esa-sceva/satcom-chunk-collection huggingface dataset:
# Neighbor-Aware Non-Orthogonal Multiple Access Scheme for Energy Harvesting Internet of Things
## 6 Conclusions  
This paper proposes NA-NOMA where each IoT device considers its energy level and other devices' transmission powers to decide its transmission policy. For example, each IoT device can transmit data with high transmission power when its energy level is sufficient and/or other devices do not transmit any data. In addition, if the energy level of an IoT device is not sufficient and/or there are lots of other devices transmitting data, that IoT device does not transmit any data. To maximize the aggregated data rate of IoT devices while keeping the acceptable average energy outage probability, a constrained stochastic game model is formulated. To achieve the optimal (i.e., best response) policies of IoT devices, the best response dynamics-based algorithm with the polynomial complexity is introduced. Evaluation results demonstrate that the proposed algorithm can find the optimal stationary  
Figure 4: Effect of \(\theta_{E}\). (**a**) Average aggregated data rate. (**b**) Energy outage probability.  
Figure 3: Effect of \(P^{H}\). (**a**) Average aggregated data rate. (**b**) Energy outage probability.  
policies within a few iterations. In addition, it can be shown that NA-NOMA can increase the average data rate up to 22% compared with a probability-based scheme while providing a sufficiently low energy outage probability (e.g., 0.05). Moreover, it can be observed that NA-NOMA optimizes its transmission policy by considering its operating environment (e.g., energy harvesting probability and target energy outage probability). As one of future works, we will investigate the spatiotemporal correlation of energy harvesting probabilities of IoT devices to further reduce the energy outage probability. In addition, we will develop the prototype of the proposed scheme for IoT-based environmental monitoring systems.  
Conceptualization, Y.K. and H.K.; methodology, Y.K. and B.K.; software, Y.K., M.P. and S.-Y.K.; validation, Y.K. and H.K.; formal analysis, Y.K. and B.K.; investigation, H.K.; resources, H.K.; data curation, Y.K. and B.K.; writing--original draft preparation, Y.K.; writing--review and editing, H.K. and S.-Y.K.; visualization, Y.K. and S.-Y.K.; supervision, H.K.; project administration, H.K.; funding acquisition, H.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation (NRF) of Korea Grant funded by the Korean Government (MSIP) (No. 2019R1C1C1004352).  
Not applicable.  
The authors declare no conflict of interest.
# Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures
## 2 Materials and Methods
### Classification and Definitions
#### 2.4.1 Regression Based Methods
Moreover, based on the Bootstrap approach, linear regression analysis and residual analysis, [PERSON] et al. [39] performed a simulation and authentication of the principal S-N curves of titanium alloy welded frames. [PERSON] et al. [40] created an experimental model and regression equations to forecast residual stresses in the Gas Tungsten Arc Welding (GTAW) procedure.
## 3 Results
### Transcriptome Analysis
#### 3.2.3 Transcriptome Analysis of Secondary Metabolite Biosynthesis Pathways  
Based on the analysis of metabolites obtained in this study, it was found that terpenoid groups dominated the biosynthesis pathway sites that have been successfully identified. Transcriptome analysis in this study using GhostKOALA software led to the identification of the major enzyme responsible for the final pathway site in the terpenoid biosynthesis, namely alpha-farnesene synthase (_AFS1_), terpenene synthase (_TPs_), and germacrene D synthase (_GDS1_). In the flower part of _H. Gagyanensis_, the _AFS1_ gene reacts with the substrate (2E,6E)-alpha farnesyl diphosphate to produce (3E,6E)-alpha-Farnesene as a product. While TPs in H. _lacunosa_ also play a role in producing terpenoid compounds. _GDS1_ in _H. coriacea_ reacted with the substrate (2E,6E)-farnesyl diphosphate to produce (-)-germacrene D as the final product. The genes involved and the final metabolites biosynthesized in the _Hoya_ flower terpenoid pathway were discovered for the first time in the genus. The study results also showed KEGG pathway analysis using MVA and MEP pathways to produce terpenoid compounds (Figure 9).  
Figure 8: Annotation of GO terms from categories of biological process (BP), molecular function (MF), and cellular components (CC) for (**a**) _H. Gagyanensis_; (**b**) _H. lacunosa_, and (**c**) _H. coriacea_. Only the top 20 terms for each category shown.
## 5 In-Line Application of LIBS in Industrial Production
### Steel Grade Detection in Casting  
In industrial steel production the casting of liquid steel into slabs, blooms, and billets is a frequently used process. The continuous casting of steel from different heats produces slabs that may have different chemical composition. The detection of different steel grades is important for the identification of the slabs. Moreover, the detection of transition zones from one steel grade to the other can improve productivity and cost-efficiency, e.g., by the reduction of steel waste. LIBS has been proposed for analyzing the chemical composition of cast steel and the at-line monitoring in the steel casting process has been successfully demonstrated [463, 464, 465].  
The steel slabs are covered by different layers of varying thickness, which poses a substantial challenge for surface-analytical techniques such as LIBS. A schematic of LIBS at-line measurement of hot steel slabs in the continuous steel casting process is shown in Figure 17a. The bulk steel material (a) is covered by a scale (oxide) layer (c), mold powder (d), and dirt. The thickness of this surface layer can exceed several 100 \(\upmu\)m. For LIBS analysis of the bulk material the surface layer has to be removed locally, e.g., by a sequence of laser pulses that precede the LIBS measurement and ablate the slab surface (\"laser cleaning\").  
Figure 16: LIBS in-line measurement of tire rubber. (**a**) Photograph of the LIBS system installed at the tire rubber production line. (**b**) Validation of S and ZnO concentrations measured in different types of rubbers (dashed line: ideal match). Adapted from [357].  
The LIBS signal of the Pb (I) line at 405.78 nm measured at-line on a hot steel slab in motion shows the transition from one steel grade to another (Figure 17b) [463]. The number of laser shots corresponds to the position of LIBS measurements on the steel slab along the casting direction (5 mm distance between two subsequent laser shots at pulse repetition frequency of 5 Hz). For comparison, the Ni (I) 341.47 nm line intensity did not change at the transition zone (inset of Figure 17b). The concentration of Pb in the two steel grades was 0 and 0.17 wt% (for Ni the concentration was 1.75 and 0.88 wt%). The LIBS signals obtained from the sample surface in real-time and the statistical analysis of signals allowed to discriminate special steel grades and to predict the distribution of elements in the intermixed transition zone of the cast slabs. The combination of LIBS measurements and Artificial Neural Network (ANN) methods for signal evaluation has also been used for the quantitative elemental analysis of cast steel along the slab length [465].
# Real-Time Communication Aid System for Korean Dysarthrie Speech
## 4 Experiments
### Experimental Results
#### 4.2.2 TTS Model  
The TTS model was trained by using the KSS dataset for a total of 150 epochs over the course of 7 days. To evaluate the performance of JETS to be used in the proposed system, we used the Tacotron2 [41] and Transformer TTS [42] models provided by ESPnet-TTS [43] to measure and compare their performance. All of these models were trained using the same dataset in the environment mentioned in Section 4.1. The evaluation metric used for performance measurement was the subjective Mean Opinion Score (MOS) [44]. The MOS is a widely used evaluation metric for TTS performance, assessing the quality of generated speech by subjectively comparing it to the original speech.  
For the MOS measurement, 10 participants from schools in Korea were asked to evaluate 20 different audio samples generated by each model. The evaluation was based on a scale of 1 (poor), 2 (fair), 3 (average), 4 (good), and 5 (excellent), and was assessed based on how natural the synthesized speech was compared to the original, unprocessed Korean speech.  
Table 3 shows the MOS results for the three TTS models. The evaluation results indicate that JETS achieved the highest score of 4.64, while Tacotron2 scored 3.03, and Transformer TSS scored the lowest at 2.51. The overall tendency in the evaluation of the synthesized speech is that the speech generated by JETS was considered natural both in terms of intonation and voice quality, whereas the speech synthesized by Tacotron2 and Transformer TTS has natural intonation but was dominated by mechanical-sounding voices.
## 3 Psychophysical Experiments
### Experimental Design  
**Independent variables:** we employed a within-subjects repeated measures design, with coding methods (CMs) as a between-subject factor and flight states as a within-subject factor ( roll-left, roll-right, pitch-up and pitch-down state).  
**Dependent variables:** recognition accuracy and reaction time were implemented to evaluate the effectiveness of vibrotactile belt for cueing angle information.  
Recognition accuracy is defined as the percentage of correct recognition of vibrotactile commands. Under the condition of correct judgment for flight states, we classified an identification with a difference between a reported and set angle no more than five degrees as a correct recognition, otherwise it was a wrong recognition. Recognition accuracy is proportion of number of correct recognitions to total number of commands.  
Reaction time defined as the elapsed time between finishing the display of the vibrotactile command and the moment when the subject has reported a given angle. It should be noted that the time spent on clicking some buttons for reporting the corresponding angles was not included in the reaction time (the time taken for clicking buttons can be recorded in experimental software).  
Information transfer rate (bits/min): to statistically analyze the preference of coding methods, we further calculate the information transfer rate (ITR) as shown in Equation (1). The most popular method for ITR calculation in brain-computer interface (BCI) research was defined by [PERSON] et al.  
Figure 5: A subject during the experiment.  
in 1998, which is a simplified computational model based on Shannon channel theory under several assumptions [36; 37].  
\[B_{t}=\frac{60}{T}(\log_{2}N+P\cdot\log_{2}P+\log_{2}(\frac{1-P}{N-1})^{1-P} \tag{1}\]  
Generally, \(B_{t}\) in bits/min is used to indicate the ITR, \(N\) is the number of possible choices and \(P\) is the probability that the desired choice will be selected, also called target identification accuracy or classifier accuracy. \(T\) (seconds/symbol) is the time needed to convey each symbol. In the current study, \(N\) can be seen as the total number of angles (60), \(T\) can be seen as the reaction time for each vibrotactile pattern, and P is obviously the recognition accuracy. The ITR is more comprehensive than recognition accuracy or reaction time for taking account of both the two evaluating criterion.
# A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems
## 4 State-of-The-Art WSN Based Air Pollution Monitoring Systems
### Community Sensor Network (CSN)
In [113], a Volatile Organic Compounds (VOCs) sensor node with high selectivity and sensitivity was developed. The authors focused more on the development of the novel tuning fork sensors than the implementation of the air quality monitoring system. Each sensor node consists of several tuning fork sensors (detecting VOCs, temperature and relative humidity) and a Bluetooth device communicating with the smart phone. A customized mobile App for visualizing the sensing data was implemented. Adavantages:  
1. Cost efficiency (The sensor node utilizes the cellphone's GPS module and the cellular network, or even the cellphone's computational power.) 2. Coupled data generators and consumers (Local or personal air pollution information is available. )Public-driven property (The cost of the sensor nodes and the data transmission can be apportioned by the users. It is costly and infeasible for a single agency to acquire all the sensor nodes.) * 4. Automatic gathering property (The sensor nodes are densely distributed at locations with gathering people automatically. Data with higher spatial resolution and accuracy are achievable in such case.) * 5. Mobility of sensor nodes (The mobility of the cellphones or users enlarges the sensor node's geographic coverage.) * 6. Public behaviors acquisition ability (Information such as the public movement patterns, and interaction between air quality and public behaviors, is achievable.) _Disadvantages or Challenges:_
* 1. Low data accuracy and reliability (The sensor nodes are typically put in pockets or handbags. Also, the users spend significant amount of time indoor or inside cars [114]). * 2. Privacy issues (The users may not want to make their location information public for privacy issues). * 3. Badly calibrated and maintained sensors (Professional calibrations of sensors performed by the public users are very unlikely. Frequent collections and calibrations of sensors by the professionals are infeasible). * 4. Serious constraint on energy consumption (The sensor nodes is typically powered by cellphone's battery or battery with small capacity). * 5. Uncontrolled or semi-controlled mobility (The routes of the sensor nodes or users are pre-determined. The sensor nodes may squeeze into a small place with crowded people and cause redundant sampling. Some locations may never be visited). * 6. 2-Dimensional data acquisition (Only the air quality of urban surface is monitored). * 7. Serious limitations on weight and size (The sensor node should be portable, which affects the accuracy, reliability and number of sensors equipped, because it is carried by user).
## 4 Multi-Angle Analysis of RGN Predicted Results
### RGN Prediction Process and Data Acquisition
#### 4.1.2 Geometry Stage of RGN  
The torsion angle data obtained in the calculation stage is transmitted to the geometry stage as the input data. The geometry stage takes the input torsion angle of the current residue and the backbone torsion angle formed in the upstream geometry stage as the input data of the geometry stage corresponding to the next residue until the final residue is reached and outputs the final predicted structure. In the geometric stage, the given torsion angle data is converted into equivalent key length, and the final tertiary structure is gradually formed through key rotation, extension, and surface rotation.
###### Abstract
Gaussian process regression (GPR) has recently become popular within the prediction of RUL due to its various advantages, including nonparametric modeling, probabilistic prediction, and low computation burden [26]. However, within feature learning, the method requires not only sufficient raw data, but also the optimization of many parameters. The artificial neural network (ANN) is a learning method for complex data composed of neural networks. The ANN model contains one or more hidden processing layers between the input and output layers. This model has a relatively stronger ability to learn compared to other methods. Therefore, the method is suitable for application to classification and regression. HSVs have complex structures whose operations include electromagnetic, mechanical, and liquid coupling processes. Furthermore, a great deal of experimentation would generally be required to validate such a model. Therefore, the data-driven method is more suitable for HSVs' state detection and RUL prediction. The objectives of the study were to: (1) construct an HSV test bench and define the HSV degradation state; (2) propose an HSV state detection method based on the FNN algorithm; (3) construct a model to predict the RUL of an HSV based on the integration of the ARIMA model and LSTM model (abbr. ARIMA-LSTM).
# Theoretical Derivation and Optimization Verification of BER for Indoor SWIPT Environments
## 6 Conclusions  
We studied a design of the algorithm for resource allocation in indoor networks with wireless power transfer on the basis of an objective optimization framework. We used wideband circular antenna arrays at the transmitter to maximize the power efficiency performance and keep the data quality. The BER should consider the inter symbol interference in a wideband system; instead, the BER only considered SNR in narrow band system. We employed SADDE and APSO algorithms to adjust the feed length and excitation voltages to synthesize the radiation pattern. Both the SADDE and APSO patterns pointed to all of the receivers at early generations, and had similar radiation patterns in the final result. The improvements in the total power harvesting ratio were about 2.45. Moreover, the results also reveal that the speed of convergence for APSO was faster than that for SADDE. The APSO uses only 100 generation for the final result.  
[PERSON], [PERSON] and [PERSON] contributed to the conception of the study. [PERSON], [PERSON] and [PERSON] contributed significantly to analysis and manusic ript preparation; [PERSON] and [PERSON] performed the data analyses and wrote the manuscript; [PERSON] and [PERSON] helped perform the analysis with constructive discussions. [PERSON] and [PERSON] major revised the manuscript in final version. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Research Initiation Project of Introducing High-level Talents from Beibu Gulf University of China, with grant number as 2017 KYQD209.  
The authors declare no conflict of interest.
# Drought Propagation in Brazilian Biomes Revealed by Remote Sensing
## 2 Materials and Methods
### Study Areas and Drought Events
#### 2.1.1 Amazon: The 2010 and 2015 Droughts  
According to [PERSON] et al. [47], most of the Brazilian Amazon has an annual \(P\) cycle with the highest rainfall in austral summer and an average annual rainfall of approximately 2200 mm/year [48; 49]. The wet/dry seasons are defined as December-March/July-October for southern Amazonia and February-May/July-October for northern Amazonia, according to the rainfall seasonal cycle [50].  
The southern Amazon, especially the southeastern region, includes the area with a large expansion of deforestation, also known as of the arc of deforestation, which is the driest portion of the Amazon Basin [51], with months with rainfall rates lower than 100 mm during the austral winter [52]. The region was affected by severe droughts in 2010 and 2015, which were associated with a record-breaking warming [53; 54; 50]. The analyzed region was located over the southern Amazon biome, a region predominantly covered by forest (76%), associated with indigenous lands and conservation units, and especially characterized by large deforestation associated with agriculture and pasture development (22% covered by farming) [51].
## 4 Design of Secure Communication Based on Synchronized Large-Scale CRNGs  
In the following, we will discuss how to apply the synchronized master-slave large-scale CRNGs to construct the secure communication system. The design diagram is given as follows:  
Figure 11: The phase planes of the controlled master and slave CRNGs.  
Figure 12: Synchronized large-scale CRNGs.  
In Figure 13, the plaintext is sent to the encryption mechanism of the transmitter. The master large-scale CRNG sends the random state to the encryption mechanism and encrypt the original signal. After the encryption is completed, the transmitter transmits the cipher-text and the information \(u_{mi}(k)\) to the receiver. At the receiver, combined \(u_{si}(k)\) with \(u_{mi}(k)\) transmitted from the transmitter, the synchronization controller \(u_{i}(k)=u_{mi}(k)+u_{si}(k)\) is constructed to achieve synchronization of the master and slave large-scale CRNGs. Finally, the decryption mechanism with the synchronization signal can complete the decryption.  
In order to verify the design of the above-mentioned secure communication system, we use Holtek's 8-bit (HT66F50) and 32-bit (HT32F1765) microcontrollers to realize the secure communication in Figure 13. The following Figure 14 shows the diagram of the secure communication system. In Figure 14, 8-bit microcontroller is used to generate plaintext and 32-bit one is used to perform large-scale CRNGs and its synchronization.  
The encryption and decryption in Figure 14 are realized by using the exclusive-or as shown in Figure 15. When the master and slave large-scale CRNGs reaches synchronization, then the master large-scale random number A and the slave large-scale random number B will be the same. The encryption can be decrypted and the plaintext m can be recovered at the receiver side because \(n=m\oplus A\oplus B=m\oplus A\oplus A=m\).  
Figure 14: Communication system design diagram.  
Figure 13: Communication system design.  
After completing the circuit design, we use some basic components to implement the circuit as shown in Figure 16, in which the dot matrix LEDs are used to display master and slave random signals, plaintext, cipher-text and decrypted text, respectively. In Figure 16, we first disable the synchronization controller. We can find that the master random number and the slave random number cannot be synchronized and the encryption cannot be decrypted smoothly. While in Figure 17, we enable the synchronization controller. Therefore, it can be found that the master random number and the slave random number are synchronized, as expected and the encryption can be decrypted.  
Figure 16: The secure communication system before synchronization.  
Figure 17: The secure communication system after synchronization.  
Figure 15: The structure of encryption and decryption mechanism.
# Tailored Nanoparticles as Vaccine Components
## 3 Types and Uses of Nanoparticles in Vaccines
### ISCOMs  
Immunostimulating complexes (ISCOMs) are spherical, perforated, cage-like, hollow micelles with a size of 40-60 nm that are made of the saponin adjuvant Quil-A(r), phospholipids, cholesterol, and the protein or peptide antigens, which are trapped in the center through nonpolar interactions [5; 19; 24][32]. ISCOMs can bind and penetrate cellular membranes and carry the antigen and the adjuvant into the cytosol of the antigen-presenting cells (APCs) such as DCs so that the antigen is transported--with activated DC migration--to the lymphnodes [19; 24]. ISCOMs are produced with the help of non-ionic detergents that are then removed by dialysis [19]. The antigens that are currently used with ISCOMs are derived from the Newcastle disease virus, herpes simplex virus, and influenza, among others [24].
# A Carrier Estimation Method Based on MLE and KF for Weak GNSS Signals
## 3 MLE Discriminator
### Dynamic Characteristics
So considering both the dynamic performance and SNR gain, 20 ms is chosen as the discriminator update time.