esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Cryptographic Key Encapsulation and Recovery Scheme in Noisy Network Conditions
## 4 CRP Mechanism
###### Operating a terminal device in noisy networks
Biometric PUFs find diverse applications, notably in secure access control systems, identity verification for financial transactions, as well as safeguarding sensitive data within healthcare and governmental contexts. In the key generation process, facial enrollment initiates the collection of facial landmarks through multiple iterations. To generate unique responses, various techniques (such as landmarking techniques) where the Euclidian distances between a substantial landmark point on the face and a random point are utilized. Another technique known as the Johnson-Lindenstrauss lemma algorithm can also be used to project a higher dimensional face to a lower dimension. In the key recovery procedure, one facial scan is performed and the challenge is used to compute distances between landmarks and the fixed point, thereby producing a subset of responses essential in key recovery. Potential errors arising from variations in lighting conditions or facial expressions are mitigated through the application of the key recovery mechanism discussed in Section 5.
## 3 Results and Discussion
### Responses of Vegetation Dynamics to Climatic Drivers
#### 3.3.3 Impacts of Time Lag on the Regression Analyses
[PERSON] _et al._[58] looked into the lag effects on the seasonal NDVI of the whole grassland in northern China by correlating the seasonal NDVI with the previous seasonal climatic factors, suggesting that the summer precipitation had lag effects on the autumn NDVI, but they did not discuss the lag effects on growing season NDVI. [PERSON] _et al._[19] calculated the distributed lag time for monthly NDVI in China, based on the number of months after which the NDVI would \"show the highest significant correlation coefficient with previous temperature or precipitation\", finding out that around 80% areas showed zero and one-month lag and only part of the southern China showed longer lags yet less than 6 months. In our case, instead of discussing at a monthly scale, we assembled NDVI and climatic drivers into growing season, with lengths of 6 to 12 months, and the spring, summer and autumn with lengths of 3 months. The longer lengths of these time periods could attenuate the effects of time lag, compared to the lag effects at a monthly scale. Specifically, except for the climatic factors 1 to 2 months ahead of the growing season that were \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Climatic Driver** & **Growing Season** & **Spring** & **Summer** & **Autumn** \\ \hline Temperature & 4.13 & 5.12 & 0.52 & 1.81 \\ Precipitation & 3.99 & 1.05 & 12.29 & 14.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Percentage of area showing significant lag effects of temperature and precipitation on the average NDVI of growing season, spring, summer and autumn (%). not considered, the lag effects of climatic factors within the growing season would not be expressed at the time scale of the whole growing season. For example, the temperature in July has been included in the growing season temperature, which may affect the NDVI in August or September, which is also included in the growing season, but would not be expressed to affect the average NDVI of the whole growing season. Hence the time lag effects may not be as significant as those in the previous studies. These different conclusions in the previous and current studies drawn with different methods reveal the complexity and uncertainty in the study of time lag itself. In this case, the effects of time lag would not be considered in the results of our regression analysis, though still deserve further investigation.
###### Abstract  
In order to improve the convenience and practicability of home rehabilitation training for post-stroke patients, this paper presents a cloud-based upper limb rehabilitation system based on motion tracking. A 3-dimensional reachable workspace virtual game (3D-RWVG) was developed to achieve meaningful home rehabilitation training. Five movements were selected as the criteria for rehabilitation assessment. Analysis was undertaken of the upper limb performance parameters: relative surface area (RSA), mean velocity (MV), logarithm of dimensionless jerk (LJ) and logarithm of curvature (LC). A two-headed convolutional neural network (TCNN) model was established for the assessment. The experiment was carried out in the hospital. The results show that the RSA, MV, LC and LJ could reflect the upper limb motor function intuitively from the graphs. The accuracy of the TCNN models is 92.6%, 80%, 89.5%, 85.1% and 87.5%, respectively. A therapist could check patient training and assessment information through the cloud database and make a diagnosis. The system can realize home rehabilitation training and assessment without the supervision of a therapist, and has the potential to become an effective home rehabilitation system.  
Keywords:stroke; upper extremity; motor function; virtual game; convolutional neural network +
Footnote †: journal: Applied
## 2 Materials and Methods
### Quantitative Criteria for the Evaluation  
Due to the complexity of the process of generating the final point clouds, it is difficult not only to predict their accuracy, but also to define the general and objective procedures to assess the IMMS  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Path 1** & **RMS (m)** & **Path 2** & **RMS (m)** \\ \hline Kaarta Stencil & 0.050 & Kaarta Stencil & 0.060 \\ Leica Pegasus & 0.060 & Leica Pegasus & 0.048 \\ GeoSlam Zeb-Revo & 0.040 & GeoSlam Zeb-Revo & 0.035 \\ \hline \hline \end{tabular}
\end{table}
Table 1: RMS of signed distances after iterative closest point (ICP) alignments of the indoor mobile mapping system (IMMS) clouds.  
considered. In any case, some operative rules have to be set beforehand for the data acquisition in order to acquire as many comparable point clouds as possible: for instance, survey resolution is related to the walking speed, the way how the handheld systems are held affects the completeness of the model, the path should be even to avoid sudden turns, large differences in the ground influence the obtainable results, and so on. Furthermore, raw data are stored in proprietary format and are not homogeneous, therefore we were not able to consider them. Data elaboration is generally performed (the Kaarta system is an exception) during post-processing, with dedicated software. Different algorithms, none of them open source or supported by accessible scientific documentation, are implemented, and a great number of parameters have to be set up.  
As said earlier, we considered the data provided to us by the producers to be the best acquired and post-processed result. The number of surveyed points varies depending on the IMMS and path, from a minimum of 7 million acquired with the Zeb-Revo in path 1 to a maximum of nearly 100 million measured with the Leica Pegasus in path 2; all of the values are later reported.  
Various analyses can be conducted on these IMMS clouds, once aligned in the same reference system as reported in Section 2.4, of course reserving a fundamental role for the ground truth cloud. More in general, the evaluation of the correctness and accuracy of a point cloud can be carried out by following at least these three approaches: cloud to cloud [30], point to point [31], and cloud to feature [32; 33] comparisons.
# Linear Algorithms for Radioelectric Spectrum Forecast
## 3 Case Study and Experiment Procedure
### Design of SARIMA Algorithm  
In Figure 7 the trend and seasonality are presented in occupancy level for the three channels. Seasonality had a period of 24 h, practically without trend and with stationary components, which makes possible the use of a SARIMA model to forecast behavior of the GSM channels [26].  
Delay difference \(s\), which for this event is selected as five (\(\Delta_{5}\)), was equivalent to the number of days of the week in which the signal was stationary [15]. Applying the augmented Dickey-Fuller test [36], in the series of three channels from Monday to Friday, the null hypothesis of unit root is rejected, which indicates stationarity. In order to find the parameters of SARIMA(p,d,q)(P,D,Q)s model, ACF and PACF were calculated for \(\Delta_{5}\) of respective channels, as shown in Figure 8.  
Figure 6: Autocorrelation for three GSM band downlink channels.  
Figure 7: Seasonality and trend components of the GSM channels.  
Using Box-Jenkins methodology [23], Figure 8 shows that PACF of \(\Delta_{5}\) decays to zero with a seasonal pattern, and crosses confidence level initially in lag 5 for negative side. This suggests that a term non-seasonal AR(1) could be used, and a seasonal MA(5) could be added.  
In order to avoid forecast overestimation (small variance and big errors), the Akaike information criterion (AIC) [37] was selected to evaluate different reasonable combinations, as is observed in Table 2. Thus, selected models were: SARIMA(1,0,5)(1,0,1)\({}_{5}\), SARIMA(1,0,5)(0,0,1)\({}_{5}\) and SARIMA(1,0,5)(0,0,1)\({}_{5}\), for occupancy levels of low, medium and high channels, respectively, and the characteristic equations, in the same order are:  
\[(1-0.0135B)\left(1-0.55B^{5}\right)\left(1-B\right)\left(1-B^{5}\right)x_{t}= \left(1-0.997B^{5}\right)\left(1-0.546B^{5}\right)e_{t} \tag{7}\]  
\[(1-0.0192B)\left(1+0.996B^{5}\right)\left(1-B\right)\left(1-B^{5}\right)x_{t}= \left(1+0.0085B^{5}\right)e_{t} \tag{8}\]  
\[(1-0.0199B)\left(1-0.016B^{5}\right)\left(1-B\right)\left(1-B^{5}\right)x_{t}= \left(1-0.994B^{5}\right)e_{t} \tag{9}\]
## 2 Description of the Mathematical Model
### Description of Fourier Neural Networks  
In this study, a Fourier-NN is used to approximate the unknown nonlinear functions with uncertainties, \(\overline{F}(\mathbf{z})\) and \(\overline{G}(\mathbf{z})\), in order to provide a stable control law for the PM-ASS. A Fourier-NN is eminently suited to the modeling and decomposition of a nonlinear function. It uses a Fourier series as the basic functions and allows much better convergence for the approximation. It is noted that the number of the hidden layers of the Fourier-NN depends on the bandwidth of the system. The weigh vector for a Fourier-NN is acquired used the Lyapunov stability theorem. The output function for a Fourier-NN is:  
\[p(\chi)=\mathbf{W}^{T}\mathbf{q}(\chi) \tag{28}\]  
where \(\chi\) and \(p(\chi)\), respectively, represent the input and the output for the Fourier-NN, \(\mathbf{q}(x)\equiv\left[\cos(w_{0}\chi),\quad\cos(w_{1}\chi),\quad\sin(w_{1}\chi ),\dots,\cos(w_{M}\chi),\quad\sin(w_{M}\chi)\right]^{T}\) is a family of orthogonal Fourier activation functions, and \(\mathbf{W}\equiv\left[W_{0}\quad W_{1}\quad\dots\quad W_{2M-1}\quad W_{2M} \right]^{T}\) is a vector for network weights.  
**Remark 2**.: _The approximation error for a Fourier-NN is bounded and is expressed as:_  
\[\varepsilon_{n}(\chi)=|f(\chi)-p(\chi)|\leq\sum_{i>n}\left(|w_{i}|+|w_{i+1}| \right)\text{; }n=2M+1 \tag{29}\]  
_Clearly, the approximation error \(\varepsilon_{n}(t)\) is eliminated as \(n\to\infty\). This implies that \(f(\chi)\) is approximated as long as \(n\) is sufficiently large._  
**Remark 3**.: _If a nonlinear function \(f(t)\) is a non-periodical function, which is written as:_  
\[f(t)=\int_{-\infty}^{\infty}F(w)e^{int}dw, \tag{30}\]_where \(F(w)=\int_{-\infty}^{\infty}f(t)e^{-jwt}dt\), then by applying [PERSON]'s theory in the frequency domain, Equation (30) is equivalent to the partition of a non-periodical function with an appropriate window in the time domain. This is written in discrete form as:_  
\[f(t)=\int_{-\infty}^{\infty}F(w)e^{jwt}dw=\sum_{n=-M}^{M}X(n\Omega)e^{jn\Omega t }\Delta w, \tag{31}\]  
_where \(\Omega=w_{n}/n\) is the base frequency that is acquired by discretizing \(w\). From an engineering point of view, the control objective is defined as a limited time interval of \([0,T]\), thus, the base frequency is simply selected as \(\Delta w=2\pi/T\)._
## 4 Results and Discussion
### Incompressible Taylor-Green Vortex
#### 4.1.2 Effects of the Explicit SGS Models and the DB Model  
The evolution of modeling dissipation terms, \(\epsilon_{sgs}\) and \(\epsilon_{a}\) are shown in Figure 5. The impact of the explicit SGS models and the DB model is investigated in the following discussion, which focuses on the \(p3\) results in Figures 2b and 5b because they are less affected by the numerical error. Before \(t=4\), the under-resolved scales are not developed in the flow, it can be observed in Figure 5b that all models produce very low dissipation. From then on the flow turns under-resolved and the modeling dissipation arises significantly and reaches the peak around \(8<t<9\). As shown in Figure 2b, during the under-resolved phase, all models bring apparent extra dissipation, which can be majorly described by \(\epsilon_{sgs}\) and \(\epsilon_{a}\) derived in Section 3. Among the SGS models, the similarity model is less dissipative than the Smagorinsky model, as expected. The DB model produces close dissipation to the similarity model, but with lower \(\epsilon_{a}\) before \(t=4\), and sharper peak around \(8<t<9\) as shown in Figure 5b. Similar trend of dissipation property can also be observed in results of the energy spectra plotted in Figure 6b. From the view of \(\epsilon_{total}\) in Figure 2b, the iLES method agrees well with the reference during \(0<t<8\) but produces lower peak value when the smallest turbulent structures occur. The DB model has the potential to improve the insufficient dissipation, with slightly extra dissipation since \(t=4\), when the flow turns under-resolved.
## 5 Conclusions  
Based on the conductive characteristics of electromagnetic waves in water, we established a mathematical model of a static electric field using a dipole pair, and generalized it to electric field-based communication systems. A DSSS BPSK communication system was designed and verified the feasibility of this approach.  
The experiment on the underwater electric field communication angle validated the accuracy of the simplified dipole model. Maximum potential difference occurred when both electrode pairs were parallel, and minimum when perpendicular. The underwater static communication experiments demonstrated that the designed DSSS BPSK wireless communication system could transmit information error-free up to 11.2 m. The DSSS BPSK system spreads the spectrum of interference and noise, thereby reducing interference power within the signal band and enhancing the demodulator's input signal-to-noise ratio. Compared to previous work, this system achieved a 40% increase in communication range and a 28% decrease in power consumption [28].  
The DSSS technique facilitates multi-access communication by allocating unique spreading codes. It leverages the autocorrelation properties of these codes to accurately receive signals from specific users. This approach holds significant potential for application in underwater robot swarm operations.  
Conceptualization, Q.H. and Y.Z.; methodology, Y.Z.; software, Y.Z., Z.Z. and X.F.; formal analysis, Q.H.; investigation, Y.Z. and X.F.; writing--original draft preparation, Y.Z.; writing--review and editing, Q.H., Y.Z., X.F. and T.Z.; visualization, Y.Z.; supervision, Q.H., Z.Z. and T.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Major Program of the National Natural Science Foundation of China, Grant No. 61890961, the General Program of the National Natural Science Foundation of China, Grant No. 61971412, the Basic Research Project of China, Grant No. JCKY2020110C074, and the Rapid Support Fund Project of China, Grant No. 61404150405.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# A Review of Neurotransmitters Sensing Methods for Neuro-Engineering Research
## 2 Neurotransmitter Detection Methods
### Optical Sensing Techniques
#### 2.2.6 Colorimetry  
This technique uses the interaction between a molecule of interest and other compounds to induce a colored reaction. The concentration of the target molecule then depends on the color and intensity of the resulting solution. Using this technique, dopamine was detected when binding with Ag-catechol [157], gold nanoparticles [158] and AHMT functionalized gold nanoparticles [159]. The reported LOD was 60 nM [157]. Other neurotransmitters were also detected using this technique, such as noradrenaline (LOD: 20 uM), adrenaline (LOD: 2.5 uM) and L-DOPA (LOD: 2.5 uM) [160].  
The colorimetry technique can also use resonance light scattering. It then induces a collective oscillation of electrons on the surfaces of plasmonic nanoparticles and results in an absorption/scattering effect at different wavelengths upon aggregation. The resonance light scattering technique can be advantageous due to its sensitivity and selectivity. Plasmonic nanoparticles based on gold and silver are highly efficient at light absorption and scattering [161]. For example, dopamine has been detected though a shift in wavelength induced by the aggregation of gold nanoparticles synthesized primarily with dopamine as reducing agent [160; 162; 163]. The indirect aggregation of nanoparticles is caused by the hydrogen bond formation within the Au-dopamine-TGA complex, thereby inducing a shift from red--purple--gray--yellow when dopamine concentration increases. However, a reversible shift (blue to red) was induced by dopamine concentration in aggregated melamine functionalized gold nanoparticles [164; 165]. Furthermore, aggregation induced by the selective binding between gold optical probes and dopamine through specific molecular bridges including aptamers avoids common interference with other neurotransmitters [159; 166]. In some occurrences, dopamine sensitivity is affected by the medium because, it is oxidized to quinone which is unstable and quickly polymerized to polydopamine in alkaline conditions. However, polydopamine nanoparticles are intrinsically fluorescent in an acid medium, allowing dopamine sensing with an  
Figure 2: Schematic representation of the fiber optic and aptamer based dopamine detection [156].  
LOD of 40 nm [167]. The reliable detection of dopamine with the low-cost resorcinol, reaching an LOD of 1.8 nM, is a good alternative for the real-time detection of this neurotransmitter [168]. Although neurotransmitter detection in the cerebral system is still challenging, the colorimetric technique to detect dopamine with functionalized gold nanoparticles is promising [158].
###### Abstract  
Construction works, especially in the case of large road or tunnel investments, require the active participation of surveyors. It applies to classic geodetic procedures such as staking out or conducting control measurements and continuously monitoring displacements and deformations. Negative factors at surveying sites may hinder the implementation or directly affect the instruments, especially total stations. Heavy dust and rainfall containing harmful and destructive substances can be mentioned here. Contemporary measuring instruments are technologically complex and expensive devices, often exposed to the sun, wind, and frost. To mainly protect robotic total stations, various preventing housings are used. Practice shows, however, that these are half-hearted solutions, often negatively influencing the accuracy of the surveys. Based on the authors' professional and scientific experience, a technological line using the modeling and 3D printing of protective housings for robotic electronic total stations has been developed. The workflow includes dedicated online modeling and printing unique instrument covers that best suit certain sites. The prototype was shielded with an additional hydrophobic layer and tested in a geodetic laboratory. The validation results confirm the high usability of the technological line while maintaining the appropriate millimeter level positioning accuracy and effectiveness of the geodetic monitoring.  
3D printing; robotic total station; structural monitoring; protection housing; conceptual modeling 22 June 2022
# Wedge-Splitting Test on Carbon-Containing Refracories at High Temperatures
## 4 Results and Discussion
### Experiments and Evaluation  
Tests with the newly developed device were carried out successfully between room temperature and 1470 \({}^{\circ}\)C. During the high-temperature experiments, the furnace chamber was purged with argon and a purging rate up to 70 L/h. Figure 6 shows the load-displacement diagrams from room temperature to 1470 \({}^{\circ}\)C.  
As the oxidation of carbon in the sample increases with increasing temperature, a purging rate was defined for each individual temperature. The purging rate at 1100 \({}^{\circ}\)C was set to 10 L/h, at 1370 \({}^{\circ}\)C 40 L/h and to 70 L/h for the test at 1470 \({}^{\circ}\)C.  
The Table 1 below shows the maximum horizontal force \(F_{H,max}\), the specific fracture energy \(Gf^{\prime}\) and the nominal notch tensile strength \(\sigma_{NT}\) according to the Equations (1)-(3).  
Figure 5: Wedge splitting test setup and sample after testing.  
Figure 6: Load/displacement diagram of the magnesia carbon material measured at different temperatures.  
For the high-temperature tests the specific fracture energy \(G_{f}\) and the nominal notch tensile strength \(\sigma_{NT}\) show an increase with increasing temperature. The test at room temperature shows the smallest nominal notch tensile strength but the specific fracture energy \(G_{f}\) is higher than that at 1100 \({}^{\circ}\)C and lower than that at 1370 \({}^{\circ}\)C. The ratio between the specific fracture energy \(G_{f}\) and the notch tensile strength \(\sigma_{NT}\) are applied as an indicator for brittleness [8].  
The following figure illustrates a sample after the experiment at 1470 \({}^{\circ}\)C. The oxidation of the carbon in the sample is relatively small, only slight traces of oxidation are visible on the surface. The ligament area of the sample (the area inside the red frame) is without any visible oxidation (Figure 7).  
The weight loss after the test at 1470 \({}^{\circ}\)C is below 1% (18 g out of 2250 g of a sample in virgin state) for a purging rate of 70 L/h. The reaction of C(s) with O\({}_{2}\)(g) decreases with an increase in the argon purging rate [9]. The weight loss after the test at 1370 \({}^{\circ}\)C is 0.8%. For the test at 1100 \({}^{\circ}\)C, the weight loss decreases to 0.5%.