esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling and Validation of Non-Homogeneous Granites
### Granite Model Validation  
To ensure the accuracy of the numerical simulation, uniaxial compression numerical simulation tests were performed on the established inhomogeneous granite model. During the simulation, the lower end of the rock model was fixed, and a downward velocity of 0.001 mm/s was applied to the upper end. The results of the comparison between the simulation and experimental results [31] are shown in Figure 3, and the obtained rock stress-strain data were compared with the experimental results in the literature [32], shown in Figure 4.  
As shown in Figures 3 and 4, the crack and stress-strain curves obtained after conducting uniaxial compression demonstrate remarkable agreement with experimental results. This consistency serves as a validation of the accuracy of the non-homogeneous granite model. Rock models play a crucial role in the numerical simulation of rock breaking. Among the commonly used models, the Mohr-Coulomb (M-C) and Drucker-Prager (D-P) models are widely adopted. In this study, the D-P model is employed [33], as it accounts for the hydrostatic pressure acting on the rock and provides a better explanation for the yielding phenomenon. The mathematical equation of the D-P model is as follows:  
\[\rho I_{1}+\sqrt{J_{2}}-K=0 \tag{1}\]  
Figure 3: Crack comparison.  
Figure 2: Rock model.  
\[I_{1}=\sigma_{1}+\sigma_{2}+\sigma_{3} \tag{2}\]  
\[J_{2}=\frac{1}{6}\Big{[}(\sigma_{1}-\sigma_{2})^{2}+(\sigma_{2}-\sigma_{3})^{2}+ (\sigma_{3}-\sigma_{1})^{2}\Big{]} \tag{3}\]  
\[\rho=\frac{2\sin\alpha}{\sqrt{3}(3-\sin\alpha)} \tag{4}\]  
\[K=\frac{6e\cos\alpha}{\sqrt{3}(3-\sin\alpha)} \tag{5}\]  
where \(\rho\) and \(K\) are experimental constants related only to the rock friction angle \(\alpha\) and cohesion \(e\).  
PDC cutters break the rock mainly in shear, and the damage starts when the plastic stress of the rock exceeds its critical value. Neglecting the effect of the broken unit on the broken rock, the plastic strain criterion of the rock is:  
\[\varepsilon^{\prime}\leq{\varepsilon^{\prime}}^{I}_{f} \tag{6}\]  
where \(\varepsilon^{\prime}\) is the equivalent plastic strain of the rock and \(\varepsilon^{\prime}\), is the equivalent plastic strain of the rock when damage occurs. \(I_{1}\) is the amount of stress in the first invariant, and \(J_{2}\) is the stress bias in the second invariant. \(\sigma_{1}\), \(\sigma_{2}\), and \(\sigma_{3}\) are the first, second, and third principal stresses, respectively.
## 5 Discussion
### RQ 4. Which Architectures Are Used to Construct MI Systems with IoT and AR?  
We identified four patterns of MI system architectures, which are illustrated in Figure 4. Three of the patterns (Figure 4a-c) were utilized in most of the reviewed studies; however, the pattern in Figure 4d and combinations of two patterns (Figure 4c,d; Figure 4b,c) were only used in one study. Similar to the combined patterns, Figure 4d was rarely observed in the reviewed studies. A reason for the infrequency of the pattern in Figure 4d may be its high complexity as compared to that of Figure 4c. For instance, an agent in Figure 4c can watch IoT devices while controlling them through an AR interface; however, an agent in Figure 4d cannot see the IoT devices that he/she is controlling, and the result of the control commands is displayed on an interaction device and/or another entity (e.g., lights in miniature buildings [42]). Consequently, Figure 4d requires an additional link between the server and another entity to allow the agent to control another entity, thereby increasing the complexity of the architecture. Similarly, system architectures with combined patterns also have a relatively higher complexity than system architectures with a single pattern, which may explain why only two studies used MI system architectures with combined patterns to support various features, namely, an AR interface for controlling IoT devices and AR navigation for visualizing a path to a destination [34]. This implies that the use of multiple MI system architecture patterns is a potential method for making the system more flexible, as discussed by [PERSON] [53].
## 5 Conclusions  
This study conducted a detailed evaluation of AES-128 encryption performance across a range of CPU and GPU hardware platforms, with a focus on designing and validating an optimized hybrid CPU-GPU encryption framework. By analyzing both the serial (CBC) and parallel (CTR) modes, we identified key performance bottlenecks such as I/O latency and workload imbalance. To address these, we proposed five enhancement strategies: data shuffling, I/O bottleneck reduction, adaptive workload distribution, ping-pong processing, and bit-slicing parallelization.  
The experimental results demonstrated that the hybrid methods notably improved encryption throughput across different file sizes and hardware setups. In particular, bit-slicing parallelization achieved the highest performance gains on large datasets, while adaptive workload distribution consistently reduced execution time variance. These findings confirm the effectiveness of combining CPU-GPU collaboration with tailored optimization techniques for high-performance cryptographic computing.  
Despite these improvements, certain limitations remain. The study focused primarily on NVIDIA GPU architectures, and the results may vary when applied to AMD or Intel GPU platforms. Additionally, the research was limited to AES-128 encryption, while different key lengths (AES-192 and AES-256) could exhibit varied performance characteristics. Furthermore, operating system and driver variations were not explored, which could impact real-world encryption speeds.  
Future research should address these limitations by extending hybrid CPU-GPU optimization techniques to heterogeneous GPU architectures and investigating performance implications for different AES key lengths. Additionally, real-time adaptive encryption models should be developed to dynamically allocate tasks based on system workload and power efficiency. The integration of emerging GPU architectures, such as those optimized for AI acceleration, into cryptographic frameworks presents another promising avenue for exploration.  
The proposed hybrid encryption strategies pave the way for more scalable, efficient, and hardware-optimized data security solutions, contributing significantly to the broader field of high-performance cryptographic computing.  
Conceptualization, M.K.Y. and J.-S.J.; Methodology, M.K.Y. and J.-S.J.; Software, J.-S.J.; Validation, M.K.Y. and J.-S.J.; Formal analysis, J.-S.J.; Investigation, J.-S.J.; Resources, M.K.Y.; Data curation, J.-S.J.; Writing - original draft, J.-S.J.; Writing - review & editing, M.K.Y. and J.-S.J.; Supervision, M.K.Y.; Project administration, M.K.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (Approval No. RS-2024-00445552).  
Not applicable.  
Dataset available upon request from the authors.  
The authors declare no conflicts of interest.
## 3 Results
### Effect of Soil Characteristics on Aroma Profiles of Grapes  
To investigate the influence of soil characteristics on the aroma profiles of the grapes, Pearson's correlation analysis was performed on soil physico-chemical properties, including 25 free aroma compounds and 8 glycosylated aroma compounds, which were selected via two-way ANOVA. The absolute value of the correlation coefficient between 0.8 and 1.0 was considered a very strong correlation. The absolute value of the correlation coefficient between 0.6 and 0.8 was considered a strong correlation. The absolute value of  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Characteristics** & **Vintage** & **CS1** & **CS2** & **CS3** & **CS4** & **V** & **P** & \(\mathbf{V\times P}\) \\ \hline hundred-grain weight (g) & 2020 & 146.8 \(\pm\) 0.7 b & 163.7 \(\pm\) 4.2 a & 166.0 \(\pm\) 5.5 a & 151.6 \(\pm\) 5.3 b & * & *** & *** \\  & 2021 & 147.7 \(\pm\) 3.5 c & 126.3 \(\pm\) 9.5 d & 161.4 \(\pm\) 1.6 b & 172.6 \(\pm\) 2.8 a & & \\ soluble solids content & 2020 & 24.0 \(\pm\) 0.9 a & 23.3 \(\pm\) 0.1 a & 23.4 \(\pm\) 0.8 a & 23.6 \(\pm\) 0.7 a & *** & ns & ns \\ (Brix) & 2021 & 22.2 \(\pm\) 1.8 a & 21.2 \(\pm\) 1.1 a & 22.4 \(\pm\) 0.8 a & 21.7 \(\pm\) 1.0 a & & \\ titralude acid & 2020 & 3.7 \(\pm\) 0.4 b & 5.4 \(\pm\) 1.1 a & 3.9 \(\pm\) 0.1 b & 4.1 \(\pm\) 0.3 b & & \\ (g tartaric acid/L) & 2021 & 5.0 \(\pm\  
the correlation coefficient between 0.4 and 0.6 was considered a medium correlation. The result showed that seven free aroma compounds and one bound aroma compound had a strong correlation with soil physico-chemical properties (Figure 3): both free styrene and free (_E_)-3-hexen-1-ol in grapes had a significant positive correlation with soil N, P, and K contents and a negative correlation with pH. Free (_E_)-3-hexen-1-ol was also positively correlated with soil Fe and EC. And free phenylacetaldehyde concentration in grapes was positively influenced by soil Ca and Mg contents. Hexanal in grapes was strongly positively correlated with soil Mg, while (_E_)-2-hexen-1-ol, also a C6 compound, was strongly negatively correlated with Mg. It was found that the bound 3-methyl-1-butanol in grapes was negatively influenced by the soil K and Mg contents.  
Figure 3: [PERSON]’s correlation coefficients between the concentration of aroma compounds in grapes and the physico-chemical properties of soil. The data from soils at a depth of 30–60 cm was used for analysis. 3-Methyl-1-butanol was the only bound aroma compound.  
Figure 2: Heat maps for statistical difference in the concentration of aroma compounds between the plots in grapes. The components listed in this figure were shown via two-way ANOVA to be significantly influenced by plot across the two vintages (Tables S5 and S6). The concentrations were converted to log2 fold for standardization. The capital letters represent the differences between the four plots in 2020 determined via one-way ANOVA (\(p\leq 0.05\)), and the lowercase letters represent the differences in 2021.
# STHarDNet: Swin Transformer with HarDNet for MRI Segmentation
## 1 Introduction
* Using the first skip connection layer in the encoder of the Swin Transformer model in the encoder-decoder model that has the U-Net structure, it accepts a feature map larger than the bottleneck as input and maintains the Swin Transformer model's shifted windows approach and hierarchical transformer characteristics. This study used the Anatomical Tracings of Lesions After Stroke (ATLAS) to conduct comparative experiments of performance. The ATLAS dataset is a standardized open dataset built for performance comparison of various algorithms that manually segment lesion locations in the MRI images of 229 stroke patients. In the ATLAS data, the MRI images of 177 patients were used as training data, and the data of the remaining 52 patients were used as validation data to conduct the comparative experiments of performance with existing state-of-the-art (SOTA) models. In the results, the proposed model's Dice, IoU, precision, and recall were 0.5547, 0.4185, 0.6764, and 0.5286, respectively, indicating that the proposed model performed better than the conventional model. Furthermore, it showed faster performance in the comparative calculation speed test of the models.
# Volterra Black-Box Models Identification Methods: Direct Collocation vs. Least Squares
## 5 Numerical Experiments
### Model 1. Periodic Signal
#### 5.1.1 Collocation Method Results for the Model 1  
Table 1 demonstrates the dependence of the residual \(\varepsilon_{N}\) on the values \(m=m_{1}=m_{2}\) for the uniform mesh \(t_{k}=\frac{k}{N},\ k=0,1,\ldots,N\), covering the segment \([0,1]\).  
All calculations were performed in the Maple system with parameter Digits:=30 (the number of digits that Maple uses when making calculations with software floating-point numbers). It should be noted that when using other values of parameter Digits, the order of the residual changes on average in direct proportion to this value. Also note that the integration during the formation of the system (11) was carried out analytically and did not introduce additional error in the calculation results. This is due to the fact that the input signal \(x(t)\) in most cases allows for the analytical calculation of the values (12). In the case of using input signals of a more complex structure, special approximation methods should be applied to the integrals (12), taking into account the possible fast oscillation of \(x(t)\). Figures 5-8 demonstrate residual error for fixed parameter \(m\).  
\begin{table}
\begin{tabular}{c c} \hline \hline \(m\) & \(\varepsilon_{N}\) \\ \hline
3 & \(1.41\times 10^{-2}\) \\
4 & \(1.14\times 10^{-6}\) \\
5 & \(4.72\times 10^{-9}\) \\
6 & \(1.77\times 10^{-12}\) \\
7 & \(1.83\times 10^{-14}\) \\
8 & \(1.53\times 10^{-18}\) \\
10 & \(2.84\times 10^{-26}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dependence of the residual \(\varepsilon_{N}\) on the values \(m,m_{1},m_{2}\) for Model 1.  
Figure 4: Input and output functions.  
Figure 5: Residual for \(m=3\).  
Figure 6: Residual for \(m=5\).  
Figure 7: Residual for \(m=7\).
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 7 Contributions to the Use of USVs for Disaster Applications
### Boat Prototypes  
N-Boat II--The Sailboat RobotThis USV is currently under development by Natalnet partner Labs. The N-Boat II is a reusable and self-sustaining USV [247]. It is a 2.5 m long vessel (0.8 m width) and weighs about 150 kg--including the hull (app. 65 kg), two 104 A/H nautical batteries (50 kg), and the keel/blade (35 kg). It has been developed since 2012, following the lessons learned from the N-boat I USV [248]. It can withstand open seas and strong winds and support long-term missions autonomously [249]. Its main applications will focus on monitoring tasks, which include environmental protection and border surveillance, as well as natural disaster mitigation through long-term extreme weather forecasting.  
Platypus USV PlatformsThe project also contains two Lutra Airboats and one Lutra Prop, with a differential drive system, from Platypus Limited Liability Company (LLC) ([[http://senseplatypus.com/](http://senseplatypus.com/)]([http://senseplatypus.com/](http://senseplatypus.com/))). Lutra boats are approximately 1.5 m long, with about 8 kg of weight, supporting a payload of about 1.5 kg. We have made modifications for both software and payload of the boats. For instance, we have adapted the Ardupilot autopilot boards to perform basic waypoint navigation, return to launch, and maneuvers compensation by software, integrated with Robot Operating System (ROS), a robotics middleware. Therefore, we intend to soon perform different path planning and obstacle avoidance according to COLREGS. We have also improved the communication capabilities between the boats: in addition to the Wi-Fi connectivity, we integrated long-range radios for basic telemetry and inter-boat communication.
## 3 Materials and Methods
### Description of Distributed Systems
#### 3.2.2 Model Parallelism  
Model parallelism (MP) is a widely employed method in distributed training that addresses the challenge posed by the limitations of a single GPU memory capacity [35]. In this approach, DL models are partitioned across multiple devices, mitigating the constraints on the maximum model size that can be accommodated. By strategically distributing model components across different devices, model parallelism enhances scalability and enables the training of larger models that would otherwise be restricted by individual GPU memory constraints. This technique is particularly valuable for tasks such as computer vision and NLP, where increasing the model size contributes to improved accuracy [36].  
Figure 3 illustrates an MP strategy in which different layers of a DNN are distributed across multiple compute nodes, where each node is responsible for processing a specific portion of the model (e.g., a hidden layer), thereby enabling the training of large-scale models that do not entirely fit into the memory of a single device.  
MP is a distributed training strategy in which the architecture of a neural network is partitioned across several devices or compute nodes. Instead of replicating the entire model in each worker, as in data parallelism, this approach assigns different layers or components of the model to separate nodes [35]. For instance, one node handles the first hidden layer, another handles the second, and so forth, as shown in Figure 3. This allows extremely large models to be trained even when their full architecture exceeds the memory limits of a single GPU and CPU.  
Multiple factors allow us to calculate the memory requirements for training a DL model, including the model architecture, batch size, and memory capacity of the devices, whether CPUs, GPUs, or Tensor Processing Units (TPUs) [37]. This is important since  
Figure 3: Model parallelism applied to the DS.  
distributed training will not be performed if the architecture does not have a large Random Access Memory (RAM) capacity on the devices used.
# Time Evolution of CO2 Diffusivity of Carbonated Concrete
## 2 Modeling of CO\({}_{2}\) Diffusivity
### CO\({}_{2}\) Diffusivity in Vapor  
The first term of Equation (2), (a) \(F(D0)\), means CO\({}_{2}\) diffusivity in vapor. CO\({}_{2}\) gas can flow through bulk pore which is not filled with solution. Diffusion theory on gases is theoretically established from kinetic molecular theory [12]. [PERSON] et al. suggested the diffusivity D in vapor has a function of both temperature and pressure [13]. They suggested gaseous diffusivity in vapor:  
\[D^{v}=\frac{10^{-7}\ T^{1.75}\sqrt{\frac{1}{M_{a}}+\frac{1}{M_{b}}}}{P\Big{[}( \sum_{a}v_{i})^{\frac{1}{3}}+(\sum_{b}v_{i})^{\frac{1}{3}}\Big{]}^{2}} \tag{3}\]  
in which, \(D^{v}\): vapor diffusivity, \(T\): temperature, \(M_{a}\), \(M_{b}\): molecular weight of components \(a\) and \(b\), \(P\): total pressure. According to this equation, the value of CO\({}_{2}\) diffusivity is approximately 0.16 cm\({}^{2}\)/s in the air pore system of concrete.
## 3 Pros and Cons about Keratin as Natural Biomaterial for Nanoparticles Production  
Millions of tons of keratin-based biomasses are produced yearly, primarily deriving from the food and wool industries and slaughterhouses. Due to the peculiar properties of these wastes, their disposal poses several issues in terms of soil, air, and water pollution. Therefore, as for other harmful wastes, companies and research institutions have focused on developing alternative uses of keratinous biomasses to reduce their health and environmental impact. Most of these activities have been based on the extraction of keratin to be used for different applications, especially in biomedical devices and drug carriers' fabrication.  
In particular, the last decades have witnessed a mounting interest in using keratin wastes as a renewable source for nanoparticle production. Indeed, keratin represents a highly abundant, biodegradable, non-toxic, biocompatible, and versatile biopolymer with very appealing physical and mechanical properties, such as strength, stability, and resistance to proteolytic degradation. Moreover, exploiting the protein hydrophobic and hydrophilic domains and the presence of several chemical moieties (-NH\({}_{2}\), -COOH, and -SH), keratin allows the incorporation or the covalent attachment of different molecules (drugs, targeting moieties, dyes).  
Furthermore, the RGD and LDV sequences on the protein backbone could ensure a certain degree of intrinsic tumor-targeting ability by specifically binding the vitronectin integrin receptors overexpressed in cancer cells. Finally, the presence of high percentages of Cys amino acids translates into a high number of intermolecular disulfide bridges. This feature, which represents a disadvantage if considering keratin waste disposal, can be exploited effectively for the controlled release of drugs at the tumor site.  
On the other side of the coin, due to keratin's poor water solubility, disulfide bridges and hydrogen inter-chain bonds must be cleaved through harsh extracting conditions, but finding an effective and environmentally friendly procedure remains an open challenge. Indeed, one further major issue arising from our analysis correlates to extraction procedures, which suffer from high variability and poor data availability on the isolated protein in terms of purity, average molecular weight, genotoxicity, and scale-up feasibility. This ultimately poses severe regulatory issues for keratin exploitation in the biomedical field.  
To summarize, using keratin as drug delivery carrier could represent a remarkable breakthrough in the field of (cancer) nanomedicine, mainly because of the natural features of this fascinating protein, such as its extraordinary drug loading ability, versatility, and functionalization properties. On the other hand, difficulties in extraction and standardization procedures still require consistent efforts both from the research and the industrial communities to bring this biomaterial closer to clinical translation.
## 3 Preliminaries  
### Real-Time System  
A real-time system is bound to provide response within pre-specified time bounds. Real-time systems can be classified along two axes, namely, hard real-time system, and soft real-time system. For the earlier system, the specified time constrains must be met with no exception, whereas, for the later, the time bound might occasionally fail with very low probability [32]. The real-time system proposed in this study is of the soft type.
# Full-Duplex MAC Protocol for CSMA/CA-Based Single-Hop Wireless Networks
## 5 Conclusions  
In single-hop wireless networks, using the FD features of the nodes can detect collisions while they are transmitting signals. The FD communication mode can improve the system throughput. In addition, the cut-through mechanism enables the nodes to decode the received frame's header independently. Based on these three aspects, a MAC protocol was designed to reduce the collision occupancy time and increase the FD opportunities in the network. To ensure backward compatibility, the protocol requires the nodes to contend for the channel using the traditional CSMA/CA mechanisms. According to the working process, we modeled the states of each node and their transitions to a Markov chain. By solving the \"active\" transmission probability of the node, we obtained the analytical performance of the protocol. Simulation results showed that the system throughput produced by our protocol was at least twice that of the traditional CSMA/CA protocol used in the HD networks.  
Conceptualization, Y.S.; Data curation, Y.S.; Formal analysis, Y.S. and W.C.; Resources, W.Z.; Supervision, W.Q.; Validation, Y.S. This research was funded in part by the National Natural Science Foundation of China (Nos. 61771368, 61871387). The authors declare no conflict of interest.  
Figure 10: System throughputs for the proposed FD MAC protocol and the MAC protocol in [5] with fixed transmission probability versus the different value of \(n\) (\(W=32\)).