esa-sceva/satcom-chunk-collection huggingface dataset:
# The Case for a More Holistic Approach to Dry Eye Disease: Is It Time to Move beyond Antibiotics?
## 3 Prebiotics
### Prebiotics and Ophthalmic Related Diseases
#### 3.3.4 Quercetin  
As a member of the flavonoid class of flavonoids, quercetin is well known for its anti-oxidant and anti-inflammatory actions. Quercetin is found in high abundance in apples, grapes, onions, artichoke, fennel, celery, beans, chickpeas, plum, turnips, peppers, strawberries, tomatoes and broccoli [117]. However, quercetin is rapidly cleared following oral consumption and it has been suggested that it has low bioavailability [118]. Given that it has recently been suggested that quercetin, as well as other flavonoids, do not have to be absorbed in order to have an effect [118], the prebiotic effects of quercetin are now being explored in addition to direct anti-oxidative and anti-inflammatory activities properties with the prebiotic-like effect on the anti-inflammatory activity of certain Bifidobacterium species reported [93]. Use of quercetin has been explored in various ocular surface diseases [118] and found to provide immunoregulation when topically applied in mouse DED models. [PERSON] and colleagues [119; 120] have reported encouraging results in both a mouse model of DED and an in vitro model using human corneal epithelial cells. Topical quercetin results in increased tear volume, restoration of smooth corneal surfaces, increased goblet cell density and reduced ROS-induced pro-inflammatory gene expression [118]. [PERSON] and colleagues report similar results with the use of quercetin in a desiccation stress DED mouse model with resulting increased tear volume, reduced corneal irregularities and increased goblet cells compared with control [121].
## 3 Experiment Setup and Results
The results show that the transmitted data have reached the effect of scrambling and masking under the encryption of the digital domain and analog domain. In addition, we also used the incorrect keys to decrypt it, and the results show that the data are difficult to recover. Figure 9: Experimental setup (AWG: arbitrary waveform generator; MZM: Mach–Zehnder modulator; EDFA: erbium-doped fiber amplifier; PM: phase modulator; VOA: variable optical attenuator; PD: photodiode; MSO: mixed signal oscilloscope). In addition, we also tested the constellation points in different scenarios, as shown in Figure 11, respectively showing the original constellation diagram (a), the encrypted data constellation diagram (b), the restored constellation diagram after 25 km optical fiber transmission (c), and the restored constellation diagram after back-to-back (BTB) (d). It is also clear from the constellation diagram that this scheme can recover the information well. In the case of the unknown key and encryption mechanism, the data are difficult to recover, and its constellation is a mess, as shown in Figure 11b. In order to verify the security of the proposed scheme, we also carried out the sensitivity test of the proposed scheme and whether the data can be recovered normally when the key is perturbed. In the concrete implementation scheme, we used the perturbed key to decrypt the transmitted data. It can be seen from Figure 12 that, when the perturbation degree of \(\mathbf{x}\), \(\mathbf{y}\) and \(\mathbf{z}\) reaches E-6, there is a large bit error in the data and the data cannot be recovered normally. The coordinate of curve \(\mathbf{x}\) at E-6 represents the BER after decrypting the information when the value 0 of the parameter \(\mathbf{x}\) in the key is changed to \(0+10^{-6}\). When the perturbation degree of the control parameter \(\mathbf{a}\) and \(\mathbf{b}\) reaches E-8, the data cannot be recovered normally. The experimental results show that the information can be recovered correctly only when the receiver obtains the correct key. For the eavesdropper, when the value difference between the cracked key and the correct key reaches E-8, the data are difficult to recover. The scheme ensures the security of data transmission. Figure 11: Constellation diagram for different scenarios. (**a**) the original constellation diagram, (**b**) the encrypted data constellation diagram, (**c**) the restored constellation diagram after 25 km optical fiber transmission, and (**d**) the restored constellation diagram after BTB. Figure 10: BER performance under different receiver power values.
## 2 LPWAN Technologies
### Sigfox  
In 2010, the French start-up Sigfox introduced the Sigfox technology, which can serve as an IoT communication network [10]. The Sigfox physical layer is also closed-source and proprietary. It utilizes Binary Phase Shift-Keying (BPSK) modulation in the unlicensed Sub-1 GHz frequency bands [10]. Sigfox exploits the Ultra Narrowband (UNB) technology with an uplink of 100 Hz bandwidth. Therefore, Sigfox signals encounter very low noise levels. Consequently, Sigfox gateways have a high receiving sensitivity, and the transceivers consume minimal power. Accordingly, a Sigfox gateway is presumed to handle up to a million connected objects with a coverage of 50 km in rural areas [2]. To achieve this low power consumption and long communication range, Sigfox networks limit the transmission throughput to 100 bps.
## 2 Materials and Methods
### 3D Fall Detection  
#### 2.4.1 Feature Extraction  
As described in the introduction, different features such as the ratio of minor axis to major axis, area, head position, shape orientation, and 3D movement have been used in many fall detection studies. Among the features, the vertical velocity is the most conspicuous feature when a fall is detected, and also after the fall occurs, the motionless time is monitored to detect an emergency situation [43]. In this study, we divide a fall event into five states and use adaptive features to distinguish falling postures, such as front falling, back falling, and side falling from confounding normal motions (squatting, sitting, and bending). We use three main features to classify the falling event into five states.  
* Silhouette features: for the ellipse model estimated by human tracking, features (orientation, positions of major and minor axes) are extracted from the silhouette of a human.
* Depth features: depth values at the two distinct end points of the major axis of the ellipse model. The depth values are generated by a supervised learning method [32; 33; 34].
* Velocity features: vertical velocity shows how the height difference changed during a certain period. The velocity is calculated by measuring the height difference (Euclidean distance) between  
Figure 5: The human tracking result based on RBPF algorithm.  
the top and bottom positions in a predefined time period (e.g., 5 frames in our study providing a clear cue of the potential fall). In addition to this, we also use the gradient of the top and the center positions to make a judgment of the no-motion phase.
# A Deconvolutional Deblurring Algorithm Based on Dual-Channel Images
## 3 The Block Layer Kernel Estimation
### The RSPI of Total Images  
Generally, when the imaging results of the two channels are considered to be weak coprime, the morphological difference of the kernel is a constant, which changes its direction and size without changing its basic topology [9]. Therefore, assuming that the input two-channel image is weakly coprime, the ideal de-noising model of the blurred image can be obtained through the de-noise operation:  
(5)  
As previously mentioned, the proposed method builds an image pyramid by down-sampling and uses the same method to estimate the blur kernel at each layer.  
As Figure 2 shows, the blur kernel estimation model Equation (4) for block layer images is divided into Equations (16) and (11), according to iterative image estimation and iterative kernel estimation, respectively. In addition, the iterative image and iterative kernel are estimated alternately using the EM method.  
The block layer kernel estimation model in the gradient domain is obtained from Equation (2) as:  
\[H_{1}^{Total}=\underset{H_{1}^{Total}\in\mathcal{H}}{\text{argmin}}\left\|h_{tr }^{Total}\right\|\text{\text@underline{ \char 100}}\left(\
abla X^{Total}\right)-\
abla Y^{Total}\right\|_{2}^{2}+ \lambda_{h}\left\|\phi(h_{tr}^{Total})\right\| \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Length of Kernel** & **Angle** & **SSIM** \\ \hline RSPI & & & 0.9818 \\ Norm\_2 & & & 0.9764 \\ RSPI & & & 0.9580 \\ Norm\_2 & & & 0.9416 \\ RSPI & & & 0.9365 \\ Norm\_2 & & & 0.9334 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of the method with RSPI.  
Figure 2: The block layer kernel estimation flow chart. According to the EM algorithm idea, iterative kernels are estimated alternatively with the iterative image, which needs to be fixed in the iterative image when estimating the kernel and vice versa.  
where \(\lambda_{h}\) is the regularization coefficient of kernel estimation, \(\mathcal{H}\) denotes the pool of allowed block layer kernel \(H_{1}^{Total}\), \(\phi(h_{tar}^{Total})\) is the regularization term function for kernel estimation as \(F(h_{iter})\), and \(h_{tar}^{Total}\) is the temp iter-kernel in the process (same as \(H^{Total}\)). Owing to its higher calculation efficiency [18], the kernel estimation model Equation (10) is solved in the sparsity gradient domain.
## 2 Methodology
### Random Forest, k-NN, SVM, SVR Technique  
Random forest (RF) is a classification or regression method that is based on the generation of multiple decision trees [45]. Currently, it is becoming an alternative to the methods used in classification problems. The idea of the RF algorithm is to build a base of random decision trees, where, unlike classical decision trees, random trees are built on the principle that a subset of analyzed features at a node is selected randomly [45]. The k-nearest neighbors (k-NN) algorithm is one of the machine learning methods [6]. This algorithm estimates the value of a point according to its neighborhood relative to the selected class, calculating the average distances between samples. The most similar samples belonging to the same class have a high probability [46; 47]. The purpose of the kNN algorithm is to search the k nearest neighbors in the learning dataset and then predict them with the selected class among the k nearest neighbors [48]. The aforementioned qualities of learning the k-NN algorithm are heavily dependent on the k parameter [49; 50]. At present, the specialized literature describes the application of the above algorithm as highly effective [49].  
SVM is a two-class linear classifier [45; 51]. SVM separates data along the decision-making border, called hypersurface [52]. In general, it outlines the border between classes and maps the input space formed by independent variables with non-linear transformation in accordance with the kernel function. If the learning vectors are separated without error by the optimal hyperplane, the expected value of the probability of error in the test is bounded by the ratio of the expected value to the value of the indicated vector and the learning vectors [51]. At present, SVM can also be applied in non-linear classification tasks [6; 53; 54]. Support vector machines are often used to classify points in Hilbert space with infinite dimensions [55; 56].  
However, three parameters determine the SVR algorithm, namely [51; 57]:  
* capacity (C), which presents a compromise between model complexity and size, to which deviations bigger than (C) are tolerated;
* epsilon (\(\epsilon\)), which controls insensitive space, and \(\epsilon\) is used to adjust training data;
* gamma (\(\gamma\)), which is the parameter of the kernel function.  
The idea of the SVR algorithm is dependent on the epsilon parameter, which can have an impact on the number of support vectors used in constructing the regression function.  
The bigger the value of epsilon, the fewer selected support vectors, which translates into less complex evaluations of the aforementioned regression and a shorter time of learning [45].
# Comprehensive Survey on VLC in E-Healthcare: Channel Coding Schemes and Modulation Techniques
## 4 Channel Coding Schemes for Error Correction in VLC in E-Healthcare Scenarios
### Advanced Coding Techniques for Dimmable VLC Systems  
Dimmable VLC systems present a unique challenge, as they must balance the dual functions of data transmission and lighting control. This is particularly important in environments such as hospitals, where lighting conditions directly affect patient care and comfort and where data transmission must be both reliable and adaptable to varying levels of light intensity. To address this, advanced coding techniques are designed to support both flicker mitigation and dimming control while maintaining high transmission efficiency.  
One such advanced approach is the curved polar code methodology, which has shown notable improvements in transmission efficiency for dimmable VLC systems. Studies have shown that the coding gains achieved using this methodology exceed those of LDPC codes and polar codes with compensation symbols, offering gains of 1.4 dB to 2.8 dB, depending on the configuration [75]. This method allows VLC systems to maintain high data integrity while adapting to varying light intensities, making it particularly suitable for healthcare applications where both efficient lighting and reliable communication are essential.  
Building on this, a new scheme involving shaped polar codes has been proposed specifically for dimmable VLC systems. The shaped polar codes scheme uses a precoder to control the dimming by adjusting the probability of 1 s in a codeword, directly correlating with the dimming value in OOK modulation systems. The simulation results for this approach reveal significant coding gains of about 2.5 dB and 2.8 dB compared to LDPC codes with compensation symbols and polar codes with compensation symbols, respectively. Furthermore, the shaped polar codes scheme maintains strong performance even at higher information rates, making it an ideal solution for applications requiring both high efficiency and adaptability to varying lighting conditions [74].  
These advanced coding techniques represent a significant evolution in dimmable VLC systems. By improving error correction capability while providing flexible dimming control, they enhance the overall performance of VLC systems in real-world environments. In healthcare settings, where dimming and flicker-free lighting are critical for patient comfort and procedural accuracy, these advanced codes offer a promising solution for ensuring reliable, high-quality data transmission.
## 4 Analyses and Discussions
### Vortex Characters of Dynamic Stall under the OFV Condition
#### 4.3.2 Dissipation Process of Separated Vortex  
Since the separated vortex has significant influence on airfoil unsteady aerodynamic force characteristics, the vortex circulation near the upper surface should be researched deeply. As a result, in order to explore the dissipation process of the separated vortex, the integration with fixed windows is accomplished in this research, and the region of integral windows is 0.1 c \(\times\) 0.08 c for LEV, and 0.08 c \(\times\) 0.08 c for TEV at different AoAs, just as shown in Figure 18. The dissipation of vortex circulation can be expressed as  
\[\Gamma=\Gamma_{0}e^{-\kappa t} \tag{9}\]  
where \(\Gamma_{0}\) denotes original vortex circulation, \(\kappa\) denotes dissipation coefficient of LEV.  
In order to analyze the influence of the integral windows on the dissipation process of the vortex, the circulation of LEV is estimated in different integral windows (i.e., 0.1 c \(\times\) 0.08 c (large window) and 0.05 c \(\times\) 0.04 c (small window)), then the variations of the LEV circulations of case 1 with \(\lambda=0.5\) are shown in Figure 19. By fitting the samples of LEV circulation, the dissipation coefficients (\(\kappa\)) of LEV for different integral windows are 0.447 for 0.1 c \(\times\) 0.08 c and 0.451 for 0.05 c \(\times\) 0.04 c, respectively. It is indicated that the dissipation rate of LEV with different integral windows is similar.  
The dissipation process of LEV circulation in Figure 20a illustrates that the circulation of LEV under the OFV condition is smaller than that under the SFV condition at the same AoA. Therefore, it is indicated that the LEV forms and sheds at smaller AoAs under the OFV condition, and this tendency accelerates with the increase in oscillating velocity. In order to explore the dissipation rate of LEV, the variations of LEV circulation with time are shown in Figure 20b. It is illustrated that the dissipation rates of different LEVs are almost the same. As a result, a new dissipation coefficient for different  
Figure 19: Comparisons of LEV dissipation between different integral windows (\(\lambda=0.5\)).  
Figure 18: Integral windows of vortex circulation with fixed windows.  
freesream velocities is calculated by fitting the three group vortexes (i.e., \(\kappa=0.426(\Gamma_{0}=0.228)\)). It is indicated that the oscillating velocity has no obvious influence on the dissipation rate of LEV. The variations of TEV circulation are shown in Figure 21. It is illustrated that the dissipation rate of TEV under the condition of OFV is slower than that under the SFV condition. This tendency could be attributed to the reason that the freestream velocity under the OFV condition is smaller than that under the SFV condition when the TEV is forming. Therefore, more energy would be supplied to the TEV from the lower surface of the airfoil since the TEV has a longer attachment time. Consequently, the dissipation rate of TEV decreased under the OFV condition.
## 4 System Demonstration  
The highly integrated wireless communication system is shown in Figure 12. The RTD was the transmitter, and bias-T was used for applying DC and PRBS signals to the RTD. Two waveguide antennas with a gain of 10 dBic were employed to transmit and receive signals. The data were demodulated by a zero bias VDI SBD detector, and then they were amplified by a low noise amplifier (LNA). The bias voltage was 1.2 V instead of 1.05 V due to the loss of the bias-T, and the amplitude of PRBS signal ranged from \(-100\) to \(100\) mV. It can be seen from Figure 7 that \(1.05+0.1\) V was in the NDR (on state), while \(1.05-0.1\) V was in the positive resistance region (off state). The transmission distance was 5 cm, and the maximum data rate was 8 Gbps. The bit error rate and eye diagram are shown in Figure 13. The BER was below \(3.8\times 10^{-3}\) up to 7 Gbps.  
Figure 11: Measured output spectrum of RTD with OOK modulation.  
Figure 10: Packaged RTD equivalent circuit including gold wire bonding.  
Finally, Table 2 lists the performance of the reported wireless communication systems that use RTD. The oscillator frequencies were higher in [9; 10; 11]. Compared with the system in [9], the proposed RTD had a higher data rate, and the proposed RTD had a higher output power than the RTD in [11]. Though the output power was higher in [12], the oscillator frequency was 24 GHz, which is difficult to use for millimeter wave and terahertz application. The RTD chip proposed in the literature [14] had a higher output power and data rate, and this was the chip that was used in our packaged RTD. However, the RTD chips mentioned in these documents are difficult to use in practical communication systems. The proposed packaged RTD biased the RTD through the SMA connector, and the output was a standard waveguide that could easily connect to a waveguide device, thus achieving system miniaturization and effectively improving system integration.  
Figure 12: High-integrated wireless communication system. (**a**) Block diagram; (**b**) Photograph.  
Figure 13: Measured bit error rate (BER) and eye diagram of RTD oscillator.
# An IoT-Based Fog Computing Model
## 2 Related Work
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**The Works** & **Work Point** & **Deficiencies** \\ \hline [PERSON] et al. [21] & They proposed a model that considers circuit, computation, offloading energy consumption to evaluate the overall energy efficiency (EE) in homogeneous fog networks. & The work only focused on the overall energy and did not consider the energy conversions across both the fog and terminal layers. \\ \hline [PERSON] et al. [22] & They proposed a latency-driven cooperative task computing in multi-user fog-radio access networks, which characterizes the trade off between communication and computing across multiple F-RAN (Fog radio access network) nodes. & They did take consider energy consumption problem in both fog layer and terminal layer. \\ \hline [PERSON] et al. [15] & In terms of the communication distance, they defined a mathematical model of a fog network and the important related parameters to clarify the computing delay and communication delay in fog architecture. & The work did not take into account the energy consumption of the whole model. \\ \hline [PERSON] et al. [23] & The authors presented a use case considering energy consumption measurements of RPL and CTP, and proposed metrics for several scenarios running both RPL and CTP. & The authors did not consider the routing protocol’s robustness and reliability. \\ \hline [PERSON] et al. [24] & The work focused on analysing different strategies to gather information from different topics. The trade-offs between the “always send” and “local buffer” methods are verified experimentally, which considering power consumption, lifetime, efficiency and reliability. \\ \hline [PERSON] et al. [25] & The authors proposed a routing protocol based on routing energy and link quality (REL). The end-to-end link quality estimation mechanism, residual energy and hop count are used to select routes to improve the reliability and energy efficiency of IoT applications. In addition, REL proposes an event-driven mechanism to provide load balancing and to avoid premature depletion of energy by nodes/networks. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related works. The IoT-based fog computing model (FCM) model proposed in this paper attempts to address the weaknesses of previous works.