esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results and Discussion
### Behavioral Analysis
#### 3.3.5 Behavioral Variation  
Based on the aforementioned analysis, we could identify the different types of behavior of the embankment materials (Figure 12) according to fines content. TFC (transition fines content) indicated a boundary of sand-like and clay-like behavior of the mixtures, and this possible range of the tested mixture was around 21% to 26%. The optimum fines content (\(f_{opt}\)), defined as the fines content that produces greatest dry density, was around 15% to 20%. The critical fines content was 21.67%.  
Figure 11: Particle shape in all mixtures with 10,000\(\times\) and 100,000\(\times\) magnification by SEM.  
Since the clayey soil (low plasticity) has its own optimum fines content which could provide a denser state, low compression index, and low void ratio, engineers could consider using the soil with \(f_{opt}\) as an embankment material. Based on the observations in this research, there could be three simple approaches to specify the value of \(f_{opt}\): (1) simple compaction test with method D, (2) identification of the possible transition zone, and (3) determination of the critical fines content. However, these three approaches are based only on the sand-kaolinite mixtures. For a more specific method to define optimum fines content, more laboratory tests are required.
# Efficiency of Pulsating Base Bleeding to Control Trailing Edge Flow Configurations
## 3 Results and Discussion  
### Trailing Edge Steady Actuation  
The effects of steady base bleed on the base pressure and the characteristic Strouhal number of the wake flow structures were studied in [PERSON] et al. [19] and are summarized in Figure 4. The evidence obtained from numerical analyses suggested to divide the trailing edge flow behavior in four distinctive phases:  
**Phase I.**: \(C_{b}\simeq 0\). Vortices shed in an alternate manner from the trailing edge when no base bleed is applied. We refer to this stage as a non-blowing configuration.
**Phase II.**: \(C_{b}<0.38\). Base pressure increases due to an initial \"filling\" effect, raising the maximum base pressure value to \(P_{base}/P_{\infty}\approx 1.1\), with a slight increment in the frequency associated to the vortices. Under these conditions, the base bleed introduces a source of asymmetry on the wake downstream, followed by a neutralization of the vortex shedding.  
Figure 3: Illustration of the actuating boundary condition for periodic base bleed.  
Phase III. \(0.38<C_{b}<0.8\)At this stage, the base pressure rapidly decays, almost eliminating the observed gains produced by a lower intensity trailing edge actuation. These changes are also accompanied by a reduction of the dominant wake flow frequency. While the trailing edge flow initially behaves as symmetric when it is time averaged, higher blowing intensities promote the onset of a non-symmetric flow topology. Phase IV. \(C_{b}>0.8\)Elevated ejected mass flows eventually force a symmetric trailing edge flow, weakening any oscillation and increasing the base pressure towards a final plateau of \(P_{base}/P_{\infty}\approx 1.07\). Further increments in the base bleed intensity do not produce significant changes in the flow topology.
# Interferometry of Gas-Phase Flows during Selective Laser Melting
## 4 Discussion
To estimate the refractive index of iron vapor, the following equation can be used [29]:  
\[\frac{n^{2}-1}{n^{2}+2}=\alpha\frac{4\pi N_{a}}{3M}d, \tag{5}\]  
where \(\alpha\) is the polarizability of atoms, \(N_{a}\) the Avogadro number, \(M\) the molar mass and \(d\) the vapor density. If we take into account the relationship of the density \(d\) with pressure \(P\) and temperature \(T\) according to the ideal gas law:  
\[d=\frac{PM}{RT}, \tag{6}\]where \(R\) is the universal gas constant; then, Equation (6) becomes:  
\[\frac{n^{2}-1}{n^{2}+2}=\alpha\frac{4\pi P}{3 kT}, \tag{7}\]  
where \(k\) is the Boltzmann constant. According to [33], the polarizability of iron \(\alpha\) is close to the atomic volume (7.7 A\({}^{3}\)). From Equation (7), the refractive index of iron vapor is \(n_{Fe}\) = 1.00127 under normal conditions (\(P\) = 10\({}^{5}\) Pa, \(T\) = 273 K). The estimations in Figure 8 show that pressure, and hence the concentration of the gas mixture in the jet, does not differ significantly from normal conditions. Therefore, the changes in gas parameters do not exceed the limit of applicability of the ideal gas model. Thus, it can be concluded that the optical density of iron vapor is an order of magnitude higher than the optical density of air. In the mixture of two gases with different polarizabilities, the contribution of each gas to the total refractive index \(n\) of the mixture can be estimated as [33]:  
\[\frac{n^{2}-1}{n^{2}+2}=V_{Fe}\frac{n_{Fe}^{2}-1}{n_{Fe}^{2}+2}+V_{A}\frac{n_ {A}{}^{2}-1}{n_{A}{}^{2}+2}, \tag{8}\]  
where \(V_{Fe}\) and \(V_{A}\) are the volume fractions of iron and air vapors in the mixture, respectively; \(n_{Fe}\) and \(n_{A}\) are the refractive indices of iron and air vapors under normal conditions; and \(n\) is the refractive index of the mixture. Equation (8) makes it possible to find the volume fraction \(V_{Fe}\) of iron vapor in air from the experimentally measured refractive index \(n\) of the mixture and the known refractive indices \(n_{Fe}\) and \(n\) of its components. In fact, in the experiments, not the absolute value of the refractive index \(n\) of the mixture was found, but its relation to the refractive index of the unperturbed air in the support arm of the interferometer (9):  
\[\langle\Delta n\rangle=n-n_{A}. \tag{9}\]  
For the above example, where \(\langle\Delta n\rangle\) = 0.000532, from Equation (8) the value of the volume fraction of iron vapor is \(V_{Fe}\) = 0.545. This shows that a significant part of the flow consists of evaporated metal.
# Enhancement of English-Bengali Machine Translation Leveraging Back-Translation
## 2 Related Work  
In this section, we review literature about methods of NMT approaches developed for low-resource languages. According to our observation, statistical machine translation (SMT) and neural machine translation (NMT) are the two most commonly used machine translation approaches. Both are corpus-based, where source and target texts are required to build the translation system. However, due to numerous issues with SMT, NMT has become the mainstream machine translation engine [2, 40, 41, 42, 43].  
We observe that in SMT, a model is separated into several sub-modules, such as the language model, the translation model, the reordering model, etc. These components work together to implement the translation function. On the other hand, NMT adopts the neural network to translate from the source language to the target language directly. NMT with an attention mechanism dynamically obtains the source language information, which is correlated to the current generating word. Therefore, NMT can derive the corresponding alignment information without setting up an alignment model, as in SMT [2, 44]. Notably, NMT models are non-linear and smaller, but SMTs are linear and larger [45]. Also, we observe that NMT outperforms SMT by a large margin in all evaluation metrics [46, 47, 48, 49, 50, 44]. The key advantage of NMT is that the source and target corpora can be trained directly in an encoder-decoder engine, which makes it fast and accurate [51]. The classic neural translation models involve sequence-to-sequence implementation with RNN encoder-decoder models [3, 4, 52, 53, 54, 55], attention mechanisms [5, 6, 7, 56, 57, 60, 61], transformers [62, 63, 64, 65], and so on. The applications include: Google Translate [7], Microsoft Translate [66], OpenNMT [67, 68, 69], and many others.
# A Comprehensive Real-Time Traffic Map for Geographic Routing in VANETs
## 5 Simulation
### CBR Performance
#### 5.1.5 Tradeoff between Efficiency and Overhead  
In the above simulations, CV is set to 1 for fast information distribution. The results show that CBR can outperform the other two protocols. In Figure 13, we vary the CV value from 1 to 5 and compare the end-to-end delay with the second best protocol, GyTAR. As shown in Figure 13a, in a low density and high speed environment, the network topology is sparse and rapidly changing. CV needs to be set to a small value to speed up the information distribution. Otherwise, the performance of the CBR may be inferior to GyTAR. As the network is dense enough, the message exchange frequency can be lower. Although the topology is still highly dynamic at high speed, but the high vehicular density can cover the effect. The CBR can outperform GyTAR in all CV setting, as shown in Figure 13b. In Figure 14, we compare the overhead with different CV settings. It is easy to image that the routing overhead decreases as the frequency of exchange decreases. In addition, the overhead of the CBR in all settings is lower than GyTAR. We can conclude that, a small CV value needs to be used only in a low density environment. In other cases, vehicles can exchange traffic information less frequently to reduce the overhead.  
Figure 13: _Cont._
## 4 CLAP Correlation and Information Sharing Services
### Information Sharing and Exporting
#### 4.3.3 Security Alarms and CAPEC Mapping  
Sharing events and/or alarms with other organizations, communities and groups encourages collaboration among researchers and help improving the detection of complex attacks in the domain of interest. CLAP outcomes are mapped with attack patterns from the Common Attack Pattern Enumeration and Classification (CAPEC ([[https://capec.mitre.org/](https://capec.mitre.org/)]([https://capec.mitre.org/](https://capec.mitre.org/)), accessed on 5 June 2021)), aiming to advance community understanding and enhance defense mechanisms. Examples of the correlated alarms mapped with CAPEC is shown in Table 4.
## 4 Simulation Analysis
### Analysis of Voltage Quality under Different Planning Schemes  
The objective of this analysis is to determine the impact of the planning results derived from the simulation on the improvement of ADN operation, with a focus on voltage quality. To this end, the voltage deviation corresponding to the five planning schemes is presented in Figure 11, which illustrates the deviation in voltage levels. The combined voltage deviation results are presented in Table 6.  
As illustrated in the table, despite the objective of optimal flexibility and economy being planned for, Schemes 2-5 all demonstrate varying degrees of improvement in voltage quality in comparison to Scheme 1. Scheme 4 shows the greatest improvement in voltage quality, with 33.7%, 4.6%, 33.5%, and 0.8% improvement over Schemes 1 and 3-5.
## 3 Results
### Simulated Case Study
#### 3.1.3 Damage Localization
To accommodate the changing reference distributions, the values are represented using the Mahalonobis distance from the healthy reference distribution. Results are reported for one earthquake instance simulated on a model with nonlinearity limited to the link 01 (**left**), link 23 (**center**), and link 45 (**right**). damage localization in the links (corresponding to floors in buildings) below the damaged one cannot be avoided. Finally, the change in the mode-shape curvature (\(\Delta\)\(M\)\(S\), shown in Figure 11 bottom right) correctly picks out the location of damage in all cases. Given the formulation of \(\Delta\)\(M\)\(S\) with respect to DOFs and not links, the specific damaged link cannot be precisely identified. In addition, changes in the MSC at a specific DOF often entail small changes in further locations, thus leading to erroneous damage localization. In Figure 11, the localization of damage is treated as a Boolean variable (either presence or absence of damage). However, when referring to the value of the computed DSFs and the corresponding probabilistic distributions, further information on the extent of damage (quantification) can be extracted, as real-world structures may sustain damage in more than one floor. For instance, the Mahalonobis distance of the TAC assumes the highest value in vicinity of the damaged link (see first row of Figure 10). A more detailed analysis of the extent of damage per floor would require the use of a structural (physics-based) model, which however lies outside the scope of this paper. While in Figures 10 and 11, localization refers to cases with a single damage location, the correlation between DSFs evaluated locally for each link and the respective value of the \(DI_{PA}\) calculated for each link is evaluated in Figure 12 for the original model (defined in Table 2 and Figure 3 in Section 3.1). In general, all DSFs yield a good correlation with damage location (correlation coefficient above 0.80), except for features \(CE\) and \(TS\), which are formulated with respect to the energy accumulation over time, and the changes in mode-shape curvature (\(\Delta\)\(M\)\(S\)), given the inability to relate damage to a single link. Very good correlation is achieved for \(W\)\(L\)\(S\)\(R\)\(S\), and \(M\)\(E\)\(R\). This confirms that while \(M\)\(E\) has a large scatter in global damage, due to the input energy of GMs, it yields good performance in relative assessment from the same GM, as required for localization. Figure 11: Overview of damage localization performance of six DSFs: _TAC_, _WLC_, _KPRX_, _TS_, _MER_, and \(\Delta\)\(M\)\(S\). While the true damage location is generally identified—except for \(K\)\(P\)\(R\)\(S\) and \(TS\) in the last floors, only \(K\)\(P\)\(R\)\(S\) does not produce false detection for linear elastic links.
# A 13.44-Bit Low-Power SAR ADC for Brain-Computer Interface Applications
## 2 Circuit Topology  
### Architectural Overview  
Figure 3 depicts the system architecture of the proposed SAR ADC. The circuit employs a 16 \(\times\) oversampling technique. During the sampling phase, neural signals are captured using bootstrapped sampling switches and stored on the CDAC array. The SAR quantization phase involves a 12-bit successive approximation conversion, facilitated by the coordinated operation of the CDAC array, an Elzakker-type dynamic comparator, and dynamic SAR logic control.  
Key functional blocks include bootstrapped sampling switches for linear signal acquisition, a segmented CDAC array with VCM-based switching to minimize power consumption, and a low-kickback Elzakker dynamic comparator to ensure high-speed and accurate decision-making. The dynamic SAR logic control unit is designed to optimize power efficiency, enabling the ADC to meet the stringent requirements of brain-computer interface applications.
## 4 Results and Discussion
### Suction Pressure Pulsation Influence on Compressor Operation for Ideal and Real Gas Models
In addition, during the compression and discharge phases of the cycle, the in-cylinder temperature of the real gas is higher than that of the ideal gas model because the in-cylinder fluid mass of the real gas is larger than that under the ideal gas assumption. The maximum temperature is about 379.5 K for the ideal gas and about 385.4 K for the real gas. Figure 4: Variation of **(a)** suction chamber pressure, **(b)** displacement of suction valve and **(c)** in-cylinder pressure based on ideal and real gas models for different suction configurations. The effects of pressure pulsations under different suction configurations on the motion of discharge valve for real and ideal gas models are illustrated in Figure 6. The figure shows a slight influence of suction pressure pulsations on the discharge valve motion. Figure 7 shows the variation of the instantaneous suction mass flow rate for real and ideal gas models under different suction configurations. Also, pressure pulsations have a clear influence on the momentum mass flow rate. The mass flow rate reaches the maximum rapidly with the opening of the suction valve. Moreover, in the same case, the mass flow rate that enters the cylinder is higher for the real gas model compared to the ideal gas model since the gas density is higher for the real gas model. Figure 8 shows the comparison of the (a) compressor mass flow rate and (b) indicates power with different suction configurations for ideal and real gas models. A direct comparison amongst the results of different configurations is made in terms of the ratio variation of both the above variables compared to the values of the compressor lumped configuration for real gas. Based on Figure 8a, gas pulsations in the suction system can considerably influence the compressor mass flow rate. If the wave action in the suction system is tuned properly, the so-called supercharging effect can be obtained. In Figure 8b it is concluded that the consumed power is higher for the real gas model compared to the ideal one, as more in-cylinder working fluid needs higher compression power. Figure 5: Variation of in-cylinder temperature based on ideal and real gas models for different suction configurations. Figure 6: Variation of displacement of discharge valve based on ideal and real gas models for different suction configurations.
# Dynamic Mixed Reality Assembly Guidance Using Optical Recognition Methods
## 4 Application
### Assembly Validation  
The application visual validation at the end of each step is based on the recognition capability of Vuforia Engine's AMTD. The Vuforia MTG can train a database of Model Targets in relation to one another, to distinguish between their appearances. For this  
Figure 8: Snapshot of the UI-Panel taken from the HoloLens.  
Figure 9: Screenshot of index finger being tracked. The white circle indicates the tracked index finger position.  
application, an AMTD was trained for the validation phase of each step, including the following Model Targets:  
* Assembly setup before placing the components of a step;
* Assembly setup with the correct positioning of components in a step;
* Assembly setup with the incorrect positioning of components in any step (if relevant).  
The Model Targets in the assembly are similar in size and number and type of features (surfaces and counter bore holes), therefore distinguishing amongst them on appearance alone--as is the behavior when training with Vuforia MTG--becomes difficult. To solve the problem, the guide view of the Model Targets was set to focus on the features with the major differences, though also limiting the effectiveness to specific camera angles and distances. The solution is the Validation Guidance Graphic, discussed in Section 4.4.  
Though the selected guide views for recognition include the specific features of the assembly models, the model recognition does not have a 100% success rate. Shared features of the Model Targets sometimes are misjudged by the trained algorithm and an incorrect Model Target is recognized. Vuforia recommends models that are trained in the MTG to be complex, because it is then easier for the algorithm to recognize them. Although the models in this application are fairly complex, zoomed and narrow guide views might mislead the Model Target algorithm to include features from other objects in the environment and lead it to false positive recognition. So, a more robust validation routine for a higher validation success rate was adopted: for a Model Target to be validated, the algorithm needs to recognize it three times from different angles. After each Model Target recognition, the user must slightly move the headset in any direction for another recognition to occur. Only with three consequent correct recognition of the same model the validation step is passed, otherwise the validation process starts over. Figure 10 shows the graphic displayed in the application, informing the user of the validation progress.
# A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks
## 6 Conclusions  
To achieve energy balance in sensor networks, a type of multi-Sink based data gathering method is proposed in this paper. By selecting the optimal leader node in each DGA, the energy efficient data uploading paths could be established and trajectories of Sinks are also optimized improving time efficiency in data collection. Moreover, with the help of the sleep scheduling and the sensing radius adjustment strategies, redundancy in network coverage and energy consumption in sensing and communication could also be effectively reduced.  
Currently in the big data era, time efficiency is topic under close review. The time delay in data gathering has been reduced with the help of multiple Sinks in LDGA, but different Sinks may move to the same traversal point simultaneously, extending the time delay in data uploading in some DGA. It should be further improved in our future work.  
The subject is sponsored by the National Natural Science Foundation of P. R. China (61373017), Jiangsu Provincial Research Scheme of Natural Science for Higher Education Institutions (14 KJB520029), Natural Science Foundation of Jiangsu Province (BK20130882), Open Project of Provincial Key Laboratory for Computer Information Processing Technology of Soochow University (KJS1327), Open Project of Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks (WSNLBZ7201517), A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPPD), Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology(CICAEET) and Innovation Project for Postgraduate of Jiangsu Province (SJLX15_0382, SJLX15_0383, KYLX15_0842).  
[PERSON] proposed the main ideas of the LDGM algorithm while [PERSON] designed and conducted the simulations of the protocol. [PERSON] and [PERSON] analyzed the data, results and verified the theory. [PERSON] served as advisor to the above authors and gave suggestions on simulations, performance evaluation and writing. The manuscript write up was a combined effort from the five Authors. All authors have read and approved the final manuscript.  
The authors declare no conflict of interest.