esa-sceva/satcom-chunk-collection huggingface dataset:
# In Vivo Efficacy of Contact Lens Drug-Delivery Systems in Glaucoma Management. A Systematic Review
## 3 Results
### Nanoparticle-Loaded CL
lowered by 4.4 \(\pm\) 0.50 mmHg after 2 h and returned after 12 h to baseline values. Conversely, after an initial burst release, the CL lowered the IOP by 6.3 \(\pm\) 1.92 mmHg 3 h later and kept below baseline values for 192 h (8 days) [46]. Other interesting polymeric nanoparticles were cyclodext that increased hydrophobic drugs' affinity to the CL polymer matrix, prolonging the drug release time [47]. In particular, the interior part of cyclodext was hydrophobic and complexed with hydrophobic drugs. Conversely, the exterior part was hydrophilic and easily penetrated the CL polymer matrix. [PERSON] et al. loaded a CL with a pueranin-cyclodextrinrin complex [47]. Puerin is a hydrophobic substance with a beta-blocker effect, used in Chinese medicine to treat ocular hypertension and glaucoma. The CL and eyedrops at 1% released a mean pueranin dose of 232.64 ug and the 500 ug, respectively. The CL and eyedrops showed the same bioavailability in the tear film. However, the mean residency time of pueranin in the CL was significantly longer (6.04 times more) in rabbits tear fluid than that of 1% of pueranin eyedrops. Data about IOP-lowering or drug concentration in the rabbit aqueous were not detected [47]. Furthermore, cyclodext allowed the incorporation of acetazolamide in the CL, opening the possibility to use this drug topically [64]. However, no in vivo study was available. Moreover, gold nanoparticles (GNP) have been investigated as CL drug delivery [48]. GNP was added to the timolol soaking solution or was merged into the CL during manufacturing, without altering the CL's physical properties. Glaucomatous rabbits were treated with timolol eyedrops, or a CL soaked with GNP or CL with GNP merged. All three treatments showed an average IOP-lowering of 2 mmHg. In the eyedrops group, the IOP returned to baseline values after 12 h. Conversely, in both CL groups the IOP returned to baseline values after 72 h. No adverse effects were reported [48]. Also, micelles have been loaded in a CL, as reported by [PERSON] et al. [49]. These authors compared the CL with micelles containing timolol or latanoprost to timolol or latanoprost eyedrops in glaucomatous rabbits. The initial burst was reduced with the CL compared to the eyedrops. The CL loaded with micelles extended drug residence time in the tear fluid for more than 120 h with timolol and 96 h with latanoprost. In particular, the mean residence time and bioavailability for both timolol and latanoprost delivered by CL were enhanced, 79.6-fold and 122.2-fold and 2.2-fold and 7.3-fold, respectively, compared to eyedrops. IOP returned to baseline after 168 h of CL application and 72 h with eyedrops (data available only with timolol because prostaglandin did not affect IOP in rabbits). However, the CL became rough with extended wearing, resulting in discomfort, red eyes, or giant papillary conjunctivitis [49].
# Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles
## 3 Improved LeNet-5 CNN Model
### Deficiency Analysis of Classical LeNet-5 Network Model  
Professor [PERSON] proposed the LeNet-5 network model in 1998, which was mainly used for digital recognition. The LeNet-5 network model consists of seven layers, including two convolutional layers, two pooling layers, two fully-connected layers and one output layer. The input image size is \(32\times 32\), and the output is a 10-dimensional classification vector, which can identify numbers from 0 to 9 [35; 36].  
The classic LeNet-5 network model has good classification and recognition effects for a single target. However, in the traffic signs recognition training, it is difficult to ensure a high enough accurate recognition rate, the training network cannot converge, and the recognition efficiency of the network decreases dramatically.  
Analysis and summary of the root causes of these problems show the following:  
1. The interference background in the traffic sign training image is much more complicated than that in a single digital image. The original convolutional kernel does not perform well in feature extraction. Consequently, the extracted features cannot be properly used for the accurate classification of the subsequent classifier.
2. Different kinds of traffic sign training images exist, and the number of datasets is large. Gradient dispersion easily occurs during network training, and the generalization ability is significantly markedly reduced.
3. The size of the ROI in the input traffic sign training image varies, and the effective features obtained by the current network model are insufficient to meet the target requirements of accurate traffic sign recognition.  
The learning rate and the iterations number of the training network are not adjusted accordingly, and the relevant parts are rationally optimized, thereby resulting to the emergence of the over-fitting phenomenon during training.
# A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks
## 5 Support Vector Machines
#### Relevant Papers  
[PERSON] et al. [35] propose a reasoning module based on Support Vector Machines (SVMs) that have been trained with previous data in offline simulations to predict the quality of transmission (QoT) of a particular lightpath in wavelength-routed optical networks (WRONs). An SVM is an algorithm used to classify unlabeled data into labeled binary categories. Pre-existing data sets are provided to the SVM to learn a function that can accurately classify lightpaths into low- or high-quality categories based on the physical and performance attributes of the lightpath cumulated in a Q-factor variable. In doing so, the authors can quickly predict high-quality or low-quality transmission in a WRON before it is deployed. They found that the SVM approach requires the least computing time compared with alternative Q-Tool and Case-Based Reasoning (CBR) methods while maintaining the highest percentage of successful inaccurate classifications.  
[PERSON] et al. [36] use SVM in the edge networking paradigm, aiming to predict and optimize the network load conventionally caused by human-based traffic, which is growing each year rapidly. In their work, they propose a method to predict the LTE network edge traffic. The analysis is based on the public cellular traffic data set, and it presents a comparison of the quality metrics. In conclusion, the proposed Support Vector Machines method allows much faster training than the Bagging and Random Forest and operates well with a mixture of numerical and categorical features.  
In [37], the authors use SVM to forecast traffic in Wireless LANs (WLANs). The main focus is a study of the issues of one-step-ahead prediction and multi-step-ahead prediction without any assumption on the statistical property of actual WLAN traffic. They evaluate the performance of different prediction models using four real WLAN traffic traces. The simulation results show that, among these methods, SVM outperforms other prediction models in WLAN traffic forecasting for both one-step-ahead and multi-step-ahead prediction.  
Figure 5: Example scatterplot showing SVM classification of data points into two classes.  
Finally, [PERSON] et al. [38] study a new hybrid network traffic prediction method (EPSVM) primarily based on Empirical Mode Decomposition (EMD), Particle Swarm Optimization (PSO), and Support Vector Machines (SVM). The EPSVM first utilizes EMD to eliminate the impact of noise signals. Then, SVM is applied to model training and fitting, and PSO optimizes the SVM parameters. The effectiveness of the presented method is examined by evaluating it with different methods, including basic SVM (BSVM), Empirical Mode Decomposition processed by SVM (ESVM), and SVM optimized by Particle Swarm Optimization (PSVM). The authors' case studies demonstrated that the hybrid SVM performed better than the other three network traffic prediction models.
# A UAV Coverage Path Planning Method Based on a Diameter-Height Model for Mountainous Terrain
## 2 Problem Statement
### The Definition of the UAV Energy Model  
The UAV energy model is a very complex research field, and the energy model calculation is closely related to various factors [26; 27; 28; 29], such as flight speed, load changes, flight altitude, etc. Because these parameters are closely related to the endurance flight distance of drones, there are numerous studies focusing on the energy savings of the UAV. Since the major focus of this research work is the coverage path planning problems of mountainous terrains, in order to avoid the research problem becoming more complex, the authors proposed a simple energy model of UAVs from a computer graphics perspective, rather than the conventional approaches of drone kinematics that are closely related to parameters such as drone flight speed, flight acceleration, flight jerk, etc.  
Intuitively, the energy calculation of UAVs is positively correlated with the flight distance (i.e., flight path length). Thus, in order to simplify the energy model calculation of UAVs, the global length of coverage planning paths generated for mountainous terrain can be used as the main energy model indicator. For the paths generated in the 3D map, the traces of UAVs are described as a series of ordered coordinate waypoints. Assuming that the generated coverage path is represented by \(Q\) and that the number of waypoints is \(q\), then \(Q\) can be expressed as (1):  
\[\mathrm{Q}=\left\{(x_{1},y_{1},z_{1}),(x_{2},y_{2},z_{2}),\ldots,\left(x_{q},y_ {q^{\prime}}z_{q}\right)\right\} \tag{1}\]  
where \((x_{i},y_{i},z_{i}),i\in[1,q]\) denotes the waypoints. Thus, the UAV energy consumption model \(E\), which is based on the trajectory length, can be defined as (2):  
\[E=\sum_{i=1}^{q-1}\sqrt{\left(x_{i}-x_{i+1}\right)^{2}+\left(y_{i}-y_{i+1} \right)^{2}+\left(z_{i}-z_{i+1}\right)^{2}} \tag{2}\]
# Apparatus and Method of Defect Detection for Resin Films
## 3 Experiments and Results
### Microscope Method  
#### 3.1.1 Experiment Setup  
For the adaptive threshold parameters, the block size was set to 205 and the constant \(c\) was set as 30 during the image processing stage by observing the defects and confirming if all the defects were captured. Contour extraction only extracts objects with areas larger than 400 pixels because under the microscope, a 25-\(\upmu\)m defect is approximately that size.  
The dataset of plastic resin film images was provided from a production line of a plastic component using a microscope. All the defect components were previously inspected using an expert examiner. During the image processing stage, we cropped 237 defects and 2457 bubbles because such irregularities would undermine the training of the neural network. Therefore, some modifications were incorporated, including rotation and flipping. In this case, the aforementioned operation increased the number of defects to 2796.  
For the classification task, 100 images were used for testing and the remaining images were used for training. The LeNet-5-adjusted network was trained for 100 epochs and the batch size was set to 100.
## 3 Results
### Evaluation of RT Values  
The RT always showed lower values than those of the IT for both the implants tested, with higher differences in 20 PCF density blocks with and without the cortical sheet. Moreover, these values were proportional to the increase in the polyurethane block density and to the decrease in the under-preparation size. In particular, the values reached 39.09 \(\pm\) 0.72 Ncm with 4 \(\times\) 13 mm implants and 2.2 mm burs and 32.38 \(\pm\) 0.68 Ncm with 3.3 \(\times\) 13 mm implants and 2.2 mm burs in the 20 PCF density block with the cortical sheet, without statistically significant differences. Conversely, in the 10 PCF density block without the cortical sheet, the lowest RT values were registered for 4 \(\times\) 13 mm TAC implants (6.81 \(\pm\) 0.82 Ncm) and for 3.3 \(\times\) 13 mm TAC implants (7.39 \(\pm\) 0.34 Ncm) in the sites prepared with the 3.2 mm bur and the 2.8 mm bur, respectively, always not reporting significant differences. However, in the same bone density, 4 \(\times\) 13 mm implants showed the highest RT values in 2.8 mm and 2.2 mm prepared sites (9.3 \(\pm\) 0.43 and 10.41 \(\pm\) 0.43 Ncm, respectively).  
Moreover, RT values reported for the two implants with all the under-prepared sizes in 20 PCF density blocks were significantly higher than the corresponding ones registered in the lowest-density block, with the exception of 4 \(\times\) 13 mm TAC implants and the 3.2 mm bur in the 20 PCF density block without the cortical sheet.  
Specifically, the increase in RT values, in concomitance with the increase in the under-preparation, showed to be about 4-14 Ncm lower than the corresponding IT values in 20 PCF density blocks with and without the cortical sheet, while about 2-6 Ncm lower for each increase in the under-preparation in 10 PCF density blocks with and without the cortical sheet (Figure 9).  
Figure 8: IT measurements derived from the two types of implants inserted in all artificial bone densities. Data are expressed as means \(\pm\) standard deviation (SD), following the one-way analysis of variance (ANOVA) test. All the values are significant, with \(p<0.05\), except for those indicated as not significant (ns).
# Advances in the Noninvasive Diagnosis of Dry Eye Disease
## 9 Thermography  
The stability of the tear film is strictly linked to the integrity of the lipid layer that controls evaporation. There is currently no commercially available instrument dedicated to the detection of tear film evaporation, but thermography permits an indirect evaluation of the evaporation rate by measuring the temperature of the ocular surface in a noninvasive manner, using a thermographic camera operating within the infrared range [83; 84; 85]. In 1995, [PERSON] et al., measured ocular surface temperature in patients with DED and reported that the surface temperature was significantly higher than in normal eyes, while the temperature at the center of the cornea of DED eyes became lower than that in normal eyes after sustained eye opening [86]. In the last decade, advances in technology have allowed the measurement of the ocular surface temperature with increasing accuracy, resolution, and speed [87]. [PERSON] et al., showed a strong spatial and temporal correlation between BUT and tear film evaporation [88].  
[PERSON] et al. reported that DED was associated with a greater decrease in the ocular surface temperature at 10 s after eye opening and suggested that measurements obtained over 10 s have sensitivity and specificity values of 0.83 and 0.80, respectively, for screening DED eyes [87].
# Dark-Center Based Insulator Detection Method in Foggy Environment
## 3 Dark-Center Fog Insulator Detection Algorithm
### Defogging Algorithm
#### 3.1.1 Optimization of Atmospheric Light Value Estimation  
Due to the dark channel, a priori algorithm is not ideal for the sky area, and there are obvious transition areas and overexposure phenomena in the sky and non-sky parts. To solve this problem, this paper adjusts the atmospheric light threshold and designs a new parameter \(K\) to modify transmittance calculation.  
The first step is to set the atmospheric light threshold. When calculating the atmospheric light, \(A\) is the value averaged by taking the top 0.1% of all luminance points, but the value is not proper due to the excessive value of atmospheric light in the sky part. Therefore, we consider setting the maximum global atmospheric light value as the threshold value for all pixel points, which is equal to the threshold value if the value of the pixel point is greater than this threshold value. In this paper, the threshold value is set to 220, which means the pixel points with gray values in the interval [220; 255] are taken in the dark channel map.  
The next step is to design the controllable parameter \(K\) for recalculating the transmittance. In normal cases, the atmospheric light value of the sky part and the \(A\) are relatively close, while the dark channel a priori algorithm is only valid for the area far from the sky. Therefore, we subtract \(A\) from the eligible pixel points, and if the result is greater than the threshold, the region is considered to be a sky region, and the transmittance needs to be recalculated; if the result is less than the threshold, the region is not considered to be a sky region, and the transmittance does not need to be recalculated. The threshold value varies by different \(K\), i.e., the transmittance at each pixel point is adjusted by the \(K\). The modified transmittance calculation formula is expressed as follows:  
\[J(x)=\frac{I(x)-A}{\min\Bigl{(}\max\Bigl{(}\frac{K}{|I(x)-A|},1\Bigr{)}\cdot \max(t(x),t_{0}),1\Bigr{)}}+A \tag{4}\]  
where the \(K\) is set to 0.22, and the \(t_{0}\) is set to 0.1 to achieve the optimal effect.
# Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution System
## 4 System Model: Proposed Framework
### Scenario 2 (Train Mlp Model at Cloud and Detect at Edge)  
The MLP model is trained in the cloud and sent down to the edge layer for detection. In this scenario, the edge servers have two functions:  
1. Continuously forward data to the cloud to continue the training.
2. Use the trained MLP model downloaded from the cloud for detection as shown in Figure 6.  
Figure 4: Proposed intelligent edge-cloud framework.  
Figure 5: Architecture for scenario 1.
# Investigating the Adsorption Behavior of Zwitterionic Surfactants on Middle Bakken Minerals
## 3 Results and Discussion
### Static Adsorption Study
#### 3.3.2 The Effect Salinity on Adsorption Study  
To evaluate the impact of salinity, the initial surfactant concentration and temperature were held constant at 0.03% and 20 \({}^{\circ}\)C, respectively. Brine with roughly 2 wt% total dissolved solids and Bakken formation brine with TDS \(\approx\) 29 wt% were utilized for comparison purposes, as depicted in Figure 7. The study was to investigate zwitterionic surfactants adsorption density on Bakken minerals under ambient pressure and at 20 \({}^{\circ}\)C.  
Figure 6: Creating calibration curves involves the following: (**a**) Analyzing absorption spectra of the KI-I2 system with various surfactants present. (**b**) Developing calibration curves to quantify surfactant levels.  
The adsorption capacity of CG3, CD2, and ME1 surfactants in Bakken formation brine were found to be 3.43 mg/g, 2.12 mg/g, and 1.89 mg/g, respectively. This indicates a decrease of approximately 1.06 \(\pm\) 0.02 mg/g in CG3 and 0.3 \(\pm\) 0.04 mg/g in CD2 and ME1 compared to their values in 2% TDS brine, which were 2.35 mg/g, 2.1 mg/g, and 1.89 mg/g. The carboxylate groups in surfactants CG3, CD2, and ME1 tend to accept protons in acidic solutions, causing the original zwitterionic structure to transform into a fully protonated form, emphasizing the dominance of the cationic segment. It is worth noting that both Bakken formation brine and 2% TDS brine display slight acidity, leading some carboxylate groups to undergo deprotonation, resulting in a scenario where the primary groups (cationic portions) outweigh the acidic groups (anionic portions) to some extent. As a result, compared to positively charged adsorbents, CG3, CD2, and ME1 exhibit more significant adsorption on negatively charged minerals. Although adsorption density decreased at elevated temperatures under the high-salinity condition, the adsorption equilibrium capacity of surfactants CG3, CD2, and ME1 remained above the proposed threshold adsorption density (1.0 mg/g). This increased adsorption can be attributed to the zwitterionic nature of the surfactants and the high quartz and clay content in the adsorbents, as discussed in subsequent sections.
## 3 Design of the Proposed Controller for the Power System
### FOPID Controller  
Based on the generalized non-integer order fundamental operator, Fractional-order Systems are established (Equation (9)) [27]:  
\[aD_{t}^{\psi}=\begin{cases}\frac{d^{\psi}}{dt^{\psi}}&\psi>0\\ 1&\psi=0\\ \int\limits_{a}^{t}(dt)^{-\psi}&\psi<0\end{cases} \tag{9}\]  
The limits of the operation, denoted by a and t, are considered in fractional-order systems. It is typically assumed that \(\psi\in R\), although it can also be a complex number. Various definitions of the integral-differential operator have been formulated. The Grunwald-Letnikov definition is commonly employed in the application of FOPID due to its suitability for numerically evaluating fractional order derivatives (Equation (10)) [27]:  
\[aD_{t}^{\psi}f(t)=\lim\limits_{h\to 0}\frac{1}{h^{\psi}}\sum\limits_{j=0}^{k} {(-1)^{j}}\bigg{(}\begin{array}{c}\psi\\ j\end{array}\bigg{)}f(t-jh) \tag{10}\]  
In Equation (10), a is set to 0, \(t\) is equal to \(kh\), where \(k\) represents the number of computation steps, and \(h\) denotes the step size. Considering zero initial conditions, the Laplace transform of the \(\psi\)-order derivative is given by Equation (11) [27]:  
\[\int\limits_{0}^{\infty}{e^{-st}}_{0}D_{t}^{\psi}f(t)dt=s^{\psi}F(s) \tag{11}\]  
In Equation (11), \(\psi\in R^{+}\), and s is the usual Laplace variable. The PI \({}^{\lambda}D^{\mu}\) controller, also known as the generalized FOPID controller [27; 28; 29], incorporates an integrator of order \(\lambda\) and a differentiator of order \(\mu\). The time domain equation for the FOPID controller can be expressed as Equation (12). In Equation (12), the terms \(D^{-\lambda}\)\(e(t)\) and \(D^{\mu}\)\(e(t)\) denote the fractional-order integral and fractional-order derivative, respectively:  
\[u_{x}(t)=K_{p}e(t)+K_{i}D^{-\lambda}e(t)+K_{d}D^{\mu}e(t) \tag{12}\]