esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design of the Platform
### Design of Persistent Datasets  
Software systems provide functionality by manipulating relevant data, and some of the data must be stored on permanent data storage or cloud space for persistence. Accordingly, our platform for diagnosing diseases with CECT scans should also manage the set of persistent datasets common among its family of applications.  
The persistent data model of the platform was constructed by applying commonly and variability analysis to a set of representative family applications, including LI-RADS, LUNG-RADS, BI-RADS, and PI-RADS applications. The class diagram capturing persistent datasets for the platform is shown in Figure 5.  
The class diagram in the figure organizes persistent objects around two core classes: _Diagnosis Session_ and _ML Model_. The _Diagnosis Session_ is a logic object class that captures essential contents for each disease diagnosis session. Hence, it aggregates other part-classes: _Lesion_, _Tumor_, _Medical Feature_, and _Disease_.  
The _ML Model_ class captures the meta-information of various machine learning models deployed in the system. This class is specialized into two subclasses: _Segmentation Model_ and _Classification Model_. Each of the subclasses is further specialized into ML models for specific purposes.  
Note that association relationships are defined between classes capturing diagnosis results and the ML models used for diagnosis. For example, the Tumor class is associated with the Tumor Segmentation Model, making it possible to trace which ML model is utilized for segmenting each specific tumor.  
Figure 5: Class diagram for persistent datasets.
# Collaborative System Usability in Spaceflight Analog Environments through Remote Observations
## 4 Results
### Playbook Usability Scoring  
Having established that self-scheduling in HERA is inherently collaborative, we aimed to quantify Playbook's usability in a spaceflight analog environment. Post-mission UEQ scores across crews and missions were amalgamated. These scores were then compared to a benchmark (v12) derived from thousands of UEQ ratings of various products, provided by [PERSON] et al. (2015). Additionally, we compared these scores to past scores from a recent, larger in-lab sample (n = 30; [18]) which serve as a satisfaction baseline specifically for Playbook. This comparative analysis enables an assessment of whether Playbook performs in the upper percentiles of software UX, even when in a complex, collaborative environment.  
Figure 4 shows that, in C6, Playbook scored highly in all aspects. It received excellent ratings (top 10% of products in the benchmark dataset) for attractiveness (M = 1.87, SD = 0.67, 95% CI = [1.54, 2.19]), perspicuity (M = 2.39, SD = 0.59, 95% CI = [2.10, 2.68]), efficiency (M = 2.05, SD = 0.53, 95% CI = [1.79, 2.31]), and dependability (M = 1.97, SD = 0.69, 95% CI = [1.63, 2.31]). Its stimulation (M = 1.59, SD = 0.75, 95% CI = [1.23, 1.92]) and novelty (M = 1.08, SD = 0.79, 95% CI = 0.69, 1.47]) were categorized as good (10% better, 75% worse) and above average (25% better, 50% worse), respectively.
## 4 Selection of Services
### An Example of Service Discovery and Dynamic Certification
The same idea can be applied in cases where services are dynamically moved from a server to another, or aggregated on the same virtual machine to optimize resources. Notably, not only changes in the hosting infrastructure require service re-certification. In fact, users can change their infrastructure thus introducing the need of a new selection process or at least of service re-certification. For instance, doing online banking in a different workspace such as mobile, owned, or shared computers may change the required authentication method and encryption algorithm. In summary, to improve service certification and selection at run time, it is important to consider the context and environment in which the services are deployed and to handle context-aware re-certification for WSD. This idea is complementary to the approach by [PERSON] _et al._ which mainly deals with static certification [9].
## 3 Results and Discussion
### Strain Contour Comparison
Consequently, combining DIC (for detailed surface strain measurements) with numerical strain contours (for internal deformations) offers a more comprehensive view of how single lap joints deform and fail. This synergy is particularly valuable for identifying the earliest stages of damage initiation, which may appear first in the subsurface regions before manifesting on the joint's external surface. Additionally, the FEA can account for the effects of different adherend thicknesses on the overall strain distribution, demonstrating that the top surface strains remain similar despite variations in thickness, which should align with the experimental observations. The identical strain pattern observed in the 25 mm overlap was likewise observed in the 10 mm overlap (Figure 18), regardless of the thickness. The anticipated strain value can be observed on the surface of the adherend. Initially, an examination of Figure 18 reveals that both the 25 mm and 10 mm overlaps display similar overall strain distributions at two different displacement levels (e.g., 0.12 mm and 0.3 mm). Nevertheless, there are key distinctions in how the strain gradients evolve once the load increases and potential plasticity arises. In particular, the longer overlap (25 mm) tends to distribute the load more uniformly, which delays the onset of localized high-strain regions. By contrast, the shorter overlap (10 mm) concentrates stresses more intensely near the edges, causing earlier local strain peaks. Furthermore, the transition from the elastic regime to a possible plastic regime can be discerned by comparing the strain contours at 0.12 mm displacement where deformation likely remains primarily elastic with those at 0.3 mm displacement where either adherend yielding may begin. When the adherends yield, plastic hinges can form at or just beyond the overlap edges, thereby redistributing strain away from the damaged areas and causing the strain contours to deviate from a simple scaled version of the elastic pattern. Conversely, if cohesive damage initiates in the adhesive, local stresses at the edges drop, and the load shifts toward the middle of the overlap, also altering the contour shape. Finally, comparing the 25 mm and 10 mm overlaps reveals that, despite both exhibiting the same general \"pattern\", the shorter overlap reaches critical stress and strain thresholds sooner. This outcome aligns with simpler analytical expectations wherein a reduced bond area elevates local loads, increases stress gradients, and hastens potential damage. Consequently, pinpointing whether strain redistribution stems from adheren density or cohesive failure requires analyzing the strain location (i.e., adheren or adhesive) and comparing the magnitude against known yield or damage criteria. These spatial strain distributions underscore how overlap length and loading magnitude interact to govern the onset and progression of deformation within single lap joints.
# The Combined Compact Difference Scheme Applied to Shear-Wave Reverse-Time Migration
## 3 Analysis of CCD and CSCD
### Dispersion Analysis
 \tag{21}\]  
where \(v_{num}\) is the numerical wave velocity, \(v\) is the true wave velocity, \(\Delta t\) is the time step, \(v\Delta t/h\) is the Courant number, and \(I=\sqrt{-1}\). Equation (21) shows that the dispersion relationship for the shear-wave equation is related to the value of spatial step, propagation speed, and time step. Based on the above dispersion relation, the corresponding \(kv_{num}\Delta t\) can be solved after \(\beta\) is determined. The ratio of numerical wave velocity to true velocity is defined as  
\[\lambda=\frac{v_{num}}{v}=\frac{kv_{num}\Delta t}{kv\Delta t}=\frac{kv_{num} \Delta t}{k^{\frac{n\Delta t}{h}}h}=\frac{kv_{num}\Delta t}{\frac{v\Delta t}{h}\beta} \tag{22}\]  
Ideally, if there is no numerical dispersion, then the velocity ratio \(\lambda\) is equal to one. The larger the velocity ratio, the more serious the numerical dispersion. For comparison, we calculate the dispersion relation between velocity ratio and \(kh\) for the central difference scheme (CFD), compact difference scheme (CD), CCD, and CSCD difference scheme. Figure 1 shows the velocity ratio curve for different \(\theta\) for the above four methods in the isotropic (\(\gamma=0\)) condition. Figure 1a-c show the velocity ratio curves for the CCD and CSCD schemes and the other two difference schemes using different \(\theta\). The Courant numbers (\(\alpha=v\Delta t/h\)) are 0.25, the horizontal axis \(\varphi\in[0,\pi]\) is the product of the wavenumber and the spatial step, and the vertical axis is the velocity ratio. A velocity ratio of one indicates that the numerical wave velocity is the same as the theoretical wave velocity, which shows that the method can suppress numerical dispersion better; otherwise, it indicates that the numerical dispersion of the method is worse. It can be seen that the dispersion phenomenon of the four methods is gradually intensified with the decrease in the number of spatial sampling points. The numerical dispersion of CSCD, CCD, and CD schemes is smaller than that of the CFD scheme, as their dispersion curves are closer to one. CSCD shows the best dispersion suppression, followed by CCD.
## 4 Light Intensity- and Spectrum-Associated Adjustment of Metabolism
### Light Control of Lipid Metabolism
#### 4.4.2 Light Quality-Associated Changes in Lipid Metabolism  
Besides the intensity of light, its spectrum is also involved in the regulation of lipid metabolism (Table 10). The RL and FRL ranges, for instance, have an important role in the control of MGDG formation in cucumber and _Arabidopsis_[23]. In addition, a low R:FR ratio modified the expression of the _phosphatidylinositol 4-kinase_ (_HbPl4K_) gene under light/dark cycles in barley (_Hordeum vulgar_ L) [180]. Interestingly, high BL influenced the expression of many genes involved in fatty acid biosynthesis, and the accumulation of several lipid metabolites differed under low, moderate, and high BL in tea [186]. These observations indicate that the light spectrum controls the lipid composition at the transcriptional level. The effect of light spectrum on lipid composition had been studied more intensively in microalgae than in higher plants. [PERSON] et al. (2020) reported that in _Phaodactylum tricornutum_, RL increased lipid content compared to WL control [187]. The level of polyunsaturated fatty acids (PUFAs) increased, whereas that of monounsaturated fatty acids decreased. In _P. tricornutum_, [PERSON] et al. (2021) reported that BL resulted in a higher production of total fatty acids, namely saturated TAGs [188]. RL, in turn, increased the cells' hexadecatrienoic acid (HTA) and eicosapentaenoic acid (EPA) content. In addition, GL treatment decreased polyunsaturated lipid content in four microalgae species [189]. Based on these results in microalgae, a strong effect of each spectral range on the lipid composition can be expected.  
Light spectrum-dependent modification of lipid composition may be useful for the improvement of stress tolerance, which was tested in barley. The additional FR illumination markedly elevated the PG and PS levels of barley leaves after 10 days at 15 \({}^{\circ}\)C compared to the WL control, whereas the levels of other lipid classes remained unchanged [190]. At 5 \({}^{\circ}\)C, the additional FR illumination led to markedly increased levels of MGDG, PG, PE, phosphatidyl inositol (PI), and phosphatidyl serine (PS) already after 1 day. PE, PS, and PG are prominent lipid classes, which have important roles in signal transduction processes [191]. In addition, the elevated level of PG may promote the proper functioning of the thylakoid membrane at 5 \({}^{\circ}\)C [192]. The important alteration caused by FR treatment was the significant increment in the number of double bonds in the different molecular classes belonging to PE, which has been correlated with cold-acclimation in cereals [193; 194; 195; 196]. However, their number should be kept under control since adverse environmental conditions may induce their peroxidation [197].
## 4 Numerical Results for Real Earthquakes  
To perform the numerical computations, we again use our method and the standard initial value problem solver NDSolve of Mathematica, see [12]. We exemplarily use a ground acceleration record from the Kobe earthquake, considered to be one of the most devastating and costly natural disasters in recent history [24]. The Kobe earthquake data, originally given as equally spaced acceleration values, on which our computations are based, are shown in Figure 3. For the NDSolve computations, earthquake data are interpolated by means of the standard procedure Interpolation of Mathematica (the interpolation order is set to 0, which means a piecewise-constant function at the output), see also Figure 3. The values from the earthquake data are used as constants in the corresponding time steps. For simplicity sake, the vertical excitation is taken proportional to the horizontal one, \(a_{\mathrm{V}}=0.3a_{\mathrm{H}}\). The set of Kobe earthquake data has the time step \(T=0.01\) s. The number of time steps used in the computations is 4090. We use two iterations with 7 Taylor terms each and with standard machine precision in the computations and compile the resulting symbolic formulas using the procedure Compile of Mathematica.  
Figure 2: (**a**) Angle \(\varphi\) over physical time \(t\) for harmonic earthquake excitation in both directions \(a_{\mathrm{H0}}=9.81\) m/s\({}^{2}\): our method, the computation time is 0.20 s (three iterations with (4, 6, 9) [PERSON] terms (for NDSolve of Mathematica the time history is visually indistinguishable and the computation time is 0.94 s). (**b**) Absolute deviation between solutions with our method and NDSolve.  
The results of the numerical integration in the case of real earthquake record in Figure 3 are plotted in Figure 4, which shows the comparison between the results of our method and the NDSolve procedure of Mathematica. We observe well agreement between the results, where the computation time is substantially shorter for the proposed method. An analogous behavior was found when using records for other disastrous earthquakes, such as the El Centro earthquake and the Bam earthquake. We note, however, that our choice of interpolating earthquake data as piecewise-constant, which was motivated by the practical requirement not to add further functional assumptions to the point-wise measured data, may impose significant performance penalty to NDSolve. Namely, it forces this algorithm to use extremely short steps at the jumps. In contrast, for the present Picard-type method the latter jumps do not result in problems, because, as mentioned already above, differentiability of the integrands is not required, only existence of the integrals.
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 3 Optimized Hierarchical Based Routing Protocols
### TPSO-CR
#### 3.5.2 Ant Colony Optimization
ACO-C is centralized, BS selects only nodes with the residual energy above the average energy of the network as possible CHs and uses the ACO algorithm to find the best solution according to a cost function. After finding the best route, the BS informs each node about its CHs and cluster it belongs to. Each CH has to coordinate the transmission of data in their corresponding clusters by using a simple TDMA schedule. At the end of each round, the CH aggregates data it received from its members, and sends the aggregated data to the BS. The algorithm ACO-C is evaluated in two network scenarios; each one contains 100 nodes with unequal initial energy. On the first scenario the BS is located in the middle of the network whereas on the second scenario it is located outside the sensor field. ACO-C is compared with LEACH, LEACH-C, and PSO-C in both scenarios. The obtained results have shown that ACO-C performs the best number of alive nodes through the time, and thus increases the network lifetime better than the three other algorithms. The issues also reveal that ACO-C outperforms LEACH, LEACH-C, and PSO-C in terms of the number of data received by the BS.
## 3 Results
### Analysis of N, P, K Fertilizer Contribution to Broccoli Yield  
3.2.1. Analysis of N Fertilizer Contribution to Broccoli Yield  
The sub-models of the yield related to both ZSHL and HL-5 were obtained by dimensionality reduction, in which P and K factors were set to 0 in the regression equation.  
\[\text{Y-N(ZSHL)}=3480.1106+39.8225\text{X}_{1}-0.0527\text{X}_{1}{}^{2} \tag{3}\]  
\[\text{Y-N(HL-5)}=4027.7660+32.5341\text{X}_{1}-0.0660\text{X}_{1}{}^{2} \tag{4}\]  
The linear graph of these two regression equations (Figure 1) can be used to analyze the changes in the yield contribution of the N fertilizer amount of two different maturity broccoli varieties. Let dy\({}_{1}\)/dx\({}_{1}\) = 0 (i = 1), then the X\({}_{1}\) values corresponding to the extreme points of the two regression equations can be obtained, respectively. For early-maturing broccoli ZSHL, when X\({}_{1}\) = 483.3 (equivalent to 483.3 kg/ha of N application), the yield contribution reached the maximum value; for late-maturing broccoli HL-5, when X\({}_{1}\) = 250 (equivalent to 250 kg/ha N fertilizer), the yield contribution reached the maximum value.  
For the early-maturing variety, ZSHL, with the increase in the N fertilizer application level, its contribution to the yield in the early stage was an almost exponential growth, and it tended to be flat to a certain extent. For the late-maturing variety, HL-5, with the increase in N fertilizer application rate, its contribution to the yield curve had a parabolic form, its contribution to yield was initially elevated, and then decreased with the increase in N fertilizer application.
# Enhanced QoS Routing Protocol for an Unmanned Ground Vehicle, Based on the ACO Approach
## 2 Method
### Firefighting UGV Terminal Transaction  
This section demonstrates the source and destination terminals (ST! DT) technique to link the other pairs. The movement direction of a terminal (source or destination firefighting UGV) and the distance between itself and its nearby junctions, determines a terminal intersection. According to these factors, each candidate junction is assigned a throughput as a cost, and the route with the greatest throughput is chosen as the optimal terminal intersection. Equations (7)-(8) demonstrate the throughput expression [27].  
\[Throughput(I_{i})=x\cdot\left(1-\frac{d(I_{i})}{L}\right)+(1-x)\cdot Dir(I_{i}) \tag{7}\]  
\[Dir(I_{i})=\left\{\begin{matrix}1,\text{the terminal FirefightingUGV moves to I\_i}\\ 0,\text{ otherwise}\end{matrix}\right. \tag{8}\]  
where:  
\(L\): Current length of road segment.  
\(D(li)\): Distance between terminal and nearby junction.  
\(Li\): Weight parameter.  
\(Dir(li)\): Direction value of the communication terminal.
## 2 Cluster Head Selection
### Self-Organized Schemes  
In these schemes, the CH selection decision is distributed within the network and each node. Either stochastically or involving some probabilistic parameters, nodes compete to be selected as CH. Based on an absolute stochastic selection or involving some resource parameters, self-organized CH  
Figure 2: The taxonomy of CH selection.  
selections can be grouped into two categories: fixed probabilistic schemes and adaptive weight-based parameters schemes.
# Smart Healthcare Applications over 5G Networks: A Systematic Review
## 3 Results and Discussion
### Correlation Analysis
The first heat map (networking technologies vs. communication protocol) shows that the D2D communication protocol is present in all technologies; there is a clear trend towards a hybrid Bluetooth-WiFi network. Apparently, these network technologies are the favorites when developing smart-healthcare-related apps. The H2M protocol uses only the cellular network, leaving aside all other technologies. This trend is interesting, because one would expect more interactions using WiFi or Bluetooth. This can be explained by knowing that apps with H2M protocols are developed for remote sites whose only access technology is the cellular network. According to the heat map, the M2M protocol is the least used, and applications developed with it use complex systems of network technologies that combine Bluetooth, WiFi, and ZigBee. Despite the importance of the M2M protocol, as it allows the interconnection of devices without the need for human actors, it is apparently not of interest to researchers and developers. Similarly, few applications exist that combine M2M and D2D protocols (only 11.36%, \(n=5\)), and in all cases, they combine Bluetooth and WiFi technology (two include Zigbee technology). This trend is striking, because smart city proposals tend to strengthen the interconnection between devices and machines, but our study reveals that smart healthcare applications do not prioritize this aspect. In the same way, the other heat maps can be analyzed and discussed, finding interesting opportunities for research and questioning current developments.
# Enhanced Spatial Stream of Two-Stream Network Using Optical Flow for Human Action Recognition
## 6 Conclusions  
The key goal of our research was to develop a reliable HAR network. Various techniques were discussed in this paper to cope with the problems faced by two-stream networks including overfitting, and their effects were measured. Different configurations of inputs were also considered, and the results were compared with published research. Here, we proposed an enhanced form of the original spatial stream. Several strategies were deployed to reduce the overfitting issue posed by the insufficient datasets. Using data augmentation and transfer learning, a critical improvement in HAR has been attained. The empirical experiments have shown that the proposed architecture's results are better than the top-ranked model in terms of accuracy, with 91.2% accuracy on the UCF101 dataset.  
While the currently applied methodology provides good results for the used dataset, future research into new alternatives for the proposed system may enhance precision. Using transfer learning in the temporal stream can provide good results. Furthermore, by adding more activities to the existing datasets, overfitting can be further minimized. Furthermore, the current study offers foundational principles for future researchers to investigate more configurations in the stated architecture, which will aid in the HAR system's high achievement level.  
S.K. Investigation, Writing--original draft; A.H. Supervision, Methodology, Writing--original draft; F.H. Methodology, Conceptualization; A.P. Resources, Validation; F.R. Resources, Validation; M.A. Methodology, Writing--review & editing; W.A. Conceptualization, Writing--review & editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Researchers Supporting Project number (RSPD2023R636), King Saud University, Riyadh, Saudi Arabia.  
Not applicable.  
The data used in this research work is a publicly available dataset available at [[https://doi.org/10.48550/arXiv.1212.0402](https://doi.org/10.48550/arXiv.1212.0402)]([https://doi.org/10.48550/arXiv.1212.0402](https://doi.org/10.48550/arXiv.1212.0402)) (accessed on 10 February 2023).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **Accuracy (\%)** \\ \hline Two-Stream Network [13] & 86.90 \\ Two-Stream Network Fusion [23] & 91.40 \\ Residual Two-Stream Network [22] & 91.70 \\ Residual Frames Two-Stream Network [29] & 91.80 \\ \hline Temporal Stream Network & 82.60 \\ Enhanced Spatial Stream Network & 79.00 \\
**Proposed Two-Stream Network** & **91.20** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison with other SOA models (mean accuracy).