esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Path-Planning Improvement
#### 2.2.3 Fusion of the FGA-RRT and PRM Algorithms
The pseudocode of fusion of the FGA-RRT and PRM algorithms is as follows (Algorithm 2):  
```
1:Input:\(M\),\(x_{start}\),\(x_{goal}\)
2:Output: A path \(\Gamma\) from \(x_{start}\) to \(x_{goal}\)
3:\(\Gamma\gets O\)
4:for\(i=1\) to \(n\)do
5:\(x_{start}\leftarrow\) FanshapedSample(\(M\))
6:\(x_{start}\leftarrow\) near(\(x_{rand}\),\(\Gamma\));
7:ifBridgeDetection(\(x_{rand}\)) then
8:\(x_{new}\leftarrow\) Steer(\(x_{rand}\), \(x_{new}\), addStepSize)
9:ifCollisionFree(\(x_{new}\), \(x_{start}\)) then
10:\(\Gamma\)addNode(\(x_{new}\))
11:if\(x_{new}=x_{goal}\) then
12:endif
13:endif
14:endif
15:endfor
16:\(q\leftarrow\) random configuration in \(\Gamma\)
17:for all \(q_{i}\in\Gamma\)do
18:\(q_{i}\leftarrow\) the closest point to \(q\) chosen from \(\Gamma\)
19:ifCollisionFree(\(q\), \(q_{i}\)) then
18:\(\Gamma\)addNode(\(q\), \(q_{i}\))
20:endif
21:endfor
22:Return\(\Gamma\)
```  
**Algorithm 2** Fusion of the FGA-RRT and PRM Algorithms  
In the fusion FGA-RRT and PRM algorithm, \(x_{start}\) and \(x_{goal}\) represent the start and goal point of the path, \(\Gamma\) represents the random tree, \(n\) represents the number of iterations of the algorithm, \(x_{rand}\) represents the random sampling points in the sector area by the fan-oriented strategy, \(x_{nour}\) represents the nearest point to \(x_{rand}\) on the random tree, \(x_{new}\) represents the point obtained by \(x_{nour}\) extending one step forward in the direction of \(x_{rand}\), \(q_{i}\) denotes the random point on the initial path generated, and \(x\) denotes the point on the path closest to \(q\). The algorithm first determines the starting point \(x_{start}\), \(x_{goal}\), and the number of iterations \(n\); initializes the random tree; and iterates through the for loop. In each step of expansion, the sampling fan-shaped target-oriented strategy first selects random sampling nodes \(x_{rand}\) from the fan-shaped space and then uses the bridge detection algorithm to determine whether it is a narrow channel area, following by connecting the point \(x_{near}\) closest to \(x_{rand}\) with a set step size for collision detection. If no collision occurs, the sampling point is added to the random tree. Finally, the PRM algorithm is integrated to optimize the path. For the random point \(q\) on \(\Gamma\), the neighborhood point \(q_{i}\) is selected according to a certain distance range, and each neighborhood point \(q_{i}\) is judged. If \(q\) and \(q_{i}\) have not yet formed a path, they are connected to form a path, and then collision detection is carried out. If no collision occurs, the path is retained, so as to obtain a better path, and then returned through the Return() function.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### Environmental Monitoring and Smart Farming
#### 3.2.8 Airborne Sensor Motes  
The airborne sensor motes [27] were tailored for atmospheric sensing applications. The nodes are endowed with a Texas Instruments microcontroller (CC430F5137) that is composed of 32 kB of programmable flash memory and 4 kB of RAM. The microcontroller combines the advantages of the features of the CC1101 sub-1 GHz RF radio module with the MSP430 CPUXV2. The MSP430 CPU has a 16-bit reduced instruction set computer (RISC) architecture with a clocking frequency of up to 20 MHz.  
The proposed nodes can communicate using 130 individual channels containing 16 time slots per second by exploiting the industrial scientific and medical (ISM) band (915 MHz) transceivers. A u-Blox Max M8 GPS receiver was also utilized for communication. A multiple frequency time-division multiple access (MF-TDMA) technique is used to facilitate the reception of data from several eMotes at once. A communication range of up to around 50 km is realized by the node. Moreover, the temperature and air pressure sensor (MS5803) and the Sensirion humidity and temperature sensor (SHT25) can be accommodated in the proposed nodes. Power supply and power consumption details were not specified by the authors.
# Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales
## Methods
### Site Descriptions  
This research spans two physiographic provinces (coastal plain and piedmont) within the APB located in central-to-northern North Carolina and southern Virginia (Figure 1, Table 1). Four LAI research sites within the APB were chosen for sampling based on forest composition and access (Table 2). These sites included: 1. EPA_Hertford (Coastal Plain)--primarily lobolly pine; 2. EPA_Fairystone (Upper Piedmont-Mountain)--primarily upland oak; 3. EPA_Umstead (Lower Piedmont)--primarily upland oak/yellow poplar; 4. EPA_Appomattox (Upper Piedmont)--primarily lobolly pine (Tables 1 and 2, Figure 1). Two sites are managed timberland (EPA_Appomattox and EPA_Hertford), the other two sites are managed state lands located in Virginia and North Carolina (Note: each research site name hereafter is truncated as: Hertford, Fairystone, Umstead, Appomattox). Selection of these LAI research sites corresponded to the 1.0 km grid associated with the 1.0 km MODIS (MOD15A2) 8-day composite LAI (collection 5) product produced by the National Aeronautics Space Administration (NASA). A detailed description of the forest composition of these four sites is also found in an initial study comparing EPIC-modeled LAI with in situ LAI (Supplementary Materials S1-S8, Table S1) [31].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & **Lat/Long** & **Elevation (m)** & **Region** & **Tree Species** \\ \hline Appomatrox & 37.22, \(-\)78.88 & 165–215 & Upper Piedmont & HDWD (21.8\%), PINE (67.6\%), \\  & & & & OV (10.6\%) \\ Hertford & 36.38, \(-\)77.00 & 8–10 & Coastal Plain & HDWD (17.8\%), PINE (72.3\%), \\ Fairstone & 36.77, \(-\)80.09 & 381–473 & Upper Piedmont & HDWD (100.0\%) \\ Umstead & 35.86, \(-\)78.73 & 75–120 & Lower Piedmont & HDWD (55.1\%), PINE (44.9\%) \\ \hline \hline \end{tabular} Note: HDWD—“hardwood”, OV—“other vegetation”.  
\end{table}
Table 1: Location, region, and tree species distribution over four 1 km LAI research sites.  
Figure 1: Virginia and North Carolina (USA) leaf area index (LAI) research sites.
# A Multi-Scale Feature Fusion Hybrid Convolution Attention Model for Birdsong Recognition
## 3 Experiment and Result Analysis
### Experimental Results and Analysis  
To validate the effectiveness of the proposed method, 12 sets of comparative experiments are designed. Using log-power spectrograms, log-Mel spectrograms and log-wavelet spectrograms, the proposed model is compared with the classic ResNet18, ResNet50 and CNN models. To ensure a controlled and comparable experimental environment, all models were trained using the same optimizer and learning rate. While model-specific tuning could potentially improve performance, our goal was to evaluate the relative performance of architectures under consistent training conditions. Each model was optimized using the Adam optimizer and trained on the training set for 50 epochs with a learning rate of \(1\times 10^{-3}\). During each epoch, the validation set was evaluated, and the best weights were saved. Finally, the evaluation metrics were calculated on the test set for comparison.  
For the log-power spectrogram, the parameters of STFT are as follows: 512 frequency points, a window length of 512 samples and a 75% frame overlap. For the log-Mel spectrogram, using the same STFT parameters, the number of Mel filters is 40. The log-wavelet spectrogram was computed with a frame length of 257 samples, 75% overlap between frames and 258 wavelet bases, enabling detailed time-frequency analysis for feature extraction.
# A Hybrid Linear Quadratic Regulator Controller for Unmanned Free-Swimming Submersible
### Related Work  
The effectiveness of the AUV depends largely on its management structure. The system's best response is analyzed through controllers. Typical examples of these controllers are lead-compensator, proportional-integral-derivative (PID) and linear quadratic regulator LQR [10]. Furthermore, a specific control mechanism is used to meet some specific temporal specifications of the desired system. Moreover, the dynamic response of a particular situation is also managed. To date, plenty of work has already been done on the controlling techniques of UUV. However, it is still continued to make it more intelligent and smart.  
The most commonly used controller is PID, but some have also used LQR to control the AUV. Due to the complex modeling of LQR, it requires accurate mathematics of AUV for tuning Q and R parameters. The Q matrix defines weights on the state and the R matrix defines control input weights. Intelligent controllers like neural networks and fuzzy logic are also designed. They provide an advantage as they do not explicitly require mathematical modeling in terms of the laws of physics [11; 12; 13]. Furthermore, they result in a relatively high accuracy. Nevertheless, these controllers have to control complex systems and result in computational complexity issues. These complexity issues are usually addressed by using hierarchies. However, these hierarchies become even more complex with the change in the input and disturbances [14].  
The work in [4] has proposed a U-model for controlling UFSS. The implementation results describe the handling of water wave disturbances. Moreover, the provided pitch and heading system responses are reasonable as compared to the PID controller. A neuro-control scheme for controlling UUV without the training phase is presented in [5]. A fuzzy logic-based approach in [6] is used to emulate the behavior of human driving. A depth and pitch feedback control of AUV is investigated in [7] by using LQR and PID controllers. Similar to [7], an interesting work is presented in [8]. Here, two PID controllers are utilized. The first PID controller acts as a supervisor to control the second one. The purpose of using two PID controllers is to achieve a more accurate response. On the other hand, the use of two controllers results in a higher mathematical complexity. It ultimately affects the calculation (or) computational time and performance of the controller. A sliding-mode approach is utilized in [9] to tolerate system uncertainties in the presence of an energy-saving mode. Similarly, the approach in [4] is effective only for a particular water region. It implies that whenever the resistance of water increases or decreases, water disturbances tend to be varied. The resistance of water can be changed due to the region or the water density. Therefore, these variations in water disturbances change the entire set of calculations.
# The Effects of Augmented Reality Companion on User Engagement in Energy Management Mobile App
## 4 Results
### Qualitative Findings
#### 4.4.1 AR Companion  
As Figure 9 illustrates, 62.50% of participants in Group A rated a higher score for the ARUI than the TUI in FA. Participants explicitly expressed their interest in the AR companion during the user evaluation. One participant (P9) noted that interacting with the AR companion was fun even after rating higher scores for the TUI in AE, PU, and RV. We then received various proposals to provide more engagement by adding content that users could enjoy. For example, the participants suggested adding more animations (P7, P17, and P29), audio dialogues or sound effects (P3), a feature that the AR companion can be changed to other animals (P2 and P10), and visual effects, such as icons, to emphasize the expression of the AR companion (P9, P17, and P20). While several participants focused on the entertainment aspect of the AR companion, two participants (P5 and P13) were interested in the AR's intuitiveness for effectively presenting data. P5 noted the following:  
\"The pet (AR companion) represents the (room) condition explicitly, which means more easy to understand and more engaging\". (P5)  
Due to these aspects of entertainment and intuitiveness, 54.17% of participants in RW scored the ARUI higher than the TUI. Participants perceived the ARUI could be valuable for other people like kids [46] or older adults [47] who might prefer a UI with improved user engagement.  
Figure 8: Means (M) of 16 questions (Appendix A) related to the overall experience and impression of the PHOENIX solution (i.e., notification message) on the mobile app with TUI after three months of use by the participants in Group B. The color of the bar implies a certain level of the mean (i.e., light purple: M \(<\) 3, normal purple: 3 \(\leq\) M \(<\) 4, dark purple: 4 \(\geq\) M ).  
Figure 9: The distribution of user preference of UI for each dimension of UES in Group A based on mean values. ‘Equal’ refers to the cases if both UIs scored the same mean value.
# Orbit Decomposition Method for Rotordynamic Coefficients Identification of Annular Seals
## 4 Numerical Model for Validation
### Computational Model and Mesh  
Due to the circumferentially nonuniform flow pattern and fluid-induced forces acting on the rotor, a full 360 deg computational model and mesh are required. Figure 9 shows the computational model of the labyrinth seal. In order to ensure fully developed flow conditions, the upstream and downstream cavities are axially extended. The computational mesh is shown in Figure 10. More nodes are placed in the regions near the rotor and stator surfaces to catch the rotation and wall effects.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of teeth, \(n\) & 5 \\ Rotor radius, \(R/\)m & 0.1374 \\ Seal cavity height, \(h/\)m & \(3.556\times 10^{-3}\) \\ Seal cavity width, \(w_{1}/\)m & \(2.286\times 10^{-3}\) \\ Tooth trip thicknesses, \(w_{2}/\)m & \(0.254\times 10^{-3}\) \\ Tooth root thickness, \(w_{3}/\)m & \(1.524\times 10^{-3}\) \\ Clearance at teeth, \(C_{r}/\)m & \(0.292\times 10^{-3}\) \\ Inlet temperature, \(T/\)K & 366.7 \\ Inlet pressure, \(P_{i}/\)MPa & 3.447 \\ Outlet pressure, \(P_{o}/\)MPa & 1.724 \\ Rotational speed, \(\omega/\)rad/s & 1162 \\ Preswirl velocity, \(V_{i}/\)m/s & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometry parameters and operating conditions [14].  
Figure 8: Geometry of labyrinth seal.  
In order to know how fine a mesh density is necessary for the accurate calculation, a mesh independence study is performed. The test is performed with a rotational speed of 1162 rad/s, inlet pressure of 3.447 MPa, outlet pressure of 1.724 MPa, and zero inlet preswirl. As shown in Figure 11, the test includes incremental adjustments to the grid size until the leakage and fluid-induced force results are independent. The final size of the mesh for the labyrinth seal is 7.69 \(\times\) 10\({}^{5}\) nodes.  
Figure 10: Computational mesh of labyrinth seal.  
Figure 9: Computational model of labyrinth seal.
## 3 Results and Discussions
### Analysis of Crosstalk  
In our experimental works, multi-wavelengths were transmitted simultaneously from OT_Tx, and therefore, another OT (OT_Rx) in the reverse direction was used to receive and operate as a tunable bandpass optical filter to select wavelength accordingly. In order to study the feasibility of using OT_Rx to be operated as tunable bandpass optical filter, the LCoS-based SLM was collaboratedwith and without the aid of physical bandpass optical filters in our experiments. The crosstalk of the system, \(C_{T}\) received at the OT_Rx was due to the fact that the other unwanted wavelengths, which should have been cut off by LCoS-based SLM, were received. Therefore, the system crosstalk was defined by the ratio of the received optical power at PD from unwanted wavelengths, which should have been cut off by the LCoS-based SLM, to the received optical power at the same PD from a selected wavelength, which was chosen to be reflected from the LCoS-based SLM [17].  
\[C_{T}=10\log_{10}\frac{P_{n}}{P_{t}}\]  
where \(P_{n}\) is the optical power of unwanted wavelengths, which should have cut off, and \(P_{t}\) is the optical power of a selected wavelength. When the LCoS-based SLM was used as a tunable bandpass optical filter to select wavelength accordingly without adding physical bandpass optical filters in front of the PD, the received light intensity of each wavelength at the PD were measured and the calculated results shown that the crosstalk is at a range of \(-3.8\) dB\(\sim\)\(-4.8\) dB. The captured images of the received light through CCD (Charge-coupled Device) camera as illustrated in Figure 5 show that the unwanted wavelengths were not completely blocked by LCoS-based SLM and therefore physical bandpass optical filters were used to improve the crosstalk. The measurement results show that with the aid of physical bandpass optical filters placed in front of the PD, the crosstalk has been dramatically improved to the range of \(-10.4\) dB\(\sim\)\(-12.1\) dB. The captured images of the received light through CCD camera as illustrated in Figure 6 have shown that the unwanted wavelengths were completely blocked. Therefore, in the following data transmission performance evaluations, physical bandpass optical filters were in turn used in front of the PD at OT_Rx.
# A Deep Learning Approach to Detect Failures in Bridges Based on the Coherence of Signals
## 2 Experimental Set Up
### Dataset Acquisition  
The dataset available for this research consists of the measurements acquired from all of the sensors summarised in Table 1. The temperatures corresponding to channels 23 and 24 are extracted from tiltmeters 2 and 14. The temperatures in channels 26 and 27 (hereafter called Temp1 and Temp2) refer instead to the internal and external temperatures measured by the weather station. All of the signals have been acquired with a sampling frequency of 1 Hz. To make the models more robust against random noise and reduce the storage space, the data have been averaged on an hourly basis, so that every channel counts one value per hour. In Figure 2, the arrangement of the data after preprocessing is reported. The dataset  
Figure 1: Candia Bridge Monitoring System.  
consists now of a matrix, where each row corresponds to a timestamp and each column corresponds to a sensor. A new row is generated every hour.  
The acquisition period spans from December 2020 to December 2022. It is important to underline that, in October 2020, the city of Candia Lomellina was hit by a flood that caused damage in this area, and, in particular, another bridge collapsed on this occasion. In the next section, it will be demonstrated how Candia Bridge was affected by this phenomenon by moving away from its equilibrium position and finding a new, stable one in the summer of 2021.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 8 Proposed Framework: Multi-Layer Threat-Defense Alignment Framework  
In contrast to earlier research that mainly lists smart grid defenses and attacks without providing a cohesive viewpoint, this study presents the MLTDAF. This approach enables comprehensive and proactive security planning for smart grid infrastructures by mapping physical and cyber threats to corresponding architectural layers and suggesting customized defense methods. This enhances the state of the art.
# Finite-Time Tracking Control of a Flexible Link Manipulator Based on an Extended State Observer
## 5 Conclusions  
The research results of this paper prove the effectiveness of the model parameter estimation method based on an adaptive ESO and the ANTSM control strategy based on an ESO applied to FLM. The adaptive ESO can effectively estimate the unknown system parameters of the FLM, including the rigid-flexible coupling coefficient and the control input gain. In addition, the ANTSM control strategy based on an ESO achieves faster, more accurate, and more perturbation-resistant trajectory tracking and vibration suppression of the FLM without requiring prior knowledge of system uncertainty and external disturbances.  
For similar flexible systems, the estimation method can obtain the unknown parameters of the system according to the feedback information. At the same time, the proposed controller can estimate and compensate for dynamic uncertainties and external disturbances, such as friction, in real-time, so as to improve the tracking accuracy and anti-disturbance ability of the system. Furthermore, the finite-time convergence characteristics of the proposed controller can improve the transient response of the system. However, in practical applications, the premise of the parameter estimation method is that the model form of the system is known. Therefore, before estimating the parameters, it is necessary to carry out a dynamic analysis and obtain the dynamic model.  
In the future, the system model parameters will be time-varying for the scenarios where FLM grabs and drops different targets. We intend to take this problem as a future research topic and design a control method with stronger environmental adaptability for FLM. The adaptive sliding mode control method based on online parameter estimation to achieve robust control of FLM with time-varying parameters is a challenging task.  
Methodology, F.J. and C.M.; Software, F.J.; Validation, F.J.; Formal analysis, F.J.; Investigation, F.J.; Data curation, F.J. and F.W.; Writing--original draft, F.J.; Writing--review & editing, F.W., Y.C. and X.F.; Project administration, M.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.