esa-sceva/satcom-chunk-collection huggingface dataset:
# High Efficiency Continuous-Variable Quantum Key Distribution Based on ATSC 3.0 LDPC Codes
## 2 Preliminaries
### Multidimensional Reconciliation Protocol  
In this work, we adopt reverse reconciliation scheme due to its longer transmission distance. The schematic diagram of multidimensional reconciliation protocol is shown in Figure 1, which can be described in the following steps:  
* [PERSON]'s side prepares Gaussian sequence \(X\) and sends it to [PERSON] through a quantum channel to get sequence \(Y\). Then, [PERSON] and [PERSON] divide \(X\) and \(Y\) into groups; each group consists of \(n\) elements, where \(n\) denotes the dimension of multidimensional reconciliation. Technically, we mainly adopt 8-dimensional reconciliation in the CVQKD system since it outperforms the other dimensions in reconciliation (\(n=1,2,4\)) [22].
* [PERSON] and [PERSON] normalize \(X\) and \(Y\) into \(x\) and \(y\), respectively, where \(x=X/\parallel\!X\!\parallel\) and \(y=Y/\parallel\!Y\!\parallel\).
* A binary sequence \(u\) is randomly generated, and then sequence \(e\) is generated by sequence \(u\) through LDPC encoding. In order to get sequence \(e\) used in the multidimensional reconciliation, \(e\) needs to be converted to a binary spherical sequence \(e^{\prime}\) as follows: \[(e^{\prime}_{1},e^{\prime}_{2},...,e^{\prime}_{n})=\bigg{(}\frac{(-1)^{e_{1}} }{\sqrt{n}},\frac{(-1)^{e_{2}}}{\sqrt{n}},...,\frac{(-1)^{e_{n}}}{\sqrt{n}} \bigg{)}.\] (3)
* [PERSON]'s side begins to calculate rotation function \(M(y,e^{\prime})=\sum_{i=1}^{n}\alpha_{i}(y,e^{\prime})A_{i}\) and syndrome \(S=H\cdot e\) by sequence \(y\) and sequence \(e\), where \(\alpha_{i}(y,e^{\prime})=(A_{i}y|e^{\prime})\) and specific matrix \(A_{i}\) can be found in [23]; \(H\) is the parity-check matrix of LDPC code. Subsequently, [PERSON] transmits the rotation function and syndrome to [PERSON] through a classical channel.
* [PERSON] receives the rotation function and maps her Gaussian sequence \(x\) to \(c\), i.e., \(c=M(y,e^{\prime})x\). Then, the parity-check matrices of the LDPC code, syndrome and sum-product algorithm are utilized to recover sequence \(c\) into sequence \(u^{\prime}\). [PERSON] and [PERSON] will share symmetrical secret key when the decoding is successful.  
Figure 1: Schematic diagram of multidimensional reconciliation protocol. \(X\) and \(Y\) denote two corresponding Gaussian sequences. \(x\) and \(y\) represent the normalized results of \(X\) and \(Y\). \(M(y,e^{\prime})\) and \(S\) are the rotation function and syndrome, respectively. \(u\) is a random binary codeword and \(e\) is the LDPC encoding result of \(u\). \(c\) denotes the result of rotation operation and \(u\prime\) represents the LDPC decoding result of \(c\).  
We can greatly improve the final secret key rate and extend the safety distance of quantum communication by combining LDPC codes and multidimensional reconciliation. From Equation (2), we can see that the higher \(\beta\) of LDPC codes, the greater the performance improvement of multidimensional reconciliation for the CVQKD system.
## 2 Theory and Formulation
### Kinematic Relations  
The cylindrical coordinate system (\(R\), \(\theta\), \(z\)) and axial symmetry in loading and geometry were taken into account. With the aid of the [PERSON]'s shear-deformation plate theory based high-order shear deformation theory (HSDT) and symmetric deformation, the displacement fields of a circular plate are expressed by the following equations:  
\[\begin{array}{c}U(R,\theta,z,t)=u(R,t)-z\frac{\partial w(R,t)}{\partial R}+ \left(z-c_{1}z^{3}\right)\left(\frac{\partial w(R,t)}{\partial R}+\phi(R,t) \right)\\ V(R,\theta,z,t)=0\\ W(R,\theta,z,t)=w(R,t)\end{array} \tag{18}\]  
where \(u\), \(w\), and \(\phi\) indicate the displacements of the midsurface in \(R\) and \(Z\) directions and rotations of the transverse normal around \(\theta\) direction, respectively. It is well known that in the higher-order shear deformation theory \(c_{1}\) is equal to 4/3 h\({}^{2}\). The nonzero components of the strain tensor can be decomposed as  
\[\left\{\begin{array}{c}\varepsilon_{RR}\\ \varepsilon_{\theta\theta}\\ \gamma_{RZ}\end{array}\right\}=\left\{\begin{array}{c}\varepsilon_{RR}^{0} \\ \varepsilon_{\theta\theta}^{0}\\ \gamma_{RZ}^{0}\end{array}\right\}+z\left\{\begin{array}{c}\kappa_{RR}\\ \kappa_{\theta\theta}\\ \kappa_{RZ}\end{array}\right\}+z^{3}\left\{\begin{array}{c}\kappa_{RR}^{ *}\\ \kappa_{\theta\theta}^{*}\\ \kappa_{RZ}^{*}\end{array}\right\} \tag{19}\]  
Figure 4: Through-the-thickness variation of mechanical properties (\(\theta=\pi/4,\ W_{CNT}=0.02,\ V_{F}=0.2\)).  
where \(\varepsilon_{RR}\) and \(\varepsilon_{\theta\theta}\) indicate the corresponding normal strains in \(R\) and \(\theta\) directions and \(\gamma_{RZ}\) represents the shear strain in the \(RZ\) plane. Equation (18) can be rewritten as  
\[\left\{\begin{array}{c}\varepsilon_{RR}^{0}\\ \varepsilon_{\theta\theta}^{0}\\ \gamma_{RZ}^{0}\end{array}\right\}=\left\{\begin{array}{c}\frac{\partial u} {\partial R}\\ \frac{\partial u}{\partial z}+\frac{\partial u}{\partial R}\end{array}\right\}, \left\{\begin{array}{c}\kappa_{RR}\\ \kappa_{\theta\theta}\\ \kappa_{RZ}\end{array}\right\}=\left\{\begin{array}{c}-\frac{\partial^{2} uv}{\partial R^{2}}+\frac{\partial^{2} uv}{\partial R^{2}}+\frac{\partial\phi}{\partial R}\\ -\frac{\partial^{2} uv}{\partial R^{2}}+\frac{\partial^{2} uv}{\partial R\partial^{2}}+\frac{\partial\phi}{\partial z}\end{array}\right\}, \left\{\begin{array}{c}\kappa_{RR}^{\kappa}\\ \kappa_{\theta\theta}^{\kappa}\\ \kappa_{RZ}^{\kappa}\end{array}\right\}=\left\{\begin{array}{c}-c_{1} \Big{(}\frac{\partial^{2} uv}{\partial R^{2}}+\frac{\partial\phi}{\partial R}\Big{)}\\ -c_{1}\Big{(}\frac{\partial^{2} uv}{\partial R\partial^{2}}+\frac{\partial\phi}{\partial z}\end{array} \right\}\right\} \tag{20}\]
## 2 sRNA as Orchestrators of Bacterial Pathogenesis
### Adherence to the Host
#### 2.1.1 PapR
**(c) The role of StxS in Shiga toxin phage regulation: Left panel (Lysogeny):** During lysogeny, the late promoters (PR\({}^{\prime}\)) of Stx1 and Stx2 phages are constitutively active, transcribing StoSL, which terminates at the intrinsic terminator \(\mathrm{tR}^{\prime}\). The StxSL transcript is processed by RNase E to generate the regulatory small RNA StxS. StxS enhances the expression of _rpoS_, the stationary phase sigma factor, while repressing _stx1B_, encoding the Shiga toxin 1B subunit, thereby reducing Shiga toxin production. **Right panel (Lytic induction):** During the lytic cycle, the phage-encoded antiterminator Q is expressed, allowing transcriptional readthrough at \(\mathrm{tR}^{\prime}\). The PR\({}^{\prime}\) promoter drives transcription beyond \(\mathrm{tR}^{\prime}\), leading to the expression of Shiga toxin genes. In the absence of StxS, _rpoS_ is constitutively expressed. (d,e) The role of FasX and MTS1338 in immune evasion and persistence:** Schematic representations of the regulatory roles of FasX and MTS1338 sRNAs in evading the host immune response and promoting bacterial persistence within the host environment. In addition to PapR, the expression of P-fimbriae is subjected to epigenetic regulation by differential methylation of two GATC sites within the 400 bp Pap regulatory region. Methylation at the distal GATC site maintains repression, whereas methylation at the proximal site promotes the ON phase by enabling PapI function ([PERSON] _et al._, 2015) (Figure 1a). Stress-responsive small RNA GcvB further modulates Lrp expression, while global regulators, including the cAMP receptor protein (CRP) and H-NS, impact P-fimbriae expression, with H-NS acting as a niche-specific repressor. The role of PapR extends beyond P-fimbriae silencing, as UPEC typically expresses only one fimbrial type at a time, leading to regulatory crosstalk ([PERSON] _et al._, 2015). The suppression of P-fimbriae by PapR may facilitate the compensatory upregulation of alternative adhesins, such as F1C fimbrie, ensuring adhesion plasticity. Together, PapR functions as a key regulatory checkpoint, restricting P-fimbriae expression to conditions where they are most beneficial for UPEC pathogenesis.
# Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data
## 3 Meteor and Background Signals
This way, non-meteor samples can be extracted from the rest of the data in a random fashion. However, this is not the way the data flow can be analyzed onboard. Besides this, the above approach will leave us with mere 1068 meteor samples, which is not sufficient to effectively train an ANN. This made us use a sliding window producing chunks that overlap by \(dt\) GTUs. In what follows, we present results obtained for \(dt=8\) GTUs that allowed us to avoid losing short meteor tracks and provided reasonable accuracy of their recognition. The procedure of labeling data chunks extracted this way will be explained in detail in Section 4.1. We split the task of meteor signal recognition into two steps. First, we trained an ANN to recognize three-dimensional data chunks that contained meteor signals. After this, we employed another ANN to select pixels containing respective signals. Each ANN thus solved a task of binary classification. This allowed us to obtain lists of meteors registered with Mini-EUSO together with their active pixels thus providing information necessary for their subsequent analysis (reconstruction of brightness, arrival directions etc. ).
# Performance Evaluation of Asphalt and Asphalt Mixtures Modified by Fuel-Resistant Admixture
## 2 Experimental Programs
### Laboratory Assessment  
#### 2.2.1 Experimental Methods for Asphalt  
(1) Preparation of FRMA  
FRMA was prepared using a high-speed shearing machine. Initially, the base asphalt was heated to a fluid state in a 165 \({}^{\circ}\)C oven. Subsequently, FRM with asphalt mass fractions of 1%, 3%, 5%, and 7% was individually added to the base asphalt. The mixture was then subjected to shear at a rate of 3600 r/min for 60 min at 180 \({}^{\circ}\)C, resulting in various FRMA compositions.  
(2) Conventional Physical Tests  
To effectively assess the impact of FRM on the conventional physical properties of asphalt, the 25 \({}^{\circ}\)C penetration, 15 \({}^{\circ}\)C ductility, and softening point of the asphalt were determined per T 0604-2011, T 0605-2011, and T 0606-2011 in Standard JTG E20-2011 [18]. Three parallel replicates are needed for penetration test, ductility test and softening point test on the same sample.  
(3) Dynamic Shear Rheometer (DSR) Test  
The purpose of the DSR test is to characterize the elastic and viscous properties of asphalt binders by measuring complex modulus \(|\)G*\(|\) and phase angle \(\delta\). The complex modulus \(|\)G*\(|\), phase angle \(\delta\), and rutting factor \(|\)G*\(|\)/ sin\(\delta\) of asphalt binders were determined at different temperatures to assess their high-temperature performance. The DSR test was conducted using the Discovery HR 20 hybrid rheometer (DHR-20) manufactured by TA Instruments (New Castle, DE, USA). Following AASHTO T 315 [20], the test employed a strain-controlled mode with a target strain level of 12% and a loading frequency of 10 rad/s. Complex modulus \(|\)G*\(|\) and phase angle \(\delta\) of the asphalt were measured at various temperatures (58, 64, 70, 76, 82 \({}^{\circ}\)C). Two parallel replicates are needed for DSR test on the same sample.  
(4) Bending Beam Rheometer (BBR) Test  
The asphalt samples were subjected to the BBR test in accordance with AASHTO T 313 [21]. The low-temperature rheological behavior of the asphalt binder was evaluated by measuring the stiffness modulus (S-value) and creep rate (m-value) of the asphalt samples. For the test, asphalt binder beams with dimensions of 127 mm \(\times\) 6.35 mm \(\times\) 12.7 mm were used. The test temperatures were set at \(-\)6 and \(-\)12 \({}^{\circ}\)C. Two parallel replicates are needed on the same sample.  
Figure 3: Design gradation of SMA-13 asphalt mix.  
\begin{table}
\begin{tabular}{c c c c c} \hline FRM content & 0\% & 1\% & 3\% & 5\% & 7\% \\ \hline Optimal asphalt content & 5.25 & 5.33 & 5.47 & 5.55 & 5.62 \\ \hline \end{tabular}
\end{table}
Table 3: Results of optimal asphalt content.
## 2 Materials and Methods
### Data Preprocessing
#### 2.2.2 Signal Processing  
Data were preprocessed differently depending on the meaningful range of variation in the biosignals. BVP is the main biosignal recorded and transmitted by the Empatica E4, providing useful information on the sympathetic and parasympathetic nervous systems that indicates episodes of autonomous and non-autonomous body behavior. Heart rate estimation, heart rate variability, and respiration rate are the most informative variables for discriminating between different PA and APS.  
Depending on age, physical condition, and emotional state, the respiration rate can take values between 13 and 57 breaths per minute. Affected by these and other factors, HR values can range from 40 to 200 BPM. Therefore, HR values outside of this range are expected to be either high-frequency noise or motion artifacts. Hence, BVP signals are passed through a 4 th order Butterworth bandpass filter with cutoff frequencies of 0.2-3.3 Hz in order to smooth BVP recordings and reduce noise.  
A 3D accelerometer is the main signal used to detect and discriminate different types of physical activities. About 98% of the human activity frequencies lie between 0-10 Hz [36]. To filter out other frequencies that do not emanate from body movement, a simple low-pass filter or a bandpass filter with a lower frequency close to zero is be an adequate solution. We used a fourth order Butterworth bandpass filter with a cutoff frequency of 0.1-10 Hz to denoise the 3D accelerometer signals.  
Electrodermal activity is typically classified into two different types, namely, the phasic skin conductance response (SCR) and the tonic skin conductance level (SCL). The SCL can be considered as the baseline of the change in electrodermal activity. It can be calculated as the rolling average of a signal over a long period of time. In the absence of external stimuli, the SCL can vary slowly over time depending on psychological condition. These changes may take as long as several minutes to occur. SCR, on the other hand, responds to rapid changes in short-term environmental stimuli, sight, noise, odor, and other factors that precede participation, such as fear, anticipation, and decision-making. The mathematical representation of this decomposition is developed by [37] as below:  
\[y=r+t+\epsilon \tag{1}\]  
where \(y\in R^{n}\) denotes the original GSR signal, \(t\in R^{n}\) is the tonic component, and \(r\in R^{n}\) stands for the phasic behavior of the signal. The parameters of the above signal  
Figure 2: Schematic representation of windowing applied to different biosignals.  
decomposition model are identifiable by solving an optimization problem [37]. Using a linear upsampling procedure, the GSR signal was doubled to 8 Hz. The purpose of signal upsampling was to ensure that the parameters of the models fitting the signal were not obtained locally because of the short rolling windows of ten seconds. For each rolling window, the phasic and tonic components were extracted and feature variables from each were extracted. Figure 3 summarizes the signal preprocessing pipeline for each signal.
# Collaborative Planning in Non-Hierarchical Networks--An Intelligent Negotiation-Based Framework
## 4 Application Case--Analysis and Results
### Overall Results  
The initial analysis of the results in the application case revealed that the proposed framework yielded promising outcomes. Coincidently, the feedback from the project's industrial partners' assessment of the framework was very positive.  
The analysis of Table 6 shows that, depending on the number of iterations in the negotiation of the collaborative plan, different strategies generate different outcomes. In the present application case for two iterations, the \"weight average\" strategy presented the best results.  
For a higher number of iterations, the best performing strategy turned out to be the \"best proposal\". Nevertheless, all four of the empirical strategy proposals presented promising results.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Intelligent Planning** & & & & \\
**Planning Strategy** & & **Weight Average** & **Best Proposal** & **Genetic Approach** \\ \hline Iteration 1 & 0.7984971 & 0.7984971 & 0.7984971 & 0.7984971 \\ \hline Iteration 2 & 0.8219750 & 0.8559725 & 0.8457682 & 0.8371205 \\ \hline Iteration 3 & 0.8430391 & 0.8651374 & 0.8799396 & 0.8753838 \\ \hline Iteration 4 & 0.8738795 & 0.8774835 & 0.9137538 & 0.9014386 \\ \hline Iteration 5 & 0.8886209 & 0.9038363 & 0.9390805 & 0.9258324 \\ \hline \end{tabular}
\end{table}
Table 5: Intelligent planning strategy results comparison.  
A preliminary analysis, using the Delphi approach, based on a results discussion with the participants in the project, points to two explanations. First, since the supply chain studied has seasonal demand peaks and the load is shared among the competitors, the strategy approaches that seek to dynamically move away faster from the overload periods present better results (i.e., best proposal). Second, not every partner is really committed in competing for a business opportunity. This means that the strategic approaches that favor the best competitors tend to obtain best results faster (i.e., best proposal and genetic approach).  
The final analysis of the results shows that, although the complexity of the multisite planning task of a collaborative network is very high, the present framework proposal has advantages compared to the traditional centralized and multi-hierarchical negotiation approach.  
Using the feedback loop enabled the system to learn from previous responses and to generate better results in an intelligent way.
## 2 Materials and Methods
### Data Preprocessing
#### 2.4.3 Processing Environment  
The Google Earth Engine (GEE) platform is currently a free answer to the matter of restricted access to processing power [19; 35]. GEE is a cloud-based platform that enables a planetary-scale analysis of petabytes of freely obtainable satellite imagery. Combining this large amount of information with the parallel computing strength of Google's infrastructure facilitates the rapid and easy evaluation of satellite images. GEE parallelises and executes code developed in JavaScript or Python, exploiting Google's cloud computing infrastructure and allowing work with intensive processes at new scales. This study used GEE for the following three reasons: (1) it is a free platform that has many built-in functions for geospatial analysis and provides quick results without downloading datasets to a local storage system; (2) it has been widely used in several environmental studies [55; 56; 57]; and (3) it uses one processing environment and may reduce the uncertainties introduced by various techniques, such as temporal aggregation, resampling (e.g., downscaling and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Band Name** & **Wavelength (nm)** & **Spatial Resolution (m)** \\ \hline Blue & 430–550 & 2.14 \\ Green & 500–620 & 2.14 \\ Red & 590–710 & 2.14 \\ Near-infrared & 740–940 & 2.14 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pleiades data from Blad Talh acquired on 16 July 2021.  
upscaling), and data reprojection. Notably, the final results from the GEE could be used to create maps (raster.tif) and tables (table.csv) in Google Drive for further processing in the Google Collaboratory (Colab) cloud service [58]. Pleiades bands (blue, green, red, and NIR) were uploaded to the cloud asset of the GEE environment because they are not yet available in the Earth Engine. Thus, the overall framework of the research can be applied to other regions to achieve related aims.
## 5 Conclusions  
In this paper, a single-channel FECG extraction method based on an instantaneous correlation coefficient and simplified coherent averaging method is proposed. The method is a completely adaptive extraction method based on the characteristics of the signals themselves, unlike the existing methods. By using the standard heart beat model for relevant calculations, the positions of the R peaks are highlighted effectively, and the identification accuracy of the R peaks is improved. By calculating the average waveform of all of the heart beats, the proposed simplified coherent averaging method can obtain the stationary information and then retain the nonstationary and nonlinear information in the residue signals. Thus, FECG information is not lost when the coherent averaging MECG is eliminated. The variance of the coherent averaging FECG can be used to identify the amplitude of the FECG nonstationary information, which is used as the threshold to eliminate the MECG residues. In this way, the FECG's useful components and nonstationary information in the remaining signals can be effectively retained, so that the nonlinear and nonstationary characteristics of the final extracted FECG can be subsequently recovered. The validity of the proposed method was verified by way of experiments using synthetic data and real database data. Through the synthetic data experiment established in this paper, it was shown that the method in this paper can effectively extract FECGs when the FECG to MECG amplitude ratio is within the range of 0.1 to 0.9. In other cases, the success rate of extraction is reduced. This is because the MECG residue is relatively large when the FECG to MECG amplitude ratio is less than 0.1, which interferes with FECG identification. When it is larger than 0.9, the R peak sizes of the FECG and MECG are similar, which affects the MECG R peak detection. When the energy ratio of FECG to noise is greater than \(-8\) dB and the heart rate ratio of FECG to MECG is greater than 1.1, changes in the noise energy and heart rate ratio have no significant effect on the performance of the method proposed in this paper. The synthetic data and real database experiment results show that FECGs extracted by this method are of high quality according to their SNR improvement, correlation coefficient, and signal waveforms. The QRS complex wave of the FECG is clear. The form of the P and T waves is reasonably enhanced. The waveform amplitude and each interval have no obvious distortion, and the nonlinear and nonstationary characteristics can be fully retained.  
Data curation, M.Z.; Formal analysis, M.Z.; Methodology, M.Z.; Supervision, G.W.; Writing--original draft, M.Z.; Writing--review and editing, M.Z. and G.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 5 Service Discovery Mechanism
### Service Unavailability  
To decrease the false service discovery ratio, besides checking the topology information table, when a node leaves the network, it sends a notification message indicating that its services will not be available anymore. A TLV structure is also used for this notification. The node will announce the service SDM_TYPE, but with a time to live VALIDITY_TIME equal to zero. When the rest of the nodes receive this message, they delete these services from their remote service tables.