esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Johnson-Cook Model
### Determination of Material Constants \(A\), \(B\), \(n\)  
For \(\dot{\epsilon}_{p}=\dot{\epsilon}_{0}\) and \(T=T_{ref}\) in Equation (1), the second and third parentheses are omitted, since the effects of strain rate strengthening and thermal softening are neglected. Modifying the remaining terms by taking the natural logarithm on both sides and using the averaged true stress-strain data from cases C1\(0\)001_NR, C2\(0\)001_NR and C3\(0\)001 NR and linearly plotting the \(\ln(\sigma-A)\) term with of Equation (4), a linear regression model was used to fit the data points, as can be seen in Figure 8.  
\[\ln(\sigma-A)=n\ln\epsilon+\ln B \tag{4}\]  
The \(A\) parameter was also calculated under reference strain conditions using the 0.2% offset method. For the linear fitting presented in Figure 8, an \(R^{2}\) factor of more than 97.5% was achieved, resulting in a good accuracy of the regression model. As a result of the latter, material constants \(A\), \(B\) and \(n\) were calculated from the slope and intercept of the curve, which can be seen in Table 3.  
Figure 8: \(\ln(\sigma-A)\) and \(\ln\epsilon\) relationship under reference conditions.
## 3 Data Synthesis and Analysis
#### 3.1.13. Harmony Search  
The harmony search (HS) is a metaheuristic optimization algorithm that imitates musicians' music improvisation process of searching for a perfect state of harmony [141]. [PERSON] et al. [141] presented a pure HS clustering algorithm for a globalized search in the solution space. The proposed HSCLUST was then hybridized with a K-means clustering algorithm in three different modes to avoid the problem of initial parameter dependence of the K-means algorithm. Each proposed hybridization depended on the stage at which the K-means algorithm is performed in the clustering process. [PERSON] and [PERSON] [142] proposed harmony K-means (HKA) based on an HS optimization algorithm for document clustering for faster global optimum convergence. [PERSON] et al. [143] hybridized the K-means algorithm with global best HS, frequent term sets, and Bayesian information criterion termed IGBHSK for automatic Web document clustering. The Global-Best HS performs the global search in the solution space while the K-means algorithm seeks the optimum value in the local search space. [PERSON] and [PERSON] [144] proposed an enhanced K-means clustering algorithm based on hybridization of the K-means with improved HS optimization technique for finding global optimum solutions. [PERSON], [PERSON], and [PERSON] [145] presented HSKH--harmony search K-means hybrid for gene expression clustering, which produced a more accurate gene clustering solution. [PERSON], [PERSON], and [PERSON] [146] proposed a combination of HS and K-means for optimizing wireless sensor network clustering. The HS was used to generate the initial solution, which is then fed into the K-means algorithm for a more precise solution. [PERSON] et al. [147] proposed a scheme for load balancing with switch migration for the distributed software-defined network (SDN) employing a combination of HS and K-means for clustering the switches.
## 4 Results and Discussion
### Organic Matter Maturity  
Vitrinite reflectance (Ro) is a primary parameter for evaluating organic matter maturity [48; 49; 50; 51]. In this study, a detailed analysis was conducted on the vitrinite reflectance data from five wells with extensive data in the sag, totaling 90 data points. The relationship between vitrinite reflectance and depth was analyzed in depth, showing a clear linear trend. As depth increases, the vitrinite reflectance values correspondingly increase. The reflectance values are primarily controlled by the maximum formation temperature and burial depth (Figure 4).  
From the thermal evolution and hydrocarbon expulsion profile of the source rocks in the Hongqi Depression, both the Carbon Preference Index (CPI) and Odd-Even Preference (OEP) decrease with increasing burial depth, approaching 1.0 around 2100 m. The hydrocarbon generation threshold is at 1350 m, the hydrocarbon expulsion threshold is at 1950 m, and the peak of hydrocarbon generation occurs at 2800 m. The vitrinite reflectance values of the dark mudstones in the Tamulangou Formation range from 0.8% to 2.0%, indicating that they are generally in the mature-to-highly mature stage. Currently, they are at the peak of hydrocarbon generation, with some areas having entered the over-mature stage (Figure 4). The maturity evolution trend of the Tamulangou Formation is significantly different from that of the overlying Tongbomiao Formation and Nantun Formation, showing a rapid increase in maturity. This is directly related to the volcanic activity that influenced the deposition of this stratigraphic sequence during its formation [52].
###### Abstract  
In this study, a Long Short-Term Memory (LSTM) model with extra variables such as weather conditions and school days was developed within a multi-scale framework in order to forecast passenger flow in both bus and rail systems, covering both regional and route-level analyses. In addition, the performance of the LSTM model was compared against three separate deep learning models. Among these, the Nonlinear Autoregressive Network with Exogenous Inputs (NARX) time series model produced the lowest error values, achieving a high level of accuracy. While no considerable changes were observed in regional rail passenger flow as a result of the inclusion of weather-related variables, a 2.2% drop in the RMSE value was achieved in bus passenger flow at the regional level; however, this improvement remains relatively modest. In contrast, at the route level, RMSE values declined by 2.4% for rail and 3.69% for bus routes. These findings reveal that the inclusion of weather-related variables significantly improves the prediction of bus passenger flow, underlining the benefits of integrating such data into forecasting models. Furthermore, the findings of this study analytically support transportation planners in making more informed, data-driven decisions regarding scheduling and capacity management.  
Keywords:public transportation; passenger flow prediction; time series; forecasting; deep learning
## 5 Numerical Results
### Performance Comparison with Other Beaconing Schemes
On the other hand, the MAB\({}^{2}\) scheme shows the best TFP performance at high vehicle densities due to its low CBR, while the BFPC and MDPRP schemes exhibit the highest TFPs in moderate to high vehicle densities. Figure 22 illustrates the minimum tracking failure distance against vehicle density. The DRL-JCBRTP scheme shows the longest mTFD among all beaconing schemes, indicating its superior tracking performance. The MAB\({}^{2}\) scheme, however, consistently shows the lowest mTFD, underscoring its limitations in tracking efficiency. These results highlight the effectiveness of the DRL-JCBRTP scheme in both minimizing TFP and maximizing mTFD. This comprehensive optimization reduces the likelihood of V2V collisions from untracked vehicles, thus improving the overall safety of driving conditions. **Vehicle Awareness Performance**: Figure 23 shows the AFP against vehicle densities. In comparison with Figures 19 and 21, the AFPs of DRL-JCBRTP, BFPC, and MDPRP schemes are primarily determined by their TFPs, whereas the AFP of the MAB\({}^{2}\) scheme is influenced more by its DFP. Figure 24 illustrates the mAFD against vehicle density. Compared with Figures 20 and 22, it is evident that mTFDs are the dominant factors determining mAFDs of MAB\({}^{2}\), BFPC, and MDPRP schemes, while both mDFD and mTFD contribute to the AFP of our DRL-JCBRTP scheme. To conclude, these numerical results underscore the comprehensive effectiveness of the DRL-JCBRTP scheme in enhancing vehicle awareness by simultaneously minimizing AFP and maximizing mAFD, ultimately improving driving safety. Figure 19: DFP vs. vehicle density. Figure 20: mDFD vs. vehicle density. Figure 23: AFP vs. vehicle density. Figure 21: TFP vs. vehicle density. Figure 24: mAFD vs. vehicle density. Figure 22: mTFD vs. vehicle density.
# Investigation of Spatial Chirp Induced by Misalignments in a Parallel Grating Pair Pulse Stretcher
## 5 Conclusions  
In this work, we analyzed the spatial chirp induced by the misalignments in the parallel grating pair pulse stretcher using the ray tracing simulation method. According to the simulation results, a simple adjustment method has been summarized that can accurately calibrate the misalignments. Measured with an imaging spectrometer, the stretched pulses of our home-made pulse stretcher show little spatial chirp, and its temporal chirp is well consistent with the designed value, indicating the stretcher we built has been well adjusted. The conclusions about the spatial chirp caused by the misalignments and the adjustment skills are also suitable for the cases when the parallel grating pair is adopted as a pulse compressor.  
Conceptualization, Z.Z. and H.J.; methodology, Z.Z.; experiment, Z.Z and W.G.; data analysis, Z.Z; validation, H.J.; writing--original draft preparation, Z.Z.; writing--review and editing, H.J., H.G., X.C., and S.L.; project administration, H.J. and S.L.; funding acquisition, S.L. and H.J. 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 number 51525502, 51975232, 51727809, and 51575214; National Key Research and Development Plan, grant number 2017 YFF0204705; Natural Science Foundation of Hubei Province Of China, grant number 2018 CFA057; National Science and Technology Major Project of China, grant number 2017 ZX02101006-004. The authors want to thank the experiment site support and technical support from the Experiment Center for Advanced Manufacturing and Technology in the School of Mechanical Science & Engineering of HUST.  
The authors declare no conflict of interest.
## 2 System Model  
MBSs and PBSs in the two-tier HCN are modelled as independent MHCP \(\Phi_{k}^{MHCP}\)(k = 1,2) in \(\mathbb{R}^{2}\). The density, transmit power, and target SIR for each tier of BS are denoted as \(\lambda_{k-MHCP}\), \(P_{Tk}\) and \(\beta_{k}\), respectively. We abbreviate the network based on this model as MHCP-MHCP. Moreover, mobile user equipment (MUEs) are modelled as PPP in \(\mathbb{R}^{2}\) with density \(\lambda_{u}\). According to [PERSON]'s theorem in random geometry, the MUE located at the represented origin is selected as the typical MUE [38]. The path loss and fast fading are taken into account when modelling the channel gain, the path loss exponent is denoted as \(\alpha\) and the fast fading experienced by the tagged UE and all the BSs are assumed to be Rayleigh fading. Assuming that the serving BS is located at point \(x_{k}\in\Phi_{k}^{MHCP}(k=1,2)\), then the received power at the tagged MUE can be written as \(P_{Tk}h_{x_{k}}\|x_{k}\|^{-\alpha}\), where \(\alpha>2\) and \(h_{x_{k}}\sim\exp(1)\) stand for the path-loss exponent and the Rayleigh fading, respectively.  
In HCNs, if orthogonal frequency division multiple access (OFDMA) is adopted, there is only inter cell interference but no intra cell interference in the network. The intercell interference will dominate noise in HCNs of even modest density [4]. As a result, the noise power has no effect on the SINR values. However, if the latest non-orthogonal multiple access techniques are adopted, HCN may not be an interference limited network [42; 43]. Considering the mathematical tractability, we assume universal frequency reuse. As such, except the serving BS of the typical MUE, all the other BSs in the HCN are potential interferers. Therefore, in this paper, we analyze SIR instead of SINR. SIR of the tagged MUE is  
\[SIR(x_{k})=\frac{P_{Tk}h_{x_{k}}\|x_{k}\|^{-\alpha}}{I_{x_{k}}}=\frac{P_{Tk}h_ {x_{k}}\|x_{k}\|^{-\alpha}}{\sum\limits_{k=1}^{2}\sum\limits_{x^{\prime}\in \Phi_{k}^{PHCPPPPPPPPPPPPPPPPPP  
The density of \(\Phi^{MHCP}\) can be calculated as [9]  
\[\lambda_{MHCP}=\frac{1-\exp(-\lambda_{PPPP}\pi r^{2})}{\pi r^{2}} \tag{3}\]  
Where \(\lambda_{MHCP}\) represents the density of \(\Phi^{MHCP}\), \(r\) represents the repulsion radius, and \(\lambda_{PPP}\) is the density of \(\Phi^{PPP}\) before removing points.
## 5 A Case Study
### Customer Requirement-Product Function Mapping
The functions \(Ft_{1}\)-Deliver cloth and \(Ft_{3}\)-Cut cloth, originally realized independently, were combined together through function analysis. In other words, the working mode was changed to \"Spread material \(\rightarrow\) Cut \(\rightarrow\) Move out of window\", to realize the function \"cut material running through window\" of cutting machine, that is, \"cutting while moving\". Consequently, the \(FR_{1}\) and \(FR_{3}\) could be converted into the functional requirement \(FR\) = \(<\)Cut material running through window\(>\). However, if the function \"cut material running through window\" was realized in actual cutting equipment by directly changing the working mode, there would be a hidden danger of transmission chain fracture. The function parameter of \"cut material running through window\", that is, \(FP\)-Cutting speed, was extracted with the cutting machine with changed working mode as research object, based on the target functional requirement. The summary of dependent function values as shown in Table 3 was obtained using the elementary dependent function calculation method in reference [27]. The \(Ft\in V_{+}\) could be obtained with the calculated dependent function value \(k(FP)\in[0,1)\) and the function domains, so modification of structure layer was needed.
## Materials and Methods
### Estimation of Antioxidant Activity (SOD, POD, and CAT) and MDA Content  
From treated seedlings, 0.5 g of leaf samples were homogenized in 2 mL of 0.05 M phosphate buffer (pH 7.8) under ice cold conditions. The homogenate was centrifuged with 6 mL of 0.05 M phosphate buffer (pH 7.8) for 20 min at 10,000\(\times\) g and at 4 \({}^{\circ}\)C [46]. The supernatant was further used to estimate the antioxidant enzymes and MDA content. The SOD activity of the treated seedlings was determined by photoreduction of nitroblue tetrazolium (NBT) [47]. To 0.05 mL of the supernatant, 1.5 mL of 0.05 M phosphate buffer (pH 7.8), 0.3 mL 0.1 mM EDTA-Na\({}_{2}\), 0.3 mL 0.13 M methionine, 0.3 mL of 0.75 mmol-L\({}^{-1}\) NBT, 0.3 mL of 0.02 mmol-L\({}^{-1}\) riboflavin and 0.25 mL distilled water were added to achieve a total volume of 3 mL. The reaction mix was exposed to fluorescent light (86.86 mmol-m\({}^{-2}\)-s\({}^{-1}\)) for 10-20 min and the absorbance was read at 560 nm wavelength on a spectrophotometer (UV-3802, UNICO, MDN, WI, USA). SOD activity was estimated as 50% inhibition of the NBT reduction by superoxide generated from the reaction of photo-reduced riboflavin and oxygen. The total SOD activity was expressed in units per gram of fresh leaves (U g\({}^{-1}\) Fw). To determine the POD activity, the guaiacol method was used [48]. To 0.5 mL extract, 50 mL of 0.05 M phosphate buffer (pH 7.8), 28 mL guaiacol, and 19 mL 30% hydrogen peroxide (v/v) was added. Increase in absorbance at 470 nm for 3.5 mL of the prepared mix was used recorded for 3 min at an interval of 30 s. Total POD activity was represented as D470 per minute per gram of fresh leaves (U g\({}^{-1}\) min\({}^{-1}\)). For the CAT activity, 0.1 mL of extract was mixed with 1.9 mL of 200 mM phosphate buffer (pH 7.0), and 1 mL of 0.3% hydrogen peroxide. The enzyme activity was determined by reading absorbance at 240 nm for 3 min. The CAT activity was obtained as OD at 240 nm min\({}^{-1}\) g\({}^{-1}\)[49].  
The MDA (Malondialdehydedehyde) content was estimated by a Thiobarbituric acid (TBA) reaction method [50]. 2 mL of the supernatant was heated with 0.6% (w/v) TBA solution [prepared in 5% (v/v) trichloroacetic acid (TCA)] for 10 min in boiling water and cooled to sediment the flocculate thus formed. The absorbance of the resulting solution was read at 450 and 532 nm and subtracted from the absorbance read at 600 nm. Total MDA content was therefore expressed as the amount of substance per gram of fresh leaves (nmol g\({}^{-1}\) Fw).
## 4 Experimental Set-Up  
### Evaluating Framework  
This study proposes a channel-wise dropout-based framework augmented with CAMs to enhance the interpretability of neural network decisions. The framework dynamically identifies spatial (channel-level) contributions that vary temporally and may contribute to misclassifications in MI-evoked brain responses.  
As shown in Figure 1, the framework comprises the following stages:  
* _Data Preprocessing and Montage Reduction._ We evaluate the impact of EEG montage size on model generalizability, hypothesizing that excessive channels promote overfitting on spatially correlated artifacts rather than task-specific MI neural dynamics. Montage sizes \(N_{c}\)\(\in\)\(\{8,16,32,64\}\) are tested separately for the best- and worst-performing subjects. Subjects are stratified into high (best)- and low (worst)-performance cohorts based on evaluated trial accuracy (\(<\) 70% or\(\geq\) 70%). This serves as a conventional reference for evaluating the benefits of DL-based frameworks in ranking subjects based on their trial-level classification accuracy.
* _Subject Grouping Based on the Classification Accuracy of MI Responses._ We evaluate three Neural network models for EEG-based classification, _EEGNet_ and _ShallowConvNet_, for their real-time applicability, alongside the advanced _TCNet Fusion_ architecture for enhanced performance. As stated above, we use _FBCSP_ as a classical baseline for extracting subject-specific spatio-spectral features. This provides a conventional reference to assess the benefits of deep learning-based approaches.
* _Spatio-temporal uncertainty estimation_. MCD is applied to assess each model's ability to learn robust, channel-independent features, thereby reducing overfitting and improving generalization. Furthermore, MCD is combined with CAMs to estimate spatiotemporal uncertainty and enhance model interpretability. Specifically, the variance computed across CAMs is overlaid onto the original CAM representation, highlighting regions where the model exhibits reduced certainty in its interpretation of MI responses.  
Figure 1: Proposed framework integrating channel-wise dropout with CAMs for spatiotemporal uncertainty estimation in MI response interpretation.
# A PUF- and Biometric-Based Lightweight Hardware Solution to Increase Security at Sensor Nodes
## 6 Experimental Results and Discussion
### Security and Privacy
Another advantage of the proposal herein is the revocability (also known as renewability or cancellable data) because the Helper and Salted Data can be replaced (by using other PUF responses) in case of information leakage. Finally, another question addressed was the capability of the RND cells in the SRAM PUF to generate the nonces employed in the communication protocol to derive the session keys. The minimum number of RND cells found in the BLE chips was 265 (these RND cells were selected in the first registration phase as those which changed 10 times their start-up values in the 20 measurements). Hence, CC2541 was able to generate nonces with 128 bits. The Hamming weight of the nonces tended  
Figure 11: Unlinkability evaluation for 2 fingers and 3 samples in enrollment and 2 fingers and 2 samples in verification for: (**a**) FVC 2000 DB2a and (**b**) FVC 2002 DB1a. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Proposal** & **No. Individuals** & **Error Rate (\%)** \\ \hline Gait [49] & 59 & EER = 2.6 \\ Electrocardiogram [50] & 28 & FMR = 5.2, FNMR = 1.9 \\ Touch Behavioral and Voice [51] & 32 & EER = 9–16.6 for touch behavioral \\ Human Body Communication [52] & 10 & EER = 4.88 for voice \\ Fingerprint (one finger) [36] & 10 & FMR = 7.06 \\ Fingerprint (one finger) [53] & 10 & FMR \textless{} 0.1, FNMR = 1 \\ Fingerprint (one finger) [53] & 96 & FMR = 1.07, FNMR = 8.33 \\ Fingerprint (one finger) [54] & 100 & EER = 8 (FVC 2000 DB2a) \\ Fingerprint (one finger) [55] & 10 & FMR = 1.52, FNMR = 20.35 (FVC 2002 DB2) \\  & & EER = 0.04 (FVC 2000 DB2a) \\ This work (two fingers) & 50 & EER = 0.22 (FVC 2002 DB1a) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of proposals for wearable sensor nodes. to be 0.5, that is, the number of '1's and '0's tended to be the same, which is interesting for the key derivation function of the protocol. Figure 12 illustrates the distribution of fractional Hamming distances (Hdist) obtained from the comparisons of pairs of RND sequences generated by the same RND cells at different start-ups (considering the 10 chips). The average fractional Hdist, calculated as in (1), is 0.48 and the dispersion of the distribution is considerable, which means that the start-up values of several RND cells do not behave as tossing a coin and, hence, they are highly but not fully entropic (if RND cells had not been selected in the first registration phase, their entropy would have been much lower, as explained in [9]). The minimum entropy calculated as in Equation (5) with different number of measurements (from 1 to 100 start-ups) from the RND cells of one of the devices was 75%, which is enough for nonces. Hence, the RND cells are suitable to generate the nonces for the key derivation function.
# The Effect of Carbon Nanofibers Surface Properties in Hydrogenation and Dehydrogen Reactions
## 1 Introduction
Apart from cinnamaldehyde, the coexistence of a hydroxyl group and an alkenyl group confers to cinnamyl alcohol high reactivity, leading to a complicated reaction pathway with many by-products that can be produced. Hydrocinnamyl alcohol (HCOH) and 1-phenyl-1-propene (PPE) are the two main by-products derived from hydrogenation and hydrogenolysis reactions respectively. The nature of the catalyst and the reaction conditions play a crucial role in directing the selectivity of the reaction. Supported metal nanoparticles are usually employed for the selective conversion of COH into CAL, and among all, Pd is one of the most used active metal for oxidation reaction, despite often suffering from deactivation phenomena [33; 34; 35; 36; 37; 38]. In this study, Pd nanoparticles were supported by the impregnation method on four CNFs with a different order of graphitisation, oxygen content and particle dimension, namely CNFs-PS, CNFs-PS-OX, CNFs-LHT and CNFs-HHT. The catalysts were then thoroughly characterised with X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDX) in order to correlate nanoparticles dimension and dispersion with the surface properties of the CNFs. The catalytic performances of the synthesised catalysts were evaluated on two Pd-catalysed reactions, namely cinnamaldehyde hydrogenation and cinnamyl alcohol oxidation. Our final goal was to study how the graphitisation order and surface oxygen content of the CNFs influence the catalytic activity in the two proposed reactions.
## 3 Wake-Up Radio Communication Protocols
### Single Hop and Multi-Hop Wake-Up Network  
In a typical sensor network scenario, the wake-up signal is sent by either the wake-up radio or main radio to a neighbor passive node. The wake-up receiver of the receiving node will accumulate and harvest enough energy from the signal to become active and then trigger an interrupt to wake-up its main radio. The main radio is turned on to perform the required communication function and the wake-up radio will immediately return to sleep for a single hop communication network. On the contrary, the wake-up radio in a multi-hop network will have to retransmit the wake-up signal to a neighboring node before going back to sleep. Hence, the wake-up radio in the multi-hop network needs to have specialized hardware functionality.