esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Validations for Ship Stiffened Panel Strain Reconstruction
### Fiber Optic Sensor Layout Based on Equivalent Neutral Layer
In the context of ship-structure-condition monitoring, optical fiber sensors, sensitive only to temperature but not force, could be strategically placed near measuring points for synchronous temperature compensation. This mitigated the influence of temperature on the measured strain data [27; 28]. In the experimental validation process of the proposed strain field reconstruction method in this study, strain data from several measurement points on the inner side of the stiffened ship panels were collected using fiber optic sensors. These collected data served as the measured strain input required for the reconstruction method. Based on this, the computed reconstructed strain data for verification points on the outer side of the ship stiffened panels, as depicted in Figure 16b, were obtained. Subsequently, a comparative analysis was conducted between this reconstructed data and the measured strain data from another fiber optic sensor attached to the outer side of the ship stiffened panels. This comparison aimed to assess the reconstruction accuracy of the method proposed in this paper.
## 6 Experiments
### Experimental Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **Classifier** & **Acc \(\pm\) Std (\%)** & **F1-Score (\(\pm\)Std) (\%)** \\ \hline \multirow{3}{*}{EfficientNet + Laser} & **SVM** & **86.75 \(\pm\) 2.45** & **87.42 \(\pm\) 2.27** \\  & RF & 80.91 \(\pm\) 2.74 & 79.37 \(\pm\) 2.65 \\  & MLP & 86.10 \(\pm\) 2.31 & 86.10 \(\pm\) 2.19 \\ \hline \multirow{3}{*}{ConvNeXt + Laser} & **SVM** & **88.31 \(\pm\) 2.93** & **87.35 \(\pm\) 2.78** \\  & RF & 78.96 \(\pm\) 4.49 & 76.01 \(\pm\) 4.33 \\  & MLP & 87.53 \(\pm\) 4.20 & 86.72 \(\pm\) 4.07 \\ \hline \multirow{3}{*}{MobileNet + Laser} & SVM & 83.25 \(\pm\) 3.37 & 80.61 \(\pm\) 3.19 \\  & RF & 77.53 \(\pm\) 5.59 & 77.30 \(\pm\) 5.43 \\ \cline{1-1}  & **MLP** & **85.71 \(\pm\) 2.36** & **85.72 \(\pm\) 2.21** \\ \hline \multirow{3}{*}{ResNet50 + Laser} & SVM & 85.71 \(\pm\) 2.72 & 83.25 \(\pm\) 2.59 \\ \cline{1-1}  & RF & 81.30 \(\pm\) 4.56 & 79.05 \(\pm\) 4.38 \\ \cline{1-1}  & **MLP** & **88.44 \(\pm\) 1.26** & **84.92 \(\pm\) 1.17** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of classifiers using fused visual and laser features. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **Classifier** & **Acc \(\pm\) Std (\%)** & **F1-Score (\(\pm\)Std) (\%)** \\ \hline \multirow{3}{*}{EfficientNet} & SVM & 83.12 \(\pm\) 2.82 & 83.77 \(\pm\) 2.91 \\  & **RF** & **83.38 \(\pm\) 1.34** & **84.11 \(\pm\) 1.28** \\  & MLP & 81.43 \(\pm\) 1.46 & 81.64 \(\pm\) 1.53 \\ \hline \multirow{3}{*}{ConvNeXt} & **SVM** & **85.97 \(\pm\) 2.74** & **84.56 \(\pm\) 2.65** \\  & RF & 82.47 \(\pm\) 3.94 & 78.87 \(\pm\) 3.79 \\  & MLP & 86.49 \(\pm\) 1.50 & 84.02 \(\pm\) 1.43 \\ \hline \multirow{3}{*}{MobileNet} & **SVM** & **82.99 \(\pm\) 3.57** & **80.20 \(\pm\) 3.34** \\  & RF & 78.44 \(\pm\) 3.45 & 78.00 \(\pm\) 3.28 \\ \cline{1-1}  & MLP & 82.08 \(\pm\) 1.20 & 79.90 \(\pm\) 1.14 \\ \hline \multirow{3}{*}{ResNet50} & SVM & 83.90 \(\pm\) 1.99 & 82.12 \(\pm\) 1.87 \\ \cline{1-1}  & RF & 82.08 \(\pm\) 2.48 & 79.93 \(\pm\) 2.35 \\ \cline{1-1}  & **MLP** & **84.29 \(\pm\) 2.77** & **83.22 \(\pm\) 2.64** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification performance using visual features extracted from CNN models. and F1-scores, with the lowest F1-score of 82.07% recorded by the EfficientNet + SVM model. These results suggest that discriminative features related to fish freshness become less distinguishable over time, and the most reliable classification performance is generally obtained on the first day.
## 2 Basic Mechanisms in Plasmon-Mediated Heterostructure
### Process of Photocatalysis
#### 2.2.1 The Generation of HCs  
There are two primary processes for the generation of HCs: the excitation of SPs and conversion of photons. In the first process [41], SPs are excited on plasmonic metal with a matched incident light, and at this moment the optical cross sections of the absorption and scatter reach a maximum under an intense interaction between plasmonic metal and the external EM field. Then, the photons, coupled to the plasmonic metal, will dissipate their energy into two forms: re-emitted photons in the radiative transition and a conversion to HCs via the non-radiative transition (Figure 2a). The ratio for generating HCs is in direct proportion to the process of absorption, which means that the effect of the scatter on the re-emission of photons should be suppressed. Furthermore, the character of absorption can be adjusted by the composition, size and shape of the plasmonic metal.  
After the light absorption, there are two distinct ways for electrons to transition in plasmonic metals: interband transitions and intraband transitions [42; 43], which are the main components of HCs (Figure 2b). In the interband transitions, electrons are excited from the filled d band below the E\({}_{\mathrm{F}}\) to empty the sp band above the E\({}_{\mathrm{F}}\), and they are insensitive to the shape and size of the metal, as well as to the polarization of the EM field. These transitions are dependent on the location of the d state, which is usually related to the component of the plasmonic metal. For example, the electrons from the d band of gold and copper can be excited by the energy of photons in the visible range, in contrast to silver with a deeper location of the d band [44]. In another electronic transition, the intraband transition, electrons come from the filled s band near the E\({}_{\mathrm{F}}\) to empty the sp band above the E\({}_{\mathrm{F}}\), and they cannot  
Figure 1: Process of the formation of the Schottky contact. (**a**) The band diagram before coming into contact with the metal and n-type semiconductor. (**b**) The flow of electrons from the n-type semiconductor to the metal and (**c**) the final equilibrium attainment with the bending of the band after contact.  
be excited directly, which results from the non-conservation of momentum. Furthermore, they need an extra momentum to be excited effectively, such as SPs [45]. These plasmon-mediated HCs are more energetic, and their behaviors can be adjusted by designing a specific morphology of the plasmonic metal, especially for anisotropic structures with sharp tips. Additionally, the ways to regulate these parts of HCs are highlighted in this review.
## 5 Conclusions  
This scientific research presents an analysis of the energy needs of a ferry fleet that uses the City Port of Split as one of its ports. Energy needs are determined on the basis of equivalent fuel consumption. They are non-periodic and non-seasonal. On a daily basis, they are extremely non-periodic due to the specifics of the sailing schedule in the high season. Non-periodic energy needs create a challenge when designing energy infrastructure, as it should be designed for the most unfavorable conditions that can occur. In order to reduce the amount of peak charging power, an optimization model was created that uses the PSO and a greedy algorithm. The goal function of this optimization task is to reduce the peak charging power. Furthermore, the model was optimized in such a way that the number of particles needed to search the area, i.e., to find the global minimum, was analyzed. The number of particles is defined, at which the optimization result is improved by 13.47% compared to the initial settings. The influence of the global weighting factor on the optimization of the peak charging power was also analyzed. For example, with an inertia weight factor of 0.55 with a defined particle number of 2048, a reduction in the peak charging power of 16.2% was achieved.  
Figure 15: Optimized charging schedule when inertia weight factor is set to 0.55 while number of particles is set to 2048.  
Recommendation for future research: Based on the obtained daily profile of the energy needs of the ship fleet, it is possible to determine the available energy capacities of the ships' ESS when the ships are not sailing. These ESS capacities can be used in the \"ship-to-shore\" system to reduce the load on the shore infrastructure when charging ferries that are priority for departure. Furthermore, the time a ferry spends in port is a significant parameter that affects the amount of charging power. Therefore, it is possible to optimize peak power by influencing the available charging time. It is also necessary to conduct a sensitivity analysis of the change in charging power in relation to the available charging time in future research.  
Conceptualization: T.P., M.K., G.K. and J.S.; methodology: T.P., M.K., G.K. and J.S.; validation: T.P. and M.K. formal analysis: T.P., M.K. and J.S.; investigation: T.P.; writing--original draft preparation: T.P. and G.K. visualization: T.P., M.K. and J.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Dataset available on request from the authors.  
The authors declare no conflicts of interest.
# Underwater Acoustic Target Tracking: A Review
## 5 Classification Underwater Acoustic Target Tracking Algorithms
### Classification Based on Instrument-Assisted Method
#### 5.1.3 UWSNs
Energy consumption and tracking accuracy are considered simultaneously in [65; 66]; however, the tracking accuracy is kept first in [66]. The most difference in [67] is considering the quantized measurements. In this section, algorithms based on different tracking instruments are reviewed. These algorithms are all oriented at or devoted to solving the inherent flaws of tracking instruments. For example, UWSNs have the limited resources, therefore, considerable papers are focused on the limitation. To make readers easier to select the appropriate tracking instrument, we compare these papers in the aspect of precision, complexity, and cost of tracking based on different instruments, as well as the strength and weakness of the corresponding tracking instrument in Table 3.
## 6 Conclusions  
The main objective of this paper was to investigate if it was possible to achieve an accurate real-world object detection system trained purely on synthetic data using UE4, NDDS, and YOLOv5. We also wanted to examine how the number of assets influenced the detection results. Throughout this paper, we have tested and evaluated the functions of domain randomization and dataset synthesizing through the game engine UE4 and the plugin NDDS. We trained an object detection model from the generated synthetic dataset utilizing transfer and supervised learning in the CNN-based YOLOv5.  
The findings of this paper show that it is possible to realize a real-world object detection system trained purely on synthetic data. However, more parameters can be adjusted to create more stability and confidence in the model. We found that the xlarge model achieved more robust detection with better generalization than the small model. Our results also provide valuable insight into how the number of assets impacts the training results. Classes with few different assets tended to be undergeneralized and harder to detect in the real world. Classes with 5-10 assets tended to be overgeneralized; further research needs to consider each object's complexity when selecting assets for their dataset. The training results manifest good precision and accuracy given synthetic data, proving the potential for further research.  
Future work will consist in closing the reality gap even further. Finding a balance in the number of assets, evaluating results with different batch sizes regarding generalization, and conducting further research around parameters such as the learning ratio for synthetic data will be a good starting point. While the dataset utilized in this article yielded promising results, research into using more than 1500 synthetic images per object should also be conducted. We hope this paper can provide valuable insight for academic and professional peers exploring this field.  
Conceptualization, I.R., S.K. and D.H.; Data curation, I.R. and S.K.; Funding acquisition, D.H.; Investigation, I.R. and S.K.; Methodology, I.R. and S.K.; Project administration, D.H.; Software, I.R. and S.K.; Supervision, P.-A.A., T.N.A. and D.H.; Visualization, I.R. and S.K.; Writing--original draft, I.R. and S.K.; Writing--review & editing, P.-A.A., T.N.A. and D.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Figure 16: Stabilization graph of a paper box over 10 s: (**a**) small model; (**b**) xlarge model.
# An Overview of Key Technologies in Physical Layer Security
## 2 Background
### Performance Metrics in PLS
#### 2.4.1 Secrecy Rate  
The rate of transmission that can reliably be supported on the legitimate channel of transmission but not decodable on the channel of the eavesdropper is termed secrecy rate. The secrecy rate of the Gaussian channel is calculated as the maximum difference between the attainable secrecy rates of Alice-Bob and Alice-Eve. Considering Figure 4, and using Equations (1) and (9), the secrecy rate of a typical PLS communication system is given by  
\[R_{s}=R_{B}-R_{E}=\log_{2}\left(1+\frac{P_{T}H_{B}}{\sigma_{B}^{2}}\right)- \log_{2}\left(1+\frac{P_{T}H_{E}}{\sigma_{E}^{2}}\right), \tag{16}\]  
where \(R_{B}\) and \(R_{E}\) are the secrecy rates of [PERSON] and [PERSON], respectively. The secrecy rate, \(R_{s}\), can be maximized using signal design and optimization techniques, which will be discussed in Section 3.
# Combining the Transformer and Convolution for Effective Brain Tumor Classification Using MRI Images
## 3 Research Methodology
### Datasets Explanation
#### 3.5.2 Figshare Dataset  
Figshare is a widely used dataset in BT classification research. This dataset was produced by Cheng in 2017 and contains 3064 brain MRI images, including three different types of BT: gliomas, meningiomas, and pituitary. The meningioma class consists of (708 images), glioma (1426 images), and pituitary tumor (930 images). Sample images of the Figshare dataset are presented in Figure 9. We split this dataset into three subgroups: training, validation, and testing, where 70% of the dataset is utilized for training, 20% for validation, and 10% for testing.
## 3 General Classification of Modern Haptic Interfaces
### Thermal-Based Haptic Interfaces
#### 3.5.1 Thermoelectric Haptic Interfaces  
Due to their ability to deliver heating, thermoelectric-based haptic interfaces are essential for thermal feedback [37]. Through the thermoelectric effect, sometimes referred to as the Peltier effect, they provide a cooling feeling to human skin. The thermoelectric devices are made up of thermoelectric pairs (constituted by p- and n-type semiconductors) linked in series and placed between two electrode layers (Figure 6b). Heat transfers from the heat sink to the junction as a result of electric charge in p-type and n-type materials diffusing to the junction, raising the temperature on the device's top surface. The figure of merit (ZT) quantifies the performance of thermoelectric materials, depending on the Z coefficient, a function of the thermoelectric properties of used materials, and T, the absolute temperature. On the other hand, the electric current flows in the opposite direction, causing the top surface's temperature to fall.  
Figure 6: Thermal-based haptic interfaces. Joule-heating interface for haptic feedback: schematic representation of Joule heaters [24] (**a**); haptic feedback interfaces utilizing thermal electric actuators. A diagram of a thermoelectric mechanism [2] (**b**); microfluidic and other thermal-based haptic interfaces. The systems for micro-fluidic heat transfer are depicted schematically (left). Thermoregulatory clothing that circulates liquid cooling through flexible, thermally conductive silicone aluminum tubes to cool the body (center and right) (**c**).  
Thermoelectric haptic interfaces may convey information through various temperature distributions in thermal pixels, in addition to controlling body temperature. For instance, [PERSON] et al. described employing a flexible thermoelectric device to create a 2D arrayed thermal haptic interface [35]. To transfer thermal information, the flexible thermoelectric version includes a 2D thermal distribution array; specifically, the device enables the modulation of each thermoelectric unit cell included in the array. The skin's thermoreceptors transmit the real-time thermal pattern to users, who can then use it as a blind-assistance tool to determine where there are obstructions. In addition, thermoelectric haptic interfaces must be stretchable to be integrated into human skin and accommodate everyday motions. A high-level combination of pixelized thermal outputs was necessary to provide high-resolution reproduction of thermal feeling in VR/AR, separately regulating groups of thermoelectric cells and thus enabling a quick temperature response. However, these criteria are difficult to meet due to the unpredictable heat flow and inefficient heat dissipation. Several devices utilize metallic finned structures to increase heating and air efficiency for heat dissipation (Figure 6b) [38]. Typical thermoelectric systems can only be used in combination with rigid heat-conductive support layers, reducing their application in flexible interfaces. Thus, for wearable thermal-based tactile devices, heat-conductive layers are essential.
# Balancing Distributed Key-Value Stores with Efficient In-Network Redirecting
## 5 Evaluation
### Feasibility Verification  
Due to the limited hardware resource in switches, especially for the ASIC pipeline, we also need to evaluate the resources required by KVSwitch in order to determine its feasibility. In Table 1, we compare the hardware resources required by KVSwitch (\(K\) = 10,000, \(M\) = 32) to that required by switch.p4 and NetCache (\(K\) = 10,000). switch.p4 [25] is a P4 program that implements most networking features (L2/L3 forwarding, VLAN, QoS, etc.) for a typical data-center ToR switch.  
Figure 9: Dynamic change of the threshold. (**a**) The change of the threshold; (**b**) Number of hot keys. (**c**) Number of replicas.  
We note that KVSwitch consumes relatively larger proportions of hash bits, SRAM and stateful ALUs (17.58%, 38.43% and 24.61%, respectively). SRAM is used for the tables and registers. Hash Bits and Stateful ALUs are mainly consumed by HashPipe and conditional operations for registers. Also, the combined usage of all resources by switch.p4 and KVSwitch remains below 70%. This means that KVSwitch can easily fit on top of switch.p4. Moreover, KVSwitch consumes much less SRAM compared to NetCache (29.79% vs. 48.98%) at the cost of little increases in demands for other resources. In other words, KVSwitch can save more resources for other network functions compared to NetCache.  
In a KVS containing 100 million items, the 10,000 items with the highest ranks account for about 49.15% of all queries under a Zipf distribution (skewness = 0.99). Therefore, KVSwitch with \(K\) = 10,000 would be able to balance a workload for a large KVS in real life.  
We also evaluate the increase in packet forwarding latency introduced by KVSwitch. Delightfully, packet latencies in original KVS and KVSwitch are 185 and 214 nanoseconds by average, respectively. The overhead in latency is only 15.7%, and this result reveals the validation of KVSwitch in the other aspect.
# Ammonia Gas Sensing Characteristic of P3 HT-rGO-MWCNT Composite Films
## 2 Experimental
### Preparation of the P3 HT-rGO-MWCNT Blends  
The P3 HT-rGO-MWCNTs blends were prepared based on methods reported in ref. [31]. To be specific, P3 HT powder was mixed in the rGO-DMF solution. The mixed solution was then ultrasonic stirred for 2 h at room temperature to produce P3 HT-rGO-DMF solutions. The MWCNTs were immersed in the DMF and ultrasonic stirred for 1 h to form the MWCNT-DMF solution. Finally, the prepared P3 HT-rGO-DMF and MWCNT-DMF solution was mixed in a small glass beaker and stirred for another 5 h at 80 \({}^{\circ}\)C. The mass ratio of the mixture P3 HT:rGO:MWCNTs was changed at 10:80:10, 20:60:20, and 30:40:30, respectively, to evaluate the NH\({}_{3}\) gas detecting sensitivity of the sensor based on rGO content. To simplify, samples of the mixture P3 HT:rGO:MWCNTs are abbreviated to PGC, where PGC-40, PGC-60, and PGC-80 are the samples with an rGO content of 40%, 60%, and 80%, respectively.
## 5 Results and Discussion
### Feature-Importance Results
#### 5.2.2 Information-Theory Feature-Importance Results  
The information gained from each weather parameter quantifies its overall impact on predicting active power. In this study, 1036 feature combinations are evaluated, each represented by a binary indicator (0 for exclusion, 1 for inclusion). For each weather parameter, we split the combinations into two groups: those that contain the parameter and those that exclude it. By training and testing an ML model on both groups and observing the performance difference, we determine how crucial that parameter is to the prediction task.  
Figure 14 illustrates both the information gain (IG) of each weather parameter (left plot) and the frequency with which each feature is included (right plot). Notably, shortwave radiation instant, direct radiation instant, global tilted irradiance instant, and terrestrial radiation instant show the highest IG values, indicating that their presence significantly improves the model's performance when predicting active power. In contrast, wind speed at 10 m and wind direction at 10 m exhibit negative IG values. On average, including these features leads to a slight decrease in predictive accuracy compared to models that omit them. This outcome can occur if the added features introduce noise, are only weakly related to the target, or overlap with other features that confuse rather than clarify the model. It does not necessarily indicate that these parameters have no real-world influence; in the context of this dataset and modeling approach, their inclusion does not help, and may even hinder, the model's ability to generalize.
## 3 Methods
### Evaluation of the Improvement in Yield Prediction Accuracy  
The improvement in prediction accuracy using the identified threshold for multiple spatio-temporal data selection is statistically quantified. More specifically, the threshold is applied to the Landsat observations (2000-2018). Firstly, we compute the temporal probability of optimally using MODIS, Landsat, and \(L_{LM}\) images for nationwide crop yield predictions during the past two decades, and then map the results to illustrate the spatial variability of multi-sensor data selection for 25-m pixel-level yield prediction across the wheatbelt. We then evaluate the area percentage of the data sources on a yearly basis and analyze their potential correlation to the annual precipitation (mm/year). Finally, the improvement in the accuracy of predicted yields is evaluated on the field level using MODIS and \(L_{LM}\) for Western and eastern Australia, against the reported data [58]. The growing season of 2015 is selected due to the availability of a larger quantity of observed yield data. The incomplete 2015 Landsat series are gap-filled with the blended values corresponding to the threshold value.