esa-sceva/satcom-chunk-collection huggingface dataset:
# Capacity-Operation Collaborative Optimization for Wind-Solar-Hydrogen Multi-Energy Supply System
## 1 Introduction
The model utilizes multi-objective optimization with carbon emissions and economic objectives and optimizes the annual load plan and system's main component capacity. This is mentioned in the outlook of many studies [24; 33], but it has not been resolved. The present study is a further extension of previous studies [35; 36; 37] and the new contributions are as follows:  
1. A novel WSH-MES system combining electricity, hydrogen, heating, and storage is constructed, which is fully driven with renewable energy and optimizes its capacity in the face of source load uncertainty. 2. A bi-level capacity-operation collaborative optimization model considering the uncertainty of source and load that integrates reliability, environmental protection, and  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Reference**} & \multicolumn{3}{c}{**System Composition**} & \multicolumn{6}{c}{**Optimization**} \\ \cline{2-10}  & \multirow{2}{*}{ \begin{tabular}{c} **Renewable** \\ **Energy** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Storage** \\ **Units** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Economy** \\ **Emissions** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Carbon** \\ **Energy** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Source and** \\ **Load** \\ **Uncertainty** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Operation** \\ **Stategy** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **Collaborative** \\ **Optimization** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Multi-** \\ **Objective** \\ \end{tabular}  } \\ \hline
[24] & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) & \(\times\) \\
[25] & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\)  
economy is established. The model is solved using non-dominated sorting genetic algorithm-II (NSGA-II) and linear programming (LP). 3. A comprehensive analysis is conducted to reveal the effects of capacity parameters on the performance of the WSH-MES system.
# Simulation of Nuclear Quantum Effects in Condensed Matter Systems via Quantum Baths
## 5 Applications of Quantum Baths to Realistic Systems
### Dynamical Properties
#### 5.3.1 Phonon Spectra in Pure and Salty Ices under Pressure  
As observed in Section 4, QTB simulations provide direct access to vibrational spectra, accounting consistently (though approximately) for anharmonic and quantum effects. This property has been exploited in different studies to confront QTB results with other, more established simulation methods [15; 18], or with experimental data. In particular, the phonon spectrum of ice was extracted from QTB simulations at various pressures, computed from the Fourier transform of the velocity-velocity autocorrelation function [1]. These QTB results are shown in Figure 10, and compared with the infra-red absorption and Raman scattering data. The QTB reproduces the experimental data well in the whole pressure range, including the softening of the O-H stretching mode close to the VII-X transition. This softening is an essential feature of the transition that can be related to the response of the system: in the classical picture, the squared frequency of the soft phonon mode is proportional to the inverse susceptibility \(\chi^{-1}\) and, in the simple one-dimensional double-well model, \(\chi\) is maximum at the transition pressure \(P_{c}\). This relates the O-H stretching mode softening with the enhanced fluctuations of the dipole moment that typically occurs at the transition. Interestingly, the O-H stretching mode softening occurs at larger pressures when a small quantity (\(\sim\)1%) of LiCl or NaCl salt is incorporated into the ice crystal, an effect that is well captured by the QTB [61].  
Contrary to previous beliefs, this effect is not due to steric hindrance, but to the a-symmetrization of the OHO triplet because of the long-range dipolar field generated by the dissociated Li\({}^{+}\) Cl\({}^{-}\) or Na\({}^{+}\) Cl\({}^{-}\) salts. Accordingly, the critical pressure for the VII-X transition in salty ice under pressure is upshifted and much closer to its classical value than in pure ice. This counterintuitive phenomenon (salty ice is \"more classical\" than pure ice) shows the subtleties of nuclear quantum effects in hydrogen-bonded systems.
# Objective Assessment of Porcine Voice Acoustics for Laryngeal Surgical Modeling
## 4 Discussion
### Frequency-Based Parameters  
Healthy pig squaels often include stronger higher frequencies as shown exemplarily in Figure 6 for the averaged energy spectrum of ten pre- and ten post-surgery squaels for pig 3. This explains the on average lower values of the frequency-based parameters (see Figure 4). PF is the position of the highest peak, which is more often within the lower frequencies since energy within higher frequencies is reduced. Similarly, Q-parameters describe the position at which half (for Q25 25%) of the total energy (in the entire squeal or one window, depending on the parameter) is reached (for details see Table 2). Less of the energy is within the higher frequencies; therefore, these positions are reached earlier, and Q parameters decrease after surgery according to our expectations (see hypotheses in Table S2).  
This trend is less consistent in the partial window-based parameters (Q502, Q5010, and Q50 min), since they are only calculated for a fraction of the entire squeal, which results in a lower frequency resolution. Therefore, a more subtle shift of spectral energy may not be shown within these parameters. Further small variations of selected starting and ending-positions between squeals may lead to these parameters being calculated for slightly different areas of each squeal. Furthermore, changes over the duration of the squeal are not shown within partial window-based parameter values; still, Q50\({}_{\text{min}}\) showed a statistically significant change in two pigs, which may hint at some usefulness of these windowed parameters for large datasets with carefully defined start and end-positions of squeals (best using a repeatable, objective method for squeal extraction).  
With the exception of PF, all investigated frequency-based parameters are defined very similarly with the general idea of making a simplified statement about spectral energy distribution. For our data, Q50 showed the most consistent change between pre- and post-surgery squeals, which may be due to this parameter being the only one of its type calculated for the entire squeal at once, allowing for maximal frequency resolution. Q50W is similar but somewhat less robust due to windowing and offers no additional insight over Q50 alone. PF on the other hand was less consistent, displaying no statistically significant change, mainly since it describes the position of the absolute maximum of the energy spectrum, which may more easily change. To avoid redundancy, among frequency-based parameters investigated, we found Q50 to be the best choice for objective assessment of pig voice health.
# Cognitive Interference Cancellation with Digital Channelizer for Satellite Communication
## 1 Introduction
One of the typical types of interference is a continuous wave (CW) tone interference which can be simply generated by a malicious user to interrupt satellite communications. In other cases, CW tone interference can be produced by inter-modulation of the internal clock and carrier frequencies used in a satellite transceiver. In this work, we focus on CW tone interference detection and cancellation. A digital channelizer based on a digital filter bank is used for interference cancellation by nulling  
Figure 1: Satellite communication networks supporting Internet of Things (IoT). the subchannel corrupted by the interference, before inputting to the synthesis filter of the filter bank. In this interference cancellation scheme, it is shown that a tone interference whose power is lower than the power of the desired signal component in a single subchannel does not need to be cancelled out because the cancellation causes more distortion of the desired signal than the interference. To detect a subchannel interfered by a CW tone signal, we employ an energy detection which measures the received signal power per subchannel and compares it with a threshold. With this simple approach, we can achieve the reasonable detection performance, i.e., low false alarm and high detection probabilities. However, this requires a long time, i.e., many samples of the signal, for accurate detection. To reduce the detection time, an improved method is also proposed. In the proposed method, we exploit the structure of a fast Fourier transform (FFT) operation which is the simplest filter bank. To verify the performance of the proposed method, we analyze the false alarm and detection probabilities of the interference detection algorithm and perform computer simulations under various conditions.
# Data-Driven Prediction of Binder Rheological Performance in RAP/RAS-Containing Asphalt Mixtures
## 3 Results and Discussion  
### Ensuring No TCE Traces in the Extracted Binders  
Prior to the characterization of the extracted binders, FTIR analyses were performed to compare the FTIR bands of the TCE and randomly selected extracted binders, as illustrated in Figure 2. The randomly selected samples comprised fifteen extracted binders: Five from field mixtures (F19, F20, F33, F36, and F42), five from plant mixtures (P2, P3, P7, P8, and P9), and five from laboratory mixtures (L5, L15, L21, L22, and L25). The major bands of TCE corresponded to the aromatic C-H stretching at 3150 cm\({}^{-1}\), C-H stretching vibration of the vinyl group at 3060 cm\({}^{-1}\), C-Cl stretching in alkyl halide between 944 and 849 cm\({}^{-1}\), and =C-H bending in alkene at 783 cm\({}^{-1}\)[51]. Figure 2 shows that no TCE traces were detected in the extracted binders. This conclusion was reached because of the absence of major TCE bands in the extracted binders.
# Research on Hybrid Relay Protocol Design and Cross-Layer Performance Based on NOMA
## 2 System Model and Signal Processing
### Signal Processing
When \(k<j\), user \(j\) detects signal \(x_{k}\) and deletes it successively from the received signal after decoding. For \(k>j\), the signal \(x_{k}\) will be treated as interference by the user \(k\). Then, the SNR of \(x_{k}\) is detected at \(\text{D}_{j}\) as follows [17]:  
\[\gamma\big{(}\text{D}_{j},x_{k}\big{)}=\frac{a_{k}\Delta_{pln}d_{\text{RD}_{k} }^{-2a}\big{|}h_{\text{RD}_{k}}\big{|}^{2}}{\sum_{i=k+1}^{n}a_{i}\Delta_{pln}d _{\text{RD}_{i}}^{-2a}\big{|}h_{\text{RD}_{i}}\big{|}^{2}+1},j\leq k\leq n-1, \tag{7}\]  
where \(\Delta_{pln}=P_{\text{R}}/N_{mt},m\in(0,1)\) is the average signal-to-noise ratio of the power line channel. \(N_{0}=N_{G}\) represents the noise power with only background noise, and \(N_{1}=N_{G}+N_{I}\) represents the noise power when both background noise and pulse noise are present. \(h_{\text{RD}_{j}}\) represents the channel fading factor for each power line channel. \(d_{\text{RD}_{j}}\) indicates the link distance between the trunk node R and the use \(\text{D}_{j}\). \(\text{D}_{n}\) performs SIC to detect its information using the following SNR:  
\[\gamma(\text{D}_{n},x_{n})=a_{n}\Delta_{p|m}|h_{\text{RD}_{n}}|^{2} \tag{8}\]
## 3 Results and Discussion
### Electrochemical Impedance Spectroscopy (EIS)  
Electrochemical impedance spectroscopy was employed to examine the interfacial charge behavior of the dye cells, illustrated through Nyquist and Bode plots in Figure 5. In Figure 5c, semicircles are evident in the high-frequency region, indicating resistance to charge transport at the interface between the electrolyte (31-/I-3) and platinum, thereby impacting the overall cell resistance. Notably, cells with thin films deposited for 3 min (Figure 4a) exhibit higher resistance [38].  
From the Bode plots (Figure 5b,d), the electron lifetimes (\(\tau\)) were calculated and are presented in Table 2. The \(\tau\) (ms) values were determined using the equation \(\tau=1/(2\pi\)fp), where fp represents the highest frequency at the ZnO/electrolyte interface [39]. It is noteworthy that all calculated electron lifetimes for the cells were relatively low, thereby influencing the incident photocurrent efficiency (IPCE) within the cells.
# SAViP: Semantic-Aware Vulnerability Prediction for Binary Programs with Neural Networks
## 4 Evaluation
### Parameter Analysis
#### 4.6.1 Semantic Features  
We apply the pre-training task from RoBERTa to extract assembly semantics and obtain instruction embeddings and then the semantic embedding of basic blocks. Instruction embeddings are similar to sentence embeddings in natural language; they are commonly obtained by selecting vector corresponding to the first special token \"\(\langle s\rangle\)\" as the sentence embedding. Considering the importance of opcodes in assembly language, we concatenate \"\(\langle s\rangle\)\" with the vector of opcodes. To demonstrate the correctness of this procedure, we distinguish several different RoBERTa settings for comparison. Table 6 and Figure 10 show the specific setting differences and the final experimental results. Among them, we also test the effects of different embedding dimensions (32, 64, 128, and 128) and compare the results when the subsequent designs are exactly the same. The result shows that, when 64 dimensions are used as the word embedding size, the result of using both \"\(\langle s\rangle\)\" and opcode is better than using either alone. When the two are used alone, the recall and time consumption are very similar, indicating that the implicit features in these two are similar but complementary. This demonstrates the superiority of our design. In the testing of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **DEV** &
\begin{tabular}{c} **Recall** \\ **TEST** \\ \end{tabular} & **GAP** & **Time (s)** \\ \hline SAViP & 0.782 & 0.7785 & 0 & 18.04 \\ without SM-F & 0.701 & 0.6905 & \(-\)8.8\% & 11.86 \\ without STA-F & 0.7515 & 0.7445 & \(-\)3.4\% & 16.56 \\ without STR-F & 0.745 & 0.7325 & \(-\)4.6\% & 13.015 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation Study.  
Figure 9: Results of the ablation study. The graph shows that the most significant contribution to our model was made by semantic features.  
embedding dimension, we can see that the higher the dimension, the higher the recall is, but the longer the time consumed. To balance the recall and time costs, we choose 64 as the final word embedding size.
## 2 State-of-the-Art in Light Dimming and Lights-Off Visible Light Communications
### Related Work
#### 2.2.1 Single Carrier Modulations
Here, there were three challenges to overcome: to generate and detect ultra-short pulses of light with off-the-shelf LEDs and photodiodes, to find a suitable modulation and demodulation design, and to solve the collision errors when pulses from different transmitters are received. The authors even conducted a study to systematically examine user's perception of the ultrashort light pulses. There was a study based on PPM [33] on the possibilities to transmit data in a smart room when the lights appear off, on the minimum illuminance needed for receiving the information, and on the factors that influence Signal-to-Noise Ratio (SNR) and Bit Error Ratio (BER), selecting the parameters and the requirements needed in terms of power, bandwidth efficiency, and dimming factor. In [34], the desired dimming control was achieved by optimizing the probability and the intensity of the PAM constellation points with a hybrid scheme that combines the analog and the digital technique. Another system was proposed in [35], where a multilevel transmission scheme has the dimming controlled through the concatenation of PAM symbols, in order to obtain an average amplitude in line with the dimming requirement.
# Effects of Ethanol-Diesel on the Combustion and Emissions from a Diesel Engine at a Low Idle Speed
## 2 Methodology  
### Test Fuels  
In this study, pure diesel fuel was blended with bioethanol with 99.9% purity. Bioethanol was blended with pure diesel fuel at 3%, 5% and 10% by volume. The test fuels were labeled DE_0 (Diesel 100% + Ethanol 0%), DE_3 (Diesel 97% + Ethanol 3%), DE_5 (Diesel 95% + Ethanol 5%) and DE_10 (Diesel 90% + Ethanol 10%). The viscosity of ethanol is about 40% lower than that of diesel fuel, and the LHV is also about 64% lower than diesel fuel. The low viscosity of ethanol is about 40% that of diesel. The cetane number of ethanol is 14.3% that of diesel. Ethanol is further known to have a higher latent heat of vaporization than diesel, by about 3-5 times [8; 21; 28]. The properties of the diesel and ethanol used in the tests are as shown in Table 1.  
When blending ethanol and diesel fuel, preventing phase separation is critically important. As shown in Figure 1, the experimental fuels of ethanol blended in diesel fuel were observed, and no phase separation occurred. A study by [PERSON] et al. [29] also reported that the phase separation of a diesel-ethanol blend does not occur when the ambient air temperature is above 10 \({}^{\circ}\)C. [PERSON] et al. [30] also reported that phase separation began to occur when the temperature drops below 10 \({}^{\circ}\)C. In this experiment, the test fuels were blended at the ambient temperature of 20 \({}^{\circ}\)C, and were kept at about 25 \({}^{\circ}\)C during tests.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **Units** & **Diesel** & **Bioethanol** \\ \hline Density at 15 \({}^{\circ}\)C & kg/m\({}^{3}\) & 836.8 & 799.4 \\ Viscosity at 40 \({}^{\circ}\)C & mm\({}^{2}\)/s & 2.719 & 1.10 \\ Lower heating value & MJ/kg & 43.96 & 28.18 \\ Cetane number & - & 55.8 & 8.0 \\ Flash point & \({}^{\circ}\)C & 55 & 12.0 \\ Oxygen content & wt.\% & 0 & 34.3 \\ Hydrogen content & wt.\% & 13.0 & 13.1 \\ Carbon content & wt.\% & 85.7 & 52.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Diesel and Bioethanol properties.
## 3 Numerical Simulation
### Verification of Numerical Model Accuracy  
The numerical simulation technique is popular among scholars because of its advantages, such as conserving resources and cost savings; the accuracy of its results is also a considerable benefit. To verify whether the Dload subroutine can accurately impose  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Item** & **Material** & **Thickness (m)** & **Unit Weight (Mg-m-3)** & **Elastic Modulus (MPa)** & **Poisson’s Ratio** & **Damping Constant (\(\alpha\)s-1)** & **Damping Constant (\(\beta\)s)** & **Mohr-Coulomb Model (MPa)** & **Mohr-Coulomb Model (\(\alpha\)s-1)** \\ \hline \multirow{2}{*}{Rigid pavement} & Cement concrete & 0.4 & 2440 & 36,000 & 0.2 & 0.1 & 0.001 & / & / \\  & Cement-stabilized & 0.4 & 2100 & 1500 & 0.25 & 0.1 & 0.002 & / & / \\ \hline \multirow{2}{*}{Flexible pavement} & Asphal concrete & 0.2 & 2380 & 1800 & 0.25 & 0.1 & 0.001 & / & / \\  & Cement-stabilized & 0.3 & 2100 & 1500 & 0.25 & 0.1 & 0.002 & / & / \\  & gravel base & 0.3 & 2000 & 200 & 0.2 & 0.1 & 0.002 & / & / \\  & base course & 0.3 & 2000 & 200 & 0.2 & 0.1 & 0.002 & / & / \\ \hline \multirow{2}{*}{Foundation course  Tunnel lining} & Soil & 80 & 1800 & 30 & 0.4 & 1 & 0.01 & 0.001 & 25 \\  & Concrete & 1.5 & 2440 & 36,000 & 0.2 & 0.1 & 0.001 & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main material parameters.  
the aircraft moving load, a dynamic analysis is conducted on the model based on the calculation example reported in the literature [34].  
A uniform infinitely long thin plate is 5 m wide and 0.1 m thick. It has a mass density of 2500 kg/m\({}^{3}\), a modulus of elasticity of 10 MPa and a Poisson's ratio of 0. The Rayleigh damping factors are \(=0.1s^{-1},\beta=0.01s\), respectively. The bottom of the slab is vertically restrained, and a sinusoidal excitation load (\(P=\sin(20\pi t)\frac{\mathrm{N}}{\mathrm{m}^{2}},t\in[0,\,0.5]\)) is applied to within 1 m of the center of the slab. The C3D8R cell is used to divide the mesh of the model. The mesh size is 0.1 m \(\times\) 0.1 m \(\times\) 0.1 m, and the analysis step time is 0.5 s. The schematics of the specific calculation example and finite element model are shown in Figure 7.  
The vertical displacement time curves for observation points A, B, and C are shown in Figure 8. The general trend of the graphs indicates that the application of the excitation load by the Dload subroutine developed using Visual Studio is generally in agreement with the results reported in the literature [34]. The aircraft excitation load acting on the model was effectively simulated by the Dload subroutine.
# Study on Impact-Echo Response of Concrete Column near the Edge
## 3 Impact Response of Concrete Column  
### Experimental Result  
In the experiment, the time history curve data obtained in the middle of the column and the data obtained at the boundary were Fourier transformed by the computer to obtain the dominant frequency corresponding to the column and the edge. The specific results are shown in Figure 4:  
Table 1 shows the test results of concrete columns via the SPC-MATS and impact-echo methods, respectively. After analyzing the collected signal, the typical dominant frequencies in the influence range of the column and the boundary were compared. From the analysis of the test results, it can be seen that the predominant frequency in the middle of the concrete column is 2.686 kHz, and the dominant frequencies at the edge are 2.563 kHz (impact-echo) and 2.441 kHz (SPC-MATS). When the two instruments detect the concrete column's edge area, the predominant frequency's value decreases. As the acquisition point is closer to the central area of the column when the acquisition point crosses a specific node, the predominant frequency mutates. This is because when the collection point is located in the edge area of the column, the tapping causes the column to undergo torsional deformation. When the collection point is closer to the middle area of the column, it is converted to the bending deformation of the column. The sensor records different modal frequencies.  
Through the above test results, it can be seen that the edge causes inevitable interference in impact-echo detection. When the acquisition point is located in the edge area, the predominant frequency corresponds to the dominant frequency generated by torsional deformation. When the acquisition point is located in the middle of the column, the predominant frequency corresponds to the dominant frequency generated by  
Figure 4: (**a**) Impact-echo spectrogram; (**b**) SPC-MATS spectrogram.  
Figure 3: (**a**) Numerical simulation diagram; (**b**) line layout diagram.  
bending deformation. In order to determine the influence range of the edge on the predominant frequency, it is necessary to further use the finite element software to simulate each working condition.