esa-sceva/satcom-chunk-collection huggingface dataset:
# Ammonia Gas Sensing Characteristic of P3 HT-rGO-MWCNT Composite Films
## 4 Conclusions  
The nanocomposite PGC sensors were successfully fabricated using the drop-casting technique. The sensitivity of the PGC gas sensor was optimized when the concentrations of P3 HT, rGO, and MWCNTs were 20 wt.%, 60 wt.%, and 20 wt.% (PGC-60), respectively. The PGC-60 sensor has a response time of 30 s, sensitivity up to 3.6% at ammonia gas concentration of 10 ppm, and relative sensitivity of 0.031%/ppm. These results are attributed to excellent electron transportation of rGO, the adsorbent activator to NH\({}_{3}\) gas of P3 HT, and holes move from P3 HT to the cathodes as a charge carrier \"nano-bridge\" of MWCNTs. In general, all three components of the PGC sensor have significantly contributed to improving both the sensitivity and response time of the NH\({}_{3}\) gas sensor. The comparison of relative sensitivity of the PGC sensor to that of the P3C sensor and the PEDOT:PSS sensor is about 2.5 and 1.5 times greater, respectively.  
Experiments, methodology, T.S.T.K., L.T.T.G. and T.Q.N.; data curation, T.Q.T.; writing--original draft preparation, N.D.L.; supervision, N.N.D. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Vietnam National Foundation for Science and Technology Development (NAFOSTED) under Grant Number 103.99-2019.19.  
**Institutional Review Board Statement:** Not applicable.  
Figure 7: Scheme of the energy levels of single components of the PGC-60 composite sensor. The dashed arrows indicate hole movement directions during the resistance measurement.
# Angle-Based Parametrization with Evolutionary Optimization for OESCL-Band Y-Junction Splitters
## 3 Optimization Algorithms
### Genetic Algorithm  
GA is a metaheuristic inspired by the process of natural selection [17], and GA is used for the design of novel optic devices such as (i) polarization beam splitters [37,38] and (ii) Y-branch waveguides [27,33,39].  
As depicted in Algorithm 1, GA has four main steps. First, it starts with \(n\) individuals represented by \(p\) parameters. Second, it will simulate the evolution of these individuals during \(k\) generations. In each generation, the simulation starts selecting a set of individuals to survive; those are called parents. Third, parents are combined following a crossover policy to produce \(n\) new individuals that share some parameters with the selected parents. Finally, the individuals randomly mutate some of their parameters.  
Algorithm 1 depicts our implementation, with the following method specifications:  
```
1\(population=generate\_population(n,p)\); // n individuals defined by p angles \(\alpha_{i}\), \(i\in[1,p]\) in Section 2
2 for\(t=0\); \(t<k\); \(t\)++do
3\(parents=select(population)\) \(children=crossover(population,parents)\); // new angles combinations (\(\alpha_{i}\), \(i\in[1,p]\)) are generated for each of the \(n\) individual \(population=mutation(children)\) // here updates with new angles \(\alpha_{i}\)
```  
**Algorithm 1**Framework of GA  
* \(generate\_population(n,p)\): returns \(n\) vectors defined by \(p\) angles (\(\alpha_{i},i\in[1,p]\) defined in Section 2 with random values from a uniform distribution in the range of the constrains of each parameter.
* \(select(population):\) chooses \(q\) individuals (i.e., we use roulette wheel selection) depending on the probability value \(prob_{i}\) given by Equation (3): \[prob_{i}=\frac{FOM_{i}-min(FOM)}{\sum(FOM_{j}-min(FOM))}\] (3) where \(FOM_{i}\) is associated with the \(i\)-th individual.
* \(crossover(population,parents)\): returns \(n\) vectors of dimension \(p\). The \(i\)-th vector is the combination of two random parents \(pa_{i}\) and \(pb_{i}\) selected from \(parents\). Its \(d\)-th parameter is defined at random by \(pa_{i,d}\) or \(pb_{i,d}\) with equal probability (i.e., we are using uniform crossover). This generates new combinations of angles to be evaluated.
* \(mutation(children)\): returns the new combinations of the previous step, but each individual may have some of its parameters added a random value \(u\in U(-r,r)\). This updates the angles \(\alpha_{i}\) of each individual.
# Experimental Study of Influence of Plant Roots on Dynamic Characteristics of Clay
## 3 Experimental Results Analysis
### Influence of Plant Root Systems on Soil Damping Ratio  
The viscous damping ratio \(\lambda\) obtained from the resonant column test is derived from the free vibration decay curve. This curve is obtained by the accelerometer installed on the resonant column drive disk. A sine wave is applied to the soil, and after ceasing the excitation, the results of free vibration are measured. The decrement of the logarithmic curve \(\delta\) can be calculated based on the logarithm of the continuous cycle amplitude ratio. The viscous damping ratio is then computed using the logarithmic decrement according to the following formula:  
\[\lambda=\sqrt{\frac{\delta^{2}}{4\pi^{2}+\delta^{2}}} \tag{7}\]  
In this study, the relationship between damping ratio and shear strain was fitted using the Hardin-Drnevich hyperbolic model. The hyperbolic formula [8] is as follows:  
\[\lambda=\frac{\gamma}{\gamma_{r}+\gamma}\lambda_{\text{max}} \tag{8}\]  
In the equation, \(\gamma\) represents the shear strain, \(\gamma_{r}\) stands for the reference shear strain, \(\lambda\) denotes the damping ratio, and \(\lambda_{max}\) signifies the maximum damping ratio.
## 1 Introduction
### Graph Neural Networks for MIP and UCP  
GNNs have attracted increasing attention in both power system optimization and MIP due to their powerful ability to model topological structures and generalize across graph-based representations.  
The authors of [24] encoded power grid topologies into graphs, utilizing GNNs to simultaneously predict active transmission constraints and commitment variables. These predictions are then refined to ensure feasibility and reduce problem dimensionality.  
The methods used in [25] exploit GNNs to estimate marginal probabilities of binary variables in MIP formulations, followed by constrained local searches near predicted solutions. However, such confined search spaces often compromise optimization efficiency and solution quality. The authors of [26] proposed a Graph Neural Network integrated with neural diving and branching strategies, achieving superior scalability and cost-efficiency in solving large-scale Unit Commitment Problems compared to conventional MIP solvers.
## 3 Our Approach  
### Overview  
We focus on the development of secure applications in future wireless sensor networks, where many sensors provide data about observable magnitudes from the environment, and many actuators let the system act on the state of the environment.  
Following the Ackoff taxonomy for the content of the human mind, we classify the content of the \"ambient mind\" into four categories:  
1. Data: Symbols. It simply exists and has no significance beyond its existence (in and of itself).
2. Information: Data that is processed to be useful; provides answers to \"who\", \"what\", \"where\", and \"when\" questions.
3. Knowledge: Application of data and information; answers \"how\" questions.  
4. Intelligence (actually, this category comprises two from the Ackoff taxonomy: understanding and wisdom): Appreciation of \"why\". It is the process by which new knowledge is synthesized from the previously held knowledge.  
The main characteristic of an intelligent ambient is the semantic enrichment of environment based on the processing of data obtained from the environment using a sensor network. This \"ambient mind\" enhances the semantics of the environment by adding meaning to the objects. The objects are conscious of the \"who\", \"what\", \"where\", \"when\", \"how\", and \"why\".  
Data is obtained by sensor nodes, but as they are not trusted, most of the remaining processing should be done in secure servers so that confidentiality attacks do not succeed (note that data has no meaning by itself). Data is sent to servers where it is processed to generate information, and then knowledge, and then understanding, and then new meaning, which is returned back to the environment. Individual nodes may be insecure, but the system should always continue its function of semantic enhancement. Moreover, attacks of individual nodes should not affect the decisions based on data from the environment. These requirements are achieved by perusing redundancy to discard data from the compromised nodes, and by changing the network structure and behavior at a speed that is fast enough to prevent a chained attack to spread.  
Figure 1 shows the data flow in the environment. As confidentiality attacks become more dangerous as data is further processed, there should be little or no processing at all in the sensor nodes, which are more vulnerable.
## 2 System Model  
In this section, we present a system architecture and channel model of LEO satellite-based NB-IoT networks. We also illustrate the principles of the RA procedure in an NTN scenario. On this basis, we focus on analyzing the problem of the CE mechanism in LEO satellite-based IoT networks.
# Communication Technologies for Smart Grid: A Comprehensive Survey
## 3 Smart Grid Communication
### Smart Grid Communication Technologies
#### 3.4.1 Wired Communication  
**Power Line Communication (PLC)**  
Power line communication utilizes the power transmission lines to transmit data. High frequency signals from a few kHz to tens of MHz are transferred over the power line [62]. The initial cost of PLC is lower since it uses the existing power line infrastructure. The technology is mature, and has already been in use for decades for commercial broadband and is highly reliable. PLC provide high throughput and low latency which makes it suitable for SG communication in densely populated areas [63]. Power line communications divides into narrowband and broadband PLC. Narrowband PLC (NB-PLC) is operating at 300-500 kHz with a data rate up to 10-500 Kbps and a range up to 3 km. This is further divided into Low Data Rate Narrowband PLC and High Data Rate Narrowband PLC. Low data Rate Narrowband PLC is single carrier based, with a data rate up to 10 kbps. High Data Rate Narrowband PLC is multi carrier based with a data rate up to 1 Mbps. Broadband PLC (BB-PLC) operates between 1.8 and 250 MHz with a data rate up to 300 Mbps. Power Line Communication can be used in nearly all parts of a SG environment, from home appliances in low voltage to grid automation in high voltage [64]. The noise created by power electronics components in the channel is a major concern with this form of communication [65; 66; 67]. Data distortion around transformers, and the need to bypass these using other communication techniques is another disadvantage with PLC [59]. Extensive field measurements show that the characteristics of PLC channel differ significantly from one environment to another, which leads to varying performance [68]. The large deployment of power cables also makes the combination of PLC technique with other communication technologies (e.g., radio frequency (RF), visible light communication (VLC), etc.) an attractive approach to extend the communication coverage, thus enabling a variety of applications such as smart home, Internet of Things, etc. [69; 70; 71]. HomePlug is a type of power line communication specifically developed in-home applications and appliances. HomePlug Green PHY (HPGP) uses PLC technology, and is developed and marketed towards HAN applications. It has a data rate up to 10 Mbps, and operates in the 2 MHz-30 MHz spectrum (BB-PLC) [72; 73].  
**Fiber Optical Communication**  
Fiber optical communication is well suited for control and monitoring, and backbone communication in WANs, although it is more expensive than other alternatives it has the advantages of long range, high bandwidth, and high data rate, and not being susceptible to electromagnetic disturbances. Limitations of fiber optic communication is the number of access points. Fiber optics are most commonly used for backbone communication, and to connect substations to the utility companies control centers [21; 34; 74].
# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## 2 Related Work
### The ETSI MEC Framework and Its Reference Architecture
#### 2.1.2 ME Management Blocks  
ME management blocks operate at both the system and host levels. At the system level, the core component is the ME orchestrator, which manages the ME app lifecycle, application rules, and requirements, and keeps track of the instantiated ME apps. To this end, it interacts with the management blocks at the host level through Mm3 and Mm4.  
The ME orchestrator must ensure that ME app requirements (such as latency or throughput) are fulfilled; thus, it selects the appropriate ME host on which ME apps should run.  
The other system level management blocks of interest for this paper are the Operations Support System (OSS) and the Customer Facing Service (CFS) portal. OSS is in charge of fault, configuration, and performance management. It receives requests to instantiate and terminate ME apps from the CFS portal, and assists the orchestrator, via Mm1, in deploying them at the desired network location.  
Figure 1: ETSI MEC framework reference architecture.  
The host level includes two management blocks, i.e., the ME platform manager, and the Virtualization Infrastructure Manager (VIM). The ME platform manager provides:  
1. ME App lifecycle management (i.e., instantiation, termination, etc.);
2. The ME platform Element Management System (EMS);
3. ME app policy management functions (i.e., traffic rule, DNS configuration, etc.).  
The ME platform manager communicates with the OSS via Mm2 for fault, configuration, and performance management, and with the ME orchestrator via Mm3 for lifecycle management and policy provision.  
As in NFV, the VIM is responsible for the virtualized resources used by ME apps [29]. Moreover, it can provide fault and performance monitoring capabilities to the ME orchestrator through Mm4.
## 5 Numerical Evaluation Results
### Evaluation Setup
#### 5.1.1 LayBack Architecture  
Initially, we consider a LayBack network architecture with \(O=2\) network operators, which we index with \(o=1\) and \(o=2\). Each network operator has three GWs for a total of six GWs. Each gateway has ten eNBs for a total of 60 eNBs.  
Each operator has an installed backhaul bitrate resource (capacity) of \(Z_{o}=10\) Mbps. Assuming that the two operators have agreements to fully share each other's backhaul capacity, the aggregate available backhaul bitrate (capacity) is \(Z=20\) Mbps. The objective of the optimization is to optimally share the available backhaul resource of \(Z=20\) Mbps among all eNBs attached to all the GWs of both operators \(o=1\) and \(o=2\).  
The round-trip propagation delays (RTTs) are set to eNB-GW RTT \(\tau_{N}^{G}=1\) ms, GW-operator RTT \(\tau_{G}^{O}=100\) ms, and operator-SDN orchestrator RTT \(\tau_{O}^{S}=1\) s.
## 1 Introduction
From previous investigations regarding dual-wavelength fiber lasers, the use of a fiber optical loop mirror (FOLM) with a high-birefringence (Hi-Bi) fiber loop has been theoretically and experimentally demonstrated as a trustworthy method for intra-cavity losses adjustment. The method is based in the wavelength displacement of the Hi-Bi FOLM periodical spectrum by applying temperature variations on the Hi-Bi fiber loop to finely balance the losses at the two simultaneously generated wavelengths [20]. In this regard, a stable high-efficiency dual-wavelength fiber laser operating in the 1.55 \(\upmu\)m waveband can be an attractive light source for different research areas. In this paper, we report a high-efficiency dual-wavelength laser emission of a linear cavity EYDCF laser with SQS and CW operation depending on the pump power range. SQS operation is observed with low pump power in a range from 322 to 890 mW. Pulses with minimum pulse duration of \(\sim\)1.5 \(\upmu\)s and maximum pulse energy of \(\sim\)3.5 \(\upmu\)J are obtained with the maximal pump power for SQS regime. With pump power up to 890 mW, stable CW laser operation is achieved. Tunable dual-wavelength laser emission with maximum separation of 7 nm is obtained by using a pair of tunable FBGs for laser wavelengths selection. The intra-cavity losses adjustment to achieve dual-wavelength laser emission with equal powers is obtained by applying temperature variations on the fiber loop of the Hi-Bi FOLM. Slope efficiency of 36.3% and maximal output power of 3.6 W with dual-wavelength laser lines spectral linewidth of \(\sim\)0.2 nm is experimentally obtained.
# Parameterized Algorithms in Bioinformatics: An Overview
## 4 Haplotyping  
Cells of _diploid_ (or, more generally, _polyloid_) species have two (multiple) copies of each chromosome. These copies are imperfect in that they can differ in a tiny percentage of their positions(\"sites\") and such positions are called _Single-nucleotide polymorphisms_ (SNPs). When sequencing a diploid genome (see Section 3 for more on sequencing), each read comes from one of the copies of some chromosome but, a priori, there is no way of knowing whether two reads came from the same copy or not. Thus, an assembled diploid genome of an individual contains SNPs where both chromosomes agree (\"homozygous sites\") and SNPs where they disagree (\"heterozygous sites\"). Since the vast majority of SNPs exist in only two states (\"bialleic SNPs\") [83; 84], the SNPs of a chromosome can be represented as a string over the alphabet \(\{0,1\}\) called a _haplotype_ and the SNPs of a diploid individual can be represented as a string over the alphabet \(\{0,1,2\}\) called the _genotype_, where 0 and 1 mean that the two chromosomes agree on one of the biallelic states, whereas 2 means that the chromosomes disagree. Now, experimentally determining the genotype of an individual is (relatively) cheap and easy while experimentally determining the haplotypes of the chromosomes is expensive [85; 86]. For many applications, however, knowledge of the haplotypes is required and, thus, computational methods have been developed to infer haplotype data (\"haplotyping\" or \"phasing\"). In Section 4.1, we consider the problem of inferring the haplotypes by overlapping reads from a diploid genome, similar to the reconstruction of DNA sequences (see Figure 7). In Section 4.2, we consider the problem of inferring the haplotypes of a population, given its genotypes (see Figure 8). This requires assuming that the distribution of haplotypes follows a parsimony criterion. Although most parsimonious solutions often do not reflect the truth [87], they can form the basis for more precise algorithms in the future.  
For surveys on both variants of haplotyping, refer to the works in [88; 89; 90; 91; 92; 93; 94; 95].
# A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams
## 2 Distance Learning
### State of the Art
#### 2.1.3 Institutions  
What moved institutions towards VLEs is their flexibility of employment, the repeatability and the consistent control they provide, the reduction of costs (in both software and hardware), the possibility of monitoring students' progress, as well as the chance to manage groups of students and of communities.  
Concerning saving costs, an important and wide-spread solution for VLEs concerns the cloud computing paradigm [5]. Such a technological choice allows for a centralized virtualization and re-use of low-performing legacy computers, as well as deploying applications without the need to buy many licenses. Moreover, some cloud-based learning environments have been devised ad hoc for virtual laboratories in different fields [18; 20], thus allowing institutions, after specific agreements, to exploit laboratories of other universities or research centers. Nevertheless, the cloud could raise some issues, such as the inability to implement a pay-per-use scheme and open-source solutions or what is called vendor lock-in. The latter regards the lack of interoperability that could block institutions from sharing data and services across different clouds or dynamically porting them from one service provider to another.
# Bayesian Estimation of Simultaneous Regression Quantiles Using Hamiltonian Monte Carlo
## 2 Simultaneous Quantile Regression  
### Asymmetric Laplace Distribution  
The ALD is a probability distribution that is used to model data with asymmetric tails and skewness. It is a continuous probability distribution defined on the real line. The distribution is characterized by three parameters: location, scale, and skewness parameters. Indeed, the ALD is often used in Bayesian statistics, particularly in the context of Bayesian regression models that involve quantile regression.  
A random variable \(U\) follows an ALD(\(\mu\), \(\sigma\), \(\tau\)) if its probability density is given by  
\[f(x;\mu,\sigma,\tau)=\frac{\tau(\tau-1)}{\sigma}\exp\left(-\frac{(x-\mu)}{ \sigma}[\tau-\mathds{1}(x\leqslant\mu)]\right),\ x\in\mathbb{R}. \tag{2}\]  
where \(0<\tau<1\) is the skew parameter, \(\sigma>0\) is the scale parameter, and \(-\infty<\mu<\infty\) is the location parameter. The mean and variance of the ALD with density (2) are  
\[\mathds{E}[U]=\mu-\frac{\tau-\frac{1}{\tau}}{\sigma}\quad\text{and}\quad \text{Var}[U]=\frac{1+\tau^{4}}{\tau^{2}\sigma^{2}}\]  
Some other ALD properties can be found in [28]. In order to simplify the posterior inference of the quantiles, it is convenient to use the location scale mixture representation. In particular, we are obliged to use it in HMC because ALD is not differentiable and to calculate the full conditional distributions (for more details, see [20]). The location-scale mixture of the ALD distribution will be used. Let \(\omega\) be an exponential latent variable with parameter \(1/\sigma\) denoted by \(\exp(1/\sigma)\) and let \(Z\) be a standard normal variable so that \(\omega\) and \(Z\) are independent, then \(\epsilon\sim\text{ALD}(0,\sigma,\tau)\) has the following representation:  
\[\epsilon=\gamma_{\tau}\omega+\delta_{\tau}\sqrt{\sigma\omega}Z,\]  
where \(\gamma_{\tau}=\frac{1-2\tau}{\tau(1-\tau)}\) and \(\delta_{\tau}^{2}=\frac{2}{\tau(1-\tau)}\).