esa-sceva/satcom-chunk-collection huggingface dataset:
# Stability and Heat Input Controllability of Two Different Modulations for Double-Pulse MIG Welding
###### Abstract  
Aluminum alloy welding frequently experiences difficulties such as heat input control, poor weld formation, and susceptibility to pore generation. We compared the use of two different modulations for double-pulse metal inert gas (MIG) welding to reduce the heat input required to generate oscillations in the weld pool. The stabilities of rectangular wave-modulated and trapezoidal wave-modulated double-pulse MIG welding (DP-MIG and TP-MIG) were analyzed by examining their welding processes and weld profiles. We found that the transitional pulse in TP-MIG welding results in smoother current transitions, softer welding arc sounds, and a highly uniform fish-scale pattern. Therefore, TP-MIG welding is more stable than DP-MIG welding. The effects of these double-pulse modulation schemes on welding input energy are presented. We propose methods for reducing welding input energy by varying the number of pulses or the pulse base time of low-energy pulse train while keeping the welding current and welding arc stable and unchanged. Compared to DP-MIG welding, TP-MIG welding reduces the input energy by 12% and produces finer grain sizes, which increases weld hardness. Therefore, TP-MIG welding offers a new approach for heat input control in DP-MIG welding of aluminum alloys. The results of this work are significant for aluminum alloy welding.  
double-pulse MIG welding; rectangular waves; trapezoidal waves Article
# Deep Reinforcement Learning for Selection of Dispatch Rules for Scheduling of Production Systems
## 5 Method Implementation and Evaluation
### Evaluation and Discussion
From the last column of the table, one can see that the DRL agent finds a better solution compared to all other algorithms by 85%. In addition, the makespan achieved with DRL is, on average, shorter than the best ones achieved by the other scheduling algorithms. In Figure 8, the graph shows better the average makespan achieved by each algorithm. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PROD** & **DRL** & **RANDOM** & **SPT** & **LPT** & **MTRJ** \\ \cline{2-6}
**RUN** & & & & **Makespan (min)** & \\ \hline
1 & **569** & 878 & 835 & 748 & 631 \\
2 & **561** & 976 & 944 & 724 & 696 \\
3 & **484** & 731 & 731 & 509 & 603 \\
4 & **393** & 614  
In Figure 9, the results of all testing algorithms are shown in a graph. Firstly, one can see that the DRL scheduling algorithm performs, on average, better than all other algorithms. At this point, it should be mentioned that the performance of an algorithm also depends on the use case. Thus, the DRL agent should be evaluated for different scheduling methods. In Figure 10, it is obvious that the DRL agent outperforms the random job selection. This is expected, since the DRL agent has been trained to choose the best actions in each situation. This also validates the training of the DRL agent and its ability to adapt to a particular production scenario and perform well in different situations. Moreover, in Figures 11-13, a comparison of the DRL with each dispatch rule is presented. In the next section, a comparison of the different algorithms' results is reported. \begin{table}
\begin{tabular}{c c c c c c} \hline
**PROD** & **DRL** & **RANDOM** & **SPT** & **LPT** & **MTRJ** \\ \cline{2-6}
**RUN** & & & & **Makespan (min)** & \\ \hline
86 & 666 & 992 & 1142 & 833 & 903 \\
87 & **484** & 496 & 557 & 610 & 507 \\
88 & **562** & 754 & 750 & 562 & 573 \\
89 & **319** & 441 & 548 & 445 & 425 \\
90 & **574** & 602 & 578 & 684 & 596 \\
91 & **991** & 1042 & 1410 & 991 & 1014 \\
92 & 587 & 628 & 778 & **546** & 587 \\
93 & **616** & 799 & 900 & 799 & 672 \\
94 & 574 & 591 & 903 & 686 & **553** \\
95 & **367** & 367 & 554 & 416 & 397 \\
96 & **493** & 797 & 795 & 660 & 584 \\
97 & 692 & **678** & 1147 & 678 & 759 \\
98 & **808** & 897 & 1164 & 808 & 851 \\
99 & **618** & 657 & 812 & 832 & 626 \\
100 & **372** & 515 & 460 & 496 & 380 \\ AVG & 554.63 & 694.61 & 795.31 & 638.73 & 599.14 \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
Figure 8: Scheduling algorithms’ average makespan achieved. Figure 11: Comparison of DRL and SPT algorithms’ performance regarding makespan achieved through 100 testing scenarios. Figure 12: Comparison of DRL and LPT algorithms’ performance regarding makespan achieved through 100 testing scenarios. Figure 10: Comparison of DRL and RANDOM algorithms’ performance regarding makespan achieved through 100 testing scenarios. Figure 9: Comparison of all algorithms’ performance regarding makespan achieved through 100 testing scenarios.
# On the Stability of Graphene-Based Aqueous Dispersions and Their Performance in Cement Mortar
###### Abstract
Based on the reported scientific literature and given the hydrophobic nature of Gr-TG and Gr-SG, a two-step process was proposed and implemented to improve their exfoliation and prevent the layers from restacking. It was characterized by an initial hydrothermal treatment (HT), aided by the addition of surfactant SC, followed by ultrasonication. The dispersions containing GO were not subjected to the HT step, as it is dispersible in water, but only to ultrasonication. The stability of the dispersions with the highest concentration was quantitatively investigated by means of the static multiple light scattering (SMLS) technique. The effect of the type and amounts of graphene-based nanomaterials on the fresh and hardened properties of mortars was evaluated. Microstructures were analyzed through field emission scanning electron microscopy (FESEM) measurements. Nitrogen and mercury intrusion porosimetry (MIP) were employed to explore the porosity and assess capillary water absorption (CWA) results.
## 3 Proposed Methodology
### Feature Selection Techniques  
In supervised classification, it is required to minimize the number of features to speed up the training and classification processes, [40]. However, fewer characteristic features may come at the expense of classification accuracy degradation [40]. However, the trick in dimensionality reduction is to trade some precision for simplicity [15]. This study considers a comparative study of two feature selection techniques, namely PCA and GA. The main reason for using these techniques in this study is due to its low noise sensitivity, less memory requirements, and high efficiency in the training process.
## 2 Profile Generation and Modification
### Profile Modification
#### 2.2.1 Single-Parameter Tooth Profile Modification  
**Example 1**.: _Roller position \(R_{p}\) modification._  
In Example 1, the desired outcome can be achieved by adjusting a correction \(\triangle R_{p}\) to the parameter \(R_{p}\) in Equation (6). A positive value of \(\triangle R_{p}\) (\(+\triangle\)\(R_{p}\)) produces a profile with a tooth thickness greater than the standard tooth thickness, causing interference between the roller and the cycloidal gear. Conversely, a negative value of \(\triangle R_{p}\) (\(-\triangle\)\(R_{p}\)) produces a profile with a smaller tooth thickness, resulting in a gap between the roller and the cycloidal disk.  
**Example 2**.: _Roller radius \(R_{rp}\) modification._  
In Example 2, the desired outcome can be achieved by adjusting a correction \(\triangle R_{rp}\) to the parameter \(R_{rp}\) in Equation (6). Contrary to Example 1, \(+\triangle\)\(R_{rp}\) produces a smaller profile while \(-\triangle\)\(R_{rp}\) is likely to produce a larger profile.
# Flavor and Functional Analysis of _Lactobacillus plantarum_
## 3 Results and Discussion
### Functional Components and Antioxidant Activity  
Total phenolic (TPC) and flavonoid (TFC) contents are the bioactive substances in the fruit that possess antioxidant activity [42; 43]. The TPC ranged from 69.61 to 82.48, and TFC ranged from 37.63 to 49.25 mg/L in seven fermentation periods of apricot juice (Table S1). TPC and TFC in apricot juice fermentation showed an upward trend. After 12 h fermentation, TPC and TFC increased by 5.82 mg/L and 4.18 mg/L, respectively. The TPC (82.48 mg/L) and TFC (49.25 mg/L) were the highest at 12 and 8 h of fermentation. The higher phenolic and flavonoid contents of fermented apricot juice can be the reason for its use in folk medicine [44].  
In recent years, the antioxidant benefits of fermented foods on human health have gained much attention from research scholars. In vitro tests based on DPPH, ABTS, FRAP and CUPRAC scavenging methods are frequently used to evaluate the antioxidant capacity of food products [34]. The same was examined for fermented apricot juice (Figure S3). Post-fermentation, DPPH, ABTS and FRAP free radical scavenging activities increased by 282.73, 149.43 and 19.43, while CUPRAC free radical scavenging activity decreased by 419.66 \(\upmu\)mol/L, respectively. Studies showed that the DPPH free radical scavenging activity of _L. rhamnus_ fermented carrot juice showed a decreasing trend, while the same increased for _L. bulgarcius_ fermented carrot juice [45]. _L. plantarum_ fermentation improved the antioxidant activity of kiwifruit pulp [46]. _L. plantarum_ fermentation can affect the transformation or protection of bioactive compounds improving the antioxidant activity of fermented foods [47]. The antioxidant compounds can act as singlet oxygen quenchers, metal chelators, hydrogen donors of free radicals and free radical terminators [48]. Importantly, antioxidant capacity may be affected by factors such as strain type, bacterial cell concentration and bacterial metabolic capacity [49].
# Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD
## 3 Experimental Results and Analysis
### Contrast Experiment
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Top-1** & **Top-5** & **GFLOPS** & **GPU** \\ \hline SqueezeNet & 57.64 & 80.40 & 0.858 & 700 ms \\ MTCNN & 63.85 & 85.03 & 0.897 & 640 ms \\ MobileNet & 69.37 & 89.22 & 0.513 & 610 ms \\ Faceness-Net & 70.31 & 84.39 & 0.858 & 720 ms \\ PVANet & 72.43 & 90.16 & 0.606 & 150 ms \\ MobileNet-SSD & 81.26 & 94.62 & 0.812 & 120 ms \\ \hline \end{tabular}
\end{table} 
Table 5: Feature extraction accuracy of each algorithm. GFLOPS =floating-point operations per second. GPU = graphics processing unit. PVANet= performance vs accuracy net. due to its unique deep convolutional structure, can learn the deep and detachable features of defects with a bigger receptive field, so it can achieve a higher positive detection rate.
## 4 Discussion
### Longitudinal VOC Data in Forensic Casework  
The VOC profile was highly dynamic over the process of decomposition. If a single experimental day from this study was chosen, it is apparent that a large portion of the profile would be uncharacterised. Therefore, the longitudinal study of decomposition odour in soil was valuable in depicting a comprehensive overview of compounds of interest.  
Previous studies have suggested using the pattern of VOC production during decomposition for PMI estimation [17, 18]. The dynamic nature of the VOC profile depicts the difficulty in establishing a reproducible pattern of VOC evolution for these purposes. While certain compounds followed general timeframes, they were not specific to a short reproducible interval that could be quantified for PMI estimation. Although the decomposition VOC profile is becoming increasingly characterised, it is still not possible to predict the exact number and types of VOCs for a particular stage of decomposition. However, decomposition VOCs have the ability to attract insects in a reproducible pattern. Such insect succession patterns are more reliable for estimating PMIs. The data obtained from longitudinal studies can provide information on potential compounds that could be signaling insect attraction during extended PMIs.  
Improved characterisation of the dynamicity of the decomposition VOC profile has valuable implications for the search and recovery of victim remains. This information can be used as a reference profile to be input into field-portable GC-MS instrumentation to provide a positive reference for decomposition odour. Electronic nose equipment generally use one or several target compounds for detection and therefore these target compounds need to be identified prior to instrument development. Similar handheld devices have been developed based on gases and VOCs released during decomposition [7, 8]. However, these instruments have yet to be employed functionally. Field instrumentation will likely never replace cadaver dogs completely, largely due to their high efficacy, sensitivity and their ability to search large area or rough terrain effectively [12]. However, they may provide valuable complementary search tools in the future.
## 4 ANN-Based Prediction Model Development and Validation
### Prediction Model Development
#### 4.1.4 Final ANN Model  
Network topology and random initialization both significantly influence the performance of the ANN. To incorporate the effect of both network topology and random weights in our model, the number of neurons in hidden layers was made to keep swapping among different numbers \((1,2,3,\ldots,n)\). Hence, for two hidden layers, swapping the number of neurons among \(n\) numbers gave rise to a total of \(c=n\times n\) combinations, as shown in Table 3. All these \(c\) combinations of network topologies were trained on \(k\) sets of training and CV datasets generated through \(k\)-_fold_ CV, resulting in \(c\times k\) ANN models. Each of these models was initialized with different random weight initialization methods \((1,2,3,\ldots w)\). The random initialization methods used are shown in Table 2. Finally, we have \(N=c\times k\times w\) models that were trained on the input dataset. The number of iterations was different because each step has a different number of input and output parameters, and for each step, a specific number of iterations was determined when the CV error was at a minimum to avoid overfitting of the model. A generalized schematic of the ANN has been given as Figure 4.  
The default learning rate in the ANN was chosen during the gradient descent in the back-propagation algorithm because the stopping criteria were the number of iterations and, as established separately for each step, how many iterations were sufficient for minimum CV error. Table 4 shows parameters of the proposed ANN model.  
Figure 3: K-Fold Cross-Validation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Step 2** & **Step 3.1** & **Step 3.2** \\ \hline \multirow{3}{*}{**Number of Layers**} & Input Layer: 1, & Input Layer: 1, & Input Layer: 1, \\  & Output Layer: 1, & Output Layer: 1, & Output Layer: 1, \\  & Hidden Layers: 2 & Hidden Layers: 2 & Hidden Layers: 2 \\ \hline \multirow{3}{*}{**Number of Neurons**} & Input layer: 2, & Input layer: 3, & Input layer: 1, \\  & Output Layer: 3, & Output Layer: 5, & Output Layer: 3, \\  & Hidden Layer: 2,3,4 & Hidden Layer: 3,4,5 & Hidden Layer: 2,3,4 \\ \hline \multirow{3}{*}{**Weight**} & \multirow{3}{*}{5} & \multirow{3}{*}{5} \\
**Initializations** & & & \\ \hline
**Learning Rate** & Default & Default & Default \\ \hline
**Activation Function** & Sigmoid & Sigmoid & Sigmoid \\ \hline
**Number of Iteration** & 10,000 & 12,000 & 10,000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of the ANN model for all PSPP spaces.  
Figure 4: Generalized Schematic of ANN.
## 4 Ground Segment
### Acquisition Plan and Operations
The image data, as well as orbit and attitude data are transmitted during several X-band contacts (320 Mbit/s downlink) per day via the Neustrelitz, Germany, multi-mission ground station. All data are available for further processing to data products at most 24 h after acquisition.
# Grating-Assisted Fiber to Chip Coupling for SOI Photonic Circuits
## 3 Factors Affecting Grating Loss
### Bandwidth Scaling  
Another vital figure of merit is the grating bandwidth. Since coupling through grating depends on fulfilling the Bragg condition, only a limited band of wavelengths are scattered to fiber. The diffracted field of a uniform grating has a wider angle of distribution owing to its short length and hence has a larger bandwidth compared to non-uniform gratings. Bandwidth can be enhanced by reducing effective grating length, which will widen the spectral distribution but it would be at the cost of a reduced overall coupling efficiency. The intrinsic 1 dB bandwidth of a generic grating coupler can be expressed as [20],  
\[\Delta\lambda_{1 dB}=\eta_{1 dB}\left|\frac{-n_{c}\cos\theta}{\frac{n_{c}^{eff}- n_{c}\sin\theta}{\lambda_{0}}-\frac{dn_{eff}}{d\lambda}}\right| \tag{11}\]  
where \(\eta_{1 dB}\) is a fiber dependent coefficient, \(\theta\) the incidence angle and \(\frac{dn_{eff}}{d\lambda}\) corresponds to the waveguide dispersion and \(n_{c}\) is the cladding index. A suitable solution to increase bandwidth  
Figure 5: A typical highly efficient conventional non-uniform grating coupler. The gratings consisting of partially etched grooves with a variable fill-factor leads to maximal mode field overlap with the Gaussian fiber mode, while the underneath bottom DBR stack ensures a high directionality.  
would be to decrease the effective grating index \(n_{g}^{eff}\) (Equation (3)). For shallow etched gratings, \(n_{g}^{eff}\) can be decreased by increasing the etch depth. This would however result in large grating reflection. An alternative way to tailor \(n_{g}^{eff}\) can be through replacing the shallow etched feature by a subwavelength grating which we shall discuss in the next section.
## 3 External Perturbations Affecting Geolocation
### Solid Earth Tidal Perturbations  
Figure 4a,b illustrates the largest effects acting on the CRs: plate tectonics and SET. The SET continuously modulates the CR position around its nominal position, whereas plate tectonics cause the local reference frame to drift away from the global frame (ITRF). In our case, the local frame used during measurement of the CR positions was the Swiss Terrestrial Reference Frame 1995 (CHTRF95), whereas the satellite positions are provided in ITRF2008. These two frames are drifting apart at a rate of \(\sim\)2.5 cm/year due to tectonics. The DGPS measurements were surveyed relative to the positions of their associated reference station(s). In our case, these reference station coordinates were provided by the Swiss national mapping agency (swisstopo) in ITRF2008. The validity date (epoch) for the reference station coordinate of both test sites was 2 July 2006, meaning that the DGPS measurements were valid within ITRF2008 on this date. For the later dates associated with the S1A products, tectonic drift had to be modelled beginning on this date, with the surveyed CR positions being updated before geolocation  
Figure 3: S1A StripMap acquisition time series over _Torny-le-Grand_ and _Dubendorf_.  
calculations were applied. The tectonic correction is simply a translation of the CR position by an amount equal to the product of the station velocity within ITRF and the time difference between epochs.  
The SET can cause vertical displacements of up to half a meter (peak-to-peak) at the equator [12], but typically no more than \(\sim\)40 cm at mid-latitudes. For the SET correction, we employed an external model, available at [13]. We used the FORTRAN code provided here to automatically update the CR position(s) according to the SET amplitude estimated for the test site at the date and time of the S1A product in question.
## 3 Results
### The Mechanism Involved  
A heterojunction photocatalyst was used in this study. According to previous studies, heterojunction photocatalysts can be arranged in a variety of ways depending on the band edge positions of their constituent semiconductors, including dominant straddling gap (type I), staggered gap (type II) or broken gap (type III) heterojunction photocatalysts [19]. The present heterojunction photocatalyst was made up of two separate semiconductor units (ZrO\({}_{2}\) and CdWO\({}_{4}\)) whose band (VB and CB) positions are different to each other. According to their band edge positions, the ZrO\({}_{2}\)-CdWO\({}_{4}\) semiconductor interface can be arranged as a straddling gap (type I) heterojunction. A Schottky barrier is formed due to different band positions or type I arrangement of the heterojunction (suggested by UV-DRS and PL spectra). Therefore, the possibility of migration of e\({}^{-}\) to the catalyst surface is increased via charge separation, and prevention of e\({}^{-}\)-h\({}^{+}\) recombination, as shown in Scheme 2[16; 18].  
When visible light falls on such a heterojunction photocatalyst, a valence band (VB) e\({}^{-}\) can be excited from one of the units (either ZrO\({}_{2}\) or CdWO\({}_{4}\)) and move to the conduction band (CB) of that unit, leading to the formation of h\({}^{+}\) in VB. Due to the difference in the positions of the bands (VB and CB) in such units (ZrO\({}_{2}\) and CdWO\({}_{4}\)) of the developed heterojunction, the possibility of migration of e\({}^{-}\) and h\({}^{+}\) from one unit to the other unit may increase. This way, the excited e\({}^{-}\) and h\({}^{+}\) do not go back and can rapidly and continuously transfer from one unit (ZrO\({}_{2}\)) to the other unit (CdWO\({}_{4}\)) of the semiconductor, thus causing charge separation, and also preventing charge recombination.  
Due to this charge separation and prevention of charge recombination, the electronic charge becomes distributed over the entire surface of the heterojunction photocatalyst. In such a situation, the e\({}^{-}\) and h\({}^{+}\) reach the surface of the catalyst and react with water, split the water, and produce H\({}_{2}\) gas. Because, in this study, a sacrificial agent such as MeOH has been used, the MeOH acts as a proton source and therefore increases the photocatalytic activity occurring in the presence of these extra protons. Based on the theory presented in the present study, the speculated mechanism has been shown through Scheme 2[16]. Our group [43; 44] has previously reported the use of Pt-Ag/Ag\({}_{3}\)PO\({}_{4}\)-WO\({}_{3}\) NCs for photocatalytic H\({}_{2}\) production from bioethanol.
# A Palm-Jacobaeus Loss Formula for Multi-Service Systems with Separated Resources
The LAG is a system composed of \(k\) independent separated resources, each with the capacity of \(f\) adopted (assumed) AUs (allocation units). It is important to underline that the resources are independent of one another and are separated, which means that the LAG can service a given call only when at least one resource, from among all \(k\) resources, has sufficient resources needed to provide service to this call. Such a definition of service excludes any possibility of a division of resources demanded by a call between a given number of different resources. The LAG is analyzed, for example, in [33; 34; 35]. In [36], a simple approximate LAG model for resources with different capacities is proposed, while [37] presents a LAG model for different resource capacities. In multi-service traffic engineering, the LAG is used to model and optimize mobile networks, cf. [10], as well as output groups of links (resources) in multi-service switching networks, e.g., [38; 39; 40; 41]. We see the main application of the elaborated extension of the Palm-Jacobaeus formula in modeling of separated wireless resources in cellular networks. However, preliminary studies undertaken by us led to the conclusion that the determination of the blocking probability of strictly determined resources in the resource group will make it possible to simplify the modeling of a large number of complex multi-service systems in mobile and optical networks, including network nodes and switching networks. This article is structured as follows. Section 2 presents the model of a group with limited availability. Section 3 includes a proposal of a method to determine the blocking probability for strictly defined resources. Section 4 provides a comparison of the results obtained in the analytical modeling with the results of the simulation experiments for a number of selected structures of multi-service resource groups. The last section summarizes the most important results of the study.