esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Cost Sensor Based on SDR Platforms for TETRA Signals Monitoring
## 4 Conclusions  
The present paper aimed to design, implement and test a sensor based on SDR technology that is able to monitor the entire electromagnetic spectrum of the TETRA signals.  
We focused specifically on achieving a sensor that would allow large-scale monitoring of the electromagnetic spectrum. We obtained a sensor made with COTS hardware components and with technical characteristics comparable to those of a high-performance monitoring system built by well-known suppliers. The advantage of this is that the cost is very low.  
The sensor has been tested both in laboratory conditions and in real outdoor operating conditions. Real-time information on all TETRA channels can solve problems such as: radio coverage determination, signal classification based on the values of channel power; intrusion or interference detection. The accuracy of the sensor was validated by comparing the measurements with a high-performance spectrum analyzer. The evaluation of the sensor will continue with the implementation of an uncertainty budget calculation procedure for the measurements. For this purpose, the combined standard uncertainty for the measured power levels will be determined by extracting probability density functions of the uncertainties of the input quantities and then applying the uncertainty equation.  
Future research directions consist of: expanding storage capacities at the level of the monitoring network by creating a database, especially with IQ signal samples; testing of Big Data and Machine Learning algorithms in order to process the data resulting from the long-term acquisition; expanding the monitoring capabilities of low-cost sensors and other signals, especially in the case of broadband signals (for which the instantaneous acquisition must cover bandwidths of the order of 100 MHz).  
Conceptualization, R.H. and P.B.; methodology, R.H., P.B. and S.M.; software, V.M. and P.B.; validation, R.H., I.B., and V.M.; formal analysis, R.H. and S.M.; investigation, R.H. and P.B.; writing--original draft preparation, R.H., P.B. and S.M.; visualization, I.B.; supervision, P.B.; project administration, R.H. and V.M.; funding acquisition, R.H., S.M. and P.B. 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.
# Dual-Frequency Linear-to-Circular Polarization Converter for Ka-Band Applications
###### Abstract  
A dual-band linear-to-circular planar polarization converter based on a multilayer printed circuit board (PCB) is proposed and demonstrated. Each cell of the periodic surface is formed by six substrate layers separated by five foam spacers. The three top layers are identical and contain an 'T'-type strip, while the three layers on the bottom side are realized with three identical Jerusalem crosses (JC). A linearly polarized (LP) wave tilted 45\({}^{\circ}\) relative to the \(x\)- and \(y\)-axis of the converter is used to illuminate the polarizer. In this configuration, right-handed circularly polarized (RHCP) waves are generated at the Ka-band while left-handed circularly polarized (LHCP) waves are generated at the K-band. An equivalent circuit model based on transmission lines is proposed and used to design the polarizer together with full-wave simulations. The simulated/measured axial ratio (AR) remains below 3 dB in the bands 19.4-21.8 GHz (12.5%) and 27.9-30.5 GHz (8.7%) with an insertion loss better than 0.5 dB.  
Keywords:linear-to-circular polarization converter; dual-band; Ka-band +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Methods
#### 2.3.5 Validation  
To validate the coastline detection results, a VHR cloud-free RapidEye mosaic (see Section 2.2.2) from the 7 th of February 2017 was utilized, for which a cloud-free Landsat scene exists with the same date and approximate acquisition time (see Table 1). The Landsat scene was cropped to the extent of the Rapid Eye mosaic. Both images were  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **Change Rates [m/Year]** \\ \hline Extreme accretion & \(>\)5 \\ Severe accretion & 5 to 3 \\ Intense accretion & 3 to 1 \\ Moderate accretion & 1 to 0.5 \\ Stable & 0.5 to \(-\)0.5 \\ Moderate erosion & \(-\)0.5 to \(-\)1 \\ Intense erosion & \(-\)1 to \(-\)3 \\ Severe erosion & \(-\)3 to \(-\)5 \\ Intense erosion & \(<\)\(-\)5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coastline change classification scheme on Level 2.  
Figure 6: Hotspot analysis method. (**a**) Coastline change classification on Level 1; coastal segments are defined hotspots when 20 consecutive transects belong to the “erosion” or “accretion” class (with 1 transect tolerance). (**b**) Coastline change classification on Level 2; hotspots showing “extreme” change rates on average (\(>\)5 m/year) are filtered. (**c**) Filtered “extreme erosion hotspot”.  
co-registered using an automatic global subpixel approach [76]. The reference shoreline was digitized manually on the RapidEye RGB mosaic. A subpixel shoreline was extracted from the cropped Landsat image using the methodology described in Section 2.3.2. The displacement between both shorelines was calculated along shore-normal transects.  
The effect of tides on the 75 th percentile MNDWI images was exemplarily assessed by extracting the sea levels in Qui Nhon (see Section 2.2.3) at each Landsat acquisition time. The resulting sea level time series was used to validate the mean-high water level captured by Landsat throughout the year.
## 3 Results and Discussion
### Effects of Operating Conditions on Reaction Responses
#### 3.4.2 Interaction Plots for \(Y_{1}\) and \(Y_{2}\)  
To visualize the impact of each factor combination and identify which factors were most influential, interaction plots were constructed. These also consider interactions among variables and can be used to optimize the operating parameters in multivariable systems [57]. The mean response of the two factors is depicted by the plot for all the possible combinations of their settings. When there is interaction between the two factors, the lines appear as non-parallel [58]. Figure 10 presents the full interactions effect plot matrix for five factors, temperature (\(A\)), liquid feed rate (\(B\)), catalyst weight (C), Ni to Co ratio (\(D\)), and phenol concentration (\(E\)) at two collocation levels on phenol conversion (\(Y_{1}\)) and hydrogen  
Figure 9: Main effect plot for (**a**) \(Y_{1}\) and (**b**) \(Y_{2}\).  
yield (\(Y_{2}\)). An interaction exists between variables when the effect of one variable on the response depends on the level of another variable [59]. Also, the presence of interaction results in the combined effect being more or less than that expected when the effects are individually added together [60]. Figure 10 shows that phenol concentration factor (\(E\)) is a predominant factor in \(Y_{1}\) and \(Y_{2}\). This means that E interacted strongly with all other factors. There was a strong two-way interaction between the main effects of \(A\) and \(E\), \(B\) and \(E\), \(C\) and \(E\), \(D\) and \(E\), and \(C\) and \(E\), as evident from the non-parallel nature of the effect lines for the interaction on \(Y_{1}\) and \(Y_{2}\) responses. However, it can be proved by regression coefficient analysis. The graph also shows that there is a possible interaction between \(C\) and \(B\) factors with respect to \(Y_{1}\) and \(Y_{2}\) due to their un-parallel lines. The rest of the graphs are parallel, suggesting a general lack of interaction or weak interaction between the different variables with respect to their influence on \(Y_{1}\) and \(Y_{2}\).
## 4 Effect of Drying on the Nutritional, Biochemical, and Physiochemical Properties of Sea Cucumbers
### Microwave Vacuum Drying  
The microwave vacuum drying method is a combined approach of a microwave and a vacuum dryer. The principle of microwave vacuum drying is to fasten the rehydration rate using the permeable microstructure. In this method, 200 W or 250 W of microwave power is applied to remove the water from the internal surface to the external surface of the body wall of sea cucumbers [82]. A microwave vacuum dryer is used to evaluate the physiochemical properties of sea cucumbers such as the rehydration ratio, water-holding capacity, and textural profile as well as nutritional composition including amino acid, hydroxyproline, and saponin content. However, high microwave power has been reported to damage the structure of collagen and accelerate amino acid degradation but did not show any significant variation in saponin content [82]. This could be due to the effect of the high temperatures (200 W to 250 W \(\cong\) 100 to 150 \({}^{\circ}\)C) used in microwaves that might degrade the collagen [88].
# Performance of Dynamic Time Division Broadcast Protocol with Rateless Coding
## 2 System Models
### System Model of Sub-DTDBC-RC  
With the same assumptions as in DTDBC-RC, we present a subslot implementation scheme for DTDBC-RC (i.e., sub-DTDBC-RC) since its DMT cannot be derived directly based on conventional outage and expected rate analysis (this will be detailed in Section 3). In sub-DTDBC-RC, the maximum allowable transmission time \(\alpha T\) at the first and second time slots is divided into \(N\) equal-length subslots, each with length \(\frac{\alpha T}{N}\). The sub-DTDBC-RC works in the same way as DTDBC-RC, with the exception that \(S_{1}\) (\(S_{2}\)) only decodes at one subslot before or at \(\alpha T\) in the former (i.e., at the \(n\)-th subslot, where \(0<n\leq N\)), whereas it decodes at any time before or at \(\alpha T\) for the latter. The working process of sub-DTDBC-RC is shown in Figure 2.  
With the above description, it is intuitively and readily seen that when \(N\rightarrow+\infty\), the sub-DTDBC-RC becomes DTDBC-RC.  
**Remark 1:** We note that the authors in [11] proposed a similar protocol as TDBC. Their protocol works in the same way as TDBC protocol with direct link, but \(S_{1}\), \(S_{2}\), and \(R\) transmit in the rateless coding way, where there are always three time slots and the lengths of three time slots are all random depending on the channels of direct and relaying links. The major differences between DTDBC-RC and the protocol in [11] are that (i) only \(R\) decodes in the first and second time slots while \(S_{1}\) and \(S_{2}\) keep silent (leading to three-time-slot transmission) in the latter, while in the former, \(S_{1}\) and \(S_{2}\) decode and \(R\) only retransmits when possible (leading to possible two-time-slot transmission); (ii) DTDBC-RC applies in the delay-constrained scenarios with given maximum allowable transmission time for one round information exchange while the latter applies in the scenarios with no delay-constraint; and (iii) we investigate the performance in terms of outage probability, expected rate, and DMT in the paper, which is more comprehensive than the performance analysis in [11] (The performance analysis method in Section 3 can be directly extended to the protocol in [11], where the two sources transmit with different data rates.).  
Figure 1: The working states of the dynamic time division broadcast rateless coding (DTDBC-RC) protocol.  
From the above description, it is also intuitive that DTDBC-RC will be more efficient in throughput than the protocol in [11]. We also point out that the protocol in [11] will never have an outage event occurring since no time delay constraint is entailed, therefore its performance is not compared with the protocols presented in this paper.
# Research on Clustering-Based Fault Diagnosis during ROV Hovering Control
## 2 Faults and Methods  
### Thruster Faults  
ROVs use propeller-type underwater thrusters as their primary propulsion system [32]. The underwater environment in which ROVs operate is unpredictable. Factors such as currents and pressure, as well as collisions with floating debris, can partially or totally damage underwater thrusters. This makes it more difficult to implement ROV motion, leading to mission failure or loss of the ROV [15; 33]. This study focused on the most frequently occurring faults caused by external factors, such as entanglement with floating debris and propeller breakage [19].  
Figure 1 illustrates the imitated fault situations. Entanglements were imitated using a net and rope (\(\mathcal{O}7\) mm), and for breakage situations, propeller blades were damaged to represent 33% and 66% of the total blade area. To apply data-based techniques, it is crucial to select appropriate fault features for underwater thrusters. According to prior research on underwater thruster FD, RPM and current data were used as fault features to diagnose propeller breakage and entanglement situations [20]. It is anticipated that changes in the area of contact with the fluid due to propeller breakage or entanglement result in torque variations [34]. A general equation representing the torque of the thruster is provided in Equation (1).  
\[Q=\rho K_{Q}n^{2}D^{5} \tag{1}\]  
where \(Q\) represents the torque of the thruster, \(\rho\) is the fluid density, \(K_{Q}\) is the torque coefficient, \(n\) represents the propeller rotation speed in revolutions per second (RPS), and \(D\) represents the diameter of the propeller. Equation (2) expresses the electrical torque of the DC motor.  
\[Q=K_{t}I-J\frac{dn}{dt} \tag{2}\]  
Here, \(K_{t}\) is the electrical torque constant, \(I\) represents the current, and \(J\) represents the moment of inertia. Assuming no change in the RPS, the current can be expressed in terms of the other variables:  
\[I=\frac{\rho K_{Q}n^{2}D^{5}}{K_{t}} \tag{3}\]According to Equation (3), it is assessed that in the case of breakage with a reduced propeller diameter, the current will decrease while the rotational speed will increase. In the case of entanglement, the current will increase while the rotational speed will decrease. Therefore, the rotational speed (RPM) and the consumption current of the thruster were selected as fault features for FD.
## 4 Emerging Lightweight Security Approaches
### Symmetric Encryption--Lightweight AES Approaches
#### 4.1.1 Lightweight AES for FPGA
Round key calculations at each cycle can be considered as simpler round keys in the block cipher primitive, as shown in Table 7.
# Airwave Noise Identification from Seismic Data Using YOLOv5
## 3 The Basic Principles of YOLOv5
### Output Layer and Loss Function
The coordinates of the ground truth bounding box \(\left(\text{b}_{\text{x}},\text{b}_{\text{y}},\text{b}_{\text{w}},\text{b}_{ \text{h}}\right)\) can be calculated using the following equations [45; 48]:  
\[\text{b}_{\text{x}}=\sigma(\text{t}_{\text{x}})+\text{c}_{\text{x}} \tag{4}\]  
\[\text{b}_{\text{y}}=\sigma\left(\text{t}_{\text{y}}\right)+\text{c}_{\text{y}} \tag{5}\]  
\[\text{b}_{\text{w}}=\text{p}_{\text{w}}\text{e}^{\text{w}} \tag{6}\]  
\[\text{b}_{\text{h}}=\text{p}_{\text{h}}\text{e}^{\text{h}} \tag{7}\]  
In these equations, the b\({}_{\text{x}}\) and b\({}_{\text{y}}\) represent the center coordinates of the bounding box, predicted as offsets from the grid cell centers and then adjusted by the grid's coordinates. The b\({}_{\text{w}}\) and b\({}_{\text{h}}\) represent the width and height of the bounding box, predicted in a log-space (using the exponential function) and scaled by anchor box sizes to ensure that the predicted bounding boxes can handle varying object sizes. The c\({}_{\text{x}}\) and c\({}_{\text{y}}\) represents the coordinates of the top-left corner of the grid, while p\({}_{\text{w}}\) and p\({}_{\text{h}}\) denote the widths and heights of the prior anchor boxes. The function \(\sigma\) is the sigmoid function, which constrains the offsets to the range (0, 1). The model's optimal weights are obtained by minimizing the overall loss function, which is a weighted sum of the localization loss and the confidence loss. The model consists of three output layers, with each layer generating a set of bounding box predictions that include the bounding boxes for objects and their corresponding confidence probabilities. When calculating the loss function, the outputs from these three feature maps are considered, and the losses from each layer are computed and accumulated to form the total loss. By conducting loss calculations and optimizations across all three feature maps, YOLOv5m effectively detects objects of varying sizes. This multi-scale loss calculation approach ensures that the model achieves robust detection performance for objects across a range of dimensions.
# Experimental Study on the Heat Transfer Performance of Pump-Assisted Capillary Phase-Change Loop
## 3 Results
### Steady-State Heat Transfer Characteristics
#### 3.2.2 Heat Transfer Characteristics  
The variation in the heat transfer coefficient (HTC) of the evaporator with the liquid-vapor pressure difference and heating power is shown in Figure 5. In Figure 5, the HTC increases first, and then decreases with an increase in heating power under with liquid-vapor pressure differences. Particularly when the liquid-vapor pressure difference was 0 and 4 kPa, the HTC increased first to the maximum and then decreased rapidly. By contrast, when the liquid-vapor pressure difference exceeded 8 kPa, the HTC decreased gradually after reaching the maximum. Regardless, the change in HTC was closely related to the transition of heat transfer modes. Under low liquid-vapor pressure difference, with the increase in heating power, the heat transfer mode switched from flooded and partially flooded to thin-film evaporation, resulting in a rapid increase in HTC. If the heating power continued to increase, the heat transfer mode switched from thin-film evaporation to overheating, leading to a decrease in the HTC. However, under a high liquid-vapor pressure difference, there would be no transition from thin-film evaporation to overheating at the current heating power. Therefore, the HTC would not decrease rapidly.  
Figure 4: Variation in baseplate temperature under different liquid-vapor pressures and heating powers.  
In summary, when the liquid-vapor pressure difference was low, the range in power required to maintain high HTC was small and overheating mode was easily entered. In contrast, a high liquid-vapor pressure difference could ensure that a high heat transfer coefficient could be maintained in a wider power range. Under a higher liquid-vapor pressure difference, the HTC would increase rapidly with heating power. In engineering applications, the liquid-vapor pressure difference could be actively adjusted according to the operation characteristics of electronic devices in order to achieve the optimum heat dissipation effect.
# A High Capacity Watermarking Technique for the Printed Document
## 2 Related Theory
### Fourier Boundary Descriptors  
The Fourier descriptor is the frequency domain coefficient obtained after the Fourier transform of the graph boundary points. Its idea is that for a closed boundary in a plane, a limited number of scattering coordinates \((x_{0},y_{0})\), \((x_{1},y_{1}),\ldots,(x_{K-1},y_{K-1})\) can be found to represent it. These coordinate points can then be treated as plural. And it is shown in Equation (3):  
\[\mathbf{s}(k)=x(k)+i\cdot y(k),\ \ \ k=0,1,\cdots,K-1 \tag{3}\]  
Thus, the closed curve boundary can be represented in one-dimensional space. For the one-dimensional sequence \(\mathbf{s}(k)\), its discrete Fourier transform is shown in Equation (4). Where \(u=0\), \(1\), \(2,\cdots,K-1,F(u)\) is the Fourier descriptor coefficient of the boundary. \(\mathbf{s}(k)\) can be regenerated by \(F(u)\) through the inverse Fourier transform, and the inverse Fourier transform function is shown in Equation (5).  
\[F(u)=\sum_{k=0}^{K-1}s(k)e^{-\beta\pi uk/K} \tag{4}\]  
\[\hat{s}(k)=\frac{1}{K}\sum_{u=0}^{P-1}F(u)e^{\beta\pi uk/P} \tag{5}\]  
When \(P=K-1\) in Equation (5), then we can conclude that \(\hat{s}(k)=s(k)\). It is a known fact that the high-frequency component of the Fourier coefficient indicates the fineness of the detail, and the low-frequency component determines the overall shape. Therefore, the boundary point can be adjusted by the parameter \(P\), and the purpose of increasing or decreasing the pixel point can be achieved without affecting the overall shape.  
The research in [26] studies the Fourier descriptor and shows its good feature in anti-RST(Rotation-Scale-Translation) attacks and its wide applications. Previous literature [19; 22; 27] used the Fourier descriptor to practice on digital watermarking. Among them, the research in [19] uses the Fourier descriptor to embed watermark on the high frequency coefficient, but the method is less robust and unblind. Reference [22] uses the Fourier descriptor to flip the boundary pixels, but the scheme can still be enhanced and optimized. Literature [27] uses Fourier descriptors to preprocess watermark information, and then applies Sudoku to embed watermarks.  
Figure 2: The improved print-scan invariant.
# Ultra-Low-Loss Silicon Waveguides for Heterogeneously Integrated Silicon/III-V Photonics
## 4 Photonic Integrated Circuits with Ultralow Loss Silicon Waveguides
### Applications  
#### Extended Cavity Lasers  
The heterogeneous silicon platform has demonstrated superior laser linewidth performance compared to typical linewidths of III/V lasers that are in a few MHz range [41]. There are at least two reasons for superior linewidth. The platform allows for the control of confinement in the quantum wells and provides a low-loss waveguide platform, opening a new possibility in improving the coherence by providing a mechanism to separate the photon resonator (in silicon) and highly-absorbing active medium [42]. The second improvement comes with to the use of ring resonators inside the laser cavity to create a widely-tunable laser. Ring resonators, provided that the utilized waveguide platform offers sufficiently low propagation losses, have an advantage as the effective cavity length at ring resonance is significantly enhanced, directly reducing linewidth. Both reasons benefit from loss reduction, and it was predicted that sub-kHz Lorentzian linewidths should be attainable with waveguide platform providing sub-0.5 dB/cm propagation loss [43].  
We extend the analysis to platform providing 0.1 dB/cm propagation and keep the same basic laser structure as in [43] comprising of three ring resonators inside the Fabry-Perot laser cavity. The estimated Lorentzian linewidth for optimized laser fabricated with such low-loss waveguides is sub-100 Hz as shown in Figure 9.  
Figure 8: (**a**) Simulation results at 1550 nm wavelength of the optical transmissions and reflections at the deep-shallow taper. The fundamental mode was launched from the ULL silicon waveguide side. Transmitted and reflected powers to the fundamental mode and high order modes were simulated using two methods. The solid lines show the results using mode expansion on the PhotoDesign FimmProp, while the markers are the data points simulated with Lumerical FDTD (**b**) Wavelength dependence of the transmission and reflection from fundamental input mode to fundamental output mode, simulated with Lumerical FDTD.  
Figure 9: Simulated Lorentzian linewidth of widely-tunable laser comprising of three rings as a function of coupling strength of the high-\(Q\) ring for four waveguide propagation loss scenarios. The analysis ignores non-linear effects.