esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic Light Detection and Recognition Method Based on YOLOv5s and AlexNet
## 2 Materials and Methods
### YOLOv5s  
[PERSON] et al. [20] proposed a deep learning framework in 2015 that can perform real-time target detection and run efficiently. YOLOv5 is divided into four versions: YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. The performance comparison of its four versions is shown in Table 1.  
YOLOv5s has a high speed of detection and accuracy, and its model is tiny [21]. The smaller the network model, the lower the performance requirements for mobile and the easier it is to deploy [22]. Therefore, for the consideration of the weight file size, recognition accuracy, and detection speed of the network model, this paper selects YOLOv5s, which has the fastest detection speed and relatively high recognition accuracy for the study. The YOLOv5s network structure can be divided into four parts: Input, Backbone, Neck, and Prediction, and its network structure is shown in Figure 3.  
The Input side consists of a total of three parts: Mosaic data enhancement, adaptive anchor frame calculation, and adaptive image scaling. The backbone network forms a convolutional neural network to extract image features by aggregating different types  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Size** & **mAP** & **mAP** & **Speed** & **Params** & **FLOPs** \\  & **(Pixels)** & **(0.50.95)** & **(0.5)** & **v100(ms)** & **(M)** & **(G)** \\ \hline YOLOv5s & 640 \(\times\) 640 & 37.4 & 56.8 & 0.9 & 7.2 & 16.5 \\ YOLOv5m & 640 \(\times\) 640 & 45.4 & 64.1 & 1.7 & 21.2 & 49.0 \\ YOLOv5l & 640 \(\times\) 640 & 49.0 & 67.3 & 2.7 & 46.5 & 109.1 \\ YOLOv5x & 640 \(\times\) 640 & 50.7 & 68.9 & 4.8 & 86.7 & 205.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of different versions of YOLOv5.  
Figure 3: YOLOv5s network structure.  
of image information [23]. It consists of three main components: Focus, the Cross Stage Partial Network (CSP) structure, and the SPP [24] module. The Neck network uses the ReLU activation function to improve network feature fusion by using the FPN [25] + PAN [26] network structure to aggregate features extracted from the backbone and detection networks [27]. On the prediction side, YOLOv5s uses the GIOU loss function to solve the problem of disjoint bounding boxes that the previous YOLO series could not optimize.
## 3 Design Optimization
### \(\mu_{\text{c}}\) Response  
The relationships between \(\text{Re}(N_{\text{eff}})\) (\(\text{Re}(\Delta N_{\text{eff}})\)), PL, \(\text{Im}(N_{\text{eff}})\) and the chemical potential \(\mu_{\text{c}}\) also have been investigated. Figure 11a shows that \(\text{Re}(N_{\text{eff}})\) of the TM mode first increases from 1.6699 to the peak value of 1.6772 as \(\mu_{\text{c}}\) increases from 0 to 0.4 eV. Subsequently, it decreases to 1.6511 as \(\mu_{\text{c}}\) continues to increase to 1 eV. Similar trends for  
Figure 10: (**a**–c) are \(\text{Re}(N_{\text{eff}})\), \(\text{Re}(\Delta N_{\text{eff}})\), modulation depth as well as propagation loss of the TM mode and complex mode in the DBOSW modulator versus \(g\), respectively; the illustration in (**c**) shows the variation of electric field distribution for both modes versus \(g\).  
Re(\(N_{\rm eff}\)) of the complex mode can also be observed. Furthermore, the Re(\(\Delta N_{\rm eff}\)) remains at a low level with an average of 3.6 \(\times\) 10\({}^{-4}\) in the range of \(\mu_{\rm c}\) = 0 to 1 eV. Figure 11b shows that the PL decreases rapidly as \(\mu_{\rm c}\) decreases from 0.2 to 0.6 eV. Therefore, the \"ON\" and the \"OFF\" state of DBOSW modulator is defined at \(\mu_{\rm c}\) = 0.6 and 0.2 eV, respectively. In this case, the MD of the TM mode up to 0.474 dB/\(\upmu\)m can be obtained, while the MD of the complex mode is 0.462 dB/\(\upmu\)m. The calculated \(\Delta\)MD (\(\,\rm{1 MD_{TM\,mode}}-\,\rm{MD_{Complex\,mode}}\,\)!) is only 0.012 dB/\(\upmu\)m at \(\lambda\) = 1550 nm.
## 4 Discussion and Conclusions
### Limitations and Potential Improvements  
Despite the overall improvement in segmentation quality, this study has some limitations:  
* Dependence on the quality of probability maps: Since probability maps are created by a separate ensemble of classification models, their accuracy directly affects the segmentation results. Improving the classification ensemble may further enhance segmentation efficiency.
* Potential generalization issues: The models were tested on a specific dataset. Additional experiments are needed to confirm the effectiveness of the method on other types of surfaces (e.g., asphalt or brick walls).
* Optimization of architectures for working with probability maps: Some models (e.g., U-Net) may require modifications to more effectively utilize additional information from the fourth channel. This could include adding attention mechanisms or adaptive channel merging.
# Robust Control of Electric Tail Reduction System: Uncertainty and Performance Index
## 5 Numerical Simulation
### Tracking Response with Uncertainty or Not  
In Sections 5.1 and 5.2, the different load states, uncertainties, optimization parameters, and the minimum performance indexes are considered. In this part of the simulation, to verify the effect of the proposed control method in dealing with the uncertainty, the tracking response with uncertainty or not is considered. Based on Section 5.1 of the parameter selection and simulation, we chose \(\kappa=0.0408\) and initial error \(\dot{\theta}_{e0}=-40\pi(rad/s)\). Figure 10 shows that when there is no uncertainty, the final tracking accuracy of the LQR control and robust constraint following control methods are very high, but the tracking error of LQR control method needs about 5 s to reach zero. When there is uncertainty, the final tracking error of the two methods increased compared with no uncertainty. The tracking error of the LQR control increased greatly (tracking error was about \(\pm 0.7\) or so), and the tracking error of robust constraint following control increased little (maximal tracking error was about \(6\times 10^{-3}\) or so). The simulation results showed that the robust constraint following the control method had a strong suppression effect on the uncertainty of the electric tail reduction system. Compared with the traditional LQR control, the proposed method had a faster response and smaller tracking error as well as better transient and steady-state performance.  
Figure 10: The step tracking speed error curve with uncertainty or not.  
Figure 9: The sinusoidal tracking control cost curve of the electric tail reduction system.
## 2 Materials and Methods
### Material Characterizations  
#### 2.2.1 Tensile Strength  
Tensile tests were conducted to determine the strength and behavior of PDMS and SE under tension. The tensile strengths of the PDMS and SE were measured using the Instron 4411 electromechanical material testing system (Instron Corporation, Canton, MA, USA). The samples were cut to a dumbbell-shape test. Figure 3 shows a schematic illustration of the sample geometry prior to testing. The materials were pulled by an applied force of less than 15 N [7]. The load and crosshead velocity of 254.00 mm/min were controlled and digitally acquired at 200 Hz using Merlin software V.22043 (Instron Corporation). From the tensile analysis, the modulus of elasticity (MOE) can be calculated using the following formula [8]:  
\[E=\frac{\sigma}{\epsilon} \tag{1}\]  
where \(E\) is the slope of the line in this region, and stress (\(\sigma\)) is proportional to strain (\(\epsilon\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**PDMS**} & \multicolumn{2}{c}{**SE**} \\ \cline{2-5}
**No.** & **Sample** & **Sample** & **Sample** & **Sample** \\  & **(Thin, 11 g)** & **(Thick, 22 g)** & **(Thin, 10 g)** & **(Thick, 22 g)** \\ \hline
1 & PDMS 9.5/1.5 & PDMS 19/3 & SE 4.5/5.5 & SE 10/12 \\ \hline
2 & PDMS 10/1 & PDMS 20/2 & SE 5/5 & SE 11/11 \\ \hline
3 & PDMS 10.5/0.5 & PDMS 21/1 & SE 5.5/4.5 & SE 12/10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Samples with various mixtures of PDMS and Silicone Elastomer (SE).
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 5 Prototype Development and Validation
### Prototype Validation and Usability
#### 5.4.1 Captive Portal with WebAuthn Compatibility Test  
Our captive-portal prototype was tested in several desktop and mobile operating systems. Taking into account the most used operating systems nowadays [25; 26], we conducted some specific compatibility tests for validating the solution. We tested Windows, macOS and Linux desktop OSs, and Android, iOS and Samsung One UI mobile OSs. Namely, the following tests were designed:  
* NET-1 (CPD): The captive portal is detected by the OS and a browser is launched.
* NET-2 (INTERNET): There is internet connection after successful captive-portal authentication.
* WA-1 (WEBAUTHN): The browser launched by the OS is compatible with WebAuthn, and the API exists.
* WA-2 (NDC): The browser was able to successfully use non-discoverable credentials.
* WA-3 (DC): The browser was able to successfully use discoverable credentials.
* WB-1 (REDIRECT): In the case of incompatibility of the default browser for captive-portal detection, the user is correctly redirected to the other browser.
* WB-2 (EXCEPTION): Exception is issued when the default browser has no practical WebAuthn compatibility.  
Table 1 shows the test results. The tests were performed with the ASUS router running the captive portal. We registered two FIDO2 security keys, one with discoverable credentials and other with non-discoverable credentials. As shown in the table, there are different operating systems that detect the captive portal and launch a specific mini-browser that usually is not compatible with WebAuthn. As analysed in [27], these captive-portal mini-browsers have limited capabilities, and even security issues. These browsers are used in smartphone OSs and Linux-based desktop OSs, which affect to our system compatibility.
# Engineering the Future: Evaluation of Virtual Reality Across Project Lifecycle Stages
## 4 Results
### Summary of Findings  
The results presented across Sections 4.1-4.6 provide a comprehensive view of how VR is perceived in systems engineering across different stages of the project lifecycle and what factors shape this perception. A profile analysis using GLM Repeated Measures revealed statistically significant variation in the perceived practicality of VR between lifecycle stages (Pillai's Trace = 0.49, F = 6.78, \(p\) < 0.001), with **Training (M = 4.00) and Proposal and Investor Recruitment (M = 3.64) rated highest, while Specification and Requirement Definition (M = 2.89) was rated lowest in perceived practicality**. Confirmatory Factor Analysis (CFA) validated a unifactorial structure for the construct \"perceived practicality of VR,\" excluding one weakly loading stage (Specification), and yielding excellent model fit (\(\chi^{2}(9)\) = 4.49, \(p\) = 0.88; RMSEA = 0.00).  
Structural equation modeling (SEM) identified three significant predictors of the perceived practicality of VR: education (\(\beta\) = \(-\)0.34, \(p\) = 0.03), familiarity with VR (\(\beta\) = \(-\)0.43, \(p\) = 0.01), and willingness to adopt VR (\(\beta\) = 0.39, \(p\) = 0.02). Interestingly, both education and familiarity exhibited a negative effect, while willingness to adopt was positively associated with the perceived practicality of VR. When these predictors were examined across lifecycle stages, education significantly reduced perceived practicality during development (\(\beta\) = \(-\)0.46, \(p\) = 0.002), whereas familiarity and adoption willingness enhanced perceived practicality during design, development, and testing stages (all \(\beta\) > 0.40, \(p\) < 0.03). The CHAID decision tree model further emphasized the role of willingness to adopt as the primary node split, followed by familiarity, education, and age. Participants with high willingness and familiarity consistently rated VR as more practical. For instance, technicians/practical engineers rated practicality highest (M = 3.03), while those with master's degrees reported more neutral evaluations (M = 0.05). Additional exploratory CATPCAanalysis revealed that participants with long engineering experience, hardware-based backgrounds, leadership roles, and initial exposure to VR in professional settings tended to report higher practicality scores--despite these variables not emerging as significant in SEM or CHAID.  
In summary, Table 2 presents the key findings across methods and stages, consolidating insights into a coherent comparison.  
Together, these findings suggest that although VR is broadly perceived as practical during later, interactive stages of engineering projects, individual attitudes--particularly openness to adoption and prior experience--are the most consistent drivers of positive evaluations. Demographic variables such as education and age exert more nuanced, and sometimes paradoxical, effects, underscoring the importance of tailoring VR implementation strategies to users' specific contexts and readiness levels.
## 5 Results and Discussion
### Comparative Analysis  
As shown in Table 7, the performance of the proposed antenna design is contrasted with past research that is currently available in the literature. The proposed design improves compactness, MIMO diversity parameters, and has relatively good time-domain properties.
# Assessment of Cardiovascular Disease in Autosomal Dominant Polycystic Kidney Disease
## 11 Conclusions  
Cardiovascular involvement is prevalent in patients with ADPKD and represents the main cause of mortality. The main pathological findings are characterized by hypertension, LVH, aneurysms, cardiac valvular defects, aortic root dilation, and other cardiovascular manifestations. These abnormalities in clinical practice correlate with disease progression in ADPKD.  
The main instrumental examination used for the assessment of cardiovascular disease in ADPKD is echocardiography, which is now performed early in all patients.  
In addition, non-invasive, easy, economic diagnostic tests to assess cardiovascular risk, such as HRV, CPET, FMD, and PWD, are also performed to better assess high and early cardiovascular risk in ADPKD patients.  
Most of the abnormalities are potentially subclinical. In fact, the early signs of the atherosclerosis process are characterized by endothelial dysfunction and high IMT, even in ADPKD with normal renal function and blood pressure. Understanding the association between ADPKD and increased cardiovascular risk leads to the establishment of an early and effective diagnostic assessment of cardiovascular changes to improve the time management of medications, to evaluate cardiovascular and kidney outcomes, and to improve the cardiovascular prognosis of ADPKD patients.  
**Author Contributions:** Conceptualization, A.G. and S.L.; validation, E.A. and A.M.P.; writing, S.L., E.A., F.T. and A.M.P.; review and editing, A.G., S.L., R.C. and M.M.; visualization, A.G. and F.T.; supervision, S.L., R.C. and M.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Simple and Robust Equalization Algorithm for Variable Modulation Systems
## 2 Conventional Equalization Algorithm  
Figure 1 shows a simplified diagram of the VM system. It mainly constructs four variable modulation physical layer (PL) frames, QPSK, 8 PSK, 16 APSK, and 32 APSK, according to the modulation method, and performs mode switching according to the link schedule. The PL frame of the VM system is shown in Figure 2, and it is mainly composed of three parts: the frame header, data block, and pilot block. The frame header consists of 26 symbols of the start of frame (SOF) and 64 symbols of the physical layer signaling (PLS) code. The data block consists of 16 slots; one slot consists of 90 symbols. The pilot block insertion is every 16 slots; the pilot block consists of 36 pilot symbols, and each pilot shall be an un-modulated symbol, identified by \(I=1/\sqrt{2}\), \(Q=1/\sqrt{2}\). The VM system can improve the data transmission efficiency of the inter-satellite link, but it brings in new requirements for the stability and reliability of the equalization algorithm performance. Conventional equalizers can be composed of two parts: a digital filter and an adaptive algorithm. The digital filter is used to improve received signals' amplitude distortion and phase distortion and to improve the accuracy of symbol decisions. An adaptive algorithm is used to adjust the filter coefficients so that the performance of the equalizer meets the design requirements. In this study, three conventional algorithms, namely CMA, CMMA, and DDLMS, were analyzed, and a new algorithm named VM-DDLMS for VM systems was proposed. The data correction was performed by the VM-DDLMS equalizer using known information from the physical layer frame.  
Figure 1: Simplified diagram of the VM system.  
Figure 2: PL frame for the VM system.
## 1 Introduction  
Due to the global distribution of factories and suppliers, the dominant supply chain model for large-scale manufacturing industries, such as civil aircraft and automobile manufacturing, is the main manufacturer-supplier model. In this model, suppliers produce parts, and the manufacturer coordinates and manages the smooth operation of the supply chain. At the same time, the main manufacturer is also responsible for the final assembly of the product. Efficient supply chain inventory management is crucial for these industries to ensure the timely material supply and production while reducing supply chain inventory costs. Many researchers have attempted to use advanced technologies to solve the problems related to this kind of supply chain inventory management.  
In recent years, with the development of machine learning-related technologies, many machine learning-based methods have been applied in supply chain demand forecasting [1; 2], risk management [3], transportation management [4], spare stock [5], inventory replacement [6; 7], and supplier selection [8]. In traditional supply chain inventory management, it is difficult to obtain, predict or estimate the information in the supply chain accurately using traditional decision rules because it is based mainly on the experience and judgment of the inventory management personnel. Reinforcement learning-based methods are well suited for sequential decision-making problems and can learn nonlinear features in problem environments. On the one hand, it provides a new solution for supply chain inventory management problems, such as the application to improve the performance in perishable inventory management [9]. On the other hand, compared to Federated Learning [10] and traditional DNN, RL method does not require a large amount of labeled data. It only requires an initial setup of the training environment, allowing the agent to interact with the environment and gather a large amount of data for training and policy updates. Therefore, we chose RL as our research method. The main related works about inventory management are listed in the Table 1. Based on the type of RL algorithm used, research on RL-based inventory management can be classified into value-based RL methods, policy-based RL methods, and Actor-Critic RL methods. We will take the literature review from these three aspects.
# Use of Surface Acoustic Waves for Crack Detection on Railway Track Components--Laboratory Tests
## 2 Test Specim and Methods
### Simulation of Elastic Wave Propagation via EFIT
The geometry parameters of the investigated crack configurations were based on the examined thin section (shown later in Figure 18) taken at the experimental measurement location in the center of the artificially produced head check band, shown in Figure 1. The inclination with respect to the surface normal was \(\sim\)103\({}^{\circ}\). The spacing between the cracks amounted to 2 mm for the two-cracks layout and 1 mm for the three-cracks layout. The crack width was limited by the grid discretization and was equal to 0.3 mm. Additionally, the geometry without cracks was simulated as a reference basis. Figure 5: (**a**) Leap-frog time integration of the elastodynamic equations. (**b**) Spatial grid by staggering stress tensor \(\sigma_{ij}\) and velocity vector components \(v_{i}\) (reprinted from [13]).
###### Abstract  
In this study, inspection planning of deteriorating bridges is optimized to determine the inspection application times and methods based on various objectives. These objectives can be formulated by considering the probabilistic structural performance and service life after inspection and maintenance. Probabilistic structural performance and service life prediction are generally based on the probability of failure (or reliability). However, there are difficulties associated with optimizing inspection planning when a low probability of failure is estimated. In this study, we address inspection planning using extrapolation approaches to efficiently compute a low probability of failure. The inspection planning method proposed in this study determines the inspection application times for a given inspection method. We investigated the applicability of direct Monte Carlo simulation (MCS), subset simulation, and two extrapolation approaches (i.e., kernel density estimation (KDE) and KDE combined with generalized Pareto distribution (GPD)) for inspection planning. The probability of failure for optimum inspection planning was based on the damage detection-based state function and extended service life-based state function. These state functions were formulated by considering damage propagation, damage detection by inspections, and service life extensions by maintenance. Illustrative applications to general examples and an existing bridge are provided to investigate the effects of approaches for computing the failure probability on the accuracy and variation of the optimum inspection application times. Finally, the most appropriate approach for optimum inspection planning was determined considering the accuracy and reliability of the solution, computational efficiency, and the applicability of the probabilistic optimization process. The presented investigations revealed that KDE is more appropriate than MCS and the combination of KDE and GPD for optimum inspection planning.  
extrapolation approach; inspection planning; probability of failure; Monte Carlo simulation; kernel density estimation; generalized Pareto distribution +
Footnote †: journal: Applied Sciences