esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Current Measurement Techniques
### Snow Property Determination Techniques
#### 3.2.4 Photogrammetry  
In the Swiss Alps, and particularly in the large avalanche test site located in \"Vallee de la Sionne\", photogrammetry has been used to precisely measure the surface of the snow cover before (when possible) and after the avalanche and to map the boundaries of avalanche events [43].  
Similarly to the method of TLS or ALS, this approach allows an estimation of the mass of snow released from the starting and deposition zones. Although it is more laborious than airborne laser scanning (and inferior in accuracy and resolution), the determination of fracture or break lines definingthe boundaries are better using photogrammetry. Thanks to the evolution of the method of sensor orientation, the requirements on _in situ_ signalization can be practically avoided for the airborne operations. Nevertheless, in comparison to ALS, the method is less automated and its performance strongly depends on the image texture as well as on the relative geometry between photographs. The latter aspect is also the main limiting factor for terrestrial-based systems (when the object to baseline ratio exceeds 5). Therefore its deployment has been limited to specific scenarios.
# Effective Surface Roughness Impact in Polarimetric GNSS-R Soil Moisture Retrievals
## 1 Introduction
The coherent and incoherent components of the GNSS-R signal will be retrieved at four different polarizations: horizontal (H), vertical (V), right-hand circularly polarized (R, for simplicity), and left-hand circularly polarized (L). Furthermore, a decoupling model will be presented for a dual-polarization retrieval approach, where the ratio between either H/V or R/L polarizations is used to estimate the soil moisture content. Finally, the errors in estimating the roughness or the decoupling model will be compared to the soil moisture model, computing the expected soil moisture error for a given error in the estimation of either the surface roughness or the decoupling parameter.
## 1 Introduction  
Smart micro-grids (MGs) enable intelligent devices to adjust usage based on dynamic pricing or incentive signals. They also facilitate the integration of intermittent renewable resource and allow storage devices like electric batteries to discharge the stored energy back to the grid when it is economical [1; 2; 3]. There are some management strategies in order to operate the smart MGs, such as central management method in which the grid manager is responsible for the operation of the system [4], or the decentralized management method in which each group entity can be a decision maker [5].  
Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) are more appropriate both in terms of environmental pollution and energy costs compared to internal combustion engine vehicles [6]. One of the important advantages of the smart MG is the easier management of EVs and PHEVs due to the availability of the appropriate telecommunication infrastructure to link with them. An EV's charging station as a load aggregator can be a good solution to manage EVs and PHEVs more easily in the smart MG and to increase consumer welfare. In this case, consumers can charge their vehicles and sell energy stored in their battery to the smart MG [7]. Therefore, unlike the typical loads, PHEVs can be discharged as the storage devices. Due to the random connection and various specifications of EVs and PHEVs, there will be severe uncertainty in their load quantities [8]. In the next section, we look for the papers in which the uncertainty of EVs loads is modeled.
## 3 Material and Methods
### Experiment Environment  
Environments where the proximity detection experiment is conducted are described. Figure 4a shows a photo from the experiment. The Chungbuk National University building is used as an experimental environment. Cellular networks and university-provided Wi-Fi networks are also available within the building. The average temperature is 26.3 \({}^{\circ}\)C when we collect the data. Proximity detection in the system measures the distance between the users with mobile devices using dynamic beaconing. So, the functions of an advertiser and a scanner are performed in the mobile device with the application. The model of the mobile device is Galaxy 7 manufactured by Samsung Electronics. They have applications that advertise and scan BLE signals. Android Studio is used to make the applications. Three different environments are used to collect the BLE signals. Because the system provides mobile-mobile interaction without physical contact, proximity detection is conducted  
Figure 3: Time diagram about proximity detection.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Seq.** & **Processing 1** & **Processing 2** & **Notation** \\ \hline
1 & - & - & Raw \\ \hline
2 & Kalman Filter & - & KF \\ \hline
3 & Kalman Filter Using AE & - & KFAE \\ \hline
4 & Denoising Autoencoder & - & DAE \\ \hline
5 & Kalman Filter & Denoising Autoencoder & KF + DAE \\ \hline
6 & Kalman Filter Using AE & Denoising Autoencoder & KFAE + DAE \\ \hline
7 & Denoising Autoencoder & Kalman Filter & DAE + KF \\ \hline
8 & Denoising Autoencoder & Kalman Filter Using AE & DAE + KFAE \\ \hline \end{tabular}
\end{table}
Table 3: Data pre-processing notation table.  
without physical contact. By placing closed doors between the mobile devices, a situation is created where there is no physical contact. Figure 4b-d show the material of doors used in the experiment. Figure 4e shows the location of the advertiser and the scanner. The distance between the advertiser and the scanner is set at 0.5 m, 1.0 m, 1.5 m, 2.0 m, 2.5 m, 3.0 m, 3.5 m, and 4.0 m. The lying mobile devices are positioned 1.0 m above the floor.  
Table 4 describes the structure and parameters of the machine learning models. Ubuntu version 18.04, Python version 3.7.12, and TensorFlow library version 2.10.0 are used to implement machine learning models. In the case of SVM models, a linear kernel is used for training. Input size and output size are fixed as 30 and 8, regardless of the types of machine learning models. The 670 RSSI set is used as the train data and the 300 RSSI set is used as the test data. A total of 10 percent of the train data is used as the validation set.  
Figure 4: The experiment environments for proximity detection. (**a**) Setting of the advertiser and scanner for RSSI data collection. (**b**) Steel door. (**c**) Wood door. (**d**) Glass door. (**e**) Location of the advertiser and scanner.
This novel concept presents significant challenges, including the performance of the lasers in space, the tracking between the main spacecraft and the retroreflectors, the refractive effects of turbulence, and the design of the telescopes to achieve a high signal-to-noise ratio for the high precision measurements. The mission concept was conceived at the Alpbach Summer School 2010. **Keywords:** UTLS; stratosphere; active limb sounding; laser occultation; spaceborne retroreflectors
## 1 Introduction
The main objective of the study is to demonstrate that economic considerations can be effectively taken into account along with retaining the overall performance of the CKD diagnosis systems. The proposed approaches are based on ensemble ranking techniques with a cost-sensitive threshold selection. The proposed heuristic rule for threshold selection takes into account both worth of a set of features and the overall incurred cost concerning the selected features. To the best of our knowledge, it is the first study that addressed the notion of data acquisition cost within the framework of cost-sensitive ensemble feature ranking. We proposed two ensemble ranking techniques that use multiple decision tree-based classifiers as heterogeneous scoring functions. A schematic diagram for an ensemble feature ranker is shown in Figure 2. The proposed techniques differ in terms of the application of threshold operation. Ensemble-1 combines all the scores and thereafter automatically selects a threshold value whereas ensemble-2 applies a threshold to individual ranks, and subsequently a set of feature subsets are generated which are later combined into a consolidated feature subset. The multiple feature subsets are combined using the majority voting scheme which is also adopted by several studies [8; 17; 29]. The major contributions of this study are as follows:  
* We propose an automatic cost-sensitive threshold selection heuristic that takes into account both the worth of a set of features and the overall accumulated cost. * Decision tree-based classifiers performed favorably on the resulting features on a benchmark CKD dataset. * The features could also yield higher average accuracy with more sophisticated classification structures, which shows that these features present high generalizability. The rest of the paper is organized as follows: Section 2 deals with the literature review on the subject. The proposed methodology is discussed in Section 3 in which the proposed ensemble techniques are elaborated. Section 4 deals with the experimentation and the case study results, in which we provide a detailed treatment of both the proposed approaches along with their comparison with other related techniques. The conclusion of the study is provided in Section 5, along with a set of future directions for extending this research. Figure 1: Cost-sensitive feature ranking and threshold selection. Figure 2: Schematic diagram of an ensemble feature ranker.
## 2 Overview of the Beiyi of Shilu Iron Mine
### Rock Mass Properties  
The surrounding rocks above and below the iron, cobalt, and copper ore bodies in the Shilu iron ore mine area are mainly hard state rocks, mainly dolomite, diorite, and silicified dolomite, and schist is only locally distributed. The stability of the ore bodies and their surrounding rocks above and below is generally good. However, the development of folding and fracturing in this area has led to the fracturing and modification of rocks along the fractures and faults and to the internal extrusion of fissures. Consequently, the strength of the rocks is significantly reduced. According to an analysis of engineering properties and the distribution characteristics of geotechnical bodies in various sections of the mine area, the geological conditions of the mine area are of a simple type, and the geological exploration type is Type III (medium type of laminated rocks). In addition, several faults of a relatively large size occur in the tectonic mine area, but, in the area studied, the fault runs through the center of the Beiyi quarry. The faults may have some influence on the movement of the rocks locally, but they will not control the rock movement pattern as a whole. Hainan Shilu iron ore involves mainly steep-dipping joints, and slow-dipping joints are not developed.  
In order to obtain accurate rock mechanical parameters, the physical and mechanical indexes of each rock in the underground and mining areas were obtained through multiple processes such as on-site sampling, the preparation of specimens, a density test, wave velocity test, mechanical experiment, and data analysis, and the mechanical parameters of rock mass were determined by combining the Hoek and Brown empirical speculation methods. The density of Shilu iron ore schist is about 2798.37 kg/m\({}^{3}\), and the wave velocity is 5337.88 m/s. The density of the double permeable rock is about 2899.95 kg/m\({}^{3}\), and the wave velocity is 5759.31 m/s. The hematite is about 3675.58 kg/m\({}^{3}\) and the wave velocity is 5781.59 m/s. The average uniaxial compressive strength of schist in the North No. 1 mining area is 72.04 MPa, the elastic modulus is 29.55 GPa, the average secant modulus is 16.55 GPa, and the Poisson's ratio is about 0.35. The uniaxial compressive strengths of bipermeable rock and hematite are 68.09 MPa and 106.44 MPa, the elastic moduli are 36.97 MPa and 65.12 MPa, and the Poisson's ratio is 0.33. The average cohesion of the joint plane of the rock mass was 0.323 MPa, the average friction angle was 37.965\({}^{\circ}\), and the average friction coefficient was 0.786. This provides important mechanical data for the subsequent mechanical analysis of engineering rock mass and the numerical simulation of mining subsidence.
# Enhancing Friction Pendulum Isolation Systems Using Passive and Semi-Active Dampers
## 2 Isolation System
### Friction Pendulum System  
The FPS consists of a concave plate slider, over which the main structure can slide during the earthquake (see Figure 2a). The design of the isolator depends mainly on its geometry and on the frictional coefficient of the material. Figure 2b describes the hysteretic behavior of this type of isolators. The fundamental period of the FPS is:  
\[T=2\pi\cdot\sqrt{\frac{R}{g}}, \tag{1}\]  
in which \(R\) is the radius of the pendulum and \(g\) the acceleration of gravity, and the equation that describes the hysteretic behaviour of an FPS is given by:  
\[F=k_{a}\cdot x\pm F_{f}\quad\text{with}\quad ka=\frac{W}{R_{eff}}, \tag{2}\]  
the first term represents the restoring force and the second one is the friction force. The isolator restoring stiffness \(k_{a}\) is obtained from the weight of the structure, \(W\), over the bearing plate of the FPS, and the effective radius, \(R_{eff}\), defined as the concave plate radius minus its height from the inner edge (Figure 2a). The variables \(x\), \(\dot{x}\) are the displacement and the velocity of the plate caused by the earthquake, with a maximum value equal to \(d_{1}\) (displacement capacity). The friction force \(F_{f}\) has two phases: _pre-sliding_ and _sliding_, which are modelled as follows:  
\[F_{f}=\begin{cases}\quad\quad k_{h}x&\text{Pre-sliding}\\ sign(\dot{x})\mu W&\text{Sliding}\end{cases}, \tag{3}\]  
in which the _pre-sliding_ phase is characterized by a very high initial stiffness (\(k_{h}\)), typically \(k_{h}\) is chosen to be of two orders of magnitude greater than the restoring stiffness \(k_{a}\) and \(\mu\) is the dynamic friction coefficient which will be assumed constant within the design methodology. For high values of \(\mu\), the pre-sliding phase and the so-called breakaway effects are of great importance in case of low-intensity (serviceability) earthquakes. High values of \(\mu\) could impede the re-centering of the FPS once the earthquake has ended, generating undesirable residual displacements [4; 11].  
Figure 2: Concave plate geometry and simplified hysteretic diagram. \(R\): Radius of the isolator. \(h_{1}\): height from the inner edge of the plate. \(R_{eff}\): Effective radius (\(R-h_{1}\)). \(d_{1}\): Displacement capacity.
## 2 Theoretical Analysis  
We analyzed the propagation process with the help of a tensor method. The tensor method developed in [59] is quite useful for treating the propagation of a partially coherent beam in random media directly. With the aid of the calculation method, the atmospheric turbulence and the object surface are embedded in the optical system. The object is supposed to be isotropic. The representative model of the object is composed of a Gaussian mirror and a thin phase screen as shown in Figure 1[56; 57]. All the optical elements in the system can be expressed in \(4\times 4\) tensor form.  
According to the unified theory of coherence and polarization, \(2\times 2\) cross-spectral density matrices are used to describe the second-order statistical properties of the EGSM beam. In this paper, we use vector symbols to denote the matrices in different parts. \(W_{ij}(\overline{\mathbf{r}})\) represents the EGSM beam generated by the source, \(W_{ij}(\overline{\mathbf{t}})\) represents the beam at the target plane, and \(W_{ij}(\overline{\mathbf{\rho}})\) represents the beam at the receiver plane.  
Figure 1: (**a**) Schematic diagram for an isotropic electromagnetic Gaussian Schell-model (EGSM) beam interacting with a semi-rough target in turbulent atmosphere, and (**b**) its equivalent (unfolded) version.
# UV-Curable Silicone-Modified Polyurethane Acrylates for Food Freshness Monitoring
## 3 Results and Discussion
### Performance of UV Coatings
#### 3.2.3 The pH-Sensitive Property  
pH-sensitive intelligent materials can serve as sensors in fields including food spoilage [11], chemical reactions [28], specific chemical substance determination [29; 30], biotechnology [31; 32], biomedical applications [33; 34], and monitoring wastewater discharged from chemical industries [35; 36]. Therefore, the pH-sensitive performance of the UV coatings was explored (Figure 6). In aqueous solutions with different pH values in the range of 2-12, it can be observed that the color of the UV coatings changed gradually from deep red to yellowish brown. When the pH values were lower than 5, the color was mainly deep red. When the pH values increased from 6 to 12, the color changed from pink to yellowish brown. Thus, the UV coatings have a good pH-sensitive property based on the color change visible to the naked eye. It is well known that the pH-sensitive range of NR is 6.4-8.0 and that the color changes from red to orange [37]. The intelligent pH-sensitive indicator films prepared by the covalent bonding of NR onto cellulose nanofibrils reported by [PERSON] et al. [20] exhibited a visible color variation at a pH value of 2-10. Evidently, the pH-sensitive range of the UV coatings has been obviously expanded.  
The covalent bonding of pH indicator groups onto a polymer matrix is a reliable strategy to improve the stability of pH-sensitive intelligent materials. It also can be seen in Figure 6 that the color of the UV coatings no longer changed when they were immersed in an aqueous solution with different pH values for 20 min. Furthermore, the color remained even when the UV coatings were immersed for over 5 h. The pH-sensitive films prepared by [PERSON] et al. exhibited a color change after 30 min of exposure to ammonia with concentrations \(\geq\) 0.1 M [9]. Similarly, the intelligent pH-sensitive indicator films reported by [PERSON] et al. [20] also changed from red to yellow after 30 min of exposure to ammonia vapor. Therefore, the UV coatings exhibited considerable variation in pH-sensitivity performance and long-term color stability.  
The color variation performance of the UV coatings was also investigated. As exhibited in Figure 7, the distinctive color variation in the UV coatings in different aqueous solutions of different solvents can be observed by the naked eye. This outstanding performance suggests that the UV coatings have the potential to act as sensors for monitoring wastewater with different solvents.  
Figure 5: DSC curves of UV coatings prepared with different NR-PUAs.
## 2 Materials and Methods
### Survivability under Simulated Gastrointestinal Conditions  
The simulated gastrointestinal conditions were obtained using basic fluid, gastric fluid, and intestinal fluid. The basic fluid was prepared by dissolving (per liter): 1.12 g potassium chloride (Sigma-Aldrich), 2.0 g sodium chloride (Sigma-Aldrich), 0.11 g calcium chloride (Sigma-Aldrich), and 0.4 g potassium dihydrogen phosphate (Sigma-Aldrich) in saline solution (0.85%). The basic fluid was sterilized by autoclaving at 121 \({}^{\circ}\)C for 15 min.  
Figure 1: Scheme of microencapsulation procedure of _S. thermophilus_ TH982 cells using the extrusion technique. (**A**) The sodium alginate alone as control. (**B**) Sodium alginate with other agents.  
The simulated gastric fluid (SGJ)) and simulated intestinal fluid (SJI) were prepared based on a modified method published by [PERSON] et al. [25].  
The gastric fluid consisted of 0.01 g/L of swine pepsin (Sigma-Aldrich) and 0.01 g/L of swine mucin (Sigma-Aldrich) added directly to the sterile basic juice. The pH was adjusted to 2.5 with 1 N HCl, and the liquid was filter-sterilized using a 0.22 um membrane filter (Sigma-Aldrich).  
The intestinal fluid contained (per liter of basic fluid): 0.01 g pancreatin (Sigma-Aldrich), 0.08 g Ox-bile extract (Sigma-Aldrich), and 0.01 g lysozyme (Sigma-Aldrich). The pH was adjusted to 7.5 with 1 N NaOH, and the juice was filter sterilized. Both gastric and intestinal fluids were prepared fresh on the day of the experiment.  
Overnight bacterial cultures were obtained after subculturing in M17 broth, and capsules were prepared as described in the encapsulation procedure. One gram of each type of capsule was added to 9 mL of SGJ and incubated at 37 \({}^{\circ}\)C for 1 h to evaluate the survivability of _S. thermophilus_ TH982 under gastric conditions. For the survival of _S. thermophilus_ TH982 under gastrointestinal conditions, 9 mL of SJJ were added following gastric fluid incubation, and the capsules and free cells with simulated gastrointestinal fluid were left at 37 \({}^{\circ}\)C for a further 2 h. The cell viability was evaluated for free and encapsulated _S. thermophilus_ after gastric and gastrointestinal incubations by plating on M17 agar and using the micro drop technique, followed by incubation at 37 \({}^{\circ}\)C for 48 h.
## 4 Rate-Distortion Analysis of Using CS Encoding in WSN  
Each quantizer bit-depth provokes a different quantization error, with different impacts in the reconstructed signal. In quantized CS, two non-linear operations occur: Measurements quantization and signal reconstruction; differently from the transform-quantization-inverse transform approach. Therefore, for a fair rate-distortion performance analysis one may look for an empirical exam [13]. For that, a range of CS measurement quantities (\(M\)) and different quantizers (having as design parameter the quantizer bit-depth \(B\), resulting in \(2^{B}\) quantization levels) are employed. The experiments employ the signals from the Intel Berkeley Research lab [7], in which 54 sensor nodes were spread inside the lab sensing environmental data for more than one month.
# Simulation of Fluid Dynamics Monitoring Using Ultrasonic Measurements
## 4 Implementation of Wave Propagation Modelling
To determine the element size for the mesh, we used the two conditions: the Courant-Friedrich-Lewy condition (CFL), described in Equation (7), and the number of elements per wavelength:  
\[C_{p}\frac{\Delta t}{\Delta x_{gll}}\leq\alpha\,, \tag{7}\]  
where \(\Delta t\) is the time step, and \(\Delta x_{gll}\) is the minimum interval between two GLL grid points. An averaged Courant number \(\alpha=0.4\) was selected. and the wave celerity was defined as the highest one \(C_{p}=2416.268\) m s\({}^{-1}\). This celerity was calculated with the lowest temperature value 274.5 \({}^{\circ}\)C in the CFD simulation. In Equation (7), \(\Delta x_{gll}\) is not the mesh size itself; rather, it is the distance interval between GLL grid points within the spectral elements. This led us to calculate a mesh size \(\Delta x=8.05\times 10^{-4}\) m and a time step of \(2.3\times 10^{-8}\) s. A total of 5.000 steps were simulated in order to have a sufficient total physical duration for the waves to travel through the entire simulation domain. The meshing used in the simulation thus comprises 3,250,000 spectral elements and a total number of GLL nodes of 215,320,764. This time step corresponds to a 43.5 MHz sampling frequency, which corresponds to a fine sampling for the ultrasonic simulated signal. Figure 6 represents some examples of the preprocesses of temperature field transfer and mesh generation for SPECEFM3D. The temperature field in the tetrahedral elements of  
TrioCFD is indicated with transparent color on the interpolated field of the hexahedral mesh for SPECFEM3D. The image on the left side is a close-up on one of these three examples.