esa-sceva/satcom-chunk-collection huggingface dataset:
# Progress in Electrodeposition of Zinc and Zinc Nickel Alloys Using Ionic Liquids
## Influence of Additives
### Ionic Liquids as Additives  
Another aspect of Zn and Zn-Ni alloy electrodeposition is that ILs themselves can be used as additives during plating from aqueous electrolytes as a grain refiner, to improve the properties of the deposit, as mentioned previously [57]. ILs as an additive can facilitate charge transfer to the metal ions by forming a cationic complex with the metal ions. Besides, adsorption of ILs on the surface resulted in a smoother and bright deposit, owing to the good charge transfer reactions at the electrodes. Studies showed that the nature of the deposit in terms of grain size and morphology can be altered by employing ILs as suitable additives [58]. Though additives have been proven to enhance the desirable properties of the deposit, the detailed role they play in the electrodeposition of zinc and zinc nickel alloys needs further attention. Deposition using ILs and DESs such as ChCl/EG/urea resulted in the formation of electrolytic breakdown products such as 2-methyl-1,3-dioxolane and chlorinated products, which included chloromethane, dichloromethane, and chloroform in small quantities [59; 60]. Further, the challenges with some ILs, such as toxicity, production complexity, and high cost in the process for use, hinders their application in the electrodeposition of zinc and zinc alloys. These need to be addressed. In addition, the high viscosity of ILs can alter the mass transportation of species and present significant issues, resulting in poor throwing power and non-uniformity of coating thickness.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 8 Research Challenges and Future Directions
### Classification of Research Challenges in Telemedicine
#### 8.1.3 Standardization  
Remote patient monitoring in telemedicine employs multiple monitoring and communication devices, potentially leading to compatibility issues. Because there are no specific technological standards in place, devices may not communicate effectively and it may not be easy to manage stored data from other devices or technologies that are not compatible [16]. There is a need to develop a standard format or protocol for medical data communication in order to mitigate this issue.
## 4 Experimental Results and Analysis  
### Datasets Description  
To verify the accuracy of our method, three typical benchmark datasets are used in this paper.  
(1) AVIRIS dataset: Salinas Valley. The image size is 512 \(\times\) 217, and it contains 224 bands and 16 classes, of which 56,975 pixels are background pixels, and 54,129 pixels can be used for classification. The false-color image and ground-truth of Salinas Valley are shown in Figure 6a,b.  
(2) Reflective Optics System Imaging Spectrometer (ROSIS) dataset: Pavia University. The size of the image is 610 \(\times\) 340, and it contains 103 bands and nine classes. The false-color image and ground-truth of Pavia University are shown in Figure 7a,b.  
(3) ITERS-CASI dataset: Houston University. It is over the University of Houston and the neighboring urban area. The data size is 349 \(\times\) 1905 and contains 144 bands in the spectral range from 364 nm to 1046 nm. It has 15 classes. The false-color image and ground-truth of Houston University are shown in Figure 8a,b.  
In the data division step, we continue to adopt the data partitioning rule we proposed in [19] to avoid potential information leakage [29]. We divide the HSI and labels into the same size of patches. Specifically, the patch size of Salina Valley is \(6\times 6\times 204\), that of the Pavia University dataset is \(8\times 8\times 103\), and that of the Houston University set is \(6\times 6\times 144\)  
Figure 6: Prediction maps of different models for the Salinas Valley dataset. (**a**) False color image; (**b**) Ground truth; (**c**) 1D-CNN; (**d**) SS3 FCN; (**e**) SSDT-net (OA = 83.13%, AA = 87.97%).  
Figure 7: Prediction maps of different models for Pavia University dataset. (**a**) False color image; (**b**) Ground truth; (**c**) 1D-CNN; (**d**) SS3 FCN; (**e**) SSDT-net (OA = 83.13%, AA = 81.55%).
# Deep Learning-Based Portable Device for Audio Distress Signal Recognition in Urban Areas
## 3 Results
### Prototype Implementation  
We assessed the performance of the real-time recognition system composed of the low-cost microphone and a Raspberry Pi with the trained model. The network model that was trained off-line was deployed on the Raspberry Pi using Tensorflow Lite, which has been shown to have a good performance behavior on this type of devices [21]. This device takes a batch of audio samples every 450 milliseconds, which is passed through a preprocessing stage that computes the Mel spectrogram and MFCCs. The result of this computation corresponds to the input of the multi-headed CNN, which provides a prediction of the class in a given window. We computed some performance indicators to measure the feasibility of computation of our classification model on the Raspberry Pi: audio recollection time, which is the time to acquire the audio samples for a given window; data preprocessing time, which is the time needed to extract the MFCC and Mel spectrogram given the audio signal; prediction time, which is the time needed to compute the network operations to provide a prediction; and RAM consumption of the model. Table 9 summarizes the mean average computational costs of the proposed device implementation for 100 audio windows.  
These results show that the portable implementation of the model in the Raspberry Pi is adequate to give a quick response in the task of event recognition, which can be considered as real-time processing. These results are comparable to the ones obtained by other works that implemented real-time applications of audio processing. Examples include audio processing times for real-time predictions of around 1000 ms in [6], and between 104 to 1360 ms in [22].
## 5 Challenges and Open Research Directions
### Data Management and Storage  
Decentralized data management in Holochain is challenging in data-intensive IoT applications. Managing large volumes of data across distributed agents can lead to issues in data consistency, storage overhead, and retrieval speeds. For example, in the healthcare IoT, real-time access to patient data is critical, and delays or inconsistencies could have serious consequences. Future research directions for optimizing data management in Holochain-based IoT systems include the following:  
* Developing hybrid architectures that combine DHTs with centralized caching and adaptive data replication strategies to improve data retrieval speeds and balance consistency and availability in decentralized environments.
* Investigating efficient indexing techniques and consensus-free synchronization mechanisms to enhance query performance and ensure data consistency in large-scale distributed networks and agent-centric architectures.
* Strengthening DHT integrity by implementing verifiable storage mechanisms, cryptographic commitments, and tamper-proof logging to ensure data authenticity and resist manipulation, including hierarchical and off-chain storage, time-series compression, and GDPR-compliant data deletion.
# Recent Advances in Photodetectors Based on Two-Dimensional Material/Si Heterojunctions
## 3 Photodetectors Based on 2D Material-Si Heterojunctions
### TMD/Si Heterojunctions
For better comparison and understanding of the TMD/Si heterojunction photodetectors, the performance parameters of some typical devices are listed in Table 2.
## 2 Literature Review and Conceptual Context
### Situation-Aware Route Planning
#### 2.2.3 Dynamic Vehicle Routing Problem
Finally, they finalized the framework by using reinforcement learning for route optimization. Evolutionary algorithms usually take long periods to finish the computations. Even if it is extensive and not optimal to include them in DVRPs, some researchers found a couple of ways to apply them due to their efficacy in solving the problem. Running the optimization in the GPU is one of the choices [65]. The authors implemented a DVRP with new customer orders throughout the day and solved it through periodic re-optimization. In other words, they were solving a classic VRP at each period, which the user defines. The results showed that running a genetic algorithm in the GPU was faster than solving the same algorithm in the CPU or an ACO. Other application includes a self-adaptive evolutionary algorithm [66]. The article states that solutions scattered over the search space can be captured better by the dynamic changes of the VRP. The changes were modeled after generating random numbers for traffic factor in edges at every time interval. The solutions for the oncoming changes are not from scratch but an evolution of configurations from the previous solution. This way, they can inherit problem-specific knowledge from their respective parent solutions. The new configurations are encoded into the new DVRP routes. The search process for the new solution can then use different configurations to effectively handle the dynamic changes and guide the search to a promising optimal.
# UAV Detection Using Thrust Engine Electromagnetic Spectra
## 2 Materials and Methods  
### Measured Objects  
In the performed experiments, two similar-sized multi-rotor UAVs were used for measuring their generated signature of an electromagnetic spectrum. Equipment was chosen according to the possibilities of performing a static flight with the flight stabilization function in use. A similar widely used motor-powered BLDC tool was also selected for comparison with the obtained EM signatures from the multi-rotor UAVs.  
First of all, the model of the four brushless electric motor-powered Eachine FPV Tyro 109 was used in the experiments. The drone has a built-in flight controller, capable of performing flight stabilization functions. After the flight controller was calibrated for the correct horizontal position, any external force applied to the drone body on any of the three rotation axes makes the engines interact individually in order to obtain the position it was calibrated for. The stabilization function results in an independent change of the thrust engine's acceleration and rotation speed. The unique electromagnetic field change influenced by the change of power of brushless motors results in an electromagnetic spectrum signature of the drone.  
The second drone that was used in the experiments is the well-known model of the Mavic 2 Enterprise from the DJI company. The drone is powered by four small-size brushless electric motors. The performed measurements and used experimental techniques were the same as with the Eachine drone.  
The electromagnetic spectrum of the Dewalt DCF809 270 brushless motor powered impact driver is also received and recorded for the evaluation of the experimental results from drones. This is the most common tool that is powered by the closest size brushless electric motor (BLDC) and has the ability to accelerate fast. Additionally, the electric impact driver has the unique load characteristics of an electric engine when the hammer force is in action. The resulted load during hammer action load on the brushless engine has a unique signature in the received electromagnetic spectrum.  
All received signatures of the electromagnetic spectrum were recorded for evaluation and comparison.
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 5 Taking a Risk-Based Approach to BVLOS Operations in Forest Environments
### Technological Considerations
#### 5.1.7 Command and Control (C2)
However, cellular networks are rarely installed with particularly robust coverage in the remote locations where forestry exists. Cellular networks also introduce a large degree of latency in the link, increasing the time between operator input and the UA responding or payload data returning to the GCS. This latency may affect DAA effectiveness and response times. Other solutions may employ using segregated bands with radio frequencies licensed and reserved for the operator (often at higher than ISM output power and, therefore, range). This, however, incurs regulator and hardware costs. Satellite links are also an option; however, these are more aimed at large, medium/high altitude long endurance (MALE/HALE) unmanned aircraft and military applications, due to their high costs and difficulties with fitting antennae to smaller aircraft [77; 78]. These may be worth exploring, due to their benefits, but only when there is a sufficient budget or if using large equipment.
## 2 Data and Methods
### Satellite to Shores  
We compare our deep learning-based approach (DSPEB) to a wave kinematics-based depth inversion model named Satellite to Shores (S2 Shores) [24; 47]. S2 Shores employs a Fourier slicing method (FS), consisting of a combined radon transform (RT) and a discrete Fourier transform (DFT). The FS technique is used to detect spectral wave characteristics such as the spectral wave phase shift and the wave number to invert water depth using the linear dispersion relation for free surface waves (2). The depth estimation procedure is repeated for each sub-window around a point where one wants to know the depth (\(h\)). Each sub-window has a user-defined size in O(100 s m), as such that it contains at least 1-2 wavelengths (\(\lambda\)). The radon transform is applied to the sub-sampled image to produce a sinogram of integrated pixel-intensities per direction. The angle corresponding to the maximum variance in the RT-sinogram corresponds to the wave direction (see [24; 47] for more details). A 1D DFT procedure per direction over the sinogram enables to pass from the spatial domain to a complex spectral domain in polar space. From the resulting polar spectrum, the wave phase and amplitude can be determined per wave number, per direction. The difference in phase (\(\Delta\Phi\)) can be found between (several) pairs of detector bands. Presuming that the wavenumber (\(k\)) is constant or near-constant over the sub-window, \(\Delta\Phi\) can be seen as representative of \(\omega(t)\), and given that the timing between the different detector bands (\(\Delta t\)) is constant, the wave celerity (\(c\)) can be determined as:  
\[c=\frac{\Delta\Phi}{2\pi k\Delta t}=\frac{\Delta\Phi\lambda}{2\pi\Delta t} \tag{1}\]  
For each wavenumber or celerity pair, (2) can be solved for depth.  
\[c^{2}=\frac{\wp}{h}tanh(kh)\Longleftrightarrow h=\frac{tanh^{-}1(\frac{c^{2 }k}{g})}{k} \tag{2}\]  
Estimates of water depth, wave celerity, wavenumber (wavelength), and direction are output by the S2 Shores algorithm at each point on an output grid with a resolution of 500 m. To evaluate and compare S2 Shores to the survey data and the DSPEB results, we use linear interpolation of the raw sparse output grid.  
Figure 1: The steps of the DSPEB method using a deep convolutional neural network. * The input and output data used in the figure are synthetic examples.
## 3 Results and Discussions
Lastly, it is worthy to note that the polarization of the electric field is along the \(x\)-axis direction (TM polarization) and the incident light is assumed to be vertically incident in the above discussion. However, the polarization angle and the incident angle are required for consideration in practical productions [31; 32; 33; 34; 35; 36]. Therefore, we further calculated the spectrum of the solar absorber by changing the polarization direction, \(\varphi\), and the incident angle, \(\theta\), and the results are shown in Figure 5. The absorption spectrum versus incident directions \(\theta\) is plotted in Figure 5a. For the lower wavelength (300-320 nm), when the angle of incidence, \(\theta\), increases, the absorption rate of light in the lower wavelength increases. For the wavelength in the range of 320-2250 nm, the incidence angle, \(\theta\), has no obvious effect on the absorption spectrum. For the wavelength range of 2250-2500 nm, when the incident angle, \(\theta\), increases, the absorption rate slightly decreases but remains at a high level. In addition, from Figure 5b, we can clearly see that the absorption spectrum has hardly changed as the angle of polarization, \(\varphi\), is swept from \(0^{\circ}\) to \(90^{\circ}\). Independent polarization is attributed to the symmetrical characteristic of the structure of the multilayer planar films. Finally, in order to demonstrate the optimization of our proposed structure and the possible structural defects, we sweep the different metals as a function of thicknesses for each F-P cavity and the thickness of the MgF\({}_{2}\) film in Figure 6, respectively. This is the basis for us to determine the parameters. These results in Figure 6 show that numerical variation within a certain range and the influence of dielectric layer on absorption spectrum are very weak. To the best of our knowledge, these numerical analyses prove that our proposed solar absorber is a wide-angle, polarization-independent, and broadband-ideal structure, which provides a superior performance in comparison to most previously reported solar absorbers, throughout the whole solar spectrum. Figure 4: Absorption power distribution at the wavelengths of \(\lambda\) = 500, 1000, 1500, 2000, and 2500 nm.
## 4 Results and Discussion
### Results of EMG by Object Intensity  
Through tests with participants using the proposed system, quantitative data including raw EMG and smoothed signals were obtained. Figure 5a is a graph of the smoothed EMG values between onset and cessation when one participant destroyed each object. Participants recorded sensor values of 215.27, 323.57, 187.22, 158.53, 208.07, and 202.75 to destroy a Level 1 object with a threshold of 0.7; 313.50, 357.88, 217.35, 221.68, 282.31, and 371.48 to destroy a Level 2 object with a threshold of 0.85; 271.31, 437.23, 231.18, 168.32, 312.55, and 292.49 to destroy a Level 3 object with a threshold of 1; and 340.01, 329.29, 339.75, 229.36, 367.53, and 331.68 to destroy a Level 4 object with a threshold of 1.05.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Q1** & **Q2** & **Q3** & **Q4** \\ \hline Subject 1 & 5 & 5 & 5 & 4 \\ Subject 2 & 5 & 4 & 5 & 5 \\ Subject 3 & 5 & 5 & 5 & 5 \\ Subject 4 & 5 & 5 & 4 & 5 \\ Subject 5 & 5 & 4 & 5 & 5 \\ Subject 6 & 5 & 5 & 5 & 5 \\ \hline Total & 5 & 4.67 (0.52) & 4.83 (0.41) & 4.83 (0.41) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Questionnaire results from six subjects.  
Figure 5b demonstrates the average of the smoothed EMG values while participants attempted to destroy each object, presented as t-test results, indicating significant differences between the two levels (\(p<0.001\)).
# An ADS-Based Sparse Optimization Method for Sonar Imaging Sensor Arrays
## 5 Conclusions  
This paper proposes the ADS method to further thin Mills Cross arrays while keeping the beam pattern unchanged, in order to reduce the complexity and cost of array sonar imaging systems. Simulations of the thinned arrays were conducted and compared with those of the original Mills Cross array. The results demonstrate that the \(PSLL\) values of the beam pattern are affected by the thinned ratio and the cycle number. The beam patterns do not decrease as long as the ADS parameters are correctly assigned. In addition, the MLW and \(PSLL\) values remain almost unchanged with azimuth angle and frequency variation. The azimuth resolution is maintained when using the given ADS sequence, but it is possible that the azimuth resolution is influenced by the thinned ratio. However, it is true that the azimuth resolution and the MLW are affected by the aperture size of the array sensor. From these experimental results, it is concluded that the ADS method can further decrease the redundancy of a Mills Cross array while maintaining the beam pattern. A mathematically deduced ADS sequence for Mills Cross array sensor sparse optimization will be provided in our future work.  
Conceptualization, J.L.; Methodology, F.S.; Software, F.S.; Validation, Y.S.; Formal Analysis, F.S.; Writing-Original Draft Preparation, P.L.; Writing-Review & Editing, J.L.; Project Administration, P.L.; Funding Acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Receiving performances of two arrays at different frequencies. (**a**) \(PSLL\) changes at different frequencies; (**b**) main lobe width (MLW) changes at different frequencies.  
**Funding:** Project supported by the science and technology development fund project of Wuxi (Grant No. N20191008), the social development project of the key development plan in Jiangsu Province (Grant No. BE2015692), National natural science foundation of China (Grant No. 41075115), and the second-phased construction projects of superior subjects in Jiangsu universities.  
**Conflicts of Interest:** The authors declare no conflict of interest. These funders provide the supports for the preliminary study of this investigation, including document retrieval, hardware system build and technical advisory. Certainly, they provide funds for the publication of this article.
## 4 Experiments
### Results
#### 4.5.3 Low-Light Enhancement Effect Comparison Experiment  
Comparative experiments with other state-of-the-art methods were performed on the SICE Part 2 public dataset and a private dataset of electric power job sites, respectively, to show the efficacy of our low-light enhancement method. Other state-of-the-art methods were selected as UNIE [13], EnlightenGAN [10], LMSPES [9], and Zero-DCE [12]. As the  
Figure 8: Ablation experiment of each loss.  
Figure 9: Comparison of \(L_{spu}\) effectiveness using mesh charts. The red boxes are two obvious and representative contrast differences.  
private dataset of the electric power operation site had no labels corresponding with the original image, it could not be judged by objective _PSNR_ and _SSIM_ indexes so the subjective visual effect and the number of correctly detected keypoints were used as the evaluation indexes. In terms of keypoint detection, we used Faster-RCNN [23] as a human detector and HRNet for keypoint detection. The comparison results are shown in Figures 10 and 11, demonstrating the subjective visual enhancement effect and the number of keypoints correctly detected; thus, this paper's method was superior to other state-of-the-art methods.  
Figures 12 and 13 show a comparison experiment of the image enhancement of a low-light scene of an electric power operation with localized strong light. Our approach was superior to existing approaches in that it improved low-light images while preventing overexposure to strong light. It also provided better overall image visualization and noise control.  
Figure 11: The second group of comparison experiments of low-light enhancement and keypoint detection in electric power operation site. The red font is the best result of keypoint detection.  
Figure 10: The first group of comparison experiments of low-light enhancement and keypoint detection in electric power operation site. The red font is the best result of keypoint detection.  
Image visualization is subjective, so we also used _PSNR_ and _SSIM_ as evaluation metrics and conducted comparison experiments using the SICE Part 2 public dataset. The metrics were calculated using MATLAB for the images enhanced by different methods and labeled images, respectively. _PSNR_ and _SSIM_ were taken as the average of 100 images. Figure 14 shows the visualization comparison with other advanced methods after low-light enhancement and Table 1 displays the findings from a comparison of _PSNR_ and _SSIM_.  
Figure 12: The first group of comparative experiments of low-light enhancement at an electric power operation site in the presence of strong light.  
Figure 13: The second group of comparative experiments of low-light enhancement at an electric power operation site in the presence of strong light.