esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Quadrature Chaos Shift Keying Communication System  
The current section introduces a communication system concept established on quadrature modulation and chaos shift keying (CSK) principles. Figure 1 illustrates the block diagram outlining the proposed Quadrature Chaos Shift Keying (QCSK) communication system. The system's core is an analog chaos oscillator operating in a drive-responseconfiguration, resulting in a coherent communication system. In the transmitter, the drive oscillator generates three state variables (\(X\), \(Y\), and \(Z\)): voltages across capacitors and currents through inductors, as defined by circuit theory. On the receiver end, the response oscillator produces only two state variables (\(X^{\prime}\), \(Y^{\prime}\)), with one variable (\(Z^{\prime}\)) being replaced by the transmitted signal derived from the drive oscillator (\(Z\)).  
Within this configuration, the signal from the state variable (\(Z\)) of the drive circuit serves as a synchronization signal. The remaining state variables (\(X\) and \(Y\)) of the drive circuit are utilized for information transmission (depicted in Figure 1 in the CSK block). Notably, all state variables represent chaotic signals, expressed as voltages across and currents through the physically implemented chaos oscillators' circuit elements.  
The information-carrying signal undergoes formation via a binary information-signal-controlled switch, alternating between \(X\) and \(Y\) signals, where bit '1' corresponds to \(X\) and bit '0' to \(Y\). Prior to forming the information-carrying signal, DC-cancellation of \(X\) and \(Y\) signals is essential, ensuring matched amplitudes. Simultaneously, the DC component is removed from the synchronization signal \(Z\). Both the formed information-carrying and synchronization signals subsequently undergo quadrature modulation and are transmitted through an Additive White Gaussian Noise (AWGN) channel.  
Upon receiving, the signal undergoes quadrature demodulation and passage through a low-pass filter. Detecting information bits relies on calculating the correlation coefficient \(\beta\) between the received signal and the \(X^{\prime}\) and \(Y^{\prime}\) signals from the response circuit. Detection of data bit '1' occurs if \(\beta_{X^{\prime}}>\beta_{Y^{\prime}}\); otherwise, '0'. Accurate bit detection necessitates signal agreement between the drive and response circuits. Information detection relies on correlation coefficient calculations, highlighting the significance of the chaos signal's frequency. Ensuring a minimum of a few oscillations per bit becomes crucial for enabling accurate detection through correlation coefficient calculations. Moreover, this factor significantly impacts the system's noise immunity. Hence, chaotic synchronization between the two chaos oscillators is imperative for precise signal detection. Consequently, the subsequent section investigates the properties of chaos oscillators. Additionally, Section 4 performs Bit Error Ratio (BER) estimation, validates results, and assesses the communication system's noise resilience.
## 2 Materials and Methods
### Evaluation Methods
#### 2.2.2 Regression and Correlation Analysis  
The relation between the output (dependent) and k input (independent) variables \(X_{i}\), \(i=1\), \(2\), \(\cdots\), \(k\), can be expressed using a multiple linear regression model in the general form  
\[Y=\beta_{0}+\sum_{i=1}^{k}\beta_{i}X_{i}+\varepsilon \tag{3}\]  
where \(\beta_{0}\) and \(\beta_{i}\) for \(i=1\), \(2\), \(\cdots\), \(k\) are the parameters of the regression model and \(\varepsilon\) is a random error.  
The parameters of the regression model are estimated using the method of least squares. The F-test of the statistical significance of the model is used to verify the statistical significance of the regression model. When testing statistical hypotheses, the p-value is used. If the p-value is lower than the specified level of significance \(\alpha\), then we reject the null hypothesis in favour of the alternative hypothesis. If the  
Values measured at the medium frequencies of 50, 63, and 80 Hz show signs of instability. Their plausibility was confirmed by repeated measurements; ten complete sets of measurements were performed for each setting, a total of several hundred measurements were performed. From the obtained average measured values of sound pressure in the transmission and receiving room, the apparent sound reduction index R' was calculated for individual excitation intervals. A graphical representation of the apparent sound reduction index for pink and white noise in the three selected excitation intervals can be seen in Figure 6.  
In all excitation intervals, the apparent sound reduction index increases for frequencies up to 500 Hz, then at a frequency of 630 Hz it slightly decreases.  
In Figure 7, we can see a graphical representation of deviations of the apparent sound reduction index when using pink or white noise in excitation at individual frequencies and excitation intervals. The most significant differences in the apparent sound reduction index between the white and pink noise occur in the range from 50 to 100 Hz.  
Figure 5: Average sound pressure level—receiving room.  
Figure 6: Apparent sound reduction index in three excitation intervals (white, pink noise).  
The most significant differences in the apparent sound reduction index between the white or pink noise occur in the range from 50 to 100 Hz. Therefore, in the subsequent analysis, we will consider three frequency intervals: up to 100 Hz, from 100 to 500 Hz and the interval from 500 to 1000 Hz.
# Enhancing Bitcoin Price Fluctuation Prediction Using Attentive LSTM and Embedding Network
### Contributions  
To solve the aforementioned problems, we propose an attentive LSTM and embedding network (ALEN) model. More specifically, an attentive LSTM network is used to capture the time dependency representation of Bitcoin price and an embedding network is adopted to incorporate the hidden representations from related cryptocurrencies. We adopt the attentive LSTM because it achieves outstanding performance in time series prediction as shown in [7, 8]. We also leverage the embedding network to further capture the relations between Bitcoin and multiple cryptocurrencies since most of the previous studies typically treat Bitcoin as an independent cryptocurrency and ignore the relations with other cryptocurrencies. The rich relations between cryptocurrencies may contain valuable clues for Bitcoin price fluctuation prediction. The work [9] shows the effectiveness in stock prediction by integrating stock relations into prediction. Therefore, we devise an embedding network to incorporate the hidden representations from different related cryptocurrencies into Bitcoin price prediction. Figure 3b shows an illustration of ALEN model. In particular, we first feed the features (see Section 3.2) into an attentive LSTM model. Then, the hidden representations of Bitcoin and other related cryptocurrencies are integrated into the final hidden representation. Finally, a fully connected layer is applied to the final hidden representation to obtain the results.  
Three key contributions of the paper are summarized as follows.  
* In this paper, we investigate the Bitcoin price fluctuation prediction problem. Meanwhile, we give a clear problem definition and introduce three kinds of features. This work can enrich the research in Bitcoin and provide investors with more tools for investment analyses.
* A novel model namely ALEN is proposed to solve the fluctuation prediction of Bitcoin price. In particular, an attentive LSTM network is used to capture the time-dependency features of Bitcoin price and an embedding network is proposed to capture the hidden representations from related cryptocurrencies.
* We empirically demonstrate the effectiveness of our ALEN on the real-world cryptocurrency market. Moreover, ALEN achieves superior state-of-the-art performance among all baselines.  
The remainder of this paper is organized as follows. Section 2 introduces the related work. Section 3 introduces the problem definition and features designation. Section 4 presents our ALEN model, which includes an attentive LSTM network and an embedding network. Section 5 describes experimental results. We conclude the paper in Section 6.
# Pulsed Nanoelectrospray Ionization Boosts Ion Signal in Whole Protein Mass Spectrometry
## 4 Conclusions  
The use of high voltage and high frequency pulses in nESI-MS can result in significant performance gains in terms of the signal intensity, background chemical noise (\(N_{\rm C}\)), and signal-to-background chemical noise (\(S/N_{\rm C}\)) in the detection of intact proteins compared to conventional DC nESI-MS. For example, the use of pulsed high voltages from 0 to \(\sim\)1.5 kV with sub-nanosecond rise times, a frequency of 200 kHz, and a duty cycle of 50-60% can increase signal and \(S/N_{\rm C}\) values by up to 82% and 154%, respectively, while the \(N_{\rm C}\) can be decreased by up to 46% compared to DC nESI. The use of pulsed high voltage waveforms in nESI-MS can also be used to significantly increase the abundances of protein ions formed from mixtures of proteins by up to 184% compared to DC nESI-MS. Given that the abundances of both small molecules (protonated angiotensin II and Fe(II)-heme) and protein ions with substantially different electrophoretic mobilities peaked at very high frequencies (200-250 kHz), these data indicate that factors other than electrophoretic mobility contribute to the enhanced performance of pulsed nESI. Alternatively, the use of pulsed nESI may result in the formation of smaller ESI droplets and less Coulombic repulsion in the ESI plume, which should result in improved ion desolvation and a more efficient transfer of ions from atmospheric pressure to under vacuum through the narrow capillary entrance of the mass spectrometer, thereby increasing the signal. Enhancing the signal for intact protein ions formed using pulsed nESI should be beneficial in many different types of tandem mass spectrometry experiments for the quantitative and qualitative analysis of complex chemical mixtures including the contents of single cells.  
ratios, Figure S3: Effects of frequency and duty cycle on average charge states and signal-to-noise ratios, Figure S4: Mass spectra for angiotensin.  
Conceptualization, W.A.D.; methodology, Q.L., E.A., X.H., K.M.M.K. and D.X.; formal analysis, Q.L. and E.A.; writing--original draft preparation, Q.L.; writing--review and editing, Q.L., E.A., K.M.M.K., X.H., D.X., J.F. and W.A.D.; supervision, W.A.D.; funding acquisition, W.A.D., K.M.M.K. and J.F. All authors have read and agreed to the published version of the manuscript.  
We thank [PERSON] for helpful discussions. We also thank the Australian Research Council for its financial support.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Analytical Figures of Merit  
The figures of merit for the developed SPME-IMS method for the determination of CWAs are summarized in Table 2. Under the optimized conditions, a series of quantitative parameters with regard to the linear range, correlation coefficient and LOD were examined. The calibration curves of each CWA were constructed by plotting the peak areas (y) of characteristic ions (MH\({}^{+}\)) _versus_ the corresponding concentration of the analytes (x). As we know, the linear dynamic range is limited in IMS [22], so we chose the stated range to adapt the method for on-site analysis. The LODs which were calculated as the concentration of the analytes at S/Ns of 2, were in the range of 1.124-1.350 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. To validate the feasibility of the method, two real water samples were analyzed under the optimized conditions. The results showed that the target CWAs were not found in the samples. To test the accuracy of the method, the recoveries of the method were investigated and the results were in the range of 72.9%-114.4%.
# Adaptive Learned Belief Propagation for Decoding Error-Correcting Codes
## 4 Weighted Belief Propagation
### Parameter Sharing Schemes  
The training complexity of WBP can be reduced through parameter sharing at the cost of performance loss. We consider dimensions \((t,v,c)\) for the ragged arrays \(\gamma_{v,c}^{(t)}\) and \(\beta_{c,v}^{(t)}\). In Type \(T\) parameter sharing over \(\gamma_{v,c}^{(t)}\), parameters are shared with respect to iterations \(t\). In Type \(T_{a}\) scheme, \(\gamma_{v,c}^{(t)}=\beta_{c,v}^{(t)}=\gamma_{v,c}\), \(\forall\ (c,v)\in E\), \(\forall\ t\in[T]\). In this case, there is a single ragged array with \(|E|\) trainable parameters \(\{\gamma_{v,c}\}_{c\in\mathbb{C}_{v},v\in\mathbb{V}_{c}}\). For the regular LDPC code, \(|E|=nd_{v}=md_{c}\). It has been observed that for typical block lengths, indeed, the weights do not change significantly with iterations [28]. In Type \(T_{b}\), there are \(T\) arrays \(\gamma_{v,c}^{(t)}=\beta_{c,v}^{(t)}\), while in Type \(T_{c}\), there are two arrays \(\gamma_{v,c}^{(t)}=\gamma_{v,c}\) and \(\beta_{c,v}^{(t)}=\beta_{c,v}\). Type \(T_{a}\) and \(T_{c}\) decoders can be referred to as BP-RNN decoders and Type \(T_{b}\) as feedforward BP. In Type \(V\) sharing, \(\gamma_{v,c}^{(t)}=\gamma_{c}^{(t)}\) is independent of \(v\). This corresponds to one weight per check node. Likewise, in Type \(C\) sharing, there is one weight per check node update, and \(\gamma_{v,c}^{(t)}=\gamma_{v}^{(t)}\).  
These schemes can be combined. For instance, in Type \(T_{a}VC\) parameter sharing, \(\beta_{c,v}^{(t)}=\gamma_{v,c}^{(t)}=\gamma\). Thus, a single parameter \(\gamma\) is introduced in all layers of the NN. This decoder is referred to as the neural normalized BP, e.g., neural normalized min-sum (NNMS) decoder when BP is based on the MS algorithm. The latter is similar to the normalized MS decoder, except that the parameter \(\gamma\) is empirically determined there. In the Type \(T_{b}VC\) scheme, \(\beta_{c,v}^{(t)}=\gamma_{v,c}^{(t)}=\gamma^{(t)}\). Here, there is one weight per iteration. In this paper, \(\alpha_{v}=1\)\(\forall\ t\in T\).
## 2 Status of Smart Grids
### Standardization and Regulations  
#### 2.2.1 North America  
In 2004, the National Electric Delivery Technology Roadmap was established as a concrete action plan in the United States. The following year, the Energy Policy Act (EPAct2005) was enacted, setting requirements for implementation, rules for smart meters and hourly rates to customers. The government investigated the installation of smart meters, responding to demand and supply.  
In 2007, the government enacted the Energy Independence and Security Act (EISA), the first legislation using smart grid terms with the aim of advancing the national transmission grid and streamlining power consumption. The Federal Energy Regulatory Commission (FERC) is focusing on smart grid-related issues to support the modernization of national electrical systems in accordance with Title XIII of the EISA. As part of it, the Grid Modernization Initiative (GMI) works with partners in industry, academia, and cities/states across the U.S. including DOE for the purpose of creating the modern grid of the future, aiming at (1) greater resilience to hazards of all types; (2) improved reliability for everyday operations; (3) enhanced security from an increasing and evolving number of threats; (4) additional affordability to maintain economic prosperity; (5) superior flexibility to respond to the variability and uncertainty of conditions; and (6) increased sustainability through energy-efficient and renewable resources. The 2017 GMI Peer Review Poster Session was the first time where all projects in the portfolio were viewed together.  
In 2009, the ARRA made the single largest investment in clean energy and supported more than 100 Smart Grid projects with subsidies of approximately $4 billion. This act focused on developing and distributing new power supplies in connection with various investment support programs for transmission and distribution, power storage monitoring, and control technology, from power generator to final consumer via power operator. Regarding cyber security for critical energy infrastructure, the Cybersecurity Research, Development and Demonstration (RD&D) for Energy Delivery Systems program takes both operational technology and information technology into account in order to protect systems against malware without any slowing down of communications.  
In Canada, the Canadian Smart Grid Standards Roadmap was released in October 2012 by the Standards Council of Canada [20]; CamnetENERGY was a major partner in the development of the roadmap. This document describes the recommendations for the future plan of smart grid technology to ensure efficient and effective development covering (1) smart grid policy, legislative and regulatory considerations; (2) privacy and security requirements; (3) transport and deployment standards; and (4) metering system standards.
## 2 Proposed Framework and Experimental Methodology
### Proposed Hybrid CPU-GPU Workflow
#### 2.4.2 Experimental Flowchart  
The experimental approach to validating the proposed hybrid workflow involved clearly defined steps, as illustrated in Figure 5. First, datasets of various sizes ranging from megabytes to gigabytes were prepared. Benchmarking scripts were executed multiple times (ten iterations each) to measure encryption performance precisely, employing Python 3.7's \"timeit\" library and NVIDIA's profiling tool \"nvprof\". Based on iterative feedback from performance data, chunk sizes, CPU-GPU task ratios, and thread counts were dynamically optimized. Statistical analyses were performed using NumPy and SciPy libraries to verify the consistency and reliability of the results. This structured experimental flow ensured accurate evaluation and reproducibility.
## 3 Database
The interface conditions were monolithic uncracked (M-U), monolithic precracked (M-P), and cold joints that were intentionally roughened (J-R) and not roughened (that is, smooth) (J-S), as shown in Figure 4. The concrete types were sand lightweight (SLW), all lightweight (ALW), and normal-weight (NW). Of the 458 test specimens given in the database, the number of normal-weight concrete specimens was 315, the number of sand lightweight concrete specimens was 91, and the number of all lightweight concrete specimens was 52 (69%, 20%, and 11% of the total, respectively). Most of the available tests (approximately 67% of the total) consisted of monolithic specimens, with the majority of tests carried out on uncracked specimens. Cold joint specimens accounted for about 33% of the total specimens collected, and most were intentionally roughened cold joints. The compressive strength of concrete varied over a wide range. The compressive strength of concrete of 13.79 through 55 MPa accounted for approximately 76% of the total. Around 24% of the total had a compressive strength of concrete of 55 through 123.81 MPa. For cold joint specimens, the lower compressive strength was only reported when the two sides of the interface had different compressive strengths of concrete. The clamping stresses ranged from 0 MPa to 13.29 MPa, with the majority of them varying in the 0 MPa to 10 MPa range. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{1}{c}{**Researchers**} & \multicolumn{1}{c}{**Year**} & \multicolumn{1}{c}{**Interface Type**} & \multicolumn{1}{c}{**Concrete Type**} & \multicolumn{1}{c}{**Number of Specimens**} & \multicolumn{1}{c}{\(f_{e}\)\({}^{\prime}\)} & \multicolumn{1}{c}{\(\rho f_{g}\)\({}^{\prime}\)} \\ \hline Rahal and Al-Khalefei [30] & 2015 & M-U & NW & 9 & 34.09 to 41.40 & 0.00 to 7.88 \\ \hline [PERSON] et al. [31] & 2016 & M-U & NW & 15 & 34.96 to 81.20 & 0.93 to 7.88 \\ \hline  & & M-U & NW & 2 & 33.37 & 5.38 \\  & & M-U & SLW & 2 & 32.89 & 5.38 \\  & & M-U & ALW & 2 & 32.41 & 5.38 \\  & & M-P & NW & 2 & 33.37 & 5.38 \\ [PERSON] et al. [32] & 2016 & M-P & SLW & 2 & 32.89 & 5.38 \\  & & M-P & ALW & 2 & 32.41 & 5.38 \\  & & J-R & SLW & 12 & 31.99 to 38.41 & 3.72 to 9.10 \\  & & J-R & ALW & 4 & 30.20 to 30.75 & 5.38 \\  & & J-S & SLW & 14 & 31.37 to 38.41 & 3.72 to 9.10 \\  & & J-S & ALW & 4 & 30.20 to 30.75 & 5.38 \\ \hline [PERSON] and [PERSON] [33] & 2016 & M-U & NW & 48 & 30.24 to 73.60 & 0.00 to 5.28 \\ \hline [PERSON] et al. [34] & 2016 & M-U & NW & 19 & 23.43 to 33.03 & 3.63 \\ \hline [PERSON] et al. [35] & 2017 & J-R & NW & 20 & 28.2 & 1.72 to 2.67 \\ \hline [PERSON] et al. [36] & 2018 & M-U & NW & 12 & 40 & 0.00 to 6.65 \\ \hline [PERSON] et al. [37] & 2021 & M-U & NW & 3 & 47 & 0.00 \\ \hline Total & 1969 to 2021 & M-U, M-P, J-R, J-S & NW, SLW, ALW & 458 & 13.79 to 123.81 & 0 to 13.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._  
Figure 4: Direct push-off with interface conditions: (**a**) monolithic uncracked, (**b**) monolithic precracked, (**c**) intentionally roughened cold joint, and (**d**) cold joint that is not roughened.
## 4 Power Scaling in MOPA System Using Cascaded Slab Module
The amplified output powers of the HG\({}_{01}\) to HG\({}_{09}\) modes are also shown in Figure 6b, and it can be seen from Figure 6b that each output power from the HG\({}_{01}\) to HG\({}_{08}\) modes is higher than 5 W.  
The pulse temporal characteristic of the amplified highest order HG\({}_{09}\) mode is monitored by a photodiode detector (DET10N/M, Thorlabs Inc., Newton, NJ, USA) connected to a 200 MHz bandwidth digital oscilloscope (SDS1204X-E, SIGLENT Inc., Shenzhen, Guangdong, China), as shown in Figure 7 (red line). It was found that the pulse interval of the output pulse was 2 ms, indicating that the laser operated at a PRR of 500 Hz. The pulse-to-pulse amplitude fluctuation is less than 1.2% (Standard Deviation). The upper right inset of Figure 7 expresses an expanded single pulse profile (blue line), showing a pulse width of 295 ns, so the peak power corresponds to 32 kW. Figure 5: The 2D spatial beam profile and the corresponding normalized beam intensity curves of HG\({}_{01}\) to HG\({}_{09}\) mode after pulse amplification. To expand the application, the following aspects, such as higher-order number \(n\) of the HG\({}_{0n}\) mode, narrow pulse width, and the conversion to vortex LG\({}_{pl}\) modes, will be the focus of our future work. In this paper, the 5 mm \(\times\) 5 mm active aperture of the AO Q-switch limits the upper boundary of order number \(n\), but with the use of the customized Q-switch with a larger aspect ratio aperture, HG\({}_{0n}\) modes with order number \(n\) higher than 9 will be generated. Meanwhile, by replacing the AO Q-switch with a saturable absorber, the mode-locking can be obtained, and the pulse width can be reduced to several hundred picoseconds. Although previous studies have shown that ultrafast high-power HG\({}_{mn}\) laser beams are affected by other parameters like self-phase modulation and bandwidth [25; 26], the relatively large dimensions of the gain medium, such as a slab with a large aspect ratio, will reduce the effect. Further conversion to vortex LG\({}_{pl}\) modes can be achieved by using AMC composed of cylindrical lenses, which will be widely used in optical communication, material processing, and many other fields [27; 28; 29]. Figure 6: Measured amplified output power. (**a**) The output power for the highest HG\({}_{09}\) mode versus pump power. (**b**) The amplified output power of the HG\({}_{01}\) to HG\({}_{09}\) modes. Figure 7: Oscilloscope traces of the pulse train (red line) for HG\({}_{09}\) mode. Inset: expanded profile of a single pulse (blue line).