esa-sceva/satcom-chunk-collection huggingface dataset:
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 5 Experimental Results and Discussion
### Experimental Setup and Encoding Configurations  
The algorithms are evaluated for a range of HD and UHD video sequences composed of content ranging from simple to highly complex motion with diverse spatial and temporal characteristics. The video sequences, encoding configurations, and QP values are selected as defined in the HEVC common test configurations [47]. For instance, test sequences are encoded using _All intra main_ configuration for QP \(\in\{22,27,32,37\}\). All experiments are carried out in an AMD 64-Core CPU @ 2.5 GHz system with 64 GB RAM and Ubuntu 14.04 64-bit operating system.  
The average percentage encoding time reduction, \(\Delta T\), is evaluated for the proposed and state-of-the-art algorithms by  
\[\Delta T=100\times\frac{T_{HM}-T_{\rho}}{T_{HM}}, \tag{13}\]  
where \(T_{HM}\) is the encoding time of HM reference software and \(T_{\rho}\) is the encoding time required for each fast encoding approach. For the proposed method, \(T_{\rho}\) includes the time taken for the data collection, feature extraction, computing weights, and online-SVM training.  
The coding efficiency performance of the proposed and state-of-the-art algorithms is measured against the HM reference encoder using Bjontegaard Delta Bit Rate (BDBR) [48]. In this case, BDBR is computed using two different quality metrics. First, Peak Signal to Noise Ratio (PSNR)--a widely used objective quality metric--is used as the quality metric in the RD curve. Then, Video Quality Metric (VQM) tools that estimate the perceived video quality of the users is utilised as the quality metric when computing the BDBR [49]. The perception of visual media is influenced by the Human Visual System, the brain, and the eye [50]. Typically, a human observer gives higher importance to certain parts of a picture, as opposed to giving equal importance to every part. Objective quality metrics such as PSNR fail to capture the perceived subjective quality in such scenarios. On the contrary, VQM is widely considered as a calibrated model of subjective video quality measurement that closely correlates to the user's QoE when consuming video content [51].
## 4 Results  
Because the study site was a mature forest we do not present an age-series analysis. In this study, we were interested in biomass production because biomass directly relates to the amount of carbon stored by the tree canopy. Our results were validated with the observed data, and a linear regression was applied to investigate accuracy.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Description of parameter** & **Symbol** & **Units** & **Default Value** & **O/L** \\ \hline \hline _Allometric relationships \& partitioning_ & & & \\ Ratio of foliage:stem partitioning at \(B=2\) cm & \(pFS_{2}\) & – & 0.03 & O \\ Ratio of foliage:stem partitioning at \(B=20\) cm & \(pFS_{20}\) & – & 0.02 & O \\ Constant in stem mass \(v\) diam. relationship & \(a_{S}\) & – & 0.06 & O \\ Power in stem mass \(v\) diam. relationship & \(n_{S}\) & – & 2.6 & O \\ _Litterfall \& root turnover_ & & & \\ Litterfall rate at \(t=0\) & \(r_{0}\) & month\({}^{-1}\) & 0.083 & O \\ Litterfall rate for mature stands & \(r_{1}\) & month\({}^{-1}\) & 0.083 & O \\ Age at which litterfall rate has median value & \(t_{F}\) & month & 24 & O \\ Average monthly root turnover rate & \(r\) & month\({}^{-1}\) & 0.083 & L \\ _Temperature modifier_ & & & & \\ Minimum temperature for growth & \(T_{n}\) & C & 8.5 & O \\ Optimum temperature for growth & \(T_{opt}\) & C & 24.5 & O \\ Maximum temperature for growth & \(T_{x}\) & C & 36 & O \\ _Frost modifier_ & & & & \\ Number of days production lost for each frost day & \(k_{F}\) & days & 1 & O \\ _VPD modifier_ & & & & \\ Defines stomatal response to VPD & \(k_{D}\) & mbar & 0.05 & O \\ _Stem Number_ & & & & \\ Basic density & \(\rho\) & t m\({}^{-3}\) & 0.6 & O \\ Maximum tree stem mass for 1,000 trees ha\({}^{-1}\) & \(w_{Sci\mathbf{1000}}\) & kg tree\({}^{-1}\) & 180 & O \\
**Conductance** & & & & \\ Maximum canopy conductance & \(g_{Cx}\) & m s\({}^{-1}\) & 0.02 & O \\ Canopy LAI for maximum canopy conductance & \(L_{Cx}\) & m\({}^{2}\) m\({}^{-2}\) & 3.33 & O \\ _Specific leaf area_ & & & & \\ Specific leaf area at stand age 0 & \(\sigma_{0}\) & m\({}^{2}\) kg\({}^{-1}\) & 16 & L \\ Specific leaf area for mature aged stands & \(\sigma_{1}\) & m\({}^{2}\) kg\({}^{-1}\) & 16 & L \\ Age at which specific leaf area = \(\
u\lambda(\sigma_{0}+\sigma_{1})\) & \(t_{\sigma}\) & yr & 2.5 & L \\ _Light interception, production and respiration_ & & & & \\ Extinction coefficient for PAR absorption by canopy & \(k\) & – & 0.565 & O \\ Maximum canopy quantum efficiency & \(\alpha_{Cx}\) & – & 0.012 & O \\ Ratio NPP/GPP & \(Y\) & – & 0.422 & O \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of parameter values as used in 3-PG for deciduous broadleaf forest. Most data are observed (O) from the experimental plot data and the others were obtained from the literature (L).
# Multi-Backup Beams for Instantaneous Link Recovery in mmWave Communications
## 6 Conclusions  
This paper presents a novel beam recovery scheme for standalone millimeter wave communications, without the reliance on sub 6 GHz microwave bands assistance. The scheme is based on novel analog beamforming architecture that radiates multiple instantaneous beam directions from a single RF chain.  
Figure 6: Power delay profile for proposed multi-backup scheme.  
Figure 7: Recovery time for the proposed multi-backup scheme at various beam numbers.  
Here, once the main beam is blocked, then a group of adjacent simultaneous beams are radiated that cover the blocked beam and promotes maximum ratio combining at the MS for enhanced signal profile and spectral efficiency. The proposed scheme features instantaneous recovery times, which can realize ultra-low latency levels. Future efforts will investigate the effects of user mobility, coherence times, and terrain in blockage modeling, and evaluating the proposed scheme under these effects.  
This work is funded by the Deanship of Scientific Research at King Faisal University, under Nasher Track (grant number 186239).  
The author acknowledges the Deanship of Scientific Research at King Faisal University for their continuous support during this project.  
The author declares no conflict of interest.
## 2 Open-Loop Tracking and Inversion Algorithm
### Mountain-Based Atmospheric Retrieval Algorithm
Therefore, when the clock error of the mountaintop receiver is eliminated by the reference star, only the ionospheric influence of the reference star needs to be eliminated, while the mountain-based occultation observation data does not need to consider its influence on the excess phase. Even with only single frequency observation data, the refractive index inversion results from the ground to the top of the mountain can be obtained. As shown in Figure 2, according to the geometric relationship of the mountain-based occultation, Excess phase \(ext_{i}^{s,r}(t)\) and Doppler \(dop_{i}^{s,r}(t)\) of transmitter (\(s\)) and receiver (\(r\)) at frequency (\(i\)) at time (\(t\)), The impact height \(a\) (the superscript and subscripts and the time label are omitted here) and the bending angle \(\alpha\) have the following relationship [44]:  
\[\frac{dop_{i}^{s,r}(t)}{f_{i}}=\frac{1}{c}\frac{\Delta ext_{i}^{s,r}(t)}{ \Delta t} \tag{6}\]  
\[dop_{i}^{s,r}(t)=-\frac{f_{i}v_{2}}{c}cos(y-\phi_{2}) \tag{7}\]  
\[a(t)=n_{2}r_{2}sin(\phi_{2})=n_{1}r_{1}sin(\phi_{1})\rightarrow\phi_{1}=arcsin (\frac{n_{2}r_{2}sin(\phi_{2})}{n_{1}r_{1}}) \tag{8}\]  
\[\alpha=\theta-\phi_{1}+\phi_{2} \tag{9}\]  
Among these, \(f_{i}\) is the frequency of frequency point \(i\), and \(\phi_{1},\ \phi_{2},\ r_{1},\ r_{2}\) are the signal incident angle and the center distance of the receiver and transmitter, respectively. \(\theta\) is the angle between the  
\[exL_{i}^{s,r}(t)=Ion_{i}^{s,r}(t)+Att_{i}^{s,r}(t) \tag{2}\]0 Geometry diagram of mountain base occultation. Coordinate point 1 represents the site of the mountain base test, at the top of Wuling Mountain. Coordinate point 2 represents the navigation satellite at an occultation event. vector from the Earth's center to the receiver and the transmitter. \(v_{2}\) is the occultation velocity, and \(\gamma\) is the angle between the occultation velocity and the vector from the Earth's center to the transmitter.
## 5 Conclusions  
The demonstrations in this paper present a clear step forward in enabling the integration of different QKD networks to deliver the keys between them via a QKDNO to manage geographically separated networks in which the different networks use different KMSs managed by each QKDNC. It is the first QKDNO to interconnect different QKD networks managed by different QKDNCs for the interoperable KMSs in the commercial network. This implementation can enable the extension of QKD network coverage by interconnecting various QKD networks equipped with different devices from different vendors. This paper presents the commercial and practical implementations of an QKDNO which acts as a central entity to manage multiple QKDNCs, ensuring end-to-end QKD service provisioning in South Korea, which is realized by SK Telecom.  
Conceptualization, methodology, software, and validation, D.-H.S., J.S. and M.H.K.; writing--original draft preparation, writing--review and editing, and visualization, D.-H.S.; project administration, J.S.; funding acquisition, M.H.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Institute of Information & Communication Technology Planning & Evaluation (IITP) grant funded by the Korean government (Ministry of Science and ICT, MSIT) (No.2020-0-00890, Development of trusted node core and interfaces for the interoperability among QKD protocols) and National Information Society Agency were funded by MSIT, Korea (NIA III-RBI-D-22066 \"Establishing and managing pilot infrastructure of quantum cryptographic communication in 2022\").  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 5 Data Imbalance
### Data Imbalance Handling Techniques Future Research Directions
#### 5.5.5 Multi-Task Learning  
Multi-task learning aims to learn multiple related tasks simultaneously to improve the model's performance on each task. Mathematically, multi-task learning can be represented as a joint optimization problem, where the model needs to be optimized simultaneously on multiple tasks. The core idea is that there are certain common features or patterns between different tasks, and by sharing these features, the learning ability of the model for each task can be enhanced [124,129].
## 4 Experimental Results
### The Experimental Results of the Proposed VCS with Grayscale Images  
The experimental results from Algorithm 5 for the case of (5, 5) are shown in Figure 8. All of the grayscale images are 512 \(\times\) 512 pixels in size. Figure 8a shows the secret image. Figure 8b-f show the cover images. Figure 8h-l show the share images generated by Algorithm 5. Figure 8g shows the revealed image via XORing all shares.  
Table 2 illustrates the PSNR of the images in Figure 8. We found that all of the PSNR values were between 19 to 20 and the original secrets were perfectly reconstructed. All of the PSNR values of our experimental results were less than 30, and we found a littleencrypted trace on the share images. However, we could still easily see that the shares were very similar to the camouflage images.  
To better understand the experimental results from Algorithm 5, we chose \(n\) images from 15 images as covers: \(n\) represents the number of shares, which were 3, 4, 5, 6, and 7. The 15 images are shown in Figure 9a-o (from [[https://pixabay.com](https://pixabay.com)]([https://pixabay.com](https://pixabay.com)), accessed date: 5 July 2022). After all possible experiments were performed, the average PSNR value was calculated. Under this assumption, we performed \(C(15,\,n)\) times for each case to calculate average PSNR. The experimental results are shown in Table 3. From Table 3, we can obviously see that as \(n\) increases, the average visual quality of shares becomes better (increases).
## 7 Conclusions and Future Scope  
HPO is an important challenge for the efficient performance of NNs. In this research work, we propose HyperGE, a two-staged GE-driven model to reduce computational resources while fine-tuning NNs on three benchmark image classification datasets and two NN architectures: VGG-19 and ResNet-50. The findings indicate the capabilities of HyperGE in reducing the hyperparameter search space by 90% in Stage 2. The immediate future scope of this research could be to test HyperGE across different NNs for different tasks. Additionally, reducing fitness evaluation complexity can be explored through the use of approximate neural networks and surrogate or approximate functions. This could provide a more efficient method for evaluating complex problems, allowing us to gain insights into how best to optimise our solutions in order to maximise performance and efficiency.  
Conceptualization, M.K.; Data curation, G.V.; Funding acquisition, C.R.; Methodology, M.K.; Project administration, C.R.; Software, G.V.; Supervision, C.R.; Visualization, G.V.; Writing--original draft, G.V. and M.K.; Writing--review editing, C.R. M.K. and G.V. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Science Foundation Ireland Grant #16/IA/4605.  
All the source and code files are available at: [[https://github.com/gaurivaidya/GE-HPO](https://github.com/gaurivaidya/GE-HPO)]([https://github.com/gaurivaidya/GE-HPO](https://github.com/gaurivaidya/GE-HPO)) (accessed on 24 June 2023).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Dataset** & **Approach** & **Model** & **\# Trials** & **\# Epochs/Time** &
\begin{tabular}{l} **Test Accuracy** \\ **(\%)** \\ \end{tabular} \\ \hline \multirow{4}{*}{CIFAR-10} & [PERSON] et al. [5] & ResNet-32 & 104 & 6 h & 93 \\  & [PERSON] et al. [24] & cuda-convnet & & 250 & 88 \\  & **HyperGE** & **VGG-19** & **80** & **50/3 h** & **84.98** \\  & **HyperGE** & **ResNet-50** & **50** & **30/2 h** & **82.80** \\ \hline \multirow{4}{*}{Caltech-101} & Autotune(BO) [30] & ResNet-50 & - & 50 & 92.01 \\  & Autotune(RS) [30] & ResNet-50 & - & 50 & 92.94 \\ \cline{1-1}  & **HyperGE** & **VGG-19** & **80** & **50** & **88.75** \\ \cline{1-1}  & **HyperGE** & **ResNet-50** & **80** & **30** & **91.89** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative analysis of HyperGE against SOTA. The bold results indicate our approach outperforms SOTA.  
Figure 13: An example illustrating flexibility of BNF grammar in scaling hyperparameters.
## 2 Pharmacology of Piperine: In Vitro and In Vivo Studies
### Anti-Diabetic Effect  
Piperine enhances hepatic oxidized glutathione (GSSG, by 100%) and decreases renal glutathione (GSH, by 35%) concentration and renal glutathione reductase (GR) activity (by 25%) in streptozotocin-induced diabetic rats when compared to healthy controls [14]. [PERSON] et al. [15] reported that piperine would suppress the body weight, and improve insulin and leptin sensitivity, thereby regulating obesity. In the study of twenty piperine derivatives containing benzothiazole moiety, nine piperine analogs exhibited higher anti-diabetic activity, in comparison with the standard, rosiglitzone [16]. Piperine exhibits anti-hyperglycemic activity in allovan-induced diabetic mice since significant blood glucose-lowering effect was registered after 14 days of oral intake at a dose of 20 mg/kg. On the other hand, the same study showed that high dose (40 mg/kg) acutely raise blood glucose level [17]. Another confirmation of piperine's ability to increase the efficacy of various drugs was demonstrated with respect to those used to treat diabetes. In another study, on allovan-induced diabetic rats, the combination of piperine with a therapeutic dose of metformin (10 mg/kg + 250 mg/kg) showed a significantly higher lowering of blood glucose level as compared to metformin alone on both the 14 th and 28 th day [18]. On streptozotocin-induced diabetic rats, the combination of piperine and glimepiride increased all the pharmacokinetic parameters (Cmax, AUC0-n, AUCtotal, T\({}_{1/2}\), MRT, and decreased the clearance, Vd) and improved overall antioxidant status [19]. Piperine reduces ligand-induced liver X receptor \(\alpha\) activity in a dose-dependent manner, showing its role as its antagonist, while its dietary effects in high-fat-diet (HFD, +0.05% piperine) rats showed decreased plasma insulin and glucose concentrations and increased insulin sensitivity [20]. Consequently, it downregulates genes involved in endoplasmic reticulum stress and upregulates GLUT2. Piperine inhibits the adipocyte differentiation of 3T3-L1 cells by decreasing master adipogenic transcriptional factors PPAR\(\gamma\), SREBP-1c, and C/EBP\(\beta\), leading to inhibition of adipogenesis [21]. It was reported that piperine inhibits uridine diphosphate-glucose dehydrogenase (UDP-GDH), UDP-glucuronosyltransferase (UDP-GT) and decreases UDP-glucuron acid (UDPGA) in rat and guinea pig liver and intestine [22].  
Piperine reduces total plasma cholesterol, low density lipoprotein (LDL) cholesterol, very low-density lipoprotein (VLDL), the activity of 3-hydroxy 3-methyl glutaryl coenzyme A (HMG CoA) reductase in the tissues, and increases lipoprotein lipase (LPL) and plasma lecithin cholesterol acyl transferase (LCAT). All these effects consequently result with the prevention of the plasma lipids and lipoproteins accumulation in antithyroid drug-induced hypercholesterolemia rats [23].
## 3 Results and Discussion
### Loss of Aqueous H\({}_{\text{2(aq)}}\) by Diffusion through the Cap Rock  
By assuming specific diffusion coefficients for the different aqueous species, the diffusion of hydrogen and related products is modeled. A non-reactive tracer, with the same diffusion coefficient as hydrogen of 5.13 \(\times\) 10\({}^{-9}\) m\({}^{2}\) s\({}^{-1}\), shows that significant amounts of hydrogen (greater than 4.0 \(\times\) 10\({}^{-7}\) mol kgw\({}^{-1}\)) were calculated only for distances less than 4 m through the cap rock (in the \(z\)-direction) over 30 years, assuming an initial cap rock porosity of 5%, no faults, and non-reactive hydrogen (Figure 4a). However, hydrogen reacts with the mineralogical assemblage of the reservoir rock and brine and is converted by BSR and methanogenesis. The loss of aqueous hydrogen by diffusion is minor. Dissolved hydrogen and corresponding aqueous species concentrations (OH\({}^{-}\)  
Figure 3: Volume changes of mineral phases (L kgw\({}^{-1}\)) in the reservoir rock after 30 years of hydrogen storage (reference scenario).  
CH4, HCO3-, Al(OH)4-, CaOH+, CaHCO3+, MgOH+, MgHCO3+, H2, NH4+, NH3, H2S, HS-, H4 SiO4, and H3 SiO4-) in the cap rock brine show no significant changes. The most significant difference has been calculated for the NH4+ concentration, which rises to around 4 \(\times\) 10\({}^{-4}\) mol kgw\({}^{-1}\) in the first meter of the cap rock (cell 182). Therefore, the effect of hydrogen storage on the cap rock is only visible in the first meter of the cap rock, which is in contact with the reservoir rock (cell 182), after 30 years. Even though each aqueous species migrates with a different rate along its concentration gradient, as described by [PERSON] et al. [58], the difference in migration is too small to see deviations in the model over the modeled time of 30 years. The loss of the non-reactive hydrogen tracer by diffusion into the cap rock is 25% and represents the maximal loss of aqueous hydrogen by diffusion over 30 years.  
In summary (Sections 3.1-3.3), the loss of hydrogen gas by bacterial conversion, gas-water-rock interactions, and aqueous diffusion is 76% over 30 years.
## 2 Related Works
### Feature Extraction  
Existing feature-extraction methods are classified into two groups: non-training-based [13; 26; 27; 28] and training-based [22; 24; 29; 30] methods. Most traditional feature-extraction methods do not require training. For example, [PERSON] and [PERSON] [13] used the Gabor filter to extract the feature maps of FV images on the preprocessed images and then used the morphological operation to enhance the definition of the feature maps. They also used the local Radon transformation to extract the features of IKP images. [PERSON] and [PERSON] [26] used the gray-level co-occurrence matrix (GLCM) to extract the second-order texture features of finger knuckle print (FKP) images. [PERSON] et al. [27] proposed a new feature expression method based on local encoding using the generalized symmetric local graph structure (GSLG) to express the position and orientation relationship of domain pixels. [PERSON] and [PERSON] [28] extracted the features of FKP and FV using a repeated line tracking method. Although these traditional feature-extraction methods do not require training of feature extractors, the quality of images affects their extraction methods. Thus, it is necessary to perform complicated preprocessing processes such as region of interest extraction, orientation correction, and image enhancement. In addition, the characteristics of each modality are different, requiring special feature-extraction methods.  
Although feature extraction through CNN requires pretraining of the network model, it has become a popular feature-extraction method in biometrics because of its less complex image preprocessing procedure and effective feature extraction. For example, [PERSON] et al. [22] extracted the features of FV and FKP through CNN. [PERSON] et al. [29] first used a five-layer CNN to extract each modal feature and then used principal component analysis (PCA) to standardize the features. [PERSON] et al. [30] proposed a multimodal finger recognition network--namely, FPV-Net--to extract the features of FVs and fingerprints. [PERSON] et al. [24] fine-tuned VGGNet [31] and ResNet [32] and used these pretrained models for the feature extraction of FV and finger shape. Regarding these works, this study utilizes a large-scale pretrained model and triplet loss function to obtain feature representations of multimodal images.