esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Bilateral Filtering for a Gaussian Pyramid Structure-Based Image Enhancement Algorithm
## 4 Gaussian-Laplacian Multi-Scale Pyramid Algorithm  
Gaussian-Laplacian pyramid [24] generation is a multi-scale image decomposition method with a sampling rate of 2, i.e., the resolution of a resulting image is halved in each successive decomposition. The resulting pyramid consists of layers of decomposed images decreasing in resolution from bottom to top, with the original image defined as the base level of the pyramid (level 0). In detail, beginning from level 0, decomposition consists of convolution of the input image with a Gaussian low-pass filter followed by a down-sample operation in order to halve the horizontal and vertical resolution of the processed image. The filtered down-sampled image is subsequently defined as the image at level 1 of the pyramid (\(F_{1}\)). This operation is repeated, as defined by the number of levels required in the Gaussian pyramid. Mathematically, this process is defined as  
\[F_{l}=\sum_{m=-2}^{2}\sum_{n=-2}^{2}G(x,y)F_{l-1}(2i+m,2j+n), \tag{11}\]  
where \(G(x,y)\) is a low-pass filter, \(l\) is the level of the pyramid, \(N\) is the number of layers in the pyramid, \(0\leq l\leq N,i\leq R_{l},j\leq C_{l}\), and \(R_{l},C_{l}\) represent the number of rows and columns in \(F_{l}\) and \(N=3\), respectively.  
As each image in the pyramid, \(F_{l}\), is of a different resolution, iterative image generation and boundary closure may be affected by noise and dispersed sampling. To ensure the structural information in the highest resolution image is retained in subsequent pyramid levels, image enhancement is required. To do this, we employed the improved Retinex algorithm to recover details in images lost in processing. Enhanced pyramid images are subsequently termed \(F_{l}^{r}\).  
To complete the image enhancement process, the modified Gaussian pyramid is reconstructed to yield a single image with the same resolution as the original input. Reconstruction is completed by generating a Laplacian pyramid from the image-enhanced Gaussian pyramid. This reconstruction process is detailed as follows:  
1. The topmost image in the Gaussian pyramid, \(F_{l+1}^{\prime}\), is interpolated to obtain an image termed \(D_{l}\), which has the same resolution as the image in the preceding layer (\(F_{l}^{\prime}\)).
2. \(D_{l}\) is subtracted from \(F_{l}^{\prime}\), with the difference, \(F_{N}\), stored in the Laplace residual set. \(F_{N}\) is subsequently added to \(F_{l}^{\prime}\) to yield \(F_{N}^{\prime}\), which is interpolated for reconstruction of the image in the preceding layer.
3. Computation of the Laplacian and image interpolation continues iteratively, until the reconstructed image is of the same resolution as the original input. Using the terminology described above, the process can be described as (12). A Gaussian-Laplacian multi-scale pyramid from intermediate results of different pyramid levels is presented in Figure 4. In our experiment, the sampling value of the pyramid was 3. \[\left\{\begin{array}{l}F_{N}=F_{l}^{\prime}-D_{l}\\ F_{N}^{\prime}=F_{N}+F_{l}^{\prime}\end{array}\right..\] (12)
# Instantaneous Beam Prediction Scheme against Link Blockage in mmWave Communications
## 4 Proposed LSTM-Based Recovery Scheme
### Operating Modes
Furthermore, the model is trained with 350 epochs over a 2-week period. Along these lines, a data structure with 60 steps (each of 10 min) and a single output is created, as the LSTM cells store long-term memory state. Thus, there are 60 previous training set elements for every recorded sample in each training stage. Hence, in the testing stage, the first 60 samples are required to achieve an accurate prediction for the subsequent best beam index. The learning computational complexity for the LSTM-based network per weight in a single time step \(t\) is gauged as \(O\) (\(W\)). Overall, the computational complexity here is at acceptable levels, since the number of inputs and memory cells is relatively small. However, this complexity increases for larger number of output units and memory cells.
## 5 Discussion
It was observed that for the first two months after the occurrence of the main earthquake, both the density and magnitude of the aftershocks were much higher than the rest. Figure 12 represents the relation distance from the mainshock versus the event's magnitude in the Eliassona, Central Greece study area. From the diagram, one can observe that a higher density of epicenters accumulated in the first 20 km from the epicenter of the main earthquake. Magnitude variations ranged from 1.0 < M\({}_{\rm L}\) < 4.0, as when seismic monitoring stations are added or removed, our ability to detect small earthquakes changes reliably. The seismic sequence's monitoring and study showed a shift in the epicenters to the northwest (Figures 13 and 14). The spatial distribution versus date indicated a displacement of earthquake density to the NW of the first three dates after the main event. Then, the density of the earthquakes decreased, and after eight days, a high-density event occurred at latitude = 39.80\({}^{\circ}\) and longitude = 21.90\({}^{\circ}\), without any further indication of spatial displacement. This fact proves the claim that the two strong earthquakes belonged to two different fault zones. Figure 11: Distribution of magnitudes with time as a function of days from the mainshock of 3 March 2021 (10:16:08, UTC) for the 3787 records. On the \(x\)-axis is the magnitude time (days from mainshock) of each event from Eliassona, Central Greece, while on the y-axis is the local magnitude (M\({}_{\rm L}\)). We determined deviatoric moment tensor solutions for 34 earthquakes with M\({}_{\rm w}\geq\) 4.0 at depths between 6 and 14 km. The depths decreased in the eastern and especially in the western termination of the sequence. The proposed methodology's application gave rise to the activation of normal faulting with the direction N-S. The depth varied 11 km < d < 15 km for the five strongest sequences, a fact that agrees with the study in [67], while the magnitudes were 5.2 < M\({}_{\rm w}\) < 6.3. Here, they were concentrated at approximately 7 km in depth, in contrast to about 14 km in the central part of the sequence. The aftershocks occurred along the entire surface rupture length but mainly north. A very high percentage of a double couple (DC) in relation emerged to the compensated linear vector dipole (CLVD) for the most significant percentage of solutions. More specifically, 80% of the solutions presented a DC > 90% solution, while the remaining 20% had a DC between 75% and 89%. The focal mechanism solutions for the strongest earthquakes determined in this study appear in Figure 15.  
Figure 12: Location with distance (km) from mainshock as a magnitude (M\({}_{\rm L}\)) function for the 3.787 events recorded. On the \(x\)-axis is the distance from the mainshock, while on the y-axis is the magnitude of each aftershock. Figure 14: Spatial distribution (latitude) of earthquake density for different dates. Figure 13: Spatial distribution (longitude) of earthquake density for different dates.
## 4 Results and Discussion
### Position of the Bolt Sliding Parallel to the Gusset Interface  
Figure 14 shows the comparison of the hysteretic curves of BRB-RCBs with the mixed sliding bolts. From Figure 14, it can be drawn that the hysteretic loops were satisfactory and stable, and no pinching effect was observed during the loading process. As seen in Figure 15, few slips between the gusset and the bolt sliding parallel to the gusset interface were found for these three bolted gusset connections, and the resulting slippage of the adjacent bolts sliding toward to the gusset interface was also limited. Therefore, it can be inferred that the decrease in the slippage of the bolt group might have a positive effect on improving the seismic performances of the BRB-RCBs. As detailed in Table 2, the elastic stiffness of specimens NG-VH-1, NG-VH-3 and NG-VH-5, compared to specimen NG-V, increased by 2.1%, 9.4% and 18.2%, respectively. Compared to specimen NG-VH-1, the cumulative energy dissipation of the specimens NG-VH-3 and NG-VH-5 increased by 2.6% and 6.2%, respectively. The main reason that was inferred was that the bolt, sliding parallel to the gusset interface, located closer to the corner of the gusset, experienced larger slippage of the bolt group, resulting in a smaller elastic stiffness and a lower energy dissipation capacity. Conversely, when the limited sliding bolt was positioned nearer to the tip of the gusset, the slippage of the bolt group was reduced, leading to a greater stiffness and a more saturated hysteretic response.  
As Figure 16 depicts, the peak Von Mises stresses at the gusset plate interfaces were all less than the yield strength of 345 MPa for the specimens from NG-VH-1 to NG-VH-5 and the maximum stress distributed near the position of the non-slip bolt (Figure 15). This is because the slip ceased when the bolt made contact with the end of the short slot and the stress concentration rapidly developed at this corresponding region. Compared with specimen NG-V, the peak stresses at the gusset interfaces of specimens NG-VH-1, NG-VH-3 and NG-VH-5 increased by 43.2%, 10.8% and 5%, respectively. It indicated that the stress level at the gusset interfaces grew due to the additional non-slip bolt, and the stress level was raised further if the position of the 'fixed' bolt moved inward to the gusset corner.  
Figure 14: Comparison of hysteretic responses of the BRB-RCBs with two-sided slotted gusset connection with bidirectional sliding bolts.
# Transmitters and Receivers for High Capacity Indoor Optical Wireless Communication
## 3 Receiver for Optical Wireless Communication
### OWC Receiver with an Array of Surface Grating Couplers
#### 3.5.2 Experiment  
Figure 15 shows the experimental setup in which wireless data transmission has been demonstrated with the array of SGC used as a light collection device. Firstly, a tunable laser emitted light at 1550 nm, and then the light was modulated with NRZ OOK PRBS15 at 1 or 10 Gbps and amplified with an EDFA to compensate for the losses in the system. The light is coupled in an into free space with a collimator. A lens with 1-inch diameter and 75-mm focal length is used to focus the light on the surface grating coupler. The total wireless path is 15 cm long. The collimator and lens were tilted 10\({}^{\circ}\) with respect to the surface of the PIC. The light was coupled out of the PIC with a cleaved fibre and passed to an optical receiver consisting of a photodiode and a trans-impedance amplifier. Transmission performance was analyzed with a BERT. A test structure consisting of two focusing surface grating couplers connected by a waveguide was used to characterize individual grating couplers. In this case, two cleaved fibres were used to couple light in and out of the PIC, and the optical power loss was measured.  
Figure 14: Simulation results of the wavelength and angle sweep of the surface grating coupler. 2D FDTD simulation in Ansys Lumerical.  
Figure 13: Photomicrograph of the “windmill” array of surface grating couplers. The windmill is used to couple light into the PIC. The solitary surface grating coupler on the right is used to couple light out of the PIC.
## 3 Analysis of Original Research Articles  
Data extraction was performed manually from each article by one reviewer and verified by a second. The extracted data included the publication year, country, commodity, model type, dataset, technique, findings, and limitations. No assumptions were made about missing data. Since no quantitative synthesis was performed, effect sizes and confidence intervals were not calculated. Risk of bias was not formally assessed. No effect estimates or measures of heterogeneity were calculated. The review does not assess the certainty of evidence (using tools such as GRADE).  
The analysis of original research articles explores the various techniques employed in wheat, corn, and rice price forecasting, focusing on machine learning, ensemble learning, deep learning, and time series methods as shown in Figure 3. Each approach is examined for its strengths and limitations in predicting price trends across different commodities.
# Experimental Study on Dynamic Response Characteristics of RPC and RC Micro Piles in SAJBs
## 3 Experimental Results
### Strains Responses  
A total of three typical strain measuring points were monitored to investigate the strain time history response of piles under the case 3 load (sine wave of 4 Hz frequency with amplitude of 0.15 g). These points were located at 212.5 mm (2.1 D) above the soil surface, at soil surface (0 D), and 425 mm (4.2 D) below the soil surface, respectively. The strain time history response of RPC and RC piles at above three points under the sine wave load are summarized in Figure 7. As seen in Figure 7, the strain time history responses of three strain measuring points at the micro-pile body are identical basically, and the strain time history response of the pile at 4.2 D below the soil surface is larger than that of the other two measuring points. It is observed that the strain response of the soil surface measuring point is second, and that of the above soil surface of 2.1 D is the minimum. It is found that RPC and RC piles are basically in the elastic stage after shaking the table test. Compared with Figure 7a,b, it is also found that the maximum strain of the RPC micro pile reaches \(200\times 10^{-6}\). However, the ultimate strain of the RC (C40) pile are between \(70\times 10^{-6}\) and \(100\times 10^{-6}\). Therefore, it indicated that the RPC micro pile has better crack resistance and deformation capacity than of the RC (C40) micro pile.  
Figure 6: Spectrum analysis results of the reactive powder concrete (RPC) pile-soil system under white noise. (**a**) The first white noise and (**b**) the fifth white noise.
## 2 Materials and Methods
### Measurement
#### 3.1.2 Conventional Asphalt Tests  
Conventional asphalt tests were conducted on pre-cut SBS-modified asphalt mixed for varying amounts of time, and the results of these tests are presented in Figure 4.  
Penetration increased initially and then decreased as mixing time increased. After 15 min of mixing, the SBS morphology displayed larger particles, leading to higher asphalt hardness. After 30 and 60 min of mixing, the SBS morphology transformed into filamentous and aggregated particles, resulting in reduced asphalt hardness. After 60 min, the narrower filaments and smaller particle diameters led to even lower asphalt hardness, hence the increase in penetration. However, after 120 min, the SBS morphology displayed foggy particles, increasing the hardness and decreasing the penetration.  
The softening point showed an initial increase and subsequent decrease with mixing time. After 15 min of mixing, the SBS morphology with larger particles formed a weaker network structure, resulting in the lowest softening point. After 30 min of mixing, the SBS morphology transformed into filamentous and aggregated particles, enhancing the network structure's strength and causing the softening point to rise. After 60 min of mixing, the reduction in filament width and particle diameter weakened the network structure, leading to a decrease in softening point. After 120 min of mixing, the foggy particles in the SBS further weakened the network structure, leading to a continued decrease in the softening point.  
Ductility increased initially and then decreased with mixing time. After 15 min of mixing, the SBS morphology with larger particles formed a weaker network structure, resulting in the lowest ductility. After 30 min of mixing, the SBS morphology transformed into filamentous and aggregated particles, enhancing the asphalt's extensibility and leading to increased ductility. After 60 min, the SBS morphology remained filamentous and  
Figure 4: Results of the conventional asphalt tests of pre-cut SBS-modified asphalt mixed for different amounts of time. (**a**) Penetration; (**b**) softening point; (**c**) ductility.  
aggregated, and the narrower filaments and smaller particle diameters further enhanced the asphalt's extensibility, causing ductility to further increase. However, after 120 min, the foggy particles in the SBS weakened the network structure further, causing a decrease in ductility.
# Temporal and Spatial Diversity of Renewable Groundwater Resources in the River Valley
## 3 Research Methods
### Model Studies  
Model tests were done using Visual ModFLOW 4.2 and applied to determine renewable groundwater resources [52; 53]. The steady state modelling made it possible to quantify the groundwater balance in a hydrogeological unit. Renewable groundwater resources (infiltration recharge) were calculated with reference to groundwater balance of the analyzed hydrogeological system from the groundwater flow model. Despite the fact that the Visual ModFLOW package is called evapotranspiration, this value actually applies to evaporation from shallow groundwater table and it is significantly lower than total evapotranspiration understood as a whole of processes that deplete groundwater resources. The values of evaporation from groundwater table are hard to measure directly, so the final values of this process were estimated during model calibration.  
The study area was discretized by assuming a space discretization step of 0.01 km\({}^{2}\) (100 \(\times\) 100 m). The step value corresponds to the detail level of the hydrogeological and geological knowledge of the area. The step also enables reliable mapping of geomorphological units in the study area.  
The model boundaries are the major rivers, which are natural hydrodynamic limits of hydrogeological units: the Vistula River in the north and the Bzura River in the west. In the south, the boundary is the edge of the Vistula valley. The eastern boundary of the model runs along the administrative borders of Warsaw. The lower boundary of the model is the top of poorly permeable Pliocene clays, occurring at depths of 30-50 m. The relationship between the aquifer formation and the environment was mapped using boundary conditions: river-type for the Vistula, Bzura and other smaller watercourses within the Vistula valley; general head boundary (GHB)-type along the erosional edge of the Vistula valley (Figure 5).  
Model calibration and error analysis were calculated based on the correlation of observed and calculated heads in 56 monitoring points. The calibration was performed for all variants of the hydrodynamic state of the aquifer formation: for the average level in the period of 1999-2013-variant 1, the highest level-variant 2 and the lowest level-variant 3 (Figure 6).  
Figure 5: The boundary conditions and the discretization net of the modelled area.  
The obtained parameters for all variants were as follows: standard error of the estimate 0.023-0.044 m; root mean squared 0.158-0.308 m; normalized RMS 0.953-1.849% (Table 2). After the calibration and verification process, the renewable resources were calculated based on recharge computed by program as a component of the groundwater balance (zone budget tool), referring to the unit area (1 square kilometer).  
Figure 6: Groundwater model calibration diagram-calculated head vs observed head. (**A**)—variant 1; (**B**)—variant 2; (**C**)—variant 3.
# Visual-Based Localization Using Pictorial Planar Objects in Indoor Environment
## 5 PicPose: A Picture-Based Pose Method
### PicPose Feature Points Extraction
#### 5.2.1 Corner Extraction by Contour Detection  
Figure 8 shows the flow of corner points extraction. After a potential region of target picture is detected using SSD, its region size will be enlarged to make sure it can include the picture as much as possible. If the object is too big, it may be reduced in size in order to improve the processing efficiency. We can convert a picture to become smaller as long as it has enough details for pose calculation. Once the optimal bounding box is generated, the exact contours of the picutre will be detected.  
Figure 7: PicPose Feature Points.  
During contour detection, many quadrilaterals on the pictorial area may be produced. We need to identify the real target picture object from these quadrilaterals. Two types of filter are used in our work. One is shape based filters. This filter removes quadrilaterals too tiny to be processed correctly and also quadrilaterals with unrealistic corner angles. The second filter is a content based filter. We use a content re-identification approach to convert a quadrilateral's picture into an ArPico marker code, and check if such a code exists in our marker librray. If we cannot find a planar object in the planar object library with the same code, we will reject that quadrilateral in the contour detection process.
## 2 Establishing and Recognizing the Maneuver Intention of HGVs
### Establishing the Maneuver Intention Model
#### 2.1.1 The Longitudinal Intention Model  
As shown in Figure 1, \(T\) is the flight time and \(H\) is the altitude of the HGV. The longitudinal maneuver intention types are divided into two categories, quasi-equilibrium gliding (\(QEG\)) and skip gliding (\(SG\)). The first maneuver intention parameter is used to describe the longitudinal maneuver intention type in the following form.  
\[A_{1}^{i}=\left\{\begin{array}{l}0,QEG\\ 1,SG\end{array}\right. \tag{1}\]Since the difference between \(QEG\) and \(SG\) trajectories is obvious, the \(A_{2}^{i}\) intention parameter is calculated differently.  
When an HGV's longitudinal maneuver is intended to be \(QEG\) (\(A_{1}^{i}=0\)), \(\dot{\gamma}\leq 0\) is maintained during the flight and the flight-path angle \(\gamma\) is negative and monotonically non-incremental. Here \(\dot{\gamma}\) is the rate of change of \(\gamma\).  
In this paper, the rate of change is defined as follows: the rate of change is the first order derivative of the variable \(x\) with respect to time \(t\), denoted as:  
\[\dot{x}=\frac{\partial x}{\partial t} \tag{2}\]  
The rate of change in altitude of HGV is described as:  
\[\dot{h}=v*\sin(\gamma) \tag{3}\]  
where \(v\) is the velocity. As such, the relationship between \(\dot{h}-v\) is determined by \(\sin(\gamma)\). Therefore, \(\sin(\gamma)\) is chosen as the characteristic parameter \(A_{2}^{i}\) in the \(QEG\) maneuver intention. \(A_{2}^{i}\) is denoted as:  
\[A_{2}^{i}=\sin(\gamma) \tag{4}\]  
When the longitudinal maneuver intention is \(SG\) (\(A_{1}^{i}=1\)), the HGV repeatedly makes slide over movements. The flight-path angle is not monotonic. Therefore, ignoring the Earth's rotation, The variation of \(\gamma\) with time is as follows:  
\[\dot{\gamma}=\frac{L\cos\sigma}{mv}+\frac{v}{r}\cos\gamma-\frac{g}{v}\cos\gamma \tag{5}\]  
where \(L\) is the lift force, \(g=9.81\) m/s\({}^{2}\) is the gravitational acceleration, \(m\) is the mass of the vehicle, and \(\sigma\) is the bank angle. \(\dot{\gamma}\) is a visual representation of the longitudinal maneuver intention of HGVs. As seen from the above equation, the parameters mainly include the longitudinal component of the lift force \(L\cos\sigma\), \(v\), and \(m\). As the parameters in the second and third terms of Equation (5) are all observable measurements, the first term \(\frac{L\cos\sigma}{mv}\) is used in this paper as the characteristic parameter \(A_{2}^{i}\) in the \(SG\) maneuver intention, which is written as:  
\[A_{2}^{i}=\frac{L\cos(\sigma)}{mv} \tag{6}\]