esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Asymptotic Outage Probability Analysis  
For further insights into the system performance, asymptotic outage probability analysis is focused on the variable-gain relaying protocol and fixed-gain relaying protocol. It is assumed that \(\tilde{\gamma}_{RF}\triangleq\tilde{\gamma}_{FSO}\). In the high SNR regime, the asymptotic outage probability expression can be presented as \(P_{out}\approx(G_{c}\cdot\tilde{\gamma})^{-G_{d}}\), where the \(G_{d}\) is the diversity order and \(G_{c}\) is the coding gain.  
For variable-gain relaying, the asymptotic expression of outage probability can be expressed as:  
\[F_{\gamma_{F_{d}}}^{\infty}(\gamma_{th})=F_{\gamma_{RF}}^{\infty}(\gamma_{th}) +F_{\gamma_{FSO}}^{\infty}(\gamma_{th})-F_{\gamma_{RF}}^{\infty}(\gamma_{th}) F_{\gamma_{FSO}}^{\infty}(\gamma_{th}) \tag{46}\]  
For the RF link, \(\lim\limits_{\tilde{\gamma}_{RF}\rightarrow\infty}e^{-\gamma/\gamma_{RF}}=1\), and the asymptotic expression for variable-gain relaying can be simplified to \(F_{\gamma_{d}}^{\infty}(\gamma_{th})=F_{\gamma_{FSO}}^{\infty}(\gamma_{th})\). Using [34] (Equation (07.34.06.0006.01)), the asymptotic expression for the second hop can be expressed as:  
\[\begin{split} F_{\gamma_{FSO}}^{\infty}(\gamma_{th})& =\sum_{k=1}^{2}\frac{\prod_{j=1,\
eq k}^{2}\Gamma\big{(}I_{2,j}-J_ {2,k}\big{)}\prod_{j=1}^{2}\Gamma\big{(}1-I_{1,j}+I_{2,k}\big{)}}{\prod_{j=3} ^{3}\Gamma\big{(}I_{1,j}-J_{2,k}\big{)}\prod_{j=3}^{3}\Gamma\big{(}1-J_{2,j}+J _{2,k}\big{)}}\\ &\times\bigg{[}\frac{(b-1)\big{(}1+m^{2}\big{)}\tau_{FSO}^{1/t}} {am2\gamma_{th}^{1/t}}\bigg{]}^{-I_{2,k}}\end{split} \tag{47}\]  
The diversity order is \(G_{d}=\min\left\{\frac{\theta}{t},\frac{m^{2}}{t}\right\}\) and it can be seen that \(G_{d}\) depends on the parameters \(a\) and \(m\).  
For fixed-gain relaying, we use [34] (Equation (07.34.06.0006.01)) to expand the Meijer's G function in (31) as:  
\[\begin{split} G_{2t,2t+1}^{2t+1,t}\bigg{[}\frac{C\gamma q_{m}^{ \prime}}{\gamma_{RF}\gamma_{FSO}}\big{|}\begin{matrix}B_{1}\\ B_{2}\end{matrix}\bigg{]}&=\sum_{k=1}^{2t+1}\frac{\prod_{j=1,j \
eq k}^{2t+1}\Gamma\big{(}B_{2,j}-B_{2,k}\big{)}}{\prod_{j=t+1}^{2t}\Gamma \big{(}B_{1,j}-B_{2,k}\big{)}}\\ &\times\prod_{j=1}^{t}\Gamma\big{(}1-B_{1,j}+B_{2,k}\big{)}\bigg{[} \frac{\gamma_{RF}\tilde{\gamma}_{FSO}}{C\gamma_{th}\varphi_{m}^{\prime}} \bigg{]}^{-B_{2,k}}\end{split} \tag{48}\]  
The asymptotic outage probability expression for fixed-gain relaying can be derived by inserting (48) and \(\lim\limits_{\tilde{\gamma}_{RF}\rightarrow\infty}e^{-\gamma/\gamma_{RF}}=1\) into (31), and \(G_{d}=\min\left\{\frac{\theta}{t},\frac{a+t-1}{t},\frac{m^{2}}{t},\frac{m^{2} +t+1}{t},0\right\}=0\).
## 3 Method
### Estimation Criteria  
The framework guarantees a watermarked image with maximum quality and minimum watermark extraction error rate to balance the imperceptibility and robustness of the algorithm. Peak signal-to-noise ratio \((PSNR)\)[21], structural similarity index \((SSIM)\)[3] and normalized cross-correlation \((NC)\)[36] are used in this work to evaluate the capability of the SSDeN algorithm. \(PSNR\) and \(SSIM\) are applied to show the algorithm's imperceptibility performance, and \(NC\) is utilized to demonstrate the robustness of watermarking. The mean square error \((MSE)\) of the image is expressed as follows:  
\[MSE=\frac{1}{M\times M}\sum_{x=0}^{M-1}\sum_{y=0}^{M-1}(I_{o}(x,y)-I_{w}(x,y))^ {2}. \tag{4}\]  
The \(PSNR\)[37] is defined as follows:  
\[PSNR=10\log_{10}\frac{MAX^{2}}{MSE}. \tag{5}\]  
where \(I_{o}(x,y)\) is the host image, \(I_{w}(x,y)\) is the watermarked image, \(MAX\) is the max pixel value of the image:  
\[L_{I}=SSIM(I_{o},I_{w})=\frac{(2\mu_{I_{o}}I_{w_{e}}+C_{1})(2\sigma_{I_{o}I_{w }}+C_{2})}{(\mu_{I_{o}}^{2}+\mu_{I_{w}}^{2}+C_{1})(\sigma_{I_{o}}^{2}+\sigma_{ I_{w}}^{2}+C_{2})}. \tag{6}\]  
where \(I_{o}\) and \(I_{w}\) denote the original and watermarked images, respectively; \(\mu_{I_{o}}\) and \(\mu_{I_{w}}\) are the average values of \(I_{o}\) and \(I_{w}\), respectively; \(\sigma_{I_{o}}\) and \(\sigma_{I_{w}}\) are the variances of \(I_{o}\) and \(I_{w}\), respectively; \(\sigma_{I_{o}I_{w}}\) is the covariance of \(I_{o}\) and \(I_{w}\); and \(C_{1}\) and \(C_{2}\) are two variables for stabilizing the division with the weak denominator.  
Robustness is used to evaluate the ability of detecting or extracting the watermark from watermarked images after normal image processing or malicious attack. The robustness of the watermarking algorithm is high when the extracted watermark is close to the original watermark. Normalized cross-correlation \((NC)\) can be adopted to measure the similarity between the original and the extracted watermarks given that the watermark is a binary image. The value range of \(NC\) is [0, 1]. The robustness of image watermarking improves asthe value of \(NC\) approaches 1. We choose \(NC\) as part of the loss function. \(NC\) is defined as follows:  
\[L_{w}=NC=\frac{\sum_{h=1}^{H}\sum_{g=1}^{G}w_{e}(h,g)\times w_{o}(h,g)}{\sqrt{( \sum_{h=1}^{H}\sum_{g=1}^{G}(w_{e})^{2})}\sqrt{(\sum_{h=1}^{H}\sum_{g=1}^{G}(w_ {o})^{2})}} \tag{7}\]  
where \(w_{0}(h,g)\) is the original watermark, \(w_{e}(h,g)\) is the extraction watermark and \(w_{*(h,g)}\) is a watermark with size \(H\times G\).  
Finally, the total training loss can be calculated as shown in Equation (8):  
\[L_{t}=\alpha\frac{1}{L_{I}+C_{3}}+\beta\frac{1}{L_{w}+C_{4}} \tag{8}\]  
where \(\alpha\) and \(\beta\) are the ratios of losses, \(\alpha+\beta=1\) and \(C_{3}\) and \(C_{4}\) are two constants of the metric, which are set to \(10^{-4}\) and \(9\times 10^{-4}\), respectively.
## 5 System Implementation and Field Testing
### Implementation Details
#### 5.1.1 Mobile App Subsystem  
The mobile app subsystem is developed in Android platform using Eclipse integrated development environment (IDE) with Android SDK. For the proposed three strategies, we use Google Maps Directions API to acquire the moving times of the tourist from his/her GPS coordinates to all the attractions' GPS coordinates in My Play List. Because Google Maps Directions API considers the moving speed as being constant, the distances are directly proportional to the moving times.
# Implementation Details for Controlling Contactless 3D Virtual Endoscopy
## 1 Introduction  
Computer assisted surgery (CAS) and contactless surgery (CS) are not new concepts in modern telesurgery and telemedicine. We have had much experience with different approaches to resolving problems that occurred during the implementation of different frameworks and surgeon needs in the operating room (OR). By applying the concept of so-called personalized medicine, during the development of our original CAS and CS concept [1], expert members of our medical and IT teams created an enormous potential for transforming diagnostic and therapeutic procedures into the best possible solutions. However, does our CS in rhinology really represent the future of smart surgery? Do we really have real technological advances in rhino surgery in our hands, as the great Professor [PERSON] once said?  
The visualization and exploration of 3D medical data (CT, MIR, or ultrasound) have great importance in real medical applications. Medical visualization system users are primarily medical doctors from a specific discipline, such as radiology, surgery, nuclear medicine, or radiation treatments, who use such tools for diagnostic support, treatment planning, intraoperative navigation, etc. [2, 3, 4].  
This paper focuses on improving the \"In the Air\" doctor-computer interaction, or HCI (human-computer interaction), during surgery in the clinical environment. The rest ofthe paper is organized as follows. In Section 2, we set the problem of navigation through the human body. In Section 3, we describe our input modalities for surgeon-computer interaction and motion recognition methods used for controlling the contactless 3D virtual endoscopy (VE). In Section 4, we present our results. In Section 5, we discuss the potential of our solution, with conclusions in Section 6.  
In modern OR, the surgeon, as a 21 st-century man, thinks differently, with a new visualization aspect and understanding of the ecosystem, visualization space, and self-and anatomy-awareness of his patients. Our approach could be an important step towards the strategy of enhancing surgeons' capacities and increasing their overall satisfaction and precision since we enable the integration of real and virtual objects in the surgical field. We started our initial research by applying virtual reality concepts in our first Tele3D CAS in rhinology where we implemented a new framework for the transfer of computer data (images, 3D models) in real-time during the surgery and, in parallel, of the encoded live video signals that were already presented in [5]. We demonstrated this approach with an example of 3D-computer assisted navigation surgery of the nose and paranasal sinuses with simulation and planning of the course of a subsequent endoscopic operation per viam virtual endoscopy, which overcomes some difficulties of conventional endoscopies, such as \"standard\" functional endoscopic sinus surgery (FESS) or Tele-FES.
# Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review
## 3 Application Areas
### Disease Diagnosis  
The distinct odor profiles detected by electronic noses stem from specific metabolic pathways associated with various diseases [140,141]. For instance, in diabetes, volatile organic compounds (VOCs) like acetone, ethanol, and isoprene are produced as metabolic byproducts of abnormal glucose metabolism [142, 143, 144, 145, 146], reflecting processes such as ketogenesis and glycolysis. Similarly, lung infections trigger the release of VOCs such as nitric oxide and volatile fatty acids due to inflammatory responses initiated by bacterial or viral pathogens [147, 148, 149, 150]. In certain cancers, metabolic dysregulation leads to the production of unique VOC signatures [151, 152, 153, 154]; for example, lung cancer often showcases elevated levels of volatile alkanes and benzene derivatives indicative of tumor-associated metabolic activities like oxidative stress and lipid peroxidation. Understanding these specific metabolic pathways and their corresponding VOC profiles enables the development of more targeted diagnostic approaches using electronic noses, thereby enhancing early detection and intervention strategies for improved patient outcomes.
## 4 Results and Discussion
### Differences between Parks  
In previous sections, a relationship was established between some tranquility viewpoints and sounds that persons visiting a park notice. Similarly, a relationship between some tranquility viewpoints and the overall assessment of the quality of the sonic environment was established. In this section, the influence of the park where the persons were encountered is studied in relation to the tranquility viewpoint and whether visitors' expectations were matched. Therefore, relative membership was also calculated for people interviewed in each park (Equation (4)).  
The results show that, although the viewpoint on tranquility may have an influence on the park that is visited, this influence seems not to be statistically significant. Indeed, one could imagine plenty of other factors such as proximity, visual environment, infrastructure, etc., that affect which park people select.  
In addition, Table 3 presents the percentage of the persons that stated that the sounds heard in the park matched their expectations. In particular, participants who stated \"agree\" or \"completely agree\" on the questionnaire statement \"the sounds in this park are the sounds that one expects to hear in a park\" (Section 3.4) were considered to have the expectations of the sonic environment of the park matched.  
Whether or not the sound environment matches the expectations of the park visitor could depend on many different factors. For example, the intrinsic quality of the sound environment in the park could match expectations of park visitors in general. However, a possible deviation in the sonic environment, as compared to the usual situation that the visitor might know from earlier visits, could also lead to an expectation mismatch. Finally, the expectations regarding the sounds a visitor would like to encounter in a park could be related to the tranquility viewpoint to which that person adheres. However, no statistically significant trends were found with Pearson correlation analysis. Therefore, at the park level, a multitude of other factors influencing whether the sonic environment matches expectations obscures the trend observed for some viewpoints seen at the level of the individual (Sections 4.2 and 4.3).
# Seismic Exploration Methods for Structural Studies and for Active Fault Characterization: A Review
## 6 Conclusions  
Seismic imaging is a fundamental tool for probing the earth's subsurface and characterizing fault zones. Seismic imaging has been extensively used in the exploration for energy resources since its development, occurred in the 1920s, and over the years, it has undergone continuous improvements in data acquisition, processing, and interpretation. These improvements have led to significant advances in accuracy, resolution, and computational efficiency.  
For example, the dense, wide-aperture field technique allows the use of several imaging procedures on the same dataset, resulting in an improved estimation of seismic velocities and in a better subsurface imaging. Seismic ray tomography and full waveform inversion are powerful techniques that integrate the seismic reflection data processing. However, the accuracy of the results can be influenced by the complexity of the subsurface geology. Full-waveform inversion provides high-resolution images of the subsurface and detailed information on geological formations, even though building an accurate initial model for FWI remains a pressing concern.  
Pre-stack depth migration, which is often used in conjunction with refraction tomography and/or with full-waveform inversion, overcomes the limitations of conventional common midpoint stacking, which relies on the hyperbolic traveltime assumption, for pre-stack reflected events, allowing a proper computation of amplitudes and traveltimes associated with non-hyperbolic reflection events originated by complex subsurface settings. Seismic attributes, seismic anisotropy and AVO have become increasingly important in improving seismic imaging and in aiding the interpretation. A few examples from recent studies focus on the use of seismic exploration methods to study active faults. Through these examples, it is shown the valuable contribution of seismic exploration in this field.  
By routinely integrating seismic data with other geophysical and geological information, we can pave the way for a more accurate imaging interpretation of the subsurface and for better seismic hazard assessments in earthquake-prone regions. In this context, the use of machine learning techniques has also shown promise in improving the consistency of seismic data processing and interpretation.  
This research received no external funding.  
Not applicable.  
No new data were created in this review paper.  
The author declares no conflict of interest.
# Damage Characteristics of Thermally Deteriorated Carbonate Rocks: A Review
## 4 Dynamic Fracture Toughness and Failure Modes
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material Properties** & **Major Findings** & **Reference** \\ \hline Dynamic & They studied the thermal effect on the dynamic behavior of carbonate rock in two different scenarios. Firstly, they tested the heated rock specimens at a temperature above 300 \({}^{\circ}\)C. & \\ Compressive & Secondly, they tested the air-cooled thermally treated rock samples. They found very similar results in both cases and concluded that, at this temperature range, thermal deterioration did not affect the dynamic strength of rocks to a great extent. & [82] \\ \hline Dynamic Elastic & They noticed a linear change in the dynamic elastic modulus of porous carbonate rocks with rising temperatures. They recorded decrements in limestones by 10\%, 60\%, and 75\% at 200 \({}^{\circ}\)C, 400 \({}^{\circ}\)C, and 600 \({}^{\circ}\)C, respectively. & [83] \\ \hline Dynamic & They performed a uniaxial impact compressive load test on limestone samples to discern their dynamic-mechanical behavior. The test results showed that the dynamic compressive strength of limestone had an exponential rise with the strain rate under an increasing impact pressure. & [93] \\ \hline Dynamic & He investigated the effect of temperature on the dynamic properties of carbonate rocks. He tested limestone at undamaged, moderate damaged (heated at 450 \({}^{\circ}\)C), and high damaged (heated at 800 \({}^{\circ}\)C) conditions under an increasing strain rate. He noted that, on account of anisotropic effects, the dynamic strength of damaged samples was found greater than the strength of undamaged samples. Furthermore, he found that the elastic modulus calculated by using ultrasonic wave velocities was dropped to 92\% at 800 \({}^{\circ}\)C. & [92] \\ \hline Dynamic Elastic & They heated the limestone from its ambient temperature to 900 \({}^{\circ}\)C and found a 70\% reduction in dynamic elastic modulus at 600 \({}^{\circ}\)C. This temperature was considered as a critical temperature after which limestone started to change its behavior from brittle to ductile. & [91] \\ \hline Damping Ratio, & They studied the cyclic effect of temperature on the dynamic properties of selected carbonate rocks. In the case of dolostone, they noticed a significant appreciation in its damping ratio, specific damping capacity, and loss factor by 15\%, 13\%, and 12\%, respectively. & [90] \\ \hline Dynamic Elastic & They observed that the thermal cycling effect significantly reduced the dynamic elastic modulus & [8] \\ Modulus & modulus of dolostone by 38\% at 200 \({}^{\circ}\)C. & [8] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A brief description of major findings regarding the variations in dynamic properties of the thermally damaged carbonate rocks.
## 3 Results and Discussion
### Spectral Processing by Window Functions  
The THz spectra were processed using Boxcar, Triangular, Happ-Genzel, Blackman-Harris 3 terms, Blackman-Harris 4 terms, Norton-Beer Weak, Norton-Beer Medium and Norton-Beer Strong window functions. Figure 2 shows the processed THz absorption spectra of 2,4-D, CPPU and IAA, respectively. They were obtained based on the same time length of 33.5 ps (with resolution of 0.008 ps) in the time-domain. To evaluate the performance of the window functions, the effect of maintaining maximum information and eliminating side lobe should be considered comprehensively. The width of the main lobe spectrum in the window function should be as narrow as possible to obtain a steeper transition band. Meanwhile, the side lobe attenuation should be as large as possible to enhance the attenuation of the stopband. If the height of the side lobe tended to zero and the energy was relatively concentrated in the main lobe, the retrieved spectrum could be similar to the true spectrum. If the side lobe was significantly higher than the noise level of the spectral signal, a pseudospectral line would be formed. Different window functions had different effects on spectrum signals, which was mainly caused by the different amounts of leakage and capabilities of frequency resolution in different window functions. It can be seen from Figure 2 that the noise of the Boxcar was large in the high-frequency region. The spectra of the Triangular had shifts of peak position and peak value compared with other seven window functions. Happ-Genzel had a good balance between signal-to-noise ratio and resolution. Blackman-Harris had high computational accuracy, small side lobe peak and small spectral interference caused by truncation. [PERSON] optimized the polynomial coefficients so that the first-order side lobes were suppressed to the maximum. The Blackman-Harris 4 term obtained the highest peak values compared with the other window functions (except for Triangular). Therefore, it was selected to process the THz spectrum when conducting FFT. Furthermore, as shown in Figure 2, the spectra in the lower frequencies (0.06-0.2 THz) were affected by the multiple reflections of the samples. At higher frequencies of 3-4 THz, high-frequency oscillation occurred in the spectra due to the interference of random noise. In these frequency bands, the signal-to-noise ratio was low. Hence, the frequency range of 0.2-3 THz was extracted as an effective band for the subsequent analysis work.  
Figure 1: THz spectra of the reference and PGRs. (**A**) Time-domain spectra; (**B**) Frequency-domain spectra.
# Contact Lenses as Drug Delivery System for Glaucoma: A Review
## 3 Contact Lens Drug Delivery in Glaucoma
### Drug-Polymer Film Embedded  
A further step in controlling drug loading and release through LC is the incorporation of a new material into the matrix that makes up CL. One of the first attempts was made by [PERSON] et al. when incorporating a PLGA (polylactic globic acid) film loaded with latanoprost, between two layers of silicone-hydrogel [60]. In-vivo studies in rabbit showed therapeutic concentrations of latanoprost for four weeks, thus increasing by 30 times the concentration in vitreous humor compared to drop treatment. The same author carried out a study in glaucomatous monkeys, obtaining sustained therapeutic values for more than eight days. In addition, the therapeutic action was higher than that obtained by the administration of drug drops (9.3 mmHg vs. 5.4 mmHg) [158].  
The use of a film, however, has the disadvantage of interfering with the optical properties of the lens and a hole in the optic zone must be made in the film in order to solve this problem [158].
# A Wireless EEG System for Neurofeedback Training
## 4 Materials and Methods
### Results
#### 4.6.2 Reading Results  
The improvements in the reading speeds were twice as high after four months of qEEG z-NF-VT training with three times fewer missed words and errors (\(p\) = 0.001; Table S2). The word reading span also improved after training (\(p\) = 0.001). Before training, the dyslexics showed slower voice response times (1497.96 \(\pm\) 30.5) during the word reading compared with post training (1266.16 \(\pm\) 27.9 ms). After training, there was a significant improvement in voice response compared with before training (\(p\) < 0.0001, \(\chi^{2}\) = 30.4; Table S2). Their performance significantly improved in the visual words and pseudoword discrimination task, reducing reading time and increasing words read correctly (\(p\) < 0.03; Table S2). The dyslexic group showed higher success rates and slower reaction times compared with both words and pseudowords after training (\(p\) < 0.03, \(\chi^{2}\) = 4.51 for words; \(p\) = 0.02, \(\chi^{2}\) = 5.29 for pseudowords). Their reaction times did not change significantly (Table S2).