esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results Analysis
### BIM Integration with Photogrammetry  
The interviewers asked 'Personal protective equipment make the construction effective, however, some construction workers do not use personal protective equipment (negligence to wear safety hat on, negligence in using safety belts in height)', how with the help of BIM can monitor and track the construction workers? Is it possible? If yes, what are the effective techniques used to overcome the accidents by using BIM? According to the interviewees, most of them believed that using PPE when working in heights was very useful and protective. They have good support for the use of photogrammetry to track and monitor construction workers. With the advantage of drone technology, photographs and videos of entire construction sites can be made and from these videos and images it is possible to detect which construction worker is not wearing a safety hat and safety belt. Moreover, automatic safety monitoring that uses photogrammetric point clouds and 4-D BIM may be the effective way to reduce the accidents. The primary goal is to employ standard camera equipment on construction sites to capture the current level of work by photographing the complete facility under construction at regular intervals. When a large number of images from diverse angles are available, photogrammetric methods can be used to reconstruct a 3-D point cloud from the data. Using this point cloud, each aspect of the construction progress (as-built) can be compared element by element against the geometry of the BIM (as-planned), producing a more accurate picture of the building progress. In this way, using camera and image processing techniques for the detection of construction workers in high-rise buildings can be useful for safer construction. Responses include 'I think the use of photogrammetry techniques through drone technology will be useful and effective' and 'I think the images and videos will make it easier for construction workers to monitor'.  
Moreover, the interviewer asked them, 'Do you think that with the help of photogrammetry, 'no identification of potential safety hazards in high-rise buildings' can be minimized?' Most of the interviews had two perspectives on the respective issue. The group of interviewees who had a positive approach to the use of photogrammetry believed that it was possible to highlight safety risky areas at construction sites when watching photographs and videos, but the other group of interviewees supported a neutral attitude. Responses included 'I believe that the identification of potential safety hazards in high-rise buildings can be made possible through photogrammetry and drone technology' and 'I believe that the identification of potential safety hazards in high-rise buildings cannot be made possible through photogrammetry because it is headache and time consuming to monitor and assess every area of building construction'.
# Perceived Usefulness of a Mandatory Information System
## 8 Conclusions  
### Summary of Key Findings  
This study examined the factors influencing the acceptance of the \"Slot\" information system within an Israeli governmental organization. By integrating advanced analytical techniques such as structural equation modeling (SEM) with bootstrapping and employing contemporary normalization methods, the study provides robust and valid insights. The findings confirmed that perceived ease of use significantly predicts perceived usefulness, which in turn influences symbolic adoption. Additionally, supervisor influence plays a crucial role in enhancing perceived usefulness, thereby indirectly affecting symbolic adoption. These relationships underscore the importance of user-friendly technology and managerial support in driving system adoption.
## 5 Conclusions  
The problem of estimating atmospheric gusts is very important from the perspective of issues related to aircraft control and also flight safety in general. For example, more accurate information about the headwind gusts may improve the performance of the auto-throttle and wind shear warning systems. The conducted flight simulations made it possible to compare the estimates with the ideal case of wind measurement. Execution tests like those presented in the paper in a real environment are difficult and involves the use of specialized measuring equipment. Installation of additional equipment on an airplane results in many practical problems, e.g., certification and obtaining formal approvals, as well as high costs. Another class of problems is it being unpredictable, unique, and difficult to measure atmospheric disturbances caused by natural factors as well as wake turbulence of other aircraft. On the other hand, in the simulation environment, we were able to check the developed methods only with the accuracy of the simulation model used. In the future, if possible, it is planned to conduct tests on a certified Class D FFS simulator.  
Initially, the study also considered the possibility of applying the signals from accelerometers to the estimation of wind gusts. However, after conducting preliminary research, it turned out that the method proposed in this paper allowed us to obtain more accurate results. The estimation method described in this article is characterised by a relatively precise representation of the original signal, as shown in Section 4. However, the signal delay may be troublesome, which in the estimation process carried out in real time, may reach the value of 1-1.5 s according to our analyses. The delay value was influenced in this case by the use of signals from simulated on-board instruments. Reading the parameters directly from the simulator core, bypassing on-board systems, would give ideal but not real results, while the data from on-board sensors are characterised by static and dynamic measurement errors [32]. For the analyses of airplane class, the 1-1.5 s delay in the real-time estimation of the headwind gust seemed to be an acceptable value. Nevertheless, in future research, the authors intend to use fusion methods and compensation algorithms that minimise these delays. At the end of the conclusions, one more limitation of the presented method should be mentioned. The proposed estimation algorithm is very sensitive to flight conditions and airplane configuration. For a transport aircraft, however, it seems to be possible to build an adaptive estimation system. Future work should assume the implementation of such a solution.  
Conceptualization, P.S. and P.R.; methodology, P.S. and P.R.; software, P.S. and P.R.; validation, P.S., T.R. and P.R.; formal analysis, P.S., T.R. and P.R.; investigation, P.S. and P.R.; resources, P.S., T.R. and P.R.; data curation, P.S. and P.R.; writing--original draft preparation, P.S.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Infiltration  
The results concerning sorptivity (cm min\({}^{-0.5}\)), cumulative infiltration (cm), and infiltration rate (cm min\({}^{-1}\)) are shown in Figure 6. Figure 6A,B depicts the cumulative infiltration and infiltration rate data for all the treatments tested. Cumulative infiltration of water through the soil was reduced with the addition of macro-biochar and nano-biochar (Figure 6). Results demonstrate no significant dissimilarity in cumulative infiltration between the control and soil treated at 0-5 cm. The average decline in cumulative infiltration ranged between 10% and 25% as compared with the control soil. The use of biochar over  
Figure 4: Effect of the application of (**A**) macro-biochar and (**B**) nano-biochar particles at four depths on cumulative evaporation (in mm) after 4 weeks of wetting/evaporation cycles.  
the rate at which water infiltrated into the soil. Again, no significant differences were noticed in the infiltration rate between the macro and nano-biochar (Figure 6). The presence of fine biochar particles in pore spaces could be a reason for the decreased infiltration rate and increased water retention. In contrast, the synergetic, positive effect of biochar and compost was noted for soil infiltration rate, water retention, and aggregate stability over the sole application of compost when it was used in sand clay loam soils, as the biochar would act as the cementing material for forming the stable soil aggregates [40].  
Usually, the improved layer of 5-10 cm made a meaningful impact on cumulative infiltration compared with unamended soil. The maximum time required for completing the (100 cm\({}^{3}\)) infiltration process within unamended soil columns was 6 min for all instances of cumulative infiltration, and the infiltration rate was 2.1 and 5.8 cm/min for the minimum and maximum points, respectively. The time for cumulative infiltration was increased considerably with the addition of both macro and nano-biochar. Therefore, the water flow in amended soil was slower than in unamended soil (control), and the time desired for the infiltration process was increased compared with the control (unamended sand soil), confirming earlier reports [10; 41; 42]. These findings demonstrate that the finer particles of the amended biochar enter more easily into the pores of sandy soil, and consequently, the width of the soil pores become reduced, resulting in a decrease in the water infiltration rate [11]. In contrast, a recent study reported by [43] that the enhanced plant available water rate and reduced bulk density previously reported during the initial years of adding biochar faded in the long term, and that this was likely due to the dilution of biochar concentration in topsoil.  
Figure 5: Effect of nano-biochar (**A**) and macro-biochar (**B**) on soil moisture content distribution.
# Using Graph Partitioning for Scalable Distributed Quantum Molecular Dynamics
## 4 Experiments
### Single-Node SM-SP2 versus Parallelized Implementation of G-SP2  
Figure 8 aims to quantify the computational savings over single-node G-SP2 when running our proposed parallel SP2 algorithm. For this, Figure 8 compares the runtime for our parallel G-SP2 on 1-32 nodes against a threaded single-node implementation of SM-SP2. We use G-SP2 here since the communication overhead exceeds the gain obtained by the extra computing power in a multi-node implementation, the main motivation for developing G-SP2 in the first place. As before, we employed _METIS_ with parameters specified in Section 4.1 together with SA for post-processing. The test system is again the polyalanine 259 molecule.  
Figure 8 shows that as expected, both an increasing number of nodes and an increasing number of blocks decreases the G-SP2 runtime. When only a few nodes are used for parallelization, the decrease in runtime is most pronounced since then, increasing the number of parallel nodes causes the runtime to drop sharply. For a higher number of nodes the curves somewhat flatten out.  
Due to the overhead from the parallel G-SP2 computation, the parallelized run of SP2 is actually slower than the SM-SP2 computation on a single node for low numbers of nodes (between 4 and 16 nodes depending on the number of blocks). The runtime decreases with an increase in the number of nodes. Eventually, the effort falls below the one of a single node implementation. For the particular physical system of the polyalanine 259 molecule, a computational speed-up is only observed for at least 4 nodes.  
Figure 8: Runtime of parallelized G-SP2 as a function of the number of nodes. Different numbers of blocks. G-SP2 applied to the polyalanine 259 molecule. Figure taken from [12]. Copyright ©2016 Society for Industrial and Applied Mathematics. Reprinted with permission. All rights reserved.
## 4 Determining the Mean Responses  
In conventional practice the maximum absolute value of deformation is first obtained for each ground motion record and then the mean value from a number of ground motion records are typically used to determine the seismic demands. [PERSON] et al. [1] recommended that the direction of the displacements should also be considered for calculating the mean response, because displacement is a vector quantity and hence the averaging procedure should consider the direction at which the maximum displacements occur. This more detailed approach enables the determination of realistic values of strains in the concrete and steel corresponding to the resultant vectorial displacement. It is noted that for each time step in the analysis for a record pair, there is an associated predicted displacement that occurs at a particular angle (referred to as the radial direction). From the results of the full time-history of displacements, the maximum displacement in each radial direction (i.e., directions around the full 360\({}^{\circ}\)) can be determined for a given ground motion. In this study radial directions at 10\({}^{\circ}\) intervals were considered. The average displacement for each radial direction can then be determined for a number of input ground motions. From the mean values in different directions the maximum displacement can be determined [1].  
In this study, both of the averaging methods (\"average of maximum absolute values\" and \"average values in different directions\") discussed above are used to determine the mean displacements of the bridge columns.
# A Novel Hybrid Edge Detection and LBP Code-Based Robust Image Steganography Method
## 3 Proposed Work
### Embedding Steps  
Step 1: Let the cover image be \(C\) and an instance of it by \(I\). We first clear \(n-\)bits of LSBs from every pixel of \(I\) by Equation (1).  
\[I(i,j)=I(i,j)-f(I(i,j),2^{n}); \tag{1}\]  
where function \(f\) returns the remainder value when one divides \(I(i,j)\) by \(2^{n}\).  
Step 2: A \(m-\)number of edge detection operators are applied, e.g., canny, sobel, fuzzy, Robert, Prewitt, log, etc., on the cleared image I to detect edge pixels, separately. The edge image is generated by Equation (2).  
\[el(i)=\psi(I,\Omega); \tag{2}\]  
where \(\psi\) is one of the \(m\) edge detection operators, i.e., \(\Omega\in\{canny, sobel,log,fuzzy,Robert,Prewitt,etc.\}\) and \(1<=I<=m\) and \(\psi\) returns the edge image \(el\) from \(I\) for a specific edge detector \(\Omega\). All possible combinations of edge images can be made if it is required. The edge images are then hybridized using the logical OR operator by Equation (3). Each edge image is a binary image. For each pixel, the edge image holds a 0 or 1. A 1-in-edge image means the corresponding pixel of \(I\) is in the detected edge.  
\[ReI=eI(1)|el(2)|.......|el(m); \tag{3}\]  
The hybridized edge image helps us in measuring the edge pixels in an image. To meet a bit higher embedding capacity, the number of edge pixels was increased by applying a morphological dilation operation by Equation (4). The emphasis was set on horizontal and vertical edges. Hence, the dilation operator, i.e., mask, is prepared by a \(3\times 3\) matrix.  
\[dReI=F(ReI,mask); \tag{4}\]  
where \(F\) returns the dilated edge images and we then select only edge pixels. Say, the detected edge pixels have formed another image \(P\). That image \(P\) is partitioned into different \(3\times 3\)-sized blocks.  
Figure 2: Proposed method scheme. (**a**) Embedding process; (**b**) Extraction process.  
Step 3: Let L be the 8-bit LBP codes that are obtained from the block using Equations (5) and (6).  
\[g(K_{c},K_{p}^{i})=\begin{cases}1&\text{if }K_{p}^{i}<K_{c}\\ 0&\text{else}\end{cases} \tag{5}\]  
\[L=g(K_{c},K_{p}^{i}); \tag{6}\]  
Step 4: Now, LBP code \(L\) was XORed with message bits \(M\) by Equation (7).  
\[H=L\oplus M; \tag{7}\]  
Step 5: Next, the XORed values were shuffled by Equation (8).  
\[HS=Shuffle(H); \tag{8}\]  
Step 6: The shuffle bits are implanted in the edge pixels of the cover image and generate a stego image by following the way. At the same time, the changes in the stego block regarding cover image pixels were surveilled.  
```
if\(HS(i)=1\)then if\(C(i)\) is odd then \(SB(i)\gets C(i)\) else if\(C(i)\) is even then \(SB(i)\gets C(i)+b\) endif endif else if\(HS(i)=0\)then if\(C(i)\) is even then \(SB(i)\gets C(i)\) else if\(C(i)\) is odd then \(SB(i)\gets C(i)+b\) endif endif endif endif endif endif endif endif
```  
Step 7: Embedding is completed.
## 3 Results and Discussion
### Technical and Economic Evaluation by Process Stage
#### 3.1.1 Technical and Economic Evaluation for the Cultivation Stage  
The process to produce biomass from microalgae begins with the cultivation stage in tubular photobioreactors. Under the parameters considered in this study, a production of 82.45 tons/ha/year of biomass is achieved. This value depends directly on the productivity considered, in this case an average value (22.66 g/m\({}^{2}\)/d) was used since it would be incorrect to assume high productivity (environmental conditions are not always favorable for the process); although there are reports that indicate that productivities of up to 45 g/m\({}^{2}\)/d can be achieved in PBRs [47]. Each photobioreactor has a diameter of 0.09 m and a length of 555 m. The diameter was established with the same value of the original design of the report on which we based [48], so that the transfer of light to the culture is not affected. For its arrangement on the surface considered for cultivation, not only the footprint of the area occupied by each system is considered, but also the projected shadows, since this area is not used to install other photobioreactors. To determine the area occupied by 1 PBR, D x L is calculated and multiplied by 2, to consider the area of the PBR footprint and the shadows that could be generated if the PBRs were installed outdoors and illuminated by sunlight. Considering that 1 ha was established as an area destined for cultivation, 86 PBRs would be necessary to cover this area.  
A photoautotrophic microalgae culture was considered, which needs CO\({}_{2}\) as a carbon source, being very useful for its use as a capture from flue gases. The flue gases from a thermoelectric plant, with a concentration of 11% _v/v_ of CO\({}_{2}\), was considered as a feed, and a capture of up to 148.4 tons/ha/year of CO\({}_{2}\) is calculated. A consumption of 16,872 m\({}^{3}\)/year of freshwater for cultivation is calculated, much less than when using an open system for cultivation. In previous work, consumption of 58,061 m\({}^{3}\)/year of freshwater was calculated for the culture in raceway pond for the same surface [56], which indicates that savings of up to 71% can be achieved when using photobioreactor systems. Another option to reduce the consumption of freshwater is the use of wastewater, it is also a source of nutrients and this could contribute to economic savings for this stage.  
The cultivation stage is highly energy consuming and even more so if photobioreactors are used. In this study, consumption of 875,053 kWh/year and a unit production cost of US $180.43/kg of biomass produced in this stage is estimated, being the highest values of all the stages of the process. The cost of producing microalgae biomass in photobioreactors has a high cost. However, it offers many advantages over an open system culture. Closed systems offer greater control of climatic conditions and better use of light, and reduce CO\({}_{2}\) losses, and all this is reflected in the high production of biomass [58]; this causes them to be the preferred systems if high-quality biomass is required.
## 3 Proposed Optimization Method
### Overview of the FLA
#### 3.1.2 Formulation of FLA  
The FLA algorithm is mathematically presented as described below.  
**Step 1:**_Initialization_. In the FLA, optimization starts based on several preferred solutions, shown by X, according to Equation (41). The generation of solutions is random and in each iteration, the best solution is selected as the best current almost optimal solution [29].  
\[\text{X}=\begin{bmatrix}x_{1,1}&x_{1,j}&\cdots&\cdots&x_{1,\text{D}}\\ x_{2,1}&x_{2,j}&\cdots&\cdots&x_{1,\text{D}}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ x_{\text{N},1}&x_{\text{N},\text{j}}&\cdots&x_{\text{N},\text{D}-1}&x_{ \text{N},\text{D}}\end{bmatrix} \tag{41}\]  
where N represents the number of solutions (population size), D represents the dimension of the problem (number of variables), and j refers to the jth decision variable.  
**Step 2:**_Clustering_. In this step, the population of the algorithm is divided into two equal groups, N1 and N2.  
**Step 3:**_Transfer function (TF)_. The efficiency of any algorithm is highly dependent on the transition from discovery to exploitation, and vice versa. In the FLA, a nonlinear transfer function (TF) is provided for this problem. TF function is defined as Equation (42) [29].  
\[TF^{t}=\sinh(t/T)^{c1} \tag{42}\]  
Here, \(t\) and \(T\) show the number of iterations and the maximum number of iterations, and C1 is considered 0.5.  
**Step 4:**_Update the molecule position:_ In this step, three phases based on transfer operators, namely DO, EO, and SSO, are presented. The transition process between the three phases is based on the following equation [31]:  
\[X_{i}^{t}=\begin{cases}\begin{array}{cc}DO&TF^{t}<0.9\\ EO&TF^{t}\leq 1\\ SSO&TF^{t}>1\end{array}\end{cases} \tag{43}\]
# An Iris Image Super-Resolution Model Based on Swin Transformer and Generative Adversarial Network
## 1 Introduction
### Research Questions  
Firstly, it is crucial to assess the feasibility of the super-resolution methodology.  
Our objective is to enhance the recognition performance of low-resolution iris images. It is essential to validate that the super-resolution method can genuinely enhance the recognition performance of low-resolution iris images.  
In the experimental section, as presented in Section 4, we conducted a comparative analysis of recognition performance between low-resolution iris images and those reconstructed using super-resolution. The results demonstrate that super-resolution reconstruction significantly enhances the recognition performance of low-resolution iris images.  
Secondly, it is imperative to deliberate upon the architecture of the model.  
In recent years, significant strides have been made in the field of super-resolution (refer to Section 2 for details). However, it is noteworthy that much of the existing work primarily focuses on super-resolution for general images, with an emphasis on optimizing the visual quality of reconstructed images.  
The inherent complexity and unpredictability of iris images distinguish the iris super-resolution task from conventional image super-resolution tasks. The unique demands of the iris super-resolution task necessitate a heightened emphasis on recovering intricate image texture details to ensure the preservation of vital identity information throughout the process.  
Throughout experimentation, we observed a notable enhancement in the recognition performance of low-resolution iris images using the model based on Swin Transformer or Generative Adversarial Network (GAN). Therefore, we aimed to design a model (refer to Section 3 for details) that effectively integrates Swin Transformer and GAN to synergistically collaborate in iris super-resolution task.  
Thirdly, the selection of appropriate metrics is paramount.  
There is no single metric that comprehensively evaluates both the reconstruction quality and recognition performance of the iris image. Therefore, distinct metrics are employed to assess the quality of generated images (such as PSNR, SSIM, PI) and the recognition performance (EER). For detailed information, please refer to Section 4.2.3.