esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
For example, high-roughness surfaces might be more conducive to cell adhesion and osseointegration, while surfaces with lower roughness but moderate hardness might be more suitable for applications requiring wear resistance. Figure 11 demonstrates cell attachment on the Ti-64 alloy under different surface treatment conditions. These results reveal the significant impact of surface treatment methods on cellular behavior, providing important evidence for optimizing the surface design of biomedical implants. Figure 11a shows cell attachment on untreated (only polished) Ti-64 surface. We can observe that cells exhibit an elongated morphology, arranged closely with a clear directionality. Cell nuclei (dark purple) are clearly visible, with well-extended cytoplasm (light purple). This indicates that while the smooth surface allows cell attachment and growth, it may lead to cell alignment in specific directions, which might not be conducive to forming natural bone tissue structure [40]. Figure 11b (2 kg/cm\({}^{2}\), 5 s shot peening + 3 M HF corrosion) displays a cell morphology starkly different from the untreated surface. Cells appear round or oval, distributed relatively uniformly but sparsely. This morphology may indicate that cells are in the initial attachment stage, not yet fully spread. The surface treatment may have increased initial attachment points but has not yet provided an ideal long-term growth environment. Figure 11c (1 kg/cm\({}^{2}\), 10 s shot peening + 3 M HF corrosion) shows a more desirable cell attachment state. The number of cells has significantly increased, with diverse morphologies, including both round initial attachment cells and spread irregular-shaped cells. Cell distribution is relatively uniform, and intercellular connections can be observed. This surface treatment may have provided the most suitable microenvironment, promoting both initial attachment and subsequent spreading and proliferation. Figure 11d (0.5 kg/cm\({}^{2}\), 20 s shot peening + 3 M HF corrosion) also shows good cell attachment effects, but compared to Figure 11c, cell morphology is more irregular, and distribution is slightly uneven. Some areas show cell aggregation, forming colony-like structures. This might suggest that lower intensity but longer duration shot peening treatment created a more complex surface morphology, providing a diverse microenvironment for cells. These observations emphasize the important influence of surface treatment parameters on cellular behavior. The shot peening treatment combined with HF corrosion seems to create surface characteristics favorable for cell attachment and growth. Different treatment parameters led to notably different cellular responses, which may reflect changes in surface morphology, chemical composition, and energy state.
## 5 Conclusions  
In this paper, an IPMSM with the optimized structure using fractional-slot single-double-layer hybrid stator windings and \(\
abla\)-type magnetic-pole rotor topology based on a basic structure was presented. Both the rated condition and limited condition were simulated and tested in the experiment, and the EMFs, cogging torque, average torque, efficiency, temperature and torque ripple were analyzed. The following was found:  
After adopting the new single-and-double-layer hybrid winding and \(\
abla\)-type magnetic-pole rotor topology, the optimized structure is basically equivalent in terms of the output torque, operating range, efficiency, etc., but the torque ripple has been significantly reduced either in the simulation or experiment.  
In addition, with the amount of material usage cut down, the manufacturing cost has also been reduced. The reduction in the motor volume and the new type of winding arrangements worsen the capability of the heat to dissipate. The temperature of the optimized structure is higher than the original one in the rated and limited conditions. The experiment shows that the steady-state temperature of the prototype motor is below the maximum limited temperature, and the optimized machine is able to operate 250 s before its temperature reaches the limit of 180 \({}^{\circ}\)C. Therefore, the optimized prototype can completely satisfy the request of aircraft drive systems.  
The findings of this comparison analysis can be used in the design stage of an IPMSM aiming to reduce the torque ripple in a high-speed situation, such as aircraft application. Furthermore, there is potential for the improvement in the rotor magnetic-pole topology, including the shape and dimensions of the magnets. In addition, the safety of the motor can also be improved in terms of new cooling methods and the motor shell structure.  
Software, F.X.; resources, Y.X.; writing--original draft, F.X. and R.Y.; test supporting; writing--review and editing, F.X., R.Y. and K.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (52237002) and (52207055), in part by the China Postdoctoral Science Foundation funded project (2022M721232) and the Key Research and Development Plan of Hubei Province (2022 BAA097) and (2022 BAA100).  
Not applicable.  
The data presented in this study are available in the article.  
Thanks for the technical advice and market application support by [PERSON].  
Figure 26: The experimental torque waveforms and harmonic distribution under rated operating condition: (**a**) torque waveforms; and (**b**) harmonic distribution.
## 2 Multilevel Edge Network Task Offloading Model
### Offloading Node Resources  
In the multilevel edge network constructed in this study, the utility functions of the terminal, the UAV, and the G-MEC sides relayed by the UAV are calculated. The UAV can be charged by solar panels, so the UAV energy storage situation can be divided into the high energy level of charging completion and the low energy level of unfilled energy. Moreover, the UAV utility function is related to its energy level. The comprehensive utility function identifies the optimal resource optimization strategy using the MDP algorithm, thereby ensuring system reliability and QOS.  
The utility function is divided into three offloading modes: local, UAV, and G-MEC offloading. That is, the task performs computation on the terminal device side, the task offloads to the UAV side, which acts as the MEC server, and the task offloads through the UAV relay to the distal G-MEC side for computation. The task has a random size of \(Z\) bit, and running 1 bit requires k CPU (Central Processing Unit) cycles. The total number of CPU cycles, \(N_{z}\), is required to process the \(Z\) bit task. \(f(a)\) is the reward function for the task to complete the calculation within the maximum task processing time and the equipment within the minimum equipment remaining charge. The decision action set is \(A\), which includes \(O_{u}\), \(O_{A}\), and \(O_{M}\), where \(O_{A}\) and \(O_{M}\) indicate that the terminal equipment offloads the task to the UAV and G-MEC. Meanwhile, \(O_{u}\) denotes the action in which the task is processed at the mobile device. A can be described by \(A=\{O_{u}O_{A}O_{M}\}\). \(R_{\text{ou}}\), \(R_{\text{ouav}}\), and \(R_{\text{oGMEC}}\) are reward values for corresponding terminal computation, UAV offloading, and G-MEC offloading, respectively.
## 7 Influence of the Damage Depth and Size on the Scattering Directivity Pattern
The numerically calculated normalized scattered field for different aspect ratios is shown in Figure 15b. In all four cases, the maximum scattered amplitude of A\({}_{0}\) mode is towards the 45\({}^{\circ}\) direction. It is observed that as the aspect ratio increases, the extent of scattered wave fields gets narrow in size. For an aspect ratio equal to 1/2 (shown in solid line), the angles of the minimum scattered amplitude are near \(\sim\)120\({}^{\circ}\) and \(\sim\)330\({}^{\circ}\). For the case of an aspect ratio equal to 2, the angles of minimum scattered amplitudes are near \(\sim\)20\({}^{\circ}\) and \(\sim\) 70\({}^{\circ}\). Except for the aspect ratio of 1/2, the scattered field along the direction 120\({}^{\circ}\) towards 330\({}^{\circ}\) (in anticlockwise manner) is negligible. The size of the extent of the scattered field can give information about the size of the base of the conical defect. From the scattering directivity pattern, one can have an idea about the base size of the conical damage inside the composite plate, once the through-thickness depth is known. Finally, the variation of amplitude of the scattered A\({}_{0}\) mode with respect to aspect ratios in the direction of 45\({}^{\circ}\) is shown in Figure 16. It is observed that with the increase in aspect ratio, the amplitude of scattered field also increases. In real experiments, it is naturally expected that the increase of the impact energy will simultaneously increase the depth and width of the damage area. According to the literature, as the damage area increases, the delamination and matrix crack contributions in the scattered wavefield will then increase with the same proportions in most directions. The contribution of the delamination is expected to be more important and the wavefield possesses a larger amplitude on the transmission side than in the reflection side [16; 25; 46]. Figure 14: Effect of the through thickness of the conical defect on the scattered A\({}_{0}\) mode towards the 45\({}^{\circ}\) direction. Figure 15: Effect of aspect ratios (2 \(\tau\)/\(\lambda\)): (**a**) Schematics for four different aspect ratios (0.5: red, 1: green, 1.5: black, 2: blue), (**b**) normalized scattered amplitude of A\({}_{0}\) mode.
## 3 Antenna Performance and Discussions
Furthermore, the antenna height is only 0.3 \(\lambda_{0}\), and it has a beam coverage of \(\pm 60\) degrees on the azimuth plane.
# A Deep Attention Model for Environmental Sound Classification from Multi-Feature Data
## 2 Related Work
### Deep Learning  
One-dimensional convolution: [PERSON] et al. [11; 12] developed a one-dimensional architecture of EnvNet v1/v2, which takes advantage of one-dimensional convolution kernel length to extract time feature information; [PERSON] et al. [13] trained an 18-layer 1D-CNN network using the original audio waveform as input, and its performance is comparable to that of the contemporaneous 2D-CNN, which has two-dimensional spectrograms as feature input. However, with the above two methods, their idea is limited to features on one level or one scale of image classification. For audio, the recognition features are usually on different levels or time scales. To address this problem, [PERSON] et al. [14], while drawing on the fact that different sound categories differ greatly in different time scales and levels, used different one-dimensional convolution kernels to extract the multi-scale temporal information of the original audio, which effectively improves the sound recognition precision. However, the original audio data, containing a lot of noise, will affect the final recognition accuracy if used directly as the input of the network. [PERSON] et al. [15] initialized the first layer of the 1D-CNN model as a Gammatone filter bank to simulate the processing of the input signal by a human hearing response mechanism, which involves fewer network parameters and further improves the recognition accuracy. Although Gammatone filtering is performed on the input audio, this model remains prone to the interference by a large amount of noise carried by the input audio. Extracting one-dimensional audio signals with 1D-CNN fails to consider the temporal structure and the frequency characteristics of the environmental sound when features are extracted at the global level.  
Two-dimensional convolution: [PERSON] et al. [16] studied environmental sound recognition using the MFCC spectrogram as the input of the network. However, the MFCC relies on discrete cosine transform (DCT) to extract coefficient features, which leads to insufficient structural information of the audio signal, resulting in unsatisfactory performance when working with deep neural networks. [PERSON] [17] fed Log-Mel and its delta spectrogram instead of MFCC as two-dimensional features into the network for classification, which has significantly improved the recognition performance. However, limited by the number of sound samples, the network cannot learn more features. [PERSON] et al. [18] proposed several data augmentation strategies to generate new training samples by stretching time, adding background noise, and transforming pitch. Its accuracy improves by 6% compared with that proposed by [PERSON] [17]. To increase the number of samples further for network learning, [PERSON] et al. [19] combined Log-Mel and Gammatone spectrogram features into the network for feature extraction, and [PERSON] et al. [20] constructed a two-way CNN model to enter the original audio and Log-Mel spectrogram into two different CNN structures, respectively, for time-frequency feature extraction.
## 3 Results and Discussions  
### XPS Analysis  
The assignment patterns of carbon, oxygen, nitrogen, and sulfur and their corresponding chemical environments in LR@1 and LR@2 at room temperature were obtained by X-ray photoelectron spectroscopy, and the results and data analysis are shown in Figure 2 and Table 2. From Figure 2, it can be observed that the carbon structures in both LR@1 and LR@2 lignite coals are mainly composed of aliphatic carbon and aromatic carbon (C-C, C-H) at binding energies of 284.80 eV, 284.20 eV, and 284.11 eV, respectively. Oxygenated carbon (C-OH, C-O-H) is also present at 286.44 eV and 286.13 eV. The oxygen in coal is mainly organic oxygen and inorganic oxygen. Inorganic oxygen in coal mainly originates from minerals, such as kaolinite, montmorillonite, quartz, carbonate, and sulfate, which are mainly formed through the crushed minerals mixed in the coal formation process or minerals precipitated in the later geological action. Organic oxygen in coal is the oxygen directly involved in the structure of coal organic macromolecules and exists in oxygen-containing functional groups. Due to the small amount of nitrogen and sulfur structures in the coal and the complex forms of their existence, in order to accurately determine the distribution and relative proportions of nitrogen and sulfur structures in the two lignite coals, the results of the X-ray photoelectron spectroscopy (XPS) peaks analysis of the experimentally obtained nitrogen and sulfur structures are summarized in Table 2 and compared with the experiments of [PERSON] et al. [27] and [PERSON] et al. [28]. It can be found that the nitrogen and sulfur structures in the two lignites are mainly dominated by pyridine, pyrrole, and thiophene. The nitrogen structure of LR@1 and LR@2 lignite is mainly pyridine-type nitrogen (399.38 eV and 398.87 eV, N-6) and pyrrole-type nitrogen (402.58 eV and 400.88 eV, N-5). The sulfur structures are primarily thiophene-type sulfur and sulfone/sulfoxide-type sulfur. In both lignites, thiophene-type sulfur constitutes the highest relative content of sulfur at 47.63% and 51.01%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & & & & & \\ \cline{2-6}
**Coal Sample** & \multicolumn{3}{c}{**N**} & \multicolumn{3}{c}{**S**} \\ \cline{2-6}
**Coal Sample** & \multicolumn{3}{c}{**Peak Value**} & \multicolumn{1}{c}{**Elemental**} & \multicolumn{1}{c}{**Relative**} & \multicolumn{1}{c}{**Peak Value**} & \multicolumn{1}{c}{**Elemental**} & \multicolumn{1}{c}{**Relative**} \\  & & **Structure** & **Content (\%)** & **State** & **Structure** & **Content (\%)** \\ \hline  & 399.38 & Pyridine & 64.37 & 164.62 & Thiophene & 47.63 \\ LR@1 & 402.58 & Pyrrole & 35.63 & 168.37 & Sulfone & 23.42 \\  & 398.87 & Pyridine & 32.99 & 164.08 & Thiophene & 51.01 \\ LR@2 & 400.88 & Pyrrole & 42.12 & 168.42 & Sulfone & 48.99 \\  & 403.20 & Oxidized nitrogen & 24.89 & & & \\ \hline \end{tabular}
\end{table}
Table 2: Analysis of C 1s, O 1s, N 1s, and S 2p in two lignite coals, LR@1 and LR@2.
## 5 Experiments
### Module Validation  
Considering that the model in this work mainly models the spatiotemporal relationships in trajectories and then uses these relationships to predict the time series of trajectories based on the trajectory space sequence, in this section, we only analyze the results. For time series prediction, we use two indicators, the RMSE and MAE, to measure the quality of the time series prediction results, as shown in (24) and (25).  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(\hat{Y_{i}}-Y_{i})^{2}} \tag{24}\]  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}|\hat{Y_{i}}-Y_{i}| \tag{25}\]  
The comparative experiments and ablation experiment that we selected are the ones mentioned in this section. The experimental results are shown in Table 3. From the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline Spatial & Temporal & STLC & STLCSS \\ \hline \multirow{4}{*}{Spa} & Attn-h & 0.7556 & 0.2129 \\  & Attn-c & 0.7450 & 0.2175 \\ \cline{1-1}  & Attn-s & 0.7531 & 0.2148 \\ \cline{1-1}  & Attn (ours) & **0.7681** & **0.2086** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of ablation study.  
experimental results, it can be seen that, compared to traditional methods such as the history and linear methods, the experimental results for the deep learning-based methods RNN and LSTM are slightly better, while the proposed method, which considers learning spatiotemporal relationships from historical trajectories, has a better experimental effect than ordinary deep learning methods. Moreover, from the experimental results for Attn-h, Attn-c, and Attn-s, it can be seen that historical trajectories play a significant role in time prediction tasks. Models that add historical trajectories perform significantly better than when directly modeling the spatiotemporal relationships of trajectories. Additionally, using deep learning methods to understand the spatiotemporal relationships in trajectories is also significantly better than using traditional statistical methods, which is consistent with the previous conclusions and further validates the effectiveness of the model.
# Simulation and Optimisation of Utility-Scale PV-Wind Systems with Pumped Hydro Storage
## 3 Computational Results and Discussion
### Optimising a PGS  
The same system as in Section 3.2 is optimised considering no load to be covered, that is, considering it as a PGS where we want to maximise NPV. The same data as in Section 3.2 were used here, with the only difference being that there was no load consumption in this case.  
Table 5 lists the results of the three optimisations: systems without PHS, systems with PHS type A, and systems with PHS type B. As for the LSS system, the optimal system without a PHS is better than that with PHS (higher NPV and lower LCOE), thereby concluding that with the costs considered for the PHS, it is not worth the energy storage. In PHS optimal systems, the pump turbine is the one of the smallest sizes (0.5 MW), which is similar to that in the LSS systems; however, the storage duration is not the minimal value, and now it is 6 h. Owing to the low power of the pump turbine, the differences between types A and B are low, as indicated in LSS.  
Figure 23: Simulation of two consecutive days, March 9–10, year 12. LSS Optimal system with PHS, type B project.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Project Type**} & **PGS—Optimal System without PHS** & \
\begin{tabular}{c} **PGS—A (Buy for** \\ **Arbitrage)—Optimal** \\ **with PHS** \\ \end{tabular} &
\begin{tabular}{c} **PGS—B (No Buy for** \\ **Arbitrage)—Optimal** \\ **with PHS** \\ \end{tabular} \\ \hline \(P_{PV\_N\_AC}\) (MWac) & 2 & 3 & 3 \\ \(P_{WT\_N}\times N_{WT}\) (MW) & 0.5 \(\times\) 5 = 2.5 & 0.5 \(\times\) 5 = 2.5 \\ \(P_{P\_N}=P_{HT\_N}\) (MW) & 0 & 0.5 & 0.5 \\ \(V_{UR\_max}\) (dam\({}^{3}\)) & 0 & 16.2 & 16.2 \\ Storage duration (h) & - & 6 & 6 \\ \(P_{FE\_Eurch\_load\_step}\), first year (EUR/kWh) & - & - & - \\ \(P_{\_}{\text{s}}\) (\%) & - & 60 & 60 \\ \(P_{FE\_Eurch\_sell\_Low\_step}\), first year (EUR/kWh) & - & 0.048 & 0.048 \\ \(P_{FE\_Eurch\_sell\_High\_step}\), first year (EUR/kWh) & - & 0.048 & 0.048 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of the different optimisations: PGS system.  
As in LSS, a minimum hydro turbine load in the PGS is high (60% of the rated power) to improve hydro turbine efficiency.
## 4 Results and Discussion
### YOLOv11n-seg Model Selection and Comparison with Other Models
#### 4.4.2 Comparison with YOLOv8n-seg  
In the initial phase, the YOLOv8n-seg model was also trained on the same dataset (519 training and 176 validation images). This model contains 85 layers and approximately 3.2 million parameters. Despite having more parameters, its computational load is higher than that of YOLOv11n.  
As shown in same figure, the following results were obtained:  
* mAP@50: 0.902 for sharp, and 0.919 for smooth.
* mAP@50-95: 0.692 for sharp, and 0.642 for smooth.  
However, the sample outputs of YOLOv8n shows that one sharp object was identified with only 0.38 confidence, and, in another case, a sharp object was incorrectly labeled as smooth.  
Comparing the output images of both models, it is evident that YOLOv11n-seg performs more reliably and accurately, especially in cases where object shapes are similar or have slight edge overlaps.
# Remote Sensing for Irrigation of Horticultural Crops
## 3 Sensors and Platforms
### Remote Sensing Platforms
#### 3.2.1 Satellite  
Satellites can be classified by (i) altitude; (ii) orbital geometry (geostationary, equatorial and sun synchronous) and (iii) timing. Regarding the first point, there are space shuttles orbiting around 250-300 km above the earth; space stations orbiting from 300 to 400 km above; low level satellites from 700 to 1500 km above, and high-level satellites, about 36000 km above the earth's surface. The first remote sensing satellite appeared in 1960 for meteorological purposes. Geostationary satellites have a 24 h-period of rotation, so they always stay over the same earth location. Usually, communications and weather satellites have often-geostationary orbits, generally located over the equator. To ensure repeatable sun illumination conditions during specific seasons, sun synchronous satellites have orbits nearly passing over the poles. Sensor and orbital characteristics determine the frequency of data acquisition (Table 1). The satellite frequency ranges from twice a day to every 16 days.
# A Novel Fiber Optic Sensor for Microparticle Velocity Measurement Using Multicore Fiber
## 3 Results
For the lower limit of particle size, the particle should not be too small to pass through the gap among cores without passing through any cores, in order to prevent the sensor from measuring any signal. According to the requirement, the calculated distance of the gap is 28 \(\upmu\)m, so the lower limit of particle diameter is 28 \(\upmu\)m. Hence theoretically, a spherical particle whose diameters range is from 28 to 91 \(\upmu\)m is measurable. Please note that the above estimation is based on the ideal case that the particle is spherical. In reality, non-spherical particles not within the above sizes may also be measured as the projection of non-spherical particles and may meet the requirements. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbf{\alpha}\) & \(\mathbf{V_{a}}\) & \(\mathbf{V_{b}}\) & \(\mathbf{V}\) \\ (\({}^{\circ}\)) & **(mm/s)** & **(mm/s)** & **(mm/s)** \\ \hline
0 & 27.53 & 14.01 & 27.53 \\ \hline
5 & 27.62 & 15.84 & 27.45 \\ \hline
10 & 26.44 & 18.06 & 27.14 \\ \hline
15 & 25.92 & 19.28 & 26.93 \\ \hline
20 & 26.26 & 21.58 & 28.01 \\ \hline
25 & 24.49 & 23.07 & 27.50 \\ \hline
30 & 24.48 & 24.48 & 27.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Direction angle \(\alpha\) and velocity data of \(V_{a}\), \(V_{b}\) and \(V\). Figure 7: Measured particle velocity versus moving direction \(\alpha\). Figure 8: Measured velocity versus actual velocity: (**a**) \(V_{a}\) with error bars; (**b**) \(V_{b}\) with error bars; (**c**) \(V\) velocity of the particle.