esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Trackside Measurement through Field Instrumentation
### Field Measurement Result Comparison
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Wheel Loads**} & \multicolumn{2}{c}{**Displacements**} \\ \cline{3-6}
**Railway Structures** & **Speed (km/h)** & \multicolumn{2}{c}{**Vertical (kN)**} & \multicolumn{2}{c}{**Lateral (kN)**} & \multicolumn{2}{c}{**Vertical Rail (mm)**} & \multicolumn{2}{c}{**Lateral Rail (mm)**} \\ \hline  & 120–130 & 88.26 & 11.12 & 1.35 & 0.21 \\ \cline{2-6}  & 130–140 & 86.81 & 13.31 & 1.31 & 0.23 \\ \cline{2-6} Type 1 & 140–150 & 87.58 & 12.22 & 1.32 & 0.31 \\ \cline{2-6}  & 150–160 & 88.46 & 11.74 & 1.43 & 0.32 \\ \cline{2-6}  & 160–170 & 89.39 & 15.08 & 1.51 & 0.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average values of maximum dynamic response by passing train for concrete track structures. With regard to the ballasted tracks, the above measurement results show varying indications that the measurement result values differ. Regression lines have been provided for the series of Figures 5 and 6 only for the purpose of visually aiding the general trend of the changing parameters in accordance with increasing speed. While some exceptions are present, in general the values for all measurement parameters were shown to increase as the speed of the train increased as well. For the load parameters (vertical and lateral wheel loads) track Types 1 and 2 show the highest range on average compared to the other types of track structures. The vertical displacement results indicate that structures where the track substructure is of an earthwork type may be subject to receiving higher wheel-rail contact load than those of the other track types due to the different track modulus properties. The higher lateral displacement perceived in Track Type 3 (tunnel structure) is estimated to be due to the enclosed wind pressure affecting the hunting oscillation, resulting in higher displacement measurement than those of the other structure types. The precise cause and effect should be further investigated and cannot be provided in the scope of these measurement results, but it was confirmed that despite the same design specification, as well as all the track structures being relatively new with minimal cumulative tonnage recorded, a variance in the measurement results can be derived. With regard to the concrete tracks, measurement results for the Type 3 (floating rail) structure was conspicuously higher than the results of the other structure types, and Type 4 (L type sleeveer, floating) structure was the lowest, on average. Results for Types 1, 2 and 5 varied without a distinct patter, indicating that further investigation is required to understand the proper relation dynamic between the track structure characteristic and the parameter measurement results, but it was evident with track
# Towards an Efficient Chipless RFID System for Modern Applications in IoT Networks
## 5 Conclusions  
The design of an efficient RFID system and a chipless tag and antenna system have been presented. The chipless tag consists of multiple resonators, which reflect its high coding capacity. The devised antenna is a step-shape rectangular patch antenna capable of operating in UWB mode. Various performance parameters of the UWB were demonstrated, which confirmed its suitability to be utilized as a tag as well as reader antennas. A measurement setup was established, which validated the feasibility of the proposed RFID system for modern applications such as Internet of Things (IoT) applications.  
Conceptualization, W.M.A. and A.F.A.S.; software, W.M.A.; methodology, W.M.A., A.F.A.S., I.E., N.N.-U. and A.M.A.-S.; formal analysis, W.M.A., A.F.A.S., I.E., N.N.-U. and A.M.A.-S.; validation, W.M.A. and A.F.A.S.; investigation, A.F.A.S., I.E. and A.M.A.-S.; resources, A.F.A.S., I.E. and A.M.A.-S.; data curation, W.M.A.; writing (original draft preparation), W.M.A. and N.N.-U.; writing (review and editing), W.M.A., N.N.-U., A.F.A.S., I.E. and A.M.A.-S.; visualization, W.M.A. and N.N.-U.; supervision, A.F.A.S., I.E. and A.M.A.-S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to acknowledge the Researchers Supporting Project number (RSP-2021/337), King Saud University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.
###### Abstract  
Land surface temperature (LST) is an important environmental parameter in many fields. However, many studies require high-spatial- and high-temporal-resolution LST products to improve the coarse spatial resolution of moderate-resolution imaging spectroradiometer (MODIS) LSTs. Numerous approaches have downscaled MODIS LST images to a finer spatial resolution using pixel-based image analysis (PBA). Meanwhile, object-based image analysis (OBIA) methods, which have developed rapidly in the analysis of high-spatial-resolution visible and near-infrared (VNIR) band data, have received little attention in the LST downscaling field. In this paper, we propose an object-based downscaling (OBD) method for MODIS LST using high-spatial-resolution multispectral images (e.g., Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)) as auxiliary data. The fundamental principle of this method is to preserve the thermal radiance of the \"object\", which is composed of several MODIS LST pixels (partly or entirely) and is unchanged after disaggregation into subpixels in the resulting LST image. The decomposition process consists of two key parts: the thermal radiance (TR) estimation of the object from MODIS LST products and the weight calculation of sub-objects or subpixels. Objects were generated from VNIR data and remote sensing indices (e.g., the normalized difference vegetation index (NDVI), the normalized difference built-up index (NDBI), and fractions of different endmembers) using a multiscale segmentation method. The radiance of subpixels or sub-objects was calculated based on the weights of their parent objects, which were estimated by the relationships between the remote sensing indices and the LST. The accuracy and the efficiency of the OBD method were validated using a pair of ASTER and MODIS datapoints that were acquired at the same time. The decomposed LST results showed that the spatial distribution of the downscaled LST image closely resembled the true LST of the ASTER, with an RMSE of 2.5 K for the entire image. A comparison with PBA methods for pixel downscaling also indicated that the OBD method achieves the lowest root mean square error (RMSE) across different landcovers, including urban areas, water bodies, and natural terrain. Therefore, the proposed OBD method significantly enhances the capability of increasing the spatial resolution of coarse MODIS LST, providing an alternative for improving the spatial resolution of MODIS LST images and expanding their applicability to studies that require high-temporal- and high-spatial-resolution LST products.  
object; land surface temperature; downscaling; MODIS; ASTER; ETM+ +
Footnote †: journal: Applied sciences
# Comparative Assessment of Shear Demand for RC Beam-Column Joints under Earthquake Loading
## 2 Fundamentals of RC Beam-Column Joints
### Joint Shear Strength
#### 2.2.1 Ec8  
The draft of the new Eurocode 8 [12] applies the principal stress criterion for the assessment of an existing RC beam-column joint. With reference to Figure 3a, the joint volume is studied in the plane stress condition. The input forces are the horizontal shear force (assumed to be equal to \(T\)) and the axial force in the column (\(N\)). The stress condition is studied via Mohr's circle as the one shown in Figure 3b. Both the cases of un-reinforced and reinforced joint are represented.  
When the principal tensile stress reaches \(f_{ct}\) the joint shear stress (\(\tau_{p}\)), for a reinforced joint, can be written as follows:  
\[\tau_{p}=f_{c}\sqrt{\left(v\rho_{sh}+\frac{f_{ct}}{f_{c}}\right)\left(v+\frac {f_{ct}}{f_{c}}\right)}, \tag{5}\]  
where:  
* \(\rho_{sh}=\frac{A_{\rm ab}}{A_{\rm b}}\) is the reinforcement ratio;
* \(\
u=\frac{N}{A_{c}f_{c}}\) is the normalized axial force.  
The shear stress is usually divided by the factor \(\sqrt{f_{c}}\). Such normalization is commonly adopted by US authors and building codes, the cracking resistance of a beam-column joint being assimilated to the tensile strength of concrete which is traditionally given as a function of \(\sqrt{f_{c}}\)[15]. Assuming \(\rho_{sh}\) is equal to zero in Equation (5) (un-reinforced joint), the tensile normalized shear strength (\(v_{j,t}\)) is written as follows:  
\[v_{j,t}=\frac{f_{ct}}{\sqrt{f_{c}}}\sqrt{1+\frac{vf_{c}}{f_{ct}}}. \tag{6}\]  
The procedure to obtain the compressive normalized shear strength (\(v_{j,c}\)) is similar to that presented above for \(v_{j,t}\). First, Mohr's circle is used to obtain the shear stress by imposing the value \(\eta f_{c}\) for the compressive principal stress, where \(\eta=0.55[min(1;30/f_{c})]^{1/3}\) is a reduction factor which takes into account the detrimental effect of transversal tensile strains. Second, \(\sqrt{f_{c}}\) normalization is used. Finally, the expression is as follows:  
\[v_{j,c}=\eta\frac{f_{c}}{\sqrt{f_{c}}}\sqrt{1-\frac{\
u}{\eta}}. \tag{7}\]
## 4 Graph Theoretic Resiliency Framework
### Resiliency Estimation Framework
#### 4.2.2 Case-II: Partially Failed State  
It is likely that some of the nodes in the network may not operate normally. As few nodes are subjected to partial failure, the partially failed state is inevitable, which is considered in this section. In this case, we consider that the direct link \(e_{1-6}\in E\) fails at time \(t_{1}\). The failure of this node decreases the \(PDR\), as can be deduced from Equation (11).  
\[PDR_{6-1}^{\gamma_{1-6}}=\frac{\sum\limits_{t=t_{6_{0}}}^{t_{1_{0}}}\alpha_{t }^{6}=\min\left\{\sum\limits_{t=t_{2_{0}}}^{t_{2_{0}}}\alpha_{t}^{2},\sum \limits_{t=t_{3_{0}}}^{t_{3_{0}}}\alpha_{t}^{3},\sum\limits_{t=t_{4_{0}}}^{t_{ 4}}\alpha_{t}^{4},\sum\limits_{t=t_{5_{0}}}^{t_{5_{0}}}\alpha_{t}^{5}\right\}}{ \sum\limits_{t=t_{1_{0}}}^{t_{1_{0}}}\beta_{t}^{1}} \tag{11}\]  
In this case, \(PDR_{6-1}^{\gamma_{1-6}}<0.8\) due to the partially failed state of a node in dominant path \(\gamma_{1_{-6}}^{1}\). Thus, all of the traffic generated at node \(v_{3}\) is re-routed to the alternative path if available, such as \(\gamma_{1-6}^{2}\). Hence, the \(PDR\) at the destination node corresponding to path \(\gamma_{1_{-6}}^{2}\) becomes \(PDR_{6-1}^{\gamma_{1-6}}>0.2\). However, there will be some time for the re-routing of the traffic over path \(\gamma_{1_{-6}}^{2}\). During this time, the \(PDR\) will continue to decrease. Once the new optimum path is detected, then the \(PDR\) either increases or maintains the present level. This is shown in the resiliency curve in Figure 6 for the time period \(t_{1}\) to \(t_{2}\). Moreover, as edge \(e_{2-3}\) is shared, the \(PDR\) corresponding to this path can never reach 1. Hence, the following inequality holds:  
\[0.2<PDR_{6-1}^{\gamma_{1-6}^{2}}<1 \tag{12}\]  
Particularly if \(0.2<PDR_{th}<PDR_{6-1}^{\gamma_{1-6}^{2}}<1\), the overall \(PDR\) at destination \(v_{6}\) corresponding to source \(v_{1}\) can be produced by,  
\[PDR_{6-1}=\sum\limits_{m=\gamma_{1-6}^{1}}^{m=\gamma_{1-6}^{1}}PDR_{6-1}^{m} \tag{13}\]  
\[PDR_{6-1}=PDR_{6-1}^{\gamma_{1-6}^{1}}+PDR_{6-1}^{\gamma_{1-6}^{2}} \tag{14}\]  
Figure 5: Resiliency curve of the SCN model for _Case-I_.  
Therefore, the following inequality holds:  
\[PDR_{th}\leq PDR_{6-1}<1 \tag{15}\]  
The corresponding resiliency curve is shown in Figure 6. As \(PDR_{6-1}\geq PDR_{th}\), the network is considered to be functional in this case. It is worth noting that, for a network \(\mathcal{N}\), \(PDR\
eq 0\) if \(\left|\gamma_{i-j}\right|>1\). Thus, for non-zero \(PDR\), there must exist an alternative path between source-destination pairs.
## 3 Results
### Lehigh Valley Control Run: Changing Climate and CO\({}_{2}\)  
The control run shows the state of current carbon and water dynamics, with the LULC of 2017 remaining constant, but with changing climate and CO\({}_{2}\). For the future period, solely as a result of the changing climate and CO\({}_{2}\), the vegetation carbon (VegC), and vegetation carbon fluxes (net primary productivity (NPP) and gross primary productivity (GPP)) show a statistically significant increase during the 21 st century (Table 2). There is also a statistically significant decrease in soil carbon. While the ecosystem carbon fluxes (NEP and NCE) have a slight increase, the increase is not significant. Overall, the water variables (evapotranspiration, runoff, and soil moisture) do not change by a large amount, with only ET showing any significant increase. These results are consistent with a warmer climate and higher CO\({}_{2}\) levels increasing vegetation productivity, warming causing more decomposition, and no large change in precipitation.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Time** & **VegC** & **SoilC** & **GPP** & **NPP** & **NCEP** & **NCE** & **Soil Moist** & **ET** & **Runoff** \\ \hline
2030–2049 & **53.33** & 2.56 & 1.72 & \(-\)3.74 & \(-\)9.35 & \(-\)11.91 & **3.80** & \(-\)2.75 & 20.70 \\ \hline
2050–2069 & **135.45** & \(\mathbf{-25.94}\) & 10.42 & 4.16 & \(-\)0.82 & \(-\)3.58 & 1.01 & 1.60 & 4.68 \\ \hline
2070–2099 & **141.72** & \(\mathbf{-13.89}\) & 15.58 & 5.54 & 0.57 & 1.06 & \(-\)1.00 & 3.88 & \(-\)6.94 \\ \hline
2020–2099 & **103.01** & \(\mathbf{-17.68}\) & **12.70** & **5.31** & 0.50 & 0.68 & \(-\)0.07 & **3.10** & \(-\)0.55 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Trends over all PFTs. Carbon units in TgCyr\({}^{-1}\); Water units in 10\({}^{12}\) mm H\({}_{2}\)O yr\({}^{-1}\). Bold values indicate a trend statistically significant at \(\alpha=0.05\) level.
## 4 Methodology
### Fuzzy Modules for Each Group of Predictors
#### 4.5.4 FuzzyEye Module  
Pupil size variation is a physiological response that is closely linked to a person's cognitive and emotional state. In the context of driving, changes in pupil size can indicate a driver's level of alertness, cognitive load, or stress, making it a valuable metric for ADAS. We added _FuzzyEye_ module that utilizes fuzzy logic to interpret these variations and provide real-time assessments of a driver's situational awareness (Figure 9). This module utilizes fuzzy logic to assess various parameters such as back mirror usage, road awareness, pupil mean diameter, pupil change rate, fMean, and fMax. Each parameter is categorized into linguistic variables--_'low'_, _'middle'_ and _'high'_, allowing for a nuanced interpretation of the driver's current state.  
There are three pairs of predictors: _backMirror_ and _road_, _pupilMean_ and _pupilChange_, as well as _fMax_ and _fMean_. Rules for their combinations are given in Table 4.  
The module utilizes separate fuzzy logic controllers (sub-systems 1, 2, and 3) in _Cascade_ 1 to process subsets of inputs and generate interim outcomes. Each sub-system generates outcomes (_crispEyeSub1_, _crispEyeSub2_ and _crispEyeSub3_). There are 27 rules for combining these fuzzified outcomes (_fuzzifiedEyeSub1_, _fuzzifiedEyeSub2_, and _fuzzifiedEyeSub3_) to produce the _fuzzifiedEye_ value. This value is then defuzzified into the final _crispEye_ output through the fuzzy inference sub-system 4. The rules are shown in Table 5.  
Figure 9: Block diagram of the _FuzzyEye_ module.
## 4 Numerical Simulation Results
### Severely Rough Road Surface (Cases 7-9)  
With respect to the scenarios with the severely rough road surface (Cases 7-9), the calculated results are shown in Figure 13. Comparing the vertical acceleration time-histories calculated at the mid-span of the third main span for Cases 7-8 (Figure 13) with those for Cases 1-3 (Figure 9), it can be observed that with the increase in road surface roughness, the structural responses are significantly amplified for the low-vehicle-speed cases (Cases 7 and 8); however, the structural response for the high-vehicle-speed case (Case 9) is not noticeably amplified after the increase in the road surface roughness, although it is still much greater than those for the low-vehicle-speed cases (Cases 7 and 8). After fast Fourier transformation, the amplitude spectra of vertical acceleration time-histories were obtained (Figure 14). Comparing the amplitude spectra shown in Figures 10 and 14, it can be found that for the low-vehicle-speed cases (Cases 7 and 8), the amplifying effects are mainly reflected in the increased energies in the low-frequency range (at round 1 Hz), indicating that the increased resonant response, rather than background response, accounts for the total response increment. However, why is the resonant response significantly amplified for the low-vehicle-speed cases after the increase in road surface roughness? This important scientific question cannot be well answered via the results obtained in this portion of study.  
Furthermore, the vehicle-induced maximum tensile stresses on Renyihe Bridge were obtained for Cases 7-9 and compared with those for Cases 1-3 in Figure 15. As shown, the maximum structural responses for most smooth road surface cases (Cases 1 and 2) are at around 0.8 MPa, which is much smaller than the tensile strength of C50 concrete (1.89 MPa); however, those for the severely rough road surface cases (Cases 7 and 8) are all at around 1.4 MPa, which is closer to the tensile strength of C50 concrete. It should be noted that the present numerical simulation simply employs one 30-ton vehicle running through the bridge as the excitation. In actual scenarios, multiple vehicles of weights greater than 30 tons usually simultaneously run on the bridge, therefore possibly causing the vehicle-induced maximum tensile stress on the concrete to be greater than the tensile material strength. This in turn leads to structural damages. The actual bridges generally have the sufficient road surface roughness required to induce structural damages. Only under this condition does the explanation given by Ref. [4] for the observation that a  
Figure 13: Vertical acceleration time-histories calculated at the mid-span of the 3 rd main span in Cases 7–9.  
beam supporting vehicles running at lower speeds suffers from more severe apparent damage (i.e., the observed phenomenon is due to different vehicle load holding durations) make sense.
## 2 Materials and Methods
### Research Methods  
#### 2.3.1 InVEST Model  
The InVEST model, jointly developed by Stanford University, the Nature Conservancy, and the World Wide Fund for Nature, is an open source ecosystem service function assessment model, which can be well used to assess regional carbon storage and its value [35]. This paper uses the carbon storage module in the InVEST model to analyze the carbon storage changes in terrestrial ecosystems in the Taihang Mountains between 2005 and 2019, which classified the carbon density of each land use type as four primary carbon pools: (1) Aboveground living biomass(C\({}_{\text{above}}\)): All living organisms above the soil. (2) Belowground living biomass(C\({}_{\text{below}}\)): All living organisms below the soil. (3) Soil carbon(C\({}_{\text{soil}}\)): the organic carbon of mineral soils. (4) Dead organic matter: dead organic matter on the soil, includes litterfall, fallen or standing dead wood. According to the distribution of land use types in the Taihang Mountains, the InVEST model analyzes and calculates the average carbon density (C\({}_{\text{total}}\)) of various land use types. The specific formulas are as follows:  
\[\text{C}_{\text{i}}=\text{C}_{\text{i,above}}+\text{C}_{\text{i,below}}+\text {C}_{\text{i,soil}}+\text{C}_{\text{i,dead}} \tag{1}\]  
\[\text{C}_{\text{total}}=\sum_{\text{i=1}}^{n}\text{C}_{\text{i}}\times\text{ S}_{\text{i}} \tag{2}\]  
i is the type of land use in type i; C\({}_{\text{i}}\) is the total carbon density of i land use type(t\(\cdot\)hm\({}^{-2}\)); C\({}_{\text{i,above}}\) ground carbon density (t\(\cdot\)hm\({}^{-2}\)) of i land use type; C\({}_{\text{i,below}}\) is the subsurface carbon density of i land use type (t\(\cdot\)hm\({}^{-2}\)); C\({}_{\text{i,dead}}\) is the carbon density of dead organic matter of i land use type (t\(\cdot\)hm\({}^{-2}\)); S\({}_{\text{i}}\) is the area of i land use type(m\({}^{2}\)); C\({}_{\text{total}}\) is the total carbon storage of terrestrial ecosystems (t).
# Improving Graphite Ore Grade Identification with a Novel FRCNN-PGR Method Based on Deep Learning
## 2 Materials and Methods
### Region Proposal Network (RPN)
#### 2.4.3 Loss Calculation Function  
The loss function of RPN consists of two parts: the classification loss and the regression loss, and the summation of them is considered as the loss value of the RPN, as shown in Equation (2).  
\[L(\{p_{i}\},\{t_{i}\})=\frac{1}{N_{cls}}\underset{i}{\sum}L_{cls}\big{(}p_{i},p_{i}^{*}\big{)}+\lambda\frac{1}{N_{reg}}\underset{i}{\sum}p_{i}^{*}L_{reg} \big{(}t_{i},t_{i}^{*}\big{)} \tag{2}\]  
Based on Equation (2), the first term represents the classification loss part, which is used to decide whether it is a positive sample. Additionally, since it does not need a positive sample-dependent category classification, it uses the cross-entropy loss function for binary classification. In Equation (2), \(N_{cls}\) and \(N_{reg}\) are the normalization; \(\lambda\) is constant with the default value of 10.  
The loss function classification formula (first term) is shown in Equation (3).  
\[L_{cls}\big{(}p_{i},p_{i}^{*}\big{)}=-\big{[}p_{i}^{*}\text{log}\,p_{i}+\big{(} 1-p_{i}^{*}\big{)}\text{log}(1-p_{i})\big{]} \tag{3}\]  
In Equation (3), \(p_{i}\) indicated the probability and \(p_{i}^{*}\) shows the predicted label of the \(i\)th positive anchor sample; based on this formula, if \(p_{i}^{*}\) is a positive sample, it is equal to one, (\(p_{i}^{*}=1\)), otherwise, it has zero value (\(p_{i}^{*}=0\)).  
As the second term in Equation (2), \(\lambda\frac{1}{N_{reg}}\) is used to keep the losses balanced between the two parts. In the case that there is no foreground and background within the detection anchor, the value is set to 0 (\(\lambda\frac{1}{N_{reg}}=0\)); and in other cases, it is the reciprocal of the sum of foreground and background. The term \(p_{i}^{*}\) in the classification prediction also appears in the regression loss part, which ensures that non-positive samples generate regression loss. This paper calculated the regression loss using Smooth L1 Loss [21], represented by a piecewise function form combining linear and quadratic functions [24]. Using the quadratic function with the bases between (\(-1\), 1) causes the image function to become smoothly near the zero point. However, it should be mentioned that the divergence is not easy for large forecast biases. In the next Equation, the calculation of the regression loss function is shown in Equations (4) and (5).  
\[L_{reg}\big{(}t_{i},t_{i}^{*}\big{)}=\underset{i\in\{x,y,w,h\}}{\sum} smooth_{L1}\big{(}t_{i}-t_{i}^{*}\big{)} \tag{4}\]  
Figure 8: Single feature extraction.  
\[Smooth_{L1}(x)=\left\{\begin{array}{ll}0.5x^{2},\;if\;|x|<1\\ |x|-0.5,\;otherwise\end{array}\right. \tag{5}\]  
In Equation (4), \(t_{i}\) represents the predicted position of the anchor, and \(t_{i}^{*}\) represents the location of the real sample. For \(x\), as the input of the function, there are two cases. When the range of \(x\) is \((-1,\,1)\), \(Smooth_{L1}\) is the first in Equation (5); in other cases, the expression for \(Smooth_{L1}\) is the second in Equation (5).