esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Grain Moisture Model Based on Improved SSA-SVR Algorithm
## 4 Results
### Improved SSA-SVR Training Model Results
the actual data points in the 3D scatter plot with the predicted surface in the 3D surface plot, one can visually assess the model's fit to the data. Ideally, the points should be closely distributed around the predicted surface, indicating that the model accurately captures the relationship between features and the target. In a three-dimensional space, it may be easier to identify anomalies that deviate from other data points or the predicted surface, which is very useful for further data cleaning and model optimization. Print the moisture values obtained after model training in an Excel spreadsheet, where each voltage and resistance corresponds to a specific moisture value. Subsequently, using this table, one can directly look up the moisture value of long-grain rice by measuring the resistance of the long-grain rice at a temperature of 30 \({}^{\circ}\)C. This will enable a more timely and accurate assessment of the moisture content of the grains. The above is based on a data model at 30 \({}^{\circ}\)C, and further explores the common relationship between temperature, voltage value, and moisture value in the range of 20 \({}^{\circ}\)C to 35 \({}^{\circ}\)C. Figure 7 is a three-dimensional surface chart of voltage, temperature, and moisture after the improved SSA-SVR algorithm. Figure 7 shows a three-dimensional surface plot combining voltage, temperature, and moisture content based on the established optimal model. It first demonstrates the training results of different voltage values on moisture content, which are non-linear, highlighting the reason for using SVR for modeling. Another dimension reveals the influence of temperature on moisture content. By utilizing the improved SSA algorithm to find the optimal parameters, predictive performance on specific datasets can be enhanced, and a color gradient is used to represent the change in grain moisture with temperature and voltage. It can be observed that as the temperature increases and the voltage decreases, the moisture shows a significant change. The moisture values rise or fall by 0.1% for each 1 \({}^{\circ}\)C increase or decrease in temperature.
## 4 Relative Uncertainty Analysis for Scattering-Rate Measurement System  
In order to quantitatively evaluate the actual measuring relative uncertainty of the system, we analyzed the influence of the relative uncertainty factors on the measuring accuracy in terms of following five main relative uncertainty sources: the laser power stability, the laser incident angle, the gap distance between the sample plate and the integrating sphere, the ambient light angle and the off-center position of the incident laser beam. The experimental results were compared with the corresponding simulation results. Then, considering the influence of all relative uncertainties on the total measuring results, the experimental measuring accuracy of the system were given.
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPPThird Generation Partnership Project  
CANController Area Network  
DNSDomain Name System  
DDNSDynamic DNS  
ECUElectronic Control Unit  
GEOGeostationary  
IPInternet Protocol  
IFsecInternet Protocol Security  
IoTInternet of Things  
LEOLow Earth Orbit  
LoRaWANLong Range Wide Area Network  
MDPIMultidisciplinary Digital Publishing Institute  
NATNetwork address translation  
OAuthOpen Authorization  
QoSQuality of Service  
QRcodeQuick Response code  
RSRPReference Signal Received Power  
RTTRound Trip Time  
SAESociety of Automotive Engineers  
SCTPStream Control Transmission Protocol  
SFSmart Farming  
SIMSubscriber Identity Module  
SRTPSecure Real-time Transport Protocol  
SSHSecure Shell  
TCPTransmission Control Protocol  
TLSTransport Layer Security  
UDPUser Datagram Protocol  
UEUser Equipment  
V2 XVehicle-to-Everything  
VPNVirtual Private Network  
Wi-FiWireless Fidelity  
WPAWi-FiProtected Access
# Guided Self-Accelerating Airy Beams--A Mini-Review
## 2 Results and Discussion
### Harmonic Potential Guidance  
In optics and photonics, an external potential embedded in the medium's index of refraction is often used as an effective tool to modulate light beams. It comes in different forms, as exemplified by vastly different photonic crystal structures. In this subsection, we investigate the management of Airy beams by a harmonic potential added to the linear medium. Typically, such a potential is easily achieved in gradient-index (GRIN) media [61; 62] and frequently utilized as a harmonic trap in Bose-Einstein condensates.
## A Low-Jitter Wireless Transmission Based on Buffer Management in Coding-Aware Routing  
**[PERSON] 1, [PERSON] 2,*  
* 1 School of Communication Engineering, Xidian University, Xi'an 710071, Shanxi, China; E-Mail: [EMAIL_ADDRESS]
* 2 Department of Information and Communication Systems, Hohai University, Changzhou 213022, Jiangsu, China
* * Author to whom correspondence should be addressed; E-Mail:[EMAIL_ADDRESS]; Tel.: +86-139-4514-9131.  
Academic Editor: [PERSON]  
_Received: 12 June 2015 / Accepted: 26 August 2015 / Published: 31 August 2015_  
**Abstract:** It is significant to reduce packet jitter for real-time applications in a wireless network. Existing coding-aware routing algorithms use the opportunistic network coding (ONC) scheme in a packet coding algorithm. The ONC scheme never delays packets to wait for the arrival of a future coding opportunity. The loss of some potential coding opportunities may degrade the contribution of network coding to jitter performance. In addition, most of the existing coding-aware routing algorithms assume that all flows participating in the network have equal rate. This is unrealistic, since multi-rate environments often appear. To overcome the above problem and expand coding-aware routing to multi-rate scenarios, from the view of data transmission, we present a low-jitter wireless transmission algorithm based on buffer management (BLJCAR), which decides packets in coding node according to the queue-length based threshold policy instead of the regular ONC policy as used in existing coding-aware routing algorithms. BLJCAR is a unified framework to merge the single rate case and multiple rate case. Simulations results show that the BLJCAR algorithm embedded in coding-aware routing outperforms the traditional ONC policy in terms of jitter, packet delivery delay, packet loss ratio and network throughput in network congestion in any traffic rates.  
**Keywords:** coding aware routing; delay jitter; buffer management; unified framework; multiple rates
# SSTNDS: A SET-Based Non-Dominated Sorting Algorithm for Multi-Objective Optimization Problems
###### Abstract  
Non-dominated sorting, used to find pareto solutions or assign solutions to different fronts, is a key but time-consuming process in multi-objective evolutionary algorithms (MOEAs). The best-case and worst-case time complexity of non-dominated sorting algorithms currently known are _O(MNlogN)_ and _O(MN2)_; M and N represent the number of objectives and the population size, respectively. In this paper, a more efficient SET-based non-dominated sorting algorithm, shorted to SETNDS, is proposed. The proposed algorithm can greatly reduce the number of comparisons on the promise of ensuring a shorter running time. In SETNDS, the rank of a solution to be sorted is determined by only comparing with the one with the highest rank degree in its dominant set. This algorithm is compared with six generally existing non-dominated sorting algorithms--fast non-dominated sorting, the arena's principle sort, the deductive sort, the corner sort, the efficient non-dominated sort and the best order sort on several kinds of datasets. The compared results show that the proposed algorithm is feasible and effective and its computational efficiency outperforms other existing algorithms.  
Keywords:dominant set; set theory; SET-based non-dominated sorting algorithm (SETNDS), time complexity +
Footnote †: journal: Applied sciences
## 4 Results  
A total of 1088 patients were enrolled in the registry, in whom we determined LVEF using echocardiography at study entry. In the HFrEF subgroup there were 862 patients with a mean LVEF of 27 \(\pm\) 7%, while in the HFmrEF group there were 226 patients with an LVEF of 43 \(\pm\) 3%, (\(p\) < 0.001). In the HFrEF group, 83.2% were men, compared to HFmrEF, where 72.1% were men (\(p\) = 0.008). The mean age was 63 \(\pm\) 12 years in the HFrEF group and 67 \(\pm\) 11 years in the HFmrEF group (\(p\) < 0.001). BMI was 29 \(\pm\) 5 in both groups. There were 424 (49.2%) non-smokers and 83 (9.6%) smokers in HFrEF, and 128 (56.6%) non-smokers and 30 (13.3%) smokers in HFmrEF. Inthe HFrEF group, patients had lower systolic blood pressure (\(p<0.001\)), higher heart rate (\(p<0.001\)), and there were fewer patients with ischemic heart disease or prior myocardial infarction compared to patients with HFmrEF (\(p<0.001\)). Of the other comorbidities, hypertension occurred in the HFrEF group in 64.7% of patients and in the HFmrEF group in 155 (68.6%) patients, followed by dyslipidemia in HFrEF in 75.6% of patients and in HFmrEF in 82.3% of patients. Diabetes mellitus occurred in HFrEF in 40.5% of patients and in HFmrEF in 32.3% of patients and chronic obstructive pulmonary disease (COPD) in HFrEF in 13.6% of patients and in HFmrEF in 12.8% of patients. There were 91 (10.6%) patients who had chronic lower extremity ischemia in the HFrEF group and 31 (13.7%) patients in the HFmrEF group. There were no statistically significant differences between the groups in the proportion of comorbidities. There were differences between the groups in the functional classification according to the New York Heart Association (NYHA), which divides patients based on a subjective assessment of dyspnea in relation to physical exertion, where patients in NYHA I are not significantly constrained in everyday life with minimal effort and usually at rest. Patients with HFrEF versus HFmrEF were represented in 9.6% vs. 26.5% in NYHA I, 59.7% versus 62.8% in NYHA II and in NYHA III and IV 30.6% versus 10.6% (\(p<0.001\)). Patients with HFrEF had a huge difference in median NT-proBNP 685 ng/L vs. 197 ng/L in HFmrEF (\(p<0.001\)). HFrEF had higher levels of uric acid (\(p<0.001\)) and urea (\(p<0.010\)), the change in creatinine was not significant. There were no significant differences in the use of ACE inhibitors or beta-blockers, as basic drugs in the treatment of heart failure significantly reduce morbidity and mortality. However, the differences in medication used between the HFrEF and HFmrEF groups were in the type of diuretic. Patients with HFrEF were more likely to use furosemide (87.4% vs. 55.3%, \(p<0.001\)) and mineralocorticoid receptor antagonists (MRA) (74.0% vs. 44.7%, \(p<0.001\)), whereas hydrochlorothiazzide was more common in the HFmrEF group (18.6% vs. 7.4%, \(p<0.001\)), as can be seen in Tables 1 and 2.
# Visible-Light CDMA Communications Using Inverted Spread Sequences
## 3 MUI Cancellation Schemes with Inverted MPSC  
### Inverted MPSC  
Although the MPSCs introduced in the previous section have a superior property of canceling MUI, the density of positive chips is \(1/q\), which is much lower than 1. Because of the low-density property, the average light intensity of each light source is equal to or lower than \(1/q\) of its maximum light intensity which is the intensity when the light source is lighting continuously. This feature is not favorable for some application systems, such as traffic signals and signs. We propose an optical CDMA system with high average light intensity. The proposed system employs MUI cancellation schemes and inverted MPSCs described later. In an inverted MPSC, each user's positive chip in the MPSC codewords is replaced with a null chip and vice versa. Table 4 shows an example of an inverted MPSC and codeword assignment for MUI cancellation schemes which are presented in the rest of this section. To date, a technique has been introduced for optical pulse-position modulation (PPM) in order to make the average light intensity higher in VLC systems [25]. This technique
## 2 Methodology  
### Review of AE and DAE  
A DAE is a variation of an autoencoder (AE) that is widely used in lossy data compression [37]. An AE aims to make the output \(\widetilde{\mathbf{x}}\) equal to the input \(\mathbf{x}\), as depicted in Figure 2a. An AE has two main parts: (1) The encoder maps the high-dimension input \(\mathbf{\hat{x}}\) into a low-dimension code \(\mathbf{z}\) via neural network layers (NNs); (2) The decoder reconstructs the high-dimension signal \(\widetilde{\mathbf{x}}\) from the low-dimension code. The formulas of these two parts can be expressed as Equations (1) and (2), where \(\mathbf{w}\) and \(\mathbf{b}\) are the weight and bias of NNs in the encoder, respectively, and \(\widetilde{\mathbf{w}}\) and \(\widetilde{\mathbf{b}}\) represent the weight and bias matrices of NNs in the decoder, respectively. \(\phi\) and \(\psi\) are the nonlinear activation functions of the encoder and the decoder, respectively.  
\[\mathbf{z}=\phi(\mathbf{w}\mathbf{\hat{x}}+\mathbf{b}) \tag{1}\] \[\widetilde{\mathbf{x}}=\psi(\widetilde{\mathbf{w}}\mathbf{z}+ \widetilde{\mathbf{b}}) \tag{2}\]  
To make \(\mathbf{x}\) and \(\widetilde{\mathbf{x}}\) be as similar as possible, the \(MSE\) is used as the cost function (3) in AE, where \(N\) and \(i\) are the number of input data and the data sample index, respectively.  
\[L=\underset{\mathbf{w},\mathbf{b},\widetilde{\mathbf{w}},\widetilde{\mathbf{b }}}{\text{argmin}}\frac{1}{N}\sum_{i=0}^{N-1}\left(x_{i}-\widetilde{x}_{i} \right)^{2} \tag{3}\]  
The DAE, an extension of the AE, is employed to reconstruct a clean signal from a corrupted signal [38]. It is commonly used in signal denoising and enhancement [39; 40]. As illustrated in Figure 2b, the input of a DAE is a corrupted signal \(\mathbf{\hat{x}}\), which consists of a clean signal \(\mathbf{x}\) and a noise signal \(\mathbf{\boldsymbol{n}}\), and the denoised signal \(\widetilde{\mathbf{x}}\) is reconstructed from \(\mathbf{\hat{x}}\). In the encoder layer, NNs attempt to isolate the features of the clean signal into code \(\mathbf{z}\), and \(\widetilde{\mathbf{x}}\) is further reconstructed from \(\mathbf{z}\) by the decoder. During the training phase, a DAE can  
Figure 2: Architecture of (**a**) AE and (**b**) DAE.  
learn the features of a clean ECG by updating the weights according to the cost function computed according to the \(MSE\) between the clean signal and the reconstructed signal.
# A Consensus-Based Likert-LMPP Model for Evaluating the Earthquake Resistance of Existing Buildings
## 3 Methodology
### Building Vulnerability Parameters (Inputs to the Proposed Model)
#### 3.3.2 Vertical Irregularities  
Buildings with columns or curtains that do not follow the height of the building create vertical irregularities, reflecting the effect of vertically discontinuous frames and changing floor areas [44]. Vertical irregularities are a major cause of building damage during strong earthquake excitation. There are a variety of factors that contribute to vertical irregularities, including steep slopes, significant wall apertures, structurally vulnerable or flexible floors, discrepancies in floor elevations, setbacks either within the same plane or outside of it [19], a column sitting on a console, vertically discontinuous columns, a curtain sitting on a column, and vertically discontinuous curtains.  
Weak Stories  
A weak story is usually characterized by vertical discontinuities or by reduced member sizes or reinforcements and tends to concentrate on inelastic activity, which may cause the story to collapse partially or completely [24].  
Buildings with soft stories have collapsed in past earthquakes around the world [53]. Buildings with soft stories have a larger share of severely damaged buildings than those with lower damage, regardless of their number of stories [54]. Soft stories typically occur when the ground floor is less stiff and stronger than the upper floors. Typically, this is the case in buildings located along the side of a main street in which the ground floor is used as a commercial or retail space while the upper floors are occupied by residences. The upper stories are strengthened and stiffened by many partition walls, but at the bottom, the commercial spaces are mostly left open between the frame members for customer circulation. Additionally, the ground stories may have taller clearances and a different axis system, causing irregularities in the construction. Euro code 8 stipulates that the displacement in the direction of seismic forces must not exceed the average story displacement by more than 20% at any given story [24].  
In RC buildings, short columns are formed by semi-infilled frames, band windows in semi-buried basements, or mid-story beams around stairway shafts. A captive column usually sustains heavy damage during strong earthquakes due to its short length not being designed to handle high shear forces. A short column is easily identified from the outside because it usually forms along an exterior axis [53].
## 4 Results and Empirical Approach
### Structural Equation Modeling  
SEM analysis is evaluated by overall and relative model fit and structural parameter estimates, depicted with one-headed arrows on a path diagram [49]. The theoretical model shown in Figure 2 is examined based on how well it reproduces the observed covariance matrix and on the significance and direction of the hypothesized paths between ISM, RSC, and ISI. The model fit indices suggested a good fit between the hypothesized framework and the data (chi-squared = 211.805, \(p\) < 0.001; \(X^{2}\)/df = 1.83; CFI = 0.96; AGFI = 0.89; TLI = 0.95; RMR = 0.029; RMSEA = 0.05) [45]. The results indicated that ISM had a positive impact on ISI (standardized coefficient = 0.512, \(p\) < 0.001). Thus, Hypothesis 1 was supported. The effect of ISM on RSC was examined and it had a negative relationship (standardized coefficient = \(-\)0.246, \(p\) < 0.001), supporting Hypothesis 2. Hypothesis 3 pertained to the relationship between RSC and ISI (standardized coefficient = \(-\)0.205, \(p\) < 0.001), which was also supported.
## 5 SDN-based VANETs Security Challenges
### Poisoning Attack on the Network Topology  
The topologies data are generally identified with upper layer programs and applications, for example, bundle steering, organization virtualization, and streamlining and versatility [75]. At the point when a system topology harming attack happens, this will affect risky circumstances since all the reliant administrations and applications will be influenced. For example, the information directing could be influenced and this will bring about a man-in-the-middle attack (MiMA) or the black hole attack. Another situation shows the effect of an assailant prevailing to capture the area of a system server so as to phish its supporters. In a brilliant stopping application utilizing the SDN-based VANETs model [36], the aggressor can capture the area of a controller to phish its administration supporters. In addition, an aggressor can even make dark gap courses by injecting bogus connections in the topology. Models in other studies [40; 67; 68; 76] are defenseless against the system topologies harming. Two well-known arrangements, Topo-Guard [75] and SPHINX [77], identify these topology harming attacks by the means of bundle checking. Topo-Guard distinguishes bogus systems using social profiling. Specifically, creators of Topo-Guard developed a topology update organizer module so as to screen system topologies and approve topology refreshes. In SPHINX, the creators proposed an abnormality discovery method dependent on confirming the irregularities in organized states. [PERSON] et al. [75] developed an augmentation to Topo-Guard referred to as Topo-Guard+. The arrangement screens the trademark control layer communication examples, and afterward shields alongside out-of-band ports amnesia attacks.
###### Abstract  
No single network solution for Internet of Things (IoT) networks can provide the required level of Quality of Service (QoS) for all applications in all environments. This leads to an increasing number of solutions created to fit particular scenarios. Given the increasing number and complexity of solutions available, it becomes difficult for an application developer to choose the solution which is best suited for an application. This article introduces a framework which autonomously chooses the best solution for the application given the current deployed environment. The framework utilises a performance model to predict the expected performance of a particular solution in a given environment. The framework can then choose an apt solution for the application from a set of available solutions. This article presents the framework with a set of models built using data collected from simulation. The modelling technique can determine with up to 85% accuracy the solution which performs the best for a particular performance metric given a set of solutions. The article highlights the fractured and disjointed practice currently in place for examining and comparing communication solutions and aims to open a discussion on harmonising testing procedures so that different solutions can be directly compared and offers a framework to achieve this within IoT networks.  
Internet of Things; performance; modelling; standardised testing; framework +
Footnote †: This paper is an extended version of our paper published in Delaney, D.Y.; [PERSON]. Modelling the Performance of a WSN with Regard to the Physical Features Exhibited by the Network. In Proceedings of the 7 th International Conference on Internet and Distributed Computing Systems, IDCS 2014, Calabria, Italy, 22-24 September 2014; Volume 8729, pp. 312-324.