esa-sceva/satcom-chunk-collection huggingface dataset:
# Constant-Beamwidth LCMV Beamformer with Rectangular Arrays
## 3 Conventional Beamformers
### SD and DS Beamformers  
Superdirective (SD) and delay-and-sum (DS) beamformers are obtained by maximizing the DF and the WNG, respectively [29; 31], both subject to the distortionless constraint. The solutions to these minimization problems are respectively given by  
\[\mathbf{h}_{\mathrm{SD}} =\frac{\mathbf{G}^{-1}\mathbf{d}}{\mathbf{d}^{\mathrm{H}}\mathbf{ G}^{-1}\mathbf{d}}, \tag{15a}\] \[\mathbf{h}_{\mathrm{DS}} =\frac{\mathbf{d}}{M}. \tag{15b}\]
# Reproducibility and Accuracy of Two Facial Scanners: A 3D In Vivo Study
## 5 Conclusions  
The conclusions of the study are as follows:  
* The Vectra 3D and the Eiscan facial scanners generate 3D models with similar, consistent, and replicable measurements, suggesting no systematic advantage of one measurement tool over the other in terms of accuracy compared to anthropometric measurements.
* The analysis of linear measurements on 3D facial scans has proved they are reliable and repeatable, showing no significant differences with respect to each other and with respect to anthropometric measurements. Similarly, all the cephalometric points analyzed turned out to be within the range from 0.5 mm to \(-\)0.5 mm, indicating a similarity between the Vectra 3D and the Einscan.
* The comparison of the overlays in the areas of the scans shows that more than 70% of the averages of overlapping surfaces of the forehead, left cheek, right cheek, tip of nose, and chin areas fell within the highly reproducible band (0.5 mm to \(-\)0.5 mm). The nose and the right and left cheeks are the area with the highest average percentage of surface in the highly reproducible band (about 80%). Except for the forehead (82.52%), more than 90% of each area analyzed fell within the reproducible band (1.5 mm to \(-\)1.5 mm).
* Three-dimensional facial scanners represent a promising tool in the field of dentistry and orthognathic surgery, allowing for a three-dimensional analysis of patients' faces, treatment predictions, and soft tissue analysis without the use of radiation.
* Future studies should aim to analyze different facial types as well as various environmental conditions to be considered for successful scanning.  
**Author Contributions:** Conceptualization, L.L. and F.P.; methodology, L.L. and F.P.; software, M.C. and G.B.; validation, L.L. and F.F.; formal analysis, F.P. and M.C.; investigation, M.C. and M.D.M.; resources, L.L. data curation, F.P.; writing--original draft preparation, F.P.; writing--review and editing, F.P. and M.C.; visualization, L.L. and F.P.; supervision, L.L. and F.P.; project administration, L.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study was conducted in accordance with the Declaration of Helsinki, and it was approved by the Institutional Review Board (or Ethics Committee) of the University of Ferrara (protocol code 08/2024, 3 March 2024).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
All authors ensured that all the data and materials, as well as software application or custom code, support their published claims and comply with field standards. The raw data supporting the conclusions of this article will be made available by the corresponding author on request.  
The authors declare no conflicts of interest.
# Automated Detection of Multi-Rotor UAVs Using a Machine-Learning Approach
## 4 Detection and Identification of a Drone Using a Machine-Learning Approach
### Creating Other Necessary Files for Training  
For successful training of the model, it is necessary to have a sufficient amount of data. Therefore, drone images were collected from freely available sources on the Internet. Their number was approximately 100. After communication with the developers of the SafeShore project, a project funded by the European Commission to detect small targets flying at low altitudes, a further data set was obtained of approximately 1000 samples. Other samples were created from video sequences capturing a flying drone.  
Next, it was necessary to create our annotation for each image showing the object of interest--the drone. The data was split so that 80% of the data was left for model training and the rest 20% for testing. The freely available LabelImg tool was used to create annotations. An example of an annotation can be seen in Figure 13.  
The.xml file generated by the annotation tool can be seen in Figure 14. From this file, the data, which are used to create the TensorFlow Record (TFRecord) file, were extracted. It is the simple file whose content is in binary form and it is used by the TensorFlow training library. Therefore, it is located in one memory block and the data access is faster. The extracted data are saved in a.csv file (right part of Figure 14). This file displays information such as the name of the file in which the drone object is located, its height and width, the name of the class, and the coordinates of the upper right corner of the located object.  
Figure 13: Annotated object (drone) in LabelImg.
# The Role of Macrophage Dynamics in Atherosclerosis Analyzed Using a Petri Net-Based Model
## 3 Results and Discussion
### Potential Clinical Relevance of the Study  
Our investigation into atherosclerosis using a Petri net-based model reveals clinical potential by elucidating the interplay between cholesterol homeostasis, macrophage dynamics, and plaque formation. The discoveries, such as the effects of inhibiting macrophage colony-stimulating factor (M-CSF) and targeting the CXCL12/ CXCR4 pathway, introduce innovative approaches to mitigating plaque formation. These insights, in line with previous studies, indicate that alternative strategies, such as regulating macrophage M1 polarization, could drive therapeutic progress.  
Nevertheless, translating these theoretical understandings into practical clinical uses encounters challenges, including verifying the accuracy of the model, considering individual patient variables, understanding the intricacies of biological processes, and carrying out thorough clinical tests. There are also ethical concerns surrounding the utilization of model-driven forecasts without solid empirical support and the necessity of effectively educating healthcare providers about the model's intricacies. In conclusion, although the Petri net model demonstrates significant promise, its integration into clinical environments requires thorough assessment and collaborative work in various fields.
# Application of Soil Moisture Active Passive (SMAP) Satellite Data in Seismic Response Assessment
## 2 Materials and Methods
### Data and Processing
#### 2.2.3 SMAP Event Window  
Considering the SMAP repeat frequency, there is a time interval of about 24 to 72 h between pre- and post-earthquake SMAP data. Table 3 lists the mainshock date and the pre- and post-event SMAP data available for each earthquake. To avoid the effects of diurnal cycles on soil moisture, only 6:00 AM SMAP surface soil moisture retrievals were used in this study and therefore, for each earthquake, the nearest available 6:00 AM SMAP data were selected for the analysis. For example, the Petrinja, Croatia 2020 earthquake happened at 11:20 AM coordinated local time [80], which equals 12:20 LST, on 29 December 2020. The nearest available SMAP data for the Croatia region before and after this earthquake were acquired on 29 December 2020 (6:00 AM LST) and 30 December 2020 (6:00 AM LST), respectively.  
Because GLDAS has a three-hour temporal resolution, soil moisture data from this platform are available less than three hours before and after earthquakes. However, since pre- and post-event SMAP soil moisture data were being compared with the GLDAS data, the soil moisture products provided by GLDAS were downloaded for the same pre- and post-event dates and times as in SMAP. The SMAP data time interval (SMAP event window), dictated by the temporal resolution of SMAP, also determined the time period for which the accumulated precipitation data (GPM) were obtained for this study.  
Soil moisture change (\(\Delta SM\)) in the SMAP event window was calculated for both SMAP and GLDAS soil moisture products as follows:  
\[\Delta SM=SM_{post}-SM_{pre} \tag{1}\]where \(SM_{Pre}\) and \(SM_{Post}\) are the soil moisture before and after the event, respectively. Figure 3 shows an example of \(\Delta SM\) maps calculated based on SMAP (\(\Delta SM_{SMAP}\)) and GLDAS (\(\Delta SM_{GLDAS}\)) in their native spatial resolution, as well as the GPM accumulated precipitation in this time window for the Petrinja, Croatia 2020 earthquake.
## 2 Analysis of MCRWPT System for RMINS
### Principle of MCRWPT System
The resonance state has an equivalent load resistance to achieve maximum PTE. It can be seen from Figure 2e that the PTE increases first and then decreases with the excitation frequency under the conditions that \(R_{L}\) = 50 \(\Omega\) and \(k\) = 0.8. The maximum PTE occurs at the resonant frequency of 100 kHz. Figure 2f shows that the PTE increases when the equivalent load resistance increases under the conditions that \(f\) is 100 kHz and \(k\) is 0.8. Therefore, increasing the PTE of the system can be achieved by increasing \(k\), making the system work in a resonant state, and changing the equivalent load resistance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Unit** \\ \hline Transmitter resistance \(R_{1}\) & 0.53 & \(\Omega\) \\ Receiver resistance \(R_{2}\) & 0.23 & \(\Omega\) \\ Coil inducances \(L_{1}\), \(L_{2}\) & 24 & \(\mu\)H \\ Compensation capacitors \(C_{1}\), \(C_{2}\) & 105.54 & nF \\ Resonant frequency \(f\) & 100 & kHz \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters used for calculations. As shown in Figure 3, when the coupling coefficient is greater than 0.35, the output power decreases with the increase of the coupling coefficient under the conditions that \(R_{L}\) is 50 \(\Omega\) and \(f\) is 100 kHz. Therefore, the design of the MCRWPT system needs to consider the influence of the coupling coefficient on output power in the case of constant input voltage. The theoretical analysis shows that the output voltage and PTE of the MCRWPT system are affected by \(R_{L}\), excitation frequency, and \(k\) when the primary and secondary coils' inductance, the resonant compensation capacitor of the primary and secondary coils, and the input voltage are constant. When MCRWPT is applied to the RMINS, depending on the internal structure in the RMINS, the power supply system can work properly and achieve higher PTE by selecting the appropriate excitation frequency for a given coil diameter and magnetic shielding structure.
## 5 Discussion
### Proposing the Model in Terms of the Rate Process Theory
In cases of double-swing tensile and compressive fatigue, such as axial-loading fatigue, the coefficient value of the loading mode is 1.47 for Vickers hardness, and in the case of rotating bending fatigue, the coefficient value of the loading mode is 1.6 for Vickers hardness. If the reference value is double-sided tensile and compressive fatigue, such as axial-loading fatigue, the coefficient ratio between the rotating bending fatigue and axial-loading fatigue is \(1.6/1.47\cong 1.09\). This coefficient ratio was calculated based on the loading mode, and it indicates that the difference is dependent on the loading mode. Therefore, this difference was considered as a value that represents the effect of loading style on the stress index. Figure 12a compares the model with the stress index in rotating bending fatigue with a three-dimensional plot of the rotary interlock fatigue limit. The following equation was used for the model, along with a stress exponent \(n\) of \(n=1.6/1.47\cong 1.09\) for Equation (14), which is as follows:  
\[E=0.334\mathrm{exp}(1.09)H_{\mathrm{V}}\mathrm{exp}\left(\frac{3.68\times 10^{5 }\times\frac{1.09}{H_{\mathrm{V}}}}{T_{0}\mathrm{ln}\left(\frac{f_{0}}{f} \right)}\right) \tag{14}\]  
Figure 12: (**a**) A three-dimensional plot showing results derived from the extended model; (**b**) a representation of the relative error for every selected rotating bending fatigue datum. The relative error of the fatigue limit estimation, provided by the \((\sigma_{\rm w}-E)/E\) calculation, was subsequently plotted, as shown in Figure 11b. The percentages of data within \(\pm 10\%\) and \(\pm 20\%\) of relative error were \(50\%\) and \(73\%\), respectively. Compared to the estimation results obtained without considering the loading mode, these results were judged to be the result of improved accuracy due to the use of the stress index. [PERSON] et al. [41] showed that, in their study, the ratio of rotating bending fatigue to axial-loading fatigue was almost identical, assuming the effect of the restraint to be \(0.9\), because, in the case of rotating bending fatigue, the plastic deformation that occurs only on the surface is restrained by the internal elastic deformation, and the actual acting stress is higher than the evaluated stress. The ratio of axial-loading fatigue to rotating bending fatigue is \(1/0.9\cong 1.11\), which is close to the ratio of \(1.09\) estimated by the equation using Vickers hardness \(H_{\rm v}\) for axial-loading fatigue and rotating bending fatigue. It is thought that the effect of the loading frequency on the fatigue limit of the rotating bending fatigue is larger than that of the axial loading fatigue because of the plastic deformation of the surface of the material. Based on the above, by using the stress index as a loading-mode coefficient, it is possible to estimate the loading frequency-dependent rotating bending fatigue test limit based on the results of axial-loading fatigue tests, which can be conducted up to high-frequency bands.
# Discrete-Time Asymptotic Tracking Control System for a Satellite with a Solar Panel
## 1 Introduction  
At present, satellite technology has become widely accessible. Earth observation satellites must perform complicated tasks with high requirements for reliability and accuracy. The attitude control of observation microsatellites (typically 50-100 kg) becomes significantly more complex when appendages such as solar panels or antennas are added, as such appendages alter their inertia and often behave like flexible structures that can vibrate or oscillate. These structural dynamics couple with the satellite's rigid body motion and introduce several technical challenges, especially in the context of high-precision pointing missions. These increasingly stringent requirements regarding pointing accuracy must be achieved despite high flexibility of the satellite structure, environmental disturbances (e.g., magnetic field vibrations and residual atmosphere), limited space for high-quality sensors and actuators, power constraints, and limited computational resources. Possible solutions include the miniaturization of sensors and actuators through the use of MEMS and the development of advanced modeling and control strategies based on AI/ML as robust and adaptive predictive algorithms. The present study falls into the category of advanced control theory, as we develop a discrete-time control algorithm that allows the attitude of a satellite with a solar panel to track a prescribed reference signal with zero asymptotic error.
## 3 Controller Design
### Position Controller Design
#### 3.1.3 Adaptive Law Design  
By introducing DO into the controller to compensate the system uncertainty, the robustness of the controller is improved. In addition, the introduction of DO greatly alleviates the requirement of switching gain in traditional sliding mode control. Further, an adaptive law is designed to compensate the observation error of the DO. The combination of the observer and the adaptive technique can effectively reduce the chattering of the system.  
\[\left\{\begin{array}{ll}\dot{\hat{D}}_{p}=\Big{[}\ \ \dot{\hat{D}}_{x}\ \ \ \ \dot{\hat{D}}_{y}\ \ \ \dot{\hat{D}}_{z}\ \Big{]}^{T}\\ \dot{\hat{D}}_{i}=\gamma_{1}\big{(}|s_{i}|-\gamma_{2}\hat{D}_{i}\big{)},i=x,y,z \end{array}\right. \tag{44}\]  
where \(\hat{D}_{p}\) is the estimation value of \(D_{p}\). The function of \(\hat{D}_{p}\) is designed to compensate for the observation error, and \(\gamma_{1}>0\), \(\gamma_{2}>0\) represents the adaptive law adjusting parameters.
## 4 Virtualization in WSN and the Concept of Overlay Networks
### Peer-to-Peer Topology  
The peer-to-peer topology is more prevalent than the ring topology in deploying overlay networks in virtualized WSNs. Peer-to-peer interactions are influenced by the mutual aid of the individual nodes that form the network. This is achieved by trusted entities that do not inject malicious activities into the network. Peer-to-peer topologies also improve network performance but further introduce restricted network competences and high failure recovery times. Their implementation lacks robustness, ease of diagnosis and it has limited privacy and security [122; 123].  
Figure 8: General Architecture of a Hierarchical Skip Graph Overlay Network.  
Figure 9 shows a general representation of an overlaid peer-to-peer topology in a VWSN model with a distributed overlay network. The virtual nodes in the overlay have interactions with nodes from different WSNs and also between themselves.  
[PERSON] et al., in [124] proposed a structured overlay network built with in-network processing in view. The architecture considers the heterogeneity of the service providers, along with an overlay global framework that spreads across the overlay structure and stream processing for packet handling. Such architecture is critical for future networks as it offers resilient frameworks to enable resource sharing. Peer-to-peer interactions are further discussed in [125] to demonstrate the need for redundant communication paths in overlay networks to achieve resilience against nodes that can drop or be attacked via DoS [126]. The architecture of [125] further improves the availability of the network through a self-healing implementation.  
Peer-to-peer topologies in overlay networks are further reinforced in [127] using a distributed model to connect end-users and gateways in mobile ad-hoc networks. Overlay networks are extended to support internet-of-things devices in a smart industrial application using a robust message exchange service for various city-wide applications such as seismic data and water leakages in the industrial distribution system [113]. The heterogeneity of the application areas in a smart industrial ecosystem renders an overlay approach very critical in addressing the scale and ubiquity of the sensor systems. It remains that the different implementation of peer-to-peer topologies presents a challenge with the vast designs that do not allowing interoperability of the different frameworks. A reference design will enable a standardized topology that can foster interoperability.
## 4 Experiments and Results
### Experimental Setup  
In the experiment, the data from 59 days in PEMS04 is divided into a training set and a test set, with the first 45 days of data used for model training and the subsequent 14 days used for testing. In PEMS08, the data from 62 days is divided into a training set and a test set, with the first 48 days used for training and the final 14 days used for testing. For each dataset, historical data from 6 time steps (half an hour) is used to predict the traffic flow change in the next time step (5 min).  
All deep learning networks in this study are implemented using Pytorch 2.3.1. The model uses the Adam optimizer with a learning rate set to 0.0001, a batch size of 64, and 50 epochs. Additionally, learning rate decay and early stopping strategies are employed to prevent overfitting.  
The network parameters of the model proposed in this paper are set as follows: the number of multi-attention heads in the GAT network num_heads is 4; the number of hidden layers num_hid is 6; and the output feature dimension out_c is 6. The number of layers num_layers of Bi-LSTM is 2, the hidden layer dimension hid_c is 16, and the output feature dimension out_c is 6; Transformer encoder has an output dimension coder_c of 8; the number of encoders and decoders num_coder are both 2, and the number of attention heads num_heads is 4. The convolutional kernel size kernel_size of the CNN is 5 in the comparison and ablation experiments.
# Simplified Scan-vs-BIM Frameworks for Automated Structural Inspection of Steel Structures
## 2 Literature Review
### Deep Learning in Construction
[2021] proposed Scan2 BIM-NET, an end-to-end deep learning method for semantic segmentation of structural, architectural, and mechanical components in point cloud data. Their approach classifies elements such as beams, ceilings, columns, floors, pipes, and walls using two convolutional neural networks and one recurrent neural network. Tested on 83 rooms from real-world industrial and commercial buildings, the method achieved an average accuracy of 86.13%, significantly improving the accuracy of semantic segmentation in the Scan-to-BIM process, particularly in under-ceiling areas with closely spaced mechanical and structural elements. [PERSON] and [PERSON] [2021] developed an alternative segmentation method tailored specifically to large-scale steel buildings. Instead of processing 3D point cloud data directly, they utilized image processing techniques on 2D \"slices\" of the point cloud. By extracting centroids of structural cross-sections using 2D convolution and projecting them back into 3D space, they constructed a 1D beamline model, including connections. This method proved robust against variations in framing systems, clutter, and point cloud sparsity, effectively handling complex geometries that traditional 3D point cloud segmentation methods struggle with. These studies demonstrate the effectiveness of deep learning in improving various traditional frameworks within the construction industry by enhancing accuracy, efficiency, and automation. However, applications of deep learning specifically in structural integrity verification, particularly within the Scan-vs-BIM framework, remain limited. Additionally, recent studies have demonstrated the effectiveness of deep learning in detecting structural changes using camera images [2019, 2020]. While camera images offer high-resolution visual data, they may lack the precise spatial and geometric information necessary for accurate structural integrity assessment. Three-dimensional laser scanning provides detailed dimensional data that capture the exact spatial relationships and dimensions of structural components, which is crucial for quantitative analysis in a Scan-vs-BIM framework. However, integrating image-based methods with laser scanning could be a potential area for future research, combining the strengths of both modalities. In conclusion, the application of deep learning may improve the traditional Scan-vs-BIM framework. Deep learning can replace the processing method that compares As-Planned BIM and As-Built scan data in a single format in the traditional Scan-vs-BIMframework. This study collects comparison data by directly comparing two types of data in the original state and proposes a deep learning model that can analyze the features of these comparison data.
## 2 Materials and Methods
### The Framework of Proposed Fusion Method
#### 2.3.1 Decomposition  
The morphology and phase images are first decomposed with NSST into a low-frequency sub-band and multiple high-frequency sub-bands, respectively. Then, they are fused using different fusion rules separately.