esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Geoeffectiveness and Arrival Time Prediction of CMEs by a Unified Deep Learning Framework
## 2 Methods  
The overall deep learning framework is shown in Figure 1. When predicting the geoeffectiveness and arrival time of the CME events, we first collect the corresponding satellite observation images from 10 min before up to 4 h after the onset time of the CME events and feed them into the deep learning framework as the only input.  
Then our framework determines the geoeffectiveness of the CME event. Specifically, we use the deep residual network embedded with the attention mechanism which plays the role of feature extraction module in geoeffectiveness prediction to extract the feature map for each observation image. Since the number of observation images collected in each CME event is not fixed (see the details in Section 3.1), we use the feature map fusion module to fuse the feature maps of each input image into a fused feature map, and predict the probability that the CME event is geoeffective or not. If the probability is greater than 0.5, it is judged as a geoeffective CME event.  
For a geoeffective CME event, our framework further predicts its arrival time by predicting the arrival time of each image in the CME event and taking the average. Specifically, we first use the deep residual regression network based on group convolution to extract the feature map of each image, and then predict the transit time of each image. The predicted arrival time of each image can be obtained through the predicted transit time plus the observation time of the image, and the average predicted arrival time of all input images in the CME event is taken as the predicted arrival time of the CME event. The reason for  
Figure 1: The overall deep learning framework of jointing geoeffectiveness and arrival time prediction. In the framework, we first predict the geoeffectiveness of CMEs and for geoeffective CMEs, we further predict the arrival time.  
adopting this strategy is to prevent the problem of under-fitting of the CME arrival time prediction network due to fewer geoeffective samples and the detailed reasons can be seen in Section 2.2.1.
# MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform
## 5 Experiments
### Algorithm Performance Analysis
#### 5.2.1 MASA in Classic DAG Environment
To further encode the complex and dynamic state space of the heterogeneous resource platform, our proposed MASA introduces the mechanism of attention, which further improves its encoding ability. The final outcomes demonstrate that the attention mechanism can significantly enhance the encoding capacity of the network model as the task volume increases and the encoding environment becomes more complex.
# A Redactable Blockchain Scheme Supporting Quantum-Resistance and Trapdoor Updates
## 2 Preliminaries
### Hierarchical Identity-Based Chameleon Hashing  
[PERSON] et al. designed a chameleon hash function based on identity encryption [21]. In IBCH, the trusted authority can extract a new secret key or trapdoor secret for an identity ID using a deterministic algorithm. As an extension of IBCH, HIBCH organizes identities into hierarchies, and keys are delegated between them.  
**Definition 8**.: _The HIBCH scheme is generally defined as the following set of algorithms:_  
Algorithm \(Setup\) generates the public parameter \(pp\) and the master key \(mk\) for the input security parameter \(\lambda\). Typically, the algorithm is run by private key generator (PKG).  
Algorithm \(Extract\) outputs the trapdoor or secret key for the user of an identity ID with the secret key of the ID's parent.  
Algorithm \(Hash\) encrypts the message \(m\) using the public parameter \(pp\) and an identity ID. It randomly chooses parameter \(r\) and outputs a hash value of \(m\).  
Algorithm \(Forge\) looks for collisions of hash values for a new message \(m^{\prime}\). Usually, the algorithm takes the hash value, an identity ID, and its key of the original message \(m\) as inputs, and outputs a new parameter \(r\).  
**Definition 9**.: _Enhanced collision resistance. A secure chameleon hash algorithm is enhanced collision resistance (CR) when challenger \(\mathcal{A}\) holds that \(Pr\Big{[}Expt_{\mathcal{A},CH}^{CR}(n,q,ID)=1\Big{]}\leq negl(n)\). The \(Expt_{\mathcal{A},CH}^{CR}(n)\) is the experiment depicted below: \(Run\;PP,MK\leftarrow Setup(1^{n},q),\mathcal{Q}\leftarrow\varnothing\); \(Run\;\mathbf{A_{SID}},\;\mathbf{S_{ID}}\leftarrow SlaveKey(1^{n},q,ID)\); \(Run\;\mathbf{F_{ID}},\mathbf{R_{ID}},\mathbf{E_{ID}}\leftarrow DelKey(\mathbf{A_{SID}},\;\mathbf{S_{ID}},ID)\); \(Run\;(M,\mathbf{r},\mathbf{h},M^{\prime},\mathbf{r^{\prime}})\leftarrow\mathcal{A}^{Expt }_{PPD(\cdot)}(PP)\); \(If\;CH(PP,M)|\mathbf{r}=CH(PP,M^{\prime})|\mathbf{r^{\prime}}\wedge(M\
eq M^{\prime}) \wedge(\mathbf{h}\
otin\mathcal{Q})\): return 1; otherwise: return 0. The \(\mathcal{Q}_{PPD(ID}^{Forge}((\mathbf{h},M,\mathbf{r}),M^{\prime})\) is defined as follows: \(If\;CH(PP,M)|\mathbf{r}\
eq\mathbf{h}\): return \(\bot\); \(If\;CH(PP,M)|\mathbf{r}=\mathbf{h}\): \(Run\;\mathbf{r^{\prime}}\leftarrow Forge(\mathbf{F_{ID}},\mathbf{R_{ID}},\mathbf{E_{ID}},M^{ \prime},\mathbf{h})\) If \(\mathbf{r^{\prime}}=\bot\): return \(\bot\); \(\mathcal{Q}\leftarrow\mathcal{Q}\bigcup\{M,M^{\prime}\}\);return \(\mathbf{r^{\prime}}\)._
# A Long-Distance WiFi Network as a Tool to Promote Social Inclusion in Southern Vercarruz, Mexico
## 3 Methodology
### Design and Implementation of a Long-Distance WiFi Network
#### 3.2.5 Network Planning  
Another fundamental aspect in the design of a long-distance WiFi link is to have a line of sight to ensure that the reception signal has enough power since the attenuation at 2.4 or 5 GHz is high. This situation implies having an infrastructure on which to place the antenna, such as houses, bridges, buildings, or towers. For this purpose, the approximate cost of the towers must be analyzed (see Table 5). For network planning, the following must be taken into account:  
* Location of the antennas;
* If towers are required, their location and height;
* Network topology;
* Type of antenna to use for each link;
* Transmission powers;
* Operation channel.  
The long-distance WiFi connection proposed (30 km) for the population of Mecayapan can be seen in Figure 16. It consists of two 18-m towers. The first tower will have an antenna built with recycled materials to transmit the internet signal, and the receiving antenna will be able to provide the service with an Ethernet connection.  
Figure 14: WiFi networks received by the laptop without connecting the implemented antenna.
# Contact Lenses as Drug Delivery System for Glaucoma: A Review
## 1 Introduction
### Physiology and Pathophysiology of Glaucoma  
Intraocular pressure is regulated by the inflow and outflow of the aqueous humor in the eye. The aqueous humor is produced by the epithelium of the ciliary body and contains electrolytes, proteins, cytokines, organic solutes and growth factors that nourish the avascular tissue of the anterior chamber [13]. The rate of aqueous humor turnover has been estimated to be \(2.4\pm 0.6\) uL/min in the adult eye [14]. The aqueous humor reaches the posterior chamber through the tissue components of the ciliary processes, then flows around the lens and through the pupil it enters into the anterior chamber [15] and a temperature gradient within the anterior chamber creates a convective flow towards the cornea [16]. The main drainage pathway for the aqueous humor out of the eye is the trabecular meshwork into the Schlemm's canal [17]. The trabecular meshwork is porous and acts as a filter that drains aqueous humor passively when a certain IOP value is reached [17]. There is another route of drainage known as unconventional or uveoscleral outflow pathway involving the ciliary body. Unlike the conventional pathway, the uveoscleral route is independent of the IOP values [18]. Recently, lymphatic channels have been identified by immunofluorescence and confocal microscopy in the human ciliary body which suggests a uveolymphatic pathway for the outflow of the aqueous humor [19].
# Energy Trading with Electric Vehicles in Smart Campus Parking Lots
## 2 Proposed System Architecture for Energy Trading in a Smart Parking Lot
### Communication Network Layer  
Information and communication technologies aim to support and manage the energy transfer between electric vehicles and the power grid. The communication network layer enables real-time data exchange between different components in the electric vehicle system. The communication infrastructure consists of communication devices, wired/wireless communication connections, routers, switches, servers, etc.
# Ni/Zn Layered Double Hydroxide (LDH) Micro/Nanosystems and Their Azorubine Adsorption Performance
## 4 Results and Discussion
### Adsorption Process from Aqueous Solutions
#### 4.2.3 Adsorbent Concentration Effect on the Adsorption Process  
Figure 6 exhibits the adsorption capacity of the Ni/Zn LDH nanohybrid at various initial concentrations of Az. It can be seen that the removal percentage decreases from 98% to 66.8% and the Az adsorption amount increases from 123.74 to 710 mg g\({}^{-1}\) when the Az initial concentration is increased from 25 to 200 mg L\({}^{-1}\). Therefore, the decrease in the dye removal efficiency with an increase in the dye concentration may be related to the initial amount of dye molecules (see pictures inset), which is lower than the available surface active sites, making the adsorption ratio independent of the initial dye concentration. Nevertheless, at higher concentrations, there would be fewer available sites than initial dye molecules in the solution which affect the Az removal percentage [46; 52].  
Figure 6: Effect of the initial Az concentration on (**a**) the adsorption capacity of Ni/Zn LDH adsorbent and (**b**) the Az removal ratio.  
Moreover, our nanoadsorbent has a high elimination rate just in the first few minutes, and the adsorption equilibrium time was 60 min. It shows that the adsorption on Ni/Zn LDH nanohybrid asa micro/nanoporous system has a short equilibrium time, and the adsorption rate of this nanohybrid is much faster than other general bulk layered compound. This can be attributed to the mesoporous micro/nanosystem morphology, which is helpful for molecular transport by virtue of its porous texture architectural feature [53; 54; 55; 56].
# Towards Understanding Driver Acceptance of C-ITS Services--A Multi-Use Case Field Study Approach
## 3 Results
### Questionnaire Results
#### 3.1.4 Willingness to Pay  
The ESRIUM project and the ASFINAG study also included questions relating to the willingness to pay for in-vehicle information. In ESRIUM, the questions asked for their individual willingness to pay and a recommendation to their employer to pay for it. The individual's willingness to pay was ranked low. Only 1 respondent agreed to be willing to pay, and 3 respondents somewhat agreed. The remaining participants (23 out of 27) did not indicate an individual willingness to pay. The results regarding the recommendation to the employer to pay for such information differ: 7 agree, 10 neither agree nor disagree, and 10 disagree.  
In the ASFINAG study, the individual willingness to pay for C-ITS-based in-vehicle information is significantly influenced by a multitude of factors. These factors include the specific use cases supported by the service, the opportunity for users to experience the service, the perceived quality of service, and the proposed pricing structure. 18% of the respondents of the online questionnaire agreed that they are willing to pay for the service. They want to pay a monthly fee or even want to pay per use in the range of 5-10 \(\upxi\) per month. Individuals who had the opportunity to experience the use cases firsthand, as demonstrated through participation in the test drives, exhibited a higher willingness to pay for such services, between 50% and 75%.
# Numerical Validation of the Radiative Model for the Solar Cadaster Developed for Greater Geneva
## 2 Materials and Methods
### Modeling Approaches for Radiative Transfers
#### 2.1.1 Ray-Tracing Methods  
Ray-tracing methods consist of following the paths taken by electromagnetic rays. There are two types of ray-tracing approaches. Forward ray-tracing methods, also called light tracing, consist of launching rays from light sources in a set of directions. Backward ray-tracing methods consist of following the light path backward. In this latter case, rays are launched in a set of directions from the element of interest (the eye of an observer or an irradiated surface). A comparison between forward and backward ray tracing is illustrated in Figure 1.  
For both of these methods, the directions can be random (probabilistic approach) or set (deterministic approach). After rays have been launched, an intersection test is carried out between each of these rays and the objects in the scene; then, new rays are cast from the points of impact while considering the properties of the materials encountered (reflection, transmission, absorption). This method makes it possible to take the effects of light into account, such as refraction of light (for example, in the case of a scene comprising glasses). The behavior of a ray at the point of intersection then depends on the refractive properties  
Figure 1: Illustration of backward and forward ray tracing.  
of the material encountered. This feature is particularly useful in the context of image synthesis or the study of transparent media with different refractive indexes.  
This method also has the advantage of making the calculation of specular reflections possible (see Figure 2). On the other hand, it is not suitable for a calculation of diffuse reflections (which are usually the case for urban surfaces), as these require a significant calculation time because of the number of rays to be launched (in all directions) at each new intersection. This method has to be coupled with a diffuse radiation model.
## 4 Intelligent Weather-Conscious AMC Scheme for Global Satellite-to-Ground Communications
### DL-Based Estimation Model
#### 4.2.3 Output Gate  
The output gate, which is responsible for selectively outputting the hidden state of the cell, has two parts. One is the control signal \(\mathbf{o}_{t}\) represented by the sigmoid function, and the other is the final output value \(\mathbf{h}_{t}\):  
\[\mathbf{o}_{t}=\sigma\big{[}\mathbf{W}_{o}\mathbf{\beta}(t)+ \mathbf{U}_{o}\mathbf{h}^{\prime}(t-1)+\mathbf{b}_{o}\big{]} \tag{10}\] \[\mathbf{h}^{\prime}(t)=\mathbf{o}_{t}\odot\tanh(\mathbf{C}_{t})\]  
where \(\mathbf{W}_{o}\) is the weight between the current input and the output gate and \(\mathbf{U}_{f}\) is the weight between the hidden state of the last moment \(\mathbf{h}^{\prime}(t-1)\) and the output gate. The predicted state is represented as  
\[s_{t}=\sigma\big{[}\mathbf{W}_{i}\mathbf{h}^{\prime}(t-1)\big{]} \tag{11}\]  
where \(\mathbf{W}_{t}\) is the weight vector of the output gate.  
The LSTM layer is followed by a fully connected layer, which is used to integrate and analyze the outputs of the LSTM layer. The output layer is connected after the fully connected layer, and since we only need to predict SNR in moment \(t\) as \(SNR_{t}\), the size of the output layer is one neuron.  
The estimation model can be pre-trained using historical information. Finally, the output of the estimation model \(SNR_{t}\) is passed to the actor-critic network as input in the decision model. Accurate prediction of the SNR at this moment is crucial, which is the basis upon which the decision model can make correct decisions.
# Late Shelf Life Saturation of Golden Delicious Apple Parameters: TSS, Weight, and Colorimetry
###### Abstract  
This work aims to estimate the shelf life of Golden Delicious apple fruit stored at room temperature by determining the changing trend in color every week using a non-destructive measurement method. Moreover, the study will measure the changes in weight loss (\(\Delta\)m) and the total soluble solids (TSS) contained in the apple. The research focuses on the last stage of ripening and the effect of shelf life affecting consumer behavior; therefore, the examined fruits were picked at the end of the season and were stored at an ambient temperature and in controlled laboratory conditions for six weeks, at 24 \({}^{\circ}\)C under 60% RH relative. Color measurements were performed with a portable color sensor, which provided a simple and effective examination method in the case of an appropriate number of fruit samples. The findings showed a significant increase in TSS and weight loss over time. Color varying (\(\Delta\)E) and chroma (C*) parameters increased with prolonged storage duration, meaning that the color of the apples became darker and more color-saturated at the end of storage. While weight loss and TSS follow a linear tendency in the given storage period, the color changes in deteriorating fruits were characterized by exponential asymptotic regression. It was found that although the moisture content reaches its limit value during the drying process of apples, the saturation of color coordinates allows for determination of the limit parameters of consumption in the linear stage of weight loss. The saturation limits (70.97; 12.77; 56.34 CIELAB L'; a'; b*), the dominant color part (b*), and the growth rate of the saturation curves allow an accurate characterization of ripening from the visual aspect, thus determining the limit parameters of shelf life and improving the critical analysis factors affecting the life of fruits after harvest. By assessing color characteristics using a non-destructive technology, customers may quickly evaluate the quality of apples and make better decisions during their purchase.  
Golden Delicious apple; storage; weight loss; color; total soluble solids; shelf life; food engineering Article  
Late Shelf Life Saturation of Golden Delicious Apple Parameters: TSS, Weight, and Colorimetry
# A Survey on Efforts to Evolve the Control Plane of Inter-Domain Routing
## 3 Background  
The Internet is a collection of tens of thousands of independently operated networks, simply called Autonomous Systems (ASs). An AS can be an Internet Service Provider (ISP), a campus, a content provider or any other independently operated networks. Thus, to carry traffic from one AS to another, an AS requires two types of routing system: intra-domain routing; and inter-domain routing.  
Intra-domain routing is the process routing network traffic inside any single autonomous system in as a fast, effective and reliable way as possible. Examples of protocols for intra-domain routing are Open Shortest Path First (OSPF), Routing Information Protocol (RIP) and internal BGP (iBGP). However, for inter-domain routing, the focus is on applying routing policies and distributing routing reachability information among ASs. For the inter-domain routing system, the external BGP [6] (BGP from now on) is the standard protocol of the Internet.
Appl. Sci._**2013**, \(3\), 55-69; doi:10.3390/app3010055  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Review_  
**Glycerol: A promising Green Solvent and Reducing Agent for Metal-Catalyzed Transfer Hydrogenation Reactions and Nanoparticles Formation**  
**[PERSON] \({}^{\dagger}\) and [PERSON] ***  
Laboratory of Organometallic Compounds and Catalysis (CSIC Associated Unit).  
Department of Organic and Inorganic Chemistry, Institute of Organometallic Chemistry \"[PERSON]\", Faculty of Chemistry, University of Oviedo, E-33006 Oviedo, Spain;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{\dagger}\) Present address: Department of Chemistry-BMC, Uppsala University, Box 576,  
SE-75123 Uppsala, Sweden.  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-985-103-453; Fax: +34-985-103-446.  
_Received: 19 December 2012; in revised form: 15 January 2013 / Accepted: 21 January 2013 / Published: 23 January 2013_  
**Abstract:** Glycerol is a non-toxic, non-hazardous, non-volatile, biodegradable, and recyclable liquid that is generated as a byproduct in the manufacture of biodiesel fuel from vegetable oils. Due to its easy availability, along with its unique combination of physical and chemical properties, glycerol has recently emerged as an economically appealing and safe solvent for organic synthesis. Recent works have also demonstrated that glycerol can be used as a hydrogen source in metal-catalyzed transfer hydrogenation of organic compounds, such as aldehydes, ketones, olefins and nitroarenes. Herein, the advances reached in this emerging field are reviewed. The utility of glycerol as solvent and reducing agent for the generation of metal nanoparticles is also briefly discussed.  
**Keywords:** glycerol; green solvent; hydrogen donor; transfer hydrogenation; reduction reactions; metal catalysis; metal nanoparticles
## 2 Method for Calculating the Lightning Electromagnetic Field over Perfectly Conducting Ground  
### Review of the Lightning Return Stroke Model  
In order to calculate the lightning electromagnetic field, the lightning return stroke model must be established firstly. Basically, there are four kinds of lightning return stroke models: The gas dynamic or physical mode, the electromagnetic model, the distributed-circuit model, and the engineering model [19; 20; 21; 22]. The engineering model is adopted here due to its wide utilization. There are several engineering models, such as the Bruce-Golde model (BG), transmission line model (TL), traveling current source model (TCS), modified transmission line exponential decay model (MTLE), and modified transmission line linear model (MTLL) [23]. In this paper, the MTLE model is adopted, which can be illustrated as Figure 1 and described by:  
\[\left\{\begin{array}{ll}i(z^{\prime},t)=i(0,t-\frac{z^{\prime}}{v})\exp(- \frac{z^{\prime}}{\alpha}),&t\geq\frac{z^{\prime}}{v}\\ i(z^{\prime},t)=0,&t<\frac{z^{\prime}}{v}\end{array}\right. \tag{1}\]  
where: \(v\)--the return stroke velocity along the lightning channel, \(\alpha\)--the decaying constant, \(i(0,t-z^{\prime}/v)\)--the delay of the channel base current, and \(\exp(-z^{\prime}/\alpha)\)--the attenuation of the channel base current.  
Figure 1: Lightning return stroke model.  
As for the channel base current, several functions are available, such as Rubinstein and [PERSON] function, [PERSON] and [PERSON] function, and Heidler function [24]. In this paper, the channel base current is represented as a sum of two Heidler functions, as given in Equation (2), and the typical parameters are listed in Table 1:  
\[i(0,t)=\left[\frac{i_{01}}{\eta_{1}}\frac{\left(t/\tau_{11}\right)^{n_{1}}}{1+ \left(t/\tau_{11}\right)^{n_{1}}}\exp\left(-\frac{t}{\tau_{12}}\right)+\frac{i _{02}}{\eta_{2}}\frac{\left(t/\tau_{21}\right)^{n_{2}}}{1+\left(t/\tau_{21} \right)^{n_{2}}}\exp\left(-\frac{t}{\tau_{22}}\right)\right], \tag{2}\]  
where:  
\[\eta_{1}=\exp\left(-\frac{\tau_{11}}{\tau_{12}}\left(n_{1}\frac{\tau_{12}}{ \tau_{11}}\right)^{\frac{1}{n_{1}}}\right)\text{ and }\eta_{2}=\exp\left(-\frac{\tau_{21}}{\tau_{22}}\left(n_{1}\frac{\tau_{22}}{ \tau_{21}}\right)^{\frac{1}{n_{2}}}\right).\]