esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Model 1--Intentions to Use Public Transport during COVID-19 Pandemic
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Unstandardized**} & \multicolumn{2}{c}{**Standardized**} & \multirow{2}{*}{**t**} & \multirow{2}{*}{**Sig.**} \\  & & **Coefficients** & & & \\  & **B** & **Std. Error** & & & \\ \hline (Constant) & \(-\)0.654 & 0.213 & & \(-\)3.068 & 0.002 \\ Gender & 0.197 & 0.054 & 0.098 & 3.615 & 0.000 \\ Marital Status & \(-\)0.093 & 0.061 & \(-\)0.048 & \(-\)1.529 & 0.126 \\ Profession & \(-\)0.014 & 0.027 & \(-\)0.015 & \(-\)0.524 & 0.600 \\ Car Ownership & 0.316 & 0.056 & 0.159 & 5.598 & 0.000 \\ Motorcycle Ownership & 0.244 & 0.052 & 0.123 & 4.655 & 0.000 \\ Trip Frequency & \(-\)0.098 & 0.027 & \(-\)0.097 & \(-\)3.598 & 0.000 \\ F1\_attitudes & 0.170 & 0.027 & 0.167 & 6.361 & 0.000 \\ F2\_awareness & 0.022 & 0.026 & 0.021 & 0.832 & 0.406 \\ F3\_responsibility & 0.092 & 0.026 & 0.090 & 3.538 & 0.000 \\ F4\_difficulty & \(-\)0.140 & 0.027 & \(-\)0.132 & \(-\)5.195 & 0.000 \\ Age & 0.089 & 0.042 & 0.073 & 2.122 & 0.034 \\ Education & \(-\)0.079 & 0.029 & \(-\)0.078 & \(-\)2.694 & 0.007 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Model 2—Parameter estimates for the multiple linear regression model. non-significant (\(\chi^{2}=9792.048\), df = 2901, \(p<0.05\)). The good-of-fit measures i.e., Cox and Snell, Nagelkerke, and McFadden pseudo-R square were 0.178, 0.233 and 0.136, respectively. The non-significant chi-square statistic (\(\chi^{2}=67.794\), df = 57, \(p=0.155\)) indicated that the test of parallel lines was satisfied. Trip frequency, marital status, age, profession and education were found to be significant explanatory variables (Table 7). More frequent travelers are less likely to use public transport while they have COVID-19 symptoms. Singles are less likely to use public transport relative to married people when they have COVID-19 symptoms. Those in the younger age groups are more likely to use public transport while experiencing COVID-19 symptoms relative to those in the oldest age group, which could be explained by the fact that the disease proved to be particularly dangerous for older people. Those with low educational qualifications are more likely to use public transport while experiencing COVID-19 relative to those having higher qualifications. As explained earlier, the respondents with lower educational qualifications had lower household incomes and lower car and motorbike ownerships making them captive public transport users. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & \(-\)**2 Log Likelihood** & **Chi-Square** & **df** & **Sig.** \\ \hline Intercept Only & 2180.824 & & & & \\ Final & 1883.619 & 297.205 & 19 & 0.000 \\ \hline \multicolumn{6}{l}{Link function: Logit.} \\ \end{tabular}
\end{table} 
Table 6: Model 3—Model fitting information.
# Smart Handoff Technique for Internet of Vehicles Communication using Dynamic Edge-Backup Node
## 2 Literature Review
### Handoff Techniques  
Some handoff techniques in the literature are the vehicular fast handoff scheme, the IP passing scheme, the cluster-based handoff scheme, the two antenna scheme and the network mobility (Nemo) scheme. The [26] vehicular fast handoff scheme is known as the layer handoff scheme, which classifies three kinds of vehicles. These are RV (relay vehicle), OSV (oncoming side vehicle) and BV (broken vehicle). The [27] discussed the vehicular fast handoff scheme (VFHS) as RV is a larger vehicle than the others. The [28] discussed that for the spreading network topology message (NTM) to BV and OSV utilize predefined frequency channel. Reference [29] explains the preferred AP and early handoff. Each AP makes a list of PAP, which gives early information and helps out the vehicle to carry out the handoff from continuing AP. In [30] they proposed an intelligent network selection (INS) method focused on enhancing the scoring function to rank efficiently from existing wireless network nodes.
# Improvement of Electrical Performance in Heterostructure Junctions TFET Based on Dual Material Gate
## 4 Conclusions  
In this paper, a dual material gate heterostructure junctionless TFET(DMMG-HJLTFET) is constructed and researched to improve the performance of JLTFET and HJLTFET. To enhance the on-state current and suppress the OFF-state current, Si/SiGe heterostructure at the source/channel interface and sandwich stack structure (GaAs/Si/GaAs) at drain region are introduced. Simultaneously, based on the gate engineered concept, the corresponding electrode is divided into three parts to further increase the tunneling barrier width at the drain/channel interface and the tunneling probabilities at source/channel interface. In our work, the effects of the device sizes, doping concentration and work function on the performance of the DMMG-HJLTFET are researched systematically to optimize the overall device performance. Simulation results indicate that DMMG-HJLTFET provides superior performance in terms of logic and analog/RF performance as compared with JLTFET and HJLTFET, the ON-state current of DMMG-HJLTFET increases up to 1.01 \(\times\) 10\({}^{-5}\) A/\(\mu\)m, and the maximum g\({}_{\rm m}\) of DMMG-HJLTFET is 1.26 \(\times\) 10\({}^{-5}\) S/\(\mu\)m at Vds = 1.0 V, meanwhile, DMMG-HJLTFET could achieve a maximum f\({}_{\rm T}\) of 9.17 GHz at Vgs = 1.4 V and a maximum GBW of 0.17 GHz at Vgs = 1.2 V, respectively. So, DMMG-HJLTFET may be one of the alternative devices for the next generation of low power applications.  
Conceptualization, H.L., T.H., W.L., S.C., S.W., and H.X.; methodology, H.X. and T.H.; software, H.X. and W.L.; validation, H.L., and H.X.; formal analysis, H.X.; investigation, H.X.; resources, H.X; data curation, H.X.; writing--original draft preparation, H.X.; writing--review and editing, H.X.; visualization, H.X.; supervision, H.X.; project administration, H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (Grant No. U1866212) and in part by Foundation for Fundamental Research of China (Grant No. JSZL2016110B003) and Major Fundamental Research Program of Shaanxi (Grant No. 2017 ZDJC-26) and Innovation Foundation of Radiation Application (Grant No. KFZC2018040206).  
The authors declare no conflict of interest.
# On the Use of Bright Scatterers for Monitoring Doppler, Dual-Polarization Weather Radars
## 3 Results
### On the Very Stable Characteristic of the 1 st Doppler Moment (Mean Radial Velocity)  
Despite the presence of a rotating antenna (constant radial velocity of 18\({}^{\circ}\)/s), the mean radial velocity is stable; however, it spans two DNs and not only one. The relative frequency of these DNs is shown in Table 1. Equation (4) in Section 2.2 can be used to convert the two DNs into the mean radial velocity. In two (out of three) days of the 2015 year, DN = 128 (zero velocity) occurred more frequently than DN = 127. Later on, DN = 127 is more frequent than DN = 128. However, any value between 127 and 129 is probably acceptable for meteorological applications (\(\pm\)0.06 m/s).  
On the Quite Stable Characteristics of the 0 th Doppler Moment (Horizontal and Vertical Polarization)  
In the case of a single transmitted signal, backscattered by a mountainous surface of uniform electromagnetic properties (ensemble of independent, randomly distributed scatterers), the incoming signal to the receiver may be assumed to be Rayleigh distributed (i.e., the received power is distributed exponentially, in a similar way to that caused by rain drops, thermal noise,... ). In order to reduce the uncertainty of the backscattered radar signal, it is necessary to average a certain number of independent samples. In the case of rain drops, since scatterers move with respect to each other, it is simpler to obtain a number of independent pulses that is larger than in the case of a terrain surface. The Swiss radars transmit 33 pulses within the HPBW at the lowest angle of elevation: in the case of ground clutter, only two or three pulses contribute to the reduction of the variance. The resulting probability distribution function (pdf) can in fact be better fitted by a lognormal or Weibull distribution. However, if a strong point target (such as the metallic tower on Cimetta) is present in the background of a radar cell that contains randomly distributed scatterers, the power distribution can be modeled using the Rice pdf [26]; in this case, the Log-transformed ratio between the (linear) average and the median power tends to be close to 0 dB. Hence, Section 3.3.1 is focused on the average and median reflectivity values (complemented by the geometric average), while Section 3.3.2 describes the relative dispersion and the Log-distance between the 90%tile and the average.
# Drilling Fluid and Cement Slurry Design for Naturally Fractured Reservoirs
## 3 Prediction of Lost Circulation
They showed that the key parameter for determination of adequate loss prevention materials are rock properties. Also, the application of an artificial neural network in the future should contain a more robust and localized data sets [27]. [PERSON] and [PERSON] (2017) studied the effect of mud weight, wellbore depth, pump pressure, and flow rate on lost circulation in the UBD of the Asmari formation, Southwest Iranian fields, using drilling data and an artificial neural network. In their research, they chose three wells with the most, moderate, and without lost circulation. They showed that the amount of lost circulation can be estimated using the created neural network with very good accuracy. Also, lost circulation has a direct relation to pump pressure and a reverse relation to flow rate [31]. [PERSON] et al. (2019) presented a study that can be used to predict a lost circulation occurrence by using artificial neural networks and Support Vector Machine (SVM). The field datasets were collected from 385 wells drilled in southern Iraq. The data included drilling operation parameters, formation type, lithology of the rock, and drilling fluid characteristics. According to their results, lithology type, mud weight, and flow rate had the most impact on lost circulation [30]. [PERSON] et al. (2020) showed that an artificial neural network (ANN) and a Functional Network (FN) can be used to predict loss zones with high accuracy. They used real-time drilling sensors and obtained data of real-time surface drilling parameters to predict the zones of lost circulation. According to their research, artificial intelligence techniques were tested by using data from one well to predict lost circulation zones with high precision. Also, their method is simple to use because the prediction of loss zones is determined from only the mechanical surface drilling parameters which are easily available for each well [1]. [PERSON] et al. (2020) used drilling data of previously drilled wells in Southern Iraq (448 cases) to establish which drilling parameters have the greatest impact on the risk level of lost circulation while drilling. Data was obtained from daily drilling reports, daily mud reports, final well reports, and master logs. They showed that the lost circulation rate can be increased by increasing the flow rate, by raising the ECD. Also, an increased rate of penetration (ROP) results in insufficient hole cleaning and negatively affects the ability to estimate losses. Other parameters that can leads to an increase in mud loss rate is mud weight because of its influence on hydrostatic pressure calculations. Their results showed that statistical analysis can be used to match the predicted and actual loss rate [29].
###### Abstract  
The aim of this study was to purify and identify peptides with antioxidant properties from protein hydrolysate of scalloped hammerhead (_Sphyrna lewini_) cartilage. Cartilaginous proteins of the scalloped hammerhead were extracted by guanidine hydrochloride, and three antioxidant peptides, named enzymolysis peptide of scalloped hammerhead cartilage A (SCPE-A), SCPE-B and SCPE-C, were subsequently isolated from the hydrolysate of the cartilaginous proteins using ultrafiltration and chromatography. The amino acid sequences of SCPE-A, SCPE-B and SCPE-C were identified as Gly-Pro-Glu (GPE), Gly-Ala-Arg-Gly-Pro-Gln (GARGPQ), and Gly-Phe-Thr-Gly-Pro-Pro-Gly-Phe-Asn-Gly (GFGPGPFNG), with molecular weights of 301.30 Da, 584.64 Da and 950.03 Da, respectively. As per in vitro activity testing, SCPE-A, SCPE-B and SCPE-C exhibited strong scavenging activities on 2,2-diphenyl-1-picryhydrazyl radicals (DPPH\(\bullet\)) (half elimination ratio (EC\({}_{50}\)) 2.43, 2.66 and 1.99 mg/mL), hydroxyl radicals (HO\(\bullet\)) (EC\({}_{50}\) 0.28, 0.21 and 0.15 mg/mL), 2,2\({}^{\prime}\)-azino-bis-3-ethylbenzothazoline-6-sulfonic acid radicals (ABTS\({}^{\bullet}\)) (EC\({}_{50}\) 0.24, 0.18 and 0.29 mg/mL), and superoxide anion radicals (O\({}_{2}^{-}\bullet\)) (EC\({}_{50}\) 0.10, 0.14 and 0.11 mg/mL). In addition, SCPE-A showed inhibition activity similar to butylated hydroxytoluene (BHT) in lipid peroxidation in a linoleic acid model system. The amino acid residues of Gly, Pro and Phe could positively influence the antioxidant activities of GPE, GARGPQ and GFTGPFNG. These results suggested that GPE, GARGPQ and GFTGPFNG might serve as potential antioxidants and be used as food additives and functional foods.  
s xalloped hammerhead (_Sphyrna lewini_); cartilage; peptide; antioxidant activity +
Footnote †: journal: Journal of Marine Sciences
# A Continuous Music Recommendation Method Considering Emotional Change
## 6 Experiments and Evaluation
### Experimental Results and Evaluation
#### 6.3.2 Evaluation of User Emotion Prediction Results Considering Emotional Changes
 \tag{28}\]  
As a result of the calculation, the result of predicting the emotional state of the user at times \(t_{0}+1\) and \(t_{0}+2\) is as shown in Equation (29). \[ \begin{split} V_{u}^{t_{0}+1}&=(0.34,0.16,0.15,0.36)\\ V_{u}^{t_{0}+2}&=(0.29,0.00,0.21,0.31)\end{split}  \tag{29}\]  
Figure 9: Example of a user’s mental model. Figure 10: Comparison of average prediction errors for each emotion at time \(t_{0}+1\). Comparing the results with the actual emotional data confirmed that the prediction error using the PMM was smaller than that using the MMM. Additionally, the average MSE, RMSE, and MAE of the total error values were calculated. Overall, it was confirmed that the error using the PMM was smaller than that using the MMM, indicating higher accuracy in the predicted values. Additionally, to further evaluate the accuracy of the predicted results, NRMSE was calculated, as shown in Table 14. Figures 12 and 13 compare the average error rates of predicted values for each emotion at times \(t_{0}+1\) and \(t_{0}+2\). Figure 11: Comparison of average prediction errors for each emotion at time \(t_{0}+2\). Figure 14 presents the comparison of overall average error rates. For predicting emotions at time \(t_{0}+1\), the error rates were 25.53% for MMM and 10.99% for PMM. For time \(t_{0}+2\), the error rates were 28.40% for MMM and 15.58% for PMM, indicating higher error rates for MMM compared with PMM. These results show a 13.68% improvement in error rate for PMM over MMM when compared with actual data, demonstrating increased accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Emotion**} & \multicolumn{2}{c}{**NRMSE (\(t_{0}+1\))**} & \multicolumn{2}{c}{**NRMSE (\(t_{0}+2\))**} \\ \cline{2-5}  & **MMM** & **PMM** & **MMM** & **PMM** \\ \hline Happy & 44.71\% & 16.91\% & 47.32\% & 26.42\% \\ Angry & 16.88\% & 9.31\% & 24.10\% & 13.86\% \\ Sad & 18.20\% & 5.26\% & 21.51\% & 10.33\% \\ Peaceful & 22.33\% & 12.48\% & 20.66\% & 11.69\% \\ Avg. & 25.53\% & 10.99\% & 28.40\% & 15.58\% \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparison of average prediction errors for each emotion at time \(t_{0}+1\) and \(t_{0}+2\). Figure 12: Comparison of average error rates of predicted values for each emotion at time \(t_{0}+1\). Figure 13: Comparison of average error rates of predicted values for each emotion at time \(t_{0}+2\).
###### Abstract  
The composition of high rockfill dam materials is complex, and the mechanical parameters are uncertain and correlated in unknown ways due to the influences of the environment and construction, leading to complex deformation mechanisms in the dam-foundation system. Statistical characteristics of material parameters are the basis for deformation and stress analysis of high core rockfill dams, and using an inaccurate distribution model may result in erroneous analysis results. Furthermore, empirically evaluated distribution types of parameters are susceptible to the influence of small sample sizes, which are common in the statistics of geotechnical engineering. Therefore, proposing a multi-dimensional joint distribution model for parameters based on small-sample data is of great importance. This study determined the interval estimation values of Duncan-Chang E-B model parameters--such as the mean value and coefficient of variation for the core wall, rockfill, and overburden materials--using parameter statistical analysis, bootstrap sampling methods, and Akaike information criterion (AIC) optimization. Additionally, the marginal distribution types of each parameter were identified. Subsequently, a multi-dimensional joint distribution model for Duncan-Chang model parameters was constructed based on the multi-dimensional nonlinear correlation analysis of parameters and the Copula function theory. The application results for the PB dam demonstrate that joint sampling can effectively reflect the inherent correlation laws of material parameters, and that the results for stress and deformation are reasonable, leading to a sound evaluation of the cracking risk in the core wall of high core rockfill dams.  
high core rockfill dam; small-sample data; Duncan-Chang model; joint distribution model; Copula function Article
# A New LoRa-like Transceiver Suited for LEO Satellite Communications +
## 3 Proposed Transceiver
### Proposed Synchronization Algorithm  
To implement our synchronization algorithm, we consider the same model of the received signal as in (12). To be more general, the global time desynchronization parameter is supposed to be \(t_{s}=KT+\Delta\tau,K\in\mathbb{N}\). Thus, the continuous version of the received signal is written:  
\[y(t)=\sqrt{P}x(t-t_{s})e^{i(2\pi(\Delta f+\frac{z_{s}t}{T})t+\theta_{0})}+w(t) \tag{23}\]  
As we already noted, due to the CFO and the DR, the ADC output signal should be sampled with \(f_{s}^{\prime}=\alpha f_{s_{min}}\), which will allow a more accurate receiver alignment with the effective start of the payload, and is necessary to correctly capture the power spectral density of the signal to process. Nevertheless, to ensure a low complexity of our proposed receiver and for a fair comparison with LoRa performance, the different processing steps are developed with a sampling rate, such that \(T_{s}=\frac{1}{f_{s_{min}}}\). Hence, The received signal, sampled at \(T_{s}\), can be written as:  
\[y(n)=\sqrt{P}x(n-n_{s})e^{i(2\pi(\Delta f+\frac{z_{s}(nT_{s})}{2})nT_{s}+ \theta_{0})}+w(n) \tag{24}\]  
where  
\[n_{s}=\lfloor\frac{t_{s}}{T_{s}}\rfloor=KM+\lfloor\frac{\Delta\tau}{T_{s}}\rfloor \tag{25}\]  
DCSS, as well as all the other modulation techniques, do not escape the need for an accurate time synchronization to avoid ISI, which strongly degrades the receiver sensitivity. However, in DCSS as well as in CSS, the presence of a time varying Doppler shift degrades the synchronization and the decoding performance, especially when increasing the symbol time \(T\). Therefore, an estimation and compensation of the DR are mandatory in some cases.  
Given these properties of DCSS, we propose to perform the synchronization of (24) by implementing the following six steps, detailed hereafter:  
1. Preamble detection;
2. Coarse time synchronization;
3. Doppler rate estimation;
4. Fractional CFO estimation;
5. Fractional STO estimation;
6. DR, fractional CFO, and fractional STO compensations.
# Systematic Review: Malware Detection and Classification in Cybersecurity
## 4 Discussion  
### Summary and Interpretation of Key Findings  
This review highlights that machine learning (ML) and _deep learning_ (DL) approaches significantly outperform traditional malware detection methods. Advanced techniques, including _deep neural networks_ and _Transformers_, have demonstrated superior accuracy in detecting _zero-day malware_ and _ransomware_, which are notoriously difficult to catch due to their evasion techniques. For example, [PERSON] et al. [35] and [PERSON] et al. [79] emphasize that visual representations and self-supervised models not only improve accuracy but also enhance interpretability, achieving detection rates near 99%. Similarly, federated learning for anomaly detection in IoT networks, as demonstrated by [PERSON] et al. [30], allows for scalable solutions without compromising data privacy.  
However, ensemble methods like those proposed by [PERSON] and [PERSON] [44], while effective, are heavily dependent on the quality of the training data, making them less reliable in environments with imbalanced datasets.
## 3 Results and Discussion
### Load Transfer Efficiency  
Based on Equation (2), the LTE results are presented in the form of boxplots for both joints and cracks in Figure 11. In general, the level of the LTE indicator is better for the joints and is more than 70% when considering the interquartile range of the whole sample, indicating satisfactory performance of the joints according to international experience [3; 4; 29]. In contrast, a greater deviation in load transfer is observed in cracks. This shows how important the results of the visual inspection are for a better interpretation of the LTE results. This means that higher LTE values were observed for cracks with a small opening width and shrinkage cracks, while lower values were observed for cracks with a larger width. This is consistent with observations from previous studies [15].  
The presence of sealing material was found to have no specific influence on the LTE ratio. This is a reasonable finding given the age of the pavement and the fact that many of the cracks observed had formed several years prior to the time of assessment. This, combined with the brittleness of the concrete, may affect the long-term effectiveness of the waterproofing-sealing material, particularly if it is not applied in a timely manner. Finally, most of the cores taken over cracks were found to be penetrated to full depth, suggesting that the continuity of the surface layer of the pavement is interrupted, which is consistent with lower values of the LTE indicator.