esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The proposed technique aims to reduce the CE delivered by a power circuit where high-side transistors are exploited, focusing on the reduction in the unwanted oscillations that take place during the high-side transistor turn-on by means of the source inductance. The paper is organized as follows: in Section 2 the source and the propagation path of CM conducted emissions are discussed in the case of a half-bridge, and a simplified model is presented. Then, in Section 3, the switching waveforms of such a leg are reported in case of a conventional gate driver and of an AGD. Section 4 introduces the effect of the source inductance and the feedback during the transistor turn-on is discussed. Then, in Section 5, simulations on the prototyped converter are reported to investigate more deeply the source inductance effect, and a method to find its optimal value is proposed. Finally, in Section 6, the experimental results are presented, and a comparison between the proposed technique and the use of a snubber is performed both in terms of CM EMI mitigation and efficiency. Concluding remarks are drawn in Section 7.
# Study of the Direct Metal Deposition of AA2024 by ElectroSpark for Coating and Reparation Scopes
## 4 Conclusions  
In the present study, AA2024 was deposited on homologous substrate by the electro-spark process. The deposits exhibit the following characteristics.  
Figure 16: Effect of spark pulse-energy _Es_ on average cracks length that propagate in the substrate from the deposit/substrate interface for three different values of frequency (F1 \(<\) F2 \(<\) F3).  
Figure 17: Number of cracks vs. cracks length of multiple-layer deposition produced using: (**a**) 0.5 J (V1, C2, 10 L/min), (**b**) 0.9 J (V2, C1, 10 L/min) and (**c**) 1.125 J (V2, C1, 10 L/min) as a function of the frequency (F1 \(<\) F2 \(<\) F3).  
1. They have layer-by-layer microstructure, and all layers are characterized by a fine and mixed microstructure both cellular and equiaxed. The cell size is not uniform along the deposit.
2. The grains are columnar and parallel to the growth direction of the deposit. Their size is not uniform and they spread through more than one layer whose microstructure is mixed.
3. The average hardness values do not change significantly with process parameters. The great standard deviation in average hardness is due to the strong variations microstructural within the thickness.
4. The defects have small dimensions whose size reduces with the increasing \(E\)s at a fixed frequency. When increasing the heat input, the defectiveness is reduced due to higher volume of re-melted substrate, the higher volume of molten splat and the lower space between the splats.
5. The substrate/deposit interface is characterized by the presence of cracks which extend into the substrate preferentially along the grain boundaries. The average length of cracks increases with frequency and \(E\)s.
6. Within the experimental plan developed in this study, the best energy value (\(E\)s) to deposit 2024 alloy on homologue substrate reducing both the inner porosity and the length of the interface cracks, is 0.9 J.  
Future studies will focus on the reduction of the defects. For example, the use of automatized depositions could solve the limits due to the manual process and/or a slight heating of the substrate during the depositions could reduce the thermal stresses crack without affecting the deposit/substrate microstructure.  
[PERSON] and [PERSON] designed research and experiments, handled the microstructure and data analysis; [PERSON] performed the fabrication of the coatings. [PERSON] revised the language and commented on the experimental results.  
The authors declare no conflict of interest.
## 2 Global Open Geo-Information and Integrated Framework
### Integrated Framework
#### Data and Information Integration  
The data are integrated for accessing and processing. Once a disaster occurs, various data on hazards, exposure levels, and disasters drawn from multiple sources must be collected within a limited amount of time for the damage estimation. Open source sharing mechanisms; local databases and new acquisition approaches such as remote sensing, crowdsourcing, surveying, and reporting, are commonly used means of data collection. Even single forms of data (e.g., post disaster remote sensing data) are typically drawn from more than ten remote sensing satellites operated by several agencies. Unlike global open data that are typically accessed through related websites, local (national) data are typically managed by several institutions. To facilitate access to a variety of information, data catalogs, information portals, dataset service platforms, and national spatial infrastructures serve as technical tools used for data collection integration. Beyond technical tools, open data policies, institutional collaboration on data sharing, and strategies for national spatial infrastructure promotion are non-technical integration approaches that can be used to improve data collection efficiency levels.  
Figure 1: Geo-information Integration Framework for Disaster Assessment.  
Datasets, particularly local ones, are typically developed by different organizations through their own strategies designed for a particular target group (e.g., basic geographic information developed by survey agencies, remote sensing information produced by space agencies, and the ground disaster information collated by disaster management agencies). The diversity of strategies used leads to many inconsistencies and heterogeneities among datasets, hindering the collaborative utilization of such datasets. Such inconsistencies not only create technical issues related to formats, accuracy levels, currencies, data, and access networks, but also create non-technical issues (e.g., licensing arrangements). Well-defined geo-information data standards, metadata, and policies for sharing and use have generally been recognized as basic means of data integration. Some web-based geo-information integration tools have been developed for specific application services to identify data inconsistencies and for automatic integration. For example, the Open Transport Net project realized the integration of multi sourced open and geospatial datasets in the transportation domain, for which both standards and tools were facilitated as the approaches of data interoperability [38]. For some major disasters, geo-databases have been shared through web services for specific groups to facilitate crowd participation for damage assessments.
# Recirculated Wave Undulators for Compact FELs
## 2 The Optical Cavity  
We consider an optical cavity [29] which is the same as in Ref. [27], composed by two Flat Mirrors (FM1, FM2), two Parabolic Mirrors (PM1, PM2) and one focusing lens. The design of such ring cavity is shown in Figure 1.  
This design allows obtaining two waists of the laser beam along a straight line, which is as well the direction where an electron beam is injected while counter-propagating with respect to the laser. The electron beam interacts at the first laser waist \(w_{1}\) and then at the second waist \(w_{2}\), with the emission of FEL radiation. The length of the cavity is \(L_{c}=2L_{l}+2L_{s}\), the long side being \(L_{l}\) and the short one \(L_{s}\). A focusing lens is placed between two parabolic mirrors at a distance \(L_{l}/2\) from both. Denoting with \(f_{p}\) the focal length of the parabolic mirrors and with \(f_{l}\) the focal length of the central focusing lens, the relative distance between one laser waist and the central focusing lens is \(d=2f_{l}\). Furthermore, the following equation must hold \(L_{l}=2f_{p}+4f_{l}\). After the first interaction the electron beam passes through the magnetic chicane (composed by the four dipoles D1, D2, D3, D4) during the time \(T_{mc}\), finally reaching the second interaction point. This time must be synchronized to the laser pulse in such a way that the two beams can \"meet\" at the waist \(w_{2}\), in formulas we obtain \(cT_{mc}=2f_{p}+2L_{s}+L_{l}\), with \(c\) the speed of light in vacuum. The strength parameter of the wave undulator is defined as:  
\[a_{0}=\frac{\lambda_{0}eE_{p}}{2\pi mc^{2}} \tag{1}\]  
where \(e\) is the elementary charge, \(m\) is the electron's mass and \(\lambda_{0}\) is the period of the wave undulator. The peak electric field \(E_{p}\) associated with the laser can be expressed in terms of the laser intensity at the waist:  
\[E_{p}=\sqrt{2Z_{0}I_{p}} \tag{2}\]  
where the vacuum impedance has been denoted as \(Z_{0}\) and \(I_{p}\) is the laser peak intensity calculated as \(I_{p}=P_{L}/\pi w_{0}^{2}\) where \(P_{L}\) is the peak power of the laser.  
Figure 1: Design of the ring cavity for the wave undulator. The laser circulates clockwise.
# On the Fidelity of NS-3 Simulations of Wireless Multipath TCP Connections
## 3 Overview of MPTCP Congestion Control Algorithms
### wVegas  
wVegas [34] is a delay-based congestion control algorithm originated from TCP-vegas. Unlike LIA and OLIA, wVegas uses packet queueing delay as a sign of congestion. On each transmission round, the congestion window of subflow \(i\) (\(W_{i}\)) is updated in two steps. In step 1, the \(W_{i}\) is incremented or decremented based on the parameters \(\alpha_{i}\) and \(diff_{i}\), as in the following:  
If \(diff_{i}>\alpha_{i}\),  
\[W_{i}=W_{i}-1 \tag{5}\]  
If \(diff_{i}<\alpha_{i}\),  
\[W_{i}=W_{i}+1 \tag{6}\]According to (7), \(diff_{i}\) is calculated as the difference between expected sending rate and actual sending rate. \(\alpha\_i\) is adjusted whenever it is smaller than \(diff_{i}\) based on the ratio between current sending rate of subflow \(i\) and total rate of all the subflows as in (8).  
\[diff_{i}=(W_{i}/base\_RTT_{i}-W_{i}/RTTi)\ base\_RTT_{i} \tag{7}\]  
\[\alpha_{i}=((W_{i}/base\_RTT_{i})/total\_rate\_of\_all\_i)\ \alpha_{total} \tag{8}\]  
In Step 2, the link queue variation is computed as  
\[q_{i}=RTT_{i}-base\_RTT_{i} \tag{9}\]  
where \(RTT_{i}\) is the average observed RTT and \(base\_RTT_{i}\) is the minimum measured RTT of subflow \(i\). When the current link queue variation is greater than \(2q\_i\), the \(W\_i\) is decreased using  
\[W_{i}=\ W_{i}\ (base\_RTT_{i}/2 RTT_{i}). \tag{10}\]  
wVegas algorithm is expected to be more responsive to the network congestion changes when compared to the other algorithms. It can achieve faster convergence with timely traffic shifting during network congestion.  
In summary, LIA, OLIA, and wVegas are designed with multipath in mind. They consider all the multipath subflows in an MPTCP connection to calculate the congestion window growth function of each subflow. The calculation of the congestion window growth function in LIA depends mainly on the RTT value. OLIA takes both the RTT and the last loss event into account to determine the appropriate increase in the congestion window. Based on TCP Vegas, wVegas identifies network congestion in an early stage (before a loss happens) by observing the minimum RTT value of each subflow using the queuing delay and then gives each flow a weight based on the congestion of its path. Alternately, Cubic is designed to be a single-path congestion control algorithm. Therefore, it increases the congestion window of a subflow based mainly on its loss events without taking the status of the other subflows into account.
# Screening of Some Romanian Raw Honeys and Their Probiotic Potential Evaluation
## 3 Results
### Microbiological Results of Raw Honey Samples  
In five raw honey samples (P1, P4, P5, P9, P10), we have identified probiotic bacteria (Table 5), processed further by DNA extraction (Figure 6) and BLASTn analysis of Bacteria 16S sequence. The probiotic bacteria were: _Bacillus myocytes_ (P1), _Bacillus thuringiensis_ (P4), _Bacillus amyloidosisaciens_ (P5), _Bacillus subtilis_ and _Bacillus velezensis_ (P9), and _B. thuringiensis_ and _Bacillus velezensis_ (P10).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Honey** & **P\(\mathbf{H}\)** & **Free Acidity** & **Lactone Acidity** & **Total Acidity** \\
**Samples** & **p\(\mathbf{H}\)** & **(meq/kg)** & **(meq/kg)** & **(meq/kg)** \\ \hline P1 & 3.78 (0.19) a & 25.3 (1.27) b & 23.3 (1.11) b & 48.6 (2.38) b \\ P2 & 4.17 (0.20) a & 13.5 (0.68) d & 10.1 (0.52) f & 23.6 (1.20) ed \\ P3 & 4.19 (0.21) a & 12.4 (0.61) ed & 14.1 (0.71) ed & 26.6 (1.28) d \\ P4 & 4.00 (0.20) a & 8.93 (0.44) f & 6.63 (0.34) g & 15.5 (0.77) f \\ P5 & 3.92 (0.20) a & 18.0 (0.92) c & 17.5 (0.90) c & 35.6 (1.74) c \\ P6 & 4.21 (0.20) a & 10.1 (0.52) fe & 9.71 (0.46) f & 19.8 (1.04) fe \\ P7 & 3.97 (0.20) a & 19.0 (0.92) c & 16.5 (0.79) de & 35.6 (1.81) c \\ P8 & 3.84 (0.19) a & 34.1 (1.77) a & 31.6 (1.53) a & 65.2 (3.17) a \\ P9 & 3.9 (0.20) a & 20.1 (0.97) c & 14.2 (0.71) ed & 34.4 (1.66) c \\ P10 & 3.89 (0.18) a & 18.7 (0.91) c & 13.3 (0.67) e & 32.0 (1.54) c \\ \hline \hline \end{tabular} The values are mean of three samples (n = 3, standard deviation in parentheses), analyzed individually in triplicate. Different letters within a column denote significant differences (\(p<\) 0.05).  
\end{table}
Table 4: pH and acidity results from raw honey samples.  
Figure 6: PCR amplification of bacteria DNA (1481 bp) Lines 1 and 15: 1500 bp DNA ladder, lines 2–6 and 8–12 bacterial DNA positive amplification (\(\sim\)1481 bp), lines 7 and 14 positive control, line 13 negative control.  
In addition to probiotic bacteria, other species have been identified in the analyzed raw honey samples (Table 5). The NTG value was between \(1\times 10^{2}\) (P1, P8) and \(100\times 10^{6}\) (P3) \(\mathrm{CFU/mL}\). This aspect means that the beekeepers do not respect the Good Apicultural Practice and a correct hygiene in their apiary. No bacteria were identified in P2 and P7 samples.
# A Data Augmentation Method for Skeleton-Based Action Recognition with Relative Features
## 4 Experiment
### Comparison of Different Frames of Skeleton Sequence  
As mentioned in Section 3.2.1, an action sequence refinement strategy is adopted to unify the length of the skeleton sequence. In this way, we can obtain fixed size images for CNN model training. The resolution of the images generated by the action sequences with different frames is also different, which could also affect the accuracy of the results. Consequently, this experiment is designed to select the optimal number of frames, N, for the skeleton sequence. In addition, the average number of frames of the Florence-3D is approximately 20, and for UTkinect-3D there are 30 frames. Consequently, the frame numbers are initialized to 30.  
As shown in the Figure 4, there are recognition results of multiple classical CNN models and our model training on images generated by data augmentation with different frame numbers on two datasets. In the charts, the X-axis represents the frame number, and the Y-axis is the classification accuracy. When the number of frames, N, is 30, the classification result of each model is at the lowest point, such that the accuracy of LeNet is 76.74% and 81.24% on the two datasets, respectively. As the number of frames increases, the accuracy of the models also increases until N = 60, reaches the highest value, and then the accuracy drops somewhat. Hence, the frame number N of each skeleton sequence is unified to 60 and the size of the generated motion image is fixed to \(45*60\).  
In detail, when the number of frames, N = 60, then DenseNet121 and our model achieves excellent and similar results on two datasets. On Florence-3D, our model achieves an accuracy of 96.59%, 0.15% lower than DenseNet121. Our model is 0.16% higher than DenseNet121 on UTkinect-3D. Although our model achieves the second-best accuracy, our model is still adopted as the final model. Because, as shown in Table 1, the space complexity of our model is 10.25 MB less than DenseNet121, the time complexity is only approximately one over ten of DenseNet121. This means our model consumes less memory and has higher efficiency than DenseNet121 [32; 33].
# Rapid Green Extractions of C-Phycocyanin from _Arthrospira maxima_ for Functional Applications
## Materials and Methods
### Green Extraction Design for Phycobilproteins Extraction  
#### 2.3.1 Microwave (MW) Design  
Microwave process was performed using a domestic microwave oven (Fensa MF-28G model, Valparaiso, Chile) which is capable of operating at a maximum input of 1000 W at a frequency of 2450 MHz; 20 mg of _A. maxima_ were used with 5 mL of independent solvents performing several extractions and the supernatant was colorless. Then the aliquots were centrifuged at 3140\(\times\)_g_ for 5 min. To optimize MW extraction conditions, a factorial experimental design 3\({}^{3}\) was used based on three factors with 3 levels, these being: power (100, 200 and 300 W), time (15, 30 and 60 s) and solvent (distilled water, Na-phosphate buffer (pH 7.0) 100 mM and Na-phosphate buffer:water 1:1, (_v/v_)). The effect of the factors and its interactions on specific response as C-PC content was studied. A total of 81 experiments (n = 3) as shown in Table 1 for _A. maxima_.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**MW Extraction Conditions**} & \multicolumn{2}{c}{**C-PC Content (mg/g)**} \\ \hline
**Exp.** & **Power (W)** & **Time (s)** & **Solvent Type \({}^{*}\)** & & \\ \hline
1 & \(-\)1 & (100) & \(-\)1 & (15) & \(-\)1 & (water) & 201.9 \(\pm\) 5.1 \\
2 & \(-\)1 & & \(-\)1 & & 0 & (Buffer) & 148.9 \(\pm\) 2.0 \\
3 & \(-\)1 & & \(-\)1 & & +1 & (Buffer:water) & 149.1 \(\pm\) 5.6 \\
4 & \(-\)1 & & 0 & (30) & \(-\)1 & & 215.0 \(\pm\) 5.5 \\
5 & \(-\)1 & & 0 & & 0 & 161.5 \(\pm\) 6.9 \\
6 & \(-\)1 & & 0 & & +1 & 165.9 \(\pm\) 9.3 \\
7 & \(-\)1 & & +1 & (60) & \(-\)1 & 169.5 \(\pm\) 3.8 \\
8 & \(-\)1 & & +1 & & 0 & 160.9 \(\pm\) 8.4 \\
9 & \(-\)1 & & +1 & & +1 & 168.6 \(\pm\) 12.2 \\
10 & 0 & (200) & \(-\)1 & & \(-\)1 & 192.5 \(\pm\) 1.3 \\
11 & 0 & & \(-\)1 & 0 & & 170.7 \(\pm\) 7.5 \\
12 & 0 & & \(-\)1 & +1 & & 143.8 \(\pm\) 16.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental design matrix including extraction conditions and results for C-phycocyanin (C-PC) content as response variable studied for the optimization of the microwaving (MW) of the cyanobacteria _Arthrospira maxima_. Values presented are mean \(\pm\) standard deviation (SD), n = 3. Power (100 (\(-\)1), 200 (\(0\)) and 300 (\(1\)), W), Time (15 (\(-\)1), 30 (\(0\)) and 60 (\(1\)), s) and Solvent Type (water (\(-\)1), buffer (\(0\)) and buffer:water (\(1\)).
# Measuring Gas Flaring in Russia with Multispectral VIIRS Nightfire
## 1 Introduction
### Context of Gas Flaring in Russia: Regulations and Reported Data Availability
However, to our knowledge, no continuous field-specific data are made publicly available by any of the sources. Field-level APG utilization rates may also be a trade secret of the companies and may thus be reported to the supervisory authorities only, contributing to the lack of publicly available detailed gas flaring data in Russia.
## 4 Baseline Models
### Neural Network Models  
* **NN**: Since the numeric and image data can both represent the surroundings and affect the decisions made by the AV, the first baseline model is a simple deep neural network that takes 12 features as input and owns two hidden layers with eight and four neurons, respectively. This  
Figure 1: Examples of two different detection tolerances applied to the same scenario, displayed on visualizations of camera images in the Waymo Open Dataset.  
Figure 2: One example of images captured by cameras of five different views from segment-11004685739714500220 of tar training_0000 in the Waymo Open Dataset.  
model aims at capturing the characteristic of the 12 features, as well as the relationship between accelerations and the surrounding environment.
* **CNN**: The second model is a traditional convolutional neural network with convolutional, batch normalization, and pooling layers. Grid search is applied to determine the optimal parameters during training. Video images of each frame in the dataset are used as inputs to predict the acceleration of the same frame. Then, the output of the multiple layers is fed into dense layers to extract feature embedding of the image and generate the output.
* **NN + CNN**: To better represent a frame using the image embedding as well as the 12 features corresponding to a frame, the third model combines the NN and CNN models. Specifically, as shown in Figure 3, the last dense layers of both models are concatenated to new dense layers that eventually generate the final output.
# IGBT Overvoltage Protection Based on Dynamic Voltage Feedback and Active Clamping
## 4 Parameter Influence of the Dynamic Voltage Feedback Circuit
### \(C_{1}\) Impact Mechanism Analysis
Figure 4 shows that the larger the value of \(C_{1}\) is, the lower the rising rate of \(V_{ce}\), which reduces the voltage spike; Figure 5 shows that the larger the value of \(C_{1}\) is, the smaller the drop rate of \(V_{ge}\) will be. Additionally, the later the active clamp circuit will be triggered to work, the longer the time for raising the gate voltage at time \(t_{3}\) will be, and at the same time, the smaller the gate voltage will be. Set \(R_{4}\) and \(U_{Z51}\) unchanged, change the value of \(C_{1}\), and then conduct multiple groups of simulations to obtain the curve of voltage spike \(V_{ce(max)}\) changing with \(C_{1}\) as shown in Figure 6. It can be seen that when \(C_{1}<400\) pF, the change of C1 has a significant impact on \(V_{ce(max)}\); when \(C_{1}>400\) pF, it has little effect on \(V_{ce(max)}\). This shows that the suppression circuit can reduce the voltage spike, but can not completely eliminate the turn off voltage spike. Figure 4: \(V_{ce}\) comparison diagram under different \(C_{1}\) values. Figure 5: \(V_{ge}\) comparison diagram under different \(C_{1}\) values.
# Development of Potato Mass Estimation System Based on Deep Learning
## 2 Materials and Methods
### Potato Mass Estimation System
#### 2.1.3 Tracking  
To accurately estimate the mass of potatoes in a field, it is necessary to accurately determine the number of potatoes entering into the collection unit. Representative object tracking algorithms include the SORT algorithm, which is a method that uses the Kalman Filter and the Hungarian algorithm. The SORT algorithm tracks the positions of objects based on their locational information; it has a disadvantage in that its accuracy is low although its calculation speed is fast. In the present study, to make up for such a drawback, the DeepSORT algorithm, which has an enhanced accuracy due to the addition of a CNN to the existing SORT, is used. Although the DeepSORT algorithm is very similar to the SORT algorithm, it can improve the object estimation accuracy by going through a process of calculating the cosine distance using the objects' feature vectors of the previous frame and the next frame and adding the Mahalanobis distance value and cosine distance value of two frames to judge the sameness within the frames [20; 21; 22].  
An ID line (I.L.) and a count line (C.L.) are designated at the middle and end points of the collection unit in the image, respectively, as shown in Figure 4, and the system is configured such that an ID is assigned when each object passes through the I.L., and counting is done when a potato, to which an ID is assigned, passes through the C.L. At this time, a cumulative method is used lest no same ID should be assigned to different potatoes [18].  
Figure 4: Baseline for potato tracking.