esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones
## 4 Performance Evaluation  
We conduct experiments in a real indoor environment to verify the accuracy and stability of the fused indoor positioning method proposed in this paper. It is worth noting that there is prior work that has proposed an indoor positioning method integrating BLE and PDR [10]. However, we will not compare their work for the following reasons. First, the fingerprint-based method is adopted for BLE positioning in [10]. However, it is labor-intensive to build and maintain the fingerprint database. Second, a robust filter based on the EKF is adopted to fuse the positioning results obtained using PDR and BLE-based methods in [10], which is totally different from the particle filtering framework adopted in our work. Actually, it is rather difficult to fully reproduce the method proposed in [10]. Therefore, we only compare the positioning results of our proposed fusion positioning method with pure PDR and BLE-based positioning methods. This section presents the experimental setting and results and makes an analysis.
## 2 Literature Review
### Research Framework and Hypothesis Development
Accordingly, SST's critical offline and online attributes, specifically perceived location convenience and ubiquity, can also predict user satisfaction and attitude [18]. Thus, we proposed the following hypothesis:  
**H6:**: _Perceived convenience of the kiosk positively impacts (H6a) satisfaction with the kiosk and (H6b) attitude toward the kiosk app._
**H7:**: _Perceived ubiquity positively impacts attitude toward the kiosk app._  
Based on the above studies, our proposed theoretical framework for understanding the adoption of SST in retail settings emphasizes the interaction between offline and online platforms. It suggests that consumers' intention to use SST is primarily driven by two key factors: satisfaction and attitude. These, in turn, are influenced by three critical perceptions: system service quality, convenience, and ubiquity. Considering offline and online SSTs, the framework covers the increasingly blurred boundaries between physical and digital retail experiences. The seven hypotheses (H1-H7) related to the conceptual framework are illustrated in Figure 1.  
Figure 1: A conceptual framework with seven hypothetical relationships of research constructs, with offline features marked in blue and online features marked in green.
## 4 Modeling the SMA-LRB Isolator
This isolator is schematically shown in Figure 4. The SMA wires were added to LRB isolators with the purposes of controlling displacement, and enhancing the re-centering property and damping capacity. The SMA wires were installed on the specific points of the isolator using the steel hooks. For simplification and avoiding numerical divergence, it was assumed that the friction between the wires and hooks was negligible ([PERSON] and [PERSON] [6]). In this paper, the constitutive model of intelligent isolators developed by [PERSON] and [PERSON] [7] was used. In this model, the SMA isolator is divided into two separate systems (i.e., SMA wires and LRB isolator) based on the superposition principle. First, the hysteresis curve is found for each device. Second, they are added to each other to form the hysteresis behavior of the SMA device. This method was assessed by performing the finite element analysis of the discrete and continuous systems, using ANSYS software ([PERSON] and [PERSON] [7]).
## 2 Datasets  
### HY-2 Series Scatterometer Data of High Wind Speed Vectors  
In this study, the Level 2B near-real-time (NRT) wind vectors retrieved by maximum likelihood estimation method are used to evaluate the quality of the HY-2B/C/D operational wind products under the condition of tropical cyclones. These data, with a spatial resolution of 25 \(\times\) 25 km, are obtained from the National Satellite Ocean Application Service (NSOAS) and are distributed in a daily orbit file. It should be pointed out that the actual spatial resolution (half power footprint diameter) of the HY-2 series scatterometer is 25 \(\times\) 35 km. In order to ensure data quality, land and sea ice data are eliminated according to the flag.  
Since the launch time of the HY-2 satellite series has a sequence as shown in Table 1, the starting times are different, and the end time is 31 December 2021, for HY-2B/C/D, as shown in Table 2. Two pencil beam antennas aboard the HY-2 satellite series can observe 1350 km by horizontal polarization (HH) at the 41\({}^{\circ}\) incident angle and 1700 km by vertical polarization (VV) at the 48\({}^{\circ}\) incident angle, and can cover the global sea surface twice per day. A wide swath is suitable for observing tropical structure globally.
# Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong--An Exploratory Study
## 2 Landslide-Related Data Inventory in Hong Kong
### Airborne Light Detection and Ranging (LiDAR) Survey  
Another key factor governing landslide susceptibility is the terrain condition. To acquire such geospatial information, the GEO carried out the first airborne Light Detection and Ranging (LiDAR) survey for the whole territory of Hong Kong in 2010 [6]. The multi-return LiDAR survey technique could overcome the problem of the views being obscured by dense vegetation, thereby obtaining the 'bare-earth' profile of the city and identifying geomorphological features such as landslide scars and debris trails more accurately. A 0.5 m-grid digital terrain model (DTM) was developed using the survey results (Figure 2c). Recently, in 2020, another LiDAR survey with point cloud density enhanced from 4 points/m\({}^{2}\) to 16 points/m\({}^{2}\) was completed, which could generate an even higher resolution DTM model. This high resolution DTM could be resampled into different grid sizes or converted into numerous types of topographic information with the use of GIS applications to serve different purposes. Examples of topographic information include slope gradient, aspect, plan curvature, profile curvature, topographic position index, and upslope catchment areas. These are essential data for landslide susceptibility mapping of an extensive area.  
Figure 2: Hong Kong’s Landslide-related data inventories adopted in the current study: **(a)** Spatial distribution of natural terrain landslides in ENTLI up to 2019; **(b)** Location plan of rain gauges for the operation of the Landslip Warning System; **(c)** Digital elevation model (DEM) based on LiDAR survey results in 2010; **(d)** Extract of the 1:20,000-scale geological map.
# Remote Sensing Multimodal Image Matching Based on Structure Feature and Learnable Matching Network
## 3 Template Matching Based on PCA-LMN
### Learnable Matching Network
#### 3.2.2 Regression Network  
The similarity map is passed through a regression network for translation estimation, which can be represented by the function F:  
\[F:R^{w_{b}\times h_{b}\times w_{t}\times h_{t}}\to R^{n} \tag{7}\]  
where \(n\) is the number of parameters to regress, \(n=2\) for translation.  
As shown in Figure 7, The regression network consists of two blocks, each block contains a convolutional layer, followed by batch normalization and ReLU. The last layer is a fully connected layer that regresses to the parameters of the transformation.  
Figure 6: Simulation map computation by correlation layer.
sulfate (SO42\({}^{-}\)), hydrogen carbonate (HCO3\({}^{-}\)), heavy metals (lead (Pb2\({}^{+}\)), chromium (Cr3\({}^{+}\)) cadmium (Cd2\({}^{+}\)), nickel (Ni2\({}^{+}\)), copper (Cu2\({}^{+}\)) and zinc (Zn2\({}^{+}\))) [3,4] and also xenobiotic organic compounds (e.g., phenols, aromatic hydrocarbons, pesticides [5], pharmaceutical compounds and personal use [6], antibiotics [7] and microplastics [8]). The disposal of solid wastes in inappropriate places can cause significant negative impacts on soil and water bodies and public health problems, namely due to the groundwater and soil contamination with LL, and; therefore, the treatment of this effluent is necessary and important [4]. In general, the treatment of LL involves the use of biological processes (e.g., activated sludge, stabilization ponds and constructed wetlands) and physicochemical processes (e.g., coagulation, AOPs, ultrafiltration (UF) and reverse osmosis (RO)) [1; 2; 3; 5; 7]. However, the most used biological and physicochemical processes have showed variation in efficiency and high operation costs due to the use of UF and RO, and, additionally, the filtered material is returned to the landfill which will become a problem in the future [1; 9]. Electrochemical processes such as electrocoagulation (EC) have proved to be a promising option in the treatment of various types of industrial effluents [10], for the removal of dyes [11], pesticides [12], heavy metals [13; 14] and pharmaceutical compounds [15]. There still exist some studies on the use of conventional EC for leachate treatment [10; 16]. Similar to other coagulation procedures, EC is based on sedimentation or flocculation of iron and aluminum multi-charged polynuclear complexes, with high adsorption properties. Nevertheless, in EC the coagulant ions, Fe3\({}^{+}\) and Al3\({}^{+}\) are electro generated by dissolution sacrifice anodes of stainless steel and aluminum, respectively [17; 18]. The EC process is a relatively low-cost method, but the inherent metal lixiviation limits the durability of electrodes, thus increasing the operational cost [19]. An inexpensive solution for replacing the electrodes is to manufacture using industrial metallic residues (chips), since they have low cost and equal effectiveness to conventional electrodes [20]. Electrocoagulation with electrodes can be a lower cost option compared with UF and RO for removing heavy metals, micropollutants and pathogens. This technology can be used downstream of biological processes or physic-chemical processes for the removal of organic matter from LL. Thus, the objective of this study was to investigate the use of electrodes obtained from metallurgical steel swarf (SfE) for the EC treatment of LL, in order to remove total coliforms and the following heavy metals: arsenic (As), barium (Ba), cadmium (Cd), chromium (Cr), cooper (Cu), iron (Fe), lead (Pb), manganese (Mn), nickel (Ni), selenium (Se) and zinc (Zn).
## 3 Reference Numerical Modelling Approach
### Material Properties  
The mechanical response of both timber and steel components is described with the support of constitutive laws and material models available in the ABAQUS library. An isotropic, elastic-plastic [PERSON] constitutive law is used for carbon steel screws (10.9 the nominal resistance class), with \(E=210\) GPa and \(\
u=0.3\) as nominal modulus of elasticity (MoE) and Poisson' ratio. Based on [6], the yielding/ultimate stress values are set in \(f_{\mathrm{y}}=f_{\mathrm{u}}=940.3\) MPa. An ultimate strain \(\delta_{\mathrm{u}}=0.5\%\) is considered. At the same time, an orthotropic constitutive law is used for spruce [21]. To ensure more realistic behaviours for the timber members, this constitutive law is integrated with a Hill plastic criterion and a brittle failure parameter. The Hill criterion allows to account for different resistance values in the principal directions of timber, and thus for different potential critical mechanisms for the examined PO setup. The additional brittle failure law, moreover, is used to include possible crushing phenomena in the timber close to the fasteners. Once attained the ultimate resistance \(f_{\mathrm{c},90}\), a linear propagation of compressive damage is taken into account for timber. Due to lack of more detailed experimental feedback, this material degradation is set to maximize at a failure deformation of \(\delta_{\mathrm{u}}=4\)mm. This value is adapted from [20], where C24 timber members have been investigated, based on the similarity in the resistance parameters for GL24h spruce. The final input parameters for timber are listed in Table 1, based on the nominal mechanical properties for GL24h strength class [24; 25].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & Parallel to the grain \(E_{\perp}\) & 11600 \\
**Elastic Moduli** & Perpendicular to the grain \(E_{\parallel}\) & 390 \\
**(Mean Values, in MPa)** & Radial \(E\) & 390 \\  & Longitudinal shear modulus G & 690 \\ \hline  & Compression parallel to the grain \(f_{\mathrm{c},0}\) & 37.5 \\
**(Mean Values, in MPa)** & Compression perpendicular to the grain \(f_{\mathrm{c},90}\) & 3.57 \\  & Shear \(f_{\mathrm{v}}\) & 3.85 \\ \hline  & Maximum stress \(f_{\mathrm{c},90}\) (MPa) & 5 \\
**Failure** & Damage evolution & Linear \\
**(Mean Values)** & Failure displacement \(\delta_{\mathrm{u}}\) (mm) & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input mechanical properties for GL24h strength class timber (ABAQUS/Explicit).  
Figure 5: Example of **(a)** typical FE assembly (1/4 th the S#1 geometry, with \(=-15^{\circ}\)) and **(b)** detail of the STS region (ABAQUS/Explicit, hidden mesh pattern). Reproduced from [21] with permission from Elsevier®, Copyright license number 4895820420991, August 2020.  
Finally, the equivalent soft layer in Figure 5 is mechanically characterized in accordance with [20; 21], in the form of an indefinitely linear elastic material with GL24h input properties (Table 2).
# Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data
## 4 Results
### Spatial and Temporal Analysis of TWSC Variation in the GRB  
Based on CMFD precipitation, simulated ET, and runoff, we obtained the TWSC estimates of the 172 sub-basins from 2002 to 2018 using the water balance equation. Figure 8 shows the spatial distribution of the average annual TWSC in the GRB from 2002 to 2018. It can be seen from Figure 8 that the TWSC is negative in some areas of the upper reaches, while it is positive in the middle and lower reaches of most sub-basins. One sub-basin in the northeast of the basin has an obvious deficit. Both the largest losses and the largest increases in the TWSC appear in the downstream area.  
In order to further analyze the variation in TWSC in the GRB, we calculated the spatial distribution of TWSC from 2002 to 2008, as shown in Figure 9. It can be seen that there is a significant loss of TWSC in the GRB in 2003. In 2003, all the TWSC in the GRB shows a negative value, and the loss decreases from the southeast to the northwest. In 2004, the TWSC in the upstream area of the GRB also presents a negative value in most sub-basins. From 2005 to 2009, the TWSC of most sub-basins in the GRB maintains a positive value, but the value is not large. In 2010, the TWSC in the northern part of the GRB increases significantly, while TWSC in the upstream area decreases significantly. In 2012 and 2015, TWSC in the GRB increases significantly, and all sub-basins show an increase in TWSC. In 2013 and 2014, most of the sub-basins manifest a significant loss in TWSC. From 2016  
Figure 8: Spatial distribution of multi-year average (2002–2018) TWSC in the Ganjiang River Basin.  
to 2018, most sub-basins in GRB show an increase in TWSC. It should be noted that the interannual variation in TWSC is in good agreement with the interannual variation in precipitation, and the TWSC in the GRB may be more influenced by precipitation.  
The time series of annual TWSC in the 172 sub-basins from 2002 to 2018 is shown in Figure 4g. According to the TWSC of 172 sub-basins in different years, it can be seen that the TWSC in most sub-basins show a relatively consistent fluctuation during the study period. TWSC decreases most in 2003, there are obvious increases in 2012 and 2015, and all sub-basins in 2012 and 2015 present positive values in TWSC. Figure 4h shows that in most sub-basins of the GRB, the interannual variation in TWSC is consistent. The monthly mean  
Figure 9: Spatial distribution in annual TWSC in the Ganjiang River Basin from 2002 to 2018 (**a**–**q**).  
TWSC in most sub-basins increases from January to June, while the TWSC is in a state of loss from July to October. The difference in TWSC among different sub-basins is large in August but is little in other months. As described in Sections 4.2 and 4.4, the variations in precipitation and runoff in different sub-basins are also large in August, which may be the reason for the large difference in TWSC.
## 3 Background and Methods
### Data Acquisition and Processing
#### 3.1.3. Feature Selection Using LASSO  
LASSO [27; 28] is a fundamental process that was utilized for eliminating insignificant features. Regularization and feature selection were the two main LASSO processes that were used while using Ordinary Least Square (OLS) regression to minimize the other sum of squares. A restriction of overall absolute parameter values was incorporated in the minimization process. Using the minimization function presented in Equation (2), the model coefficient \(\beta\) is determined.  
\[LASSO(\beta_{i},\beta_{0})=\underset{\beta}{\text{argmin}}\sum_{i=1}^{n}\left(RV _{i}-(\beta_{i}Input_{newi}+\beta_{0})\right)^{2}+\alpha\sum_{j=1}^{k}\lvert \beta_{j}\rvert \tag{2}\]  
where the feature data is denoted as \(Input_{newi}\); the coefficient of the feature I is denoted as \(\beta_{i}\); the regularization parameter is denoted as \(\alpha\); \(RV_{i}\) denotes the response vector; and \(n\) is  
Figure 2: Dataset generation using IoT devices.  
the number of features. The smaller number of features were acquired with larger values of \(\alpha\) which made the coefficients controlled to be zero.
# The Microfabricated Alkali Vapor Cell with High Hermeticity for Chip-Scale Atomic Clock
## 4 Conclusions  
This paper presents a fast and flexible way of fabricating MEMS cells through improved MEMS technology and anodic bonding. The two hermetic cavities are fabricated by the etching process and glass-silicon-glass single chip anodic bonding in a buffer gas atmosphere. The middle channels are formed by laser micromachining. The detection result of the leak rate was 5 \(\times\) 10\({}^{-8}\) mbar L/s, which is consistent with the MIL-STD-883E standard (5 \(\times\) 10\({}^{-8}\) mbar L/s). A specially designed table-top system for the microfabricated cell has been built to characterize the absorption characteristics and CPT resonance signal. To optimize the short-term frequency stability of the chip scale atomic clock, the MEMS cell was integrated in the table-top CPT systems to investigate the maximized ratio contrast/FWHM. The MEMS cell is deemed suitable for the atomic clock application. Compared with other MEMS vapor cells produced by the research groups, such as NIST and FEMTO-ST, the leak rate and CPT contrast are at the same level. However, the linewidth of CPT is much broader with an inadequate N\({}_{2}\) buffer gas. Therefore, the CPT linewidth will be further narrowed by optimizing the N\({}_{2}\) pressure in the process of dispenser activation. An atomic clock based on a microfabricated cell will be fabricated in the future. Furthermore, the fatigue behavior and frequency stability will also be studied thoroughly by our group.  
Conceptualization, S.J. and Z.J.; methodology, B.J., X.L. and Z.S.; investigation, S.J.; validation, S.J., Y.P. and Z.J.; formal analysis, Z.J.; writing--original draft preparation, S.J. and Z.J.; writing--review and editing, S.J. and Z.J.; supervision, Z.J. and J.Q.; funding acquisition, Z.J. and Y.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (grant nos. 61805226 and 62075206).  
Not applicable.  
Figure 9: The ratio contrast/linewidth as a function of (**A**) laser power and (**B**) temperature.
## 3 DoFs Angles Estimation
### Sensor Fusion and Orientation Estimation
The \(atan2(Y,X)\) denotes the operation \(\arctan\Big{(}\frac{Y}{X}\Big{)}\) of inverse tangent, which accept solutions in the angle range of \(\big{[}-\frac{\pi}{2},\frac{\pi}{2}\big{]}\). The estimation of the rotation \(\psi_{a}\) in \(z\) axis is derived from the compass measurements along with the already estimated orientation angles \(\phi_{a}\) and \(\theta_{a}\), as shown in the following equation. This is performed in order to improve the accuracy of the rotation estimation in this axis:  
\[\psi_{a}=\text{atan2}\big{(}C_{y},C_{x}\big{)} \tag{6}\]  
where  
\[ \begin{split} C_{x}&=M_{x}\cos\left(\phi_{a} \right)+M_{y}\sin\left(\phi_{a}\right)\sin\left(\theta_{a}\right)+M_{z}\sin \left(\phi_{a}\right)\cos\left(\theta_{a}\right)\\ C_{y}&=M_{y}\cos\left(\theta_{a}\right)-M_{z}\sin \left(\phi_{a}\right)\end{split}  \tag{7}\]  
and the parameters \(M_{x}\), \(M_{y}\), and \(M_{z}\) represent the compass measurements along the corresponding axes. Subsequently, sensors' orientation can be estimated based on the gyroscope measurements on a short-term horizon, due to the resulting drifting, and by relying on the accelerometer and magnetometer measurements for a long-term orientation estimation. A complementary filter is suggested, in order to provide more accurate estimations of the orientation angles for the sensor node. This filter accounts for drifting compensation and noise reduction and is formed as:  
\[ \begin{split}\phi[n]&=\Bigg{[}\phi[n-1]+\frac{\delta \phi_{g}[n]}{\delta t}T\Bigg{]}k+(1-k)\phi_{a}[n]\\ \theta[n]&=\Bigg{[}\theta[n-1]+\frac{\delta\theta_{g }[n]}{\delta t}T\Bigg{]}k+(1-k)\theta_{a}[n]\\ \psi[n]&=\Bigg{[}\psi[n-1]+\frac{\delta\psi_{g}[n]}{ \delta t}T\Bigg{]}k+(1-k)\psi_{a}[n]\end{split}  \tag{8}\]  
Thus, concluding to the next form taking into account the Equations (3), (5) and (6):  
\[ \begin{split}\phi[n]&=\Bigg{[}\phi[n-1]+\frac{ \delta\phi_{g}[n]}{\delta t}T\Bigg{]}k+(1-k)\text{atan2}\bigg{(}\frac{A_{x}[n]} {A_{z}[n]}\bigg{)}\\ \theta[n]&=\Bigg{[}\theta[n-1]+\frac{\delta\theta_{g }[n]}{\delta t}T\Bigg{]}k+(1-k)\text{atan2}\bigg{(}A_{y}[n],\sqrt{A_{x}[n]^{2}+ A_{z}[n]^{2}}\bigg{)}\\ \psi[n]&=\Bigg{[}\psi[n-1]+\frac{\delta\psi_{g}[n]}{ \delta t}T\Bigg{]}k+(1-k)\text{atan2}\big{(}C_{y}[n],C_{x}[n]\big{)}\end{split}  \tag{9}\]  
where \(k\in(0.5,1)\) is a variable weighting the effect of its term and its typical value is close to 1.
# Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR
## 4 Discussion  
### The Need for Thermal Infrared Imaging Combined with High-Resolution Optical Data and LiDAR Data  
The technical limitations of TIR instruments mean that the resolution of TIR images is coarser than optical and LiDAR data generated from UAV-based sensors [39]. Only temperature is contained in the canopy information of TIR data, and additional data sources are needed to provide the required data regrading the tree crown. In TIR data, temperature information is available for reverse water content, stomatal conductance, evapotranspiration, and other ground-feature parameters [40; 41; 42]. However, optical data contain more information on the physiological and biochemical parameters of plants, which could cause spectral changes [43; 44]. In contrast, LiDAR data contain rich spatial structural characteristics for distinguishing the geometric information of surface features, which is used mainly for extracting the structural parameters of plants at the canopy level [33; 45].  
Therefore, using a combination of different remote sensing data can exploit the advantages of each source and the different plant information, providing a more accurate evaluation of the health status of vegetation. The improved accuracy for monitoring forest infestations via the comprehensive use of multi-source, high-resolution remote sensing data may be conducive to quickly estimating the outbreak of forest pests in a large area and taking appropriate actions to limit the spread of the pests [30; 46].