esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancement of the DRPE Encryption Algorithm for Phase CGHs by Random Pixel Shuffling
## 4 Results Discussion and Performance Analysis
### Key Sensitivity  
Key sensitivity is a crucial property in image encryption algorithms, as it quantifies how small variations in the key can lead to significantly different results. This study has not focused on this characteristic, as phase masks or pixel shuffling vectors can be generated using algorithms described in the literature that inherently possess this property [18; 21].
# Pursuit-Evasion Problem of Unmanned Surface Vehicles in a Complex Marine Environment
## 2 Problem Formulation and Tracking Strategy
### Kinematics and Dynamics of USV  
A reasonable mathematical model of motion is the basis of ship motion simulation and control algorithm design. The motion model of USV is simplified to a three degree of freedom (DOF) motion model of the water surface. The motion needed to be taken into consideration is in the horizontal plane, including the surge, the sway, and the yaw. The vessel motion model in the horizontal plane is shown in Figure 2.  
Figure 1: Schematic diagram of guidance method.  
Figure 2: Coordinate systems of USV.  
The model of a USV with three DOFs can be described as follows:  
\[\left\{\begin{array}{l}\dot{x}=u\cos\psi-v\sin\psi\\ \dot{y}=u\sin\psi+v\cos\psi\\ \dot{\psi}=r\end{array}\right. \tag{4}\]  
In the above expressions, \(x\), \(y\), and \(\psi\) denote the position and orientation in the earth-fixed coordination system, and \(u\), \(v\), and \(r\) are the velocity vectors of the USV with respect to the body-fixed frame, which represent the surge, the sway, and the yaw.  
The dynamic equation of the USV when encountering an external disturbance is as follows:  
\[\left\{\begin{array}{l}\dot{u}=\frac{m_{12}}{m_{11}}\upsilon r-\frac{d_{11}} {m_{11}}u+\frac{1}{m_{11}}(\tau_{u}+\tau_{uu})\\ \dot{v}=\frac{m_{11}}{m_{22}}\upsilon r-\frac{d_{22}}{m_{22}}v+\frac{1}{m_{22}} (\tau_{v}+\tau_{uv})\\ \dot{r}=\frac{m_{11}-m_{22}}{m_{32}}\upsilon-\frac{d_{31}}{m_{33}}r+\frac{1}{ m_{33}}(\tau_{r}+\tau_{uv})\end{array}\right. \tag{5}\]  
In the above expressions, \(m_{ii}\) is the positive definite inertia matrix and \(d_{ii}\) is the damping matrix. \(\tau_{ui}\) and \(\tau_{ri}\) represent the longitudinal force and yaw moment of the USV, and \(\tau_{uu}\), \(\tau_{uv}\), and \(\tau_{uv}\) represent the influence of an external time-varying uncertain disturbance on the USV motion.
## 4 Case Studies
### Three-Node Islanded Microgrid System  
OpenDSS currently does not support detailed models for grid-forming inverters. To validate the behavior of the introduced grid-forming inverter model (Section 2.4), it was compared against transient simulation results from MATLAB(r)/Simulink, using the Simscape Electrical Specialized Power Systems acausal blockset [14]. A three-phase inverter with control in a stationary reference frame [74] was implemented based on the model described in [57]. This model is referred to as the \"Three-Node Islanded Microgrid System\" model and illustrated in Figure 12.  
Based on [23], a hysteresis approach is applied to limit current during faults. When the root-mean-squared (rms) phase current of the inverter exceeds the hysteresis threshold, the inverter current control loops switch from the output of the voltage control loops to a fixed current reference. Because a three-leg, two-level voltage-source inverter topology is selected, the inverter is not capable of providing zero-sequence current such as needed to supply a ground fault. To overcome this, the inverter is interfaced to the rest of the microgrid with a 480 \(V_{ll}\)-480 \(V_{ln}\) delta-grounded-wye transformer. Tables 4 and 5 lists the parameters of the transient model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Subsystem** & **Symbol** & **Value** \\ \hline Voltage loop & kpv & 0.35 \\ Voltage loop & krv & 400 \\ Voltage loop & kvh5 & 4 \\ Voltage loop & kvh7 & 20 \\ Voltage loop & kvh11 & 11 \\ Current loop & kpi & 0.7 \\ Current loop & kri & 400 \\ Current loop & kih5 & 30 \\ Current loop & kih7 & 30 \\ Current loop & kih11 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Inverter controller parameters.  
Figure 12: Oneline diagram of the Three-Node Islanded Microgrid System.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fault Node** & **Fault Phasing** & **OpenDSS Fault Current (A)** & **PMsP Fault Current (A)** & **Difference (\%)** \\ \hline
13 & LG & 4385.2 & 4490.6 & +2.40 \\
13 & LL & 5108.1 & 4995.3 & \(-\)2.21 \\
13 & 3P & 5315.0 & 5578.8 & +4.96 \\
67 & LG & 2478.3 & 2495.2 & +0.68 \\
67 & LL & 3173.0 & 3132.2 & \(-\)1.29 \\
67 & 3P & 3433.5 & 3538.0 & +3.04 \\
113 & LG & 1532.0 & 1545.9 & +0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of results for configuration Gfli-M-GC of the IEEE 123-Node System.
# A Survey on Emerging Blockchain Technology Platforms for Securing the Internet of Things
## 4 Analysis and Discussion: IoT Security and BC Components
### Cryptography  
Another key security component for blockchain-based IoT security solutions is cryptography. Cryptography contributes highly to the security of information in blockchain. By employing cryptographic mechanisms including hashing, digital signatures, zero-knowledge proof, and encryption, blockchain systems can foster increased levels of security and trust among users and things. This has been adopted for IoT security in areas of authentication, data encryption during communication, and data validation. Existing blockchain  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**S/N** & **Consensus** & **Blockchain** & **Mechanism** & **Threat** & **Throughput** \\ \hline
1. & PoS & Ethereum & Locked assets & Sybil \& 51\% attack & 20 TPS \\ \hline
2. & Pure PoS & Algorand & Random selection & long range attack & 6000 TPS \\ \hline
3. & DAG PoW & IoTA & Tip Selection & 34\% attack & 12 TPS \\ \hline
4. & Roll- DPoS & IoTeX & Random selection & Collusion Attacks & 10,000 TPS \\ \hline
5. & Round Robins & Multichain & Rotational &
\begin{tabular}{c} Nothing at stake \\ attack \\ \end{tabular} & 2 million TPS \\ \hline
6. & PBFT & Hyperledger & Voting &
\begin{tabular}{c} 1/3 faulty node \& \\ DoS \\ \end{tabular} & 20,000 TPS \\ \hline \hline \end{tabular}
\end{table}
Table 3: Consensus algorithm properties and threats.  
Figure 7: Consensus mechanisms in the studied literature.  
technology has been seen to use different cryptographic settings for digital signatures, e.g., Ethereum uses an Elliptic Curve Digital Signature Algorithm (ECDSA), which is a type of Elliptic Curve Cryptography (ECC); others are the Edward Digital Signature Algorithm (EdDSA) and Rivest-Shamir-Adleman (RSA). ECDSA is adopted in most works, and while some of these works proposed a lightweight ECC to adapt it to resource-constrained IoT devices, this is not recommended as it impacts the security strength of the device. Cryptographic Sortition is another approach used by Algorand. The Winternitz One-Time Signature (WOTS) and Merkle Signature Scheme were also used and are considered quantum resistant. Figure 8 illustrates the percentage breakdown of reliance and security contribution provided by the predominant key tools across the six platforms under examination. The calculation of this percentage involves a standard fixed allocation of 30% for all major security tools, namely, consensus, digital signature, and hashing. The remaining portion is derived from the collaborative security features integrated to augment the overall security framework of the platforms.
## 4 Experiment and Result Analysis
### Implementation Details  
In this experiment, we trained our system on the training split of the BDD dataset from the scratch. For each image, we first performed image normalization using the value \(\mu\) = (0.485, 0.456, 0.406) and \(\sigma\) = (0.229, 0.224, 0.225). The model consists of 1.52 M parameters. The input images were resized to 720 \(\times\) 720 using a random crop technique. We used AdamOptimzer with a learning rate of 0.1 \(\times\) 10\({}^{-3}\) and a minibatch size of 30. All modules were implemented using the Pytorch framework [25]. We used an Intel(R) Xeon Gold 6148 CPU and an NVIDIA GTX Titan Xp to run the experiment.
## 4 Simulation Results
### Simulation Results of the Smart Inverter Control
#### 4.1.2 Per-Unit Value of the Mains Voltage Was between 1 p.u. and 1.03 p.u.  
When more renewable energy connects to the mains, the power of the grid-connected photovoltaic system feeding into the mains would also rise. This would cause the mains voltage to rise and appliance damage at the user end. Therefore, when the per-unit value  
Figure 11: The output PF, real power, reactive power, and apparent power of the smart inverter when the per-unit value of mains voltage equals 1 p.u.  
Figure 12: The three-phase output voltage and current waveforms of the smart inverter when the per-unit value of mains voltage equals 1 p.u.  
of mains voltage detected by the system was between 1 p.u.-1.03 p.u., the smart inverter should start to be controlled to absorb the reactive power of the system. From Figure 13, it could be observed that when the photovoltaic system fed excessive power to the mains, the mains voltage \(V_{grid}\) would gradually increase if not regulated. However, should regulation be conducted with the voltage-power control technique of the photovoltaic smart inverter mentioned, the mains voltage \(V_{grid}\) would reduce to the root-mean-square value of 220 V (1 p.u.). Therefore, the increase in mains voltage could be inhibited. As shown in Figure 13, the PF of the smart inverter was adjusted to 0.9 (lagging) to absorb the redundant reactive power. Figure 13, by conrast, also displayed the output power of the smart inverter when the per-unit value of mains voltage was between 1 p.u. and 1.03 p.u. It could be observed that the reactive power of mains absorbed by the inverter was 774 VAr (Q > 0); then, the apparent power S still remained at 1600 VA as the apparent output power of PVMA, while the output real power P of the smart inverter reduced to 1440 W. Figure 14 displayed the three-phase output voltage and current waveforms of the smart inverter when the per-unit value of mains voltage was between 1 p.u. and 1.03 p.u. From Figure 14, the three-phase balance could be observed, but the current was lagging behind voltage; thus, the PF was lagging.  
Figure 13: The mains voltage \(V_{grid}\) before and after regulation, output PF regulation, real power, reactive power, and apparent power of the smart inverter when the per-unit value of mains voltage was between 1 p.u. and 1.03 p.u.
# Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks
## 2 Related Work
The proposed model could successfully simulate sign damage, grace period, blanket replacement, spot replacement (initiated beyond regular inspection), daytime inspections, and retro-reflectivity degradation. Results showed that the grace period was effective in reducing sign maintenance and replacement costs. [PERSON] and [PERSON] (2020) presented a detailed literature review of different predictive analytics used for highway maintenance activities as life expectancy and deterioration models, pavement marking prediction models, and sign retro and degradation models [27]. The survey of the included studies indicated that the deterministic models have mainly dominated the field of traffic sign deterioration, their life expectancy, and retro-reflectivity prediction. In another study, [PERSON] and [PERSON] evaluated the performance of different sign management practices considering data for three case studies in the US: South Carolina, North Carolina, and Virginia [28]. It was concluded that both blanket replacement and expected sign life significantly reduce the need for the day and night time inspections, with North Carolina having the most mature sign management program. More recently, a study conducted by [PERSON] (2021) emphasized the proper selection of application of retro sign sheeting for the specific application [29]. This report thoroughly summarized the key issues involved in the process of decision-making from different aspects, such as retro-reflectivity science, retro-reflectivity materials, application of fluorescent materials, minimum retro-reflectivity standards and specifications, sign performance and degradation, field measurement of sign retro-reflectivity, safety analysis, and economic benefits. The study reported that the roadway environment should be one of the key factors in selecting the sign sheeting. The recommendation concluded that sheeting with the highest grades should be deployed in urban areas since there is a significant demand for drivers' attention, particularly during the night. It is evident from previous works that mostly simple regression models were explored to investigate traffic sign degradation. Advanced Machine techniques have not been explored enough in the domain of sign retro-reflectivity. In addition, previous studies have separate models for each of the different combinations of sheating and colors. To the best of our knowledge, no study has introduced a unified and robust model incorporating different sheating characteristics, aging conditions, and other predictor variables. This study was undertaken to address this research gap.
## 4 Result and Discussion
### Measurement Errors and Methods to Reduce them
#### 4.4.3 The Effect of Antenna Beamwidth  
For a radiometer, the observed TB is a weighted average of the TB over the antenna pattern, but the radiometer does not have an infinitesimal beamwidth, the observed TB will turn out greater than the TB at the antenna beam center [15; 18]. Assuming a Gaussian antenna pattern, the amount of calibration \(\delta T_{b}\) can be given as [18]:  
\[\delta T_{b}(\theta)=\frac{BW^{2}}{16\ln(2)}\Big{(}T_{m}-T_{bg}\Big{)}e^{-\tau (\theta)}\Bigg{[}2+\frac{2-\tau(\theta)}{\tan(\theta)^{2}}\Bigg{]}\tau(\theta), \tag{15}\]  
where \(BW\) is the full width half-maximum power of the power pattern. This correction is proportional to the zenith opacity and it is generally small (<0.1 K). However, the effect of the beamwidth grows exponentially for elevation angles lower than 30\({}^{\circ}\)[18], which is best calibrated. In practice, the side lobes of antenna beam may pick up radiation at low elevation from the ground source. For this reason, the observation should avoid low elevation angles.
# An Efficient Steganographic Protocol for WebP Files
## 3 Results
### Data-Based Methods  
#### 3.2.1 Transparency  
The purpose of this experiment was to test the transparency method. Tests included the embedding of two secrets; the first one was in the form of bytes, while the second one was an image. In the subsequent part, the secrets were extracted. Additional testing served for comparing the results of WebP and PNG embedding in transparent pixels. To test this method, we used a WebP image of size \(588\times 97\) with 18,622 transparent and 38,414 non-transparent pixels. This gives about 32.64% of the container available for data hiding. We conducted two experiments. In the first one, red, green, and blue components of each transparent pixel were replaced with secret data. In the second experiment, we concealed a secret image in one of the transparent regions.  
The resulting images were of a lossless type of WebP, so their header was marked with 'VP8L' chunk. In the first experiment, we used all possible capacity, which means that we were able to embed 55,866 bytes of data. As a result of this, the image size increased from 1.8 kB to 61.3 kB. For comparison, we also conducted the same test for the PNG image. The sizes before and after embedding were the same, namely 67.7 kB.  
In the second experiment, we prepared a secret image containing text in the transparent region of the container. In this case, only part of the available capacity was used, so the size of the stego file increased only to 2.2 kB. The stego image and recovered secret are presented in Figure 5.
## 5 Conclusions  
In this study, the problems of existing image fusion-based low-light image enhancement methods are first analyzed. Then, to address these problems, we propose an exposure fusion cross-attention network (EFCANet) for low-light image enhancement. A single low-light image is input to the model, and an exposure-corrected image is generated using an exposure image generator (EIG). EFCANet enhances low-light images in a progressive recursive manner, with a single Cross-Attention Fusion Module (CAFM) acting as a recursive unit and a total of four recursive units. In order to maintain the balance of luminance and color, the recursive unit fuses the exposure-corrected image and the low-light image in the YCbCr color space to achieve image enhancement. We evaluate the enhanced images qualitatively and quantitatively. Finally, the effectiveness and rationality of the network structure are verified by ablation experiments. In the experimental analysis, EFCANet has advantages in several indexes compared with other methods. Although EFCANet achieved better enhancement results, it can only enhance static images but not real-time enhancement and video image enhancement. In future work, our research goal is to improve the real-time performance of the algorithm and put it into applications with real-time requirements such as unmanned driving.  
Conceptualization, J.L.; methodology, Z.Y.; software, F.L.; validation, Z.Y.; formal analysis, J.L.; investigation, J.L.; resources, F.L.; data curation, F.L.; writing--original draft preparation, Z.Y.; visualization, F.L.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (62272281, 61972235), Shandong Natural Science Foundation of China (ZR2021 MF107, ZR2022 MA076), Yantai science and technology innovation development plan (2022 JCJ031).  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Failure Modelling of CP800 Using Acoustic Emission Analysis
## 3 Results and Discussion
### Failure Behaviour
As for the stress state, the equivalent plastic strain was evaluated on the specimen surface for the mechanical and the optical evaluation method, but inside the specimen for the acoustical method. Based on the experimental and numerical results, the JC failure model was parametrised using the least-squares method and a Generalized Reduced Gradient Nonlinear Solver. The coefficients of the JC failure model for the three evaluation methods are summarised in Table 3.  
In Figure 12, the JC failure models are shown for the different evaluation methods. The JC failure model of the mechanical evaluation method has a similar course as the JC failure model of the acoustical evaluation method, but proceeds at higher equivalent plastic strains. In contrast, the slope of the JC failure model is lower and, therefore, different for the optical evaluation method. Until triaxialities of 0.25, the JC failure model of the acoustical evaluation method has the lowest values, which changes for higher triaxialities above 0.25. Here, the JC failure model of the optical evaluation method has the lowest equivalent plastic strains of the three failure models. This progression of the curves is due to the different equivalent plastic strains and the variations in the stress states. Since the equivalent plastic strains for the acoustical evaluation method are lower at the loading angles \(-3^{\circ}\) to \(59^{\circ}\) and higher at \(74.5^{\circ}\) and \(90^{\circ}\) than for the optical evaluation method, the slope of the JC failure model of the optical evaluation method is higher than that of the acoustical evaluation method. Assuming that the JC failure model of the acoustical evaluation method is the most precise model, the material failure of a component would be predicted to late in a process simulation using the JC failure model of the mechanical evaluation method. Comparing the JC failure model of the acoustical evaluation method with that of the optical evaluation method, the material failure would be estimated too late for triaxialities up to 0.25 and too early for higher triaxialities by the JC failure model of the optical evaluation method. Figure 11: Stress state (**A**) and equivalent plastic strain (**B**) of the butterfly specimens for the three evaluation methods. \begin{table}
\begin{tabular}{c c c c} \hline
**Coefficient** & \(\mathbf{D_{1}}\) & \(\mathbf{D_{2}}\) & \(\mathbf{D_{3}}\) \\ \hline Mechanical & 0.5158 & 0.2617 & \(-3.02\) \\ Optical & 0.3485 & 0.4664 & \(-3.03\) \\ Acoustical & 0.4691 & 0.2345 & \(-2.99\) \\ \hline \end{tabular}
\end{table} 
Table 3: Coefficients of the JC failure model for the different evaluation methods.
# Metalens Antenna Based on an Ultrathin Dual-Polarized Double-Layer Huygens' Metasurface
## 4 Design and Discussion of a Two-Channel Focusing Metalens
Figure 9b,c show the normalized electric field distributions of \(E_{x}\) and \(E_{y}\) to describe the response of the metalens to specific field components, which illustrate that each of the focal points originates from the incident fields of the corresponding polarized wave. The two focal points are relatively independent, demonstrating the successful realization of the designed two-channel functionality. Then, the field distributions on the focal plane under \(x\)- and \(y\)-polarized incidences are plotted separately in Figure 9d,e, which confirm that the positions of the focal points are consistent with the preset design. Furthermore, we define the focusing efficiency as the ratio of the energy of the central spot region (within a width of three times the full width at half maxima) to the total energy on the incident side. The operational bandwidth ranges from 24 GHz to 27.8 GHz. Thus, the focusing efficiencies of the designed two-channel focusing metalens are calculated to be 52.4% for \(x\)-polarization and 48.6% for \(y\)-polarization, respectively, achieving an excellent focusing effect in each channel. Figure 8: Actual phase mapping results of the two-channel focusing metalens. (**a**) For \(x\)-polarized channel; (**b**) For \(y\)-polarized channel. After completing numerical simulation verification, the designed metasurface can be fabricated and experimentally tested. The fabrication process is as follows: Firstly, preparation of a Rogers RT5880 substrate with a thickness of 0.8 mm as the substrate material. Secondly, using PCB fabrication techniques, including masking and photolithography, copper patches with a thickness of 0.018 mm are deposited on both sides of the substrate according to the designed metasurface patterns. Finally, clean the fabricated sample and apply anti-oxidation treatment to ensure the performance of the metasurface and prevent phase errors caused by copper oxidation. After that, the electromagnetic properties of the fabricated metasurface can be tested and verified under experimental conditions [36,37]. It is worth noting that the Huygens' metalens operates based on electromagnetic resonances between both sides of metasurface, precise alignment of the patterned copper layers during the photomask process is critical. The fabrication process of the designed metalens in this work is relatively simple, based on mature techniques, and cost-effective.