esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation and Results
### PTZ Panorama  
The camera can be placed in the center, side, or corner of the room to obtain horizontal 360\({}^{\circ}\), 180\({}^{\circ}\), or 90\({}^{\circ}\) angles of a panorama. Based on the specification of our PTZ camera used and multiple experiments, we decided that two rows (vertical two images) are enough to cover the important area of the room. The camera that we used has a horizontal FOV of 63.7\({}^{\circ}\) and a vertical FOV of about 36\({}^{\circ}\). Thus, we needed 20 images to cover the scene from the center (FOV 360\({}^{\circ}\)\(\times\) 56\({}^{\circ}\)), 10 images for the side (FOV 180\({}^{\circ}\)\(\times\) 56\({}^{\circ}\)), and 6 images for the corner (FOV 90\({}^{\circ}\)\(\times\) 56\({}^{\circ}\)). A single vertical image can also cover the main scene of the hall, so we can also have a panorama with an FOV of 360\({}^{\circ}\)\(\times\) 36\({}^{\circ}\), 180\({}^{\circ}\)\(\times\) 36\({}^{\circ}\), and 90\({}^{\circ}\)\(\times\) 36\({}^{\circ}\) from 10, 5, and 3 images for the center, side, and corner, respectively.  
We captured images with about 25% overlapping regions. Movement of camera and capturing was controlled from a computer connected through serial communication and  
Figure 7: Screenshots of the proposed JPEG 360 converter application.  
using VISCA (Video System Control Architecture) commands specified by the camera manufacturer. The average capturing time for the camera placed in the center was 30.476 s for 20 images and 23.263 s for 10 images, respectively. Similarly, when the camera was placed on the side, 17.919 s and 13.761 s were used for capturing 10 images and 5 images, respectively. Finally, when the camera was placed in the corner, 14.582 s and 11.685 s were used for taking six and three images, respectively.  
Images captured using the above setup were then stitched into a panorama. Stitching was completed using the most popular method, i.e., feature-based image stitching. The feature-based stitching consisted of features' detection, features' matching, homography estimation, image warping, and seam-binding processes. The OpenCV 4.4.0 library downloaded from the official website [36] with 'contrib' modules was used for stitching. Key features of captured images were detected using Speeded Up Robust Features (SURF) [37], Scale Invariant Feature Transform (SIFT) [38], Oriented FAST and Rotated BRIEF (ORB) [39], and KAZE [40] algorithms. Each algorithm has its pros and cons. SURF and SIFT detect features robustly invariant to image scale, rotation, and illumination [41]. Random sample consensus (RANSAC) [42] was the technique used for feature-matching of adjacent images and estimating homography transform. Based on homography, the adjacent images were warped. To remove seams, we used multi-band blending [43], the most efficient technique for removing seams and preserving quality and a smooth transition.  
Users can adjust different parameters and use the application easily with our developed Windows graphical user interface software, shown in Figure 8.
# Investigation of the Dynamic Buckling of Spherical Shell Structures Due to Subsea Collisions
## 3 Dynamic Buckling Criteria
### Relationship between Static Force and Dynamic Force
#### 3.3.4 Effect of Elastic Modulus and External Pressure  
Numerical simulations have been carried out for evaluating the effects of elastic modulus and external pressure. The shell models have adopted the radius of 2.0 m, a Poisson ratio of 0.15, the external pressure of 15 MPa, the velocity of 3.0 m/s, and thicknesses of 0.04 m and 0.05 m, with a variety of elastic moduli and different pressures, respectively. Note that \(e\) is the elastic modulus ratio, which is defined as \(e\)= \(E/210\).  
Table 8 presents the relationship between the contact forces and elastic modulus ratios together with external pressures. It should be noted that the duration time of impulse contact decreases with the incremental increase of elastic modulus (or contact stiffness), but the duration time remains the same over a variety of external pressure values.  
Figure 15a illustrates the relationships between force and elastic modulus ratios, while Figure 15b illustrates the relationships between forces and external pressures. Based on the results obtained, the contact forces comply with Equation (10), while the impulses remain the same over different  
Figure 14: Effect of shell thickness on forces and impulse at impact velocities of 4.0 m/s and 8.0 m/s. (a) 4.0 m/s; (b) 8.0 m/s.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Velocity** & \(k_{t}\) & \(C_{t}\) & \(Km_{t}\) & \(Cm_{t}\) & \(k_{t}/km_{t}\) & \(C_{t}/Cm_{t}\) \\ \hline
4 m/s & 449 & \(-0.33\) & 265 & \(-0.38\) & 1.69 & 1.69 \\
8 m/s & 950 & \(-2.5\) & 556 & \(-1.5\) & 1.67 & 1.72 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The regression analysis into the effect of shell thickness.  
cases. These results imply that the exchange of impact momentum for every case under various elastic modulus ratios or different external pressures is identical.  
\[f_{p}=k_{e}\cdot e+C_{e},f_{m}=km_{e}\cdot e+Cm_{e} \tag{10}\]  
where \(k_{e}\) and \(km_{e}\) are the proportional coefficients. The results of regression analysis are shown in Table 9. It is apparent that the ratio of \(k_{e}/km_{e}\) and \(C_{e}/Cm_{e}\) can be considered as a constant value of 1.70, and the slope of the force over the pressure relationship is relatively very small. It can be evident that the dynamic forces are constant, regardless of the changes in external pressure. The maximum force and the mean force can be expressed in accordance with the analytical formulation, \(f_{p}=k_{s}\cdot f_{m}\), where \(k_{s}\) is 1.70 without the effect of the elastic modulus ratio. It is also clear that the contact forces during collision impacts have an identical shape function for all of the cases of the various elastic modulus ratios.
## 3 Methodology  
In this section, we describe the new \"Automated Water Area Extraction Tool\" (AWAX) in detail. First, we start with the initialization, followed by a monthly pre-processing of the Landsat and Sentinel-2 images. Then, monthly land-water masks with remaining data gaps caused by clouds, cloud shadows, or snow are created. Also, data voids caused by sensor errors are considered. This is achieved by calculating five different water indexes and a classification to separate land and water pixels. Then, monthly land-water masks with remaining data gaps are computed by combining 5 water indexes (see Section 3.2.2). Based on all available land-water masks with data gaps between January 1984 and June 2018, a water probability mask is computed. In the next step, the long-term water probability mask is used in an iterative approach to fill remaining data gaps in the monthly data. Finally, based on all gap-filled land-water masks a surface area time series is computed. Figure 2 shows the flowchart of the AWAX approach in detail.  
Figure 2: Flowchart of the AWAX with processing steps (blue) and resulting data sets (red).
## 2 Immunomodulatory Function of Extracellular Hsp60, Hsp70, and Hsp90
### Parkinson's Disease  
PD is characterized by movement disorders and loss of dopaminergic neurons in the brain's substantia nigra pars compacta [67; 68]. The disease is also characterized by aggregated \(\alpha\)-synuclein that forms nuclear inclusions called Lewy bodies [69]. A study in yeast cells has shown that null mutations in the Hsp60 gene are linked with defects in the folding of mitochondrial proteins, with accumulations of misfolded peptides analogous to the \(\alpha\)-synuclein aggregates of PD [70]. Hsp60, Hsp70, and Hsp90 interact with \(\alpha\)-synuclein in the Lewy bodies in PD patients. These inclusions consist not only of \(\alpha\)-synuclein aggregates but also contain molecular chaperones which have been sequestered in the aggregates while attempting to impede or correct protein misfolding and aggregation [71; 72]. This sequestration leads to a deficit of chaperones available for maintaining protein homeostasis, namely, a chaperopathy by defect occurs, which contributes to the aggravation of the pathologic process leading to neurodegeneration. The interaction between Hsp70 and \(\alpha\)-synuclein involves the central hydrophobic region of the pathological protein and the substrate-binding domain Hsp70 and is crucial for inhibiting assembly before the elongation stage [73]. The neuroprotective function of overexpressed Hsp70 has been confirmed in experimental models in vivo [74]. There is less information regarding the protective role of Hsp90 in the regulation of \(\alpha\)-synuclein aggregation. Like in AD, Hsp60, Hsp70, and Hsp90 contribute to neuronal toxicity in PD. Hsp90 abolishes the binding of \(\alpha\)-synuclein to vesicles and promotes the formation of fibrils [75]. In in vivo and in vitro models of PD, it was found that Hsp60 expression gradually decreased after 6-hydroxydopamine (6-OHDA) injection into dopaminergic neurons (DA). This result may be explained by the release of Hsp60 by the damaged neurons, as suggested by its presence in the cell culture medium [76]. In PD models and patients, activation of microglia plays a key role in the release of proinflammatory factors that aggravate the loss of DA neurons [77]. Astrocytes, which are the predominant glial cell type in the CNS, are also critically affected by stressors. The expression of Hsp60 on the surface of activated microglia suggests that Hsp60 is involved in the progression of PD. Extracellular release of Hsp60 from CNS cells undergoing necrotic or apoptotic death activates microglia in a TLR4- and MyD88-dependent manner [21]. Hsp60 was released from degenerated neurons to activate microglia in a rat PD model, providing a novel idea for developing a therapeutic strategy to slow or stop PD progression by preventing the release of Hsp60 or interfering with the interaction between Hsp60 and microglia [76].
## Data and Methods
### Methodology
#### 3.3.2 Vapor Pressure Deficit  
When plants transpire, the mixture of air and water exiting the stomata is saturated at a relative humidity of \(\sim\)100% [53]. The maximum amount of water vapor the air can hold at a particular temperature is a function of the temperature itself, so there is potential for higher transpiration at higher temperatures. The climate of the Sahel is generally hot, with a mean annual air temperature of 30 \({}^{\circ}\)C across the three sites. The average relative humidity (RH) across the three sites is 18% in the dry season and 65% in the growing season according to the field measurements. Vapor pressure deficit (VPD) [54] is the difference between the amount of water in the air and the maximum amount of water the air can hold when it is saturated, and is a limiting factor for Sahelian GPP [25; 26; 55]. VPD was calculated from field-measured air temperature (T\({}_{a}\), \({}^{\circ}\)C) and relative humidity (RH, %) following the approach described in [PERSON] et al. [56]:  
\[e_{s}=0.611\times\exp\left[17.27\times\frac{T_{a}}{T_{a}+237.3}\right] \tag{2}\]  
where \(e_{s}\) is the saturation vapor pressure (kPa) at a certain T\({}_{a}\). Then, the actual vapor pressure (\(e_{a}\), kPa) is calculated from RH and \(e_{s}\):  
\[e_{a}=\left(\frac{RH}{100}\right)\times e_{s} \tag{3}\]  
Finally, VPD (kPa) is estimated as the difference between the two:  
\[VPD=e_{s}-e_{a} \tag{4}\]
# On the Physics of Kayaking
## Appendix A
### Skin Friction \(F_{s}\)  
Skin friction \(\underline{F}_{s}\) is opposed to the motion and its magnitude can be written as \(F_{s}=1/2\rho SC_{g}V^{2}\) where \(S\) is the wetted surface and \(C_{s}\) is the skin friction coefficient. This coefficient depends on the Reynolds number \(Re_{L}=\rho VL/\eta\) based on the length of the hull \(L\) (Figure 11) and the fluid density \(\rho\) and dynamic viscosity \(\eta\) (for water \(\rho=10^{3}\) kg/m\({}^{3}\) and \(\eta=10^{-3}\) Pa.s). In the range \(10^{4}<Re_{L}<10^{6}\), the laminar coefficient is evaluated at \(C_{s}=1.33/\sqrt{Re_{L}}\) while at a larger Reynolds number, the commonly accepted formula is the one from the International Towing Tank Conference (ITTC) [18; 22]: \(C_{s}=(1+k)\)\(0.075/\left(\log Re_{L}-2\right)^{2}\). In this expression, \((1+k)=2.76\left(L/\Omega^{1/3}\right)^{-0.4}\) is the shape factor, which depends on the length of the boat \(L\) and the immersed volume \(\Omega\)[23].  
In kayaking, the typical velocity is \(V=5\) m/sand the hull length \(L=5\) m, so that \(Re_{L}=2.5\times 10^{7}\). Using \(\Omega=80\times 10^{-3}\) m\({}^{3}\), one finds \((1+k)=1.035\). With this Reynolds number and shape factor, the above formula leads to the value \(C_{s}=0.0027\), which is not far from the value of \(0.0028\) estimated by [PERSON] [2]. Since the wetted surface is related to the immersed volume by the relation \(S=2.5\sqrt{\Omega L}\)[2], we finally get  
\[F_{s}=\frac{1}{2}\rho SC_{s}V^{2}\ \ \ \text{with}\ \ \ SC_{s}=\frac{0.5175L^{2}}{ \left(\log Re_{L}-2\right)^{2}}\Big{(}\frac{\Omega}{L^{3}}\Big{)}^{0.633}. \tag{1}\]  
For a given load \(\Omega=M_{\rm f}/\rho\), since the log term remains almost constant, we deduce that the skin friction increases with the square root of the length \(L\) and almost quadratically with the velocity. For a typical load \(M_{\rm f}=76\) kg, a boat length \(L=5.2\) m, and a boat speed \(V=5\) m/s the above Equation (1) leads to \(SC_{s}=4.08\times 10^{-3}\) m\({}^{2}\) or \(F_{s}=51\) N for Athlete A1. The same calculation for Athlete A2 (\(M_{\rm f}=84\) kg, \(L=4.5\) m, \(V=5\) m/s) leads to \(SC_{s}=4.38\times 10^{-3}\) m\({}^{2}\) or \(F_{s}=54.8\) N.
## 3 Results  
### Antimicrobial Effect of Enterocin Gr17 and EOs Combinations on Bacterial and Fungal Strains  
The MIC and FICI of ENT and the EOs against bacterial and fungal strains are shown in Table 1. The MIC of ENT was higher than that of most EOs when they were against the same strain, which indicated that the inhibition of ENT on the indicator strains was weaker than most EOs. Among the EOs single treatment, CEO had the most obvious antimicrobial effect on most bacterial and fungal strains, excluding _P. expansum_. The inhibitory effect of CEO and TEO on _C. albicans_ was significantly higher than that of the other EOs (\(p<0.05\)), with MICs of 0.0195 g/L and 0.1563 g/L, respectively. Meanwhile, an additive or indifferent effect was observed in most combinations of ENT and EOs, and no antagonism was found in the combinations (Table 1). The fungi exhibited decreased sensitivity compared to the bacterial strains under the treatment of ENT and EOs, while the combination of ENT and CEO had the best synergistic effect, which was effective against all of the indicator strains, including bacteria and fungi. Therefore, the combination of ENT and CEO was selected to conduct the subsequent study.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Indicator Strains**} & \multicolumn{6}{c}{**MIC (g/L)**} & \multicolumn{6}{c}{**FICI**} \\ \hline  & **ENT** & **EEO** & **TEO** & **CEO** & **CEO** & **MEO** & **LEO** & **ENT** & **ENT** & **ENT** & **ENT** & **ENT** & **ENT** \\  & **ENT** & **EEO** & **TEO** & **CEO** & **CEO** & **MEO** & **LEO** & **and** & **and** & **and** & **and** & **and** \\  & & & & & & & & & & **EEO** & **CEO** & **CEO** & **MEO** & **LEO** \\ \hline _S. aureus_ & 5 & 2.5 & 5 & 2.5 & 0.3125 & 1.25 & 1.25 & 1.031 & 0.531 & 0.563 & 0.125 & 1.25 & 1.031 \\ _B. cereus_ & 1.25 & 2.5 & 5 & 1.25 & 0.3125 & 1.25 & 0.625 & 0.125 & 0.156 & 1.5 & 0.063 & 0.5 & 1.25 \\ _L. monocytogenes_ & 1.25 & 1.25 & 0.3125 & 0.3125 & 0.1563 & 0.625 & 0.3125 & 0.625 & 1.125 & 0.156 & 0.094 & 0.563 & 0.625 \\ _S. enterica_ & 1.25 & 0.3125 & 5 & 0.1563 & 0.3125 & 2.5 & 0.3125 & 1.125 & 1.25 & 0.625 & 0.125 & 0.75 & 1.063 \\ _E. coli_ & 5 & 2.5 & 5 & 0.3125 & 0.3125 & 2.5 & 0.625 & 0.125 & 0.563 & 0.75 & 0.063 & 0.625 & 1.5 \\ _P. aeruginosa_ & 5 & 1.25 & 0.625 & 0.3125 & 0.3125 & 5 & 0.625 & 1.5 & 0.625 & 1.031 & 0.125 & 1.125 & 0.563 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The minimum inhibitory concentration (MIC) and fractional inhibitory concentration index (FICI) of enterocin Gr17 and essential oils (eugenol [EEO], thymol [TEO], carvacrol [CAEO], cinnamaldehyde [CEO], methionine [MEO] and linalool [LEO]) against bacterial and fungal strains.
## 2 Literature Review
### Rephotography  
Rephotography is of great value for research in historical studies because it tracks changes over time. In the past, historical scenes were rephotographed manually [70]. Manual rephotography is complex and requires special photographic skills and time to achieve good results. A good rephotograph depends on the precise location, angle, and perspective from which the historical image was taken. Therefore, recapturing a historical scene from the same perspective has been challenging. Computer vision research enables many assisting applications [28] that help the photographer to capture the image from the same viewpoint. Besides these applications, many approaches have been proposed to achieve rephotography based on image collections [71]. The advances in computer technology allow researchers to address this problem using deep learning [29]. The approach of extracting the facade and performing rephotography works well for images of houses where the facade is prominent, but detecting the facade of complex building structures makes this task even more difficult. By using computer vision techniques such as SIFT, ORF, SURF, Cirtorch, or DELF we can solve the problem to some extent, but improvements are still needed to learn and match features from low-quality historical images.
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 4 5G and Healthcare Opportunities
### Virtual Reality + Haptic/Tactile Internet  
The Tactile Internet is an ultra-responsive and ultra-reliable network connectivity that is envisioned to transmit touch and actuation in real time. It will revolutionize almost every segment of society with unprecedented applications and also truly shift the paradigm from content-delivery to control communications (skill-set or labour-delivery) also called haptic communications. It will create an Internet of an entirely new dimension to machine-machine and human-machine interaction by providing low latency and high reliability, secure with a supper coverage network; which are the daunting requirements for real-time interactive systems [43]. Tactile Internet underpinned by the zero-delay network will provide virtual reality-based headsets, which can facilitate doctors in performing operations through telepresence.
# An Improved Back-Projection Algorithm for GNSS-R BSAK Imaging Based on CPU and GPU Platform
## 3 Methods
### Parallelized BPA on GPU
#### 3.4.1 Parallelized Range Compression on GPU  
It can be seen from Section 3 that the range compression is realized by converting to the frequency domain. As shown in Figure 5, the range compression module is all processed on the GPU, which includes main operations such as FFT and IFFT operation, complex multiplication, and complex addition. FFT and IFFT operations can be completed through the cuFFT library provided by Compute Unified Device Architecture (CUDA). The parallel complex multiplication and complex addition in the frequency domain are completed by constructing a kernel function. It is worth noting that the operation of FFT and IFFT will take up a lot of video memory resources, and the data size needs to be reasonably planned according to the size of the video memory to prevent memory overflow.  
In the CUDA architecture, a thread is the smallest execution unit of a GPU that corresponds to the streaming processor (SP) of hardware. The threads are allocated to the number of range gates sampled according to the range, and each thread is solely responsible for the complex multiplication of the echo signal and the reference signal by a range gate data. The main steps of range compression on GPU include:  
1. \(N_{a}\) and \(N_{r}\) are determined according to the size of one segment data, where \(N_{a}\) and \(N_{r}\) are the number of range and azimuth samples, respectively. The memory of CPU and GPU are requested for the host for the corresponding data, respectively, and the data are transferred from the memory of the CPU to the memory of the GPU.
2. The cuFFT library function of CUDA is used to complete the FFT of the reference signal and echo signal, respectively.
3. The kernel function of parallel complex multiplication of reference signal complex conjugate matrix and echo signal matrix in the frequency domain is designed. Threads are allocated to compressed data based on range gate, and kernel function is called to complete parallel complex multiplication.
4. Use the cuFFT library function to develop the IFFT solution to convert the range compressed data into the time domain, generate correlation values \(F(\eta,\tau)\) that match the slant range.
5. Repeat the steps (1)-(4) until all echo data are processed.  
Figure 5: GNSS-R BSAR signal processing flowchart.
## 4 Performance Evaluation
### Test Simulation Setup  
The simulation test setup, using 7 sensors, is shown in Figure 5, and the sensor locations are listed in Table 1.  
In the tests, two source positions were used: a near-range position \(P_{s}=(157.3\) m, \(113.9\)m, \(0\)m) and a long-range position \(P_{l}=(250.2\) m, \(1280.4\) m, \(0\) m). Notice that the source positions are known to be on the ground level, according to the 2.5D problem.  
In the tests, \(c=340\) m/s was utilized (speed of sound), and the measurement errors were modeled by additive white noise. Two different noise levels were used: \(\sigma_{i,1}=210\) s and \(\sigma_{i,1}=2.1\) ms, resulting in \(\sigma_{i,1}=0.1\) m and \(\sigma_{i,1}=1\) m, respectively. The final resolution \(D_{final}\) of the F-HHT and H-HHT was 0.1m, 1m, and 5m. The parameters of the six scenarios are summarized in Table 2.  
Scenarios 1-3 are near-range, while scenarios 4-6 are long-range. In both ranges, different noise levels and final grid sizes were applied, as shown in Table 2.  
All of the tested methods were implemented in Matlab v. 9.11. Notes on the utilized algorithms:  
* The LS algorithm was Matlab's built-in quasi-Newton solver in function fminunc. The gradient search was started from a random point within a 50m radius of the true source position.
* The F-HHT algorithm used final grid size parameter \(D_{final}\). In the H-HHT, the same grid size was used in the second stage of the algorithm, where the search area was \(50\cdot D_{final}\times 50\cdot D_{final}\) around the result provided by the R-HHT in the first stage.
* The search area for the HHT methods was set to [0, 0, 500, 500] in the near-range cases and [0, 0, 1500, 1500] in the long-range cases.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sensor ID** & **Position (m)** \\ \hline
1 & (10, 10, 100) \\
2 & (430, 190, 50) \\
3 & (450, 350, 200) \\
4 & (70, 300, 150) \\
5 & (400, 10, 150) \\
6 & (200, 50, 50) \\
7 & (250, 470, 20) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensor placement in the 7-sensor experiment.  
Figure 5: Sensor placement in the simulation setup with 7 sensors.  
* The R-HHT and H-HHT algorithms were implemented using the HHT as defined in section III-B.
* The likelihood function (7) includes the measurement noise estimate \(\sigma_{i,1}\). In practical cases, the exact value of the noise is often unknown. Therefore, it is recommended to overestimate the noise level and use the overestimated value in the HHT. To replicate this procedure in the simulations, we used \(\sigma_{i,1}\) to generate the measurements, and for the HHT algorithms, we provided \(3\sigma_{i,1}\) as the noise level estimate.