esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Localization of Underwater Nodes Using Airborne Visible Light Beams
## 1 Introduction
The system is validated through extensive simulations, demonstrating high localization accuracy and robustness under different deployment and mobility conditions. It is worth noting that while recent research such as the HOENN-based DoA estimation method [9] also utilizes magnitude measurements for angular inference, its objective, methodology, and application context are fundamentally different from ours. Specifically, HOENN is designed for aerial scenarios and employs hybrid optical-electronic neural networks and diffractive metasurfaces to estimate the direction of arrival of RF signals. In contrast, our proposed 3D-LUNA framework addresses the challenge of cross-medium 3D localization of underwater nodes by modeling the propagation of visible light beams across the air-water interface. It reconstructs node coordinates based on measured light intensity without relying on learning-based architectures or angular classification. Unlike HOENN, 3D-LUNA requires no pre-training or metasurfaces and is particularly suited for infrastructure-free and covert underwater applications. The rest of this paper is organized as follows. Section 2 provides a review of related work, discussing existing underwater localization techniques and their limitations. Section 3 presents the system model and the theoretical foundation of our proposed 3D-LUNA approach, including key assumptions and mathematical formulations. Section 4 details the 3D-LUNA localization protocol, explaining how airborne VLC beams are utilized for accurate underwater node positioning. Section 5 evaluates the proposed method through extensive simulations, analyzing its accuracy, efficiency, and robustness under various conditions. Finally, Section 6 concludes the paper by summarizing key contributions and outcomes.
## 2 Materials and Methods
### Cellular Network  
The communication infrastructure is considered one of the most vital infrastructures to a society remaining functional. Due to the ubiquity of cell phones, cellular base stations are considered significant elements in the communication infrastructure. Furthermore, the authors of [9] discussed how the cellular network could be reconfigured to be used to provide disaster recovery communications. This study identified the most critical cellular network base stations by employing three factors: (1) geographic coverage, (2) population covered, and (3) power requirements. The geographic coverage for each base station was classified into short, medium, and long in terms of distance in miles.  
Through natural disasters, it is critical to provide emergency shelters with power. Governments commonly utilize event centers as a shelter when emergencies occur. For instance, the George R. Brown Convention Center in Houston, TX, covered thousands of people throughout hurricane Harvey [29]. Three inputs were selected to classify emergency shelters: energy usage, size, and capacity. Both data regarding the capacity and size of emergency shelters are openly accessible online. Nevertheless, power consumption was determined by calculating the size in \(ft^{2}\) and then by multiplying it by the power average required for universal non-residential property space, which is 14 kWh per \(ft^{2}\)[30].  
Total Weighted Value (\(TWV_{SI}\))  
The total weighted value \(TWV_{SI}\) related to every node \(v\) in a standalone infrastructure can be defined as follows:  
\[TWV_{SI}(v)=\sum_{n=1}^{m}\mathit{Parm}_{n}(v) \tag{8}\]  
where \(\mathit{Parm}_{n}\) is the context-dependent factor for that infrastructure. As in the interconnected infrastructure case, feature scaling is used to put the parameters on the same scale.
## 4 Methods and Models for Sound Classification in Concrete Pouring Work Monitoring
### Classifiers (CNN and RNN)
RNN is primarily used to recognize sequential information, such as voice and letters, because it effectively processes only relatively short sequences. In addition, the biggest advantage of RNNs is that various and flexible structures can be created as necessary, as it is a network structure that can accept inputs and outputs regardless of the length of the sequence (Figure 11). However, RNNs have an inherent issue in determining relatively long sounds or long sentences. As an alternative, LSTMs incorporate a gate that controls the flow of data, enabling improved determinations. This enhanced RNN structure reflects long-distance dependencies by adding a gate that controls the amount of information transmitted without unconditionally reflecting the input value in the state. Nonetheless, the application of the LSTM algorithm was not considered in this study, as sound data at construction sites do not possess the characteristics of long sound data that require reflection of long-distance dependencies, such as words or sentences. CNN is particularly useful in capturing local patterns (e.g., spectral characteristics of sound) within the data, while RNN is suitable for processing sequential data and captures temporal patterns of audio signals well. Through these two algorithms, sound related to concrete pouring work can be classified more accurately. Therefore, this study analyzed the characteristics of CNN and RNN, which are representative deep-learning algorithms in the field of sound recognition, constructed a site sound data classification model by inputting pre-processed sound data to both CNN and RNN, and compared and verified the accuracy of the classification model using various indicators. Figure 9: Image of sound data pre-processed for CNN model training.
## 3 Incident Detector Using Big Data Analytics and Neural Networks
### Challenges Facing the Detection of Road Accident Using AI/ML  
The difficulties in applying AI and ML for incident detection on the road are ILD installation, upkeep, replacement, and some recurring upkeep. The accuracy is bad, especially when it comes to crucial road infrastructure. The sample size restriction makes accurate measurements of the speed and travel duration difficult [73]. With the IoV's development tendency, large-scale sensors and vehicles frequently connect to the internet, which may cause a network capacity issue [37]. With the help of this study, it is possible to analyze the driving characteristics of drivers, the days with the highest accident rates, and the roadways with the worst accident rates. The distribution by season and vehicle type was shown on the heat maps, which added more complex variables to the research. Even with a three-year time limit, the prediction model produced good results. A longer time frame and more variables included in the predictive model's eligibility could help to improve accuracy [124]. It summarizes the state of the art, the challenges, and the opportunities for researchers in the linked domains of context sensing, human affect sensing, and social signaling analysis. According to some, the most promising method for obtaining precise context sensing in realistic surroundings is to develop multimodal, multi-aspect context sensing. The W5+ (who, where, what, when, why, and how) approach provides context sensing [16].  
Interference and collisions by concealed nodes present a significant obstacle to efficient data sharing between vehicles. Vehicles are unaware of other vehicles that are simultaneously broadcasting due to the ad hoc nature of vehicular communications for safety applications, which can lead to crashes. When there is a high vehicle density and a high data traffic load, this is a severe concern of the network [70], which causes accidents; another issue that needs to be addressed is liability. For the various levels of automation, a distinct line defining the scope of liability must be established to determine the cause of the accident. As a result, legislation, traffic laws, and insurance regulations may need to be changed. The existing restrictions are based on the idea that a human driver is aboard while the vehicle is driven. However, automation technology is supposed to completely or partially replace the driver, shifting the responsibility [120].  
Another challenge is the utilization of AI-based transport systems in low-income and unstable nations, which is extremely difficult because of the lack of readiness for use of their infrastructures and the inability to provide maintenance and repairs. This barrier includes a shortage of dependable power sources and frail communication networks. Countries that invest less of their GDP in technology research and physical infrastructure may find it more difficult to take advantage of AI [6].
###### Abstract  
The acquisition of real-world sonar data necessitates substantial investments of manpower, material resources, and financial capital, rendering it challenging to obtain sufficient authentic samples for sonar-related research tasks. Consequently, sonar image simulation technology has become increasingly vital in the field of sonar data analysis. Traditional sonar simulation methods predominantly focus on low-level physical modeling, which often suffers from limited image controllability and diminished fidelity in multi-category and multi-background scenarios. To address these limitations, this paper proposes a Conditional Dual-Branch Attention Generative Adversarial Network (CDBA-GAN). The framework comprises three key innovations: The conditional information fusion module, dual-branch attention feature fusion mechanism, and cross-layer feature reuse. By integrating encoded conditional information with the original input data of the generative adversarial network, the fusion module enables precise control over the generation of sonar images under specific conditions. A hierarchical attention mechanism is implemented, sequentially performing channel-level and pixel-level attention operations. This establishes distinct weight matrices at both granularities, thereby enhancing the correlation between corresponding elements. The dual-branch attention features are fused via a skip-connection architecture, facilitating efficient feature reuse across network layers. The experimental results demonstrate that the proposed CDBA-GAN generates condition-specific sonar images with a significantly lower Frechet inception distance (FID) compared to existing methods. Notably, the framework exhibits robust imaging performance under noisy interference and outperforms state-of-the-art models (e.g., DCGAN, WGAN, SAGAN) in fidelity across four categorical conditions, as quantified by FID metrics.  
sonar data generation; dual-branch attention feature fusion mechanism; layer-skipping operation; fidelity; generative adversarial network
# Improving VulRepair's Perfect Prediction by Leveraging the LION Optimizer
## 3 Methodology
### Improving VulRepair: ImpVulRepair
#### 3.3.2 Optimizer  
Optimizers are algorithms that are used to minimize the loss function by changing the weights and learning rates of neural networks, thus reducing the loss between the true values and the predicted values [34]. Stochastic gradient descent, momentum, RMSprop, and Adam are several examples of optimizers. Google developed a PyTorch optimizer in 2023, as reported in [32]. It is supposedly better for transformer architectures. It is also memory-efficient. It seems like a viable option that we can use for ImpVulRepair. To obtain more information and a better understanding, we visited GitHub, which explained the optimizer and its implementation [35].  
The Evolved Sign Momentum (LION) is an optimization algorithm for deep neural network training. It is a stochastic gradient descent method and utilizes the sign operator to control updates [36]. It is memory-efficient compared to Adam. Currently, VulRepair uses AdamW, a popular optimizer that adds L2 regularization to the Adam optimizer. However, LION outperforms Adam, reduces training times, and is 2.3 times faster [32]. LION uses momentum and requires a smaller learning rate, larger batch sizes, and greater weight decay. It is simpler, faster, and has fewer hyperparameters than AdamW, with the optimal settings including a learning rate of 0.0001, weight decay of 0.01, momentum Beta1 set to 0.9, and momentum Beta2 set to 0.99 [34]. Therefore, we used the LION optimizer to determine whether it was a good fit for ImpVulRepair.
## 5 Genetics
### Genetic Resilience to Mental Health Conditions in the Context of ACEs  
The serotonin transporter promoter polymorphism (5-HTTPLR) variant of the SLC6A4 gene has variant long (L) and short (s) alleles that affect the level of serotonin expression [47]. Serotonin is an important neurotransmitter primarily associated with regulating neurocognitive processes by directing when satiation of the reward system is achieved; the L allele of the 5-HTTPLR has historically been described as the more beneficial allele [47]. However, in a review that captured six studies on the role of the 5-HTTPLR gene and mental health resilience in the presence of adversity, three reported that the L/L genotype was protective while three reported that the s/s genotype was protective [52]. The oxytocin receptor gene (OXTR) plays an important role in stress regulation, promoting cognitive functioning, social relationships, and one's self-esteem [50]. Two studies reported a risk effect for children with the OXTR G/G genotype when exposed to adversity [53; 54], whereas another team reported protective effects of the G/G genotype [48]. Interestingly, another study revealed a significant interaction between mothers' prenatal stress and infants' OXTR genotype, such that infants with two or more risk genotypes (e.g., single-nucleotide polymorphisms Rs53576 GG and rs2254298 A) exhibited less self-regulation when exposed to prenatal stress; these findings support gene-by-environment interactions as early as the prenatal period [55]. However, there is also evidence that the Rs53576 GG genotype is associated with positive outcomes (e.g., increased trust behavior), supporting the differential susceptibility hypothesis whereby homozygosity of this genotype (G/G) may prove advantageous or disadvantageous in the absence or presence, respectively, of ACEs [56]. Lastly, the brain-derived neurotrophic factor (BDNF) gene has been identified as an important contributor to children's resilience, playing an important role in neuroplasticity, neuronal growth and maintenance, and glucose utilization [51]. Three studies observed protective effects for those with the Val (Valine)/Val genotype as compared to those with other genotypes, such as the Met (Methionine) allele variation [52; 57].  
The corticotropin releasing hormone receptor 1 (CRHR1) is an important gene as it is a fundamental gene that helps govern the HPA axis system through its effect on cortisol production [48]. The TAT haplotype of the CRHR1 has been observed to be protective in the face of adversity based on two studies [48; 58], while others show additional risk effects based on other genetic groups such as those with the GGT haplotype or the G allele [58; 59]. Other genes, such as the FK506-binding protein 5 (FKBP5) gene, are also involved in HPA functioning and potentially mental health outcomes [49]. A meta-analysis revealed that individuals with specific allele variations for the FKBP5 gene had higher risk of developing certain mental health conditions, such as depression or post-traumatic stress disorder [60].
# Pluripot Stem Cells for Transgenesis in the Rabbit: A Utopia?
## 2 Rabbit Pluripotent Stem Cells (rbPSCs)  
### Rabbit Embryonic Stem Cells (rbESCs)  
Rabbit embryonic cell cultures were described by American teams in 1993 [42; 43], but the first lines of rbESCs were not published until 15 years later by two teams from China and Japan [44; 45]. RbESCs are derived from ICM cells of early blastocysts (E3.5-E4.0). They are cultured on feeder cells and form flat colonies (Figure 1). The self-renewal of rbESCs depends on the activin A/TGF\(\beta\)/SMAD2-3 and FGF2 pathways [46; 47]. FGF2 appears indispensable by inducing the PI3K/AKT and MAPK pathways [48; 49], while the WNT/\(\beta\)-catenin pathway may also be indirectly activated by FGF2 [50]. It is possible to derive cells without any growth factor in the medium if they are cultured on feeder cells, showing that LIF is not necessary for the maintenance of rbESCs [51]. However, the addition of LIF to the culture medium of rbESCs has often been used [48; 49; 52], and several studies have described the effect of LIF on the derivation of rbESCs and the induction of LIF-receptor expression [52; 53]. Unlike the derivation of rodent ESCs stabilized in the naive state [54], MEK and GSK3B inhibitors do not enhance epiblast cell differentiation in vivo [55] or rbESC derivation in vitro [51]. In addition, the maintenance of dome-shaped naive-like rbESC colonies in the presence of these two inhibitors requires both FGF2 and feeder cells [56].  
RbESCs are unstable and their enzymatic single-cell dissociation induces higher rates of cell proliferation than clump passages [51], but generates more chromosomal abnormalities as described for human ESCs [57]. The inhibitor of the rho kinase (ROCKi), which acts by blocking the apoptotic  
Figure 1: Schematic representation of the rabbit pluripotent stem cell lines produced by our research team [47; 51; 57] and placed on a scale of primed pluripotency. Methods and media are indicated. rbESC: rabbit embryonic stem cell; rbiPSC: rabbit induced pluripotent stem cell; ICM: inner cell mass; KOSR: knockout serum replacement; FGF2: fibroblast growth factor 2; FBS: fetal bovine serum; LIF: leukemia inhibitory factor; CKF: clumps/KOSR/FGF2; CKL: clumps/KOSR/LIF; AKF: accurate/KOSR/LIF; AKSL: accurate/KOSR + FBS/LIF; AKSF: accurate/KOSR + FBS/LIF; AKSF: accurate/KOSR + FBS/LIF; AKSgff: accurate/KOSR + FBS/growth factor free; rbEKA: rabbit enhanced KLF activity. Scale bar = 30 \(\mu\)m.  
response induced by cell dissociation [58], does not increase the clonogenicity of rbESCs, unlike human ESCs [59], but leads to their arrest of proliferation and differentiation (our own unpublished data). This difference can be explained by the high expression levels of _ROCKI_ and _ROCK2_ genes in rbESCs [60], which may be essential to maintain cell pluripotency by eliminating cells engaged into differentiation.
## Appendix A Analysis Using Two Counterweights
### Numerical Results  
After proving that the counterweight that has the less influence on the optimization is Counterweight-2, three solutions are selected from the Pareto front that use only Counterweights-1 and 3. Solutions are chosen as follows:  
1. If the interest is to optimize the index related to the ShF (\(\beta_{ShF}\)) without giving importance to the index related to the ShM (\(\beta_{ShF}\)), the selected solution is: \[\begin{array}{ll}x_{c1}=-0.210649187&y_{c1}=0.002597417&t_{c1}=0.005812833\\ x_{c3}=-0.133324421&y_{c3}=7.62E-05&t_{c3}=0.039931556\end{array}\] By using this solution, the ShF can be reduced by 99.67%, while the ShM by 24.34%.
2. If the interest is to optimize the index related to the ShM (\(\beta_{ShM}\)), without giving importance to the index related to the ShF (\(\beta_{ShF}\)), the selected solution is: \[\begin{array}{ll}x_{c1}=-0.273728043&y_{c1}=-0.029116001&t_{c1}=0.005\\ x_{c3}=-0.11860947&y_{c3}=0.10186805&t_{c3}=0.04\end{array}\] By using this solution, the ShM can be reduced by 79.22%, while the ShF by 1.55%.
3. If the interest is to optimize both indexes, \(\beta_{ShF}\) and \(\beta_{ShM}\), the selected solution is: \[\begin{array}{ll}x_{c1}=-0.207213869&y_{c1}=0.031661306&t_{c1}=0.006993731\\ x_{c3}=-0.126559077&y_{c3}=0.058396603&t_{c3}=0.04\end{array}\] This solution reduces the ShF and the ShM by 55.05% and 57.27%, respectively.
## 4 Results and Discussion
### Error Analysis  
To validate the generalization behavior of our models and gain deeper insight into their performance, we performed error analysis using confusion matrices (Figures 3-5). These matrices demonstrate the benefits and drawbacks of using ML, DL, and LLM-based classifiers. The SVM and Logistic Regression ML models provide high true-positive and true-negative counts, along with balanced sensitivity and specificity. Conversely, Naive Bayes had more false positives and false negatives, consistent with its lower performance metrics.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline LLaMA 2 (Fine-Tuned) & 96.58 & 96.50 & 96.58 & 96.50 \\ ModernBERT (M-BERT) & 94.20 & 94.19 & 94.10 & 94.67 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of LLaMA 2 and M-BERT.  
Thus, the deep learning models (CNN and Bi-LSTM) showed much better class separation, with the CNN having the lowest number of misclassifications. But Bi-LSTM showed slightly' more false positives, suggesting some over-classification of neutral content as offensive content. This knowledge is instrumental in interpreting the contextual misinterpretation behavior of DL models.  
The classification was almost symmetric, suggesting that LLaMA 2 was particularly the best among LLMs with high precision and was least confused between classes. ModernBERT, achieving slightly lower accuracy but high false-negative rates and low overall error, still performed well. These results validate that, particularly for fine-tuned LLMs on task-specific datasets, the model is able to capture the nuanced semantics of Roman Urdu-English code-mixed content.  
Figure 4: DL Model Confusion matrix.  
Figure 3: ML model confusion matrix.
## 3 System Model
### The Wireless Link Model  
The received signal vector of MAP/MP/ONU \(j\) from user/human/machine \(i\) via one single hop is \(\mathbf{y}_{j}\). \(j\in\mathit{Neb}(i,1)\).  
\[\mathbf{y}_{j}=\sqrt{p_{0}}\sum_{j\in\mathit{Neb}(i,1)}(\mathbf{h}_{ji}*s_{i})+\mathbf{n} \tag{1}\]  
where \(p_{0}\) is the transmit power of user/human/machine \(i\), \(s_{i}\) is the transmit signal satisfying \(E[s_{i}^{2}]=1\), \(\mathbf{h}_{ji}\) is the wireless transmit channel from user/human/machine \(i\) to MAP/MP/ONU \(j\) via one single hop. \(\mathbf{h}_{ji}\) is also a \(M\times 1\) vector, and \(\mathbf{n}\) is the Gaussian noise-obeying \(N(0,\sigma^{2})\). \(s_{i}\) is a tactile service form user/human/machine \(i\). The tactile service, such as remote digital gloves or robots, always requires the long-term transmission of interactive data between user/human/machine and task-computing cloud. \(s_{i}\) is considered to be related to the data transfer rate. Note that the signals of any two different users, \(s_{i}\) and \(s_{i}\), are independent from each other, i.e., \(E[s_{i},s_{i}]=0\), \(\forall i\
eq l\).  
For the multi-hop, the signal is only relayed by the intermedia MAPs and MPs, without de-modulation and re-modulation. The received signal vector of MAP/MP/ONU \(j\) from user/human/machine \(i\) via a \(K\)-hop path (\(K\geq 2\)) is  
\[\mathbf{y}_{j}=\alpha*\mathbf{y}_{k}+\mathbf{n} \tag{2}\]  
where \(j\in\mathit{Neb}(i,K)\), \(k\in\mathit{Neb}(i,K-1)\), and \(\alpha\) is the coefficient of signal attenuation (\(0<\alpha\leq 1\)).  
Combining Equations (1) and (2), no matter the value of \(K\) is, \(\mathbf{y}_{j}\) is  
\[\mathbf{y}_{j}=\alpha^{K-2}\sqrt{p_{0}}\sum_{j\in\mathit{Neb}(i,K)}(\mathbf{h}_{ji}*s_ {i})+\frac{1-\alpha^{K-1}}{1-\alpha}\mathbf{n} \tag{3}\]  
Figure 2: The task and offloading in the FiWi network equipped with three tiers of MEC services.