esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Smaller Scale Turbulence  
Next, small-scale turbulence is discussed in this section. The small-scale turbulence is represented by the energy dissipation rate, which governs small-scale turbulence, which is more responsible for the energy dissipation. The energy dissipation rate is expressed as Equation (36) [35]. The energy dissipation rate is related more to the small-scale turbulent structure. In the energy cascade process,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Wall Type** & \(\delta\) & \(\delta^{\star}\) & \(\theta\) & \(Re_{\delta}\) & \(Re_{\theta}\) \\ \hline Smooth Wall & 10.2 & 1.63 & 1.24 & \(2.66\times 10^{4}\) & \(3.22\times 10^{4}\) \\ Riblet & 10.2 & 1.56 & 1.20 & \(2.66\times 10^{4}\) & \(3.12\times 10^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Boundary layer characteristics covering boundary layer thicknesses and Reynolds numbers.  
Figure 11: Normalized integral time and length scales normalized by the value in the freestream region.  
assuming that there is no energy loss, the energy produced by the large-scale turbulent motions is transferred to the small scales, which dissipate them:  
\[\varepsilon=0.697\frac{\sigma_{u}^{3}}{L_{0}}. \tag{36}\]  
Figure 13 compares the energy dissipation rates for the smooth wall case and the riblet wall. The probe height was also normalized by each boundary layer thickness for the smooth wall and riblet wall, respectively. Obviously, the riblet surface shows a much smaller distribution. Up to an 18% reduction is obtained using riblets. This physical interpretation indicates that the energy production was reduced in the riblet case. According to [PERSON] (2016), this can be attributed to the fact that the ejection motion was reduced by the riblets.  
Figure 14 compares the profiles of the turbulence intensity for the smooth wall and the riblet wall. The turbulence intensity was calculated as \(\sigma_{u}/\overline{U}\) and normalized by the value in the freestream region. Though the turbulence intensity in the region above \(H/\delta>0.6\) is identical between the smooth wall and riblet wall cases, the turbulence intensity near the wall for the riblet case appears to be smaller than that for the smooth wall case. This is consistent with the observation described in Table 3. Along with the observation obtained from Figure 13, the turbulence near the wall was reduced by the presence of the riblets.  
This study showed that the large-scale turbulent structure in the streamwise direction became larger and the turbulence intensity became smaller by applying a riblet surface. A more detailed analysis, such as computer simulation or three-dimensional measurement with highly temporally and spatially resolved diagnostics, is needed to identify the key physics behind those observations and those responsible for the reduction of skin friction drag by applying the riblet surface, especially the present three-dimensional riblet surface.  
Figure 14: Profiles of turbulence intensities.  
Figure 13: Profiles of energy dissipation rate.
# Immuno-PET for Glioma Imaging: An Update
## 5 Discussion
Meanwhile, red-fluorescent dyes such as 5-ALA are gaining consideration for their potential application in fluorescent-guided surgery for gliomas [70]. Bioluminescence imaging (BLI) relies on detecting and quantifying light emission resulting from the conversion of chemical energy into photon energy. This phenomenon occurs when a substrate undergoes oxidation by the enzyme luciferase in living cells and tissues [71; 72]. Coelenterazine (CLZN), for instance, serves as the substrate for a group of bioluminescent enzymes derived from marine organisms. Various luciferase genes, such as those from fireflies, renilla, gaussia, and most recently Vargula hilgen-dorifi, have been cloned and utilized, with firefly luciferase being the most commonly employed. BLI has found extensive use in assessing and optimizing therapeutic strategies for GBM. One of the significant advantages of bioluminescence is its ability to offer real-time, non-invasive evaluation of specific physiological processes. However, conventional BLI is limited to providing two-dimensional (2D) and qualitative information about the tumor region due to the light scattering effect, in contrast to immuno-PET technology. Recognizing these limitations, efforts have been directed towards transitioning from BLI to bioluminescence Tomography (BLT). Along this path, [PERSON] et al. developed a new reconstruction method that accurately locates the tumor and defines its morphology in three dimensions. Thisapproach was successfully tested in orthotopic glioma mouse models [73]. Further studies are essential to better understand the potential contribution of immuno-PET to glioma research compared to non-radionuclide-based alternatives. Additionally, it is crucial to define its practical implementation in the clinical workup of gliomas.
## 3 Results and Discussion
### Optical Properties
When compared with the VO\({}_{2}\)(60 nm)/glass, the samples of VO\({}_{2}\)(60 nm)/Cu\({}_{50}\)Zr\({}_{50}\)(40 nm)/glass and VO\({}_{2}\)(60 nm)/Cu\({}_{50}\)Zr\({}_{50}\)(80 nm)/glass have higher \(\Delta T_{sol}\) and  
Figure 5: Optical transmittance curves of the VO\({}_{2}\)/Cu\({}_{50}\)Zr\({}_{50}\)/glass thin films in the range of 200–2650 nm by varying temperature, for various thicknesses of Cu\({}_{50}\)Zr\({}_{50}\) buffer layer. (**a**) No layer; (**b**) 40 nm thickness; (**c**) 80 nm thickness; and (**d**) 160 nm thickness. \(\Delta\)\(T_{2500\rm nm}\) values. According to [28], Cu\({}_{50}\)Zr\({}_{50}\) possesses a metallic property in the infrared region. For the sandwich structure VO\({}_{2}\)/Cu\({}_{50}\)Zr\({}_{50}\)/glass, p-polarized SPPs are supported on the metallic film interfaces in the corresponding range. The appearance of the SPPs probably effectively modulates the optical properties. This mechanism will be further studied in the future [38; 39]. When increasing the Cu\({}_{50}\)Zr\({}_{50}\) buffer layer thickness to 160 nm, the \(\Delta T_{sol}\) and \(\Delta T_{2500\rm nm}\) values clearly decreased. The main reason for this is the effect of crystallization quality, which corresponds to the above XRD data analysis. Favorable thermochromic properties are achieved in the sample of VO\({}_{2}\)(60 nm)/Cu\({}_{50}\)Zr\({}_{50}\)(40 nm)/glass. The sample of VO\({}_{2}\)(60 nm)/Cu\({}_{50}\)Zr\({}_{50}\)(40 nm)/glass shows better optical thermochromic performance, and the \(\Delta T_{sol}\) value is as high as \(\sim\)14.3% with the \(T_{vis}\) value up to 52.3% and the \(\Delta T_{2500\rm nm}\) value up to 60.2%, which represents a good balance between \(\Delta T_{sol}\) and \(T_{vis}\) for smart window applications. These results can be compared with previous experimental results, such as those for periodic and aperiodic porous VO\({}_{2}\)(M1) films that were prepared through complex chemical and physical processes in multiple layers of TiO\({}_{2}\)(or SiO\({}_{2}\))/VO\({}_{2}\)/substrate films [40; 41; 42] and VO\({}_{2}\)-based composite thin films [43; 44]. All in all, optimizing optical properties of VO\({}_{2}\) films had indeed been enhanced by the introduction of Cu\({}_{50}\)Zr\({}_{50}\) buffer layer.
# Robust Burst Detection Algorithm for Distributed Unique Word TDMA Signal
## 3 Algorithm Description  
### Traditional Centralized Dual Correlation Detection Algorithm  
TDC detection algorithm [20; 21] is mainly based on centralized UW to detect and synchronize burst signals. The detection principle is shown in Figure 4.  
As shown in Figure 4, the sliding detection window is concentrated and continuous in TDC detection algorithm. So, the signal selected by this window is continuous in waveform and frequency offset.  
TDC detection formula[21] based on burst signal is:  
\[L=\sum\limits_{d=1}^{D-1}\left\{\frac{\left|\sum\limits_{k=d}^{D-1}r^{*}(k)a( k)r(k-d)a^{*}(k-d)\right|^{2}}{\sum\limits_{k=d}^{D-1}\left|r^{*}(k)r^{*}(k-d) \right|^{2}}\right\} \tag{3}\]  
Herein, \(D\) is the total length of UW, \(L\) is correlation coefficient, \(*\) in \(x^{*}\) is complex conjugate.  
Figure 4: TDC detection principle.  
Figure 3: The known local UW structure.  
Figure 2: Distributed unique word TDMA frame structure.  
The numerator in Equation (3) can be written as:  
\[corr=\left|\sum_{k=d}^{D-1}r^{*}(k)r(k-d)\{a^{*}(k)a(k-d)\}^{*}\right|^{2} \tag{4}\]  
Equation (4) is the core of dual correlation algorithm. Bring Equation (1) into Equation (4), for the convenience of discussion, let \(d=1\), consider the non-correlation between noise and UW, ignore the influence of noise, we can obtain:  
\[corr=\left|\sum_{k=1}^{D-1}r^{*}(k)r(k-1)\{a^{*}(k)a(k-1)\}^{*} \right|^{2} \tag{5}\] \[=\left|\sum_{k=1}^{D-1}s^{*}(k)s(k-1)\{a^{*}(k)a(k-1)\}^{*}e^{- \mathrm{j}\omega_{0}T}\right|^{2}\]  
Since the signal in sliding detection window is continuous on frequency offset, the delay conjugate multiplication \(r^{*}(k)r(k-1)\) in Equation (5) converts the frequency offset to one fixed phase offset \(e^{-\mathrm{j}\omega_{0}T}\). Then this phase offset is eliminated by the modulo operation, and when the sliding detection window is aligned with the UW of received signal, \(s(k)=a(k)\) for any \(k\), then \(s^{*}(k)s(k-1)\{a^{*}(k)a(k-1)\}^{*}=1\), \(corr\) will be maximum.
# Object-Tracking Algorithm Combining Motion Direction and Time Series
## 4 Experimental Results and Analysis
### Speed, FLOPs, and Params  
Table 5 compares our algorithm with the baseline algorithm, STARK-ST50, in terms of Speed, Params, and FLOPs. STARK-ST50 achieves a Speed of 27 fps, requires 28.2 M Params, and has 12.8 G FLOPs, while our algorithm achieves 25 fps, requires 29.8 M Params, and has 12.9 G FLOPs. Compared to STARK-ST50, our algorithm shows a slight increase in both FLOPs and Params. Importantly, our algorithm maintains real-time tracking performance.
# Superior Control of Spacecraft Re-Entry Trajectory
## 2 Optimization Mathematical Model for Re-Entry Trajectory and Superior Control Algorithms
### Constraints on Re-Entry Trajectory  
The differential equation of flight mechanics during re-entry flight is as follows:  
\[\frac{dx(t)}{dt}=f[t,\mathbf{x}(t),\mathbf{u}(t)], \tag{2}\]  
\(\mathbf{x}=[V,\gamma,\psi,h,\theta,\phi]\) is the status parameter; \(\mathbf{u}=[\alpha,\sigma]\) is the control parameter; \(t\) is time. The differential equations of flight motion can be found in [4; 5; 6; 7; 8].  
Heating rate \(\dot{Q}_{S}\) and overload \(n_{Y}\) are subject to the following constraints:  
\[\dot{Q}_{S}\leq\dot{Q}_{SMax}, \tag{3}\] \[n_{Y}\leq n_{YMax},\] (4) \[\dot{Q}_{S}(t)=\frac{17600}{\sqrt{R_{N}}}\sqrt{\frac{\rho}{\rho_{ 0}}}\left(\frac{V}{V_{0}}\right)^{3.15}, \tag{5}\]  
In the above equation, \(V_{0}=10.8\) km/s; \(\rho\) and \(\rho_{0}\) are the atmospheric densities at local and sea levels, respectively; and the subscript \"Max\" is the maximum allowable value. The control variables are constrained as follows:  
\[\alpha_{Min}\leq\alpha\leq\alpha_{Max}, \tag{6}\]  
\[\sigma_{Min}\leq\sigma\leq\sigma_{Max}, \tag{7}\]  
The subscript 'Min' represents the minimum allowable value. The subscript 'Max' represents the maximum allowable value. From starting point 0 to ending point F,  
\[V(t_{0})=V_{0},\gamma(t_{0})=\gamma_{0},\psi(t_{0})=\psi_{0}, \tag{8}\] \[\theta(t_{0})=\theta_{0},h(t_{0})=h_{0},\phi(t_{0})=\phi_{0};\]  
\[V(t_{f})=V_{f},\gamma(t_{f})=\gamma_{f},\psi(t_{f})=\psi_{f}, \tag{9}\] \[\theta(t_{f})=\theta_{f},h(t_{f})=h_{f},\phi(t_{f})=\phi_{f};\]  
The subscript '\(0\)' represents the initial time. The subscript '\(\prime\)' represents the terminal time.
# Natural Convection of Dusty Hybrid Nanofluids in an Enclosure Including Two Oriented Heated Fins
## 4 Results and Discussion
### Effects of Hybrid Nanofluids  
Figure 12 shows effects of the nanoparticle volume fraction for hybrid nanofluid on contours of the isotherms, streamlines, horizontal and vertical velocities of the dusty fluid at \(L_{1}=L_{2}=0.5,\;D_{s}=10,\;\alpha_{s}=0.01,\;Ra=10^{5}\;,\;\gamma_{1}=45^{\circ}\) and \(\gamma=0.1\). Generally, an increase in the nanoparticle volume fraction enhances the viscosity of the fluid and hence the fluid activity is reduced. This can be proved from observing the maximum value of the stream function, at \(\phi_{Al_{2}O_{3}}=\phi_{Cu}=1\%\), \((|\psi|_{\max}=2.8613)\) and at \(\phi_{Al_{2}O_{3}}=\phi_{Cu}=4\%\), \((|\psi|_{\max}=2.7907)\). Similar trends occur on the maximum values of horizontal and vertical velocities, when \(\phi_{Al_{2}O_{3}}=\phi_{Cu}=1\%\) (\(|u_{s}|_{\max}=0.1485,\;|v_{s}|_{\max}=0.2422\)) and at \(\phi_{Al_{2}O_{3}}=\phi_{Cu}=4\%\), \((|u_{s}|_{\max}=0.1414,\;|v_{s}|_{\max}=0.2192)\). The isotherms are slightly affected by adding the nanoparticles inside the enclosure.  
Figure 13 presents the isothermal dusty contours under the effects of the mixture densities ratio at inclination angle \(\gamma_{1}=45^{\circ}\), \(\phi_{Al_{2}O_{3}}=\ \phi_{Cu}=0.02\), \(\alpha_{s}=0.001\), \(Ra=10^{5}\), at \(L_{1}=L_{2}=0.5\) and \(\gamma=0.1\). From these invitigations, effects of the mixture densities ratio on the isothermal dusty contours appear only at low dusty parameter (\(\alpha_{s}=0.001\)). From this figure, the distributions of the isothermal dusty contours decrease when the mixture densities ratio \(D_{s}\) increases from 100 to 1000. The variations of mixture densities ratio from 1 to 100 have slight effects on the isothermal dusty contours.  
Figure 12: Effects of nanoparticle volume fraction for hybrid nanofluid on contours of (**a**) isothermal lines; (**b**) streamlines; (**c**) horizontal dusty velocity; (**d**) vertical velocity of dusty fluid at \(L_{1}=L_{2}=0.5,\ D_{s}=10,\ \alpha_{s}=0.01,\ Ra=10^{5}\), \(\gamma_{1}=45^{\circ}\) and \(\gamma=0.1\).
# Algorithmic Techniques for GPU Scheduling: A Comprehensive Survey
## 2 Models
### Evaluation Metrics and Validation Methods for Scheduling Models
By stress-testing algorithms under diverse conditions--such as workload surges or heavy-tailed job sizes--simulation reveals behavior that analytic methods cannot easily capture. Simulators vary in fidelity, from lightweight discrete-event frameworks that abstract away low-level details to comprehensive cluster emulators incorporating network topology and GPU memory contention [131]. Many studies feed in production traces to ensure their experiments mirror operational realities. Comparing strategies side by side in simulation highlights each approach's strengths and weaknesses (for example, excelling under high load but degrading when job sizes are unpredictable) and often drives further model refinement, such as adjusting assumed workload distributions to better fit observed data. Real-world deployment: The most definitive validation of a scheduling model comes from deploying the scheduler on an actual GPU cluster and observing its performance under real workloads. Research prototypes are often tested on hardware ranging from small academic setups (tens of GPUs) to slices of production systems (hundreds of GPUs). For instance, the Tiresias scheduler was evaluated on a 60-GPU production testbed and demonstrated substantial reductions in median job completion time compared to the existing policy [74]. By running experiments on real clusters, one captures all the operational details and overheads that models and simulators may omit -including OS and driver overheads, resource-sharing effects, job startup latencies, and unpredictable user behavior. In these studies, new algorithms are typically benchmarked against standard baselines such as FIFO (first-in-first-out), dominant job first (DJF--a parallel-job variant of shortest-job-first), default Kubernetes or Slurm schedulers, and simple backfilling policies [36]. Researchers report not only average improvements but also gains at various percentiles (e.g., tail latency reductions) and analyze potential side effects such as whether optimizing for mean JCT disproportionately penalizes the longest jobs. These real-world experiments provide the strongest evidence that modeling and algorithmic innovations deliver tangible benefits in production environments. In summary, validating GPU-scheduling models requires a three-pronged approach--proofs for ideal guarantees, simulations for controlled exploration, and real deployments for production realism--together providing the strongest evidence of practical utility.
# Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms
## 3 Proposed Search Algorithm, a Set of Behaviors: A1
### Search Behavior
#### 3.1.1 Pheromone Dynamics  
The dynamics of the virtual pheromones have been implemented following the bi-dimensional heat flow (or gas diffusion) equation:  
\[\frac{\partial\phi(\mathbf{r},t)}{\partial t}=-\
abla\mathbf{F}+S \tag{15a}\] \[\mathbf{F}=-D(\mathbf{r},t)\
abla\phi(\mathbf{r},t) \tag{15b}\]  
where \(\phi\) is the concentration of the virtual pheromones, \(\
abla\) is the divergence operator, \(\mathbf{F}\) is the pheromone flux and \(D\) is the diffusion coefficient and represents how well the pheromones are transported. \(S\) is the source of pheromones, i.e., the creation of pheromones at each discretization cell.  
Using the divergence theorem in Equation (15) and considering as volumes of control the discretization cells, we can solve the obtained integral equation with an explicit scheme, forward in time and centered in space (FTCS). We consider therefore mean values of \(\phi\) and \(S\) inside those cells  
Figure 3: Correction factor \(\beta_{f}\) of the efficiency as a function of its standard deviation. For the dashed line, the value of \(\sigma_{c}=0.03\).  
and mean values of \(\mathbf{F}\) and \(D\) along their boundaries. The problem is then properly posed by finally defining the initial and the boundary conditions:  
\[\phi(x,y,t=0)=\phi_{0}(x,y) \tag{16a}\] \[\phi_{x}(0,y,t)=\phi_{x}(L_{x},y,t)=0\] (16b) \[\phi_{y}(x,0,t)=\phi_{y}(x,L_{y},t)=0 \tag{16c}\]  
The first equation represents the initial concentration of pheromones over the search space, whereas the next four are von Neumann conditions, which state that the pheromone's flux through the boundaries must always be zero. Obstacles inside the scenario are impermeable to the pheromones flux, similarly to the boundaries of the area.
# A Multi-Step Image Pre-Enhancement Strategy for a Fish Feeding Behavior Analysis Using Efficientnet
## 2 Methodology  
### Overview  
The methodological framework proposed in this paper, as illustrated in Figure 1, primarily consists of three modules: data collection and processing, image pre-enhancement, and classification based on lightweight CNN models.  
The data collection and processing module involves first acquiring videos of fish feeding behavior in a production environment, followed by frame selection according to specific rules to form an image dataset. Within this module, data augmentation techniques such as random cropping and color enhancement are employed to increase the diversity of  
Figure 1: The overall process of the fish school feeding behavior identification method.  
image samples and the model's adaptability to geometric transformations and illumination changes. This, in turn, enhances the model's accuracy and generalization ability in recognizing fish feeding behavior in complex natural environments. The image pre-enhancement module includes three main steps: (1) MSRCR, which is primarily used to address issues of water surface reflections in images [34,35]; (2) the Multi-Metric-Driven Contrast Limited Adaptive Histogram Equalization (mdc) technique, which aims to improve the contrast between fish, bait, and the background in the images; and (3) Unsharp Masking (USM) sharpening [36], which is employed to enhance the details within the images.
## 4 Simulation Results and Attack Test
### Security of Transmission on Public Channels  
From many hyperchaotic encryption schemes presented in recent years, although various boosts have been applied to improve the security of the scheme, for both sides of the communication, vital and sensitive data related to the generation of the hyperchaotic system and other confidential things must be transmitted through the dedicated  
\begin{table}
\begin{tabular}{c c c} \hline  & **NPCR** & **UACI** \\ \hline Ours & **99.6124** & **33.4600** \\
[1] & 99.6109 & 33.4354 \\
[20] & 99.5705 & 33.4781 \\
[33] & 99.6078 & 33.4404 \\
[34] & 99.6013 & 33.6531 \\
[37] & 99.6262 & 33.4384 \\
[38] & 99.6192 & 33.5316 \\ \hline \end{tabular}
\end{table}
Table 4: The comparison between the different algorithms for the plaintext sensitivity.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Index** & **Lena** & **Cameraman** & **Pepper** & **Baboon** & **Theoretical Value** \\ \hline NPCR & 99.6124 & 99.6118 & 99.6124 & 99.6031 & 99.6094 \\ UACI & 33.4600 & 33.4697 & 33.4618 & 33.4589 & 33.4635 \\ BACI & 26.7639 & 26.7681 & 26.7619 & 26.7674 & 26.7712 \\ \hline \end{tabular}
\end{table}
Table 3: The analytical results for the plaintext sensitivity.  
secure channel or agreed in advance to avoid the potential risk of the eavesdropping from the malicious participants on this communication channel. In order to increase the convenience for the transmission, according to the scheme proposed in this article, sensitive hash data will be encrypted through the ECIES before transmission through the public channel. With their combination, strong defensiveness is provided by spec256k1 ([[https://en.bitcoin.it/wiki/Secp256k1](https://en.bitcoin.it/wiki/Secp256k1)]([https://en.bitcoin.it/wiki/Secp256k1](https://en.bitcoin.it/wiki/Secp256k1)), accessed on 31 March 2021) and the advanced encryption standard-256-Galois/counter mode (AES-256-GCM), which have been extensively used due to their soundness in resisting a variety of different attacks. Moreover, the usage of the one-time key pair and the MAC function which offers validation for the tag in the cipher message also strengthens the security of the ECIES system. In conclusion, it is obvious that this encryption scheme shows improvements in the security of transmission on public channels.
## 3 The Proposed Multimodal Model
### Stage 1: Data Pre-Processing  
#### 3.2.1 Text Pre-Processing  
Text pre-processing involves extracting the useful information from raw text data and normalizing it to make it suitable for further analysis or modeling tasks. Figure 3 illustrates an example describing the text pre-processing procedure. Our proposed model pre-processes the text in the following order:  
1. Case-folding: The model converts all words to lowercase to ensure case insensitivity for all text.
2. Tokenization: The model first splits the text in a document into individual sentences. The model then tokenized all sentences into a sequence of tokens by using the word_tokenize of the NLTK module in Python.
3. Stop-word removal: Stop-words are common words frequently occurring in all documents but do not provide meaningful information [39]. In order to retain only meaningful word tokens from the data, the model removes stop-words. We used the stop-words predefined in the NLTK module in Python and eliminated stop-words from all generated tokens. Additionally, we removed non-alphabetic tokens such as numbers.