esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Performance Evaluation
### Evaluation for the Designed Protocol
#### 6.1.1 Protocol Comparison Simulation Setup
* Random Timer Forwarding (RTF): For each hop relay vehicle selection, the timer is independent of the positions of the sender and receiver; it is drawn according to a uniform probability density on the interval \([t_{\text{min}},t_{\text{max}}]\)[57,65]. * Link State-aware Geographic Routing protocol (LSGR): LSGR incorporated the link state's influence into the routing protocol design [28]. LSGR contrived a routing metric called Expected One-transmission Advance (EOA) to improve the greedy forwarding algorithm by explicitly incorporating the link state and packets advance. However, the EOA weighting parameter might not be easily estimated for the beacon-less routing protocol design. To take a suitable case for our system scenario, we assume the link state can be represented by the link duration parameter considered in our paper. For the path construction process, the neighboring vehicle that can provide the best link state will be selected as the next hop relay vehicle. Definitely, this can improve the transmission efficiency by diminishing transmission failures, while the end-to-end delay might not be guaranteed at the same time. In the following, we first present a comparison of our proposed protocol, LBRP, with DBF, RTF and LSGR (Section 6.1). Then, we give results to assess the accuracy of the analytical model as opposed to the simulations (Section 6.2). A final wrap-up discussion of the performance evaluation results is given in Section 6.3.
## 3 Analytical Model
Equation (10) represents the estimated SNR after MISO transmission, which takes into account the channel response of multiple transmitted signals and, hence, enhancement in the overall performance. Then finally the capacity (SNR dependent) of the MISO system can be given as  
Figure 1:1  
Finally, in the presence of interference signal, the optimum receiver configuration can be obtained by first estimating the decision static (\(x^{*}\)) of the information signal using the waveform-spreading correlator. Here, the cost function (\(\Gamma\)) represents the quality measurements of the system as a sum of the Mean Square Error (MSE) of all MISO channels that are required to be determined for the encoded sequence (\(c\)) used for the transmitting signal, given by  
Figure 1:2  
where r represents the cross-correlation function and \(T\) is the time delay with respect to the direct path signal. Thus, by using the correlation function (\(K\)) of the composite signal consisting of the information signal, noise and interference signal, the SNR for the optimized receiver becomes  
Figure 1:3  
Considering the vertical and horizontal movements of the vehicles due to road surface and traffic conditions [39], a set of four LEDs is arranged in a square shape to ensure the availability of adequate amounts of illumination in different conditions. A lower number of units will cause an insufficient level of received power [40]. On the other hand, increasing the number of LED units will result in high cost and complexity. The next section will present the simulations performed on the basis of the proposed system and an optimized receiver for MISO transmission.
## 3 Data
### Swarm Data  
The Swarm SH solution (truncated to a degree and order 40) was derived from the International Combination Service for Time-variable Gravity, covering the period January 2014 to December 2020. It is a combination model constituted by using variance component estimation, consisting of four Swarm models from the Astronomical Institute at the Czech Academy of Sciences, the University of Bern, the Institute of Geodesy at the Graz University of Technology and Ohio State University [31]. Like GRACE SH solutions, the Swarm SH solutions need to be preprocessed. In addition to the different filtering parameters, the processing flow and methods are basically the same as GRACE data. In our study, 1000 km Fan filter was used to process the Swarm SH solution.
# Multi-Frame Blind Super-Resolution Based on Joint Motion Estimation and Blur Kernel Estimation
## 6 Conclusions  
In MFSR practical applications, the reconstructed HR images are greatly affected by motion, blur and noise. Currently, it is assumed that the blur kernel is known in most of the super-resolution methods. Multi-frame blind super-resolution under an unknown blur kernel is challenging. We develop a novel multi-frame blind super-resolution method in this paper. In contrast to deep learning methods, our method deals with multi frame super-resolution issues, but not through training and learning. We first calculate the motion estimation and blur kernel estimation of the image independently. Then, we use the result and the confidence weight of multi-frame blind super-resolution to correct the motion estimation and blur kernel estimation. In the iterative motion estimation, the Jacobian matrix of motion parameters is introduced to estimate the motion more accurately. The final estimated HR image is gradually obtained in the iterative correction process. This method of combining the estimation of motion parameters, blur kernel estimation and parameters in image reconstruction overcomes the poor convergence of the simple alternating minimization method.  
Our proposed method can better preserve the details of the image without obvious artifacts compared to the existing methods. We mainly study the MFSR of natural images. We assume that the next task for us to perform is to study the MFSR of special images or specific domains, such as low light and multi-modal MFSR. Our future work will be directed to the development of new priors for special applications.  
Figure 8: Visual comparison of algorithms for the IMG_1480 image when the magnification factor is 5. (**a**) The LR image; (**b**) Bicubic interpolation; (**c**) BayesSR; (**d**) SRB; (**e**) DeepSR; (**f**) FBSR; (**g**) Ours.  
**Author Contributions:** Conceptualization, S.L. and M.W.; methodology, S.L.; software, Q.H.; validation, S.L. and Q.H.; formal analysis, M.W.; writing--original draft preparation, S.L.; writing--review and editing, M.W.; visualization, Q.H.; supervision, M.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available from the corresponding author. Send a request in corresponding author's email, then you will receive the data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Result and Discussion
### Thermal Storage Performance Analysis
The effect of GNPs content to thermal storage performance is shown in Figure 6. As the GNPs content increases, the latent heat gradually decreases while the thermal conductivity increases, implying that the enhancement of thermal conductivity using GNPs will be accompanied by decreased latent heat in the composites. Whereas adding 2 wt. % GNPs results in a 146% increase in thermal conductivity and only a 6.3% reduction in the latent heat of the PEG used in the current work. Therefore, it may be beneficial to add a low loading ration of GNPs to obtain the suitable latent heat as well as enhance the thermal conductivity of composites. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sample** & \(\mathbf{T_{\text{ms}}}\) (\({}^{\circ}\)C) & \(\mathbf{T_{\text{mp}}}\) (\({}^{\circ}\)C) & \(\mathbf{\Delta H_{\text{m}}}\) (\(\mathbf{\text{\text{/g}}}\)) & \(\mathbf{T_{\text{ss}}}\) (\({}^{\circ}\)C) & \(\mathbf{T_{\text{sp}}}\) (\({}^{\circ}\)C) & \(\mathbf{T_{\text{se}}}\) (\({}^{\circ}\)C) & \(\mathbf{\Delta H_{\text{g}}}\) (\(\mathbf{\text{/g}}\)) \\ \hline PEG & 58.8 & 66.9 & 69.1 & 180.7 & 38.9 & 33.8 & 27.5 & 161.3 \\ PEG/GNPs-0.5\% & 57.5 & 63.8 & 65.9 & 178.5 & 37.6 & 34.6 & 29.3 & 158.4 \\ PEG/GNPs-1\% & 57.9 & 63.4 & 65.7 & 175.2 & 36.4 & 33.7 & 29.4 & 155.6 \\ PEG/GNPs-1.5\% & 56.4 & 63.2 & 65.6 & 172.1 & 36.5 & 32.1 & 28.6 & 152.8 \\ PEG/GNPs-2\% & 56.1 & 62.6 & 64.8 & 170.6 & 37.1 & 34.2 & 29.5 & 151.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: DSC melting and solidifying characteristics of pristine PEG and PEG/GNPs-X% composites. Figure 5: Solidifying DSC curves of the pure PEG and PEG/GNPs composites with the different contents of GNPs.
## 6 Conclusions and Future Study  
In this study, we proposed a VM consolidation model to induce stable operation of a DC by exploring VM combinations, where the sum of the workloads remain stable, to reduce the power consumption of the DC. The HS scheme is designed to efficiently solve the proposed VM consolidation model, and the adaptive HS, where the parameters change dynamically, is applied. The adaptive HS shows better results than the original HS, and comparative analysis with the heuristic algorithm FFD shows that the proposed algorithm also has superior performance compared to the conventional approaches. In particular, when the SC policy, which searches for a combination of VMs in which the workload of the PM is stably maintained, showed excellent results in terms of the quality of the DC operation environment than other policies.  
Benchmark data were used for the VM workload, and the VM workload was forecast to apply the proposed VM consolidation model to real world applications. In dynamic cloud computing environments, workload forecasting is an essential process for resource assignment, capacity planning, and energy savings. In a future study, an integrated VM consolidation model that combines the proposed VM consolidation model and a workload prediction subsystem will be explored. Through this, we intend to improve the limitations of this study, assuming the VM workload as a definite value.  
The workload prediction subsystem in the integrated VM consolidation model would be expected to cluster similar VMs through behavioral analyses, and each cluster would be used as a unit of the learning model. The clustering forecast technique is aimed at maintaining unique patterns of VMs while increasing the management efficiency of the learning model with appropriate numbers of clusters. In the future study, the integrated VM consolidation model combining the proposed model in this study and a clustering forecasting technique that is appropriate for large-scale DC environments will be considered.  
Till date, DCs have generally adopted conservative operation measures that prioritize environmental stability owing to uncertainties. As a large number of organizations are migrating to cloud-based computing environments, the scale of the DC, which has been characterized as \"Bigger is Better\" is expected to grow exponentially in the future. Therefore, intelligent cloud integrated management platforms utilizing advanced technologies, such as artificial intelligence and big data analysis, along with stable and efficient automatic operation strategies must be considered so that future researches will be aimed at these goals.  
[PERSON] developed the model and wrote the majority of the manuscript. [PERSON] performed the experiments and analyzed the data. [PERSON] developed the overall idea and the basic outline of the paper. All authors have read and agreed to the published version of the manuscript.  
This work has supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2019R1F1A1059276).  
Not applicable.  
The authors declare no conflict of interest.
# Bell-Clauser-Horne-Shimony-Holt Behavior Under Quantum Loss and Decoherence
## 3 Qubit Quantum Channels
### Phase-Damping Channel  
This is also called a dephasing channel, and acts as follows:  
\[\mathcal{E}\Bigg{(}\begin{pmatrix}\varrho_{0,0}&\varrho_{0,1}\\ \varrho_{1,0}&\varrho_{1,1}\end{pmatrix}\Bigg{)}=\begin{pmatrix}\varrho_{0,0}& (1-p)\varrho_{0,1}\\ (1-p)\varrho_{1,0}&\varrho_{1,1}\end{pmatrix}. \tag{20}\]  
This channel depletes the coherences (off-diagonal elements) of the density matrix in the given basis for \(p=1\), keeping the diagonals only. Hence, it is a unital channel: it keeps the completely mixed state \((1/2)\mathbb{I}\) invariant. The Choi matrix of the phase-damping channel reads  
\[X_{\text{PD}}=\begin{pmatrix}1&0&0&1-p\\ 0&0&0&0\\ 0&0&0&0\\ 1-p&0&0&1\end{pmatrix}. \tag{21}\]
## 4 Evaluation
### Computation Overhead  
In many cloud systems, large-scale file-encryption tasks (e.g., securing logs, analytical datasets, or backups) are performed asynchronously in the background, while real-time computation workloads continue to run concurrently. Although small-sized file operations typically incur minimal and short-lived system costs (e.g., transient memory usage or localized I/O), large-scale file encryption can introduce sustained I/O pressure, memory contention, and CPU competition. To identify the practicality of our scheme in cloud systems, we evaluate whether background encryption interferes with concurrently running applications under realistic cloud workloads.  
While our proposed scheme enhances security by integrating PQC and QKD with AES, it may affect overall performance by incurring computational overhead. To evaluate  
Figure 11: QKD Runtime Variability.  
this impact, we measured the workload running time across four YCSB workloads: A, B, C, and D. Each workload reflects a distinct access pattern: workload A (update-heavy), B (read-mostly), C (read-only), and D (read-latest). In addition, each workload was executed using the YCSB with request distribution set to Zipfian. Three configurations were compared: a baseline YCSB workload execution with no encryption (Vanilla), execution under AES-only encryption (AES (Original)), and execution under our proposed scheme integrating PQC, QKD, and AES. In all configurations, the YCSB workload was executed on the identical master to ensure consistency. While Vanilla and AES (Original) performed encryption on a single node, our proposed scheme executed distributed encryption across 4 nodes, with the master handling the workload and coordination.  
Table 2 presents the runtime results for all three configurations, averaged over five runs. As shown in the table, both AES (Original) and our proposed scheme increase workload processing time compared to Vanilla, but our proposed scheme consistently incurs lower overhead than AES (Original). In workload A, the runtime for Vanilla, AES, and Proposed is 255.65, 291.43, and 267.74 s, corresponding to a 14.01% increase for AES and 4.72% for Proposed. In workload B, the runtimes are 168.86, 209.65, and 182.34 s, resulting in a 24.17% increase for AES and 7.98% for Proposed. In workloads C and D, the runtime for Vanilla, AES, and Proposed is 115.55, 173.61, and 153.32 s in workload C, and 111.06, 146.62, and 132.17 s in workload D. These correspond to increases of 50.23% (AES) and 32.73% (Proposed) in workload C, and 31.98% (AES) and 19.02% (Proposed) in workload D, respectively. These results show that while both AES and our proposed scheme incur overhead compared to unencrypted execution, our proposed scheme consistently reduces the overhead impact, demonstrating its efficiency and practicality.
## 4 Local Refinement Near Immersed Boundaries Using Algebraic Level Sets
### Brief Review of Algebraic Level Sets  
Here, we propose using unsigned and signed distance from parametric boundaries for local refinement near immersed boundaries. In general, estimating the shortest distance to a parametric boundary requires numerical iteration using the Newton-Raphson method. Such iterations are generally non-robust, as Newton-Raphson iterations yield non-unique foot (nearest) points on the parametric surface near regions of large curvature. In order to circumvent these challenges, we have recently proposed techniques founded on algebraic geometry to estimate unsigned and signed distance measures from parametric boundaries, termed algebraic level sets [34; 35], which have been further developed for point projection [41] and phase merging [42]. Algebraic level sets are briefly reviewed below prior to describing mesh refinement algorithms based on algebraic level sets.  
The main idea is to convert a parametric NURBS entity into its implicit form using the resultant theory and to use the level set of the \"implicitized\" function as a measure of distance. Pre-processing by decomposing the NURBS entity into constituent Bezier patches and post-processing by blending using R-functions are utilized to generate the level sets from complex NURBS entities. The algebraic level sets have the following properties:  
Figure 10: \(n\)-stripe refinement on a \(4\times 4\) domain. The left boundary of each refinement region \(\Omega^{i}\) is provided by \(\xi=4-3\cdot 2^{1-i}\) and the right boundaries coincide at \(\xi=4\). The splitting lines during kd-tree subdivision are labeled with \(l_{i},i=1,2,\cdots,n\).  
Exact locally near the surface  
Monotonic function of exact distance  
Sufficiently smooth for engineering applications  
Efficiently obtained without numerical iterations  
Thus, algebraic level sets can serve the role of a distance measure from the parametric boundary. Therefore, we refer to the algebraic level sets interchangeably as algebraic distance fields. We briefly review the computation of algebraic distance fields and illustrate the procedure through simple examples. Although, the examples illustrated here are of NURBS curves, the procedure is naturally applicable to parametric surfaces, as demonstrated in [34; 35].
## 3 Performance Analysis
### Throughput  
The throughput, which is related to the effective information transmission time of the system, can be calculated as [41]  
\[\text{Throughput}=\frac{(1-\tau)T/2}{T}\bar{C}=\frac{(1-\tau)}{2}\bar{C}, \tag{26}\]  
where \((1-\tau)T/2\) is the effective information transmission time between the source and the destination for the proposed communication system model.
# Feedback Approach for the Relay Channel with Noisy Feedback and Its Security Analysis
## 3 An SK-Type Feedback Scheme for the Gaussian Relay Channel with Noisy Feedback
### Our Achievable Scheme  
#### 3.2.1 Message Mapping Method  
For a given coding blocklength \(N\) and decoding error probability \(\epsilon\), let \(|\mathcal{M}|=2^{NR(N\epsilon)}\), where \(M\) is the transmitted message uniformly distributed over the set \(\mathcal{M}=\{1,2,\ldots,2^{NR(N\epsilon)}\}\). Divide the interval \([-0.5,0.5]\) into \(2^{NR(N,\epsilon)}\) equally spaced sub-intervals, and the center of each sub-interval is mapped one-to-one to a message value in \(\mathcal{M}\). Let \(\theta\) be the center of each sub-interval with respect to the message \(M\). Since \(M\) is equi-probably distributed over the set \(\mathcal{M}\), \(\theta\) is approximately uniformly distributed over the interval \([-0.5,0.5]\) (i.e., \(\theta\sim\text{Unif}[-0.5,0.5]\)) and has a variance of approximately \(1/12\), i.e., \(E[\theta^{2}]=1/12\).
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### Human Monitoring and Tracking  
#### 3.1.1 InfiniTime  
InfiniTime [18] is a wrist-wearable sensor node that was tailored to record images, audio, video, accelerometer data and temperature for tracking purposes. It utilizes a near-field communication/radio frequency identification (NFC/RFID) tag-integrated circuit (IC) with 16 kB electrically erasable programmable read-only memory (EEPROM) and inter-integrated circuit (I2C) bus interface. Regarding the node's communication capabilities, wireless communication is facilitated by the M24 LR16E-R transceiver manufactured by ST Microelectronics. The data rate is variable from 6.6 kbps to 26 kbps. Furthermore, the node contains an on-board ultra-low-power 3-axis accelerometer (ADXL362) by Analog Devices, temperature sensor, INMP801 MEMS analog microphone and an external 112 \(\times\) 112 pixel analog camera.  
Concerning the node's power supply specifications, the node is powered by solar and thermal energy harvesters. The solar panel consists of a parallel connection of eight small amorphous silicon solar cells (AM-1417 CA) from Sanyo Energy. The thermal energy harnessing is facilitated by seven thermoelectric generator (TEG) modules. A single 40 mAh, 3.7 V lithium-polymer (LiPo) rechargeable battery is used for energy storage. Regarding the node's power consumption, at 8 MHz MCU clock frequency, the current draw during ultra-sleep, idle, display update and data sending (radio only being active) modes is 1.225 \(\upmu\)A, 859 \(\upmu\)A, 4278 \(\upmu\)A and 0.442 \(\upmu\)A, respectively.
# Applications of Deep Learning to Neurodevelopment in Pediatric Imaging: Achievements and Challenges
## 4 Discussion  
### Advancements in Deep Learning Applied to Pediatric MRI  
This study reviews pediatric MRI studies for recognition, segmentation, and prediction tasks in neurodevelop. Throughout the review, CNN is the most commonly utilized model. Variations and advancement based on the basic architecture have been proposed to improve the performance in multi-tasks. Multi-view 2D CNN and 3D CNN have been proposed to deal with the 3D volumes in neuroimaging [57, 82, 84]. The multi-view 2D CNN processes 3D volumes with slices generated from sagittal, axial, and coronal sections while 3D CNN utilizes 3D kernels in the networks. Multi-branch CNN models also utilize multimodal imaging to study the brain from different perspectives. Structural  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Study** & **Year** & **Task** & **Population** & **Technique** & **Preprocessing** & **Method** & **Results** \\ \hline
[156] & 2022 & Image &
\begin{tabular}{c} \begin  
connectomes and functional connectomes were combined for age prediction in study [129] and cognitive function prediction in study [139]. Multimodal studies classified children with ASD from healthy controls using combinations of sMRI and rs-fMRI [75; 76; 81]. sMRI provides structural information, fMRI provides information based on brain activity, and DTI provides information regarding quantitative anisotropy and orientation. Multimodal neuroimaging allows researchers to understand the brain from different perspectives and plays an essential role in investigating the brain functional and structural changes in pediatric neurodevelop. Variations of U-net dominate in the segmentation tasks. Dilated-Dense U-Net and U-net with attention mechanism achieved great performance in brain structure segmentation [104; 120]. Meanwhile, semi-supervised learning and transfer learning initiated studies with a small number of training data [103; 122]. GAN shows its superiority in image generation tasks. Variations of GANs have been proposed to synthesize pseudo-images from low-resolution images or latent space [149; 155; 156]. Overall, the development of computational powers has enabled deep learning models to have more complex structures and greater ability to process 3D volumes for a variety of tasks.