esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This is believed to be due to operator-related factors and morphological features (cusp height or steepness and the cavity floor) affecting the radiant exposure [6, 34]. The recommended minimum irradiance is now 500-550 mW/cm\({}^{2}\) with a wavelength bandwidth of 400-515 nm on the tip of the light curing device [37]. In practice, irradiance level values above 400 mW/cm\({}^{2}\) are considered sufficient with an exposure time of 40-60 s. Currently, however, many dental manufacturers recommend a shorter exposure time; therefore, an LCU should produce a light irradiance of at least 500 mW/cm\({}^{2}\)[31, 38]. The exposure distance is one of the most frequently analysed variables influencing polymerisation in the literature. For a point-source spherical wave, the irradiance is inversely proportional to the square of the distance. As the distance of the light-curing tip from the material surface increases, the light intensity decreases [29, 30]. Irradiance figures described by manufacturers are usually inaccurate because their measurements are made at zero source distance, which is rarely seen in clinical practice [22]. There are recommendations in the literature regarding the optimal distance of the lamp from the surface of the composite filling. Polymerization must be effective if the tip of the light-curing device is at least 2 mm from the resin area and no further than 6 mm. Therefore, dentists in their daily clinical work should polymerise the resin composite from as short a distance as possible [39]. However, there is no data in the literature on the kinetics of the lamp's light irradiance (power curve shape) for the exposure modes proposed by manufacturers, including the increasing distance of the light tip from the composite material surface. It seems that our study can be an excellent complement to previously performed analyses and provide clinicians with clear guidelines for selecting the optimal polymerisation mode.
# The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines
## 2 IoT and Wind Energy  
The efficacy and magnitude of wind technologies are advancing at a rapid pace. The energy specialists have ambitious goals for the future integration of wind energy into the industry. The primary challenge in the advancement of wind energy lies in the inherent unpredictability of these resources. The enormous amount of data generated by devices, as well as the need for real-time data, has led to the widespread use of IoT devices [12]. Therefore, real-time operation can make the necessary arrangements in the power system to counteract fluctuations without experiencing sudden changes in power output. Furthermore, the availability of real-time data sharing is essential for effective collaboration between energy storage facilities and wind units [12]. Figure 1 illustrates IoT's contribution to the wind energy sector, using weather data as input and providing real-time, accurate information for energy prediction.  
Furthermore, the use of IoT technology in conjunction with information and communications technology (ICT) infrastructures enables wind farm operators to effectively implement precise predictive maintenance schedules, thereby mitigating the risk of incurring substantial losses. A timetable of this nature can be implemented through the utilization of ML and data mining methodologies. Timely maintenance can lower the levelized cost of energy (LCoE) index for wind assets [13]. The LCoE index quantifies the discounted value of the average cost of electricity over the whole operational lifecycle of the turbines. The indispasability of IoT in harnessing wind energy lies in the prompt collection and analysis of data pertaining to wind turbines and wind farms. Currently, the challenges of data transfer latency for offshore wind farms and the restricted capacity to transmit information to distant areas are two significant difficulties that need to be resolved. Hence, by gathering and examining crucial data in real-time, the process of making decisions, such as the prompt shutdown of a turbine to prevent further damages, can be expedited or even automated [5]. The integration of IoT technologies in the wind industry emphasizes the necessity for more holistic approaches to develop economical, secure, and reliable frameworks for the planning, operation, installation, and maintenance of wind farms and turbines.  
Typically, wind farms are placed in distant areas, resulting in control centers being situated many hours away from the wind farms. Utilizing an IoT network, remote data transmission connectivity can enable control centers to effectively monitor the condition of a wind turbine and exert control over its operation [5]. Wind turbines, being located in remote areas, necessitate the use of wireless networks like cellular or satellite networks for IoT network access. Figure 2 illustrates a combined system consisting of wind turbines and a wireless IoT network.  
Figure 1: IoT in wind energy sector.  
Figure 2: Utilization of an IoT network to transfer data from turbines to the control center.
# Retinal Vessels Segmentation Techniques and Algorithms: A Survey
## 4 Retinal Vessels Segmentation Techniques
### Model-Based Techniques
#### 4.5.2 Geometric Deformable Models  
Fast Marching methods and level sets methods are considered numerical techniques devised to track the propagating interfaces, the starting point for geometric deformable methods comes from the evolution analysis of the curves and surfaces, considered _interfaces_, and was first proposed by the mathematician, [PERSON] [111, 112]. Afterwards, [PERSON] et al. [110] suggested representing the curve depending on Euclidian distance rather than parameters-dependency by representing a curve as a level set, which means the contour is represented as zero-level set of an auxiliary distance function \(\varnothing\). Level sets theory paved the way to represent contours in flexible manner, where it can join or break apart without the need of reparameterization.  
[PERSON] et al. [75] used a novel level set technique does not need level set function initialization, by using local region area descriptor. The proposed technique involves two major steps: (1) a contour C is found, and then used to divide the entire retinal image into several parts based on whether the pixel position is inside the contoured area or not; and (2) a clustering algorithm is applied on the sub-regions that result from the first step, which, in turn, yield local cluster value, namely, a new region information used to redefine an energy functional in the first step until the algorithm converges. The second step represents key contribution of this paper, since it eliminates the effect of inhomogeneity of retinal image pixels' intensities. Moreover, it gives more information about the local intensity information at level of image pixel, which eliminates the need for level set function re-initialization, considered a major drawback of level sets-based techniques. The proposed technique was tested on DRIVE dataset and an accuracy of 0.9360, sensitivity of 0.7078 and specificity of 0.9699 are reported.  
A novel modification to level set based retinal vessels segmentation was presented by [PERSON] et al. [73] in terms of initialization and edge detection phases, which are needed as a pre-requirement for level set based techniques. In the initialization phase, the seed points were determined by sampling centerlines of vessels based on ridge identification, and then accurate boundaries of the retinal vessel tree are determined through phase map built based on ridge detection technique. The proposed algorithm was tested on both DRIVE dataset and a dataset created by paper's authors. The algorithm achieved values of 0.9412, 0.9743, and 0.7181 for accuracy, specificity and sensitivity, respectively, for DRIVE, and 0.9453, 0.9640 and 0.6130, respectively, on the proposed dataset. As a conclusion, both deformable models, either parametric or geometric, share a common problem: both require, as essential step, a set of seed points that are determined either manually or automatically.
# GPR55 and GPR119 Receptors Contribute to the Processing of Neuropathic Pain in Rats
## 4 Materials and Methods
### Drugs
#### 4.2.1 Induction of Spinal Nerve Ligation and Measurement of Tactile Allodynia  
Rats were prepared according to the method previously described [45]. In brief, animals were anesthetized by an intraperitoneal injection of ketamine (50 mg/kg)/xylazine (20 mg/kg) combination. After surgical preparation, the dorsal vertebral column was exposed and the left L5-L6 spinal nerves were isolated and firmly tied with a 6-0 non-absorbable silk suture proximal to the dorsal root ganglion. For sham rats, the surgical procedure was similar in every detail, but the L5-L6 spinal nerves were not ligated after exposure.  
Fourteen days after surgical procedure, the rats were placed in elevated, clear-acrylic, wire mesh-bottomed chambers. After a 30 min period of acclimatization, tactile allodynia was tested using a series of standardized von Frey fibres with logarithmically incremental stiffness (Stoelting, Wood Dale, IL, USA) according to the paradigm previously described [46]. In each evaluation, a 2 g von Frey fibre was applied perpendicular to the plantar surface of the left hind paw with enough force to produce a buckle during 5 s. A positive response was registered when the rat abruptly withdrew its paw and indicated the use of the previous fibre in the series, whereas a negative response was recorded in the absence of paw withdrawal response after 5 s and prompted testing of the next fibre in the series. This procedure continued until four additional measurements were obtained after the first response change in the paw withdrawal behaviour or until five consecutive negative (15 g) or four consecutive positive (0.25 g) responses were obtained. The resulting pattern allowed the calculation of the 50% paw withdrawal threshold by the following equation.  
\[50\%\ \text{g threshold}=\frac{10^{(\text{Xf}+\ \kappa\delta)}}{10,000}\]  
where Xf = the logarithmic value of the last von Frey fibre utilized, \(\kappa\) = the value obtained in table for the response pattern, and \(\partial\) = the mean logarithmic difference between stimuli. All rats were evaluated for 50% paw withdrawal threshold before drug administration and 0.5, 1, 2, 3, 4, 5, 6, 7, and 8 h after drug administration. Tactile allodynia was present when the 50% paw withdrawal threshold was below 4 g.
## 2 Materials and Methods  
### Sea Surface Temperature, Ozone and Meteorological Data  
The sea surface temperature (SST), ozone and meteorological data used in this study were derived from ERA5, the fifth-generation reanalysis of global climate and weather from the ECMWF (European Centre for Medium-Range Weather Forecasts) ([[https://cds.climate.copernicus.eu/cdsapp#](https://cds.climate.copernicus.eu/cdsapp#)]([https://cds.climate.copernicus.eu/cdsapp#](https://cds.climate.copernicus.eu/cdsapp#))!/dataset/reanalysis-era5-pressure-levels-monthly-means?tab=form, accessed on 1 November 2023). These data are used to identify El Nino events and analyze changes in the ozone valley and other atmospheric systems [48]. The horizontal grid resolution of the data is 0.25\({}^{\circ}\) by 0.25\({}^{\circ}\), and the vertical height is divided into 37 layers, covering from the ground (1000 hPa) to the stratopause (1 hPa), with a time resolution of months. The period selected in this study includes summer monthly data from 1979 to 2022, and the variables used include SST, the ozone mass mixing ratio, horizontal wind speed, vertical velocity, geopotential, etc.
## 3 Feature Extraction
### Audio Features  
For the audio features, likelihoods given by a global model trained for the audio data are used. The likelihood features are obtained as follows. First, during an offline training phase, 13 mel-frequency cepstral coefficients (MFCCs) along with their first- and second-order derivatives are calculated from audio training data [15]. The MFCCs are calculated from audio data recorded at 16 kHz sampling rate using a window of 30 ms and a frame-skip of 40 ms. After this, a single Gaussian mixture model (GMM) with 32 component densities is trained. Once the above offline training phase is complete, the likelihood features can be calculated for input audio data. This is done by, first, calculating MFCCs for the input audio data and then calculating the mixture likelihoods of the GMM for the MFCC feature vectors. The mixture likelihoods for the whole audio clip are then output as the likelihood features. Thus, for each input audio clip, we obtain a vector of 32 likelihood values. By using likelihood features we can represent a whole audio clip with a single feature vector instead of a sequence of MFCC vectors. An alternative way to represent an audio clip with a single feature vector would be to, for example, use the mean of the calculated MFCCs. Also, a covariance matrix could be calculated. However, information is lost when using only the mean of the MFCCs and for shorter audio clips, there might not be enough data to reliably calculate the variances or a covariance matrix.
# P4-KBR: A Key-Based Routing System for P4-Programmable Networks
## 3 P4 Ecosystem
Each state extracts header fields (e.g., Ethernet, IPv4, TCP/UDP), which are declared in the P4 program and, depending on the field value, the state will change to another state. Once the packet is parsed, it passes through the P4 programmable _Ingress pipeline_ where match-action tables are used. A table compares the matching fields in flow entries with packet headers or metadata to determine the appropriate actions. These actions include the possibility of modifying the header fields of the current packet (stateless or transient information) and perform stateful operations that store state persistently, i.e., for more than a single packet. P4 allows the programmer to maintain stateful or persistent information in the form of counters (for counter events associated to entries in tables), meters (for data rate measurement) and registers (counters that can be operated from actions in a general way). After the _Ingress pipeline_, the packet can be transferred to a non-P4 programmable block that represents the non-programmable traffic manager including fixed functions provided by the target vendor. Next, the packets pass through the _Egress pipeline_, which could define additional match-action tables for further modifications if necessary. Finally, the packets pass through the _Deparser_ for serialization and then they exit the switch.
# Triangular-Shaped 5-DOF Piezoelectric Robot for Optical Lens Positioning
## 1 Introduction
However, the robot cannot rotate and move the payload in the plane independently. [PERSON] et al. proposed a two-DOF piezoelectric rotary motor [19]. The design of the motor is based on a flat cross-shaped actuator and a cylinder placed at the center of the actuator. The spherical rotor is placed on top of the cylinder using an additional positioning frame. The operation principle is based on an inertial stick-slip principle. It was shown that the motor can achieve a rotation speed around \(X\) and \(Y\) axes up to 268.48 mrad/s and resolution of 6.34 \(\upmu\)rad. However, the actuator provides only 2-DOF angular motion of payload, while the planar motion of the payload was not foreseen. [PERSON] et al. introduced a piezoelectric 3-DOF rotary motor [20]. The design of the motor is based on four piezoelectric beams joined in a cross-shaped structure. The spherical rotor is placed in the center of the cross structure. The operation principle of the motor is based on the excitation of longitudinal and bending modes of the beams using harmonic electric signals with a phase difference. Numerical and experimental investigations showed that the rotor can be rotated around the \(X\) and \(Y\) axes with a speed of 363 RPM, while the rotation speed around the \(Z\)-axis was 171 RPM. Literature review showed that there are no piezoelectric robots developed using a single actuator that can provide planar and rotational motion. On the other hand, few dual-function piezoelectric actuators and motors are proposed [21; 22]. However, in most cases, these devices can provide angular and planar motion of the cylinder-shaped slider [23; 24]. This paper represents a novel 5-DOF piezoelectric robot that can provide unlimited 2-DOF planar motion and 3-DOF angular motion of the payload independently. The main advantages of the proposed robot are unlimited planar motion, simple design based on a single piezoelectric transducer, and angular and planar motions of the robot are controlled using a single excitation signal. The proposed piezoelectric robot can be used for optical lens positioning in the holder.
# Geographic Routing Decision Method for Flying Ad Hoc Networks Based on Mobile Prediction
## 4 Evaluation  
### Experimental Platform  
The simulation environment of this paper is Ubuntu 18.04 and Network Simulator 3 (NS-3.29) [37], and it is compared with other routing protocols. The simulation scenarios are distributed within a 1000 m \(\times\) 1000 m \(\times\) 1000 m area. The speed of the nodes varies between 10-100 m/s, and the number of nodes is set to 20-100. Each node has the same maximum transmission power of 15 dBm, with a receiving sensitivity of \(-80\) dBm [38]. The corresponding communication radius at maximum transmission power is approximately 250 m. Each node communicates using IEEE 802.11p [39], and all nodes have the same initial energy. Table 1 shows the experimental parameter settings for the simulation.
## 4 Performance Evaluation
### Performance Metric  
* Packet Reception Rate (PRR): ratio of the total number of received packets \(R\) over the total number of packets sent from sources, \[PRR=\frac{R}{T_{source}}\times 100\] (19)
* Probability of collision (_P_col_): The likelihood or chance that two or more vehicles or devices are attempting to transmit data within the same communication channel simultaneously. \[\begin{split} P\_col=P(UE1_{T})P(UE2_{T}|UE1_{T})+\\ P(UE2_{T})P(UE1_{T}|UE2_{T})\end{split}\] (20)
## 3 Results and Discussion
### Viability of Lacticaseibacillus rhamnosus GG Encapsulated and Added to Food Matrices
[PERSON] and [PERSON] [44] present in their study viability of _Lacticaseibacillus rhamnus_ encapsulated and added to orange juice stored at 4 \({}^{\circ}\)C of approximately 80% at the end of the third week, while the viability of the free cell was 75%. These results show similarities with those obtained in the present study of the AM systems and free cells, being 86.16% and 80.5%, respectively, but not with those obtained by the UT System, which had 100% viability at the end of the evaluation. [PERSON] and [PERSON] [26] analyzed the viability of _Lacticaseibacillus acidophilus_ ATCC 4356 encapsulated and added to yogurt during storage at 4 \({}^{\circ}\)C, obtaining viability data for their encapsulated system of 86.68% and 90% for free cells. The viability of the free cells had values close to the results obtained in this study; however, the encapsulated systems differ in the values presented by AM and UT systems, being higher than those obtained in our work. They highlighted that high probiotic survival could be attributed to the size of the capsules, the strain used, or to post-acidification processes during storage, related to the diffusion capacity of components within and outside the capsule caused by the porosity of the alginate, which suggests that the passage of nutrients can be used by the encapsulated bacteria favored their viability during the storage process [45]. Figures 5-8 show the micrographs of the capsules during their storage in juice and yogurt obtained by SEM. Figure 5: Encapsulated _Lacticaseibacillus rhamnus_ GG: AM treatment in apple juice: 7 days (column (**a**)), 15 days (column (**b**)), and 22 days (column (**c**)). During their evaluation, the capsules did not show macroscopic differences. The alginate capsules exhibited resistance and stability until the second week of the study. When exposed to SEM, the electron beam likely managed to pierce the capsules, thus releasing the components after a prolonged focus time. However, the encapsulation and the wall material can offer protection to the bacterial cells against conditions that could affect their viability, such as the stress that the gastrointestinal tract may cause when consuming a food inoculated with probiotics or due to the shear forces caused by the homogenization processes [19, 46, 47]. Figure 6: Encapsulated _Lacticaseibacillus rhamnosus_ GG: AM treatment in yogurt: 15 days (column (a)), 22 days (column (b)), and 30 days (column (c)). Figure 7: Encapsulated _Lacticaseibacillus rhamnosus_ GG: UT treatment in apple juice: 7 days (column (a)), 15 days (column (b)), and 22 days (column (c)).
## 3 Results and Discussions
### Power Generation Characteristics  
The power generated in patterns B and C varies significantly depending on the reference time used during the generated power calculation. Hence, it is not easy to evaluate the generated power accurately. Therefore, the characteristics of the generated power are discussed for pattern A only.  
Figures 14 and 15 depict the relationships between power generation efficiency \(k_{e}\) and \(D/L\), and \(k_{e}\) and \(H_{l}/L\) depending on \(T/T_{f}\) and \(h/h_{f}\), respectively. \(T/T_{f}\) denotes the ratio of the wave period \(T\) and the natural period of the flap \(T_{f}\), and \(h/h_{f}\) is the ratio of the initial water depth \(h\) and the flap height \(h_{f}\).  
Figure 12: Screenshots for pattern D for the case of \(D=1.09\) m, \(h=0.475\) m, \(T=1.4\) s, and \(H_{l}=0.12\) m at (**a**) \(t/T=1/4\), (**b**) \(t/T=2/4\), (**c**) \(t/T=3/4\), and (**d**) \(t/T=4/4\). \(D/L\) was 0.4306. The blue solid line denotes the water surface.  
Figure 13: Relationship between \(D/L\) and \(k_{r}\) in patterns A, D, and E.  
As depicted in Figure 14a, the maximum \(k_{e}\) (=0.77) was obtained at \(D/L\approx 0.32\), which was the case for \(T/T_{f}\) = 0.9 and 1.1 close to the natural period of the flap. However, confirming the results for \(T/T_{f}\) = 0.9 and 1.1 at \(D/L\approx 0.25\) and 0.43, \(k_{e}\) exhibited a low value (\(k_{e}<0.20\)) in comparison with other results. Therefore, it is considered that \(k_{e}\) is affected more by the installation position than by the natural period of the flap. Focusing on the relationship between \(D/L\) and \(k_{e}\), it is theoretically well known that the maximum \(k_{e}\) is obtained at the position of the node of the standing wave formed by the reflecting wall (\(D/L\) = 0.25), when the flap is extremely thin and the behavior of the flap does not affect the standing waves [39]. In contrast, the flap adopted in this experiment was the shape of an inverted triangular prism with a wide upper part, thereby overtopping or overflowing on the top of the flap occurs when incident waves propagate over the flap. This phenomenon leads to the loss of incident wave energy, and a slight phase delay is likely to occur in the rotation of the flap when the wave force acting on the flap is not enough to rotate owing to the energy loss; i.e., the flap rotates later than the propagation of the waves. Hence, a phase discrepancy can occur between the motion of the flap and the standing waves. Furthermore, not only the shape of the flap but also the damping of the PTO system contributes to the phase discrepancy. As a result, the position where the motion of the flap was maximized by synchronizing the direction of rotation of the flap and the motion direction of the water particles in the standing waves was considered to be \(D/L\approx 0.32\) in this study. The phase difference between the rotation of the flap and the standing wave is dependent on the ratio of the initial water depth and the flap height \(h/h_{f}\), as depicted in Figure 14b. For example, the maximum \(k_{e}\) appears at the position of \(D/L\approx 0.32\), at the same (\(h/h_{f}\) = 1