esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Properties of Aluminum Alloy and Galvanized Steel by AC Pulse MIG Braze Welding
###### Abstract  
In response to global environment and fuel efficiency regulations aiming to reduce CO\({}_{2}\) emissions, multi-material structures that use lightweight materials are currently being developed to realize the weight reduction of vehicles in automotive manufacturing. The dissimilar welding of aluminum alloy to steel has great importance, but it is still challenging due to their widely varying thermo-physical properties and the formation of intermetallic compounds. This study aimed to investigate the effect of process parameters on the wettability, mechanical properties, and microstructure in AC Pulse MIG welded joints of AA6061-T6 and galvanized steel sheets. A parametric study on torch aiming position and welding current with EN ratio variation was performed to optimize the process parameters. The result showed that the amount of metal deposition increased with EN ratio. When the EN ratio was higher, the wire feeding speed increased and the heat input process lowered. Moreover, the wetting length increased, ranging from 6.6 to 8.4 mm, and the wetting angle increased from 31.2 to 67.6\({}^{\circ}\), respectively. As a result of the tensile shear test, the maximum tensile shear load of dissimilar welded joints produced at 70 A with a 20% EN ratio was approximately 8.8 kN. From the result of scanning electron microscopy with energy-dispersive spectrometry, FeAl\({}_{3}\) LMC was observed at the joint interface, and the IMC layer thickness decreased with EN ratio at 70 A.  
dissimilar metal joining; AC pulse MIG welding; EN ratio; wettability; joint strength; intermetallic compounds +
Footnote †: journal: Applied  
doi: 10.3390/app1115105
## 5 The Principle of Intelligence Techniques
### Particle Swarm Optimization (PSO) Algorithm  
PSO is a swarm algorithm inspired by the behavior of the particles/social animals, such as fish or birds. It was introduced and developed by [PERSON] and [PERSON] [90] and classified as one of the metaheuristic techniques. It was considered as an evolutionary computation technique in the statistical community [41]. The PSO algorithm implements six steps for optimal searching as the following procedure. In which, the velocity and local positions are continuously updated using Equations (9) and (10), for sharing their experience.  
1. Generate the particle swarm size with random locations and speeds on \(d\) dimensions in the subject space.
2. Estimate the proper optimization fitness function for each particle in \(d\) factors.
3. Compare the fitness evaluation with _pbest_ of the particle. If the present value is better than _pbest_, then set _pbest_ value equal to the present value, and the _pbest_ position equal to the current position in \(d\)-dimensional space.
4. Compare the evaluation of the fitness with the overall previous best of the population. If the current value is better than _gbest_, then reset _gbest_ to the current population index and value of particle.
5. Change the velocity and local of the particle as Equations (9) and (10) as follow: \[\begin{array}{c}v_{j}^{i+1}\ =\ ww_{j}^{(i)}+(c_{1}\times r_{1}\times(\textit{ local best}_{j}-x_{j}^{(i)}))+(c_{2}\times r_{2}\times(\textit{global best}_{j}-x_{j}^{(i)})),\\ v_{\min}\leq v_{j}^{(i)}\leq v_{\max}\end{array}\] (9) \[\begin{array}{c}x_{j}^{i+1}\ =\ x_{j}^{(i)}+v_{j}^{(i+1)};j\ =\ 1,2, \ldots,n\end{array}\] (10) where \(x_{j}^{(i)}\) denote the position of particle \(j\) at iteration \(i\); \(v_{j}^{(i)}\) denote the velocity of particle \(j\) at iteration \(i\); \(w\) is the inertial weight coefficient; \(i\) is the iteration number; \(r_{1}\) and \(r_{2}\) represent a random number uniformly distributed in [0,1] which is randomly generated at iteration and for each particle.
6. Loop to step (2) until a criterion is met. Usually, the maximum number of iterations or sufficiently good fitness, the searching process will stop.  
In this study, the PSO algorithm was used/applied to optimize the hyper-parameters of the SVR model with different kernel functions (i.e., radial basis function, polynomial, linear) for predicting PM\({}_{10}\) concentration from drilling operations.
## 4 Simulation Experiment Example of FE Model Updating of Antenna
Then, we calculate the RMS of the BUS upper chord nodes' half-path length errors of the original and updated FE models under gravity loading under 10\({}^{\circ}\) and 70\({}^{\circ}\) conditions and compare them with the RMS of the antenna primary reflector surface nodes half-path length errors under these two elevation angle conditions. The results are shown in Figures 15 and 16. The error between the RMS of the FE model and the antenna at a 10\({}^{\circ}\) elevation angle is reduced from 0.051 mm to 0.04 mm, 21.6% lower than the original error. The error between the RMS of the FE model and the antenna at a 70\({}^{\circ}\) elevation angle is reduced from 0.049 mm to 0.045 mm, which is 8.1% lower than the original error. As shown in Table 5, in the whole model updating process, the FE model is used to calculate once, with an average time of 7.73 s. The RBF surrogate model calculates once, with an average time of 0.081 s. The calculation time of the RBF model is 1% of that of the FE model. Figure 16: The error distribution under the condition of 70\({}^{\circ}\) elevation angle and gravity load: **(a)** the antenna and the original FE model; **(b)** the antenna and the updated FE model. Figure 15: The error distribution under the condition of 10\({}^{\circ}\) elevation angle and gravity load: **(a)** the antenna and the original FE model; **(b)** the antenna and the updated FE model.
## 2 Project Background and Calculation Method
### Calculation Method
#### 2.2.2 Determination of Rock Reduction Parameters
The radar reflection signal of the R4 section is strong, and the frequency change of its electromagnetic wave signal is typical, dominated by medium- and high-frequency signals, with the same direction axis being continuous, without an obvious shock wave signal. Combined with the situation of the R4 section's palm face, it is concluded that the range of 0-25 m, in front of the R4 section, is dominated by mudstone, which is a soft rock. Figure 8: R\({}_{1}\) cross-section radar data analysis map. The joints and fissures are undeveloped and the integrity of the surrounding rock is typical; as such, it is a class IV deviation surrounding rock. The photo of the new tunnel palm face, as well as the results of the analysis of the overtopping geological prediction radar data, are in good agreement with the grading profile of the rock layers and the surrounding rocks. Therefore, it is, finally, determined that the malmstone layer of the cross-tunnel project (assuming that the grading of the surrounding rocks of the existing tunnels is the same as that of the new tunnels) is a Class IV surrounding rock, while the mudstone layer is a Class IV deviated surrounding rock and, at the same time, with reference to the GSI index diagram (Figure 9), it is determined that the malmstone and mudstone layers have a GSI value of 55 and 45.
## 2 Results and Discussion
### Essential Oils
Published studies have come to conflicting conclusions regarding the influence of the EO isolation method on the chemical profile of EO. While some of them report significant differences between samples, especially in terms of EO yield [2; 5], others confirm that EOs are not significantly affected by the extraction method [4; 21]. However, in general, a higher content of oxygenated compounds and a lower number of hydrocarbons are reported in EOs isolated by the MHD method. This can be explained by the higher dipole moment of the oxygenated compounds, which is why they interact more strongly with microwaves and are, therefore, more easily extracted than the monoterrene hydrocarbons with weak dipole moment [4]. Agreement with these observations was found in studies by [PERSON]  
Figure 1: GC-MS total ion chromatogram of sea fennel essential oil obtained hydro-distillation (**a**) and microwave-assisted hydro-distillation (**b**). Peak assignment is given in Table 1.  
et al. [22] on basil, garden mint, and thyme; [PERSON] et al. [5] on citrus peel's EO; [PERSON] et al. [6], [PERSON] et al. [23], and [PERSON] et al. [4] on rosemary; and [PERSON] et al. [2] on mint EO. [PERSON] et al. [21] studied the influence of the method used and different extraction parameters on the chemistry of basil essential oil and reported a greater number of detected compounds in the oil obtained by MHD, as well as [PERSON] et al. [5] in the citrus peel oils obtained by MHD compared to HD and cold pressing. Finally, almost all the mentioned studies reported significant advantages of MHD distillation over conventional hydrodistillation, especially in terms of short duration and lower energy consumption.
# Performance Evaluation of the New Compound-Carrier-Modulated Signal for Future Navigation Signals
## 3 Evaluation Criteria  
This section presents the analytical methods and evaluation criteria applied to quantify and assess the signal performance and provide essential references for signal design.
## 2 Materials and Methods  
### Sample Selection  
This preliminary study was approved by the Ethics in Research Committee of the Centre of Health Sciences of the University of Rome \"Tor Vergata\", Italy (register No 25-30-29). Fifteen single-rooted teeth extracted for orthodontic reasons from young adult patients between 15 and 25 years old were selected. Teeth met the following inclusion criteria: no carious lesions, cracks, resorptions or previous restorative and endodontic treatment. Only teeth with intact and mature root apices and similar length (20-22 mm) were selected. Teeth were then radiographed buccolingually and mesiodistally. Teeth with root canal curvatures greater than \(5^{\circ}\) or calcified root canals were discarded. The degree of root canal curvature was calculated according to [PERSON]'s method. After extraction, teeth were stored in a solution of 2% thymol in distilled water at room temperature and used within one week. Inclusion and exclusion criteria were verified under a \(20\times\) magnification laboratory microscope (Stemi DV4 Spot; Carl Zeiss, Oberkochen, Germany). After the access cavity was created, a #15 K-file (Dentsply Sirona) was inserted into the canal until the instrument tip was barely visible at the apical foramen and the working length was established 1 mm shorter than that point. The apices of the teeth were sealed with a resin-based composite material (SDR flow+, Dentsply Sirona) with the aim of simulating clinical conditions to prevent passive extrusion of the irrigant, creating a closed-end system [27]. The specimens were stabilized in a dedicated device, ProTrain (Simit Dental, Mantova, Italy), to standardize the operative procedures (Figure 1). A single endodontist was selected for all treatment procedures to eliminate interoperator variability and procedural bias.
## 2 Problem Description and Modelling
All these target allocations constitute a candidate _solution_\(\mathbf{a}=\)\(\left[\mathbf{a}_{1}\right,\)\(\mathbf{a}_{2}\),..., \(\mathbf{a}_{m}\)\(\left[\mathbf{a}_{m}\right]\) for the AEOS constellation observing schedule. For target \(g\in\mathbf{a}_{s}\), let \(t_{g}\) be the time that \(s\) begins to observe \(g\) within the VTW \(\mathbf{\mathit{W}}_{\mathit{S}}\)\({}^{k}=\)\(\left[\mathbf{\mathit{s}}_{\mathit{S}}\)\({}^{k}\), \(\mathbf{\mathit{ets}}_{\mathit{S}}\)\({}^{k}\right]\) in an orbit \(k\in R\), and the end time of observation is \(t_{g}+d_{g}\). The following VTW condition must be satisfied:  
\[t_{g}\geq\)\(\mathit{st}_{\mathit{sg}}^{k} \tag{1}\]  
\[t_{g}+d_{g}\leq\)\(\mathit{et}_{\mathit{sg}}^{k} \tag{2}\]  
Note that there may be cases where an AEOS \(s\) cannot observe a task \(g\) in \(\mathbf{a}_{s}\) due to conflicting VTMs (see Section 3.1). For completeness, we assume the start-observing or start time of this assigned but unobserved target is 0, i.e., \(t_{g}=0\). Then, let \(\Gamma_{s}\subseteq\mathbf{a}_{s}\) be the sequence of observable targets by \(s\) (\(\Gamma_{s}\) can be obtained by Algorithm CGF in Section 3.3). Based on the characteristics of AS-01 satellite [29], the look angle of AEOS \(s\) should be adjusted as \(\Gamma_{\mathit{sg}}(t_{g})\) before observing target \(g\) at the time \(t_{g}\), and this angle remains unchanged during the subsequent observation. Therefore, if AEOS \(s\) intends to observe two consecutive targets \(g\) and \(h\) in the same orbit, a transition time denoted as _trans_(\(t_{g}\), \(t_{h}\)) is required between the observation of \(g\) and \(h\).
## 4 Experiment II: Listener-Preferred \(\text{SBR}\) for Speech-Background Mixing
### Results  
For each listener, the SBR for each audio scene was averaged over the four excerpts. The mean SBR level chosen by listeners for each type of audio content is displayed in Figure 6 as open circles with error bars indicating 95% confidence intervals of the means. Across the five audio scenes, the listener-preferred SBRs for mixing are between \(-6.1\) to \(15.6\) dB, with that for the 'News Broadcasting' and 'Radio drama' scenes being the highest and lowest, respectively. One-way within-subject ANOVA confirmed that the listener-preferred SBR significantly varied between audio scenes [\(F(4,116)\)=\(47.347,p<0.001,\eta^{2}=0.620\)]. Post-hoc pairwise comparisons with Bonferroni correction further suggested that while listeners chose the same SBRs for the 'Football commentary', 'Public speech' and 'Radio poetry' scenes [all \(p>0.05\)], the SBRs for 'News broadcasting' [\(p<0.001\)] and 'Radio drama' [\(p<0.001\)] scenes were higher and lower respectively than those in the other scenes.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Scene** & **Foreground Speech** & **Background Sound** \\ \hline Football commentary & Commentary by a _male_ commentator & Crowds in a big football stadium \\ News broadcasting & Speech by a _female_ broadcaster & Speech by a male broadcaster \\ Public speech & Campaign speech by a _male_ politician & Public crowd babble sound \\ Radio drama & Narrative by a _male_ narrator & Music and other sound effects \\ Radio poetry & Poetry performed by a _female_ reciter & Piano and string instruments \\ \hline \hline \end{tabular}
\end{table}
Table 2: Foreground speech and background sound in the five audio scenes presented in Expt. II.  
In Figure 6, the model-predicted SBRs which led to a BiDWGP score of 0.5 are shown as '+'. A discrepancy between the listener-preferred and predicted \(\text{SBR}_{min}\) is evident: SBRs predicted as the minimum requirement for 100% speech intelligibility by the BiDWGP metric is clearly lower than those chosen by listeners, especially for 'News broadcasting', which differed from the model prediction by 23.1 dB. Interestingly, except for 'News broadcasting', the differences between the listener-preferred and predicted SBRs appear to be the same across background sounds. By minimising RMSE across the four scenes (i.e., 'News broadcasting' was excluded), it was found that applying a constant gain of 5.5 dB to the predicted \(\text{SBR}_{min}\) achieves a SBR level close to level preferred by listeners (RMSE of 0.9 dB). This is illustrated with 'x' markers in Figure 6.
# Comparison of L1 and L5 Bands GNSS Signals Acquisition
## 3 Assessment of Processing Time and Memory Requirements
### Acquisition of a Pilot Channel without Assistance
#### 3.2.4 Use of Non-Coherent Accumulation with Parallel Implementation  
This implementation is very similar to the one of the data channel acquisition; there are only two differences:  
* The number of branches is \(N_{\rm S}\) instead of \(N_{\rm D}\).
* For a pilot channel, the coherent accumulations for the different delays start and finish at the same time, therefore the latency and the length of data needed is slightly reduced.  
The implementation is shown in Figure 8. Since the accumulations start and finish at the same time, the timing diagram is the same as for the case of coherent integration only, shown in Figure 5, except  
Figure 6: Implementation of a pilot channel acquisition, without non-coherent accumulation, in a serial way (timing diagram available in Figure 7).  
Figure 7: Timing diagram of the pilot channel acquisition shown in Figure 6, with \(N_{\rm S}=4\) and \(K=3\).  
that the portion repeated is for further coherent and non-coherent accumulations (and the number of repetition is still \(K\)).  
Regarding the memory, the memory-based accumulators require  
\[\begin{array}{l}M=N_{\text{P}}2N_{\text{P}}B_{\text{C}}+N_{\text{S}}N_{\text{P} }B_{\text{NC}}\text{ bits}\\ =N_{\text{S}}N_{\text{P}}(2B_{\text{C}}+B_{\text{NC}})\text{ bits}\\ =N_{\text{S}}N_{\text{P}}[3B_{\text{R}}+3\lceil\log{}_{2}(KN_{\text{S}}/N_{ \text{NC}})\rceil+\lceil\log_{2}(N_{\text{NC}})\rceil]\text{ bits},\end{array} \tag{15}\]  
with  
\[B_{\text{C}}=B_{\text{R}}+\lceil\log{}_{2}(KN_{\text{S}}/N_{\text{NC}})\rceil, \tag{16}\]  
and  
\[\begin{array}{l}B_{\text{NC}}=B_{\text{C}}+\lceil\log_{2}(N_{\text{NC}}) \rceil\\ =B_{\text{R}}+\lceil\log{}_{2}(KN_{\text{S}}/N_{\text{NC}})\rceil+\lceil\log_{ 2}(N_{\text{NC}})\rceil.\end{array} \tag{17}\]
# Kalman Filter and Wavelet Cross-Correlation for VHF Broadband Interferometer Lightning Mapping
## 2 Materials and Methods
### Simulation  
In this sub-section, numerical simulations are conducted to assess the performance of the proposed mapping methods using simulated VHF-ITF signals. An actual measured signal (from antenna B is used as a reference to simulate the other perpendicular antennae (C and D). Given the hand-crafted azimuth and elevation incident angles, the signals of antennae C and D are copies of antenna B with a  
Figure 3: VHF broadband digital interferometer data acquisition system.  
phase shift for each temporal sliding window. This simulation can provide an insight into the best methodological approach for estimating lightning mapping. Figure 4 illustrates the proposed steps to simulate the lightning mapping signals and to evaluate the performance of the estimation methods.
# Adaptive Natural Gradient Method for Learning of Stochastic Neural Networks in Mini-Batch Mode
## 4 Adaptive Natural Gradient Learning in Mini-Batch Mode
### Adaptive Natural Gradient Learning Algorithm in Mini-Batch Mode
```
Input: A partition of training set \(\mathbf{D}\) with \(\mathbf{K}\) disjoint subsets \(\mathbf{D}_{\mathbf{k}}(\mathbf{k}=1\ldots\mathbf{K})\)

Learning parameter: \(\mathbf{\theta}\) User-defined parameter: \(\mathbf{\lambda}\leftarrow\) 1/(number of learning parameters)
1:\(\mathbf{\theta}_{\mathbf{K},0}\leftarrow\) random vector with small values
2:for\(t=1\) to max_iteration do
3:\(\overline{G}_{0,t}^{-1}\leftarrow\mathbf{\lambda}\mathbf{I}\)
4:\(\mathbf{\theta}_{0,t}\leftarrow\mathbf{\theta}_{\mathbf{K},t-1}\)
5:for\(k=1\) to \(\mathbf{K}\) do
6: calculate \(\overline{\mathbf{G}}\left(\mathbf{D}_{k},\mathbf{\theta}_{k-1,t}\right)\) using (18) * or (19) **
7: update \(\left(\overline{G}_{k}\left(\mathbf{\theta}_{k-1,t}\right)\right)^{-1}\) using (23)
8: update \(\mathbf{\theta}_{k-1,t}\) using the Equation (21): \[\mathbf{\theta}_{k,t}\leftarrow\mathbf{\theta}_{k-1,t}-\eta\left(\overline{G}_{k}\left( \mathbf{\theta}_{k-1,t}\right)\right)^{-1}\frac{\partial\underline{E}\left(\mathbf{D} _{k},\mathbf{\theta}_{k-1,t}\right)}{\partial\mathbf{\theta}}\]
9: end for \(k\)
10: end for \(t\)
```  
* Equation for additive Gaussian model  
** Equation for logistic regression model ```  
**Algorithm 1**Adaptive natural gradient (ANG) for mini-batch learning mode  
As shown in Algorithm 1, the proposed method needs to keep an additional matrix with \(\mathbf{N}\times\mathbf{N}\) size where \(\mathbf{N}\) is the number of learning parameters. Therefore, it uses \(\mathbf{O}\left(N^{2}\right)\) additional memory compared to the standard gradient method. Also, to obtain the update term in the Equation (21), we need \(\mathbf{O}\left(\mathbf{M}\mathbf{N}^{2}\right)\) computations where \(\mathbf{M}\) is the number of training samples in a mini-batch subset. This complexity is generally the same as the conventional quasi-Newton methods, and thus the proposed method is practically applicable to the problems that can be dealt with the conventional second-order methods. One main difference from the conventional second-order method is that the natural gradient uses Fisher information matrix instead of Hessian that is defined by the whole data set for exact computation. In addition, the natural gradient method is originally designed for online learning with stochastic gradient, whereas the quasi-Newton method requires a complicated process to obtain a stable approximation of Hessian using a small mini-batch subset. From this point, we can expect that the proposed method is more efficient than the second-order methods, especially for the problems with a large data set. This is confirmed by the comparative experiments in the next section.
## 3 Overview of the Proposed Method
### Generate Situations and Events at the Functional Level
#### 4.2.1 Generate the Functional Situations  
Although we defined the situation as describing the state of the AV and of the other entities of its operational environment, when generating the functional situations, we have to remain consistent with the definition of the functional level given by [PERSON] et al. At this stage of the identification, the detailed information on the parameters and their values is not to be filled in, the aim being simply to know the configurations of situations that will have to be considered when generating the test scenarios. Therefore, the identification of the functional situations proposed here is mainly based on the information collected during the definition of the automation function, its usage context or the operational maneuvers.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **Physical Infrastructure** & **Other Road Users** & **Weather Conditions** & & **Ego Vehicle** \\ \hline \(\bullet\) & Type of infrastructure: & & & & \(\bullet\) & Equipped with TJC \\  & divided lanes (highway, & & & & \(\bullet\) & Speed range: \(\leq\)60 km/h \\  & expressway) & & & \(\bullet\) & Type of precipitation: & \(\bullet\) & Presence of Driver = Yes \\  & Roadway type: & & & not specified & \(\bullet\) & No lane changing \\  & separate carriageway & \(\bullet\) & No predestrian, no & & \(\bullet\) & Type of brightness: not & \(\bullet\) & Vehicle dynamic: no \\  & No intersection, no & obstacle, no wrong & & specified & & strong deceleration, no \\  & roundabout, no pedestrian & & way driver & \(\bullet\) & Temperature: not & & strong acceleration, no \\  & crossing, no stop lights, no & & & specified & & strong action on the \\  & giving way, no priority entry, & & & & & steering wheel \\  & no U-turn, no working zone, & & & & \(\bullet\) & No trailer and no towing \\  & no tunnel & & & & & vehicle are attached \\ \hline \hline \end{tabular}
\end{table}
Table 1: TJC’s ODD description.  
Figure 1 shows the process for obtaining the functional situations. These are obtained by combining the information provided by the ODD (type of traffic density, type of infrastructure), and the operational modes and maneuvers.  
Examples for the TJC function: Traffic density can be fluid, congested or accordion-like. It is related to the number of users present in the traffic (i.e., around the AV). If Number_of_users < 3, then traffic is fluid, otherwise traffic is congested or accordion-like.  
For the infrastructure, several types can be encountered: divided lanes (motorway, expressway), toll area, work zone, road tunnel, etc.  
By considering the TJC function and the description of its usage context, we can deduce the functional situations which need to be considered for the generation. Table 2 illustrates the six functional situations obtained.  
In the reminder of this example, we focus on a specific functional situation: Ego in a \"Car following\" situation in the \"Activation\" mode on a motorway and in congested traffic.