esa-sceva/satcom-chunk-collection huggingface dataset:
# VDGA-Based Resistorless Mixed-Mode Universal Filter and Dual-Mode Quadrature Oscillator
## 3 Proposed Resistorless Mixed-Mode Universal Filter  
Figure 2 illustrates the proposed mixed-mode universal biquadratic filter, which is designed using two VDGA units and two grounded capacitors, eliminating the need for any external passive resistors. In IC design, employing grounded capacitors is helpful for reducing stray parasitic capacitance at their terminals, which is alleviated by external grounded capacitors.  
With the same designed topology, the proposed circuit can realize mixed-mode universal filter functions in VM, TAM, CM, and TIM, as characterized below.
## 4 Cryptographic Analysis
### Peak Signal-to-Noise Ratio  
Peak signal-to-noise ratio (PSNR) is another way to compute the power of the clean signal against the power of noise [29]. The PSNR is more commonly used in image encryption algorithms [30] and can be used to test the quality of the proposed encryption scheme in this article. PSNR is calculated as follows:  
\[PSNR=10 log_{10}\frac{MAX^{2}}{MSE}dB \tag{15}\]  
where the maximum possible value of audio stream is MAX. In this case, the maximum value can be 65,535. There is possibly a square error between the plain and encrypted files and this mean square error (MSE) can be computed as follows:  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}\left(x_{i}-y_{i}\right)^{2} \tag{16}\]  
The SNR and PSNR values for nine different audio files are represented in Table 3.  
In this proposed method, we obtained a negative SNR value which shows that the encrypted files are very noisy and the clear signal is destroyed. As can be seen, PSNR values are small, which means that the encrypted audio files have a very high level of noise. Compared with the other audio encryption schemes [5,8,27], we can see that the proposed scheme has comparable and better SNR and PSNR values.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Audio File** & **NSCR** \\ \hline usb-headset-weird & 99.994\% \\ cartoon-mumble-speak & 100.000\% \\ grito-wav & 99.998\% \\ a-strange-dream & 99.996\% \\ radio & 99.999\% \\ insult & 99.999\% \\ sharpening-blade-long-blade & 99.998\% \\ airport-security-message & 99.998\% \\ shakuhachi-grave-5 & 99.998\% \\ Ref. [5] & 99.998\% \\ Ref. [8] & 99.998\% \\ Ref. [27] & 99.998\% \\ AES [27] & 99.603\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of the sample change rate.
## 5 Numerical Results
### Coverage Probability Verification  
Figure 10 depicts the relationship between the threshold and the coverage probability for different \(\beta\). As \(\beta\) increases, the negative correlation between points increases, so the distribution of nodes becomes more regular and most of the disturbances are restricted to areas closer to a point. Therefore, the regularity leads them to cover a wider area. Figure 11 describes the coverage probability for different beam widths. The directionality of the beam helps to increase the connection probability but decreases the coverage probability, which is due to the fact that assigning a shape to the beam reduces the width of the beam. Therefore, we need to consider that the beam width can be adjusted to keep the performance of the whole network balanced.  
Figure 8: The ergodic capacity as a function of \(P\) for different numbers of relay hops.  
Figure 9: The ergodic capacity as a function of \(P\) at different beam width.
# A Key Management Scheme for Mobile Wireless Sensor Networks
## 4 Evaluation and Comparison
### Connectivity  
The connectivity is calculated as the probability of successfully establishing a link with another node. Table 3 shows the formula of the connectivity for the considered schemes.  
SSL, FPWK, PGK, SKKE, LEAP+ and FLEAP provide the same level of connectivity as PCAT: all the nodes are able to establish a link with any other node in the network. For the schemes based on the random key distribution, the parameter \(r\), which represents the quantity of keys owned by a node, and \(p\), which represents the total quantity of keys, determine the level of connectivity. EG, RSDTMK  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **Messages** & **Size of the Transmitted Data** \\ \hline FPWK and PGK & 0 & 0 \\ LEAP+ and FLEAP & 2 & \(2\cdot l_{ID}+l_{k}\) \\ SKKE & 2 & \(3\cdot l_{ID}+4\cdot l_{k}\) \\ RSDTMK & 2 & \(r\cdot l_{sID}+l_{kID}+2\cdot l_{ID}+l_{k}\) \\ EG & 2 & \((r+1)\cdot l_{kID}+2\cdot l_{ID}+l_{k}\) \\
2C & 2 & \(2r\cdot l_{kID}+2\cdot l_{ID}+l_{k}\) \\
1–5C & 2 & \((r+5)\cdot l_{kID}+2\cdot l_{ID}+l_{k}\) \\ SSL & 4 & \(4\cdot l_{ID}+2\cdot l_{sign}+2\cdot l_{pu}+2l_{enc}\) \\ PCAT & 4 & \(4\cdot l_{ID}+2\cdot l_{pu}+2l_{enc}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Communication efficiency.  
and 1-5C provide a lower connectivity, since only the nodes that share at least a key can establish a link. In 2C, two nodes are able to establish a link if they share at least two starting keys.
# Key Recovery for Content Protection Using Ternary PUFs Designed with Pre-Formed ReRAM
## 2 One Way Unclonable Functions with Ternary States
### One Way Unclonable Functions
#### 2.1.1 Random Number (\(\mathbf{T}\))  
* The random number \(\mathbf{T}\) feeds an extended output function (XOF) pointing at a set of addresses \(\mathbf{A}\) contributing to the generation of stream \(\mathbf{K}\). For example, the XOF can be a SHAKE from the message digest \(\mathbf{MD}\) of the SHA-3 hashing function.
* \(\mathbf{T}\) can be concatenated with password \(\mathbf{PW}\), as shown in Equation (2): \[\mathbf{A}\leftarrow\text{XOF}\leftarrow\mathbf{MD}\leftarrow\text{Hash }\left(\mathbf{T}\oplus\mathbf{PW}\right)\] (2)
* With the use of a password, or another multi-factor scheme, \(\mathbf{T}\) can be freely disclosed through insecure communication channels.
## 4 Algorithms
With this information, both methods can tune their performance to different traffic profiles which results in different decisions in terms of the transponder placement. Without the detailed knowledge on the traffic profile, usually a uniform traffic profile is assumed, which results in a particular placement of transponders which may not be the best for traffic profiles different than uniform. Secondly, both methods during their run (i.e., simulation of network routing over a period of time) collect data on transponder usage for each node in subsequent iterations of the considered time period which allows to find some statistics such as maximum usage (MSU) or average usage (SAUR). Next, these statistics are directly applied to make the final decision on transponder placement. In terms of time complexity of the above described algorithms, the time complexity of the UNI and ND methods is simply \(O(|V|)\), where \(|V|\) denotes the number of nodes. In the case of RO, CONF, and COMB, the time complexity is dominated by the shortest path calculation and can be estimated as \(O(|V|^{3})\) assuming the Floyd-Warshall algorithm. Finally, the time complexity of the two last methods, MSU and SAUR, depend on the execution time of the ARBR algorithm and the size of network traffic to be simulated. However, the final placement of transponders has simple complexity of \(O(|V|)\). Thus, the overall complexity of MSU and SAUR can be formulated as \(O(|V|+time(\text{ARBR}))\), where \(time(\text{ARBR})\) denotes the execution time required to simulate the analyzed traffic with the use of the ARBR method.
# Monocular Vision-Based Depth Estimation of Forward-Looking Scenes for Mobile Platforms
## 2 Methodology
### Depth Estimation Network  
The proposed depth estimation network is built upon the UNet++ architecture with an encoder-decoder structure [24]. The encoder extracts the detailed and semantic features by improving the model of ResNet-18. Meanwhile, the decoder transforms these features into a pixel-level depth map. Additionally, the attention mechanism ECANet is integrated into the decoder, allowing for the adjustment of weights assigned to different feature channels. This mechanism enhances the significance of important channels, facilitating the extraction of more prominent features. The overall structure of the proposed depth estimation network is illustrated in Figure 2.  
In the encoder, the input image undergoes multiple down-sampling operations, as illustrated in Figure 3a, leading to a continuous reduction in the dimensions of the feature maps. During this down-sampling process, the number of channels is sequentially increased from 3 to 64, then to 128, 256, and finally 512, allowing the model to extract the high-level features. However, this process often results in the loss of crucial information that is equally important for depth estimation, such as object edges and small target regions. These areas typically suffer from low brightness and blurred textures, which makes it challenging for the model to accurately extract their feature information. The proposed method addresses this issue by incorporating multiple Feature Aggregation Nodes (FANs), as illustrated in Figure 3b, and replacing the skip connection between the encoder and decoder with FANs. This modification results in improved model performance in complex and challenging visual environments. The primary function of the FAN is to integrate multiscale features extracted from the encoder, facilitating the fusion and reuse of low-level and high-level features through intermediate nodes. This module effectively minimizes the loss of critical feature information during both down-sampling and up-sampling.  
Figure 1: The framework of the proposed approach.  
In the decoder, the high-level features extracted by the encoder are up-sampled multiple times to reduce the number of channels from 512 to 256, 128, 64, 32, and finally to 16, ultimately generating a single-channel depth map with the same resolution as the  
Figure 3: Components of the depth estimation network. (**a**) Down-sampling operations with convolution in the encoder. (**b**) Feature aggregation nodes. (**c**) Up-sampling operations with ECA in the decoder.  
Figure 2: The structure of the depth estimation network proposed in this paper.  
input image. The up-sampling operation of the decoder is illustrated in Figure 3c. The feature maps in the decoder are derived from the previous layer, the corresponding encoder layer, and the outputs of the FAN. Multiscale feature fusion is achieved through ECANet, which plays a crucial role in accurately extracting depth information under challenging visual conditions.
## 3 Results and Discussion
### Optical Properties
Be it in the visible or the ultraviolet region, the absorption effect after pressure is better, which indicates that the photoelectric performance after pressure is better and more suitable for solar cells materials. Moreover, we found that the absorbed light has a blue shift after pressure, indicating that the absorbed energy increases, which is consistent with the increase of band gap after pressure. Perovskite CsGeI\({}_{3}\) is highly absorptive in a wide energy range near the main peak, so it is suitable for use as the absorption layer of solar cells.
## 4 Experiments and Analysis  
Following the description of the proposed FiMa-Reader system, we move over to the experiments. The experiments are divided into four parts, each focusing on different aspects related to the proposed solution. First, we investigate the performance of the proposed DAH pattern, as described in Section 3.1.1. Second, we compare the repeatable docking process positioning accuracy using the FiMa-Reader system and a LiDAR-based navigation process. Third, we compare the position accuracy of different FiMa-Reader system output rates to validate the effectiveness of the multithread framework described in Section 3.1.3. Finally, we test the performance of the FiMa-Reader system in both light-on and light-off scenarios to validate the proposed infrared camera and 940 nm fill-lights. For each experiment, we describe how it was conducted and the obtained results. Finally, we provide comments on the obtained results.
## 2 Design Considerations of Supercritical CO\({}_{2}\) Heat Exchanger
### Heat Exchanger Type Selection  
Heat exchanger types can be classified based on the number of working fluids, compactness, flow arrangements, and heat transfer mechanisms. Tubular, plate type, extended surface, and printed circuit heat exchanger types are typical heat exchangers used in industrial areas. Among the heat exchangers, the tubular heat exchanger is popular due to its flexibility: the core shape can be easily changed by the tube diameter, length, and arrangements. In addition, tubular heat exchangers are usually used in high-temperature and high-pressure conditions. A plate type heat exchanger consists of two flow membranes, and a number of plates are compressed or welded with a gasket. Therefore, it is not appropriate to use it in extreme operating conditions due to the possibility of leakage. Compared to the tubular and plate type heat exchangers, a higher effectiveness can be achieved by using extended surface heat exchangers. However, a high pressure drop can appear on extended surface heat exchangers. For compact size heat exchangers, printed circuit heat exchangers (PCHE) have been widely studied. The volume of a PCHE can be minimized up to 1/30 compared to conventional shell-and-tube heat exchangers with the same heat duty [22]. However, maintenance and inspection of a PCHE are difficult because these heat exchangers are manufactured by a diffusion bonding process. In addition, there are limitations in material selection for diffusion bonding processes.  
The type of SCO\({}_{2}\) heat exchanger can be determined by the desired operating condition. Because the target operating condition of the SCO\({}_{2}\) heat exchanger is at a high temperature and high pressure (600 \({}^{\circ}\)C and 200 bar), the heat exchanger should endure high thermal stress and thermal shock. In addition, the maintenance and inspection of the heat exchanger should be easy. The tubular type heat exchanger has a low pressure drop and offers the least-risk design for the thermal shock resistance, and it has modest effectiveness. Because the flue gas was considered as the heat source, the pressure drop on the hot side should also be minimized. The flexible design of the tubular heat exchanger can offer a pressure drop on the flue gas side. Therefore, the tubular type was selected as the SCO\({}_{2}\) heat exchanger.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Design Parameters** & **Operating Condition** \\ \hline Mass flow rate (SCO\({}_{2}\)) & 1 kg/s \\ \hline Outlet pressure (SCO\({}_{2}\)) & 200 bar \\ \hline Inlet \& Outlet temperature (SCO\({}_{2}\)) & 300 \& 600 \({}^{\circ}\)C \\ \hline Flue gas inlet temperature & 800 \({}^{\circ}\)C \\ \hline Outlet pressure (flue gas) & atmospheric pressure \\ \hline \hline \end{tabular}
\end{table}
Table 1: Operating condition of the SCO\({}_{2}\) heat exchanger.
## 4 Application Fields of the A25 Integrated MIoT
### Maritime Traffic and Navigation Safety  
1. Ship monitoring system: By installing sensors and communication equipment on a ship, the real-time monitoring of the ship's position, speed, heading, and other parameters is possible. These data can be transmitted to the monitoring center on land. Such systems provide real-time position information on the vessel, helping to monitor the ship's course and avoid collisions.
2. Ship health monitoring system: By installing sensors and data acquisition equipment on a ship, the operating status and health status of the ship's mechanical, electrical, and other equipment can be monitored in real time. The system automatically identifies equipment failures and abnormalities, providing early warnings for maintenance and repair to ensure the normal operation and navigational safety of the ship.
3. Ship communication and emergency rescue systems: By using IoT technology, real-time communication between ships and land or other ships can be realized. When the vessel is in an accident or distress, it can quickly send a distress signal and provide accurate location information so that rescuers can respond quickly and provide assistance.
4. Intelligent maritime transportation: Global maritime trade inevitably impacts economies, transportation systems, and markets on a global scale [145]. However, centralized optimization for ships underway faces numerous challenges, such as potential breaches in data security, excessive signaling overhead, and delayed responses due to high latency. Facing this challenge, using the federated learning in the A2S integrated MIoT has been proposed as an efficient solution for promoting data privacy, low latency, and high communication efficiency.
## 5 Sensitivity Analysis  
Sensitivity analysis is performed to better understand the impact of each criterion on the decision. This analysis shows which criteria are more dominant and which criteria are more affected by changes in their weights. Thus, decision-makers can better understand which criteria are the most critical. Furthermore, sensitivity analysis increases the transparency of the model. It shows under what conditions and to what extent the results change, facilitating understanding of the decision process and supports the scientific validity of the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Alternatives** & \(d^{*}(x_{j})\) & \(d^{-}(x_{j})\) & \(DoC(x_{j})\) & **Ranking** \\ \hline \(A_{1}\) & 7.950 & 1.174 & 0.129 & 8 \\ \(A_{2}\) & 6.383 & 2.966 & 0.317 & 7 \\ \(A_{3}\) & 5.181 & 4.182 & 0.447 & 5 \\ \(A_{4}\) & 5.654 & 4.287 & 0.431 & 6 \\ \(A_{5}\) & 4.754 & 4.661 & 0.495 & 4 \\ \(A_{6}\) & 3.510 & 5.782 & 0.622 & 3 \\ \(A_{7}\) & 1.674 & 6.947 & 0.806 & 2 \\ \(A_{8}\) & 1.696 & 8.044 & 0.826 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of IVT2F-TOPSIS for international dispute resolution alternatives.  
results. Therefore, sensitivity analysis provides decision-makers with a flexible perspective. Understanding how to make decisions under different scenarios makes it possible to make more adaptive and robust decisions under alternative conditions. For this purpose, the closeness indexes for each case scenario are computed using Equations (1)-(16). The case scenarios are presented in Table 11. Figure 2 shows the computed criteria weights for several scenarios. This allows decision-makers to see how changes in criteria weights impact the overall decision outcome, leading to more informed and confident decisions. By presenting the results, decision-makers can easily compare and evaluate different scenarios to make the best possible choice.  
Table 11 represents a sensitivity analysis across different cases for decision-making alternatives labeled \(A_{1}\) to \(A_{8}\). The current case shows the baseline performance or preference values for each alternative (\(A_{1}\) to \(A_{8}\)) without any modifications to the expert evaluation, which are stated in the application of the proposed methodology. Case-1 to Case-7 indicate different scenarios where changes in criteria weights have been applied to observe how sensitive the outcomes appear. In this analysis, the results show how robust or stable the alternatives' ranks are by comparing the values of each case. According to the value changes of alternatives across cases, this situation suggests that the ranking is sensitive to specific criteria weights. Alternatives with fluctuations across cases indicate areas that are more affected by changes in criteria. For instance, \(A_{1}\), \(A_{4}\), and \(A_{8}\) show substantial variation across cases; they could be critical for the decision-making outcome. \(A_{3}\) and  
Figure 2: Sensitivity analysis results.  
maintain relatively consistent values across cases; this situation could imply that they are robust choices, less affected by variations in criteria weights.
## 4 Discussion
### Multi-Source Data and Methods Reveal a More Comprehensive Picture in China's Terrestrial Ecosystems
Due to the direct correlation between photosynthetic physiology and carbon assimilation, GPP\({}_{\text{max}}\) in this study is closely related to GPP and accounts for most of its interannual variability. The maximum rate of photosynthesis (GPP\({}_{\text{max}}\)) is primarily determined by factors such as leaf area index (LAI), chlorophyll content, and enzyme activities involved in the Calvin cycle. Results from biased correlation analyses by [PERSON] et al. [3] also indicated that GPP\({}_{\text{max}}\) contributes more to GPP's interannual variability than CUP in most ecosystem types. This suggest that while the duration of the photosynthetically active period (CUP) is crucial, the efficiency and capacity of photosynthesis during this period are more significant determinants of overall productivity. It has been suggested that vegetation GPP\({}_{\text{max}}\) has the potential to continue to increase, and modern plant trait measurements have found that leaf photosynthetic capacity varies greatly among vegetation functional types. These variations can be attributed to differences in stomatal conductance, nitrogen use efficiency, and the capability of plants to acclimatize to changing environmental conditions through physiological adjustments. Studies under warming scenarios have also shown that the relationship between vegetation growth activity and temperature variability tends to weaken, and so does the extension of the CUP [50]. This may be due to the saturation of photosynthetic rates at higher temperatures and the increased occurrence of thermal stress, which can offset the benefits of longer growing seasons. The role of controlling vegetation carbon sequestration by GPP\({}_{\text{max}}\) will be further strengthened. Given that GPP\({}_{\text{max}}\) exhibits trends over time, greater focus should be placed on plant physiological changes to better explain GPP variability and enhance monitoring in terrestrial ecosystems.
# Investigation on Numerical Simulation of VIV of Deep-Sea Flexible Risers
## 4 Results and Discussion
### The Displacement-Time History Curves  
It can be seen from Figure 13 that the vibration amplitude variation of each node in the inline (IL) and crossflow (CF) directions gradually increased from both ends of the riser to the center of the riser. The maximum value of the average value of the IL displacement appeared at node 37 instead of node 35 at the midpoint, with an amplitude of approximately 2.081D. The maximum average value of the IL displacement was located about 2-4% below the midpoint of the riser, which was due to the gradual decrease in the top tension of each riser node along the riser axis from top to bottom.  
The CF displacement reciprocated at y/D = 0, but the vibration amplitude was not stable. The vibration displacement-time history curves in the CF direction and the IL direction did not have smooth and regular sine or cosine curves similar to those in the two-dimensional VIV of a cylinder, but rather approximated the vibration trace of the two-dimensional cylinder when multi-frequency flapping occurred, which also reflects the randomness and complexity of three-dimensional VIV.