esa-sceva/satcom-chunk-collection huggingface dataset:
# Weld Defect Detection of a CMT Arc-Welded Aluminum Alloy Sheet Based on Arc Sound Signal Processing
## 3 Experiment and Result
### Welding Experiment and Analysis
#### 3.2.2 Time Domain Analysis of the Arc Sound Signal  
Figure 5 presents the arc sound, welding current, and arc voltage signals that were collected simultaneously. As can be seen in the rising and falling edges of the peak current, the amplitude of the arc sound signal increases sharply, then decreases slowly and maintains a certain amplitude of vibration. Especially in the rising edge of the current, the arc sound signal performance is more violent. On the falling edge of the voltage signal trough, the amplitude of the arc sound signal increases and then decreases slowly. These effects can be explained by the CMT welding process combined with the sound generation mechanism.  
Sound is a longitudinally propagating mechanical wave that is generated by the vibration of an object. The change in arc power is the source of excitation of the arc sound. The greater the instantaneous rate of change of the arc power, the greater the amplitude of the corresponding arc sound. The CMT welding process is characterized by alternate wire feed and retraction movements with the hot and cold processes being alternatively repeated [28]. A CMT cycle can be divided into three phases: peak current phase, background current phase, and short-circuiting phase [3], as shown in Figure 6. In the peak current phase, a high current pulse corresponding to a constant arc voltage ignites the welding arc and then heats the wire electrode to form a little liquid droplet on the wire tip. At the beginning of this phase, the arc power changes drastically. From the peak to the background phase, the arc power is decreased by the reducing current to inhibit the globular transfer, and the arc power changes are relatively large. In the short-circuiting phase, the arc voltage changes to zero, the arc energy is zero, and the arc is extinguished. At the same time, a reverse motion of the wire, liquid fracture, and transfer of the material into the welding pool occur. As arc short-circuiting occurs, the arc power changes less.  
During the CMT welding process, the alternating changes in the arc power produce the arc sound. The reciprocating motion of the wire causes changes in arc length and shape, and the entry of the wire tip into the molten pool in the short-circuit phase causes vibrations and changes in the shape of the molten pool. All of these significantly affect the  
Figure 4: Weld seam states.  
Figure 5: CMT arc sound, current, and voltage.  
arc sound. Therefore, the arc sound signal contains richer information related to the quality of the weld.
## 3 Test Results and Observations
### Summary of Verification Results and Observations  
It is confirmed that the area accumulated with carbon powder or with ash powder appeared white based on CR X-ray images and that the color of the area of accumulated ash powder was whiter. In addition, it is verified that it is possible to analyze the accumulation status by comparing pixel values produced by ImageJ J. Therefore, this study shows that it is confirmed to be technically possible to diagnose the accumulation status of soot and ash using the CR X-ray imaging technique and ImageJ J. However, although the qualitative evaluation through CR X-ray images has been sufficiently proven, the numerical values obtained through ImageJ J are only pixel values, so it is not enough to be called a quantitative evaluation. In order for the pixel values obtained to be demonstrated by quantitative evaluation, additional studies are needed, such as uncertainty analysis. In addition, as the substrate without canister was photographed, research is necessary to analyze whether soot and ash are accumulated in the DPF of in-use vehicles, and more thought should be given to the adaptability of the technique.  
In recent years, various radiation studies have been conducted, including experiments to evaluate X-ray-radian and gamma-radian-based systems using Monte Carlo radiation transmission tools [17; 18; 19; 20]. The CR X-ray imaging technique is expected to make contributions to the environment by diagnosing whether a DPF is damaged and detecting illegal remodeling at vehicle inspection stations. It is also expected to be used for various purposes, such as for DPF cleaning shops and retrofit manufacturers to determine whether the DPF is sustainably usable after a cleaning process or needs to be repaired. As portable X-ray systems and flexibly bendable IP are produced and sold in mass quantities, the technique is deemed easily accessible.
# Model Updating Using Measurements from Sensors Installed in Arbitrary Positions and Directions
## 2 Materials and Methods  
### Measurement-Model Link  
The eigenvector obtained by modal identification corresponds to that at the output measurement location. Besides, the eigenvector obtained by the analytic model is the one at the node DOF. In general, the output measurement location and the location of the node do not coincide, which means that these eigenvectors are at different locations. Moreover, the measurement direction is likely to differ from the DOF direction of the node. The type of eigenvector also differs between the measurement and the analytic model when the strain is measured. Accordingly, a solution is required to remedy the mismatch in the location, direction, and type between the measurement and the analysis. To that goal, the relation between the node DOFs of the analytic model and the measured values were derived.  
Figure 1: Mismatch of measurement location and finite element mesh.  
The analytic model can be placed in any arbitrary position in the space, and various sensors such as accelerometers and strain gauges can be installed in each element of the analytic model (Figure 2). The spatial position in the analytic model is defined in the global coordinate system (GCS), and can be transformed into the element coordinate system (ECS). The location of the sensors and the measurement direction can be defined in the GCS or ECS. The measurement direction in the GCS is related to the measurement direction in the ECS as follows.  
\[n^{\prime}=Rn \tag{1}\]  
\[n=R^{T}n^{\prime} \tag{2}\]  
where the rotation matrix \(R\) is as follows:  
\[R=\left[\begin{array}{ccc}\cos(x^{\prime},\ x)&\cos(x^{\prime},y)&\cos(x^{ \prime},z)\\ \cos(y^{\prime},x)&\cos(y^{\prime},y)&\cos(y^{\prime},z)\\ \cos(z^{\prime},x)&\cos(z^{\prime},y)&\cos(z^{\prime},z)\end{array}\right] \tag{3}\]
# Modified Pseudo-Dynamic Bearing Capacity of Shallow Strip Foundation near Slope on Non-Uniform Soil
## 4 Results and Analysis
### Parameter Analysis
#### 4.3.3 Influence of \(L\) and \(\beta\)  
For adjacent-slope foundations, unlike foundations located on level ground, the bearing capacity is affected by the slope angle \(\beta\) as well as the setback distance \(L\). Figure 11 illustrates the variation in seismic bearing capacity \(p\) as a ratio \(L/B_{0}\) of setback distance to foundation width for different \(\varphi\). For \(\varphi=25^{\circ}\), the bearing capacity \(p\) increases by about 34% when \(L/B_{0}\) increases from 0 to 3. In addition, it can be seen that there is a tendency for \(p\) to converge slowly as \(L/B_{0}\) increases. However, as \(\varphi\) increases, the convergence trend becomes less pronounced. Therefore, the further the foundation is from the slope surface apex, the higher its stability.  
Figure 12 shows the trend of seismic bearing capacity \(p\) with the slope angle \(\beta\) for different \(\varphi\). It is obvious that \(p\) decreases as \(\beta\) increases. For \(\varphi=25^{\circ}\), the bearing capacity \(p\) decreases by about 54% when \(\beta\) increases from 0 to 40\({}^{\circ}\). For \(\varphi=35^{\circ}\), the bearing capacity \(p\) reduces by about 66%. Therefore, the effect of slope angle \(\beta\) on bearing capacity is significant, the steeper the slope, the lower the bearing capacity of the foundation located on it. In addition, the curves in Figure 12 also have a tendency to converge.
## 4 Conclusions and Future Work  
This research focuses on the health monitoring of elevator systems using a novel fault detection technique. The goal of this research was to develop generic models for profile extraction and automated feature extraction for fault detection in the health state monitoring of elevator systems. Our approach in this research provided above 90% accuracy in fault detection and in the case of analyzing false positives for all floor combinations with new extracted deep features from sensor data including both acceleration and magnetic signals. The results support the goal of this research of developing generic models which can be used in other machine systems for fault detection. The results are useful in terms of detecting false alarms in elevator predictive maintenance. The approach will also reduce unnecessary visits of maintenance personnel to installation sites if the analysis results are used to allocate maintenance resources. Our developed models can also be used for different predictive maintenance solutions to automatically generate highly informative deep features for solving diagnostics problems. Our models outperform others because of new deep features extracted from the dataset as compared to existing features calculated from the same raw sensor dataset. The automated feature extraction approach does not require any prior domain knowledge. It also provides dimensionality reduction and is robust against overfitting characteristics. The experimental results show the feasibility of our generic models, which will increase the safety of passengers as well as serve the public interest. Visualization of the extracted profiles and features support our goal of developing generic models for profile and feature extraction for fault detection.  
In future work, we will extend our approach on other real-world big data cases to validate its potential for other applications and improve its efficacy.  
Conceptualization, K.M.M. and K.H.; Data curation, K.M.M.; Formal analysis, K.M.M.; Investigation, K.M.M.; Methodology, K.M.M.; Resources, K.M.M.; Software, K.M.M.; Supervision, K.H.; Validation, K.M.M. and K.H.; Visualization, K.M.M.; Writing-original draft, K.M.M.; Writing-review and editing, K.M.M. and K.H.  
Figure 11: Extracted deep features (only healthy profiles).  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract
Flame Retardant-4 (FR-4) material is commonly used to design substrates. Modern technologies demand compounds that are cost-effective, compact, and lightweight, which led to the fabrication of low-cost and easily designed substrates. For example, an ENG metamaterial with an EMR value of 3.5 exhibited EM cloaking operation as it covered the C-band [17]. In another study [18], a dual SRR of hexagonal shape with a gap (GCHSRR) metamaterial (10 \(\times\) 10 mm\({}^{2}\) dimension) exhibited microwave absorbance uses as it covered the C- and X-bands. In recent times, metamaterials have been aimed at supporting various frequency bands. Each frequency band has widespread use; the S-band covers a frequency range of 2-4 GHz; the X-band covers 8-12 GHz, and the Ku-bands covers 12-18 GHz. The three bands are extensively utilised in satellite communications [19]. Among them, the terrestrial signals can only slightly affect the Ku-band, which operates in a high-frequency range that allows the use of trivial-sized dish antennas for VSAT uses. Meanwhile, a metamaterial with a harmonising SRR (5 \(\times\) 5 mm\({}^{2}\)) and EMR of 8 demonstrated a resonance frequency of 7.5 GHz [20]. This study proposed a new and novel metamaterial assembly whose unit cell consists of one SRR with a modified hexagonal-shaped metal stripe. It demonstrated resonance frequencies in the S-, X-, and Ku-bands (2-4 GHz, 8-12 GHz, and 12-18 GHz) in the microwave spectra, apart from indicating negative permittivity and negative refractive index at the same frequencies. Furthermore, the reliability of the performance of the proposed metamaterial was tested using different validation tests. Equivalent circuits were also designed in ADS, HFSS design, 2 \(\times\) 1 and 2 \(\times\) 2 arrays to simulate validation tests. The analyses were performed using different effective parameters like permittivity, permeability and the refractive index, together with transmission and reflection coefficients. The EMR was also used as a comparison of performances in the result section.
# Orbital Angular Momentum Generation and Detection by Geometric-Phase Based Metasurfaces
## 3 Metasurfaces for OAM Generation
### Metasurfaces for Multiple OAM-Beam Generation  
Figure 11 depicts two types of geometric-phase metasurfaces for multiple OAM-beam generation [90,97]. The metasurface in Figure 11a generates two collinear OAM beams. The dielectric nanofins forming the topological charges of \(q=2.5\) and \(q=5\) are interleaved each other. The dielectric nanofins consisted of TiO\({}_{2}\) and were fabricated based on atomic layer deposition and electron beam lithography. In Figure 11b, the nanoantennas with different topological charge are interleaved randomly.
# A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks
## 3 Definitions  
Traffic prediction techniques can often be applied across various timescales or time-independently, so criteria are needed to classify techniques into short-term or long-term categories. For the purpose of this paper,'short-term' refers to the prediction and application of techniques in the timeframe of minutes, hours, and days. 'Long-term' refers to the prediction and application of techniques for weeks, months, and years. A specific technique is classified as short-term or long-term depending on whether it:  
1. specifically discusses short-term or long-term prediction purposes;
2. is applied in simulation for the short term or long term;
3. predicts traffic for short-term or long-term increments of time;
4. is time-dependent or -independent.  
In the case where the technique is time-independent, as is often the case in predicting blocking probability, this paper will classify the technique as long-term. It is important to note that short-term predictions may become long-term predictions if deployed for a longer duration. Additionally, a technique's purpose of prediction may be applicable for the long term, such as blocking probability estimation or power usage reduction. Henceforth, time-independent techniques are classified as long-term traffic prediction because they may be applied regardless of time duration.  
Moreover, all techniques discussed in this paper apply either to Local Area Networks (LAN) or Wide Area Networks (WAN). LAN are networks where devices are connected within small geographical locations, such as a building or a group of buildings, as shown in Figure 2. Networks are considered WAN when they span larger geographical locations across large distances, such as a country-wide network or a global network, as shown in Figure 3.  
Figure 2: Local Area Network diagram.
# Research on Named Entity Recognition Based on Gated Interaction Mechanisms
###### Abstract  
Using long short-term memory (LSTM) networks to build a named entity recognition model is important for the task of named entity recognition. However, traditional memory networks lack a direct connection between input information and hidden states, leading to key feature information not being fully learned during training and causing information loss. This paper designs a bidirectional variant of the long short-term memory (BiLSTM) network called Mogrifier-BiGRU, which combines the BERT pre-trained model and the conditional random field (CRF) network model. The Mogrifier gating interaction unit is set with more hyperparameters to achieve deep interaction of gating information, changing the relationship between input and hidden states so that they are no longer independent. By introducing more nonlinear transformations, the model can learn more complex input-output mapping relationships. Then, by combining Bayesian optimization with the improved Mogrifier-BiGRU network, the optimal hyperparameters of the model are automatically calculated. Experimental results show that the model method based on the gating interaction mechanism can effectively combine feature information, improving the accuracy of Chinese-named entity recognition. On the dataset, an F1-score of 85.42% was achieved, which is 7% higher than traditional methods and 10% higher for the accuracy of some entity recognition.  
named entity recognition; deep learning; GRU; gated interaction mechanism; Bayesian optimization +
Footnote †: journal: Applied
# Optimization of Image Capture Distance for Facial Thermograms in Dentistry
## 2 Materials and Methods
### Thermography Procedure and the Thermograms
#### 2.3.1 Thermal Camera  
A smartphone attachment thermal imaging camera (FLIR One Gen 3--IOS, Teledyne FLIR LLC, Wilsonville, OR, USA) was used in this study. The camera was equipped with multi-spectral dynamic imaging (MSX(r)) technology, which embosses details from the visual camera onto the thermal images. The camera has a thermal resolution of 80 \(\times\) 60 and detects readings from an object temperature range between \(-\)20 \({}^{\circ}\)C and 120 \({}^{\circ}\)C at an operating temperature between 0 \({}^{\circ}\)C and 35 \({}^{\circ}\)C. The reported accuracy of the camera is a \(\pm\)3 \({}^{\circ}\)C (\(\pm\)5%) difference between the ambient and scene temperature (applicable 60 s after starting the unit and lies in the range of 15 \({}^{\circ}\)C-35 \({}^{\circ}\)C with the scene temperature within 5 \({}^{\circ}\)C-120 \({}^{\circ}\)C). The optical specifications of the camera include a fixed focus at 15 cm till infinity, a frame rate of 8.7 Hz, horizontal (HFOV) and vertical/diagonal (VFOV) field of views of 50\({}^{\circ}\)\(\pm\)1\({}^{\circ}\) and 38\({}^{\circ}\)\(\pm\)1\({}^{\circ}\), respectively, with an automatic/manual shutter. The camera is equipped with a thermal sensor with a pixel size of 17 \(\upmu\)m, and a 150 mK (0.15 K) thermal sensitivity, capable of capturing video as well as still images with a visual resolution of 1440 \(\times\) 1080.  
The camera was attached to an iOS smartphone (Apple Inc., Cupertino, CA, USA). The captured images were analyzed using an analysis software (FLIR Research Studio for Windows, Ver 1.1.3, FLIR systems Inc., Wilsonville, OR, USA).
## 6 Conclusions  
A transit vehicle tracking service prototype was developed based on an ITS architecture that facilitates interoperability with other mobility services that consider the used architecture as a base. The prototype was built using LoRa between the critical modules. Six experiments were performed to evaluate the operation of LoRa in the prototype. The evaluated parameters in the experiments were spreading factor (SF), bandwidth (BW), and coding rate (CR), and the ideal values found for the service were SF = 9, BW = 250 kHz, CR = 4/5. This setting of parameters scaled well in terms of distance, number of vehicles, and vehicle speed in most of the six performed experiments. The results obtained were compared to a baseline (SF = 9, BW = 125, and CR = 4/5) obtaining similar values in the best setting of each experiment. Some aspects affecting the results were evaluated: (a) LoS between devices and gateway, which seriously affects operation; (b) number of devices; by increasing this number, the percentage of lost packets increased between 10% and 20%; and (c) the increase in vehicle speed (from 20 to 60 km/h) causes a lost packets increase of 6%. In future works, it is recommended to validate with additional tests the operation of LoRa for the tracking service, using the best identified setting. Additionally, it is recommended to work on a mechanism that allows dynamically modifying LoRa parameters.  
Conceptualization, R.S.-C. and A.P.d.l.C.; methodology, F.J.M., J.S.Q.Y., A.D.V.L., R.S.-C., and A.P.d.l.C.; validation, F.J.M., J.S.Q.Y., A.D.V.L., and R.S.-C.; formal analysis, F.J.M., J.S.Q.Y., A.D.V.L., and R.S.-C.; investigation, F.J.M., J.S.Q.Y., A.D.V.L., and R.S.-C.; resources, A.P.d.l.C. and J.M.M.M.; writing--original draft preparation, R.S.-C., F.J.M., J.S.Q.Y., and A.D.V.L.; writing--review and editing, A.P.d.l.C. and J.M.M.M.; supervision, A.P.d.l.C. and J.M.M.M.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Acknowledgments:** Authors wish to thank Universidad Icesi (ICT Department) and Universidad del Cauca (Telematics Department) for supporting this research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# The Active Segmentation Platform for Microscopic Image Classification and Segmentation
## 4 Materials and Methods
### Image Classification Task  
The image classification workflow is presented in Figure 7. In order to classify objects, we first enriched the feature space by extracting pixel features (i.e., LoG and ALoG) at several scales. The filters, features and parameters used for the task are summarized in Tables 5 and 6. In a third step, we have extracted various regional features (Table 4). Instrumental for the practical applicability of this step is the feature selection, which was performed in the same way as in the segmentation workflow. Next, the SVM classifier was used to train the model. For the present task, we have used the SMO algorithm [34]. The outcome of the classification was evaluated against the ground truth by computing standard metrics--true positive rate (TP), false positive rate (FP), precision, recall, \(F_{1}\) score, Receiver operating characteristic (ROC) area and Precision-Recall (PR) area.  
For classification, we have used 10 fold cross validation and calculated several evaluation metrics in each iteration i.e., true positive (TP), false positive (FP), precision, recall, \(F1\) score, area under ROC curve, area under PR curve. All classification metrics were computed pixel-wise (respectively voxel-wise).  
classification into cytoplasm and background (i.e., membranes and unclassified organelles). Table 7 shows training and testing results of the AS/IJ and Ilastik. Remarkably, both Ilastik and our platform achieved score 1.0 in \(V^{rand}\) and \(V^{info}\) metrics on the training data set. On the other hand, Ilastik outperformed AS/IJ by 0.04 in terms of \(V^{rand}\), whereas \(V^{info}\) was comparable for both platforms.
###### Abstract  
In this paper, a simulation model for Selective Laser Melting (SLM) technology is established to simulate the additive manufacturing process of a turbine impeller for an aerospace engine. By utilizing the simulation model, variations in laser power and scanning speed are employed to obtain simulated results of thermal deformation for the turbine impeller under different laser power and scanning speed conditions. The results indicate that the thermal deformation of the component increases with the augmentation of laser power, decreases with the escalation of scanning speed, and eventually stabilizes. Based on the relationship between thermal deformation and energy, the energy utilization efficiency of the SLM process under different conditions is calculated. The findings demonstrate that, within a certain range of power, the synergistic effect of laser power and scanning speed allows for an increase in energy utilization efficiency and a reduction in processing time while ensuring the mechanical performance of the formed parts. Consequently, this approach proves effective in lowering production costs for complex components based on SLM technology.  
additive manufacturing; Selective Laser Melting; simulation; thermal deformation; energy utilization +
Footnote †: journal: Applied sciences