esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
We evaluated our proposed methods using the eye and eyebrow images from four face datasets. The combination of the multiscale analysis methods with the curvature-based methods performed well. We achieved the goal from this work, as the scale-space method and discrete wavelet transform successfully exposed details at finer scales, and these details were given in-depth characteristics with a curvature-based method.  
Furthermore, the best discriminative features between the eyes and eyebrows were investigated. The variations in the datasets took an important role in the system's performance. Based on the accuracy results, the eyebrows were suitable for datasets with more controlled conditions, while the eyes were ideal for uncontrolled and larger datasets. The  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Accuracy (\%)**} & \multicolumn{2}{c}{**Total Images**} \\  & & & **(Class/Testing per Class/Training per Class)** \\ \hline \multirow{3}{*}{RFFMDS v1.0} & Eyes * & 81.25 & 160 (8/10/10) \\  & Eyebrows * & 95.00 & 160 (8/10/10) \\  & [PERSON] [32] & 87.50 & 176 (8/2/20) \\ \hline \multirow{3}{*}{ABD} & Eyes * & 69.05 & 84 (21/2/2) \\  & Eyebrows * & 57.14 & 84 (21/2/2) \\  & Eyes * & 39.34 & 244 (61/2/2) \\  & Eyebrows * & 23.77 & 244 (61/2/2) \\  & [PERSON] [67] on SVM & 74.83 & 687 (90:10-CV) ** \\ \hline \multirow{3}{*}{EYB dataset} & Eyes * & 76.04 & 434 (7/31/31) \\  & Eyebrows * & 98.61 & 434 (7/31/31) \\  & Eyes * & 96.88 & 2242 (38/29/30) \\  & Eyebrows * & 93.22 & 2242 (38/29/30) \\  & [PERSON] [68] & 93.96 & 70 (10/1/6) \\  & [PERSON] [69] & 67.42 & 2432 (38/1/63) \\  & [PERSON] [70] & 96.56 & 2404 (80:20) *** \\  & [PERSON] [71] on L-SVM & 97.10 & 2432 (38/32/32) \\  & [PERSON] [72] on SVM & 97.70 & 2432 (38/32/32) \\ \hline \hline \end{tabular}  
* Results from the proposed methods. ** On 90/10 training/testing cross-validation total of 687 images with unbalanced dataset. *** On 80/20 training/testing division for 2404 images.  
\end{table}
Table 12: Comparison of accuracy (%) results with other methods.  
highest accuracy results were 76.04% and 98.61% for the limited dataset, and 96.88% and 93.22% for the larger dataset for the eye and eyebrow images, respectively. The performance results were comparable and achieved similar high accuracy results to other works.  
Conceptualization, R.L.; methodology, D.G.; software, R.L.; validation, C.A. and D.G.; investigation, R.L.; writing--original draft preparation, R.L.; writing--review and editing, C.A.; supervision, C.A. and D.G.; project administration, C.A. All authors have read and agreed to the published version of the manuscript.  
This research is funded by Directorate of Research and Development, Universitas Indonesia under Hibah PUTI 2022 (Grant No. NKB-682/UN2.RST/HKP.05.00/2022).  
Not applicable.  
The datasets which support this article are from the RFFMDS v1.0 data set [32], the cropped extended Yale B dataset [34; 35], and the Aberdeen and pain expression subset data sets [36].  
The authors declare no conflict of interest.
## 4 Stability Analysis of Slopes Under Freeze-Thaw Cycles
### The Influence of Freeze-Thaw Cycles on Slope Stresses  
The nephogram of maximum principal stress for the slope after undergoing 0, 30, 60, and 90 freeze-thaw (F-T) cycles is presented in Figure 7.  
As illustrated in Figure 7, stress redistribution was observed with increasing freeze-thaw (F-T) cycles, accompanied by stress concentration phenomena at the slope toe and crest regions. Comparative analysis between the initial gravitational stress field and post-90-cycle F-T conditions revealed a significant stress amplification, with the maximum tensile stress increasing by 0.73 MPa. Progressive stability degradation at both the toe and crest was attributed to the F-T-induced deformation of near-surface geomaterials, coupled with the sustained propagation of tensile stresses within the slope body.
# DMCNet-Pro: A Model-Driven Multi-Pilot Convolution Neural Network for MIMO-OFDM Receivers
## 5 Experimental Design and Results
### Results and Analysis of DMCNet-Pro  
It can be seen that at 32 pilots, the accuracy of the model-driven scheme is slightly improved compared with the data-driven method and the traditional method, but at 8 pilots, the accuracy of the model-driven scheme is very close to that of DMCNet. The main reason for this is that when there is a large number of pilot sequences, the receiver can use traditional methods to perform channel estimation and signal detection comparatively. Therefore, LS initialization and ZF initialization provide guidance to the neural network, and the neural network can gradually adjust parameters in the right direction to reduce errors. However, in eight-pilot mode, due to the small pilot frequency, it is difficult for traditional methods to perform signal estimation and signal detection, and the accuracy of LS initialization and ZF initialization is not high, so the gain brought by changing to a model-driven model is not large.  
During the experiment, it is found that in the model-driven receiver, the number of parameters of the model can be greatly reduced and the accuracy is close to that of the data-driven model. This is reflected in the number of output channels of Res-Block three-layer convolution. For this reason, an experiment was designed to explore the influence of the number of output channels of Res-Block on the performance. The BER of each group is shown in Figure 12, where 512-512-256* indicates the BER of the data-driven model DMCNet at 32 pilot frequency. It can be seen from the results that in the model-driven network, when the number of Res-Block output channels reaches 200-200-100, the accuracy is already better than that in the data-driven network with more parameters. When the number of parameters continues to increase, the gain in accuracy is not obvious. Therefore, the set of parameters of 200-200-100 is selected at 32 pilot frequency to set the number of channels in each layer of Res-Block. By comparing the parameter number and FLOPs of the 32-pilot model-driven network DMCNet, it can be seen that the model-driven scheme can achieve a better or similar receiving accuracy with less computation, which indicates that in the data-driven scheme, a large number of parameters and operations is needed to fit the signal transmission process. By making full use of the known information, part of the network parameters can effectively be replaced, and the method used for sub-module  
Figure 11: Accuracy comparison of different signal detection algorithms.  
training also makes the training of each module's parameters more efficient. The same experiment was also performed at eight pilots. Due to the low accuracy of traditional algorithms for channel estimation and signal detection at eight pilots, the model-driven scheme does not bring significant accuracy or parameter number gains at eight pilots.
# A Preemptive-Resume Priority MAC Protocol for Efficient BSM Transmission in UAV-Assisted VANETs
## 4 Problem Formulation and Performance Metrics
### UAV Communication Latency
#### 4.1.2 UAV MAC Layer Service Delay
Therefore, the actual time interval can be simplified as follows:  
\[E(Slot)=PT_{s}+(1-P)\sigma\,. \tag{23}\]  
The probability of no packet arrival within a time slot interval \(1-\text{q}\) can be written as:  
\[1-q=exp(-\lambda\times E(Slot)). \tag{24}\]  
The average MAC layer delay can be expressed as [30]:  
\[E(T_{MAC})=\Bigg{[}\frac{(1-2P)(W-1)+PW\big{(}1-(2P)^{m}\big{)}}{2(1-2P)(1-P)} \Bigg{]}E(Slot)+\frac{T_{s}}{1-P}\,. \tag{25}\]  
In the equation, m represents the maximum backoff stage specified by the 802.11 standard, and W represents the initial contention window size. It is important to note that the variable \(T_{MAC}\) varies for each priority level.
## 7 Real Data Applications
### Analysis of Second Dataset
For the data on the tensile strength of polyester fibers, Figure 10 draws the profile log-likelihood of the UEHLD for each parameter by fixing one parameter and changing the others. This figure demonstrates the excellent behavior of the aforementioned data, since the two roots of the parameters are global maxima. Figure 11 shows a contour plots with variable parameters, the log-likelihood function, and the log-product spacing function of the UEHLD to verify that the estimates have unique points. Figure 10: Profile likelihood for parameters of the UEHLD for polyester fibers’ tensile strength data. Figure 9: Plots of the estimated PDFs, CDFs, and P-P of the UEHLD of polyester fibers’ tensile strength data. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**ML**} & \multicolumn{4}{c}{**MPS**} & \multicolumn{4}{c}{**Bayesian**} \\ \hline  & **Estimates** & **SE** & **Lower** & **Upper** & **Estimates** & **SE** & **Lower** & **Upper** & **Estimates** & **SE** & **Lower** & **Upper** \\ \hline \(\varphi\) & 1.1281 & 0.2051 & 0.7262 & 1.5301 & 1.0058 & 0.2074 & 0.5994 & 1.4124 & 1.1354 & 0.1931 & 0.7826 & 1.5369 \\ \hline \(\delta\) & 1.2346 & 0.2951 & 0.6563 & 1.8130 & 1.0606 & 0.2407 & 0.5889 & 1.5325 & 1.2423 & 0.2932 & 0.7414 & 1.8473 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Estimates, SE, and CI of the UEHLD parameters for polyester fibers’ tensile strength data. Figure 12 shows trace plots of the posterior distributions of the parameters to track the convergence of the MCMC outputs. Additionally, they display the histograms for the marginal posterior density estimates of the parameters based on 10,000 chain values and the Gaussian kernel, demonstrating how effectively the MCMC process converges. All of the produced posteriors are symmetric with regard to the theoretical posterior density functions, as shown by the estimations.
## 3 Experiment Method
### Post-Processing
#### 3.3.4 Judgment of the Puncture Moment  
In order to automatically detect the moment of puncture, it is crucial to determine an appropriate threshold value. Ideally, the needle length should gradually decrease from 100% when puncturing occurs. However, in the actual experimental setup, accurately calculating this decline from 100% proves challenging due to variations in operator technique and the varying angles of the needle resulting from different postures. Therefore, it becomes necessary to determine a suitable threshold value for this experiment. To achieve this, a comparative analysis was conducted by smoothing the needle puncture data described in Section 3.3.3, and the results were evaluated using four threshold values: 95%, 96%, 97%, and 98%. The outcomes are depicted in Figure 17.  
Figure 16: Moving average processing.  
Figure 17: Relation between predicted puncture time (from our system) and ground truth for each threshold.  
The primary objective was to strike a balance between minimizing false positives, where a puncture is incorrectly detected when the needle has not actually punctured the skin, and minimizing false negatives, where a puncture goes undetected despite the needle successfully puncturing the skin. To determine the optimal threshold, we compared the standard deviation (SD) and mean absolute error (MAE) of four different data sets, as shown in Table 3.  
By conducting a comparative analysis of the standard deviation (SD) of the predicted puncture time and the mean absolute error (MAE), calculated based on the difference from the ground truth, the extent of disparity between the predicted and true values can be evaluated. Consequently, when the threshold value is set to 96%, the standard deviation reaches its minimum, indicating a relatively concentrated distribution of predicted values and minimal deviations from the true values. This signifies the superior stability of the model. Furthermore, by comparing the mean absolute error (MAE) among different thresholds, one can assess the average level of disparity between the predicted and true values. Similarly, at the threshold value of 96%, the mean absolute error reaches its minimum, underscoring the heightened average accuracy of the model. Therefore, this study opted for a threshold value of 96% to achieve the utmost stability and accuracy in the model.
# WUE and CO2 Estimations by Eddy Covariance and Remote Sensing in Different Tropical Biomes
## 2 Materials and Methods
### MODIS Data  
Remotely sensed GPP data obtained from the MODIS sensor onboard the Terra satellite were used. These data were calculated based on the concept of light use efficiency [47], through the relation between incident photosynthetically active radiation (PAR), the fraction of photosynthetically active radiation absorbed by plants (FPAR) and the actual light use efficiency (\(\varepsilon\)) of vegetation [49]:  
\[\text{GPP}=\varepsilon\ast\text{APAR} \tag{6}\]  
where APAR is the absorbed photosynthetically active radiation, which is calculated as the product between the FPAR--derived from the MOD15A2H product [50]--and the PAR. PAR values are obtained from the Global Modeling Assimilation Office (GMAO) reanalysis [51] set and correspond to 45% of the total incident solar energy in the visible spectrum (0.4 to 0.7 \(\upmu\)m). In the MOD17A2 product, the values of \(\varepsilon\) are derived from the attenuation of its maximum value (\(\varepsilon\)max) due to two environmental stresses: (1) minimum temperature (Tmin), which can inhibit photosynthesis, reducing enzymatic activity, and (2) vapor pressure deficit (VPD), because high VPD can reduce stomatal conductance [52]. The GPP data for the Caatinga biome were derived from the MOD17A2 version 6.0 product [53] from the MODIS sensor on board the Terra satellite. This algorithm provides 8-day composite data in a 500 m spatial resolution.  
Reanalysis data from the GMAO dataset ([[https://gmao.gsfc.nasa.gov/GMAO_products/reanalysis_products.php](https://gmao.gsfc.nasa.gov/GMAO_products/reanalysis_products.php)]([https://gmao.gsfc.nasa.gov/GMAO_products/reanalysis_products.php](https://gmao.gsfc.nasa.gov/GMAO_products/reanalysis_products.php)), accessed on 25 February 2022) used as input parameters for the MOD17A2 product were Tmin and VPD. Therefore, we carried out an overall assessment to check for inconsistencies in this dataset in relation to the same parameters measured at the Cerrado, Caatinga, Pantanal and Amazon biomes. FPAR and leaf area index (LAI) data from the MOD15A2H product were also verified. Furthermore, the MOD12 product [54] for land cover was also discussed regarding the years 2014 and 2015 in the region.  
The MOD12 [54] product for land cover was also discussed regarding the Cerrado and Caatinga site for the period from 1 January 2014 to 31 July 2015, the Pantanal site for the period from 1 January 2015 to 31 December 2016, and the Amazon site from 1 January 2009 to 31 December 2011.
## 3 Results & Discussion
### Implications of Anode Deposits  
Given the spatial distribution of Ba-containing material deposited on the anode, a crude estimate of the mass deposited on the anode can be made. Approximating the deposited volume as a uniform, 100% dense, 5 \(\upmu\)m thick layer of pure BaO (the densest species potentially present, \(\rho\) = 5.72 g/cm\({}^{3}\)) within the primary deposition region (2 mm diameter) yields an estimate of \(\sim\)0.05 mg of deposited mass. This is likely an overestimate of the amount of deposited material as it ignores porosity in the deposited layer, assumes the deposited material is the most dense of likely species, and neglects potential thinning of the deposited layer towards the edges of primary deposition region. This estimate also assumes that the bare ring evident in Figure 7 was coated, and the deposited material in this region flaked off during post-operation handling. In addition, it should also be noted that it is not clear _when_, during cathode activation and/or operation, deposits on the anode were formed. Despite these limitations, the present calculation is at least a reasonable order-of-magnitude estimate of the mass deposited on the anode. For comparison, similarly manufactured scandate cathodes contain a total of \(\sim\)4 mg of impregnate prior to activation, as determined by the manufacturer. This suggests that on the order of 1 wt.% of the impregnate is desorbed and deposited on the anode over operating times on the order of 10,000 h.  
In addition, EDS analysis strongly indicates that the deposited material primarily consists of Ba/Al/Ca oxide. The presence of an oxide deposit on the anode directly across from thecathode could have significant implications for the apparent emission performance of the cathode. Specifically, a non-conducting, potentially time-dependent coating on the anode will modify details of the applied electric field used to collect emitted current. As noted above, the time and compositional evolution of deposited layers on the anode could not be directly assessed in the present study, but may warrant further examination.
## 3 Results and Discussion
### Microstructure and Morphology
All these characterizations implied that the Ag\({}_{3}\)PO\({}_{4}\)/SnO\({}_{2}\)/CC heterojunction was successfully prepared. Figure 6a shows the UV-vis diffuse reflectance spectra of SnO\({}_{2}\), Ag\({}_{3}\)PO\({}_{4}\), Ag\({}_{3}\)PO\({}_{4}\)/CC, SnO\({}_{2}\)/CC and Ag\({}_{3}\)PO\({}_{4}\)/SnO\({}_{2}\)/CC. As illustrated in Figure 6a, Ag\({}_{3}\)PO\({}_{4}\) showed photoabsorption in the visible light region, and SnO\({}_{2}\) demonstrated no absorption of visible light. SnO\({}_{2}\)/CC was similar to the absorption edge of pure SnO\({}_{2}\), while there was an obvious absorption tail in the long-wavelength region (400-800 nm), which could have resulted from the scattering and absorption of light among the texture and pore structure in CC [41]. Ag\({}_{3}\)PO\({}_{4}\)/CC differs from the absorption edge of pure Ag\({}_{3}\)PO\({}_{4}\) in the short-wavelength region (200-400 nm). This may be related to the low content of Ag\({}_{3}\)PO\({}_{4}\) deposited on carbon cloth. The obtained Ag\({}_{3}\)PO\({}_{4}\)/SnO\({}_{2}\)/CC heterojunction performs a wide and strong absorption in the visible region, and it also has an increased absorption tail in the long-wavelength region (400-800 nm). Figure 6b displays the corresponding Tauc's plots of (\(\alpha\)hv)\({}^{2}\) vs. (hv) of Ag\({}_{3}\)PO\({}_{4}\) and SnO\({}_{2}\), which showed that the band gap of Ag\({}_{3}\)PO\({}_{4}\) and SnO\({}_{2}\) was estimated to be 2.42 and 3.6 eV, respectively. Figure 4: TEM image of Ag\({}_{3}\)PO\({}_{4}\)/SnO\({}_{2}\)/CC (**a**). HRTEM image of Ag\({}_{3}\)PO\({}_{4}\)/SnO\({}_{2}\)/CC (**b**). Figure 5: XPS spectra of the Ag\({}_{3}\)PO\({}_{4}\)/SnO\({}_{2}\)/CC: (**a**) survey spectrum, (**b**) Sn 3d, (**c**) Ag 3d, (**d**) P 2p. Figure 6: UV-vis diffuse reflectance spectra of the as-prepared samples (**a**). Plots of (\(\alpha\)hv)\({}^{2}\) versus energy (hv) of the as-prepared Ag\({}_{3}\)PO\({}_{4}\) and SnO\({}_{2}\) (**b**).
# Flow Dynamics of Sulfate-Modified Water/Polymer Flooding in Micromodels with Modified Wettability
## 3 Results and Discussion
### Polymer Flood-Oil Recovery and Pressure Response for Tertiary Mode Polymer Flood
#### 3.3.2 Polymer Viscosity Equal to the Oil Viscosity in Complex-Wet Model  
As a further step, the impact of increased polymer viscosity (equal to the oil viscosity) on oil recovery was investigated. To do so, 1500-ppm polymer was injected through the micromodels described in the previous sections. Figures 20 and 21 describe the additional oil recovered and the impact on pressure response.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Experiment**} & \multirow{2}{*}{**Flooding Brine**} & **Soi** & **Swc** & **RF** & **Additional RF** \\ \cline{3-6}  & & & & **(\%)** & \\ \hline
1 & SSW & 91.15 & 8.85 & 55.01 & - \\
2 & SSW+2 SO4 & 96.49 & 3.51 & 55.40 & 0.39 \\
3 & DSSW & 95.32 & 4.68 & 54.43 & \(-\)0.58 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Summary of real structure water-wet micromodel with initial oil saturation, connate water saturation and final oil recovery factors.  
Figure 19: Oil recovery and pressure drop versus PV injected for complex-wet artificial structure micromodel. Polymer (half to the oil viscosity) flooding in tertiary mode after the brine flood in secondary mode.  
Polymer diluted in SSW resulted in additional oil recovery of 4.33% (Figure 20), and the polymer prepared in SSW+2 SO42- resulted in additional oil recovery of 6.91% (Figure 21). Overall recovery from both micromodels was approximately 50%. Brine flooding was also performed after post-tertiary-mode polymer flooding, to observe further recovery due to any bypass flow through the micromodel, but, in both cases, no oil was recovered. The reason for this could be a loss of polymer viscosity, due to mechanical degradation while flowing through flow lines and the pump. This degradation caused a reduction in polymer viscosity, resulting in lower viscosity than that of the oil. Later, brine flooding was performed for the pre-flushed flow paths of polymer solutions, due to lower viscosity of the aqueous phase, compared to the viscosity of the trapped oil. This investigation and the comparison of its results with those outlined in the previous section suggest injecting polymer solution with a viscosity at least equal to the oleic phase in porous media. Lower polymer viscosity (half that oil) does not contribute to the economics of a polymer injection project.  
Figure 21: Oil recovery and pressure drop versus PV injected for the complex-wet artificial structure micromodel. Polymer (equal to the oil viscosity) flooding in post-tertiary mode after the SSW+2 SO42- brine flood in secondary mode.  
Figure 20: Oil recovery and pressure drop versus PV injected for complex-wet artificial structure micromodel. Polymer (equal to the oil viscosity) flooding in post-tertiary mode after the SSW brine flood in secondary mode.
## 2 Background
### GIFT Block Cipher  
GIFT [19] is a lightweight block cipher that corrects the well-known weakness of PRESENT and improves its efficiency. It is an SPN-based cipher with two variants: GIFT-64 and GIFT-128. 128-bit key GIFT-64 operates with 28 rounds and GIFT-128 with 40 rounds, and each round consists of three steps: SubCells, PermBits, and AddRoundKey as shown in Figure 2. GIFT is similar to PRESENT, but the half round key is computed and no addroundkey is performed at the start of encryption.  
* sBoxLayer: S-box with 16 values (Substitute box) to replace from 4 bits to 4 bits in the state.
* pLayer: Applying a bit-level shift to the state.
* AddRoundKey: Adding state to the 64-bit word of the round key using finite field arithmetic.  
Figure 1: PRESENT structure.  
The key schedule and round constants are the same in both versions of GIFT. The difference is round key extraction. The round key is first extracted from the key state before the key state update. In the case of GIFT-64, two 16-bit words of the key state are extracted as round keys.  
\[RK=U\parallel V,\ U\gets W6,\ V\gets W7\]  
In the case of GIFT-128, four 16-bit words in the key state are extracted as a round key.  
\[RK=U\parallel V,\ U\gets W2\parallel W3,\ V\gets W6\parallel W7\]  
The key state is updated as follows:  
\[\begin{array}{c}W_{0}\parallel W_{1}\gets W_{0}\ggg 2\parallel W_{1}\ggg 12\\ W_{2}\parallel W_{3}\gets W_{2}\parallel W_{3}\\ W_{4}\parallel W_{5}\gets W_{4}\parallel W_{5}\\ W_{6}\parallel W_{7}\gets W_{6}\parallel W_{7}\end{array}\]