esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation of Smart NFC Door Access System for Hotel Room
## 1 Introduction  
Security is acknowledged as one of the most vital factors and words in the world for people [1] and it is supposed to be supplied anywhere in various businesses, making it the first concept that should be offered in every location with fulfilment and asset. Furthermore, security is a primary goal in the hotel sector because it is significant in a variety of additional fields, and sectors [2].  
Hotel industries are considered the top contributors to the national economy. Hotels have become an important key growth in industrial economic assistance. As a result, the hotel's maintenance, and the best experience is a major focus for both the visitors and representatives in the area [3]. Most people believe that the technological limits and shortcomings in hotels throughout global, which is assumed unusual, mainly in the modern market and the transformation potential of New Technologies. The hotel access points, and their management are one of the most significant components in hotels that are responsible for security and management efficiency [4].  
Access points management systems apply a consistent approach to protect and control the access points, such as hotel doors placed in the staff or in the background of a hotel, or entryways for guests located in elevators or room doors in the hotel industry. With the advent of smartphones and technological advancement, technology is now required for many routine daily chores. The survey poll reveals that many hotel managers and visitors desire and call for further implementations of cutting-edge technologies in lodging [5].  
Moreover, the epidemic of COVID-19 has resulted in basic and dramatic changes in the hotel industry [6] with a pessimistic effect. The most considerable of these are hotelguests' anxieties about social distancing, cleanliness, and well-being. Since COVID-19 is a disease that spreads through the air, it is inevitable that the virus would adhere to objects and migrate through physical touch. In hotels, there are several doorknobs and switches that might be pressed, increasing the likelihood of contracting an infection. Consequently, the recent ordinary for the potential hotel industry has transformed to \"high-tech\" to \"low-touch\" [7]. NFC offers exceptionally innovative applications that are utilized smartphone Host Card Emulation (HCE) running in Google Android operating systems only in different fields and applications that underline security levels of contactless activities. Most NFC frameworks incorporate a label peruser, which ought to have the option to peruse any inactive or dynamic tag to trade information or the previous. There are three kinds of tasks or as known to be conceivable application situations, which incorporate read/compose labels, distributed labels, and the card emulator [8]. Each type's determinations are point by point and represented to give a worked-on clarification in Figure 1.
## 3 Results
### Experimental Analysis of Distributed Learning Classification Module
#### 3.4.1 Selection of Training Models for Clients  
In the distributed learning module we created, each client trains its own network in each round and then uploads the iterated model parameters to the server. To investigate the impacts of diverse network models on training results, we conducted comparison tests with many standard deep learning models, including artificial neural network (ANN), BP neural network, convolutional neural network (CNN), and residual network (ResNet). Figure 12 depicts the results of 30 rounds of training. As demonstrated in the figure, using ResNet results in the best performance, with an accuracy of 98.96%. As a result, ResNet was chosen as the network for client training.  
Figure 11: UNET denoising effect diagram. The average spectral curves of the original data, generated data, and denoised data are plotted.  
Figure 12: The results of different training models on the client.
## 2 Sites and Data  
The study area covered most of western Taichung and the neighboring regions in the west-central part of Taiwan (Figure 1). According to [PERSON] and [PERSON] [20], this area includes four geological regions: the foothills on the east (i.e., Western Footballs) and the coastal plain on the west (i.e., Western Coastal Plain) with the Taichung Basin and the Tatushan-Pakushan hills (i.e., Tatushan Anticline and Pakushan Anticline) between them (Figure 1). The Taichung Basin is a structural basin. The NS-trending Chelungpu Fault is the boundary fault between the foothills and the Taichung basin. The Tatushan-Pakushan hills (anticlines) separate the Western Coastal Plain from the Taichung piggyback basin because of the thrusting of the NS-trending Changhua Fault. The Tatushan-Pakushan hills, extending from the Dajia River (in northern Taichung) to the Choshui River (in southern Changhua), are mainly composed of conglomerates of the upper Toukoshan Formation. However, the thick-bedded sandstones, beneath the conglomerates in stratigraphic sequence, are only exposed in the area near the Tatu River. Widespread alluvium, which consists of gravel, sand, and clay, covers the Taichung Basin and the coastal plain [20].  
In the study area, we performed microtremor array measurements at 53 sites. These sites covered most of western Taichung and most lie on alluvium (Figure 2). The distances between neighboring sites were between 2 and 5 km. At each site, microtremor arrays of two sizes (small [S] and large [L]) were used for measurements. At each array, 10 stations were deployed in the form of 3 triangles of varying apertures around the center station. Here, the site DTE on the Western Coastal Plain is employed as an example. Figure 3 exhibits the S- and L-array configurations at site DTE. The center station is fixed for the S and L arrays (min. and max. radii: 25 m and 100 m and 100 m and 400 m, respectively) at each site.
## 4 Experimental Results  
We evaluate our method in three stages. First, we assess the scaling process without applying extrinsic calibration to enable comparison with an existing benchmark. In this stage, we transform between LiDAR and camera coordinates using the parameters provided in the dataset for both the benchmark and the scaled point clouds. Next, we evaluate the extrinsic calibration, setting a benchmark for estimating OSDaR23-specific extrinsics using only a single image. Finally, we analyze the accuracy of the scaled and extrinsically calibrated 3D point clouds to determine the overall performance of our approach. Itis important to note that while the first stage can be compared against an established benchmark, no benchmark currently exists for extrinsic calibration to the OSDaR23-specific coordinate system.
# Tensile Properties and Tensile Failure Criteria of Layered Rocks
## 2 Experimental Study on Layered Rocks by the Brazilian Test  
### Test Equipment and Samples  
Layered rock samples (shale in Figure 1) with different dip angles, \(\theta\) (the angle between the bedding plane and the horizontal plane), as shown in Figure 2, were selected for the experiments. According to the method suggested by the International Society for Rock Mechanics (ISRM), the samples were carefully drilled from a large fresh shale block. They were machined into Brazilian disc cylinders (25 mm in thickness and 50 mm in diameter) with seven different dip angels (\(0^{\circ},15^{\circ},30^{\circ},45^{\circ},60^{\circ},75^{\circ},90^{\circ}\)). These samples are displayed in Figure 1. The experiments were performed using a Rock Mechanics Testing System (RMT-150C). The axial displacement loading mode was selected, with a controlled rate of 0.0005 mm/s until the failure of the tested shale sample. A photograph and a schematic diagram of the experimental setup is presented in Figure 2.  
According to the theory of the Brazilian disc test, the tensile strength of rock samples can be expressed as (negative sign represents tensile stress):  
\[\sigma_{\rm t}=-\frac{2P_{t}}{\pi Dt} \tag{1}\]  
where \(P_{t}\) is the failure load of the rock sample, \(D\) is the diameter of the rock sample, and \(t\) is the thickness of the rock sample.
# Scale-Model Experiment on the Delayed Failure Mechanism of Deep Tunnels in Brittle Rock
## 2 Background Engineering and Scientific Issue  
Jinping II is a hydroelectric project located on the Yanlong River in Sichuan Province, southwest of China [17]. There is a total of seven tunnels in this project: twin transportation tunnels serving as accesses, one drainage tunnel, and four water-conveying (headrace) tunnels (Figure 1a). The length of each tunnel is about 16.7 m, and the total length of the tunnels is around 117 km. All the tunnels are located beneath Jinping Mountain, with a summit of 4455 m elevation. The maximum depth of the headrace tunnels is 2525 m from the mountain surface. The tunnels mainly go through huge blocks of marble (Figure 1b), except about 5 km weak rocks as slate and schist near west portal. Three tunnel-boring-machines (TBMs) drilled from the east portal of the drainage tunnel, No. 1 and No. 3 headrace tunnels. The other two headrace tunnels (No. 2 and No. 4) have been excavated by the traditional drill and blasting (D&B) method from the east portal and the west portal, respectively.  
During the excavation of these tunnels, many time-delayed rockbursts occurred. The following phenomena could be found about the delayed time of the time-delayed rockbursts [19]:  
1. Most of the time-delayed rockbursts occur within 7 days to 1 month after excavation;
2. The relationship between frequency of the time-delayed rockbursts and their delayed blasting time matches a power function, and the peak frequency of rockburst count is within 4 h after excavation.  
The key scientific question is why the peak frequency of rockbursts is within 4 h after excavation, and why sometimes the delayed time can up to last one month. Solving this question would provide more key evidence for the mechanism of the rockbursts. In this paper, we review the delayed failure mechanism, design a scale-model test and conduct numerical simulations to seek the answer.
## 3 Historical Review to State-of-the-Art
### Ceres
#### 3.3.2 CERES-Terra
As an approach to validate both the CERES-TRMM and CERES-Terra shortwave ADMs, it was noted that the average all-sky albedo at a given solar zenith angle should be independent of satellite viewing geometry. This provided a useful consistency test. Both the CERES-TRMM and CERES-Terra ADMs show improved self-consistency compared with ERBE ADMs (Figure 9). While ERBE ADMs show 4-8% variation in albedo with viewing zenith angle, CERES-TRMM and CERES-Terra ADMs show variations closer to 1-2% (40; 43). Figure 8: Sigmoidal fit between shortwave radiance and \(\ln(f\tau)\) for liquid cloud over ocean in angular bin (\(\theta_{\rm{S}}\): \(34^{\circ}\)–\(36^{\circ}\), \(\theta_{\rm{P}}\): \(50^{\circ}\)–\(52^{\circ}\), \(\varphi\): \(6^{\circ}\)–\(8^{\circ}\)). Individual points are instantaneous observations colored by cloud fraction. Inset is the sigmoidal function with 5 free parameters: I\({}_{0},~{}\chi_{0},~{}\rm{a},~{}b,~{}c\). Adapted from [PERSON] et al. (2013) © American Meteorological Society. Used with permission. Figure 9: Variation in mean all-sky albedo with viewing zenith angle for (**a**) January and (**b**) July 2004. Adapted from [PERSON] et al. (2013) © American Meteorological Society. Used with permission.
# On the Definition of Quantum Programming Modules
## 3 Towards Modular Quantum Programming
### Background on Modularity in Quantum Programming
According to [17], given \(U_{x}\), we can say that a circuit \(P\) is _modular_ with respect to \(U_{x}\) if it is possible to construct a \(U_{x}\)-independent module with different implementations provided that: (1) there is independence with respect to the infrastructure used for its realization (this requires that the physical systems implementing \(P\) and \(U\) are well-known, including the respective Hilbert spaces and computational basis), and (2), no changes on \(P\) are needed for replacing one \(U_{x}\) implementation by other equivalent. There is a constraint that must be satisfied if we want to have \(U\) modular with respect to \(P\). Here, the classical concept of black box is needed. A black box [27] is a device or system which can be viewed in terms of its inputs and outputs, without any knowledge of its internals. Two algorithms are equivalent and indistinguishable if, given any inputs, they generate the same outputs. Thus, if \(P\) is modular with respect to \(U\), then \(P[U]\) does not reveal which algorithm has actually been used for implementing \(U\). This is a constraint limiting what kind of quantum computations can be made modular. As described in [17], there are many algorithms (like phase estimation, quantum factoring, solution of linear equations, among others) that are not modular (because they work by extracting information beyond the black-box properties of the embedded module), thus precluding the implementation of circuits that work for every possible input and forcing to be adapted for each possible entry. In these cases, some redesign is needed in order to make them modular. Thus, in spite of the importance of being able to define QP as a set of separately constructed modules that are combined as needed without knowing their internal implementation, scarce theoretical results have demonstrated in which situations the modularity is not possible, although they also have provided methods to avoid the situation for certain algorithms. In some cases, modularity can be achieved at the cost of losing part of the information computed, while, in other cases, the information sacrificed is not crucial (for more detail about this subject, readers can refer to [17]). In any case, more guidelines for modular design are needed in order to facilitate the development of QP.
# Automatic Identification of Landslides Based on Deep Learning
## 3 Materials and Methods
### Evaluation Metrics  
In deep learning methods, recall rate and accuracy rate are indicators that can evaluate the recognition effect of the model, and they are associated with the confusion matrix; As Table 1 shows, T and F represent the prediction of true or false; P (positive) and N (negative) represent the type of prediction; TP (true positive), TN (true negative), FP (false positive), and FN (false negative) are used to classify pixels. Taking this article as an example, TP means that the pixel is identified as a landslide pixel; the identification is correct. TN means that the pixel is identified as a nonlandslide pixel; the identification is correct. FP indicates that the pixel was identified as a landslide pixel and identified incorrectly. FN indicates that the pixel was identified as a nonlandslide pixel and identified incorrectly.  
Precision represents the proportion of the actual landslide pixels in the pixels predicted by the model as landslides, as shown in Equation (1):  
\[Precision=\frac{TP}{FP+TP}, \tag{1}\]  
Recall represents the proportion of landslide pixels predicted by the model in all actual landslide pixels, as shown in Equation (2):  
\[Recall=\frac{TP}{FN+TP}, \tag{2}\]  
In addition, mIoU is a widely used metric in semantic segmentation tasks and is used as a standard measure to measure semantic segmentation models. Intersection over union (IoU) represents the ratio between the intersection and union of the predicted results of landslide pixels and the actual landslide pixels, and mIoU represents the average of all categories of IoU. The IoU is shown by Equation (3):  
\[mIoU=\frac{1}{k+1}\sum_{i=0}^{k}\frac{p_{ii}}{\sum_{j=0}^{k}p_{ij}+\sum_{j=0}^ {k}p_{ji}-p_{ii}}, \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Actual Values**} & \multicolumn{2}{c}{**Predicted Values**} \\ \cline{2-3}  & **Positive** & **Negative** \\ \hline Positive & TP & FN \\ Negative & FP & TN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix of classification results.  
Figure 3: PSPNet architecture. First, a CNN is used to obtain the last convolutional feature map given an input image. A pyramid parsing module is then applied to collect different subregion representations, followed by upsampling and concatenation layers to form the final feature representation. Finally, the representation is fed into convolutional layers to obtain the final per-pixel predictions.  
In this formula, \(i\) represents the real pixel, \(j\) represents the predicted pixel, \(p_{ij}\) represents that \(i\) will be predicted as \(j\), \(p_{ii}\) represents that \(i\) will be predicted as \(i\) and \(p_{ji}\) represents that \(j\) will be predicted as \(i\).
## 2 Results and Discussion
### The Effect of SPME Fiber Coating on Analytical Results
When Carboxen/PDMS and DVB/CAR/PDMS were used, the peak area of esters in FST1 (9.96% and 5.27%) is bigger than that in FST2 (5.44% and 3.90%). However, when PDMS/DVB and PDMS were used, the peak area of esters in FST1 (5.63% and 5.04%) is smaller than that in FST2 (10.41% and 12.49%). The reason may be that the peak area is a relative value. It is affected by a number of factors, such as the kind of fiber, the thickness of fiber, the number of identified constituents, the polarity of constituents, _etc._  
The extraction efficiency of Carboxen/PDMS on aldehydes and ketone is best among the four fibers. The total peak areas in FST1 and FST2 were 1.54% and 0.59%, respectively. When PDMS/DVB and DVB/CAR/PDMS were used, aldehydes and ketone were identified only in FST1. When PDMS was used, aldehydes and ketone were not identified in FST1 and FST2. The results indicated that the total content of aldehydes and ketone was lower in FST, especially in FST2. The extraction efficiencies of Carboxen/PDMS and PDMS/DVB on sulfide and DVB/CAR/PDMS and PDMS on sulfide are close. When these fibers were used, the peak area of sulfide in FST1 (22.79% and 38.39%) is bigger than that in FST2 (21.66% and 22.49%). However, when DVB/CAR/PDMS and PDMS were used, the peak area of sulfide in FST1 (15.13% and 23.05%) is smaller than that in FST2 (16.34% and 33.40%). The main reason may be that the peak area is a relative value, too. The relative peak areas of indole in FST1 and FST2 were close when using Carboxen/PDMS, PDMS/DVB and PDMS, but the value by PDMS was smaller than those by Carboxen/PDMS and PDMS/DVB. Maybe PDMS was better for extracting fatty acids, and this made the relative peak areas of indole become smaller. Among the four fibers, the relative peak areas of indole in FST1 (64.79%) and FST2(45.55%) were biggest by using DVB/CAR/PDMS. Maybe the reason was that indole was adsorbed easier by DVB/CAR/PDMS and the total number of identified compounds was less.
# Does Below-Above Canopy Air Mass Decoupling Impact Temperate Floodplain Forest CO2 Exchange?
## 3 Results
### Influence of Decoupling on above Canopy Derived Carbon Fluxes in the Study Years 2019-2020  
The patterns of above and below canopy fluxes were analyzed to determine how much of the below canopy fluxes are represented by the above canopy fluxes. On average, above canopy fluxes exhibit CUP for the days of the year 110-281 (Figure 4, on the left). At nights, often under conditions of very low or no turbulence when CO\({}_{2}\) accumulates close to the ground, below canopy fluxes were slightly higher than above canopy fluxes (Figure 4, on the right).  
**u* two-level filtering**  
For 2019, we derived the filtering thresholds of u* above the canopy 0.43 m s\({}^{-1}\) and below the canopy 0.08 m s\({}^{-1}\) (Figure 5). Periods with both u* above and below the canopy above their thresholds indicate full coupling, i.e., sufficient mixing across the whole canopy.  
Figure 4: Daily average exchange of CO\({}_{2}\) above and below the canopy during the years 2019–2020 (on the **left**) and averaged diurnal patterns covering the years 2019–2020 (on the **right**).  
Figure 5: Relation between below canopy u* vs. above canopy u* (black circles; 30 min resolution; m s\({}^{-1}\); both axes are in logarithmic scale). The yellow vertical and horizontal line indicate the estimated above and below the canopy u* thresholds, respectively, which distinguish weak turbulent periods from the period when the canopy layers were fully coupled.  
**CO\({}_{2}\) flux diurnal variation based on single- and two-level filtering**  
The mean diurnal variation of single- and two-level u* filtered CO\({}_{2}\) exchange showed a typical behavior characterized by CO\({}_{2}\) uptake from around 5:00 till about 19:00, on average, and CO\({}_{2}\) emission only during the night hours. Small differences between the single- and two-level u* filtered data can be observed. The two-level filtered data indicate higher respiration during nighttime which depicts the effect of accounting for decoupling with the two-level filtering (Figure 6).  
**Sensitivity analysis of decoupling filtering thresholds**  
As the differences between the single- and two-level u* filtered data seemed only marginal, we conducted a sensitivity analysis of the additional below canopy u* filtering threshold. With this procedure, we wanted to evaluate how sensitive the above canopy derived annual C exchange is for the additional decoupling filtering (Table 2).  
In the case that decoupling would significantly hinder the vertical CO\({}_{2}\) transport from forest floor through canopy, the annual cumulative C results should become less negative with increasing strictness of decoupling filtering, as stricter filtering should better account for below canopy respiration. Our results, however, suggest that at the study site, decoupling occurs regularly, but has no significant effect on above canopy derived annual cumulative C exchange. The annual storage flux in the year 2019 equals 0.9 g m\({}^{-2}\), thus, would not change this result.