esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Cone Transcriptomes Are at the Top among Retinal Cells after T3 Treatment  
The extraction of DEGs in each cell cluster was conducted, and the data show that treatment with T3 greatly altered the expressions of the genes in the cones. There were 450 DEGs, representing 38.5% of the total amount of DEGs detected, in the cones after the T3 treatment based on the change fold and \(p\)-value of 0.05. The numbers of DEGs in the rods, bipolar cells, ganglion cells, Muller cells, microglia, and astrocytes were 51 (4.4%), 96 (8.2%), 101 (8.6%), 179 (15.3%), 100 (8.6%), and 163 (14%), respectively (Figure 2A; Table 1). Collectively, a total of 1169 genes were detected in 11 cell clusters (Table 1). The profound alteration of the gene expression in the cones after the T3 treatment is also shown on the t-SNE plots (Figure 2B). Thus, cone transcriptomes were at the top among retinal cells after the T3 treatment. Treatment with T3 led to the upregulation of 229 DEGs and the downregulation of 221 DEGs in cones (Supplementary Table S2). The top 25 down-regulated DEGs included genes involved in phototransduction, such as _Arr3_, _Opn1 sw_, _Opn1 mw_, and _Pde6h_, and the top 25 upregulated DEGs included genes involved in metabolism and transcriptional regulation, such as _Mt2_, _Mt1_, _Lrrc2_, and _Cepd_, and genes encoding the long non-coding RNAs, such as _Xist_, which mediates chromosome-wide gene silencing (Figure 2C-E).  
Figure 2: Cone transcriptomes are at the top among retinal cells after the T3 treatment. (**A**) A pie plot showing the number and percentage of DEGs in different retinal cell types after the T3 treatment. (**B**) A t-SNE plot showing the cone cell distribution profiles in the retinas of the control and T3-treated mice. (**C**) A heatmap showing the top 50 DEGs in cones after the T3 treatment. (**D**) A diagram showing the log2 FC change amplitudes of the top 50 DEGs in cones after the T3 treatment. (**E**) Violin plots showing the change amplitudes of the top 48 DEGs in cones after the T3 treatment.
## 3 System Model  
This section presents a system scenario for calculating the information security awareness of users on the Internet and in network spaces. First, before we define the system model's components, we should know that cybercrime is a crime committed on the Internet, on networks, or even against stationary computers. The cybersecurity awareness web program is significant software among the growing information security trends. To prove the limitations of the lack of cybersecurity basics, the system model consists of four parts:  
* An initial knowledge-check
* A lower cybersecurity awareness module
* An advanced knowledge check
* A high cybersecurity awareness module  
The initial knowledge check module checks the information security basics known by the user. The lower cybersecurity awareness module is responsible for identifying the low awareness of the user and giving appropriate feedback for the user's bottlenecks. The advanced knowledge check module assesses the advanced information security basics possessed by the user. The high cybersecurity awareness module also identifies the high awareness of the user and gives appropriate feedback for the current user's bottlenecks.  
These four parts work consecutively through logical examinations and result retrievals, as shown through the blocks and arrows in the dataflow diagram depicted in Figure 2. First, it is necessary to ensure that the user has good knowledge of information security, and the program initially checks the current knowledge. If the user does not have this knowledge, then the situation is classified as \"poor awareness\" and the result is provided as \"low cybersecurity awareness\". The next step is to check if the tested user has good knowledge. If the result is positive, the aggregation concludes that the user has excellent information security awareness (high cybersecurity awareness). Therefore, using this system model, we can determine the user's cybersecurity awareness level. The system model realizes the need for increasing the cybersecurity awareness of users. If this model is implemented in the real world, the risks of cybercrimes can be minimized as users become more aware of information security basics. The use of a cybersecurity awareness web program not only ensures a high cybersecurity awareness level while testing users but also improves cybersecurity training efficiency.
## 4 Conclusions  
In summary, we proposed an SOA-based MPS-OOK transmission for FSO communication. At the transmitter end, an MPS-OOK signal was modulated with a constant SOP at the high-intensity level and various SOPs at the low-intensity level for the sake of the SE enhancement with the transmitted power efficiency. At the receiver end, a polarization-independent SOA was used to optically suppress the scintillation effect and equalize the intensities of the various SOPs in the deep gain saturation state using the high dynamic nonlinear gains without the polarization distortion. An LP with a high ER was applied after the SOA to convert the equalized SOPs into the different intensity levels. The converted MPS-OOK signal was detected using a single PD and distinguished using an M-FTD. The proposed technique was verified via experiments. An MZM-based fading simulator was used to accommodate the different scintillation effects. The experimental results demonstrated that the SE was enhanced up to 2 bit/s/Hz with effective scintillation mitigation using the proposed technique. Therefore, it has high potential and is a feasible technique for FSO communication.  
Conceptualization, Y.-Q.H.; data curation, Y.-Q.H.; formal analysis, Y.-Q.H., D.-H.K., J.-Y.C., I.-H.H. and W.-H.S.; project administration, S.-K.H.; writing--original draft, Y.-Q.H.; writing--review and editing, Y.-Q.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Institute for Information and Communications Technology Promotion (IIITF) grant funded by the Korean government (MSIT; Ministry of Science and ICT) (No. 2019-0-00685, Free-space-optical-communication-based vertical mobile network).  
Not applicable.  
Figure 5: Bit error rate (BER) performance of the on–off keying (OOK), two-level polarization shift on–off keying (2 PS-OOK), 3 PS-OOK, and 4 PS-OOK transmission: (**a**) \(\sigma_{l}^{2}\) of 0.11 and (**b**) \(\sigma_{l}^{2}\) of 0.59.  
Figure 6: Spectral efficiencies (SEs) of the proposed technique at a BER of \(1~{}\times~{}10^{-3}\).
# A Network Traffic Prediction Method for AIOps Based on TDA and Attention GRU
## 4 Experiment
### Evaluating Indicator  
To accurately evaluate the experimental effect, the evaluation indexes used in this paper were _MSE_, _MAPE_, \(R^{2}\), 20% _ACC_, 15% _ACC_, and 10% _ACC_.  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}\left(\hat{y}_{i}-y_{i}\right)^{2} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Send Traffic** & \begin{tabular}{c} **Transmission** \\ **Rate** \\ \end{tabular} & **Total Flow** & \begin{tabular}{c} **Total Received** \\ **Packets** \\ \end{tabular} & \begin{tabular}{c} **Receiving** \\ **Rate** \\ \end{tabular} & **Total Packets** & \
\begin{tabular}{c} **Bandwidth** \\ **Utilization** \\ \end{tabular} &
\begin{tabular}{c} **Receive** \\ **Traffic** \\ \end{tabular} \\ \hline Memory usage & 0.31 & 0.31 & 0.3 & 0.32 & 0.29 & 0.32 & 0.32 & 0.25 \\ Send traffic & 1 & 1 & 0.99 & 0.94 & 0.97 & 0.94 & 0.94 & 0.81 \\ Transmission rate & 1 & 1 & 0.99 & 0.94 & 0.97 & 0.94 & 0.94 & 0.81 \\ Total packet error rate & \(-\)0.06 & \(-\)0.06 & \(-\)0.07 & \(-\)0.04 & \(-\)0.08 & \(-\)0.04 & \(-\)0.04 & \(-\)0.08 \\ Total flow & 0.99 & 0.99 & 1 & 0.94 & 0.99 & 0.94 & 0.94 & 0.83 \\ Total broadcast traffic & \(-\)0.06 & \(-\)0.06 & \(-\)0.07 & \(-\)0.04 & \(-\)0.08 & \(-\)0.04 & \(-\)0.04 & \(-\)0.08 \\ Total received packets & 0.94 & 0.94 & 0.94 & 1 & 0.94 & 1 & 1 & 0.8 \\ Receiving rate & 0.97 & 0.97 & 0.99 & 0.94 & 1 & 0.94 & 0.94 & 0.83 \\ Total packets & 0.94 & 0.94 & 0.94 & 1 & 0.94 & 1 & 1 & 0.8 \\ Total packets sent & \(-\)0.11 & \(-\)0.11 & \(-\)0.12 & \(-\)0.16 & \(-\)0.13 & \(-\)0.16 & \(-\)0.16 & \(-\)0.13 \\ Bandwidth utilization & 0.94 & 0.94 & 0.94 & 1 & 0.94 & 1 & 1 & 0.8 \\ Run time & 0.22 & 0.22 & 0.21 & 0.28 & 0.2 & 0.28 & 0.28 & 0.2 \\ Receive traffic & 0.81 & 0.81 & 0.83 & 0.8 & 0.83 & 0.8 & 0.8 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristic correlation analysis.  
Figure 4: Data conversion diagram.  
\[MAPE=\frac{100\%}{n}\sum_{i=1}^{n}\left|\frac{\hat{y}_{i}-y_{i}}{y_{i}}\right| \tag{4}\]  
\[R^{2}=1-\frac{\sum_{i}\left(\hat{y}_{i}-y_{i}\right)^{2}}{\sum_{i}\left(\overline {y}_{i}-y_{i}\right)^{2}} \tag{5}\]  
\[20\%ACC=1-\frac{1}{n}count(\frac{|\hat{y}-y|}{y}>\frac{20}{100}) \tag{6}\]  
\[15\%ACC=1-\frac{1}{n}count(\frac{|\hat{y}-y|}{y}>\frac{15}{100}) \tag{7}\]  
\[10\%ACC=1-\frac{1}{n}count(\frac{|\hat{y}-y|}{y}>\frac{10}{100}) \tag{8}\]  
where \(\hat{y}\) is the predicted value, \(y\) is the actual value, \(n\) is the number of samples, and \(count(A>B)\) is the number of samples that meet the conditions of \(A>B\).
## 4 Results
### Clustering Results  
As the best number of clusters was not known beforehand, the k-means clustering technique was used to divide the dataset several times. In Table 1 it can be seen that nine different hybrid topologies were created, dividing the dataset into 2, 3, 4, 5, 6, 7, 8, 9, and 10 clusters. It is also shown that the first column corresponds to the global model (no clusters).  
The training of the k-means algorithm was made with random initial centroids, and for all the configurations the training was repeated 20 times to avoid local minimum. Moreover, the training phase includes a final condition to avoid clusters with less than 15 samples; however, in this research, the smallest cluster has 611 samples.
# Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs
## 5 Discussion
### Selection of Data Sources
Findings reported here based on different data source combinations are applicable to other spatial-temporal data fusion models that rely on Landsat-MODIS image pairs.
# Effect of Temperature on Channel Compensation in Optical Camera Communication
## 3 Optical Camera Communication Link Model
### Emitted Spectrum versus Temperature  
The photons emitted by an LED present a wavelength that is related to the energy gap of the semiconductor substrate. In addition, the energy gap is affected by the p-n junction temperature, which is usually modeled using Equation (7). According to the authors of [37], in most semiconductor materials, the energy gap diminishes as temperature increases. Therefore, as the wavelength is inversely proportional to this gap, the LED's peak wavelength increases with temperature.  
\[E_{g}=E_{0}-\frac{\alpha T^{2}}{T+\beta} \tag{7}\]  
where \(T\) is temperature, \(E_{0}\) is energy gap at 0 K temperature condition, and \(\alpha\) and \(\beta\) are semiconductor-dependent constants, which are empirically determined.  
Nonetheless, the LED substrate must be taken into consideration. In [12], the authors demonstrated that depending on the Indium (In) content in InGaN alloys (green and blue LEDs), the band gap may not decrease with temperature as many materials do, but can even increase with temperature. Furthermore, junction temperature affects both spectral width and luminous efficiency. In general, as temperature increases, the conversion efficiency decreases and the spectral width increases [13].  
As has been already mentioned, temperature produces spectral changes on the LED emission. In this study, these variations would affect \(S_{px}^{(i,j)}\) from Equation (4) as the terms \(\tilde{S}^{(i)}(\lambda,T)\) are temperature-dependent. Consequently, the SINR will also be affected.
# Challenges for Teaching Wireless Communications Standards at the Graduate Level
## 2 Problems in Teaching Wireless Communications Standards
The security aspects areconsidered in the IEEE 802.11i version, and other versions signed with letters (e, f, k, p, r, s, T, u, v) concern the extensions of the basic services and improvements of services already available, such as IEEE802.11e for QoS or IEEE 802.11p for wireless ability in vehicular environments. An important aspect to highlight in teaching of standards is the use of radio frequency spectrum resources in the different countries and the adoption of different regulations. For example, the IEEE802.11a on the 5.4 GHz bandwidth can be used in the U.S. without a license according to Part 15 of the FCC Rules and Regulations, while in Europe, this is not allowed as it can create interferences with radar and satellite services operating in this bandwidth. Therefore, the 802.11h protocol was introduced in 2004 to satisfy the requirements of the ETSI EN 301 893 regulation by using DFS (Dynamic Frequency Selection) and TPC (Transmit Power Control) techniques. It is also important to inform the students of the different standardization activities of the main actors such as IEEE, 3 GPP, and 3 GPP2, e.g., the activities of IEEE starting from IEEE 802.11a based on OFDM have developed the IEEE 802.16m system based on OFDMA, while LTE was derived from the experiences of 3 GPP in the previous 3G systems.
## 3 Results and Discussion
### Effect of Drying on Cassava Leaves Quality
#### 3.2.2 Effect of Drying Temperatures on Cassava Leaves Quality
Similarly, drying curry leaves in a hot-air oven at 60 \({}^{\circ}\)C retained 51.61% of lutein [37], while drying Moringa oleifera leaves at 50 \({}^{\circ}\)C for 12 h retained 47.7% of _trans_-lutein [13]. In guabiju and red guava, lutein content was reduced to 25.42% and 33.85%, respectively, after being hot-air dried at 70 \({}^{\circ}\)C [38]. Moreover, lutein content in marigold flowers decreased to approximately half of the fresh samples concentration when dried at 60 \({}^{\circ}\)C for 4 h [39]. Lutein concentration can change due to isomerization, epimerization, oxidation, or other degradation processes during heat treatment [40]. Chlorophyll-_a_ and chlorophyll-_b_ were significantly reduced after drying with a cabinet dryer, with contents decreasing to 66.03-72.85% and 66.94-73.88%, respectively. A similar result was found in a previous study by [PERSON] et al. [34], who found that hot-air drying dill greens at 50 \({}^{\circ}\)C for 6.5 h reduced chlorophyll-_a_ and chlorophyll-_b_ to 60.29% and 80.95% of their values in fresh samples. These reductions in chlorophylls in dried cassava leaves were due to the degradation caused by heat and chlorophyllase enzyme activity from the leaf tissues [22]. Crude proteins content of cassava leaves after drying remained comparable to that of fresh samples. This finding aligns with a previous study, which reported that the crude proteins content remained at 99.63% in cassava leaves dried in a solar tunnel dryer [41]. Most of the amino acids remained stable after drying, though some showed significant changes (Table 3). Histidine, alanine, and cysteine content in dried cassava leaves significantly increased at all drying temperatures (40-80 \({}^{\circ}\)C). These results align with a previous study, which found that the amino acid profile (g amino acid/kg total proteins) of cooked cassava leaves was not significantly changed from raw cassava leaves, except for a significant increase in alanine after thermal cooking [4]. Cooking cassava leaves in boiling water reduced total proteins and amino acid content by around 58% due to diffusion and thermal degradation [4]. [PERSON] et al. [42] reported that the eight amino acids (glycine, cysteine, aspartic acid, asparagine, glutamic acid, glutamine, arginine, and histidine) decomposed when heated between 185 \({}^{\circ}\)C and 280 \({}^{\circ}\)C. However, drying cassava leaves at the highest temperature (80 \({}^{\circ}\)C) specifically degraded aspartic acid content.
# Predicting the Effect of Processing Parameters on Caliber-Rolled Mg Alloys through Machine Learning
## 4 Conclusions  
There have been no studies that exploit machine learning to model the mechanical properties of MPCR-processed Mg alloys. This study employed two machine-learning approaches (i.e., the ANN and GAN-aided ANN) to predict the effect of the MPCR processing parameters on the microhardness of AZ31 Mg alloy. The ANN approach was prepared by hyperparameter optimization. A constitutive approach failed to provide precise equivalent strains as it disregarded the complex stress states and redundant strains of MPCR deformation. FEA suggested the radical V-shaped distribution of strains induced by diamond calibers, resulting in the similar distribution of microhardness values. The center of V-shaped distribution was shifted towards the higher distance owing to the imperfect manufacturing, thereby increasing the predictive difficulty for the present study. The ANN provided a good prediction for the center area where the microhardness values showed the uniform distribution with the small deviation (\(\pm\)5 HV). Integrating GAN gave rise to the negligible improvement in prediction, since the ANN itself was sufficient to predict this regime. However, the predictive difficulty exceedingly increased in the side sections due to the increased deviation (\(\pm\)13 HV) and the shifted center of V-shaped hardness variation. The ANN plotted the symmetric hardness variation, suggesting that it failed to provide a useful prediction for this regime. In contrast, the GAN-aided ANN successfully anticipated the shift with the reduced RMSE compared with the ANN approach. Thus, integrating the GAN with the ANN can improve the predictability of hardness distribution  
Figure 6: Comparing predictability for Vickers microhardness between ANN and GAN-aided ANN approaches: **(a)** RMSE values for the left section (0-2 mm in distance from the left edge), center (2–5.5 mm), and right section (5.5–7.5 mm); **(b)** the variation in error rates per datum as a function of distance.  
even when it is distorted by imperfect manufacturing conditions. The applicability of the GAN-aided ANN approach needs to be confirmed for other mechanical properties, such as mechanical strength, ductility, fatigue limit, and toughness.  
Conceptualization, T.L.; methodology, J.Y., S.B. and T.L.; software, J.Y. and S.B.; investigation, S.J.O.; resources, J.K. and T.L.; writing--original draft preparation, J.Y., S.B., J.K. and T.L.; writing--review and editing, T.L.; visualization, J.Y., S.B. and T.L.; supervision, T.L.; project administration, T.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by 2021 BK21 FOUR Program of Pusan National University.  
The data presented in this study are available on request.  
The authors declare no conflict of interest.
# Intra-Mode Decision Based on Lagrange Optimization Regarding Chroma Coding
###### Abstract  
The latest generation of standard versatile video coding (VVC) continues to utilize hybrid coding architecture to further promote compression performance, where the intra-mode decision module selects the optimal mode to balance bitrate and coding distortion. With regard to chroma intra modes, a scheme that uses a cross-component linear model (CCLM) is involved by utilizing the component correlation between luma and chroma, which could implicitly introduce distortion propagation from luma blocks to subsequent chroma prediction blocks during coding, impacting the result of a Lagrange optimization. This paper presents an improved intra-mode decision-based modified Lagrange multiplier for chroma components in VVC. The characteristics of chroma intra prediction are examined in depth, and the process of an intra-mode decision is analyzed in detail; then, the coding distortion dependency between the luma and chroma is described and incorporated into a Lagrange optimization framework to determine the optimal mode. The proposed method achieves an average bitrate-saving effect of 1.23% compared with the original scheme by using a dependent rate-distortion optimization in an All-Intra configuration.  
Lagrange optimization; chroma coding; intra-mode decision; versatile video coding +
Footnote †: journal: Applied  
18 June 2020  
Revised: 22 July 2024  
Accepted: 23 July 2024  
Published: 25 July 2024
# Characteristic Length and Time Scales of the Highly Forward Scattering of Photons in Random Media
###### Abstract  
Background: Elucidation of the highly forward scattering of photons in random media such as biological tissue is crucial for further developments of optical imaging using photon transport models. We evaluated length and time scales of the photon scattering in three-dimensional media. Methods: We employed analytical solutions of the time-dependent radiative transfer, \(M\)-th order delta-Eddington, and photon diffusion equations (RTE, dEM, and PDE). We calculated the fluence rates at different source-detector distances and optical properties. Results: We found that the zeroth order dEM and PDE, which approximate the highly forward scattering to the isotropic scattering, are valid in longer length and time scales than approximately \(10/\mu_{t}^{\prime}\) and \(40/\mu_{t}^{\prime}v\), respectively, where \(\mu_{t}^{\prime}\) is the reduced transport coefficient and \(v\) the speed of light in a medium. The first and second order dEM, which approximate the highly forward-peaked phase function by the first two and three Legendre moments, are valid in the longer scales than approximately \(4.0/\mu_{t}^{\prime}\) and \(6.3/\mu_{t}^{\prime}v\); \(2.8/\mu_{t}^{\prime}\) and \(3.5/\mu_{t}^{\prime}v\), respectively. The boundary conditions less influence the length scales, while they reduce the times scales from those for bulk at the longer length scale than approximately \(4.0/\mu_{t}^{\prime}\). Conclusion: Our findings are useful for constructions of accurate and efficient photon transport models. We evaluated length and time scales of the highly forward scattering of photons in various kinds of three-dimensional random media by analytical solutions of the radiative transfer, \(M\)-th order delta-Eddington, and photon diffusion equations.  
radiative transfer equation; highly forward scattering of photons; diffusion and delta-Eddington approximations; characteristic length and time scales of photon transport