esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Cell Proliferation  
The antiproliferative activity was studied on MCF-7, T47-D, LNCaP, HepaRG, and Caco-2 cancer cells and on normal fibroblasts (NHDF) by the MTT assay [45]. Figure 2 summarizes the results of the initial screening at 30 \(\upmu\)M for all compounds. IC\({}_{50}\) was calculated when a reduction in cell proliferation was higher than 50% (Table 1). The most cytotoxic compound was molecule **5**, with the lowest determined IC\({}_{50}\) values for all tested cells. Interestingly, higher selectivity index (SI) was determined for compound **5** (Table 2)in hormone-dependent (MCF-7, T47-D, and LNCaPP) cancer cells. A SI value higher than 2 indicates a high selectivity for cancer cells [46]. In addition, compounds **4** and **5** were tested on T47-D (ER\({}^{+}\)) cells to evaluate their potential estrogenic capability (Figure 3) [33; 47] in comparison with E2, which led to an increase in proliferation of T47-D cells in all concentrations studied. Unfortunately, compound **5** also led to cell proliferation at 0.1 uM (140%) when compared with the negative control. On the other hand, and interestingly, compound **4** did not exhibit proliferative action.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Compounds** & **MCF-7** & **T47-D** & **LNCaP** & **HepaRG** & **Caco-2** & **NHDF** \\ \hline
**E1** & 41.93 & ND & ND & 29.53 & 42.69 & 61.82 \\
**2** & ND & 29.24 & ND & 46.54 & ND & ND \\
**4** & ND & ND & ND & 32.04 & ND & ND \\
**5** & 5.87 & 7.40 & 5.30 & 10.91 & 14.12 & 12.14 \\
**6** & 53.59 & 44.56 & ND & 36.06 & ND & ND \\
**5-FU** & 1.71 & 0.54 & 7.79 & 1.78 & 1.31 & 3.61 \\ \hline \end{tabular}  
* Results from MTT assay using different concentrations (0.1, 1, 10, 25, 50, and 10 uM) for 72 h. The data shown are representative of at least two independent experiments. ND: not determined.  
\end{table}
Table 1: IC\({}_{50}\) (uM) of compounds **2, 4–6** and 5-fluorouracil (5-FU) against MCF-7, T47-D, LNCaPP, HepaRG, and Caco-2 cancer cells and normal fibroblasts (NHDF) 1.
# A Spectral-Aware Convolutional Neural Network for Pansharpening
## 4 Experiment Results
### Experimental 4--Pavia University Dataset
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **SAM** & **ERGAS** & **RMSE** & **SCC** \\ \hline Reference & 0 & 0 & 0 & 1 \\ \hline EXP & 7.5634 & 6.9160 & 502.8104 & 0.8063 \\ SFIM & 6.8461 & 4.9617 & 404.0341 & 0.8831 \\ CNMF & 8.9287 & 6.0287 & 528.3205 & 0.8176 \\ MTF-GLP-HPM & 6.7015 & 4.6096 & 391.0704 & 0.8934 \\ GSA & 10.0496 & 5.6724 & 440.2654 & 0.8867 \\ GFPCA & 9.8152 & 7.3872 & 578.1234 & 0.7529 \\ Bayesian Sparse & 10.6064 & 5.4058 & 472.9086 & 0.8857 \\ PNN & 15.2681 & 9.8561 & 515.6730 & 0.9203 \\ DRPNN & 6.7640 & 4.2221 & 367.0981 & 0.9135 \\ DiCNN1 & 6.8786 & 3.9914 & 343.6299 & 0.9230 \\ \hline SA-PNN & **6.5109** & **3.9451** & **327.4077** & **0.9241** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Quality indexes at reduced resolution on a \(150\times 75\) sub-scene of the Pavia dataset (best result is in bold). approximate to the reference line, which demonstrates the excellent spectral fidelity ability of our SA-PNN again. Figure 7: Pansharpening results for the Pavia dataset: (**a**) ground truth; (**b**) EXP; (**c**) SFIM; (**d**) CNMF; (**e**) MTF-GLP-HPM; (**f**) GSA; (**g**) GFPCA; (**h**) Bayesian Sparse; (**i**) PNN; (**j**) DRPNN; (**k**) DiCNN1; and (**l**) SA-PNN.
## 2 Collision Avoidance Method  
Figure 1 shows the collision avoidance procedure in accordance with the COLREGs, and the following subsection could be four steps [2]. Firstly, the TS and/or obstacle in the vicinity of the OS are detected by using radar plotting according to \"Rule 5\". Secondly, the collision risk is assessed on the basis of the computed _CRI_ according to \"Rule 7\". Thirdly, encounter relation is decided according to \"Rule 13\", \"Rule 14\", and \"Rule 15\". Finally, taking an action for collision avoidance via optimal course and speed taking into consideration the collision risk is executed by a control command according to \"Rule 8\", \"Rule 16\", and \"Rule 17\".
## 4 Experimental Results
### Trajectory Prediction Results  
This paper provides a coarse pose for the intelligent vehicle in the underground parking scene through trajectory prediction, which helps to narrow the scope of the nearest map node retrieval and serves as a prior pose for metric localization. To improve the computational efficiency, the following formulas [52] were employed during the trajectory prediction process to approximate the values of the Fresnel cosine function \(C(x)\) and the sine function \(S(x)\):  
\[\left\{\begin{array}{l}C(x)=\frac{1}{2}+\omega(x)sin\left(\frac{\pi}{2}x^{2 }\right)-\phi(x)cos\left(\frac{\pi}{2}x^{2}\right)\\ S(x)=\frac{1}{2}-\omega(x)cos\left(\frac{\pi}{2}x^{2}\right)-\phi(x)sin\left( \frac{\pi}{2}x^{2}\right)\\ \omega(x)=\frac{1+0.926x}{2+1.792x+3.104x^{2}}\\ \phi(x)=\frac{1}{2+4.142x+3.492x^{2}+6.670x^{3}}\end{array}\right. \tag{15}\]  
The results of the trajectory prediction experiments based on 16 sets of data are shown in Table 3. In the industrial park's underground scene, the average error for each group of experiments fell within the range of [0.31 m, 0.51 m], with the maximum errors not exceeding 2.85 m. In the mall's underground scene, the average error for each group of experiments was within the range of [0.39 m, 0.47 m], with the maximum errors not exceeding 2.23 m. The experimental results indicated that the trajectory prediction algorithm achieved an average prediction accuracy of approximately 0.4 m across multiple datasets in two typical underground parking scenes. Furthermore, the algorithm demonstrated a high precision, robustness, and consistent performance with a difference in the average error of no more than 0.11 m between the different scenes. Additionally, the maximum error of this trajectory prediction algorithm was less than 3 m. Therefore, in this paper, the threshold \(\delta\) for the vehicle trajectory prediction error was set to 3 m, which adequately covered the actual errors occurring in the trajectory prediction algorithm. It is worth noting that the trajectory prediction aims to provide a coarse pose, so the algorithm was considered reasonable when the error was within a manageable range.  
Once the coarse position of the vehicle was obtained, it could be checked whether the vehicle is within the coverage range of the map. The radius \(\psi\) for searching the map nodes was set to 3 m. Therefore, the actual search radius for the map nodes was (\(\delta+\psi=6\) m), and the map nodes within this radius constituted the localization candidate set \(M_{\text{candi}}\). If the localization node was within the coverage range of the map, the next step was to retrieve the nearest map node.
# Enhanced Upward Translations for Systems with Clusters
## 4 Numerical Results for Typical Kernels of BIE
### 3D Navier BIE
Table 7 presents the comparison of _relative_ errors for conventional and modified translations when the far field is generated by a cluster of radius \(r_{cl}\). Its center is at the relative distance \(\epsilon\) from the ES. It may be seen that for the most unfavourably located cluster with its center close to the ES (\(\epsilon=0.02\)), the modified method provides two orders greater accuracy for 386 nodes at ES. The error is incomparably less than the mentioned error of 70% reported in [24] for this number of nodes. Thus the modified approach avoids the need in notable increase the number of nodes on ES to bring the accuracy of starting S2M translations to an acceptable level. The said above referred to a single cluster. The modified S2M translations may be applied to each of the \(N_{cl}\) clusters distinguished. This significantly improves the accuracy of far fields from sources located in the spherical layer, as compared with the far fields of these sources, translated conventionally. Underline that, as established, when any of the clusters contains only one source, the error of its modified translation is zero. This may serve to simplify the modified translations. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{4}{c}{**Conventional**} & \multicolumn{4}{c}{**Modified**} \\ \hline \(\epsilon\) & **0.02** & **0.1** & **0.2** & **0.02** & **0.1** & **0.2** & \\ \hline \(r_{cl}lR_{e}\) & **0.01** & **0.07** & **0.07** & **0.12** & **0.01** & **0.07** & **0.07** & **0.12** \\ \hline max & 0.02166 & 0.00809 & 0.00212 & 0.00189 & 8.80 \(\times\) 10\({}^{-5}\) & 0.00058 & 0.00023 & 0.00063 \\ \hline
**average** & 0.00312 & 0.00094 & 0.00035 & 0.00029 & 1.11 \(\times\) 10\({}^{-5}\) & 9.23 \(\times\) 10\({}^{-5}\) & 3.99 \(\times\) 10\({}^{-5}\) & 0.00011 \\ \hline \end{tabular}
\end{table} 
Table 6: Maximal and average relative errors for translations by means _singular_ kernel (3D _hypersingular_ Navier BIE). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{4}{c}{**Conventional**} & \multicolumn{4}{c}{**Modified**} \\ \hline \(\epsilon\) & **0.02** & **0.1** & **0.2** & **0.02** & **0.1** & **0.2** & \\ \hline \(r_{cl}lR_{e}\) & **0.01** & **0.07** & **0.07** & **0.12** & **0.01** & **0.07** & **0.07** & **0.12** \\ \hline max & 0.06542 & 0.03746 & 0.03044 & 0.02570 & 0.00046 & 0.00647 & 0.00299 & 0.00837 \\ \
## 2 Materials and Methods
### Study Protocol  
The device (SIXPAD; MTG Co., Ltd., Nagoya, Japan) and WB-NMES protocol have been previously described [6; 10]. NMES was applied to the anterior and posterior upper arm, chest, back, abdomen, abdominal oblique, gluteus, and anterior and posterior thigh muscles. The intensity of the electrical stimulation for WB-NMES was determined after three training sessions for WB-NMES. All participants underwent the experiment for a total of three days. NMES was performed at 20 Hz, with 4 s of stimulation followed by 4 s of pause. The output voltage and effective current of the device ranged from level 1 (10.6 V, 1.7 mArms) to level 100 (70.0 V, 11.4 mArms), and the intensity of the stimulation was adjustable for each site. With a numeric rating scale of 7, the stimulus intensity was set to be able to pedal a CPX warm-up electrically braked cycle ergometer (20 W). The stimulation intensity was the same from warm-up to the maximum load. In the second and third sessions, CPX using a cycle ergometer or hybrid exercise with cycle ergometer loaded on WB-NMES were randomly performed. The first and second sessions were conducted over a period of three days; the second and third sessions were conducted one week apart. The protocol for CPX is shown in Figure 1.  
Participants refrained from drinking alcohol or beverages containing caffeine for 12 h prior to the study. All tests and CPX were performed under the supervision of a doctor. CPX for the cycle ergometer protocol comprised 2 min of rest and 4 min of warm-up at 20 W. After the warm-up exercise, the ergo-load was gradually increased by 20 W per minute. CPX for the hybrid exercise with a cycle ergometer loaded on a WB-NMES device comprised 2 min of rest, followed by 2 min of WB-NMES, and 2 min of warm-up at 20 W; the exercise test was then performed, with a gradual increase in ergo load of 20 W per minute. Participants were instructed to pedal at 50-60 revolutions per minute. Symptom-limited CPX was performed with ramp protocols commonly used in CPX; the session was terminated when they could no longer pedal at more than 50 revolutions per minute. Therefore, exercise time slightly varied depending on the subject. The assessment was conducted such that participants could not see the load on the cycle ergometer. Post-exercise measurements were taken just after symptom--limited CPX. In the hybrid CPX, the same electrical stimulation intensity was used from warm-up to maximum exercise load.  
Blood sampling and the observation of sublingual microcirculation and nailfold microvascular flow were performed at three points: at rest, immediately after exercise, and 20 min after exercise. Whole blood passage time, blood glucose level, white blood cells, hematocrit, platelets, blood lactate levels (Lactate Pro 2; Arkray, Kyoto, Japan), plasma levels of derivatives of reactive oxygen metabolites (d-ROMs), and biological antioxidant potential (BAP) were assessed using d-ROMs and BAP test kits (Diacron International, Grosseto, Italy).
## 1 Introduction
Based on our early work, this paper reveals that the PCC voltage amplitude is also a key factor affecting the maximum power transfer capability of the system, especially in the resistive grid case. It was found that a higher PCC voltage than the rated value in a resistive grid is beneficial to increase the power transfer capability of grid-connected inverters. The rest of this paper is organized as follows. Section 2 introduces the study system and its simplified model. Then, the maximum power transfer capability of the system at the rated PCC voltage is analyzed. Section 3 analyzes the maximum power transfer capability of the system at varying PCC voltage. After that, optimal parameters for maximum power operation in the inductive and resistive grids are illustrated in Section 4. The simulation results are illustrated in Section 5. Finally, concluding remarks are drawn in Section 6.
# Combining Dilution of Precision and Kalman Filtering for UWB Positioning in a Narrow Space
## 3 Experiments and Results  
### Experimental Protocol  
#### 3.1.1 Experimental Scene  
In order to verify the effectiveness of the proposed algorithm, two narrow scenes with different length-width ratios were selected for experimental verification, as shown  
Figure 2: Flow chart of the fusion positioning algorithm.  
in Figure 3. Scene A was the dormitory corridor of South China Normal University. The whole experimental scene was 24 m long and 2 m wide. Scene B was the dormitory corridor junction of South China Normal University. The whole experimental scene was 20 m long and 3 m wide.
# Swin-MRDB: Pan-Sharpening Model Based on the Swin Transformer and Multi-Scale CNN
## 2 Methods
### Swin Transformer Extracts Branches
#### 2.2.5 Algorithm Overview  
Algorithm 1 gives the overall framework and training process of Swin-MRDB. Specifically, in the data preparation stage, the number of model iterations M is read first, and then hyperspectral and panchromatic images are randomly read from the training sets \(H_{train}\) and \(P_{train}\) (Line4-5). In the feature extraction phase, image features \(Swin_{hs-pm}\), \(MRD_{ls}\), and \(MRD_{pan}\) are extracted based on the Swin transformer and multi-scale feature extraction block (Line7-9). In the feature fusion stage, features \(Swin_{hs-pan}\), \(MRD_{hs}\), and \(MRD_{pan}\) are fused using the residual feature fusion block to obtain a high-resolution hyperspectral image \(D\) (Line11). During the parameter calculation stage, a formula is utilized to determine the disparity between \(G\) and \(D\), resulting in the objective evaluation indicator \(V\)'s calculation (Line3). Define the value of \(V_{best}\) as 0. If the \(V\) obtained by some epoch is greater than \(V_{best}\), the value of \(V\) is used instead of the value of \(V_{best}\) (Line14-17).  
```
Input: hyperspectral training set \(H_{train}\), panchromatic image training set \(P_{train}\), hyperspectral test set \(H_{test}\), panchromatic image test set \(P_{test}\), ground truth image G, number of iterations M. Output: Objective evaluation indicator \(V_{best}\).
1 % Data preparation
2 \(V_{best}\gets 0\)
3 Read epoch value \(M\)
4 For\(m=1\dotsm M\)do
5 Randomly obtain image data \(H\), \(P\) from \(H_{train}\) and \(P_{train}\).
6 % feature extraction stage  The Swin Transform feature extraction block was utilized to extract the feature \(Swin_{hs-pan}\) of the hyperspectral and panchromatic images.  The multi-scale feature extraction block was utilized to extract the feature \(MRD_{ls}\) of the hyperspectral images.  The multi-scale feature extraction block was utilized to extract the feature \(MRD_{pan}\) of the panchromatic images. \(\%\) feature fusion stage  The high-resolution hyperspectral image \(D\) is obtained by extracting \(Swin_{hs-span}\), \(MRD_{hs}\) and \(MRD_{jun}\) based on the residual feature fusion block. \(\%\) Parameter calculation stage  The difference between the ground truth image \(G\) and \(D\) is calculated according to the formula to obtain the objective evaluation index \(V\) IF \(V>V_{best}\)then
7 \(V_{best}\gets V\)
8 end for
9 Return\(V_{best}\)
```  
**Algorithm 1** Swin-MRDB
# Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT
## 4 Case Study
### Compression Performance Metrics  
The performance of the compression algorithms was evaluated by the compression rate (CR), normalized residue energy (NRE), and normalized filtered residue energy (NFRE) metrics. The CR is defined as the ratio between the number of bytes that compose the input signal and the number of bytes that compose the output signal, defined in Equation (6), in which \(P\) is the number of bytes of the input signal and \(R\) is the number of bytes in the output signal.  
\[CR=\frac{P}{R} \tag{6}\]  
The NRE is the ratio between the energy of the residue, the difference between the original signal and the decompressed signal, and the energy of the original signal, as defined in Equation (7), in which \(x_{i}\) is the \(i\)th sample of the original signal and \(\hat{x}_{i}\) is the \(i\)th sample of the reconstructed signal.  
\[NRE=\frac{\sum_{i=0}^{N}(x_{i}-\hat{x}_{i})^{2}}{\sum_{i=0}^{N}x_{i}^{2}} \tag{7}\]  
By computing the NRE after filtering the original and decompressed signals to extract only their contents in a specific frequency band, the NFRE in said frequency band is obtained.
## 4 Conclusions  
This paper proposes an analytic design and optimization method for electric propulsion motors specifically designed for small EVs. Based on the proposed analytical sizing and optimization model, a 36-slot, 32-pole, and a 2.5 kW outrunner BLDC motor was designed and simulated where essential issues such as output power, shaft torque, cogging torque, speed range, efficiency, slot fill factor, current density, specific magnetic loading, and cost were considered. The sizing parameters of the produced motor were determined from the optimization results. The experimental results of the motor were derived from the optimum design results. The simulation and experimental results are presented comparatively. The deviation ratio is quite small, indicating that the experimental results are in good agreement with the simulation results. A FEM multiphysics simulation structure is used to couple 3D FEM electromagnetic and steady-state thermal analysis. Also, using 2D FEM and experimental results validates the analytical design and analysis. These results show that the motor is suitable for the natural cooling method. The proposed in-wheel motor performs satisfactorily with experimental studies, and the results obtained show that the most appropriate structure is the outrunner, which allows many poles to achieve high efficiency at low speeds.  
This research was funded by the scientific research project's participation in the Tubitak Efficiency Challenge, grant number KU-BAP01/2020-74 by Kastamonu University.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
I would like to express our sincere gratitude to Aegean Dynamics Corp. for their valuable support in the experimental phase of this study. Their contributions have been instrumental in facilitating the research process and enhancing the overall quality of our findings.  
The author declares no conflicts of interest.  
The following abbreviations are used in this manuscript:  
BLDC Brushless direct-current motor EV Electric vehicle FEM Finite element method FFT Fast Fourier transform  
\begin{table}
\begin{tabular}{c c} \hline
**Object** & **Temperature** \\ \hline Rotor & 22 \({}^{\circ}\)C \\ \hline Stator yoke & 66.015 \({}^{\circ}\)C \\ \hline Stator teeth & 67.366 \({}^{\circ}\)C \\ \hline Stator winding & 65.975 \({}^{\circ}\)C \\ \hline Magnet & 22 \({}^{\circ}\)C \\ \hline Rotor (minimum) & 22 \({}^{\circ}\)C \\ \hline Stator teeth (maximum) & 72.874 \({}^{\circ}\)C \\ \hline All body (average) & 53.026 \({}^{\circ}\)C \\ \hline \end{tabular}
\end{table}
Table 5: Temperature values of motor parts.
# A Framework for Bluetooth-Based Real-Time Audio Data Acquisition in Mobile Robotics
## 6 Results and Discussion
### Audio Acquisition Performance  
The audio acquisition system demonstrated strong performance across varying acoustic environments. Table 2 summarizes the quantitative measurements for each environment, including packet delivery rate, calculated signal-to-noise ratio, and subjective quality assessment.  
The packet delivery rate showed a slight decline as ambient noise levels increased, potentially due to increased processing demands for higher-amplitude signals. However, even in the most challenging environment (a busy street), delivery rates remained above 90%, providing continuous audio with minimal perceptible dropouts.  
Figure 6 illustrates representative audio waveforms captured in each environment, highlighting the system's ability to capture both subtle ambient sounds and louder environmental events. The dynamic range compression algorithm successfully adapted to each environment, maintaining a consistent output level despite dramatically different input amplitudes.  
The noise floor calibration implemented in the Android application proved particularly effective in the quiet room environment, reducing the audibility of system self-noise by approximately 6 dB based on a spectral analysis. This improvement was less pronounced in noisier environments, where environmental sounds naturally masked system noise. We acknowledge that the subjective audio quality assessment was conducted with only three evaluators, which provides limited statistical power. Future work should incorporate objective metrics such as PESQ (Perceptual Evaluation of Speech Quality) and STOI (Short-Time Objective Intelligibility) to provide more robust and statistically significant quality assessments. The subjective quality scores represent averaged ratings from three independent evaluators using a standardized five-point scale, where one indicates poor quality and five indicates excellent quality. While this provides initial validation of system performance, future studies should employ larger sample sizes and standardized objective metrics for a more comprehensive quality assessment.
## 2 Materials and Methods
### Materials
#### 2.4.7 System Usability Scale (SUS)  
The System Usability Scale (SUS) was employed to assess the usability of the TMT-VR. The SUS includes 10 items scored on a 5-point Likert scale, with scores converted into a total usability score out of 100. An example item is \"I think I would like to use this tool frequently\" [68]. The SUS is widely used for its simplicity and strong psychometric properties. It has demonstrated excellent internal consistency, with Cronbach's alpha values typically ranging from 0.85 to 0.92. In terms of validity, the SUS has shown to correlate well with other usability measures, and a SUS score above 68 is considered above average for usability, while scores above 80 are considered excellent.