esa-sceva/satcom-chunk-collection huggingface dataset:
# A Topological Sensitive Node Importance Evaluation Method in Aerospace Information Networks
## 5 Experimental Simulation and Analysis
### Simulation Experiment and Analysis of Node Importance
#### 5.1.2 Node Importance Algorithm Discrimination Evaluation  
The satellite network is characterized by high dynamics, its topological structure changes regularly during the period, and the structure changes extremely small. We use the method in this paper and the existing four node importance evaluation indicators (degree centrality, evaluation indicators based on natural connectivity, jump-surface node method, and mutual information method) to calculate the importance of nodes in Figures 4 and 5. Its experimental results are shown in Figure 7.  
It can be seen from Figure 7 that when the network topology in Figure 4 becomes the structure in Figure 5, the importance of node 1 calculated by degree centrality, jump-surface node method and mutual information method does not change. These methods fail to  
Figure 7: Comparison of node importance: (**a**) Mutual information method; (**b**) Degree centrality; (**c**) Natural connectivity; (**d**) Jump-surface node method; (**e**) Our method.  
reflect that node importance changes with network topology reconfiguration. According to Figure 4, when no link is established between nodes 9 and 10, nodes 11 and 12, and nodes 13 and 14, the communication between nodes 2 and 15 needs to pass through node 1. Once node 1 fails, the spatial information network will become two unconnected symmetric subnets. When the link between node 9 and node 10, node 11 and node 12, and node 13 and node 14 is established, node 2 and node 15 can communicate through node 1. In addition, node 2 and node 15 can also communicate through the link between node 11 and node 12, therefore the importance of node 1 decreases. This conclusion is consistent with the result of the method proposed in this paper. It can be seen in Figure 7c that the importance of node 1 in Figure 5 has increased instead, indicating that the calculation result of natural connectivity is contrary to the actual situation. This is also true for node 2. At the same time, it can be found from node 11 that after the network topology changes, node 11 forms a link with node 12. When node 1 fails, node 11 can connect the left and right subgraphs. Therefore, the importance of node 11 is improved compared with that before, which is consistent with the results obtained by our method. It fully shows that the algorithm based on node importance proposed in this paper can effectively distinguish nodes with similar network topology.
## 3 Results and Discussion
### Influence of Biochar Incorporation on Plant Physiological Parameters  
Incorporation of biochar at distinct application rates led to a significant increase in all parameters measured (Figure 2). _P. tomentosa_ height increased by 45.7-49.8% at 2.5-5.0% biochar application rates, peaking at 10% biochar (by 82.0%). Stem diameter was significantly influenced by 10% biochar only, increasing by 54.4% (Figure 2b). Meanwhile, aboveground biomass (AGB) DW gradually increased with increasing biochar doses by 85.5, 182, and 336%, respectively (Figure 2c). Indeed, stem DW was more sensitive to biochar incorporation with a significant increase even at 2.5% biochar, whereas a significant difference in leaf DW was observed at higher application rates. Root biomass DW increased equally without dependence on biochar application rates by up to 243% (Figure 2d). In the case of leaf length, the biochar effect mirrored the pattern observed in plant height, increasing the parameter by up to 74.1% (Table S2). Considering leaf width, biochar incorporation exerted an impact similar to that observed for stem diameter, with an increase of up to 78.5% (Table S2).
## 4 Optimization Process
### 3D Optimization Stage
#### 4.4.3 Cost Function  
Once the control points and the deformation function were implemented, the next step was the definition of the cost function to be optimized. Traditionally, the adjoint optimization processes are focused on one single parameter. Nevertheless, the high restrictions derived from the correct performance of the compressor generated the need to consider other main variables whose evolution may be inversely proportional to the pressure drop optimization. Because of that, in the present paper, an algorithm is proposed where a specific weight is imposed to each one of the main variables. By modifying this weight, the limits of the range in which each variable can oscillate are set.  
Starting from the values coming from the 2D optimization in its 3D configuration, after setting the objective value of each variable, the relative weight of each of them was imposed with respect to the main variable, defined as the total pressure at the outlet. In Table 3, the weights and the possible variations are summarized. As it can also be observed in Table 3, a change of 10% of the mass flow must imply a reduction in the pressure drop of 50% to be worth it, due to the high restrictions in the nominal value of the mass flow used in the controller of the outlet, as defined in Section 3.3, whereas another value such as the uniformity index (UI), defined in Equation (13), which apparently is less restrictive in the context of achieving nominal points, did not present such resistances to the changes. The bypass ratio, defined before in Section 3.4 was also controlled, in order to ensure as much as possible our hypotheses were satisfied.  
\[\Delta P_{t}=P_{t,inlet,mixer}-P_{t,outlet,mixer}=\frac{P_{t,inlet,core}\cdot \dot{m}_{core}+P_{t,inlet,bypass}\cdot\dot{m}_{bypass}}{\dot{m}_{total}}-P_{ t,outlet,mixer} \tag{11}\]  
\[F_{cost}=\Delta P_{t}+K_{BPR}\cdot(BPR-BPR_{obj})^{2}+K_{UI}\cdot(UI_{u}-UI_{u, obj})^{2}+K_{tt}\cdot(\dot{m}-\dot{m}_{obj})^{2} \tag{12}\]  
being  
\[UI_{u}=1-\frac{\sum_{f}^{n}\left|u_{f}-\overline{u}\right|A_{f}}{2|u|\sum_{f}^ {n}A_{f}} \tag{13}\]  
Figure 10: Control points of the adjoint method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Weight** & **Obj. Value** & \(\Delta\)**Variable** & \(\Delta\)**Pressure** \\ \hline \(BPR(-)\) & 1120 & 2.66 & 0.1 & 0.3 \\ \(\dot{m}\) (kg/s) & 130 & 5.92 & 0.1 & 0.5 \\ \(UI_{u}(-)\) & 3224 & 1 & 0.1 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Weights of the cost function.
## 4 Results
### Multimodal-Based DL Experiments  
To ensure a comprehensive evaluation while maintaining data distribution integrity, we implemented two validation protocols: holdout validation and stratified 5-fold cross-validation. In holdout validation, the dataset was randomly divided into training (80%) and testing (20%) subsets. Preprocessing and evaluation followed the same procedures described in the methodology section.  
The result of the holdout validation is shown in Table 8. The hybrid-level fusion model achieved the highest accuracy (0.9958) and F1-score (0.9958), making it the most balanced  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **FAR** & **FRR** & **EER** \\ \hline MobileNet & 0.9747 & 0.9751 & 0.9747 & 0.9747 & 0.9734 & 0.0350 & 0.5042 \\ DenseNet121 & 0.9557 & 0.9596 & 0.9557 & 0.9545 & 0.0003 & 0.0542 & 0.0272 \\ InceptionV3 & 0.9241 & 0.9291 & 0.9241 & 0.9226 & 0.0005 & 0.0894 & 0.0449 \\ VGG16 & 0.8601 & 0.8680 & 0.8601 & 0.8559 & 0.0010 & 0.1654 & 0.0832 \\ VGG19 & 0.8544 & 0.8649 & 0.8544 & 0.8475 & 0.0100 & 0.1790 & 0.0945 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Unimodal FP performance using lightweight DL models.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **FAR** & **FRR** & **EER** \\ \hline MobileNet & 0.9754 & 0.9779 & 0.9754 & 0.9740 & 0.0002 & 0.0291 & 0.0146 \\ DenseNet121 & 0.9662 & 0.9699 & 0.9662 & 0.9654 & 0.0002 & 0.0438 & 0.2200 \\ InceptionV3 & 0.9079 & 0.9180 & 0.9079 & 0.9055 & 0.0007 & 0.1052 & 0.0529 \\ VGG16 & 0.5914 & 0.6182 & 0.5914 & 0.5743 & 0.0029 & 0.4242 & 0.2135 \\ VGG19 & 0.6582 & 0.6661 & 0.6582 & 0.6388 & 0.0025 & 0.3655 & 0.1840 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Unimodal FV performance using lightweight DL models.  
and accurate among the evaluated strategies. The score-level fusion model achieved the lowest FRR (0.0127) and EER (0.0094), indicating strong reliability in recognizing genuine users. In contrast, the feature-level model, while slightly less accurate (0.9705), recorded the lowest FAR (0.0254), demonstrating stronger resistance to impostors.  
Table 8 shows that score-level fusion yields the lowest FRR (0.0127) by allowing flexible decision-making based on individual modality scores, which minimizes incorrect rejections of genuine users. However, this flexibility can lead to a higher FAR of 0.0062 as the system may become sensitive to inconsistent scores or noise. In contrast, feature-level fusion achieves a lower FAR (0.0254) and greater resistance to impostors, enhancing robustness but slightly increasing FRR (0.0391) due to stricter matching. These results highlight a trade-off between user acceptance and error control as the score-level fusion enhances user acceptance and convenience, while feature-level fusion favors tighter error control, and the optimal choice depends on the security and usability requirements of the deployment context.
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 3 Understanding Large Language Models
### Capabilities Relevant to Maritime Safety  
The capabilities of LLMs hold significant potential for addressing challenges in maritime safety. One key application lies in natural language processing tasks relevant to maritime operations. LLMs can analyze and synthesize information from vast amounts of unstructured text data, such as safety manuals, incident reports, maintenance logs, and regulatory documents. By extracting insights and identifying patterns, they can aid in risk assessment and the development of preventative strategies [96; 97].  
Multilingual understanding is particularly pertinent in the maritime context, where crews are often composed of individuals from diverse linguistic backgrounds [98]. LLMs possess robust translation capabilities [99], enabling real-time interpretation and communication across languages. This can mitigate misunderstandings and enhance coordination among crew members, thereby reducing the likelihood of errors attributable to language barriers.  
Furthermore, LLMs can assist in automating the generation of reports [100] and documentation [101; 102; 103], ensuring consistency and compliance with international standards and regulations. They can provide interactive support for crew training and education by generating scenarios, answering queries, and explaining complex concepts in accessible language. In emergency situations, LLMs could offer real-time guidance based on standard operating procedures and best practices.  
The ability of LLMs to process and understand regulatory texts [104] can also facilitate compliance by highlighting relevant regulations and summarizing changes in legislation. By integrating LLMs into decision support systems, maritime operators can enhance situational awareness and make more informed decisions that prioritize safety.
## 3 EHO-Based Optimization Algorithms
### Alpha Tuning of aEHO  
Careful investigation of EHO parameters recommends setting the scale factor \(\alpha\) to be adaptive is more promising than being a constant value in the range [0, 1].  
Putting it simply, making alpha adaptive and related to the population number is more convenient and matched to the notion of evolution in Equation (11). In the original EHO algorithm, the scale factor-alpha is a constant value. Now, \(\alpha\) is varying with the generation number by this function:  
\[\alpha_{new}=\alpha+\frac{\alpha_{\max}-\alpha_{\min}}{n}. \tag{11}\]
## 2 Design Goals and Procedure
From top-left to bottom-right: 3D view from the top, lateral view of the device, top view of the device, ground side of the microstrip layer, and line view of the microstrip layer. Main dimensions: \(L_{s}=120\) mm, \(W_{s}=110\) mm, \(L_{p}=52.586\) mm, \(W_{p}=62.457\) mm, \(d_{rad}=3\) mm, and \(h_{air}=8.13\) mm. Color legend: white is the substrate, dark yellow/brown is copper, and background gray is air. The feeding of the patch is made with a coaxial probe that has been implemented as a pin rod connecting the microstrip line with the patch through the ground plane by using the through-wire technique stated in [23]. With the aim of using the pin rod as the holding post of the patch, and considering the size of the patch, a 3 mm diameter metallic rod has been selected to ensure the robustness and stability of the device. To make the manufacturing process easier, the values of the pads and gaps necessary for the through-wire technique have been forced to the values of 1 mm for the gap in the ground plane and 2 mm for the pads around the metallic rod. As the rod must trespass the microstrip layer, a through-hole must be made; that hole needs to be metalized (with 9 um of copper electrodeposition) to achieve good connectivity with the rod, so that procedure needs to be completed before printing the microstrip line. The insertion position of the pin rod into the patch is critical. According to the considerations stated in Chapter 21.6 of [26], the insertion position for a linearly polarized antenna should be at the middle of one of the radiating edges and separated a distance \(\Delta x\) from the edge toward the center of the patch. It is well-known that the impedance strongly varies along the antenna, and, furthermore, the impedance and bandwidth are dependent on the ratio \(W_{p}/L_{p}\) because it changes the quantity of energy stored in the cavity formed by the patch and the ground plane. Consequently, it is stated in [26] that the precise insertion position is very sensitive to \(L_{p}\) and \(\Delta x\) and only initial approximations can be given that must later be adjusted empirically through a simulator. Taking the former into account and considering that the air is the dielectric of the patch, the initial position of the insertion pin rod is estimated by \(\Delta x\approx 0.25L_{p}\). This device has been simulated (including losses), and the values of \(\Delta x\) and \(\Delta L\) (and therefore \(L_{p}\)) have been optimized for adjustment of the insertion position of the pin rod that maximizes the radiation at 2.4 GHz. The optimum point indicates that the antenna has a bandwidth of 10.46% around 2.4 GHz (according to the band with \(S_{11}<-10\) dB), with a directivity of 9.95 dBi and a total efficiency of 99.78%; those values are quite promising, as they reflect the high-performance indexes of the bandwidth, efficiency, and gain at the same time for just a single radiating element. The final dimensions of the optimized device are those in Table 1.
# Handover Management for Drones in Future Mobile Networks--A Survey
## 4 Handover Management for Drone Networks
### Handover Decision Algorithms  
A variety of handover decision-making algorithms are used in cellular networks, such as RSRP, Received Signal Strength Indicator (RSSI) of the Serving Base Station (S-BS), the Signal-to-Interference-Plus-Noise Ratio (SINR), mobile movement speed, distance between the UE and BS, limited capacity of BSs, weight functions, cost functions, fuzzy logic control, and machine with deep learning technology. The same handover decision algorithms can be used with drones, but the performance will differ due to the different characterization of drones [63; 64; 65; 66; 67; 68; 69; 70]. Moreover, the requirements of 6G technology will be ultra-high compared to those of the previous mobile systems. This also creates the need for more robust, efficient, dynamic, and smart handover decision algorithms for drones' networks. Several studies have been conducted in the literature that deal with this matter.  
For example, the authors in [67] created a method for establishing drone connectivity with IoT. The model architecture consists of two main nodes: the sensor node and the data processing node. Two different modes of communication are utilized: Wi-Fi and satellite communication. The handoff was performed based on several parameters: network accessibility, RSSI, QoS, cost of data transmission, and network performance. If one of the previous criteria indicated that the Wi-Fi interface is not the optimal choice, vertical handover is performed to switch to the satellite communication mode. If neither of the interfaces correctly operate, buffering is then performed to avoid packet loss until one of the interfaces becomes available.  
The authors in [23] investigated a method that analyzes the impact of heterogeneous movement Device-to-Device (D2D) drone-supported Mission-Critical Machine-Type Communication (mcMTC) in 5G. Due to the rapid increase in the use of IoT systems, mcMTC's role has become extremely significant. Therefore, fulfilling these extensive requirements is necessary. The paper examined the influence of various movement patterns on heterogeneous users. The study verified that, as long as alternative connectivity options are in use, availability will increase. The WINTERsim simulator was applied for the evaluation.  
The impact of a heterogeneous device's movement is based on the multi-connectivity options, which introduce three measured cases: vehicular connection, manufacturing automation, and city communications. The UEs included in the multi-connectivity system can utilize D2D, cellular, and drone-supported connections. Ref. [68] proved that low and limited mobility of the device has no effect on the connection availability and reliability. Since the packet sizes are diverse, the use of D2D-assisted communications and drones greatly enhances reliability and data rates. In contrast, performance degradation was detected for cases where movement was high.
## 3 Results and Discussion
### Streamflow Simulation Scenarios and Results
#### 3.3.1 Scenario I: Hydrological Model Calibrated by Gauge Observations
Furthermore, it can be found that the uncorrected products exhibited greater error propagation at the outlet of the catchment [72]. Moreover, the growing magnitude of floods diminished the effect of model error as the drainage area increased [73]. As a result, the bigger streamflow magnitudes generally tended to exhibit better hydrological model performance in the Beijing River Basin (with its larger basin area). Additionally, the best performance of the selected eleven precipitation products for hydrological modeling was different over the Beijing and Dongjiang River Basins.
# Cooperative Reception of Multiple Satellite Downlinks
###### Abstract
The quality of service (QoS) at mobile devices when receiving signals from satellites was investigated and tested for service continuity in [31] under two scenarios, with and without multi connectivity to 5G cellular networks. The multi-connectivity to a satellite and to cellular networks has ensured a reliable and continuous service at the mobile receiver, the investigation revealed. Nowadays, the number of Internet of things (IoT) applications is exponentially growing, and hence recent research efforts have been more focused on maintaining reliable services through satellite communications [32]. In this context, this article proposes a receive diversity scheme based on the maximum likelihood with suggested SP algorithms as a basic study for the future implementation of omnidirectional ground stations network with continuous ad-hoc allocation of receiving sites along satellite trajectories to virtual ground stations. Multiple copies of the same transmitted signal are received by multiple geographically separated receivers. These multiple receivers cooperatively share these uncorrelated received versions of the transmitted signal with a virtual ground station to help detect the message signal more reliably. Doing so, even when one or more receiving sites are heavily affected by noise or other disturbances, there is still a possibility to retrieve the original signal from other cooperative sites with lower impairment levels. Hence, the outage probability of the whole system is significantly decreased. Therefore, the suggested network contributes mainly to the improvement of the small satellites' downlink reception by reducing the BER, tracking more than a satellite at once, extending the communication window, and providing resistance against outages,  
This paper is organized as follows: Section 2 describes the conventional single radio system and moves to the intended SIMO configuration with the suggested system model. The findings are presented and discussed in Section 3. Finally, Section 4 draws a brief conclusion.
# Invisible Shield: Unveiling an Efficient Watermarking Solution for Medical Imaging Security
## 4 Results  
The suggested model is evaluated using a diverse range of medical images, encompassing X-ray, ultrasound (US), magnetic resonance imaging (MRI), and computed tomography (CT). These images, each with dimensions of 1024 \(\times\) 1024 pixels, are sourced from the MedPixTM Medical Image Database [33]. All test medical images in the evaluation are grayscale images of 8-bit depth. The watermarking process in the algorithm is anticipated to be both efficient and robust, ensuring minimal distortion to the medical images while maintaining the watermark's integrity against various image processing operations. This dual focus on efficiency and robustness is essential for preserving the diagnostic quality of the images while securing patient data.