esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliable Semantic Communication System Enabled by Knowledge Graph
## 3 System Model
### System Process
If \(X\) is sent, the received signal can be represented as  
\[Y=HX+N \tag{19}\]  
where \(H\) is the channel coefficient and \(N\sim\mathcal{CN}\big{(}0,\sigma_{n}^{2}\big{)}\) denotes the additive white Gaussian noise. After obtaining the received signal, the receiver will decode it to recover the transmitted knowledge graph. Defining \(C^{-1}(\bullet)\) and \(T^{-1}(\bullet)\) as the channel decoder and the source decoder, respectively, the received knowledge graph \(\hat{G}\) can be represented as  
\[\hat{G}=T^{-1}\Big{(}C^{-1}(Y)\Big{)} \tag{20}\]  
Then we use the proposed semantic restoration method \(G2S_{\varphi}(\bullet)\) to obtain the restored sentence \(\hat{S}\). \[\hat{S}=G2S_{\varphi}(\hat{G}) \tag{21}\]  
The process of the proposed semantic communication system is shown in Algorithm 3. ```
1:The transmitted sentence \(S\)
2:Transmitter:
3:Extract the knowledge graph by Algorithm 1
4:for\(i=1\) to \(n\)do
5: Compute the semantic importance of \(g_{i}\) by Equation (17)
6:endfor
7: Sort the knowledge graph according to the semantic importance
8: Adjust the number of transmitted triplets according to the channel quality
9:\(C(T(G))\to X\)
10:Transmit \(X\) over the channel
11:Receiver:
12: Receive \(Y\)
13:\(T^{-1}\big{(}C^{-1}(Y)\big{)}\rightarrow\hat{G}\)
14: Restore the sentence \(\hat{S}\) by Algorithm 2
15:Output: The restored sentence \(\hat{S}\)
```  
**Algorithm 3** Process of the proposed semantic communication system.
# Evaluation of a Microporous Acoustic Liner Using Advanced Noise Control Fan Engine +
###### Abstract
The two extreme responses from the microporous materials of specific particle size groups lead to the conclusion that both high sound absorption and transmission loss (energy dissipation) cannot be achieved by single-size particle distribution through the thickness. An alternative approach of different size particle distribution, in other words, a microstructural controlled graded pore size distribution approach using different size particles, is needed. This was demonstrated through both experimentation and predictive acoustic analyses in references [21; 24]. Also, the specific particle size distribution was established [24]. A graded (large to small pore size) \(\upmu\)PHB composite material (layers of different thicknesses but with a total thickness of 50 mm) was designed and developed to achieve an absorption coefficient (\(\alpha\)) \(\geq\) 0.50 and transmission loss (TLn) \(\geq\) 20 dB in the broad frequency range above 500 Hz. A novel concept of graded \(\upmu\)PHB composite material development, acoustic testing, and validation was demonstrated in the normal incidence case, but fabricating an acoustic liner and its assessment in the case of duct or grazing flow inside an aircraft engine needs to be tested and validated. The current study reports the fabrication of a microstructurally controlled graded \(\upmu\)PHB composite material liner that was designed and validated for normal incidence impedance and met the design requirements, showcasing successful results. The material technology was subsequently evaluated as an inlet liner in an Advanced Noise Control Fan (ANCF) engine. The NASA Glenn Research Center's ANCF low-speed test rig [25] was chosen. The ANCF engine is well known and has been used for many years in numerous research works. The liner specs include an inner diameter of 1219 mm (48\({}^{\prime\prime}\)), an outer diameter of 1321 mm (52\({}^{\prime\prime}\)), an axial length of 229 mm (9\({}^{\prime\prime}\)), and a liner thickness of 50 mm (2\({}^{\prime\prime}\)). We chose a two-layer graded material system with each layer being 25 mm (1\({}^{\prime\prime}\)) thick; the innermost layer was made of large-size particles with a mean particle diameter (d\({}_{\rm m}\)) of 625 \(\upmu\)m, and the outer layer was made of medium-size particles with a mean particle diameter of 160 \(\upmu\)m. The details are provided in the next section. The liner was tested at the Notre Dame Turbomachinery Lab Whitefield [26] test facility. The decibel (dB) loss in the sound pressure levels from this liner technology relative to the baseline configuration was evaluated to assess the impact of the proposed concept.
# Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data
## 2 Study Area  
Shanxi Province, located on the central Loess Plateau of Northern China, is characterized by typical loess hills and gully topography (Figure 1). The surface area of Shanxi Province is 156,700 km\({}^{2}\), and approximately 80.1% of the land area is covered with mountains and hills. As the largest coal-producing province, Shanxi Province accounts for one fourth of the total coal production in China [30]. Groundwater is exploited for food production, as well as for coal mining. The climate in most areas of Shanxi Province is semi-arid and is under the influence of the continental monsoon, with obvious zonal and vertical variations. The annual average temperature varies between \(-1\)\({}^{\circ}\)C and 14 \({}^{\circ}\)C, decreasing from south to north and from plain to mountain areas. The annual average precipitation varies from 400 mm in the northwest to 650 mm in the southeast. Approximately 70% to 80% of the annual precipitation occurs during the monsoon season from June to September.  
Figure 1: Location of Shanxi Province **(a)**; digital elevation map **(b)** and spatial distribution of irrigated and rainfed observation sites and survey points **(c)** across the study area. The irrigated and rainfed observation sites represent agrometeorological stations, while the survey points represent random selected ground survey points.  
In the study area, crop cultivation is vulnerable to drought due to little precipitation during critical growth stages. Smallholders comprise a large force in Shanxi's agricultural sector, and the most commonly planted crops include wheat, corn, millet, beans and tubers. Wheat is the most widely-grown staple crop throughout south central Shanxi; it is usually sown during mid-September to late October and harvested from mid-May to early July. The wheat-planting area in south central Shanxi Province was about 710,100 ha during 2010 to 2011. In general, irrigated areas are distributed throughout south central Shanxi. Irrigation activities mainly occur before dormancy, greening, jointing and grain filling, and irrigation is largely determined by the availability of surface and groundwater.  
Irrigated areas can be divided into the area equipped for irrigation and the actual irrigated area. The irrigated area is typically smaller than the area equipped for irrigation due to water availability, climatic conditions, economic factors, labor availability, land management decisions, _etc._ In this study, we focus on the actual irrigated area, which reflects the real irrigation conditions.
# Simulation-Based Performance Analysis for a Novel AVS/RS Technology with Movable Lifts
## 6 Conclusions  
In this paper, a novel AVS/RS with movable lifts (AVS/RS/ML) is proposed as an alternative design to existing AVS/RSs. In the proposed design, it is aimed to provide an inexpensive AVS/RS solution with highly utilized AGVs, capable of warehouse operations and can travel out-of-warehouse as transportation service. Two alternative designs based on I/O locations are experimented by simulating the systems under different tracking configurations and with different number of AGVs in the model. The results are compared mainly based on their throughput rate outputs under high utilization levels of AGVs (i.e., 95%). Two designs are experimented under different warehouse aisle volumes: 900 and 1800 totes. Besides, for each capacity, different number of tiers and columns are also experimented.  
From the simulation results, it is observed that in the proposed AVS/RS/ML, average utilization of AGVs is much higher than average utilization of the shuttle vehicles in the traditional SBS/RS. Hence, the performance of the proposed system should be explored more, by comparing its performance throughput rate with the other existing system designs. Besides, integrating smarter operating as well as routing congestion algorithms by comparing with the current simulated ones may also be explored to check whether or not it assures further significant improvements in the system performance.  
As a future work, more AGVs can be allowed to travel in an aisle by integrating optimal collision prevention algorithms. Increased number of AGVs can further be balanced with additional MLs. Besides, different tracking designs as well as velocity profiles can also be experimented as future works. Other aspects that would be worth focusing on as future works are operating and initial investment costs, safety costs, energy expenses, and space utilizations of that novel system.  
Conceptualization, B.J. and T.L.; methodology, B.Y.E. and M.K.; software, M.K.; validation, B.Y.E. and M.K.; formal analysis, B.J., B.Y.E., M.K., and T.L.; investigation, B.J.; resources, B.J., B.Y.E., M.K., and T.L.; data curation, B.J., B.Y.E., M.K., and T.L.; writing--original draft preparation, B.J., B.Y.E., M.K., and T.L.; writing--review and editing, B.J., B.Y.E., M.K., and T.L.; visualization, B.J. and M.K.; supervision, B.J., B.Y.E., and T.L.; project administration, B.J. and T.L.; funding acquisition, B.J. and T.L. All authors have read and agreed to the published version of the manuscript.  
This research work was supported by the Slovenian Research Agency (ARRS) in the framework of the applied research project entitled: \"Warehousing 4.0--Integration model of robotics and warehouse order-picking systems\"; Grant number: L5-2626.
## 2 Background: Electric Vehicle Charging Systems and Standards
### IEC 61851: Basic Signaling  
IEC 61851 [6; 7] defines how an EV and an EVSE can synchronize their states during a charge. It can only express simple states, such as connection status or readiness for charging, thus called _basic signaling_, and more complex information is left to the high-level communication defined in ISO/IEC 15118. In IEC61851, EV and EVSE exchange information by voltage levels and Pulse Width Modulation (PWM) through the _control pilot_ line. PWM indicates a state by _duty cycle_, the proportion of voltage-on time over a period.
## 3 Results
### Beer-Lambert Law at Low and High Energies  
Figure 2 demonstrates the log ratio plots generated to evaluate the validity of the Beer-Lambert law in the sample. To compare results with experimental measurements of Beer-Lambert behavior in STEM as demonstrated by [PERSON] et al. [22], simulations to produce the plots in Figure 2 were performed with a top-focused beam on silicon dioxide, with a collection angle of 8.8 mrad. The 300 keV beam (Figure 2A) produced a maximum linear thickness on the order of 350-550 nm, slightly underestimating the experimental value of 650 nm. Error may have been introduced since [PERSON] et al. employed an energy filter while this simulation did not, so these maximum thicknesses should be treated as order-of-magnitude estimates in comparison to experimental results. With this stipulation, the correct order of magnitude was achieved for lower energy STEM, so qualitative conclusions about the effect of incident energy may be drawn based on results from MeV-STEM.  
The 3 MeV beam (Figure 2B) produced a maximum linear thickness on the order of 4 \(\upmu\)m. For the same reason as the lower energy simulation, an exact value was not calculated, as a single camera distance and convergence semi-angle were not chosen. From the order-of-magnitude estimate, keeping all other factors equal, increasing incident energy from 300 keV to 3 MeV resulted in the applicability of the Beer-Lambert law to nearly 10 times the thickness range.  
The maximum linear depth of other materials at 300 keV was also estimated. For amorphous ice and a bottom-focused beam, this value was found to be 1.52 \(\upmu\)m. Compared to silicon dioxide, silicon had a maximum linear depth on the order of 300-400 nm with a top-focused beam. These results are compared to the maximum linear depth with 3 MeV beams in Section 3.5.  
Figure 1: Electron beam profile in 10 \(\upmu\)m thick sample. (**A**,**B**) Trace plot and calculated transverse beam profiles in crystalline silicon. (**C**,**D**) Trace plot and calculated transverse beam profiles in amorphous carbon. Dimensional values are in nm.
## 5 Experiments
### Experimental Results and Comparison on the Synthetic Data  
We evaluate the performance of the HGA on the synthetic data. Compared with TLBO-RTS and ABC-QAP, the proposed method HGA consistently outperforms the state-of-the-art algorithms in all databases. Table 13 shows the best-found solution (BFS) from trials, the averaged fitness value, averaged percentage deviation from BFS, and the averaged time to reach the reported fitness over 10 repeated executions for each database. All best-found solutions are derived by the proposed HGA.  
We observe that the proposed HGA method consistently outperformed the other methods in all the databases in terms of fitness and APD. As can be seen above, HGA produced better solutions (higher fitness values) with substantially low deviation while keeping up competitive computational time for the tested problems in all instances. These results demonstrate that our proposed algorithm is effective, in particular, for weighted item assignment situations. Finally, we show averaged fitness values with 50 repetitions according to the maximum time limit from 30 to 150 min with step size 30 for DB2 in Figure 7 to demonstrate the convergence of the proposed method. The values increase as the maximum time limit increases with a trend of saturation.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & \multirow{2}{*}{**BFS**} & \multicolumn{3}{c}{**TLBO-RTS**} & \multicolumn{3}{c}{**ABC-QAP**} & \multicolumn{3}{c}{**Proposed HGA**} \\ \cline{2-11}  & & **Fitness** & **APD** & **Min.** & **Fitness** & **APD** & **Min.** & **Fitness** & **APD** & **Min.** \\ \hline DB1 & 2306 & 2281.6 & 1.058 & 4.5 & 2295.8 & 0.442 & 45.3 & \begin{tabular}{c} * \\ 2306.0 \\ \end{tabular} & 0.000 & 16.2 \\ DB2 & 5162 & 5027.0 & 2.615 & 29.6 & 5037.8 & 2.406 & \begin{tabular}{c} * \\ 5105.8 \\ \end{tabular} & 1.089 & 49.7 \\ DB3 & 8086 & 7898.8 & 2.315 & 29.0 & 7925.2 & 1.989 & 76.5 & \
\begin{tabular}{c} * \\ 8040.4 \\ \end{tabular} & 0.564 & 69.8 \\ DB4 & 12,554 & 12,301.6 & 2.011 & 39.3 & 12,310.0 & 1.944 & 57.5 &
\begin{tabular}{c} * \\ 12,464.8 \\ \end{tabular} & 0.711 & 78.7 \\ \hline \hline \end{tabular} Column “APD” stands for the average percentage deviation from the best-found solution from all trials. Columns “fitness” and “min.” represent averaged fitness and average time to reach the found fitness values in minutes, respectively. “’ means that rectangular partially matched crossover (RPMX) is statistically significant with significance level of 0.01 in comparison with ABC-QAP by paired \(t\) test for each instance.  
\end{table}
Table 13: Comparison of the TLBO-RTS, ABC-QAP, and HGA on synthetic data.  
Data Availability Statement: Available via [[https://github.com/bzantium/HGA-RPMX](https://github.com/bzantium/HGA-RPMX)]([https://github.com/bzantium/HGA-RPMX](https://github.com/bzantium/HGA-RPMX)) (accessed on 19 January 2021).  
The authors acknowledge no other support given than what is covered by the authors contribution or funding sections.  
The authors declare no conflict of interest.
## 1 Introduction
The study aims to elucidate the effects of the PAA on physicochemical properties of CA membranes with a special focus on the impact of the blended PAA/CA membranes in the Cadmium (Cd) remediation. Overall, the blending of PAA/CA addresses two main issues associated with UF membranes: (i) mitigation of the fouling phenomena and (ii) the improvement of the efficiency of the CA membrane on heavy metal rejection.
# Design of a Piezoelectrically Actuated Ultrananocrystalline Diamond (UNCD) Microantilever Biosensor
## 2 Materials and Methods
### Coupled Simulation Actuator  
Using the same methodology described in Section 2.1, the resonant frequency of the integrated piezoelectric actuator structure combined with either a UNCD or Si microcantilever was estimated. A mass of 1 picogram (pg), uniformly distributed across the surface of the UNCD or Si microcantilever, was then modeled using the Added Mass node in the Solid Mechanics module. This approach allows for the evaluation of the resonant frequency shift resulting from surface mass loading.  
Figure 3: Geometric model of the piezoelectric actuation-based structure for the microcantilever biosensor. The dotted line represents the fixed boundary conditions.  
If the resonant frequency does not shift by at least 2 kHz, the microcantilever structure is modified by reducing the beam length. Shortening the beam increases its resonant frequency due to the corresponding reduction in mass. Additionally, since the sensitivity of a rectangular microcantilever (as shown in Equation (4)) is inversely proportional to the cube of its length (\(1/L^{3}\)), decreasing the beam length significantly enhances the frequency shift and, consequently, the overall sensitivity.  
After the optimization was finished, a more realistic mass distribution model was implemented to test the frequency response of the microcantilever. In this case, the mass was simulated by using the Added Mass Node but using a Gaussian distribution function to constrain the mass distribution to a certain area on the microcantilever. Equation (7) was used as the mass distribution function.  
\[m=\Big{(}\,1\times 10^{-15}\,\mathrm{kg}\Big{)}\times\,\exp\!\left(-\frac{(x-x_ {0})^{2}+(y-y_{0})^{2}}{r^{2}}\right) \tag{7}\]  
where \(x\) and \(y\) represent the coordinates across the microcantilever beam, \(x_{0}\) and \(y_{0}\) are the coordinates where the center of the mass distribution is located, \(r\) is the variance in the mass distribution, which constrains the distribution to a certain region, and \(\exp\) represents the Euler number. The maximum value of the mass distribution is reached exactly at the center of the region and decreases as the function takes a value away from the center. The value of \(r\) was set to 5 \(\upmu\)m to evaluate the distribution at \(y_{0}\) 40 \(\upmu\)m, 30 \(\upmu\)m, and 20 \(\upmu\)m. The value of \(x_{0}\) was kept constant at 0 \(\upmu\)m.
# Super-Resolution Lensless Imaging of Cells Using Brownian Motion
## 3 Results and Discussion  
The cell images obtained by the lensless imaging system were registered using two-step motion estimation. Then, through the above-mentioned method, the final red blood cell images, which had an 8\(\times\) super-resolution reconstruction, were obtained as shown in Figure 3.  
Figure 3 shows that the resolution of the direct reconstruction of the focus plane from the raw hologram was very low, and the resolution greatly improved after super-resolution reconstruction. The high resolution reconstruction of the focused cell images was comparable to the 10\(\times\) objective lens imaging results. The numerical aperture (NA) of the objective lens was 0.45, so the spatial resolution of the objective lens was 0.61 \(\upmu\)m. The pixel size of CIS in the proposed system was 2.2 \(\upmu\)m. According to the [PERSON] criterion, the spatial resolution of the original image obtained by the lensless imaging system was 4.4 \(\upmu\)m. The proposed algorithm improved the resolution of the image by eight times, and the spatial resolution could reach 0.55 \(\upmu\)m. Therefore, the 10\(\times\) objective lens (NA = 0.45) is used for the reference system in this paper.  
To establish a clearer representation of the increasing resolution, Figure 4 presents the grayscale change of the different resolution result in Figure 3. As the multi-frame super-resolution does not require cell flow, this super-resolution method can image the same cell over a long period of time. In some special cases, the single-cell super-resolution algorithm based on Brownian motion can solve the Brownian motion problem of cells in suspension, and lays the foundation for the application of a lensless system.
## 4 Experimental Validation
### Comparison with Other Methods  
To further verify the effectiveness of the methods proposed in this paper, other benchmark algorithms are used as a reference for comparison. Including the results of analyzing the original data and multi-parameter (mean value, root mean square value, variance, kurtosis value, minimum value, maximum value, and peak-to-peak value) sample set by using the DAE algorithm. It also includes results obtained by inputting raw data as well as un-enhanced, WSC pre-processed 2D images into the CNN model. The results are shown in Figure 9. The original time-domain data are directly inputted into the deep autoencoding model, and the classification accuracy rate is about 83.54%. Further extract multiple feature parameters of the time domain signal and input them into the deep auto-encoding model; although the classification accuracy has been improved, the average accuracy of the multiple classification results is only 88.29%. If the time domain signal is directly input to the one-dimensional CNN model, only 71% of the recognition accuracy can be obtained. Applying the method proposed in this paper, the one-dimensional time-domain signal is converted into two-dimensional data by calculating the WSC and then inputted into the two-dimensional CNN model for analysis, which can accurately identify 93.18% of the fault types. Further using the method of image segmentation to augment the data can improve the recognition accuracy to 97.86%.  
Use t-SNE to visualize the characteristic manifolds obtained by the one-dimensional CNN model, as shown in Figure 10. The monitored vibration data under the healthy condition can be gathered together well, and it is more distinguishable from other fault types. Compared with Figure 8, the other three types of fault data have relatively small intra-class aggregation and inter-class differences. This is attributable to the fact that in the case of large external interference, the early fault features are weak and easy to be overwhelmed by random noise components, which affects the matching of target information. After the pre-processing of the feature enhancement, the characteristic information related to the fault is clarified. The difference of different fault feature information is used to improve the recognition ability of the model for different types of faults.  
Figure 8: Visual graph after state feature learning by t-SNE.
# A Low-Cost Video-Based System for Neurodegenerative Disease Detection by Mobility Test Analysis
## 2 Materials and Methods
### Acquisition Setup  
For the data acquisition, a low-cost RGB monocular camera was installed in two different nursing institutes [38]. The camera used in this work was the HIK-Vision DS-2 CD2385 FWD-I camera with a 2.8 mm focal length and a 4K resolution at 20 fps. The camera was installed in the gym of the institutes, where people usually execute mobility tests, so the medical staff periodically can monitor the motion abilities of older people following a defined protocol. The camera was placed as shown in Figure 2, at a height \(h\) = 2 m above the floor and a horizontal distance \(d\) = 3 m from the wall against which the chair was leaning. The camera was tilted down to an angle of about 45\({}^{\circ}\).