esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products
## 1 Introduction  
Compared to conventional manufacturing, which often involves machining or other techniques to extract surplus material, additive manufacturing (AM) creates components layer by layer [1]. AM uses CAD/CAM software for model generation, then the model is inputted into a 3D printer for slicing and G&M code generation, after which the 3D printer forms a 3D component. There are several types of AM processes that include fused deposition modeling (FDM), stereolithography (SLA), digital light processing (DLP), selective laser sintering (SLS), etc. [2]. The application range of AM is wide; it is used in the field of manufacturing, healthcare, aerospace engineering, fabrication, fashion, etc. [3, 4]. Because of the low cost of materials and the state of material available, Fused Deposition Modelling (FDM) is the most popular AM method. Despite the diversity of components AM can produce, AM is still susceptible to various defects due to the material properties and structural diversity of printed components. FDM 3D printers are subjected to multiple defects. During printing, due to material property or process failure, the component gets printed with several defects, such as warping, blistering, porosity, cracking, and residual stress.  
The research provides an experimental comparative analysis of real-time defect detection. The objectives of this research are:  
* To provide a real-time fault detection system for FDM 3D printers.  
* To provide a comparative study of model algorithms for fault detection on their computational accuracy results.
* To provide a density-wise classification of printed components.
* To provide ensemble learning results of model algorithm combinations.  
The paper overview includes the system methodology in which the experimental approach, algorithms, and pre-trained model used are explained. Further, it includes the experimental setup used, including the data collection technique and the result obtained by experimental analysis and comparative results of model algorithms.  
Different defects that occur in printed components and their causes and effects are shown in Table 1. Out of all the defects in 3D printed components, most are because of the material property or printing technique used. Warping gets introduced during the printing of a long component. The material extrusion layer-by-layer technique used in FDM components often requires post-processing since the printed component has a poor surface finish. Sometimes formation of small voids can lead to crack generation and, later on, failure of a component. Various defects that occur, such as clogging of the nozzle, improper bed leveling, misalignment of the printing platform, lack or loss of adhesion of the print platform etc., can be solved by manual adjustment. These errors are mainly due to carelessness of the operator and are not part of the research.
# Gesture-to-Text Translation Using SURF for Indian Sign Language
## 3 Research Methodology
### Image Preprocessing
#### 3.3.2 Edge Detection Using Canny Edge  
The most important information in a photograph is image edge data, which can indicate the relative position inside the target region, target frame, and other pertinent details. Among the most important processes in image processing is edge discovery, and the outcomes of the discovery directly impact picture analysis. In 1986, Canny introduced three performance metrics for edge detection operators and found the most effective to be the Canny edge detection operator [26]. The conversion of the image from the BGR format to the Canny edge image happens in the hierarchal format in the order explained in Figure 5. The output of the Canny edge detection is shown in Figure 6, where it can be observed that the boundary of the hand gesture is extracted in the Canny edge output.  
The algorithm for the Canny edge detection [26].  
* Smooth the picture with the Gauss function: \[\mathrm{G(x,\ y)}=\frac{exp\big{[}-\big{(}x^{2}+y^{2}\big{)}/2\sigma^{2}\big{]} }{2\pi\sigma^{2}}\] (11) where \(\sigma\) denotes a gauss filter parameter.
* Differential operators are used to determine the amplitude gradient and orientation. \[\mathrm{Ex[i,j]}=(\{[I+1,j]-I[i,j]+I[I+1,j+1]-I[i,j+1]\}/2\] (12) \[\mathrm{Ey[i,j]}=(\{[i,j+1]-I[i,j]+I[I+1,j+1]-I[[I+1,j]\}/2\] (13) \[G_{x}=\begin{pmatrix}-1&1\\ -1&1\end{pmatrix}\] (14) \[G_{y}=\begin{pmatrix}-1&1\\ -1&1\end{pmatrix}\] (15)  
It is possible to quantify the size and direction of the slope. Its image gradient is of the following magnitude:  
\[\left|\left|\mathrm{M(i,\ j)}\right|\right|=\sqrt{E_{x}[i,\ j]^{2}+E_{y}[i,\ j ]^{2}} \tag{16}\]  
The azimuth of the image gradient is:  
\[\mathrm{\theta(i,\ j)}=\tan^{-1}\bigg{(}\frac{E_{y}[i,j]}{E_{x}[i,\ j]}\bigg{)} \tag{17}\]
* Select a non-maximal number reduction approach (NMS) to obtain potential edge points.  
Figure 5: BGR to Canny edge conversion.  
Figure 6: Character “D” after Canny edge detection.  
* Double threshold these potential candidate edge points.
* Join the remaining potential edge points that are linked to edge points to obtain the picture edge detection findings.
## 3 Research Method
### Model Development
#### 3.2.3 Monitoring Scenario 3: Estimation of Spoil Volume Changes
Specifically, the disturbance boundary of the SDA can be considered as consisting of multiple pixels, where the base size of each pixel is determined by the spatial resolution of SAR data, with a base area of \(d^{2}\). The dynamic changes in spoil volume can be calculated using Formula (1):  
\[\Delta\
u=\sum_{i}^{n}\Delta h_{i}\times d^{2} \tag{1}\]  
where \(\Delta\
u\) is the total volume change of spoil, \(n\) and \(i\) are the total number of pixels with SAR elevation changes contained in the SDAs disturbance boundaries and the \(i\)th pixel, \(\Delta h_{i}\) is the SAR-based elevation change of the \(i\)th pixel, and \(d\) is the pixel size of SAR. The average elevation change can be calculated as shown in Formula (2). \[\overline{h}=\frac{1}{n}\sum_{i}^{n}\Delta h_{i} \tag{2}\]  
where \(\overline{h}\) is the average elevation change obtained using the D-InSAR technique. Based on the average elevation change and dynamic volume change obtained using the D-InSAR technique, as well as the actual measured values, the errors in average elevation change and dynamic volume change are calculated.
# Analysis of Subharmonic Oscillation and Slope Compensation for a Differential Boost Inverter
###### Abstract  
This paper focuses on the steady-behavior of a differential boost inverter used for generating a sinewave AC voltage in rural areas. The analysis of its dynamics will be performed using an accurate approach based on discrete time models and Floquet theory and adopting a quasi-static approximation. In particular, the undesired subharmonic oscillation exhibited by the inverter will be analyzed and its boundary in the parameter space will be predicted and delimited. Combining analytical expressions and computational procedures to determine the quasi-static duty cycle, subharmonic oscillation is accurately predicted. It is found that subharmonic oscillation takes place at critical values of the sinewave voltage reference cycle, which can cause distortion to the input current and degrade the harmonic content of the output voltage. The results provide useful information for the design of the boost inverter to avoid distortion caused by subharmonic oscillation. Namely, the minimum value of the compensation slope and the maximum proportional gain of the AC output voltage controller guaranteeing a pure sinewave voltage and clean inductor current during the entire AC cycle will be determined. Numerical simulations performed on the switched model implemented using PSIM(\(\copy\)) software confirm the theoretical predictions.  
differential boost inverter; current mode control; nonlinear behavior; subharmonic oscillation; slope compensation Article
# Mitigating the Scintillation Effect on GNSS Signals Using MP and ROTI
## 1 Introduction
The 1/30 Hz data can be acquired from around 500 stations daily, while 1 Hz data can be acquired from less than 170 stations each day. Furthermore, the International GNSS Service (IGS) started to provide 1/30 Hz and 1 Hz data in 1991 and 2001, respectively. In addition, the file size of 1/30 Hz data is substantially smaller than that of 1 Hz data. Therefore, the ability to use 1/30 Hz data for scintillation research opens opportunities by using archived data for more detailed studies of past scintillation events. The rate of change of total electron content index (ROTI) has been used to show the presence of scintillation instead of the scintillation parameters used in past research [20; 21; 22; 23; 24]. In addition, multipath parameter (MP) also has the ability to show a part of scintillation events [20; 25; 26]. Both ROTI and MP (standard parameters) can be generated using 1/30 Hz. Based on MP and ROTI, we propose three strategies for improving the positioning quality of PPP:  
1. remove the satellite with the largest MP or ROTI value;
2. remove observations where MP or ROTI values exceed a threshold;
3. weight the measurement noise matrix of the Kalman Filter (KF) using MP or ROTI values. To evaluate the effectiveness of MP and ROTI, the S4 and \(\sigma_{\Phi}\) values were also utilized in the three strategies for comparison and validation.
# Application of Improved LightGBM Model in Blood Glucose Prediction
## Materials and Methods
### Blood Glucose Prediction by HY_LightGBM Model  
Through the data preprocessing in Sections 2.4 and 2.5 and the Bayesian hyper-parameter optimization algorithm, the optimal parameters of the LightGBM model were determined and input into the LightGBM model for training and prediction. The blood glucose values output by it and the blood glucose values in the test set were evaluated by three evaluation indicators, namely: mean square error (MSE), root mean square error (RMSE), and determination coefficient R2 (R-Square).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Name** & **Default Value** & **Optimal Parameters** & **Parameter Implication** \\ \hline learning\_rate & 0.1 & 0.052 & Learning rate \\ n\_estimators & 10 & 376 & Number of basic learners \\ min\_data\_in\_leaf & 20 & 18 & The smallest possible record tree for a leaf \\ bagging\_fraction & 1 & 0.9 & Data scale for each iteration \\ feature\_fraction & 1 & 0.5 & Proportion of randomly selected features in each iteration \\ \hline \hline \end{tabular}
\end{table}
Table 2: The optimal parameters of the LightGBM model.  
Figure 9: Scatter diagram of aspartate aminotransferase and blood glucose value.
## 5 Numerical Evaluation Results
### Separated Peak Demands
#### 5.2.5 Impact of Random eNB Traffic Bursts
For the high load (which corresponds to a long-run average overall backhaul use of \(10/12\)), the assumption of the Lyapunov optimization is satisfied (i.e., the queues are stable), allowing the increased \(V\) to reduce the queue lengths. Figure 7d considers the medium load scenario for short eNB bursts of 0.5 s (with corresponding 0.75 s off state sojourn time). Comparing Figure 7b,d, we observe that the operation without SDN achieves shorter eNB queue lengths with the short bursts in Figure 7d compared to the long bursts in Figure 7b. For example, an eNB queue length under 100 kB is achieved with over 0.8 probability in Figure 7d, but only less than 0.65 probability in Figure 7b. Intuitively, the shorter eNB traffic bursts create only smaller eNBs queue backlogs that are easier to clear with the limited operator bandwidth \(Z_{o}\). We also observe from the comparison of Figure 7b,d that the gap between SDN control with \(V=100\) and with \(V=1000\) has slightly widened in Figure 7d, mainly due to the CDF curve for \(V=100\) reaching only lower values in Figure 7d compared to Figure 7b. This is primarily because the shorter burst in Figure 7d require more flexibility from the SDN control; however, the \(V=100\) control provides only limited flexibility and can therefore not perform quite as well as for the longer bursts in Figure 7b. Nevertheless, even though the gap between operation with SDN control vs. operation without SDN control has slightly shrunken in Figure 7d compared to Figure 7b, the SDN control still achieved substantial eNB queue length reductions.
# Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning
## 2 Data Collection and Preprocessing
### Noise Reduction  
Several algorithms and applications are available for speech enhancement. We performed noise suppression using spectral subtraction and voice activity detection [37] over noisy audio samples. We have obtained spectral estimates for the background noise from the input signal. Figure 2 demonstrates the performance of the proposed method; Figure 2a shows the time-waveform of the alphabet with background noise whereas Figure 2b shows the time-waveform of the clean alphabet.
# PIFNet: 3D Object Detection Using Joint Image and Point Cloud Features for Autonomous Driving
## 4 Experimental Results
### Experiments on KITTI Dataset  
We evaluated our PIFNet on KITTI Dataset following the common protocol to report the Average Precision (AP) of class Car with 0.7 (IoU) threshold. Table 1 presents quantitative results on the KITTI test set [34] for several state-of-the-art methods. Our proposedmethod (PIFNet) outperforms multi-sensor-based methods MV3D [31], F-Point Net [30], MMF [24], and EPNet [27] by 20.96%, 14.31%, 6.48%, and 3.94% in terms of 3D average mAP. The results for other methods are brought from the KITTI leaderboard [34].  
In Table 1, our method showed the best results with mAP of 85.16% in 3D detection. The second best method was EPNet [27] with mAP of 81.22%.  
One can observe that the proposed PIFNet has achieved the significant performance gains over other camera-LiDAR fusion-based detectors in the KITTI leaderboard. In particular, our approach improved the average mAP by up to 4.27% compared to that of EPNet [27], the best published fusion-based method so far. While PIFNet outperforms the MMF [24] for easy and moderate cases in BEV detection, MMF [24] showed the best performance for the hard case. Compared with Lidar-only methods, PIFNet also outperforms most of 3D detectors except PV-RCNN [6] for the hard case in BEV detection. Since PV-RCNN [6] combines the point-wise feature and voxel-wise feature, it might have stronger Lidar feature.  
Furthermore, we evaluated the inference time on a GPU board, and the running time is 0.21 s per frame on average. In Table 2, the running times are compared with several other methods which are based on the fusion of Lidar point clouds and camera images. The time-consuming is a bit longer than other methods because our network utilizes rather a large number of intermediate parameters and features.
## 4 Results and Discussion  
### Combination of Image and Text Basically Achieved Quality Grading  
Multiple regular expression matching patterns were designed for each feature based on all of the possible formats in which each feature could appear in the descriptive text, obtaining a value for each feature present in each sample. Numerical features extracted from the textual data were solely utilized to train the model (Experiment (Exp) 1, Figure 4a). The influence of the layer number of the Transformer Encoder was studied (insert of Figure 4a). As the layer number increased, the accuracy of test set generally increased first, and reached the maximum (59.36%) at three layers, then sharply fell. Therefore, the structure of Transformer Encoder was optimized with three layers. Due to the prevalent features missing in most samples within the orchid dataset, the feature matrix exhibited significant sparsity, with most elements being empty [21]. A few non-zero elements cannot sufficiently represent the grading pattern of orchids. The sparsity led to the model overfitting to limited available and frequently occurring features, resulting in poor generalization ability [22]. The subjectivity of sellers or different grading standards contributed to inconsistency in the dataset. _Rchb. f._ with similar grades often had vastly different numerical features. It is difficult for the model to stably learn quality grading patterns from a dataset [23]. After 224 epochs, peak accuracies of 63.02% and 59.36% on validation set and test set were achieved, respectively (Figure 4b).  
Figure 3: Computing process of SHAP algorithm for explainable features representation.  
SHAP analysis was integrated to quantify the importance of seven orchid numerical features based on their contribution to the model's grading decisions. The analysis revealed that 'Leaf number' had the most significant impact on the model, whereas 'Bud Number' had the least. However, the SHAP values for all seven features were closely aligned, indicating a challenge for the model in distinguishing the differential impact of each feature on grading outcomes, as depicted in Figure 4c.  
To address the limitations brought by numerical features extracted from textual data, visual datasets were incorporated into dataset. UNet was utilized to segment the petals from orchid images. After manually annotating all orchid sample images and conducting 42 epochs, the test set achieved a maximum mIOU of 89.16%. The average RGB values were computed from the \(i\)th segmented petal images by averaging the pixel values across the red, green, and blue channels, defined as  
\[\text{Average\_RGB}_{i}=\left(\frac{1}{N_{i}}\sum_{j=1}^{N_{i}}R_{j},\frac{1} {N}\sum_{j=1}^{N_{i}}G_{j},\frac{1}{N}\sum_{j=1}^{N_{i}}B_{j}\right) \tag{5}\]  
where \(R_{j}\), \(G_{j}\), \(B_{j}\) are the red, green, and blue pixel values of the segmented petals, respectively, and \(N_{i}\) is the total number of pixels of the \(i\)th image in the segmented area.
## 6 Conclusions  
This work investigates leaky Lamb wave radiation from a waveguide plate with a finite width with consideration for the width modes of the Lamb wave and their superposition. Dispersion curves obtained using the SAFE method showed that Lamb waves in the plate strip have numerous width modes, in contrast to the case of an infinitely wide plate. These width modes were very close to each other in the dispersion curve, and thus it could be inferred from these results that the multiple width modes were bound to be excited and propagated together by the high modal density. In the beam profile measurement, one could observe that characteristics of the leaky radiation beam from the waveguide plate were noticeably changed with variation of the excitation source position. This characteristic was strongly estimated to be affected by the spatial beating induced by wave velocity differences of the superposed modes. Changes to the wave phase on the waveguide plate were validated by a simple computational result and then the leaky radiation beam profiles were simulated with variation of the wave phase of the superposed modes on the radiation surface. From this simulation, it was demonstrated that leaky Lamb wave radiation from the waveguide plate was affected by superposition of the width modes. In particular, the lateral beam was dominantly influenced by the wave phase on the radiation surface, changed by the excitation source position. Perfect avoidance of the spatial beating caused by the mode superposition of the width modes in the strip-like plate is expected to be physically difficult. Therefore, the excitation conditions, including the excitation frequency and the geometry of the waveguide plate, need to be properly designed for the waveguide sensor using leaky Lamb wave radiation from a waveguide plate with a finite width. Further study is necessary to analyze the radiation characteristics of practical plate-type waveguide sensors based on this preliminary work.  
Conceptualization and methodology: [PERSON] and [PERSON]; investigation: [PERSON]-[PERSON] and [PERSON]; SAFE analysis: [PERSON]-[PERSON]; experimental validation: [PERSON]-[PERSON] and [PERSON]; writing--original draft preparation, [PERSON]-[PERSON]; writing--review and editing: [PERSON], and [PERSON]; supervision: [PERSON]; All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Nuclear Research & Development Program of the National Research Foundation with a grant funded by the Korea Ministry of Science and ICT.  
The authors declare no conflict of interest.  
Figure 18: Measured beam profiles of the leaky Lamb wave excited at \(d=350\) mm (1.0 mm thickness and 15 mm width, 1.5 MHz center frequency).
## 3 Results and Discussion
### Characterization of the Rods and Rod with ARP
#### 3.3.3 \(M_{n}\) Study  
This study revealed the influence of the HME process on the decrease in the \(M_{n}\) value of terpolymers based on d,l-LA or l-LA, GA, and TMC. This issue has already been extensively discussed in previous studies. The decrease in the \(M_{n}\) of poly(d,l-lactide-_co_-glycolide-_co_-trimethylene carbonate) and poly(l-lactide-_co_-glycolide-_co_-trimethylene carbonate) was 41.68% and 24.15%, respectively [12]. For comparison, other authors revealed a \(M_{n}\) decrease in the range of 27-69% during melt processing for poly(d,l-lactide), poly(l-lactide), d,l-PLGA, and poly(l-lactide-_co_-glycolide) (l-PLGA) [32; 33; 34; 35; 36; 37; 38; 39; 40]. The literature also points out the shearing forces caused by the rotating screw during HME as a reason for the \(M_{n}\) decrease in aliphatic polyesters [33; 34; 35; 37; 38; 40].  
In this study, the noted changes reflect the pertinent literature data. A decrease in \(M_{n}\) in the range of 2.9-39.1% for blank rods was noted. Less susceptible to a \(M_{n}\) decrease were blank rods based on terpolymers with a lower initial value, _i.e._, the T_d_,\(l\) 21 rod and T_l_ 59 rod. The highest loss of \(M_{n}\) was observed for the T_l_ 77 rod (39.1%, _i.e._, 30 kDa). The introduction of ARP resulted in a decrease in \(M_{n}\), which was insignificant from the point of view of formulation stability (Table 3).
## 4 Numerical Examples and Illustrations
### Bifurcation and Stability Analysis  
This section presents numerical examples that investigate qualitative and quantitative changes and bifurcations in thermal environments. Force-response curves are obtained by selecting the external excitation amplitude as the control parameter, while frequency-response curves are obtained by adopting the detuning parameter as the control factor. The stable (unstable) solution branches are indicated by solid (dashed) lines, and the vibration behaviors in cooling and warming conditions are represented by blue and red lines (circles), respectively, using the perturbation (numerical) method.