex measures such as power \(\mathrm{P_{90}}\)[41], \(\mathrm{a_{rms}}\)[41], characteristic intensity (\(\mathrm{I_{c}}\)) [41], the potential damage measure according to [PERSON], [PERSON] and [PERSON] (\(\mathrm{I_{FVF}}\)) [49] and the IM after [PERSON] and [PERSON] (\(\mathrm{I_{RG}}\)) [50].  
It has to be mentioned that seismic parameters that depend on the fundamental structural period, such as individual spectral values, were not calculated. These parameters could not be used due to the elongation of the elastic period during the first seismic event. Instead, the Housner intensity [51] (\(\mathrm{SI_{H}}\)) which accumulates pseudo-spectral velocities (PSV) to a constant range of possible eigen periods and demonstrates high correlation with the structural damage [23; 52; 53] is employed. All of the mathematical expressions of the examined IMs are summarised in Table 1. The elastic spectra are defined using OpenSeismoMatlab [54], while values of the IMs are computed through Python [55] code.
###### Abstract
The signal processing stage makes use of the WTS; the extracted features are then fed into an LSTM network for training and testing phases. Parallel with the mentioned classification method, the same features are introduced to an SVM model. The results of these techniques are compared with the conditions where the raw signals are used as input to the LSTM algorithm as well as the input to the feature extraction stage before applying the SVM. The pioneering elements of this research are the use of ABAQUS/CAE modeling for bowed rotor systems and the combination of WTS with LSTM and SVM for classifying these systems with a limited number of sample tests, which has not been documented in the literature before for such a mechanical system.