esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 RegTech Applications in Non-Financial Sectors
### Logistics  
RegTech solutions can enhance tracking and traceability capabilities in logistics operations. Through technologies such as RFID (Radio Frequency Identification) and IoT, RegTech tools enable real-time visibility of shipments, inventory, and transportation conditions. Logistics companies must comply with regulations governing transport operations and driver safety. RegTech solutions can automate driver compliance checks, including driver qualifications, licensing, hours of service, and vehicle maintenance records.
# A Review of Convolutional Neural Network Applied to Fruit Image Processing
## 9 Conclusions  
In this review, we studied different works on the use of CNN-based approaches for fruit image processing. In previous reviews of computer vision applications for fruit analysis, we observed that CNN was not identified as a relevant approach. Besides, it should be noted that most of these studies collected information before 2019.  
We were able to identify three basic application areas in our study. The first is fruit classification, where this process is directly applied to the classification of fruits by their type in applications for markets, supermarkets, wholesalers, and retailers. The second is fruit quality control, which is used in applications to identify internal and external damages on fruits, its degree of maturity, and also to detect a lack of nutrients or diseases. The third is an area that we called fruit detection, which is applied for the harvesting of fruits in the orchards and also to estimate its location for automatic harvesting.  
We noted that the architectures of the CNN-based approaches vary by different applications, works, and authors. Besides, we cannot establish one CNN architecture as superior over the rest. Hence, it is possible to use a pre-trained CNN modifying some layers and parameters to design a new CNN model, as well as starting from scratch. Evaluation results show CNN-based approaches achieved excellent results, up to 100% in some cases. Moreover, we observed that the greatest growth of CNN applications is in the robotic harvesting sector.  
J.N.-T. Investigation, Software, Resources and Writing--original draft; M.M. Funding acquisition, Conceptualization, Methodology and Writing--review & editing; R.H.-G. Software, Methodology and Writing--review & editing; R.J.B. Writing--review & editing ; C.F. Funding acquisition; A.V. Writing--review & editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Innovation Fund for Competitiveness--FIC, Government of Maule, Chile--Project Transfer Development Equipment Estimation Quality of Raspberry, code 40.001.110-0. (Esta investigacion fue financada por el Fondo de Innovacion para la Competitividad--FIC, Gobierno de Maule, Chile--Proyecto de Transferencia de Desarrollo de Equipo de Estimacion de Calidad de la Frambuesa, codigo 40.001.110-0).  
The authors declare no conflict of interest.
# A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph
## 2 Related Works  
There are some works that already use panoramic radiograph image segmentation. The authors in [1] propose a fully automatic approach using a statistical model together with a deep learning model. The deep learning model generates a mask of the tooth area and the statistical model segments and labels the 28 teeth. The proposed model was evaluated in 14 test images with average accuracy and recall of 79.0% and 82.7%, respectively, and Dice of 74.4%. The authors in [16] proposed a region-based convolutional neural network (R-CNN) to detect and locate dental structures using 846 dental notes from 30 panoramic radiographs for training and 20 radiographs for validation and testing. The results obtained were 85.8% accuracy and 89.3% recall and an average intersection-over-union (IoU) of 87.7%.  
[PERSON] et al. [17] proposed a machine learning to analyze 120 panoramic radiographs selected from the Department of Oral and Maxillofacial Sciences of the Sapienza University of Rome, Italy. The analyses performed resulted in an overall sensitivity of 0.89 and an overall specificity of 0.98. The downside of the work is that it does not detail the configuration of the machine learning model.  
[PERSON] and [PERSON] [4] conducted a literature review on segmentation methods applied in dental images. In the study, they noticed a gap in the panoramic radiograph images and, to fill this gap, the authors presented a new dataset containing 1500 panoramic radiograph images with great variability and proposed a deep learning model to segment images in this set of images. The initial results obtained were 84% precision, 76% recall and 92% accuracy. Subsequently, using this same database, the authors in [15] proposed the TSASNet (two-stage attention segmentation network) which is divided into two stages. The first stage contains an attention network, which can be global and/or local, to obtain preliminary information on the radiograph and, in the second stage, in fact, a segmentation network. This method was evaluated and reached 96.94% accuracy, 94.97% precision, 93.77% recall and 92.72% Dice. The authors in [18] proposed a fully convolutional neural network (FCN) model based on the U-Net architecture to improve segmentation performance. The final result was 93.4%, 93.5% and 93.7% of Dice, precision and recall, respectively. Other modelsusing ensemble strategies were proposed, in which the best model obtained results of 93.6%, 93.3% and 94.3% of Dice, precision and recall, respectively.  
This work is very similar to the works of [15; 18], but it uses other segmentation models that are gaining prominence in the literature with other medical segmentation datasets [19; 20; 21; 22].
# High-Accuracy Calibration Based on Linearity Adjustment for Eddy Current Displacement Sensor
## 3 Calibration Method Based on Linearity Adjustment Model  
In order to calibrate the ECDs accurately and effectively, we propose a calibration method based on cyclic linearity adjustment, and the calibration flow is shown in Figure 5. The linearity adjustment model established in this paper is divided into the following steps:  
(a) Firstly, adjust the sensitivity of the original calibration result by regulating the voltage \(u\) to the required output range;  
(b) Then, partition adjustment is adopted on the account of targeted adjustments to the deviations of different regions; moreover, mid-scale, zero-scale and full-scale adjustments are determined for operation convenience and universality;  
(c) Finally, the iterate adjustment function to perform overall fine-tuning until the sensitivity, linearity and required voltage output range meet the requirements.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Sensor 1** & **Sensor 2** & **Sensor 3** & **Sensor 4** \\ \hline
**Output range (V)** & \(-0.9593\)–\(1.038\) & \(-0.9682\)–\(0.9760\) & \(-0.9906\)–\(0.9522\) & \(-1.0075\)–\(0.9813\) \\
**Sensitivity (V/mm)** & \(0.3329\) & \(0.3240\) & \(0.3238\) & \(0.3315\) \\
**Linearity** & \(96.77\%\) & \(95.13\%\) & \(98.00\%\) & \(97.16\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Original sensitivity and linearity of each sensor probe.  
Figure 4: Original calibration data.
# Flexible Phased Antenna Arrays: A Review
## 4 Major Challenges
### Electrical Performance Limitations
\begin{table}
\begin{tabular}{c c c c c} \hline
**Paper** & **Design** & **Phase Compensation** & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{[62]} & Probe-fed patch antennas mounted & A phase-based distance & Sensor-free approach, Applicable to & Reduced accuracy at close spacing \\  & on an AMacrylonitrile budaadiene & measurement using phase & arbitrary deformations & \\  & styrene (ABS) frame & differences at two frequencies & & \\ \hline \multirow{4}{*}{[71]} & Patch array built on thin flexible & Phase shifters are implemented as & & \\  & composite laminate made of E-glass & extended microstrip line lengths, & & \\  & fiber and a highly conductive & introducing phase delays & & \\  & mesh-style fabric & & & \\ \hline \multirow{4}{*}{[53]} & Rigid-flex flexible membrane for RF & No phase compensation but & & \\  & and DC routing, with rigid tiles as & element-level electronic beam & & \\  & substrates for antenna and & steering & & \\ \hline \multirow{4}{*}{[54]} & Rigid–flex checkerboard structure & & & \\  & combining rigid PCBs and flexible & No dynamic phase compensation & Flexible in 2D, Dual polarization & \\  & substrate & & & \\ \hline \multirow{4}{*}{[59]} & Patch array with integrated magnetic & Phase control is achieved by tuning & & \\  & particles based Composite & the permeability (per-element phase & \\  & Right/Left-Handed (CRLH) & & tuning) & \\  & metamaterial phase shifters & & \\ \hline \multirow{2}{*}{[64]} & Double-layer flexible PCB & Integrated digital phase shifters & Low loss under bending, Integrated & No dynamic phase calibration, \\  & & active electronics & Assembly complexity \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._mutual coupling between elements by modifying the patch dimensions and utilized a two-level multiple-section feeding network for improved impedance matching and spectral performance. Wide angle scanning requires the achievement of a stable beam steering over wide angles, but this often results in grating lobes or gain reduction. This hurdle was mitigated by using a simplified phase-shifting network [73]. The suppression of grain lobes is also possible with another technique of utilization of a triangular lattice configuration, particularly the offset rows in the triangular configuration mitigated the grating lobes, but this resulted in the increased design complexity for modular integration [72].
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 4 Methodology and System Architecture
### Enhanced Security Analysis
#### 4.5.4 Error Rate Analysis  
The bit error rate (BER) \(P_{b}\) in a frequency hopping system is given by:  
\[P_{b}=Q\Bigg{(}\sqrt{\frac{2E_{b}}{N_{0}}}\Bigg{)} \tag{18}\]  
where \(E_{b}\) is the energy per bit, \(N_{0}\) is the noise power spectral density, and \(Q(\cdot)\) is the Q-function.  
Frequency hopping spreads the signal over multiple frequencies, effectively reducing \(N_{0}\) and improving \(P_{b}\).
## 3 Numerical Analysis of the Influencing Mechanisms of Prodder-Object System Parameters
### Effects of the Prodder-Object Coupling Part on Plastic Box Vibrational Characteristics
#### 3.3.1 Effects of \(k_{3}\), the Stiffness of Steel Rod  
As shown in Figure 6, when the value of \(k_{3}\) is increasing from 100 kN/m to 3213 kN/m, the main resonance frequency of the plastic box increases slightly while the related amplitude decreases, which means that a steel rod with smaller stiffness may lead to greater measured signal strength. Thus, steel rods for a better performing prodder should have smaller stiffness.  
Figure 5: Effects of \(c_{1}\) on the frequency response of the plastic box.  
Figure 6: Effects of \(k_{3}\) on the frequency response of the plastic box.
# A Truly Second-Order and Unconditionally Stable Thermal Lattice Boltzmann Method
## 4 Numerical Tests
### Mixed Convection in a Lid-Driven Cavity  
In this section, we present the example of mixed convection in a lid-driven cavity. The physical configuration of the problem is a square cavity with moving top lid and three stationary walls. The top and the bottom walls are maintained at constant cold and hot temperatures (\(T_{c}\) and \(T_{h}\)), respectively (see Figure 4). Neumann boundary condition for the temperature with its normal gradient equal to zero is implemented on the right/left walls. Typical non-dimensional parameters defined for this problem include Reynolds number, Grashof number, Prandtl number and Richardson number, which are expressed as  
\[Re=\frac{U_{lid}L}{v} \tag{71}\]  
\[Gr=\frac{g\beta(T_{h}-T_{c})L^{3}}{v^{2}} \tag{72}\]  
\[Pr=\frac{v}{\chi} \tag{73}\]  
\[Ri=\frac{Gr}{Re^{2}} \tag{74}\]  
Referring to set-ups in previous studies [26; 27], here we set \(Gr=10^{6}\) and \(Pr=0.71\), and three cases with different Richardson numbers of 10, 1.0 and 0.1 are simulated. Uniform mesh with the size of 251 \(\times\) 251 is adopted in our simulations.  
Figure 5 presents isotherms and streamlines at steady state obtained by USTLBM. As can be seen, smoothness of the isotherms and streamlines is well maintained by the present USTLBM. It is also observed that, at larger \(Ri\) number (e.g., \(Ri=10\)), two major vortices show up in the flow pattern. With the decreasing of the Ri number, the lower vortex is gradually compressed. When \(Ri\) number is sufficiently small (e.g., \(Ri=0.1\)), only one major vortex exists and the flow pattern is quite similar to the classic lid-driven cavity flow at the same Reynolds number. The physical mechanism of such phenomenon is the relative importance of natural convection to the forced convection, which is the essence of Richardson number. At low \(Ri\) number, the effect of the natural convection is quite small or even negligible, due to which the final flow pattern is mainly shaped by the shear force induced by  
Figure 4: Physical configuration of mixed convection in a lid-driven cavity.  
the moving lid. Therefore, the streamlines at low \(Ri\) number are very much like those of isothermal lid-driven cavity flows at the same Reynolds number. In high \(Ri\) number scenario, natural convection has comparable or even larger effect on the flow field as the forced convection does. Subsequently, the buoyancy force induced by the temperature gradient enlarges the vortex on the right corner and eventually makes its size comparable to that of the main vortex.  
Figure 5: Isotherms and streamlines of mixed convection in a lid-driven cavity: (**Left**) isotherms; and (**Right**) streamlines.  
For quantitative comparison, the average Nusselt numbers on the hot wall at different Richardson numbers are presented in Table 5. Apparently, the computational results in our simulations agree well with the reference data in the literature. Therefore, the robustness of USTLBM in this mixed convection problem is validated.
# Siamese Tracking with Adaptive Template-Updating Strategy
## 3 Our Approach
### System Design  
We propose utilizing our method in an intelligent real-time tracking system that can be remotely operated and monitored. In this system, images are taken in a timely manner by cameras on aircraft in the sky, and our targets are vehicles on land. Once the system is started, users can manually select the tracking target by clicking on it on the monitor screen. Then, the system automatically learns which object to track by detecting contours and abstracting semantic information according to the position of the initial point. After extracting target features, our system begins to track targets through features in the following frames and saves target features as a high-confidence template. If the similarity between template and tracking object is smaller than a threshold due to target deformation or occlusion, which suggests that the initial template is not able to precisely match object features in the current frame, our system updates the template with strategies that are discussed further down. While the tracker fails or finishes processing all images in a video sequence, the system finishes its task as well. A brief flowchart of the whole system is shown in Figure 3.
## 2 Spectrum Sharing Processes
### Implementation  
A spectrum sharing scheme can be implemented along the dimensions of _space_ (e.g., use of directional antennas and MIMO--Multiple Input Multiple Output--technologies, power control), _frequency_ (proper channel selection), _code_ (with Code Division Multiple Access--CDMA), _time_ (application of TDMA), _power_, and combinations of those. Next, we briefly discuss how such a scheme can be built using these dimensions.  
Space: Coexistence in the same spectrum can be achieved by means of smart sharing in the space domain. This can be implemented by using directional antennas so as to minimize coverage overlaps, in particular with smart antenna technologies, such as MIMO, as well as by applying transmission power control schemes, where the transmission range is dynamically adjusted for the purpose of maximizing the number and quality of wireless links by minimizing interference. The power dimension is further discussed below.  
Power: Depending on the employed coding technology for the available spectrum, different CR users can exploit the same frequencies simultaneously. CDMA makes it possible to maximize utilization in the available spectrum, though only if the proper power window is maintained, _i.e._, it is mandatory to define, assign and enforce the channel power assignment. Too much power in a band interferes with other users and might push them out.  
Time: If a specific channel (defined by frequency, coding and modulation) in a particular coverage area is to be shared amongst different users, time slot allocation is natural, yielding the Time Division Multiple Access (TDMA) scheme. Guard intervals should be employed between time slots to compensate for a possible lack of time synchronization with the subsequent risk of unintended collisions.  
Channel: In a specific coverage area, several channels can be used at the same time. In this case, each user is assigned a specific channel, which can actually be composed of other (sub-)channels. Thus, we arrive at Frequency Division Multiple Access (FDMA). From a single user's point of view,isolating the different channels from each other is of the utmost importance. This helps in maintaining the throughput which otherwise is reduced by interference. A typical example is the use of channels in the unlicensed 2.4 GHz band by Wi-Fi technology: There are 11 (in the US) available 22 MHz channels (13 in the EU and 14 in Japan), but there are only three non-interfering channels (e.g., 1, 6, 11 in the US). This limitation demonstrates the need for careful frequency assignment.  
Furthermore, all the above-described methods can be combined to achieve a seamless functionality while being aware of all CR dimensions [25]. No matter which method is applied, the user needs to be sure that it can use the spectrum according to the negotiated sharing plan without (un-)intentional disturbance.
# On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview
## 3 FDD of Railway S&C Systems
### Data-Driven FDD Methods for Railway S&C Systems  
A detailed review of the existing data-driven FDD methods for railway S&C systems is presented in this subsection. Data-driven approaches need only output signals (historical data) to detect and diagnose (and/or prognose) possible faults in an S&C system as can be seen in Figure 13.  
Data-driven techniques are gaining in popularity since they are - as can be seen in Figure 13--model-free methods. Several features (indices, criteria) are generated in the time, frequency, time-frequency domain, and/or envelope spectrum features, which can usually be physically interpreted, or statistical features, which cannot be physically interpreted [84]. Different techniques from different disciplines are commonly used for data-driven FDD, including techniques from (digital) signal processing, cluster analysis, data mining, statistical pattern recognition, modern artificial intelligence (i.e., machine learning and deep learning), and image processing [84; 85]. These techniques can be used separately or in combination (e.g., hybrid techniques). They aim to synthesize the information available from the raw data (output signal) gathered, to better represent the system's health condition [84].  
Contrary to the lack of published papers using a model-based FDD method for railway S&C systems, as mentioned in the previous subsection, dozens of research works have dealt with the detection, diagnosis, and (sometimes) prognosis of the different possible faults in S&C systems. In an attempt to better review the existing data-driven FDD methods for S&C systems, three categories are considered: (1) FD methods, which deal only with detecting the fault; (2) FDD methods that detect the  
Figure 13: Data-driven FDD method for S&C system.  
presence or not of the fault and diagnose its location and roots; and finally (3) FP (prognostics and health management (PHM)) methods in which RUL prediction is performed.
# A Real Model of a Micro-Grid to Improve Network Stability
## 3 Description of the DC Micro-Grid  
A number of small or miniature power plants using renewable energy sources are connected to the grid; some of these can be found within the automation laboratory where the SEM is located. Importantly, PV panels are installed on the roof of the building; for practical reasons, however, a significant portion of the plants are based at remote locations. After being produced, the electrical energy (power) from these distant suppliers is measured by a data logger, which then communicates the obtained information via GPRS to the control centre (CC). The physical equivalent of the energy generated in such external power plants is simulated by a relevant remote energy source model--a remote energy source (RES)-controlled power box (available at the automation laboratory) connected into the SEM by means of the CB1 modules (Figure 1). The RES is managed by the CompactRIO control system according to the data received from the wireless data logger.  
Figure 1: A block diagram of the smart energy model (SEM) (CB1—control block 1; CB2—control block 2; BAT (BAT1, BAT2)—battery; RES—physical model of a remote energy source; EL—electrolyte; S—valve to switch and control the hydrogen flow to the fuel cell (FC); ST—vehicle charging station; G1—wind turbine generator, 12 V\({}_{\text{DC}}\) and 160 W; G2—water turbine generator with 230 V, 50 Hz, and 500 W; CC—control centre, CWP—central workplace.
## 2 Related Work
Another drawback of asynchronous FL is the high communication cost, especially for faster clients since they more frequently exchange their models with the FL server. A potential solution could be to enable the FL server to update the global model after it receives a number of local models instead of receiving each local model. The concept of client clustering has been proposed to mitigate model accuracy reduction caused by non-IID data. For example, [PERSON] [32] suggested clustering clients based on the similarity of their local updates to the global model. The clients within the same cluster are trained independently and in parallel on a specialized model. Similarly, [PERSON] et al. [33] proposed a customized generative adversarial network for grouping clients into clusters without sharing raw data. Based on that, they also developed heuristic algorithms to dynamically adjust cluster numbers and client associations based on loss function values. While clustering reduces the impact of non-IID data, these methods overlook differences in client capacities, leaving the straggler problem unresolved.