esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### The Proposed Solution
Our selected dataset is smaller than large datasets such as ImageNet used to train large-scale CNNs [41]. As a result, we relied on transfer learning from models trained for the ImageNet challenge in order to benefit from their visual feature extraction. A deeper analysis of these networks revealed that they are very good at extracting and learning low-level image features even for data that were not used to train them. In fact, the majority of domain-specific image recognition tasks rely on transfer learning [42]. For the visual features part, we used pretrained CNNs such as ResNet50 with the ImageNet weights as initialization. We designed a set of deep-learning-based classifiers using transfer learning (for the visual feature learning part). We conducted several tests and compared the classification performance for different base CNNs, including the ResNet50 [39], VGG16, VGG19 [37] and InceptionV3 [43] CNNs (these networks were the winners of the ImageNet challenge). We designed four networks based on the following CNNs: ResNet50, VGG16, VGG19 and InceptionV3. The InceptionV3 network has an input image resolution of 299 \(\times\) 299 pixels, and we therefore increased the patch size in the online data augmentation process while training the network. For each of the networks we conducted the following procedure: We removed the top dense layersand connected the last convolutional layer to a global average pooling of 2048 units; this layer was regularized with 50% dropout; this average pooling layer was then connected to a ReLU activated dense layer of 1024 units (visual features layer); and finally, we connected the ReLU dense layer to two output SoftMax activated layers to perform the multitask learning (for artist attribution and year estimation). It is worth noting that the convolutional layer weights were initialized to the ImageNet weights and all network layers were fine-tuned. The hyperparameters used are described in Table 3, and the architecture of the networks is outlined in Figure 8.  
Following our tests, we chose the ResNet50 (residual networks) CNN as our image feature extractor. This network was mainly selected for its training and validation performance compared to the others. The comparison results of the single input network are outlined in Section 3.
## 5 Conclusions  
During the present research work, it was possible to conclude that:  
* The four-point flexural test and scratch testing are expeditious methods to determine the mechanical and tribological properties of newly developed composite resins for restorative dentistry.
* Of all the composites analyzed, Activa(tm) was the one with the lowest hardness, followed by Filtek(tm) and consequently Ketac(tm). However, toughness presented the inverse order, with Activa(tm) showing greater resistance to fracture than Filtek(tm) and in turn than Ketac(tm).
* The stiffness or elastic modulus determined by the two different methods show the same trend although with different amplitudes.
* For the tribological test an increasing normal load up to 20 N was applied. A COF varying between 1 and 1.5 was observed with the maximum value for the Activaspecimen and the minimum value for FilekTM. Regarding the wear rate, this was lower for ActivaTM, followed by FilekTM and KetacTM.
* Related with the wear mechanism it was shown that ActivaTM presented a smooth surface without visible cracks characteristic from a ductile removal mode, while FilekTM and KetacTM presented a brittle peeling of great portions of material characteristic of brittle removal mode.
* ActivaTM can absorb impact better than FilekTM and KetacTM.
* ActivaTM performed better in relation to fracture toughness, wear rate and impact absorption than FilekTM and KetacTM. Moreover, thermocycling had little effect on this bioactive composite.  
Further studies are needed, with standardized protocols, to establish clear relations between the evaluated parameters for each material and to confirm their clinical performance.  
Conceptualization, L.V., A.R. and E.C.; methodology, A.S.C., L.V., A.R. and E.C.; software, E.R.C., A.S.C. and L.V.; validation, A.S.C., M.M.F., L.V., A.R. and E.C.; formal analysis, E.R.C., A.S.C., L.V., A.R. and E.C.; investigation, E.R.C., A.S.C., L.A., A.B.P., C.M.M. and J.S.; data curation, E.R.C., A.S.C., I.A., L.V., A.R. and E.C.; writing--original draft preparation, E.R.C., A.S.C., I.A., A.B.P., C.M.M., L.V. and J.S.; writing--review and editing, A.S.C., I.A., M.M.F., L.V., A.R. and E.C.; supervision, L.V., A.R. and E.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Radio Planning Aspects
### Methodology for Planning the New LTE Network  
The proposed flow-chart to plan the new LTE network can be observed in Figure 5.  
Figure 5: The proposed flow-chart to plan the new LTE network.  
First of all, the LTE radioelectric coverage for each municipal area is calculated using the existing TETRA sites and the existing sites with other radiocommunication systems (not TETRA). In our case, this leads to 34 sites in total (16 TETRA base stations and the 18 existing sites) (Figure 3).  
* If the QoS (see Section 2.5) is fulfilled, the following optimization algorithm can be applied: Initially, individual radioelectric coverages were calculated for the available sites and are ordered in a table from the least to the greatest radioelectric coverage. Then, an iterative process for each site was conducted starting with the first element (the one with the least radioelectric coverage) and the same sequential order (from top to bottom). In each iteration, the following steps are carried out:
* The element of the corresponding table is discarded.
* The global radioelectric coverage is calculated with the sites not discarded or eliminated.
* If the macro scalar quality is accomplished for the region and the municipal areas, the site discarded in step 1 is eliminated and, if not, the site is kept in the table.
* If the QoS is not fulfilled, this fact means that more sites are needed. Therefore, the sites which do not exist yet but can be built with the necessary infrastructure (telecommunication tower, electrical line, stand, etc.)--the location of which is estimated according to the dark zones observed in the global radioelectric coverage map--should be progressively added until the QoS is accomplished. Then, the previously mentioned optimization algorithm can be applied.
# A Multi-Scale Feature Fusion Hybrid Convolution Attention Model for Birdsong Recognition
## 2 Research Method  
This study mainly includes two parts. The first part introduces the construction method of time-frequency spectrum features, that is, three spectral representations are extracted from the original audio signal: logarithmic power spectrum (log-power), logarithmic Mel spectrum (log-Mel) and logarithmic wavelet spectrum (log-wavelet). These features can describe the frequency distribution and time variation characteristics of birdsong from different dimensions. The second part describes, in detail, the network structure of the proposed MUSCA model (multi-scale feature fusion hybrid convolutional attention model). The model combines a hybrid convolution module with channel attention and spatial attention mechanisms and improves the effectiveness and classification performance of feature extraction through a multi-scale feature fusion strategy.
# Event-Based Emergency Detection for Safe Drone
## 4 Case Study
### Abnormal Case: Too Early (Extreme Wind)  
The next case is an extreme wind condition. We set the wind speed in this case to 7.0 m/s, which is more than the drone can normally handle. This speed limit is about 5.0 m/s, and is an assumption based on the drone specs and a considerable recommendation for real-world applications. Figure 6a demonstrates how the drone maneuvers during extreme wind conditions. Since the wind was heading parallel to the drone's direction, we can observe that the drone was moving faster than expected. After a few waypoints, our event-based controller was able to detect the emergency. As shown in Figure 6b, when the drone missed the time window during the path from c to d, the drone detected a time-window out of bounds and called for an emergency landing. The red mark points to the moment of detection. Further in Table 2, the actual numerical time data including T\({}_{\text{start}}\), T\({}_{\text{min}}\), T\({}_{\text{max}}\), and the actual arrival time T\({}_{\text{arrival}}\) is displayed.  
The strong tailwind that exceeded the drone's capability of control forced the drone to maneuver faster than expected. Even from the first waypoint, the drone nearly made it within the normal arrival time-window. It might be controversial to consider tailwind an abnormal situation, but if the drone is not to move in a straight line, there is a chance of it possibly crashing into obstacles in urban surroundings. This case study proved the capability of our event-based controllers' ability to handle hard-to-detect abnormal situations.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Path** & **T\({}_{\text{start}}\)** & **T\({}_{\text{min}}\)** & **T\({}_{\text{max}}\)** & **T\({}_{\text{arrival}}\)** \\ \hline
**A** to **b** & 0.00 s & **4.50 s** & **5.50 s** & 5.23 s \\
**b** to **c** & 5.23 s & **9.73 s** & **10.73 s** & 10.26 s \\
**c** to **d** & 10.26 s & **14.76 s** & **15.76 s** & 15.14 s \\
**d** to **e** & 15.14 s & **19.64 s** & **20.64 s** & 19.92 s \\
**e** to **F** & 19.92 s & **24.42 s** & **25.42 s** & 24.59 s \\ \hline \end{tabular}
\end{table}
Table 1: The actual numerical time data for each waypoint, including T\({}_{\text{start}}\), T\({}_{\text{min}}\), T\({}_{\text{max}}\), and the actual arrival time T\({}_{\text{arrival}}\) for the normal case. All five T\({}_{\text{arrival}}\) resulted in arriving inside the time-window without any issue. Each start time was calculated after the T\({}_{\text{arrival}}\) data were retrieved and did not consider computational delays.  
Figure 6: _Cont._
# An Integrated Access and Backhaul Approach to Sustainable Dense Small Cell Network Planning
## 5 Conclusions
**Abbreviations**  
**3 GPP** 3 rd Generation Partnership Project  
**5G**  the fifth generation of mobile networks  
**6G**  sixth generation of mobile networks  
**ACO**  Ant Colony Optimization  
**AI**  Artificial Intelligence  
**AR**  Augmented Reality  
**BB**  Base Band  
**BBU**  Base Band Unit  
**BCI**  Brain Computer Interface  
**BER**  Bit Error Rate  
**BS**  Base Station  
**BW**  bandwidth  
**C-RAN**  Cloud Radio Access Networks  
**CAPEX**  Capital Expenditure  
**CoMP**  Coordinated Multipoint  
**CPU**  Central Processing Unit  
**CR**  Cognitive Radio  
**CRN**  Cognitive Radio Network  
**D2D**  Device-to-Device  
**DA**  Digital Avatar  
**DAC**  Digital-to-Analog Converter  
**DAS**  Distributed Antenna Systems  
**DBA**  Dynamic Bandwidth Allocation  
**DC**  Duty Cycle  
**DL**  Deep Learning  
**DRAM**  Dynamic Random Access Memory  
**DRL**  Deep Reinforcement Learning  
**DSA**  Dynamic Spectrum Access  
**DT**  Digital Twin  
**D-RAN**  Distributed Radio Access Network  
**FSMC**  Filterbank Multicarrier  
**FEC**  Forward Error Correction  
**FFR**  Fractional Frequency Reuse  
**FSO**  Free Space Optics  
**GA**  Genetic Algorithms  
**GPU**  Graphic Processing Unit  
**HAP**  High Altitude Platform  
**HL**  Higher Layer  
**HARQ**  Hybrid-Automatic Repeat Request  
**IoT**  Internet of Things  
**IAB**  Integrated Access and Backhaul  
**KPI**  Key Performance Indicator  
**LAN**  Local Area Network  
**LAP**  Low Altitude Platform  
**LL**  Lower Layer  
**LOS**  Line of Sight
# Resilient Formation Reconfiguration for Leader-Follower Multi-UAVs
## 5 Articulation of Formation Reconfiguration Strategies for Multi-UAVs with Variable Swarm Scale
### Design of Formation Reconfiguration Strategy for Multi-UAVs under New Member Addition  
The formation reconfiguration strategy for leader-follower multi-UAVs in the case of an increasing swarm scale covers the update of follower amount, the renewal of the swarm communication topology, the parameter configuration of the new member and the reconstruction of the intended geometry, which can be further summarized as the following steps with the renewing process of the swarm communication topology illustrated in Figure 6.  
**Step 1.** Update the amount of the followers inside the multi-UAVs by N = N + 1.  
**Step 2.** Set the number of the new member to N.  
**Step 3.** Establish a bidirectional connection between node N and node N \(-\) 2.  
**Step 4.** Set the desired distance of node N with respect to node 0.  
**Step 5.** Reform the expected geometry under the formation control law (30).  
Figure 5: The restoration of the swarm communication topology under follower disengagement.
## 2 Materials and Methods
### Expert Validation
For the ML masks that required correction, a ground-truth mask was generated by technicians based on the surgeon's comments, with verification of the final mask carried out by the surgeon. The differences between the ML-generated masks and the surgeon-corrected masks were quantified using the Dice coefficient, distance map (as defined by the percentage of the surface mesh of the ML-generated mask with a normal distance of more than 0.5 mm from the ground-truth mask), correction ratio (as defined by the percentage of corrected volume to ground-truth volume), and percentage error in volume and FI. A non-inferiority analysis tested whether the error in ML segmentation and quantification was substantially worse than the variation between the surgeons reviewing a common set of masks. For the non-inferiority analysis, we followed a framework proposed by [PERSON] et al. [27], which requires definition of a non-inferiority margin (\(\Delta\)). For non-inferiority of the Dice coefficient, \(\Delta\) was defined as 1 -- the minimum inter-surgeon Dice coefficient. For the non-inferiority of all other error metrics, \(\Delta\) was defined as the maximum of inter-surgeon error. The following summarizes our null hypothesis for the non-inferiority analysis. \[Dice_{inter-surgeon} \geq Dice_{ml-surgeon}+\Delta_{Dice} \tag{1}\] \[Error_{inter-surgeon} \leq Error_{ml-surgeon}-\Delta_{Error} \tag{2}\]  
Finally, a non-parametric one-sided Wilcoxon rank-sum test was used to test the hypothesis, with a significance level of (\(p<0.05\)).
## 6 Creating and Deploying Applications Over SARA: The Case of a Collaborative Voxel-Based Game
Thus, the HoloLens participant can only inspect the AR content. we can imagine a scenario in which the playing field of some e-sport (e.g., League of Legends [36]) is injected into SARA. The spectators could then see an AR copy of that land where they wanted, but without the ability to interact. However, specialists could have access to a set of tools that allow adding annotations on the map. Figure 14: Capture of the voxel-based prototype taken from the iOS client. 5. **Selecting and applying the _Collaboration Model:_** the fifth and final step is to establish the _Collaboration Model_ that the session will present. In the voxels example, several possibilities are viable. For example, it is possible to use a _Turn-based_ model, which will only allow the user who has the turn to make changes on the world. Another possibility is to limit the interaction capabilities of the users, allowing only one user to add cubes and another one to only delete them. However, it is also possible to set aside any rule by choosing the _Unconstrained model_. In that case, all participants may add and remove cubes from the world at their will, in a manner similar to the original version of the game where no control was performed. In summary, in step 1 we launch the _Communication Service_ which will allow the rest of the system to work. In step 2 we implement the application logic and prepare the connection with the SARA platform. With step 3, users' clients are generated, through which they will be able to see the AR content that represents the session status. In step 4, if required, the specific logic of the users' clients is implemented, which will depend on the scope of the application. Finally, in step 5 we select a Collaboration Model for the session that will make the appropriate regulation of the events of the session. This prototype can be seen in action in the following link [37]. As shown in Figure 6, there are three stages that are common among multiple developments over SARA: the configuration and launching of the _Communication Service_ (step 1), the creation of the consumer clients (step 3) and the selection of the Collaboration Model (step 5). Thus, SARA provides artifacts with the basic functionality associated with each of these stages, which supports the application development by reusing them.
## 3 Validation of the Numerical Scheme  
To examine whether the designed numerical scheme is physically reliable, an experiment was conducted to obtain a result comparable with the numerical data. The experimental rig in the National Products Quality Inspection and Testing Center for Pump (Zibo) was used. The closed loop is schematically shown in Figure 4. The tested pump was operated at a rotational speed of 980 rpm. Glass beads with an identical diameter of 6.0 mm were selected as solid particles. The density of the glass beads was 2500 kg/m\({}^{3}\). Two solid volume fractions, i.e., 0.002 and 0.011, were selected.  
Based on the geometric parameters of the tested pump, a computational domain was constructed accordingly. Boundary conditions were defined according to the operating conditions set for the performance test. Hence, the pump head was numerically calculated at the two solid volume fractions. A comparison between the numerical and experimental results is illustrated in Figure 5. At \(C_{V}=0.002\) and 0.011, when the ratio of the flow rate increases, the pump head decreases continuously, which is shared by the experimental and the numerical results. The numerical data are consistently greater than their counterparts. At \(C_{V}=0.002\), the largest relative deviation between the two pump head curves, namely, 4.6%, emerges at \(q/q_{d}=0.4\), as marked in Figure 5. When \(C_{V}\) increases from 0.002 to 0.011, the pump head decreases on the whole, and the discrepancy between the experimental and numerical results is enlarged. The maximum of 9.6% appears at \(q/q_{d}=1.2\). Apart from the consistency in the variations in the pump head with the flow rate and solid volume fraction, the difference between the numerical and experimental results is reasonable since some practical factors, such as the fluctuation in the rotational speed and unevenly distributed  
Figure 4: Experimental rig for testing the performance of a solid–liquid two-phase flow pump.  
roughness over the blade surface, could not be numerically described. Therefore, high physical fidelity of the designed numerical scheme was ensured.
# Design of a Low-Resolution Gamma-ray Spectrometer for Monitoring Radioactive Levels of Wastewater
## 2 Materials and Methods
### System Description  
In this study, a 38-mm-diameter and 38-mm-height NaI(TI) scintillation detector (GS-1515-NaI, Bee Research Pty, Ltd., Mascot, Australia) connected to a photomultiplier tube, preamplifier, and power supplier was used to measure the amount of radiation. The output of the preamplifier was connected to a shaping amplifier in a multichannel analyzer (MCA; GS-USB-PRO, Bee Research Pty, Ltd., Mascot, Australia).  
Free gamma spectroscopy software PRA 24.0.2.0 ([PERSON], Sydney University, Sydney, Australia) [19] was used as a data logger for Windows 10 (Microsoft Ltd., Raymond, WA, USA). This software controls an analog-to-digital converter (ADC), including an MCA, through the USB communication interface, analyzes pulses received from the shaping amplifier, and records them in real time.  
The specifications of the continuous radioactivity monitoring system for the radioactive wastewater decay tank are listed in Table 1.
## 3 A Proposed Approach
### Planning Space Representation
#### 3.2.1 Fast Marching (FM) Method  
The FM method was first proposed by [PERSON] [23] to solve the eikonal equation by using a single-pass algorithm. The eikonal equation is defined as follows:  
\[|\
abla U(\mathbf{p})|W(\mathbf{p})=1, \tag{1}\]  
where \(\mathbf{p}\) represents the point in metric space, i.e., \(\mathbf{p}=(x,y)\) in 2D space and \(\mathbf{p}=(x,y,z)\) in 3D space. \(U(\mathbf{p})\) denotes the travel time of interface front at point \(\mathbf{p}\), whereas \(W(\mathbf{p})\) indicates the local propagating speed at point \(\mathbf{p}\).  
The FM method calculates the value at every point in accordance with a strategy that is similar to the Dijkstra algorithm [24]. In addition, the FM method is usually applied on a grid map with three categories of points: (1) Far points, which denote the points at which the interface front has not yet arrived, and whose travel times are not yet computed; (2) Trial points, which represent the points at which the front will arrive soon, and whose travel times have already been calculated but may be updated in later computations; and (3) Accepted points, which indicate the points that the front has already passed, and whose travel times will not be changed.  
Starting from the original Accepted point, the travel times of the points on the grid map are iteratively calculated. In the beginning, all points of the grid map are marked as Far points and infinity travel times, and the start point is marked as the Accepted point, whose arrival time is set to zero. During each iteration, points adjacent to the Accepted point are marked as Trial points, and travel times of these points are calculated. After that, the Trial point with the shortest travel time will be marked as the Accepted point, and a new iteration begins. This strategy is based on the fact that the travel time \(U\) at any point only depends on the neighboring points. In fact, such neighboring points generally have smaller values. The specific calculation process refers to [7,8].  
In this research, the FM method is used to model the planning space with different local propagating speed function \(W(\mathbf{p})\).