esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Human Role in Teleoperation
### Frame Rate  
The frame rate (FR) is the number of images displayed per time unit, indicating the image refresh rate of the system (frames per second (fps) or Hz). Studies reveal that generally, a low frame rate degrades operators' performance. [PERSON] and [PERSON] [55] analyzed the efficiency of moving a robot arm to a target via a camera view, on a placement style task (accuracy and speed peg in hole task), for three frame rate levels (3 fps, 5 fps, and 30 fps). They found that increases in the frame rate improved the teleoperation efficiency significantly, and for levels below 5 Hz, there was a significant performance deterioration. In Chen [56], the participant experienced a significant performance degradation in a simulated target acquisition task with a frame rate of 5 Hz. [PERSON] and [PERSON] [57] assessed the impact of different frame rates (60 Hz, 15 Hz, 10 Hz, 5 Hz, 3 Hz, 2 Hz, and 1 Hz) on a 3D target acquisition task (Fitts' law style task) and also concluded that lower frame rates, 5 Hz or below, decreased users' performance; however, they suggested that 10 Hz was enough for the tested task. They also noticed that the effect of frame rate and the lag were closely related. The impact of frame rate in the sense of presence was analyzed in [PERSON] et al. [58] for a virtual environment (VE) placement task at 10 Hz, 15 Hz, 20 Hz, and 30 Hz. They found that presence increased significantly from 15 Hz to 20 Hz and kept growing from 20 Hz to 30 Hz. They also reported that lower frame rates, besides impairing the sense of presence can also cause balance loss and a heart rate increase. [PERSON] [59] found that first-person-shooter video game users significantly improved their target acquisition task performances while the frame rate varied from 3 Hz to 60 Hz. In [PERSON]'s rover teleoperation driving task [51], several display rates were evaluated at 10 Hz, 15 Hz, 20 Hz, 25 Hz, and 30 Hz. Negative impacts were more noticeable at lower frame rates, namely on speed and motion perception. There was a drop of 37% in the average rover's speed when the FR condition changed from 20 Hz to 10 Hz. Participants felt comfortable teleoperating at 25 Hz and started experiencing an initial discomfort at 20 Hz due to flickering. They found it difficult to perceive the rover's speed at frame rates under 15 Hz. [PERSON] and [PERSON] [60] conducted a survey involving 50 studies, and they found that generally higher FR and a small standard deviation of the frame rate benefit users' psychomotor performances (\(\geq\)17.5 Hz for placement tasks, \(\geq\)10 Hz for tracing tasks, \(\geq\)16 Hz for navigation and tracking targets). In summary, they concluded that 15 Hz is a reasonable threshold for most of the tasks, including perceptual and psychomotor. The sense of presence and that of immersion benefit from higher values of FR (60 Hz to 90 Hz), which enable improved realism [61; 62].
# Model Updating Using Frequency Response Functions Based on Sherman-Morrison Formula
## 4 Case Study
### Truss Structure
#### 4.1.2 Comparison of Sensitivity Analysis  
To further more intuitively illustrate the accuracy and efficiency of the proposed method in sensitivity calculating, we make \(10^{-1}\), \(10^{-2}\), and \(10^{-3}\) of the perturbations in the elastic modulus respectively and compare the proposed method with the analytic derivatives method of Equation (4) to obtain the results shown in Figure 4.  
Figure 4: Sensitivity of FRFs to elastic modulus.  
Figure 3: Truss elements and nodes diagrams.  
The following error index \(\mathbf{O}\) is used to quantify the accuracy of the sensitivity of the FRFs:  
\[\mathbf{O}=\frac{\sum_{i=1}^{N}\left(\left|\mathbf{S}_{i}^{Exact}-\mathbf{S}_{i}^{Proposed} \right|/\mathbf{S}_{i}^{Exact}\right)}{N}\times 100\% \tag{31}\]  
where \(N\) is the number of points on the curves. \(\mathbf{S}_{i}^{Exact}\) and \(\mathbf{S}_{i}^{Proposed}\) are the sensitivities of the FRFs obtained by analytical derivatives method of Equation (4) and the proposed method, respectively. A smaller \(\mathbf{O}\) indicates a more accurate result.  
The results in Figure 4 show that under different perturbations, the sensitivity calculated by the proposed method compares reasonably well with that calculated by the analytical derivatives. The small figure in the blue dashed box in Figure 4 is a local magnification comparison of the sensitivity curves calculated by the proposed method and the analytical derivatives method under different perturbations. With the decrease of the perturbation value, the sensitivity calculated by the proposed method is closer to that of the analytic derivatives method.  
As shown in Table 1, the error is only 0.01% for the perturbation of \(10^{-3}\), which shows that the proposed method can carry out sensitivity analysis sufficient accurately for small variations in the design parameters. It can also be found that although the sensitivity calculated by the two different methods is basically the same, the calculation efficiency of the proposed method in this paper is higher than that of the analytic derivatives method as is shown in Table 2. This advantage will be of great use for model updating of large-scale engineering structures, which can save much computation time.
# Use of the Codon Table to Quantify the Evolutionary Role of Random Mutations
## 1 Introduction  
It is a fundamental tenet of evolutionary biology that evolution is driven by mutations in the carrier of the genetic code, the DNA, and, consequently, the RNA. While a simplistic view of Darwinian evolution would assume all mutations to be equally probable, the current work in this field is dominated by characterizing various biases in the mutation rates. A recent paper reviewed the research on these biases indicated that biases are categorized as mutation bias, i.e., the difference in the rate of changes at the RNA level (e.g., C-G bias [1]), and composition bias, i.e., the difference in the fitness of the mutated structure, showing bias at the protein level, and showed that they act synergistically [2]. Another recent work [3] emphasized the non-randomness of mutations.  
The aim of this paper is to divert the attention from the various biases, and instead attempts to quantify the randomness of the mutations observed during evolution. For this purpose, the results of the mutations will be examined at the protein level. In support of the protein-centric view of mutation, it should be noted that while mutations at the RNA level are interesting biochemical problems, the biological significance of the mutations manifests itself at the protein level.  
It has been recognized that if evolution were driven by unbiased random mutations, then the amino acid (AA) frequencies in proteins should fully correlate with the number of codons that code each AA; a correlation was indeed found, but it was not very strong [4]. The number of codons were also found negatively correlated with the AA size and were correlated with the AAs hydrostatic pressure asymmetry index [5]. This paper will examine the codon multiplicity-AA frequency correlation according to families of organisms.  
One important use of sequence alignment algorithms is for the detection of the evolutionary relations of proteins. Sequence alignments are based on so-called substitution matrices that measure the similarity of various AAs; in the evolutionary context they should measure the likelihood of one AA to be mutated into another one. The more the RNA mutation are unbiasedly random, the more these substitution matrices should be correlated with the likelihood of AA mutations resulting from unbiased random RNA mutations. To get at these correlations, the codons representing each AA will be used to derive the unbiased likelihood of AA mutations resulting from single or double nucleotide changesfollowed by the calculation of the correlation between the mutation likelihoods derived from the codon table and various AA substitution matrices. The larger the correlation, the larger the role of randomness in the AA mutations.
## 4 How Is the Practice of Astronomy Impacted by Increased Satellite Numbers?
These interferences arguably impact the practice of astronomy by making the capturing of telescope images of the sky more difficult, tedious, and time-consuming. The information being lost behind these streaks of light was also highlighted as having a concerning impact on important astronomical research projects, as outlined in the SATCON1 Report (American Astronomical Society, 2020)10 as follows:  
\"Many astronomical investigations collect data with the requirement of observing any part of the sky needed to achieve the research objective with uniform quality over the field of view. These include studies that are among the highest priorities in the discipline: stellar populations in the Milky Way and neighbouring galaxies; searches for potentially hazardous near-Earth objects; identification of gravitational wave sources such as neutron star mergers; and wide-area searches for transiting exoplanets\" (American Astronomical Society, 2020, p. 3). Footnote 10: American Astronomical Society (2020). The recommendations of which were elaborated upon at SATCON 2 in 2021. Thus, streaks of light from satellites reflecting sunlight interrupt the consistency of the quality of these important observations. Furthermore, these streaks of light could prevent the detection of \"a time-critical aspect and/or a rare, scientifically critical target\" (American Astronomical Society, 2020, p. 3), which, if this were to occur, would undermine the aims of these astronomical projects. Astronomical observations and images being interrupted by satellites reflecting sunlight impact the practice of astronomy by requiring additional software to fix astronomical images, affecting the quality of observation research projects or even preventing the detection of one-off space events. Therefore, the impact experienced by professional astronomers is considerable, but the impact is arguably more acute for the Indigenous Peoples, to whom the practice of astronomy is integral.
## 3 Datasets and Analyses
### Datasets  
**Telecom Dataset**: This dataset is collected by one of the biggest telecom operator in Shanghai, China [47]. It contains app usage records of mobile users in Shanghai. Each record contains: anonymous user ID (Identity Document), time, base station ID, its latitude and longitude coordinates, and current app ID. This dataset contains 9.4 billion records generated by 1.37 million users from 20 to 26 April 2016, and covers the 2000 most popular apps in the App Store and Google Play. In the dataset, the records of 10,000 users who visited more than 10 locations and used more than 5 apps in each location are selected. Finally we obtain this dataset, called Telecom dataset, with over 40 million records, containing 11,584 locations and 1327 apps.  
**TalkingData**: This dataset is collected by TalkingData SDK (Software Development Kit, which is integrated in mobile applications) and published on the Kaggle website [48]. Each record includes anonymous device ID, time, latitude, longitude, and app ID, which reflects users' app usage behavior. The dataset is pre-processed as follows. First, the coverage area is divided into grids of 1 km \(\times\) 1 km, and the latitude and longitude coordinates are converted into grid IDs. Second, the 40,000 densest area in a square and users with more than 30 records are selected. Moreover, the locations and apps accessed by less than fiveusers are filtered out in order to reduce noise in the dataset. In the end, an app usage dataset with 256 users, 689 apps and 439 locations is obtained.  
Table 1 summarizes the key information of Telecom and TalkingData datasets.
# Performance Analysis of Rate Splitting in Massive MIMO Systems with Low Resolution ADCs/DACs
###### Abstract  
In this paper, we propose rate-splitting (RS) multiple access to mitigate the effects of quantization noise (QN) inherent in low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). We consider the downlink (DL) of a multiuser massive multiple-input multiple-output (MIMO) system where the base station (BS) is equipped with low-resolution ADCs/DACs. The BS employs the RS scheme for data transmission. Under imperfect channel state information (CSI), we characterize the spectral efficiency (SE) and energy efficiency (EE) by deriving the asymptotic signal-to-interference-and-noise ratio (SINR). For 1-bit resolution, the QN is very high, and the RS scheme shows no rate gain over the non-RS scheme. As the ADC/DAC resolution increases (i.e., 2-3 bits), the RS scheme achieves higher SE in the high signal-to-noise ratio (SNR) regime compared to that of the non-RS scheme. For a 3-bit resolution, the number of antennas can be reduced by 27% in the RS scheme to achieve the same SE as the non-RS scheme. Low-resolution DACs degrades the system performance more than low-resolution ADCs. Hence, it is preferable to equip the system with low-resolution ADCs than low-resolution DACs. The system achieves the best SE/EE tradeoff for 4-bit resolution ADCs/DACs.  
m _Applied Sciences_  
**Citation:** [PERSON]; [PERSON].  
Performance Analysis of Rate Splitting in Massive MIMO Systems with Low Resolution ADCs/DACs.  
_Appl. Sci._**2021**, _11_, 9409. [[https://doi.org/10.3390/app11209409](https://doi.org/10.3390/app11209409)]([https://doi.org/10.3390/app11209409](https://doi.org/10.3390/app11209409))  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
**Keywords:** massive multiple-input multiple-output; rate-splitting; low-resolution digital-to-analog converters (DACs); low-resolution analog-to-digital converters (ADCs); energy efficiency
## 3 Results
### Partialling out of ENSO and IOD Teleconnections from SST-SIF Correlations  
To avoid interferences from the ENSO and IOD events, we calculated the partial correlation among the selected SST-SIF relationships and only statistically significant correlations are discussed here (Table 5). All the highlighted ACZs maintained the link with the tropical Indian Ocean regions. Even after excluding the influences of ENSO and IOD, the degree of the SST-SIF association did not diminish greatly compared to the standard linear correlations (Table 4), and most of those relationships remained statistically significant. The intensity of the ENSO and IOD effects on the SIF variability across India was found to be less in June, but it increased as the monsoon proceeded. Even with the removal of NINO3 SST effects and the DMI from the respective months, most of the ACZs across the northern and central-eastern portions of India maintained their relationship with WIO during the monsoon onset period. Similarly, the significance of the NIO and CIO SST anomalies is still considerable in all of the selected ACZs. No considerable correlation was observed among WIO SST and SIF anomalies after June. As the monsoon peaks in August, NIO showed significant connections only with WCPG, and CIO exhibited links with GPH and WDR. During September, the linkage of the CIO SST with the SIF anomalies of ECPH and EPH was maintained. However, many SST-SIF correlations discovered after June become insignificant after removing ENSO or IOD effects (Table 5). However, certain ambiguities may still exist in the reported SST-SIF correlations. On the other hand, the composite of SIF anomalies illustrates how India's terrestrial photosynthetic activity specifically responds to the anomalous positive and negative SST years in the Western, Northern and Central Indian Oceans. This would clearly represent the impact of the tropical Indian Ocean SST variability on the Indian vegetation anomalies.
# A Deep Learning Technique for Optical Inspection of Color Contact Lenses
## 2 Materials and Methods
### Optical Transmittance Measurements for Colored Contact Lenses  
The transmittance of the colored contact lenses was measured in a wavelength range of 250-700 nm using a Shimadzu UV-1650 PC spectrophotometer (Shimadzu, Tokyo, Japan). The measurements for each sample were iterated four times, and the results were averaged.
# Optimization-Based Approaches to Control of Probabilistic Boolean Networks
## 5 Discussion  
In this section, from the viewpoint of numerical experiments, we compare two introduced methods [23; 24] with other existing methods.  
In many existing methods, transition probability matrices with \(2^{n}\times 2^{n}\) in discrete-time Markov chains must be manipulated (see, e.g., [19; 20; 21; 30; 32; 36]). In Example 2, the transition probability matrix with \(u(0)=0\) can be obtained as  
\[\begin{bmatrix}0.8&0&0&0&0.2&0&0&0\\ 0.2&0&0&0&0.8&0&0&0\\ 0.56&0.24&0&0&0.14&0.06&0&0\\ 0.14&0.06&0&0&0.56&0.24&0&0\\ 0&0&0.24&0.56&0&0&0.06&0.14\\ 0.06&0.14&0&0&0.24&0.56&0&0\\ 0&0&0.56&0.24&0&0&0.14&0.06\\ 0.14&0.06&0&0&0.56&0.24&0&0\\ \end{bmatrix}.\]See also the state transition diagram in Figure 1. In recent years, analysis and control methods for BNs and PBNs using the semi-tensor product approach have been widely studied (see, e.g., [34; 36]). In this approach, matrices with \(2^{n}\times 2^{n}\) must be manipulated. It is hard to manipulate matrices with such size. In MATLAB, matrices with \(2^{16}\times 2^{16}\) cannot be generated. Although matrices with \(2^{15}\times 2^{15}\) can be generated, computation of the product of matrices requires a long time. This is a weakness of many existing methods.  
In two methods introduced in this review paper, matrices with such size are not manipulated. As a result, we can compute the optimal control problem. In [24], we presented a numerical experiment for the polynomial optimization approach. Then, we reported that for a PBN with 15 states and three control inputs, the computation times for solving Problem 1 in the cases of \(N=2,3,4,5\) were 0.6 s (\(N=2\)), 19.6 s (\(N=3\)), 218.8 s (\(N=4\)), and 334.4 s (\(N=5\)), respectively. In this numerical experiments, we used SparsePOP [45] for solving a polynomial optimization problem. The computer environment is CPU: Intel Core i7 1.2 GHz, Memory: 4 GB, Windows 7 Professional 64 bit. In the integer programming approach, we presented in [23] a numerical experiment for a context-sensitive PBN with 15 states and three control inputs. Then, we reported that the computation time for solving Problem 2-A with \(N=10\) was 18.96 s. In this numerical experiments, we used ILOG CPLEX 11.0 for solving an ILP problem. The computer environment is CPU: Intel Core 2 Duo CPU 3.0 GHz, Memory: 4 GB, Windows 7 Professional 64 bit. We remark that it is hard to solve the optimal control problem of a PBN with such size by using the other existing methods such as [20; 21; 34; 36]. Thus, two methods introduced here enable us to compute the optimal control input for relatively large-scale PBNs.
# Optimal Base Station Density of Dense Network:
## 2 System Model
### Distribution of BSs in HetNets  
Unlike the traditional network, the BSs in HetNets are deployed irregularly to meet the uneven data demand. Thus, we can use mutually independent PPP to model the spatial locations of the MaBSs, SmBSs and users in HetNets. Its appropriateness has been proved in [23]. PPP offers a method to analyze the performance of HetNets in a statistical point of view. The distribution of MaBSs, SmBSs and users are \(\Phi_{\text{M}}=\left\{M_{i};\ i=1,2,3,...\right\}\), \(\Phi_{\text{m}}=\left\{m_{i};\ i=1,2,3,...\right\}\) and \(\Phi_{\text{u}}=\left\{u_{i};\ i=1,2,3,...\right\}\), respectively, with the corresponding densities \(\lambda_{\text{M}}\), \(\lambda_{\text{m}}\) and \(\lambda_{\text{u}}\). \(M_{i}\), \(m_{i}\) and \(u_{i}\) are the location of the \(i\)th MaBSs, SmBSs or users. With the PPP model, the two-tier HetNets is abstracted as a Voronoi tessellation with two independent tiers, as shown in Figure 1. The stars stand for the MaBS and their cell ranges are denoted by solid lines. The dots are SmBSs and dashed lines indicate their cell ranges. In such HetNets, each user falls in an overlapping zone of an MaBS and an SmBS, and chooses the BS with strongest long-term received signal strength to connect.
# Flooding Conceptual Review: Sustainability-Focalized Best Practices in Nigeria
## 5 Conceptual Framework
### Urbanisation and Floods  
A significant amount of research over the past two decades has shown a strong relationship between urban areas and local micro-climate. The \"urban heat island\" (UHI) effect is now well-established, whereby urban areas have higher temperatures than surrounding regions [41]. In many cases, UHI increases rainfall in the vicinity of cities. A number of studies have found an increase in rainfall in regions downwind of urban areas, with some cases recording increases as high as 25% [41; 42]. In urbanized areas, huge amounts of anthropogenic waste heat is emitted due to human activities; the increase of energy consumption is causing environmental problems and temperature rise in the urban atmosphere [43]. Hence even without long term climatic changes, urban extreme rainfall intensities may be increasing with severe impacts on society at large.  
In Nigeria, the rehabilitation of rivers, channels and sewers lags far behind the development of municipal construction. Consequently, the existing drainage capacities are insufficient in draining runoff discharge, increasing flood risk. Moreover, there is a lack of adequate infrastructural provision, especially within Edo State. Over the last decade, Benin City and other parts of Edo State have witnessed rapid territorial expansion, in which successive administrations until recently have failed to match growth with infrastructural development--particularly in the expansion of its drainage network [44]. Currently, with an annual urbanization rate of 5.5%, the highest in the world, Nigerian cities face numerous problems, including: deterioration of the environment, urban decay, un-cleared refuse, flooding, erosion and pollution [45; 46]. At present, it is easy to acknowledge casual factors of urban flooding and gully erosion are multifarious.