esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Grating Fabrication Process  
UV lithography for manufacturing of two-dimensional (2D) gratings was carried out using the process chain similar to the contrast curve sample preparation, but with several application-related changes. The main overview of the process is shown in Figure 2. Thinner silicon wafers were used for grating fabrication in order to achieve a higher wavefront modulation. The substrates were 200 \(\upmu\)m silicon wafers with few nanometers of Ti and TiOx to create a conductive contact layer for the concluding electroplating. Substrate cleaning was performed to guarantee that it was free of impurities and moisture. Oxygen plasma cleaning was employed with subsequent baking at 120 \({}^{\circ}\)C and cooling to room temperature immediately before coating. Spin-coating of TI PRIME (MicroChemicals GmbH, Ulm, Germany) adhesion promoter was performed prior to photoresist application. TI PRIME forms a physically-bonded sub-monolayer of the active compound after solvent evaporation. A subsequent baking step of the primed substrate at 120 \({}^{\circ}\)C chemically activated the adhesion promoter, thus forming the desired hydrophobic surface allowing the subsequent photoresist coating with improved wetting and adhesion between the TiOx and the photoresist. Immediately after the primed substrate baking was finished, spin-coating of the negative mr-X 10 photoresist was performed to achieve layers of 10 \(\upmu\)m, 20 \(\upmu\)m, 30 \(\upmu\)m, and 40 \(\upmu\)m thicknesses. A soft bake to remove the solvent content from the sample was carried out at 95 \({}^{\circ}\)C on hot plates for time periods from 10 min to 20 min depending on the photoresist layer thickness.  
Figure 1: Sensitivity curves obtained for mr-X 10 photoresist layers of thickness: 10 \(\upmu\)m (black), 15 \(\upmu\)m (red), 20 \(\upmu\)m (green), and the respective colored pattern overview for 10 \(\upmu\)m (gray), 15 \(\upmu\)m (red), and 20 \(\upmu\)m (green). The curves were plotted using the approach reported in [18]. In the figure, contrast values for different curves are defined according to Equation (2). The dose at the maximum slope serves as a quantitative value for a comparison of the curves’ shift.
###### Abstract  
In wireless sensor networks (WSN), most sensor nodes are powered by batteries with limited power, meaning the quality of the network may deteriorate at any time. Therefore, to reduce the energy consumption of sensor nodes and extend the lifetime of the network, this study proposes a novel energy-efficient clustering mechanism of a routing protocol. First, a novel metaheuristic algorithm is proposed, based on differential equations of bamboo growth and the Gaussian mixture model, called the bamboo growth optimizer (BFGO). Second, based on the BFGO algorithm, a clustering mechanism of a routing protocol (BFGO-C) is proposed, in which the encoding method and fitness function are redesigned. It can maximize the energy efficiency and minimize the transmission distance. In addition, heterogeneous nodes are added to the WSN to distinguish tasks among nodes and extend the lifetime of the network. Finally, this paper compares the proposed BFGO-C with three classic clustering protocols. The results show that the protocol based on the BFGO-C can be successfully applied to the clustering routing protocol and can effectively reduce energy consumption and enhance network performance.  
Keywords:wireless sensor networks; energy-efficient clustering mechanism; bamboo forest growth optimizer +
Footnote †: journal:  
Article
# 3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video
## 2 Related Works
### Imaging Photoplethysmography
#### 2.1.4 Machine Learning  
Research that covers the development of machine learning models dedicated to PPG signal measurement or biomedical parameter assessment from video streams are quite rare. Supervised machine learning techniques like linear regression and k-nearest neighbors showed better results than methods based on blind source separation [53]. The trained models are user-dependent. Support vector machine models have also been proposed to detect heart beats [54] and assess pulse rate [55].  
[PERSON] et al. were the first to employ a standard deep convolutional neural network architecture (VGG with 15 layers). The network is trained to predict pulse rate based on the time-frequency representation of processed PPG signals [56]. [PERSON] et al. proposed DeepPhys [57] and DeepMag [58], two deep convolutional network architectures trained to respectively predict pulse wave and magnify color/motion variations produced by the periodic changes in blood flow. The convolutional layers are guided using attention masks to ensure the robust estimation of PPG signals under lighting fluctuation and motion. [PERSON] et al. proposed a deep convolutional neural network architecture to robustly segment skin regions and assess vital signs [59]. [PERSON] et al. proposed a two-stage deep convolutional neural network (CNN) architecture [60] with an extractor stage that takes temporal sequences and outputs a signal. The latter is then fed to a heart rate estimator that predicts the pulse rate. [PERSON] et al. employed spatiotemporal maps to train a pulse rate estimator with transfer learning [61] using both synthetic and real video data.  
Contact pulse signals can also be identified using restricted Boltzmann machine and deep belief networks [62]. Deep recurrent neural network architectures, and in particular multilayerl ong short-term memory (LSTM), can be trained to predict arterial blood pressure from contact PPG and electrocardiogram signals [63].
# Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks
## 4 Proposed Energy-Aware Path Planning (EAPP) Approaches  
This section deals with the energy-aware path planning problem of an automated guided vehicle (AGV). The distance between every two sensor node pairs is the issue at hand. The most famous NP-hard optimization problem is thought to be the TSP [40; 41]. With multiple visits to each city, the TSP can create a route for a salesman that starts at his house, visits several locations, and then returns to the starting point with the least amount of travel time [42].  
Figure 1: In this wireless sensor network (WSN), an automated guided vehicle (AGV) is utilized to collect data from all fifteen sensors (S1, S2,..., S15) that are monitoring the environment with the purpose of identifying anomalies and dangers in order to decrease the likelihood of accidents occurring. Following the completion of the compilation of all of the data, they should be transmitted to the data sink, which is the entity that is accountable for collecting all of the data and evaluating them.  
In the energy-aware path planning problem, most of the studies use the metaheuristic or approximation algorithms. Therefore, we use these commonly used algorithms for the comparative study in this section. Some other works propose different algorithms for different scenarios; however, they are not applicable for the TSP-type path planning problem investigated in this paper.  
The genetic algorithm is known as an efficient metaheuristic for the TSP problem so it is applied to the TSP problem at hand. On the other hand, as a recently proposed technique in 2014, the Grey Wolf algorithm is also considered for the TSP problem at hand. Finally, as an approximation algorithm, the Nearest Neighbour algorithm is applied to this problem because it is much faster and more robust than the metaheuristic techniques. Therefore, we consider the problem with an obstacle avoidance issue by using the Nearest Neighbour algorithm, which shows the best performance among all the three algorithms.  
By tackling the EAPP problem as a TSP problem, we present a Nearest Neighbour (NN)-based approach, Grey Wolf Optimizer (GWO)-based approach, and Genetic Algorithm (GA)-based approach.
# Semantic 3D Reconstruction with Learning MVS and 2D Segmentation of Aerial Images
## 4 Experimental Evaluation  
### Experimental Protocol  
**Dataset:** We carry out the training process of semantic segmentation on UDD [[https://github.com/[PERSON]/UDD](https://github.com/[PERSON]/UDD)]([https://github.com/[PERSON]/UDD](https://github.com/[PERSON]/UDD)) [7], an UAV collected dataset with five categories, containing 160 and 40  
Figure 4: Comparison between the point clouds before and after refinement. (**a–c**) Top: coarse result. Bottom: refined result. (**d**): The panoramic scene for (**a–c**).  
images in the training and validation sets, respectively. The categories are _Building_, _Vegetation_, _Road_, _Vehicle_, and _Background_. The performance is measured on its test set called PKU-M1, which is a reconstruction dataset also collected by a mini-UAV at low altitude. PKU-M1 consists of 288 RGB images at \(4000\times 3000\) resolution. We down-sample the result to \(1000\times 750\) to accelerate the prediction speed.  
**Coloring policy:** Cityscapes [[https://www.cityscapes-dataset.com](https://www.cityscapes-dataset.com)]([https://www.cityscapes-dataset.com](https://www.cityscapes-dataset.com))[32] is the state-of-the-art semantic segmentation dataset for urban scene understanding, which was released in 2016 and received much attention. We borrow the coloring policy of semantic labels from Cityscapes [32].  
**Training:** UDD [7] is trained by Deeplab V2 [3] network structure implemented on TensorFlow [33]. We use the stochastic gradient descending [34] optimizer with weight decaying parameter \(5\times 10^{-5}\). Learning rate is initialized to \(1\times 10^{-3}\) with a momentum of 0.99. The entire apparatus is conducted on a Ubuntu 18.04 server, with an Intel core i7-9700K CPU, 32 GB memory, and a single Titan X Pascal GPU.  
**Measurements recap:** Assume the number of non-background classes is k. The confusion matrix \(\mathbf{M}\) for foreground categories can be denoted as below:  
\[\mathbf{M}=\begin{pmatrix}c_{11}&c_{12}&...&c_{1k}\\ c_{21}&c_{22}&...&c_{2k}\\...&...&...&...\\ c_{k1}&c_{k2}&...&c_{kk}\end{pmatrix} \tag{8}\]  
For a specific foreground semantic label \(l_{x}\in\mathcal{L}\), the problem can be formulated to a binary classification problem, where:  
\[TruePositive(TP)=c_{xx}, \tag{9}\]  
\[TrueNegative(TN)=\sum_{i=0}^{k}\sum_{j=0}^{k}c_{ii},i\
eq x,j\
eq x, \tag{10}\]  
\[FalsePositive(FP)=\sum_{i=0}^{k}c_{xi},i\
eq x, \tag{11}\]  
\[FalseNegative(FN)=\sum_{i=0}^{k}c_{ix},i\
eq x. \tag{12}\]  
Then, Pixel Accuracy, precision, recall, and F1-score can be deducted as below:  
\[Pixel\,Accuracy(PA)=\frac{\sum_{i=0}^{k}c_{ii}}{\sum_{i=0}^{k}\sum_{j=0}^{k}c_ {ij}}\, \tag{13}\]  
\[Precision=\frac{TP}{TP+FP}\, \tag{14}\]  
\[Recall=\frac{TP}{TP+FN}\, \tag{15}\]  
\[F1-score=2\times\frac{Precision\times Recall}{Precision+Recall}. \tag{16}\]
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 5 The Telco Cloud Enabled 5G Applications
### Telco Cloud Industrial Applications
#### 5.5.3 Smart Transportation  
Nowadays, cities face numerous challenges, including a lack of public transportation, a limited number of parking spaces, and pedestrian and driver safety [205]. Telco Cloud can be used to automate traffic at the network's edge by collecting real-time data from devices such as cameras and various sensors installed on both sides of the road, allowing for full control of traffic and the ability to alert drivers to congested areas or dangerous roads, as well as assisting in the smart parking process, using sensors to determine available parking spaces. There are numerous Edge Cloud use cases, including [177,204,205].
## Materials and Methods
### Objective Function
\(R\) and \(\left(x_{\text{o}},y_{\text{o}}\right)\) are circular curves corresponding to circle radius and the center \(O\) coordinate. \(d_{\text{zh}}\) and \(d_{\text{hz}}\) express the distance from the point cloud data to the first and second intermediate straight line, and \(d_{\text{hy}}\), \(d_{\text{yh}}\) stand for the distance from point cloud data to the first and second transition curve. \(d_{\text{yy}}\) is the distance from the point cloud data to the circular curve. Then, the track lining distance between the measuring point to the reconstructed railway is the shortest distance from the point cloud data to the five line elements, as is shown in Equation (4). \[D=MIN(d_{\text{zh}},d_{\text{hz}},d_{\text{hy}},d_{\text{yy}}) \tag{5}\]  
Figure 2: The basic unit of a track curve. \(o\), \(R\) are the center and radius of the circle; \(l_{1}\), \(l_{2}\) are the length of the first and second transition curve ZH, HY, YH, HZ and QZ, respectively, represent the intersection of the first straight line and the first transition curve, the first transition curve and the circular curve, the circular curve, the circular curve and the second transition curve, the second transition curve and the second straight line, and the midpoint of the circular curve. JD is the intersection of extension lines of the first and second straight lines. Based on Equations (3)-(5), it is not difficult to find that the optimizing model is closely related to the circle curve radius \(R\); the circle center \(O\); the first and second intermediate straight line slope, \(k_{1}\) and, \(k_{2}\);and intercepts \(b_{1}\) and \(b_{2}\); and the first and second transition curves \(l_{1}\) and \(l_{2}\). Thus, the distance function \(D(R,O,k_{1},k_{2},b_{1},b_{2},l_{1},l_{2})\) is established for \(R,O,k_{1},k_{2},b_{1},b_{2},l_{1},l_{2}\). Suppose: \(\mathbf{X}=(R,O,k_{1},k_{2},b_{1},b_{2},l_{1},l_{2})^{T}\), then objective function is expressed as:  
\[Q=MIN\sum_{i=1}^{n}\left(D_{i}(\mathbf{X})\right)^{2} \tag{6}\]
## 8 The SCS Instrument
### Requirements  
The monochromatic-beam operations described in Ref. [92] are key to the success of the SCS instrument. The SASE3 soft X-ray monochromator is equipped with two gratings and a flat mirror that allows for monochromatic beam operation at high (\(\Delta\)E/E = 2.5 \(\times\) 10\({}^{-5}\)) and medium energy resolutions (\(\Delta\)E/E = 1 \(\times\) 10\({}^{-4}\)) as well as non-monochromatized beam operations without changing the beam transport to the sample. A tunable grating illumination concept is therefore implemented to provide a minimum spectral bandwidth-time duration product [92]. In this way, RIXS experiments with high energy-resolution and lower time resolution as well as ultrafast dynamics studies at reduced energy resolutions (e.g., femtosecond surface chemistry and magnetism) can be performed at the same experiment station of SCS. New developments for the FEL source, such as full polarization control and undulator gap scanning techniques, will be implemented at SCS. These are nowadays standard capabilities at synchrotron facilities for X-ray spectroscopy investigations.  
The majority of the experiments will require X-rays to impinge on fixed solid targets that cannot easily be replenished between X-ray pulses, in contrast to liquid jet or particle injection schemes. This sets particular constraints on the optical and X-ray pulse energies in pump-probe experiments when sample damage or degradation by the radiation and heating have to be mitigated to a level that sufficient data acquisition is possible between the sample exchanges. Heat dissipation schemes have to be developed in order to reach the ultimate 4.5 MHz burst mode operation, where the sample relaxation time and the heat dissipation in the probed area must be shorter than 220 ns.  
While time-resolved spectroscopy experiments on fixed targets require high average photon flux, CDI and nonlinear X-ray-matter-interaction experiments need the highest pulse energies. In single shot imaging experiments the number of incoming photons determines the attainable resolution and therefore, depending on the damage threshold, requires the experiment to be carried out in a \"diffraction before destruction\" mode [93]. In this case, a new sample has to be repositioned in the beam between the X-ray bursts at 10 Hz repetition rate and a pulse selection mode is necessary.
# Gas-Liquid Mixability Study in a Jet-Stirred Tank for Mineral Flotation
## 3 Results and Discussion
### Influence of Feed Volume on Flow Field
#### 3.1.1 Influence of Feeding Amount on Velocity Field in Stirred Tank  
The instantaneous liquid-phase velocities of XY sections taken at different heights in the stirred tank are depicted in Figure 7. It is observed that despite varying inlet velocities across the five working conditions, the liquid-phase velocity follows a consistent distribution trend with spatial height: as height increases, the liquid-phase velocity decreases. This phenomenon results from gravity exerting a downward force on the liquid, countering the upward driving force of the jet and gradually reducing velocity. Across all planes, the liquid-phase velocity gradually increases from the center of the vessel body towards the vessel wall.
# Performance Analysis of Dual-Hop Hybrid RF-UOWC NOMA Systems
## 3 Outage Probability Analysis
### Outage Probability \(Op_{2}\)  
The outage \(OP_{2}\) occurs if \(R\) or \(D_{2}\) fails to decode \(x_{1}\) or \(x_{2}\); this is due to NOMA SIC concept that involves receiving \(x_{1}\) and cancels it before receiving \(x_{2}\). It is formulated as  
\[\begin{split} OP_{2}&=1-Pr(\gamma_{R}^{1}>\gamma_{1},\gamma _{R}^{2}>\gamma_{2},\gamma_{D2}^{1}>\gamma_{1},\gamma_{D2}^{2}>\gamma_{2})\\ &\stackrel{{(b)}}{{=}}1-Pr(|h_{w}|^{2}>\frac{\tau_{1}}{ \rho_{s}d^{-w}},|h_{w}|^{2}>\frac{\tau_{2}}{\rho_{s}d^{-w}})\times Pr(|h_{2}|^ {2}>\frac{\beta_{1}}{\rho_{R}},|h_{2}|^{2}>\frac{\beta_{2}}{\rho_{R}})\\ &=1-Pr(|h_{w}|^{2}>\frac{\tau}{\rho_{s}d^{-w}})\times Pr(|h_{2}|^ {2}>\frac{\beta}{\rho_{R}}),\end{split} \tag{6}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(\mathbf{BL}\) & \(\mathbf{TG}\) & & & & & \\ (L/min) & \(\mathbf{C\cdot}\mathbf{\mathrm{cm}^{-1}}\) & \(\mathbf{w}\) & \(\mathbf{\lambda}\) & \(\mathbf{a}\) & \(\mathbf{b}\) & \(\mathbf{c}\) \\ \hline
2.4 & 0.05 & 0.2130 & 0.3291 & 1.4299 & 1.1817 & 17.1984 \\
2.4 & 0.15 & 0.1807 & 0.1641 & 0.2334 & 1.4201 & 22.5924 \\
4.7 & 0.1 & 0.4539 & 0.2744 & 0.3008 & 1.7053 & 54.1422 \\ \hline \end{tabular}
\end{table}
Table 1: EGG parameters for temperature gradient water [5].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(\mathbf{BL}\) & \(\mathbf{W}\) & \(\mathbf{\lambda}\) & \(\mathbf{a}\) & \(\mathbf{b}\) & \(\mathbf{c}\) \\ \hline
2.4 & 0.1770 & 0.4687 & 0.7736 & 1.1372 & 49.1773 \\
4.7 & 0.2064 & 0.3953 & 0.5307 & 1.2154 & 35.7368 \\ \hline \end{tabular}
\end{table}
Table 2: EGG parameters for thermally uniform salty water [5].  
where \((b)\) stems from the independence between \(h_{w}\) and \(h_{2}\), \(\gamma_{2}=2^{R_{2}}-1\) with \(R_{2}\) is the target data rate of \(x_{2}\), \(\tau_{2}=\gamma_{2}/(a_{2}-a_{1}\eta\gamma_{2})\) under condition that \(a_{2}>a_{1}\eta\gamma_{2}\) or \(a_{1}<1/(1+\eta\gamma_{2})\), similarly \(\beta_{2}=\gamma_{2}/(b_{2}-b_{1}\eta\gamma_{2})\) under condition that \(b_{2}>b_{1}\eta\gamma_{2}\) or \(b_{1}<1/(1+\eta\gamma_{2})\), \(\tau=\max(\tau_{1},\tau_{2})\), and \(\beta=\max(\beta_{1},\beta_{2})\). With the aid of CDFs in (1) and (3), we obtain a closed-form expression of \(OP_{2}\) as in (7).  
\[OP_{2}=1-e^{\frac{-\tau}{\rho_{g}d^{-\eta}}}\Bigg{(}1-wG_{1,2}^{1,1}\Bigg{(} \frac{1}{\lambda}(\frac{\beta}{\rho_{R}h_{r}})^{\frac{1}{r}}\mid\begin{array} []{c}1\\ 1,0\end{array}\Bigg{)}-\frac{1-w}{\Gamma(a)}G_{1,2}^{1,1}\Bigg{(}\frac{1}{b^{ \varepsilon}}(\frac{\beta}{\rho_{R}h_{r}})^{\frac{\varepsilon}{r}}\mid\begin{array} []{c}1\\ a,0\end{array}\Bigg{)}\Bigg{)}. \tag{7}\]
## 3 UAV-Based Targeting for Emergency Material Delivery  
In emergency material delivery, UAV path planning and target detection tasks are interdependent and constitute an efficient material delivery system. UAV path optimization aims to shorten flight distances to reduce energy consumption and ensure that the UAV reaches each predefined emergency drop station quickly and accurately. At the same time, target detection technology helps the UAV identify and locate drop points to ensure that supplies are delivered accurately. The collaboration of these two tasks is crucial for enhancing the effectiveness and precision of emergency supplies.
## 4 Future Trends
### Cloud Computing  
Nowadays, cloud computing is becoming a trend for many aspects of our everyday life. More and more businesses are migrating several of their systems to the cloud because of its availability, security, scalability, among others. When discussing businesses' status, payments, or stock inventory, the amount of time required to get a result may be enough if a human perceives it as instantaneous or even tolerated if a small delay occurs. However, in application domains such as autonomous driving, real-time results are a must if accidents or life-threatening scenarios have to be avoided. Thus, the need to explore concepts such as edge and fog within the cloud computing domain. Edge and fog computing consists of moving the location where the information is processed closer to where it is generated [73]. Any device with an internet connection that can store and process data can be used as a fog node (Cisco, \"Fog Computing and the Internet of Things: Extend the Cloud to Where the Things Are\", _Cisco_, computing-overview.pdf (cisco.com)). This way, the cloud server might rely on these nodes for most analysis, reducing latency and increasing privacy. The authors considered three characteristics of an application that needs fog computing:  
1. Data is gathered on an edge. In this case, an AV or a group of AVs can be considered an extreme edge.
2. There is a lot of data being generated in a large geographic area.
3. The information gathered needs to be analyzed, and it has to provoke a response in less than a second.  
An average cable provider in 2008 allowed 16 Mbps ([PERSON], Nielsen's Law of Internet Bandwidth, _Nielsen Norman Group_, Nielsen's Law of Internet Bandwidth (ngroup.com)). Two years later, bandwidth increased to 31 Mbps. The last measurement was from 2019 with 325 Mbps. This behavior can be described by [PERSON]'s law, which says that each year, bandwidth increases by approximately 50%. Some authors even compare [PERSON]'s law with [PERSON]'s law, which describes computer power. Despite recent advances in bandwidth capacity, edge computing is still the most critical step for real-time cloud analysis. As shown in [45], a DDNN that involves the edge brings several benefits, such as fault tolerance, privacy, and reduced communication costs.  
All and all, it is concluded that edge and fog computing are the best ways to implement a NN in the cloud for applications that require fast analysis like AVs. Despite requiring more powerful end devices, or edge servers, these techniques reduce network traffic, allowing faster analysis of information, more privacy, and fewer transmission errors.