esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficient Routing Protocol in Novel Schemes for Performance Evaluation
## 1 Introduction
### Research Methodology  
To complete this examination work, we continue through a few phases. Our first methodology begins with a starter investigation of WSNs, distinguishing its significant difficulties and main points of contention, regarding executing the steering methods on asset imperative networks. The second stage remembers the direct review for steering techniques in WSN and examines the existing arrangements and approaches proposed for the best in class. We proposed novel directing techniques. The direction's fundamental objective is to diminish energy utilization in the WS networks, as well as recognizing which steering approach will influence the execution of the WSN, as far as its lifetime is concerned. As indicated by our proposed directing approaches, the best answers for advancing energy utilization in the WS networks are based on the grounds that different methodologies (FEARM [29], ELEACH [30], EESRA [34], and DMEERP [35]) actually have a few constraints that are not pertinent for creative and brilliant applications in WSNs. Our third stage recognizes the energy utilization issue in directing, and a calculation was recommended that meant to diminish the energy exhausted by sensor hubs, which, in turn, increments the organization's life expectancy. The fourth stage starts with fostering a clever strategy for productive energy cost capability, which decides the energy adjustments.  
The reenactment is achieved by looking at the presentation of the proposed system with two unique strategies. The NS2 test system is being utilized in this examination, since it is an occasion-driven, discrete, and object-arranged network test system that addresses organizing research. On every remote organization, steering, UDP, and multicast convention copying are upheld.
## 2 Model Development
### Latent Heat Loss due to Sweating during Walking/logging
The foot sole temperature rises \(\left(T_{f}-T_{f0}\right)\) were calculated for three speeds with and without considering the sweating evaporative heat loss. The results are compared with experimental measurements based on [3], as presented in Figure 6.  
A good agreement can be observed between the experimental and numerical results (Figure 6). According to Figure 6, the maximum increase in foot sole temperature after 30 min is 6 \({}^{\circ}\)C for a gait speed of 3 km/h. This value is 8 \({}^{\circ}\)C and 11.5 \({}^{\circ}\)C, for 6 km/h and 9 km/h, respectively. Figure 6 shows that for a gait speed of 3 km/h, the effect of sweating on cooling is not strong and all curves, i.e., the numerical results with and without sweating and experimental measurements for ball and arch, are approximately overlapping. However, at higher speeds, especially for 9 km/h, the effect of sweating is very considerable. For a gait speed of 9 km/h, after the first 15 min, sweating as a thermoregulation mechanism comes into play and cools the arch area. The predicted temperature-time trend with sweating much closely replicate the temperature measured under the arch area while temperatures predicted without sweating more closely match the temperature-time trend at the ball of the foot. For a gait speed of 6 km/h, sweating-induced cooling is triggered at a later time around 20 min, while at this speed the sweating at the ball zone seems to be negligible (Figure 6b). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Temperature** & \(c_{p_{a}}\) & \(k_{a}\) & \(\rho_{a}\) & \(\mu_{a}\) \\ (\({}^{\circ}\)C) & (\(\mathbf{j}\cdot\mathbf{kg}^{-1}\cdot\mathbf{K}^{-1}\)) & (\(\mathbf{W}\cdot\mathbf{m}^{-1}\cdot\mathbf{K}^{-1}\)) & (\(\mathbf{kg}\cdot\mathbf{m}^{-3}\)) & (\(\mathbf{Pa}\cdot\mathbf{s}\)) \\ \hline
25 & 1006.96 & 0.0261 & 1.171 & \(1.84\times 10^{-5}\) \\
35 \({}^{*}\) & 1007 & 0.0269 & 1.135 & — \\ \hline \hline \end{tabular}  
* is used in Section 4.  
\end{table} 
Table 2: Air thermo-physical properties at 25 \({}^{\circ}\)C based on what was reported in [30]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Speed** & **3** & **6** & **9** \\
**(km/h)** & & & \\ \hline Max. Contact force & & & \\ \(\%\)B.W. & 100 & 110 & 170 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Maximum contact force in percent of body weight (%B.W) for different gait speeds as reported in [2]. Figure 6: Comparison of foot sole temperature (numerical simulation and experimental measurements). (**a**) Gait speed \(3\) km/h; (**b**) Gait speed \(6\) km/h; and (**c**) Gait speed \(9\) km/h.
# Deep Learning for Laryngopharyngeal Reflux Diagnosis
## 1 Introduction  
Laryngopharyngeal reflux disease (LPR) may involve about 50% of all patients with laryngeal or voice disorders [1]. Misdiagnosis and over-diagnosis of LPR are common phenomena in general otolaryngology clinics. To diagnose LPR both conveniently and effectively is a big challenge for the laryngologists. The Reflux Score Index (RSI) and Reflux Finding Score (RFS) are the subjective diagnostic tools that applied widely today. As for the objective method, the gold standard is the multichannel intraluminal impedance 24 h pH monitoring (MI-24 h pH) currently. However, pH monitoring has several drawbacks, such as aggressive approach, the inconvenience and discomfort for a patient, and the disagreement on the placement of proximal probe. Hence, both scales and MII-24 h pH monitoring are not ideal diagnostic methods, so analyzing laryngoscope images appears to be a promising direction that is convenient, effective and low-cost.  
[PERSON] et al. proposed to extract color and texture features from seven specific regions in laryngoscope images for distinguishing LPR [2]. However, their approach relies on manually drawn regions to extract features, so large variations might occur during this kind of manual works and this is not an end-to-end approach. Moreover, the single hidden-layer network used in their approach may not capture hierarchical structures in laryngoscope images. [PERSON] et al. showed that deep learning with multilayer neural networks had permeated the entire field of medical image analysis [3]. Therefore, in this work, we focus on leveraging deep learning methods in an end-to-end fashion for an LPR diagnostic system [4]. However, it is well-known that deep learning requires massive data to beeffective, which may pose a great challenge for this medical image application due to the time-consuming and costly endeavor of collecting data and making annotations for the dataset. This work explored how recent deep-learning approaches perform on this small-sample problem.
# Driver Clustering Based on Individual Curve Path Selection Preference
## 3 Results
### Online Parameter Learning  
In the previous section, a dynamic and a cautious driving style group were defined. The reference parameter vectors are given in (26). In this section, we implement the EKF algorithm introduced in Section 2.4. To illustrate the algorithm's accuracy, a test is executed. For this test, the data of driver no. 1 is taken from the reference dataset [19]. The parameters \(p^{\text{left}}\) and \(P^{\text{right}}\) are identified by the EKF algorithm. The initial guess of the outputs (i.e., the lane offset values) is taken from the first sample of the measurement, whereas the initial parameter values are assumed to be zero. The initial covariance matrix is given in (42).  
\[\Sigma_{0}=I^{21\times 21}\sigma_{0}^{2} \tag{42}\]  
where \(\sigma_{0}\) is the initial covariance value for all states, and \(\sigma_{0}=0.1\) is chosen. The results of the test are shown in Figure 6. In the upper plots, the final estimate of the parameter vectors is visualized. Besides the estimator results \(\hat{P}^{*}\), the reference parameters \(P^{*}\) calculated by (20) and the moving parameters \(\bar{P}^{*}\) given in (22) are plotted. In the bottom plot, the NRMS values (23) of the parameter estimation error and the moving parameter error to the reference parameters are plotted. To help visualize \(\text{NRMS}=1\), a reference line is added to this plot. The results show that the EKF algorithm reaches convergence fast, together with the moving parameters. EKF algorithm estimates the parameter values with a small terminal error. Interestingly, the estimation error does not reach its minimum value at the very end of the simulation. The reason is that even the reference parameters change during driving as the behavior of the driver is not constant, either. The EKF can slowly react to these changes, but that causes a continuous rise and fall in the estimation error. In general, it is observed that the EKF can predict the parameter values accurately, giving a good basis for executing the classification based on the estimated parameters.  
Further runs with different initial covariance values \(\sigma_{0}\) have been conducted to prove the robustness of the algorithm. The results are shown in Figure 7. Selecting various initial covariances in the range of 0.01 and 10, the estimation error does not change significantly. This proves that the proposed EKF algorithm is robust against this parameter and may be applicable for real-time parameter learning.  
Figure 6: Estimation results of \(P^{\text{left}}\) and \(P^{\text{right}}\), illustrated on the parameters of driver 1.
## 3 Classical Routing Protocols
### Modification of LEACH Based on CH Selection
#### 3.2.14 R-Leach  
The R-LEACH protocol is a variation of LEACH that chooses the CH based on two essential parameters: the individual node's remaining energy and the network's ideal number of CHs [50]. After each round, the non-CH nodes' residual energy is examined, and the one with the highest energy level in relation to the others has a higher possibility of being chosen as CH for the current round. This would prevent the network from dying out too soon, extending its lifespan. The threshold value for CH selection is modified as:  
\[T(n)=\left\{\begin{array}{l}\frac{p}{1-P\left(rmd\frac{1}{p}\right)}\times \frac{E_{residual}}{E_{initial}}k_{opt};for\text{ all }n\in G\\ 0;\text{ Otherwise}\end{array}\right. \tag{16}\]  
where \(E_{residual}\) is the remaining energy level of the node and \(E_{initial}\) is the initial assigned energy level. The optimal number of cluster \(k_{opt}\) can be written as in [88].  
\[k_{opt}=\sqrt{\frac{n}{2\pi}}\sqrt{\frac{E_{fs}}{E_{amp}d^{4}(2m-1)E_{initial} -mE_{DA}}}M \tag{17}\]  
\(M\) is the network diameter, \(m\) is the number of CHs in each cluster and \(d\) is the distance between nodes.  
The R-LEACH was further modified using Fuzzy logic to evaluate the value of \(k_{opt}\) using a Fuzzy base rule called FR-LEACH [89]. An exponential member function based on energy is used to find the number of clusters adapting to the network energy changes.
## 3 Results and Discussion
### FTIR Spectra
Finally, other studies have also evaluated the accuracy of the FTIR analytical method for the estimation of proteins, carbohydrates, lipids of microalgae and cyanobacteria (single batch cultures) and reported good correlations by plotting the actual values versus the predicted one or by fitting the data in a first order equation [46; 47; 49; 53; 54].
## 1 Introduction
According to the research conducted by [PERSON] et al. [18], low CO\({}_{2}\) emission High-Performance Concretes reaching a compressive strength of 100 MPa after 90 days of curing can be successfully designed using multicomponent Portland and slag cements. The non-air-entrained HPCs with a clinker content of 211-269 kg per 1 m\({}^{3}\) of concrete and addition of silica fume were considered to be frost-resistant after 28 days of curing with an average compressive strength loss not greater than 3.5% after 150 cycles of freezing and thawing (according to Polish procedure PN-B-06265). High resistance to water penetration under pressure was also observed for these low-clinker High-Performance Concretes. In the paper of [PERSON] and [PERSON] [19], high-strength concretes with a water/binder ratio equal to 0.30 and 20-40% replacement of OPC with GGBFS were characterized by a compressive strength above 80 MPa after 28 days of curing, exceeding the values obtained for a reference concrete mixture with 100% OPC as a binder. [PERSON] et al. [20] stated that it was possible to obtain low-clinker self-compacting HPCs with OPC content of 134-204 kg in 1 m\({}^{3}\) of concrete mix by incorporating fly ash, limestone, silica fume, and metakaolin. The designed HPCs reached compressive strength ranging from 77 to 88 MPa after 28 days of curing. Research into the synergistic effect of GGBFS, FA and SF addition on the microstructure, as well as mechanical and durability properties, has already been conducted for normal strength concretes by [PERSON] et al. [21]. Based on the literature search, it can be stated that in the vast majority of studies, the properties of low-clinker concretes are evaluated for up to 90 days. The amount of research concerning longer periods of curing is limited. Therefore, further investigation needs to be done in this field, especially in the case of low-clinker HPC. In this paper, the properties of High-Performance Concretes with low Portland cement clinker content were investigated. Slag cement (CEM III/B) was used as the main binder in the designed concrete mixtures. SCMs such as Ground Granulated Blast Furnace Slag (GGBFS), Siliceous Fly Ash (SFA) and Silica Fume (SF) were added in various proportions,maintaining a low water/binder ratio (0.30) in order to achieve high compressive strength in the composite. The concretes' long-term mechanical performance was investigated on the basis of compressive strength and modulus of elasticity tests conducted 2 years after specimen preparation. Additionally, SEM microstructure analysis was performed on 2-year-old samples. Durability properties were assessed on the basis of freeze-thaw tests and water penetration under pressure tests, along with a hardened concrete pH value testing after 56 days of curing.
_Article_  
**Two-Color Quantum Correlation between Down-Conversion Beams at 1.5 and 3.3 \(\upmu\)m from a Singly Resonant Optical Parametric Oscillator**  
**[PERSON] 1**, [PERSON] 1,2, [PERSON] 1,2 and [PERSON] 1,2*  
Received: 26 March 2020; Accepted: 11 April 2020; Published: 14 April 2020  
**Abstract:** We demonstrated a two-color quantum correlation between the down-conversion beams with a telecommunication wavelength at 1.5 \(\upmu\)m and mid-infrared wavelength at 3.3 \(\upmu\)m generated by a singly resonant optical parametric oscillator (SRO) operated above the pump threshold with a magnesium-oxide doped periodically-poled lithium niobate crystal in the cavity. A maximum of 1.8 dB noise reduction of the intensity difference of the twin beams was measured at the analysis frequency of 5 MHz. Based on a theoretical model for the quantum correlation between the twin beams given by a semi-classical approach, the influence of the analysis frequency and pump parameter on the quantum correlation between the twin beams was discussed theoretically and experimentally. The quantum correlation between the twin beams was degraded at the analysis frequencies above 5 MHz due to the limitation of the bandwidth of SRO cavity and was degraded at the analysis frequencies below 5 MHz due to the excess intensity noise of the pump. The two-color quantum correlated twin beams at 1.5 and 3.3 \(\upmu\)m have potential applications in high-precision measurements beyond the shot noise level.  
**Keywords:** quantum correlation; twin beams; singly resonant optical parametric oscillator; telecommunication wavelength at 1.5 \(\upmu\)m; mid-infrared wavelength at 3.3 \(\upmu\)m
## 2 Background and Prior Art
### Orbital Angular Momentum  
Orbital angular momentum (OAM) in electric fields was discovered by [PERSON] et al. [7]. They found that under certain conditions, the Laguerre-Gauss beam could transition from a standard plane wave propagation to a helical path. Consequently, the Gaussian-shaped distribution frequently exhibited by lasers becomes a doughnut shaped pattern when an OAM mode is adopted. The OAM azimuthal dependency is expressed by \(exp(i\ell\phi)\), where \(\ell\) is the topological charge or mode number. When \(\ell=0\), the wavefront is a plane. When \(|\ell|>0\), the wavefront travels in a helical path, where the direction of rotation about the z-axis is controlled by the sign on \(\ell\). The radial distance from the z-axis to the helix is controlled by the mode number. The larger the mode number, the greater the radius.  
A significant property exhibited by OAM modes is that they are orthogonal to each other [7]. Consequently, multiple OAM beams with different modes can be multiplexed together and be completely recovered at the receiver. Leveraging this property allowed [PERSON] et al. to achieve terabit data rates in ideal conditions [8]. While promising, communications in non-ideal conditions with turbulence and attenuation present hurdles in actually achieving these rates.  
Various of approaches have been used to detect modes at the receiver, including conjugate mode sorting [9; 10], Doppler effect measurements [11], dove prism interferometers [12], optical transformers [13], and spiral fringe counting [14]. In 2017 [PERSON] and [PERSON] applied ML to the problem and found significant improvements in demultiplexing accuracy and simplification of hardware setup over existing approaches [2].  
While Ref. [2] provided a proof of concept in applying CNNs to determining OAM modes, they left an exploration of the best CNN for future work. That investigation is completed here in addition to addressing other questions.
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 2 Classification of Whole-Genome Alignment Algorithms
### Hashing Techniques in Genome Alignment
#### 2.4.2 Efficiency and Applications  
Hashing techniques contribute to the efficiency of genome alignment tools by reducing the computational complexity of searching for sequence matches. This efficiency is critical for analyzing the vast datasets generated through NGS technologies, enabling real-time data processing and analysis.  
* Efficiency in SNP discovery: Tools like SOAP leverage hashing for rapid identification of potential SNP locations via quickly mapping reads to the reference genome [54], a process integral to variant calling and genotyping studies;
* Handling genetic diversity: Stampy's use of hashing to accommodate high sequence variability allows researchers to study populations with significant genetic differences, making it invaluable for evolutionary biology and conservation genetics [55];
* Comparative genomics with BLAST: BLAST's hashing-based search mechanism is efficient in identifying homologous sequences across species, aiding in the annotation of novel genomes and the discovery of evolutionary conserved regions [56].  
In conclusion, hashing techniques enhance the capability of genome alignment tools to manage the complexities and demands of modern genomic analyses. Through facilitating rapid and accurate alignment of sequencing reads to reference genomes, tools employing hashing are indispensable for a wide range of genomic studies, from variant discovery and comparative genomics to transcriptomics and beyond. Their continued development and integration into bioinformatics software will remain a key factor in the advancement of genetic and genomic research.
# A New Indicator to Assess Public Perception of Air Pollution Based on Complaint Data
## 3 Results
### Spatiotemporal Analysis of Public Perception of Air Pollution
#### 3.2.2 Spatiotemporal Analysis of APPI  
To evaluate the public's perception of air pollution more reasonably and comprehensively, we used Formula 9 to calculate the index APPI based on the public's complaint density and complaint emotional intensity. The spatial distribution results of APPI are shown in Figure 4.  
As shown in Figure 4, the APPI of different years is normalized and comparable. Moreover, to make the interannual changes of public perception comparable, the maximum density of APPI in 2015 was taken as the standard, and the color changes in other years were set. Red is the high-density area of APPI, which indicates the strongest public perception of air pollution. At the same time, green is the low-density area of APPI, which means the weakest public perception of air pollution.  
Figure 4 indicates that, from 2012 to 2015, public perception of air pollution first increased and then decreased. For example, the public's perception of air pollution had increased year by year in Shandong Province, reaching the peak in 2015. Since 2015, the public's perception of air pollution had been a progressive decrease every year.  
When coupled with the results in Table 4, the increase in the number of public complaints and the perceived intensity of air pollution may be due to the increased public awareness of environmental protection. It may also indicate the aggravation of air pollution  
Figure 4: The results of APPI in Shandong Province from 2012 to 2017.  
lution. However, the decrease in the number of public complaints and the weakening of public perception of air pollution, to a certain extent, reflects that the air quality has been improved. In addition, existing studies have shown that Shandong Province and even China's air quality have become better in recent years [1].  
Figure 4 also shows that Zibo, Jinan, Laiwu, Zaozhuang, Linyi, and Qingdao are the areas with the strongest public perception of air pollution. Except for Qingdao, other cities are inland cities with developed industry in Shandong Province. Naturally, air pollution cannot be avoided in cities with developed industries, especially in cities where chemical products mainly by refineries are the critical project, which will also be the birthplace of air pollution. Qingdao is not only a coastal city, but also a tourist city. At the same time, there are many industries and enterprises in Qingdao. Therefore, the public's strong perception of its air pollution may be due to the public's higher requirements on air quality in Qingdao or the impact of industrial emissions. That may be related to the distribution of industries or enterprises that are prone to air pollution.  
This paper will further verify public complaints' content through word cloud analysis and POI data of industries and enterprises in the discussion section.