esa-sceva/satcom-chunk-collection huggingface dataset:
# Amplitude and Phase Information Interaction for Speech Enhancement Method
## 5 Result and Analysis
### Comparison of Models Using Different Magphase  
The Magphase unit is used to extract the amplitude spectrum and phase spectrum features of speech in the full-band unit and combine the information provided by them. Table 2 presents ablation experiments with different numbers of Magphase units, demonstrating the effectiveness of these units in improving the model's performance. In the model structures used in Table 2, apart from the varying number of concatenated Magphase units, all other units and parameters are kept the same. The Magphase column indicates the number of concatenated information interaction units in the FullSubNet-pMix model. The results of experiments 1 to 6 in Table 2 demonstrate that adding these units amplifies the effect of speech enhancement. Furthermore, the data from experiments 7 to 11 in Table 2 indicate that the best amplification effect is achieved when the number of Magphase units is 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Num** & **Model** & **Magphase** &
\begin{tabular}{c} **Lookahead** \\ **(ms)** \\ \end{tabular} & **Sb\_num** & **WB-PESQ** & **NB-PESQ** & **STOI** & **SI-SDR** \\ \hline
1 & FullSubNet-pMix & 4 & 8 & 10 & 2.771 & 3.265 & 95.91 & 17.17 \\
2 & FullSubNet-pMix & 4 & 6 & 10 & 2.737 & 3.252 & 95.65 & 17.00 \\
3 & FullSubNet-pMix & 4 & 4 & 10 & 2.760 & 3.255 & 95.85 & 17.17 \\
4 & FullSubNet-pMix & 4 & 2 & 5 & 2.726 & 3.218 & 95.76 & 17.09 \\
5 & FullSubNet-pMix & 4 & 2 & 7 & 2.764 & 3.244 & 95.84 & 17.17 \\
6 & FullSubNet-pMix & 4 & 2 & 13 & 2.817 & 3.300 & 96.04 & 17.27 \\
7 & FullSubNet-pMix & 4 & 2 & 15 & 2.838 & 3.325 & 96.08 & 17.51 \\
8 & FullSubNet-pMix & 4 & 2 & 10 & 2.866 & 3.342 & 96.24 & 17.66 \\
9 & FullSubNet-pMix & 3 & 20 & 8 & 2.785 & 3.273 & 96.00 & 17.27 \\
10 & FullSubNet-pMix & 3 & 20 & 32 & 2.756 & 3.275 & 95.78 & 17.11 \\
11 & FullSubNet-pMix & 3 & 25 & 32 & 2.745 & 3.234 & 95.60 & 17.95 \\
12 & FullSubNet-pMix & 3 & 20 & 16 & 2.861 & 3.353 & 96.15 & 17.54 \\
13 & FullSubNet-pMix & 3 & 32 & 15 & 2.835 & 3.329 & 96.07 & 17.49 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The effect of different hyperparameters on speech enhancement ability.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
4-NBD\({}^{+}\) & 4-Nitrobenzenediamineion ions \\ Amp & Amperometry \\ AO & Acridine Orange \\ AuAT-SPE & Screen-Printed Gold Electrode (High temperature cured ink) \\ AuBT-SPE & Screen-Printed Gold Electrode (Low temperature cured ink) \\ AuNPs & Gold nanoparticles \\ BSA & Bovine Serum Albumin \\ BDD & Boron-Doped Diamond \\ CC & Catechol \\ CA & Chronoamperometry \\ CB4 & Carbon Black Ink \\ CBPE & Carbon Black Paste Electrode \\ CdTe-QDs & Cadmium Telluride Quantum Dots \\ CGGE & Chitosan Biopolymer, Graphite Powder and Glycerol Mixture Electrode \\ CMS & Carboxymethyl Starch \\ Cu-CMCS2 & Enzyme-less-Cubic Mesoporous Carbon Stage 2 (Tyrosinase-mimicking) \\ CV & Cyclic voltammetry/Cyclic voltammogram \\ CVs & Cyclic voltammograms \\ DA & Dopamine \\ Diazo & Surface modified by diazotization reaction \\ DHP & Dihexadecylphosphate \\ DPV & Differential Pulse Voltammetry \\ E\({}_{\text{pa}}\) & Anodic peak potential \\ E\({}_{\text{pc}}\) & Cathodic peak potential \\ GCE & Glassy Carbon Electrode \\ GLU & Glutaraldehyde \\ GNPs & Graphene Nanoplatelets \\ GR & Graphene \\ GRAFT & Surface modified with aminophenyl groups \\ IL & Ionic Liquid \\ i\({}_{\text{pa}}\) & Anodic peak current \\ i\({}_{\text{pc}}\) & Cathodic peak current \\ ITO & Indium Tin Oxide \\ LDR & Linear Dynamic Range \\ LOD & Limit of Detection \\ LOQ & Limit of Quantification \\ LSV & Linear Sweep Voltammetry \\ MLN & Natural Molybdenum \\ MNP & Magnetic Nanoparticles \\ MWCNTs & Multiwalled Carbon Nanotubes \\ NDs & Carbon Nanodiamonds \\ PANI & Polyaniline \\ PBS & Phosphate-Buffered Solution \\ PEDOT & Poly(3_A_-ethylenedioxythiophene) \\ PET & Polyethylene Terephthalate \\ PTA & Poly-3-Thiophenemalonic Acid \\ PS & Potato Starch \\ \end{tabular}
# Quasi-All-Passive Thermal Control System Design and On-Orbit Validation of Luojia 1-01 Satellite
## 3 TCS Design Proposal
### Heating Design  
Since limited mass and power can be allocated to the TCS, there are only two heating regions for the whole satellite, as shown in Table 11. One is on the camera and the other is on the battery, both with backup for the heating instruments. The heater band is made of Constantan foil and the temperature measurement device is a thermistor.
## 7 Datasets
### Astronomy
Analytics for Solar Flares) [83] is a multivariate time series benchmark dataset designed for the field of solar physics. The dataset contains 4075 solar active regions, spanning the 9-year operational period of the Solar Dynamics Observatory (SDO). Accurate prediction of solar flares is crucial for astronauts, space equipment, and satellite communication systems.
# Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm
## 1 Introduction  
### General Context  
Direct current (DC) networks have increased their participation in the electrical sector due to DC distribution's advantages regarding energy losses, voltage profile behavior, and controllability properties [1, 2]. In addition, DC distribution technologies are becoming a real alternative for providing electrical service to multiple users due to the advances made in power electronic converters, small-scale renewable energy sources, and energy storage systems, as most of these devices work with DC power. Therefore, this makes their connection to DC distribution networks more natural in contrast with classical AC networks, where power electronic inverters are mandatory [3, 4]. Electrical distribution networks with DC technology can be built with two topologies, i.e., monopolar and bipolar configurations. Monopolar DC networks correspond to distribution grids with a positive pole and a return wire (neutral) that allow multiple linear and non-linear loads to be interconnected between both conductors, which are supplied with a single voltage magnitude [5]. Bipolar DC grids comprise two poles (positive and negative poles) and a neutral wire that make it possible to connect loads with a monopolar voltage magnitude as well as bipolar loads between the positive and negative poles that experience twice as much voltage in their terminals [2].  
Due to construction costs, most of the existing DC distribution networks have been built with a monopolar structure, as this requires 33% fewer investments in conductors than the bipolar configuration. Therefore, this research focuses on the analysis of monopolar DC networks while considering the optimal power injection from renewables in order to reduce the expected annual operating costs in terminals of the substation, i.e., the power electronic converter that interfaces the conventional AC network with the monopolar DC network.
# Towards a Heat-Resistant Tethered Micro-Aerial Vehicle for Structure Fire Sensing
## 2 Related Works  
Although many works are related to UAVs in emergency and fire scenarios, none have been designed explicitly for structure fires. Many unmanned aerial systems (UASs) have been proposed for emergency restoration work [3], wireless communications [4; 5; 6], recovery of drowning victims [7], marine incidents [8], floods [9], and collapse of buildings [10].  
Regarding fires, most of the works are centered around firefighting [11] and wildland fires [12; 13; 14; 15; 16]. Another relevant application of UASs is search and rescue [17]. For the use cases mentioned above, only one fire-resistant drone has been documented: the FAROS project [18]. It offers a significant step forward in heat-resistant UAV technology. Nevertheless, its complexity and lack of propeller protections leave room for improvement. Similarly, FireDrone's most recent work [19] proposes a clever use of aerogels to insulate the UAV, but the complexity of the system makes it a large and heavy UAV, difficult for firefighters and rescuers to transport during an emergency. No commercial solution has been found that is capable of operating above 40-50 \({}^{\circ}\)C.  
When it comes to thermal modeling, a satellite design methodology has been followed [20; 21; 22]. No similar thermal analyses were found in the bibliography regarding drones/UASs.  
Equipping sensors that allow situational awareness in such a small vehicle is also challenging. Research has demonstrated that visibility is significantly lower in the visible spectrum compared to the near-infrared spectrum, indicating that certain technologies may be more effective in smoke conditions depending on the wavelength used [23]. There are some thermal infrared remote (TIR) sensing options available [24]. Traditional aircraft or ground TIR platforms can provide valuable regional-scale environmental information. However, these platforms have limitations, such as expensive costs, complicated manipulation, etc. In comparison, small unmanned aircraft systems (UASs) have many advantages in TIR sensing applications over traditional platforms, especially in structure fires and local-scale fires [25; 26].
## 4 State-of-the-Art Review
### Water Electrolysis
By regulating the size and shape of their structure, materials such as nanotubes, nanowires, nanorods, and nanosheets can boost the overall conversion from solar light to hydrogen in terms of energy efficiency; with the inclusion of these nanomaterials in semiconductors, one observes a clear increase in the absorption of solar light. The biggest drawback of such technology resides in its efficiency--which is still very low--and the need to develop cost-effective materials to overcome said performance. Finally, [PERSON] et al., in their study regarding current status and research trends in water electrolysis science and technology, give us the future outlook of the next generation of electrolyzers: increasing the operating current density while improving efficiency [129]. Ideally, the authors believe water electrolyzers could be used for grid-balancing services and energy storage systems, with market applications foreseen in the short-term period. Artificial intelligence and neural network methods could even be used for efficiently designing, planning, and controlling the operation of these types of systems, something that poses very interesting and daring challenges for the future of these technologies [130; 131; 132]. The various hydrogen production methods along with their advantages, disadvantages, efficiency, and capital costs--based on the literature review done so far--are provided in Table 3.
## 4 Experiment and Verification
### Component Health Assessment  
The health indicator HDDD is calculated separately for the two parameters. The results of the two parameters are then weighted and fused using the assessment model. The results are shown in Figure 10.  
Figure 10: The results of the calculation of the component health indicator HDDD.  
Figure 9: Positive normalization of the current and bearing temperature.  
During the initial phase of the orbital run, the HDDD of the current data shows minimal changes, while the HDDD of the temperature data exhibits a significant downward trend. Starting from approximately day 700, there is a rapid decline in the health of the current data, surpassing the rate of decline in the temperature data. After day 1050, the health of the current data falls below that of the bearing temperature parameter. These changes align well with the original data and the method accurately describes the variations in health for both parameters, demonstrating its validity.  
As these data are for the full life of the momentum wheel component in stable operation, the component is not in a state of complete \"failure\". Therefore, the health status of the component has gone through three levels: excellent, good, and qualified. The combined HDDD of the momentum wheel component is used as a feature and the health status classes are classified using spectral clustering. The results of the threshold classification are presented in Table 2.  
Based on the thresholds, the curve for the change in the health status of the momentum wheel component is shown in Figure 11.  
Figure 11 clearly demonstrates the rapid degradation of the component's performance, reaching a passable condition within approximately five years. Despite this degradation, the component is still capable of performing certain work tasks. By conducting real-time in-orbit health assessments of key components, it becomes possible to strategically allocate work tasks based on the component's status. This approach effectively prevents unnecessary waste of satellite resources.
# Automated Multiple Concrete Damage Detection Using Instance Segmentation Deep Learning Model
## 4 Experimental Evaluation  
### Damage Detection Results  
An evaluation of the trained model was carried out using 25 actual images containing damage as well as various environmental objects. These were obtained from the Internet or captured from real structures. Among the 25 images, 5, 5, 5, and 10 images contain cracks, efflorescence, rebar  
Figure 4: Examples of image augmentation: (**a**) original image of crack, (**b**) flipping left-right, (**c**) flipping up-down, (**d**) rotating 90°, and (**e**) zero padding at right and bottom corner.  
exposure, and/or spalling and their collocations (e.g., spalling with rebar exposure, spalling with cracks, and efflorescence with cracks), respectively. Before applying the trained model, all test images were resized to 1024 \(\times\) 1024, which is the input size of the Mask R-CNN model, regardless of their own resolutions. This was accomplished to infer the images by a single application of the trained model. The detection results only includes the bounding boxes with confidence scores higher than 0.5, which is the marginal value for the optimal object detection with Faster R-CNN-based models [41]. In Figures 5-8, some examples of damage detection are illustrated and compared with the ground truths. Figures 5-7 show examples for single types of damage (i.e., cracks and efflorescence), while Figure 8 shows collocated damage. In the figures, the ground truths of the damage are marked in the left columns, and they are visually compared with the detection results in the right columns. The ground truths and the detected bounding boxes and masks are colored as follows: red for cracks, green for efflorescence, cyan for rebar exposure, and purple for spalling. To consider the field environment with other surrounding objects, all test images were selected to include various objects as well as damage. This includes a stair railing in Figure 6, trash in Figure 7, and scaffolding in Figure 8. Please note that the results of the other tested images are listed in Table 1.
# Efficient Support Vector Regression for Wideband DOA Estimation Using a Genetic Algorithm
## 4 Performance Analysis  
To demonstrate the effectiveness of the proposed algorithm and analyze its performance, a series of simulation experiments are conducted. Assuming that the receiving array is a ULA with six elements, the center frequency of the far-field wideband signal is 8 MHz, the bandwidth is 4 MHz, and the spacing of the elements is 15 m.
# Self-Organized Conductive Gratings of Au Nanostripe Dimers Enable Tunable Plasmonic Activity
## 3 Conclusions  
We have demonstrated a cost-effective nanofabrication method able to achieve quasi-1D arrays of plasmonic nanostripe antennas, extending homogeneously over a large area (cm\({}^{2}\)). This self-organized approach enables the tailoring of the nanostripes' nanoscale morphology, as well as tuning the plasmonic response over a broadband VIS and near-IR spectral range. In parallel, we have shown the possibility to combine high electrical conductivity with strong optical dichroism in semi-transparent nanoelectrodes behaving as wire grid polarizers, thus opening a variety of applications for these templates in active plasmonic devices.  
The nanofabrication control characteristic of this self-organized approach has been exploited for developing a multi-step growth of quasi-1D arrays of Au-SiO\({}_{2}\)-Au nanodimers characterized by hybridized plasmon resonances with superior near field confinement capabilities. This figure, combined with the possibility of tuning the plasmonic modes over a broadband spectrum, opens a variety of applications for these plasmonic metasurfaces in linear and nonlinear optical spectroscopies, advanced microscopy and biosensing.  
Figure 4: (**a**) FEM calculations of nanostripe dimers’ optical behavior as a function of the Au nanostripes aspect ratio _lhw_, which is varied by acting on \(w\) and keeping _lh_ fixed to 22 nm. The silica spacer is set to 24 nm thickness and the array periodicity is 210 nm; and (**b**) TM-polarized optical extinction spectra of nanostripe dimer arrays, where the Au-SiO\({}_{2}\)-Au layers thickness was tailored as follows: 30-44-30 nm (red curve), 22-44-22 nm (green curve) and 22-22-22 nm (black curve).  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/10/4/1301/s1](http://www.mdpi.com/2076-3417/10/4/1301/s1)]([http://www.mdpi.com/2076-3417/10/4/1301/s1](http://www.mdpi.com/2076-3417/10/4/1301/s1)).  
**Author Contributions:** [PERSON] and [PERSON] conceived the idea, [PERSON] and [PERSON] performed the experiments, [PERSON] performed the calculations, [PERSON] wrote the original draft of the manuscript and all the authors contributed to the final version of the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Ministero dell'Universita e della Ricerca Scientifica (MIUR) through the PRIN 2015 Grant 2015 WTWT3, from Compagnia di San Paolo in the framework of Project ID ROL 9361, and from MAECI in the framework of the Italy-Egypt bilateral protocol.  
**Acknowledgments:** Financial support is gratefully acknowledged from Ministero dell'Universita e della Ricerca Scientifica (MIUR) through the PRIN 2015 Grant 2015 WTWT3, from Compagnia di San Paolo in the framework of Project ID ROL 9361, and from MAECI in the framework of the Italy-Egypt bilateral protocol.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Survey from Real-Time to Near Real-Time Applications in Fog Computing Environments
## 2 Material and Methods
### Material
#### 2.1.3 Differences Between Edge and Fog Computing  
Fog computing is often referred to as edge computing, but, according to [PERSON] et al. [13], these two approaches have key differences, which are:  
* Fog computing works with cloud computing while edge communicates only with the local network;
* Fog computing is hierarchically structured, while the edge has a limited number of peripheral layers;
* Unlike edge, the fog computing has support for network, storage, control, and acceleration of data processing.  
What is Fog computing and how it differs from Edge computing are questions widespread among the scientific community mainly because these are new concepts in the consolidation process. The paper [PERSON] et al. [14] explores these questions and sheds light on their clarification. According to the authors, Fog computing is the highest evolution of the Edge computing principles. This strong affirmation is based on the idea that cloud computing could be extended to the network edge, near the data sources (IoT devices), to bring the computation inside these devices. This idea can be implemented in different ways, like Mobile Edge Computing, Mobile Cloud Computing, and Cloudlet Computing. However, in the authors' view, Fog Computing is not yet another implementation of Edge computing because it is not limited to only the edge of the network. It incorporates the Edge computing concept, offering support to a well-organized intermediate layer that fully bridges the gap between IoT and Cloud computing.
## 3 Analysis and Discussion
### Study on GRU Parameter Settings
#### 3.1.1 Network Structure  
The \"number of cells in the hidden layer\" and \"number of hidden layers\" are key parameters that influence the network's structure. As shown in Table 1, 15 models are trained to analyse the network structure parameters. The accuracy of the listed models is an average value obtained on the validation set after three Monte Carlo training runs for each network. The last column shows the trainable parameters of each network, which can be used as an index of network complexity when accuracies are calculated on the same platform. In addition, four additional GRU networks are trained, and the inputs to these networks are GM data (long time series data) without having the features extracted by hand, similar to the inputs of the study [18]. However, our inputs are in 3074 dimensions, and the network structure parameters are the same as networks 1, 5, 9, and 13 in Table 1.  
Although a marginal error may theoretically be introduced during data preprocessing, MFCCs accurately reflect the main features of ground motion and provide good Mo model performance. Therefore, we use it as a baseline to compare and analyse whether the features extracted by AFEM are more suitable for this model. In addition, compared with the model that uses truncated long time series as the input, the MFCC features lead to an average improvement of 5.1% in model validation accuracy and a 60.4% reduction in training time, which are significant improvements in both. However, it still does not prove that the hand-crafted features are the most suitable features for the target task (structural seismic response prediction). After all, MFCCs correspond to human ear structures, not building structures.  
On the other hand, better performance of the task network can be achieved by balancing the number of hidden layers, the number of cells in the hidden layers, and the network complexity, as shown in Table 1. Here, models 4, 6, 7, 9, and 13 have fewer parameters and a higher verification accuracy.
## 3 Experimental Results and Analysis  
### Temperature Change of Bulk Grain Pile  
Temperature is an important factor affecting the safety of grain storage. To study the temperature variation of grain piles under different pressures, the temperature change data of grain pile at different measuring points for 72 h were measured. The temperature variation curves of nine measurement points on the vertical plane along the direction of temperature gradient, under different vertical pressures, are shown in Figure 2.  
Figure 2 shows that under the same pressure, owing to the continuous heat exchange between the side-wall temperature boundary and the grain pile, the grain temperature near the temperature boundary changes linearly under the influence of the large temperature-difference between the temperature control boundary and the grain pile. Further, with the elapse of time, the slope of temperature change gradually decreases, indicating that owing to the continuous heat transfer, the rate of change of temperature in the bulk grain pile slows down. When the static storage time is 72 h and the vertical pressure is 50 kPa, the average rate of change of temperature change at a4, b4, and c4 in the middle section (parallel to _YOZ_, \(X\) = 300 mm, hereinafter referred to as \"the middle plane\") are the smallest at 0.002 \({}^{\circ}\)C/h; when the vertical pressure is 100 kPa, the average rate of change of temperature change is 0.016 \({}^{\circ}\)C/h; when the vertical pressure is 200 kPa, the average rate of change of temperature change is the maximum at 0.038 \({}^{\circ}\)C/h. The results show that the porosity of the grain pile decreases with the increase in the vertical pressure, and the heat transfer rate increases through the grain particles.