esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Response Surface and Contour Analysis
#### 3.3.2 Effect of SP and SF Interaction  
Similarly, keeping the PFA at an intermediate level, the response surface and contour in SP-SF are shown in Figure 7. The response surfaces of Figures 6a and 7a both have an \"arch\" shape. Unlike Figures 6b and 7b has a higher degree of symmetry above and below the contour lines. In addition, as can be seen from the denseness of the contours in Figure 7b that the change in SF content has a greater effect on the fluctuation of the compressive strength when the SP content is high and the amount of SF is low. This means that when the PFA content is 15% of the cement mass and the SF content is less than 3.5% or greater than 6.5%, the effect of the change in SF content on the compressive strength fluctuates more than if the SF content is greater than 3.5-6.5%.  
Figure 6: Response surface and contour in SP-PFA. (**a**) Response surface. (**b**) Contour.
## 4 Case Study
### Control Group Design and Results
#### 4.2.4 Planar/Curve Projection Surface  
The planar and curve projection surfaces were tested in order to be compared. The tested models were made as a cylinder type and cube type by applying several textures of building materials on it  
Figure 5: Comparison of projection results with illumination condition. (**a**) Projection with environmental electric light; (**b**) Projection without environmental light.  
Figure 6: Comparison of projection results in different projection angles.  
(Figure 7). The typical building textures, such as brick, stone, and column pattern were projected on both planar and curved surfaces. On the curved surface, there was a distortion issue, which affected the level of texture definition and slightly changed the design. Furthermore, there were discontinuous areas of the texture on the curved surface, but this was caused by the discontinuous part of the cylinder flank on the UV map. Likewise, the curved surface wrapping around the object might need to be separately placed on the UV map, which will lead to the discontinuity in texture. Moreover, on the transitional area of the curved surface that cannot be projected on (Figure 7f), there was huge distortion, and the pixel quality deteriorated extremely close to the edge lines.
# Coexistence of 5G/6G and Digital Terrestrial Television Networks
###### Abstract  
This paper analyzes practical solutions for implementing 5G/6G and Digital Terrestrial Television (DTT) network coexistence in the UHF band. Two potential scenarios are explored using the case study of a nationwide DTT network: spectrum splitting and spectrum interleaving. Both scenarios are thoroughly examined based on current UHF spectrum usage and the DTT network structure based on a case study of Poland. The analysis includes potential future benefits and limitations of each scenario. The results presented in this paper can guide administrations in implementing decisions made at the International Telecommunication Union--Radiocommunication Sector, World Radiocommunication Conference 2023 (ITU-R WRC23), improving spectrum efficiency and 5G/6G networks development.  
5G; 6G; DTT; network coexistence; UHF; radio spectrum; interleaving spectrum; network planning +
Footnote †: journal: Accepted: 24 June 2024
# Development of a Calibration Strip for Immunohromatographic Assay Detection Systems
## Methodology  
### Image Acquisition and Processing  
#### 3.1.1 Structure of Test Strip  
In general, an ICG assay test strip consists of three sections, including the sample pad, analytical membrane, and absorption pad, as shown in Figure 2. The sample pad includes a sample hole, through which the test samples are placed by drops. In a conjugate pad, colloidal gold nanoparticles are used as the markers for the specific target antigen. On the analytical membrane, there are a T (Test) line and C (Control) line where the antibody is placed. The T line indicates the concentration of the test sample, whereas the C line confirms the validity of the test. The absorption pad, which is located at the other end of the test strip, creates capillary action. As the test samples starts to flow from the sample pad to the absorption pad, as indicated by the arrow, immunoreaction occurs in the conjugate pad to form the conjugated particles (colloidal gold-labelled antigen-antibody complex). Then, the conjugated particles wick along the analytical membrane where another immunoreaction occurs on the T line and the C line. The rest of the particles will continue their journey until they reach the absorption pad. Finally, test results can be interpreted from the color of the T line and C line.  
Figure 1: (**a**) Schematic diagram of photoelectric detection system; and (**b**) the 3D structure of the photoelectric detection system; with 1: test strip; 2: photoelectric module; 3: mechanical stage; 4: central board; and 5: driving module.
# A Time Integration Method Based on Galerkin Weak Form for Nonlinear Structural Dynamics
## 3 Analysis of Numerical Stability and Accuracy
### Stability  
To analyze the stability of the proposed algorithm, the following single-degree-of-freedom system is considered:  
\[\ddot{u}+\omega^{2}u=0 \tag{26}\]  
where \(\omega\) is the circular frequency of free vibration. When the proposed algorithm is applied to solve this linear dynamic equation, Equations (16) and (17) can be expressed as:  
\[K_{ij}=-\frac{4}{\Delta t^{2}}\int\limits_{-1}^{1}\dot{N}_{i}\dot{N}_{j}\mathrm{ d}\zeta+\omega^{2}\int\limits_{-1}^{1}N_{i}N_{j}\mathrm{d}\zeta,P_{i}=0. \tag{27}\]  
Taking the linear element as an example, all the integral terms in Equation (27) could be integrated exactly by more than two Gaussian points. If we only use one Gauss integral point, i.e., the reduced Gauss integration, the recursive difference equation at any two successive time points would be derived as follows:  
\[\left\{\begin{array}{l}u_{n+1}\\ v_{n+1}\end{array}\right\}=\frac{1}{\Omega^{2}+4}\left[\begin{array}{cc}- \Omega^{2}+4&4\Delta t\\ -\frac{4\Omega^{2}}{\Delta t}&-\Omega^{2}+4\end{array}\right]\left\{\begin{array} []{l}u_{n}\\ v_{n}\end{array}\right\}=\mathbf{A}\left\{\begin{array}{l}u_{n}\\ v_{n}\end{array}\right\}. \tag{28}\]  
In this case, the present algorithm automatically degrades into the so-called 'GW' (Galerkin Weak form) method' which has been already proposed [34] for linear elastodynamic problems, and turns out to be an unconditionally stable time integration method with the spectral radii \(\rho(\mathbf{A})=1\), similar to quadratic and cubic elements. [PERSON] [25] discovered that, when reduced Gauss integration was used in calculation of the stiffness matrix, the GW method for linear elastodynamic equations is unconditionally stable, otherwise, it is conditionally stable. This conclusion might be understood as the loss of accuracy earns the improvement of stability.
## 2 Principle and Mathematical Model  
### Principle of FBG Sensor  
This study employed FBG sensors for data acquisition. The fundamental principle involves measuring the shift in the reflected central wavelength to quantify temperature and strain variations [20]. For an FBG sensor (Figure 1), the Bragg wavelength \(\lambda_{B}\), defined as the central wavelength reflected when incident light interacts with the grating, is expressed as [21]  
\[\lambda_{B}=2n_{eff}\Lambda \tag{1}\]  
where \(n_{eff}\) represents the effective refractive index of the fiber core, and \(\Lambda\) denotes the grating period.  
The wavelength shift in an FBG induced by temperature (\(\Delta T\)) and strain (\(\epsilon\)) is mathematically represented as  
\[\frac{\Delta\lambda_{B}}{\lambda_{B}}=(\alpha_{s}+\xi_{s})\Delta T+(1-P_{e})\epsilon \tag{2}\]  
Figure 1: Schematic of FBG sensor.  
where \(\alpha_{s}\) is the thermal expansion coefficient of the optical fiber, \(\xi_{s}\) is the thermo-optic coefficient of the optical fiber, and \(P_{\varepsilon}\) is the effective photoelastic coefficient of the optical fiber.  
A further simplified equation is  
\[\frac{\Delta\lambda_{B}}{\lambda_{B}}=\alpha_{T}\Delta T+\alpha_{\varepsilon}\varepsilon \tag{3}\]  
where \(\alpha_{T}=\alpha_{s}+\xi_{s}\) is the temperature sensitivity coefficient, and \(\alpha_{\varepsilon}=1-P_{\varepsilon}\) is the strain sensitivity coefficient.  
For FBG temperature sensors, under the assumption of negligible external mechanical loading, where wavelength shifts are exclusively caused by temperature variations, the governing relationship is expressed as  
\[\Delta\lambda_{B}=\alpha_{T}\Delta T\lambda_{B} \tag{4}\]  
This unique characteristic enables the fabrication of FBG temperature sensors, where temperature monitoring is achieved by detecting variations in the reflected wavelength.  
For FBG strain sensors, calibration experiments determine the temperature sensitivity coefficient (\(\alpha_{T}\)) and strain sensitivity coefficient (\(\alpha_{\varepsilon}\)). By deploying auxiliary temperature sensors adjacent to the strain sensors to monitor thermal fluctuations, structural strain can be computationally derived through thermal compensation algorithms.  
The FBG sensors utilized in this study are schematically illustrated in Figure 2.  
Figure 2: FBG sensors of this study. (**a**) Horizontal strain sensor; (**b**) vertical strain sensor; (**c**) Earth pressure sensor; (**d**) temperature sensor.
## 4 Discussion
The red band (665 nm) makes use of the [Chl-\(a\)] absorption maximum and acts to normalize the effects of [Chl-\(a\)] absorption and backscattering by particulate matter [15]. Algorithms for perceived PIM are very similar to those for TSM (especially in sediments with high inorganic rates) and use red and NIR bands [76; 77]. The reason is that inorganic particles contribute greatly to scattering in the NIR [78]. Our study has determined that, for a range of 0.188-12.84 mg/L, PIM can be estimated through the use of the R\({}_{705}\) band algorithm with a high R\({}^{2}\). There are several publications [76; 77; 79] showing that the elevated signals in the NIR part of the spectrum are a good predictor of TSM. Watersamples used in these studies were very rich in mineral particles (250 mg/L). Therefore, if the water has a high amount of mineral particles, it is attributed to an elevated NIR signal. Despite the fact that the automatic products offered by the C2 RCC processor may present an acceptable correlation (R\({}^{2}\) = 0.5506) with the data measured in situ and suitable statistics (RMSE = 14.41 mg/L; NRMSE = 30%), these results can be improved by the algorithms proposed in this study, which present both better correlations (R\({}^{2}\) = 0.7918) and lower error compared to the real data (RMSE = 2.7 mg/L; NRMSE = 10.3%). The use of remote sensing as a tool for capturing information applied to Limnology and hydrological planning represents a saving in time and costs for water resources' evaluation and, therefore, for the management, rationalization, optimization and modernization of hydrological planning. The experience acquired from the beginning of remote sensing programs to the present day in the study and monitoring of Spanish water bodies using multispectral images, indicates the need to incorporate data collection in situ, and to adjust specific algorithms in areas as unique as those chosen for this study.
## 2 Materials and Methods
### Instrumental Set-Up  
GC-MS analysis was performed on an Agilent 6890 series gas chromatograph equipped with an Agilent 7683 automatic liquid sampler and interfaced to an Agilent 5973N mass selective detector (Agilent Technologies, Little Falls, NY, USA). A fused silica capillary column (30 m length \(\times\) 0.25 mm I.D. \(\times\) 0.25 \(\upmu\)m film thickness; Zebron ZB-5; 5% diphenyl, 95% dimethyl polysiloxane; Phenomenex, Torrance, CA, USA) was used. Helium was used as the carrier gas in the constant pressure mode (14.3 psi). For all analyses, a solvent delay of 3.2 min was used. The transfer line, ion source, and quadrupole temperatures were maintained at 280 \({}^{\circ}\)C, 230 \({}^{\circ}\)C, and 150 \({}^{\circ}\)C, respectively. In the full-scan mode, electron ionization mass spectra in the range of 45-550 Da were recorded at 70 eV of energy. Data acquisition and instrument control were performed by the MSD ChemStation software (G1701 CA, version E.02.02.1431; Agilent Technologies, USA). The identification of each compound was addressed through the comparison of their retention indices (RIs) relative to a standard mixture of C\({}_{7}\)-C\({}_{30}\)\(n\)-alkanes [18] and the characteristic features of their mass spectra in comparison with Wiley's library spectral data bank (G1035B, Rev D.02.00; Agilent Technologies, USA). The calculation of each assay was performed by comparing the average peak areas of the extracted compounds with the IS peak area. A split/splitless (S/SL) injector operating at 270 \({}^{\circ}\)C was used in the SL mode (injection volume of 1 \(\upmu\)L) for the HS-BA\(\upmu\)E/GC-MS assays. The oven temperature was programmed to start at 54 \({}^{\circ}\)C (held isothermally for 2 min) then heated up to 170 \({}^{\circ}\)C at 20 \({}^{\circ}\)C/min (held isothermally for 2 min) and, finally heated up to 250 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min (held isothermally for 5 min), resulting in a run time of 42.17 min. For the HS-SPME/GC-MS assays, the injector (thermal desorption (TD) at 270 \({}^{\circ}\)C for 5 min) and oven temperature were programmed according to previous work [7].
# Numerical Study on Transient Annular Pressure Caused by Hydration Heat during Well Cementing  
[PERSON]  
1 College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266555, China; [EMAIL_ADDRESS]  
2 School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266555, China; [EMAIL_ADDRESS] (B.S.); [EMAIL_ADDRESS] (Z.W.); [EMAIL_ADDRESS] (Z.Z.)  
3 CNPC Chuanqing Drilling Engineering Co., Ltd. (CCDC), Chengdu 610051, China; [EMAIL_ADDRESS] (M.X.); [EMAIL_ADDRESS] (Y.R.); [EMAIL_ADDRESS] (Y.F.)  
Correspondence: [EMAIL_ADDRESS]  
[PERSON]  
2 School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266555, China; [EMAIL_ADDRESS] (B.S.); [EMAIL_ADDRESS] (Z.W.); [EMAIL_ADDRESS] (Z.Z.)
a two-phase seepage model and some prediction models for annular pressure. However, research into the annular pressure caused by thermal expansion is limited. [PERSON] [6] and [PERSON] [7] proposed a numerical model to predict the trapped annular pressure caused by thermal expansion.  
During the well cementing process, a large amount of heat is released as a result of the cement hydration reaction [14; 15]. The sealed fluids in the annulus, including the drilling fluid and cement slurry, are heated up, and this leads to annular pressure [16]. Therefore, an abnormal increase in temperature and casing pressure is frequently observed during the cement hydration stage. Therefore, it is essential to study the transient annular pressure caused by hydration heat during well cementing.  
Currently, studies on the annular pressure caused by hydration heat during well cementing are very limited. In order to study the transient annular pressure caused by hydration heat during well cementing, a kinetics model of cement hydration is established in this study. On the basis of cement hydration kinetics, a transient model of annulus pressure is established. A series of numerical simulations are performed using the new model established in this paper. The transient temperature in the wellbore, the cement hydration process, and the annular pressure are analyzed. Suggestions are drawn from the numerical simulations that can provide safety guidance for the management of annular pressure.
# Dissolved Gas Analysis for Fault Prediction in Power Transformers Using Machine Learning Techniques
## 3 Methodology and Machine Learning Methods (MLMs)
### MLMs
#### 3.1.3 The K-Nearest Neighbors (KNN) Algorithm  
The K-nearest neighbors method is indeed one of the most efficient OML strategies for classification tasks in many different application scenarios [27]. The key idea behind this approach is to search, for every class, for the samples in the training dataset that are closest to any given query position [28]. Bayesian optimization (BO) is used to find the optimal hyperparameters of the KNN algorithm so that the highest achievable classification accuracy is reached. The hyperparameters of the optimization of KNN include the number of neighbors, the metric of distance, the weight of distance, and normalization of the dataset. The main steps of the KNN method are as follows:  
For each Test_Point in Test Data  
distances = []  
For each Train_Point in Train Data  
distance = calculate_distance(test_point, train_point)  
distances.append((distance, train_point.class_label))  
Sort distances by the first element (distance)  
nearest_neighbors = distances[0:K]  
class_votes = {}  
For each neighbor in nearest_neighbors:  
class_label = neighbor.class_label  
if class_label in class_votes:  
class_votes[class_label] += 1  
else  
class_votes[class_label] = 1  
predicted_class = argmax(class_votes)  
Assign predicted_class to test_point
# Implementation of a Breast Phantom with Acoustic Properties for Ultrasonic Thermometry
## 2 Theoretical Framework  
### Thermometry as a Method of Diagnosis and Detection of Breast Anomalies  
Ultrasound-based thermometry methods can be classified as either passive or active. The first ones involve the transducer transmitting acoustic waves focused on the region of interest; the echo signals are received and processed to extract the desired parameters, which depend on the temperature. Passive thermometry methods are based on the detection of acoustic radiation emitted from tissues experiencing thermal changes and generated by the thermal agitation of atoms and molecules within the tissue [41; 42].  
Within the presently active methods, there are several approaches: some are based on speckle tracking techniques, which focus on estimating time delays of echo signals due to local changes in the SOS and to the thermal expansion of the medium, produced by temperature increase [20; 21; 39; 43; 44; 45; 46; 47; 48; 49; 50]. Another well-known methodology is the measurement of the change in backscattered energy (CBE) [23; 24; 51], which utilizes the temperature-dependent nature of backscattered energy as an acoustic parameter to be related to tissue temperature. Others are based on characterizing the changes in the statistical properties of ultrasonic signals acquired from the medium [52; 53]. Other methods exist, such as methods using changes in the nonlinear parameters of the medium and methods using changes in the attenuation parameter, but these are not discussed in this study. All of them offer advantages such as real-time capabilities or non-ionizing radiation. However, there are limitations to them, such as tissue motion or diffraction effects that must be considered before advancing to pre-clinical trials. To validate thermometry methods in rigorous controlled conditions, they must be tested on trial media called phantoms, which replicate required tissue properties, avoiding interference from non-desirable effects. Phantom-based experimentation is essential for evaluating the effectiveness of thermometry approaches in the first evaluation stage. In this work, we aim to develop and validate low-cost, lab-made ultrasonic breast phantoms for ultrasonic thermometry-based diagnosis applications assessment.
# Sensitivity Analysis of Numerical Coherency Model for Rock Sites
###### Abstract  
Characterization of ground motion incoherency can significantly reduce the seismic load imposed on large scale infrastructures. Because of difficulties in developing an empirical coherency function from a site-specific dense array, it is seldom used in practice. A number of studies used numerical simulations to develop generic coherency models. However, they have only been developed for idealized profiles. A comprehensive parametric study evaluating the effect of various parameters influencing the calculated coherency function has not yet been performed. We utilized the measured shear wave velocity (\(V_{s}\)) profile at Pinyon Flat, located in California, to perform a suite of time history analyses. This site was selected because the empirical coherency function developed here has been used as a reference model for rock sites. We performed several sensitivity studies investigating the effect of both the site spatial variability and numerical analysis parameters in order to provide a guideline for developing a coherency model from numerical simulations. The outputs were compared against the empirical coherency model to better illustrate the importance of the parameters. The coefficient of variation (CV) of \(V_{s}\) was revealed to be the primary parameter influencing the calculated plane-wave coherency, whereas the correlation length (CL) has a secondary influence. Site-specific convergence analyses should be performed to determine the optimum numerical parameter, including the number of analyses and depth of numerical model. Considering the importance of CV and \(V_{s}\), it is recommended to perform field tests to determine site-specific values to derive numerical coherency functions.  
Keywords:spatial variability; coherency model; hard rock site; numerical simulation; plane-wave coherency +
Footnote †: journal: Applied sciences