esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Installation of the Structural Monitoring System  
The span #8 of the aqueduct is located right in the middle of the riverbed and has the highest moment frame supports among all spans at the upstream and downstream ends. It is selected as the target span for this study.  
Part of the monitoring system is shown in Figure 2. The static level gauge detects the vertical displacement at its location by measuring the liquid level variation inside its chamber. Due to its work mechanism, all gauges in the same assembly set should  
Figure 1: Flowchart of research methodology.  
be installed at about the same elevation. For the sake of field installation and future maintenance, two sets of static level gauges are mounted on the top of the left and right sidewalls of the trough above the composite tied-arch, and each set has one gauge at the upstream end, mid-span, and downstream end of the trough, respectively, which are designated as (1) in Figure 2. An ultrasonic water level gauge is fixed onto the side surface of the first upstream transverse link beam at the top of the trough, which is designated as (2) in Figure 2.  
Four joint meters (LVDTs), two on each side of the composite tied-arch, are installed across the expansion joints at the upstream and downstream abutments, which are designated as (3) in Figure 2. Vibrating-string strain gauges are installed at the upstream end, mid-span, and downstream end of the arch ring and bottom tension rod on each side of the composite tied-arch, which are designated as (4) in Figure 2. To avoid the complex stress condition at the joints between the arch ring and tension rod, the gauges at both ends of those two members are set at least one cross-dimensional size away from the joints. A detailed layout of monitoring devices at the upstream abutment of the composite tied-arch is shown in Figure 3.  
The structural monitoring system was installed in November 2023 and started data collection on \(1\) December of the same year. The sampling interval for all monitoring devices is set to be about \(1\) min (\(60\) s), and the monitoring data are transmitted to the control center via a 4G wireless network. This research covers the analysis of the monitored data for over a year from \(1\) December 2023 to \(31\) December 2024, during which the aqueduct underwent operation (water conveyance) from \(9\) March 2024 to \(2\) May 2024.  
Figure 2: Installation scheme of part of the structural monitoring system. (1) Static level gauges, (2) ultrasonic water level gauge, (3) joint meters (LVDT), (4) vibrating-string strain gauges.
# Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
## 3 Experimental Result  
In this section, extensive experiments are conducted to demonstrate the performance of proposed FHRR-Net. Results on real-world road image and synthetic road data are used to evaluate the performance of our model against several state-of-the-art neural network methods. The synthetic dataset restoration score is quantitatively measured by PSNR (peak signal-to-noise) and SSIM (structural similarity index) which are often used as indicators for evaluating performance in several image restoration works. Real-world road image experiment results are also compared qualitatively. We also show the effectiveness of proposed method through ablation study.
# Modeling and Optimization of NO2 Stations in the Smart City of Barcelona
## 3 A Case of Study: Barcelona City
### Meteorological Stations  
To obtain data on the meteorological variables measured in Barcelona during the study period, the corresponding files for each year are downloaded from Open Data Barcelona. Each file contains the measurement date, hour (if applicable), station code, meteorological variable code, and the corresponding value. A separate file from Open Data provides the station identifiers and coordinates, while another file in the same repository lists the codes for meteorological variables and the units in which they are measured. Table 2 displays the stations' identifiers and coordinates.  
The selected variables for the analysis of meteorological data are Daily Mean Temperature (TM), measured in \({}^{\circ}\)C; Daily Mean Relative Humidity (HRM), measured in %; Daily Mean Atmospheric Pressure (PM), measured in hPa; Accumulated Daily Precipitation (PPT), measured in mm; Daily Solar Radiation (RS24h), measured in MJ/m\({}^{2}\); Daily Mean Wind Speed at 10 m (VVM10), measured in m/s; and Daily Mean Wind Direction at 10 m (DVM10), measured in degrees. Each variable's maximum, minimum, and mean values are measured at each meteorological station. For the study, we work with the mean value of each variable. Station X2 only has records for the temperature and relative humidity  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Station ID** & **Longitude** & **Latitude** \\ \hline
50 & 2.1874 & 41.38640 \\
43 & 2.1538 & 41.38530 \\
44 & 2.1534 & 41.39870 \\
57 & 2.1151 & 41.38750 \\
4 & 2.2045 & 41.40390 \\
42 & 2.1331 & 41.37880 \\
54 & 2.1480 & 41.42610 \\
58 & 2.1239 & 41.41843 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Air quality stations and their locations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Station ID** & **Longitude** & **Latitude** \\ \hline D5 & 2.12379 & 41.41864 \\ X2 & 2.18847 & 41.38943 \\ X4 & 2.16775 & 41.38390 \\ X8 & 2.10540 & 41.37919 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Meteorological stations and their locations.  
variables throughout the entire period, and the reasons for this data gap are unknown. Due to the lack of data, this station is not considered in the study. Figure 1 shows the location of all the air quality and meteorological stations in Barcelona that are considered in the study.
# Off-Line Camera-Based Calibration for Optical See-Through Head-Mounted Displays
## 3 Methods
### Calibration Software
#### 4.2.1 Intrinsic Camera Calibrations  
The viewpoint camera and the tracking camera were calibrated with a conventional calibration technique [23] that required storing multiple camera views of a planar pattern (i.e., OpenCV checkerboard). Linear parameters (i.e., intrinsic camera matrix) and non-linearities due to camera lens distortion (i.e., distortion parameters) were computed using non-linear least-squares minimization (i.e., the Levenberg-Marquardt algorithm). This procedure was performed using the MATLAB camera calibration toolbox (R2018b MathWorks, Inc., Natick, MA, USA).
## 1 Introduction
Different types of ZIFs are synthesized in which ZIF-8 has gained remarkable attention because of its unique properties. ZIF-8 composed of Zn (II) linked with 2-methylimidazolate bridges forming a sodalite (SOD)-like structure similar to Y and X types of zeolite structures. ZIF-8's porous structure is composed of large cavities with 1.16 nm diameters, connected through 0.34 nm pore openings [29; 33; 34]. ZIF-8 has high thermal and chemical stability that cannot be seen in many MOFs and other ZIFs [29; 31; 35]. ZIF-8 is stable in refluxing benzene, methanol, water, and aqueous sodium hydroxide for seven days at different temperatures, from 25 \({}^{\circ}\)C to the boiling point of each solvent [31]. The crystal structure of ZIF-8 is stable in air for 5 h at 300 \({}^{\circ}\)C, in argon for 5 h at 400 \({}^{\circ}\)C, and in N\({}_{2}\) at 550 \({}^{\circ}\)C [36; 37], and under pressure [38]. For the first time, [PERSON] et al. synthesized ZIF-8 in 2006 by dissolving 2-methylimidazol and zinc nitrate tetrahydrate in dimethylformamide (DMF) at 140 \({}^{\circ}\)C for 24 h [31]. Trapping of DMF molecules inside the ZIF-8 cavities was one of the disadvantages of [PERSON]'s method [32]. Since then, many other research teams synthesized ZIF-8 using different solvents with smaller kinetic diameters [32], such as methanol [39], and straight and branched aliphatic alcohols [40]. These organic solvents are toxic and expensive. Recently, a great deal of effort has been made to synthesize ZIF-8 in aqueous solution by using metal to ligand ratios of 2 (Zn:Hmim = 2) [32; 41]. [PERSON] et al. synthesized ZIF-8 from stoichiometric Zn and Hmim in the presence of ammonium hydroxide at room temperature with Zn:Hmim:NH\({}_{3}\):H\({}_{2}\)O molar ratio of 1:2:32:157 [32]. In this study, we synthesized ZIF-8 using an environmentally-friendly method in aqueous solution at room temperature. ZIF-8 samples were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), and FT-IR techniques. The effect of humidity on adsorption properties of toluene on ZIF-8 samples were studied. The effect of humidity on adsorption capacity/efficiency is an important factor for application of sorbents in real conditions. The adsorption experiments were carried out in a fixed-bed adsorption setup. Adsorption behavior of the ZIF-8 samples (as synthesized and thermally-activated) toward toluene was studied at different RH. The experimental data were fitted into the [PERSON] and [PERSON] mathematical models.
# A Novel Resource Allocation Scheme in NOMA-Based Cellular Network with D2D Communications
### Proposed Tree-Based Search Algorithm (TSA) for Power Allocation
\begin{array}{c}n\\ 1\end{array} \right)+\cdots+\left( \begin{array}{c}n\\ U_{\max}\end{array} \right)\right)+\left( \begin{array}{c}L\\ 1\end{array} \right)+\cdots+\left( \begin{array}{c}L\\ U_{\max}^{D2D}\end{array} \right)\right\}\),  
Figure 4: Computational complexity cost comparison of the tree-based search algorithm and the exhaustive search algorithm with \(\Delta=0.01\). Figure 3: Flow chart of the tree-based search power allocation algorithm (TSA). ```
1:Input: \(\bullet\) Initial the judgement criteria: \(\Omega_{0}=0\) and \(\Gamma_{0}=0\). \(\bullet\) Initial list of the channel states of the CUs in the same SC \(\{h_{1,m},\ldots,h_{n,m}\}\). 2:Tree-based search: for\(k\in\{1,\ldots,n_{m}\}\)do repeat Calculate \(\beta_{k,m}\) according to survival nodes of the previous layer and the minimum interval \(\Delta\) of the power allocation coefficients which satisfies the condition (26) and (27). Calculate \(\Omega_{k}\) and find out the nodes of the same \(\Omega_{k}\) to put them into \(z\) groups. for\(r\in\{1,\ldots,z\}\)do Calculate the set of \(\Gamma_{r}\) and \({\Gamma_{r}}^{*}=\max\{\ |\) and make the new set of \(\Gamma_{k}\). Save the nodes of \({\Gamma_{r}}^{*}\) and delete the others. endfor until\(k=n_{m}\). If\(k=n_{m}\)then Calculate \(\beta_{n_{m}}\) and \(g=1\). \({\Gamma_{n,m}}^{*}=\max\{\Gamma_{n,m}\}\). Save the nodes of \({\Gamma_{n,m}}^{*}\) and delete the others. Finally get the final survival path. endif endfor
```  
**Algorithm 2** Tree-based Search Power Allocation Algorithm (TSA)
## 4 Handover Dataset Collection and Deep Learning Model Performance Analysis  
The most common mobile network handover method is based on RSRP measurements. Therefore, the main essence of the proposed handover mechanism is to predict the RSRP from neighboring eNBs using deep learning models. The implementation of the deep learning models is achieved with Python, using Keras (version 2.2.2) and Tensorflow (version 1.8.0), as a backend. We compare the performance of four deep learning models, including MLP (multilayer perceptron), CNN (convolutional neural network), RNN (recurrent neural network), and LSTM (long short-term memory), and we apply the deep learning models with the best prediction accuracy.
## 2 Project Overview and the Synchronous Propulsion and Assembly Method  
### Project Overview  
The airport connecting line (west section) project of the Shanghai rail transit city line is the trunk line connecting Shanghai's international airports. The tunnel runs east-west as a whole, with a total length of about 68.6 km, including an underground section of 56.7 km. The maximum overburden of the tunnel top is 33 m, the minimum is 14 m, the minimum radius is R520, the maximum longitudinal slope is 2%, and the length below the radius of R600 is about 660 m. The construction sequence of the tunnel is shown by the red arrow in Figure 1. The main crossing strata are \(\copyright\) A Silt clay, \(\copyright\) A Silt clay, \(\copyright\) A Silt clay, and \(\copyright\) A Siltly sand mixed with silty clay. The specific soil layer structure is shown in Figure 1.  
Figure 1: Longitudinal profile of excavation soil layer in synchronous propulsion and assembly mode.  
The outer diameter of the tunnel lining ring is \(\Phi\)13.6 m, the inner diameter is \(\Phi\)12.5 m, and the thickness of the segment is 550 mm. The lining adopts a universal wedge-shaped ring, in the form of a \(6+2+1\) (1/2) block, which consists of six standard segments, two adjacent segments, and one capping block. The average length of the lining ring is about 2 m. The segment is made of high-performance concrete with a strength of C60 and an impermeability grade of P12. Taking the standard block as an example, the outer side of the segment contact surface is wrapped with an elastic gasket covering \(42.352^{\circ}\). The inner side of the contact surface of the segment is covered with a packer, and the packer coverage angle is \(40^{\circ}\). Each standard block is directly loaded by four jacks, and the contact area between the jack and the segment is protected by a shoe plate.
# The Soundscape Hackathon as a Methodology to Accelerate Co-Creation of the Urban Public Space
## 3 The Soundscape Hackathon Event
### Equipment
#### 3.4.3 Audio Rendering Techniques  
Two 3D audio rendering techniques were available and recommend to use during the Soundscape Hackathon: wave field synthesis (WFS) and ambisonics. These are physics-based audio reconstruction techniques that aim to create a particular acoustical pressure field at the location of the listener [35] using loudspeaker arrays. Both techniques provide 3D localized sound to reconstruct virtual environments, based on a definition of the room, the audio signal and the desired playback location.  
Ambisonics, introduced by [PERSON] et al. [36] is based on the decomposition of a sound field into spherical harmonics. Using higher order ambisonics, an enlarged'sweet spot' can be achievedfor multiple listeners; however, the effect is limited to the center of the room [37]. Wave field field synthesis is achieved through superposition of elementary spherical waves, inspired by the Huygens Principle [38]. The advantage of this technique is the reproduction of physically correct sound fields up to a certain spatial aliasing frequency in an extended sweet spot area, making it suitable for multiple listeners and allowing them to move away from the center [39]. A dedicated WFS sound renderer (Barco IOSONO Core [40]) was available to accurately render different types of 3D sound objects [41; 42]: inside or outside the room (point sources) and sources at infinite distance (plane waves). The IOSONO is a user friendly audio processor which translates any incoming audio object (consisting of the audio signal, sound type and playback location) to the discrete speaker outputs. DANTE presets were provided for this configuration. Location data of all audio signals was provided using Open Sound Control (OSC) signals to the IOSONO core. The IOSONO was only available in the ASIL Lab.
## 3 Materials and Methods
### Vehicular CO Prediction Model  
#### 3.4.1 Vehicular CO Model Parameters  
The presented model aims to predict the daily traffic CO emissions and create prediction maps at different times of a day by using GIS techniques. The traffic CO descriptor (i.e., the dependent parameter) in the current study is the vehicular CO emissions measured every 15 min. The contributing parameters to vehicular CO emission were first selected depending on the previous studies and with consideration of traffic condition, weather characteristics, the surrounding (LULC), topography, and the building heights in the study area. These parameters are the number of vehicles, number of heavy vehicles, the number of motorbikes, temperature, humidity, wind speed, wind direction, LULC and digital surface model (DSM). Many studies have been conducted based on these parameters [32; 33; 34; 35; 36]. There are many factors that could affect the vehicular emissions such as engine condition and the fuel  
Figure 4: Location of traffic CO and meteorological parameter measurement on a highway section.  
Figure 3: The sampling locations.  
type (gasoline, diesel); however, these data cannot be detected through field survey. On the other hand, the data collection did not contain vehicle speed information because the study area is very small and the variation between the vehicles speed is difficult to differentiate. Consequently, the vehicle speed profile is stable during the day. There are many vehicular emissions studies conducted without adopting vehicle speed, such as [25]. The parameters' statistics are shown in Table 1. The NN model was used to find out the degree of contribution of each mentioned parameter for estimating the vehicular CO emission. The NN has the ability to model the complex simulations of non-linear problems such as vehicular CO emission. However, contributing parameters to vehicular CO emission may not be directly used as inputs in the NN model, because of high levels of correlation between the factors resulting in multicollinearity, which can reduce the precision of estimating the vehicular CO emission. Moreover, using a large number of contributing parameters to vehicular CO emission (traffic CO predictors) as input layers of NN can generate over-fitting problems and increase the complexity ratio to run NN model. In the proposed NN model, only relevant and low-correlated parameters were used as inputs. The relevant and significant factors were selected using the CFS model. Table 2 shows the traffic CO measurements results.
## 5 Conclusions  
IFGD process identification problem has always been a challenging task in the industrial field. In our work BEMD-TCN approach is proposed to address this problem, where BEMD is used to decrease the non-stationary degree of the IFGD process, while TCN model is modified to model each subprocess. Comparative studies suggest that, among the models that appeared in the previous literature (i.e., RNN, BP network, Hammerstein model and TCN), our approach yields the best modeling performance in terms of MAPE and RMSE in all seasons. To be specific, the score of MAPE/RMSE reaches 0.00058/0.0059 in spring, 0.00089/0.0058 in summer, 0.00091/0.0063 in autumn, and 0.00062/0.0043 in winter. In this sense, the objective of accurate modeling industrial flue gas desulfurization process is achieved with the proposed approach. The proposed modeling approach is easily understandable while still assuring high modeling accuracy, and the authors believe that the method will find wide application in fault detection and intelligent control problems in the future.  
Conceptualization, X.L. and K.W.; methodology, X.L.; software, Q.L.; validation, X.L., Q.L. and K.W.; formal analysis, Q.L.; investigation, X.L.; resources, K.W.; data curation, X.L.; writing--original draft preparation, Q.L.; writing--review and editing, K.W.; visualization, X.L.; supervision, X.L.; project administration, X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 61873006; the Beijing Natural Science Foundation grant number 4212040.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Season**} & \multirow{2}{*}{**Metrics**} & \multicolumn{6}{c}{**Approaches**} \\ \cline{3-8}  & & **RNN** & **BP Network** & **Hammerstein** & **TCN** & **BEMD-TCN** \\ \hline \multirow{2}{*}{Spring} & MAPE & 0.0089 & 0.0128 & 0.0126 & 0.0041 & 0.00058 \\  & RMSE & 0.0645 & 0.0829 & 0.0801 & 0.0321 & 0.0059 \\ \multirow{2}{*}{Summer} & MAPE & 0.0061 & 0.0257 & 0.0103 & 0.0039 & 0.00089 \\  & RMSE & 0.0414 & 0.0851 & 0.0761 & 0.0299 & 0.0058 \\ \multirow{2}{*}{Autumn}{} & MAPE & 0.0051 & 0.0274 & 0.0109 & 0.0031 & 0.00091 \\  & RMSE & 0.0348 & 0.0860 & 0.0775 & 0.0236 & 0.0063 \\ \multirow{2}{*}{Winter} & MAPE & 0.0058 & 0.0125 & 0.0102 & 0.0025 & 0.00062 \\  & RMSE & 0.0404 & 0.0826 & 0.0758 & 0.0199 & 0.0043 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparisons between different modeling methods.  
**Acknowledgments:** The authors would like to thank the Editor-in-Chief for reading the manuscript and providing valuable comments. The authors would also like to thank the anonymous reviewers for their valuable comments and suggestions, which helped improve this paper greatly.
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 3 Road Extraction Based on Different Data Sources
### Road Extraction Based on Hyperspectral Images
#### 3.2.2 Status and Prospects  
Only a few reports on extracting road information from spaceborne hyperspectral images are available. This is because the spatial resolution is insufficient to extract road information accurately (e.g., the spatial resolution of Hyperion and Gaofen-5 hyperspectral imagers is 30 m), especially for urban roads and narrow roads. In addition, spaceborne hyperspectral images are difficult to acquire, and public datasets for road extraction are not available. Airborne hyperspectral images are still the main data source for the study of road extraction, because of their higher spatial resolutions and lower costs compared to those of the spaceborne data.  
Hyperspectral images have shown considerable potential for road condition detection, road material identification, road pothole detection and crack detection. However, the preprocessing accuracy of the hyperspectral images should be improved to promote their applications. Hyperspectral data with geometric correction and relative radiometric calibration can meet this requirement for qualitative applications such as road detection. However, for quantitative applications such as pavement material recognition, more steps are required, such as high-precision absolute radiometric calibration, spectral calibration and atmospheric correction.