esa-sceva/satcom-chunk-collection huggingface dataset:
# 5G New Radio Open Radio Access Network Implementation in Brazil: Review and Cost Assessment
## 2 Background
### Open RAN Architecture
#### 2.2.2 Latency and Reliability  
Virtualization of RAN functions in c-RAN and v-RAN architectures can introduce additional latency due to the network delays associated with the centralized processing and transport of data over the fronthaul links.  
To address this challenge, the use of advanced techniques, such as edge computing and adaptive functional split, can help to optimize the latency budget and ensure the reliable delivery of critical applications [26].  
In contrast, d-RAN architecture generally offers lower latency, as processing is accomplished locally at the cell site, reducing transmission delays.  
Open RAN, with its focus on open interfaces and the potential for a more diverse ecosystem of vendors, may introduce new challenges in terms of integrating and coordinating the different components to ensure end-to-end reliability.
# Design of Remote Upgrade System for Data Processing Unit in Marine Engine Room Simulator
## 4 Firmware Remote Upgrade Design
### Function Design  
#### 4.2.1 Flash Partitions  
Flash memory is a safe and fast storage entity that has become the most essential carrier of data and programs in embedded systems. In this paper, it mainly stores two parts of code: the bootloader and the user application [23]. The bootloader is the initial code when the device is started up. The user application is the code executed by the embedded system after the bootloader has completed [24]. There are eight sectors in the flash main memory block comprising four 32 KB sectors, one 128 KB sector, and three 256 KB sectors.  
Combining the size of Flash space required for each part of this system, the Flash of the internal chip of the DPU is divided into four parts: the bootloader program area; the information reserved area; the APP program running area; and the APP program backup area [25]. The Flash memory partitions are shown in Table 1.  
The Flash size of the chip is 1024 K bytes, so the addresses reserved for the APP program running area and program backup area are enough. Since the reserved information area will not be erased during normal work, it can be for storing key information, such as upgrade flags, upgrade file versions, program download addresses, etc. To prevent the loss of critical information after an upgrade, we should allocate an appropriate amount of space to the reserved area.
# Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Parana State, Brazil
## 2 Material and Methods
### Data
#### 2.2.2 Satellite Data: IMERG  
Precipitation data from remote sensing were acquired as daily and monthly temporal resolution and 0.1\({}^{\circ}\) spatial resolution, from the satellite constellation of the GPM, IMERG version 6 product, distributed by Goddard Earth Sciences Data and Information Services Center Distributed Active Archive Center (GES DISC DAAC), available online on [[http://mirador.gsfc.nasa.gov/com](http://mirador.gsfc.nasa.gov/com)]([http://mirador.gsfc.nasa.gov/com](http://mirador.gsfc.nasa.gov/com)), accessed on 15 March 2020. The daily and monthly products \"Final Run\" were used, and the time series analyzed was coincident with that of the gauges (1 June 2000 to 31 December 2018). The IMERG algorithm operates to intercalibrate, merge, and interpolate all satellite microwave precipitation estimates, microwave-calibrated infrared estimates, gauge observations, and other data from potential sensors from the TRMM and GPM eras [31]. The \"Final Run\" product includes microwave-infrared estimates without gauge adjustment and the calibrated product based on the Global Precipitation Climatology Centre monthly gauge analysis [30]. In general, the \"Final Run\" products present bias correction and more accurate results than the other products supplied in near real-time (Early and Late Run) [39].  
Figure 2: Mesoregions of Parana state demarcated by the Brazilian Institute of Geography and Statistics (IBGE).
## 5 Conclusions  
This study introduces a deep learning-based approach to determine the optimal gain of a DAC in scenarios where an SDR generates multiple baseband signals for aircraft communications. We identified an analytical distribution that closely models the voltage distribution of the composite signal using the RSS fitting method and found that the GGD fits the composite signal particularly well. We trained a DNN on simulated composite signal data to estimate the parameters of the GGD. This trained DNN allows rapid prediction of the composite signal's voltage distribution as it varies with changes in the SDR configuration. Utilizing the predicted voltage distribution, the DAC's gain can be dynamically adjusted, maximizing its range utilization and minimizing potential signal distortion.  
Conceptualization, K.L.K.; data curation, V.K.G.; formal analysis, V.K.G.; investigation, V.K.G.; methodology, V.K.G.; project administration, K.L.K.; software, V.K.G.; supervision, K.L.K.; validation, V.K.G. and K.L.K.; visualization, V.K.G.; writing--original draft, V.K.G.; writing--reviewing and editing, V.K.G. and K.L.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The methodology used to simulate the data is described in the manuscript; further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Fortenedia Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 1 Introduction  
In recent years, the concept of space-air-ground integrated network (SAGIN), which integrates satellite, aerial, and terrestrial communications, has emerged as a noteworthy architectural paradigm [1]. This integrated approach has received significant research attention in an evolving and compelling area of study such as the sixth-generation (6G) communication network [2]. SAGIN aims to address the connectivity challenges that arise in remote and hard-to-reach areas by offering a cost-effective and high-capacity solution. Therefore, this type of network seems to be the only path towards realizing the Internet of remote things. However, despite the undoubted benefits of these networks, they also come with certain disadvantages, including unbalanced distribution of resources [3], channel impairments [4], complexity and integration challenges [5], and security concerns [2]. SAGIN can overcome some of the limitations associated with traditional communication methods by incorporating free space optical (FSO) systems. In general, combining FSO and radio frequency (RF) communications in hybrid systems is a promising approach,addressing limitations and enhancing performance for improved throughput, reliability, and energy efficiency [6].  
As far as FSO-assisted SAGINs are concerned, these types of systems will lead to improved performance, reliability, and adaptability in diverse operational scenarios, since they will offer increased bandwidth availability, low latency, increased security, and immunity to electromagnetic interference [7]. However, FSO communications are also prone to various environmental and channel attenuation effects, e.g., atmospheric turbulence, that result in severe degradation of the performance, e.g., [8; 9]. An alternative approach to mitigating the impact of atmospheric turbulence involves integrating RF links alongside the FSO ones to exploit their complementary attributes. This hybrid RF/FSO communication strategy allows for the advantages of both RF and FSO technologies, as a result effectively minimizing the detrimental effects associated with adverse weather conditions [10]. The performance of these systems can be further enhanced if unmanned aerial vehicles (UAVs) or high-altitude platform stations (HAPS) are used as relays [11; 12]. The cooperation of HAPS and low Earth orbit (LEO) satellites is expected to guarantee higher capacity with lower propagation delay.
# Multilayer Backbones for Internet of Battlefield Things +
## 2 Formulation of the Problem
#### Multilayer Connected dominating set problem  
INSTANCE: A positive integer \(K\), and a multilayer network consisting of \(n\) layers, i.e., a set of \(n\) pairs \((G_{i},E_{i})\), where \(G_{i}\) is the node set of a usual network and \(E_{i}\) is a set of edges, for \(1\leq i\leq n\), and a set of interlayer links \(E^{ML}=\{E_{i,j}\subseteq G_{i}\times G_{j};i,j\in\{1,\ldots,n\},i\
eq j\}\).  
QUESTION: Is there a dominating set of size \(K\) for \((G^{ML},E^{ML})\), i.e., a subset \(V^{\prime}\subseteq(\cup V_{i})\) with \(|V^{\prime}|=K\) such that for all \(u\in(\cup V_{i})-V^{\prime}\) there is some \(v_{j}\in(\cup V_{i})\) for which \((u,v_{j})\in((\cup E_{i})\cup E^{ML})\), and moreover the induced subgraph defined by \(V^{\prime}\) is connected?
## 4 Conclusions  
To guarantee the operation and maintenance of long-span bridges, an intelligent vision-based method for crack identification based on CNN and GAT is proposed. Firstly, a 224 \(\times\) 224 window slides over images of bridge decks or box girders, and the patch inside the window along with its neighborhoods is input into the CNN model to extract features and reduce dimensions, which generates the central node and its neighboring nodes. Then, a graph structure can be established by connecting the central node with its eight neighboring nodes. Subsequently, the graph structure is treated as the input of the GAT model to classify the patch within the sliding window. After the whole image is scanned, the patches classified as \"crack\" by the CNN-GAT model compose the defect area, which highlights the position and the shape of cracks in the original bridge structures.  
The main contribution of this work is that GAT is introduced into crack identification. In general, CNN is commonly used to detect the patch within the sliding window while its relationship with neighborhoods is ignored. Since cracks are spatially extended, the neighborhoods of crack patches are likely to contain cracks. In this study, we construct a graph structure to establish a connection between the concerned patch and its neighborhoods and use GAT to extract features synthetically. In addition, the pre-training strategy of convolutional kernels is adopted to increase convergence speed during the model training process. The results in the test images demonstrate that the proposed CNN-GAT model has an excellent capability to identify cracks. Compared to the traditional CNN, the TPR of the test results from the CNN-GAT model is much higher, while the TNR is at almost the same high level, which implies that the proposed model is more sensitive to cracks while maintaining the resistance to interference factors. It also indicates that the introduction of GAT could make the whole framework concentrate more on the crack features, which encourages the researchers to add extra GAT modules to other networks for improvement in the field of crack visual detection.  
Conceptualization, F.C.; methodology, F.C.; software, C.C.; validation, J.H.; formal analysis, T.T.; investigation, J.H.; resources, F.C.; data curation, C.C.; writing--original draft preparation, F.C.; writing--review and editing, T.T.; visualization, T.T.; supervision, J.H.; project administration, C.C.; funding acquisition, F.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Henan, grant numbers 242300420054 and 242300420663.  
Not applicable.  
Figure 14: Confusion matrix of the test results by the pre-trained CNN model.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 4 Application Example
### Motion-Based Design of Passive Damping Devices under Uncertainty Conditions
#### 4.3.2 Elastomeric Damper  
Subsequently, the motion-based design of the elastomeric damper under uncertainty conditions may be addressed. The design problem of this elastomeric damper may be defined as follows:  
\[\begin{split}\text{find}\ \theta=\begin{bmatrix}c_{d,e},\ k_{d,e} \end{bmatrix}\ \text{to minimize}\ f(\theta)=\begin{bmatrix}f_{1},\ f_{2} \end{bmatrix}=\begin{bmatrix}c_{d,e},\ k_{d,e}\end{bmatrix}\\ \text{subject to}\Bigg{\{}\begin{array}{l}c_{\min}<c_{d,e}<c_{ \max}\\ k_{\min}<k_{d,e}<k_{\max}\\ \beta_{s}(\theta)\geq\beta_{t}=1.35\;.\end{array}\end{split} \tag{22}\]  
As result of the design process, the parameters of the elastomeric damper (\(c_{d,e}\) and \(k_{d,e}\)) were obtained. The best solution among all the elements of the Pareto front was \(c_{d,e}=1.22\times 10^{5}\) sN/m and \(k_{d,e}=1.30\times 10^{5}\) N/m. The reliability index associated with this solution is, \(\beta_{s}(\theta)=1.49\), which met the design requirements. Figure 7 shows the maximum displacement at the mid-span of the cable damped by the elastomeric damper for the different elements of the sample.  
Figure 6: Maximum displacement at the mid-span of the stay cable damped by the viscous damper for the different elements of the sample.
## 4 Experimental Results
### 2-DE Images Classification
#### 4.3.2 Classification Performance of CNN (Convolutional Neural Network)  
To investigate the effectiveness of our method in comparison to other DNNs, we applied it to a CNN and evaluated its performance by using 2-DE images as the source domain data. Figure 5A shows the CNN structure, which was determined on the basis of the ten-fold cross validation while changing the hyperparameters (the number of channels, strides, and layers) shown in this figure.  
Table 4 lists the classification performance, which indicates that our method performed better than the conventional ATDL [2] and the full-scratch method. These results suggest that our method is applicable to CNNs as well as SdAs. The CNN is widely used in image recognition and achieves high classification accuracy in terms of several standard sets of data [21; 25; 26]. Therefore, our method appears to be comparable and can be applied to various image recognition problems.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **PPV** & **NPV** & **MCC** & **F1** & **ACC** \\ \hline \(m_{l}\)[2] & 0.878 & **0.985** & **0.885** & **0.921** & **0.949** \\ Full & **0.963** & 0.944 & 0.879 & 0.912 & **0.949** \\ Ours & **0.966** & **0.971** & **0.923** & **0.949** & **0.969** \\ \hline \end{tabular}
\end{table}
Table 4: Classification performance when using CNN. The red bold is the best performance of each evaluation. The bold is the second best performance.  
Figure 5: (**A**) CNN structure. (**B**) Distances of different source domains.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **PPV** & **NPV** & **MCC** & **F1** & **ACC** \\ \hline
[6] & **0.962** & 0.944 & 0.879 & 0.912 & **0.949** \\
[8] & 0.931 & **0.957** & 0.879 & 0.915 & **0.949** \\
[11] & **1** & 0.932 & **0.880** & 0.909 & **0.949** \\
[12] & 0.931 & 0.957 & 0.879 & 0.915 & 0.949 \\ \(m_{l}\)[2] & 0.931 & **0.957** & 0.879 & **0.915** & **0.949** \\ L-SVM & 0.871 & 0.956 & 0.833 & 0.885 & 0.929 \\ K-SVM & 0.931 & **0.957** & 0.879 & **0.915** & **0.949** \\ Full & 0.929 & 0.943 & 0.854 & 0.897 & 0.939 \\ \(\backslash\)tuning & 0.857 & 0.914 & 0.756 & 0.828 & 0.898 \\ Ours & **1** & **0.971** & **0.952** & **0.966** & **0.980** \\ \hline \end{tabular}
\end{table}
Table 3: Performance of actual sepsis data classification. The red bold is the best performance of each evaluation. The black bold is the second best performance.
## 5 Numerical Results and Discussion
### Calculation and Analysis of SIFs  
The SIFs indicate the cracking tip state extent in the form of a numerical value, which can characterize the strength of the stress and strain field near the crack tip. The SIFs are determined by the size of the external force, loading mode, crack size and shape, and the geometry and size of the work pieces. The SIFs in the heating and cooling processes are obtained by the XFEM. The SIFs of the crack are shown in Figures 20 and 21.  
It can be seen from Figure 20 that the absolute value of \(K_{\rm I}\) is almost linear in growth in the welding heating process. The \(K_{\rm I}\) value is negative because the obvious stress exists when the welding heating expansion of the crack tip is hampered by the nearby area metal. The value of \(K_{\rm II}\) firstly has a small and slow decrease, then appears to fluctuate and has a tendency to decrease after reaching the maximum. The value of \(K_{\rm III}\) is basic linear growth at the beginning of the welding; the value becomes larger as the distance between the heating source and the crack tip becomes far. The value of \(K_{\rm III}\) begins to decrease when the welding is over.  
It indicates that the SIFs are changed under the effect of the moving heating source. Three types of SIFs reach the maximum and the minimum value in the heating process with different variation trends.  
Figure 21: The stress intensity factors (SIFs) of the crack in the cooling process. (**a**) \(K_{\rm I}\); (**b**) \(K_{\rm II}\); (**c**) \(K_{\rm III}\).  
Figure 20: The stress intensity factors (SIFs) of the crack in the heating process. (**a**) \(K_{\rm I}\); (**b**) \(K_{\rm II}\); (**c**) \(K_{\rm III}\).  
It can be seen from Figure 21 that the \(K_{\rm I}\), \(K_{\rm II}\) and \(K_{\rm III}\) all tend to be a certain value in the welding cooling process. The reason is that the welding heating stress of the crack tip is reduced when the effect of the moving heating source disappears. However, the three types of SIFs are not reduced to zero, because there still exists a certain value of welding residual stress after the welding cooling. Due to an obvious temperature gradient caused by the moving heating source, the values of \(K_{\rm I}\), \(K_{\rm II}\) and \(K_{\rm III}\) do not form a very smooth curve, which is due to the slight fluctuation. The SIFs of the crack are all gradually stabilized when the temperature of the workpiece tends to be balanced.
## 2 Theory and Simulation
The noise from the optical components, as shown in Figure 4b, arises from the thermal expansion (\(\delta_{2}(t)\)) and thermo-optic effects (\(\delta_{3}(t)\)), both proportional to \(\Delta\Gamma(t)\) per Equations (8) and (9). This noise is independent of the substrate material but becomes the dominant contributor when \(\delta_{1}(t)\) is minimized, such as with ULE or enhanced thermal shielding. The refractive index-related noise \(\delta_{3}(t)\), though minor, increases slightly with more shielding layers due to residual temperature gradients. However, as thermal shielding reduces \(\Delta\Gamma(t)\), the overall noise from the optical components decreases, as predicted by Equation (10). The total system noise \(\varphi_{t}(t)\), depicted in Figure 4c, decreases linearly with the reduction in \(\Delta\Gamma(t)\) afforded by the additional shielding layers. This behavior directly follows from Equation (10), where \(\varphi_{t}(t)\propto\Delta\Gamma(t)\). The progressive noise reduction with the one-, \begin{table}
\begin{tabular}{c c} \hline \hline
**Material** & **Thermal Expansion Coefficient \(\alpha(\times 10^{-6}\) K\({}^{-1})\)** \\ \hline Aluminum Alloy & 23.6 \\ \hline Quartz & 3.2 \\ ULE & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Thermal Expansion Coefficients of Substrate Materials. Figure 4: Interferometer Thermal Noise Simulation. (**a**) Thermal noise generated by optical substrate. (**b**) Thermal noise generated by optical element. (**c**) System thermal noise. (**d**) Contribution of each noise without thermal shielding. (**e–g**) Contribution of each noise with single-layer/double-layer/triple-layer thermal shielding. two-, and three-layer shielding configurations confirms the efficacy of multi-layer designs in attenuating thermal fluctuations and, consequently, suppressing noise. By integrating a three-layer thermal shielding system with a ULE substrate, the system's thermal time constant was extended to 78.5 h, significantly reducing \(\Delta\)T(t). Coupled with ULE's minimal \(\alpha_{1}\), this design suppressed thermally induced optical path noise to sub-picometer levels. These results are in strong agreement with the theoretical framework established in Equation (10), validating the approach and highlighting the synergistic benefits of shielding and material selection for stabilizing interferometric measurements.
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 6 Challenges and Limitations
### Ethical and Legal Considerations
#### 6.2.3 Broader Ethical and Societal Challenges  
Ethical challenges associated with the implementation of Large Language Models (LLMs) in maritime operations extend beyond bias and fairness, encompassing critical concerns about data ownership, privacy, and the potential misuse of sensitive information. Maritime operations generate vast amounts of proprietary and operationally sensitive data, and the integration of LLMs raises questions about who owns these data, how they are secured, and whether they could be misused by malicious actors. For instance,  
Figure 6: Ethical and regulatory challenges framework.  
unauthorized access to operational data processed by LLMs could expose vulnerabilities, jeopardizing both vessel and crew safety.  
Another ethical challenge lies in the over-reliance on AI for safety-critical decisions. While LLMs offer advanced capabilities for anomaly detection and predictive analysis, entrusting them with full autonomy in critical scenarios risks oversights that may arise from the models' inherent limitations, such as hallucinations or inaccurate outputs. This underscores the importance of maintaining a human-in-the-loop approach to ensure accountability and mitigate risks.  
The societal implications of LLM deployment also merit consideration, particularly regarding crew job security and decision-making accountability. As LLMs take on increasingly complex tasks, there is a risk of de-skilling the workforce, reducing opportunities for crew members to exercise their expertise. Additionally, the ambiguity surrounding decision accountability when LLM recommendations influence critical outcomes raises important legal and ethical questions. To address these challenges, robust governance frameworks must be developed, ensuring clear guidelines for the ethical use of LLMs in maritime operations. These frameworks should emphasize transparency, accountability, and the necessity of human oversight to build trust in AI-driven systems.
# 3D Positioning of Drones through Images
## 4 Experimental Setup  
### Training Configuration  
In this experiment, PCPVT-S was used as the backbone network for the MH-FPI model during the training process. PCPVT-S was pre-trained on the ImageNet dataset with its weights loaded, and the original classification layer was removed. Unless otherwise specified, the feature fusion network in MH-FPI employed the FPN network for fusion operations.  
The AdamW optimizer was used to update the model parameters during training. The initial learning rate was set to \(4\times 10^{-4}\), with a weight decay parameter of \(5\times 10^{-4}\).  
The model was trained for 45 epochs with a batch size of 16. To prevent overfitting, the learning rate was reduced by a factor of 10 at the 10 th and 20 th epochs.  
The model was trained on the UL14 plus dataset and validated on its test set. During training, the input image size for the UAV image branch was set to 256 \(\times\) 256, while the satellite image map input branch used a size of \(384\times 384\). Data augmentation with random scaling and center point, as described in Section 2.3, was applied during training. All experiments in this section were conducted on an NVIDIA GeForce RTX 3060 GPU with mixed precision enabled.