esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The nonlinear model and energy dissipation of a rotary axial eddy current damper (ECD) and the dynamic responses to harmonic and seismic base excitations of a linear elastic SDOF system with the nonlinear ECD (SDOF-ECD) are investigated. Firstly, the nonlinear force-velocity relationship of the ECD is studied using finite element simulation, experimental testing and mathematical model fitting. Secondly, the energy dissipated by the nonlinear ECD under a cycle of harmonic motion is derived analytically and its optimal critical velocity is determined such that the energy dissipation is maximized. Finally, the responses of the SDOF-ECDs subjected to harmonic and seismic base excitations are calculated using numerical algorithm, where the displacement and acceleration control performance and the energy dissipation capacity of the ECD are compared with those of the conventional fluid viscous dampers (FVDs). The results indicate that the seismic control performance of ECDs outperforms that of FVDs in most cases and it is anticipated that the ECDs can be used as good alternative devices to conventional FVDs for seismic control applications.  
Keywords:eddy current damper; passive vibration control; nonlinear damper; seismic response mitigation; energy dissipation +
Footnote †: journal: Applied
## 2 Modeling of the Dynamic System
### Vibration Mechanism of the Yaw System
\[\frac{d^{2}x}{dt^{2}}=\frac{d^{2}\theta}{dt^{2}}t+2\frac{d\theta}{dt}=0 \tag{7}\]  
Substitute \(\omega t\) from Equation (6) into Equation (2); rewrite Equation (2) as:  
\[J\frac{\mathrm{d}^{2}S}{\mathrm{d}t^{2}}+2\xi_{1}\omega_{\mathrm{n1}}\frac{ds} {dt}+k_{1}s-k_{1}R\frac{dx}{dt}+k_{1}R\theta-T\Delta\mu=0, \tag{8}\]  
Substitute \(\frac{d^{2}\theta}{dt^{2}}\) from Equation (5) into Equation (7); then, rewrite Equation (7) as:  
\[(k_{2}/\omega_{\mathrm{n2}}^{2})\frac{\mathrm{d}^{2}x}{\mathrm{d}t^{2}}+2(\xi_ {2}-\eta)\omega_{\mathrm{n2}}\frac{dx}{dt}-2(\xi_{2}-\eta)\omega_{\mathrm{n2}} \theta-2l\frac{d\theta}{dt}+\omega_{\mathrm{n2}}^{2}x=0, \tag{9}\]  
Considering that, in conjunction with Formulas (8), (5), and (9) in turn, the state variables vector is \(X=\left[x_{1}\ x_{2}\ x_{3}\right]^{\mathrm{T}}=\left[s\ \theta\ x\right]^{ \mathrm{T}}\), we can obtain the following governing system:  
\[M_{0}\ddot{X}+C_{0}\dot{X}+K_{0}X=B_{0}, \tag{10}\]  
where \(M_{0}\), \(C_{0}\), and \(K_{0}\) are \(3\times 3\) matrices; \(B_{0}\) is \(3\times 1\) matrix and can be expressed as \(B_{0}=\left[T\Delta\mu\ 0\ 0\right]^{\mathrm{T}}\). In the present study, in Equation (5), we assume that the force yaw moment applied to the right side of Formula (5) is \(u\), based on the position of the equilibrium point. In Figure 1, the yaw driving auxiliary motor was also used for the active drive to overcome the LS vibration, with the driving action taking place exactly at the position of object 2 perpendicular to the direction of \(O-O_{1}\) in Figure 2a. Assuming that the auxiliary driving torque of the yaw auxiliary motor is \(0.2u\) (not exceeding \(0.5u\), otherwise it will greatly affect the yaw torque), then the governing system in Equation (10) can be rewritten as:  
\[M_{0}\ddot{X}+C_{0}\dot{X}+K_{0}X=\left[0.2\ \ \ 1\ \ \ 0\right]^{\mathrm{T}}u=Q_{0}u, \tag{11}\]  
Note that, due to \(B_{0}=\left[T\Delta\mu\ 0\ 0\right]^{\mathrm{T}}\), the practical auxiliary driving torque is \(0.2u-T\Delta\mu\). To carry on with the subsequent solutions, assume \(Y=\left[X\ \dot{X}\right]^{\mathrm{T}}\); Equation (11) can be transformed into the state space as follows:  
\[ \begin{cases}\dot{Y}=AY+Bu\\ y=CY\end{cases} , \tag{12}\]  
where \(A= \begin{bmatrix}0_{3\times 3}&I_{\mathrm{E}(3\times 3)}\\ -M_{0}^{-1}K_{0}&-M_{0}^{-1}C_{0}\end{bmatrix} \), \(B= \begin{bmatrix}0_{3\times 1}\\ M_{0}^{-1}Q_{0}\end{bmatrix} \), \(C=I_{\mathrm{E}(6\times 6)}\); herein, \(I_{\mathrm{E}}\) is the identity matrix.
## 4 A Method for Configuring VWSNs to Use the Properties of Brain Networks
### Human-Brain Networks
#### 4.1.1 Modular Community Structure  
A brain network is spatial, which means that the length of a connection is limited by the metabolic costs associated with establishing and maintaining the connection [14; 22; 27]. As a consequence, many short-distance links are constructed and maintained in preference to long-distance links. Within the modular community structure possessed by brain networks, connections are dense and short within modules, but sparse and long between modules. Further, this type of structure is hierarchical [15; 22].  
The modular structure in human-brain networks offers various advantages. The high density of connections within each module ensures robust connectivity, including many alternative routes between pairs of nodes within the same module. Moreover, connection distance and communication delay can be adjusted quickly by configuring long-distance inter-module links [22]. When cognitive demand increases, costly long-distance links are constructed so as to acquire a more efficient structure; when demand decreases, the structure becomes more highly clustered and, hence, less costly. This feature contributes most especially to evolvability.  
The introduction of a hierarchical modular structure to WSNs is expected to result in a topology that can evolve adaptively to changes in resources and traffic demand. Because WSNs, like brain networks, are spatial networks, sensor nodes deployed in a close area will be densely interconnected when a hierarchical modular structure is used. Therefore, the topology of a WSN generally possesses many alternative routes, resulting in robust connectivity. Moreover, the hierarchical structure is suitable for the horizontal integration of virtual networks, which increases the reusability of virtual resources. To integrate multiple VWSNs into one VWSN, a new tier can be overlaid onto the existing VWSNs, which are then connected in the added tier. In the application that uses this integrated VWSN, each node can reuse most of the routing tables assigned by the component VWSNs.
## 5 Rebars' Detection Methodology and Results
### Training Database Preparation  
The dataset used for analysis was derived from thermographic images generated through numerical modeling. These images simulate microwave heating scenarios and serve as the foundation for training the neural network. Two distinct heating modes were represented in the dataset: modulated and continuous. In addition, the images were categorized according to the angular orientation of the sample relative to the antenna, with four angles considered: 0\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\), and 45\({}^{\circ}\). These parameters allowed for the creation of a diverse dataset, encompassing variations in both the heating strategy and geometric configuration, thereby increasing the robustness of the training process.  
To prepare the data for input into the convolutional neural network, thermal images were segmented into smaller vertical strips measuring 8 \(\times\) 239 pixels. These fragments corresponded to regions containing heated reinforcement bars and were selected based on reference data that precisely identified the location of the rebars within the sample. To ensure a balanced dataset for binary classification, an equal number of image strips representing areas without reinforcement were randomly extracted (Figure 12).  
As a result, the rebar detection task was formulated as a binary classification problem, distinguishing between regions with and without embedded steel bars. The final dataset comprised two balanced classes, each containing 9600 examples. For the training process, the data were split into three subsets: 70% for training, 15% for validation, and 15% for testing. This partitioning ensured that model performance could be objectively evaluated on unseen data, supporting reliable generalization of the trained network.  
Figure 12: Illustration of input data preparation for neural network training: (**a**) reference image with annotated rebar positions and (**b**) corresponding thermal image with selected 8 \(\times\) 239-pixel vertical strips. The red strip represents a region containing reinforcement, while the blue strip corresponds to a background area without rebar.
## 3 Validation
### Tidal Water Level Validation  
Figure 5 shows the water levels on the western bank of the Taiwan Strait during the passage of Soudelor and Dujuan, including the tidal gauge observations (black solid dots) and predictions  
Figure 4: Time series of central pressure (blue dotted lines), wind speed (dashed lines), and significant wave height (solid lines) in the Taiwan Strait during the passage of Typhoon Dujuan. (**a**) Buoy No.2, (**b**) Buoy No.3, (**c**) Buoy No.4, (**d**) Buoy No.5, (**e**) Buoy Gulei, (**f**) Buoy Douweigang, (**g**) Buoy Huangqi, (**h**) Buoy Beishuang.  
of water level that considered the interaction between the tide and the storm surge (red solid lines). The tidal range is <3 m in the south of the Taiwan Strait but it reaches >6 m in the north. On the western bank of the Taiwan Strait, the storm surge reached over 1 m during the passage of both Soudelor and Dujuan. The arrival of Typhoon Dujuan occurred in conjunction with the astronomical spring tide, which revealed the considerable potential danger of inundation. However, Dujuan made landfall at the time of lower tide elevations and thus severe storm surge damage was fortunately avoided.  
Figure 5: Time series of observed water level (solid dots) and predicted water level simulated with consideration of tide-surge interaction (red solid lines) of Typhoon Soudelor (**a–e**) and Typhoon Dujuan (**f-j**) on the western bank of the Taiwan Strait.
## 2 Processing Blocks in SDR Transmitter
### Sample-Wise Pulse Shaping
#### 2.3.1 Mixing
Modulation impairments that can be incorporated with this proof-of-a-concept framework include phase noise/errors, amplifier compression, noise, in-band spurious, IQ gain imbalance, and IQ quadrature error.
## 3 Imaging Method and Configuration
### Experimental Setup  
The considered plate is made of stainless steel and is immersed in water (schematic configuration and picture of the experimental setup are represented in Figure 6). Its thickness and length are defined by \(e=7.8\) mm and \(L=70\) cm. A linear phased array transducer with 16 elements that defines the surface \(\Gamma_{\text{m}es}\) parallel to the plate, is used as a transmitter-receiver sensor. The central frequency is \(f=320\) kHz. The pitch of the linear array is \(p=3\) mm.  
A delay law is applied to the linear array that imposes the beam angle at the emission, and thus the Lamb wave mode is generated in the plate. The linear array is positioned very close to the plate (\(d=0.5\) mm) to lower successive reflections between the linear array and the plate that would lead to the generation of multiple Lamb waves in the experience. The topological energy is calculated inside the plate that implies the use of the horizontal \(u\left(\overset{\rightarrow}{r},t\right)\) or vertical \(v\left(\overset{\rightarrow}{r},t\right)\) displacements in the calculation of the latter.  
In an initial stage, a plate without any defect is studied. The objective is the detection of the edge of the plate at \(L=70\) cm. Then, the application of the method on a second plate containing a notch at \(X_{\text{n} notch}=52\) cm is carried out.  
The measurements are performed with a Sonaxis(r) ultrasonic linear array composed of sixteen 1.8 mm wide elements and a Lecoeur(r) OPEN system. The experimental acquisition and the simulations are done for a beam angle \(\theta_{\text{i}}=12.6^{\circ}\) that corresponds to the mode A1.  
Figure 6: (**a**) Geometry of the experimental setup; (**b**) Experimental setup.  
The emitted signal is a ten-period sinus signal filtered by a Hanning window in order to lower the dispersion phenomenon. The mode A1 has been chosen because it presents a low attenuation along the propagation so that it is easily detectable. The linear array transducer is set at 40 cm of the left edge of the plate.
## 2 Materials and Methods
### Assessment of Biofilm-Forming Ability of Listeria monocytogenes Strains  
The biofilm formation ability of the _Listeria monocytogenes_ strains was tested using the method of [PERSON] et al. [27] with modifications. _Listeria monocytogenes_ strains were inoculated in tryptic soy broth (TSB) with 1% glucose and incubated for 24 h at 37 \({}^{\circ}\)C. Subsequently, the turbidity of the inoculum was adjusted to 0.5 McFarland using a nephelometer (Grand Instruments, Cambridge, UK) and diluted 1/100 in TSB with 1% glucose to achieve approximately a 10\({}^{6}\) CFU/mL inoculum. Furthermore, 100 \(\upmu\)L of the inoculum was transferred to wells of polystyrene microtiter plates (Boettger, Bodennais, Germany) containing 100 \(\upmu\)L TSB with 1% glucose to achieve a final concentration of \(5\times 10^{5}\) CFU/mL. For each _Listeria monocytogenes_ isolate, 3 replicates were conducted, while a strip of wells served as the negative control (non-inoculated 200 \(\upmu\)L sterile TSB with 1% glucose). The microplates were hermetically sealed with parafilm to prevent desiccation and incubated at 37 \({}^{\circ}\)C for 24 h. Then, the wells were washed gently with normal saline, air-dried for 1 h at 60 \({}^{\circ}\)C, and stained with 50 \(\upmu\)L 0.06% crystal violet solution for 5 min at room temperature. The stained crystal violet was solubilized with 200 \(\upmu\)L of 33% acetic acid for 10 min at room temperature. Finally, the optical density (OD) was measured at 600 nm using an automatic spectrometer (MicroDigital Co., Seongnam-si, Republic of Korea). Depending on the size of the OD of each isolate and the OD of the controls (ODc), the _Listeria monocytogenes_ isolates were classified as no biofilm producers (OD \(<\) ODc), weak biofilm producers (ODc \(<\) OD \(\leq\) 2*ODc), moderate biofilm producers (2*ODc \(<\) OD \(\leq\) 4*ODc), or strong biofilm producers (OD \(>\) 4*ODc).
## 5 Conclusions  
In this paper, we investigated the state-of-the-art approaches of link correlation measurement and proposed a novel link correlation estimation framework (called LACE). Compared with existing beacon-based approaches (BCM), LACE: (1) directly uses data packet receptions for link correlation calculation; and (2) considers both historical data and the instant SINRs. Therefore, LACE provides more accurate link correlation estimation. Moreover, due to the elimination of beacon messages, LACE is much more lightweight than BCM. Experimental results show that LACE achieves more accurate link correlation estimation and improves the performance of existing correlation-based protocols. The future direction lies in finding a theoretical optimal tradeoff between the historical correlation value and the instant value to meet various requirements from upper-layer protocols.
## 4 Results  
### Microscopic Characteristics of Sandstone  
1. Composition Analysis of Sandstone  
Small samples of the obtained top-layer water-saturated sandstone were crushed to a fine powder by passing through a 325-mesh sieve for XRD analysis. The analysis was compared with the powder diffraction file standard card database. The XRD spectrum of the water-saturated sandstone from the top layer of the roadway is shown in Figure 10, and the component analysis results are presented in Tables 2 and 3.  
According to the XRD analysis, the sandstone primarily consisted of quartz (54.1%), sodium feldspar (15.8%), potassium feldspar (8.8%), and clay minerals (21.3%). Among the clay minerals, kaolinite (57.26%) and illite (42.74%) were dominant. The water absorption and weathering characteristics of these minerals significantly influence the mechanical properties of sandstones. Kaolinite exhibits strong water absorption, whereas illite expands upon water absorption, further affecting the rock stability. Moreover, after water penetration, illite weathers into montmorillonite, which exhibits strong swelling and high water-absorption properties. This results in volumetric expansion and strength reduction of the rock, leading to the deformation and strength weakening of the top-layer sandstone of the roadway.  
2. Microscopic Structural Analysis of Sandstone  
In this experiment, SEM was conducted using a VEGA COMPACT device to examine the samples and reveal the microstructural characteristics of the water-saturated top sandstone layer in the roadway. As shown in Figure 11, at 50\(\times\) magnification, the surface of the top-layer sandstone appeared dense with no obvious cracks. At 2000\(\times\) magnification, irregularly developed cracks were observed with no clear directional patterns. At 3000\(\times\) magnification, the pore channels were clearly visible, and the spaces between the particles were relatively large, facilitating the movement of water.  
Water absorption by rock triggers a series of physical and chemical changes. From a physical perspective, water molecules fill micropores and cracks on the rock surface. Chemically, water interacts with the mineral components within rock, leading to dissolution, ion exchange, and other reactions. These processes alter the chemical composition and structure of the rock, resulting in a reduction in its strength and a significant impact on its mechanical properties.
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 4 Design Issues in Gait, Balance and RoM Wearable Systems
### Magnetic Disturbances  
A challenge in the application of IMUs is that magnetic field is known to be inhomogeneous in indoor environments and near ferromagnetic materials. These disturbances limit the accuracy of measured parameters in two ways: sensor orientation estimates are deteriorated, and magnetic disturbances may limit the accuracy of the sensor-to-segment calibration [98]. To avoid magnetic disturbances, there are researchers that consider to use non-magnetic equipment to perform the assessment, such as a couch with wooden frames [22]. There are others that use Kalman filters and sensor fusion techniques to minimize the disturbance applied to the signals being evaluated [23; 58; 98; 99].
# Using Dynamo for Automatic Reconstruction of BIM Elements from Point Clouds
## 4 Results
The elements that presented a minor variation were the walls and structural beams; however, for these cases, the automatic method was still more accurate, at 18.2% and 12.5%, respectively. In terms of the average accuracy for the entire project, the building model created with the automatic methods was 30% more accurate than the one created manually. This value increases to 55.6% if the topography surface is considered (Table 4). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Element**} & **Automatic** & **Manual** & **Accuracy** & **Accuracy** \\  & **Method** & **Method** & **Difference (mm)** & **Increment (\%)** \\ \hline Columns & 6 mm & 9 mm & 3 mm & 33.3\% \\ Beams & 7 mm & 8 mm & 1 mm & 12.5\% \\ Walls & 9 mm & 11 mm & 2 mm & 18.2\% \\ Floors & 4 mm & 11 mm & 7 mm & 63.6\% \\ Topography & 8 mm & 24 mm & 16 mm & 66.7\% \\ Building (except terrain) & 7 mm & 10 mm & 3 mm & 30.0\% \\ Entire Model & 8 mm & 18 mm & 10 mm & 55.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The average accuracy of models created by automatic and manual methods, the difference in accuracy between methods, and rate of accuracy increase. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multirow{2}{*}{**Approach**} & \multicolumn{2}{c}{**Accuracy (\% of Points)**} & **Average** & \multirow{2}{*}{**Time Consumed**} \\  & & **Up to 12 mm** & **Up to 25 mm** & **Accuracy** & \\ \hline Columns & Manual & 77.8\% & 93.6\% & 9 mm & 1 h 59 min \\ Beams & Manual & 82.3\% & 92.8\% & 8 mm & 3 h 20 min \\ Walls & Manual & 62.8\% & 89.7\% & 11 mm & 42 min \\ Floors & Manual & 69.9\% & 87.8\% & 11 mm & 25 min \\ Topography & Manual & 32.4\% & 57.4\% & 24 mm & 40 min \\ Building (except terrain) & Manual & 70.5\% & 90.8\% & 10 mm & 6 h 26 m \\ Total & Manual & 49.5\% & 73.1\% & 18 mm & 7 h 6 min \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Level of accuracy and the time consumed in the modeling using a manual approach.
# AlGaN Ultraviolet Metal-Semiconductor-Metal Photodetectors with Reduced Graphene Oxide Contacts
## 4 Conclusions  
In summary, we successfully fabricated and analyzed rGO/AlGaN MSM photodiodes. After a facile fabrication of highly transparent rGO layers, excellent rectifying properties were obtained from the rGO/AlGaN MSM structures. When the photoresponsivity was investigated along with the various Al mole fractions, high photo/dark current ratios and sharp cut-off wavelengths were observed, which were systematically associated with the energy bandgaps of the three different AlGaN samples with Al mole fractions \(x\) = 0.95, 0.134, and 0.179. The results suggested that the rGO as a transparent Schottky electrode has potential applicability for visible-blind optical sensors when combined with AlGaN semiconductors.  
Original draft preparation with experiment and analysis, B.P.; Supervision and writing-review, J.C.  
This research was funded by the basic science research program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education (No. 2017R1D1A1A09000684) and by the research funds of Chonbuk National University in 2018.  
The authors declare no conflict of interest.
## 1 Introduction
Some progress has been made in the Amazon [35; 36; 37; 38; 39] and African forests [40; 41; 42]; however, the Southern China region has typically been neglected in remote sensing phenology studies [43; 44; 45; 46]. Consequently, the phenological character of forests in this region remains uncertain, with additional complexity driven by fragmentation [47], high species diversity, and complex topography [48]. The lack of detailed validation studies in Southern China means that there is little information regarding the suitability of global EO LAI products for the forests in this region, which play a major role in the carbon sink across China, and span the climate gradient from the subtropics to tropics [49; 50]. In this study, we take advantage of a network of 6 sites with \(\sim\)8000 ground-based measurements of LAI for forests located across the tropical and subtropical Southern China region to fully evaluate three satellite-based remote sensed LAI products with global coverage (Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m and PROBA-V GEO2 1 km and V3 300 m). Specifically, we address the following questions:  
1. How well do EO-based estimates of LAI capture temporal dynamics observed in ground measurements? 2. How robust are EO LAI error estimates?To answer these questions, we evaluate errors across multiple LAI retrieval algorithms and spatial resolutions and their associated temporal dynamics. We use additional fine resolution satellite data to evaluate the heterogeneity of canopy cover at the scales intermediate between in situ data and the satellite products at each location. Finally, we discuss the importance of robust error characterization for LAI products in the context of constraining models of the terrestrial carbon cycle and other ecosystem processes. We aim to characterize and understand errors and bias in the EO LAI products but do not make retrievals over the entire region. Whilst our scientific questions are focused on the Southern China region, the approach used can be applied elsewhere if in situ data is available. Assessing the uncertainties in LAI products through comparison with in situ measurements, i.e., direct validation is critical for their proper use in land surface models [51; 52]. A better understanding of the uncertainties embedded in current LAI products will improve the assimilation of the LAI into land surface modeling studies [53; 54] by providing a more robust error weighting [55].