esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### GF and MODIS Fusion Results  
By inputting a base GF-1 WFV image, two days of MOD9 GA data and two days of multi-spectral GF-1 WFV images, a synthetic multi-spectral GF-1 WFV image was generated by STDFA. By inputting a base GF-1 WFV image, two days of multi-spectral MOD9 GA data and two days of multi-spectral MODIS data and GF-1 WFV datasets, a synthetic multi-spectral GF-1 WFV image was also generated by ESTARFM. These synthetic multi-spectral GF-1 WFV images contained four bands, including blue, green, red and NIR. Figure 3a shows the actual observed MODIS surface reflectance in the red band acquired on 7 October 2013 in Kuche and Luntai. Figure 3b, c show the synthetic GF-1 WFV surface reflectance imagery in the red band generated by STDFA and ESTARFM, respectively, for the two study areas. Figure 3d shows the actual observed GF-1 WFV red-band surface reflectance acquired on 7 October 2013 in Kuche and Luntai.  
Through visual interpretation, we found that the synthetic and actual GF-1 WFV data are very similar and were not distinguishable with the naked eye. Table 5 shows the results of the quantitative evaluation using the correlation analysis method. From Table 5, we also find that both STDFA and ESTARFM can generate synthetic GF-1 WFV data that are very similar to actual GF-1 WFV data, with \(r\) values greater than 0.8643. ESTARFM performed slightly better than STDFA in Kuche, while the accuracies of these two methods were very similar in Luntai.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Kuche**} \\ \hline \multirow{2}{*}{**Parameters**} & \multicolumn{6}{c}{**STDFA**} & \multicolumn{6}{c}{**ESTARFM**} \\ \cline{2-10}  & Blue & Green & Red & NIR & Blue & Green & Red & NIR \\ \hline \(r\) & 0.9493 & 0.9642 & 0.9759 & 0.9231 & 0.9463 & 0.9601 & 0.9714 & 0.8989 \\ Var & 0.0001 & 0.0002 & 0.0002 & 0.0001 & 0.0002 & 0.0002 & 0.0002 & 0.0002 \\ MAD & 0.0091 & 0.0104 & 0.0096 & 0.0086 & 0.0093 & 0.0110 & 0.0106 & 0.0100 \\ RMSE & 0.0494 & 0.0422 & 0.0166 & 0.0364 & 0.0425 & 0.0305 & 0.0218 & 0.0177 \\ bias & \(-\)0.0479 & \(-\)0.0399 & \(-\)0.0109 & \(-\)0.0344 & \(-\)0.0406 & \(-\)0.0270 & \(-\)0.0169 & \(-\)0.0115 \\ \hline \hline \multicolumn{10}{c}{**Luntai**} \\ \hline \multirow{2}{*}{**Parameters**} & \multicolumn{6}{c}{**STDFA**} & \multicolumn{6}{c}{**ESTARFM**} \\ \cline{2-10}  & Blue & Green & Red & NIR & Blue & Green & Red & NIR \\ \hline \(r\) & 0.9591 & 0.9640 & 0.9761 & 0.9608 & 0.9585 & 0.9598 & 0.9724 & 0.9475 \\ Var & 0.0002 & 0.0002 & 0.0002 & 0.0001 & 0.0002 & 0.0003 & 0.0002 & 0.0002 \\ MAD & 0.0098 & 0.0114 & 0.0102 & 0.0081 & 0.0099 & 0.0119 & 0.0108 & 0.0108 \\ RMSE & 0.0461 & 0.0404 & 0.0336 & 0.0226 & 0.0466 & 0.0344 & 0.0275 & 0.0175 \\ bias & \(-\)0.0441 & \(-\)0.0375 & \(-\)0.0308 & \(-\)0.0197 & \(-\)0.0446 & \(-\)0.0305 & \(-\)0.0235 & \(-\)0.0098 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the correlation analysis between synthetic and actual HJ CCD data.
## 4 Experiments and Analysis
### Experiment Setting
#### 4.2.3 Network Fine-Tuning  
A compact network with lower accuracy is obtained after network pruning. Then the pruned network is fine-tuned for three epochs with a learning rate of 0.001.
## 3 Materials and Methods
### QoS Routing  
The shortest path algorithm determines the path with the lowest connection cost, specified by a value inversely proportional to the available bandwidth of each connection. [PERSON]'s algorithm is usually used for this type of search. In our case, we create variations of the shortest path using [PERSON]'s algorithm to consider the path metrics with the lowest bandwidth cost and minimum hops, delay, and packet loss. [32].  
* The minimum hops path computes the shortest path with the minimum number of links between the source and destination node. \[H=min\;(h(P))\] (7)
* Minimum cost of available bandwidth path: \[B=min(\lambda(P))\] (8)
* Minimum delay path: \[D=min(\mu(P))\] (9)
* Minimum packet loss path: \[PL=min(\rho(P))\] (10)
## 3 Results and Discussions
### Optimization of the \(InGaP/GaAs\) DjSCs with Buffer and New Top BSF Layers
#### 3.3.5 Stability Challenges and Enhancement Strategies for InGaP/GaAs DjSCs  
Ensuring the long-term stability of \(InGaP/GaAs\) (DjSCs) is essential for their durability and commercial viability. This can be met through the design optimization of different used materials and their structure, minimizing the impact of the degradation factors such as thermal stress, moisture, light exposure, and mechanical stress. Indeed, it was proved that the use of optimized wide-bandgap materials like \(AlGaAs\) and \(AlGaInP\) enhance performance and stability of solar cells by reducing recombination losses. Additionally, optimizing the layers' properties and applying protective coatings can help significantly in preventing the degradation. However, it was proved that regular monitoring and testing can ensure early detection of different anomalies, allowing for timely corrective measures to maintain optimal performance and reliability.
# A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data
###### Abstract  
Temperature is a core element of the regional climate system and plays a key role in energy exchange and weather evolution. The current reanalysis of temperature data faces difficulties in providing more accurate geographical temperature data due to insufficient spatial resolution (0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\)). In this study, a lightweight downscaling method incorporating a convolutional neural network is proposed to construct a high-resolution temperature prediction model for the Macau region based on ERA5 reanalysis data. Aiming at the existing data due to insufficient resolution, a two-stage convolutional feature extraction module is introduced to optimize the model parameters by combining them with the observation data of Macau meteorological stations. The experimental results show that the accuracy of this method is 21.4% higher than that of the traditional interpolation method in the instantaneous prediction, and the prediction effect in the next 3 h is also very good. The model is expected to be extended to other regions in the future, providing an effective solution for obtaining long-term high-resolution temperature data in other regions, which can support the refinement of meteorological services and climate research.  
ERA5 reanalysis data; convolutional neural network; temperature prediction Article  
Academic Editor: [PERSON]  
Received: 5 May 2025  
Accepted: 7 May 2025  
Published: 9 May 2025  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]. A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data. _Appl. Sci._**2025**, 15,5321. [[https://doi.org/10.3390/app15105321](https://doi.org/10.3390/app15105321)]([https://doi.org/10.3390/app15105321](https://doi.org/10.3390/app15105321))
# RSS Indoor Localization Based on a Single Access Point
## 2 Related Work  
This section provides an overview of fingerprint-based localization methods and the related work that considers multipath and polarization effects in indoor localization procedures. The section also summarizes relevant localization evaluation techniques.
## 3 AYOLO Model (Attention YOLO)
### AYOLO Architecture
In order to prevent information loss in the layer-to-layer transmission process, we propose a logical aggregation and information extraction mechanism (Semantic Aggregation, Enhancement, and Distribution Module--SAEDM) by integrating new location and channel information, inspired by the Visual Transformer (ViT) module. We propose an integrated module that takes information from all levels, integrates them, and then takes advantage of the attention mechanism to use it at different levels. With this model, \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Methods** & **AP0** & **AP95** & **APs** & **APm** & **API** & **Para** & **FLOPS** \\ \hline Methods & AP0 (\%) \(\uparrow\) & AP95 (\%) \(\uparrow\) & APs (\%) \(\uparrow\) & APm (\%) \(\uparrow\) & API (\%) \(\uparrow\) & Para (M) \(\downarrow\) & FLOPS (G) \(\downarrow\) \\ DETR & 79.9 & 4.60 & 14.7 & 34.4 & 61.8 & 39.37 & 86 \\ YOLOv5s & 79.6 & 46.3 & 17.6 & 37.2 & 58.3 & 6.72 & 15.9 \\ YOLOv5m & 80.9 & 47.3 & 17.4 & 38.2 & 62.0 & 19.94 & 48 \\ DAMO-YOLOs & 81.6 & 48.7 & 19.0 & 40.8 & 57.5 & 16.3 & 37.8 \\ DAMO-YOLOm & 81.8 & 50.1 & 18.7 & 40.3 & 61.8 & 28.2 & 61.8 \\ YOLOv6m & 81.0 & 49.3 & 20.3 & 39.7 & 62.7 & 34.86 & 103.4 \\ YOLOv6x & 83.0 & 49.2 & 21.6 & 39.3 & 62.6 & 67.62 & 188.3 \\ YOLOv8n & 80.3 & 47.7 & 15.8 & 37.8 & 62.7 & 3.01 & 8.1 \\ YOLOv8s & 81.8 & 49.2 & 18.7 & 38.7 & 62.5 & 11.13 & 28.5 \\ YOLOv8m & 82.2 & 49.9 & 18.3 & 39.9 & 63.0 & 25.85 & 78.7 \\ YOLOv11 & 82.5 & 48.9 & 20.8 & 38.6 & 62.9 & 35.60 & 92.1 \\ AYOLO & 83.2 & 49.4 & 21.7 & 39.8 & 63.1 & 32.8 & 89.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Technology comparison. we prevent the information loss inherent in the traditional YOLO structure and increase information fusion and inference capabilities without significantly increasing latency. In our application, the SAEDM module consists of the following parts:  
* Dilated convolutions: Using \"Dilated Convolutions\" that expand the convolution filter to obtain non-neighboring and more distant information. * Pyramid architecture: Using the pyramid structure to better integrate features at different scales and obtain semantic information. In the YOLO architecture, information inputs come from the layers formed as a result of downlinking the backbone. In addition, the sizes of the feature maps from each layer are, respectively, R, \(\frac{1}{2}R\), \(\frac{1}{4}R\), and \(\frac{1}{8}R\). Batch size: N; channels: C; and dimensions: R = H \(\times\) W.
# Four-Port MIMO Antenna System for 5G n79 Band RF Devices
## 4 Conclusions  
This article presented an inverted L-shaped antenna with a defected ground structure. The proposed antenna was designed to resonate at the resonance frequency of 4.8 GHz allocated for the 5G n79 band. Furthermore, the proposed antenna was transformed into a four-element MIMO array system in such a way that all four radiating elements were perpendicular to each other, so that the minimum isolation achieved was 25 dB. The proposed MIMO system was fabricated and tested at an in-house facility, and it was found that the simulated and measured results were in close agreement in satisfying the basic MIMO parameters. The proposed MIMO antenna is compact in size and can therefore be considered a promising candidate for future 5G RF devices.  
Conceptualization, H.A. and I.B.; methodology, H.A. and I.B.; software, H.A. and I.B.; validation, S.I.M., M.R.A. and I.B.; formal analysis, S.I.M. and N.J.; investigation, X.-C.R. and W.U.K.T.; resources, X.-C.R. and A.M.H.; data curation, S.I.M. and W.U.K.T.; writing--original draft preparation, M.A.B. and M.A.K.; writing--review and editing, M.A.B. and M.A.K.; visualization, M.R.A.; supervision, X.-C.R. and A.M.H.; project administration, H.A. and N.J.; funding acquisition, X.-C.R. and M.R.A. All authors have read and agreed to the published version of the manuscript.  
This project was supported by the National Natural Science Foundation of China (61861043).  
All the data have been included in the study.  
The authors declare no conflict of interest.
## 3 Research Methodology
### Data Collection  
The questionnaire was developed using the SurveyMonkey platform and distributed primarily through the Saudi Engineering Council, as well as via email and various social media channels. A total of 142 responses were received, of which 106 were fully completed. The survey specifically targeted participants with substantial professional experience to ensure the reliability of the data, reflecting real-world conditions in the field. To qualify for participation, respondents were required to meet the following criteria: hold a specific professional role within their organization, such as architect, engineer, project manager, contractor, or consultant, with an option to specify their role if it was not listed; demonstrate an awareness of digital twin technologies and AI applications; and possess at least five years of experience in the Saudi construction sector. These criteria ensured that the survey captured insights from knowledgeable and experienced professionals, thereby enhancing the validity of the findings.
## 4 Conclusions  
This paper presents the shape optimization of a PMSM using an MLP, a kind of deep learning method. To maximize the performance of the PMSM, the average torque and THD of the back EMF were set as the multi-objective function, and efficiency and torque ripple were set as constraints. FEA was used to improve the accuracy of the design results, and DOE was performed based on changes of the design variables. As a result of the parametric study by ANOM, the rib thickness was determined to be a design variable that greatly affects the responses. Based on the DOE results, metamodels were generated by applying the proposed MLP technique and the conventional Kriging technique. Shape optimization was performed by applying the generated metamodels and HMA, and the results were compared. Average torque was increased by 2.2% with the MLP and by 1.3% with the Kriging model. In both cases, the THD of the back EMF, efficiency, and torque ripple were nearly equal. Therefore, in this design model, the metamodel using the MLP achieved better prediction performance than the conventional Kriging model. The back EMF was also increased by 4.3% compared with the initial model owing to the improvement of the magnetic flux flow. The structural stability of the optimal model was verified by  
Figure 12: Mechanical stress generated in the rotor of the optimal model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Items**} & \multicolumn{1}{c}{**Unit**} & \multicolumn{1}{c}{**Initial (FEA)**} & \multicolumn{3}{c}{**Optimal (MLP)**} \\ \hline Weighting ratio (Average torque: THD of the back EMF) & - & - & 0.50.5 & 0.40.6 & 0.3:0.7 \\ \hline \multirow{2}{*}{Design variables} & Inner angle between PM & \({}^{\circ}\) & 136 & 142 & 142 & 142 \\  & Rib thickness & mm & 2.0 & 1.6 & 1.6 & 2.0 \\ \hline \multirow{4}{*}{Design results} & Average torque & N-m & 60.05 & 61.36 & 61.36 & 60.29 \\  & THD of the back EMF & \% & 3.519 & 3.526 & 3.526 & 3.488 \\ \cline{1-1}  & Efficiency & \% & 91.43 & 91.59 & 91.59 & 91.46 \\ \cline{1-1}  & Torque ripple & \% & 6.951 & 7.196 & 7.196 & 6.963 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Optimization results according to the weighting change.  
mechanical stress analysis. Optimal design analysis was carried out by changing the weighting of the multi-objective function, and it was confirmed that the THD of the back EMF was difficult to improve compared to the average torque. This study shows that an MLP, a type of deep learning technique, is useful as a metamodeling technique for shape optimization of PMSM. It is expected that MLPs can be used for the shape optimization of various electric machines in the near future.  
This study was supported by research fund from Honam University, 2018 and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT). (No. NRF-2018R1C1B5046117). The author express gratitude to PIDOTECH and FRONITS for their technical support.  
The author declares no conflict of interest.
# A Modeling Design Method for Complex Products Based on LSTM Neural Network and Kansei Engineering
## 4 Discussion  
The perceptual evaluation model based on the LSTM neural network (KE-LSTM) is the basis of the proposed CP modeling design method (CP-KEDL). In order to verify the validity of the LSTM neural network, two kinds of neural networks were built as controlled experiments to compare the evaluation results. As a well-known basic traditional machine learning tool [59], Deep Neural Networks (DNNs) have been applied in many controlled experiments to validate the efficiency of research [59; 60; 61]. In addition, CNNs were reported that have good performance in processing data that come in the form of multiple arrays [62]; for example, many data modalities are in the form of multiple arrays: 1D for signals and sequences, including language [63; 64]; 2D for images or audio spectrograms [65]; and 3D for video or volumetric images [66]. Therefore, DNN and CNN were chosen to be the compared models. The structures of the two networks were set as follows:  
(1) DNN, a two-layer network structure, has 32, 24, and 3 neurons in the input, hidden and output layers, respectively.  
(2) CNN has two convolution layers and two pooling layers and finally outputs the results through the full connection layer.  
During training and testing, both DNN and CNN used the same coded number data in Table 7 as LSTM, and the training set and test set of each model are consistent with LSTM. Both of them used Formula (3) in Section 3.6 as the performance evaluation index.  
In order to quantitatively evaluate the performance of the above models, the root mean square error (_RMSE_) [67] and _MSE_ were used to evaluate the error between the model output and the measured value. The smaller _RMSE_ and _MSE_, the smaller the model deviation and the better effect of performance evaluation. The formulas of _RMSE_ and _MSE_ are as follows:  
Figure 12: Modeling feature set of the optimal truck-crane design scheme.  
\[RMSE =\sqrt{\frac{1}{m}\sum_{i=1}^{m}(y_{i}-\hat{y}_{i})^{2}} \tag{4}\] \[MSE =\frac{1}{m}\sum_{i=1}^{m}(y_{i}-\hat{y}_{i})^{2} \tag{5}\]  
where \(\hat{y}_{i}\) and \(y_{i}\) are the output value and the actual value of model, respectively. The evaluation results of the testing set are shown in Table 8.  
According to Table 8 and Figure 13, the LSTM neural network performs best in evaluating and predicting the users' perceptual preference of CPs, followed by the CNN, and DNN is the worst. We suppose the reason is that, compared with CNN and DNN models, LSTM model takes more product modeling features into account when modeling and adds an observation time sequence so that the product-feature-coding set can be more comprehensive and multi-dimensional. Therefore, the results show that, after adding users' visual observation sequence features of CPs to product-modeling-feature data, the neural network can better grasp product-modeling features, showing better accuracy and efficiency. In addition, the results also verified the necessity of adding the visual sequence to the feature processing of CPs.
# Aminated Covalent Organic Polymers for Anionic Dye Adsorption in Aqueous Systems
## 3 Results and Discussion
### Adsorption Isotherm
The prepared ACOP offers a reasonably high maximum adsorption capacity relative to other well-known adsorbents. Even though there are several other materials with higher \(q_{\rm m}\) values, our material is still worth reporting on because of its catalyst-free one-pot synthesis as well as charge selective feature. Notably, the adsorption equilibrium was achieved in less than 30 min, as depicted in Figure 5. Therefore, the prepared ACOP has great potential for use as an adsorbent with fast kinetics and large adsorption capacity. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & & **Langmuir** & & & **Freundlich** & \\ \hline
**Parameter** & \(q_{\rm m}\) **(mg/g)** & \(a_{\rm L}\) **(L/mg)** & \(\mathbf{R^{2}}\) & \(\mathbf{K_{F}}\) & \(\mathbf{n}\) & \(\mathbf{R^{2}}\) \\ \hline MO & 351.9 & 0.0806 & 0.8341 & 97.34 & 3.753 & 0.9510 \\ \hline OG & 227.9 & 0.0498 & 0.7170 & 43.59 & 3.072 & 0.9293 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detailed parameters obtained from the adsorption isotherms fitted using Langmuir and Freundlich isotherm models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**adsorbent** & **Dye** & **Dosing (g/L)** & **Initial Concentration (mg/L)** & **Contact Time (min)** & \(q_{\rm m}\) (mg/g)** & **Ref.** \\ \hline Carbon nanotubes & & 0.3 & 20 & 180 & 35.4–64.7 & [60] \\ Activated carbon & & 0.5 & 25–75 & 350 & 238.1 & [61] \\ ZnAl-LDO & & 0.5 & 100 & 120 & 200.0 & [62] \\ Benzodiimidazole-COF & Methyl orange & 1.0 & - & 160 & 256 & [63] \\ CMP-Im & & 0.25 & 100–600 & 720 & 588 & [64] \\ MOF-808 & & 0.2 & - & 240 & 540 & [65] \\ ACOP & & 1.0 & 180–350 & 180 & 351.9 & This study \\ \hline MgFe-LDO & & 1.0 & 50–800 & 1440 & 378.8 & [66] \\ Activated clay & & 0.2 & 400 & 300 & 128.6 & [67] \\ Fe\({}_{\rm C}\)/ML-101(Cr) & Orange G & 0.6 & 40–80 & 120 & 200.0 & [68] \\ MOF-808 & & 0.2 & - & 240 & 197 & [65] \\ PANI@AS Discopposites & & 0.5 & 10–500 & 120 & 191.0 & [69] \\ ACOP & & 1.0 & 80–350 & 180 & 227.9 & This study \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summarized maximum adsorption capacities (\(q_{\rm m}\)) of various dyes on different adsorbents.
## 2 Materials and Methods
### Clinical Assessment  
Clinical evaluation was always performed by the same examiner following this sequence.  
1. The OSDI questionnaire was administered to all volunteers. This instrument allows DED screening [42] and also DED severity classification: \"mild\" (score 13-22), \"moderate\" (score 23-32) and \"severe\" (score 33-100).
2. A NRS was used to rate the severity of pain, as previously reported [43]. LASIK patients were indicated to rate their average ocular pain severity on a 10-point scale ranging from \"no pain\" (score 0) to \"the most severe pain\" (score 10). Scores ranging from 2 to 4 was considered \"mild\" pain, from 5 to 7 \"moderate\" pain, and from 8 to 10 \"severe\" pain.
3. Corrected distance visual acuity (CDVA) was evaluated using an ETDRS chart at a 4 m distance.
4. TBUT was assessed after the instillation of 5 mL of 2% sodium fluorescein. The measurement was repeated three times to obtain an average value.
5. CFS was evaluated using the Oxford scheme (grade 0-5).
6. Confocal microscopy assessment of the cornea. The IVCM images of the cornea were obtained using the Rostock cornea module of the Heidelberg Retina Tomograph III (Heidelberg Engineering GmbH, Heidelberg, Germany). Non-overlapping images of the central cornea focused on the sub-basal nerve plexus were obtained using sequence and/or volume scans. Each image was comprised of \(384\times 384\) pixels, which represents a coronal section of \(400\times 400\)\(\mu\)m (0.16 mm\({}^{2}\)). The grade of nerve tortuosity was subjectively evaluated according to the scale (0-4) reported by [PERSON] and [PERSON] [6]. The observer who assessed each image was blinded to the LASIK group classification.  
In the case of CDVA, TBUT and CFS, both eyes were assessed, and the mean was computed for analysis.