esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
[12] analyzed existing research and presented a user-friendly AOMA methodology that consists of three stages: spurious mode removal, clustering and physical mode selection. [PERSON] et al. [13] presented a method to automatically analyze the stabilization diagram through checking of the reasonable damping ratio, a modal complexity check and clustering, as well as the introduction of a self-adaptable dynamic threshold to keep track of the modal parameter. As methods for estimating the uncertainty of modal parameters [14; 15; 16; 17; 18; 19] were developed, methods considering the uncertainty of modal parameters [20; 17; 21] were presented and methods using machine learning techniques [22; 23; 24; 25] were also studied. The present study intends to propose a method for extracting real modes from a pool of candidates obtained by SSI-COV. In particular, values of criteria that can be applied consistently to various bridge types are also suggested by exploiting the uncertainty information of individual measured data. Section 2 delivers a summary of SSI-COV that can provide the modal uncertainty. Section 3 introduces the method filtering the real modes and clustering them with respect to the modal uncertainty. Finally, Section 4 validates the effectiveness of the proposed method through its application to actual bridges.
## 5 Conclusions  
Based on the progress in mathematical modeling, experimentation, and application research of relative permeability hysteresis, researchers have gained some understandings of relative permeability hysteresis effects. Relative permeability hysteresis is a phenomenon that physically exists and is influenced by various factors such as rock properties and development parameters. It has a significant impact on production in reservoir development. A deeper understanding of relative permeability hysteresis could provide valuable guidance for on-site development. Fluid flow under actual reservoir conditions of temperature and pressure differs greatly from room temperature conditions. Currently, there is limited research on relative permeability hysteresis under high temperature and pressure conditions. Future research on relative permeability hysteresis under high temperature and pressure should be a target, providing theoretical support for practical reservoir development. Furthermore, CCUS have become a recent hotspot. Considering relative permeability hysteresis in CCUS research is of great significance. Gas storage's cyclical injection and production natural involves a lot of hysteresis phenomena. Experimental results and specific models applicable to large numbers repeated injection and production remain to be studied. Y.L. ([PERSON]): Conceptualization, Methodology, Formal analysis, Supervision, Project administration; P.G. : Conceptualization, Methodology, Formal analysis, Methodology, Resources, Writing--Original Draft, Writing--Review & Editing, Supervision, Project administration, Funding acquisition; Y.L. ([PERSON]): Conceptualization, Methodology, Formal analysis, Supervision, Project administration; S.W. : Conceptualization, Methodology, Formal analysis, Resources, Writing--Original Draft, Writing--Review & Editing; S.C.: Conceptualization, Formal analysis, Supervision, Project administration; J.Z. : Formal analysis, Formal analysis, Project administration; W.S. : Formal analysis, Project administration; D.Q. : Conceptualization, Project administration; Q.J. : Methodology, Formal analysis, Resources, Writing--Original Draft, Writing--Review & Editing, All authors have read and agreed to the published version of the manuscript. This work is support by Daqing Oilfield Co. Ltd. Funding number DQYT-1201002-2022-JS-703. This work would like to express recognition of the research did by Sichuan Natural Science Foundation, China [grant number 2022 NSFSC1029]. Authors [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company Daqing Oilfield Co. Ltd. The remaining authors declare
## 3 Proposed Method
### Template Update  
Intuitively, object appearance may remain unchanged only for a certain period of time; thus, fixed templates will not be available all the time. So they must be updated to deal with the appearance variation. However, error is inevitable to be introduced when templates are updated. If we update them too frequently, the tracker is easy to drift from the ground target due to accumulated error.  
A method of updating templates dynamically is employed to alleviate the problem. This means that a target template is updated only when it is not similar to the tracking result \(\hat{y_{k}}\). The similarity function is defined as \(\cos(\theta)\), where \(\theta\) is the angle between the two normalized vectors. A suitable threshold \(\theta_{thr}\) is selected to determine whether to update the target template. Once the maximal angle \(\theta_{max}\) exceeds the threshold, this target template can be replaced by the optimal estimation to be updated. The whole algorithm is summarized in Algorithm 1.  
```
0: current frame \(F_{t}\), sample set \(X_{t-1}\), target template \(T_{t-1}\);
0: the optimal state \(\hat{x_{t}}\), sample set \(X_{t}\), target template \(T_{t}\);
1:for\(t=1\) to the number of frames do
2: Draw the new samples \(x_{t}^{i}\) from \(x_{t-1}^{i}\) with respect to \(p(x_{t}|x_{t-1})\);
3:for\(i=1\) to the number of templates do
4: Each target template is represented sparsely with all the target candidates (4);
5: The reconstruction error is treated as the weight of each target template (5);
6:endfor
7: The fused coefficient \(\hat{\alpha_{t}}\) is obtained by (6) then normalized as (7);
8: The optimal state \(\hat{x_{t}}\) is estimated as (8);
9: Resample to obtain \(X_{t}\) to avoid degeneracy phenomenon;
10:if\(\theta_{max}>\theta_{thr}\)then
11: Target template with maximal angle \(\theta_{max}\) is replaced by the optimal estimation to be updated;
12:endif
13:endfor
```  
**Algorithm 1** PS-L1 tracker
## 3 Results and Discussion
### Effect of Pomelo Cultivars on Flavonoid Composition of the Juice  
Flavonoids compounds exist widely in citrus fruits, and they are the main reason for the bitter taste and bioactive properties of citrus fruit juices [46]. Therefore, the flavonoid composition of different cultivars' pomelo juices was investigated, and the results were presented in Table 5. The narirutin, naringin, hydroxynaringenin, naringenin, sinensistein and hesperidin concentrations of pomelo juices were measured. Only the narirutin and naringenin were detected in all the pomelo juices, which were the main bitter substances of pomelo juices [47]. Moreover, these flavonoids contents of most HP juices were higher than those of LP juice, which might be due to these flavonoids mainly existing in the pulp. HPGT juice obtained the highest concentration of flavonoid composition, which was consistent with the result in Figure 4. The highest concentrations of narirutin and naringenin were observed in HPLU and LP grapefruit juice (LPGT), respectively. Among these pomelo juices, the HPLU exhibited higher contents of narirutin and naringenin, indicating that the taste of HPLU was more bitter. [PERSON] et al. also found that the HP citrus juice had higher contents of narirutin [48]. Meanwhile, sinensistein was found in grapefruit juices and _cv._ Wendanyu juices, while the hesperidin and hydroxynaringenin were only detected in _cv._ Liangpingyu juice and grapefruit juice, respectively. These results indicated that the contents and types of flavonoid compounds of pomelo juices were significant influenced by their cultivars. Therefore, _cv._ Guanxiyu juice was much better for consumption for people in comparison with other pomelo juices.  
Figure 4: Effects of cultivars on TP, TF and ascorbic acid content of pomelo juices. (**A**) LP juice; (**B**) HP juice; data are expressed as means \(\pm\) standard deviation (\(n\) = 3); different letters indicate significantly different means at \(p\) < 0.05.
# An Embedded System Based on Raspberry Pi for Effective Electrocardiogram Monitoring
## 3 The ECG Circuit Design
### The Schematic Diagram of the ECG Circuit
#### 3.2.6 The Summing Amplifier  
To make the ECG signal compatible with the analog to digital converter, we have used the summing amplifier to do level shifting.  
Adding a DC offset is necessary to remove the dc component and make all parts of the signal above the ground. After the summing amplifier, an extra amplifier with a gain = 2 was added to increase the amplitude of the ECG signal before the peak detector.
# Parameter Identification of Structures with Different Connections Using Static Responses
## 4 Parameter Identification for a Rigid Frame  
### Member Stiffness Matrix of a Rigid Frame  
Various structures, such as bridges and multi-story industrial plants, are composed of frames. A frame is a rod structure consisting of beams and columns to resist loads. In contrast to the plane truss element, the plane frame is subjected to shear forces and bending moments in addition to axial forces. The member stiffness matrix of the plane frame [25] is given by Equation (7). This matrix has three degrees of freedom per joint and includes axial effects, as well as shear force effects and principal bending moment effects [27]. To consider the damage incurred in the structure, the cross-sectional area \(A_{u}\) and moment of inertia \(I_{u}\) of the damaged member are included in the matrix, which constitute the unknown parameter \(P\) that needs to be identified.  
\[\mathbf{k^{\prime}}=\left[\begin{array}{cccc}\frac{A_{u}E}{L}&&&\\ 0&\frac{12E_{I_{u}}}{L^{3}}&&SYM\\ 0&\frac{6E_{I_{u}}}{L^{2}}&\frac{4E_{I_{u}}}{L}&&\\ -\frac{A_{u}E}{L}&0&0&\frac{A_{u}E}{L}&\\ 0&-\frac{12E_{I_{u}}}{L^{3}}&-\frac{6E_{I_{u}}}{L^{4}}&0&\frac{12E_{I_{u}}}{L ^{3}}\\ 0&\frac{6E_{I_{u}}}{L^{2}}&\frac{2E_{I_{u}}}{L}&0&-\frac{6E_{I_{u}}}{L^{2}}& \frac{4E_{I_{u}}}{L}\end{array}\right] \tag{7}\]  
The terms in Equation (7) in addition to \(A_{u}\) and \(I_{u}\) are the modulus of elasticity (\(E\)) and length (\(L\)) of the member.
# PPP and PPP-AR Kinematic Post-Processed Performance of GPS-Only, Galileo-Only and Multi-GNSS
###### Abstract  
Precise point positioning (PPP) has been used for decades not only for general positioning needs but also for geodetic and other scientific applications. The CNES-CLS Analysis Centre (AC) of the International GNSS Service (IGS) is performing PPP with phase ambiguity resolution (PPP-AR) using the zero-difference ambiguity fixing approach also known as \"Integer PPP\" (IPPP). In this paper we examine the postprocessed kinematic PPP and PPP-AR using Galileo-only, GPS-only and Multi-GNSS (GPS + Galileo) constellations. The interest is to examine the accuracy for each GNSS system individually but also of their combination to measure the current benefits of using Galileo within a Multi-GNSS PPP and PPP-AR. Results show that Galileo-only positioning is nearly at the same level as GPS-only; around 2-4 mm horizontal and aound 10 mm vertical repeatability (example station of BRUX). In addition, the use of Galileo system--even uncompleted--improves the performance of the positioning when combined with GPS giving mm level repeatability (improvement of around 30% in East, North and Up components). Repeatabilities observed for Multi-GNSS (GPS + GAL) PPP-AR, taking into account the global network statistics, are a little larger, with 8 mm in horizontal and 17 mm in vertical directions. This result shows that including Galileo ameliorates the best positioning accuracy achieved until today with GPS PPP-AR.  
GNSS; GPS; Galileo; precise point positioning (PPP); PPP with phase ambiguity resolution (PPP-AR); integer PPP (IPPP); ambiguity resolution; zero-difference ambiguity fixing; multi-GNSS 2019 11 2477; doi:10.3390/rs11212477
## 6 Software Architecture of the Multi-Sensor Platform
### Application Phase
#### 6.2.2 UDP Server Task  
The UDP server task is an event-triggered task to receive data to be sent to the cloud from the sensors. This task sets up a network socket using the LWIP stack to send the data packets as a UDP message. The task first creates two sockets and sends the data to these sockets to achieve data redundancy on the cloud application side. After successful data transmission, it moves to sleep mode and waits for the next trigger for data. A unique data format is considered for each sensor to differentiate sensor data.  
The external sensors, for example, vibration sensor _ADCMXL3021_, after configuration, send data array packets directly stored on the RAM of the microcontroller via the DMA channel. After successfully writing data to memory, an interrupt is generated, triggering the sensor task to acquire the data from the RAM cache, preprocess the data, pack it with a timestamp, and notify the UDP server for data packet transmission. The UDP server packs the data as a UDP packet with necessary headers and information and sends it to the destination address in two ports to achieve redundancy.
## 3 Materials and Methods  
### Dual Transform for Moving Objects  
In general, the geometric dual transform maps a hyper-plane \(h\) from \(R^{m}\) to a point in \(R^{m}\), and vice versa. In this section, we briefly present how the duality transformation operates in a one-dimensional case. A line from the plane \((t,y)\) or \((t,x)\) is mapped to a point on the dual plane (see Figure 1).  
1. **Hough-X**: The equation \(y(t)=ut+a\) is mapped to a point \((u,a)\), where axes \(u\), \(a\) represent the slope (that is, velocity) and intercept of an object's trajectory, respectively. Thus, we get the dual point \((u,a)\), the so-called Hough-X transform.
2. **Hough-Y**: The equation \(y(t)=ut+a\) is rewritten as \(t=\frac{1}{u}y-\frac{a}{u}\), a different dual representation, the so-called Hough-Y transform. The point in the dual plane is represented as \((b,c)\), where \(b=\frac{-a}{u}\) (the intersection with the line \(y=0\)) and \(c=\frac{1}{u}\).  
It is worth mentioning that the Hough-X transform cannot represent vertical lines, while horizontal lines cannot be represented using the Hough-Y transform. Nonetheless, both transforms are valid, since in our setting, velocity is bounded by \([u_{min},u_{max}]\), and thus lines have a minimum and maximum slope.
# Ground Based Hyperspectral Imaging to Characterize Canopy-Level Photosynthetic Activities
## 2 Materials and Methods
### Data Acquisition  
#### 2.2.1 Hyperspectral Data Acquisition  
The GPhenoVision system was configured with a spectral module for spectral data acquisition [48]. The spectral module consisted of one spectrometer (Flame VIS-NIR, Ocean Optics Inc., Largo, FL, USA) and one hyperspectral camera (MSV500, Middleton Spectral Vision, Middleton, WI, USA) (Figure 1). Both sensors were calibrated radiometrically and spectrally (also spatially for the hyperspectral camera), covering the spectral range from 400 nm to 1000 nm with a spectral sampling resolution of 2 nm. For the spectral calibration, the spectrometer was calibrated by the manufacturer, whereas the hyperspectral camera was calibrated by the authors using three calibration lamps. Details of spectral and spatial calibration can be found in a previous study [37]. For the radiometric calibration, the spectrometer, the hyperspectral camera, a spectroradiometer (Ocean Optics Inc., Largo, FL, USA), and an illumination source (DC-950, Fiber-Lite, Dolan-Jenner Industries, Boxborough, MA, USA) were attached to an integrating sphere (4P-GPS-060-SF, Labsphere, North Sutton, NH, USA). By adjusting the illumination intensity at different levels, calibration models between digital counts and irradiance were established for the spectrometer and hyperspectral camera. The spectrometer was equipped with a cosine corrector (field of view of 180\({}^{\circ}\)) facing towards the sky, whereas the hyperspectral camera was positioned nadir to the ground. The spectrometer acquired the irradiance spectra of the sunlight. Depending on the solar irradiance intensity, the sampling frequency of the spectrometer varied from 20 Hz to 50 Hz, so that signal intensities could stay in the optimal range without saturation. The hyperspectral camera was positioned 2.15 m above the ground, collecting radiance spectral images of plant canopies of two plots at a time. To ensure the spatial aspect ratio, the hyperspectral camera ran at 100 frames per second (FPS) and the platform moved at an approximate speed of 0.5 m/s. The system moved sunward along the plot direction (east-west direction) to avoid potential issues caused by shading effects. Six data collection trials were conducted on 9 August 2018 (beginning approximately 24 h after Diuron application) at 08:00, 10:00, 12:00, 14:00, 16:00, and 18:00, respectively.
###### Abstract  
The functionality of buried metallic pipelines can be compromised by the electrical lines that share the same right-of-way. Given the considerable size of shared corridors, computer simulation is an important tool for performing risk assessment and mitigation design. In this work, we introduce an open-source computational framework for the analysis of electromagnetic interference on large earth-return structures. The developed framework is based on FLARE--an efficient finite element solver developed by the authors in MATLAB(r). FLARE includes solvers for problems involving static electric and magnetic fields, and DC and time-harmonic AC currents. Quasi-magnetostatic transient problems can be studied through time-marching or--for linear problems--with an efficient inverse-Laplace approach. In this work, we succinctly describe the optimization of time-critical operations in FLARE, as well as the implementation of a transient solver with automatic time-stepping. We validate the numerical results obtained with FLARE via a comparison with the commercial software COMSOL Multiphysics(r). We then use the validated time-marching analysis results to test the accuracy and efficiency of three numerical inverse-Laplace algorithms. The test problem considered is the assessment of the inductive coupling between a 500 kV transmission line and a metallic pipeline buried in the soil.  
numerical simulation; pipeline integrity; AC interference; earth-return; finite element analysis; inverse-Laplace; optimization +
Footnote †: journal:  
Article  
FLARE: A Framework for the Finite Element Simulation of Electromagnetic Interference on Buried Metallic Pipelines
# Calibration of Mean Wind Profiles Using Wind Lidar Measurements
## 4 Results and Discussion
### Optimization of the Choice of Measurement Elevations  
Assuming that the Lidar lowest measurement point is at 30 m above the ground, and a maximum of 12 measurement points is available, four additional scenarios are considered, indicated as setups #1 through #4 in Table 2. The choice of the measurement points in the additional setups is driven by the idea that a larger number of points close to the lowest elevation should bring an improvement in the accuracy of the derivatives of the identified profile, therefore improving the accuracy of its extrapolation to the lower elevations.  
In particular, limited to Case (a) in Table 1, the results are presented in Figure 6 in terms of bias and Coefficient of Variation (CoV) within an ensemble of \(M=1000\) cases of the mean values \(\mu_{20}(N)\) and \(\mu_{2d}(N)\). These results highlight that the strategy of gathering measurement points close to the lowest point does not lead to any substantial increase in the accuracy of the estimated values of \(z_{0}\) and \(z_{d}\). Similar results were obtained for cases (b), (c) and (d) in Table 1, which are not shown here for brevity.  
Figure 6: Bias and Coefficient of Variation of \(\mu_{20}\) and \(\mu_{2d}\) as a function of the sample size \(N\) of available measured profiles for the different measurement Setups of Table 2: Case (a) \(z_{0}=0.05\) m, \(z_{d}=5\) m.
## 3 Methodologies
### Multi-Channel Emotion Recognition Algorithm for the Classroom
#### 3.2.1 EfficientNetv2 Network  
EfficientNetv2 offers several advantages, including a lightweight architecture, rapid processing speed, and high accuracy. These characteristics render it particularly well-suited for efficient image feature extraction tasks. Consequently, this study utilizes EfficientNetv2 to extract image features.  
EfficientNet was introduced by Google in 2019, and its core innovation lies in the \"composite scaling factor\" approach. This method enhances the computational efficiency and accuracy by simultaneously optimizing the network's depth, width, and resolution [29]. While EfficientNetv1 has demonstrated a significant performance on the ImageNet dataset, it suffers from slow inference times and is heavily dependent on large volumes of training data [30].  
To address these challenges, the Google team introduced EfficientNetv2 in 2021 (Figure 5). This new architecture is designed to adapt to application scenarios that demand higher speed and efficiency by balancing design elements to enhance the training speed while reducing the number of parameters. EfficientNetv2 presents two significant advancements over its predecessor, EfficientNetv1: First, it employs the Fused-MBConv module instead of the MBConv module for shallow networks. This modification optimizes the balance between the performance and efficiency by eliminating ascending convolutions and enabling shortcut branches under specific conditions. Secondly, EfficientNetv2 introduces a non-uniform scaling strategy for the network. This approach allows differential adjustments based on each stage's contribution to the overall network performance; certain stages are allocated more resources while others are simplified accordingly. Such refinements enhance the overall network efficiency without compromising or even improving its performance.  
Figure 4: Impact of WIoUv3 hyperparameter selection on gradient gain curve [27].  
Figure 5: Adjustment module diagram of EfficientNetv2.
# Experimental Study of 6 LoPLC for Home Energy Management Systems
## 3 Hanadu 6 LoPLC
In terms of use-cases, CoAPwas intended for smart energy, building automation and control applications, which are in line with this study. Figure 1: Hanadu low-power PLC system over the IPv6 network (6 LoPLC) test bench showing (**a**) the main functional components and (**b**) the communication among system components. As seen in Figure 3, the system log shows the time-stamped message about communication activities between the HEMS controller and the lamp. For example, the log shows that a control command is used to increase the luminance of the lamp to 74%; such low-level information is useful for system maintenance and support.