esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### VPItransmissionMaker Simulation Results
(**a**) Measured MI curves versus OSNR; (**b**) Measured MAE of residual phase error curves versus OSNR. Figure 7: Simulation system setup (EA: electrical amplifier; ECL: external cavity laser; MZM: Mach-Zehnder modulator; EDFA: Erbium-doped fiber amplifier; LO: local oscillator). be improved by at least 0.85 bit/symbol, which indicates that the proposed constellation diagram structure significantly improves the estimation accuracy of the PCPE algorithm. As shown in the insets of constellation diagrams and the MAE curves in Figure 9b, the residual phase noise of R-128 QAM after PCPE recovery is much greater than GS-128 QAM. In the OSNR interval from 25 dB to 40 dB, the MAE reduction percentage that can be achieved using the proposed GS constellation surges from 35.55% to 52.42%, which proves that the proposed scheme can achieve the most significant improvement in phase estimation accuracy and reliability under the channel condition of high OSNR.
# A Review of Research on Spectrum Sensing Based on Deep Learning
## 7 Conclusions  
As an important component of cognitive radio, spectrum sensing is also a key technique for improving the efficiency of spectrum utilization and achieving reasonable and efficient allocation of spectrum resources, which has important research value and application prospects. While traditional spectrum-sensing algorithms no longer satisfy the increasingly complex sensing environment, researchers continue to incorporate novel approaches such as deep learning and deep reinforcement learning to address emerging problems in this area. In this paper, we review the basic principles, strengths, and weaknesses of several traditional SS algorithms and briefly describe recent optimization and improvement based on the existing traditional methods by scholars. Next, a comprehensive overview of the current research status and application of several commonly used deep neural networks in deep learning in spectrum sensing is presented, reflecting the advantages of deep-learning-based spectrum-sensing methods in terms of their adaptive ability, detection probability accuracy, and complex data processing ability by comparing them with traditional algorithms. Subsequently, we further summarize the state of the art and recent applications of deep learning and deep reinforcement learning in cooperative spectrum sensing. Finally, the current difficulties in the application of deep learning in the field of spectrum sensing are pointed out, and future research ideas and directions are envisioned, with the aim of providing research ideas for researchers working in the field of deep-learning-based spectrum sensing.  
Conceptualization, Z.L.; methodology, investigation, Y.Z. ;writing--original draft preparation, Y.Z.; writing--review and editing, Z.L.; supervision, Z.L.; project administration, Z.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 61801319; in part by Sichuan Science and Technology Program under Grants 2020 JDQ0061 and 2021 YFG0099; in part by the Innovation Fund of Chinese Universities under Grant 2020 HYA04001; and in part by the Innovation Fund of Engineering Research Center of the Ministry of Education of China, Digital Learning Technology Integration and Application (No. 1221009).  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
## 5 Long-Short-Range Communication
### Long-Range Communication  
Low-power networks (LPWANs) are a subgroup of long-range transmission standards with considerable applicability in IoT applications. The coverage area of LPWANs is several kilometers in urban areas, which is considerably longer than the traditional range of IoT communication technologies such as Bluetooth or Wi-Fi, the range of which is only a few meters. Therefore, a costly or nearby network is required for reasonable applicability in health care.  
LBWANs offer advantages over cellular networks such as 3G, which are constructed for transmission of small amounts of data and are rarely suitable for the massive quantity of data required for healthcare purposes, including for general health tracking, receiving regular updates, emergency calls, etc., as well as rehabilitation applications for which monitoring updates may be necessary on a daily basis [73].  
The principle of construction is also suggested for low-energy healthcare devices in order to ensure their function for a long duration before human intervention is necessary to change or recharge batteries.  
This can reduce the patient risk of being disconnected, in addition to increasing convenience for the wearer. With such advantages, a low-power network is best-suited for the transmission of data from the master node to the cloud for data processing and storage. Lora WAN and Sigfox were found to be the most promising standards for low-power networks or LPWAN. Such standards are well-established and are suitable for upcoming standards including narrowband IoT [74].  
In terms of suitability, the existing standard LPWAN was found to be efficient for IoT-based healthcare systems, which are listed and compared in Table 1.
# Agreement Technologies for Coordination in Smart Cities
## 3 Applications
### Coordination of Traffic Flows through Intelligent Intersections
* _Network of Intersections_: we built a computational market where drivers must acquire the right to pass through the intersections of the urban road network, implementing the intersection managers as competitive suppliers of reservations which selfishly adapt the prices to match the actual demand, and combine the competitive strategy for traffic assignment with the auction-based control policy at the intersection level into an adaptive, market-inspired mechanism for traffic management of reservation-based intersections.
# Color Simulation of Multilayered Thin Films Using Python
## 3 Results and Discussion
(**b**) SnO\({}_{2}\) thin film on a Si substrate: the photograph of the SnO\({}_{2}\) thin film with thickness gradient (upper) and simulated color as a function of the SnO\({}_{2}\) film thickness by the Python code (lower). The grid in the photograph is a masked area for line profile measurements—the value below each dot is the film thickness from the line profile measurements nearby. The thickness of a native SiO\({}_{2}\) layer was set to 2 nm in the simulation. The two white dashed lines correspond to the identified thickness range of the experimental samples (i.e., 76–385 nm). simulated colors of the SnO\({}_{2}\) and ZnO thin films represented in both the sRGB and CIE _xy_ color spaces. In the sRGB representation, the fact that the color variations are a function of thickness is easily observable. In contrast, the CIE _xy_ representation provides insight into the color gamut achievable from a given material. For example, although the colors of SnO\({}_{2}\) and ZnO appear similar in the RGB color space, the CIE color space reveals that SnO\({}_{2}\) can be used to produce a slightly more vivid blue. Figure 5: Comparison of experimental colors and simulated colors of the ZnO thin film. (**a**) Experimental colors of the ZnO thin film on a Si substrate: photographs of the ZnO thin film with a thickness gradient taken from various angles (5, 30, and 45\({}^{\circ}\)). The dots marked on the Si substrate are approximately 1 cm apart and the values below each dot correspond to the thicknesses obtained from line profile measurements nearby. (**b**) Simulated colors of the ZnO thin film on a Si substrate, using the Python code, as a function of the thickness and incident angle. The thickness of the native SiO\({}_{2}\) layer was set to 2 nm in the simulation. The three white dashed boxes correspond to the color ranges expected from the experimental samples (thickness range: 200–410 nm; incident angle: 5, 30, and 45\({}^{\circ}\)). Figure 6: Simulated color gamuts of SnO\({}_{2}\) and ZnO thin films on a Si substrate, presented as sRGB (upper, colors as a function of thickness) and CIE 1931 _xy_ chromaticity diagrams (lower): (**a**) SnO\({}_{2}\) and (**b**) ZnO. The thickness of the films was simulated in the range of 0 to 2000 nm, with an incident angle of zero. The points in the CIE xy diagram correspond to the obtained sRGB values demonstrated in the color bar. The results above demonstrate the ability of the Python code to create accurate reflectance spectra and corresponding color simulations. This code could be developed further to estimate the thickness of a film based on known \(n\) and \(k\) values, and vice versa. Moreover, recent advances in materials research have increasingly made use of AI-driven approaches, such as optics-based inverse design [39; 40; 41; 42; 43; 44; 45] and active learning [46]. In this context, this code, implemented in Python, which has been primarily used in AI research, serves as an accessible and easily modifiable, yet powerful, tool.
## 4 Proposed System Models for Cardiovascular Disease Prediction
### Description of DB-Net and DVR-Net for Cardiovascular Disease Prediction
#### 4.4.1 Dense Belief Network for Cardiovascular Disease Prediction  
DB-Net processes input data independently through dense layers and dense blocks in parallel. The working of DB-Net is provided in Algorithm 1. Different feature representations are extracted by the dense layers and dense blocks operating independently. While dense layers concentrate on capturing fundamental feature patterns, dense blocks which are made up of interconnected layers enhance and refine more intricate representations. To create an accurate prediction, the outputs from the dense layers and dense blocks are concatenated at the last layer. Because of this parallel methodology, DB-Net is able to efficiently learn a wide variety of feature abstractions that are essential for the prediction of CVD. By enhancing gradient flow within the dense blocks, the parallel design helps to avoid vanishing gradients and promotes more stable training. DB-Net is very good at extracting deep abstract features in both structured and unstructured data that can be used to find complex patterns in CVD datasets. This is particularly helpful in the healthcare industry, as the models predictions may be influenced by subtle relationships between features like blood pressure, cholesterol levels, and other indicators. DB-Net is well-suited to handle high-dimensional health data in CVD prediction because it reduces the risk of the vanishing gradient problem, improving stability during training. Densely connected layers in DB-Net facilitate effective information and gradient flow by allowing direct connections between early and subsequent layers. DB-Net learn complex data dependencies more efficiently in order to capture the relationships between different health indicators in CVD datasets.  
```
0: Dataset \(X\), labels \(y\)
0: Predicted labels \(\hat{y}\) for CVD classification
1:Input: CVD data \(X\)
2:Initialize Dense layers and Dense blocks
3: Pass \(X\) through a series of dense layers to extract fundamental feature patterns:
4: Dense Layer Output \(\leftarrow\) Dense Layers(\(X\))
5: Pass \(X\) through a series of dense blocks to capture complex representations:
6: Dense Block Output \(\leftarrow\) Dense Blocks(\(X\))
7: Concatenate the outputs of the dense layers and dense blocks:
8: Concatenated Output \(\leftarrow\) Concatenate(Dense Layer Output, Dense Block Output)
9: Pass the concatenated output through the final prediction layer for CVD classification:
10:\(\hat{y}\leftarrow\) Prediction Layer(Concatenated Output) return\(\hat{y}\)
```  
**Algorithm 1**Dense Belief Network for Cardiovascular Disease Prediction
# Design of SWB Antenna with Triple Band Notch Characteristics for Multipurpose Wireless Applications
## 8 Conclusions  
A compact concentric structured SWB antenna with triple notch bands using an E-shaped stub, split elliptical-shaped slot, and C-shaped resonator has been demonstrated experimentally. The presented antenna offers a broad impedance bandwidth of 45.83 GHz (1.67-47.5 GHz), with triple notches at 1.8-2.2 GHz, 4-7.2 GHz, and 9.8-10.4 GHz to eliminate the interference from co-existing AWS, C, and X bands, respectively. Based on empirical formulas, three notch bands are developed at the intended frequencies with appropriate dimensions. In order to understand the triple band-notched functions, the surface current distribution at the band-notched frequencies are computed and examined. The band notches of the designed antenna can be tuned autonomously through their particular governing parameters. The simulated and measured results of VSWR, gain, and radiation pattern are compared, and a close agreement between simulated and measured results is observed. The presented antenna has an elementary linear structure with a concise dimension of 45 \(\times\) 40 mm\({}^{2}\), allowing it to be embedded within portable devices conveniently. The designed antenna offers excellent time-domain performances, such as constant group delay, minimum pulse distortion, and an abruptly reduced gain at the stop bands, which makes it appropriate for pulse-based SWB communications.  
Conceptualization: T.A. and W.B.; methodology: T.A., W.B., A.S., P.K., S.D. and J.A.; software: W.B. and A.S.; validation, M.S., W.B., T.A., P.K., A.S., S.D. and J.A.; data citation: W.B. and A.S.; writing: W.B., T.A., S.D. and A.S.; supervision: [PERSON], M.S., P.K., and J.A. 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.
## 3 Proposed Methodology
### Multi-Preference Search Mode Based on Evolutionary Demand  
Constrained multi-objective optimization problems (CMOPs) often contain conflicting objectives and constraint satisfaction requirements. The algorithms' different preferences for handling objective optimization and constraint optimization at different stages of population evolution directly affect the viability, diversity, and convergence of the population. It is challenging for CMOPs to maintain a balance between using objective information to accelerate convergence and locating feasible regions. Thus, to address the above challenges, we use a multi-preference model to rationalize the scheduling of offspring generation for populations. Based on the idea of the multi-preference model proposed by [24], this paper designs three different preference models according to the computational resources consumed by the algorithm and the ratio of diversity, convergence, and feasibility, as shown in Figure 1. Different offspring generation strategies are used for each model to guide the evolutionary direction of the population.  
\[search\_mode=\begin{cases}1,&\text{if FE}<\alpha\cdot\text{maxFE}\wedge\text{ feasible}(P)<k,\\ 3,&\text{if FE}<\alpha\cdot\text{maxFE}\wedge\text{feasible}(P)\geq k,\\ 2,&\text{if FE}\geq(1-\alpha)\cdot\text{maxFE}\wedge\text{feasible}(A)<k,\\ 3,&\text{if FE}\geq(1-\alpha)\cdot\text{maxFE}\wedge\text{feasible}(A)\geq k. \end{cases} \tag{8}\]  
where FE represents the number of function evaluations currently used, maxFE represents the maximum number of function evaluations, \(\alpha\) is the threshold adjustment parameter, and feasible\((\cdot)\) represents the number of feasible solutions in the population.
# On the Fairness of Internet Congestion Control over WiFi with Deep Reinforcement Learning
## 5 Experimental Methodology, Results, and Discussion
### DRL Model
#### 5.5.1 Training Reward  
Figure 17a, showing the convergence of training rewards, illustrates the evolution of training rewards over 1000 epochs during the training of the DRL agent. The y-axis represents the convergence rewards accumulated by the agent in each epoch, while thex-axis corresponds to the number of training epochs. The blue curve shows the sample rewards from a single training run, reflecting the variability in the agent's learning process, while the red curve represents the average rewards over 50 training runs, providing a smoothed view of the agent's performance over time.  
The curves indicate a general upward trend, signifying the agent's progress towards a near-optimal policy. As the epochs increase, the total rewards rise, demonstrating that the agent is improving its decision-making capabilities. However, the blue curve exhibits fluctuations and occasional plateaus, suggesting that the learning process involves an exploration of the state-action space, which is inherent to DRL algorithms. The eventual stabilization of the red curve toward the latter epochs suggests that the agent is converging towards an optimal policy, where further improvements become marginal.  
Figure 17b, showing the distribution of training rewards, visualizes the distribution of rewards across different training episodes. The blue boxplot represents the sample rewards from a single training run, while the green boxplot illustrates the average rewards across 50 training runs.  
The median reward, located around 70, signifies the typical performance level of the agent during training. The small inter-quartile range (IQR) suggests that the agent's performance was consistent across episodes, with the rewards generally clustering around the median. There are a few outliers visible at the lower end of the range, indicating instances where the agent received significantly lower rewards. However, the presence of these outliers is limited, suggesting that the agent was mostly successful in selecting high-performing CCAs that matched the current network conditions. This consistency in performance highlights the model's effectiveness in minimizing network issues like unfairness and flow starvation.
## 3 Concepts and Methods
### Bitmaps and Seeds Synchronization and Task Scheduling  
#### 3.3.1 Bitmaps Synchronization  
In the ensemble fuzzing framework proposed in this paper, due to the division of the global seed corpus, each fuzzer's instance maintains only its local bitmap, without knowledge of the current global branch coverage status during the fuzzing process. When the testcase generated by a fuzzing instance covers a new branch in its local bitmap, it is considered that a new seed has been generated. However, other subtasks may already have seeds covering the same branch.  
To this end, a strategy for bitmaps synchronization was designed: inter-process shared memory was used to store the bitmap, and the intersection of the coverage branch sets of all fuzzing instances was taken regularly (based on our experience with fuzzing, we set the synchronization interval to 5 min during the evaluation) to form a global bitmap, which was then directly written into the shared bitmap of each fuzzer's instance. In this way, each fuzzer was able to obtain complete branch coverage of the tested program without knowledge of the seeds used by other fuzzers.
## 2 Materials
### Development of Embedded Control System  
The system configuration diagram and the actual product of the control system developed in this study are shown in Figure 2. As shown in Figure 2a, the developed control system utilizes only a knee angle sensor and a 3-axis IMU sensor to recognize the user's gait intentions. These components are integrated within the drive module, eliminating the need for external wiring, which reduces the risk of failure and simplifies repairs.  
Figure 1: The system configuration of the developed KAEGO: (**a**) Gait training for a hemiplegic patient wearing the developed KAEGO; (**b**) The system structure of the KAEGO. It is similar to the KAFO structure except for the knee actuator module.  
Figure 2: The system configuration of the developed embedded control system: (**a**) The control system configuration diagram; (**b**) The manufactured knee actuator module and the developed embedded controller system including the sensor and communication module.  
The control system primarily consists of a microcontroller (STM32F405), sensor module (EBIMU and absolute encoder), Bluetooth module (115,200 bps, 8 bit), and a motor driver module (maxon EC) interconnected via a CAN bus to facilitate potential future expansions. The microcontroller, IMU, and Bluetooth module are designed to operate at 100 Hz.  
The Bluetooth module is included to monitor the system's operational status and enable remote control. Additionally, the operational state of the gait orthosis can be audibly indicated by a buzzer sound. This feature allows patients to be aware of the orthosis's operation status, complementing the visual feedback.
# Blind Spoofing GNSS Constellation Detection Using a Multi-Antenna Snapshot Receiver +
## 6 Results and Analysis
### Eigenvalue Comparisons  
In this section, the Eigenvalues of the correlation matrix \(\mathbf{C}_{\mathrm{norm}}\) were evaluated. As the number of steering vectors is dependent on the number of acquired satellites, the number of Eigenvalues and vectors are inconsistent. As such, the ratio of the maximum-to-mean Eigenvalues was evaluated. The CDFs for the maximum-to-mean Eigenvalues are shown in Figure 11.  
Figure 11: CDF comparison for the maximum-to-mean of the Eigenvalues for a six-element array.  
Figure 10: CDF comparison for the percentage of detections for a two-element array.  
Figure 9: CDF comparison for the percentage of detections for a six-element array.  
From the Eigenvalue results, it can be shown that a good separation between the real and spoofed data is achieved. The maximum-to-mean ratio exploits the fact that the signal subspace of the Eigenvectors is small for a spoofing scenario, as all of the array steering vectors are similar. This detector also achieves perfect detections, like what was observed by the fraction of detections approach. However, this method only requires a single threshold to be set, making it simpler to tune and to optimize. The disadvantage of the Eigenvalue approach is that it requires more processing than the fraction of detections approach.