esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of a Sound Signal for Quality Monitoring in Laser Microlap Welding
###### Abstract  
This study focused on correlation analysis between welding quality and sound-signal features collected during microlaser welding. The study provides promising features for developing a monitoring system that detects low joint strength caused by a gap between metal sheets after welding. To obtain sound signals for signal analysis and develop the monitoring system, experiments for laser microlap welding were conducted on a laser microwelding platform by installing a microelectromechanical system (MEMS) microphone away from the welding point, and an acoustic emission (AE) sensor on the fixture. The gap between two metal sheet layers was controlled using clamp force, a pressing bar, and the appropriate installation of a thin piece of paper between the metal sheets. After sound signals from the microphone were collected, the correlation between features of time-domain sound signals and of welding quality was analyzed by categorizing the referred signals into eight sections during welding. After appropriately generating the features after signal analysis and selecting the most promising features for low-joint-strength monitoring on the basis of scatter index \(J\), a hidden Markov model (HMM)-based classifier was applied to evaluate the performance of the selected sound-signal features. Results revealed that three sound-signal features were closely related to joint-strength variation caused by the gap between two metal-sheet layers: (1) the root-mean-square (RMS) value of the first section of sound signals, (2) the standard deviation of the first section of sound signals, and (3) the standard deviation to the RMS ratio of the second section of sound signals. In system evaluation, a 100% classification rate was obtained for normal and low-bonding-strength monitoring when the HMM-based classifier was developed on the basis of the three selected features.  
laser microwelding; quality monitoring; audible sound +
Footnote †: journal: Applied Sciences
# Memory-Non-Linearity Trade-Off in Distance-Based Delay Networks
###### Abstract  
The performance of echo state networks (ESNs) in temporal pattern learning tasks depends both on their memory capacity (MC) and their non-linear processing. It has been shown that linear memory capacity is maximized when ESN neurons have linear activation, and that a trade-off between non-linearity and linear memory capacity is required for temporal pattern learning tasks. The more recent distance-based delay networks (DDNs) have shown improved memory capacity over ESNs in several benchmark temporal pattern learning tasks. However, it has not thus far been studied whether this increased memory capacity comes at the cost of reduced non-linear processing. In this paper, we advance the hypothesis that DDNs in fact achieve a better trade-off between linear MC and non-linearity than ESNs, by showing that DDNs can have strong non-linearity with large memory spans. We tested this hypothesis using the NARMA-30 task and the bitwise delayed XOR task, two commonly used reservoir benchmark tasks that require a high degree of both non-linearity and memory.  
information processing capacity; memory capacity; memory-non-linearity trade-off; reservoir computing; distance-based delay networks; echo state networks +
Footnote †: journal:
## 3 Results
### Compression Analyses  
#### 3.8.1 Heckel Analysis  
Based on the measured true densities, for which no significant change within the range 1.643-1.648 g/mL was detected, for compacted and non-compacted Avicel PH-101 powders, Heckel plots and Heckel parameters were determined for non-compacted Avicel PH-101, compacted-1 st cycle Avicel PH-101, compacted-2 nd cycle Avicel PH-101, and compacted-3 rd cycle Avicel PH-101. Results are presented in Figure 10 and Table 3, respectively. The linear portion of the Heckel plots was selected in the compression pressure range from 104 to 156 MPa. The extent of plastic deformation of compacted Avicel PH-101 is higher (as Py is lower) than the non-compacted original form. In fact, the extent of Avicel's plastic deformation increases as a function of the number of times the compaction is repeated. However, the Db value undergoes a decrease for the powders comprising the 1 st and 2 nd compared to the Db value of the 1 st compaction cycle. For the 3 rd cycle, the Db value underwent a slight increase and was slightly higher than the Db value of the 1 st compaction cycle.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Powder Type**} & \multicolumn{6}{c}{**Heckel Parameters**} \\ \cline{2-7}  & **Line Slope** & **Line Intercept (A)** & **Py** & **D\({}_{\mathbf{a}}\)** & **D\({}_{\mathbf{0}}\)** & **D\({}_{\mathbf{b}}\) = D\({}_{\mathbf{a}}-{}_{\mathbf{0}}\)** \\ \hline Non-Compacted & \multirow{2}{*}{0.0044} & \multirow{2}{*}{0.860} & \multirow{2}{*}{227.3} & \multirow{2}{*}{0.577} & \multirow{2}{*}{0.41} & \multirow{2}{*}{0.167} \\ Avicel 101 & & & & & & \\ Avicel-1 st & \multirow{2}{*}{0.0048} & \multirow{2}{*}{0.760} & \multirow{2}{*}{208.3} & \multirow{2}{*}{0.532} & \multirow{2}{*}{0.45} & \multirow{2}{*}{0.081} \\ Compaction Cycle & & & & & & \\ \hline Avicel-2 nd & \multirow{2}{*}{0.0048} & \multirow{2}{*}{0.775} & \multirow{2}{*}{208.3} & \multirow{2}{*}{0.539} & \multirow{2}{*}{0.46} & \multirow{2}{*}{0.077} \\ Compaction Cycle & & & & & & \\ Avicel-3 rd & \multirow{2}{*}{0.0054} & \multirow{2}{*}{0.829} & \multirow{2}{*}{185.2} & \multirow{2}{*}{0.563} & \multirow{2}{*}{0.47} & \multirow{2}{*}{0.096} \\ Compaction Cycle & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters derived from the Heckel plots of non-compacted and compacted Avicel®® PH-101.  
Figure 9: Porosity of tablets made of non-compacted and compacted Avicel PH-101.
# Predictive Control for Current Distortion Mitigation in Mining Power Grids
## 4 System Implementation and Results
### Case 2: Compensation of Total Current Distortion  
In this case, \(\lambda_{p}\) was relaxed to provide evidence of the effect of the THD constraint. Similar to case 1, this test was divided into two periods, shown in Figure 6. The first period began when \(THD_{max}\) was settled to 5%, reducing the current harmonics, as shown in Figure 6a,b. Unlike case 1, none of the three current harmonics were totally mitigated, but current THD reduced from 25.7% to 2.45%; therefore, distortion on the PCC currents and power was allowed, as shown in Figure 6c.  
Figure 5: Power and currents delivered by the PCC and the DG for case 1: (**a**) PCC and DG power-Time domain. (**b**) Frequency spectrum and (**c**) time domain PCC signals when 3 th harmonic was not compensated. (**d**) Frequency spectrum and (**e**) time domain signals when PCC was totally compensated.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Period** & \(\mathbf{I_{1}}\) & \(\mathbf{I_{2}}\) & \(\mathbf{I_{3}}\) & \(\mathbf{I_{4}}\) \\ \hline
0 & 7.1 & \(\leq\)0.8 & 0.5 & \(\leq\)0.5 \\  & 100\% & 11.42\% & 7.143\% & 7.143\% \\ \hline  & \(P_{0}^{\text{DC}}=0\) & \multicolumn{3}{c}{\(THD^{\text{PC}}=25.7\%\)} \\ \hline
1 & 4.65 & \(\leq\)0.01 & 0.5117 & \(\leq\)0.01 \\  & 100\% & 0.2\% & 11\% & 0.02\% \\ \hline  & \(P_{0}^{\text{DC}}=2\) & \multicolumn{3}{c}{\(THD^{\text{PC}}=\)} \\  & & & 11.43\% & \\ \hline
2 & 5.1 & \(\leq\)0.01 & 0.0183 & \(\leq\)0.01 \\  & 100\% & 0.2\% & 0.358\% & 0.2\% \\ \hline  & \(P_{0}^{\text{DC}}=2\) & \multicolumn{3}{c}{\(THD^{\text{PC}}=0.75\%\)} \\ \hline \(\lambda_{u}[1/W^{2}]\) & \(\lambda_{0}[1/W^{2}]\) & \(\lambda_{p}[1/W^{2}]\) & \(\lambda_{c}[1/W^{2}]\) & \(\lambda_{T}\) \\ \hline
1 & 1 & 1e4 & 1e8 & 1e3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measured currents and weighting factors.  
The second period began when the active power component supplied by the DG increased, but \(THD_{max}\) was preserved. In this case, distortion caused by harmonic components should also be reduced, because the PCC current at the fundamental frequency is reduced, preserving the power THD into the range stated by Inequality (18). Tuned weights and magnitudes of current harmonic components are shown in Table 4. According to the results, the current THD was lower than 5% in both periods; therefore, TRD requirements from IEEE std 1547 were satisfied as well.
# Deep Multimodal Emotion Recognition on Human Speech: A Review
## 2 Modalities and Feature Extraction
### Audio
#### 2.1.2 Audio Representation Learning  
The development of deep learning has resulted in a variety of representation learning techniques for different modalities. Convolutional neural networks (CNNs) have been widely used to \"learn\" image-related features in a supervised manner. Therefore, given a way to map audio signals into 2D representations (images), deep audio features can be learnt through CNNs. The most straightforward and obvious approach to achieve this is through spectrograms. For example, in [26; 27; 28], spectrograms were used as image inputs to CNNs in order to train classifiers for emotion recognition, and since then, this approach of combining spectrograms (or melgrams) with CNNs has been widely adopted for speech emotion recognition tasks.  
Lately, a variety of representation learning techniques and architectures has been proposed in order to learn audio representations from the initial waveform. The SincNet [29], a network that replaces the one-dimensional convolution kernels with sinc filter functions on the audio signal, is a supervised learning technique in such a direction. On the other hand, unsupervised methods for speech representation learning from audio are rapidly evolving and produce efficient architectures such as wav2 vec [30], wav2 vec 2.0 [31] and the newly produced HuBERT (Hidden-Unit BERT) [32], which is the currently state-of-the-art architecture for such representations.
## 4 Simulation Realization of Rock Bolt Pile Unit
### Method of Applying Pretension Force of Pile Unit  
In FLAC\({}^{\text{3D}}\), there is no direct command to apply the pretension force to the rock bolts simulated by the pile unit. Through the built-in fish script of FLAC\({}^{\text{3D}}\), this paper proposes the method of \"break\(-\)tension\(-\)suture\", realizing the application of the pretension force to the rock bolts simulated by the pile unit.  
The rock bolt is 2.4 m long and is simulated using 24 pile units. The anchorage section is 0.8 m long, and the free section is 1.6 m long. Two sections of pile units are created at the position where the rock bolt is set. The shorter section contains eight pile units and simulates the anchorage section. The parameters are set according to the parameters of the anchorage section pile unit in Table 3. The longer section contains 17 pile units and simulates the free section of the rock bolt. The cs-scoh and cs-sk of the pile unit in the free section are both set to 0, and other parameters are the same as those of the anchorage section. A rigid connection is established between the tail of the pile unit in the free section and the surrounding grid to simulate the rock bolt tray (Figure 9a).  
Tension loads are applied to the nodes at the openings of the anchorage section and the free section of the rock bolt. The magnitude of the tension load is the preset pretension force of the rock bolt. The pile unit is stretched, and the operation is balanced to simulate the application process of the pretension force of the rock bolt (Figure 9b,c). Taking the two nodes at the openings of the anchorage section and the free section as the starting and ending points, a new pile unit is established. The parameters are set according to the pile unit in the free section. The opening of the rock bolt is \"sutured\", and the tension load applied to the nodes is deleted. The operation is balanced to complete the pretension of the rock bolt. The grid is released, and the operation is carried out to realize the diffusion of the prestress in the surrounding rock (Figure 9d). It should be noted that due to the calculation of flexible structures and ultimate bearing capacity involved in the simulation, a large deformation calculation mode is adopted in the simulation.  
The shape of the support stress field after the pretension of the rock bolt by the above method is observed. The overall compressive stress zone is in a \"pomegranate shape\". The magnitude and shape are quite consistent with the support stress field shapes in the similar simulation by [PERSON] [28] and the numerical simulation by [PERSON] [29]. This proves the effectiveness of the \"break-tension-suture\" method.
## 1 Introduction  
The assessment and establishment of a safe period of operation for industrial pipeline systems in the transportation of oil and gas is an urgent problem for the petrochemical and thermal energy industry [1; 2; 3; 4; 5]. The assessment of the stress-strain state by classical approaches does not consider the interaction of the metal with an aggressive working environment, particularly hydrogen, which significantly affects the bearing capacity of the pipeline. As a result of the embrittlement of the metal of the pipe with hydrogen, there is a change in the characteristics of resistance to strain and destruction.  
Given that hydrogen interacting with metal on the one hand facilitates the nucleation and development of defects in local volumes [6; 7; 8; 9], and on the other hand accelerates its degradation due to the flooding ability of the media, which is caused by their electrochemical interaction with the metal and the release of hydrogen [10; 11; 12; 13; 14; 15; 16], this process is enhanced by a change in the stress-strain state in local volumes due to its change along the length of the pipe (bends, bends). All changes are most fully manifested on the inner surface of the pipe in contact with the environment, which affects the operational degradation that occurs under the combined action of stresses under the working pressure and corrosive-wetting environments, which reduces the material's resistance to strain and destruction.  
Mechanical and structural-fractographic methods [17,18] are known to assess the operational degradation of pipe steels; these are based on establishing a change in the basic mechanical characteristics, particularly a decrease in the characteristics of plasticity and resistance to brittle fracture, which are determined for samples made from operational steels via a comparison with the material's initial state.  
Corrosion and hydrogen-mechanical methods are based on the assessment of hydrogen embrittlement, which is established by comparing the mechanical properties of pre-flooded steels in the initial and operational states [10]. Electrochemical methods are based on establishing the changes that occur in sensitive indicators, particularly the polarization resistance and corrosion current density before operational changes in the state of steels [10].  
It should be noted that the metal flooding process occurs under operating conditions where the stress-strain state in the pipeline can change from elastic to elastic-plastic, which affects the concentration of hydrogen in local volumes and reduces the bearing capacity [19].  
This paper developed a theoretical and experimental approach to evaluating the safe service life of the pipeline system during the transportation of hydrogen mixtures. The basis of the proposed approach is the energy approach, according to which the main characteristic of the resistance of the material is the specific energy of strain and destruction, which is invariant to the load conditions and the influence of the hydrogen environment.
## 2 Bus Impedance Power Flow Method
### Tap-Changing Transformer Model  
The bus impedance power-flow calculation method, originally presented in [40], is usually not used in power-flow studies, or the method is used in analyzing a system without transformers because the method cannot analyze tap-changing transformers. However, most transmission and distribution systems include tap-changing transformers to change the secondary-side voltage. Therefore, such a transformer should not be ignored in a power-flow calculation algorithm.  
If a tap-changer with a turns ratio of \(a\) is on the low-voltage (or secondary) side in Figure 3, the admittance matrix, \(\mathbf{Y_{bus}}\), is  
\[\left[\begin{array}{c}I_{m}\\ I_{n}\end{array}\right]=\left[\begin{array}{cc}Y_{eq}+Y_{m0}&-aY_{eq}\\ aY_{eq}&-a^{2}(Y_{eq}+Y_{ex})+Y_{n0}\end{array}\right]\left[\begin{array}{c} V_{m}\\ V_{n}\end{array}\right]=\mathbf{Y_{bus}}\mathbf{V}. \tag{8}\]  
where \(Y_{m0}\) and \(Y_{n0}\) are the line capacitances beside the transformer.  
To model the tap-changing transformer depicted in Figure 3 in the proposed method, this study proposes decomposing the transformer model into two parts: the series and parallel elements in Figure 4.  
\[I_{m}=aY_{eq}(V_{m}-V_{n})+((1-a)Y_{eq}+Y_{m0})V_{m}, \tag{9}\]  
\[I_{n}=aY_{eq}(V_{m}-V_{n})-((a^{2}-a)Y_{eq}+a^{2}Y_{ex}+Y_{n0})V_{n}. \tag{10}\]  
Figure 3: Transformer with a secondary-side tap.  
Figure 2: Currents that flow in a bus.  
The proposed method builds the \(\mathbf{Z_{bus}}\) matrix of the series elements in Figure 4. The matrix also includes lines without transformers. The final \(\mathbf{Z_{bus}}\) matrix is used in (1) during iterations. Additionally, the proposed method builds the \(\mathbf{Y_{bus}}\) matrix of the parallel elements in Figure 4, calculates \(I_{m^{\prime}0}\) and \(I_{n^{\prime}0}\) by (5), and adds them to Equation (6). As a result of this compensation (\(I_{m^{\prime}0}\) and \(I_{n^{\prime}0}\)), the proposed method can solve the problem of applying the conventional bus impedance, matrix power-flow calculation method to tap-changing transformers.
# Towards a Heat-Resistant Tethered Micro-Aerial Vehicle for Structure Fire Sensing
## 3 UAV Design
### Component Selection and Payloads  
According to the operational requirements described in Section 3.1, the components selected are those described in Table 1. Design criteria focus on minimizing overall weight and energy consumption while maintaining full operational capabilities. In order to minimize costs and facilitate maintenance, we tried to use most of the standard components in FPV micro-drones.  
After all the necessary equipment, the estimated margin for the cable and payload is 265.42 g.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Component** & **Unit Weight (g)** & **Units** & **Total Weight (g)** \\ \hline Emax RS1108 (EMAX, Brea, CA, USA) & 8.20 & 4 & 32.80 \\ \hline HQ Prop 3 \(\times\) (HQProp, Ningbo, China) 3 \(\times\) 3 & 1.57 & 4 & 6.28 \\ \hline Tramp nano VTX (ImmersionRC, Wanchai, Hong Kong) & 1.01 & 1 & 1.01 \\ \hline Lumenier 20A \(\times\) 4 ESC (Lumenier, Sarasota, FL, USA) & 12.00 & 1 & 12.00 \\ \hline Kakute F7 AIO (Holybro, Hong Kong) & 11.20 & 1 & 11.20 \\ \hline Aluminium Hex Spacer 3 \(\times\) 35 mm & 3.05 & 4 & 12.19 \\ \hline Carbon fiber frame & 62.66 & 1 & 62.66 \\ \hline Propeller duct & 12.00 & 4 & 48.00 \\ \hline TBS Nano rx + antenna (TBS Avionics, Hong Kong) & 3.90 & 1 & 3.90 \\ \hline MaxSonar EZ4 2C & 8.00 & 2 & 16.00 \\ \hline FC-ESC Hardware & 15.00 & 1 & 15.00 \\ \hline RunCam Nano 2 FOV170\({}^{\circ}\) (RunCam, Aberdeen, Hong Kong) & 3.72 & 1 & 3.72 \\ \hline Heat-protection lateral felt & 10.00 & 1 & 10.00 \\ \hline Total weight (g) & & & 234.76 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Componentselection.  
Figure 1: Three-dimensional render of the heat-resistant quadcopter fully assembled.  
For the sensor requirements, both an FPV and a thermal camera are needed. At a weight of only 3.72 g, the RunCam Nano 2 FOV 170 deg offers a wide-angle lens that enables flying beyond the line of sight even in confined spaces. For the thermal camera, the FLIR Lepton 3.5 (Teledyne FLIR, Wilsonville, OR, USA) offers the best performance at up to 450 degC with a 160 \(\times\) 120 pixel sensor, with only 0.9 g of weight. The DroneThermal V4 module (GroupGets, Reno, NV, USA) (2.3 g) converts the Flir signal to analog video that can be transmitted through a standard video transmitter (VTX). To switch between FPV and thermal video, the VIFLY 2 channel Cam Switcher (VIFLY, Wuhan, China), at only 0.5 g, is employed. Finally, the ZMR Electronic Switch (ZMR, Shenzhen, China) allows powering off the payloads via PWM when needed to avoid excessive power consumption and optimize heat dissipation. This component weight 3 g.  
The total payload weight is 10.82 g, including cables, leaving a margin of 254.42 g for the tether. A diagram of the electronic components, sensors, and camera connections is presented in Figure 2.
## 3 Experimental Results
### Mineral Characterization
#### 3.3.2 Coarse Aggregate: Dolomite and Granite  
Dolomite is a carbonate mineral (calcium magnesium carbonate, CaMg(CO\({}_{3}\))\({}_{2}\)) that is formed by chemical or biological precipitations from seawater reacting with CaCO\({}_{3}\)[36]. Typical oxide analysis of dolomite indicates 47.7% CO\({}_{2}\), 30.4% CaO, and 21.9% MgO by weight [37; 38; 39; 40; 41]. SEM images of dolomite are shown in Figure 8a,b. The images show dolomite deposition in beds, layers, and strata, which are characteristic of sedimentary rock [42]. The EDX data shown in Table 4 confirm that the calcium and magnesium are more abundant than any other impurity element.  
Granite is a type of igneous rock consisting of different minerals. The SEM image of granite (Figure 8c) shows a partial platy structure. In addition, the granite shows special features in the form of three separate zones identified by backscattered electron image analysis (see Figure 8d). The mineral assemblage of igneous rocks is shown in Figure 9a. The igneous rocks mainly consist of feldspar, quartz, mica, and amphibole minerals. These forms interlock in a somewhat equigranular matrix of feldspar and quartz with darker biotite mica and amphibole scattered within the lighter color minerals. By definition, granite is an igneous rock with at least 20% quartz and up to 65% alkali feldspar by volume. The EDX data shown in Table 4 indicates the percentage of Si, and the granite used in this study can be located in the mineral assemblage of igneous rocks (see the vertical dashed line in Figure 9a).  
Figure 7: SEM images of fine aggregates (note: 1 KX = 1000 X).  
EDX analysis results of the three distinct zones in the granite sample are shown in Figure 9b-d. The brighter Zone 1 contains a higher percentage of iron and magnesium. It may contain a mica  
Figure 8: SEM images of dolomite and granite with magnification (note: 1 KX = 1000 X).  
Figure 9: Mineral assemblage of igneous rock and chemical compositional analysis results.  
group of sheet silicate (phyllosilicate) minerals. The darker Zone 2 is rich in silicon, aluminum, sodium, and cobalt. It may contain an aluminosilicate and a part of the feldspar family, mainly plagioclase. The darkest Zone 3 is rich in silicon and mostly contains quartz. Niobium and tantalum were predominantly found in the granite sample by elemental analysis. Niobium and tantalum are generally found as primary minerals in alkaline igneous rocks like granite [41].