esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Defect Detection Using Threshold Value Classification Based on Thermographic Inspection
## 4 Results and Discussion
### Automatic Defect Detection  
For automatic defect detection of amplitude and phase signal processing images, binarization processing using the Otsu algorithm was performed on images to which median filtering was applied. The optimum threshold values for amplitude and phase for binarization are 25 and 27, respectively, as shown in Figure 10.  
Figure 8: Images with filtering (median, mean, Gaussian, NLmean) applied to raw images for first de-noising: (a) amplitude and (b) phase.  
Figure 10: Images with second de-noising applied after the binarization process: (**a**) amplitude and (**b**) phase.  
Figure 9: SNR comparison of each filtered image.  
The process for second de-noising after binarization is as follows. Firstly, by using the 'bwareapen' morphology function, all objects with fewer than 50 pixels that did not belong to the desired object were removed. Secondly, by using the'strel' function, the gaps of defects in the image were filled in. Thirdly, boundary lines were traced to recognize defects in the image. In order to improve the processing speed, the 'noholes' function was used, which prevented finding inner contours. These functions were applied individually in MATLAB software.  
After performing the second de-noising, a metric equation for automatic defect detection was used. The area and perimeter of each defective object were measured, and based on the results, a simple metric representing the roundness of the object was established, as follows [37]:  
\[\text{metric}=\frac{4\pi\times\text{area}}{\text{perimeter}^{2}} \tag{9}\]  
This metric is 1 for circles only and less than 1 for all other shapes. This classification process can be adjusted by setting an appropriate threshold value. The roundness threshold value for defect detection was set to 0.75. Figure 11 shows an image with the automatic defect detection of amplitude and phase. The roundness of the 'B3' defect in the amplitude image was calculated as 0.37; therefore, it was not recognized as a defect. In the raw amplitude image of Figure 5, the defect in the 'B' column was visually undetectable. Therefore, no defects were detected in the 'B' column, even in the binary image. The phase image can confirm that more defects than amplitude images were detected.
# Research on Energy Storage Optimization Operation Schedule in an Island System
## 4 Results
### Case 2  
For case 2, the total cost of power generation was 4957 kNTD, the total startup cost was 28 kNTD, and the total economic cost was 4985 kNTD with a total number of four startups. Figure 8 shows the hourly power flow for the day with a maximum load while considering three heavy oil plants (Plant1_1, Plant1_8, and Plant1_9) on shutdown mode. Again, the sumP (violet) is the net load that also considers the charging and discharging of the PSS while the SOC (brown) is expressed in percentage.  
Figure 8: Case 2 power flow result.  
Figure 7: Hourly power generation cost for the day with minimum load (case 1).  
As shown in Figure 9, when the load was heavy at the 12 th and 13 th hour, two LFOs (Plant2_1 and Plant2_4) were turned on. Plant2_1 was turned on from the 12 th to the 15 th hour, while Plant2_4 was turned on from the 13 th to the 21 st hour. In addition, the startup and shutdown time of each generator all met the constraint of minimum startup time and shutdown time in the period.  
In the case of peak load, because many generators were working at/near their maximum capacity, the down-spinning reserve was sufficient for the system. From Figure 10, the operating up-reserve (green) was maintained at the up-spinning reserve constraint, especially for the 16 th to 18 th hours where the charging and discharging of the PSS just met the spinning reserve constraint. As seen in the crisscrossing of the up-reserve limit (orange), and the diesel's maximum output (yellow), the PSS provided for additional reserve so that the use of the diesel generator could be reduced. This result highlights the importance of the PSS.  
Figure 9: Case 2 hourly power generation of each fuel generator.  
Figure 11 shows the hourly cost of power generation. From the 0 th to 11 th hours, if there were only heavy fuel units, the power generation cost was just around 4.5 NTD/kWh. When the LFO units were turned on at the 12 th and 13 th hours, during peak load hours, the power generation cost increased significantly, as shown in the red block in Figure 12.  
Figure 11: The hourly average power generation cost for the day with maximum load (case 2).  
Figure 12: Hourly average cost of energy and PSS power output for case 2.  
Figure 10: The operating reserve and load for the day with maximum load (case 2).  
By comparing the average fuel cost with the charging and discharging power of the PSS, as seen in Figure 12, it can be noticed that during the peak hours (12 th-21 st hour) the cost was high when the PSS discharged, and decreased when the PSS charged at the 16 th to 18 th hours. To reiterate, at the 12 th and 13 th hours, the LFOs turned on, and the PSS in this case provided power to transition into the system. When the system had stabilized, the PSS charged to lessen the cost during the peak hours, then discharged again when support was needed during generator shutdown. This can prove that the PSS had the function of peak-shaving in this case.
## 4 Discussion
### Ablation Study
#### 4.5.2 Analysis of Saliency-Preserving Strategy  
To evaluate the effectiveness of the proposed saliency-preserving strategy (SAP) for multiscale information fusion, we conducted ablation experiments comparing fusion performance with and without SAP. As shown in Figure 18, the fused images without SAP exhibited noticeable quality degradation, demonstrating the importance of the saliency-preserving strategy for effective multiscale feature integration. The quantitative results in Table 6 further confirmed this observation, where the SAP strategy consistently outperformed the baseline across all evaluation metrics. For example, significant improvements can be seen in metrics like Average Gradient and Spatial Frequency, particularly for images such as \"Kaptein_1123\" and \"01432\". These results clearly validate the necessity of incorporating the saliency-preserving strategy in our fusion framework.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Image** & & **VIF** & **SF** & **AG** & **CC** & **EN** \\ \hline \multirow{2}{*}{“Queens Road”} & Without SAP & 0.761 & 0.031 & 3.217 & 0.436 & 6.325 \\  & With SAP & **0.82** & **0.045** & **3.683** & **0.675** & **6.998** \\ \hline \multirow{2}{*}{“Kaptein\_1123”} & Without SAP & 0.838 & 0.029 & 3.467 & 0.34 & 6.76 \\  & With SAP & **0.874** & **0.047** & **5.01** & **0.585** & **6.882** \\ \hline \multirow{2}{*}{“01432”} & Without SAP & 0.714 & 0.055 & 6.664 & 0.488 & 7.43 \\  & With SAP & **0.724** & **0.136** & **11.10** & **0.619** & **7.546** \\ \hline \multirow{2}{*}{“01454”} & Without SAP & 0.987 & 0.046 & 4.217 & 0.631 & 7.327 \\  & With SAP & **0.993** & **0.063** & **4.621** & **0.697** & **7.433** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation experimental results on the saliency-preserving, with the best results highlighted in bold.  
Figure 17: The fused images with and without adaptive brightness enhancement (ADB).
# The Effect of Pharmaceutical Excipients for Applying to Spray-Dried Omega-3 Powder
## 3 Results
### Determination of Particle Size, Moisture and Free Oil, Encapsulated Oil, and Total Oil  
The moisture (%) of formulations was about 4% and there is no significant difference between formulations. The diluted emulsion formulation (P18-6) had lower moisture content than that of P18-4. The droplet size of SE and RE was ranged from 300 to 400 nm. Droplet size of RE increased than droplet of SE. But, there was no difference significantly between SE and RE under the various spray-drying operating condition. The content of free oil was ranged from 30.33 to 42.23% (Table 4).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Formal**} & **Moisture** & \multirow{2}{*}{**SE (nm)**} & \multirow{2}{*}{**RE (nm)**} & **Free Oil** & **Encapsulated** & **Total Oil** \\  & **(\%)** & & & **(g/100 g)** & **Oil (g/100 g)** & **(g/100 g)** \\ \hline P18-1 & 4.17 \(\pm\) 0.50 & 337.17 \(\pm\) 8.32 & 351.77 \(\pm\) 9.13 & 42.23 \(\pm\) 0.03 & 10.20 \(\pm\) 0.64 & 56.95 \(\pm\) 0.70 \\ P18-2 & 3.67 \(\pm\) 0.40 & 281.70 \(\pm\) 1.93 & 388.13 \(\pm\) 13.88 & 39.30 \(\pm\) 4.60 & 11.14 \(\pm\) 0.19 & 54.63 \(\pm\) 2.11 \\ P18-3 & 3.87 \(\pm\) 0.31 & 305.13 \(\pm\) 3.45 & 323.20 \(\pm\) 7.37 & 37.98 \(\pm\) 4.67 & 12.35 \(\pm\) 1.74 & 52.84 \(\pm\) 3.52 \\ P18-4 & 3.40 \(\pm\) 0.30 & 341.80 \(\pm\) 4.95 & 344.73 \(\pm\) 11.86 & 35.33 \(\pm\) 1.21 & 14.12 \(\pm\) 0.88 & 52.10 \(\pm\) 1.52 \\ P18-5 & 3.67 \(\pm\) 0.25 & 352.77 \(\pm\) 24.8 & 378.33 \(\pm\) 10.82 & 33.67 \(\pm\) 0.07 & 14.22 \(\pm\) 2.78 & 51.58 \(\pm\) 1.85 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The effect of various spray drying condition on moisture amount, particle size and oil amount (free oil, encapsulated oil and total oil) of spray-dried olive oil powder using \(\gamma\)-CD and hydrogenated lecithin (n = 3).  
Figure 1: SEM image of spray-dried olive oil powder, P8, P16, P18, and P21. P18 showed the spherical shape with many pores but its surface was smooth.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Formal**} & **Inlet Temp.** & **Outlet Temp.** & **Atomization** & **Olive** & **DW** & \(\gamma\)**-CD** & **Hydrogenated** & **Total** \\  & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **Pressure** & **Oil (g)** & **(mL)** & **(g)** & **Leithin (g)** & **(g)** \\ \hline P18-1 & 180 & 85–90 & 15 & 9.3 & 200 & 4.8 & 4.5 & 18.6 \\ P18-2 & 180 & 85–90 & 15 & 9.3 & 200 & 5.8 & 3.5 & 18.6 \\ P18-3 & 180 & 85–90 & 15 & 9.3 & 200 & 6.8 & 2.5 & 18.6 \\
## 2 Background
### IEEE 802.11 ah RAW  
Although IEEE 802.11 ah inherits most of the basic IEEE 802.11 MAC features, several innovative MAC mechanisms are proposed to support the general requirements of the IoT applications. One of these novel MAC features is Restricted Access Window (RAW).  
The RAW mechanism aims to mitigate collisions in dense wireless networks, where a large number of stations are contending for channel access simultaneously. Specifically, the channel time is split into several intervals, namely the RAW periods and the shared channel airtime. As shown in Figure 3, only a portion of stations, namely the RAW stations, from a specific group are allowed to contend the channel in a particular RAW period. By contrast, all stations can compete for the channel in the shared channel airtime. The AP is responsible for assigning each RAW period to a group of stations by a beacon frame carrying a RAW Parameter Set (RPS), which is an information element that specifies the RAW related information, including the stations belonging to the group and the group start time. Besides, the RPS also contains the slot format, the number of RAW slots (\(N_{s}\)) and slot duration count sub-fields, which jointly determine the RAW slot duration as follows:  
\[D=500\ \upmu\text{s}+C\times 120\ \upmu\text{s}, \tag{1}\]  
where \(C\) is slot duration count sub-field, and \(D\) is the RAW slot duration. The number of RAW slots \(N_{s}\) and \(C\) are determined by the slot format sub-field. If the slot format sub-field is set to 1, each RAW period consists of at most eight RAW slots and the maximum value of \(C\) is 2047. Otherwise, each RAW period consists of at most 64 RAW slots and the maximum value of \(C\) is 255.  
To make EDCA compatible with the RAW mechanism, each station adopts two backoff states of EDCA to manage data transmission inside and outside its assigned RAW slot, respectively [13]. The first backoff state is adopted outside RAW slots, in which all stations are permitted to compete for the channel. For the first backoff state, the station freezes its backoff timer at the start of each RAW period, and resumes the backoff timer at the end of the RAW period. The second backoff state is adopted inside RAW slots, where only the designated group of stations is permitted to contend for channel access. For the second backoff state, stations start backoff procedure at the start of their own RAW slot, and terminate their backoff procedure at the end of their RAW slot.
## 3 Data and Methods
### Validation  
Cluster results of k-means, spectral clustering, and hierarchical clustering validated with different measures are shown in Figure 4. It can be seen from (1)-(6) and (10)-(12) that all the three cluster methods performed best when the cluster number is 2, and decreased when the cluster number increased. In the mathematics view, the data should be divided into two classes as they performed best. However, we should consider our geological needs as an important classification factor as well. This sea area can be divided into two classes, with the seabed sediments mutating at about a 14 m water depth.  
Nevertheless, we need a more accurate range of submarine landslide susceptibility with acceptable mathematical accuracy. The cluster number of 3 is still insufficient to classify the study region because the classification result may be easily inferred from the hydrodynamic characteristics. As a result, the final cluster number is 4, which corresponds to the classifications very high, high, low, and very low.  
As seen from (7) to (9), the clustering result performed best when gamma was equal to 0.01 with the Calinski-Harabasz index and gamma equal to about 1 using the silhouette index and Davies-Bouldin index. Therefore, the kernel function parameter gamma is 1. It is shown in (13)-(15) that the result performed best when the affinity method is Manhattan. The specific parameters used in the three cluster models can be seen in Table 1.  
Figure 4: Cluster results of k-means, spectral clustering, and hierarchical clustering were validated with the Calinski–Harabasz index, silhouette index, and Davies–Bouldin index. (1)–(3) Validate cluster numbers of k-means. (4)–(6) Validate cluster numbers of spectral clustering. (7)–(9) Validate kernel function parameter of spectral clustering. (10)–(12) Validate cluster numbers of hierarchical clustering. (13)–(15) Validate affinity of hierarchical clustering. The red circle indicates the location where the model achieved the best prediction.  
All machine learning calculations in the article were carried out using the scikit-learn machine learning package [26], an open-source python library, via a laptop with windows 10, 16G RAM, CPU R 5800H, and GPU 3060.
# A Wideband Digital Pre-Distortion Algorithm Based on Edge Signal Correction
## 5 Conclusions  
This paper proposes a wideband DPD algorithm based on edge signal correction. By collecting signals from the central frequency band and utilizing only the filtered frequency domain information for estimating the DPD model parameters, the proposed algorithm reduces both data volume and processing complexity. Furthermore, by incorporating cross-terms into the model structure and continuously calibrating the entire model using band-edge sampled signals, the fitting accuracy of the model is significantly enhanced. The proposed algorithm was simulated and analyzed using MATLAB R2024a and compared with four typical wideband DPD algorithms. The simulation results demonstrate that the proposed scheme exhibits a significantly lower secondary channel power within the frequency offset range of 80 MHz to 180 MHz, demonstrating superior anti-interference performance.  
The proposed algorithm has demonstrated significant advantages in mid-frequency wideband scenarios. However, to meet the demands of future communication systems (6G, satellite communications, etc.), several challenges must be addressed, including operation at higher frequency bands, lower power consumption, and multi-channel coordination. Future research could integrate machine learning, novel hardware architectures, and standardized testing to advance DPD technology toward intelligent and full-spectrum adaptive development.  
**Author Contributions:** Formal analysis, Y.L., H.Z. and Z.G.; Writing--original draft, Y.L., H.Z. and Z.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Key Scientific Research Projects of Universities in Anhui Province (2023 AH052798).  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# The Impact of Antenna Height on 3D Channel:
## 4 Conclusions  
In this paper, the impact of antenna height on EAoD and EAoA is investigated under typical outdoor scenarios: straight street, fork road, and crossroad. At the beginning, we briefly introduced the IRLA and the simulation scenario (the center of Paris). Then, the closed-form expressions of the PDF of EAoD and EAoA were derived under various street scenarios, the related AS and DS are also presented. Finally, the discussion of the results was given. According to the results, we conclude that the PDFs of EAoD and EAoA are modelled as exponential functions whose parameters are observed as a linear function of the antenna height under the straight street scenario. Meanwhile, the characteristics of EAoD and EAoA change dramatically when the BS antenna height is half of the building's height in the forked road and crossroad scenarios. Moreover, the results of the AS and the DS show that in order to obtain the maximum or minimum value of the AS and the DS, the BS antenna should be deployed at half of the building's height. We also conclude that the single reflection path dominates the distribution of EAoD and EAoA. In our future works, the PDFs of EAoD and EAoA with different antenna patterns and tilt will be considered under typical outdoor scenarios.  
The research is funded in part by the European Union's EUROSTARS project Build-Wise (11088), in part by the European Union's Horizon 2020 research and innovation programme is 3 DMIMO (734798), and in part by National Natural Science Foundation of China (61501137). [PERSON] acknowledges the foundation of China Scholarship Council.  
[PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed reagents/materials/analysis tools; and [PERSON], [PERSON], and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# A Computer-Vision Based Application for Student Behavior Monitoring in Classroom
## 1 Introduction
### Existing Systems
Recently, a school in Hangzhou, China, is using facial recognition to monitor the behavior of their students [16]. The technology that classifies the students is generally based on their range of emotions--from antipathy to happiness (and a whole host of others). The system also cross-checks the faces of all students against the school database to mark the attendance and has the ability to predict if a student is feeling sick. Unfortunately, the results of most actions have not yet been published. However, this showed the possibility to use facial recognition technology to help and monitor students.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 5 Studies toward Clinical Applications of TD-NIRS
### Time-Domain Fluorescence Diffuse Optical Spectroscopy (TD-FS) and Tomography (TD-FT)
#### TD-FT Using Early Arriving Photons  
Many studies of TD-FT have been done using early arriving photons. Some of them to name a few are reviewed below.  
[PERSON] et al. used a streak camera for TR measurements of early photons, TOF distributions of which were Laplace transformed with an optimum parameter, and the fluorophore targets were accurately localized under the presence of the noise of background fluorescence [205].  
[PERSON] et al. reconstructed TD-FT images of lung carcinoma in mice in vivo from early photons detected by a high-speed gated ICCD with a gate time of 100 ps to obtain 360\({}^{\circ}\) projections [206]. Light propagation was described by a cumulant solution of the TD-RTE to generate three-point Jacobians, and a standard inversion process was employed to reconstruct 3D images of the fluorescence targets.  
[PERSON] and [PERSON] developed a TD-DOT system to quantitatively reconstruct \(\mu_{a}\) and \(\mu_{s}{}^{\prime}\) images inside small animals such as mice for the purpose of improvement of TD-FT images [207]. They used high-speed time-gated ICCD with a time gate less than 300 ps to obtain TR transmission data which were converted to FD data for image reconstruction of \(\mu_{a}\) and \(\mu_{s}{}^{\prime}\) using a non-linear iterative inversion algorithm.  
[PERSON] et al. studied early-photon TD-FT to explain improved spatial resolutions of the images reconstructed from time-gated measurements with 200-400 ps time-gates by mathematical formulations and in silico phantoms [208]. They used the TD-DE for the forward problem and singular-value analysis for the inverse problem. [PERSON] et al. reported that using not only early photons but also late photons improve both the spatial resolution and image contrast in 3D TD-FT on a heterogeneous mouse model [209]. They employed the normalized Born-ratios as the featured data type, derived the Jacobians of the featured data to the changes in the absorption coefficient of fluorophores, and solved the non-linear iterative inversion problem to reconstruct the TD-FT images.  
[PERSON] et al. developed a reconstruction algorithm of TD-FT for small animals using the first order time-derivative (or slope) of the rising portion of the measured TOF distributions in the early time regime [210]. Using the first derivative provided robustness to measurement noises and high-spatial resolutions in the reconstructed TD-FT images. As the forward model, they employed the telegraph equation (TE) of Equation (5) having the term, \(\partial^{2}\phi(\mathbf{r},t)/\partial t^{2}\), because the TD-DE was derived by assuming \(\partial^{2}\phi(\mathbf{r},t)/\partial t^{2}<<\partial\phi(\mathbf{r},t)/ \partial t\) which may breakdown for early photons.