esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Electrostatic-Atomic Accelerometer for Future Space Gravity Missions
## 3 Results and Discussions
### Performance Aspects
#### 3.4.4 SWaP  
The elaborated budget for the hybrid instrument is given in Table 6.  
We reach therefore a total budget of 109 L, 75 kg and 145 W for the hybrid instrument based on an electrostatic accelerometer and a molasses-based CAI.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Physics Package**} & \multicolumn{3}{c}{**Electronics/Laser/MW**} & \multicolumn{3}{c}{**Whole Instrument**} \\ \cline{2-9}  & **Size** & **Weight** & **Size** & **Weight** & **Power** & **Size** & **Weight** & **Power** \\ \hline EA & 7 L & 6 kg & 9 L & 6 kg & 45 W & 16 L & 12 kg & 45 W \\ \hline CAI & 36 L & 24 kg & 57 L & 39 kg & 100 W & 93 L & 63 kg & 100 W \\ \hline Hybrid & 43 L & 30 kg & 66 L & 45 kg & 145 W & **109 L** & **75 kg** & **145 W** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Size, weight, and power budget for the hybrid instrument. Note that the CAI budget for the electronics, laser, and microwave system was extrapolated by taking into account the PHARAO’s flight model budget [87; 88].  
Figure 30: (**a**) Global hybrid instrument architecture. (**b**) Exploded view of the hybrid architecture.  
In a more prospective way, the molasses source for the CAI could be upgraded to a BEC source [17; 18; 89; 90; 91; 92; 93]. In such an approach, the budget of a hybrid instrument was evaluated to have a the less favorable SWaP of 156 L, 102 kg, and 245 W (contribution of the EA: 16 L, 12 kg, 45 W), but this should lead to better performances with respect to acceleration measurements. This is mainly due to a better control of the kinematics of the atomic cloud, with a position/velocity stability that should be better than \(\pm 10\)\(\upmu\)m/\(\pm 100\)\(\upmu\)m/s [25], and should lead to an expected gain of roughly one order of magnitude regarding acceleration stability.  
Note that concerning the potential implementation of a CAI in a satellite, it seems that the electrical power consumption could constitute a severe limit. One possible mitigation strategy could be the implementation of an intermittent mode of operation where the CAI alternates between a standard mode of operation with full power consumption (245 W) and a standby mode characterized by reduced power consumption (95 W) (cf. Figure 31). A preliminary analysis has shown under some assumptions (e.g., a CAI limited by the QPN, a BEC atomic source, \(T=2\) s, \(T_{c}=6\) s, etc.) that the average electrical power consumption could be lowered to around 110 W considering an effective measurement cycling time of 42 s (\(n=7\)). This mode of operation seems compatible in terms of noise with an objective of hybridization at a level of \(10^{-10}\) m.s\({}^{-2}\).Hz\({}^{-1/2}\) (see Figure 32).
## 4 Discussion
### Different Performances of H-LUE in Different PFTs  
We found statistically significant differences in the performance of the H-LUE among PFTs (Figure 6). The H-LUE showed good performance for crops and deciduous broadleaf forests, and poor performance for evergreen broadleaf forests, savannahs, and shrubs. This conclusion is supported by previous studies [16; 52]. [PERSON] et al. [16] assessed the performance of seven LUE models in six ecosystem types and found that all seven models showed good performance for deciduous broadleaf forests and poor performance for shrublands and evergreen broadleaf forests. Based on 17 models against observations from 36 North American flux towers, [PERSON] et al. [52] revealed that the models performed best for deciduous broadleaf sites, but not well for evergreen sites. In general, deciduous broadleaf forests show distinct seasonal dynamics of leaf phenology, and satellite data can accurately capture the phenology change [16], which is beneficial to GPP modeling. Meanwhile, evergreen broadleaf forests show subtle changes in the seasonal leaf phenology, and various environmental variables jointly determine the vegetation photosynthesis, which increases the difficulty of modeling [6].
## 3 Numerical Examples  
### Cantilever Beam  
Since the analytic solution for the cantilever problem is known, it is often employed as a benchmark for numerical methods. The sensitivity and accuracy of the improved RPIM based stress solutions for general cantilever beam problem (without stress concentration effect) were presented here. Consider a cantilever beam problem shown in Figure 6. Actually, because there is no stress concentration effect, the stress (\(\sigma_{m}+\sigma_{b}\)) here can be viewed as generalized nominal stresses.  
The thickness of the beam is 1 m. [PERSON]'s modulus is 3E7. Poisson's ratio is 0.3. The model is considered as a plane stress problem. The parabolic distributed traction \(P\ =\ 1000\)KN was enforced at the right free end as natural boundary condition. The analytic solution of this model was listed in [36]. A total of 175 (\(25\times 7\)) field nodes were distributed in the domain of the model. The domain was discretized into 40 (10 m \(\times\ 4\) m) rectangular background cells for Gauss numerical integrations. Penalty method was carried out to exert essential boundary conditions on the left end. The analytical solution, stress results of RPIM, EFG, improved RPIM and improved EFG at coordinate position (24 m, 6 m) were listed in Table 1. It can be seen that there are good agreements among the stress solutions of improved RPIM, improved EFG and the analytical results.  
The performance of meshfree method is often affected by parameters used in the method. The stress solution sensitivity to parameters used in these methods was investigated below. In this study, the linear polynomial terms were used in the RPIM-MQ expressed as Equation (1). The linear basis and the cubic spline weight function were used in the moving least squares (MLS) approximation.
# Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
## 4 Experimental Design
### Benchmark Datasets  
The NASA datasets have been widely used in previous studies [3; 27; 67]. Because the original NASA datasets have the data quality problems [5; 78], the cleaned version from the tera-PROMISE repository [43] is used as the benchmark datasets. Table 1 shows the statistics of eleven cleaned NASA datasets. For each dataset, the statistical information includes the project name, the number of software metrics (# Met., for short), the number of instances (# Ins.), the number of defective instances (# Def. Ins.), and defect ratio (i.e., the proportion of the number of defective instances to the total number of instances). Each data instance (or sample) includes two kinds of information: the metrics of one software module (e.g., a function) and the corresponding class label of whether this module contains defects. The module metrics include Halstead metrics [10], McCabe metrics [9], and other metrics. The details of software metrics that are used in these datasets can be seen in [3]. We can see that all of the datasets are very imbalanced, especially for MC1 and PC2. With varying degrees of sample size, metrics, and defect ratio, these datasets provide an extensive scenario for the evaluation of difference class-imbalance learning methods.
## 7 Open Issues and Opportunities for Future Research
### ML Collaboration across Management Domains  
A single ML engine lacks the scalability for global management of a large network consisting of multiple management domains. Furthermore, a global ML process for the entire multi-domain network might not be realistic due to the administration autonomy of different network operators/service providers. Both the hierarchical structure as specified by the ZSM framework and alternative structures (e.g., peer-to-peer federative structure) assume that each domain has its own choice of ML/analytics models and techniques. The combination of domain-specific knowledge and the aggregation of different analytics models across management domains are important issues that are still open for future research.  
Recent progress in new machine learning technologies such as federated learning [48] offers some promising approaches to addressing these issues. The application of federated learning in networking has started attracting researchers' attention [49]. On the other hand, federated learning in large-scale networks faces some challenges such as expensive communications, system diversity, statistical heterogeneity, and privacy concerns [50]. How to design a management architecture that is able to fully leverage ML capabilities for cross-domain management while meeting the heterogeneity and scalability requirements becomes an important problem for future research.
# Exposure to NO\({}_{2}\) and PM\({}_{2.5}\) While Commuting: Utility of Low-Cost Sensor
## 4 Discussion
### Differences in Pollututant Concentrations
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mode of Transport**} & \multirow{2}{*}{**Person**} & \multirow{2}{*}{**Location**} & \multicolumn{2}{c}{**Mean (Min-Max) Concentration**} & \multirow{2}{*}{**Mean (Min–Max) Dose, \(\mathrm{\SIUnitSymbolMicro g}\)/km**} & \multirow{2}{*}{**References**} \\  & & & \multicolumn{2}{c}{Per Trip, \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\)**} & & \\ \cline{5-6}  & & & PM\({}_{2.5}\) & NO\({}_{2}\) & PM\({}_{2.5}\) & NO\({}_{2}\) \\ \hline \multirow{6}{*}{Adults} & \multirow{6}{*}{Milan, Italy} & Female: & & \\  & & & & Winter: & \\  & & & & 11.9–121.8 \(\mathrm{\SIUnitSymbolMicro g}\) & \\  & & & & Summer: & \\  & & & & 9.2–66.5 \(\mathrm{\SIUnitSymbolMicro g}\) & [44] \\  & & & & Male: & \\  & & & & Winter: & \\  & & & & 13.7–127.0 \(\mathrm{\SIUnitSymbolMicro g}\) & \\  & & & & Summer: & \\ \cline{2-6}  & & & & 10.6–76.8 \(\mathrm{\SIUnitSymbolMicro g}\) & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \hline \hline \end{tabular}  
*—in ug per trip, *–in ug during the trip time from 15 to 90 min. N/A—not available. WO—window closed. WC—window open.  
\end{table} 
Table 2: _Cont._Walking during the heating season resulted in an average PM\({}_{2.5}\) concentration of 7.3 \(\pm\) 10.0 \(\mu\)g/m\({}^{3}\), with a range between 2.0 and 44.7 \(\mu\)g/m\({}^{3}\), while NO\({}_{2}\) concentrations reached 33.0 \(\pm\) 23.4 \(\mu\)g/m\({}^{3}\), ranging from 0.7 to 78.6 \(\mu\)g/m\({}^{3}\). During the non-heating season, PM\({}_{2.5}\) levels remained at 7.3 \(\pm\) 17.0 \(\mu\)g/m\({}^{3}\) (2.0-95.4 \(\mu\)g/m\({}^{3}\)), while NO\({}_{2}\) levels were lower at 21.4 \(\pm\) 15.4 \(\mu\)g/m\({}^{3}\) (0.6-59.5 \(\mu\)g/m\({}^{3}\)). This seasonal trend is consistent with the previously observed increases in pollutant levels during colder months due to heating emissions.
# An Experimental Study on the Large-Volume Liquid Hydrogen Release in an Open Space
## 2 Experiment Introduction
### Test Conditions and Classification  
Tables 3 and 4 summarize the release conditions and environmental conditions for the first six tests and the second six tests, respectively. It is worth noting that the first six tests are on concrete grounds, while the last six tests are on cobblestone grounds.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test ID** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline Release volume (m\({}^{3}\)) & 2.5 & 2.5 & 1.5 & 3 & 3 & 0.5 \\ Release duration (s) & 30 & 48 & 25 & 4 & 5 & 20 \\ Ambient temperature (K) & 277.76 & 279.17 & 283.06 & 282.05 & 307.82 & 307.67 \\ Relative humidity (\%) & 27.63 & 29.21 & 27.46 & 46.11 & 79.55 & 67.13 \\ Wind velocity (m/s) & 0.04 & 0.05 & 0.61 & 0.11 & 0.01 & 0.15 \\ Wind direction to release axis (\({}^{\circ}\)) & 135 & 135 & 90 & 270 & 270 & 0 \\ Release method & 100 mm & 50 mm & 50 mm & \multirow{2}{*}{Pouring} & \multirow{2}{*}{Pouring} & \multirow{2}{*}{50 mm} \\  & hole & hole & hole & & hole \\ \end{tabular}
\end{table}
Table 4: Release conditions of liquid hydrogen of the last 6 tests.  
Figure 4: The liquid hydrogen tank with a level indicator rod.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sensor ID**} & \multicolumn{3}{c}{**Tests 1–6**} & \multicolumn{3}{c}{**Tests 7-10**} & \multicolumn{3}{c}{**Tests 11–12**} \\ \cline{2-10}  & \(x\) **(m)** & \(y\) **(m)** & \(z\) **(m)** & \(x\) **(m)** & \(y\) **(m)** & \(z\) **(m)** & \(x\) **(m)** & \(y\) **(m)** & \(z\) **(m)** \\ \hline N21/T21 & 0 & 15 & 20 & 4 & \(-\)0.7 & 2 & 2 & 0 & 2 \\ N27/T27 & \(-\)15 & 0 & 20 & 4 & 3.7 & 2 & 2 & 3 & 2 \\ N28/T28 & 0 & \(-\)15 & 20 & \(-\)4 & 3.7 & 2 & \(-\)2 & 3 & 2 \\ N29/T29 & 15 & 0 & 20 & \(-\)4 & \(-\)0.7 & 2 & \(-\)2 & 0 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The coordinates of sensors N21/T21, N27/T27, N28/T28, and N29/T29.  
The environmental conditions in the above 12 tests are classified to compare specific issues influencing the experimental results. The details are shown in Table 5.
# Semantic Communication on Digital Wireless Communication Systems
## 3 IER--A Novel Physical Layer Semantic Metric  
For the traditional wireless system, the BER (bits error rate) is a basic physical layer metric to measure the reliability of the E2E communication link. One of the main principles for designing communication systems is to reduce the BER metric to as low as possible from 1G to 5G. However, the BER is not suitable to measure the quality of the physical layer semantic communication because semantic communication is not anticipated to recover information without errors in the bit level. Thus, a new semantic metric should be defined and should focus on the preservation of the semantics in the regenerated content. In this chapter, we will try to define a novel loss function to measure the semantic transmission quality and verify this novel loss function with our simulation platform.
# Rubber Creep Model and Its Influence on Mounting Stiffness
## 2 Rubber Creep Mechanism and Theoretical Model
### Viscoelastic Model of Rubber
#### 2.2.1 Maxwell Model  
[PERSON]'s model consists of a simple linear spring connected in series with a Newton sticky pot, as shown in Figure 2[10; 12]. For a linear spring, when the external force is loaded, it can produce an instantaneous deformation response, and when the external force is removed, the deformation can be fully recovered. Therefore, the spring follows [PERSON]'s law, and its stress-strain relationship is  
\[\sigma=E\varepsilon \tag{1}\]  
where \(\sigma\), \(\varepsilon\) represent the normal stress and the normal strain respectively, and E is the elastic modulus of tension and compression. The sticky pot is contrary to the spring; when the external force is withdrawn, due to internal energy consumption, its deformation cannot be fully recovered, leading to permanent deformation, and its stress-strain relationship follows [PERSON]'s law of viscosity  
\[\sigma=\eta\dot{\varepsilon} \tag{2}\]  
where \(\eta\) represents the coefficient of viscosity; \(\dot{\varepsilon}\) represents strain; \(\varepsilon\) derivative with respect to time being the strain rate. According to Equations (1) and (2), its constitutive equation can be obtained as follows  
\[\dot{\varepsilon}=\frac{\dot{\sigma}}{E}+\frac{\sigma}{\eta} \tag{3}\]  
where \(\dot{\sigma}\) represents the derivative of stress \(\sigma\) with respect to time, namely stress rate.  
In [PERSON]'s model, when stimulated by the outside world, its instantaneous response is determined by the spring, and the sticky pot cannot generate an instantaneous response, as shown in Figure 3. Therefore, at the initial state t = 0, the stress-strain relation corresponding to the model is  
\[\varepsilon_{0}=\frac{\sigma_{0}}{E} \tag{4}\]  
When the excitation is constant stress, that is, with increasing time, the Maxwell model produces a creep response. It can be obtained from Maxwell constitutive equation  
\[\dot{\varepsilon}=\frac{\sigma}{\eta} \tag{5}\]  
By integrating the formula, the strain state under constant stress loading is obtained.  
\[\varepsilon(t)=\frac{\sigma_{0}}{\eta}t+\frac{\sigma_{0}}{E} \tag{6}\]  
Figure 2: Maxwell model.  
Rubber material is a cross-linked polymer; under constant external excitation, its deformation will eventually tend to a stable state with the increase of time. Figure 3 shows that under constant stress loading, the model can deform infinitely with time, which represents the ideal creep response of a viscous body. Therefore, the Maxwell model cannot describe the creep performance of rubber materials.
# Unveiling the Strain Rate Sensitivity of G18 NiCrMo3-6 CAST Steel in Tension/Compression Asymmetry
## 2 Materials and Methods
### Determination of SRS  
After obtaining the strain-stress response at specific strain rates, these data were processed properly to obtain the SRS parameter (as a general convention of theory of plasticity, SRS parameter is often denoted as \(m\) parameter). When a power-law-based strain rate effect (Equation (1)) is used [28], then parameter \(m\) can be automatically obtained via processing of two plastic responses at distinct strain rates. (Readers should give attention to the fact that the first component of Equation (1) is the rate-independent hardening functions, and the second term contributes to the rate sensitivity effect). In other words, when two flow curves are present (in general, one of them is the reference train rate data), it is possible to determine \(m\) with the help of Equation (2) [23,29]:  
\[\sigma=\gamma\ (\varepsilon_{p})\times\left(\frac{\dot{\varepsilon_{p}}}{ \varepsilon_{0}}\right)^{m} \tag{1}\]  
\[m|_{(\varepsilon_{0},\ \dot{\varepsilon_{p}})}=\frac{\partial ln(\frac{ \sigma_{1}}{\varepsilon_{0}})}{\partial ln(\frac{\dot{\varepsilon_{1}}}{ \varepsilon_{0}})} \tag{2}\]  
In Equation (1), \(\dot{\varepsilon_{p}}\) is the strain rate which is apart from the reference strain rate \(\dot{\varepsilon_{0}}\). Within the scope of this contribution, \(\dot{\varepsilon_{0}}\) was taken as 0.001 (s\({}^{-1}\)), in the same way as the general rule-of-thumb of plasticity, which is based on the fact that beyond this limit materials do mostly not possess any rate dependency. Furthermore, in Equation (2), \(\sigma_{0}\) and \(\sigma_{1}\) are experimentally obtained true stress values which correspond to the same strain value at the strain rates of \(\dot{\varepsilon_{0}}\) and \(\dot{\varepsilon_{1}}\), respectively.  
As can be observed in Equation (2), \(m\) is not constant, and it is generally a function of the plastic strain and strain rate. However, it is still possible to obtain it with proper data processing operations. This task is also fulfilled by a specific MatLab(r) code. Since the experimental data were obtained with high frequency (may reach up to \(10^{5}\) Hz), a linear interpolation approach was accepted as a proper solution. While using the above-mentioned formulation and the procedure, \(m\) was determined regarding tension and compression asymmetry, as in Figure 5. The built-in function _interp_1 of MatLab(r) and the above-mentioned formulation were used, for that purpose (in order to capture the asymmetry of SRS in tension and compression zones, \(m\) was computed separately for the designated stress states specifically). The obtained \(m\) parameter data were depicted as an advanced version (surface data) at Section 4 since they create a 3D surface through having two independent variables).  
Figure 4: (**a**) True strain vs. true stress data of Sample-1; (**b**) detailed view of the designated (red dotted) area.
## 3 Results and Discussion
### Micro Hardness  
Figure 12 describes the micro hardness distributions along the height direction of these six thin wall and block samples. It can be found that the variation of micro hardness values of thin wall samples is between 70 HV and 80 HV, and evidently changes along the height direction of the deposition with the CMT-P process. However, this change is not obvious with the CMT-ADV process. The hardness value from the bottom to the middle of the samples vibrated around 75 HV, and it gradually decreased to about 68 HV in the top two layers. The mean hardness value is 72 HV (P2) and 74 HV (P1) for the block samples formed with the CMT-P process, and 72 HV (A2) and 77 HV (A1) for the block samples formed with the CMT-ADV process. The micro hardness of the block samples periodically varies along the vertical direction.  
Figure 11: The microstructure of block samples formed with the CMT-ADV process.  
It is well known that the micro hardness is affected by the microstructure characteristics of the material. With a finer microstructure, there are more grain boundaries, which make the micro hardness increase by improving the material's ability to resist deformation. From the analysis of Section 3.2, it can be found that the hardness values of the samples are in accordance with the grain distribution. As shown in Figure 12c,d, with the same process, the micro hardness value of the sample can be enhanced by reducing the heat input.  
Compared with thin wall deposits, the molten pool of block samples can distribute heat to the lower and former layers effectively. In addition, there is a certain interval of cooling time between layers for the block samples, which can weaken the preheating effect of the former layer. As a result of these two aspects, the cooling rate and temperature gradient in the vertical direction of the deposition layer changed moderately, resulting in the periodical change of hardness values in block deposits.
## 2 Materials and Methods
### Modeling
#### 2.2.3 ARIMA Model  
The ARIMA model, developed by [PERSON] and [PERSON] in 1970 [30], combines autoregressive (AR) and moving average (MA) models. Since it requires the stationary time series, differencing (integrating) the time series is necessary. Using the Augmented Dickey-Fuller (ADF) [31] unit-root test, we can determine whether or not the time series is stationary. The ARIMA model is represented as ARIMA (\(p\),\(d\),\(q\)) with three parameters, such as the order of AR (\(p\)), the degree of differencing (\(d\)), and the order of MA (\(q\)). We estimated the parameters of the ARIMA model using a graph of the autocorrelation function (ACF) and a correlogram of partial autocorrelation (PACF). We chose the best ARIMA model using an automated function in the \(pmdarima\) package [32], which utilizes a stepwise approach to search multiple combinations of \(p\), \(d\), and \(q\) parameters. The statistical significance level for the ARIMA model was set at 0.05. An Akaike information criterion (AIC), which is an extensively used measure in the evaluation of ARIMA models, was used to evaluate the ARIMA model. It measures the goodness of fit of the model as well as the simplicity of the model. The AIC is defined as:  
\[\text{AIC}=-2 lnL(\overset{\wedge}{\theta})+2p \tag{6}\]  
where \(L(\overset{\wedge}{\theta})\) is the likelihood of the model evaluated at the maximum likelihood estimate (MLE), \(p\) is the total number of parameters, and \(n\) is the number of observations. Lower AIC values indicate a better model fit.