esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Implementation
#### 3.2.4 Entity Enrichment & Graph Representation
Due to the rich internal structure that characterizes labeled property graphs, allowing each node or relationship to store more than one property (thus reducing the graph's size), we used the Neo4J graph database based on labeled property graphs for storing, representing, and enriching the extracted relationships [82]. Neo4J is a Java-based graph database management system, described by its developers as an ACID-compliant transactional database with native graph storage and processing. It provides a web interface allowing relationships to be queriedvia CYPHER, a declarative graph query language that allows for expressive and efficient data querying in property graphs. The final structure of the enriched triple extraction output following the above processes is shown in Table 3 for a single triple. The graph representation consists of three distinct types of nodes which are linked via three types of edges:  
* **Corpus** nodes (purple): they signify the ingested text (e.g., scientific articles), are represented by the _article_title_ and the unique _article_id_ properties, and are connected to one or more triples via the _contains triples_ one-directional edge,
* **Subject** nodes (green): they denote the subject of the extracted triple and are connected to one or more corpora (as mentioned above), as well as to one or more **Objects**, via _predicate_ one-directional edges. The **Subject** node has the following properties: _value_: the natural language text of the subject; _subj_entity_name_: the name(s) of the entities linked to UMLS concepts; _subj_entity_coded_: the coded ID(s) of the entities linked to UMLS concepts; _subj_entity_description_: a short description of the linked entities; _subj_entity_confidence_: the reported confidence of the entity linking process; _article_id_: the unique ID of the extracted article; _article_title_: the title of the extracted article; _sentence_text_: the text of the processed sentence; _sent_num_: the serial number of the sentence in the whole corpus; _triple_num_: the serial number of the triple extracted from the given sentence; and _engine_: the OIE engine that from which the triple was extracted from. * **Object** nodes (red): they denote the object of the extracted triple and are connected to their respective **Subject** through the _predicate_ edge (as mentioned above), as well as with the \"Subject\" of the following triple in the corpus (if there exists one) via the _followed by_ one-directional edge. Their properties are identical to those of the **Subject** nodes. An example figure of the graph representation that concentrates the aforementioned nodes and edges of one CORD-19 article is in Figure 3.
## 3 Empirical Results and Analysis
### Robustness Test
#### 3.2.2 Counterfactual Test  
The premise of using the DID method is that the treatment group and the control group are comparable. If there is no influence of the APCP Action Plan policy, the \(GTFP\) of the treatment group and the control group will not change with time and produce significant differences. In this paper, the time of policy implementation is revised from 2014 to 2009 and 2010. According to benchmark Models (1) and (2), we conducted a test that was consistent with the benchmark regression.  
Figure 1: Box plot of parallel trend test (based on FE (4)).  
The estimated regression results of the counterfactual test are shown in Table 5. If the time of policy implementation is advanced to 2009 or 2010, the coefficients of Policy2009 and Policy2010 are not significant. The results of the counterfactual test show that before 2014, the APCP Action Plan policy has no significant impact on the _GTFP_ of the treatment group and the control group; this means that the actual policy pilot year can significantly improve _GTFP_. Therefore, the conclusions of this paper are quite robust.  
Figure 3: The graph of the policy estimation coefficient of 10,000 random sampling experiment panels with a bandwidth of 0.1387 (fixed-effect model).  
Figure 2: The panel OLS estimation graph of the policy estimation coefficient of 10,000 random sampling experiment panels with a bandwidth of 0.1449.
## 4 Mobility and Environmental Data Integration: System Overview
### System Architecture
#### 4.2.1 Georeferenced Data Collector  
Mobility, pollution, meteorological and geographical maps data are fed from online databases and websites to the EMDI system's data collector component. Mobility data pours as a series of Comma Separated Value (CSV) files containing a set of geographical coordinates and mobility measurements. Also, pollution and meteorological data are collected as series of NetCDF or GRIB files encompassing two-dimensional tuples of pollution and meteorological variables. It is composed of a set of geographical coordinates  
Figure 5: Architecture overview of EMDI system.  
and a value indicating the concentration of the pollution or meteorological target variable in those locations (for example, PM\({}_{10}\) concentration). The geographical maps are represented as GeoJSON or shapefiles containing a set of polygons that represent a geographic area, from which pollution and mobility data have been captured.  
This heterogeneous data is gathered automatically by the data collector. In addition to retrieving data from online sources, this component can convert pollution and meteorological GRIB and NetCDF formats into CSV counterparts for subsequent analytical tasks.  
The transformations applied to GRIB and NetCDF data are executed through appropriate libraries provided over-the-counter by our system. Data integrator is also responsible for storing the output CSV files of pollution, meteorological and mobility data, and the GeoJSON/shapefiles maps in the distributed cloud storage where they can be imported by analytical engines and other analytical services.
## 2 Quality Assessment of Water Level through Calibration and Validation
#### 2.1.3 Absolute Calibration, Validation of SARAL/AltiKa over the Lake Issykkul
We then compare the GPS vertical coordinates along the track 554 with the individual measurements of the lake altitude above the ellipsoid using AltiKa measurements. We thus perform the calculation of the absolute bias using the ocean and the ice1-OCOG (Offset Centre Of Gravity) retrackers:  
* With the ocean retracker, the water level bias, which is averaged along the track 554 and using the first 15 cycles of the satellite is given in Figure 7. The absolute bias obtained is: \(-\)52 \(\pm\) 24 mm. * With ice1-OCOG retracker, the water level bias, which is averaged along the track 554 and using the first 15 cycles of the satellite, is given in Figure 8. The absolute bias obtained is: \(+\)34 \(\pm\) 19 mm. Figure 7: Differences between the GPS profiles and SARAL/AltiKa water altitude above the ellipsoid with ocean retracker at reference points corresponding to the GPS points along the SARAL/AltiKa track 554.
## 4 Numerical Simulation Model
### Grid Independence Verification  
The grid size plays a pivotal role in determining the accuracy of numerical simulations, a phenomenon commonly known as grid independence verification. In this study, grid independence is validated through the utilization of three distinct grid datasets. Each of the three datasets adheres to separate grid reconstruction criteria, as detailed in Table 3. As depicted in Figure 6, with consistent cross-sectional parameters in relation to the prior specifications, the time-averaged fluid velocity profiles of the three grid sets during a given time period were observed. By considering a fine mesh, which boasts the highest mesh count, as the reference benchmark, the deviations of the remaining two types of grids from the fine mesh's average fluid velocity over time were computed.  
\[\text{Deviation 1}=(\text{coarse mesh}-\text{fine mesh})/\text{fine mesh}) \times 100\% \tag{14}\]  
\[\text{Deviation 2}=(\text{medium grid}-\text{fine mesh})/\text{fine mesh}) \times 100\% \tag{15}\]  
As illustrated in Figure 7, it is evident that both deviations exhibit a rapid ascent from 0 at the initial time, signifying substantial disparities in the calculated results of this model when subjected to different grid sizes during the initial phase. However, within a span of 0.05 T, both deviations experienced a considerable reduction. During this time frame, the deviations did not surpass 5%, and Deviation 1, in particular, remained below 1% and exhibited a smaller value compared to Deviation 2. In light of a combined assessment that considers the magnitude of deviation, the average rate, and operation hours, the grid configuration of coarse mesh is designated as the optimal grid model for subsequent numerical simulation computations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Grid** & **Number of Grids** & **Grid** & **Minimum** & **Local** & \\  & & **Quality Range** & **Grid Size** & **Encryption Size** & **Operation Hours** \\ \hline coarse mesh & 10,289 & 0.67, 0.98 & 1 mm & 0.1 mm & 12 h \\ medium grid & 23,867 & 0.68, 0.98 & 0.5 mm & 0.05 mm & 16 h \\ fine mesh & 196,515 & 0.54, 0.97 & 0.1 mm & 0.01 mm & 24 h \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter settings for dynamic mesh models.
# Photo-Thermoelectric Conversion of Plasmonic Nanohole Array
## 2 Materials and Methods  
Figure 1a shows a schematic of a Ag thin-film photodetector with nanoholes fabricated on a substrate. A positive resist film (Nihonzeon ZEP520A) with a thickness of 216 nm was prepared on a glass substrate by spin-coating. A pattern of nanoholes was fabricated over an area of \(500\times 500\)\(\upmu\)m\({}^{2}\) by electron beam lithography (JEOL; JBX-6300 FS). The periodicity of the nanohole array was varied; 250, 300, and 400 nm were all used for inter-hole spacings in different versions of the sample. Then, Ag thin film (40 nm thickness, 2.0 mm width, and 6.0 mm length) was deposited on the resist film by thermal evaporation. This fabrication process created a Ag nanodot at the bottom of each nanohole. Figure 1b shows a field-emission scanning electron microscopy (FE-SEM; Hitachi High-Technologies Corporation; SU8010) image of a top view of the nanohole pattern. Reflection spectra for the Ag nanohole array were measured using a microscopic spectrometer (Ocean Optics HR4000). To carry out thermoelectrical testing of the nanohole-array Ag device, an area encompassing the nanohole pattern was irradiated by a diode laser (Thorlabs) (Supplementary Materials Figure S1); the beam cross-section diameter and the beam full width at half maximum (FWHM) were 70 \(\upmu\)m and 50 \(\upmu\)m, respectively. The optical power and power density of the laser beam were 10 mW and \(2.6\times 10^{2}\) W/cm\({}^{2}\), respectively. Gold wires were connected to both edges of the Ag thin film using Ag paste. The distance between the two areas of Ag paste was 4.5 mm, and the edge of the nanohole area was located at 0.5 mm away from the edge of the nearest Ag paste area. The photocurrent generated in the Ag thin film was measured using a digital multimeter ([PERSON] 2000). In fact, the location of the Ag nanohole area was optimized to maximize photocurrent because the location of the illuminated area determines the photocurrent flowing between the two Ag paste islands, based on thermoelectric principles. For this reason, the Ag nanohole area was placed very close to one of the Ag paste islands.
## 7 Discussion
### Potential Interference of the Power Grid  
As mentioned earlier, this method is designed specifically for transformers that experience power outages, ensuring that the demagnetization process does not interfere with the power grid. The generated current pulse is confined within the transformer winding and will not propagate to the power grid. This isolation is crucial for maintaining grid stability and preventing any unexpected electromagnetic interference. In addition, the rapid execution of this method (usually within one minute) further reduces the risk of any residual impact on the surrounding electrical environment.
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 5 Simulations and Discussion of Results
Likewise, we present the performance comparison of the estimators assuming Non-PR condition, as depicted in Figure 25a,b. In a similar trend, the RLS CE scheme demonstrates the best performance as compared with the other estimators. In addition, all the adaptive CE schemes offer better performance in comparison with the linear CE schemes. Moreover, comparing the NPR filter design performance curves (Figure 24a,b) with the performance curve of the Non-PR filter design (Figure 25a,b) for the GFDM-OQAM system set-up, we observe that the CE schemes offer better performance when the NPR filter design is assumed. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Specification**} \\ \cline{2-3}  & **OFDM-OQAM** & **GFDM-OQAM** \\ \hline No. of Sub-carriers & 512 & 512 \\ Signal Constellation & 16-QAM-OQAM & 16-QAM-OQAM \\ Sampling Frequency & 10,000 Hz & 10,000 Hz \\ Doppler Frequency & 10 and 200 (Hz) & 10 and 200 (Hz) \\ Sampling Rate & \(\frac{1}{10^{3}}\)s & \(\frac{1}{10^{3}}\)s \\ Doppler Rate(s) & 0.001 and 0.02 & 0.001 and 0.02 \\ Overlapping Factor & 4 & 4 \\ Filter Coefficient (\(\hat{P}[1]\)) & 0.97195983 & 0.97195983 \\ Channel Type & Rayleigh fading & Rayleigh fading \\ Cyclic Prefix Overhead & \(-\) & 25\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Simulation parameters for FBMC-OQAM waveforms. Figure 22: Performance comparison in terms of NMSE versus Non-PR error at SNR = 5 dB.
# Secure Outsourced Blockchain-Based Medical Data Sharing System Using Proxy Re-Encryption +
## 4 The Proposed Scheme
### The Re-Encryption Processes
#### 4.3.2 User Registration  
After system initialization, the system is ready to receive user registration. In this process, secret and public keys are generated, where each key is composed of two variables.  
The system verifies the validity of user \(u_{i}\), who tries to register;  
If the validity test is passed, then the system assigns param to \(u_{i}\);  
User \(u_{i}\) selects \(x_{i,1},\ x_{i,2}\in\mathds{Z}_{q}^{*}\), and sets \(\mathtt{sk}_{i}\) as \((x_{i,1},\ x_{i,2})\);  
User \(u_{i}\) calculates \(pk_{i,1}=g^{x_{i,1}}\mod q\) and \(pk_{i,2}=g^{x_{i,2}}\mod q\), and sets public key \(\mathtt{pk}_{i}\) as \((pk_{i,1},pk_{i,2})\);  
User \(u_{i}\) sends the public key to the system manager.  
Specifically, we let \(\mathtt{sk}_{M}=\{x_{M,1},\ x_{M,2}\}\) and \(\mathtt{pk}_{M}=\{g^{x_{M,1}},\ g^{x_{M,2}}\}\) be the secret and public keys of the system manager, respectively.
## 2 Background and Literature Review
### Benchmarking Methods
If poorly designed, human interactions with a machine can lead to excessive operator workload due to the difficulty understanding the machine's current intent during a task. * Situational awareness of human and machines: Humans must have awareness of the tasks, systems, and environments, as well as adequate awareness of the status of the machine and vice versa [42]. The desired incremental gains in safety and efficiency produced by HMT could be less than expected unless they are designed properly, taking into account the factors noted above. A poorly designed HMT might even create new risks and hazards that threaten the safety and efficiency of a team. Therefore, it is important to measure the impact of HMT on operator and system outcomes. Evaluations of HMT designs necessitate methods and metrics for assessing effectiveness and efficiency, yet such metrics are currently lacking. The effectiveness of HMT must be measured across several dimensions, such as mission effectiveness, machine behavior, human performance, and human-machine collaboration.
# Synchronization, Control and Data Assimilation of the Lorenz System
## 2 Master-Slave Synchronization
### Conditional Coupling  
In the original Pecora-Carrol implementation [15], the signal from the master is fully extracted, i.e., \(p=1\), so that, for the \(\mathbf{R}\) system, either a component is untouched or it is derived from the replica (\(\mathbf{r}\)).  
To be explicit, for the Lorenz system we have  
\[\dot{x} =\sigma(y-x), \tag{16}\] \[\dot{y} =-xz+\rho x-y,\] \[\dot{z} =xy-\beta z.\]  
For instance, for a full coupling along the \(x\) direction (\(\mathbf{C}=[1,0,0]\)), the replica will follow the equation  
\[X =x, \tag{17}\] \[\dot{Y} =-xZ+\rho x-Y,\] \[Z =xY-\beta Z.\]It is possible to define the sub-Lyapunov [14] or, better, the conditional Lyapunov exponents [15], by iterating the equation for the difference \(\delta r\) in the tangent space. For instance, for the Lorenz system coupled along the \(x\) direction (\(\mathbf{C}=[1,0,0]\)), we have  
\[\frac{\mathrm{d}}{\mathrm{d}t}\binom{\delta y}{\delta z}=\binom{-1}{x}\cdot \binom{\delta y}{\delta z}, \tag{18}\]  
giving two conditional exponents. The system synchronizes if both of them are negative.  
In Figure 1, we report the value of the maximum conditional Lyapunov exponent as a function of the coupling strength \(p\) for the \(\mathbf{C}_{x}=[1,0,0]\), \(\mathbf{C}_{y}=[0,1,0]\), and \(\mathbf{C}_{z}=[0,0,1]\) coupling directions.  
As noted also in Ref. [15], the Lorenz system synchronizes if coupled along the \(x\) and \(y\) directions, but not along the \(z\) one, even for \(p=1\).  
Our observable was the average distance \(d\),  
\[\begin{split} d(t)&=||\mathbf{r}(t)-\mathbf{R}(t)||=\sqrt{ (x(t)-X(t))^{2}+(y(t)-Y(t))^{2}+(z(t)-Z(t))^{2}},\\ d&=\frac{1}{T}\int_{0}^{T}d(t)\mathrm{d}t,\end{split} \tag{19}\]  
computed after a proper transient.  
In Figure 2, we show \(d\) as a function of the coupling strength \(p\) for different coupling directions. For all the simulations, we set \(\sigma=10\), \(\beta=8/3\), \(\rho=28\), and we use the Euler integration scheme to integrate the equation with a temporal step \(\mathrm{d}t=10^{-3}\). After a transient time of free evolution, we couple the master and the slave system at every time step. At \(T=100\) (simulation end time), we compute the distance \(d\) between the two systems.  
Figure 1: Conditional Lyapunov maximal exponent for different coupling directions: \(\mathbb{C}_{x}=[1,0,0]\), \(C_{y}=[0,1,0]\), \(C_{z}=[0,0,1]\). The black dotted lined marks the zero value while the blue continuous line marks the value of the Lyapunov exponent. In the last one, in orange, we also show the distance \(d\) (normalized at its maximum value obtained in the simulation) between the master and slave state variables for different coupling strengths (see also Figure 2).  
Figure 2: Asymptotic distance \(d\) as a function of the coupling strength \(p\) for different coupling directions. From left to right: \(\mathbf{C}=[1,1,1]\), \(\mathbb{C}_{x}=[1,0,0]\), \(\mathbf{C}_{y}=[0,1,0]\).
## 2 Theoretical Analysis and Improvement Research on Finite Element Models
### Theory of Finite Element Analysis for Electromagnetic Fields
This process results in the assembly of the global matrix equation: [K] [X] = [P], where [K] represents the global stiffness matrix, [P] denotes the applied conditions, and [X] is the vector of unknown variables to be solved for. By solving for the large sparse matrix within this global matrix equation, the electromagnetic field distribution across the entire domain can be obtained.
## 3 On-Site Measurements
### Tunnel Section
#### 3.1.3 Force Density Spectra  
Using the admittance and immission measurements, the force density spectra were determined for each train class as described in Section 2.3. Point transfer admittances of the relevant receiver points were converted to line transfer admittances using correction factors according to the relationship described in Section 2.1.1. Figure 11 shows the resulting force density spectra in root-mean-square amplitudes for different train types along the tunnel section. The solid lines indicate the mean force per speed class, and the surrounding hatched area represents the range between the 5 th and 95 th percentiles of the respective category. Again, the recorded number of trains per class is shown in brackets.  
Figure 10: Examples of vibration velocity spectra in one-third octave bands for different train types recorded at the abutment sensors over 4 weeks inside the tunnel at Q1.
# A Real-Time GNSS/PDR Navigation System for Mobile Devices
## 3 Results
### GNSS/PDR Integration
#### 3.1.1 Best Performance  
In the best performance case, the GNSS signal is continuously detected and therefore for each detected step the KF updates the state estimate by merging the inertial data with the last GNSS reading. It is important to note that the GNSS chipset, in addition to the position solution expressed in latitude and longitude values, provides a value that represents the accuracy associated to the position and therefore the reliability of the data at that instant of time. In this way, the KF gives more or less importance to the value read from the GNSS.  
Figure 9 shows the results for this case. It is possible to notice how the path that is reconstructed only from the inertial data (blue line) deviates from the others due to the accumulation of errors, as foreseen by the PDR method. The position results related to GNSS data, on the other hand, is strongly influenced by the accuracy with which the position solutions are determined and being an urban route it is not always sufficiently high.  
It is also possible to note that the signal reconstructed by the KF inherits the advantages of both methods: the smoothness of the PDR navigation and the bounded position error provided by the GNSS. In the area where the satellite signal has poor accuracy, the filter properly weights the GNSS information without following the latitude and longitude readings.  
On the other hand Figure 10 shows the GNSS position accuracy along the test: the average value is less than 50 m, but in some conditions the accuracy can rise up to 250 m, making the relative latitude and longitude readings useless.  
Figure 9: Comparison between the reconstructions performed by the PDR data, the GNSS data and the best performance fusion algorithm.
# Effects of Nonlinear Damping on Vibrations of Microbeam
## 3 Results
### Non-Resonant Hard Excitation  
When the frequency of the load \(\Omega_{1}\) is further than the modal frequency \(\omega\), the condition for eliminating secular terms of Equation (17) can be written as:  
\[i2\omega\big{(}A^{\prime}+c_{1}A\big{)}+(i2c_{2}\omega+6d)\Lambda^{2}A+(ic_{2} \omega+3d)A^{2}\overline{A}=0 \tag{18}\]  
Suppose  
\[A=\frac{1}{2}\alpha\exp(i\beta) \tag{19}\]  
where \(\alpha\) and \(\beta\) are real. Substituting Equation (19) into Equation (18), and separating the real and imaginary parts, we obtain:  
\[\alpha^{\prime}=\Big{(}-c_{1}-c_{2}\Lambda^{2}\Big{)}\alpha-\frac{1}{8}c_{2} \alpha^{3},\ \alpha\beta^{\prime}=\frac{3d}{\omega}\bigg{(}\Lambda^{2}+\frac{1}{8}\alpha^{ 2}\bigg{)}\alpha \tag{20}\]  
For steady-state solutions of Equation (20), we obtain \(\alpha^{\prime}=\beta^{\prime}=0\). Therefore, \(\alpha\) and \(\beta\) are the solutions of  
\[\Big{(}-c_{1}-c_{2}\Lambda^{2}\Big{)}\alpha-\frac{1}{8}c_{2}\alpha^{3}=0,\ \frac{3d}{\omega}\bigg{(}\Lambda^{2}+\frac{1}{8}\alpha^{2}\bigg{)}\alpha=0 \tag{21}\]  
The first-order approximate solution of the non-resonant hard excitation can be written as:  
\[\eta=\varepsilon[\ \alpha\cos(\omega T_{0}+\beta)+\Lambda\cos(\Omega_{1}T_{0})] \tag{22}\]  
From Equation (21), we obtain \(\big{(}-c_{1}-c_{2}\Lambda^{2}\big{)}\alpha-c_{2}\alpha^{3}/8=0\). This indicates \(\alpha=0\) due to \(c_{1}>0\) and \(c_{2}>0\). Therefore, the free-oscillation solution will decay with time and the first-order approximate solution will only include the composition excited by the external loads. This outcome can be confirmed by numerical simulations of Equation (11), as shown in Figure 2. While the free-oscillation term is decaying, its frequency is a function of the amplitude \(\alpha\).