esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Image Pre-Processing
#### 2.4.1 Geometric Correction  
Specifically, the image orthorectification process was carried out to spatially fuse the image scenes captured on each spectral channel, to form a single image based on the common rows and columns. Subsequently, geometric correction was carried out in order to spatially register the orthomosaic imagery. The imagery was spatially registered to WGS-1984-UTM-Zone-36S spheroid. The atmospheric correction process was not carried out in this study because the UAV system operated below the atmosphere; the recorded reflected radiations were not subjected to interaction with the atmosphere. The imaged scene obtained from the UAV system extended beyond the asphalt road to cover crop fields  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System** & **Characteristics** & **Specification** \\ \hline \multirow{8}{*}{Platform} & Mass & 6 kg \\  & Flying height & 120 m \\  & Areal extent & 8711 m squared \\  & Scanning duration & 13 min 28 s \\  & Speed & 10 ms\({}^{-1}\) \\  & Visible satellites & 13 \\  & Overlap & 75\% \\  & Side lap & 75\% \\  & Dimension & 1.2 m \\ \hline \multirow{8}{*}{Red-Edge Sensor} & Spectral channels & B, G, R, NIR, R\_Edge \\  & Focal length & 5.5 mm \\ \cline{1-1}  & Field of view & 7.2 degrees \\ \cline{1-1}  & Mass & 150 g \\ \cline{1-1}  & Image resolution & 1280 \(\times\) 960 mm \\ \cline{1-1}  & Spatial resolution & 8.5 cm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the UAV system employed to acquire image for this study.  
and adjacent vegetation. As such, the image sub-setting process was carried out to extract only the surface covered by asphalt road, using the \"Extract By Mask\" tool embedded in ArcMap GIS environment.
Therefore, it is very necessary to develop an automatic signal classification system which can automatically identify the most relevant features of ultrasonic signals with defects and eliminate the subjective selection. Recently, because of the great strides in computing power, deep learning, which can extract and identify the effective features of images automatically, became a potential solution to deal with the above issue. In the field of deep learning, the convolutional neural network (CNN) has proved to be highly qualified for visual recognition tasks and is widely implemented [12]. A deep CNN with a linear SVM top layer was used to automatically extract features for each signal from wavelet coefficients and complete signal classification tasks for composite materials [13]. [PERSON] et al. [14] converted laser ultrasonic signals into the scalograms (images) via the wavelet transform which were then used as the image input for the pretrained CNN to extract the defect features automatically to quantify the width of defects. All above works have to utilize the wavelet transform technique to convert time-domain signals into images (scalogram) since traditional CNNs require high-resolution images as input. Such a process is still involved with the manual selection of parameters for wavelet transform and will be time-consuming with large volumes of raw data. In this paper, a CNN using raw time-domain data without the wavelet transform as input is designed to automatically extract features for the classification of ultrasonic signals. The approach is validated by the mockup with internal slots. The results demonstrate that our neural network architecture with independent features performs considerably better than the ones with handcrafted features. Finally, we visualize the shape, location and orientation of defects with a C-scan image based on classification results to validate the effectiveness of the results. Figure 1 shows the outline of the paper.
# A Trustworthy Communication Hub for Cyber-Physical Systems
## 6 Evaluation
### CPS Hub with Physical CPS Assets
However, when sharing a larger set of content such as multimedia, domain, or even vendor specific information content/protocols, then some more advanced methods, such as data plane negotiation, are maybe needed (see Section 5.4). For the realizations of the publish/subscribe method, we had two options: XMPP pub-sub nodes or MQTT-based solution. The MQTT-based publish/subscribe method proved to be more simple and usable than XMPP pub-sub node, and thus it was selected. However, it does not have any support for trust-based conditional delivery of content, nor presence management of physical resources. Therefore, presence and buddy subscription means of XMPP were selected and applied for enabling dynamic support for presence management and negotiation of trust relationships. In addition, the concept of the Jabber identifier (JID), that is, notation _<owner>@<domain.server>/<physical resource>_, applied in the XMPP proved to be very practically usable, see Sections 3.2 and 4.2. However, we enhanced it a step forward in this research, see (1) in Section 4.2, and also applied it for the MQTT-based publish/subscribe and identities of the trust checking service. Figure 19: Polar and Valopaa devices of the smart mobile M2M demonstration in the digital innovation forum (DIF) event. Figure 20: Visualization of the presence and trust relationship in the CPS hub. The realization of the trust checking services proved to be the most challenging part in this step. We were capable for taking the dynamic presence of resources, their identities, owner identities, and grid of agreements into concern in the DIF demonstrator. However, it proved during the work that aspects such as, for example, situation (alarm and so on), time, information, and subscriber require more capabilities in order to enable their definition in the owner specific way. Therefore, application of external authorization service relying on SAFAX and XACML technology [22; 23] was included in the trust checking services of the CPS hub.
# Tracking of Stiffness Variation in Structural Members Using Input Error Function Observers
## 5 Numerical Simulation for Stiffness Tracking: Two-Dimensional Three-Bay Truss Structure
The mean value of the stiffness estimation in Table 4 is less sensitive to the number of data points used when compared to the sensitivity of the stiffness estimation data for % rms noise considered in Table 3.  
For brevity, the stiffness variation of only one member, #E10, is tracked here. This member will be a representative of a member critical to the structural integrity of the system under consideration. Without loss of generality, the stiffness variation in any of the truss members can be tracked using the proposed algorithm in this study. However, the proposed algorithm will have limitations inherent to the structural system under consideration. Changes in output measurements will not have the same level of sensitivity to the changes in stiffness of all the structural members and will be a function of load path and location of input-output measurements. The proposed methodology has the limitation that the number of independent sensors needs to be at least equal to the number of structural members so that failure among each structural member can be tracked. For practical applications, implementing Monte Carlo simulations will result in expensive computational costs and might delay the decision process to support real-time tracking depending upon the complexity of the structural system under monitoring. If necessary, computational burden can be addressed by tracking only members critical to the structural integrity. A structural health monitoring system can be envisioned where an alarm can be triggered once a certain threshold is crossed for critical members, and from then onwards, the whole structural system will be closely monitored. In this study, analytical redundancy because of structural stiffness changes is considered in this study. The influence of other factors such as variations in the mass, damping values or environmental factors that will result in a different system response when compared to the assumed theoretical system are beyond the scope of this paper. However, if the effect of such factors is local and will result in stiffness change locally, the algorithm proposed in this paper will be able to \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time Interval** & **200** & **400** & **600** & **800** \\ \hline
0–500 & 0.9830 & 0.9924 & 0.9877 & 0.9931 \\ \hline
500–1000 & 0.7994 & 0.8155 & 0.8143 & 0.8001 \\ \hline
1000–1500 & 0.7047 & 0.7048 & 0.7077 & 0.7125 \\ \hline
1500–2000 & 0.6031 & 0.5986 & 0.6133 & 0.6077 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Estimation of stiffness with different data points and 5% rms noise in output measurements. Figure 10: Stiffness variation in truss member # E10 for different data points. identify the stiffness variation, but further work is needed to make a distinction between structural stiffness changes and other changes such as variations in mass, temperature or damping values.
# WIG-Net: Wavelet-Based Defocus Deblurring with IFA and GCN
## 3 Methods
### Dataset  
The DPDD dataset [1] was employed to train our network, which is a commonly used end-to-end defocus dataset. In addition, our own dataset (Figure 6) was created. To this end, a Canon EOS 800D camera was utilized to capture a pair of identical static images at various apertures (F1.8-F22). As the aperture size varied, the depth of field in the captured images differed, resulting in both clear (Ground Truth) and defocus blurred images. The specific capture procedure was as follows: First, the camera was securely positioned on a tripod to maintain stability throughout the entire shooting process while keeping the lens focal length constant. The aperture was adjusted to its minimum value to acquire fully clear images and subsequently switched to its maximum value to obtain blurred images. Throughout the entire capture process, the automatic exposure mode was employed to adjust the exposure time, ensuring consistent brightness across all images. To capture diverse scenes and different types of defocused and blurred images, 100 scenes (indoor and outdoor settings) and varying focal lengths were examined for image acquisition. Similarly, our custom-made dataset comprised a total of 500 pairs of images. These images were divided into training, validation, and test sets, with each set containing 400, 50, and 50 scenes, respectively.
# Solar Concentration for Wastewaters Remediation: A Review of Materials and Technologies
## 3 Advanced Oxidation Processes and Photocatalysis
### Principles of Solar Photocatalysis
#### 3.1.2 Homogeneous Photocatalysis  
In the homogeneous photocatalysis process, important efforts were devoted to the investigation of the Fenton reaction in an aqueous solution containing metal ions and hydrogen peroxide, capable of providing hydroxyl radicals. When UV/visible radiation (wavelength \(\leq\) 600 nm) is introduced in the process, it becomes catalytic; this is known as \"photo-Fenton\" [7; 111; 112]. In the photo-Fenton reaction, the metal ion initially reacts with the H\({}_{2}\)O\({}_{2}\) added to the contaminated water, producing OH\({}^{\bullet}\) radicals (Equation (2)):  
\[\mathrm{Metal}^{2+}+\mathrm{H}_{2}\mathrm{O}_{2}\rightarrow\mathrm{Metal}^{3+ }+\mathrm{OH}^{-}+\mathrm{OH}^{\bullet} \tag{2}\]  
\[\mathrm{Metal}^{3+}+\mathrm{OH}^{-}+hv\rightarrow\mathrm{Metal}^{2+}+\mathrm{ OH}^{\bullet} \tag{3}\]  
The absorption of a photon (Equation (3)) then not only restores the initial Metal\({}^{2+}\), the crucial catalytic species for the Fenton reaction, but also produces additional radicals that can contribute to the oxidation of organic pollutants [113].  
Though less interesting from an industrial point of view due to the difficulty of separating and recovering the catalyst, a photo-Fenton process in the homogeneous route has been recently demonstrated for SODIS technology, using ethylendiamine-N\({}^{\prime}\),N\({}^{\prime}\)-disuccin acid as a complex agent to prevent iron precipitation as ferric hydroxide [114].
# Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization
## 5 Discussion  
The EECP-MFO model was presented in this research to overcome the routing problem in FANETS. Highly energy efficient and optimized solutions in search space can be found using the MFO method. In FANET, the EECP-MFO method improves cluster building time, reduces energy consumption, and has a high probability of packet delivery success with a balanced number of cluster heads. In addition, it decreased broadcasting efforts that were  
Figure 19: Consistency in number of cluster heads.  
Figure 18: Cluttering lifetime vs. number of nodes (\(3000\times 3000\) m\({}^{2}\)).  
not essential and helped to minimize the cost of routing and preserve the energy of UAVs by regulating their transmission range. Figure 20 depicts a side-by-side comparison of the three existing methods with our own proposed protocol. Every assessing parameter shows that EECP-MFO outperforms other approaches since it can give an optimum node for CH selection and the best neighbor nodes selection for a cluster in an efficient and optimal way. However, if we talk about the second most superior algorithm, we cannot reach a single algorithm as GWO [14; 25] performs better than the PSO [18] and ACO [17; 20] in the case of clustering time. However, in the case of energy efficiency and probability of success, ACO performs better than GWO and PSO [36].  
One of the critical limitations of the proposed algorithm is tuning parameters, as it can bias the fitness function for selecting a CH and also leads to the skewing problem. Improper selection of those parameters will result in a poor clustering mechanism, leading to high energy consumption and a weak node lifetime [37; 38; 39; 40]. However, secondly, the proposed algorithm does not provide any aggregation technique by which it can ignore the identical packet for processing as they consume substantial computation efforts and UAV's energy. A packet scheduling system is required to overcome the issues mentioned above, also tackling the congestion control concern. These limitations can be addressed in future studies [41]. Moreover, these limitations will encourage the researchers to take up this research problem. As part of our future work, we will also attempt to construct a dynamic model in a heterogeneous environment using the Moth flame optimization technique to make our proposed model more widely applicable for improving the lifetime of WSNs and providing an energy-efficient for the application and development of WSNs and IoT [42].
## Materials and Methods
### Use of 3D-Elements in Critical Frequency Analysis  
The use of one-dimensional elements, which are based on plate theory or plane stress assumptions, can be found in the literature for determining the critical frequencies of the rotating components. [PERSON] [7] and [PERSON] et al. [8] have calculated critical speeds of critical frequencies for metallic shafts using one-dimensional elements based on [PERSON] and [PERSON]'s beam theories. Similarly, [PERSON] and [PERSON] et al. [9] also used these theories to analyze walled composite shafts.  
On the other hand, by combining one-, two-, and three-dimensional methods, several rotor settings were designed by [PERSON] and [PERSON] [10]. Their approach was to combine the FEM with the sub-structure synthesis in the definition of the spindles of the rotating disks in order to find critical frequencies.  
The 2D elements, which were based on [PERSON]'s theory, were used to model the disk, whereas the stationary components and spindle were modeled using the [PERSON] and [PERSON] theories. [PERSON] and [PERSON] [10] later extended this strategy to a disk spindle. The bases were designed with three-dimensional components so as to guarantee compliance with the beam components. [PERSON] et al. [11] defined the dynamics of the spinning blade disks on the flexible disks. Transition elements were used in order to model structural elements such as shaft, blades and disks. [PERSON] and [PERSON] [12] utilized a combination of 2D Fourier formulation with 3D FE elements for analyzing big rotating machine components. For asymmetric structures, 3D finite element modeling is often employed to capture a true picture of their kinematic behavior. Despite such advancements in the computing strength, a large number of sophisticated 3D models still face complicated computational issues [13].  
The simplified approach of the one- and two-dimensional elements can be used in many cases, but the use of three-dimensional elements is the best method to achieve the goals of a dynamic analysis [14]. One of the advantages of using three-dimensional elements is to directly develop a finite element model of the rotating component rather than using any other mathematical approximation. The 3D elements can also address the non-linearities introduced in the rotor system of asymmetric rotors [15], and can also ensure the accuracy of the structure [16]. The asymmetric rotor can be because of the blade-off condition due to bird strike, or it can also be due to the loss of an entire blade. Another advantage of using a 3D element is that the gyroscopic effects of a rotating system can also be studied. Therefore, the use of a 3D element is the most viable solution to determine the critical frequencies of a rotating component [17].
# A Parametric Computational Study of RC Building Structures under Corner-Column Removal Situations
## 2 Description of the Study
### Parametric Study  
The parametric study analysed the influence of five different structural response parameters of the building under study. The results are given and discussed in the following sections. The five parameters considered were (i) tensile concrete strength (f\({}_{\text{ct}}\)), (ii) flexural reinforcement ratio around the P2/P6 column-slab joint (\(\rho\)), (iii) number of floors (nf), (iv) column removal time (cnt) and (v) the applied superimposed load (q). Ten different models were processed, establishing the model described in Section 2.3 as the reference model but considering the same tensile strength (2.0 MPa) for both slabs. Models built for the parametric study vary by only one parameter at a time. Three different f\({}_{\text{ct}}\) values were considered: 2.0, 2.5 and 3.0 MPa, the lowest value being similar to the experimental one and adopting two higher levels. The flexural reinforcement ratio around the P2/P6 column-slab joint was approximately equal to 0.8%, which could be considered relatively high, so a value of 0.5% was also considered. The number of floors was also taken into account due to its possible influence in the Vierendeel action and the dynamic response, adding two and five extra levels to those of the test (i.e., four and seven floors). As the column removal time clearly affects the dynamic response of the structure, for this parameter, three extra levels were considered: 0.01 s, 0.05 s (both smaller than that in the test) and 0.2 s (higher than that in the test). The lower value is acceptable according to the limit of T/10 [36], where T (vibration period) was 0.28 s according to the experimental results. The applied superimposed load was also increased from 5.3 kN/m\({}^{2}\) to 9 kN/m\({}^{2}\), which represents the maximum design load for a Ultimate Limit State (ULS) in persistent and transient design situations and was also near the flexural capacity of the slab. Table 3 shows the parameter values in each model.
# A Graph-Based Approach to Recognizing Complex Human Object Interactions in Sequential Data
## 3 Materials and Methods
### Feature Extraction
#### 3.4.1 Full Body Feature: Dense Trajectory  
For full-body silhouettes, dense trajectories are obtained, as they are robust to abrupt motion [45]. Every point \(P_{t}=(x_{t},y_{t})\) on the silhouette is tracked from frame \(I_{t}\) until the next frame \(I_{t+1}\) by computing its dense optical flow field \(\omega_{t}=(u_{t},v_{t})\). Points of subsequent frames are concatenated to form trajectories: \((P_{t},P_{t+1},\ldots)\). For each frame, if no tracked point is found in a \(W\times W\) neighborhood, a new point is sampled and added to the tracking process so that a dense coverage of trajectories is ensured. A sample of the dense trajectories is shown in Figure 5. Given a trajectory of length \(L\), which is set to 15 as in [45], its shape is described by a sequence S of displacement vectors \(\Delta P_{t}\), given in Equation (4), where each displacement vector is calculated using Equation (5). The resulting 30-dimensional vector for each point is normalized by the sum of displacement vector magnitudes as shown in Equation (6).  
Figure 5: Dense trajectories. (**a**) Trajectory at frame 310, (**b**) trajectory at frame 430, (**c**) trajectory at frame 700.  
\[S=(\Delta P_{t},\dots\ \Delta P_{t+L-1}) \tag{4}\]  
\[\Delta Pt=(P_{t+1}-P_{t})=(x_{t+1}-x_{t},y_{t+1}-y_{t}) \tag{5}\]  
\[T=\frac{(\Delta P_{t}\dots\Delta P_{t+L-1})}{\sum_{j=t}^{t+L-1}\bigl{|}\bigl{|} \Delta P_{j}\bigr{|}\bigr{|}} \tag{6}\]
###### Abstract  
To effectively and objectively evaluate the spatial efficiency of rail transit station areas, **seventeen** typical rail station areas in Xi'an were selected as the research object. An evaluation system for spatial efficiency was constructed based on data from field research, satellite images, Baidu heat maps, and station passenger flow statistics. Key factors such as land use, transportation systems, social aspects, and spatial efficiency are considered in the framework. A data envelopment analysis (DEA) method was used to evaluate the spatial efficiency of these sample station areas. The results are as follows. 1 An incomplete symmetric relationship exists between the Constant Returns to Scale Technical Efficiency (Crste) and the Variable Returns to Scale Technical Efficiency (Vrste) of station area spatial efficiency. The keys to improving station area spatial efficiency include reducing redundant resource investments and establishing a rational resource allocation structure. 2 For high-efficiency station areas, the Crste and Vrste are relatively high, with an overall increasing return to scale efficiency (Scale). In medium-efficiency station areas, the Crste is relatively high, but either Vrste or Scale is low. In low-efficiency station areas, the Crste is moderate, and both Vrste and Scale are low. The findings provide a reference for the intensive use of land around Xi'an rail stations, as well as support for the sustainable operation of rail transit.  
spatial efficiency; DEA; rail transit station area +
Footnote †: journal: Applied sciences  
Article
## 4 Results and Discussion
### Data Collection and ANN Results  
AAN was utilized to predict the coefficient of friction using MATLAB code that was executed on 81 measured data in the experiments. Table 4 displays samples of these data in tabulated form. The table displays both the input parameters (slide time, load, number of nodules, and nodule diameter) and the output parameter (coefficient of friction). Altogether, 30% of these datasets were applied to test and verify the model, whilst the remaining 70% were used to train the ANN model. Thus, 12 datasets were utilized for each testing and validation stage, while approximately 57 datasets were used for the training stage. The ANN model's specifications are summarized below:  
* The use of the Levenberg-Marquardt back-propagation algorithm enabled the training stage to be reached;
* Two ANN structures were employed, one of which had a single hidden layer and the second of which contained a double hidden layer;
* To assess the ANN model, the mean squared error (MSE) and coefficient of determination (R\({}^{2}\)) must be calculated;
* A trial-and-error technique was used to control the number of neurons in the hidden layer;
* The sigmoid function enabled the hidden layer to be activated, whilst the purine activation function was used to activate the output signal.  
Figure 7: Effect of microstructure on the wear rate at a load of 52.015 N and sliding speed of 70 cm/s.