esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Procedures and Analytics
#### 3.1.6 Ln Stripping Section  
For the Ln stripping section, 0.5 mol L\({}^{-1}\) TEDGA in 0.5 mol L\({}^{-1}\) HNO\({}_{3}\) was tested. Figure 6 shows the Am, Cm, and Eu distribution ratios as a function of the experimental run time and equilibrium values. Distribution ratios \(D\) and mass balances calculated from the transient and equilibrium samples and stage efficiencies for all metal ions are shown in Table S6, together with the concentrations of metal ions in the organic feed (combined organic effluents from the An stripping section test). The stage efficiencies were calculated by dividing the equilibrium distribution ratio by the mean value of the transient samples distribution ratios at 50, 60, and 65.5 min.  
Ln(III) and An(III) distribution ratios were all around 0.2 in steady state. The distribution ratios were much lower in equilibrium, resulting in very low calculated stage efficiencies of ca. 1-2%. Nevertheless, the Ln(III) and An(III) distribution ratios were believed to be low enough to enable back-extraction during the full countercurrent demonstration test. As the distribution ratios for Ln(III) and An(III) were very similar, a separation was not observed in this step.  
The fission product's distribution ratios show that Zr, Mo, Fe, Rb, Pd, and Ru would stay extracted in the organic phase and would be routed to the spent solvent in a full countercurrent test if not scrubbed in earlier stages. Any Sr and Y present would be stripped together with the Ln(III).  
During the Ln stripping single centrifugal contactor test, a turbidity of the effluent organic phase was observed, which was attributed to the relatively high concentration of 0.5 mol L\({}^{-1}\) TEDGA used in the test. That turbidity had not been observed in the batch tests, probably due to intense centrifugation to facilitate phase separation. TEDGA may form partly extractable Ln complexes, causing the observed turbidity [36; 37; 38]. Therefore, it was decided to reduce the TEDGA concentration in the full  
Figure 6: Am, Cm, and Eu distribution ratios as a function of the experimental run time (data points) and equilibrium values (horizontal lines) in the Ln stripping section single centrifugal contactor test. “g”, “a”, and “I” indicate values measured by gamma and alpha spectrometry as well as ICP-MS, respectively.  
countercurrent demonstration test to 0.2 mol L\({}^{-1}\) TEDGA in 0.5 mol L\({}^{-1}\) HNO\({}_{3}\), as this composition showed comparable results in the batch tests. Therefore, the lower TEDGA concentration was used in the full countercurrent demonstration test. To compensate for the lower TEDGA concentration, the flow rate was increased to 72 mL h\({}^{-1}\) in the flowsheet test.
## 4 Model Prediction Results and Analysis
### Parameter Optimization of SVR Model  
As the machining process is non-linear, in order to enhance the performance of the support vector regression model, a radial basis function (RBF) is selected. The parameter \(gamma\) can be treated as the reciprocal of the radius of influence exerted by the selected samples of the support vector regression. A lower parameter \(C\) is capable to make the decision surface smoother, and a high \(C\) allows the model freedom to choose more samples as support vector. Grid search is a relatively simple approach to determining the \(gamma\) and \(C\) values for SVR regression model parameters. Figure 10 presents a heat map of the goodness of fit which is determined by the cross-validation process and demonstrates the effect created by the parameters \(gamma\) and \(C\) on the radial basis function, it can be seen from the figures that the smaller training error conforms to an irregular band-like distribution. With the upper and lower limits of the search (search interval) and the transition interval set, a set of values for the most accurate parameters \(gamma\) and \(C\) can be found in this study. The kernel coefficient \(gamma\) of the radial basis function is selected as [\(1\times 10^{-6}\), \(10\)], and the penalty parameter \(C\) of the error term is [\(0\), \(1\times 10^{6}\)]. With the grid search conducted, when the goodness of fit (\(R^{2}\)) reaches 0.840, the optimal parameters \(gamma=1\times 10^{-5}\) and \(C=1\times 10^{6}\) are determined. Ten sets of test data were predicted using SVR, as shown in Figure 11. Whether in the smaller feed or the larger feed, the prediction error is found obvious.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **LM** & & **BR** & & **SCG** \\ \hline \multirow{2}{*}{RMSE} & Optimized & \multirow{2}{*}{RMSE} & Optimized & \multirow{2}{*}{RMSE} & Optimized \\  & Configuration & & Configuration & & Configuration \\ \hline
0.1226 & 3-4-1 & 0.1130 & 3-10-1 & 0.1868 & 3-7-1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of training algorithms.  
Figure 9: BR prediction results.
## 1 Introduction
Having demonstrated its applications, it may be possible to adopt an OIM in an audio system as an intelligibility meter and further as a guide to select appropriate mixing levels for the speech content. There are several reasons that OIMs may be useful for these purposes: (1) OIMs are physically and physiologically plausible--they are built based on valid auditory models, (2) their prediction is evaluated against human listeners' perceptual performance in various conditions, (3) they can be treated as an independent input-output component, and hence can be plugged into existing systems without substantial alteration to other components in the entire system, and (4) from word to sentence, they can make predictions over time and thus enable dynamic online operation. [PERSON] attempted to adopt the Speech intelligibility Index (SII) [20] to control the background level in a 5.1 system, in order to increases overall comprehension and to reduce listening effort for elderly people [32]. However, the intelligibility criterion used to alter the background level was not detailed and the proposed system was not systematically evaluated. Thus, its performance is unknown. In this article, we propose an approach to automate mixing levels based on intelligibility predictions from an OIM for an object-based audio system. In Section 2, the framework of the proposed system is described. The first attempt to predict an appropriate SBR mixing level uses the OIM Binaural Distortion-Weighted Glimpse Proportion (BiDWGP) [9]. This is detailed in Section 3, where the predictive accuracy is also compared to four other OIMs including two standard intelligibility measures--SII and Speech Transmission Index (STI) [21]--the Normalised-Covariance Measure (NCM) [33], and the Short-Time Objective Intelligibility (STOI) [23]. Section 4 details a psychoacoustic experiment where listeners give their preferred SBR. It is found that the SBR is not the same as that expected from the OIM work. A gain adjustment to the SBR is required and this depends on the type of background sound. Section 5 details a subjective experiment to access the performance of the OIM system with the suggested gain adjustment from the previous experiment. Finally, conclusions are drawn in Section 6.
# A Lifestyle-Based Perspective on the Mobility Choices and Preferences of Urban Travelers
## 4 Results
### Attitudinal Clustering
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Standardized Variable/Factor**} & **Cluster 1** & **Cluster 2** & **Cluster 3** & **Cluster 4** & \multirow{2}{*}{**r-Ratio**} \\  & **(Eco-Conscious** & **(Digitally Engaged** & **(Cautious Green** & **(Socially Driven** & **p-Values** & **F-Ratio** \\  & **Social Adapters)** & **Conventionalists)** & **Conversversives)** & **Digital Enthusiasts)** & **p-Values** \\ \hline Recycling habits & 4.3 & 4.4 & 4.4 & 3.5 & 0.000 & 33.974 \\ Trust\_state\_for\_recycling & 2.7 & 2.8 & 2.4 & 1.8 & 0.000 & 21.161 \\ Care\_for\_energy\_savings & 4.1 & 4.2 & 4.3 & 3.6 & 0.000 & 14.386 \\ Trust\_products\_from\_media & 2.5 & 2.9 & 1.6 & 1.6 & 0.000 & 99.014 \\ Trust\_products\_from\_socialmedia & 2.6 & 2.8 & 1.5 & 1.9 & 0.000 & 77.449 \\ Close\_people\_affect\_my\_choices & 3.6 & 3.5 & 2.6 & 3.7 & 0.000 & 38.421 \\ Importance\_to\_adjust\_to\_ & 3.5 & 3.6 & 3.1 & 3.4 & 0.006 & 4.202 \\ social\_developments & 3.7 & 3.3 & 3.6 & 3.9 & 0.001 & 5.429 \\ Nurse\_social\_distancing & 2.8 & 2.9 & 2.6 & 1.6 & 0.000 & 27.984 \\ No\_meat\_in\_diet & 2.1 & 1.5 & 1.8 & 1.3 & 0.000 & 15.473 \\ number\_electronic\_activities & 3.1 & 5.8 & 4.9 & 5.7 & 0.000 & 172.913 \\ number\_electronic\_devices & 2.4 & 2.9 & 2.9 & 2.7 & 0.000 & 9.183 \\ Cluster population & 87 & 126 & 107 & 79 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Clustering results. Individuals in cluster 3 demonstrate low trust in (social) media and opinions of others. While they show strong care for recycling and energy savings, they are skeptical about state actions toward recycling. They score the lowest among all clusters in valuing the importance of adapting to social developments. They consider exercise important for health but they are less inclined than clusters 1 and 2 to pursue social distancing. Although they own as many electronic devices as cluster 2, they perform fewer electronic activities than clusters 1 and 4 do. Cluster 3 can be labeled as \"Cautious Green Conservatives\". Individuals in cluster 4 do not trust the products promoted through the media and social media nor the state for recycling processes. However, their choices are strongly influenced by those of their closest acquaintances. They have the lowest score among all clusters in recycling habits, care for energy savings (though still relatively high), and pursuit of social distancing. They score very highly in viewing exercise as essential for health but rely heavily on meat in their diet. They regularly engage in a high number of electronic activities. Cluster 4 can be labelled as \"Socially Driven Digital Enthusiasts\".
###### Abstract  
_Pinus massoniana_ is a pioneer reforestation tree species in China. It is crucial to evaluate the seedling vig of _Pinus massoniana_ for reforestation work, and leaf moisture and nitrogen content are key factors used to achieve it. In this paper, we proposed a non-destructive testing method based on the multi-scale short cut convolutional neural network (MS-SC-CNN) to measure moisture and nitrogen content in leaves of _Pinus massoniana_ seedlings. By designing a reasonable short cut structure, the method realized the transmission of loss function gradient across the multi-layer structure in the network and reduced the information loss caused by the multi-layer transmission in the forward propagation. Meanwhile, in the back propagation stage, the loss caused by the multi-layer transmission of gradient was reduced. Thus, the gradient vanishing problem in training was avoided. Since the method realized cross-layer transmission error, the convolutional layer could be increased appropriately to obtain higher measurement accuracy. To verify the performance of the proposed MS-SC-CNN non-destructive measurement method, the near-infrared hyperspectral data of sample leaves of 219 _Pinus massoniana_ seedlings were collected from the Huangping Forest Farm in Guizhou Province. The correlation coefficient between the measured and real values of the prediction was as high as 0.977 and the root mean square error was 0.242 for the moisture content of _Pinus massoniana_ seedling leaves. For the nitrogen content of _Pinus massoniana_ seedling leaves, the correlation coefficient between the measured and real values of the prediction was 0.906 and the root-mean-square error was 0.061. The results showed that the non-destructive testing method based on MS-SC-CNN that we proposed can accurately measure the moisture and nitrogen content in leaves of _Pinus massoniana_ seedlings.  
Pinus massoniana seedlings; moisture content; nitrogen content; near-infrared spectroscopy (NIRS); multi-scale short cut convolutional neural network (MS-SC-CNN) +
Footnote †: journal: Applied Sciences
# Autonomic Semantic-Based Context-Aware Platform for Mobile Applications in Pervasive Environments
## 3 Kali-Smart: Autonomic Semantic-Based Context-Aware Adaptation Platform
### Platform Architecture
We choose to follow the rules-based approach for the basic advantage that a user can simply reconfigure the behavior of the environment by defining new rules, without having to change the source code. * _Action Reasoner_: which is based on the JESS inference engine, as well [15], executes the SWRL service rules and determines the appropriate actions according to the deduced situations. * _Prediction Reasoner_: is responsible for generating predictions about the activity and situations happening in the environment. * **Service Controller** is responsible for re-routing the data transfer to another path when there is poor QoS, connection failure, low battery, etc., in order to ensure service continuity and perform reconfiguration changes at the Kalimucho server. * _Automatic Semantic Service Discovery:_ all gateways and services in the same local network are automatically discovered by sending SNMP (Simple Network Management Protocol: which represents the management information control that describes the router state) broadcast. Only mobile devices hosting the Kalimucho platform with SNMP will send a response to the SNMP request broadcast by ASSACR. SNMP responses will be processed by ASSACR to establish the neighbors/services individuals of the _OWL Host and Services Classes_ in the ontology repository. * _Semantic Service Analyzer_: analyzes equivalent semantic services based on location, time and activity with different qualities (execution time, modality input/output). Two cases are possible for equivalent semantic services with different qualities: (1) semantic equivalent services with different service qualities at the same Kalimucho gateway; in this case, redundancy is supported by the Kalimucho gateway; and (2) semantic equivalent services with different service qualities selected from two (or more) gateways. In this case, redundancy is supported by the Kalimucho server. * _Reconfiguration Generator:_ SNMP is a simple protocol to ensure dynamic service reconfiguration; ASSACR can automatically create configuration files to be saved in a Kalimucho repository. * _Service Deplorer:_ the configuration file created in the previous phase is copied in the Kalimucho cloud server, and all mobile devices affected by the equivalent path and the new services configuration are saved. * 4. The **Kalimucho Platform Layer:** offers service-level functions: the physical (re-)deployment and dynamic incremental reconfiguration strategy according to its system (Android, laptop with QoS requirement, dynamic supervision of adaptation components and communication protocols between mobile nodes). Figure 1: Kali-Smart: platform architecture. ASSACR (Autonomic Semantic Service Adaptation Controller and Reconfiguration).
## 2 Theoretical Background
### State Space Method Theory
 \tag{19}\]  
\[\left[ \begin{array}{c}\dot{x}_{1}\\ \dot{x}_{2}\\ \dot{x}_{3}\\ \dot{x}_{4}\\ \dot{x}_{5}\\ \dot{x}_{6}\end{array} \right]=\left[ \begin{array}{cccccc}0&1&0&0&0&0\\ 0&0&0&0&0&0\\ 0&0&1&0&0&0\\ 0&0&-w_{2}^{2}&-2\xi_{2}w_{2}&0&0\\ 0&0&0&0&0&1\\ 0&0&0&0&-w_{3}^{2}&-2\xi_{3}w_{3}\end{array} \right]\left[ \begin{array}{c}x _{1}\\ x_{2}\\ x_{3}\\ x_{4}\\ x_{5}\\ x_{6}\end{array} \right]+\left[ \begin{array}{c}0\\ F_{p1}\\ 0\\ F_{p2}\\ 0\\ F_{p3}\end{array} \right]u \tag{20}\]  
In the output equation under principal coordinates, the number of columns of the output matrix \(\mathbf{C}\) is the same as the number of state variables. \[\mathbf{y}_{p}=\left[ \begin{array}{cccccc}1&0&0&0&0&0\\ 0&1&0&0&0&0\\ 0&0&0&1&0&0\\ 0&0&0&0&1&0\\ 0&0&0&0&0&1\end{array} \right]\left[ \begin{array}{c}x_{1}\\ x_{2}\\ x_{3}\\ x_{4}\\ x_{5}\\ x_{6}\end{array} \right]+\left[ \begin{array}{c}0\\ 0\\ 0\\ 0\\ 0\\ 0\end{array} \right]u \tag{21}\]The output equation under the main coordinate is inverse-transformed back to the physical coordinates:  
\[\left[ \begin{array}{c}z_{1}\\ z_{1}\\ z_{2}\\ z_{3}\\ z_{3}\end{array} \right]=z_{n}\mathbf{y_{p}}=\left[ \begin{array}{c}z_{n11}y_{p1}+z_{ n12}y_{p3}+z_{n13}y_{p5}\\ z_{n11}y_{p2}+z_{n12}y_{p4}+z_{n13}y_{p6}\\ z_{n21}y_{p1}+z_{n22}y_{p3}+z_{n23}y_{p5}\\ z_{n21}y_{p2}+z_{n22}y_{p4}+z_{n23}y_{p6}\\ z_{n31}y_{p1}+z_{n32}y_{p3}+z_{n33}y_{p5}\\ z_{n31}y_{p2}+z_{n32}y_{p4}+z_{n33}y_{p6}\end{array} \right] \tag{22}\]  
At this point, the output matrix in physical coordinates is:  
\[\mathbf{y}\mathbf{=}\mathbf{Cx}=\left[ \begin{array}{ccccc}z_{n11}&0&z_{n12}&0&z_{n13}&0\\ 0&z_{n11}&0&z_{n12}&0&z_{n13}\\ z_{n21}&0&z_{n22}&0&z_{n23}&0\\ 0&z_{n21}&0&z_{n22}&0&z_{n23}\\ z_{n31}&0&z_{n32}&0&z_{n33}&0\\ 0&z_{n31}&0&z_{n32}&0&z_{n33}\end{array} \right] \tag{23}\]  
Since there is no feedback from the system, the feedback matrix \(D=0\). The above theory demonstrates that the coupled vibration equations of a multi-degree-of-freedom system are decoupled by modal coordinate transformation and converted to state space form. The obtained single-degree-of-freedom solution is used as the coefficient to superposition the various modes of the structure. Finally, the total frequency response of the structure is obtained.
## 3 Results and Discussion
### Effect on the Mechanical Behavior
#### 3.2.1 Stress-Strain Behavior  
The stress-strain behaviors of the samples that were subjected to F-T action are presented in Figure 10, indicating that the F-T action significantly affects the pre-peak stress-strain relations of the sandstone samples. All the curves showed a non-linear behavior in the initial compressive stage and exhibited a consistent variation due to the increasing F-T treatments. The stress-strain curves became gradually more non-linear with an increasing number of applied F-T cycles, indicating that the number of pores or cracks in the samples increased with F-T action. On the other hand, the crack closure strain increased with more F-T cycles, which can be used to evaluate the density of cracks and further identify the amount of cracks damage in the rocks [31]. Moreover, the non-linear stage near the peak stress showed a clear increasing trend with the F-T action, signifying that part of the energy was consumed after the elastic stage in the loading process; these energies were closely related to the porosity, damage degree, mineral particles, and cement strength of the sample. [PERSON] et al. [32] considered that the crack propagation and frictional increase among mineral particles in samples were primarily responsible for the energy consumption near the peak stress. For the samples that were subjected to F-T action, the microstructures inside the rock changed from relatively dense to loose (Figure 7). Meanwhile, the peak strain corresponding to peak stress tends to increase with F-T action, signifying a larger deformation in rocks with more F-T cycles (Figure 10). These variations that were induced by F-T action facilitated the development of cracks, so the variations of non-linear behavior near peak stress reflected the significant effect of F-T action on the microstructure in the tested sandstone. Besides, the slope in the linear stage of the stress-strain curves decreased with the increase of F-T action number, indicating a reduction in the elastic modulus.  
Figure 10: Stress-strain curves of the tested samples that were subjected to different F-T cycles.
## 2 Materials and Methods
### Experimental
#### 2.5.1 Experiments in the kHz Range  
An inductor based on a ferrite yoke was connected to the amplifier and set on top of a slab of the material to be investigated (Figure 3a). To avoid a slow rise-time of the induction current and to work easily at different induction frequencies, the induction system is nonresonant. Given by the amplifier used, induction frequencies between 7.5 and 20 kHz were applied for a total time of 5 s. The voltage amplitude over the inductor was kept constant and checked for absence of possible nonharmonic distortions. Owing to the frequency dependent impedance of the inductor, excitation current and efficiency decreased with increasing frequency. Images with a frame rate of 100 Hz from an evaluation area between the yoke arms were recorded for the time of the excitation burst. All following evaluations of the temperature as a function of time were obtained as an average over the area marked in Figure 3a.  
One sample was a slab of stainless steel 316 with a thickness of 19 mm. Using the values shown in Table 1, the thermal penetration depth after 5 s was 8.3 mm and the skin depth at \(10\) kHz was 4.6 mm. For comparison, slabs of ferritic steel and of CFRP were studied. Their thickness was 3 and 4 mm, respectively. The steel samples measured were thermally and electromagnetically thick, the CFRP sample was thermally thick and electromagnetically thin for the parameters of the experiments.  
As the excitation power was limited and the signals of stainless steel and CFRP were relatively small, averaging of several repeated experiments was performed, followed by slight smoothing of the signal curves by a gliding average.  
Figure 3: **(a)** Photo of the inductor for the kHz range experiment with the sample underneath and marking of the evaluation area. **(b)** Photo of the inductor for the low-frequency experiment with the sample between the adjustable yokes. **(c)** Thermographic image of the sample in **(b)** with marking of the evaluation area.  
The stainless steel sample had a quite homogeneous natural surface. The measurement on this sample was repeated with a black paint coating and led to similar results. The ferritic steel sample was measured with a thin black coating. The CFRP sample was not coated.