esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The purpose of this study was to verify the effect of deep-sea water thalassotherapy (DSWTT) on recovery from fatigue and muscle damage. The same exercise program is conducted in general underwater and deep-sea water to confirm the characteristics of deep-sea water through fatigue recovery and muscle damage enzymes. A total of 30 male college students were studied, including 10 belonging to the control group (CG), 10 in the water exercise group (WEG), and 10 in the deep-sea water exercise group (DSWEG). The DSWTT treatment consists of three components--preheating, treatment, and cooling--and the DSWTT program stretches and masses the entire upper body, lower body, back, and the entire body for a total of 25 min in a deep-sea tank. After the DSWTT program, blood tests were conducted to confirm the level of fatigue-related parameters and muscle damage enzymes. Fatigue-related parameters including glucose, lactate, ammonia, and lactate dehydrogenase (LDH), and the levels of muscle damage enzymes such as creatinine kinase (CK) and aspartate aminotransferase (AST) were measured. The results revealed that fatigue had a primary effect (\(p<0.001\)) and exhibited strongly significant interaction (\(p<0.001\)) with lactate, ammonia, and LDH levels, whereas the glucose level remained unchanged. The post hoc results showed a significant decrease in these parameters among DSWEG compared to CG and WEG (\(p<0.01\)). Muscle damage enzymes showed a main effect (\(p<0.001\)) and significant interaction (\(p<0.001\)) with CK and AST (\(p<0.001\)). The post hoc results showed a significant decrease in DSWEG compared with CG and WEG (\(p<0.01\)). In conclusion, the DSWTT program applied to this study showed significant effects on muscle fatigue and muscle damage recovery. When the DSWTT program is applied in hot springs, it can have a positive effect on muscle fatigue and muscle damage recovery and can contribute to improving national health and quality of life. Further studies are needed to investigate DSWTT programs with various research subjects at different program temperatures, exercise times, and frequencies of treatment and exercise.  
fatigue rehabilitation; thalassotherapy adverse effects; blood test; lactate dehydrogenase; creatinine kinase 22 September 2020; Accepted: 23 November 2020; Published: 25 November 2020
# 4D Building Reconstruction with Machine Learning and Historical Maps
## 4 Methodology
### Machine and Deep Learning for Regression Models  
Within the machine and deep learning applications, the regression problem is a common task. Regression analysis is a predictive modelling technique that predicts numerical variables \(y\), typically called target, based on one or multiple variables \(x\) (predictors) [47]. Thus, a regression model aims to build a mathematical equation that defines the target \(y\) as a function of the predictor \(x\). Once the model is trained, it can use new predictor values to infer \(y\). In this work, several and common regression models, mainly available in the scikit-learn library [48], were tested for inferring the building heights from a set of predictor variables (Section 4.2), in particular:  
1. Ordinary Least Squares Linear regressor: this model minimises the residual sum of squares between the observed and target variables. It assumes a linear connection between outputs and predictor variables, and it is sensitive to random errors when variables are not independent [49].
2. Random Forest regressor: it is a supervised learning algorithm based on ensemble learning. Random Forest combines multiple decision trees (reducing the variance and overfitting) resulting in an averaged prediction of the individual classifiers. It also provides straightforward methods for the features' importance analysis and selection [50].
3. CatBoost regressor: it uses gradient boosting on decision trees. The decision tree is used as a weak base learner, while gradient boosting iteratively fits a sequence of these trees [51].
4. Support Vector regressor with the Radius Basis Function (RBF) kernel: it produces a model depending only on a subset of the training data. The employed cost function ignores samples whose prediction is close to their target [52].
5. Multilayer Perceptron regressor: it is a neural network model where neurons are arranged in different layers, connected by differently weighted joints [53]. The model optimises the squared loss using, e.g., stochastic gradient descent.  
Figure 5: The general workflow based on machine learning regression to learn building heights.
# SACT: A New Model of Covert Communication Based on SDN
## 3 Materials and Methods
### Watermark Conversion Analysis
#### 3.1.2 Watermark Demodulation Analysis  
There are constraints between groups of convolutional codes. Viterbi decoding reduces decoding error rate [38] by using the algebraic structure of convolutional code and the probabilistic statistical properties of the channel. When the inter group constraint of convolutional codes is small, the decoding speed of Viterbi decoding is fast and the algorithm complexity is relatively low. The algorithm complexity Viterbi decoding is \(O(n*d^{2})\), \(n\) is the number of sequences, and \(d\) is the length of sequences [36].  
After extracting the binary watermark sequence from the data stream, our system compares it with all sending sequences. Then finding a path with the smallest Hamming distance [39] between the corresponding coding sequence and the received sequence of watermark information. The symbol information corresponding to this path is taken as the encoding output, which is the watermark information sequence before convolution expansion.  
In order to verify the correlation between the extracted watermark sequence \(B\) and the original watermark sequence \(A\), we need to calculate the similarity of the two watermark sequences and set their threshold range. Then the similarity is compared with the threshold parameter of the confidence interval. If the similarity calculation result is within the confidence interval, it is determined that the watermark information embedded in the current detected data stream is associated with the source data stream. That is, there is a communication relationship between the current detected communication parties, otherwise, there is no communication relationship.
# Superhydrophobic Polypyrrole-Coated Cigarette Filters for Effective Oil/Water Separation
## 3 Results and Discussion
### Oil Absorption Capacity  
Figure 8a shows the oil absorption capacity of different superhydrophobic cigarette filter samples. The 3D porous fiber structures could absorb large amounts of oil [42]. The original cigarette filter and filters prepared using 0.1 mol/L Py, 0.2 mol/L Py, 0.5 mol/L Py, 1 mol/L Py, and 2 mol/L Py had absorption capacities for petroleum ether of 4.6 g/g, 5.4 g/g, 6.8 g/g, 6.95 g/g, 8.1 g/g, and 9.4 g/g, respectively. The absorption capacity of the as-prepared cigarette filter was larger than that of the original cigarette filter for petroleum ether. As the pyrrole concentration increased, the oil absorption capacity increased. The filter prepared using 2 mol/L Py had the largest capacity for petroleum ether. We introduced this indicator to illustrate that the filters can absorb oils up to many times their own mass. The comparison between before and after treatment indicated that the filter could absorb more oil after treatment. Figure 8b shows the oil absorption capacity for different oils and organic solvents; in these experiments, we used filters prepared using 2 mol/L Py. The superhydrophobic cigarette filter had different absorption capacities for different oils and organic solvents because of their physical and chemical properties [43]. The \(Q_{\text{ab}}\) values for petroleum ether, engine oil, vegetable oil, n-hexane, and chloroform were 9.4 g/g, 13.6 g/g, 13.9 g/g, 9.6 g/g, and 22.7 g/g, respectively. Thus, the high roughness provided by Pyp particles obviously enhanced the filters' absorption capacity for oils or organic solvents. Herein, the filter we made has a large oil absorption range and the ability to adsorb a variety of oils.
## 3 Methods
### Resampling of Explanatory Variables from Calendar Time to Thermal Time  
To ensure year-to-year comparability of vegetation conditions, the explanatory variables were resampled from calendar time (day of year) to thermal time, which denotes cumulated mean daily air temperatures at 2 m a.g.l above a crop-specific threshold. Thus, a thermal time is a good proxy for the crop development stage [34; 35; 36]. Analysis of vegetation indices in respect to thermal time allows derivation of temporal anomalies by referring instantaneous values of an index to a multiannual average calculated for the same thermal time (i.e., the same crop development stage). If the calendar time was used instead of the thermal time, the temporal anomalies could be related to the shift in a vegetation season (e.g., a delay in biomass accumulation), however not to the actual crop conditions that are to be used to forecast crop yields.  
Thermal time was calculated for a day \(d\) of the year as so-called Growing Degree Days (GDD) from daily maximum (\(T_{\text{max}}\)) and minimum (\(T_{\text{min}}\)) air temperatures using a formula:  
\[\text{GDD}_{d}=\sum_{i=1}^{d}\left[\left(\frac{T_{\text{max},i}+T_{\text{min}, i}}{2}-T_{\text{base}}\right)\times\underbrace{\left[\left(\frac{T_{\text{max},i}+T_{ \text{min},i}}{2}-T_{\text{base}}\right)>0\right]}_{\text{conditional}}\right] \tag{2}\]where \(T_{\rm base}\) stands for crop-specific temperature threshold: 5 \({}^{\circ}\)C for winter wheat and winter rapeseed, and 10 \({}^{\circ}\)C for maize. In addition, \(T_{\rm min,i}\) equals \(T_{\rm base}\) if \(T_{\rm min,i}<T_{\rm base}\), and \(T_{\rm max,i}\) equals 30 \({}^{\circ}\)C if \(T_{\rm max,i}>30\)\({}^{\circ}\)C. The conditional part of the equation equals 1 if the condition is met, and 0 otherwise, which implies that only positive values (mean air temperature values reduced by the threshold) are summarized.  
Based on daily GDD values, all yield predictors were resampled for eight GDD values ranging from 150 \({}^{\circ}\)C to 1200 \({}^{\circ}\)C with a step of 150 \({}^{\circ}\)C. Since GDD were calculated at a daily time step, all predictors had to have the same 1-day temporal resolution prior resampling. Therefore, the 8-day MODIS NDVI and LST products were converted to 1-day resolution using the spline function.  
Resampling of NDVI and LST to thermal time allowed derivation of normalized indicators proposed by [PERSON] (1997) [37] such as: Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) defined as:  
\[{\rm VCI_{GDD}}=100\times\frac{{\rm NDVI_{GDD}-NDVI_{min,\,GDD}}}{{\rm NDVI_{ max,GDD}-NDVI_{min,\,GDD}}} \tag{3}\]  
\[{\rm TCI_{GDD}}=100\times\frac{{\rm LST_{max,GDD}-LSTM}}{{\rm LST_{max,GDD}- LST_{min,\,GDD}}} \tag{4}\]  
where GDD indicates the growing degree days (thermal time), NDVI\({}_{\rm GDD}\)--an instantaneous NDVI value, and NDVI\({}_{\rm min,\,GDD}\) and NDVI\({}_{\rm max,\,GDD}\)--minimum and maximum NDVI recorded in the period 2000-2019 at a particular location for a given GDD, respectively. Definition of \({\rm TCI_{GDD}}\) follows the same logic as \({\rm VCI_{GDD}}\).
## 5 Results and Discussion
### Assimilation Frequency  
An important physical parameter to consider in DA is the frequency at which data are assimilated into our model. To test the sensitivity of our DA approach to the assimilation frequency, we performed a series of experiments maintaining the same configuration as in the Base-Test with eight drifters but altering the value of the frequency (\(\Delta t\)) at which drifter velocities were assimilated into the model. Error estimates in terms of RMSE for both velocity components are presented in Figure 4. For assimilation intervals <2 min, our model remains less sensitive to the specific interval used, likely due to data correlation effects. However, increasing the assimilation interval beyond 2 to 10 min shows a high degradation in DA performance. This infers that DA performance is highly responsive to the frequency of data assimilation. Assimilating drifter data every 1 min resulted in ~27% improvement while assimilating every 10 min produced essentially no improvement in model output. Performing assimilations every 5 min provided an 11% and 13% improvement in \(V_{x}\) and \(V_{y}\), respectively, notably less than the improvement gained in the Base-Test, with an assimilation frequency of 1 min. However, an assimilation frequency of 2 min led to a slight degradation in DA performance improvement compared to the Base-Test. In fact, increasing the time interval beyond the Lagrangian integral time scale of the lake (here, ~50 s) causes some important spatiotemporal velocity information to be lost, leading to deterioration in the model performance [16]. In general, the results denote that using  
Figure 3: Sensitivity of DA performance to ensemble size. Five different ensembles were examined according to the Base-Test configuration.  
a DA time interval close to the Lagrangian integral time scale of the domain leads to better outcomes.
_Applied_  
_sciences_  
_Article_  
**Stochastic Analysis of the Gas Flow at the Gas Diffusion Layer/Channel Interface of a High-Temperature Polymer Electrolyte Fuel Cell**  
**[PERSON] 1,*\({}^{\text{(c)}}\), [PERSON] 1, [PERSON] 1 and [PERSON] 1,2,3**  
1 Forschungszentrum Julich GmbH, Institute of Energy and Climate Research, IEK-3: Electrochemical Process Engineering, D-52425 Julich, Germany; [EMAIL_ADDRESS] (J.Y.); [EMAIL_ADDRESS] (U.K.); [EMAIL_ADDRESS] (W.L.)
2 Modeling in Electrochemical Process Engineering, RWTH Aachen University, D-52056 Aachen, Germany
* 3 Julich Aachen Research Alliance--High Performance Computing (JARA-HPC), D-52425 Julich, Germany
* 4 Correspondence: [EMAIL_ADDRESS]; Tel.: +49-2461-61-6676 check for updates  
Received: 16 November 2018; Accepted: 5 December 2018; Published: 7 December 2018  
**Featured Application: Featured applications are channel or cell simulations that can consider information about the distribution of steam coming out of the GDL.**  
**Abstract:** Gas diffusion layers (GDLs) play a significant role in the efficient operation of high-temperature polymer electrolyte fuel cells. They connect the electrodes to the gas channels of the bipolar plate by porous material with a meso-scale geometric structure. The electrodes must be sufficiently supplied by gases from the channels to operate fuel cells efficiently. Furthermore, reaction products must be transported in the other direction. The gas transport is simulated in the through-plane direction of the GDL, and its microstructure created by a stochastic model is equivalent to the structure of real GDL material. Continuum approaches in cell-scale simulations have model parameters for porous regions that can be taken from effective properties calculated from the meso-scale simulation results, as one feature of multi-scale simulations. Another significant issue in multi-scale simulations is the interface between two regions. The focus is on the gas flow at the interface between GDL and the gas channel, which is analyzed using statistical methods. Quantitative relationships between functionality and microstructure can be detected. With this approach, virtual GDL materials can possibly be designed with improved transport properties. The evaluation of the surface flow with stochastic methods offers substantiated benefits that are suitable for connecting the meso-scale to larger spatial scales.  
**Keywords:** HT-PEFC; GDL/channel interface; lattice Boltzmann; stochastic modeling; upscaling  
**PACS:** 88.30.gg; 88.30.J-; 88.30.pd
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 3 Methodology  
This section introduces a systems engineering method to investigate if nanogrids can increase the resilience of critical loads on military installations and lower the mission impact from potential threats. Figure 1 illustrates the method.  
Figure 1: Overview of Methodology. This methodology is created for base energy managers to follow, starting with the collection of system information and ending with final design recommendations.
# MIMO Signal Detection Based on IM-LSTMNet Model
## 2 Methods
The space-time coding and spatial multiplexing technology of MIMO can also be applied to each subcarrier of OFDM. To sum up, in the MIMO-OFDM system, increasing diversity and multiplexing in the frequency domain bring greater system gain and system capacity. ### MIMO-OFDM Signal Detection Model Based on FrFT  
In order to carry out signal detection in the FrFT-based MIMO-OFDM system, we built an IM-LSTMNet model, as shown in Figure 4. The IM-LSTMNet model proposed in this paper combines the traditional neural network CNN and LSTM, and adds the Squeeze-and-Excitation Network on the basis of this to improve the model's capability of capturing vital information. As shown in Figure 4, the signal is sent to the CNN module and the Squeeze-and-Excitation Network as inputs. The two outputs are taken as inputs for the point-by-point attention layer. The information obtained through the point-by-point attention layer is entered into the LSTM module for processing. Figure 4: IM-LSTMNet frame diagram.
## 3 Experimental Results and Analysis  
### Flow Discharge  
Flow discharge is a critical control factor in the design of a hydraulic structure. A satisfactory flow rate ensures the safe operation of a dam in a hyperconcentrated sediment-laden river, so the effect of sediment on flow discharge is an important and urgent issue. The experiments in this study were conducted to determine the relationship between flow rate and upstream water head for sediment-laden flow. The experimental results are presented in Figure 2, in which the dark line is the flow rate of fresh water and the dots are the flow rates of the sediment-laden flow of various sediment concentrations. The flow rate was observed to increase exponentially with the square root of the upstream water head. The dots were close to the line, indicating that the flow rate is stable regardless of sediment concentration, which is the same conclusion reached by references [44; 45]. Therefore, when a hydraulic structure is designed, the flow rate need only be evaluated in terms of fresh water.
The evaluation of structural reliability of structural systems equipped with base isolation solution has been widely investigated [14] also considering random seismic excitations [15]. Similarly, referring both to 3D RC structures and to equivalent models, deterministic [16] and seismic reliability [17; 18; 19] analyses have been carried out. Specifically, in [17; 18; 19] the seismic reliability-based design (SRBD) approach has been proposed as a strategy to define some important design parameters for base-isolated systems. The aim of this work is to investigate the influence of the retrofitting intervention composed of single-concave friction pendulum devices on the seismic response on an existing reinforced concrete building located in central Italy, which is widely recognized as an area characterized by a very high seismicity. After the characterization of the geometrical features and material properties according to the \"knowledge levels\" approach [2], a non-linear numerical model of the structure has been defined using a fiber-approach. Then non-linear dynamic analyses have been carried out considering multiple recorded ground motions with the three accelerometric components, scaled to the design seismic intensity [2], to assess the seismic response of the building with and without the retrofitting intervention composed of the FP isolators. The results from the non-linear dynamic analysis have been useful to evaluate the effectiveness of the retrofitting technique in probabilistic terms adopting a log-normal probabilistic model for the output variables expressed as both the interstory drift index and horizontal relative displacement of the FP devices. Finally, the probabilities of exceedance of the different values of the output variables are evaluated.
## 5 Simulation Experiments and Result Analysis  
In this section, the simulation experiments are divided into two parts, i.e., data restoration on University of California Irvine (UCI) standard datasets and heterogeneous data integration on intelligent power distribution and utilization datasets. The former one is performed to verify the validity and stability of our proposed GANs-HDI algorithm, while the latter one is performed to test the actual effect of our proposed GANs-HDI algorithm for intelligent power distribution and utilization heterogeneous data in TensorFlow platform. All of the following simulation experiments were performed in Matlab 2012a and JetBrains PyCharm 2017.2 environment with Core-TM [EMAIL_ADDRESS] GHz and NVIDIA GeForce 840M processor, respectively.
###### Abstract  
This paper proposes a reciprocal-selection-based 'Win-Win' overlay spectrum-sharing scheme for device-to-Device-enabled cellular networks to address the resource sharing between Device-to-Device devices and the cellular users by using an overlay approach. Based on the proposed scheme, the cell edge users intend to lease part of its spectrum resource to Device-to-Device transmission pairs. However, the Device-to-Device users have to provide the cooperative transmission assistance for the cell edge users in order to improve the Quality of Service of the uplink transmission from the cell edge users to the base station. Compared to the underlay spectrum-sharing scheme, overlay spectrum-sharing scheme may reduce spectrum efficiency. Hence, Non-Orthogonal Multiple Access technology is invoked at the Device-to-Device transmitter in order to improve the spectrum efficiency. The Stackelberg game is exploited to model the behaviours of the cell edge users and Device-to-Device devices. Moreover, based on matching theory, the cell edge users and Device-to-Device pairs form one-to-one matching and the stability of matching is analysed. The simulation results show that the proposed reciprocal-selection-based 'Win-Win' overlay spectrum-sharing scheme is capable of providing considerable rate improvements for both EUs and D2D pairs and reducing transmit power dissipated by the D2D transmitter to forward data for the EU compared with the existing methods.  
**Keywords:** overlay spectrum-sharing; Device-to-Device; Stackelberg game; matching theory +
Footnote †: journal:  
_Article_  
A Reciprocal-Selection-Based 'Win-Win'  
Overlay Spectrum-Sharing Scheme for Device-to-Device-Enabled Cellular Network [PERSON] _et al._ (2018); [PERSON] and [PERSON]
# A Haar Wavelet Decision Feedback Channel Estimation Method in OFDM Systems
###### Abstract  
Channel estimation is a key technology in improving the performance of the orthogonal frequency division multiplexing (OFDM) system. The pilot-based channel estimation method decreases the spectral efficiency and data transmission rate. Some conventional channel estimation methods cannot suppress the noise effectively, which affects the quality of the final received signals. To solve these two problems, a Haar wavelet decision feedback (DF) channel estimation method, also named the Haar wavelet method, is proposed in this paper. The proposed Haar wavelet method can suppress the noise existing at the estimated channel impulse response (CIR) effectively, based on a time-domain threshold which is a standard deviation of noise obtained by wavelet decomposition. At the same time, the proposed Haar wavelet method just requires inserting one block pilot symbol in front of the first OFDM symbol, which therefore improves the data transmission rate and spectrum efficiency greatly. Simulation results are shown to verify the effectiveness of the proposed Haar wavelet method in multipath channel propagation conditions.  
Keywords:channel estimation; orthogonal frequency division multiplexing (OFDM); decision feedback (DF); wavelet decomposition +
Footnote †: journal: Applied Physics Letters