esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Validation  
### Experimental Layout  
An outer-race fault experiment in a gearbox is carried out at Shanghai Jiao Tong University, of which the test layout is demonstrated in Figure 13. The shaft rotating frequency (SRF) is controlled by the inverter motor (YVP80M1) to generate the time-varying rotating speed signal. Four acceleration sensors (manufactured by Wuxi Houde Automation Meter Co., Ltd. with the type of HD-YD-221 and the sensitivity of 100 mV/g) are arranged on the test points as shown in Figure 13a. The sampling frequency is set as 51,200 Hz. There are two shafts in the gearbox, where three healthy bearings (corresponding to test points 1, 2 and 4) and one fault bearing (corresponding to test point 3) are illustrated as displayed in Figure 13a,b. Thus, the vibration signal measured on test point 3 is used for outer race fault feature extraction. Two standard spur gears with tooth numbers 28 and 39 respectively are meshed in the gearbox, which means that the measured signal on the measuring point will contain not only the rolling bearing fault component but also the gears meshing component.  
Figure 12: The extraction results of Case 2 under the quadratically changing rotation speed condition. The dash lines in green, red and blue indicate the shaft rotating cyclic order, corresponding BPOO and its multipliers and side cyclic orders. (**a**) the denoised OFSC obtained by the proposed joint sparsity and low-rankness constraint; (**b**) the EEOS obtained by the proposed joint sparsity and low-rankness constraint; (**c**) the denoised OFSC obtained by single sparsity constraint; (**d**) the EEOS obtained by single sparsity constraint; (**e**) the denoised OFSC obtained by single low-rankness constraint; (**f**) the EEOS obtained by single low-rankness constraint.  
In this experiment, the rolling bearing (type: 6203) with the outer race point corrosion defect (seen in Figure 13c) is processed by electric discharge machining (EDM). The tested rolling bearing parameters are listed in Table 2. Note that the \(\alpha_{\text{BPOO}}\) in this experiment can be calculated as  
\[\alpha_{\text{BPOO}}=\frac{n}{2}\left(1-\frac{d}{D}\cos\theta\right), \tag{20}\]  
where \(n\) is number of rolling balls, \(\theta\) is the contact angle, \(d\) and \(D\) denote the pitch diameter and rolling element diameter respectively.
###### Abstract  
In this work, we present a novel four-channel coherent optical chaotic secure communication (COCSC) system, incorporating four simultaneous photonic reservoir computers in tandem with four coherent demodulation units. We employ a quartet of photonic reservoirs that capture the chaotic dynamics of four polarization components (PCs) emitted by a driving QD spin-VCSEL. These reservoirs are realized utilizing four PCs of a corresponding reservoir QD spin-VCSEL. Through these four concurrent photonic reservoir structures, we facilitate high-quality wideband-chaos synchronization across four pairs of PCs. Leveraging wideband chaos synchronization, our COCSC system boasts a substantial 4 \(\times\) 100 GHz capacity. High-quality synchronization is pivotal for the precise demasking or decoding of four distinct signal types, QPSK, 4 QAM, 8 QAM and 16 QAM, which are concealed within disparate chaotic PCs. After initial demodulation via correlation techniques and subsequent refinement through a variety of digital signal processing methods, we successfully reconstruct four unique baseband signals that conform to the QPSK, 4 QAM, 8 QAM and 16 QAM specifications. Careful examination of the eye diagrams, bit error rates, and temporal trajectories of the coherently demodulated baseband signals indicates that each set of baseband signals is flawlessly retrieved. This is underscored by the pronounced eye openings in the eye diagrams and a negligible bit error rate for each channel of baseband signals. Our results suggest that delay-based optical reservoir computing employing a QD spin-VCSEL is a potent approach for achieving multi-channel coherent optical secure communication with optimal performance and enhanced security.  
Keywords:quantum-dot (QD) spin-vertical-cavity surface-emitting laser; photonic reservoir computing; chaotic synchronization; coherent optical chaos secure communication +
Footnote †: journal: _Photonics_
# Navigation Control and Signal Processing Methods for Multiple Autonomous Unmanned Systems
###### Abstract
The fixed-time stability technique [58; 59; 60] has emerged as a valuable approach for designing a controller which can ensure a guaranteed converging time for the system, irrespective of the initial conditions of the agents. While the concept of fixed-time formation control holds promise, current research efforts have predominantly focused on addressing the consensus problem in general multi-agent systems and fully actuated AUVs, leaving room for further investigation and development in this specific area.
# Searching for the Ideal Recipe for Preparing Synthetic Data in the Multi-Object Detection Problem
## 3 Results
### Estimation of Synthetic Data Amount  
This research also investigated the optimal proportion of generated synthetic data in the training set. For hybrid data and the transfer learning approach, various ratios of synthetic data to real data were examined. Firstly, only synthetic data were used for training, indicating the worst results. On the other hand, when only real data were used,  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Class** & **Bicycle** & \multicolumn{2}{c}{**Trolley**} & \multicolumn{2}{c}{**Wheelchair**} & \multicolumn{2}{c}{**Box**} & \multicolumn{2}{c}{**Suitcase**} & \multicolumn{2}{c}{**Bag**} \\
**mAP \(\uparrow\)** & **0.5** & **:0.95** & **0.5** & **:0.95** & **0.5** & **:0.95** & **:0.5** & **:0.95** & **:0.5** & **:0.95** & **:0.5** & **:0.95** \\ \hline real & 0.98 & 0.79 & 0.98 & 0.81 & 0.96 & 0.67 & 0.89 & 0.64 & 0.94 & 0.71 & 0.86 & 0.61 \\ tf. real & 0.99 & 0.85 & 0.99 & 0.87 & 0.98 & 0.77 & 0.96 & 0.77 & 0.97 & 0.80 & 0.94 & 0.74 \\ hybrid & 0.99 & 0.87 & 0.99 & 0.88 & 0.99 & 0.81 & 0.98 & 0.81 & 0.99 & 0.85 & 0.97 & 0.77 \\
**tf. hybr.** & **1.00** & **0.91** & **1.00** & **0.92** & **1.00** & **0.87** & **0.99** & **0.89** & **1.0** & **0.90** & **0.99** & **0.86** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of mAP parameters (0.5 and 0.5:0.95) for selected classes in multi-object detection, proving that transfer learning applied for hybrid data is more accurate than real data with a limited number of images. The best results were marked in bold.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Method** & **Prec. \(\uparrow\)** & **Rec. \(\uparrow\)** & **mAP0.5 \(\uparrow\)** & **mAP0.95 \(\uparrow\)** & **F1 sc. \(\uparrow\)** & **Box \(\downarrow\)** & **Object. \(\downarrow\)** & **Class. \(\downarrow\)** \\ \hline real & 0.9162 & 0.8908 & 0.9360 & 0.7052 & 0.9033 & 0.0422 & 0.0081 & 0.0076 \\ tf. real & 0.9562 & 0.9472 & 0.9718 & 0.8018 & 0.9517 & 0.0350 & 0.0091 & 0.0039 \\ hybrid & 0.9646 & 0.9656 & 0.9848 & 0.8350 & 0.9651 & 0.0320 & **0.0074** & 0.0027 \\
**tf. hybr.** & **0.9850** & **0.9862** & **0.9952** & **0.8940** & **0.9856** & **0.0228** & 0.0077 & **0.0018** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of different approaches in the training of object detection models with the application of real and hybrid data concerning Precision, Recall, mAP parameters, F1 score, Box loss, Objectness, and Classification. The best results were marked in bold.
## 3 Materials and Methods
### Methodology
#### 3.3.1 HF Measurement  
Human activities primarily affect the ecological environment via factors such as land use, human and economic activities, and infrastructure [30]. The HF index method accounts for the interaction between human impact and the affected area. Therefore, this study selects the land use intensity index (LUI), POP, NL, and GDP to construct the HF indicator system. The equivalent coefficients for different land use types in the LUI are shown in Table 1[31]. Given that the indicators represent different but complementary aspects of human activities, and that no authoritative weighting system currently exists, this study calculates the human impact index using an equal-weighting method after normalization. This approach has been widely applied in the existing literature and helps maintain the model's practicality when prior quantitative relationships among indicators are lacking [32; 33]. The formula for calculating HF is as follows:  
\[HF(i,t)=LUI(i,t)+POP(i,t)+NL(i,t)+GDP(i,t) \tag{1}\]  
where \(HF(i,t)\) denotes the cumulative HF at grid \(i\) in year \(t\); this index combines the impacts of different human factors, including LUI, POP, NL, and GDP. According to the  
Figure 2: Research framework. Note: The methodological framework consists of four sequential stages: (1) Constructing a multi-period composite atlas of HF and EQ: (2) Analyzing the multiscale evolutionary trends of HF and EQ: (3) Assessing the spatiotemporal coupling effects between HF and EQ: (4) Implementing a static–dynamic integrated zoning management approach for HF and EQ.  
study area's characteristics, this paper categorizes HF into five levels: very weak (0-0.05), weak (0.05-0.1), moderate (0.1-0.15), strong (0.15-0.2), and very strong (0.2-1).
## 4 Experimental Result and Discussion
### Line Search Algorithm Comparison and Correctness Verification
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**Parameters**} \\  & _E (km)_ & _N (km)_ & _Depth_ & _Strike_ (deg.)_ & _Dip (deg.)_ & _Length_ & _Width_ & _Rake_ (km)_ & _Slip (cm)_ \\ \hline Armijo & 6.7756 & \(-8.0579\) & 3.7138 & 203.9841 & 38.7941 & 5.0784 & 5.3181 & 115.3062 & 12.5695 \\  & \(\pm\) 0.1814 & \(\pm\) 0.1563 & \(\pm\) 0.2603 & \(\pm\) 6.5069 & \(\pm\) 2.3886 & \(\pm\) 0.4434 & \(\pm\) 0.4994 & \(\pm\) 4.7805 & \(\pm\) 2.0428 \\ MT & 6.8278 & \(-7.9336\) & 3.6811 & 201.7411 & 38.5657 & 4.9229 & 5.3982 & 112.9773 & 12.9347 \\  & \(\pm\) 0.8900 & \(\pm\) 0.9688 & \(\pm\) 1.0870 & \(\pm\) 33.9702 & \(\pm\) 7.9274 & \(\pm\) 0.8697 & \(\pm\) 1.0252 & \(\pm\) 20.5970 & \(\pm\) 5.2915 \\ BWW & 6.7314 & \(-8.0493\) & 3.7909 & 202.7314 & 38.7331 & 5.0749 & 5.6685 & 115.2043 & 12.2949 \\  & \(\pm\) 1.0308 & \(\pm\) 0.1263 & \(\pm\) 0.8933 & \(\pm\) 31.26251 & \(\pm\) 8.3463 & \(\pm\) 0.8959 & \(\pm\) 0.9614 & \(\pm\) 18.3948 & \(\pm\) 5.4515 \\ cuLBFGSB & 6.7366 & \(-7.9948\) & 3.6931 & 204.2539 & 38.4440 & 5.0455 & 5.4938 & 114.8131 & 12.3322 \\  & \(\pm\) 0.3813 & \(\pm\) 0.4724 & \(\pm\) 0.7714 & \(\pm\) 12.2758 & \(\pm\) 4.1287 & \(\pm\) 1.4588 & \(\pm\) 1.8056 & \(\pm\) 10.1586 & \(\pm\) 12.0922 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The best-fit parameters and a 95% confidence interval of the Monte Carlo results. Therefore, as the final step, we verified the correctness of our source parameter estimation framework with the best-fit parameters derived by Armijo. Figure 12 compares the geodetic displacement and the modeled displacement acquired from the final source parameters. Each row of the figure represents the surface displacement for the E-W direction, the N-S direction, and the depth direction. The third column also shows the residual displacement between the geodetic and the modeled displacements. The residual displacement demonstrates that our proposed framework approximates the geodetic displacement quite precisely, where the RMSE for each direction is 0.49, 0.31, and 0.33 cm for the E-W, the N-S, and the depth direction, respectively. Figure 11: Parameter distribution of the Monte Carlo results.
Even if this tag, made of a metallic pattern with a very small size of 1.6 mm\({}^{2}\) is not \"chipless\" and remains out of the context of this study, it well illustrates the necessity to increase the useful frequency in order to reduce the size of the structure. On the other hand, this study is also positioned in the perspective of very rapid developments that affect the size of potential technical solutions to read such tags in the THz range. Indeed, a low cost and compact solution have been proposed by [PERSON] et al. [20] in 2015. This THz-TDS system uses cheap and commercially available components, such as for example, those taken from a DVD drive. In order to reduce the cost of the THz-TDS system, it is possible to replace the femtosecond laser by a cheaper and more classical Fabry-Perot laser diode [21]. Even if the performance of such THz-TDS systems is reduced, especially in terms of bandwidth, it remains sufficient for tag reading. Moreover, such an evolution can also be observed on continuous wave systems (THz-CW), which do not require a femtosecond laser. Such a spectrometer, based on DFB (Distributed Feedback Laser), can be of course less expensive and more compact, for example to be carried on a drone [22]. Some ongoing developments are also very encouraging to continue to reduce their cost and size, for example, by using multimode laser integrated on glass to be used for THz generation by frequency mixing [23]. In this context, we show that the prospect to operationally read and reliably identify a low-cost and easy to manufacture tag, is now made possible using commercially available THz spectrometers. We based our study on the structure we proposed in 2011 [10], fabricated using polyethylene to make it low-cost and compatible with industrial manufacturing techniques [11; 24]. In this study, we experimentally show the possibility to read such an 8-bits tag in a quasi-real-time duration (<64 ms), with a rapid commercial Terahertz Time Domain Spectrometer (THz-TDS), while keeping a bit error rate smaller than 1%. We deduced from these results a criterion to ensure minimum reading reliability, that takes into account the reader performances, such as, for example, its signal to noise ratio. We also theoretically explore the impact of the losses of the material used to fabricate the tag on the coding capacity. Thus we show that the performances of such a tag can realistically extend up to about 60 bits by only using materials exhibiting an absorption coefficient of about 1 cm\({}^{-1}\). Taking into account the size of the tag, much smaller than in the radiofrequency range, it corresponds to a surface density of about 300 bits/cm\({}^{2}\), almost one order of magnitude higher than demonstrated in [14]. Finally, we experimentally evaluate that angular acceptance inversely decreases with the coding capacity, but tends to a limit of 5 degrees, despite the coding capacity from 20 bits. Let's noticed that such angular acceptance remains sufficient for application using, for example, smart cards type objects.
## 3 Results and Discussion
### Spatiotemporal Variations of Mangrove Forests in the Study Area
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Zones** & **Increase** & **Decrease** \\ \hline Transition & - & 314 \\ Buffer & 669 & 350 \\ Core & 579 & 40 \\
**Total** & **1248** & **705** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The significant changes in mangrove area through three ecological zones from 2015 to 2019 (ha). Figure 5: Spatiotemporal changes in mangrove population during the study period in Can Gio (\(p<0.05\)). transformation occurred regarding other components in the core zone. Moreover, about 133.03 ha and 366.9 ha of forest area switched to aquaculture and water bodies around the buffer zone. Furthermore, the built-up area was transformed from the forest area (about 6.97 ha), compared to the rest of the land. It can be concluded that the interconversion of LULC was aimed on the influence of economic development, which led to mangrove change, and these impacts gradually threaten the core zone. Figure 6: The land use/land cover change in the study area between 2015 and 2019. According to [67], there was a varying perception among local farmers on mangrove vegetation. The fishermen clearly understood the negative influences of forest degradation, while the large group of aqua-farmers ignored the regulation of forest protection and management. Moreover, a weakness of strategies and management in the planning of livelihood conversion exerted negative impacts on the mangrove population [68; 69; 70]. Furthermore, it is necessary to mention a proposed project of the local government for ecotourism development [17; 18], well-known as the sea-encroached urban-tourism zone with a total area of 2870 ha, which is being conducted in Can Thanh town, located in the buffer zone (presented in Figure 7). A large number of mangrove species will be destroyed around the mentioned project for the construction of the supporting infrastructure (i.e., resort, bridge, road, and entertainment services) as well as to connect the local function sites for ecotourism development. It can be demonstrated that a large number of investors have a demand for earning benefits from the mangrove area because of its ecological and economic value [64]. However, the change in LULC in the study area provided evidence for consequences of anthropogenic factors on mangrove dynamics, and this also contributes to the control of forest management in the future. For instance, the enlargement of intensive aquaculture in the transition zone, and especially in the buffer zone, has led to a decline in mangrove area, water pollution, and the spatial distribution of species in water bodies [8]. Moreover, the location of the new ecotourism project in the south pole of the forest may
# Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks
## 4 Algorithm Design
### Routing Neighbor Discovery  
Vehicle nodes periodically broadcast beacons, and the receiving vehicle uses beacon information to update the link quality information between the sender and receiver, thereby establishing a neighbor table. The time interval for this process can be fine-tuned based on system requirements or network conditions. Algorithm 1 outlines the handling of vehicle beacon messages by the EARR protocol. In the third stage, neighbor messages are extracted and employed to update the neighbor node table. If the neighbor's address is found within the received neighbor beacon, the node's data is updated. Otherwise, a general neighbor entry is added, using the neighbor's address as the index, and the link-stability factor, link-duration factor, received signal-strength factor, and available-bandwidth factor are calculated.  
```
0: Graph \(\mathcal{G}=(\mathcal{V},\mathcal{L})\), \(\mathcal{P}_{v_{n}}^{i}\), \(v_{n_{i}}\), and Beacon
0: Neighbor tables
```  
**Phase 1: Broadcast Beacon**  
```
1:for each \(v_{n_{i}}\in\mathcal{V}\), \(i=1,2,\cdots,|\mathcal{V}|\)do
2: Broadcast\((Bacon)\)
3:endfor
4:Phase 2: Location estimation
5:for each \(v_{n_{i}}\in\mathcal{V}\), \(i=1,2,\cdots,|\mathcal{V}|\)do
6:if vehicle \(v_{n_{i}}\) position update time arrived then
7:\(v_{n_{i}}\) obtains its location via GPS
8:endif
9:endfor
10:Phase 3: Neighbor vehicle discovery
11:for all received Beacondo
12: Gets the source node address \(ADDR(v_{n_{i}})\) contained in the beacon
13:if\(ADDR(v_{n_{i}})\in\mathcal{N}_{ADDR}\)then
14: Update \(v_{n_{i}}\) neighbor record
15:else
16: Add a new record for neighbor \(v_{n_{i}}\)
17:endif
18: Calculate link stability factor using Equation (5)
19: Calculate link duration factor using Equations (6)-(8)
20: Calculate received signal strength factor using Equation (9)
21: Calculate available bandwidth factor using Equations (10) and (12)
22: Keep the \(\mathcal{Q}_{max}(v_{n_{i}})\) value
23: Keep the beacon receiving time \(BRT(v_{n_{i}})\)
24:endfor
```  
**Algorithm 1** Beacon exchange for neighbor discovery  
Figure 7: Busy channel.
## 5 Implementation
### System Operation  
Our proposed system uses lasers and light-emitting diode (LED) sensors to detect vehicles and send data to Arduino. Each side of the road has three sensors. Each pair includes a laser and an LDR. The laser sends a beam of infrared radiation through the path, and the LDR receives this beam. When a vehicle crosses the road, it blocks the laser beam, resulting in less light reaching the LDR. Arduino detects this change and knows that the vehicle is present. This information can be sent to IoT platforms like Thing$Peak for monitoring and analysis. This setting helps better manage traffic by providing real-time information about vehicle availability, which can reduce congestion and improve mobility.  
The traffic control system works in real time to control traffic lights according to current conditions. The sensors detect the front of the car and send these data to Arduino. Arduino immediately processes this information to determine which traffic light should be green. This real-time decision helps to adjust traffic signals according to current traffic conditions, allowing for smoother movement and reduced delays. By regularly updating traffic signal timings based on live data, the system ensures that traffic flows efficiently and reduces congestion.
###### Abstract  
Aiming to investigate the problem that dam-monitoring data are difficult to analyze in a timely and accurate automated manner, in this paper, we propose an automated framework for dam health monitoring based on data microservices. The framework consists of structural components, monitoring sensors, and a digital virtual model, which is a hybrid of a finite element (FE) model, a geometric model, a mathematical model, and a deep learning algorithm. Long short-term memory (LSTM) was employed to accurately fit and predict the monitoring data, while dynamic inversion and simulation were used to calibrate and update the data in the hybrid model. The automated tool enables systematic maintenance and management, minimizing errors that are commonly associated with manual visual inspections of structures. The effectiveness of the framework was successfully validated in the safety monitoring and management of a practical dam project, in which the hybrid model improved the prediction accuracy of monitored data, with a maximum absolute error of 0.35 mm. The proposed method can be considered user-friendly and cost-effective, which improves the operational and maintenance efficiency of the project with practical significance.  
safety monitoring; mechanism model; deep learning algorithm; back analysis; data integration; maintenance system; automated framework +
Footnote †: journal: Applied sciences