esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Signal Transmission System
### System Structure  
The developed signal transmission system based on the human body medium is composed of a transmitter, a Mach-Zehnder EO sensor and a receiving circuit, as shown in Figure 1. In the developed system, a baseband signal is input to the transmitter through the input port first, and then it is modulated and amplified in the transmitter. The processed signal is coupled into the human body through the signal electrode, while it is also coupled into the earth ground through the ground electrode. Subsequently, signal transmission within the human body is received by a Mach-Zehnder EO sensor through the signal electrode. In the EO sensor, signal is coupled to the ground electrode of the Mach-Zehnder modulator, and then it transmits to the ground electrode of the receiver. Finally, signal is coupled into the earth ground, and thereby a signal loop has been established.  
On the other hand, the functions of the Mach-Zehnder electro-optical modulator can be described as follows: as shown in Figure 1, the Mach-Zehnder EO sensor consists of a laser diode, a Mach-Zehnder EO modulator and a photodetector. As the laser light (\(\lambda\) = 1,550 nm) from the laser diode passes through the two arms of the Mach-Zehnder EO modulator, the refractive index of the arm changes with the voltage of the received signal which is applied on it. Meanwhile, the modulator sums the optical waves from each arm, and thereby the phase change is converted into amplitude change. Subsequently, the change of optical amplitude is converted into the corresponding change of electric signals by the photodetector. Finally, the output signal of the photodetector is processed by the receiving circuit. As  
Figure 1: The developed signal transmission system based on the human body medium.  
shown in Figure 1, the ground electrode is insulated from the photodetector and the receiving circuit, which will help to decrease signal noise and waveform distortion.
# Ferry Scheduling Optimization Considering Arrival Time Uncertainty and In-Place Time Differences
## 2 Problem Description and Model Construction
According to the CAA, when the flight ferry service requires multiple ferry vehicles to provide service, the first ferry vehicle \(m=0\), and the in-place time \(a_{j,r}^{m}\) needs to be in front of the arrival time \(t_{j}^{0}\) of the flight ferry service and at least \(d_{0}\) in advance, as shown in Figure 3c. The non-first ferry \(m\
eq 0\), and the arrival time \(a_{j,r}^{m}\) needs to arrive within the maximum time interval \(d_{1}\) after the previous ferry leaves. Figure 2: Optimization objective diagram. Therefore, the requirements for the arrival time of each sub-service of the ferry are as in Equation (8). \[a_{j,r}^{m}z_{j,r}^{m}\leq \begin{cases}t_{j}^{0}-d_{0}+M\Big{(}1-z_{j,r}^{m}\Big{)},m=0\\ t_{j}^{m-1}+d_{p}+d_{1}\quad,1\leq m\end{cases} \forall j\in J,r\in R \tag{8}\]  
\[t_{j}^{m}=\max\left(\sum_{r\in R}a_{j,r}^{m}z_{j,r}^{m},t_{j}^{m-1}+d_{p} \right),\forall j\in J,m\in\big{[}1,u_{j}\big{)} \tag{9}\]  
Correspondingly, Equation (9) represents the requirement of the start time of each sub-service of the ferry. \(z_{j,r}^{m}\) takes the value of 1, which means that ferry \(r\) executes the sub-service \(m\) of ferry service \(j\). When its in-place time \(a_{j,r}^{m}\) arrives before the departure of the previous sub-service \(m-1\) ferry, its start time \(t_{j}^{m}\) is equal to the time of the departure of the \(m-1\) service ferry; otherwise, it is the actual arrival time of the ferry \(r\) while satisfying the constraints of Equation (8).
## 3 Results and Analysis
### Water Absorption Rate  
As shown in Figure 12, the effect of varying the replacement levels on the water absorption of cement mortar was analyzed under a fixed water-to-cement ratio of 0.5: At 3 days, the water absorption of the control group was 8.8%. For a 5% replacement, the absorption increased to 9.0% (+2.3% compared with the control group). At 10% replacement, it rose to 9.7% (+7.8% compared with 5%). At 15% replacement, the absorption reached 10.2% (+5.2% compared with 10%). At 91 days, the water absorption rates for the replacement levels of 0%, 5%, 10%, and 15% were 7.1%, 7.4%, 8.3%, and 8.5%, respectively. Compared with the control group, the increases were +4.2%, +16.9%, and +19.7% for 5%, 10%, and 15%, respectively.  
Figure 11: Ultrasonic pulse velocities of cement mortars with different TPSW amounts at different ages.  
Figure 12: Water absorptions of cement mortars with different TPSW amounts at different ages.  
The water absorption showed a decreasing trend with prolonged curing periods due to the completion of hydration reactions. In the early stages, incomplete hydration led to a higher porosity and a less dense structure, which resulted in higher absorption rates. By 28 days, the hydration reactions were more complete, which reduced the porosity and consequently lowered the water absorption. However, as the TPSW replacement level increased from 0% to 15%, the water absorption rose. This increase was attributed to the lower water absorption capacity of the TPSW compared with the natural sand, which caused excess water molecules in the mortar, which created more internal pores and ultimately increased the water absorption.
# Artificial Intelligence and Employment: A Delicate Balance Between Progress and Quality in China
## 3 Material and Method
### Definition of Variables
#### 3.2.4 Macroeconomic Controls and Model Specification  
In order to reduce the error of these variables in the research results, this paper selects the following control variables with reference to existing studies: the urbanization rate (Urb), which is measured by the ratio of urban population to the total resident population of the region, has a positive impact on employment, and can reduce the disadvantaged employment rate and improve the quality of employment [30]; the degree of fiscal expenditure (Gov) is measured by the ratio of local fiscal expenditure to regional GDP, whereby an increase in fiscal expenditure can create a large number of jobs with relatively low a cost of creating jobs, which in turn affects the quality of employment [31]; the degree of trade openness (Ope), measured by the ratio of total import and export trade to regional GDP, will change the employment structure of the society, which will further affect the quality of employment [32]; the level of financial development (Tra) is measured by the ratio of the year-end loan balance of financial institutions to the year-end loan balance of financial institutions to the year-end deposit balance of financial institutions in each region, and the development of finance will lead to more financing for SMEs and thus create more employment opportunities [33].  
The econometric model accounts for four macroeconomic factors shaping regional labor markets:  
1. Urbanization Rate (Urb): Higher urbanization concentrates skilled labor and tech infrastructure, amplifying AI's positive impacts in cities like Shenzhen.
2. Fiscal Expenditure (Gov): Increased public spending mitigates displacement risks, validating **H3** by showing that policies like reskilling programs buffer low-skilled workers.
3. Trade Openness (Ope): Global integration exacerbates skill polarization, as foreign competition accelerates automation in labor-intensive sectors.
4. Financial Development (Tra): Robust financial systems enhance access to AI-driven opportunities, underscoring the interdependence of technological and economic infrastructure.  
Theoretical Implications: Controls isolate AI's \"direct effect\" from confounding macroeconomic trends, ensuring unbiased estimates. The negative coefficient for trade openness (\(-0.0090433\)) highlights globalization's dual role: fostering innovation while exacerbating inequality--a critical consideration for policymakers in developing economies.
## 3 Temperature-Aware Routing Protocols
### LTR (Least Temperature Routing)  
LTR, or the Least Temperature Routing protocol [22], is developed based on the TARA protocol. The setup phase is similar to that of TARA: every node communicates with its neighbors and gathers information about their temperature by observing their activities. The improvement lies in how the packet is forwarded in the network. Unlike TARA, which buffers the packet if the destination node is a neighboring node, LTR forwards the packet directly to the destination node. Additionally, as its name implies, in LTR, each node tries to forward the packet to the \"coolest\" neighbor since the beginning of transmission from the sender node. There is also a packet-discarding mechanism, in which a parameter, named MAX HOPS, is defined, and if the received packet's hop count exceeds this value, the packet will be dropped. The purpose of this mechanism is to prevent a packet from going around the network too far. Another mechanism to reduce unnecessary hops and loops is also presented. If the \"coolest\" neighbor has been recently visited, the packet will be forwarded to the \"second coolest\" neighbor. To support this mechanism, a list of recently visited nodes within some time window is included in the packet. Figure 4 shows a sample of how the LTR protocol works in the network.  
Figure 3: An example of Thermal-Aware Routing Algorithm (TARA).
## 3 Multi-Sensor-Based Remote Autonomous Navigation and Close-Range
### Automatic Redarging Path Planning Algorithm
#### 3.2.1 Improved A*
The yellow lines in the figure represent the paths generated by the traditional A* algorithm, while the purple lines indicate the paths generated by the improved A* algorithm. Table 1 summarizes the key metrics for each experimental group, highlighting significant differences in performance. In the first experimental group, the traditional A* algorithm recorded a planning time of 0.020610 s, while the improved A* algorithm had a slightly longer planning time of 0.027963 s. Although the improved A* algorithm exhibited a longer planning time, it demonstrated substantial advantages in turn angles and turn counts. Notably, it achieved a turn angle of 0 degrees and a turn count of 0, indicating a smoother trajectory compared to the traditional A* algorithm, which had a turn angle of 45 degrees and a turn count of 1. Figure 4: Path planning using traditional A* and improved A* from different starting positions to the charging station. (**a**) Traditional A* from Position 1. (**b**) Improved A* from Position 1. (**c**) Comparison of A* and improved A*. (**d**) Traditional A* from Position 2. (**e**) Improved A* from Position 2. (**f**) Comparison of A* and improved A*. (**g**) Traditional A* from Position 3. (**h**) Improved A* from Position 3. (**i**) Comparison of A* and improved A*. In terms of path length, the improved A* algorithm achieved a length of 13.0384, significantly shorter than the traditional A* algorithm's length of 13.8995. This represents an approximate reduction of 6.2%, suggesting that the improved A* algorithm effectively optimizes the path. The node count further illustrates this enhancement; the traditional A* algorithm processed 81 nodes, whereas the improved A* algorithm required only 53 nodes, indicating a reduction of about 34.6% in node evaluations, thereby enhancing search efficiency. Overall, the improved A* algorithm shows substantial relative improvements in both path smoothness and computational efficiency, making it a more effective choice for path planning tasks.
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 5 Experimental Results and Discussion
### Performance Comparison with Related Methods  
We have compared our proposed method with most of the related joint compression and encryption methods. The experimental results demonstrate that our method outperforms the existing methods in terms of compression, as shown in Table 1, and security, according to the statistical results. Joint compression and encryption of a SPIHT bit stream with modified Kd-tree and varying parameters chaotic maps provide efficient security with compressed data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Joint Compression and** **Encryption**} & \multicolumn{2}{c}{**Diffusion and** **Permutations of**} & \multirow{2}{*}{**SPIHT Encoding and** **Bits Packing**} & \multicolumn{2}{c}{**Permutations with**} \\  & & **Permutations of** **Wavelet Coefficients** & & **Kd-Tree and Chaotic Bit** **Stream Encryption** \\ \hline Computational complexity & \(O(n)\) & \(O(n)\) & \(O(n\times\log(n))\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Computational complexity.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Image**} & **Wavelet** & **SPIHT** & **Compression** & **Compression with** & **Total** \\  & **Coefficient** & **Compression** & **and** & **Wavelet Tree and** & **Compression and** \\  & **Encryption Time** & **Time** & **Encryption** & **Huffman Coding** & **Encryption Time** \\ \hline Lena & 3.3 s & 2.4 s & 12.3 s & 1.2 s & 19.2 s \\ Baboon & 3.1 s & 2.1 s & 12.1 s & 1.4 s & 18.7 s \\ Peppers & 3.4 s & 2.3 s & 12.5 s & 1.3 s & 19.5 s \\ Airplane & 3.0 s & 2.1 s & 12.3 s & 1.2 s & 18.6 s \\ House & 3.1 s & 2.2 s & 12.4 s & 1.4 s & 19.1 s \\ Lake & 3.2 s & 2.3 s & 12.2 s & 1.3 s & 19.0 s \\ \hline \hline \end{tabular}
\end{table}
Table 11: Compression and Encryption Times.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Images/Tests** & **Lena** & **Baboon** & **Pepper** & **Airplane** & **House** & **Lake** \\ \hline NPCR & 99.47\% & 99.36\% & 99.52\% & 99.38\% & 99.25\% & 99.46\% \\ UACI & 33.23\% & 33.21\% & 33.34\% & 33.46\% & 33.14\% & 33.18\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: NPCR and UACI results.
# Managing Redundancy in a Multiple-Robot Orienteering Problem for Equilibrium and Robustness
## 2 Materials and Methods
### Algorithm for RMOPWB
#### 2.3.1 Path Replan  
Two heuristic methodologies are explored: one for solitary robotic operations and the other for collaborative robotic endeavors. These approaches are implemented through the _CutCircle_ and _BackWalks_ functions, respectively. Algorithm 2 formalizes the Path Replanning process using a while-loop structure, where each iteration processes a walk \(\mathcal{S}_{1}(i)\) via the _CutCircle_ function. Following loop termination, the complete walk set undergoes optimization through the _BackWalks_ function. Implementation details for both functions are provided in Algorithm 3.  
* Implementation of the _CutCircle_ function. When a robot exhausts its traversal budget, it may return to a previously visited vertex. This revisit does not necessarily constitute redundancy--in some cases (e.g., example 3 in Figure 5), the robot may restart exploration in a new direction to access high-reward vertices. A cycle is identified when the current vertex connects back to a previously visited counterpart. Such a cycle is deemed redundant and removable only when all constituent vertices are revisits (as demonstrated in example 2 of Figure 5).
* Implementation of the _BackWalks_ function. In crowded environments with multiple robots, walk redundancy primarily manifests as inter-robot path overlaps. The solution involves backtracking walks from terminal vertices to starting points (see examples 1, 4, and 5 in Figure 5). The algorithm sequentially checks each walk's terminal vertex for presence in other robots' walks (example 4). For implementation, we must verify whether removing overlapping terminal vertices preserves solution optimality relative to Problem 1.
# Demonstration of Triband Multi-Focal Imaging with Optical Coherence Tomography
## 4 OCT Angiography of Human Skin  
The extended-focus imaging can be used to better resolve smaller vessels in OCT angiography acquisitions across substantial depth ranges [26]. To demonstrate this, we imaged the microvasculature in human skin on the palm of a healthy volunteer. A scanning protocol providing 7 measurements at each of 1184 transverse locations in the X-axis was used. The vascular contrast was computed using previously described complex differential variance algorithm that has high flow sensitivity while minimizing sub-pixel bulk motion artifact [27; 28]. Vascular projections over the entire imaging depth were obtained separately for each band, and on the merged extended-focus dataset. Figure 3a-c displays the individual projection images. As highlighted by the magenta arrows, the superficial vessels are most clearly resolved in the 1060 nm band projection. The cyan arrows mark the deeper vessels that are captured more uniformly by the 1550 nm imaging band. Such deeper vessels have a better contrast in the longer wavelength spectrum due to the improved signal-to-noise ratio supported by the shifted focal plane. In the 1310 nm imaging band, the vessels have a consistent but modest contrast over entire imaging depth. Figure 3d,e compare the depth-encoded colorized projection of the 1310 nm imaging subsystem to the combined projection. We note that it is difficult to appreciate the improved transverse resolution in the deeper dermis as the vessel diameter is, in general, larger in the deeper dermis compared to the papillary dermis. However, the features in the shifted focal plane imaging shown in Figure 3a,c are still manifest in the extended-focus projection.  
Figure 2: Projection images of polyurethane sponge. (**a–c**) MIP of the imaged volume from the individual interferometers where the source spectrum is centered at 1060 nm, 1310 nm, and 1550 nm, respectively. The arrows (a–c) highlight the in-focus depth in the imaging field. (**d**) MIP of the merged volume. The first row shows MIPs in grayscale, and the second row shows colorized MIPs with pseudo-colormap encoding the depth. Scale bar = 1 mm.
# A Multiple Criteria Decision Analysis Framework for Dispersed Group Decision-Making Contexts
###### Abstract  
To support Group Decision-Making processes when participants are dispersed is a complex task. The biggest challenges are related to communication limitations that impede decision-makers to take advantage of the benefits associated with face-to-face Group Decision-Making processes. Several approaches that intend to aid dispersed groups attaining decisions have been applied to Group Decision Support Systems. However, strategies to support decision-makers in reasoning, understanding the reasons behind the different recommendations, and promoting the decision quality are very limited. In this work, we propose a Multiple Criteria Decision Analysis Framework that intends to overcome those limitations through a set of functionalities that can be used to support decision-makers attaining more informed, consistent, and satisfactory decisions. These functionalities are exposed through a microservice, which is part of a Consensus-Based Group Decision Support System and is used by autonomous software agents to support decision-makers according to their specific needs/interests. We concluded that the proposed framework greatly facilitates the definition of important procedures, allowing decision-makers to take advantage of deciding as a group and to understand the reasons behind the different recommendations and proposals.  
group decision-making; consensus reaching processes; consensus support systems; multicriteria group decision-making; multiple criteria decision analysis; multi-agent systems; microservices +
Footnote †: journal: Applied Sciences
## 4 Numerical Simulation
### Percent Residual Deviation (PRD)  
This parameter measures the deviation of the encrypted speech signal from the original signal [41]. For the original voice signal \(x(i)\) and the encrypted voice signal \(y(i)\), the PRD is calculated as:  
\[PRD=100(\sqrt{\frac{\sum(x_{i}-y_{i})^{2}}{\sum(x(i))^{2}}}) \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Number** \\ **of Levels** \\ **(N)** \\ \end{tabular} & **SNR** & **PSNR** & **PRD** & \
\begin{tabular}{c} **Correlation** \\ **Coefficient** \\ **CC** \\ \end{tabular} &
\begin{tabular}{c} **Encryption** \\ **Time (te)** \\ **(td)**\(\updownarrow\)**s \\ \end{tabular} \\ \hline
1 & \(-60.10\) & \(11.15\) & \(1.0124\times 10^{+5}\) & \(0.0027\) & \(0.32\) & \(0.23\) \\
2 & \(-65.41\) & \(12.39\) & \(1.8650\times 10^{+5}\) & \(0.0011\) & \(0.47\) & \(0.30\) \\
3 & \(-69.02\) & \(12.56\) & \(2.8242\times 10^{+5}\) & \(0.0006\) & \(0.74\) & \(0.46\) \\
4 & \(-71.68\) & \(12.47\) & \(3.8411\times 10^{+5}\) & \(0.0004\) & \(0.81\) & \(0.47\) \\
5 & \(-73.99\) & \(11.95\) & \(5.0087\times 10^{+5}\) & \(0.0005\) & \(1.1\) & \(0.6\) \\
6 & \(-75.98\) & \(11.37\) & \(6.3008\times 10^{+5}\) & \(0.0006\) & \(1.2\) & \(0.7\) \\
7 & \(-77.76\) & \(10.73\) & \(7.7353\times 10^{+5}\) & \(0.0008\) & \(1.3\) & \(0.7\) \\
8 & \(-79.34\) & \(10.18\) & \(9.2707\times 10^{+5}\) & \(0.0011\) & \(1.8\) & \(0.8\) \\
9 & \(-79.34\) & \(10.18\) & \(1.1057\times 10^{+6}\) & \(0.0014\) & \(1.8\) & \(0.9\) \\
10 & \(-80.87\) & \(9.56\) & \(1.3088\times 10^{+6}\) & \(0.0017\) & \(1.9\) & \(1\) \\
15 & \(-82.33\) & \(8.95\) & \(2.5864\times 10^{+6}\) & \(0.0020\) & \(3\) & \(1.6\) \\
20 & \(-92.02\) & \(7.44\) & \(3.9904\times 10^{+6}\) & \(0.0018\) & \(4\) & \(2\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Security analysis of N-level cascaded coupled chaotic systems.  
The calculation of the (PRD) for different voice signals is given in Table 3. PRD quantifies the relative difference between the original and encrypted signals. This metric can be used as a security metric since an encryption system with high PRD ensures that the original data is sufficiently concealed, making it more difficult for attackers to distinguish or reverse-engineer the encrypted content. The large obtained value of PRD shows that the encrypted signal has highly deviated from the original voice signal, in which case represents better security. In other words, increasing the number of levels results in a greater deviation between the original voice signal and the encrypted signal.