esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards an Automated Design Evaluation Method for Wire Arc Additive Manufacturing
## 2 State of the Art
### Current Methods for Manufacturability Evaluation
#### 2.1.1 Heuvers' Circles Method for Casting  
Heuvers' circles is a method to analyze cast parts for their manufacturability, first published in 1929 by [PERSON] [23]. In casting, different cross-section sizes lead to different cooling and shrinking rates. The solidification of the material starts at the component's surface while the material on the inside is still liquid. The solidified material can be compared to a shell, which cannot follow the shrinking rates of the inner material. Therefore, cavities can occur which cannot be filled by the liquid material anymore. To reduce the risk of cavities, the part has to be analyzed with the method of Heuvers' circles.  
To do so, circles are drawn into different cross-sections of the part (see Figure 1). The gradient of the diameters can be compared to a specific material factor. With this method, oversized cross-section changes can be detected and the risk of cavities can be reduced.  
In general, the diameter ratio _D/d_ of two contacting circles has to be lower than 1.6 [24]. For specific materials, lower diameter ratios have to be used. Typical Heuvers' factors \(k_{h}\) are 1.0 to 1.5 [25].  
An automated version of the Heuvers' circles method has been proposed by [PERSON] et al. [15] to predict hot spots. They extend the method of Heuvers' circles for three-dimensional geometries by using a _medial axis transformation_ (3D-MAT). The MAT generates a medial axis for two-dimensional cross-sections (2D-MAT) or a medial surface for three-dimensional parts (3D-MAT), respectively, which are the centers of the generated Heuvers' circles/spheres, and estimates their corresponding circle/sphere radii [15].
# Automatic Digital Modulation Classification Based on Curriculum Learning
## 4 Results and Discussion  
In this section, a series of measurements are implemented to verify the classification accuracy of the automatic digital modulation classifier. In our experiment, various modulated signals were tested, including 2 ASK, 4 ASK, 8 ASK, 2 FSK, 4 FSK, 8 FSK, 2 PSK, 4 PSK, 8 PSK, 16 QAM and 64 QAM. The relative parameters are shown in Table 1. We generated a training set and test set by using Matlab2018a. Every training set included 110,000 samples, while each validation set and test set included 11,000 samples. All these samples possessed the same length of 1024 and various SNRs obeying uniform distribution. The training, validation and test sets were used to implement the training, evaluation and exam of classifier, respectively. In addition, the classifier with only StudentNet was named as the Baseline classifier, and the one containing both StudentNet and MentorNet was called the MentorNet classifier.
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 6 ML Applications in Energy Prediction
### Non-Intrusive Load Monitoring  
Without the need for individual appliance meters, NILM approaches break down the overall energy signal of a building into consumption profiles that are particular to individual devices [103; 104]. In NILM systems, ML techniques like deep neural networks and hidden Markov models are crucial. In their discussion of the use of ML approaches in NILM, ref. [105] emphasized the importance of these techniques in obtaining comprehensive insights into energy usage.
## 3 Test Results and Discussion
### Results of Repeated Impact Tests  
#### 3.2.1 Typical Stress Waveform Analysis  
Figure 8 shows that the amplitude of the reflected wave and the area surrounded by the reflected wave and abscissa axis gradually increase with increasing impact times, while the amplitude of the transmitted wave and the area surrounded by the transmitted wave and abscissa axis gradually decrease. The reason is that the wave reflection is related to the change in the wave impedance of the specimen. Using the idea of a limit, when the wave impedance of the specimen is completely consistent with the SHPB bar system, reflection will not occur, and the shape and size of the transmitted wave are completely consistent with the incident wave; when the wave impedance of the specimen is zero, the reflected wave is opposite to the incident wave, and there is no transmitted wave. The wave impedance of a rock is related to its internal cracks. The more cracks there are, the smaller the wave impedance is. This shows that the pores and cracks of sandstone gradually increase with the increase in the number of impacts, while the longitudinal wave velocity and wave impedance gradually decrease. The reflected wave carries the strain information of the specimen, which indicates that the strain and strain rate of sandstone gradually increase with increasing impact times. The transmitted wave carries the stress information of the specimen; thus, the stress of sandstone gradually decreases with increasing impact times. This shows that the damage of sandstone under repeated impacts continues to develop, eventually leading to failure.
# A Note on the Soft Group Category
## 3 Soft Group Category
For any \(x\in A\), we write \(F(p_{1}(x))=F(x)=\hat{1_{G}}(F(x))\) and so \((F\circ p_{1})(x)=(\hat{1_{G}}\circ F)(x)\). Secondly, if \(a=y\), then we obtain \(F(p_{1}(y))=F(x)=F(y)=\hat{1_{G}}(F(y))\) and again \((F\circ p_{1})(y)=(\hat{1_{G}}\circ F)(y)\). Therefore, \((1_{G},p_{1})\) is a soft group homomorphism. In the similar way, we can prove that the map \((1_{G},p_{2})\) is a soft group homomorphism. Eventually, we have\[(f,p)\circ(1_{G},p_{1}) =(f\circ 1_{G},p\circ p_{1})\] \[=(f\circ 1_{G},p\circ p_{2})\] \[=(f,p)\circ(1_{G},p_{2}).\]  
However, \((f,p)\) is not monic since \((1_{G}\circ p_{1})\
eq(1_{G}\circ p_{2})\). This completes the proof. Theorem 4: _Let \((f,p):(F,A)_{G}\rightarrow(H,B)_{K}\) be a morphism in the soft group category \(SGp\). If both \(f\) and \(p\) are injective, then the morphism \((f,p)\) is monic._  
Proof: Assume that \(f\) and \(p\) are injective. Let \((f_{1},p_{1}),\ ((f_{2},p_{2}):(L,C)_{M}\rightarrow(F,A)_{G}\) be any two morphisms in \(SGp\) such that \((f,p)\circ(f_{1},p_{1})=(f,p)\circ(f_{2},p_{2})\). Thus, it can been easily seen that \((f\circ f_{1},p\circ p_{1})=(f\circ f_{2},p\circ p_{2})\). Then, we obtain \(f\circ f_{1}=f\circ f_{2}\) and \(p\circ p_{1}=p\circ p_{2}\). Since both \(f\) and \(p\) are one-to-one, we obtain \(f_{1}=f_{2}\) in \(Gp\) and \(p_{1}=p_{2}\) in \(Set\), where \(Gp\) and \(Set\) represent the group category and set category, respectively. Therefore, the morphism \((f,p)\) is monic in \(SGp\). Theorem 5: _If a soft group morphism \((f,p)\) in the \(SGp\) is split monic, then both \(f\) and \(p\) is injective._  
Proof: Let \((F,A)_{G}\) and \((H,B)_{K}\) be any two soft groups, and let \((f,p):(F,A)_{G}\rightarrow(H,B)_{K}\) be a soft split monic morphism. Since \((f,p)\) is a split monic morphism, then there is \((g,q):(H,B)_{K}\rightarrow(F,A)_{G}\) such that \((g,q)\circ(f,p)=1_{(F,A)_{G}}=(1_{G},1_{A})\). Because of Definition 11, we have \(g\circ f=1_{G}\) and \(q\circ p=1_{A}\). It follows that \(f\) is a split monic morphism in the group category \(Gp\) and \(p\) is a split monic morphism in the set category \(Set\). Thus, both \(f\) and \(p\) are injective. Theorem 6: _Let \((f,p):(F,A)_{G}\rightarrow(H,B)_{K}\) be a morphism in the soft group category \(SGp\). If both \(f\) and \(p\) are surjective, then the morphism \((f,p)\) is epic._  
Proof: Assume that \((g_{1},q_{1}),\ (g_{2},q_{2}):(H,B)_{K}\rightarrow(T,D)_{N}\) are any two morphisms in \(SGp\) such that \((g_{1},q_{1})\circ(f,p)=(g_{2},q_{2})\circ(f,p)\). Thus, we deduce that \((g_{1}\circ f,q_{1}\circ p)=(g_{2}\circ f,q_{2}\circ p)\). From this, we obtain \(g_{1}\circ f=g_{2}\circ f\) and \(q_{1}\circ p=q_{2}\circ p\). We obtain \(g_{1}=g_{2}\) in \(Gp\) and \(q_{1}=q_{2}\) in \(Set\) due to the fact that \(f\) is an epimorphism and \(q\) is onto. Hence, the morphism \((f,p)\) is epic in \(SGp\).
# Evaluation of Ride Comfort under Vortex-Induced Vibration of Long-Span Bridge
## 4 Result and Discussion
### The Amplitude Limit of VIV Based on OVTV and MSI  
During the initial phases of constructing long-span bridges, the bridge deck is built according to highway standards. Simultaneously, the bridge mentioned in Section 4.1 enforces a maximum speed limit of 80 km/h and a minimum speed limit of 40 km/h. Considering that higher vehicle speeds result in an increased OVTV and decreased MSI, we select an A-grade road surface and a vehicle speed of 80 km/h for calculating the comfort index OVTV under various frequency amplitude vortex vibrations. Based on the relationship between the OVTV and human subjective perception, we set a limit of 0.315 m/s\({}^{2}\). After interpolation, we determined the driving comfort amplitudes for four vehicle types, which serve as the vortex-induced vibration limits. The results are presented in Table 5.  
Similarly, we used interpolation to find corresponding amplitude values when MSI ranges from 1% to 10%. The minimum value for each vehicle type is set as its corresponding vortex-induced vibration limit based on MSI. These findings are shown in Table 6.  
Figure 11: Relationship between MSI and frequency and amplitude in different vehicles. (**a**) Sedan car (**b**) Light truck. (**c**) Mini van. (**d**) Motor bus.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frequency (Hz)** & **Sedan Car** & **Light Truck** & **Mini Van** & **Motor Bus** \\ \hline
0.233 & 0.79 & 1.45 & 0.76 & 1.58 \\
0.276 & 0.25 & 0.41 & 0.25 & 0.45 \\
0.328 & 0.15 & 0.21 & 0.15 & 0.22 \\
0.381 & 0.12 & 0.15 & 0.11 & 0.15 \\
0.496 & 0.07 & 0.07 & 0.07 & 0.06 \\
0.557 & 0.06 & 0.06 & 0.05 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Vortex-induced vibration limit based on OVTV (unit: m).  
MSI values were calculated at a vehicle speed of 40 km/h. To provide a more intuitive representation of the results, Figure 12 have been added to illustrate how the amplitude limits vary with frequency.
## 3 CFD Modelling as Digital Twin
### CFD Models for Filling and Emptying Processes
#### 3.3.3 Numerical Resolution  
Solving the governing equations in the CFD modelling of two-phase flows is necessary to obtain good numerical accuracy when compared with measurements made in experimental or industrial application scenarios. Various algorithms have been developed to solve the coupled pressure-velocity equations in the Navier-Stokes equations. Among these, the SIMPLE (Semi-Implicit Method for Pressure-Linked Equations) algorithm is widely used for its simplicity and effectiveness in laminar and low-turbulence flows; it iteratively corrects pressure and velocity to achieve convergence [60; 61; 62]. In contrast, the PISO (Pressure Implicit with Splitting of Operators) algorithm enhances this approach by allowing multiple corrections within a single time step, making it particularly suitable for unsteady and turbulent flows [63]. The PIMPLE (Pressure Implicit with Multiple Predictor-Corrector Steps) algorithm combines the benefits of both SIMPLE and PISO, offering flexibility in handling transient simulations while maintaining robust convergence properties [64; 65]. The authors have developed CFD models using solution algorithms to solve them and obtain results numerically. Table 5 shows a summary of the applicability and characteristics of each solution algorithm to understand the differences between each.  
Figure 7: Types of elements for the discretisation of two- and three-dimensional CFD geometries: (**a**) structured elements and (**b**) unstructured elements.
# COMS Visible Channel Calibration Using Moon Observation Data
## 5 Conclusions  
This study reports the long-term characteristics of the COMS MI visible channel for the first time through the application of GSICS concepts, implementation of GIRO, and the use of moon observation data from April 2011 to December 2017. Results indicated that the irradiance ratio between GIRO and lunar observations of COMS MI showed strong seasonal variation due to seasonal variation in the phase angle of the Moon. Furthermore, the phase angle of the Moon showed a similar pattern every half year, which was related to Moon observation attitude and time. For COMS MI, the number of Moon observations was limited because of the attenuation tendency with respect to the limited phase angle range. However, when Geo-KOMPSAT-2A (Geostationary-Korea Multi-Purpose Satellite-2A) AMI (Advanced Meteorological Imager), the next generation geostationary satellite of KMA, is launched at the end of 2018, the Moon phase angle will be further subdivided through an analysis of the degradation ratio. It is anticipated that Moon observation data will be obtained more frequently than with COMS MI. Therefore, it is expected that the effect of seasonal variation on the phase angle will be reduced.  
T.-[PERSON] processed COMS MI Moon observation data, plotted, and analyzed results of Moon calibration, and prepared the manuscript. [PERSON] formulated and directed the methodology, validation, results, and analysis. Both authors contributed to the methodology, validation, results, analysis, and reviewed the manuscript.  
**Acknowledgments:** This study was supported by the project entitled \"Development of Meteorological Satellite Operation and Application Technology\" of the KMA/NMSC (Korea Meteorological Administration/National Meteorological Satellite Center).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Applying Particle Swarm Optimization Variations to Solve the Transportation Problem Effectively
## 4 The Basic PSO for Solving the TP
The value of the Coefficient of Variation (CV), which is the basic measure for proving stability of stochastic algorithms, is, for all PSO variations, quite small; more precisely, the mean CV value is for each PSO variation is as follows: Classic PSO, 2.12%; DWPSO, 1.32%; TVAC, 0.87%; TrigAC-PSO, 0.66%; and FSVAC, 1.26%. These values show that TrigAC-PSO, which is one of the new PSO variations presented in this work, is the most stable one. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & & **PSO** & **DWPSO** & **TVAC** & **TrigAC-PSO** & **FSVAC** \\ \hline \multirow{6}{*}{Pr01} & Mean & 894.7666 & 884.3 & 883.6 & 882.86667 & 884.4 \\  & St.Dev & 22.51847 & 10.61278 & 11.2544 & 7.41263 & 8.76366 \\  & Min & 880 & 880 & 880 & 880 & 880 \\  & Max & 965 & 928 & 940 & 910 & 917 \\  & cv\% & 2.51668658 & 1.200133 & 1.273701 & 0.8396096 & 0.990905 \\ \hline \multirow{6}{*}{Pr02} & Mean & 743 & 743 & 743 & 743 & 743 \\  & St.Dev & 0 & 0 & 0 & 0 & 0 \\  & Min & 743 & 743 & 743 & 743 & 743 \\  & Max & 743 & 743 & 743 & 743 & 743 \\  & cv\% & 0 & 0 & 0 & 0 & 0 \\ \hline \multirow{6}{*}{Pr03} & Mean & 5600 & 5600 & 5600 & 5600 & 5600 \\  & St.Dev & 0 & 0 & 0 & 0 & 0 \\  & Min & 5600 & 5600 & 5600 & 5600 & 5600 \\  & Max & 5600 & 5600 & 5600 & 5600 & 5600 \\  & cv\% & 0 & 0 & 0 & 0 & 0 \\ \hline \multirow{6}{*}{Pr04} & Mean & 59 & 59 & 59 & 59 & 59 \\  & St.Dev & 0 & 0 & 0 & 0 & 0 \\ \cline{1-1}  & Min & 59 & 59 & 59 & 59 & 59 \\ \cline{1-1}  & Max & 59 & 59 & 59 & 59 & 59 \\ \cline{1-1}  & cv\% & 0 & 0 & 0 & 0 & 0 \\ \hline \multirow{6}{*}{Pr05} & Mean & 28.266666 & 28 & 28 & 28 & 28 \\ \cline{1-1}  & St.Dev & 0.4497764 & 0 & 0 & 0 & 0 \\ \cline{1-1}  & Min & 28 & 28 & 28 & 28 & 28 \\ \cline{1-1}  & Max & 29 & 28 & 28 & 28 & 28 \\ \cline{1-1}  & cv\% & 1.591190254 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistical measures for 20 particles.
## 1 Introduction
Using ceramic spheres manufactured via SLA in alumina as reference elements for the calibration of medium- and high-range optical sensors is a potential application worth considering. In this study, alumina spherical artifacts [25] produced by stereolithography were evaluated using a candidate measuring technique: conoscopic holography. The spheres were first measured by contact with a coordinate measuring machine (CMM) to obtain reference values for diameter and form error. These parameters were also recorded from point clouds generated through digitalization using the conoscopic holography sensor, and the results were compared with the reference values. This comparison, between contact (CMM) and HC, will allow us to obtain the overall feasibility of this measurement technique in measurements of this type of ceramic material when it is manufactured with this new additive manufacturing process (SLA). The following sections first describe the equipment and materials used in the study, as well as the methodology for assessing the obtained measurements. Subsequently, the results for the evaluated parameters--diameter and form error--are presented, compared, and discussed in relation to the reference values. The final section summarizes the conclusions drawn from the data presented in the previous sections.
###### Abstract  
A simple methodology was developed for the determination of four Endocrine Disrupting Chemicals (EDCs) in dairy products. The EDCs included alkylphenols (4-tert-octylphenol, technical nonylphenol isomers, 4-nonylphenol) and bisphenol-A. The methodology consisted of a quick, easy, cheap, effective, rugged and safe (QuEChERS) extraction followed by liquid chromatography (LC) coupled to the hybrid LTQ/Orbitrap mass spectrometer (MS). The high resolution (HR) analysis provided the required selectivity demonstrating excellent sensitivity and enabled the high-mass accuracy of the analytes within short time of analysis, after a chemometric optimization of the instrument parameters. An experimental design was employed for the estimation of the effect of different parameters on the QuEChERS extraction efficiency to obtain the optimum conditions. Method validation proved that analysis exhibited excellent linearity (R\({}^{2}\) > 0.9966), low enough precision (0.6 to 13.3%) and recoveries in the range of 91 to 108%. Limits of detection (LOD < 6.5 ng g\({}^{-1}\)) and quantification (LOQ < 20 ng g\({}^{-1}\)) as well as matrix effects (ME) were also evaluated. The optimized method was successfully applied to analyze dairy commodities varying in fat content and packaging material including milk, yogurts and infant formulae. Detected concentration levels (MDL-10.4 ng g\({}^{-1}\)) for bisphenol-A BPA in milk samples resulted in 0.36% of TDI for the medium case (average BPA concentrations) and 1.15% of TDI for the worst case (maximum BPA concentration).  
QuEChERS; LC-LTQ/Orbitrap MS; bisphenol A; nonylphenol; octylphenol; milk; dairy +
Footnote †: journal: Applied Sciences
# DScanNet: Packaging Defect Detection Algorithm Based on Selective State Space Models
## 6 Discussion  
The DSCanNet model proposed in this study shows significant advantages in the task of logistics packaging defect detection, but there are still many issues to be explored.  
In terms of model performance, DSCanNet performs well in terms of accuracy, recall, mean average precision (mAP) and detection speed (FPS). Compared with the original YOLOv5 algorithm, the detection precision is improved by 4.3 percentage points to reach a high accuracy of 96.8%, the recall is also improved by 2.9 percentage points, and the detection speed reaches 97.1 FPS. This is due to the synergistic effect of the components of the MEFE Module, the PCR Module, and the Mampa Block. Although the model performs well on existing datasets, it may face more challenges in real-life complex and changing logistics scenarios. For example, factors such as different lighting conditions, complex backgrounds, and the diversity of packaging materials may have an impact on the model performance. Subsequent studies may consider training and testing on more diverse datasets to further improve the generalization ability of the model.  
Analyzing from the perspective of model structure, the introduction of Mampa Block is a major innovation point of DSCanNet. The Mampa architecture was originally applied to long sequence modeling, and its direct use in visual detection tasks would lead to the loss of global receptive field information, which is effectively solved by the Mampa Block designed based on the VSS Block in this paper. Through the SS2D mechanism, linear complexity is realized while maintaining the global receptive field. However, in practical applications, how to further optimize the implementation of the Mampa Block so that it can run more efficiently on some edge devices with limited computational resources is an issue.  
In terms of dataset, the BIGC-LP defect dataset used in this study contains multiple defect types and has been processed with data enhancement, which effectively improves the generalization ability of the model. However, there is still room for improvement in the scale and diversity of the dataset. There are many types of defects in real logistics packaging, and in addition to tears, scratches, deformations, and stains covered in the dataset, there may be other types of defects. Meanwhile, there are differences in different materials, sizes, and designs, and these factors may affect the detection effect of the model. Therefore, subsequent consideration can be given to expanding the size of the dataset to collect more images of different types of logistics packaging defects to further enhance the adaptability of the model to various real-world situations.
## 3 Proposed Method
### Homography and Image Correction  
Based on the corner detection results, this study applies homography and image correction techniques to convert distorted license plate images into a normalized form. This process plays a crucial role in improving the accuracy of license plate character recognition. Homography is the process of converting a distorted license plate image into a frontal view using the four corner coordinates of the detected license plate. This is performed by calculating a homography matrix and using it to transform the image. The homography matrix is a 3 \(\times\) 3 matrix that defines the mapping between the four corner coordinates of the original image and the target image. Through this homography and image correction process, license plate images captured at various angles and lighting conditions are converted into a standardized form. This contributes significantly to improving the accuracy of the subsequent character recognition step.