esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Field Modal Testing for PV Support Structure
### Data from Field Modal Testing  
Computer vision-based measurement was used in tests 1 and 2. The vibration displacement signals at the measurement points were identified from videos captured by a monocular camera. The camera view for test 1 is shown in Figure 9, where three artificial markers and their corresponding measurement point numbers are indicated. The camera view for test 2 is shown in Figure 9, where eight artificial markers and measurement point numbers are labeled. A clearer layout of the paper marker positions can be found in Figure 8a,b.  
The free decay segment of the signal from test 1 and 2 was extracted, as shown in Figure 10a,b. The amplitude ratios among measurement points remain stable, and the power spectra all exhibited a single peak, which is close to the frequency from the FE modal analysis results. Therefore, it can be concluded that tests 1 and 2 effectively and independently excited the target mode.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Cases** & **Target Mode** & **Excitation Method** & **Sensor Type** & **Placement Method** & **Test Duration** \\ \hline \multirow{2}{*}{1} & half-wave vertical & \multirow{2}{*}{1/2 span, vertical harmonic loads} & Vison-based & \multirow{2}{*}{Figure 8a} & \multirow{2}{*}{5 min} \\  & bending & & measurement & \\ \hline \multirow{2}{*}{2} & first-order torsion & \multirow{2}{*}{1/2 span, vertical synchronized \(\,\) opposite-direction \(\,\) harmonic loads} & Vison-based \(\,\) measurement & \multirow{2}{*}{Figure 8b} & \multirow{2}{*}{5 min} \\  & & & & \\ \hline \multirow{2}{*}{3} & full-wave vertical & \multirow{2}{*}{1/4 and 3/4 span, vertical \(\,\) synchronized opposite-direction \(\,\) harmonic loads} & Velocity sensor & \multirow{2}{*}{Figure 8c} & \multirow{2}{*}{5 min} \\  & & & & \\ \hline \multirow{2}{*}{4} & second-order \(\,\) torsion & Ambient excitation & Velocity sensor & \multirow{2}{*}{Figure 8c} & \multirow{2}{*}{120 min} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the test cases.  
Figure 9: Camera view.  
In test 3, velocity sensors were used, and the free decay segment was extracted. The velocity signals and corresponding power spectra are shown in Figure 10c. Although the target mode is full-wave vertical bending, the power spectrum indicates that the vibration was dominated by the half-wave vertical bending mode. The full-wave vertical bending mode exhibited low vibration energy and was difficult to excite independently. The modal frequency of the full-wave vertical bending mode was 2.98 Hz, which is close to the FE modal analysis result of 3.04 Hz.  
In test 4, velocity sensors were used, and 50 min of vibration signal under ambient excitation were selected. The time history and power spectra are shown in Figure 10d. The ambient excitation effectively excited the second-order torsional mode, with a modal frequency of 3.53 Hz.
## 3 System Model and QoT Estimation
### Modulation Format Selection  
For each dynamically arriving request, a suitable modulation format can be selected for it based on the length of its selected path using distance adaptive modulation, which can be calculated by Equation (5).  
\[RFS_{s}=[\frac{r_{b}}{M\cdot Slot}]+Gb, \tag{5}\]  
where \(M\) represents the modulation level associated with a given request \(r\), while \(RFS_{s}\) represents the FSs allocated to that request. The paper took into account four modulation formats: binary phase-shift keying (BPSK), quadrature phase-shift keying (QPSK), 8-QAM, and 16-QAM, and their corresponding modulation levels were as follows: \(M\) = {1, 2, 3, 4}. The modulation format selected by a request was determined by the distance between its selected paths. The slot denotes the optical network channel capacity whose size is 12.5 GHz.  
\(Gb\) denotes the guarded bandwidth between the neighbor lightpaths. The correlation between modulation format and transmission distance are summarized in paper [19].
## 1 Introduction
### Methods for Estimating the Sound Speed in the Seafloor
In these experiments, a horizontal aperture is usually obtained using a fixed or towed multichannel Horizontal Line Array (HLA), or by deploying multiple sources at different distances from a set of hydrophones [33]. Alternatively, the possibility of head-wave-data inversion for estimations of the sub-bottom sound speed in shallow water using a Vertical Line Array was demonstrated by [PERSON] et al. [24]. [PERSON] and [PERSON] [34] and [PERSON] [35] suggested a simple technique for interpreting the velocity structure of the upper 500-800 m of young oceanic crust from the travel-time data of sonobuoy and ocean-bottom receiver-refraction experiments. The model consisted of a homogeneous water layer over a sub-seafloor layer in which the velocity increased linearly with depth. In addition, a simple analytical expression was used to estimate the positive sound-speed gradient from the travel time, i.e., the source-receiver distance (offset) curve of the caustic that was formed (Section 4.5 in [36]). Recently, with the increased scope of deep-water operations, which are supported by acoustical orienteering and communication, the need has risen to determine the geoacoustic parameters of the deep seafloor. Together, the works cited above lay the theoretical framework for such an investigation. However, the practical implementation of seafloor sound-speed-estimation methodologies, which are based on the usage of the arrival times of head waves, in deep waters is challenging and costly. Here we demonstrate the cost-effective use of standard commercial 2D seismic-reflection data, which are commonly acquired along ocean margins, for the geoacoustic characterization of relatively deep (\(\sim\)1000 m) seafloor sediments.
## 4 Results and Discussion
### Possible Example Use-Cases
#### 4.2.1 Design of Probe Signal for Predicting System Performance  
Let us represent the multi-tone signal used for channel sounding as  
\[\bar{x}(t)=\sum_{\hat{f}=\hat{f}_{1}}^{\hat{f}_{W}}\cos(2\pi f\delta\hat{t}+\psi_ {\hat{f}})=\sum_{\hat{f}=\hat{f}_{1}}^{\hat{f}_{W}}\mathcal{R}\bigg{\{}\hat{X}_ {\hat{f}}e^{i(2\pi f\hat{\beta}\hat{t})}\bigg{\}} \tag{30}\]  
where \(\delta\) is the frequency separation between tones, \(\hat{T}=1/\delta\) is the fundamental period of \(\bar{x}(t)\), \(\hat{f}_{1}\) and \(\hat{f}_{W}\) are the limits of the channel bandwidth and \(\hat{X}_{\hat{f}}=e^{i\psi_{\hat{f}}}\), where \(\psi_{\hat{f}}\) is the relative phase of each tone; \(\bar{x}(t)\) corresponds to a Fourier series with complex coefficients \(\hat{X}_{\hat{f}}\). \(\hat{X}_{\hat{f}}\) can be designed using the Zadoff-Chu sequence as  
\[\hat{X}_{\hat{f}}=\exp\bigg{(}-j\frac{\pi\hat{\alpha}f^{2}}{W_{ZC}}\bigg{)} \tag{31}\]  
where \(W_{ZC}\) is the length of the Zadoff-Chu sequence and \(\hat{\alpha}\in\mathbb{W}\), such that \(gcd(u,W_{ZC})=1\) where \(gcd(\cdot)\) stands for the greatest common divisor function.  
Next, the multi-tone signal \(\bar{x}(t)\) is communicated over our Simulink-based channel model. The received signal, in that case, is given by  
\[\bar{y}(t) =\int_{0}^{\infty}\bar{x}(t-\tau_{q})h(t,\tau_{q})\mathrm{d}\tau_ {q}=\sum_{\hat{f}=\hat{f}_{1}}^{\hat{f}_{W}}\mathcal{R}\bigg{\{}e^{i(2\pi f \hat{\beta}\hat{t}+\psi_{\hat{f}})}\int_{0}^{\infty}h(t,\tau_{q})e^{i(2\pi f \hat{\beta}\tau_{q})}\mathrm{d}\tau_{q}\bigg{\}}\] \[=\sum_{\hat{f}=\hat{f}_{1}}^{\hat{f}_{W}}\mathcal{R}\bigg{\{}H(t, \hat{f}\delta)e^{i(2\pi f\hat{\beta}t+\psi_{\hat{f}})}\bigg{\}} \tag{32}\]  
where \(H(t,\rho)\) is the time-varying channel frequency response.  
Using the method outlined in (31) and (32), a multi-tone signal is first generated for probing the channel. The sampling frequency of both the transmitter and receiver is set to 30 kHz. The frequency spacing parameter \(\hat{\delta}\) determines the range of channel delays that can be estimated. In this case, \(\hat{\delta}\) is set to 100 Hz since the max delay spread of the simulated channel is 10 ms. The total bandwidth of the transmitted signal is \(B=4\) kHz, with the lower frequency bound \(\hat{f}_{L}=1\) kHz, and the upper frequency bound \(\hat{f}_{H}=5\) kHz. Accordingly, the number of tones is set to 40, and the phase parameter of each tone is generated by the  
Figure 7: Generated channel samples using our formulated channel emulator platform (left-hand side) compared with the CIRs generated using the BELLHOP platform (right-hand side of the figure).  
Zadoff-Chu sequence. Figure 8 shows the waveform and the frequency spectrum of the multi-tone signal. Next, the probe signal is sent through a channel generated using our Simulink model. A total of 100 groups of probe signals were sent (matching the number of snapshots of the simulated channel). Each probe signal contains a multi-tone signal with 3 periods, and its duration is 30 ms. A sample of the received signal is shown in Figure 9.
###### Abstract  
The self-comparison method is a powerful tool in the uncertainty evaluation of optical lattice clocks, but any drifts will cause a frequency offset between the two compared clock loops and thus lead to incorrect measurement result. We propose a drift-insensitive self-comparison method to remove this frequency offset by adjusting the clock detection sequence. We also experimentally demonstrate the validity of this method in a one-dimensional \({}^{87}\)Sr optical lattice clock. As the clock laser frequency drift exists, the measured frequency difference between two identical clock loops is (\(240\pm 34\)) mHz using the traditional self-comparison method, while it is (\(-15\pm 16\)) mHz using the drift-insensitive self-comparison method, indicating that this frequency offset is cancelled within current measurement precision. We further use the drift-insensitive self-comparison technique to measure the collisional shift and the second-order Zeeman shift of our clock and the results show that the fractional collisional shift and the second-order Zeeman shift are \(4.54(28)\times 10^{-16}\) and \(5.06(3)\times 10^{-17}\), respectively.  
optical lattice clocks; strontium atoms; optical lattices; laser cooling and trapping +
Footnote †: journal: Applied Sciences
# Design, Modeling, and Analysis of Piezoelectric-Actuated Device for Blood Sampling
## 3 Finite Element Modeling of the Micropump
### Fluid-Structure Interaction (FSI)  
The FSI was used to create interaction between solid and fluid surfaces inside the pump chamber due to the actuation of FQPB. Near the first natural frequency, harmonic voltage was applied to the FQPB. Actuation of FQPB caused pumping action to provide the required flow of blood into the pump chamber due to the combined effect of the pressure and suction cycle.  
In ANSYS, solution for fluid and solid regions was carried out by different solvers. CFX fluid solver solved the fluid dynamic problem while multi-physics solid solver solved the structural dynamic problem. Two-way coupling was used to exchange information between the fluid-solid solvers. In one-way coupling, only fluid pressure acting at the structure was transferred to the MPS solver. In two-way-coupling, in addition to the above, the displacement of the piezoelectric bimorph was also transferred to the CFX solver.
## 4 Results and Discussion
#### Forecasting Results Based on Data Set 1: Parking Lot Canopy Array Datasets
This application showed that the VAE method for PV power forecasting has superior performance. Also, it is noticed that RNN and its extended variants LSTM, BiLSTM, and GRU achieve slightly comparable performance to the VAE in terms of the statistical indexes (RMSE, RMSE, MAE, EV, and NRMSE). Table 3 indicates that deep learning models exhibited improved forecasting performance compared to the baseline methods (i.e., LR and SVR).
###### Abstract  
A new optical encryption algorithm, called the random-phase encryption algorithm, is proposed in this study. When this algorithm was applied in constructing computer-generated holograms, the out-of-focus image crosstalk was effectively reduced, and the image quality was greatly enhanced. In this study, the researchers encrypted each multi-depth or multi-view random phase sub-image with the phase-locked key to generate multi-channel encryption phase information. During the reconstruction, the switch of the phase-locked key of the sub-image was found to achieve different image reconstruction effects with different views or depths. This algorithm proved to substantially reduce the out-of-focus image crosstalk and to enhance the reconstruction quality of the original computer holography without concerning the mutual interference among the information of each view for multi-view and multi-depth holograms.  
optical encryption algorithm; reducing defocused-information crosstalk; computer-generated holography +
Footnote †: journal: Applied sciences
# Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications
## 5 EIT Clinical-Based Applications
### Breast Imaging
All the admittivity distributions obtained are much lesser in compressed breasts in EIT as compared to the previous experiments for imaging whole chest. They have projected a three-layered examining a progressive model of the breast: the highest layer of thin low admittivity, the lowest layers representing skin and a comparatively thicker high-admittivity middle layer portraying breast tissue. The authors have suggested a progressive solution to this layered portrayal relating this solution with the homogeneous case. They have concluded that the layered forward solution performed better reconstruction as compared to the homogeneous forward solution [90]. The authors have studied the use of EIT in comparison to the other techniques such as mammography (MMG); ultrasound (US) and biopsy. They have concluded that the EIT had 64.4% sensitivity and is recommended to be a promising method for scanning and detecting breast irregularities before breast self-examination. Nevertheless, MMG has to be done to confirm the analysis [89]. In [91] to get tissue impedance maps the authors have demonstrated the usage of adjustable higher density microelectrode arrays. Exact reconstruction in EIT is primarily based on spatial resolution and arrays to obtain tissue impedance maps. Flexible arrays with high density which are suitable to the surface of the breast can give high flexibility in the reconstruction of conductivity maps of advanced resolutions as compared to that have been formerly achieved [91].
## 3 Results  
### Occupant Behaviors  
#### 3.1.1 Spatial Patterns  
Occupancy sensors were installed in the measurement space to analyze occupancy patterns in each zone over five weekdays in the winter season. The FMCW sensor generates data using a coordinate system when occupancy occurs, calculating the distance and angle of the occupant relative to the sensor's center point at a rate of one data point per second. The measured data are converted back into coordinate values (x-y distance) using a microprocessor and can be displayed on a planar representation of the space through location mapping. Missed and lost signals are represented as null values to prevent confusion with unoccupied conditions (Zone 0). For cases where the position changes at each measurement, the most frequent position for 15 min is determined as the occupancy location at that measurement time. Figure 6 illustrates the spatial distribution of occupants within the target space during five weekdays (4-8 December 2023). By employing the plane of the space and predefined zones, the measured data, including the sensor's measurable angle (60 degrees) and distance, were converted into location coordinates and visualized. Zone 1, characterized by the presence of a desk, is primarily designated for sedentary activities such as work and study. Zone 2, where the bed is situated, is predominantly utilized for sleep. Zone 3, comprising bookshelves and closets, represents activities beyond those in zones 1 and 2, primarily standing. Over the five-day period, the occupancy pattern was predominantly centered in Zone 2, reflecting the prevalence of sleep-related activities. Zones 1 and 3, associated with study and work activities and standing activities, respectively, exhibited lower occupancy frequencies.  
Table 6 presents the frequency of each activity over a week, including weekends. Out of a total of 672 measurements, approximately 65% of the time was occupied, while the remaining 35% was unoccupied. Analysis of occupancy-related activities revealed that sleep accounted for about 70%, study and office work for approximately 20%, and other  
Figure 5: Schematic structure of study condition.  
activities for around 10%. These findings suggest that the primary use of this space is for sleep.  
These occupancy patterns indicate the need for energy conservation measures such as heating cessation or the resetting of the set temperature during unoccupied periods, and the implementation of optimal heating operations during sleep, the primary activity while occupied, to ensure comfort and reduce heating energy consumption.
# The Shear Behavior of the Curved Interface in Polyurethane-Concrete Composite Structures
###### Abstract  
Polyurethane grouting trenchless technology has been widely applied to the rehabilitation of concealed defects in engineering structures. The interfacial properties between polyurethane and engineering structures are key factors determining the stability of the composite structure. In practical applications, the interface shapes of different engineering structures vary significantly, and the influence of the interface shape on interfacial properties should not be overlooked. This study focuses on engineering structures with curved interfaces, such as pile foundations, pipelines, and tunnels. Direct shear tests were conducted on polyurethane and concrete composite specimens with curved interfaces. A comparative analysis of the shear behavior between curved and planar composite specimens was performed, and the influence of arc diameter and polyurethane density on the shear behavior of curved and planar composite specimens was investigated. Additionally, SEM (Scanning Electron Microscopy) was used to conduct a microscopic examination of the interfaces with different polyurethane densities after failure, and the microscopic shear mechanisms between polyurethane and concrete materials were explored. The results revealed that the shear behavior of curved specimens was significantly higher than that of planar specimens. The shear strengths of curved specimens with diameters of 400 mm, 500 mm, and 700 mm were approximately 1.50, 1.39, and 1.10 times those of planar specimens, respectively. With increasing polyurethane density, the variation trend of shear strength in curved specimens was similar to that of planar specimens. However, significant differences were observed in the shear modulus, peak displacement, and shear residual strength between curved and planar specimens as the polyurethane density varied. Different diameter curved interface specimens exhibited a similar trend of shear strength variation with polyurethane density, gradually decreasing as the curvature diameter increased, and ultimately approaching that of planar specimens.  
polyurethane; concrete; interface; rehabilitation; shear behavior; direct shear tests; interfacial microstructure
# Ordered Subspace Clustering for Complex Non-Rigid Motion by 3D Reconstruction
## 4 Solutions
0: The data representation \(Z\). 1: Initialization: \(Z^{0}=0\), \(U^{0}=0\), \(E^{0}=0\), \(X^{0}=0\), \(t=0,G=0\), \(F=0\), \(\rho=1.3\). 2:while\(t<N\)do
3: Calculate \(X^{t}\) by (7);
4: Find \(Z^{t}\) by solving (9);
5: Find \(U^{t}\) by solving (13);
6: Find \(E^{t}\) by solving (16);
7: Update \(F\gets F+\gamma(U-ZS)\) ;
8: Update \(G\gets G+\gamma_{1}(W-RX^{\#}-E)\) ;
9: Update \(\gamma\leftarrow\rho\gamma\), \(\gamma_{1}\leftarrow\rho\gamma_{1}\);
10: Update \(t\gets t+1\);
11:endwhile
```  
**Algorithm 1** Solving data representation of the proposed ordered subspace clustering (OSC)-non-rigid structure-from-motion (NRSfM).
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 6 Research Challenges
### Trade-Off Between Spatial and Temporal Resolutions  
A trade-off between spatial and temporal resolution is nearly always present in SUHI research due to technique limitations. Some sensors like Landsat series and ASTER can yield LST at a relatively high spatial resolution (60-120 m), but are restricted by the lack of data for the nighttime period and low temporal resolution. High-temporal resolution sensors such as AVHRR and MODIS can only provide LST at a medium or coarse resolution (\(\geq\)1 km). The choice of the image was often determined with a research focus [223]. For example, high-spatial resolution images were preferred for understanding the SUHI variations within a city and, thus, provide insights for local city planners. In contrast, high-temporal resolution data were required to calculate energy parameters or to examine the relationships between SUHI and bioclimatic factors (e.g., air pollution).  
However, the current satellite-borne sensors can never entirely meet the needs of scientific research and practitioners because of the high spatio-temporal variability of the SUHI phenomenon [108; 224; 225]. Sensors with a 50 to 100 m pixel size have been proven to be most suitable in urban thermal environment studies [39; 226; 227], which means that the resolution of the most widely used sensors known as Landsat TM/ETM+/TIRS is close to the optimal spatial resolution. However, they are commonly impeded by limited thermal calibration, no nighttime data, and a long revisiting cycle.  
In addition, remote sensing satellites orbit the Earth in a sun-synchronous orbit and, thereby, show a region at a constant specific time of a day/night known as the local sun time. However, SUHI experiences great variations during the day/night. [PERSON] et al. [228] showed that a SUHI study should cover the two times of day: early noon hours (12:00-13:00 p.m.) and early morning hours before sunrise (5:00 a.m.). However, satellite images are likely not available at these specific times. Geostationary satellites like GOES and SEVERI sensors serve as the ideal platforms for understanding diurnal cycles of SUHI, but their coarse spatial resolution, limited coverage area, and low accuracy have impeded their wide application in SUHI studies [129].  
Some spatial downscaling and temporal upscaling algorithms have been proposed to increase the spatial [223; 227; 229; 230] and temporal [208; 210; 231] resolutions of LST data, respectively. Those methods have revealed a wealth of information that can partially compensate for the resolution problems, but at the cost of accuracy [223]. Therefore, new satellite missions focusing on reducing pixel size and increasing temporal frequency should be the most fundamental way to promote SUHI research and practice. Besides the upcoming large satellite missions such as Landsat 9, increasing \"small satellites\" with high spatial, temporal, and spectral resolutions may provide valuable data sources for local or regional SUHI studies in the future [232].