esa-sceva/satcom-chunk-collection huggingface dataset:
## Impact of Indoor Environment on Path Loss in Body  
**Area Networks**  
**[PERSON] and [PERSON] ***  
Institute of Electronics, Lodz University of Technology, Wolczanska 211/215, PL 90-924, Lodz, Poland; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +48-602-359-950.  
External Editor: [PERSON]  
_Received: 1 May 2014; in revised form: 22 August 2014 / Accepted: 16 September 2014 / Published: 20 October 2014_  
**Abstract:** In this paper the influence of an example indoor environment on narrowband radio channel path loss for body area networks operating around 2.4 GHz is investigated using computer simulations and on-site measurements. In contrast to other similar studies, the simulation model included both a numerical human body phantom and its environment--room walls, floor and ceiling. As an example, radio signal attenuation between two different configurations of transceivers with dipole antennas placed in a direct vicinity of a human body (on-body scenario) is analyzed by computer simulations for several types of reflecting environments. In the analyzed case the propagation environments comprised a human body and office room walls. As a reference environment for comparison, free space with only a conducting ground plane, modelling a steel mesh reinforced concrete floor, was chosen. The transmitting and receiving antennas were placed in two on-body configurations chest-back and chest-arm. Path loss _vs._ frequency simulation results obtained using Finite Difference Time Domain (FDTD) method and a multi-tissue anthropomorphic phantom were compared to results of measurements taken with a vector network analyzer with a human subject located in an average-size empty cuboidal office room. A comparison of path loss values in different environments variants gives some qualitative and quantitative insight into the adequacy of simplified indoor environment model for the indoor body area network channel representation.  
**Keywords:** Body Area Networks; FDTD; body phantoms; on-body sensor network; wearable antennas
## 4 Evaluation
### Training DGRL  
For training purpose, we built six traffic intensity models ranging from 20% to 125%. The traffic intensity is reflected as the time interval between packet generation. Figure 6 shows the traffic model when the traffic intensity is 125%. Figure 6a shows the time interval distribution of data packets, and Figure 6b shows the number of data packets sent.  
The shortest path algorithm Dijkstra is used to generate the initial packet transmission path, as well as the relevant environment data, including channel delay, packet loss rate of nodes, the occupancy of buffers, and the generated rewards value.  
After the pre-training, the model is used to predict the transmission direction in the simulation environment. We will collect the environment data and the corresponding reward value and store them in the experience pool. The weights of all the neural networks in the model will be updated by experience playback and gradient descent. The loop will be repeated 200 times, each of which comprises 100 steps.  
Figure 7 shows the loss and reward during the training process. The loss curve shows a downward trend on the whole. However, the loss often increases throughout the training. This can be explained by the difference between reinforcement learning and supervised learning based on fixed data sets. The training data of reinforcement learning come from the experience pool, and the data in the experience pool are collected from the environment, so they are in constant change. The loss curve fluctuates when encountering a new state space that leads to better reward. In the beginning, the reward is lower because the controller does not have enough knowledge about the network and explores the environment. After some training episodes, the reward increases rapidly. It reflects that routing policies made by the controller are able to guide packets forwarding to obtain better returns.  
Figure 6: Traffic model under 125% traffic intensity. (a) Time interval of packet transmission. (b) Number of data packets sent.
## 1 Introduction
[19] compared and analyzed the thickness of the mixed layer between the primary flow and the secondary flow in a steam ejector calculated with the wet steam model and the ideal gas model. They also explained why the ejection ratio calculated with the wet vapor model was higher than that calculated with the ideal gas model. [PERSON] et al. [20] employed the wet steam model to optimize the steam ejector's geometry. Compared to the ideal gas model, the use of the wet steam model dramatically decreased the entrainment ratio error from 16.24% for single-phase steam to 3.92% when compared to experimental data. [PERSON] et al. [21] analyzed in detail the condensation process inside steam ejectors. They found that the most intensive condensation happened at the primary nozzle downstream and nozzle exit region. [PERSON] et al. [22; 23] modified the nonequilibrium condensation model. The results showed that the modified model could describe the flow characteristics inside the steam ejector more accurately. Therefore, an accurate calculation of the flow characteristics inside the steam ejector was found to be key to improving its performance. However, most current studies have assumed steam as an ideal gas and ignored the effects of the nonequilibrium phase change. This has resulted in a large deviation between simulation results and the actual phenomenon [24]. Moreover, the effect of the nonequilibrium phase change on the characteristics inside the steam ejector is also lacking. In this paper, a wet steam model considering the nonequilibrium phase change is established to analyze the flow characteristics inside a steam ejector. The effects of the wet steam model and the ideal gas model on the pressure distribution, temperature distribution and Mach number distribution inside the steam ejector under different \(P_{m}\) are compared and analyzed. At the same time, the influence of the phase change on the flow characteristics of the steam ejector is explained through the distribution of the supercooling degree and wetness fraction.
# Assessing Groundwater Level with a Unified Seasonal Outlook and Hydrological Modeling Projection
## 4 Results and Discussion
### Model Validation
In short, the simulation model demonstrated both stability and feasibility as its results agreed with the observed groundwater table. The validation results confirmed that these parameters are practical and adequate for subsequent seasonal assessment.
# Rubber Creep Model and Its Influence on Mounting Stiffness
## 3 Test Method for Suspension Creep  
Rubber creep test according to the national standard GB/T19242-2003--\"vulcanized rubber under compression or shear determination of creep\". The rubber creep test adopts the sample with a diameter of 29.0 \(\pm\) 0.5 mm and height of 12.5 mm \(\pm\) 0.5 mm. The loading mode is vertical compression. Before the test, the sample and its device shall be adjusted in the following ways:  
(1) the compression device is required to keep two steel plates parallel, the roughness of two steel plates \(\leq\) 0.2 \(\upmu\)m.  
(2) The lower pressure plate shall be fixed in the middle of the bottom of the box, and the distance between the lower pressure plate and the thermostat shall be kept above 40 mm.  
(3) It is required that the sample should be placed in the thermostat in, the compression device, so that all components reach thermal balance.  
(4) The sample was compressed to (25 \(\pm\) 2)% of the initial strain at the speed of 25 mm/min, and then the load was unloaded. After the static sample returned to the initial state, the operation was performed again five times. The test load is 1000 N, and the direction is vertical downward. During the test, all the load should be loaded on the sample within 6 s, and then the deformation of the sample should be measured at different times. In the compression process, the initial thickness \(\delta_{1}\) should be measured after the sample is compressed to 10 min \(\pm\) 0.2 min, and the final thickness \(\delta_{2}\) of the sample can be measured according to the recommended termination time (such as 1 h, 2 h, 4 h, etc.). Then, the calculation formula of compressive creep increment \(\Delta\varepsilon\) is  
\[\Delta\varepsilon=\varepsilon_{2}-\varepsilon_{1}=\frac{\delta_{1}-\delta_{2}} {\delta_{0}} \tag{9}\]  
\[\varepsilon_{1}=\frac{\delta_{0}-\delta_{1}}{\delta_{0}} \tag{10}\]  
where \(\varepsilon_{1}\) represents the compressive strain of the compressed sample measured at 10 min; \(\varepsilon_{2}\) denotes the strain of the compressed sample at the end of the test; \(\delta_{0}\) represents the initial thickness of the compressed sample, in mm; \(\delta_{1}\) denotes the thickness of the compressed sample measured at 10 min (in mm); \(\delta_{2}\) represents the final thickness of the compressed sample at the end of the test. In order to avoid the accidental test results and improve the accuracy of the test, the above rubber creep test should be carried out independently on more than three rubber samples under the same conditions. The test results show that the static stiffness corresponding to no creep, 2.98 mm creep and 3.83 mm creep are 210 N/mm, 262 N/mm and 281 N/mm respectively.  
Figure 5: Kelvin creep model.
# Rotational Speed Control Using ANN-Based MPPT for OWC Based on Surface Elevation Measurements
## 2 Theoretical Considerations
### Back-to-Back Converter Model  
The BTB converter is formed of a Grid-Side Converter (GSC) connected to the Grid and a Rotor-Side Converter (RSC) connected to the windings of the rotor in the DFIG [28].  
The main role of the RSC is delivering voltage to the windings of the rotor in the DFIG. Its objective is aligning the rotor-flux with the stator-flux through the rotor-current control to obtain the required torque. Therefore, the RSC enables the stator voltages and generated power regulation [29].  
The active and reactive powers across the stator are defined by [30; 31]:  
\[P_{s} = \sfrac{3}{2}\left(v_{ds}i_{ds}+v_{qs}i_{qs}\right) \tag{19}\] \[Q_{s} = \sfrac{3}{2}\left(v_{qs}i_{ds}-v_{ds}i_{qs}\right) \tag{20}\]  
The grid voltages can be defined as:  
\[\left\{\begin{array}{l}v_{dg}=R_{g}i_{dg}+L_{g}\frac{di_{dg}}{dt}-\omega_{g} L_{g}i_{qs}+u_{dg}\\ v_{qg}=R_{g}i_{qs}+L_{g}\frac{di_{qg}}{dt}+\omega_{g}L_{g}i_{dg}+u_{qg}\end{array}\right. \tag{21}\]  
where \(R_{g}\) and \(L_{g}\) are the grid coupling resistance and inductance, \(\omega_{g}\) is the grid's angular frequency, \(i_{dg}\) and \(i_{qg}\) are the \(d\)-\(q\) grid currents and \(u_{dg}\) and \(u_{qg}\) are the GSC voltages.  
The equations describing the active and reactive powers between the GSC and the Grid are:  
\[P_{g}=\sfrac{3}{2}\left(v_{dg}i_{dg}+v_{qs}i_{qs}\right) \tag{22}\] \[Q_{g}=\sfrac{3}{2}\left(v_{qs}i_{dg}-v_{dg}i_{qs}\right) \tag{23}\]  
The energy conserved within the capacitor has been defined in [32] as:  
\[W_{dc}=\int P_{dc}dt=\frac{1}{2}Cv_{dc}^{2} \tag{24}\]  
with \(C\), \(v_{dc}\), \(W_{dc}\) and \(P_{dc}\) are respectively the DC-link capacitance, voltage, energy and power.
Because of the flow coefficient of Figure 5, the obtained turbine torques differ at the crest of each wave as illustrated in Figure 6. In fact, no stalling was observed for the first scenario but a significant decline at the crest in the second scenario due to the stalling phenomenon that decreases the average value of the torque.  
Figure 4: Variations for different waves. (**a**) Airflow speed. (**b**) Rotational speed.  
Figure 5: Flow coefficient vs. time for two wave amplitudes [31].  
Figure 6: Turbine torques vs. time for two sea conditions [31].
# Dynamic Investigation of a Solar-Driven Brayton Cycle with Supercritical CO2
## Material and Methods
### Modeling of the Solar Tower
The average specific heat of the molten salt (C\({}_{\text{p,s,avg}}\)) at constant pressure is calculated based on its average temperature [15]. \[\text{Q}_{\text{u}}=\dot{\text{m}}_{\text{s}}\times\text{C}_{\text{p,s,avg}} \times(\text{T}_{\text{s,out}}-\text{T}_{\text{s,in}}) \tag{7}\]  
The thermal losses from the surface of the central receiver to the ambient (Q\({}_{\text{loss}}\)) are the sum of the radiation (Q\({}_{\text{rad}}\))  
\(\mathrm{h_{out}}\) is the heat transfer coefficient due to convection between the central receiver and the ambient and \(\mathrm{T_{amb}}\) is the ambient temperature [13]. \[\mathrm{Q_{conv}}=\mathrm{A_{r}}\times\mathrm{h_{out}}\times(\mathrm{T_{r}}- \mathrm{T_{amb}}) \tag{9}\]  
The radiation thermal losses (\(\mathrm{Q_{rad}}\)) can be calculated using the equation below, where \(\mathrm{\varepsilon_{r}}\) is the emissivity of the outer surface of the central receiver and \(\mathrm{\sigma}\) is the Stefan-Boltzmann constant [23]. \[\mathrm{Q_{rad}}=\mathrm{A_{r}}\times\mathrm{\varepsilon_{r}}\times\mathrm{ \sigma}\times\left(\mathrm{T_{r}}^{4}-\mathrm{T_{amb}}^{4}\right) \tag{10}\]  
The heat transfer coefficient due to convection between the central receiver and the ambient (\(\mathrm{h_{out}}\)) can be calculated based on the average Nusselt number using the equation below, where \(\mathrm{k_{air}}\) is the thermal conductivity of air and \(\mathrm{D_{r}}\) is the diameter of the cylindrical surface of the central receiver [30]. Figure 1:0  
The average Nusselt number for a cross flow on a circular cylinder (\(\mathrm{Nu_{D}}\)) can be calculated according to the following equation (Churchill and Bernstein), considering that the product between Reynolds and Prandtl number is greater than 0.2 [30]. Figure 1:1  
The Reynold number for a cross flow on the outer surface of a circular cylinder (\(\mathrm{Re_{D}}\)) can be calculated based on the following equation [30]. Figure 1:2  
The Prandtl number (\(\mathrm{Pr}\)) for air can be calculated using the following equation [31]. Figure 1:3
# Design of XOR Photonic Gate Using Highly Nonlinear Fiber
## 3 HNLP Output  
Note that the main HNLF design parameters used in this design are as follows. The length is 1.007 km, the nonlinear coefficient is 12.5 1/W.km, the dispersion slope is 0.0074 ps/nm\({}^{2}\)/km, and the scattering section dispersion is 100 m. The calibration of the BPF1 and BPF2 controls the output of the HNLF in order to achieve XOR operation. The output of the HNLF when using pump B, yields probe A to propagate via the optical circulator A, as an input to BPF1. Meanwhile, the output when using pump A, results in probe B to be directed towards OC B, and acts as an input to BPF2. In order to perform the photonic XOR logic gate at the output of the designed circuit, the values of the BPFs should be calibrated in a way that function A \(\times\) B\({}^{\prime}\) (pump A and probe B) is achieved after BPF1 and (A\({}^{\prime}\times\) B) is resultant after BPF2 (see Table 1).  
Figure 1: Different stages for the propagating pump and probe in HNLF.  
Figure 2: Design setup of the XOR logic gate.  
BPF outputs are coupled via the optical coupler which will result in the optical XOR logic gate for the input binary sequence of signals A and B. The adjustment of the BPFs conforms to the following criteria:  
If the BPF keeps the original signal wavelength components at \(\lambda(\lambda_{\text{A}}\,\text{or}\,\lambda_{\text{B}})\) and removes the wavelength spectral components that were generated by the XPM effect at \(\lambda^{\prime}(\lambda^{\prime}_{\text{A}}\,\text{or}\,\lambda^{\prime}_{ \text{B}})\), then the logical operation A\({}^{\prime}\times\)B is performed [14]. The BPF1 input port is composed of an original probe A wavelength spectrum at \(\lambda_{\text{A}}\)\(=1555.2\) nm and the identical shifted XPM replica at \(\lambda^{\prime}_{\text{A}}\)\(=1558.08\) nm. BPF1 will clip \(\lambda^{\prime}_{\text{A}}\) and keep \(\lambda_{\text{A}}\). As a result, the cut-off wavelength of BPF1 will be \(\lambda_{\text{BPF1}}\)\(=1555.08\) nm. Figures 3 and 4 illustrate the time domain and spectral output of BPF1, respectively, i.e., composed of the filtered spectral component at 1555.08 nm with adjacent negligible low power side band around 1500-1530 nm. Furthermore, the optical power for the main output spectrum is recorded here as \(-11.151\) dBm.
## 3 Experimental Results
### Microstructure Analyses
The black dashed line depicts the highest possible packet area \(A^{\text{2D}}_{\text{CPP}}(A^{\text{2D}}_{\text{PAG}})^{-1}=1\) in this diagram. A wide spread of packet areas is identified. In order to evaluate whether the cooling rate has an influence on the distribution of packet sizes, an attempt is made to represent the holistic trend of the data. Based on the data recorded, as shown in Figure 8a, for each quenching rate, a curve of type  
\[g(x)=c_{1}\,x^{c_{2}}+c_{3} \tag{3}\]  
with the fitting constants \(c_{1}\), \(c_{2}\) and \(c_{3}\) is fitted with the nonlinear least squares method to represent the data. The resulting curves are shown in Figure 8b. Based on this, the packet size depends considerably on the PAG areas. Accordingly, a larger austenite grain tends to contain larger packets. However, a clear correlation of the quenching rate with the packet areas as a function of PAG areas is not identified. Figure 6: Comparison of mean and median prior austenite grain cross-section areas in the measured EBSD domain; numbers above bars indicate the number of evaluable grains in the domain. Figure 7: Empirical cumulative distribution function: (a) normalized packet cross section areas, (b) normalized block cross section areas. The same evaluation procedure is applied to the martensite block areas; see Figure 9. The curves fitted in Figure 9b show a lower dependency of the martensite block areas on theirs PAG area. However, again, no systematic influence of the cooling rate can be identified. The evaluation of the numbers of packets and blocks in each PAG leads to the same results; see Appendix B.
## 3 Results and Discussion  
### Batter Analysis  
#### 3.1.1 Color Evaluation  
Color can be used for the prediction of quality attributes for a large number of food products. The colorimetric study of batter was performed to obtain lightness values (\(L^{\star}\)), and color opponent dimensions such as \(a^{\star}\) and \(b^{\star}\) (Figure 1). The values of \(L^{\star}\) range from 0 (dark) to 100 (light). \(L^{\star}\) values showed a significant decrease from the control in the case of the partial (SaB-C, SpB-C) and complete replacement (Sa-C, Sp-C) of butter with chosen oleogels. The decrease in the \(L^{\star}\) value suggests a reduction in the batter's lightness. This supported a direct relationship between the decreased lightness and the replacement of animal fat as per the previous work [21]. The value of two chromatic components ranges from \(-\)120 to +120 [22]. For the \(a^{\star}\), the values range from green to red, and for \(b^{\star}\), it is blue to yellow. The \(``-\)ve\(\"\)\(a^{\star}\) values of all the batter samples indicated the presence of a green hue. These values remained similar for all the samples. \(b^{\star}\) is a measurement of hue where the \(\"+\)ve\(\"\) value indicates a yellow hue contrary to the \(``-\)ve\(\"\)\(b^{\star}\) value for the blue hue. The values of \(b^{\star}\) for all the batter appeared \(``+\)ve\(\"\), suggesting the existence of yellowish in the samples. The yellow pigmentation of lutein in wheat flour is the possible reason for such results [23]. Interestingly, the replacement of butter with oleogels, either partially or completely, resulted in the increased yellowness of the batter. Since the major portion of these oleogels is SO, which is light yellow, it may have contributed to the increased yellow chromaticity [24]. The quantification of the shade of yellow can also be completed through the calculation of YI. Similar to \(b^{\star}\) values, the \(YI\) values of all the batter samples showed a rise (\(p\) < 0.05) from the control. The \(\Delta E\) values were found in the range 30-50. The \(\Delta E\) values in the range 0.0-2.0 correspond to differences that are impossible to spot, \(\Delta E\) values in the range 2.0-3.5 can be spotted by a skilled observer, and \(\Delta E\) values higher than 3.5 corresponds to obvious changes in the samples' colors [25]. An observer with no expertise in evaluating colors could already be able to detect \(\Delta E\) greater than 2 [26].
###### Abstract  
In this work, high-performance pore-filled anion-exchange membranes (PFAEMs) with double cross-linking structures have been successfully developed for application to promising electrochemical energy conversion systems, such as alkaline direct liquid fuel cells (ADLFCs) and vanadium redox flow batteries (VRFBs). Specifically, two kinds of porous polytetrafluoroethylene (PTFE) substrates, with different hydrophilicities, were utilized for the membrane fabrication. The PTFE-based PFAEMs revealed, both excellent electrochemical characteristics, and chemical stability in harsh environments. It was proven that the use of a hydrophilic porous substrate is more desirable for the efficient power generation of ADLFCs, mainly owing to the facilitated transport of hydroxyl ions through the membrane, showing an excellent maximum power density of around 400 mW cm\({}^{-2}\) at 60 \({}^{\circ}\)C. In the case of VRFB, however, the battery cell employing the hydrophobic PTFE-based PFAEM exhibited the highest energy efficiency (87%, cf. AMX = 82%) among the tested membranes, because the crossover rate of vanadium redox species through the membrane most significantly affects the VRFB efficiency. The results imply that the properties of a porous substrate for preparing the membranes should match the operating environment, for successful applications to electrochemical energy conversion processes.  
pore-filled anion-exchange membranes; double cross-linking structures; alkaline direct liquid fuel cells; vanadium redox flow batteries; porous PTFE substrates Article
## 5 Discussion
### Convolution Patches and Number of Layers  
Although we approached this study expecting the pooling strategies to play the most relevant role performance-wise, we also observed patterns of improvement depending on the selected convolutionpatch (as presented in Figure 4) and the number of layers. Considering less information in the time domain for the convolution process leads to better mean accuracy rates. We found the difference between the highest and lowest mean accuracy rate in the convolution patch to be 0.036, which is slightly bigger than the difference between the lowest and highest mean accuracy rates between the tested pool strategies (0.032).  
On a related matter, the models with only one layer outperformed those with two layers in 21 (58.3%) of the 36 cases. As each time the pool patch is applied, the size of the input is reduced significantly, a large number of layers might produce an oversimplification of the input. In our preliminary research, models with three and four layers were tested for different tasks using the datasets described in this work; however, they performed poorly in comparison to models with only one or two layers. This situation might be different if the input we used did not consist of down-sampled data, therefore not falling into the oversimplification problem with the proposed CNN models.  
To analyze whether the down-sampling negatively affects the performance of the CNN, we used non-down-sampled data to test the model that achieved the highest accuracy as reported in Section 4. The results, which favor the down-sampled data, can be seen in Figure 5.  
By using down-sampled data, we could not only boost the accuracy with respect to the non-down-sampled data, but also shorten the training/testing times inherent in the size of the input.