esa-sceva/satcom-chunk-collection huggingface dataset:
# A New One-Dimensional Compound Chaotic System and Its Application in High-Speed Image Encryption
## 2 The Proposed New One-Dimensional Chaotic Map
### Logistic-Tent Map (LTM)
#### 2.3.2 Lyapunov Exponent and Bifurcation Diagram of LTM System  
The Lyapunov exponent of system (4) is positive in all ranges of the parameter interval of \(b\) (see Figure 3a, where \(a=6\)). When \(b=0.35\), the Lyapunov exponent has the maximum  
Figure 2: Lyapunov exponent and bifurcation diagram of tent map. (**a**) The graph of Lyapunov exponent versus parameter \(b\); (**b**) bifurcation diagram of system state quantity versus parameter \(b\).  
value of \(LE_{\text{max}}\) = 0.6934. The bifurcation diagram (see Figure 3b, where \(a\) = 6) of state values of the system also proves that the system is aperiodic in the whole parameter value range (unlimited values/no repetition), and the state values can evenly fill the whole value space in (0, 1). These characteristics of the LTM system will bring better security for information encryption.
# Design of SWB Antenna with Triple Band Notch Characteristics for Multipurpose Wireless Applications
## 6 Time-Domain Characterization  
SWB antennas must be capable of operating over the entire bandwidth with good efficiency and acceptable radiation properties. Simultaneously, the transmitted pulse from the SWB antenna should have a good impulse response with minimum distortion. In addition, it is necessary that the pulse be identified at the receiver end with minimal  
Figure 14: Simulated and measured far field radiation patterns of presented antenna at (\(\varnothing=90^{\circ}\)) and (\(\varnothing=0^{\circ}\)) for (**a**) 2.53 GHz, (**b**) 8.43 GHz, (**c**) 11.88 GHz, and (**d**) 18.51 GHz.  
distortion. Therefore, to endorse the significance of the antenna for pulsed communication systems, time-domain analysis was performed. The time-domain characterization of the antenna parameters confirmed that the designed antenna was an excellent candidate for triple band-notched SWB applications. To realize the time-domain behavior of the signal at band-notched frequencies, two similar antennas were arranged in face-to-face and side-by-side configurations 75 mm apart, as shown in Figure 15. In both the configurations, Gaussian pulses, as shown in Figure 16, were taken into consideration. A few key time-domain metrics were investigated in this following subsection.
## 4 Results and Discussion
### Coverage Prediction via Ray-Tracing  
We performed simulations using the ray-launching algorithm presented in Section 3 for access point locations corresponding to the TX antenna locations used in the measurements, and we analyzed the multipath components for a grid point corresponding to the RX locations used in the measurements. The simulation time using the settings from Table 2 is 67 min on a notebook computer with a 2.3 GHz Intel Core i5 processor.
# Detecting Banana Plantations in the Wet Tropics, Australia, Using Aerial Photography and U-Net
## 2 Materials and Methods
### U-Net Inference  
[PERSON] et al. [37] found in previous studies that the edges of each image chip have a lower accuracy than the center region. To overcome this, a two-pass ensemble inference strategy was adopted. This was done by breaking the whole image into \(256\times 256\) image chips, iteratively applying the model to the original patch and three rotated versions of the patch and averaging the results. The second pass of the image was conducted, offset by 128 pixels. The result from the two passes are combined using a weighted average with pixels toward the center of the patch given a higher weight than the pixels toward the edge.  
A prediction probability threshold of 90% was used to classify areas of banana plantations. Small features and gaps of 0.01 hectares or less were removed or filled and the data were converted into a polygon feature class with the edges smoothed to remove the square edges of individual pixels.  
To increase the performance of the classification, the original aerial images were split up into overlapping tiles, allowing the inference to be conducted on all eight Nvidia Tesla V100 GPUs. It took approximately 12 h to run the model on the 2015 imagery.  
Figure 4: The U-Net architecture, starting with an input image patch (top-left) and ending with an output segmentation patch (top-right) [33].
# Blind Recognition of Forward Error Correction Codes Based on Recurrent Neural Network
## 4 Simulation Results  
In this section, the recognition of the four FEC codes based on the improved RNN network is first simulated. Further considering that the performance of neural network identification is affected by many factors, such as network type, data size, etc., this paper compares different network types, data sizes and the lengths of data, makes comprehensive comparison and summarizes in the end.
## 8 Performance Analysis
### Simulation Setup  
To carry out the simulation experiment, we select a real urban traffic scene in Cologne, Germany, and use Java open street map (JOSM) [46] to extract the main traffic network in Figure 4a. The number and distribution of charging infrastructures are shown in Figure 4b, where the number and distribution of CSs follow the information in [47]. ESs are randomly distributed, and considering that the deployment cost of ESs is higher than that of CSs, the number of ESs will be less than the number of CSs in the paper. The details of the map and other parameters required for the simulation are shown in Table 4.  
We perform our evaluations using MATLAB and Simulation of Urban Mobility (SUMO), which is a traffic pattern and an open source, highly portable, microscopic, and continuous multi-modal traffic simulation. Our evaluations use data on all-day traffic conditions in the city of Cologne, Germany described by a TAPASCologne simulation scenario [48]. The capacity of the battery is 45 kWh based on Qirui Ruilin [49] and the maximum mileage of the battery capacity is 140 km. We initialize the experimental AEV with an SoC of 40% and set the arrival rate of vehicles at the charging infrastructure to 45 vehicles/hour. We set the small, medium, and large charging rates as 30 kWh, 50 kWh, and 70 kWh. The AEV is charged to at least 90%. Both MATLAB and SUMO are run on a Windows 11 computer with AMD R7 processor and 16 GB memory.
# A Review of Advanced CMOS RF Power Amplifier Architecture Trends for Low Power 5G Wireless Networks
## Advanced RF PA Architectures
### Traveling Wave RF PA (TWA)
The achievable bandwidth in CMOS is comparable to that of the designed in _III-V_ group semiconductors. Reported CMOS, _SiGe_ and \(GaAs/GaN\) BiCMOS TWAs can achieve a similar bandwidth, although power-wise _III-V_ group semiconductors are more superior;
* Linearization and predistortion possible. DPD algorithms can be used to extend the linearity of the whole TWA as well as linearizer diodes at the gate of each segment can be placed;
* Concept simplicity. TWA concept is based on transmission line theory, which has matured and is thoroughly investigated;
* No additional impedance matching network. Due to the innate transmission line impedance of 50 \(\Omega\), there is no need to include impedance matching networks at the input or output;
* A choice of different architecture variations. Single-stage, multi-stage, parallel, matrix in both uniform and non-uniform arrangements are at the disposal of the designer. TWA architecture disadvantages:
* Large area due to multiple inductors. This makes integration into transceiver chips (ASICs containing not only a single PA) very difficult and impractical if not impossible;
* Efficiency of basic PA classes. TWA has an outstanding bandwidth, but the _PAE_ is naturally decreasing with the increase of the frequency. The architecture itself is not aimed at improving _PAE_ but elements from other advanced PA architectures can be incorporated (ex. ET/EER modulator);
* Additional noise due to source and drain termination resistors. The latter noise can be reduced by integrating reported termination noise reduction techniques.
# GNSS-R Observations of Marine Plastic Litter in a Water Flume: An Experimental Study
## 3 Results
### Marine Plastic Debris  
Figure 12 shows the temporal series of the amplitude of the reflected signals [dBfs], and the histograms of the calibrated reflectivity at RL and RR polarizations for the different marine plastic debris tested, at 30\({}^{\circ}\) elevation angle. As it will be shown the detectability of marine plastic litter based on amplitude measurements is challenging, even when plastic debris are occupying a significant fraction of the first Fresnel zone (Table 2). This is due to the small dielectric constant change with respect that of water), and the small surface roughness change induced by the floating debris. However, in some cases, the presence of debris may flatten the surface, as in the case of nets or plastic bags (Figure 12a, red ellipse), while in others small capillary waves are induced when a wave crest impinges in the debris (Figure 12b-g, blue ellipses). This is discussed in more detail in Section 4. These small  
Figure 11: Incidence angle variation of reflection coefficients at RL and RR polarizations for clean sinusoidal roughed water surface (significant wave height = 9 cm) without capillary waves.  
Figure 10: Incidence angle variation of reflection coefficients at RL and RR polarizations for clean roughed water surface (significant wave height = 9 cm) without capillary waves.  
changes are very difficult to detect in terms of the average received power, but induce some detectable changes in the standard deviation of the reflected signal power (i.e., reflectivity), its kurtosis, and/or skewness.  
Figure 12: _Cont._  
Figure 12: (**a,b**) Bottles and fixed net, (**c,d**) Plastic Bottles, (**e,f**) Nets, (**g,h**) Food wraps and bags, (**i,j**) Caps and lids, (**k,l**) Straws, (**m,n**) Pellets, and (**o,p**) Styrofoam.  
In general, when the kurtosis increases in the amplitude histograms, the skewness decreases, and vice-versa, and in the phase histograms, both tend to either increase or decrease simultaneously. Most detectable changes occur at RL polarization, but often the signature in the RR polarization is evident as well, as cross-polarization is very sensitive to surface roughness.
# Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach +
## 4 Results
### Simulation Results
#### 4.1.2 Comparative Analysis  
We present now a comparative analysis between a standard EKF, the SEKF and the SEKF\({}^{2}\) reported using four RFID tags only. As stated above, the fixed-lag SEKF window has been fixed to \(n=55\) samples. The length of the trajectories (i.e., the number of available measurements) used are different and range from 180 to 270 samples, i.e., about 36 to 54 s. A qualitative analysis using one actual trajectory is proposed in Figure 7.  
It has to be noted how visually the SEKF and SEKF\({}^{2}\) performs better than the EKF, as expected, while mild differences exist among them. This fact is further substantiated by the RMSE quantitive comparisons among the three different algorithms reported in terms of the box-and-whiskers plot in Figure 8.  
It is now quantitatively evident how the smoother (either SEKF or SEKF\({}^{2}\)) ensures a lower error for the trajectory estimates. As reported in Figure 6, being the SEKF\({}^{2}\) unfeasible in real time but yielding de facto the same uncertainties of SEKF, the rest of the results report the latter only.  
Figure 6: Computation time for different window sizes of the SEKF. The horizontal dashed line is the available sampling time.  
Figure 7: Sampled estimated trajectories by (**a**) EKF, (**b**) SEKF and (**c**) SEKF\({}^{2}\) based on the four RFID tags reported in the figure with star markers.
# Hybrid Deep Learning Approach for Automated Sleep Cycle Analysis
## 3 Methodology
### Model Training
#### 3.4.5 Regularization  
Regularization techniques reduce overfitting and improve the ability of a model to generalize to unseen data [49]. Among the most widely used are the following.  
* Dropout: This technique randomly turns off subsets of neurons during each training iteration [50], reducing the dependence on individual neurons.
* L2 Regularization: This technique penalizes large weights to reduce overfitting [51], minimizing the prediction error and weight size. A penalty is introduced into the cost function, scaling with the total of weight squares.
* Early Stopping: This method works by tracking the validation performance during training [52], which is stopped if the validation loss is not progressing or degrading, preventing overfitting and unnecessary iterations.
* Batch Normalization: This method normalizes layer activation, ensuring a standard normal distribution for activation values within each mini-batch [53]. This approach improves convergence, mitigates internal covariate shifts, and acts as a regularizer [4].  
The effectiveness of these strategies varies based on the model structure and the nature of the data; they contribute to better generalization and improved performance.
# DMCNet-Pro: A Model-Driven Multi-Pilot Convolution Neural Network for MIMO-OFDM Receivers
## 3 The Structure of DMCNet
### Signal Detection Network  
The signal detection module's details are shown in Table 2. For the data part, a tensor of length 1024 is also extracted. These 1024 data points represent the information of the data part after passing through the channel, so no further extraction is needed. Similarly, these data are reshaped into a (1, 256, 4) tensor and concatenated with the implicit channel estimation results of the pilot part in the third dimension. This combined tensor serves as the input to the signal detection network. For eight pilots, the dimensions of the merged data are (1, 256, 12). In this data with 12 channels, the first 8 channels contain the results of the pilot part through the pilot module and the last 4 channels contain the original data part after reshaping. The merged tensor of size (1, 256, 12) is then expanded in the channels through two consecutive convolutions, resulting in a tensor of size (1, 256, 100). It is then processed through three Res-Block residual refinement modules. Each Res-Block consists of three cascaded convolutional layers, and the output after the first convolution is directly connected to the final output in a shortcut manner. Finally, the intermediate results are dimensionally reduced to a (1, 256, 4) tensor through convolution, flattened into 1024 floating point data, and the final estimation result is obtained through a bit decision function. It is worth noting that due to the relatively large number of channels in the data of the Res-Block part, a channel attention (CA) module is added to each Res-Block to weight the multi-channel data and improve system performance.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & **Module Type** & \begin{tabular}{c} **Output Shape (8 Pilots)** \\ \end{tabular} & \begin{tabular}{c} **Output Shape** \\ **(32 Pilots)** \\ \end{tabular} \\ \hline \hline \begin{tabular}{c} Merge data \\ \end{tabular} & – & [1, 256, 12] & [1, 256, 20] \\ \hline \begin{tabular}{c} Convolution \\ layer 0 \\ \end{tabular} & \(1\times 9\) & [1, 256, 50] & [1, 256, 128] \\ \hline \begin{tabular}{c} Convolution \\ layer 1 \\ \end{tabular} & \(1\times 9\) & [1, 256, 100] & [1, 256, 256] \\ \hline \begin{tabular}{c} Res-Block 0 \\ \end{tabular} & Res-Block & [1, 256, 100] & [1, 256, 256] \\ \hline \
\begin{tabular}{c} Res-Block 1 \\ \end{tabular} & Res-Block & [1, 256, 100] & [1, 256, 256] \\ \hline
\begin{tabular}{c} Res-Block 2 \\ \end{tabular} & Res-Block & [1, 256, 100] & [1, 256, 256] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Signal detection module details.
# Anatomical Characteristics of the Masseter Muscle in
## 4 Discussion
We also thought about the possibility that the muscular difference is due to the secondary effect of mandibular difference. However, it is well-known that mandibular prognathism is affected by both heredity and environmental influences [31; 32]. In addition, there are many studies or examples related to the action of soft tissue on hard tissue. [PERSON] suggested the concept of a functional matrix, saying that all mandibular units are influenced by nearby soft tissue [5; 6]. Muscular dystrophy, a neuromuscular disorder that results in a greater mandibular angle and excessive vertical growth of the mandible, is a good example of a muscle affecting craniofacial growth [33]. Another example is the decrease in mandibular bone quality after botulinum injection [34]. In addition, a study on the _Foxc1_ gene, which causes bony fusion of the maxilla and mandible and defects in the craniofacial musculature when missed, shows the relationship between bones and muscles during development [35]. Even though it is uncertain whether prognathism affects nearby muscles, it is certain that differences in the action of nearby muscles affect mandibular growth. There are many studies on whether the vertical dimension affects the mandibular morphology and masticatory muscles. [PERSON] et al. showed a positive correlation between the thickness of masticatory muscle and vertical dimension of the mandible [36]. [PERSON] et al. suggested that the patients with higher mandibular plane angle showed an increase in the thickness of the soft tissue pogonion after bimaxillary surgery [37]. However, [PERSON] et al. showed that the vertical facial types do not cause any differences in relapse after mandibular setback surgery [38]. Vertical dimension is one of the important topics in this field. However, in our study, we focused on the difference in masseter muscle between normal and prognathic group, following our previous study [15]. Further studies considering vertical dimension are needed. This study is one of the first to analyze the differences in masseter muscle force between participants of normal and prognathic groups. Although the exact etiology of mandibular prognathism remains to be elaborated, it is certain that thin masseter muscles with inefficient orientation are closely related to mandibular prognathism. As remodeling of the mandible occurs throughout life, understanding masticatory muscles in relation to prognathism will be useful in regulating mandibular growth in young patients, surgical or non-surgical orthodontic treatment in adult patients, preventing relapse after orthognathic surgery, and even estimating the mandibular changes in the elderly. Further studies on lateral pterygoid muscle, which is another key muscle related to prognathism, are required.
# Experimental Study on Cavity Formation and Ground Subsidence Behavior Based on Ground Conditions
## 3 Experimental Results
### Experimental Results with Respect to Fines Content
Table 7 presents the measurement results of the amount of soil discharged until cavity formation. Clearly, the cavity formation tended to be inhibited when the fines content was 10% or higher. However, when the fines content was less than 10%, relatively large cavities occurred at a groundwater level of 10 cm. This indicates that the cavity expanded over a relatively long duration under a groundwater level of 10 cm but did not lead to ground subsidence in the absence of a load over it. When the fines content was 10% or higher, ground subsidence did not occur in most cases, making it difficult to identify significant cavity volumes.
# A PINN-Based Nonlinear PMSM Electromagnetic Model Using Differential Inductance Theory
## 3 Nonlinear Electromagnetic Modeling of PMSM  
The nonlinear PMSM electromagnetic model based on differential inductance is applicable not only to steady-state excitation (SE) conditions where \(\mathbf{i}_{dq0}\) is a constant value, but also to unsteady-state excitation (USE) conditions. (27) shows that accurate prediction of PMSM torque requires characterizing the distributions of inductance, flux linkage, and magnetic energy as functions of electrical angle; for each specified current operating point, these distributions can be evaluated via finite element analysis (FEA). However, in real motor drive applications, \(\mathbf{i}_{dq0}\) is not a constant quantity, and the PMSM generally operates at USE conditions. Since \(\psi_{dq0\_PM}\) and \(W_{dq0\_PM}\) are generated only by the permanent magnets, and are consistent for any current operating point, it is only necessary to solve for the armature flux linkage \(\psi^{{}^{\prime}}_{dq0\_AM}\) and armature magnetic energy \(W^{{}^{\prime}}_{dq0\_AM}\) for the USE conditions at the non-FEA operating point. Depending on the solution method, there are the Global Linearization Model (GLM) and the optimal linear approximation model (OLAM).
# Multipoint Thermal Sensing System for Power Semiconductor Devices Utilizing Fiber Bragg Gratings
## 6 Conclusions  
This study examines the performance capabilities of multipoint FBG temperature sensing for IGBTs and other power semiconductor modules. A static thermal resistive network is employed to formulate the equivalent junction temperature of IGBT and rectifier modules, avoiding the need to dismantle the IGBT housing for sensor placement. The comparison between the air interface under study and the FBG-IGBT interface calibration indicates that the mechanical stress during its operation has not influenced the sensitivity of the sensors. Furthermore, the load power vs. temperature curve demonstrated the use of a single optical fiber with multiplexed sensor wavelengths for multipoint temperature sensing; this enables the comprehensive thermal profiling of the entire power system. The analysis of the airgap revealed that the FBG-IGBT sensor interface needed to be free of  
Figure 11: (**a**) Thermal imaging of IGBT at time T1. (**b**) Thermal imaging of IGBT at time T2 (where T2 > T1).  
Figure 12: Predictions of hotspots using neural networks.  
an airgap or fitted with an appropriate thermal pad to enhance the temperature reading accuracy. Additionally, AI-powered neural networks processed thermal images of IGBTs to detect subtle heat anomalies, enabling predictive maintenance and improving component reliability. These findings were validated through experiments on a custom-built three-phase inverter system. In the future, a dedicated fiber sensing port could facilitate the integration of multiple FBG sensors into a distributed monitoring system. Furthermore, machine learning techniques can be employed to improve sensor calibration and accuracy by leveraging ambient temperature data and pre-trained models.  
Conceptualization, K.K.Q. and R.I.; methodology, R.I., K.K.Q., J.M. and M.A.; validation, R.I. and N.I.; formal analysis, R.I. and K.K.Q.; investigation, K.K.Q., N.I. and R.I.; resources, K.K.Q.; data curation, K.K.Q. and R.I.; writing--original draft preparation, R.I. and K.K.Q.; review and editing, K.K.Q., J.M. and N.I.; supervision, K.K.Q.; project administration, K.K.Q.; funding acquisition, K.K.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by DROC at KFUPM through grant no. ISP23219.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
[PERSON] would like to acknowledge the support provided by DROC at KFUPM through an outbound international visiting program (Project no. ISP23219).  
The authors declare no conflicts of interest.