esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Pathogenesis of AD
### The Cholinergic Hypothesis  
The cholinergic hypothesis was the first molecular approach to the pathophysiology of AD. This hypothesis states that AD is a primary degenerative process capable of selectively damaging groups of cholinergic neurons in the amygdala, hippocampus, nucleus basalis, frontal cortex, and medial septum regions, which play important roles in attention, awareness, learning, memory, and other mnemonic processes [59]. Furthermore, ROS may be responsible for the formation of A\(\beta\) plaques, resulting in a decrease in the production of the cholinergic neurotransmitter acetylcholine (Ach) in the cortical and basal ganglia of the brain. This might alter synaptic communication, which may lead to inflammatory processes, memory loss, and cell death [23]. ACh is immediately hydrolyzed after discharge at the synapse by AChE to prevent the negative consequences of excessive cholinergic stimulation. On the other hand, BuChE is a related enzyme with the same functionality. ACh does not hydrolyze as fast when cholinesterase is blocked, and ACh levels increase [2; 98]. As a result, AchE and BuChE inhibitors are potential targets for AD.  
Interestingly, apolipoproteins are major players in regulating A\(\beta\) pathology. The ApoE gene encodes three alleles, namely ApoE2, ApoE3, and ApoE4 It, where ApoE4 is the major genetic risk factor for sporadic AD and is associated with cognitive deficits [99]. The effect of ApoE on AD was studied in the context of the \"cholinergic hypothesis\" in 1976, by recognizing that cholinergic neurons are not the crucial target of AD. In specific areas of the brain, cholinergic receptor binding decreases with minor to severe AD, which is associated with neuropsychiatric symptoms [59]. In vivo binding of the cholinergic receptor may reveal links to other key changes in the brain connected with aging and AD and may provide a potential target for molecular treatment. In this case, a significant cholinergic neuron loss in the forebrain nucleus leads to a decrease in the acetylcholine-mediated neurotransmission. In addition, drugs that contribute to the regulation of acetylcholine transmitter levels, such as donepezil and cholinesterase inhibitors (ChEIs) have laid the foundation for symptomatic AD therapy for more than 20 years [10].
## 4 Analytical Analysis of the Transverse Cracks in BFRP-CRCP
### Comparison and Analysis  
The field observation and analytical calculation results for both the crack spacing and crack width of CRCP with BFRP bars without an asphalt overlay are presented in Table 4.  
As for the crack spacing, the result of the analytical calculation was 4.47 m, which is less than but close to the field observation result. Meanwhile, for crack width the analytical result was 1.78 mm, a little higher than the field measured one (1.30 mm). Theoretically, this difference can be produced by the assumption of the linear bond-slip relationship between the concrete and the BFRP reinforcement, and the friction between the concrete slab and the base. Furthermore, the differential moisture and temperature effects in the field that are not included in the model, which can cause differences in the prediction results and the field-measured ones as well. In addition to the theoretical explanations, the transverse cracks in CRCP develop with time and tend to be fully developed in around two to three years, according to the field surveys from McGhee [19]. However, the crack survey of this CRCP test section with BFRP reinforcement is only conducted as long as 36 days after the construction and, therefore, the field-measured results must be lower than the prediction values, which are normally used to estimate the final crack spacing and crack width for the CRCP. In general, a reasonable agreement was achieved from the field observations and analytical calculations, with a slight overprediction of crack spacing and crack width at earlier ages occurring in the model.  
For traditional CRCP with steel bars, the transverse crack spacing usually ranges from 1.2 to 1.8 m [20; 21] with a crack width less than 0.5 mm, which is suggested in the American Association of State Highway and Transportation Officials (AASHTO) standard [22]. However, since the corrosion resistant property of the BFRP reinforcement, a less harsh requirement for crack width in CRCP with BFRP bars can be accepted. To provide a better performance for the CRCP structure, the influence of the BFRP design parameters (the elastic modulus, the reinforcement content of BFRP bars (both BFRP  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Crack Spacing (m)** & **Crack Width (mm)** \\ \hline Final field observation & 4.85 & 1.30 \\ Analytical results & 4.47 & 1.78 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the field-measured and predicted crack spacing and crack width.  
Figure 6: Predictions of the crack spacing (**left**) and crack width (**right**) in CRCP with BFRP bars.  
spacing and diameter-related), and the coefficient of bond stiffness between the concrete and the BFRP reinforcement) on the transverse crack behavior of CRCP needs to be thoroughly investigated.
# Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks
## 3 Related Work
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Year** & **Technique** & **Problem** & **Considered Parameters** & **Limitations** \\ \hline
[15] & 2018 & LSTM with Euclidean distance & To improve content delivery in host centric network & Accurate prediction of next caching server to utilise cache, reduce delay & High mobility, scalability, V2X \\ \hline
[27] & 2021 & Harmony search routing protocol & To find the shortest route using OLSR & & Blind flooding \\ \hline
[28] & 2020 & PSO & Enhancing BW efficiency in IZV communication & Minimises broadcast time & Convergence time is slow \\ \hline
[14] & 2019 & GA with Markov chain & Cache popular content with accurate path prediction on the basis of past data or history & Reduces latency and data traffic load & \\ \hline
[16], & 2015, & Comparison of nature-inspired algorithms and CS & Comparison between metaheuristic optimisation algorithms in terms of TSP & Initial parameter, dependency, memory and time complexity & CS performs better but recognition of egg by host bird can cause problem. \\ \hline
[34], & 2019 & Comparison of meta-heuristic algorithm & Minimum path & Delay, hit ratio & CS performs better \\ \hline
[37] & 2022 & Optimized routing with CS-WKCA & K-medoids clustering to find dissimilarities & Avoid road accident & K-medoids is more complicated and unable to recover from database \\ \hline
[38] & 2022 & Whale dragonfly with RI-CS & Rider-integrated CS is used to find best rout with minimal cost & Provide bandwidth to dropped BW devices, evaluate energy consumption & No clustering therefore lack of cooperation among end users \\ \hline
[39] & 2022 & Giraffe-kicking optimization in VANETs & Hybrid C-means iterative clustering & Avoid energy consumptions & No pre-caching to reduce delivery delay \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Parameters** & **Complexity** & **Justification** \\ \hline GA & Population size, mutation, crossover & \(O\) (\(n^{\star}\)\(m^{\star}\)\(c\)) & All three operations will conducted multiple times, which increases its complexity \\ \hline PSO & Number of particles (N), acceleration co-efficient, inertia weight, neighboring range, number of iterations, random values & \(\Omega\) (\(n\)/\(log\)\(n\)) and \(O\) (\(n\)\(log\)\(n\)) & Stability problem restricts the success rate, secondly need memory to update velocity \\ \hline \multirow{2}{*}{ACO} & Number of ants, evaporation rate, alpha and beta (control parameters) & Average case: \(O\) (\(n\)) & n \(\rightarrow\) number of ants \\  & & Worst case: \(O\) (\(m^{\star}\)\(n\)) & If n increases complexity will also increase \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Complexities comparison of significant algorithms.
# Passwordless Authentication Using a Combination of Cryptography, Steganography, and Biometrics
## 3 Methods
### User Interaction Flow
#### 3.3.2 Account Recovery  
In password-based accounts, security questions are one method employed to help users regain access to their accounts if they forget the password or lose their credentials. SA relies on the unique attribute of the user (their typing pattern) to restore access to their account.  
Since the credentials for each account are saved in secure storage on the device and never shared, loss of the device means the user can no longer access the device. To  
Figure 7: Ecom user authentication flow.  
again access to the account, the user will need to provide the stego-image that was pre-embedded with their typing ID and the public key for that account according to the steps described thus:  
* User installs the app on a new device and navigates to the account import screen (AIS).
* The user types in their email address in the input field and uploads the stego-image holding the account credentials. The AIS captures the typing pattern of the user in the background while they are typing.
* On biometric authentication success on the device, the typing pattern is encrypted using the server public key (this was received during the app installation) and, along with the stego-image, is sent to the SA server.
* The SA server confirms that the image contains data for that account after extracting the typing ID and decrypting it, and the public key. The server checks the database for that typing ID, to confirm that a previous relationship existed between the public key and typing ID; if the public key was not previously mapped to the typing ID, the request is rejected. If there was a previous mapping, the typing ID and the typing pattern are encrypted and sent to the TypingDNA server for verification.
* Based on the response from the TypingDNA server as in Figure 8, the account import request will be accepted (assuming there was a match) or rejected.
* If there is a match, the user is routed to the accounts list screen, and the imported account is added, allowing the user to authenticate to the account as in Section 3.3.1.
# Bioelectrical Signals for the Diagnosis and Therapy of Functional Gastrointestinal Disorders
## 3 Stomach
### Diagnostics
One study showed that abnormal spatial metrics obtained through high-resolution EGG recordings correlated with the severity symptoms of gastric disorders such as gastroparesis and functional dyspepsia [77]. Magnetogastrography (MGG) is the magnetic counterpart of EGG and is a non-invasive and noncontact alternative [78,79]. MGG provides a higher signal-to-noise ratio (SNR) compared to EGG as magnetic fields are not as greatly attenuated by muscle and fat layers surrounding the body, and hence detects sources located deeper within the body than electrical-based recordings [80]. A recent study showed that extracting SW propagation parameters such as direction and speed from the experimental MGG recordings is feasible [81]. These parameters were altered in diabetic gastroparesis patients where reduced propagation velocities associated with retrograde propagation patterns were reported, while the dominant SW frequency did not show a significant difference [82].
# Effects of Wave-Induced Processes in a Coupled Wave-Ocean Model on Particle Transport Simulations
## 2 Methods
### Observational Data  
#### 2.2.1 Drifter Data  
The HE 445 cruise was performed between May and July 2015 (see Figure 1b for the trajectories and deployment period of the drifters) [26]. The R/V Heincke deployed nine Albatros drifters corresponding to two models (Figure S1): MD03i (drifters 1-6) and ODi (drifters 7-9). The drifters provided their current positions by a Global Positioning System (GPS), which were transmitted to the R/V Heincke via Iridium (a bi-directional satellite communication network). The MD03i is a cylinder-shaped drifter, with a diameter of 0.1 m and length of 0.32 m, but only approximately 0.08 m remains above the water surface. The MD03 drag ratio is 33.2, and according to the parametrization [26] the MD03 slippage is around 1.1 to 1.6 cm/s, for 10 m/s wind speed and velocity difference across the drogue (\(\Delta U\)) equal to 0.1 cm/s. The ODi is a spherical drifter with a diameter of 0.2 m, but only approximately 0.1 m is above the water surface. A sail (0.5 m in length and diameter, Figure S1) was attached to every drifter to enhance the drag below the water surface, and it was 0.5 m below the surface. Due to the small drifter surface above the water compared to the sail surface below the water, the drifter is designed to follow the ambient current in the upper meter of the water column. Due to the meteorological conditions, only some of the drifters were recovered at the end of the campaign. The recovered drifters corresponded to the short data-set, while the non-recovered drifters from the long data-set transmitted data until the batteries drained (Figure 1b).
###### Abstract  
In vitro anti-proliferative activity of _Pinus palustris_ extract and its purified abietic acid was assessed against different human cancer cell lines (HepG-2, MCF-7 and HCT-116) compared to normal WI-38 cell line. Abietic acid showed more promising IC\({}_{50}\) values against MCF-7 cells than pine extract (0.06 \(\mathrm{\SIUnitSymbolMicro mL}\) and 0.11 \(\mathrm{\SIUnitSymbolMicro m}\), respectively), with insignificant cytotoxicity toward normal fibroblast WI-38 cells. Abietic acid triggered both G\({}_{2}\)/M cell arrest and subG\({}_{50}\)-G\({}_{1}\) subpopulation in MCF-7, compared to SubG\({}_{0}\)-G\({}_{1}\) subpopulation arrest only for the extract. It also induced overexpression of key apoptotic genes (_Fas, FasL, Casp3, Casp8, Cyt-C_ and _Bax_) and downregulation of both proliferation (_VEGF, IGFR1, TGF-_f_) and oncogenic (_C-myc_ and _NF-xB_) genes. Additionally, abiotic acid induced overexpression of cytochrome-C protein. Furthermore, it increased levels of total antioxidants to diminish carcinogenesis and chemotherapy resistance. _P. palustris_ is a valuable source of active abietic acid, an antiproliferative agent to MCF-7 cells through induction of apoptosis with promising future anticancer agency in breast cancer therapy.  
abietic acid; P. palustris; anti-cancer; MCF-7; apoptosis Article
# GazeGuide: An Eye-Gaze-Guided Active Immersive UAV Camera
## 7 Applications  
UAVs are becoming increasingly refined in terms of their hardware capabilities. Improvements in sensor systems, on-board computational platforms, energy storage, and other technologies have made it possible to build a huge variety of UAVs for different applications [60], such as aerial surveillance, environmental monitoring, traffic monitoring, precision agriculture and inspection of agricultural fields, aerial goods delivery, natural disaster areas surveillance, and forest, border, and fire monitoring [61; 62]. In these applications, UAVs are equipped with some specific sensors (LiDAR and cameras) to continuously monitor the ground ToIs. Basically, in video-based surveillance and monitoring applications, the onboard camera provides a rich visual feed to the monitor. These application tasks can be very exhausting for a human operator because of such rich visual feeds. Hence, any reduction of the operator workload frees the cognitive capacity for the actual surveillance task. In video-based surveillance and monitoring applications, a frequently occurring task is to keep track of a moving target. If the target moves out of the displayed scene, then the operator has to readjust the camera viewpoint to bring the target within the scene. Moreover, aerial cinematography systems require considerable attention and effort for the simultaneous identification of the actor, the predication of how the scene is going to evolve, and UAV control; thus, avoiding obstacles and reaching the expected viewpoints is very challenging. In such situations, GazeGuide appears compelling and keeps the target focused just by looking at the target. Hence, the operator's visual attention could be focused on the primary (surveillance) task, and the secondary (navigational) task is achieved effortlessly at the same time.
# A Novel 3D Analytical Scattering Model
## 2 Geometry of the Proposed Analytical Channel Model
The total length of a signal from GS to AS (via a single bounce) can thus be given as,  
\[l=r_{b}+r_{a}. \tag{11}\]  
\(r_{b}\) is the distance of a scatterer from GS and can be written in simplified form as,  
\[r_{b}=\sqrt{d^{2}+r_{a}^{2}\cos^{2}\beta_{a}-2r_{a}d\cos\beta_{a}\cos\phi_{a}+ (h_{\rm AS}-h_{\rm GS}+r_{a}\sin^{2}\beta_{a})}. \tag{12}\]  
Substituting the value of \(r_{b}\) in (11) and solving for \(r_{a}\), the equation for \(r_{a}\) as a function of path delay and angles seen on AS can be rearranged as,  
\[r_{a}=\frac{l^{2}-d_{\rm LCB}^{2}}{2(l-d\cos\beta_{a}\cos\phi_{a}-(h_{\rm AS}- h_{\rm GS})\sin\beta_{a})}. \tag{13}\]  
\(V\) is the volume of TESR, which is an important parameter for the case of uniform scatterers, which can be calculated by the same approach as given in [21] for a similar geometry. The solution for \(V\) can thus be expressed in compact form as given below,  
\[V=\frac{1}{3\sqrt{(a_{\rm\tau}^{2}+b_{\rm\tau}^{2}+(-a_{\rm\tau} ^{2}+b_{\rm\tau}^{2})\cos(2\theta_{\rm los}))^{3}}}\times\Big{\{}\sqrt{2}a_{ \rm\tau}b_{\rm\tau}^{2}\pi(\sigma_{c}+\sigma_{\rm\rm\tau}+\psi_{w}) \tag{14}\] \[\big{(}3a_{\rm\tau}^{2}+3b_{\rm\tau}^{2}-2(\sigma_{c}+\sigma_{ \rm\tau}+\psi_{w})^{2}+6(\sigma_{c}+\sigma_{\rm\tau}+\psi_{w})(h_{\rm AS}- \frac{d_{\rm los}}{2}\ \sin\theta_{\rm los})\] \[\quad-6(h_{\rm AS}-\frac{d_{\rm los}}{2}\ \sin\theta_{\rm los})^{2}+3(-a_{\rm\tau}^{2}+b_{ \rm\tau}^{2})\cos(2\theta_{\rm los})\big{)}\Big{\}}.\]  
Figure 1: **(a) Path of a radio propagation signal from the air station (AS) to the ground station (GS) with details of the angles in the azimuth and elevation planes; (b) overall geometric composition of the 3D air-to-ground (A2G)/ground-to-air (G2A) radio propagation scenarios. **
## 4 Surrogate-Based Optimization (SBO)  
The process of surrogate-based optimization is shown in Figure 7. First, the orthogonal array (OA) method is adopted to generate initial sample points. The simulation results obtained by the CFD method are used to construct the initial KM. The root mean square error (RMSE) is used as the infilled criterion, and the genetic algorithm (GA) is adopted to search for the maximum RMSE to determine new infilled points. The infilled points are used to reconstruct the KM. Then, the decreasing ratio of RMSE is used as the termination criterion to avoid wasting computational cost and time. The following sub-sections will introduce more detailed information.  
Figure 5: Comparison between numerical and experimental results: (**a**) OH radical distribution and flame image at \(Re=1000\); (**b**) The height of premixed cone at different \(Re\).  
Figure 6: Variation of total heat transfer (\(\bar{Q}_{T}\)) for different \(Re\), with fixed \(\phi=1\) and \(H/d=2\).
# Aggregate Type and Concrete Age Effects on Anchor Breakout Performance: Large Database and Insights
## 3 Results
### Anchor Results
#### 3.2.2 Shear Loading  
Summary of Experimentally Determined Edge Breakout Capacities  
Anchor shear tests were performed on the same concrete slabs used for unconfined tension tests. For each slab, first anchor shear tests were performed in order to minimize damage of the concrete member, preventing potential splitting cracks of the member due to the tensile tests and its effect on shear load capacity. Anchor for the shear tests were spaced sufficiently (fulfilling code requirements) to prevent interactions. Experimentally obtained shear load capacities for all concretes and ages are shown in Figure 5, where each marker represents the mean of four tests for CDL concretes and five tests for CSTB concretes. For some CSTB concretes, only the first age was tested due to logistical reasons. Table 7 summarizes all experimentally obtained mean shear capacities plotted in Figure 5, including their respective coefficients of variation.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Batch/Agg. Type**} & \multicolumn{2}{c}{\(N_{cap}\)**at 1 st Age**} & \multicolumn{2}{c}{\(N_{cap}\)**at 2 nd Age**} \\  & & **kN (kip)** & **kN (kip)** \\ \hline \multirow{6}{*}{low strength} & CSTB 1a/Agg. 4 & 113.0 (25.4) \(\pm\) 14.7\% & 132.3 (29.7) \(\pm\) 8.6\% \\ \cline{2-4}  & CDL 1/Agg. 1 & 111.2 (25.0) \(\pm\) 8.3\% & 143.4 (32.2) \(\pm\)4.9\% \\ \cline{2-4}  & CSTB 2a/Agg. 5 & 95.0 (21.4) \(\pm\) 11.0\% & 109.6 (24.6) \(\pm\) 12.1\% \\ \cline{2-4}  & CDL 2/Agg. 2 & 135.6 (30.5) \(\pm\) 9.6\% & 183.4 (41.2) \(\pm\) 7.0\% \\ \cline{2-4}  & CSTB 3a/Agg. 6 & 98.9 (22.2) \(\pm\) 7.3\% & 122.7 (27.6) \(\pm\) 6.9\% \\ \cline{2-4}  & CDL 3/Agg. 3 & 124.4 (28.0) \(\pm\) 1.2\% & 173.8 (39.1) \(\pm\) 5.6\% \\ \cline{2-4}  & CSTB 4a/Agg. 7 & 111.4 (25.0) \(\pm\) 11.0\% & 107.7 (24.2) \(\pm\) 14.5\% \\ \hline \multirow{6}{*}{High strength} & CSTB 1b/Agg. 4 & 186.8 (42.0) \(\pm\) 10.3\% & 204.8 (46.0) \(\pm\) 11.4\% \\ \cline{2-4}  & CSTB 2b/Agg. 5 & 160.0 (35.9) \(\pm\) 13.1\% & 128.8 (29.0) \(\pm\) 3.5\% \\ \cline{2-4}  & CSTB 3b/Agg. 6 & 144.3 (32.4) \(\pm\) 5.2\% & 160.9 (36.2) \(\pm\) 13.1\% \\ \cline{2-4}  & CSTB 4b/Agg. 7 & 159.8 (35.9) \(\pm\) 15.9\% & 176.9 (39.8) \(\pm\) 12.1\% \\ \hline \end{tabular}
\end{table}
Table 6: Experimentally obtained concrete cone capacities.  
Figure 5: Experimentally obtained edge breakout capacities for all tested concretes and ages (note: 1 kN = 0.225 kip).  
In line with the results of the tensile tests, the analysis of the shear tests also reveals the absence of any correlation between shear capacity and L.A. coefficient. As expected, high-strength concretes (red diamond markers) result in the highest ultimate loads.
# A Study on the Channel Expansion VAE for Content-Based Image Retrieval
## 4 Experiment
### Dataset
#### 4.1.2 MNIST Dataset  
This dataset included 70,000 images of hand-written digits from 0 to 9. All images belonged to 10 classes and were 28 \(\times\) 28 sized gray scale. For comparison with past papers, we randomly picked 100 images per class as a test query set. The remainder of the dataset belonged to the database (gallery set).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Block** & **Kernel Size** & **Output Size** & **Repeat** \\ \hline Conv\_in & \(3\times 3\) & \(64\times 64\times 32\) & \(\times 1\) \\ Block\_1 & \(3\times 3\) & \(64\times 64\times 16\) & \(\times 1\) \\ Block\_2 & \(5\times 5\) & \(32\times 32\times 40\) & \(\times 2\) \\ Block\_3 & \(3\times 3\) & \(16\times 16\times 80\) & \(\times 3\) \\ Block\_4 & \(5\times 5\) & \(16\times 16\times 112\) & \(\times 3\) \\ Block\_5 & \(5\times 5\) & \(8\times 8\times 192\) & \(\times 4\) \\ Block\_6 & \(3\times 3\) & \(4\times 4\times 320\) & \(\times 1\) \\ Block\_7 & \(3\times 3\) & \(2\times 2\times 512\) & \(\times 1\) \\ Layer\_out & GMP \(|\) FCL & 512 & \(\times 1\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Encoder in channel expansion VAE.  
Figure 3: Channel Expansion VAE structure: (**a**) encoder with block unit, (**b**) VAE with convolution, (**c**) VAE with channel expansion. BN is batch normalization and DW_Conv is depth-wise convolution.