esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
### Optimization Algorithms
#### 2.2.2 Whale Optimization Algorithm (WOA)  
The Whale Optimization Algorithm (WOA) is a metaheuristic optimization technique inspired by the social behavior of humpback whales. Proposed by [PERSON] and [PERSON] in 2016 [46], WOA has become one of the most widely used algorithms across various fields. Its design is based on the natural hunting strategies observed in humpback whales [47].  
As in PSO, initially, WOA randomly initializes a population of solutions (referred to as \"whales\") within the search space. The fitness function will be used to evaluate the quality of each solution. Additionally, each whale's position is updated towards the global best solution found so far [48; 49].  
As in most optimization algorithms, WOA has exploration and exploitation phases. For each whale \(i\) in the population, with its current position \(\mathbf{x}_{i}\), the whales updating their positions as follows:  
\[\mathbf{D}_{i}(t)=|C\mathbf{G}(t)-\mathbf{x}_{i}(t)| \tag{2}\]  
\[\mathbf{x}_{i}(t+1)=\mathbf{G}-A\mathbf{D}_{i}(t) \tag{3}\]  
where \(A\) and \(C\) are coefficients, which are calculated as in Equations (4) and (5).  
\[A=2 ar_{1}-a \tag{4}\]  
\[C=2r_{2} \tag{5}\]  
where \(r_{1}\) and \(r_{2}\) are two random numbers in \([0,1]\) and \(a\) is a parameter that linearly decreases from two to zero over the course of the iterations, as follows:  
\[a=2-\frac{2t}{Max_{Iter}}\]  
where \(t\) is the current iteration and \(Max_{Iter}\) is the maximum number of iterations.  
The exploration and exploitation phases of WOA are as follows:  
* **Exploration phase:** if \(|A|\geq 1\), the whales explore the space and they update their positions as follows: \[\mathbf{D}_{i}(t)=|C\mathbf{x}_{rand}-\mathbf{x}_{i}(t)|\] \[\mathbf{x}_{i}(t+1)=\mathbf{x}_{rand}-A\mathbf{D}_{i}(t)\] where \(\mathbf{x}_{rand}\) is a random position chosen from the current population.
* **Exploitation phase:** If \(|A|<1\), the whales move towards the best candidate using two types of movements (shrinking encircling mechanism and spiral updating position), as follows: \[\mathbf{x}_{i}(t+1)=\begin{cases}\mathbf{G}(t)-A\mathbf{D}_{i}(t)&\text{ if }r_{4}<0.5\\ \mathbf{D}_{i}(t)e^{by_{3}}cos(2\pi r_{3})+\mathbf{G}(t)&\text{ if }r_{4}\geq 0.5 \end{cases}\] where \(r_{4}\) is a random number in \([0,1]\), \(r_{3}\) is a random number in \([-1,1]\), and \(b\) is a constant for defining the shape of the logarithmic spiral.  
To ensure that the updated positions remain within the search space, techniques for handling boundary conditions should be applied. As the algorithm progresses, the exploration rate should be gradually reduced to shift the focus from exploration to exploitation. The fitness function is then used to evaluate the new positions of each whale, and if any whale discovers a position better than the current global best, this global best solution (\(\mathbf{G}\)) will be updated. These steps are repeated until the maximum number of iterations is reached or satisfactory solutions are found [50; 51]. More details about WOA are in [39; 46].
## 5 Experiments and Results
### Quantitative Analysis of Prediction Results  
In this section, we compare the developed model and baseline models in terms of RMSE as presented in Figure 9. We can observe that in the first second of the prediction horizon, the RMSE value is approximately 0.65 m, where our proposal performs slightly better than others. Nevertheless, when the prediction time increases to 3 s, the baselines are failing to handle the prediction issue, and its evaluation errors gradually exceed 2.00 m. In contrast, the TPASTGCN model remains robust and maintains the RMSE value at a lower level. Note that the deviation grows sharply for the baseline model in 3-5 s, demonstrating that these models are not suitable for long-term anticipation tasks. However, our proposal constantly outperforms its partner models with over 20.00% accuracy by raising its RMSE value slowly. This indicates that our model acts out for generating future trajectories whether for short-term or long-term windows.  
In general, the traditional CV model has the lowest prediction performance, since it does not capture the spatial-temporal features of multi-agents and is only focused on intrinsic states. Compared with C-LSTM and GRU models, the GAIL-GRU exhibits a notable improvement especially in the previous 4 seconds. This is because the GAIL-GRU method has successfully considered the impact of surrounding vehicles and incorporates them into the model. However, as shown in Figure 9, one of the main drawbacks of the GAIL-GRU model lies in its limited capacity in describing longer sequence features and ignoring traffic rules, which results in a rapid growth in RMSE value at later stages. In contrast, our scheme highlights excellent prediction performance with over 23% accuracy in the baselines, owing to the fact that we benefit from a graph topology approach to fully synthesize spatial-temporal trajectory information and multi-agent intersection mechanisms, which are critical features in extremely dynamic and variable intersection scenarios.
# Low-Latency Short-Packet Transmission over a Large Spatial Scale
## 6 Conclusions  
In this paper, we studied the optimal policies and performance bounds for low-latency short-packet transmission over a large spatial scale, in which both the propagation loss of the SNR and the propagation delay should be taken into consideration. Although DF relays can be deployed to regenerate packets reliably and mitigate their SNR loss due to propagation, they induce a severe decoding delay. To address these issues, we optimized the relay deployment, power allocation, and blocklength for each relay. Moreover, we investigated the performance limits of the distant transmission of short packets via asymptotic analysis. Given the overall transmission power, we found the optimal distance-latency-reliability tradeoff. Our results may provide some engineering insights into the design of integrated LEO satellite communications in 6G. Important future work includes the study of amplify-and-forward short-packet relaying, multi-user short-packet transmission, and two-way short-packet relaying over a large spatial scale. Furthermore, we will consider the situation where the relay nodes are not at fixed positions. We will investigate whether it is possible to allocate the resources in real time based on the positions of the nodes.  
Conceptualization, W.C. and H.V.P; formal analysis, L.H.; methodology, L.H. and X.Z.; software, L.H.; writing--original draft, L.H.; writing--review and editing, X.Z., W.C., and H.V.P. All authors read and agreed to the published version of the manuscript.  
This research was supported in part by the U.S. National Science Foundation under Grants CCF-0939370 and CCF-1908308, the Beijing Natural Science Foundation under Grant No. 4191001, the National Natural Science Foundation of China under Grant No. 61971264, and the National Key R&D Program of China under Grant 2018 YFB1801102.  
Not applicable.  
Figure 8: The optimal distance–latency–reliability tradeoff.  
**Data Availability Statement:** Data is contained within the article or supplementary material.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.
# Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval
## 2 Related Works
### Unsupervised Hashing  
Unsupervised hashing methods do not leverage label information for the training dataset. For example, Local Sensitive Hashing (LSH) [25] and its variants [26; 27] use random projections to map instances into a Hamming space. Iterative Quantization (ITQ) [28] optimizes the projection by PCA, and then learns an orthogonal rotation matrix to bridge the quantization gap. Unsupervised semantic deep hashing (USDH) [29] uses semantic information to guide the training of hash mapping function. Unsupervised Deep Video Hashing (UDVH) [30] learns the hash codes in a self-taught manner by jointly integrating discriminative video representation with optimal code learning. Neighborhood Discriminant Hashing (NDH) [31] learns hash function by preserving the neighborhood discriminative information in Hamming space. Collective Matrix Factorization Hashing (CMFH) [32] learns a shared common latent space by a collective matrix factorization algorithm, and then adopts a thresholding operation to generate the hash codes. Fusion Similarity Hashing (FSH) [33] proposes a novel fusion similarity method for hash learning by capturing the latent relations between different heterogeneous modalities.
## 3 Simulation Results and Discussion
### Effects of Particle Shape  
In this subsection, the influence of particle shape varying between one (circular particle), two, and three on the mechanical properties of MGMCs with a 25% particle volume fraction is studied under uniaxial compressive loading along the y-axis. Five different microstructural finite element models containing ellipse particles with random orientation are generated through the developed program for different particle shapes, respectively. It can be seen from Figure 6 that particle shape plays a small role on the stiffness and strength of MGMCs. However, circle particles can bring more ductility than ellipse particles. The extensibility brought by particles decreases with the increasing aspect ratio of the particle. In Figure 7, it can be seen that the shear band is always generated from the tips of ellipses. Particles that have a large respect ratio may have more stress concentration and stress misfit, which will promote the generation of shear bands.  
Figure 4: The influence of particle orientation on the stress-strain curves of MGMCs (metallic glass matrix composites).  
Figure 5: The influence of particle orientations on normalized free volume in BMG. (**a**) \(\alpha=0^{\circ}\); (**b**) \(\alpha=22.5^{\circ}\); (**c**) \(\alpha=45^{\circ}\).
# Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach
## 3 Results
### Uncertainties Analysis
#### 3.3.2 Comparison between GPS Measurements and Sentinel-2  
Comparison of annual glacier surface velocities obtained from in-situ differential GPS measurements and from Sentinel-2 2017-2018 time-averaged map shows good overall agreement as illustrated in Figure 5. The color of the GPS points in the figure characterizes the width of the glacier. We note that the largest discrepancies are observed for glaciers with width of less than 250 m, which are not well captured by our processing chain. We also note important differences for three GPS points located in the accumulation area of the Mer de Glace glacier, where only a small number of successful pairs were obtained, hence with a large weighted standard deviation due to the poor statistic (see Sections 3.3.1 and 3.1). Similar pattern can also be observed for Argentiere Glacier (Figure S5). Our mapping agrees within 5% in the ablation zone (close to point A', km 3.5 to km 4.25 in Figure S5) where a robust statistic of more than 200 estimates is found and weighted standard deviation is about 10 m/year. Higher upstream (close to point A, km 0.25 to km 1 in Figure S5), image matching is more complex because of the limited number of surface features and the persisting shadows cast by steep surrounding mountains. The poor statistic in this area translates into noisier pattern of ice velocity and larger difference between GPS and Sentinel-2 measurements of about 10 m/year.  
Figure 5: Comparison between satellite-derived and differential GPS glacier surface velocities between 2015 and 2018 from GLACIOCLIM database in the Mont-Blanc massif and from [18] in the Ötztal Alps. Displayed error bars are the local weighted standard deviation (1-sigma) as in Figure 2B. Data points are color-coded as a function of the glacier width.  
Overall, the mean difference, calculated as the weighted average of the differences between the GPS and Sentinel-2 points, is 6 m/year. The weights are defined as the inverse of the local precision estimated with Sentinel-2 (see Section 3.3.1), GPS measurements are assumed to provide the actual displacement and therefore error-free. If we do not consider glaciers less than 250 m wide, the difference is only 3.7 m/year because, as mentioned above, these glaciers are not well captured by our processing chain. Although the number of comparisons with GPS measurements is statistically low, this suggests that the accuracy of the Sentinel-2 time-averaged map would be less than 10 m/year. The remaining discrepancies could also be due to the difference in time periods between the GPS measurements acquired between 2015-2017 and our map, which is representative of the years 2017 and 2018. Another reason for the differences between in-situ and remote measurements would obviously be the resolution of our map (50 m) which will necessarily spatially \"smooth\" the motion compared to local GPS measurements.
# A Variational Neural Network Based on Algorithm Unfolding for Image Blind Deblurring
## 2 Related Work
### Image Restoration Model  
To enhance image detail restoration and improve training efficiency, [PERSON] et al. [20] proposed the trainable nonlinear reaction diffusion (TNRD). The TNRD effectively addresses various image restoration tasks, including denoising, deblurring, and super-resolution. This algorithm employs a trainable nonlinear reaction-diffusion mechanism combined with deep learning techniques, enabling flexible applications across different image restoration scenarios. TNRD not only improves the visual quality of images but also optimizes the training process, demonstrating excellent performance in handling complex images. Its mathematical representation can be expressed as follows:  
\[E(u)=\frac{1}{2}\|k*u-f\|_{2}^{2}+\sum_{i=1}^{N_{k}}\Phi_{i}(K_{i}*u) \tag{4}\]  
where \(\Phi_{i}\) is a nonlinear function, \(K_{i}\) is the convolution kernel, and \(N_{k}\) is the number of filters.  
A variety of restoration tasks can be addressed by TNRD, and image details can be better preserved compared to traditional linear filtering methods. However, the performance and effectiveness of the model are greatly influenced by the choice of parameters, such as the design of filters and the setting of regularization parameters, which necessitates more tuning and optimization.
## 3 Analysis Plan
### The Problem of Class Imbalance  
The focus of our analysis is to train our binary classifiers by addressing the class-imbalance issue. In classification, a classifier predicts categorical labels (or classes). The classes are imbalanced if there are many more instances of some classes than others in a dataset [18]. The class imbalance is prevalent because many real-world applications, such as fraud detection, spam detection, anomaly detection, and psychological diagnosis, are composed of a large number of normal examples with only a small number of abnormal or interesting examples [19]. The ratio between minor and major classes can be 1:100, 1:1000, or even 1:10,000 depending on the applications. For example, fraudulent cases in retail banking are about 0.1 percent [20].  
The class imbalance poses a difficulty in classification because the classifiers trained on the imbalance dataset tend to show a higher predictive accuracy on the major classes, but a poorer predictive accuracy on the minor classes. This bias toward the major classes happens because the performance metrics tend to treat the minor classes as the noise, and therefore guide the classifiers to ignore the misclassification of the minor classes during the learning process [9]. Table 3 shows a hypothetical confusion matrix illustrating the case where the accuracy of a binary classifier can be deceptively excellent when the classifier completely ignores the misclassification of the minor classes. In this example, the proportions of dropouts and non-dropouts are imbalanced, i.e., 100 dropouts (1%) vs. 9900 non-dropouts (99%). The accuracy of the binary classifier in this example is 0.99 despite the complete misclassifications of the dropouts. In this way, when classes are imbalanced, the minor classes are often more misclassified than the major classes. Because the minor class is usually the class of interest in classification, the class imbalance has been a challenging problem in data mining community [21; 22].  
The problem of class imbalance is a well-known issue in the machine learning community. However, less attention has been paid to this issue when developing dropout early warning system. As presented in Table 1, high school dropout rates in South Korea are less than 2%. According to the National Center for Education Statistics, high school dropout rates in the United States have decreased from 27.2% in 1960 to 6.1% in 2016. Because the class of interest in the dropout prediction is a minor class (i.e., dropout), the issue of class imbalance needs to be properly handled when building a predictive model for the dropout early warning system.
# A Review of Artificial Intelligence Research in Peer-Reviewed Communication Journals
## 3 Method  
We used the content analysis method to analyze published articles about AI in the communication field because analyzing published articles' content provides knowledge about the development of academic research in a given field [36]. We used Web of Science, the world's oldest and most widely used database that indexes leading research publications and citations [37], to find peer-reviewed journal articles in the communication field. Web of Science is one the most recognized multidisciplinary proprietary databases for peer-reviewed journal content. The Web of Science follows a comprehensive journal selection process that takes into account publication standards, expert evaluations, consistent publication, and the quality of citation data [38]. The unit of analysis for this study was peer-reviewed journal articles in the communication field. This study specifically focused on articles published within the broader communication discipline, encompassing areas like mass communication, public relations, advertising, and journalism. Therefore, we used \"mass communication\", \"new media\", \"social media\", \"public relations\", \"strategic communication\", \"advertising\", \"journalism\", \"marketing communications\", \"Facebook\", \"Instagram\", \"Twitter\", \"TikTok\", and \"YouTube\" as keywords to search relevant articles along with \"artificial intelligence\". These social media platforms are the most popular social networks worldwide [39] that can benefit from the use of AI. Unlike automated systems, manual review enables subjective judgments and decisions based on the individual assessment of each article. In the systematic review search and selection process, a manual search of identified sources to select candidate papers based on abstract and title is a common way to exclude papers if they are clearly irrelevant [40]. As a result, the authors carefully examined each article to decide whether it should be included or excluded from the data analysis. The authors set up clear and standardized criteria for inclusion and exclusion (for instance, the written language of the articles must be English, and it must be empirical articles instead of review articles). Moreover, multiple reviewers (the two coders and the two authors) performed each step of the selection, data collection, and analysis to ensure greater objectivity and consistency in the process. Thus, we manually reviewed all of the articles published in peer-reviewed journals and removed articles (N = 35) unrelated to AI. Thus, this study consists of all the articles (N = 137) that addressed AI in the communication field published in peer-reviewed journals between 2006 and 2022. The year 2006 is the start date because the first article related to AI was published in the communication field in 2006 in _New Media & Society_[41].
# Numerical Simulation of Micro-Bubbles Dispersion by Surface Waves
## 4 Results and Discussion
### Carrier Flow Modification  
Figure 2 compares distributions of a vorticity modulus field obtained using DNS in different planes at dimensionless time moment \(t\) = 300 in the cases with and without bubbles (Figure 2a-c and 2d-f, respectively). The figure shows that in both cases, vorticity streaks, oriented in the direction of the surface wave propagation, are present in the near-surface water layer indicative of turbulence generated by surface wave motion [17]. The figure also shows that rising bubbles create vertical streaks of vorticity in their wakes.  
Figure 3 presents distributions of phase-averaged water velocity components, \(\langle U_{x}\rangle\) and \(\langle U_{z}\rangle\), obtained using DNS with wave slope \(ka\) = 0.1 and 0.2 (left and right panels), both with and without bubbles. The components are compared against an analytical, potential-flow, solution for the velocity field in the surface deep-water gravity wave (in dashed line) [1]. The figure shows that the computed fields agree well with the analytical solution except a near surface layer where the wave-induced turbulence modifies the water velocity. The influence of bubbles on the phase-averaged surface-wave flow field is found to be insignificant (in Figure 3, the full black and blue lines practically coincide).  
Mean profiles of the fluctuations of \(x\),\(y\),\(z\) water-velocity components, \([U_{i}^{\prime}]\), evaluated as a root mean square deviation,  
\[[U_{i}^{\prime}]=\left(\left[U_{i}^{2}\right]-\left[U_{i}\right]^{2}\right)^{1 /2}, \tag{30}\]  
and obtained employing DNS for different bubble diameter, \(d\) = 200, 300 and 400 \(\upmu\)m, with the same reference void fraction (\(C_{0}\approx\) 5 \(\times\) 10\({}^{-5}\)), both with and without wind-induced surface drift, are compared in Figure 4 with the corresponding bubble-free flow cases. The comparison shows that bubbles enhance wave-induced turbulence, and, in both cases (with and without surface wind drift), mostly \(z\)-component of water velocity is affected (Figure 4c,f). This turbulence enhancement is caused by the presence of wakes induced by rising bubbles (Figure 2f). Figure 4f also indicates that the influence of a wind- driven surface stress synchronizes the mean velocity fluctuations for different bubble sizes. The explanation of this observation requires further research and is to be reported elsewhere.
# Towards a Real-Time Description of the Ionosphere:
## 3 Measured and Modeled Data Used for Validation  
### Observations from Ground-Based Ionosondes  
Observations of the F2-layer peak ionospheric characteristics, _f_oF2 and _I_m_F2, measured by 40 ground-based ionosonde stations, located at different latitudes in both hemispheres, during the last two solar cycles (from 1 January 2000 to 31 December 2019), are considered as reference. The selected ionosonde stations are listed in Table 1 with the corresponding geographic coordinates, quasi-dipole (QD) magnetic latitude [55], modify [36], and time coverage of the dataset. Figure 1 illustrates the spatial distribution of the 40 ionosonde stations.