esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Results Using the 'First' Part of the Dataset
Figure 7: Depth coordinate changes during upper arm retraction (Test 1): (**a**)—right elbow joint; (**b**)—right wrist joint. error persists, with smoother and more centered trajectories in the final result, similar to the first test. The third test of upper arm adduction is presented in Figure 10, focusing on the same joints. Synchronization occurs between frames 10 and 50, with the exercise starting at frame 60. The largest depth error appears during the drawing of the upper arm toward the body, yet the final result shows smoother and more centered joint trajectories. Figure 10: An illustration of the 3D human skeleton of drawing the upper arm in the first dataset. Figure 9: Depth coordinate changes during upper arm retraction (Test 2): (**a**)—right elbow joint; (**b**)—right wrist joint. In standing arm adduction exercises, the mean error for the right elbow is between 0.0752 m and 0.0803 m, with standard deviations from 0.0446 m to 0.0463 m, while the right wrist errors range from 0.1127 m to 0.1214 m, with standard deviations between 0.0647 m and 0.0745 m.  
Finally, Figure 11 illustrates the depth coordinate variations during the first upper arm adduction test. Video synchronization occurs between frames 1 and \(55\), with the exercise starting at frame 60. A discrepancy arises from frame 90 onward, rendering these results invalid. The second test, shown in Figure 12, also focuses on the right elbow and wrist joints. Synchronization occurs between frames 10 and 50, with the exercise beginning at frame 60. Despite a large predicted depth error, the final result shows smoother and more centered trajectories, which is consistent with the previous trials. Figure 13 depicts the depth coordinate variations during the third upper arm adduction test. The same synchronization occurs between frames 10 and 50, with the exercise beginning at frame 60. The largest depth error appears when the upper arm is drawn toward the body. However, as with earlier tests, the final result shows smoother and more centered trajectories, consistent with previous tests. Figure 11: Depth coordinate changes during upper arm adduction (Test 1): **(a)—right elbow joint; (b)—right wrist joint**  
Figure 12: Depth coordinate changes during upper arm adduction (Test 2): **(a)—right elbow joint; (b)—right wrist joint. **  
For this standing arm adduction test, the right elbow shows a mean error between 0.0582 m and 0.0614 m, with standard deviations from 0.0482 m to 0.0494 m, and the right wrist errors are between 0.0857 m and 0.0884 m, with standard deviations from 0.0857 m to 0.0876 m.
## 6 Simulation Results and Discussions
### Analysis of Underlying Causes and Correlations of Results  
The proposed protocol outperforms others in network lifespan, energy consumption, throughput, and delay due to its strategic design. The use of a hexagonal DFG ensures uniform coverage and minimal signal loss, which conserves energy and extends network lifetime. The MPA and dynamic RMs optimize PDFH selection, balancing load and preventing node failures. Multi-hop routing reduces communication distance, conserving energy, while the RDCF mechanism selects optimal relay nodes to minimize energy usage. Data fusion grouping reduces control overhead, and backup mechanisms maintain data flow during node failures. These factors collectively enhance throughput, minimize delay, and optimize energy usage, especially for latency-sensitive IoT applications.  
The correlation between network lifespan, energy consumption, throughput, and average delay reveals several important correlations as follows:  
* Network lifespan and energy consumption: There is an inverse correlation between energy consumption and network lifespan. Protocols that minimize energy usage extend the operational time of SNs, thereby prolonging the overall network lifespan. The proposed protocol achieves this by optimizing routing and PDFH selection, reducing unnecessary energy expenditure.
* Network lifespan and throughput: A longer network lifespan directly contributes to higher throughput, as the network remains functional for a more extended period of time, allowing more data packets to be transmitted to the sink node. This correlation is evident in the proposed protocol, where efficient energy management ensures sustained data transmission over time.
* Energy consumption and average delay: Lower energy consumption correlates with reduced average delay. Efficient energy usage ensures that SNs and PDFHs remain operational and responsive, minimizing the time required for data transmission. The proposed protocol's optimized scheduling and routing strategies contribute to this correlation by enhancing both energy efficiency and transmission speed.
* Throughput and average delay: While high throughput generally indicates efficient data transmission, it can sometimes lead to increased network congestion and higher delays. However, the proposed protocol balances this by employing multi-hop routing, which is a major challenge in the future.  
Figure 14: Average delay: A comparison with counterpart protocols for Scenarios 1 and 2 (PSO-EC [39], LDIWPSO [40], OFCA [41], and NPSOP [42]). (**a**) Scenario #1: Network area: 100 \(\times\) 100 m\({}^{2}\) with 100 nodes, 10% and 5% PDFHs. (**b**) Scenario #2: Network area: 500 \(\times\) 500 m\({}^{2}\) with 300 nodes, 10% and 5% PDFHs.  
ing and adaptive scheduling, which maintain high throughput without significantly increasing delay.
# Vision-Based Distance Measurement in Advanced Driving Assistance Systems
## 4 Distance Measurement between the Target and the Subject Vehicle
### Target Distance Measurement
#### 4.2.2 Distance Measurement of the Target Pedestrian  
Different from a target vehicle, the object points of the pedestrian in the camera body coordinate system cannot be fitted as a plane. Consequently, this method first uses the histogram to count the number of object points with different depth values. Specifically, suppose that \(\left(x_{l}^{i},y_{l}^{i},z_{l}^{i}\right)\) is the coordinate of the object points of the target pedestrian, \(S\) is the number of object points, \(min\_z\) and \(max\_z\) are the minimum and maximum depth values of the object points, respectively, the statistical range of depth values in the histogram is from \(\left\lfloor min\_z\right\rfloor\) to \(\left\lceil max\_z\right\rceil\), and the interval is \(1\), where \(\left\lfloor\bullet\right\rfloor\) and \(\left\lceil\bullet\right\rceil\) indicate rounding down and rounding up to an integer, respectively. Secondly, the peak of the histogram and the corresponding depth range are obtained. Finally, the distance between the target pedestrian and the subject vehicle is the average of the depth values in this range.
## 5 Results and Interpretation
### Random Walk-Based Clogging
#### 5.2.2 Multi-Phase Clogging
When \(q\) was gradually decreasing, the particles reached closer and closer to the outlet surfaces. In Estaillades, even with rather small \(q\), i.e., \(q=0.0001\), there were no precipitated voxels after layer \(k=700\). Therefore, Estaillades may not be an appropriate rock formation to use for geological sequestration. Only in Ketton, even with \(q=0.01\), did the precipitated voxels reach the outlet surface; this can be explained by its high permeability (see the sub-figures in the last row of Figure 3. One of the reasons for the highest permeability being in Ketton was its relatively high number of medium-size pores compared to the others, as indicated by its right-most (compare to those of the other rocks) shown in Figure A2 in Appendix A. It is observed that geometric properties, such as the physical size of pores, are as important to fluid flow as topological properties like connectivity. Despite having the highest permeability, the mineralized trapping efficiency in Ketton was lower than in Bentheimer and Doddington (by comparing the decrease in porosities in Figure 9. The reason for this may have been partly due to Ketton's low porosity. Figure 10: Distribution of clogged voxels along injection direction at the end of the multi-phase clogging process with respect to different values of deposition probability \(q\). We also implemented the simulations with the additional assumption that the deposition occurred only at the grain boundary. The derived results show behavior similar to those without the above assumption; see Appendix B.
# GPU-Accelerated Fock Matrix Computation with Efficient Reduction
## 1 Introduction
In quantum chemistry, contemporary efforts have focused on accelerating targeted computations with high parallelism such as ERI evaluation and Fock matrix construction using GPUs [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]. Although GPUs offer high-performance machinery with thousands of cores tailored for matrix computations, capitalizing on their capabilities requires nontrivial and efficient implementations that address GPU-specific architectures and programming challenges. Among these, memory contention arising from parallel implementations of Fock matrix construction has been a persistent challenge. In the parallel processing of Equation (1), multiple threads concurrently update a single Fock matrix, leading to frequent conflicts in memory access at the same addresses. Although data inconsistency due to conflicts can be prevented with mutual exclusion, the atomic additions on the same address are executed sequentially, significantly degrading the overall parallel computation performance. Previous studies [15, 16, 18, 20, 27] have mitigated the memory contention issue by accumulating the updates within thread-local buffers and by reducing the number of the atomic operations to the Fock matrix in global memory. However, the number of ERIs aggregated within each thread is limited, even when using the thread-local reduction approaches, and \(O(M^{4})\) atomic operations are still required. The above leads to a severe memory contention due to the \(O(M^{4})\) operations concentrated into a limited \(O(M^{2})\) memory space of the Fock matrix.
# Dynamic Multi-Task Graph Isomorphism Network for Classification of Alzheimer's Disease
## 4 Experiments  
### Benchmark Dataset  
To conduct our experiments, a total of 303 fMRI scans from the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) were selected as the dataset. ADNI is a multi-center, longitudinal, observational study funded jointly by the National Institutes of Health, pharmaceutical companies, and non-profit organizations. It aims to discover, optimize, evaluate, and track the neuroimaging, clinical and psychological data, and biomarkers of AD and mild cognitive impairment (MCI) patients [32; 42; 43]. According to the dataset we chose, a comprehensive analysis was conducted, as presented in Table 1. Our dataset consists of 118 patients with AD and 185 cognitively normal (CN) control participants. We found that in the AD group, the mean age was 74.69 years with a standard deviation of 7.44 years. In the CN group, the mean age was 74.79 years with a standard deviation of 5.97 years. The age difference between the AD and CN groups was significant, indicating age as an important factor influencing the onset of AD. Furthermore, we examined the distribution of gender in the AD and CN groups. In the AD group, 47.5% of the participants were female, and 52.5% were male. In the CN group, 54.6% were female, and 45.4% were male. The gender distribution difference between the AD and CN groups was also significant, suggesting gender as an important factor influencing the onset of AD. These individuals underwent resting-state fMRI scans, and all the scan data have been preprocessed to enhance data quality and analyzability. The preprocessing steps include noise removal, motion artifact correction, and spatial normalization, ensuring data consistency and comparability.  
To improve the generalization and robustness of our model, we also utilized clinical information provided by the ADNI, including the participants' age and gender. Age and gender are important factors influencing the onset of AD and can also affect the characteristics of fMRI signals [14; 44]. Therefore, by simultaneously learning the AD classification task and predicting the age and gender of the participants, our DMT-GIN model can comprehensively understand and analyze neuroimaging features associated with AD.
## 3 Results
### Xrd
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{**Sample A (Bare Fe\({}_{3}\)O\({}_{4}\) NPs)**} & \(\mathbf{a_{0}}\) (nm) & \(\mathbf{<}\)D\(>\) (nm) & \(\sigma\) (nm) \\ \hline Scherrer & 0.837 & 8.4 \(\pm\) 0.3 & - \\ Williamson-Hall & - & 8.6 \(\pm\) 0.7 & - \\ [PERSON] & - & 8.7 \(\pm\) 0.5 & 3.8 \(\pm\) 0.6 \\ Rietveld (PM2K) & 0.836 & 9.7 \(\pm\) 0.1 & 2.4 \(\pm\) 0.1 \\ \hline \multicolumn{1}{c}{**Sample B (Fe\({}_{3}\)O\({}_{4}\) NPs cit. ex situ)**} & & \\ \hline Scherrer & 0.837 & 9.2 \(\pm\) 0.5 & - \\ [PERSON] & - & 9.6 \(\pm\) 1.5 & - \\ [PERSON] & - & 8.9 \(\pm\) 0.8 & 4.1 \(\pm\) 0.7 \\ Rietveld (PM2K) & 0.838 & 10.3 \(\pm\) 0.7 & 2.6 \(\pm\) 0.2 \\ \hline \multicolumn{1}{c}{**Sample C (Fe\({}_{3}\)O\({}_{4}\) NPs cit. in situ)**} & & \\ \hline Scherrer & 0.837 & 3.4 \(\pm\) 0.3 & - \\ [PERSON] & - & 4.0 \(\pm\) 1.0 & - \\ [PERSON] & - & 3.7 \(\pm\) 0.3 & 1.6 \(\pm\) 0.5 \\ [PERSON] (PM2K) & 0.838 & 4.6 \(\pm\) 0.2 & 0.9 \(\pm\) 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of the analysis of the diffractograms by using the various methods. The accuracy on the lattice parameter is \(\pm\) 0.001 nm. \(\textless}\)D\(>\) calculated with all methods is the average weighted by the volume. For [PERSON] and Pielaszek methods, the values of \(\textless}\textgreater{}\)D\(>\) are the average of the values derived from the widths of the most intense peaks in the diffractograms. \(\sigma\) is the standard deviation of the PSD. which is convenient from the computational point of view, is a good approximation of the lognormal distribution curve, as shown in Figure 2, dashed lines. The best fit of the diffractograms of the three samples by means of the Rietveld method implemented in the PM2K software are shown in Figure S4, Supplementary Materials. The average values of the relevant parameters determined by all the methods are reported in Table 2. The analysis of the XRD data clearly show that the average diameter of magnetite. NPs co-precipitated in the presence of citrate (sample C) is smaller than those of samples A and B. On the other hand, the size of bare NPs is not significantly different from that of the ex situ NPs. This result suggests than when magnetite NPs are immersed in the citrate solution at pH 6 there is not dissolution of the oxide. The standard deviations of sample A and B are larger than that of sample C but the relative standard deviations (standard deviation divided by the average diameter) are barely the same. Hence, we can conclude that the presence of citrate during co-precipitation of magnetite leads to smaller primary NPs than those prepared in absence of citrate but with the same level of dispersion.
# Designing Dual-Function Nanostructures for Water Purification in Sunlight
## 3 Results and Discussion
### Fourier Transform Infrared Spectroscopy  
Figure 3 illustrates the infrared spectra of Zn-Al LDHs, graphene and their composites Zn-Al-GO. Spectrum of Zn-Al LDHs (Figure 3a) reveals a broad peak at 3418 cm\({}^{-1}\) indicating the vibration mode of O\(-\)H groups which belongs to the metal hydroxide layer and the interlayer water molecules [29]. This broadness is due to the presence of hydrogen bonds that was confirmed by weak shoulder at 2988 cm\({}^{-1}\). In addition, the two bands observed at 1429 cm\({}^{-1}\) and 1360 cm\({}^{-1}\) confirmed the presence of the interlayered carbonate anions. It is known that the vibration mode (\(\
u_{3}\)) of the free carbonate species is recorded at 1450 cm\({}^{-1}\). It means that the splitting and shifting of the vibration of the interlayered carbonate anions occurred because of lowering of their symmetry. In addition, the band at 832 cm\({}^{-1}\) was attributed to the mode \(\
u_{4}\) of carbonate anion, while the peak observed at 744 cm\({}^{-1}\) was attributed to the bending mode of carbonate anions [30]. The other absorption bands at low frequency 650-500 cm\({}^{-1}\) were due to the metal-oxygen vibrations such as M\(-\)O\(-\)H vibration and O\(-\)M\(-\)O stretching (M = Zn or Al) in the lattice of Zn-Al LDHs [31].  
On the other hand, the Zn-Al-GO composites (Figure 3b-d) showed similar spectra of the pure Zn-Al LDHs. The wave number of vibration of hydroxyl groups (\(\
u_{\text{O--H}}\)) shifted from 3418 cm\({}^{-1}\) to 3433 cm\({}^{-1}\) with increasing the percentage of GO suggesting that there is interaction between the sheets of the GO and the layers of LDHs.  
In the composite Zn-Al-GO1, the bands in the carbonate-vibration region between 1360 cm\({}^{-1}\) and 1429 cm\({}^{-1}\) presented splitting to three bands comparing to the pure Zn-Al LDHs indicating growth of different kinds of LDHs over GO. This finding agrees with XRD results. However, the other two composites Zn-Al-GO2 and Zn-Al-GO3 showed only one band at 1361 cm\({}^{-1}\) indicating growth of one kind of LDHs over GO.  
Interestingly, the two peaks of the metal-oxygen vibrations of Zn-Al LDHs at 612 cm\({}^{-1}\) and 554 cm\({}^{-1}\) changed to one peak at 555 cm\({}^{-1}\), 557 cm\({}^{-1}\) and 559 cm\({}^{-1}\) with increasing the concentration of GO indicating that chemical interaction may be occurred during the growth of LDHs with GO [32].
# Artificial-Intelligence-Based Charger Deployment in Wireless Reshargeable Sensor Networks
## 5 Simulation Results
### Comparison of Number of Chargers  
In this section, we compare the number of chargers while the number of sensor nodes increased from 50 to 400. The x-axis represents the number of sensors, and the y-axis represents the number of chargers. To ensure objectivity and accuracy, all of the algorithms performed 1000 iterations.  
Figure 3 shows a comparison of the number of chargers for SABC and LSABC. In the LSABC, if the number of sensor nodes increases, the curve of LSABC will tend to signify an exponential function. This phenomenon shows that the LSABC can effectively avoid falling into a local optimum. SABC's deployment cost will increase when the number of sensor nodes increases.  
The growth curves of LTSBC and TSBC are like those of LSABC and SABC. According to the coding method of SA and TS, they can be classified as a single solution. The layoff algorithm can help the single solution to have better quality; the benefit will be notable  
Figure 3: Comparison of the number of chargers.  
when the number of sensor nodes increases because the most important concept of the layoff algorithm is that all the sensor nodes will be covered in each iteration. The original solution is randomly created in SABC and TSBC. If the sensor nodes cannot be covered in the beginning, it is hard to guarantee that the sensor nodes can be covered in some other iteration.  
Moreover, LGABC can strongly reduce the number of deployed chargers compared to GABC. To have better convergence in every iteration, the status of the candidate charger can only change from zero to one. In LGABC, the selection step will perform better in the condition of convergent data. Conversely, many deployed chargers will lead to a worse solution in the selection step; that is the reason why the number of chargers of GABC is double that in LGABC.  
We can see that the simulation shows that LACOBC can reduce the deployment cost by at least a third, proving that the layoff algorithm can enhance the ACOBC's ability to find a better solution. In ACOBC, the pheromones retain any candidate charger that is a good choice for next path, but ants will stop searching when all of the sensor nodes are covered. In other words, the sensor cannot check most of the paths. The deployment problem is continuous. This means that there are many combinations of paths, and ACOBC cannot check most of them, which is why ACOBC cannot converge.  
Finally, the simulation indicates that the layoff algorithm can enhance SA, TS, GA and ACO in the deployment problem to reduce deployment cost. The enhanced performance is obvious in the LGABC and LACOBC.
# Resource Allocation in C-V2X Mode 3 Based on the Exchanged Preference Profiles
## 4 Resource Allocation via Matching
### Matching-Game Formulation  
As can be observed from problem (13), the two participating agents in the matching process are the CUE set \(\mathcal{M}\) and the VUE set \(\mathcal{K}\), respectively.  
Since problem (13) is also an MINLP problem, to address this challenge, we formulate problem (13) as a many-to-one matching game with two-sided preferences and propose a novel matching algorithm based on the exchanged preference profiles. The many-to-one matching means that each agent from the CUE set can be matched to more than one member from the VUE set, while members from the VUE set can only be matched to at most one CUE. The two-sided preferences mean that each agent of the VUE or CUE set ranks the members in the opposite set in the order of its preference. The definition of the corresponding matching \(\mu\) is as follows:  
**Definition 1**.: _A matching game \(\mu\) is defined by two sets of players \(\mathcal{M}\cup\mathcal{K}\), which satisfies for \(\forall k\in\mathcal{K},m\in\mathcal{M}\):_  
1. _If_ \(k\in\mu(m)\)_, then_ \(\mu(k)=m\)_._
2. _If_ \(\mu(k)=m\)_, then_ \(\mu(m)\in\mathcal{M}_{k,m}\)_._
3. \(|\mu(k)|\leq 1,0\leq|\mu(m)|\leq n_{q}\)_._  
_where \(\mathcal{M}_{k,m}\) denotes the set of vehicles who prefers the CUE \(m\), and \(|\cdot|\) is the cardinality of a set._
## 8 Experimental Results and Analysis
### Inversion of Measured Speed Profiles  
To verify the validity of the EOF, the measured velocity distribution samples were compared to the samples inversion by EOF. The measured speed profiles are shown in Figure 8. Due to equipment failures and environmental factors, there was an error in the measured sound speed profiles. As it can be seen in Figure 8, the sound speed fluctuated at the depth in the range 15-100 m and tended to be stable after the depth reached 100 m. The samples were measured at a maximum depth of about 200 m.  
In the simulation experiment, we performed the first three steps of the EOF. The eight sound speed profiles was extracted for fitting, and after the inversion, the newly obtained profile was compared with the measured sound speed profile, Figure 9.  
Figure 7: The traditional acoustic correction method.  
The mean square error (MSE) of the sound speed profile is presented in Figure 10. In Figure 10, it can be seen that the MSE was below 0.5 m/s [30].  
\[\text{MSE}=\frac{1}{N}\sum_{i=1}^{N}\left(\textit{observed}_{i}-\textit{predicted}_{i} \right)^{2} \tag{28}\]  
The MSE can evaluate the degree of change of the data. The smaller the value of the MSE, the better the accuracy of the prediction model describing the experimental data. At depths in the range 0-100 m, the MSE fluctuations were more frequent, and at depths larger than 100 m, the MSE was reduced to 0.2 m/s. Accordingly, it can be concluded that the EOF inversion of sound speed profile met the experimental requirements.  
Figure 8: Measured speed profiles.  
Figure 9: Comparison of the measured speed profiles and EOF inversion profiles.