esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Laboratory Tests
### Mechanical-Acoustic-Optical Results  
The stress-strain relationships of all rock-like samples are shown in Figure 3, and the results in each group show good consistency in sample strength and curve slope; the maximum and minimum average UCS values occur in Design-3 and Design-2, respectively. When the samples reach the UCS status, the corresponding axial strains are commonly lower than 0.01.  
The mechanical-acoustic-optical results captured in three groups of samples (the intermediate value is selected in each group) are shown in Figures 4-6; the maximum UCS belongs to the Design-3 sample (21.49 MPa), and the minimum UCS appears in the Design-2 sample (13.87 MPa).  
Figure 3: Strain–stress relationships of all groups of samples.  
Figure 2: Design of the uniaxial compression test and the monitoring systems.  
Figure 4: Test results of samples with fracture inclination of 30\({}^{\circ}\).  
Figure 5: Test results of samples with fracture inclination of 45\({}^{\circ}\) (modified from the reference).  
The majority of AE events densely appear neighboring to the UCS status, and the corresponding peak AE rate is over 950 events/0.1 s; as the flaw inclination angle increases, the proportion of accumulative AE events during the UCS period gradually decreases, weakening the severity of the final destruction. Several local failures (illustrated as high AE rates) appear before the final destruction in the Design-1 and Design-2 samples, and the occurrence of local failure may not necessarily be accompanied by fluctuations in the stress curves.  
The maximum principal strain fields obtained from all samples indicate that the LMPS regions initiate from the tips of the 2D preset flaws, show a typical S-shaped distribution, and contact with the right-top corner of the 3D internal flaws. In addition, when the loadings approach the UCS status, the upper part of LMPS regions (above the 2D flaw) extends to the left corner of the 2D flaws (in all samples), and the lower part of LMPS regions remains in a stable distribution.  
As the flaw inclination angle increases, the final surface cracks continuously connect the right tips of the twin flaws, and the cracks start from the left tip of the 2D flaw and stably extend upwards; cracks initiate from the middle point of the 2D preset flaw (Design-1 and 2 samples) and develop downwards and finally connect to the right tip of the 3D flaw; all cracks in the Design-3 sample only start from the flaw tips.  
Accordingly, the above results prove that changes in the inclination angle of preset twin flaws will gradually change the intensity of the final failure of the sample, surface strain field, and distribution characteristics of secondary cracks. Moreover, the internal 3D flaws show little influence on the distributions of the surface strain fields and cracks, which are mainly controlled by the 2D preset flaws.
## 2 Materials and Methods
### Data Analysis  
Temporal variations in the data were analyzed after selection of conditions of APAR > 300 umol m\({}^{-2}\) s\({}^{-1}\) and sun elevations > 25\({}^{\circ}\). These filters were applied to show seasonal dynamics in NDVI, SIFA, SIFB, and FYSIFA with FYSIFB. Then, days with clear skies, identified from the parabolic daily trend in incoming PAR, were picked up for further analysis of diurnal data trends. These data were divided into four subsets by month and averaged at half-hour intervals to show the dynamics of the means for measured variables during four characteristic periods of the year and to show variations in the variables with shadow fraction (\(\alpha_{\mathrm{S}}\)). Intervals between dates in each data group do not exceed 30 days. Thus, we isolated groups of data collected under similar SZA simultaneously at 30 min steps in the day. Mean values were calculated for 3- to 5-day blocks, depending on the availability of cloudless days in the data set.  
For the primary regression analysis, we selected data from days with clear sky conditions in time intervals between 10:00 am and 2:00 pm (April-October) and extracted the midday averages across 2 h intervals for data analysis. These data are characteristic, most with APAR > 800 umol m\({}^{-2}\) s\({}^{-1}\). For better comparison, relationships between variables were compared also between half-hourly averaged data on selected days between 10 and 14 h. The linear regressions were evaluated at \(p\) < 0.05, \(p\) < 0.01, and \(p\) < 0.001.
## 4 Conversion Device Characterization
The application of the fiber-coupled waveguide module limits the efficiency of the conversion device. On one hand, the insertion loss of the \(2\times 1\) combiner and the coupling loss of the waveguide module to output fiber are introduced compared to a free-space coupled PPLN waveguide. On the other hand, the pumping power is limited by the maximum allowable power of the fiber end face (500 mW). The maximum pumping power coupled into the \(2\times 1\) combiner is 350 mW due to the coupling efficiency of the input fiber of the \(2\times 1\) combiner (0.7). The corresponding maximum pumping power entering the waveguide is only 117 mW, due to the transmission of the \(2\times 1\) combiner (0.61) and the input coupling efficiency of the waveguide (0.55). In the case of free-space coupled waveguide, the internal conversion efficiency can be further improved by increasing the pumping power. The SNR of the converted pulse is also related to the duration of the input pulse. For a pulse of long duration, a large measurement window is needed to obtain its complete information, which means that more noise is included in the measurement process and the SNR of converted pulse is decreased. The noise level of the converted pulse with different durations can be represented by parameter \(\mu_{1}\), which is the photon number contained in the input pulse to make the SNR of converted pulse equal to 1. We measure \(\mu_{1}\) as a function of the temporal FWHM of input pulse \(\Delta t_{in}\), as shown in Figure 6. The measurement window is taken to be three times the temporal FWHM of the output pulse after filtering system, in which more than 99% of the converted pulse can be included. Although the external device conversion efficiency is low (\(\eta_{ext}=0.51\%\)), the value of \(\mu_{1}\) is less than 1 for \(\Delta t_{in}=250\) ns, which benefits from strong noise suppression. The solid line in Figure 6 is a linear fitting with no offset for the experimental data. Except for \(\Delta t_{in}=50\) ns, the other data points are in good agreement with the linear fitting. The reason is that the temporal FWHM of output pulse is broadened twice when \(\Delta t_{in}=50\) ns, while the output pulse width of other data points is close to that of the input pulse. Figure 6: The mean number of photons in the input pulse to achieve SNR = 1 (\(\mu_{1}\)) as a function of the FWHM of input pulse. The pumping power is 315 mW. The solid line is a linear fitting with no offset.
# A Robust Routing Protocol in Cognitive Unmanned Aerial Vehicular Networks
## 6 Simulation Results
### Performance Evaluation
Figure 7: Comparison of packet delivery ratios for packet sizes of 512 and 1024.  
for packet transmission. This analysis evaluates two distinct packet sizes: 512 bytes and 1024 bytes, within a simulation environment featuring UAVs with a transmission range of 550 m.  
For packet sizes of 512 bytes, the normalized routing load ranges from 5.142 to 18.072, while for packet sizes of 1024 bytes, it varies from 5.646 to 16.426. The findings suggest that as the number of trials increases, the normalized routing load tends to fluctuate. This fluctuation may stem from changes in network topology, traffic patterns, and packet sizes, all of which influence the routing overhead experienced by individual nodes. Additionally, it is observed that the normalized routing load exhibits different trends for each packet size. For instance, while there is a general increase in routing load with higher trial counts for both packet sizes, the trend is less consistent, with instances of both increase and decrease in routing load.
# Speech Enhancement Based on Unidirectional Interactive Noise Modeling Assistance
## 4 Results and Discussion
### Performance Analysis over Different Signal-to-Noise Ratios (SNRs)
In contrast, the baseline model BiInterNet in this work achieves similar overall performance while ensuring causal inference. In addition, BiInterNet has fewer model parameters and lower computational cost. This demonstrates the benefits of our proposed system. Furthermore, compared to SN-Net, the proposed UniInterNet reduces the model parameters by about 77% and computational cost by about 80%, while retaining the strength of causal inference. Meanwhile, the performance degradation of UniInterNet is acceptable. Therefore, UniInterNet has significant advantages over SN-Net, especially in scenarios with limited computing resources and processing latency.
## 4 Results and Analysis
### Case of Wheat Futures
#### 4.1.1 Decomposition Results  
This subsection illustrates the decomposition results of wheat futures prices using EMD, WPT, ITD and VMD approaches, respectively. More specifically, according to the EMD method, the price data series of wheat futures is decomposed into seven IMFs, defined as IMF1, IMF2,..., IMF7, and a residual sub-series, named RES, which are shown in Figure 7. As to the WPT method, two typical decomposition levels, namely two levels and three levels, are taken into consideration in our study, for the sake of finding out the better decomposition level that will improve the PSO-BPNN's forecasting accuracy more. Finally, the more suitable level turns out to be three, and the three-level decomposition results, including an approximation component L and seven detail components defined as H1, H2,..., H7, of the WPT technique is given in Figure 8. As to the ITD method, the upper limit on the number of decomposition, namely ten, is set manually in our study. Under such a preset, the ITD method automatically divides the sample data into five PRs, and a RES as well. See Figure 9 for decomposition results in detail. As to the VMD method, this study compares the decomposition effect of different number of modes, namely from six modes to nine modes. Given the hybrid models' forecasting accuracy, eight modes, namely y1, y2,..., y8, are determined eventually, as shown in Figure 10.  
Figure 8: Decomposition results of the WPT method (wheat).  
Figure 7: Decomposition results of the empirical mode decomposition (EMD) method (wheat).
# Toward High Bit Rate LoRa Transmission via Joint Frequency-Amplitude Modulation
## 6 Conclusions  
We present the design, implementation and evaluation of QR\(-\)LoRa, which is a novel PHY-layer scheme that leverages the _joint frequency-amplitude modulation_ to improve the bit rate of LoRa transmissions. On top of the frequency-modulated information of traditional LoRa PHY, QR\(-\)LoRa also modulates another dimension of data in the amplitude domain simultaneously. We prototype QR\(-\)LoRa on the USRP N210 and conduct extensive evaluations. We believe QR\(-\)LoRa represents a significant step in a line of work that could improve the bit rate gain by a factor of 2 compared with existing LoRa devices.  
Conceptualization, G.T. and Z.Z.; Methodology, G.T.; Software, J.W.; Validation, G.T., Z.Z. and L.W.; Formal analysis, G.T.; Investigation, G.T.; Resources, C.Z.; Data curation, N.J.; Writing--review and editing, all authors. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China [Grant No. 62472369], the Natural Science Foundation of Hebei Province [Grant No. F2022203045], the Beijing Natural Science Foundation [Grant No. L222004], the Hebei Provincial Science and Technology Research and Development Program [Grant No. 23620801G, 22567637H], and the Hebei Key Laboratory Project [Grant No. 202250701010046, 22567637H]. Data Availability Statement: Data sharing is not applicable to this article as the study did not generate new data and the used data are subject to commercial confidentiality agreements  
We would like to express heartfelt gratitude to the research team at Yanshan University's School of Information Science and Engineering, whose collaborative discussions and technical support were instrumental in the development of this work. Special thanks are extended to the Hebei Key Laboratory of Information Transmission and Signal Processing for providing the experimental facilities and academic environment that enabled the implementation and validation of QR\(-\)LoRa.Finally, we appreciate the constructive feedback from reviewers, which helped refine the manuscript and strengthen the technical contributions described herein. All authors disclosed no relevant relationships.
# A Methodology for the Design of Application-Specific Cyber-Physical Social Sensing Co-Simulators
## 2 State of the Art
### State-of-the Art on Co-Simulation  
The term co-simulation may be understood in various ways. However, for this work, we are following [PERSON]'s proposal, which defines co-simulation as any simulation of a coupled system performed through the composition of various simulators [53]. However, contrary to these previous proposals focused on the coordination of tools considered as black boxes, in this work domain specific tools could be slight modified if required and possible.  
Traditional works on co-simulation investigate the coordination between simulators with different time representations (discrete time and continuous time simulators, an approach usually named as hybrid co-simulation) [30]. Problems such as the multi-level modeling (reality may be modeled in different ways depending on the abstraction level) [54] or the stability of dynamical systems [55] have been also addressed.  
In relation to the coordination of different simulation tools various standard technologies have been proposed. Basically, three of them are the most important: High-level architecture (HLA) [56], Functional Mock-up Interface (FMI) [57] and Discrete Event System Specification (DEVS) [58].  
HLA standard is mainly focused on continuous time (CT) simulations. This standard defines an interface, an object model and a set of rules in order to allow simulation tools to communicate regardless the underlying platform. FMI, on the contrary, is usually employed with discrete-event oriented simulations. This standard was specifically designed to simulate Cyber-Physical Systems, and it is based on the creation of a simulation model or a software library called Functional Mock-up Unit (FMU). Finally, DEVS is also focused on discrete systems, although it is different from the previously mentioned proposal as it is a formalized rather than an available technology.  
CPSS co-simulation is natively hybrid. Social and physical simulations are (in general) continuous in time, while cyber (network) simulations are based on discrete events. In that way, proposed standards are not completely useful as, as indicated by [PERSON] [53], both standards have limitations for hybrid co-simulation. In fact, some extension of these standards for certain applications have been proposed [59; 60], although these proposals do not totally resolve the problem.
## 3 Results and Discussion  
### Prediction of Putting Parameter (G*/sin\(\delta\)) Using Unaged Parameters  
The results of the prediction of the Superpave(r) rutting parameter are presented in Table 3 and Figure 2. The main motivation of this section is to explore the possibilities of using an unaged input parameter to predict the Superpave(r) (G*/sin\(\delta\)) rutting parameter. As stated in the previous section, the G*/sin\(\delta\) parameter is valuable in estimating rutting susceptive in asphalt binder. However, experimental measurements to determine the values of G*/sin\(\delta\) at different PG (performance grade) temperatures is expensive and involve advanced equipment.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & \multicolumn{3}{c}{**Training Results**} & \multicolumn{6}{c}{**Simulation Results**} \\ \hline  & & & & & \multicolumn{3}{c}{**3\% Late Dataset**} & \multicolumn{3}{c}{**6\% Late Dataset**} \\ \hline  & R\({}^{2}\) & RMSE & MAE & R\({}^{2}\) & RMSE & MAE & R\({}^{2}\) & RMSE & MAE \\ \hline ANN & 0.96 & 0.024 & 0.013 & **0.99** & **0.007** & **0.006** & 0.96 & 0.012 & 0.008 \\ \hline GPR & 0.95 & 0.026 & 0.012 & 0.97 & 0.015 & 0.008 & 0.97 & 0.010 & 0.007 \\ \hline RNN & 0.97 & 0.019 & 0.010 & 0.96 & 0.016 & 0.008 & **0.96** & **0.008** & **0.005** \\ \hline SVM & 0.95 & 0.028 & 0.013 & 0.95 & 0.016 & 0.012 & 0.95 & 0.011 & 0.008 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance results for the prediction of G*/sin\(\delta\) parameters using unaged dataset.  
Figure 2: The comparison of predicted vs. measured G*/sin\(\delta\) parameters using the unaged dataset.  
Four different approaches were used for modelling of the G*/sin\(\delta\) parameter at different PG temperatures. The proposed GPR algorithm, RNN, SVM and the ANN were the modelling tools used.  
In the results, it is worth noting that the overall training performance of the four methods was satisfactory considering the normalized RMSE, MSE and MAE values and coefficient of determination (R\({}^{2}\)) of the models. The statistical goodness of fit parameters is presented in Table 3. A comparison could be made between the GPR and RNN predictive accuracy and traditional ANN and SVM models. Figure 2 shows the point by point comparison of the combined (M-L-3 and M-L-6) simulation dataset results of the unaged dataset prediction of rutting parameter. As seen from the graph, the ANN model was able to simulate the corresponding measured rutting parameters better than the GPR, RNN and SVM with respect to R\({}^{2}\) values of 0.99 and 0.96 for the M-L-3 and M-L-6 simulation subsets respectively. The normalised RMSE and MAE were also found to be lower than the other models, The overall performance of all the models was substantially high.
# A Review of Posture Detection Methods for Pigs Using Deep Learning
## 2 Data Acquisition and Sub-Tasks for Pig Posture Detection
### Data Acquisition for Pig Posture Detection
#### 2.1.2 Two-Dimensional Images from Optical Cameras  
With the rapid advancement of image processing techniques, ordinary 2D monitor cameras, which are characterized by convenience and inexpensiveness, have gradually become the most popular data acquisition equipment in animal behavior research [26]. Two-dimensional cameras used for pig posture detection, mainly including RGB cameras and gray cameras, are usually fixed above the pen with the lens pointing down to obtain a vertical top view [6]. Researchers can input certain images or a whole video sequence into a machine vision model to detect and estimate the individual posture of every pig within a pig pen in the pixel space.  
Researchers have long been attempting to detect and analyze pig postures in a visual image for intuitive interpretation. With the help of image processing technologies, such as ellipse-fitting or segmentation algorithms, individual pigs in color images can be segmented and then detected under sub-optimal conditions [27], or changes in posture pattern and location of grouped pigs can be found in grey images [6]. Apart from the traditional methods, superabundant deep learning pig posture models based on the 2D camera system have been emerging in recent years. Application of deep learning can obtain considerably high average precision, above 98%, in pig behavior and posture identification at the individual level [28].
# Energy Harvesting over Rician Fading Channel:
## 3 System Performance
### Asymptotic Analysis  
The asymptotic analysis is significant to provide further insights into the impact of hardware impairments on the network performance. It can also be used to verify the correctness of the exact analysis. In this section, the asymptotic outage probability and SER at the high SNR regime are going to be derived rigorously.
## 3 Deliberate Attacks on IECPS
### Cyberattacks
#### 3.1.2 Man-in-the-Middle Attacks  
MITM is an indirect attack method, and its attack principle is shown in Figure 4. The attacker exploits vulnerabilities in the communication protocol, employing techniques such as malware, domain name system spoofing, and address resolution protocol spoofing, to forge identities, impersonate legitimate nodes within the energy system, and establish  
Figure 3: Schematic of the RA.  
an illicit communication channel within the pre-existing communication network. At this stage, the attacker gains unauthorized access to the system's operational information, all while remaining undetected by the system. The attacker is capable of intercepting and injecting erroneous system data and control commands at will, thereby disrupting the normal operation of the energy system, upsetting the balance between supply and demand, and potentially causing damage on system components. In contrast to general data tampering, MITM attacks are more clandestine and challenging to detect, as they occur within the network communication link, with the attacker remaining concealed throughout the process.  
From the perspective of the attack process inherent in MITM attacks, the attacker injects erroneous data into the corresponding control unit. Once the control unit executes based on the faulty instruction, the attacker proceeds to intercept and modify the feedback operation data, thereby facilitating long-term stealth and undetected manipulation [22]. Consequently, the MITM attack can be mathematically modeled as follows:  
\[\tilde{u}_{\text{da}}(t)=u_{\text{da}}(t)+\gamma \tag{2}\]  
\[\tilde{y}_{\text{da}}(t)=y_{\text{da}}(t)-\delta, \tag{3}\]  
where \(\tilde{u}_{\text{da}}(t)\) and \(u_{\text{da}}(t)\) denote the control signals after and before tampering, respectively; \(\tilde{y}_{\text{da}}(t)\) and \(y_{\text{da}}(t)\) denote the operation data after and before tampering, respectively; \(\gamma\) is the modification amount of the control instruction; and \(\delta\) is the modification amount of the operation data.
# Performance Evaluation of VANET Routing Protocols in Madinah City
## 3 Methodology
### Traffic Characteristics  
The dataset used to perform experiments were generated using the SUMO traffic modeling simulator due to the lack of real-world datasets available for Madinah city. The synthetic dataset contained modeling of autonomous vehicles and human-driven vehicles with different traffic densities and distributions to measure the performance difference in each test case.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & \multicolumn{3}{c}{**Routing Protocols**} \\ \cline{2-4}  & **AODV** & **DYMO** & **DSDV** \\ \hline Protocol type & Reactive & Reactive & Proactive \\ Routing scheme & On-demand & On-demand & Table-driven \\ Routing loop & Loop-free & Loop-free & Loop-free \\ Control message overhead & Low & Low & High \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of Ad-hoc routing protocols.