esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 VLC-IoT: Is VLC Really Necessary for IoT Networks Deployment?  
In this section, we ask the important question: Does VLC improve IoT performance? Clearly, ubiquitous IoT needs massive connectivity, high reliability, high data rate, and high positioning accuracy as well as low latency and low power consumption with improved security [33; 34]. Furthermore, the ever-growing number of IoT devices imposes a new challenge on the IoT network to deliver high-speed data transmission to support the different application services of devices, things, and machines. This is in spite of millimeter wave (mmWave) being capable of supporting high-speed short-range communications [33]. However, the rapid growth of wireless communications is poised to overwhelm the mmWave and RF spectrum. On the other hand, the license-free VLC spectrum supports high-speed transmission with boundless capacity. The network capacity in massive IoT deployment can be improved by executing small cells in 5G wireless networks [18; 26; 28]. Yet, co-channel interference in densely deployed small cells will limit the extent to which IoT network coverage can be extended through small cells deployment [28]. Similarly, cross-layer interference in heterogeneous 5G networks can limit network capacity for massive IoT devices [35]. However, VLC does not generate electromagnetic interference nor does it interfere with RF communications. Therefore, it can be deployed in scenarios that are electromagnetic interference sensitive. Currently, high power consumption in 5G networks with a massive number of IoT devices is a big challenge in wireless communications. As a result, the current RF-based 5G wireless networks cannot support massive connectivity with high-speed data rate transmission of massive IoT devices. Similarly, the high cost of hardware and design complexity of mmWave communications due to multiple antennas implemented at both the receivers and transmitter deters massive connectivity of massive IoT devices [16]. However, VLC is cost-effective because it deploys existing lighting infrastructure also due to the low cost of its front-end components. Moreover, VLC offers high accuracy localization for object tracking and navigation through visible light positioning (VLP) [36; 37]. Moreover, VLC is safe for indoor environments unlike RF-based positioning systems [37]. Furthermore, security is a key challenge in massive IoT as a result of the large number of devices involved which attracts an equally large number of attackers. Conversely, VLC offers high communication security because VLC does not penetrate walls like RF waves that easily penetrates walls and buildings making it vulnerable to eavesdroppers [26; 34]. Due to these unique features, VLC can either be deployed to complement or totally replace RF-based solutions for numerous IoT-based indoor and outdoor applications to provide more bandwidth and coverage. Another approach is to develop IoT devices that can use a hybrid of VLC and 5G networks to achieve envisioned high data rates.
## 4 Discussion
The conversion of 8-_epi_-deoxyloganic acid to catalpol is considered the second period of catalpol production. Cytochrome P450s play important roles in this stage. A total of 44 unigenes of the CYP450 family met the screening criteria based on the FPKM analysis. Given that the known CYP450s in _route l_ belong to the CYP72 and 76 families, 12 unigenes of the CYP72 family and 7 members of the CYP76 family were further identified in this study. The top three most highly expressed sequences of the CYP72 family were annotated as dehydrogenase, oxidoreductase, and SLS, respectively. The highest expressed unigenes of the CYP76 family were DLS and G10H. The identification of genes in the iridoids biosynthesis suggested that the keyword searching method and the gene screening criteria were reliable in discovering putative genes in catalpol biosynthesis. As shown in Figure 1, hydroxylases are responsible for three steps in catalpol formation. The step from 8-_epi_-deoxyloganic acid to unsaenoside acid is similar to the reaction catalyzed by DL7H in _route l_. Herein, two unigenes (Cluster-11149.14301 and Cluster-11149.10607) were annotated as DL7H, and their expression profile showed a positive correlation with the accumulation of catalpol (Figure 7), suggesting these two unigenes might catalyze the hydroxylation of 8-_epi_-deoxyloganic acid. Geraniol synthase is the first branch enzyme in the biogenesis of catalpol. In the _R. glutinosa_ transcriptomic databases, Cluster-11149.40193 was selected as a _GES_ candidate for functional characterization. Using the PCR approach, _RgGES1_ and _RgGES2_ with several mutations were isolated from the young leaves. Since _R. glutinosa_ is an autotetraphoid plant [28], it is reasonable to isolate two similar sequences from _R. glutinosa_ for one unigene. The phylogenetic analysis revealed RgGES1 and RgGES2 belonged to the TPS g group, which specifically produce acyclic terpen like geraniol and linalool. As the general structural feature of the members from the TPS g group, RgGES1 and RgGES2 lacked the RRX8W motif, which has been present in many, but not always found in, monoterynes synthases [49]. Like the geraniol synthase from \(\mathcal{O}\). _basilicum_ and _Dendrobium officinal_[32; 50], the purified recombinant RgGES1 and RgGES2 catalyzed GPP to uniquely produce geraniol in the in vitro assay (Figure 10), suggesting RgGES1 and RgGES2 were classified as geraniol synthase in _R. glutinosa_. In addition, given that _RgGES_ (Cluster-11149.40193) is highly expressed in the young leaves, it was reasonable to assume that _RgGES1_ and _RgGES2_ participate in catalpol biosynthesis in _R. glutinosa_.
## 2 Materials and Methods
### Phenotypic Screening and In Vitro Assays
#### 2.4.2 SARS-CoV-2 Viral RNA Copy Determination in A549-hACE2 Cells  
Eight thousand cells per well were seeded on pre-spotted compound plates 24 h before inoculation with SARS-CoV-2 at MOI 0.1. After incubation for 2 h at 37 \({}^{\circ}\)C, cell supernatants were removed, and the plates were washed once with DPBS. Plates were re-filled with compounds diluted in culture medium to the same concentration as before the wash. Plates were incubated for an additional 48 h in a humidified incubator at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\). Supernatants were collected and treated with MagNA Pure 96 external lysis buffer (Roche Diagnostics, Basel, Switzerland) and used as input for automated RNA extraction using the MagNA Pure 96 instrument and the complementary DNA and Viral NA Small Volume Kit (Roche Diagnostics, Basel, Switzerland). Extracted RNA was used as a template in one-step RT-qPCR in a master mix containing LightCycler(r) Multiplex RNA Virus Master (Roche Life Science, Penzberg, Germany) components and a SARS-CoV-2 primer/probeset, ordered from Integrated DNA Technologies (Leuven, Belgium), targeting the viral nucleocapsid genetic sequence. Primer and probe sequences were as follows: Forward: 5\({}^{\prime}\)-GACCCCAAAATCAGCGAAAT-3\({}^{\prime}\), Reverse: 5\({}^{\prime}\)-TCTGGTTACTGCCAGTTGAATCTG-3\({}^{\prime}\), Probe: 5\({}^{\prime}\)-FAM-ACCCCGATTACGTTTGGTGGACC-BHQI-3\({}^{\prime}\). In-house prepared SARS-CoV-2 nucleocapsid RNA was included as a standard to allow virus copy quantitation. RT-qPCR itself was performed using the LightCycler 480 (Roche Life Science, Penzberg, Germany) and consisted of the following cycling steps: reverse transcription; 50 \({}^{\circ}\)C--10 min, denaturation, and polymerase activation; 95 \({}^{\circ}\)C--30 s, followed by 45 cycles of denaturation; 95 \({}^{\circ}\)C--5 s and annealing plus extension; 60 \({}^{\circ}\)C--30 s. Resulting copy numbers, as well as the viral yield reduction (log\({}_{10}\) scale), were plotted in function of the compound concentration using GraphPad Prism v8.
# Image Quality Assessment of Augmented Reality Glasses as Medical Display Devices (HoloLens 2)
## 4 Discussion  
### Evaluation of the Overall Image Quality  
This study is the first to evaluate the overall image quality of HoloLens 2 in a medical context based on participants' subjective ratings, allowing users to directly assess the perceptibility of image elements.  
The findings reveal that HoloLens 2 struggles to display dark gray shades effectively. Participants had difficulty distinguishing the first two luminance surface elements (image element 6). However, upon distinguishing between the first and third, as well as the second and third, surface elements were generally achievable. Additionally, participants noted perceiving an edge in the black area of the gradient bars (image element 7).  
[PERSON] et al. [25] evaluated the image quality of HoloLens 2 and compared it to AR glasses utilizing the organic light-emitting diode (OLED) technology. Their findings revealed deficiencies in contrast display within dark image regions and inadequate differentiation of highly bright images. Nonetheless, these issues were not identified in the present study.  
[PERSON] et al. [26] investigated the influence of ambient illumination on the contrast displays of both HoloLens and HoloLens 2 across environments with illuminance levels ranging from 0 to 20,000 lx. They reported that contrast--and consequently image quality--diminished in brightly lit settings.  
Conversely, ambient lighting had minimal effect on the visibility of image elements in this study. Participants performed similarly in both the examination room and office, with only two individuals showing a decreased ability to discern luminance surface elements. Notably, the office illuminance (138.7 lx) was markedly lower than that in [PERSON] et al.'s [26] study. Therefore, the visibility of image elements in exceedingly bright environments, such as operating rooms, warrants further investigation.
## 3 Proposed Methods
### Asymmetric Loss Function  
Class imbalance is common in detection problems. To solve this problem, we propose an asymmetric loss function. This section will introduce the loss in detail.  
In the field of defect detection, the number of positive samples (defective images) is usually smaller than the number of negative samples (defect-free images). This imbalance will cause two problems: (1) the training efficiency is low because negative samples take up a large portion of time during the training process; and (2) the training performance is poor because the network learns features from a large number of negative samples instead of the effective defect features, which leads to model degradation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Enhanced Operation** & **Probability** & **Intensity** \\ \hline Horizontal (or vertical) zoom & 0.1 & 0–10\% Pixel \\ \hline Add Gaussian noise & 0.1 & Variance 0.005–0.02 \\ \hline rotation & 0.2 & 0–15\({}^{\circ}\) \\ \hline Horizontal (or vertical) translation & 0.3 & 0–10\% Pixel \\ \hline \hline \end{tabular}
\end{table}
Table 2: Second stage augmentation operation.  
A common solution is to perform a form of hard negative mining [32] or to give different weights to different classes during training [33]. In contrast, certain scholars attempted to solve the problem by designing special loss functions to allow efficient training on all examples without sampling or weights. The most common loss function used for binary classification is cross-entropy (CE), which expression as follows:  
\[L_{CE}=\left\{\begin{array}{ll}-\log(\ y^{\prime})&if\ y=1\\ -\log(1-y^{\prime})&if\ y=0\end{array}\right. \tag{1}\]  
where \(y^{\prime}\) is the prediction and \(y\) is the expected value. \(y=1\) is a positive sample, \(y=0\) is a negative sample. However, whether the ground truth of the sample is defective or not, the attenuation degree on CE loss is the same.  
The focal loss function [34] modified the CE-loss function to reduce the relative loss of well-classified samples and placed more attention on bad-classified samples. A remarkable characteristic of both CE-loss and focal loss is that although the loss of defective samples is large, since the number of defective samples is less, network training does not focus on defective samples. Thus, we redesigned the loss to give more attention to the loss value of the defective sample by using the exponential function as the attention mechanism. It is written as:  
\[L_{asy}=\left\{\begin{array}{ll}-e^{y^{\prime}}\log(\ y^{\prime})&if\ y=1\\ -e^{-y^{\prime}}\log(1-y^{\prime})&if\ y=0\end{array}\right. \tag{2}\]  
Figure 3 compares three kinds of loss functions. Intuitively, they are symmetric in the center of (a) and (b), which means that no matter the positive sample or the negative sample, their loss value is the same. Thereby, it pays more attention to the samples of minority class, alleviating the imbalance of positive and negative samples.
## 3 Results
### Exposed Aggregate Cement Concrete  
The basic analysis of the noise levels for the EACC 8 surface is given in Table 1. Noise level is specified to one decimal place, standard deviation to two. Noise level ranges from about 97.5 dB(A) in the year of construction to 99.5 dB(A) in the sixth year of road service life. During this time, there was a 2 dB(A) increase in noise. In years 0-5, the standard deviation indicates that, on average, all values are delineated by almost half from the mean. The difference between the minimum and maximum measured values varies between 1.5 and 2.0 dB(A) in these years. This is evident in the box plot as outliers (years 0, 4 and 5) or the long lines. The smallest variance of noise values is found for surfaces in the sixth year of service life, when the difference between the minimum and the maximum is only 0.5 dB, which also corresponds to the small standard deviation. The box plot in Figure 7 shows the outliers found in the input data. In this case, outliers cannot be excluded from the analysis, as they represent the sections where the measurements correctly followed each year--so they are not incorrect.  
Median value for each year of service life was inserted in a chart (Figure 8). Tyre/road noise increases as the surface ages. A linear or logarithmic character can therefore be expected. When the points are fitted to a linear equation, the coefficient of determination equals 0.973; when they are fitted to exponential function, it equals 0.991. The closer the coefficient of determination is to 1.0, the more reliably and accurately the equations describe the data behaviour. The coefficient of determination is very close to one for the logarithmic interpolation, so this equation describes the data behaviour quite accurately. From this equation it is possible to predict the behaviour of the road surface for the following years. As can be seen in Figure 8, for the EACC 8 surface, noise increases more markedly during the first four years and then the increase is less steep.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year after** & **Mean** & **Median** & **Minimum** & **Maximum** & **Standard** \\
**Laying** & **(dB(A))** & **(dB(A))** & **(dB(A))** & **(dB(A))** & **Deviation** \\ \hline
0 & 97.5 & 97.5 & 96.7 & 98.7 & 0.46 \\
1 & 98.0 & 97.9 & 97.1 & 99.1 & 0.43 \\
2 & 98.4 & 98.3 & 97.7 & 99.3 & 0.42 \\
3 & 98.7 & 98.8 & 97.8 & 99.5 & 0.42 \\
4 & 99.0 & 99.0 & 98.0 & 99.6 & 0.43 \\
5 & 99.2 & 99.2 & 98.2 & 99.7 &
# Plasmonic Filter and Demultiplexer Based on Square Ring Resonator
###### Abstract  
A ring resonator is a basic component of traditional photonic integrated circuits (PIC), which has been, however, found difficult to be applied efficiently in high-compact plasmonic metal-insulator-metal (MIM) systems. Here, based on a plasmonic band-stop filter with a square ring resonator (SRR), a novel side-coupling method is introduced both numerically and theoretically to achieve a drop in the resonant wavelength in the SRR with considerable efficiency. By introducing the reflector structure, the performance can be appreciably improved. Besides, this structure also has potential for sensing and switching. Finally, a dual demultiplexer based on SRRs is realized at telecommunication wavelengths with comparable performance, which makes it possible to apply ring resonators in on-chip plasmonic wavelength division multiplex (WDM) networks. This work is valuable for PIC design, and will promote the on-chip plasmonic system progress.  
metal-insulator-metal structure; square ring resonator; filter; demultiplexer Article
# Modeling of Key Specifications for RF Amplifiers Using the Extreme Learning Machine
## 3 Modeling Process
### ELM Training Algorithm  
In Section 3.1, SLFNs can be approximated with zero error for \(d\) samples. Therefore, [25],  
\[\sum_{j=1}^{d}\|o_{j}-t_{j}\|=0 \tag{2}\]  
\[t_{j}=\sum_{i=1}^{L}\beta_{i}g_{i}(x_{j})=\sum_{i=1}^{L}\beta_{i}G(a_{i},b_{i},x_{j}) \tag{3}\]  
Figure 4: Single-hidden layer feedforward network.  
Equation (2) can be expressed as [25]  
\[\mathbf{H}\mathbf{\beta}=\mathbf{T} \tag{4}\]  
where \(\mathbf{H}\) is the hidden layer output matrix (randomized matrix) [28]  
\[\mathbf{H}=\left[\begin{array}{c}h(x_{1})\\ \vdots\\ h(x_{d})\end{array}\right]=\left[\begin{array}{ccc}h_{1}(x_{1})&\cdots&h_{L} (x_{1})\\ \vdots&\vdots&\vdots\\ h_{1}(x_{d})&\cdots&h_{L}(x_{d})\end{array}\right] \tag{5}\]  
and \(\mathbf{T}\) is the training data-target matrix [28].  
\[\mathbf{T}=\left[\begin{array}{c}t_{1}^{T}\\ \vdots\\ t_{d}^{T}\end{array}\right]=\left[\begin{array}{ccc}t_{11}&\cdots&t_{1n}\\ \vdots&\vdots&\vdots\\ t_{d1}&\cdots&t_{dn}\end{array}\right] \tag{6}\]  
The ELM training algorithm can be summarized as follows [28; 29].  
1. Randomly assign the hidden node parameters, e.g., the input weights (\(a_{i}\)) and hidden layer weights (\(b_{i}\)), \(i=1,\,\ldots,\,L\).
2. Calculate the hidden layer output matrix \(\mathbf{H}\).
3. Obtain the output weight vector. \[\mathbf{\beta}=\mathbf{H}^{\mathsf{T}}\mathbf{T}\] (7)  
where \(\mathbf{H}^{\mathsf{T}}\) is the Moore-Penrose generalized inverse of matrix \(\mathbf{H}\).  
The orthogonal projection method can be efficiently used for the calculation of MP inverse [28]:  
If \(\mathbf{H}^{\mathsf{T}}\mathbf{H}\) is nonsingular,  
\[\mathbf{H}^{\mathsf{T}}=\left(\mathbf{H}^{\mathsf{T}}\mathbf{H}\right)^{-1}\mathbf{H}^{\mathsf{ T}} \tag{8}\]  
if \(\mathbf{H}\mathbf{H}^{\mathsf{T}}\) is nonsingular,  
\[\mathbf{H}^{\mathsf{T}}=\mathbf{H}^{\mathsf{T}}\left(\mathbf{H}^{\mathsf{T}}\mathbf{H}\right)^ {-1} \tag{9}\]
# A Study of Breast Cancer Classification Algorithms by Fusing Machine Learning and Deep Learning
## 5 Analysis of Indicators  
### Evaluation Indicators  
Since models often make various changes according to different requirements in real-world application scenarios, we use different evaluation metrics to determine whether the model can function properly under the existing real-world task requirements and environment. Introducing and considering a variety of different evaluation metrics simultaneously when studying real-world problems will be of great help in the model selection exercise conducted in this paper.  
In connection with the practical application scenario of this paper, we chose to introduce two different concepts (precision and recall) to assist in solving the relevant problems involved in this paper.  
\[\begin{array}{c}\mathrm{Precision}=\frac{TP}{TP+FP}\\ \mathrm{Recall}=\frac{TP}{TP+FN}\end{array} \tag{8}\]  
As shown in Equation (8), precision is mainly based on the actual prediction results of this paper, indicating the proportion of correctly predicted samples in the positive class prediction results. Recall is mainly targeted at the original sample of this paper, indicating the proportion of correctly predicted positive cases in the sample to all positive cases.  
After further research, we find that to obtain more-desirable model action results in a practical application setting, in addition to precision and recall, another two quantitative methods, the F1 score and ROC curve, need to be introduced to combine the advantages of precision and recall.  
\[\mathrm{F1}=2*\frac{\mathrm{precision}*\mathrm{recall}}{\mathrm{precision}+ \mathrm{recall}} \tag{9}\]As shown in Equation (9), the F1 score includes both accuracy and recall in the calculation, helping both indicators to reach the highest point at the same time, maximizing the values of both metrics while maintaining the relative balance between them as much as possible.  
\[\begin{array}{l}TPR=\frac{TP}{P}=\frac{TP}{TP+FN}\\ FPR=\frac{FP}{N}=\frac{FP}{FP+TN}=1-TNR\end{array} \tag{10}\]  
As shown in Equation (10), the ROC curve can easily and quickly help discover how well a classifier can analyze the samples at a certain threshold value.
# Smart Handoff Technique for Internet of Vehicles Communication using Dynamic Edge-Backup Node
## 2 Literature Review
### Handoff Techniques
#### 2.1.4 Network Mobility (NEMO) Scheme  
In this method collection of vehicles is set up on the premise of the multi-bounce transfer idea. For communication with BS, NEMO utilizes a mobile router (MR). Its idea is same as that of a bus that contains two MRs in which the front switch is known as Front MR and a back switch is known as Rear MR. Front MR is appointed to handoff and Rear MR gives administrations to systems.
# A Comprehensive Study on EDFA Characteristics: Temperature Impact
## 3 Results and Discussion
### Emission Cross-Section Variation with Temperature  
In order to investigate the influence of temperature on the emission cross-section, the wavelength at which the two cross-sections spectrums are equal is of great importance. From the experimental work, this wavelength was found to be 1537.9 nm [34] and the net free energy, \(\varepsilon\) required to excite one E\({}_{\text{r}}^{+3}\) ion from the \({}^{4}\)I\({}_{15/2}\) to the \({}^{4}\)I\({}_{13/2}\) has been calculated from its experimental definition (\(\
u=\varepsilon/\text{h}\)) to be 0.8078 eV. Finally, the McCumber relation has been applied to the emission cross-section, and its variation with temperature in the range from \(-40\)\({}^{\circ}\)C to \(+80\)\({}^{\circ}\)C is shown in Figure 2. A small variation in gain amplitude with a slight variation in temperature is noticed, but with no shift toward longer or shorter wavelengths. Hence, it can be revealed that the window at which the signal is  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Symbol** & **Value** \\ \hline Core effective area & A\({}_{\text{eff}}\) & \(1.96\times 10^{-11}\) m\({}^{2}\) \\ \hline E\({}_{\text{r}}^{+3}\) ion doping radius & B & 2.5 \(\upmu\)m \\ \hline Pump wavelength & \(\lambda_{\text{P}}\) & 1480 nm \\ \hline Signal wavelength & \(\lambda_{\text{s}}\) & 1532 nm \\ \hline Pump absorption cross-section & \(\sigma_{\text{P}}^{\text{a}}\) & \(2.4312\times 10^{-25}\) m\({}^{2}\) \\ \hline Pump emission cross-section & \(\sigma_{\text{P}}^{\text{e}}\) & \(0.7164\times 10^{-25}\) m\({}^{2}\) \\ \hline Signal absorption cross-section & \(\sigma_{\text{s}}^{\text{a}}\) & \(6.2778\times 10^{-25}\) m\({}^{2}\) \\ \hline Signal emission cross-section & \(\sigma_{\text{s}}^{\text{e}}\) & \(5.5644\times 10^{-25}\) m\({}^{2}\) \\ \hline Fluorescence life time & \(\tau\) & 10.8 ms \\ \hline Total E\({}_{\text{r}}^{+3}\) ion density & N & \(1\times 10^{25}\) ion/m\({}^{3}\) \\ \hline Core refractive index & N & 1.5 \\ \hline Numerical aperture & NA & 0.18 \\ \hline \end{tabular}
\end{table}
Table 1: Al/P-Silica erbium-doped fiber amplifier (EDFA) parameters used in simulation [21].  
amplified is not affected by the temperature variation. Another observation from Figure 2 is that the wavelength at which all of the emission cross-section curves meet the absorption cross-section curve is 1537.9 nm. At shorter wavelengths, an increase in temperature causes an increase in the value of the emission cross-section. At longer wavelengths, the effect is opposite, and the emission cross-section decreases with temperature. Our results are found to be in a good agreement with those obtained by [PERSON] et al. [19].