esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Results
### Simulation Results and Analysis
#### 4.2.2 Case 2: Second-Best Location for FEVCS  
The second-best location of the FEVCS is estimated to be at node 22. In Case 2, FEVCSs at nodes 22 and 25 act as energy storage for the grid; compared with Case 1, one more node is active in the network, reducing the VOLL of Case 1. The mobility of the fleet vehicles is presented in Table 2. As shown in the Figure 10, two master DGs form microgrids MG2 and MG3 to energize the active grid sections. While the generators PV2 and PV3 act as master DGs, GT4 follows the generator PV3. Moreover, both FEVCSs support the microgrids in moving energy from an energy surplus microgrid to an energy deficient microgrid. The fleet vehicle EV-1 is not used for restoration during 14:00-16:00 h; instead, the vehicle is located at node 3 of the transportation network, which overlaps with the distribution network at node 25. From Figure 11, EV-1 is used from 18:00 to 21:00 h, during which the storage of EV-1 reduces to the minimum value. Mobility is used by the model for vehicles EV-2 and EV-3. The demand to upkeep microgrid 3, controlled by the DG at node 25, is higher than the demand for microgrid 2, controlled by node 22. Therefore, the model uses EV-3 at node 25 for 16:00 to 17:00 h and then sends EV-3 to node 22 because the available storage cannot provide sufficient energy to node 25. Then EV-2, which has higher storage, is used to energize node 25 to provide sufficient energy to manage the supply-demand balance of the presented configurations.  
Figure 8: Generation load balance in microgrid 3 with optimal EVCS in Case 1.  
Figure 9: Variation in SOC and charging power of fleet EVs in Case 1.
# Synthesis of Mesoporous MWCNT/HKUST-1 Composite for Wastewater Treatment
## 2 Experimental Procedures
### Adsorption Experiment  
The adsorption experiment was carried out on methylene blue (MB), a commonly used organic pollutant in industry [12]. A stock solution of methylene blue (1000 mg/L) was prepared by dissolving 1 g of methylene blue in 1 L of distilled water. The solution pH was adjusted with 0.1 M HCL An adsorbent dosage of 0.015 g was treated with 25 mL of 20 ppm of MB solution and placed inside a thermostatic bath shaker for 24 h. After the experiments, the amount of MB adsorbed was monitored through UV-VIS spectroscopy.
## 5 Experimentation, Results, and Analysis
### Analysis
#### 5.4.1 Quantitative Analysis
* **Custom Loss Weights:** The third subplot reveals that the model is sensitive to the weights used in the custom loss function. The CES reaches its maximum value at a weight of 0.7, emphasizing the importance of finely tuning this hyperparameter for optimal performance. The sensitivity analysis provides valuable insights into the robustness and sensitivity of the DCAT model to different hyperparameters, validating our choices and underlining the areas that require careful tuning for achieving superior performance. Overall, the quantitative analysis corroborates the model's robustness and superiority in generating high-quality, contextually rich captions. It excels not just in standard metrics like BLEU and METEOR but also in our custom metric, CA, designed to evaluate contextual accuracy.
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 1 Introduction
The omnidirectional aspect of the antenna is then maintained and the surrounding UAVs will have nearly the same signal strength if the distances are equal;
* A channel based on Rice propagation model using line-of-sight (LOS) was analyzed by simulation employing a synthetic signal associated with XBee devices to define how farther one aircraft can stay away from another while maintaining minimum QoS, that is, to ensure that the signal strength at reception has a power exceeding \(-100\) dBm;
* Tests were performed in a controlled environment to transmit images between the multi-UAV network and the base station, where the image is split into multiple packets. To simulate the tests, XBee sensors embedded in the UAV model DJI Phantom 3 are employed. A preliminary version of this work was published as a conference paper in [34]. The present extended version of this study includes the area scan strategies detailed as follows. In order to compare them properly and define an appropriate architecture for the application, an analysis of possible network characteristics for multi-UAV systems is carried out. A detailed description of a FANET developed for a multi-UAV system can then be applied to the detection of intruder boats in the predicted impact area of suborbital rockets launched from the CLBI. In this context, the scanning procedure can be performed in an efficient and reliable manner, thus reducing cost and time. The remainder of this paper is organized as follows. Section 2 presents the main characteristics of multi-UAV communication systems. The scanning strategies are presented and discussed in Section 3. Section 4 describes the multi-UAV system, the image processing system, as well as the proposed network architecture for the addressed application in terms of the software and hardware required to support the network. The scenarios for which the tests are carried out are described in Section 5. Section 6 presents the results of tests performed on a data transmission network with ZigBee devices. The main conclusions are given in Section 7.
# Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs
## 4 Linear Optimization  
_Linear Optimization_ is a mathematical optimization model widely used to gain the best possible result. It consists of an objective function in which we formulate problem with the aim of minimizing or maximizing the result under a set of linear constraints.  
In this section, two linear problems are formulated, the first one is to minimize the energy consumption and the second one is to maximize the throughput of the network.
## 3 Results
### Cyclogenesis Parameters Such as RH, VWS, Vorticity, and PI
The favorable VWS between 0 and 5 m/s is very favorable and ideal for tropical cyclone formation. Overall, the average value of VWS for the recent decade is around 0.7 m/s, contributing to circulation and increasing moisture from the AS to the western coast of India (Figure S2). Figure 6: The same as Figure 5, except for vertical wind shear (shaded; ms\({}^{-1}\)) at 850–200 hPa levels derived from ERA5 reanalysis at a 25 km resolution. Figure 7: The same as Figure 5, except for a low-level relative vorticity anomaly (\(\times 10^{6}\) S\({}^{-1}\)) at the 850 hPa level derived from ERA5 reanalysis at a 25 km resolution. These trends highlight the growing potential for TC formation in the AS region during 1992-2021. Time series of vorticity, VWS, and MRH are depicted in Figure 9. These trends  
Figure 8: The same as Figure 5, except for potential instability (PI) anomaly (K) for three decades derived from ERA5 reanalysis at a 25 km resolution. Figure 9: Time series of (**a**) mean relative humidity, (**b**) vertical wind shear, (**c**) 850 hPa relative vorticity, and (**d**) potential intensity anomaly over the Arabian Sea during the OND season during 1992–2021 derived from ERA5 reanalysis. reinforce the hypothesis that decadal variability in these key parameters has driven the recent rise in TC activity in the AS. The potential instability (PI) is one of the TC genesis parameters depicted in Figure 8. In recent decades (2012-2021), the central AS showed higher potential instability than the previous two decades (1992-2001) and (2002-2011). The westward shift of warming from the South China Sea has been clearly evident in recent decades, as shown in Figure 8c. Figure 9 shows the interannual variations and trends of the fundamental variables required for the formation and then sustenance of the tropical cyclones over the Arabian Sea. In general, the wind shear and moisture are quite high during the summer monsoon, which decreases substantially after the post-monsoon season.
# Static Strength and Buckling Analysis of an Aircraft Support
## 2 Methods  
Structural strength design [7] is one of the most important parts of aircraft structure design [8]. The strength analysis, in this paper, includes both numerical calculation and strength test to verify whether the strength requirements are met. Structural strength test is an indispensable part of aircraft development. On the basis of HB 7713-2002 general requirements of static strength test for aircraft structure [9], we used the design load of 30.151 KN as the strength test load. In the strength test, it is necessary to reasonably select the loading mode of the test load, which should be the same as the real loading state of the aircraft [10]. The research object of this paper is the load-bearing assembly connected by the fuselage. Due to the complex geometry and loading conditions, the test fixture needs to be designed specifically.  
As shown in Figure 1a, yellow frame area is the main bearing surface and the main loading area for strength analysis and buckling analysis. Connection of the support is shown in Figure 1b, where white area in the middle is a fixed connection, and the green area is an adjacent connector. In the strength test, considering the force of the connection in the aircraft, these two areas are considered as the fixed ends of the strength test. Yellow and blue areas are the loading parts of the support, which are considered as the loading end of the test fixture.  
In this paper, buckling analysis is used to study the structural stability of bearing parts. The purpose of buckling analysis is to determine the critical load when the structure changes from a stable state to an unstable state. According to the size of the deformation state before instability, the linear analysis or nonlinear analysis before instability is used. In this paper, the eigenvalue method is used to analyze the linear eigenvalue buckling, and then, the first order critical load obtained by linear buckling is selected as the load reference value of nonlinear buckling analysis. Considering the case of geometric imperfections, 1% and 10% of the initial imperfection values are used, and the Riks arc length method is used to analyze the nonlinear buckling without imperfection and with initial imperfection, respectively. Based on the results of strength analysis and buckling analysis, this paper discusses its strength and instability to obtain its structural strength and structural stability, which can provide a basis for studying its bearing capacity and structural optimization design. The main process is shown in Figure 2.  
Figure 1: Connections on the fuselage. (**a**) Main loading area, (**b**) Connection of the support.
# Efficient Melody Extraction Based on Extreme Learning Machine
## 4 Experimental Results and Discussions
### Parameter Setting  
There are some parameters that needed to be set before evaluation. As mentioned before, the musical signals are down-sampled to reduce the data quantity and accelerate the processing speed. In this work, the mixtures were re-sampled to 16 kHz. The MATLAB CQT toolbox implemented by [PERSON] et al. was utilized herein for multi-resolution spectral analysis [24]. The spectral analysis range was [0, 8 kHz]. As the frequency tolerance is half semitone range of the ground truth, there are 12 semitones per octave, hence the CQT bins are geometrically spaced with 36 bins per octave, enough to satisfy the tolerance. The melody pitches were experientially set, ranging from 110 Hz to 1000 Hz. There were 40 notes with one semitone interval within this range. Hence, the ELM output one-hot vector was of dimension 41, since the unvoiced frames are also assigned with one pattern. The search radius \(\delta\) of fine-tuning was set to be 2/3 semitones (i.e., two bins). If \(\delta\) is set greater than 2/3 semitones, the fine-tuned pitch might shift to other notes rather than the coarse one, and if it is set less than 2/3 semitones, i.e., 1/3 semitone, the searching range does not cover the frequency tolerance. Moreover, the tiny margin between 2/3 semitones and tolerance can help track occasional singing glides. To make use of contextual information, the input feature vectors of the ELM cover 7 adjacent frames centered at the current frame.  
Except for the aforementioned parameters, the hidden neuron number of ELM also need to be set. In this work, we evaluated the training accuracy, validation accuracy, training time, and validation time when the neuron number ranged from 1000 to 7000. The experimental results are given in Table 2.  
It can be seen from Table 2 that training accuracy grows with the increase of the hidden neuron number. This phenomenon might because more complicated non-linear mapping functions can be approximated with more neuron numbers. However, the validation accuracy first grows then declines slightly with the increase of the neuron number. This observation reveals that the ELM network suffers from over-fitting to a small extent. As far as the processing times are concerned, the training time was prolonged dramatically with the increase of a hidden neuron number and the validation time was also extended approximately linearly. Considering both accuracies and time efficiencies, the hidden neuron number was set as 5000 in the following experiments.
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 5 Enhancing Safety Through Large Language Models
### Compliance and Regulatory Support
#### 5.2.2 Audit Preparation and Support  
Compliance audits are a routine aspect of maritime operations, requiring meticulous preparation and extensive documentation. LLMs can streamline audit preparation by assisting in the compilation of necessary documents [153], such as certificates, logs, and procedural manuals. By searching and organizing information from various sources, the models can assemble comprehensive audit packages efficiently.  
Additionally, LLMs can identify areas requiring attention before audits by analyzing previous audit reports, compliance records, and operational data. The models can generate checklists or summaries of potential non-compliance issues, enabling organizations to address them proactively. This support not only enhances the effectiveness of audits but also reinforces a culture of continuous improvement and adherence to safety standards.
# Quantum Image Steganography Schemes for Data Hiding: A Survey
## 3 Quantum Image Steganography Schemes
### Inverted Pattern Approach  
Inspired by the classical inverted pattern approach by [PERSON] et al. [30], recent research for applying quantum steganography aimed to achieve excellent performances in embedding capacity and visual effects while simultaneously achieving a low computational complexity by using a quantum version of the inverted pattern scheme along with LSB [9]. The benefit of applying the inverted pattern approach [30] to a scheme is to improve the steganographic image's resolution after being degraded from applying the LSB method.  
In this scheme, [PERSON] et al. [9] first used LSB to hide the secret message content into an NQER image model by embedding each qubit into each image pixel's least significant bit. The algorithm then applies the inverted pattern approach by transforming some of the secret message pixels into an inverted qubit sequence, where it records the produced inverted pattern using an empty quantum binary image. Next, to improve the steganographic image quality, the algorithm compares the original embedded image with the inverted one and uses the most appropriate sequence out of the two to create the ideal qubit sequence and produce a better image. When extracting the secret message, the algorithm inverts the embedding process and adds it to an empty quantum image.  
Along with achieving a lower computational complexity than other quantum steganography schemes and its superiority in visual quality and capacity, [PERSON]'s approach of using the inverted pattern approach to enhance LSB is straightforward to execute.
## 2 Results
### Correlations among Main Variables  
Pearson correlation coefficients among the different variables are calculated and shown in Table 9 (vegetative stage) and 10 (flowering stage), using the SAS software. In the vegetative stage, a highly positive correlation (above 0.9) is seen between SC and RC, BAF and RC, and BAF and SC. The TF\({}_{\rm veg}\) is poorly-correlated with SC, RC and BAF, indicating that at the vegetative stage, the HMs obviously are largely concentrated in the roots. These correlations are true for both common bean and faba bean at the vegetative stage. However, at the flowering stage, medium, positive and negative correlations were respectively seen between the varieties (common bean or faba bean) on the one hand, and RC, FIC and BAF on the other. Nevertheless, the translocation factor for flowers has a highly-positive correlation with both the HM concentration in the flowers (0.87**) and TF\({}_{\rm shoots}\) (0.78**) (Table 9). The correlation coefficients and the results tabulated in the earlier tables clearly show that common bean and faba bean behave very differently when it comes to translocation of the HMs from roots and shoots to flowers. At the pod stage (fruiting), BAF is positively and significantly correlated with variety (0.70**) and RC (0.78**). (BAF and RC (0.77**)) and (TF\({}_{\rm B}\) * Fl C (0.87**)) are two other highly-positive correlations observed (Table 10). All this leads to inference that _Vicia faba_ is a hyper-accumulator of HMs at the fruiting stage (when there is a greater transfer of HMs from below the ground to the aerial parts of the plants). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Species** & **Treatment** & **RC (ppm)** & **SC (ppm)** & **PC (ppm)** \\ \hline \multirow{4}{*}{Common bean} & Control & 0.34 \({}^{\rm d}\)\(\pm\) 0.02 & 0.02 \({}^{\rm d}\)\(\pm\) 0.0003 & 0.005 \({}^{\rm d}\)\(\pm\) 0.0003 \\  & Lead & 8.63 \({}^{\rm b}\)\(\pm\) 1.45 & 3.04 \({}^{\rm b}\)\(\pm\) 0.72 & 0.52 \({}^{\rm c}\)\(\pm\) 0.04 \\  & Copper & 9.96 \({}^{\rm a}\)\(\pm\) 1.56 & 13.17 \({}^{\rm a}\)\(\pm\) 1.86 & 9.48 \({}^{\rm a}\)\(\pm\) 1.51 \\  & Cadmium & 0.65 \({}^{\rm c}\)\(\pm\) 0.07 & 0.60 \({}^{\rm c}\)\(\pm\) 0.05 & 0.73 \({}^{\rm b}\)\(\pm\) 0.08 \\ \hline \multirow{4}{*}{Faba bean} & Control & 0.34 \({}^{\rm d}\)\(\pm\) 0.03 & 0.02 \({}^{\rm c}\)\(\pm\) 0.0003 & 0.005 \({}^{\rm d}\)\(\pm\) 0.0004 \\  & Lead & 25.07 \({}^{\rm a}\)\(\pm\) 2.05 & 3.04 \({}^{\rm b}\)\(\pm\) 0.42 & 2.87 \({}^{\rm b}\)\(\pm\) 0.28 \\ \cline{1-1}  & Copper & 14.34 \({}^{\rm b}\)\(\pm\) 1.18 & 5.25 \({}^{\rm a}\)\(\pm\) 0.57 & 27.04 \({}^{\rm a}\)\(\pm\) 2.19 \\ \cline{1-1}  & Cadmium & 5.84 \({}^{\rm c}\)\(\pm\) 0.89 & 3.58 \({}^{\rm b}\)\(\pm\) 0.38 & 1.44 \({}^{\rm c}\)\(\pm\) 0.17 \\ \hline \hline \end{tabular} RC: root concentration (\({}^{\rm a,b,c,d}\)); SC: shoot concentration (\({}^{\rm a,b,c,d}\)); PC: pod concentration (\({}^{\rm a,b,c,d}\)).  
\end{table} 
Table 7: The HM concentrations in roots, shoots and pods at the pods-stage.
# Error Performance of Amplitude Shift Keying-Type Asymmetric Quantum Communication Systems
## 5 Error Performance  
In this section, we present the results that were obtained numerically when using the error probability (15) for the ACC system and the analytical expression for the error probabilities (63) and (66) for the AQC system when using the quasi-Bell states. Whenusing the TSVS, the calculation of the error probability \(P_{\text{e}}^{\text{(TSVS)}}\) in the case in which the average number of photons is small (\(\langle n\rangle_{\text{TSVS}}\leq 5\)) is performed based on the conventional numerical calculation method, i.e., using an equation that approximates Equation (13) to a finite value of \(n\). In other words, as described in references [32; 33], the calculation should be performed after suitable truncation of the Hilbert space by taking both the average number of photons and the order of the error probability into consideration. However, in the case where the average number of photons is large (i.e., \(\langle n\rangle_{\text{TSVS}}>5\)), it is difficult to treat the eigenvalue problem in Equation (22) numerically because the dimensions of the received quantum state (i.e., its density operator) are large. Therefore, the AQC system's performance is evaluated using the upper bound \(P_{\text{UB}}^{\text{(TSVS)}}\) and the lower bound \(P_{\text{LB}}^{\text{(TSVS)}}\) on the error probability \(P_{\text{e}}^{\text{(TSVS)}}\) given in reference [9] when the TSVS is used, as follows:  
\[P_{\text{LB}}^{\text{(TSVS)}} =\frac{1}{2}\Bigg{[}1-\sqrt{1-\left\{\left(1-\sqrt{R_{0}R_{1}}- \sqrt{(1-R_{0})(1-R_{1})}\right)N_{\text{S}}+1\right\}^{-2}}\Bigg{]}, \tag{70}\] \[P_{\text{UB}}^{\text{(TSVS)}} =\begin{cases}\frac{1}{2}\{\left(1-\sqrt{R_{0}}\right)N_{\text{S }}+1\}^{-2}&(R_{1}=1)\\ \frac{1}{2}(\Sigma\sigma^{z}-\Theta\bar{\sigma})^{-1}&(R_{1}\
eq 1)^{\prime} \end{cases}, \tag{71}\]  
where  
\[\sigma =\frac{(1-R_{0})N_{\text{S}}+1}{(1-R_{1})N_{\text{S}}+1}, \tag{72}\] \[\theta =\frac{1-R_{0}}{1-R_{1}},\] (73) \[\Sigma =\frac{\left\{\left(1-\sqrt{R_{0}R_{1}}\right)N_{\text{S}}+1 \right\}^{2}}{(1-R_{0})N_{\text{S}}+1},\] (74) \[\Theta =(1-R_{1})N_{\text{S}},\] (75) \[z =\begin{cases}0&\left((\Sigma-\Theta)^{-2}(\ln\theta^{\Theta}- \ln\sigma^{\Sigma})\geq 0\right)\\ 1&\left((\Sigma-\Theta)^{-2}(\ln\theta^{\Theta}-\ln\sigma^{\Sigma})<0\right) \end{cases}. \tag{76}\]  
This paper also provides a comparison with the universal lower bound \(P_{\text{U-LB}}\) on error probability given in reference [9] when the use of any multimode pure input state is permitted, as follows:  
\[P_{\text{U-LB}}=\frac{1}{2}\Bigg{\{}1-\sqrt{1-\left(\sqrt{R_{0}R_{1}}+\sqrt{ (1-R_{0})(1-R_{1})}\right)^{2N_{\text{S}}^{\prime}}}\Bigg{\}}, \tag{77}\]  
where \(\langle n\rangle_{\text{U-LB}}=N_{\text{S}}^{\prime}\) is the average number of photons in mode S.