esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring of Structures and Infrastructures by Low-Cost GNSS Receivers
## 2 Concepts  
### What We Want to Monitor  
First, we have to identify on the structure a certain number of material points, the motion of which significantly represent its mechanical behavior. This choice cannot be done by the GNSS expert alone nor by the structural engineer alone, but they have to collaborate in designing the monitoring network, taking into account the structural importance of the motion of the analyzed material points and sky-visibility-related issues, i.e., direct access to the signals coming from the satellites. For instance, in the example analyzed in this paper, the position of the GNSS antennas was dictated by the need of monitoring structural elements potentially affected by a landslide motion. Of course, in other circumstances, different suitable positions could be chosen. Let us recall that the point monitored by GNSS is, as a matter of fact, the phase center of the antenna of the receiver, so it is usually necessary to install a mechanically rigid connection between the antenna and the material point on the structure of the object being monitored.  
The result of the monitoring should then be a time series of the estimated coordinates for all points of the network and, if possible, the covariance matrices of the estimation errors. As a minimum, the variances of the estimation errors should be known.  
One remark concerns the kind of coordinate system we are looking at: one coordinate should always be in the vertical direction \(Z\); as for the other two horizontal coordinates, they can be chosen to be meaningful for the structure. For instance, for a bridge, it is obviously interesting to have one axis longitudinal and the other transverse to the direction of the bridge.
## 4 Results and Discussion
### Fracture Resistance Values of Tested Samples
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multirow{2}{*}{**Temperature**} & \multirow{2}{*}{**Pure Mode I**} & \multirow{2}{*}{**Mixed Mode I**} & \multirow{2}{*}{**Pure Mode II**} & \multirow{2}{*}{**Mode II**} & \multirow{2}{*}{**Mode II**} & \multirow{2}{*}{**Mode III**} & \multirow{2}{*}{**Mixed Mode I/III**} \\ \cline{5-8}  & & & **K\({}_{\text{Hf}}\)** & **K\({}_{\text{Hf}}\)** & **K\({}_{\text{IIIf}}\)** & **K\({}_{\text{IIIf}}\)** & **K\({}_{\text{Hf}}\)** & **K\({}_{\text{IIIf}}\)** \\ \hline \multirow{4}{*}{**SCB**} & \multirow{2}{*}{Asymmetric SCB} & Low & 0.82 & 0.54 & 0.54 & 0.78 & - & - & - \\ \cline{2-8}  & & Medium & 0.38 & 0.21 & 0.21 & 0.34 & - & - & - \\ \cline{2-8}  & \multirow{2}{*}{Inclined cracked SCB} & Low & - & 0.51 & 0.57 & 0.68 & - & - & - \\ \cline{2-8}  & & Medium & - & 0.20 & 0.23 & 0.29 & - & - & - \\ \hline \multirow{4}{*}{**SCB**} & \multirow{2}{*}{ENDB} & Low & 0.78 & - & - & - & 0.45 & 0.41 & 0.48 \\ \cline{2-8}  & & Medium & 0.40 & - & - & - & 0.21 & 0.22 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Critical values of stress intensity factors obtained from different specimens under low and medium temperatures.
## 1 Introduction
[56] investigated the use of a LiDAR survey during a major flood event and a digital terrain model to document flood water depths. [PERSON] et al. [57] also used LiDAR-derived DEM for the extraction of uncultivable trails, ditches, and cultivated field parcels within flood plains in order to evaluate flood risk. Furthermore, [PERSON] et al. [58] investigated the effect of various sources of digital terrain models (DTM) on the delineation of topographic depressions and their hydrological impacts. The main objective of this study is to further investigate the application of remote sensing techniques, using optical and SAR open data in conjunction with other data sources and GIS techniques, for monitoring and evaluating the impacts of a natural disaster. The case study was a flash flood event that took place from 20 to 22 May 2016, following an intense and heavy rainfall that occurred in the central-eastern part of West Thessaly, in Central Greece, including a part of the Thessalian plain, which is characterized by intensive agricultural activity. A L7 image, acquired on May 23, one day after the phenomenon and two Sentinel-1 SAR images (S1), acquired on May 21 (in the middle of the storm event), and were processed. For L7 the Tasseled Cap Transformation (TCT), the Modified Normalized Difference Water Index (MNDWI), the Difference between Vegetation and Water (DVW), and the Red and Short Wave Infrared (RSWIR) index, were applied and tested. Their results were compared, and from their analysis, the inundated area was delineated. As far as the S1 SAR data are concerned, a Level-1 Ground Range Detected (GRD) product has utilized. Also, a method setting a threshold value (that is established manually) of radar backscatter, which is followed by a binary algorithm to determine whether a given raster pixel is flooded or not, was implemented. A detailed DEM with 5 m spatial resolution was also used to assess the flood risk in the affected area, to improve the delineation of the inundated areas, and to estimate flood water depth. Finally, four Sentinel-2 (S2) images were utilized so as to map the land use/cover and classify the existing crop types in the flooded area [59]. Specifically, this study set out to: (a) examine the utility, the accuracy, and the limitations of high resolution optical and SAR data for the detection and delineation of floodplain water, (b) investigate the use of additional spatial data to improve the obtained results, (c) recommend a suitable methodology for mapping inundated area extent and flood water depth to better assess flood impacts, (d) determine the best data processing required technique for such cases, and (e) explore the advantages of the synergistic use of remote sensing (optical and radar) and GIS (detailed DEM) techniques to overcome limitations in flood monitoring and damage assessment.
# Recent Developments of Solar Cells from PbS Colloidal Quantum Dots
## 1 Introduction  
Usually, material properties are defined by the atomic or molecular composition of a material tested at the macro scale. This rule no longer holds when structures become very small, in the range of nanometers, where scaling effects lead to significantly different optical [1; 2; 3], chemical [4; 5; 6], magnetic [7; 8; 9] or other properties of nanoparticles or quantum dots. An interesting method to combine the bottom-up assembly of devices from nanoparticles with the mechanical flexibility of the device is now accessible, with the use of relatively simple chemical procedures based on colloidal semiconductor quantum dots [10]. A general reason for semiconducting quantum dots research and development is that they are universal technologically tunable systems, with adjustable energy levels for lighting and sensing applications.  
The core of colloidal semiconductor quantum dots often consists of nanocrystals from some hundred to a thousand atoms of II-VI, III-V or IV-VI semiconductors [11]. On their surface, they bear organic molecules or metal complexes as ligands, as visible in Figure 1[12].  
These ligands build the surface layer between the semiconductor quantum dot and the outside, in this way both influencing and being influenced by an inner and an outer interface. The inorganic/organic surface between quantum dots and ligands thus impacts the electronic structure of the quantum dot [13], as well as interactions with neighboring quantum dots [14].  
A highly interesting material to produce such quantum dots is PbS. This IV-VI semiconductor typically has a sodium chloride crystal structure [15] and a temperature-dependent direct band gap between approx. 0.29 eV and 0.45 eV, making it useful for detecting infrared and visible light [16]. Besides macroscopic crystals and epitaxial thin films, PbS is often prepared in the form ofnanocrystallites or quantum dots by diverse methods, from chemical routes [17] to electrodeposition [18] and microorganism-based synthesis [19]. One of the main areas of research and development in which PbS nanocrystallites--typically as colloidal PbS quantum dots--are used is photovoltaic applications. Similar to PbSe, PbS has high photosensitivity in the near-infrared spectrum [20], and allows for the tuning of the optical band gap to a broad range of around 0.7-2.1 eV [21; 22], as well as the electronic structure of the quantum dot films and significantly shifting the band edges without large modifications of the band gap [23; 24]. PbS colloidal quantum dots also show multiple exciton generation [25; 26]. All these properties make PbS quantum dot solar cells belong to the quantum dot solar cells with the highest performance [27; 28; 29], and thus making PbS a highly interesting material for solar cells.  
This review paper gives an overview of most recent studies on solar cells prepared with PbS colloidal quantum dots, production methods, possible material combinations and the resulting solar cell efficiencies.
## 3 Results
### Needleleaf and Broadleaf Forest Identification Results
With prior knowledge, adding prior statistics using the self-adaptive thresholding segmentation method can maximize the number of pixels required for each class and effectively improve the segmentation accuracy. The results presented above were obtained using GF-1 imagery. We also evaluated other datasets imaged during the summer of 2020, including GF-2 and Landsat OLI, utilizing the proposed model (F-Pix2 Pix). The segmentation accuracies of the three datasets were similar, accurately identifying both needleleaf and broadleaf forests and effectively avoiding class imbalance. Specifically, GF-2 had the highest accuracy and the greatest differentiation between needleleaf and broadleaf forests, which might be attributed to superior resolution. Further investigation was required to determine the exact extent of its impact. These results demonstrated that the proposed model performed well on various datasets, achieved cross-scale segmentation, and exhibited strong generalizability. Figure 7: Comparison of the segmentation results for needleleaf and broadleaf forests.
# Vegetation Loss Measurements for Single Alley Trees in Millimeter-Wave Bands
###### Abstract  
As fixed wireless access (FWA) is still envisioned as a reasonable way to achieve communications links, foliage attenuation becomes an important wireless channel impairment in the millimeter-wave bandwidth. Foliage is modeled in the radiative transfer equation as a medium of random scatterers. However, other phenomena in the wireless channel may also occur. In this work, vegetation attenuation measurements are presented for a single tree alley for 26-32 GHz. The results show that vegetation loss increases significantly after the second tree in the alley. Measurement-based foliage losses are compared with model-based, and new tuning parameters are proposed for models.  
Keywords:foliage attenuation; millimeter-wave propagation; vegetation attenuation; radiative transfer equation +
Footnote †: journal: Sensors
## 5 Experiments and Discussion
### Comparative Evaluation and Analysis of Performance
#### 5.3.4 Influence of Different Components in EIAFM  
The model architecture of EIAFM covers different properties of feature learning. In order to investigate how EIAFM can increase model performance from the four components at the same time, the components of the model were changed from 1 to 4. The other hyperparameters were defaulted to match the values provided in Table 3, and the linear, attention, explicit, and implicit components are represent as LC, AC, EC, and IC, respectively. Fifteen different combinations of the components were obtained, and the experimental results are shown in Table 5. From the table, it can be seen that the model performance is optimal with four components, which captures the individual feature interaction components.  
It can be observed that EC performs best in the one-component model, which indicates that the explicit higher-order interactions between inputs and outputs are more important than the other relationships. EC + IC performs best in the two-component model, which indicates that the explicit and implicit higher-order interactions between the inputs and outputs are more effective than the other two-component combinations of the model; this conforms to the design idea behind the xDeepFM model. It is worth noting that the importance of the combination of EC and IC is used in the comparison of the three-component models; the key point is that IC + EC + AC is more effective compared to the IC + EC + LC model, as the former contains an attention component, indicating that the combination of explicit and implicit higher-order feature interactions and the attention component can improve the model performance. To summarize, it can be concluded that more components result in better model effectiveness and that each component contributes to the recommendation performance of the EIAFM model.  
Figure 4: Effects of hyperparameters in EIAFM.
## 3 Numerical Simulations
### Domain Discretization and Boundary Conditions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(R_{\text{si}}\) (m\({}^{2}\cdot\)K/W) & & & 0.13 & & \\ \hline \(R_{\text{se}}\) (m\({}^{2}\cdot\)K/W) & 0.00 & 0.04 & 0.08 & 0.13 & 0.16 & 0.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluated internal (\(R_{\text{si}}\)) and external (\(R_{\text{se}}\)) surface thermal resistances. * _Second stage_  
Based on the results presented in Table 3, the second step of the analysis involved confirming the computational precision of temperature and flux values to meet the criterion specified in condition C2.e of standard ISO 10211 [33]. This condition necessitates that for iterative solution methods, iterations should proceed until the ratio of the sum of all thermal fluxes (positive and negative) entering the object to the semi-sum of their absolute values is below 0.0001. Figure 3: Mesh refinement stages in thermal simulation with visualization of temperature field for the LSF partition wall: (**a**) 16.0 mm; (**b**) 8.0 mm; (**c**) 4.0 mm; (**d**) 2.0 mm; (**e**) 1.0 mm; (**f**) 0.5 mm; (**g**) 0.2 mm; (**h**) temperature field at 0.2 mm. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**N. of nodes** & 234 & 500 & 910 & 1739 & 3795 & 7546 & 19,468 \\
**Step size** (mm) & 16.0 & 8.0 & 4.0 & 2.0 & 1.0 & 0.5 & 0.2 \\ \hline \(\phi_{\text{si}}\) (W/model length) & 2.436365 & 2.373234 & 2.346557 & 2.331952 & 2.322983 & 2.318495 & 2.315669 \\
**Differences** & — & \(-2.7\%\) & \(-1.1\%\) & \(-0.6\%\) & \(-0.4\%\) & \(-0.2\%\) & \(-0.1\%\) \\ \hline \(R_{\text{cond}}\) (m\({}^{2}\cdot\)K/W) & 1.471790 & 1.515464 & 1.534625 & 1.545301 & 1.551924 & 1.555257 & 1.557363 \\ \(R\)**-value** (m\({}^{2}\cdot\)K/W) & 1.641790 & 1.685464 & 1.704625 & 1.715301 & 1.721924 & 1.725257 & 1.727363 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Analysis of convergence of thermal flux sensitivity to discretization node variation, regarding the LSF partition PSIPLAN model, for \(R_{\text{si}}\) and \(R_{\text{se}}\) values equal to 0.13 and 0.04 m\({}^{2}\cdot\)K/W, respectively. Taking into account the stipulation in section C.2.b of standard ISO 10211 [33], which states that the subdivision degree of the object (specifically, the number of cells or nodes) is at the discretion of the user rather than being predetermined by the computational method, and referring to the outcomes highlighted in stage 1, a subdivision size of 0.5 mm was consistently utilized throughout this case study. Observing the precision row in the results (see Table 4), it can be seen that the precision of the iterative calculations consistently improves, eventually achieving and surpassing the required threshold of less than 0.0001. This is evidenced by the final entries in the precision row, which meet the rigorous precision requirement laid out in condition C2.e of standard ISO 10211 [33].
###### Abstract  
Due to increased complexity and interactions between various subsystems, higher-order MIMO systems present difficulties in terms of stability and control performance. This study effort provides a novel, all-encompassing method for creating a decentralized fractional-order control technique for higher-order systems. Given the greater number of variables that needed to be optimized for fractional order control in higher-order, multi-input, multi-output systems, the modified flower pollination optimization algorithm (MFPOA) optimization technique was chosen due to its rapid convergence speed and minimal computational effort. The goal of the design is to improve control performance. Maximum overshoot (Mp), rising time (tr), and settling time (ts) are the performance factors taken into consideration. The MFPOA approach is used to improve the settings of the proposed decentralized fractional-order proportional-integral-derivative (FOPID) controller. By exploring the parameter space and converging on the best controller settings, the MFPOA examines the parameter space and satisfies the imposed constraints by maintaining system stability. To evaluate the suggested approach, simulation studies on two systems are carried out. The results show that by decreasing the loop interactions between subsystems with improved stability, the decentralized control with the MFPOA-based FOPID controller provides better control performance.  
decentralized control; modified flower pollination optimization algorithm; fractional-order proportional-integral-derivative controller; sensitivity function; parameter optimization
# Seismic Multi-Parameter Full-Waveform Inversion Based on Rock Physical Constraints
## 2 Materials and Methods
### Construction of Rock Physical Prior Information
#### 2.2.2 The Inversion Objective Function of Petrophysical Relationship Constraints  
The incorporation of novel constraint terms mandates a redefinition of the inversion objective function:  
\[Q_{R}(\mathbf{m})=Q(\mathbf{m})+\lambda_{R}\Phi_{R}(\mathbf{m}) \tag{8}\]  
where \(\Phi_{R}(\mathbf{m})\) is the model term based on the model penalty function norm, and \(\lambda_{R}\) is the petrophysical relationship regularization factor, which controls the trade-off parameter between the weight of the data misfit and the model term in the objective function under petrophysical relationship constraints \(Q_{R}(\mathbf{m})\). In order to integrate the petrophysical relationships (commonly in explicit forms) among different parameters, this study formulates the model constraints as follows:  
\[\Phi_{R}(\mathbf{m})=\frac{1}{2}{\sum_{x}}[m_{1}-f(m_{2})]^{2} \tag{9}\]  
where \(\mathbf{x}\) is the model space coordinate, \(m_{1}=m_{1}(\mathbf{x})\) and \(m_{2}=m_{2}(\mathbf{x})\) are different physical parameters, and \(f=f(\mathbf{x})\) is a predefined function that maps \(m_{2}\) to \(m_{1}\). Insights from geological exploration indicate that, governed by the uniform sedimentary environment and sedimentary facies, a robust correlation exists between the seismic wave velocity of strata and rock density. Moreover, drilling data can provide a reliable petrophysical parameter relationship. Therefore, \(m_{2}\) is the density, \(m_{1}\) is the velocity, and Equation (9) becomes  
\[\Phi_{R}(\mathbf{m})=\frac{1}{2}{\sum_{x}}[\rho-f(v)]^{2} \tag{10}\]  
where \(\Phi_{R}(\mathbf{m})\) plays a pivotal role in constraining the updated model to remain within the trend delineated by \(\rho-f(v)\).  
The gradient of \(\Phi_{R}(\mathbf{m})\) relative to \(\mathbf{m}\) is:  
\[\
abla_{\mathbf{m}}\Phi_{R}(\mathbf{m})=[\rho-f(v)]\frac{\partial[\rho-f(v)]} {\partial\mathbf{m}} \tag{11}\]  
where \(\partial\) is the partial derivation operator, and \(\
abla\) represents the gradient.
## 4 Experimental Verification
### Conventional OMA Methods  
The conventional OMA methods, such as EFDD, FSDD, data-driven SSI (SSI-DATA), and covariance-driven SSI (SSI-COV), are used to identify the modal parameters of the beam (Figure 8b) under white noise (Figure 11) and non-white noise excitations (Figures 12 and 13). However, as shown in Figure 12c, the SSI-COV method has first-order modal leakage in identifying the natural frequency of the beam excited by excitation spectrum 2. As shown in Figures 12d and 13d, the SSI-DATA method also has first-order modal leakage in identifying the natural frequencies of the beam excited by excitation spectra 2  
Figure 10: Excitation spectrums.  
and 3. Additionally, according to Figure 13a-c, the EFDD, FSDD, and SSI-COV methods have mode leakages at the first natural frequency and false modes at the second natural frequency when identifying the modal frequency of the beam excited by excitation spectrum 3. Normally, the result of modal parameter identification excited by excitation spectrum 1 is considered to be accurate [21]. The analysis of Figure 11a,b and Figure 12a,b, shows that the EFDD and FSDD methods have no modal leakages and false modes when identifying the natural frequencies of the beam excited by excitation spectrum 2, but they are inconsistent with the identification results excited by excitation spectrum 1. More specifically, the first five natural frequencies of the beam under white (excitation spectrum 1) and non-white noise excitation (excitation spectrum 2) identified by EFDD are (16.83, 96.17, 306.41, 622.40, 997.5) Hz and (16.26, 97.42, 308.91, 619.90, 987.51) Hz, respectively, and the first five natural frequencies of the beam under white (excitation spectrum 1) and non-white noise excitation (excitation spectrum 2) identified by FSDD are (16.83, 97.46, 316.25, 617.75, 996.3) Hz and (16.83, 97.8, 311.0, 618.4, 1011.5) Hz, respectively. Briefly, different methods have different identification results for the same beam under a different excitation, the same method has different identification results for the same beam under a different excitation, and different methods have different identification results for the same beam under the same excitation.
## 3 Materials and Methods
### Methods
#### 3.2.2 Preparation of Signal Conditioning, Acquisition, and Transmission  
The procedure implemented to utilize the signal conditioning, acquisition, and transmission stages is as follows. First, the wiring of the instrument is performed, as shown in Figure 4a. It is relevant to note that this step defines the performance of the signal acquisition stage, if an external voltage is used. In our case, this parameter was set to 5 V using the microcontroller code. Then, the offset voltage of the instrument is tuned by cautiously adjusting the potentiometer, RV1 in Figure 3, thus accounting for the ADC configuration previously mentioned. Finally, BLE transmission of sensed data is facilitated by means of the microcontroller code, which implements two modalities: _Calibration_ and _Measurement_. In _calibration_ mode, the user is enabled to characterize the sensor (refer to Section 3.2.1) by employing the three buffer solutions. The resulting parameters are stored in the instrument memory for future use. In _measurement_ mode, the instrument acquires arbitrary voltage values, and converts such information to pH values. pH information is then sent serially to the Bluetooth radio, which transmits wirelessly the data to a gateway (i.e., personal computer or smartphone) for further processing.