esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Hyperspectral Imaging System  
The hyperspectral images of the samples were obtained by the reflectance mode of a laboratory-based pushbroom hyperspectral imaging system which records the hyperspectral information of a two-dimensional matrix. The hyperspectral imaging system consists of a high-performance CCD (charge coupled device camera) camera, a mobile platform used for samples shifting, an imaging spectrograph (ImSpectorV10, Spectral Imaging LTD., Oulu, Finland) and a computer supported with Spectral-Cube data acquisition software (Spectral Imaging Ltd., Oulu, Finland,2007) which controls the motor speed, exposure time, binning mode, wavelength range and image acquisition. An illumination unit containing two 150 W quartz tungsten halogen lamps (Oriel Instruments, California, USA) was fixed above the mobile platform at 300 mm ground height. The camera spectral range was from 874 nm to 1734 nm divided into 256 bands. The camera had 672 \(\times\) 256 (spatial \(\times\) spectral) pixels with a spectral resolution of 2.8 nm. UnscramblerX 10.1 (CAMO Process as, Oslo, Norway, 2004) software, ENVI 5.2 (Research System Inc., Boulder, CO, USA, 2014) and MATLAB R2016b (The MathWorks Inc., Natick, MA, USA, 2016) were used to analyze the spectral data in the study. Figure 1 shows the process of hyperspectral image acquisition in our experiment.
## 3 Sensing Performance Tests and Discussions
### Temperature Dependence of Refractive Index of Water
And also, the measurement accuracy could be further improved by using a set of standard RIs of to-be-measured liquid to strictly pre-calibrate the spectra of the hybrid grating at different RIs and temperature.
## 4 UTS-mAR Implementation
### UTS-mAR Context Preparation  
The first stage of UTS-mAR is the preparation of the context data. The context related to the hotel recommendation service is obtained from different sources. The environmental context is obtained from geospatial data. This information contains both spatial and non-spatial information. Spatial information includes the position of candidate hotels, transportation, restaurants, and attractions. Non-spatial data also contain hotel information, such as the number of hotel stars and cost. Geospatial vector data were obtained from the free data available on the website [44], in shape file format. Subsequently, a portion of the data concerning Seoul was selected for the simulation. This information contained locations of candidate hotels, transportation (e.g., bus, subway), restaurants (e.g., restaurants, fast food), and historical attractions (usual (park), museum, historical (castle)). This information was inputted into the GIS software (ArcGIS 10.2) for completion. Several features in the layers of candidate hotels and historical attractions were added from the data obtained via Google Earth software. In addition, the candidate hotel information, such as the cost and the number of hotel stars, was determined using information available in Google Maps. Notably, hotel cost information varies at different times, and the data used herein were at the time of extracting this information. Finally, the geospatial data were entered into the SpatialLite database for use in the system. Figure 5 shows geospatial vector data created with ArcGIS software. The user context is entered via the system interface developed in the Android Studio environment. This interface contains different textbooks to enter the transportation type (e.g., bus and subway), restaurant type (e.g., restaurant, fast food), and historical attraction type (usual (park), museum, historical (castle)). This interface also allows the user to track location data by pressing the tracking button to create user context.  
In addition, the context data related to the AR visualization section were extracted in different ways. Images related to the target object for visit were captured. In this study, the main gate of Sejong University was selected as the target object for visit. In addition, Wikipedia was used to prepare information related to Sejong University. The position of the Sejong main gate was defined in the system using a tracking method. The time of day for system testing, along with data such as age and the presence of vision problems, was entered via the system interface. These data were entered by the user using the list box. This interface also allows the storage of user position data based on the tracking model. These data were stored in the system and added to the context. The exact locations of the user and main gate are defined using GPS during the experiment.
# Pruning Optimization over Threshold-Based Historical Continuous Query
## 4 Pruning Optimization
### Performance Analysis  
The time complexity of the best-first traversal algorithm comes from the processes of creating the E3 DR-tree and iterative searching. Suppose that the query trajectory \(T_{q}\) and the moving object database \(D\) contain respectively \(n\) and \(m\) trajectory segments. First, in the aspect of creating the E3 DR-tree, since the E3 DR-tree is created in the same way as the traditional R-tree, the running time of creating it is O(\(n\)log\(n\)). Second, in the aspect of iterative searching, all candidate trajectory segments are concentrated on just one leaf node of the \(d\)_rtree_ in the best case, so the time complexity is O(\(n\)+log\(m\)). Conversely, in the worst case, each trajectory segment in \(T_{q}\) needs to match each trajectory segment in \(D\), it takes O(\(mn\)) rounds of computation. Certainly, it is difficult to achieve the theoretical worst-case complexity when actually performing iterative searching. In summary, the total time complexity of the best-first traversal algorithm is O(\(n\)log\(n\)+log\(m\)) to O(\(n\)log\(n\)+\(mn\)).  
Figure 7 shows an example of the pruning process of THC query, where \(T_{q}\) contains six trajectory segments, each trajectory in \(D\) contains four trajectory segments, and the distance threshold \(d\) is one. The maximum number of children of a node in the 3 DR-tree that indexes \(T_{q}\) or \(D\) is three. Considering that there are not many trajectory segments in the example, we do not consider the time attribute, and only performed pruning based on the spatial distance.  
The best first traversal algorithm (shorted as BFT) takes a total of 105 pruning checks to get the pruning results, in which the number of pruning is 100 times by checking the overlap between the MBRs, and the number of pruning is only five times by checking the Mindist. As a comparison, the pruning method proposed in literature [10] that takes each segment of the query trajectory as a reference object (shorted as TDS) takes 114 pruning checks, while the pruning method proposed in literature [12] (shorted as CDQLTD) that regards the whole query trajectory as the pruning reference takes only 31 pruning checks. From the pruning results shown in Table 2, benefiting from the segment-based pruning way, the pruning results of TDS and BFT are closer to the final results than that of CDQLTD. Moreover, compared to TDS, due to the pruning strategy that combines MBR overlap and Mindist calculation, the pruning accuracy achieved by BFT is more than that of TDS.  
Figure 7: Example of the pruning process of THC query.
## 2 Analytical Solutions of Pavement Response under Random Moving Loads and Pavement Temperatures  
### Analytical Solutions of Viscoelastic Multilayered System  
Asphalt pavement is generally simplified as a viscoelastic multilayered system. The analytical solutions of the viscoelastic multilayered system under a moving load have been derived based on Galilean and Fourier transforms, and the numerical calculations of analytical solutions have been implemented using a Gaussian integral [15].  
A viscoelastic three-layered system under a rectangular uniform moving load as illustrated in Figure 1 was considered as an example; the three layers from top to bottom correspond to the asphalt layer, base layer, and subgrade. The property of the asphalt layer was assumed as viscoelasticity, and the properties of the base layer and subgrade were considered as elasticity. The mechanical responses at any point within the pavement can be expressed as Equation (1).  
\[R(x,y,z,t)=\frac{P}{4\pi^{2}}\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}F \left(\widetilde{E}_{1},E_{2},E_{3},\mathbf{\mu},\mathbf{\rho},h_{1},h_{2},\xi_{1},\xi_ {2},z\right)\cdot e^{i\xi_{1}(x-Vt)}\cdot e^{i\xi_{2}y}\mathrm{d}\xi_{1} \mathrm{d}\xi_{2} \tag{1}\]  
where, \(x\), \(y\), and \(z\) = coordinates of the calculation point; \(\xi_{1}\) = coordinate in the complex plane with respect to \(x\); \(\xi_{2}\) = coordinate in the complex plane with respect to \(y\); \(t\) = movement time of load; \(V\) = speed of moving load; \(P\) = load pressure; \(\mathbf{\mu}\) = [PERSON]'s ratios of the three layers \(\left[\begin{matrix}\mu_{1}&\mu_{2}&\mu_{3}\end{matrix}\right]\); \(\mathbf{\rho}\) = densities of the three layers \(\left[\begin{matrix}\rho_{1}&\rho_{2}&\rho_{3}\end{matrix}\right]\); \(h_{1}\) and \(h_{2}\) = depths of the asphalt and base layer; \(E_{2}\) and \(E_{3}\) = elastic moduli of the base layer and subgrade; \(\widetilde{E}_{1}\) = viscoelastic modulus of the asphalt layer in the complex plane, which is related to \(\xi_{1}\) and \(V\), and can be written as Equation (2) using Prony series; \(F\) = function describes a viscoelastic three-layered system in the complex plane; and \(R(x,y,z,t)\) = mechanical responses, such as displacements, strains, and stresses at any point within the pavement.  
\[\widetilde{E}_{1}(\xi_{1}V)=E_{\infty}\left(1-\sum_{j=1}^{J}\frac{g_{j}}{1-i \xi_{1}V\tau_{j}}\right) \tag{2}\]  
where, \(E_{\infty}\) = instantaneous elastic modulus; \(g_{j}\) = Prony series; and \(\tau_{j}\) = reduced time.  
The dimension of \(-\xi_{1}V\) is frequency (Hz). The Prony series equation in the time domain can be expressed as Equation (3), and Equation (2) is the frequency domain expression of Equation (3).  
\[E_{1}(t_{0})=E_{\infty}\left[1-\sum_{j=1}^{J}g_{j}\cdot\left(1-e^{-t_{0}/\tau_ {j}}\right)\right] \tag{3}\]  
where, \(t_{0}\) = load time; and \(E_{1}\) = viscoelastic modulus of the asphalt layer in the time domain.
# Advancing GAN Deepfake Detection: Mixed Datasets and Comprehensive Artifact Analysis
## 4 Methodology of the Novel Deepfake Detection Framework
### Classification Techniques
It predicts based on the 'k' closest training points, using majority vote (classification) or averaging (regression). KNNs is easy to implement and adapts well to the structure of the data but is sensitive to the choice of 'k' and irrelevant features. It can also be slow for large datasets, as it calculates distances to all points during prediction [59]. * **Logistic Regression** predicts the probability of an input belonging to a category by applying the logistic function to a linear combination of features, yielding a value between 0 and 1. It is simple, interpretable, and efficient for small datasets but sensitive to outliers and assumes linearity, limiting its ability to model complex, non-linear relationships. It can also struggle with high-dimensional or highly correlated data, risking overfitting. For face deepfake detection, it has been used in [34; 60] as a classifier. * **Naive Bayes** uses [PERSON] theorem to calculate class probabilities, assuming feature independence. This makes it efficient in speed and memory, ideal for large datasets. However, it struggles with correlated features and zero probabilities, often requiring techniques like Laplace smoothing for unseen feature-class combinations. * **Extra Trees (Extremely Randomized Trees)** is an ensemble method that builds decision trees with added randomness, selecting features and split thresholds randomly. This reduces overfitting and improves generalization. Extra Trees are faster to train than methods like Random Forests while maintaining high accuracy, making them effective for large datasets and providing useful feature importance insights [59].
# Multi-Device Security Application for Unmanned Surface and Aerial Systems
### Related Work  
#### Messaging Layer Security Protocol  
MLS is an Internet Engineering Task Force (IETF) end-to-end encryption standard built from the ground up to support communications for group sizes ranging into several thousands of devices [3]. While traditional protocols provide point-to-point secure channels, MLS provides a high degree of scalability by leveraging a tree-based key hierarchy to maintain a single, shared group key. This hierarchy supports key updates with \(O(log(N))\) overhead.  
MLS is also designed to provide Post-Compromise Security (PCS) and Forward Secrecy (FS). PCS is the protection of data after a compromise, also known as \"self-healing\" or \"future secrecy\", and is conditioned on the rotation of various keys as well as passive adversarial behavior [4; 5]. On the other end, FS refers to the protection of data sent before a point of compromise [4].  
Iterations of MLS have already been implemented in the commercial sector for communication services applications such as Cisco Webex and RingCentral [6], yet further applications of the protocol outside of instant messaging have yet to see much investigation. This includes the utilization of the MLS protocol for UxS. To date, one work has looked at the application of MLS within small UAS swarms [7]. However, that research focused on the viability of use (i.e., that small devices could handle the encryption) and tested within a single platform.
# Behavior of Sedimentary Rock Tunnel against Rigid Projectile Impact
## 5 Conclusions  
The present study of finite element simulation for rock tunnel resistance against impact loading has four different sandstone rocks surrounding a 5 m diameter reinforced concrete tunnel lining. The major conclusions drawn from the present study are as follows:  
Figure 11: Deformation contours of steel bar reinforcement cage for (**a**) Jhingurda, (**b**) Singrauli, (**c**) Jamrani, and (**d**) Kota sandstone tunnel when an impact loading occurs due to a 100 kg missile.  
1. The Kota sandstone has 2.22-times, 0.77-times, 0.35-times more impact resistance than Jhingurda, Singrauli and Jamrani sandstone, respectively. Therefore, the impact resistance of a rock tunnel is a function of cohesion and friction angle.
2. The deformation profile for Jhingurda sandstone follows a smooth curvature while the curvature becomes distorted and non-uniform for other types of sandstone. It has also been observed that as the strength of sandstone increases, the peak deformation decreases, while the length of tunnel under disturbance increases with the increase in the strength of sandstone. In addition, it has been concluded that weaker sandstone requires strengthening for a smaller area after an impact loading event while high-strength sandstone will require repair for a longer portion of the tunnel.
3. The magnitude of peak velocity and acceleration is maximum in case of Jhingurda sandstone at 20.41 milliseconds. Moreover, the pattern of variation in acceleration and velocity remains similar, and therefore, it is independent of the type of sandstone.
4. It can be concluded that Kota sandstone has minimum deformation and is therefore the safest sandstone rock under impact loading conditions. However, the area of disturbance is greatest in the case of Kota sandstone, and vibrations may reach the tunnel crown in a shorter time in comparison to other rocks.
5. The effect of impact loading has reached the tunnel lining when constructed in strong and brittle sandstone, or vice versa. In addition, the tensile damage in all the different types of sandstones considered in the present study remains concentrated at the outer periphery of the liner. The steel reinforcement cage experiences maximum deformation at the crown position in all the cases, while the maximum magnitude of deformation occurred in the case of Jhingurda sandstone.  
M.R.S.: drafting--data collection and preparation of the manuscript, writing review and editing, S.A.: drafting--preparation of the manuscript, revision and correction; A.H.A.: composing--reviewing, fund acquisition and modifying; A.S.A.: composing--reviewing, and modifying; W.S.: reviewing and modifying. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the support provided by Researchers Supporting. Project Number (RSP2022R473), King Saud University, Riyadh, Saudi Arabia.  
Not applicable.  
Authors acknowledge the support provided by King Saud University, Saudi Arabia, and Aligarh Muslim University, India, to carry out this study.  
The authors declare no conflict of interest.
# Experimental Study on a Prediction Model of the Shrinkage and Creep of Recycled Aggregate Concrete
## 3 Test Results and Analysis
### Analysis of Creep Test Results  
The creep curve of RAC at 180 days is shown in Figure 3. The creep of RAC is similar to that of ordinary concrete. At 180 days, the creep coefficient of RAC with a 50% and 100% substitution rate in group I increased by 19.6% and 39.6%, respectively, compared with that of ordinary concrete. The creep coefficient of RAC with a 50% and 100% substitution rate in group II was 23.6% and 44.3% higher than that of ordinary concrete, respectively. At the initial stage of loading, the creep of RAC with a 50% and 100% substitution rate increased faster than that of ordinary concrete, and the creep coefficient was always higher than ordinary concrete. The creep growth rate decreased with the passage of time, but the decrease rate of the creep growth rate of ordinary concrete that was close to the creep coefficient of RAC was small. At 120 days, the creep development of all three creeps tended to be flat, and about 90% of the total creep variable was completed, which roughly shows that the creep of the RAC tended to be stable.  
Figure 3: Creep coefficient curve of RAC. (**a**) \(w\)/\(c\) = 0.527; (**b**) \(w\)/\(c\) = 0.4. Note: \(\varepsilon_{sh}\) indicates shrinkage strain and is dimensionless; \(\overline{k}\) indicates the slope and is dimensionless; \(t_{0}\) indicates the loading age of concrete; \(t\) indicates the age of the concrete.  
At the initial stage of loading, the creep of normal concrete with \(w\)/\(c\) = 0.4 was faster than that of normal concrete with \(w\)/\(c\) = 0.527, and the creep coefficient was higher than the latter. After that, the creep growth rate of normal concrete with \(w\)/\(c\) = 0.4 decreased and that of ordinary concrete with \(w\)/\(c\) = 0.527 increased rapidly. When loading to 180 days, the creep coefficient of normal concrete with \(w\)/\(c\) = 0.527 increased by 43.8% compared with that of RAC with \(w\)/\(c\) = 0.4. For the RAC with \(w\)/\(c\) = 0.4 and a substitution rate of 50% and 100%, the creep development trend was the same. At 180 days, the creep coefficient of RAC with \(w\)/\(c\) = 0.527 increased by 40% and 38.7%, respectively, compared with that of RAC with a substitution rate of 50% and 100% with \(w\)/\(c\) = 0.4. This shows that the water-cement ratio is an important factor affecting the creep of RAC. The high water-cement ratio and the large amount of recycled mortar have a significant effect on creep.
## 3 Related Parameters  
### Responses of Tunnels to External Loads  
In practical engineering, the shield tunnel lining system is composed of segments connected by steel bolts. The longitudinal flexural stiffness of the shield tunnel is clearly lower than that of the continuous lining tunnel due to the existence of intersegment joints. Different researchers have proposed different analytical methods to evaluate the equivalent flexural stiffness of shield tunnels [22; 23; 24]. Among the existing methods, the Shiba [24] method is more commonly used and widely applied in engineering practice. Figure 7 shows the calculation model of the Shiba method. According to the Shiba theory, the connecting bolt and the lining segment bear tension and pressure, respectively. Under the action of the bending moment, as shown in Figure 7, the upper part of the lining segment bears tension, and the lower part bears pressure. According to the force balance principle, [PERSON] derives the equivalent bending stiffness formula of the shield tunnel:  
\[(EI)_{eq}=\frac{\cos^{3}\psi}{\cos\psi+(\psi+\pi/2)\sin\psi}E_{c}I_{c} \tag{16}\]  
\[\cos\psi+\psi=\pi\left(0.5+\frac{nk_{b}l}{E_{c}A_{c}}\right) \tag{17}\]  
\[k_{b}=E_{j}A_{j}/l_{j} \tag{18}\]  
where \(k_{b}\) is the elastic stiffness of longitudinal joints of the segment ring, \(E_{j}\) is the elastic modulus of bolts, \(A_{j}\) is the bolt cross-sectional area, \(l_{j}\) is the bolt length, \(n\) is the longitudinal number of bolts, \(l\) is the segment width, \(E_{c}\) is the elastic modulus of the segment, and, as shown in Figure 7, \(\psi\) is the angle of the neutral axis. \(I_{c}\) is the longitudinal moment of inertia of the segment cross-section, and \(A_{c}\) is the segment cross-sectional area.
# On the Trade-Off between the Main Parameters of Planar Antenna Arrays
## 2 Planar Arrays with Regular Lattices
### The Choice of the Lattice  
Once the synthesis problem is defined, the first question we do have to answer is: how many antennas do we need? For square lattices (Figure 1a) we could use a Tchebyshev-based approach used in [55] for linear arrays, finding that the maximum inter-element distance between elements must be:  
\[d_{S}=\frac{\lambda}{1+w_{1}+w_{s}} \tag{4}\]  
It is also possible to use a triangular lattice (Figure 1b) for the placing of elements on the plane; in this case, the required maximum distance must be:  
\[d_{T}=\frac{2\lambda}{\sqrt{3}(1+w_{1}+w_{s})} \tag{5}\]  
It is interesting to note that \(d_{T}\) is about 15% lager than \(d_{S}\).  
The inter-element distances \(d_{S}\) and \(d_{T}\) are such to have the grating lobes focused just outside the region (\(w\leq 1+w_{s}\)) that could be in the visible range when scanning the beam.  
In the case of a square lattice with \(d_{S}\), we could use a grid of \(N_{L,S}\times N_{L,S}\) elements, with  
\[N_{L,S}=1+\left\lceil\frac{\cosh^{-1}SLL}{2d_{S}/\lambda\cosh^{-1}((\cos(\pi w _{1}/2))^{-1})}\right\rceil \tag{6}\]  
where \(\lceil x\rceil\) approximates \(x\) to the smallest integer larger than \(x\).  
It turns out that to radiate a beam with a circular footprint, the entire \(N_{L,S}^{2}\) elements square array is not necessary, so we could take only the elements of the rectangular lattice that fall into the circle of radius \(R_{MAX}=d_{S}N_{L,S}/2\) (see Figure 1a). The approximate overall number of elements to use would be equal to  
\[N_{S}\approx\frac{\pi}{4}N_{L,S}^{2} \tag{7}\]  
but the exact effective number of radiators to use needs to be found numerically.  
Similarly, with the triangular lattice, we could start from a grid of \(N_{L,T}\times\left\lceil N_{L,T}\sqrt{3}/2\right\rceil\) radiators, with  
\[N_{L,T}=1+\left\lceil\frac{\cosh^{-1}SLL}{2d_{T}/\lambda\cosh^{-1}((\cos(\pi w _{1}/2))^{-1})}\right\rceil \tag{8}\]  
and then taking only the elements of the lattice that fall into the circle of radius \(R_{MAX}=d_{T}N_{L,T}/2\) (see Figure 1b). The approximate overall number of radiators to use would be equal to  
\[N_{T}\approx\frac{\pi\sqrt{3}}{8}N_{L,S}^{2} \tag{9}\]  
but, even in this case, the exact number of radiators to use needs to be found numerically.  
It is, again, interesting to observe that the triangular lattice allows a reduction of the number of radiating elements of about 15% with respect to the square lattice.  
The relationships shown above allow us to make a first dimensioning of the parameters for two types of regular lattice arrays.  
Given the beam-width, the SLL, and the scanning range, the found element number is the minimum that a regular lattice would require. As it will be shown later, this is not true for sparse or thinned arrays, but the number of elements of the regular lattices will represent a benchmark for the \"sparsification\" of the algorithm: it is possible to claim a reduction of the number of radiators only if the obtained layout uses fewer elements than the triangular equispaced lattice.