esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Magnetic Inversion Based on an Adaptive Quadtree Data Compression
## 4 Conclusions  
In this paper, an adaptive quadtree decomposition method is proposed to compress magnetic data, which is decomposed recursively until the data in each quadrant meets the regional consistency standard. The model tests show that the method can be used for fine sampling where the gradient of magnetic anomaly data is large, and for sparse sampling at the place with a gentle anomaly change, to realize the effective compression of magnetic data and greatly improve the calculation efficiency of 3D inversion. In addition, the mean method can suppress noise and improve the resolution of inversion results.  
The method was applied to the magnetic data for the Ma'anshan mining area, and five possible areas of magnetite orebodies were delineated in the surrounding and deep part of the existing mining area, which provides some significant guidance for the next prospecting work.  
The 3D magnetic inversion algorithm is an important means of data interpretation. However, the calculation efficiency is very low. The adaptive quadtree decomposition calculation is only used to compress the data space. When the amount of data is large, the underground subdivision grid is very large, which still leads to the calculation speed being very slow. The calculation efficiency can be further improved by combining wavelet compression technology, adaptive mesh generation technology, parallel computing, and so on.  
Conceptualization, Z.Z. and S.Z.; methodology, S.Z. and D.J.; software, D.J.; validation, T.L. and S.Z.; formal analysis, Y.G., D.J. and T.L.; investigation, S.Z. and T.L.; resources, S.Z.; data curation, S.Z. and Y.G.; writing--original draft preparation, D.J.; writing--review and editing, S.Z., P.L. and Z.Z.; visualization, D.J.; supervision, T.L.; project administration, S.Z.; funding acquisition, Y.G., S.Z. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and Technology of the People Republic of China under Grant 2017 YFC0602203, the Natural Science Foundation of Jilin Province under Grant 20200201211 JC, the Innovation and Entrepreneurship Project for College Students of Jilin University under Grant 201910183426, and Geological Survey Project under Grant DD20201118.  
We would like to thank the editor and reviewers for their reviews that improved the content of this paper.  
The authors declare no conflict of interest.
# Moving Object Tracking and Its Application to an Indoor Dual-Robot Path
###### Abstract  
This paper presents an application of image tracking using an omnidirectional wheeled mobile robot (WMR). The objective of this study is to integrate image processing of hue, saturation, and lightness (HSL) for fuzzy color space, and use mean shift tracking for object detection and a Radio Frequency Identification (RFID) reader for confirming destination. Fuzzy control is applied to omnidirectional WMR for indoor patrol and intruder detection. Experimental results show that the proposed control scheme can make the WMRs perform indoor security service.  
fuzzy color classification; mean shift tracking; fuzzy control; Wi-Fi communication
## 3 Results  
### Synthesis and Characterizations of GO-SSS and PVDF-g-SSS Composite Membrane  
As shown in Figure 2a, small clusters existed in the casting solution, and GO was not compatible when 0.5% unmodified GO was directly mixed with the PVDF-g-SSS casting solution. In Figure 2b, it can be seen that the casting solution doped with GO-SSS had  
Figure 1: Schematic drawing of the permeation cell.  
a darker color, was more uniform, and had no cluster existence compared to the casting solution doped with the same amount of GO.  
Figure 3 shows the photographs of M1-M6 membranes, which are quite uniform. It was noticed that the higher the proportion of GO-SSS incorporated, the darker the color of the membrane material.  
Figure 4 depicts the FT-IR spectra of GO and GO-SSS powders after freeze-drying. Compared with bare GO powder (a), the freeze-dried GO-SSS powder (b) shows absorption bands at 1300 cm\({}^{-1}\) to 1550 cm\({}^{-1}\), representing the benzene ring's skeleton stretching vibration. The absorption peak shown at 2800 cm\({}^{-1}\) to 3000 cm\({}^{-1}\) represents the stretching vibration peak of the methylene group on the benzene ring. In the FTIR spectrum of GO-SSS powder, the absorption peaks at 1010 cm\({}^{-1}\) and 1150 cm\({}^{-1}\) represent the symmetric stretching vibration peaks of the sulfonate [23]. This indicates that the surface of GO has been successfully grafted with sodium p-styrene sulfonate.  
Figure 5a shows that GO shows a strong diffraction peak at 20 = 11.34\({}^{\circ}\), indicating that the interlayer spacing based on Bragg's law (2 disin\(\theta\) = n \(\times\) \(\lambda\)) is about 0.78 n. In comparison, no obvious sharp diffraction peak was observed in the case of GO-SSS (Figure 5b), indicating that the polyelectrolyte molecular brush destroyed the periodic structure of GO and effectively inhibited the aggregation of the graphene sheets. The original GO's sharp diffraction peak at 20 = 11.34\({}^{\circ}\) shifted to 20 < 5.0\({}^{\circ}\) after GO modification, and the corresponding interlayer spacing was greatly increased from 0.78 nm to above 1.75 nm. These results indicate that the polyelectrolyte molecular brush was successfully grafted onto the surface of the GO sheet.  
The SEM image in Figure 6 shows the topography of GO and GO-SSS. Figure 6a clearly shows the lamella structure of GO, while Figure 6b displays the hairy two-dimensional molecular brush flaky structure of GO-SSS.  
The SEM image in Figure 7 shows the section surface morphology of the original PVDF membrane and the composite membrane. Clearly, the surface of the original membrane is dense and non-porous, while the composite membrane has many wrinkles on the surface due to the presence of GO-SSS sheets.  
Figure 3: The morphology of M1–M6 membranes.  
Figure 2: (**a**) GO mixed PVDF-g-SSS membrane solution, (**b**) GO-SSS mixed with PVDF-g-SSS membrane solution at the same concentration of 0.5%.  
Figure 4: FT-IR spectra of (**a**) GO, (**b**) modified GO.  
Figure 5: XRD pattern of (**a**) GO, (**b**) GO-SSS.  
Figure 6: SEM image of (**a**) GO surface, (**b**) GO-SSS surface.
## 3 Methods of Determining of Image Number Lines
### Methods of Evaluating Contrast of Reconstructed Images  
In order to determine the number lines of reconstructed images from the revealing emission signals, four basic methods were used to evaluate image contrast. At this point, it should be emphasized that these methods effectively determine the level of contrast of images of satisfactory quality, e.g., from data display systems in the form of photos [38,39]. The data obtained in the process of electromagnetic infiltration are recreated and presented in the form of images. The images obtained in this way are very often highly noisy, containing graphic elements that strongly contrast with the background and have no relation to the processed information [11,17,19,35]. This largely hinders the analysis of such images and the possibility of direct use of typical image processing methods [40,41,42]. The input data of the reconstructed image line \(B_{Corr}\) algorithm are the \(B_{Entr}\) quantities, which are determined at the preliminary stage of analyzing the time course of the amplitude variation of the revealing emission signal. The authors' experience shows that the \(B_{Entr}\) estimation's accuracy is \(\pm 10\), hence the need to clarify this value, which allows for effective processing of the obtained image in the process of tens of coherent summations. The algorithm for determining the correct number lines of image assumes carrying out appropriate calculations of the values of the adopted measures for the number of lines:  
\[B=B_{Entr}\pm n, \tag{1}\]  
where \(n=0,1,2,\ldots,20\). In this way, 41 values are obtained:  
\(B=B_{Entr}-20\),  
\(B=B_{Entr}-19\),  
\(B=B_{Entr}-18\),  
\(\ldots\),  
\(B=B_{Entr}\),  
\(\ldots\),  
\(B=B_{Entr}+18\),  
\(B=B_{Entr}+19\),  
\(B=B_{Entr}+20\),  
selected measures that allow for the selection of the \(B_{Corr}\) value that meets the adopted criterion, which is the maximum value. It corresponds to the correct number of lines of the reconstructed image. 2.1 Contrast Evaluation Based on the Value of the Average Amplitude of Pixels of the Reconstructed Image--Method I  
\[Contrast_{I\_Un}(B)=\frac{Contrast_{I}(B)}{maximum_{I}}, \tag{2}\]  
where  
\[Contrast_{I}(B)=\frac{I_{B\_max}(B)-I_{B\_min}(B)}{\overline{I_{B}}(B)}, \tag{3}\]  
\[maximum_{I}=\underset{B}{\text{max}}(Contrast_{I}(B)), \tag{4}\]  
\[\overline{I_{B}}=\frac{1}{B\cdot M}\sum_{m=0}^{M-1}\sum_{b=0}^{B-1}I_{B}(b,m), \tag{5}\]\[l_{B\_max}(B) = \max_{b,m}(l_{B}(b,m)), \tag{6}\] \[l_{B\_min}(B) = \min_{b,m}(l_{B}(b,m)), \tag{7}\]  
\(M\)--columns number of reconstructed image;  
\(m\)--number of columns of reconstructed image (\(m=0,\,1,2,\ldots,\,M-1\));  
\(B\)--rows number (lines) of reconstructed image calculated according to (1);  
\(b\)--number of rows (lines) of reconstructed image (\(b=0,\,1,2,\ldots,\,B-1\));  
\(l_{B}(b,m)\)--value of image pixel amplitude for coordinates (\(b,m\));  
\(l_{B\_max}(B)\)--the maximum value of the image pixel amplitude for the number \(B\) line;  
\(l_{B\_min}(B)\)--the minimum value of the image pixel amplitude for the number \(B\) line.
# Connectivity Analysis in VANETs with Dynamic Ranges
## 4 Discussion
### Big Vehicle Shadowing Effect  
#### 4.3.1 Connectivity with Big Vehicle Prob  
The impact of large vehicles on network connectivity is illustrated in Figure 9. In this analysis, the network operates with a constant transmission power of \(P_{t}=0.5\) W and a total of \(N=40\) vehicle nodes. A proportion, \(P_{k}\), of these vehicles are classified as large vehicles.  
Figure 9a,b depict the connectivity probability for path-loss exponents of \(\alpha=2\) and \(\alpha=3\), respectively. As \(P_{k}\) increases, connectivity probability declines sharply in environments modeled by [PERSON] and Nakagami-\(m\) channel distributions. This decline is primarily due to the additional communication obstructions introduced by large vehicles, which block signal propagation between communicating nodes.  
The synchronized traffic model helps maintain more stable connectivity compared to the exponential traffic model, even in the presence of large vehicle obstructions. However, as the number of communicating vehicles increases and the path-loss exponent rises, connectivity deteriorates further due to the compounded impact of signal obstructions from the large vehicle bodies.  
Figure 8: Impact of pathloss on connectivity probability analysis at \(\rho=0.025\).  
Overall, the presence of large vehicles significantly reduces VANET connectivity, particularly in environments with higher path-loss conditions.
# Effective Strategies for Enhancing Real-Time Weapons Detection in Industry
## 4 Results
### Third Phase
#### 4.3.3 Comparison on Different Combinations  
In a final experiment, we conducted several tests with different possible combinations, where each possibility represents different subsections of the experiment carried out. The results obtained can be seen in Table 11. This approach allows us to observe, in a more comprehensive manner, the impact of the different combinations on the model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scenario** & **Combination** & **AP50** & **AP75** & **AP** & **F1** & **FP** & **mFPS** \\ \hline \multirow{4}{*}{Bright} & All & 61.14 & 59.28 & 51.36 & 40.12 & 0 & 34 \\  & + temp. wind. & 52.66 & 52.49 & 48.10 & 11.82 & 0 & 47 \\  & + 2 nd clas. & 75.72 & 71.31 & 61.09 & 71.71 & 48 & 34 \\  & Baseline & 77.68 & 73.88 & 63.41 & 73.60 & 164 & 48 \\ \hline \multirow{4}{*}{Dark} & All & 58.95 & 55.37 & 46.33 & 37.77 & 0 & 34 \\  & + temp. wind. & 50.75 & 50.19 & 44.36 & 10.04 & 0 & 47 \\ \cline{1-1}  & + 2 nd clas. & 73.10 & 66.08 & 54.90 & 67.16 & 32 & 34 \\ \cline{1-1}  & Baseline & 74.11 & 67.99 & 56.73 & 65.45 & 84 & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 10: General comparison between combinations in bright and dark scenes (temp. wind. refers to the temporal window and 2 nd clas. refers to the second classifier). The metrics used were Average Precision (AP), AP50, AP75, F1 score (F1), false positives (FP), and mean frames per second (mFPS). These were calculated using an IoU of 0.7 and a confidence threshold of 0.5. Note that the results in red are the best and those in blue are the second best.  
Figure 12: Comparison between original images (**first row**) and images with a brightness reduction of 75% (**second row**).  
As can be seen, the temporal window module helps us to minimise the number of false positives at the cost of lowering the number of true positives and their metrics (F1, mAP50, mAP75, and mAP). The best combination using the temporal window is the marked middle row with an F1 score of 68.89. If we seek to maximise detections, even if we detect some false positives, we can see how the marked penultimate row, in which all techniques (background images, data augmentation, scale match, and the second classifier) except the temporal window are used, achieves good results in all metrics (F1 score: 75.72) and reduces false positives to 48.
## 2 Materials and Methods
### Data Sources and Processing
#### 2.2.1 Data for Constructing the VMFDI
It has become a key parameter for monitoring vegetation growth and drought stress [38]. However, original Orbiting Carbon Observatory-2 (OCO-2) SIF data are spatially discontinuous and are not suitable for regional-scale studies. Therefore, global OCO-2-based SIF product (GOSIF) data were adopted [48]. GOSIF data were downscaled to 1 km to match the VPD and SM data for VMFDI calculation.
# Steganalysis of Quantization Index Modulation Steganography in G.723.1 Codec
## 3 The Effect of Index Distribution  
QIM steganography is related to the linear vector quantization of speech coding, so the linear predictive vector quantization process is first described.
## 6 Conclusions  
We used a variation streaming method to advance the field's understanding of tele-abrasive systems. On this basis, we propose a new approach to tele-abrasive systems, consisting of two steps: navigating the arm movement pattern, and using a linear estimation with a Kalman filter. The jitter pattern we propose minimizes the error in accuracy, while the Kalman filter helps to improve precision. Consequently, the total variation is significantly reduced as compared to that generated by the conventional system. Our approach was validated using a virtual tele-abrasive system, which could be different from the real system.  
Our approach can be an efficient alternative for tele-abrasive and similar tasks to assist operators, especially those prone to show high variation in the abrasive nozzle position. Additionally, all the participants stated that our approach is more comfortable for them to perform compared with the conventional approach, such that it leads to less stress and fatigue during the operation. Our approach can be applied to other similar applications, and we expect to see this happen over time.  
In future work, we believe that this approach can be applied to path shapes other than a straight line as long as the path shapes can mathematically be approximated or the movement orientation is continually tracked. Other filtering techniques could be used to solve more complex path shapes, such as the Extended Kalman filter (EKF), the Madgwick filter, and the Mahony filter. Furthermore, the human arm movement pattern may be changed to suit the path shape such that the operator can comfortably perform the task.  
Conceptualization, R.J. and C.Y.; methodology, R.J.; software, R.J.; validation, R.J., C.Y. and S.C.; formal analysis, R.J.; investigation, R.J.; resources, C.Y.; data curation, R.J.; writing--original draft preparation, R.J.; writing--review and editing, C.Y. and S.C.; visualization, R.J.; supervision, C.Y.; project administration, C.Y.; funding acquisition, R.J. and C.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by funding from the School of Engineering, KMITL grant no. 2560-01-01027, and the National Science and Technology Development Agency grant no. FDA-CO-2563-12494-TH.  
Not applicable.  
We would like to thank [PERSON] for technical support. We also thank all the participants who were involved in our study.  
The authors declare no conflict of interest.
# QoE Optimization in a Live Cellular Network through RLC Parameter Tuning
## 6 Proof of Concept
### Simulated Scenario
#### 6.1.1 Experiment Setup  
The simulation scenario used to assess the optimization algorithm proposed in this paper was the same as that used in [17]. This scenario consisted of two LTE cells with a distance of 100 m between them. Each cell served 10 users randomly located in the coverage area of the cells. One of these users was used to evaluate the algorithm. In the first case, this user was the client of a real-time video streaming service. The streamed video was in MPEG-4 format, with a size of 3.6 MB, a duration of 10 s, and a rate of 30 fps. Packets with a delay of more than 150 ms were considered lost, thus complying with the requirements of real-time video transmission [29]. In the second case, the user was the client of a file transfer service. For these tests, 5 MB files were used. The rest of the users were in both cases clients of a constant bitrate service. Table 1 lists the main configuration parameters of the simulations.
## 4 Discussion
### Application of AI in Predicting Facial Symmetry following OGS  
OGS is considered with a therapeutic goal to achieve functional and esthetic corrections. Evaluation of facial asymmetry is very crucial in planning and executing OGS. A study conducted by [PERSON] et al. assessed the degree of facial asymmetry in patients before and after OGS using a Deep Convolutional Neural Network (DCNN) based model. This model demonstrated an accuracy of 78.85% in held-out patterns and facial symmetry degree assessment within 1 degree was 98.63%. Comparison was made for assessing the difference between pre- and post-surgery facial symmetry using this model. The mean pre-operative facial symmetry degree was higher than postoperative with a significant improvement (\(p<0.02\)). The limitation of this study was that the model was built using a small sample number and the validation was confined to data obtained from one particular center [26]. In their study, [PERSON] et al. reported on using an AI-based model for predicting the facial morphology post OGS using 3D facial images. The study reported an average system error of 0.94 mm. The success rates when the success was defined by a system error of \(<\)1 mm was 54% and for \(<\)2 mm it was 100%. However, the limitation of this study was that the data used for developing this model was obtained from only two hospitals, so more studies need to be conducted using data from other hospitals to know the performance of this model [29]. [PERSON] et al. reported on a Convolutional Neural Networks (CNNs)-based AI model for assessing the facial symmetry before and after OGS using CBCT images. The model demonstrated an excellent performance, with accuracy of 90% in assessing facial symmetry. Although the model demonstrated excellent performance, the authors suggested that there is a need for large number of data sets in order to enhance the accuracy of the AI model [30]. In their study, [PERSON] et al. reported on applying a CNNs-based AI model for predicting the facial symmetry before and after OGS using 3D facial images. This model was efficient in evaluating the facial symmetry. However, there were dissimilarities between the patient's subjective view and the ML score since the assessment score represent general results [31]. A similar study conducted by [PERSON] et al. reported on applying a deep learning model for predicting soft tissue profile for planning OGS using CBCT data. This model was efficient in predicting the 3D soft tissue profile. However, this model tended to under predict the displacements in asymmetrical cases and in cases with cranial/caudaldisplacements. Another limitation was the smaller number of samples used for training the model [32].
# Hyperparameter Optimization of Ensemble Models for Spam Email Detection
## 3 Methodology  
### Dataset  
The Enron dataset was used in this research because it is the only substantial collection of an actual email that is public and also because of its high level of usage among researchers. The Enron dataset is made up of 6 main directories, each directory has several subdirectories, each containing emails as a single text file [28]. In this study, the Enron1 dataset was used. These emails were converted to a single CSV file by [PERSON] [29]. The CSV file contained about 33,000 emails. However, during conversion, some of the email messages were not correctly aligned with their labels. The non-aligning messages were removed alongside the orphaned labels through Microsoft Excel. On completion of the removal, the CSV file contained a total of 32,860 emails. Of the total emails, 16,026 (49%) are legitimate emails (ham) and 16,834 (51%) are spam. The CSV file contained five columns labeled message ID, subject, spam/ham, and date. The subject and date columns were not used in this study. The needed columns were readjusted as serial numbers. Column two contains the class label of each of the emails and column three contains the text of each email.
## 4 Design and Electrical Performance Characterization
### Input, Inter-Resonator, and Output Coupling Characterization
#### 4.4.4 Mechanical Adjustment  
Mechanical tuning elements have been implemented to compensate for measurement-simulation discrepancies, manufacturing tolerances, and temperature/vibration effects, necessitating adjustable components for both frequency and coupling optimization. A thorough characterization of these mechanical tuning elements was conducted, encompassing resonant frequency control, input-output coupling optimization, inter-resonator coupling management, and cross-coupling effect mitigation.  
For this purpose, a basic microstrip line (\(Z_{0}\)) was designed (Figure 1(a)) and fabricated to validate field distribution modifications through tuning screw insertion. A bridge-shaped structure was incorporated to enable precise screw positioning and depth control.  
Figure 25: Electromagnetic cross-coupling characterization.  
The characterization included the following three test configurations: no tuning element, screw positioned 0.3 mm from the transmission line, and screw at 0.15 mm proximity.  
Figure 27a,b presents the measured \(S_{21}\) transmission and \(S_{11}\) reflection responses, respectively. The results reveal resonant frequency shifts, evidenced by rotation of the imaginary component within a constant-radius \(\Gamma\) circle on the Smith chart, with decreasing screw-to-line distance. This behavior, apparent in both Figures 26b and 27b, results from direct field distribution manipulation (affecting both amplitude and phase). The tuning screws exhibit limited \(Q_{u}\), which directly impacts final filter performance. However, as Figure 27a demonstrates, improper embedding structure design can induce in-band screw resonance when penetration depth exceeds \(\lambda_{\text{eff}}/4\), observed in both 0.3 mm and 0.15 mm proximity cases through \(S_{21}\) response analysis (resonance at 11.15 GHz).