esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Non-orthogonal multiple access (NOMA) emerges as a promising candidate for 5G, which radically alters the way users share the spectrum. In the NOMA system, user clustering (UC) becomes another research issue as grouping the users on different subcarriers with different power levels has a significant impact on spectral utilization. In previous literature, plenty of works have been devoted to solving the UC problem. Recently, the artificial neural network (ANN) has gained considerable attention due to the availability of UC datasets, obtained from the Brute-Force search (BF-S) method. In this paper, deep neural network-based UC (DNN-UC) is employed to effectively characterize the nonlinearity between the cluster formation with channel diversity and transmission powers. Compared to the ANN-UC, the DNN-UC is more competent as UC is a non-convex NP-complete problem, which cannot be entirely captured by the ANN model. In this work, the DNN-UC is first trained with the training samples and then validated with the testing samples to examine its mean square error (MSE) and throughput performance in an asymmetrical fading NOMA channel. Unlike the ANN-UC, the DNN-UC model offers greater room for hyper-parameter optimizations to maximize its learning capability. With the optimized hyper-parameters, the DNN-UC can achieve near-optimal throughput performance, approximately 97% of the throughput of the BF-S method.  
deep neural network; non-orthogonal multiple access; throughput maximization; user clustering; machine learning 202113507. [[https://doi.org/10.3390/sym13081507](https://doi.org/10.3390/sym13081507)]([https://doi.org/10.3390/sym13081507](https://doi.org/10.3390/sym13081507))  
1
# A Constrained Coding-Aware Routing Scheme in Wireless Ad-Hoc Networks
## 3 Routing Implementation
### Routing Coding Benefit  
In the available paths, we need to choose the best path. Therefore, we design a novel routing metric to quantify all the available paths in this subsection. The existing routing metrics are roughly divided into two categories to measure the routing coding benefit (i.e., topology-based metrics and load-based metrics). In the CAR schemes, topology-based metrics include the three following measures. (1) hop count: it is often used because of its simple application. It only counts the hop numbers of the link and ignores the link quality and other link characteristics. (2) ETX(the expected transmission count) [39]: it can calculate the number of expected successful transmissions and reflect the link quality. (3) ETT (the expected transmission time) [40]: it is extended from ETX and includes the link bandwidth and flow rate. The load-based metrics often take into account the current interfering traffic in the routing decision. In [41], [PERSON] et al. proposed an expected number of coded transmission (ECX) metric that considered the number of encoded packets, the channel capacity, and the packet loss probability. [PERSON] et al. proposed a group-aware cooperative routing metric (GAR) that applies the group feature and the packet delivery probability to reduce the network load in resource-constrained  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline Flow (4b) & & & \(f_{2}\) & & \\ Hops (4b) & & & 4 & & \\ Path (32b) & S1 & R3 & R4 & R5 & D2 \\ Neighbors (32b) & D1 & - & R2 & S1 & - \\ Overheard flows (32b) & \(f_{1}\) & \(f_{1}\) & \(f_{1}\) & - \\ Coding opportunities (16b) & - & - & T & - & - \\ Coding flows (16b) & - & - & \(f_{1}\) & - & - \\ Flow rate (32b) & \(R_{f_{2}}\) & \(R_{f_{1}},R_{f_{2}}\) & \(R_{f_{2}}\) & \(R_{f_{2}}\) \\ Link quality (16b) & & & \(Q_{1},Q_{2},Q_{3},Q_{4}\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The routing information stored in the RREP message.  
networks [42]. However, most load-based metrics are too difficult to implement and have high costs. The coding-aware routing metric (CRM) was proposed by [PERSON] et al. [10], which takes into account the encoded queue length. The CRM showed that the estimation of the queue length consumed less power. Moreover, in [37], we discussed the queue state of the coding nodes with the Markov model. In this paper, we propose a new metric called expected transmission count based on queue length (EXQL) that combines the topology-based and load-based metrics to choose a good path. EXQL consists of two sections in which (1) a coding benefit function is used to calculate the coding gain of the encoded packets by the routing information, and (2) based on the queue length, a weight factor is used to reflect the state of the coding node. In addition, the EXQL metric can adjust to the constrained coding conditions and simplify the routing implementation.
Sensors_**2015**, _15_, 23706-23726; doi:10.3390/s150923706  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
A Novel Component Carrier Configuration and Switching Scheme for Real-Time Traffic in a Cognitive-Radio-Based Spectrum Aggregation System  
**[PERSON] 1,2,\({}^{\dagger}\), [PERSON] 1,2,\({}^{\dagger,\star}\) and [PERSON] 1,2,\({}^{\dagger}\)**  
\({}^{1}\) Harbin Institute of Technology Communication Research Center, Harbin 150001, China;  
E-Mails: [EMAIL_ADDRESS] (Y.F.); [EMAIL_ADDRESS] (Y.X.)  
\({}^{2}\) Key Laboratory of Police Wireless Digital Communication, Ministry of Public Security,  
Harbin 150001, China  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-451-8641-3513 (ext. 8202); Fax: +86-451-8641-8070.  
Academic Editor: [PERSON]  
_Received: 31 July 2015 / Accepted: 11 September 2015 / Published: 17 September 2015_  
**Abstract:** In spectrum aggregation (SA), two or more component carriers (CCs) of different bandwidths in different bands can be aggregated to support a wider transmission bandwidth. The scheduling delay is the most important design constraint for the broadband wireless trunking (BWT) system, especially in the cognitive radio (CR) condition. The current resource scheduling schemes for spectrum aggregation become questionable and are not suitable for meeting the challenge of the delay requirement. Consequently, the authors propose a novel component carrier configuration and switching scheme for real-time traffic (RT-CCCS) to satisfy the delay requirement in the CR-based SA system. In this work, the authors consider a sensor-network-assisted CR network. The authors first introduce a resource scheduling structure for SA in the CR condition. Then the proposed scheme is analyzed in detail. Finally, simulations are carried out to verify the analysis on the proposed scheme. Simulation results prove that our proposed scheme can satisfy the delay requirement in the CR-based SA system.  
**Keywords:** RT-CCCS; real-time traffic; spectrum aggregation; resource scheduling; cognitive radio
# Phytoplankton as CO\({}_{2}\) Sinks: Redirecting the Carbon Cycle
## 2 Photosynthesis and Global Balances of Matter and Energy
### Photosynthetic Efficiency  
Despite a large amount of energy continuously reaching the Earth's surface, only a small percentage of this is finally stored as reduced carbon by photosynthesis. Initially, only 53% of the incident radiation belongs to the PAR (Photosynthetic active radiation) region, which is suitable for its photochemical transformation. Thirty percent of this effective light is lost by hitting non-photosynthetic cell structures of most primary producers, ending up in available 37%. Ignoring photoinhibition scenarios, 24% of entered photons in PSI and PSII are lost in the adjustment to reach the optimal conversion wavelengths, yielding 28% concerning initial radiation. During the chemical conversion of this remaining light, 68% is lost along the different electronic chains and enzymatic reactions, and only 9-11% of the initial energy is stored as proper monosaccharides for the metabolism (GPP). Finally, 40% of these compounds are employed in mitochondria or involved in photorespiration processes [19], meaning a 5% theoretical PE (Photosynthetic Efficiency). Photorespiration is partially avoided and improved in C4 and CAM plants, shifting the final theoretical efficiency to 6-7%. Alternatively, C3 plants show passive control against photorespiration/carboxylation balance, reaching 4.7% of theoretical PE [20; 21]. However, these values become even lower in natural environments and rarely exceed 2.4% and 3.4% for C3 and C4 crops, respectively, in what is usually called NPP [22].  
Microalgae and cyanobacteria present some differences in this general diagram. Their complexity and the lack of specialised structures allow the reduction of photon impact on non-photosynthetic structures. Furthermore, in these organisms, RuBisCo works in a CO\({}_{2}\)-rich environment provided by the carboxosomes (cyanobacteria) or the pyrenoid (microalgae), which largely avoid the photorespiratory activity of RuBisCo [23; 24]. Therefore, the PE of some microalgae species has been established at approximately 20% under highly controlled scenarios [25]. Nevertheless, the annual averages of outdoor biomass production systems rarely offer a PE value above 2.5% due to the broad ranges of temperatures and fluctuations in photoperiods and light intensities, among other handicaps [26].
## 3 Stability Analysis of Equilibria
### The Commensurate Case
: _Local asymptotic stability of \(E_{*}\) is guaranteed if either one of the following two conditions is verified._  
\[A^{2}\geq 4B,\quad R_{0}>1,\quad|\frac{-A\pm\sqrt{A^{2}-4B}}{2}|<2^{\eta}, \tag{28}\]  
_or_  
\[A^{2}<4B,|\gamma_{3,A}|<\left(2\cos\frac{|\arg\gamma_{3,A}|-\pi}{2-\eta}\right) ^{\eta},\quad|\arg\gamma_{3,A}|>\eta\frac{\pi}{2}. \tag{29}\]  
_where \(A=\frac{R_{0}}{d}\) and \(B=\lambda b(1-\frac{1}{R_{0}})\)._  
Proof. : The Jacobian matrix is computed  
\[\mathcal{J}^{*}= \begin{pmatrix}-\lambda I_{*}-d&-\lambda S_{*}\\ \lambda I_{*}&\lambda S_{*}-\epsilon-d\end{pmatrix} , \tag{30}\]  
The characteristic equation determines the eigenvalues of \(\mathcal{J}^{*}\)  
\[\gamma^{2}+A\gamma+B=0, \tag{31}\]  
with  
\[A=\frac{R_{0}}{d}\quad\text{ and }\quad B=\lambda b(1-\frac{1}{R_{0}}), \tag{32}\]  
which implies  
\[\gamma_{3,A}=\frac{1}{2}\Big{(}-A\pm\sqrt{A^{2}-4B}\Big{)}, \tag{33}\]  
for \(A^{2}\geq 4B\) The eigenvalues \(\gamma_{3,4}\) have arguments of \(\pi\) when \(\gamma_{3,A}<0\) which is fulfilled if  
\[ \begin{cases}\frac{R_{0}}{d}>0,\quad\text{(holds true because all the parameters are positive)}\\ \lambda b(1-\frac{1}{R_{0}})>0,\quad\text{i.e, }R_{0}>1,\end{cases}  \tag{34}\]  
this leads to \(\gamma_{3,A}=\pi>\frac{\eta\pi}{2}\). The condition \(|\gamma_{3,A}|<\left(2\cos\frac{|\arg\gamma_{3,A}|-\pi}{2-\eta}\right)^{\eta}=2 ^{\eta}\) is valid if \(|\frac{-A\pm\sqrt{A^{2}-4B}}{2}|<2^{\eta}\). For \(A^{2}<4B\), in this case, \(\gamma_{3,A}\) are complex. The requirements for stability hold true if  
\[|\gamma_{3,A}|<\left(2\cos\frac{|\arg(\gamma_{3,A})|-\pi}{2-\eta}\right)^{ \eta},\quad\text{and}\quad|\arg(\gamma_{3,A})|>\frac{\eta\pi}{2}.\qed \tag{35}\]  
**Example 1**. : _Consider System (12) with parameters \((b,\lambda,d,\epsilon)=(5,0.01,0.2,0.13)\) and \(\eta=0.9\), which implies that \(R_{0}=0.75<1\). Figure 1a demonstrates the stability of \(E_{0}=(25,0)\)._  
**Example 2**. : _Let System (12) with parameters \((b,\lambda,d,\epsilon)=(5,0.04,0.2,0.13)\) and \(\eta=0.9\), which implies that \(R_{0}=3.03>1\). In Figure 1b, \(E_{*}=(8.25,10.15)\) is confirmed to be stable._  
**Example 3**. : _Consider System (12) with parameters \((b,\lambda,d,\epsilon)=(20,0.04,0.2,0.13)\) and \(\eta=0.4\), which implies that \(R_{0}=12.1212\). Figure 1c highlights the chaotic dynamics of the system characterized by irregular and unpredictable fluctuations in its states. This behavior is typical of systems with sensitive parameters and fractional order variations, where small changes can lead to significantly different outcomes._
## 8 Simulation Environment
In the optimized power structure (OPS), power factors are allocate d for user devices in proportion to the analytical formula concluded previously for every device in Section 4. In the simulation files, the transmission distance for each user device with respect to BS is assigned as follows: \(d_{f}=900\) m, \(d_{m}=400\) m, and \(d_{n}=100\) m. Data and pilot symbols are modulated using Quadrature phase shift keying (QPSK) as the modulation format and the applied transferred power is mostly varying from 0 to 30 dBm.
# STPA-RL: Integrating Reinforcement Learning into STPA for Loss Scenario Exploration
## 4 Case Study: Platform Screen Door
### Schematizing Control Structure  
The PSD system is composed of several components and interactions, including an input value called 'train motion' that provides information about the train operation, an 'obstacle sensor' that detects people and objects passing through doors, a 'door position sensor' that provides feedback on the current state of the door, a 'door controller' that determines the control command to open and close the doors, and a 'door actuator' that moves the actual physical door with mechanical force [30].  
To identify the Process Model of the PSD system, the command from the 'door controller' to open and close the door is determined by the values of the 'door position sensor', the 'train motion' state, and the 'obstacle sensor'. The system also utilizes three types of feedback. First, the 'train motion' state is identified by inputting the state of stopped, ready, and moving. Second, the 'door position sensor' recognizes whether the door is opened, opening, closed, or closing and provides feedback accordingly. Third, the 'obstacle sensor' detects the presence or absence of an object and feeds it back to the system. Figure 5 displays the control structure of the PSD system.
## 3 Results and Discussion
### Remote Sensing and IR Camera
The use of GIS can provide the general location of the leak, with infrared technology being used for accurate leak location. The integrated hybrid approach can be very suitable for large water distribution networks. Previous studies have either used IR thermography or GIS separately for water leakage detection [12; 13; 23]. However, this study implemented two different techniques for leak detection. This paper used buffering analysis and intersection methods to create a GIS-based system to locate potential leakages by considering three operational conditioning factors for leakage: flow, pressure, and water quality. The model was validated by using thirteen DMAs of the SEWA WDN. The software generated an intersection map by identifying locations where low residuals intersected with low pressure and flow. This generates an intersection map that highlights areas that have all three conditioning factors for leakage. This intersection map can be used by water utilities for corrective maintenance and preventive measures. In addition, the findings have shown that the IR camera was able to accurately detect leaks in a PPR pipeline with a thickness of 5.7 mm. IR thermography has some disadvantages, such as being influenced by soil moisture and weather conditions [4; 23]. However, these disadvantages can be mitigated by integrating IR thermography into a GIS-based system.
## 6 Using MEC as Enabler for Vehicular Applications  
The deployment of vehicular applications will place a high demand on communications networks, which must ensure that data are transmitted securely and on time. These applications are used for safe and comfortable autonomous driving, to avoid hazards, and for tele-operated cars. MEC is a crucial facilitator for such vehicular applications. In this section, the recent works on applications of MEC-enabled vehicular networks in various domains are presented.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Methodology** & **Key Metrics** & **Focused Component** \\ \hline \(\left[115\right]\) & Real-time energy-aware offloading using MINLP & Energy consumption & Vehicles \\ \(\left[116\right]\) & Resource management optimization with DRL & Network traffic and overhead & MEC server and vehicles \\ \(\left[117\right]\) & Threshold-based computation & Computation resource & MEC server \\  & resource configuration & demand and processor power & MEC server \\ \(\left[122\right]\) & Adaptive resource allocation & Processing capacity, & MEC server \\  & SDN-enabled IoV resource allocation with & Processing capacity, & MEC server and vehicles \\  & PSO algorithm & radio resources & MEC server and vehicles \\ \(\left[124\right]\) & A contract-Stackelberg approach & Idle resources of vehicles & Vehicles \\ \(\left[125\right]\) & Dynamic resource management with MDP & Dynamic network conditions & MEC server \\ \(\left[127\right]\) & Mobility-aware resource allocation based on & Mobility of users and & MEC server \\  & matching theory & constrained resources & MEC server and vehicles \\ \(\left[130\right]\) & Intelligent offloading and caching strategy & Energy efficiency & MEC server and vehicles \\ \(\left[131\right]\) & Mobility-aware EECOC task scheduling & Task latency & MEC server \\  & with DRL & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: A comparison among recent works on resource allocation in MEC-enabled VNs.
## 3 Results and Discussion
#### 3.3.2 Folic Acid of Gluten Free Muffins (GFM)  
The folic content of GFM manufactured with OR and SP sourdoughs at 0, 12, and 24 h of fermentation are illustrated in Figure 8. The folic content of GFM was influenced by the addition of the OR and SP, GFM produced with SP being statistically different (\(p<0.05\)) from GFM produced with sourdough from spontaneous fermentation. This could be explained by the higher content in folic acid in sample SP 24 h (10.48 \(\mu\)g/g f.w) compared with the amount found in OR 24 h sample (3.2 \(\mu\)g/g f.w). This finding is consistent with [79] who proved that the use of sourdough fermented with Lactobacillus strains in bread manufacturing could counteract the thermal loss of bioactive compounds through baking process.  
It is noteworthy to mention that the content of folic acid in final products like bread, noodles, and cookies was improved when QWF was used [48]. Even if, folate is a temperature sensitive vitamin and baking process could diminish its amount, [48] reported a total folate of 17-98 \(\mu\)g/100 g d.m. (dry matter) in noodles, 18-62 \(\mu\)g/100 g d.m. in cookies, and 26-41 \(\mu\)g/100 g d.m. in breads, respectively.  
Folate is the generic descriptor for folic acid, used to describe the folic acid and its derivatives, and it is involved in cell essential metabolism function. Folic acid is defined as a chemical form of folate and it is often used in the food fortification process [92]. The lack of folates in human body could lead to the development of different diseases, such as neural tube defects, malformations, megaloblastic anemia, cardiovascular diseases, and could play an important role in lung carcinogenesis [40; 92]. Therefore, in the US, the fortification of food with folic acid is mandatory and the daily intake recommendation between 200 and 400 \(\mu\)g [40; 92]. Moreover, recently [4] reported a lack of folate content in the diet of children diagnosed with celiac disease highlighting the necessity for mandated gluten free folate food fortification policy.  
Figure 8: Folic acid content of gluten free muffins. Small letters in common indicate no significant differences between OR and SP samples withdrawn at different moments; Big different letters indicate significant differences between SP and OR samples withdrawn at the same moment.
## 1 Introduction
After arriving at a fire point, the UAV swarms need to judge the severity of the fire point, take preventive and control measures, and then detect the next fire point. Currently, there is a lack of research on systematic forest fire detection strategies based on fire point detection, fire assessment, and prevention measures based on UAV swarms. The main contributions of this paper are presented as follows:  
* An application strategy for UAV swarms in forest fire detection is put forward. In contrast to other detection methods, this study establishes a systematic forest fire detection strategy, including fire point detection, fire assessment, and control measures. The fusion detection method is conducted in the modeling of forest fire environments to verify the feasibility of the strategy. This strategy lays the foundation for research on large-scale forest fire detection and prevention based on UAV swarms in the real world. * A fusion detection method based on the PSO and ABC algorithms is proposed. The fast convergence property of the PSO algorithm enables the UAV swarm to reach the fire point quickly. Upon reaching the fire point, the fusion detection method utilizes the ABC algorithm to make a judgment on the severity of the fire and make control measures, then continuously detects multiple fire points. The fusion detection method provides a new idea for the research of efficient and systematic forest fire detection methods. * A forest fire 3D modeling environment has been established. Forest fires are characterized by a large fire range and multiple fire points, so the simulation of the fire environment needs to realize the fire scenarios with different heights of mountain peaks, different fire locations, and different fire ranges. The forest fire 3D modeling environment provides a good research condition for implementing the application strategy of UAV swarms in forest fire detection. The rest of this paper is organized as follows: Section 2 formulates the problem of UAV swarm applied in forest fire detection. Section 3 introduces the methods of modeling forest fire 3D environment, detecting fire points, judging fire conditions, and making fire control measures. Section 4 offers the analysis and results through modeling and simulation to verify the feasibility of the application strategy of UAV swarm in forest fire detection. Finally, Section 5 presents the conclusion and outlines avenues for the next research.
## 3 Results and Discussion
### The Validation of the Methods  
#### 3.3.1 Linearity  
Under the optimal HS-SPME conditions (extraction fiber 50/30 \(\upmu\)m DVB/CAR/PDMS, ion concentration 20%, extraction time 87 min, and extraction temperature 50 \({}^{\circ}\)C) and the conditions of experimental methods and instrumental parameters determined in Section 2.4, the mixed standard stock solutions of the three DMSs were diluted step by step using ultrapure water to formulate concentrations of 10, 50, 100, 200, 500, 750, and 1000 ng/L, and analyzed by linear regression using the external standard method. According to the results of the peaks of the three DMSs at different concentrations, the usual fitting method was used to create scatter plots with the peak area y as the vertical coordinate and concentration x/(ng/L) as the horizontal coordinate. However, to facilitate the calculation and analysis of subsequent experiments, the graphing and fitting methods were adjusted. The peak area and concentration were linearly fitted using corresponding natural log (ln) values. The standard working curves were plotted, and the results are shown in Table 3. The peak areas of DMS, DMDS, and DMTS were well-fitted to the corresponding concentration ranges, with fitting coefficient R\({}^{2}\) values of 0.9967, 0.9907, and 0.9994, respectively. The fitting coefficients R\({}^{2}\) of 0.99, or above, and the \(p\)-values of 0.001 indicate that the peak areas of the three DMSs showed an exponential increase with an increase in their concentration.