esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
A novel methodology for designing SPUF for dual application modes has been proposed. Our analysis shows that the memory and PUF modes of operation are affected in a contradicting way by the device geometry of SRAM cells. Four circuit-level techniques are applied to alleviate the design constraints and expand the design space. With the proposed systematic approach to optimize the SRAM cell design for both memory and PUF requirements, the memory failure probability is reduced by 75% while the BER of PUF response is maintained below \(10^{-6}\). Meanwhile, the area, leakage current and dynamic power are kept to an acceptable level.
# Face Swapping Consistency Transfer with Neural Identity Carrier
## 3 Methods  
Existing face-swapping methods take identity and target image/video pairs as input. In this paper, we treat the face-swapping problem from a novel perspective. We focus on consistency inheritance in the whole process. Given an identity \(X_{id}\) and a target \(X_{t}\), here \(X_{id}\) and \(X_{t}\) can be any portrait image or video, we first use existing face-swapping methods to generate a face-swapping proxy, denoted as \(X_{ref}\).  
Taking \(X_{ref}\) as references, we train a _U-Net_ as a neural identity carrier to carry the primary information of the face-swapping proxy. During the training stage, we introduce a coarse encoder \(E_{c}\) and a detail encoder \(E_{d}\) to reconstruct a series of face parameters, including albedo coefficients \(\alpha\), separate linear identity shape \(\beta\) and detail \(\delta\), which will be used as constraints of the transfer learning to generate a photo-realistic result \(X_{o}\).
## 3 Experiment and Simulation of V-Shaped Bending at Room Temperature
### Simulation Procedure  
To predict the spring-back after V-bending of an SS400 steel sheet, both pure isotropic and kinematic hardening laws were employed using ABAQUS software. The input parameters for the simulation are presented in Tables 2 and 3. The simulation model is shown in Figure 4a, where the workpiece dimensions are 5 mm \(\times\) 35 mm \(\times\) 110 mm (thickness \(\times\) width \(\times\) length). The results of the V-bending simulation at room temperature are presented in Figure 4b-d. A comparison between the simulation results using pure isotropic and kinematic hardening laws and the corresponding experimental data (Table 4) are presented in Table 5. The deviation of the measured angle (\(\Delta\theta\)) after spring-back was calculated as Equation (10).  
\[\Delta\theta=\theta_{sim}-\theta_{exp} \tag{10}\]  
where \(\theta_{sim}\) and \(\theta_{exp}\) are the simulated and experimental measure angles, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**H (mm)**} & \multirow{2}{*}{**Bending Angle** (\({}^{\circ}\))} & \multicolumn{6}{c}{**Measure Angle 0 exp (\({}^{\circ}\))**} & \multicolumn{6}{c}{**Spring-Back (\({}^{\circ}\))**} \\ \cline{3-10}  & & & **Levels** & & & **Levels** & & & **Average** \\ \cline{3-10}  & & **1** & **2** & **3** & & **1** & **2** & **3** & \\ \hline
10 & 136 & 141.5 & 142.5 & 141.5 & 141.833 & 5.5 & 6.5 & 5.5 & 5.833 \\ \hline
16 & 112 & 117.67 & 117.88 & 119.72 & 118.423 & 5.67 & 5.88 & 7.72 & 6.423 \\ \hline
22 & 88 & 96 & 95 & 94.5 & 95.167 & 8.0 & 7.0 & 6.5 & 7.167 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of measurements before and after spring-back in V-bending experiment.  
Figure 3: Experimental setup for V-bending at room temperature (**a**) and results of spring-back with various punch vertical displacements of 10 mm (**b**), 16 mm (**c**), and 22 mm (**d**).  
Figure 4: Simulation of V-bending process (**a**) and results of simulations with different punch vertical displacements of (**b**) 10 mm, (**c**) 16 mm, and (**d**) 22 mm.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**H (mm)**} & \multirow{2}{*}{**Bending Angle** (\({}^{\circ}\))} & \multicolumn{3}{c}{**Measure Angle (\({}^{\circ}\))**} & \multicolumn{3}{c}{**Deviation (\(\Delta\theta\))(\({}^{\circ}\))**} \\ \cline{3-6}  & & \multirow{2}{*}{**Experiment (\(\theta_{\mathrm{exp}}\))**} & \multicolumn{3}{c}{**Simulation (\(\theta_{\mathrm{sim}}\))**} & \\ \cline{3-6}  & & & **Isotropic** & **Kinematic** & **Isotropic (\(\Delta\theta_{\mathrm{iso}}\))** & **Kinematic (\(\Delta\theta_{\mathrm{kine}}\))** \\ \hline
10 & 136 & 141.83 & 142.50 & 139.05 & 0.667 & \(-\)2.783 \\ \hline
16 & 112 & 118.42 & 119.98 & 117.11 & 1.557 & \(-\)1.313 \\ \hline
22 & 88 & 95.17 & 96.47 & 93.94 & 1.303 & \(-\)1.227 \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of experimental and simulation results for spring-back angle.
## 3 Numerical Simulation
### Governing Equation
#### 3.1.4 Displacement Field Control Equation  
As illustrated in Figure 2, considering the reliability of the model calculation and the need to simplify the calculation speed, this study simplifies the hydrothermal three-field coupling relationship [33; 34]. Specifically, the full coupling of hydrothermal processes is considered first, which is followed by sequential unidirectional coupling through the inheritance of the solution. The corresponding control equation is as follows:  
\[\varepsilon_{vf}=\theta_{i}+\theta_{u}-n \tag{12}\]  
\[\varepsilon_{vT}=3\Big{[}\beta_{s}\theta_{s}(T-T_{0})+\beta_{u}\theta_{u}(T-T _{0})+\beta_{i}\theta_{i}\Big{(}T-T_{f}\Big{)}\Big{]} \tag{13}\]  
where \(\varepsilon_{vf}\) is the volumetric strain due to the ice-water phase transition, \(n\) is the initial porosity; \(\varepsilon_{vT}\) is the volumetric strain due to temperature change, and \(\beta_{s}\), \(\beta_{u}\), and \(\beta_{i}\) are the coefficients of linear expansion for the soil particles, unfrozen water, and ice, respectively.  
Figure 2: Physical field coupling relationship diagram.
## 5 Conclusions  
In this work, we propose an oil slick detection method for SLAR images using a strategy based on image processing and computer vision techniques without using the characterization of oil slicks obtained from the multi-polarization features of a radar. We use only the intensity image obtained from the backscatter signal without considering radar parameters. The strategy consists of two stages. First, SLAR images are processed to identify noise regions caused by aircraft maneuvers. This step is required to avoid the detection of false positives (i.e., pixels which are not the pixels of an oil slick but have similar brightness features). Second, SLAR images, which have been processed beforehand, are used to detect oil slicks. This process can be performed automatically and can also be carried out by on-boards sensors once the radar scan is digitalized as an SLAR image.  
Although the state of the art almost never contains approaches with which to detect the dark spots of oil slicks in SLAR images, we have made an effort to make a brief comparison between our method and other approaches for the same task using SLAR images as inputs.  
As future work, we would like to add deep learning techniques as data augmentation in order to artificially increase the size of the training set and thus improve the detection. Data augmentation consists of transforming the original SLAR image to obtain synthetic samples when the example of missions is not sufficient to design and implement an automatic detection method. In this line, our idea is to also prove the behavior of Convolutional Neural Networks (CNN) in order to simultaneously detect more targets. For example, CNN will provide more flexibility for use in new situations and will make it possible to detect other targets, such as coastlines or ships, at the same time as oil slicks.  
The authors would like to thank Babcock Helicopters SAU, which is part of Babcock MCS Spain, for the provision of data. This work was supported by the Spanish Ministry of Economy and Competitiveness through the ONTIME research project (RTC-2014-1863-8).  
The contribution presented in this work is the result of the joint work of all the authors comprising the research team. Each of the members contributed in varying degrees to each step of this research work, such as analysis, design, development, implementation and the testing of the system. In particular, the research concept and the design of the system's architecture were carried out by [PERSON], and the implementation by [PERSON], respectively. Both authors contributed to the tests and the analysis of the results.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Comparison of Performance between Features and Skeleton Data  
In the present study, we compared the performance of the manual feature extraction method and the skeleton-data-based method in FMS assessment. As shown in Table 3, the manual feature extraction method has better performance in FMS assessment. Compared with the skeleton-data-based method, the manual feature extraction method can capture the key features of the FMS movement more accurately, thus assessing the quality of the movement more accurately. The manual feature extraction method circumvents the impact of skeleton data quality differences on action scoring to a certain extent because the skeleton data are screened and cleaned through bespoke processing. In addition, the manual feature extraction method has good interpretability, which helps us better understand the FMS movement quality. Specifically, the manual feature extraction method generally scores each action with higher accuracy than the skeleton-data-based method; for example, the scoring accuracy of m09 improved from 0.44 to 0.88. The average accuracy of the manual feature extraction method is 0.8, while the average accuracy of the skeleton-data-based method is only 0.63, indicating that the manual feature extraction method has better performance in FMS assessment.  
Figure 6: (a) F1-micro-average of FMS movements. (b) F1-macro-average of FMS movements.
# Optical Angle Sensor Technology Based on the Optical Frequency Comb Laser
## 1 Introduction
### Methods Based on the Chromatic Aberrations of a Simple Lens  
According to the thin-lens equation, the focal length of a simple lens depends on the light wavelength of an incident light via the lens refractive index [73]. When a collimated mode-locked femtosecond laser is made incident to a simple lens in such a way that the laser axis is aligned to be coaxial with respect to the optical axis of the lens, the optical modes in the femtosecond laser are focused at different points on the optical axis of the lens. The light ray from an off-axis point will arrive at a different height above the optical axis; namely, the frequency-dependent lens focal length causes a frequency dependence of the transverse magnification as well [73]. This characteristic, which is referred to as the lateral chromatic aberration, can be employed for measurement of the small angular displacement of an object with the enhancement of the laser autocollimation [25; 58]. Figure 11 shows the optical setup for measurement of the small angular displacement of an object based on the chromatic aberrations of a simple lens, where the principle of the laser autocollimation is integrated. A mode-locked femtosecond laser is employed as the light source, while the laser  
Figure 10: An example of the absolute angular position measurement by the optical frequency-domain femtosecond laser autocollimator [57]. Figure 9: Absolute angular position measurement by the mode-locked femtosecond laser autocollimator based on the principle of the optical frequency-domain angle measurement with the enhancement of an assisted angle sensor [57]. In the setup, the angle of the normal of the grating reflector with respect to the incident femtosecond laser is a final measurand. autocollimation unit composed of a collimator objective and a single-mode fiber connected to a spectrometer is employed as the detector for angle measurement. The mode-locked femtosecond laser from the light source is collimated by a collimating lens and is then made incident to a target reflector. The reflected beam is then made incident to the laser autocollimation unit. The fiber detector in the laser autocollimation unit is placed at an off-axis position \(d_{\text{Fiber}}\) (\(\
eq\)0) with respect to the optical axis of the collimator objective; namely, the optical axis of the laser autocollimation unit has an angle with respect to the optical axis of the collimator objective. This arrangement enables the optical setup to detect the small angular displacement of the target reflector with the effect of the lateral chromatic aberration of the lens.
# CKGAT: Collaborative Knowledge-Aware Graph Attention Network for Top-N Recommendation
## 4 Proposed CKGAT Method
### Model Learning and Recommendation Generation  
CKGAT adopts the negative sampling strategy to improve the efficiency of model learning, and the number of negative samples is the same as that of positive ones for each user. As in CKAN [4], the loss function of CKGAT is defined in Equation (15).  
\[\mathcal{L}=\sum_{u\in\mathcal{U}}\left(\sum_{v\in\{v|(u,v)\in\mathcal{P}^{+}\}} \mathcal{J}(y_{uv},\hat{y}_{uv})-\sum_{v\in\{v|(u,v)\in\mathcal{P}^{-}\}} \mathcal{J}(y_{uv},\hat{y}_{uv})\right)+\lambda\|\Theta\|_{2}^{2} \tag{15}\]  
where \(\mathcal{J}(y_{uv},\hat{y}_{uv})\) is the cross-entropy loss. \(\mathcal{P}^{+}\) represents the positive user-item pair set, \(\mathcal{P}^{-}\) is the opposite. \(\Theta=\{\mathbf{E},\mathbf{R},\mathbf{w}_{1},\mathbf{w}_{2},\mathbf{b},\mathbf{ W}_{i},\forall i\in\{0,1,2\}\}\) represents the set of model parameters, where \(\mathbf{E}\) and \(\mathbf{R}\) are the embedding table of all entities and the embedding table of all relations, respectively. \(\|\Theta\|_{2}^{2}\) is the L2-regularizer. Hyper-parameter \(\lambda\) is used to balance the regularizer.  
CKGAT uses the stochastic gradient descent (SGD) algorithm to minimize the above loss function to learn all the parameters of CKGAT. Once the learning process is completed, CKGAT can obtain the trained user embedding and item embedding. Given a target user, CKGAT first uses the user embedding and the embeddings of all candidate items that the user has not interacted with to calculate the predicted probabilities of the items. Then the predicted probabilities are sorted in descending order, thereby generating a top-N recommendation list for the user.
## 5 Results and Discussion
### Descriptive Statistics
The findings shown in Figure 3 indicate that the participants in the preliminary survey reported low engagement in behaviors (X13) and (X16) to (X21). Although risky behavior (X13), crossing a stop-junction without entirely stopping, showed low engagement in the preliminary survey, it was included in the subsequent survey and analysis because it is a critical risky behavior that Malaysian road users engage in without the awareness of it being a traffic offense. As a result of this observation, behaviors (X16) to (X21) were excluded from the research. Figure 4 shows the mean value of self-reported risky riding behavior (X1 to X15), risk perception (Y1 to Y15), and positive affect (Z1 to Z15). Similar to the percentage of self-reported risky behavior, the mean of self-reported risky riding behavior illustrated in Figure 4 reveals that drinking alcohol before riding (X6) was the least common riding behavior among motorcyclists, with a mean value of 1.211. The results also reveal that most participants frequently rode during peak hours (X9) (mean value: 3.216). In terms of risk perception, the majority of the motorcyclists perceived (Y15), riding without switching on the headlights during the daytime, to be the least risky behavior, with a mean value of 3.015. In contrast, the majority of the respondents believed drinking alcohol before riding a motorcycle (Y6) to be an exceptionally risky behavior (mean value: 4.201), as summarized in Figure 4. As shown in Figure 4, most motorcyclists had a positive effect regarding the behavior of frequently changing lanes to overt  
Figure 4: Results from self-reported surveys on the mean value of risky behavior, risk perception, and positive affect. Generally, this behavior saves traveling time for motorcycle riders, thus creating positive affect among motorcyclists. In addition to this finding, riding during peak hours (Z9) had the second-highest mean of positive affect. The risky behavior with the lowest mean of positive affect was (Z6) as the majority of Malaysians practice a religion that forbids the consumption of alcohol. The mean values of all three measured constructs are compared in Figure 5. The results reveal that only behavior X9 (riding during peak hours) had higher mean values compared to respondents' mean risk perception of this behavior (Y9), showing that a lower risk perception results in a higher engagement rate.
# Non-Local Spatial and Temporal Attention Network for Video-Based Person Re-Identification
## 4 Experiment
### Datasets and Evaluation  
We evaluated the proposed model on two commonly used video-based person Re-ID datasets: MARS, DukeMTMC-VideoReID.  
MARS: The MARS [30] dataset is an extended version of the Market1501 [3] dataset and is also the first large-scale video-based dataset. Since all bounding boxes and tracks are automatically generated, it contains disruption terms, and each identification may contain multiple tracks. It is the largest video-based person Re-ID dataset with 1261 identities and 20,478 videos, with multiple frames per person captured across six non-overlapping camera views. Among the total identities, about half of the identities are used for training, and the other half are used for testing. Additionally, the MARS dataset includes 3248 identities (disjoint with the train and test set) that are used as distractors.  
DukeMTMC-VideoReID: The DukeMTMCVideoReID [31] is a subset of the DukeMTMC multicamera dataset [32], which was collected on an outdoor scenario with varying viewpoints, illuminations, backgrounds, and occlusions using eight synchronized cameras. The dataset contains 1404 identities for training and testing and 408 identities as distractors. In total, there are 2196 videos for training and 2636 videos for testing. Each video contains person images sampled every 12 frames. During testing, a video for each ID is used as the query, and the remaining videos are placed in the gallery.  
In Table 2, a detailed comparison of the two datasets MARS and DukeMTMC-VideoReID is shown from the following aspects:  
* The total number of people included;
* The number of people used for training;
* The number of people used for testing;
* The number of people used as distractors;
* The total number of videos contained in the dataset; and
* The number of cameras used in data collection.  
We used the same evaluation indicators as those used in the literature [12; 13; 30; 33]: CMC (cumulative matching characteristics) and mAP (mean average precision). CMC refers to the probability of finding the correct identity among the first k matches based on the retrieval ability of the algorithm. We chose to use CMC when only one gallery instance exists for every identity. We tested the  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Dataset** & **Identity** & **Train** & **Test** & **Distractor** & **Video** & **Camera** \\ \hline MARS [30] & 1261 & 625 & 636 & 3248 & 20478 & 6 \\ Duke [31] & 1404 & 702 & 702 & 408 & 4832 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of MARS and DukeMTMC-VideoReID, Duke = DukeMTMC-VideoReID dataset.  
probability of rank-1, rank-5, and rank-20. The mAP metric is used when there are multiple instances of the same identity in the gallery.
## 3 Results and Discussion
### Translocation Factor  
Translocation factors of Pb, Zn, Cu, and Cd from root to stem, stem to leaf, and leaf to fruit showed significant variations (Table 2). The resulting variations in all the plant functional parts indicate high mobilities of metals inside the plant tissues. Here, we recorded Pb-TF \(>\)1 from leaf-fruit (L-F) at six abandoned sites, four for Cd (2 each for S-L and L-F), and two sites for Cu (L-F), respectively. However, Zn-TF \(>\) 1 was not recorded in any functional part of _X. strumarium_; hence its mobility remained inactive at abandoned sites. The higher mobility of other metals indicates an ecologically inexpensive phytoremediation potential of this IAPS. As mentioned earlier, almost all the heavy metals except Zn moved to fruits in higher quantity in the majority of abandoned sites, possibly originating new resistant populations capable of tolerating heavy metal stress in time-space. TF factor or mobility index (MI) is considered one of the important factors in assessing hyperaccumulator plants (TF \(>\) 1) reveals phytoremediation potential, particularly for heavy metals [61]. Mobilization inside plants increases the level of metals in aboveground parts which may be attributed to aerial deposition and absorption of metals through surfaces of the leaves [62] as depicted by our results at few abandoned sites (e.g., TF \(>\) 1 for L-F for Pb in PC, MKI, MSM, TFN, TBM, Cu in HIZ and BKP, and Cd in BKP and CUA sites and S-L for Cd in KTH and SKR sites). Concerning phytoextraction and phytostabilization strategies often utilized in assessing phytoremediation [63], _X. strumarium_ has efficient phytoextraction ability in terms of bioaccumulation and translocation of the aforementioned heavy metals into various below and aboveground parts. However, in phytostabilization, the plant changes pollutants nature in the rhizosphere, thus making it harmless or tending to make it harmless for plant growth development [64], but this phenomenon is not yet known for this IASP.
# Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey
## 3 Related Work
### Trust Management Models in VANETs
#### 3.3.2 Data-Centric Trust Models  
Data-centric confidence models concentrate on approximating the reliability of the data received. To reliably check the consistency of the information obtained, the prototypes need mutual data from several bases, such as national automobiles or RSUs. Reference [63] constructs a trust typical to estimate the reliability of a communication directly constructed on several variables, including gratified resemblance, content clash and path resemblance. Reference [64] suggest a deterministic method to calculate the self-reliance near the transmitted communication by using the transmitted signal strength (RSS) for the measurement of spaces and the geolocation of the automobile (location coordinate). Reference [65] Propose email-based joint dependence and social network trust to create and maintain data trust. Latency and data sparsity are the main disadvantages of data-centric models. Great records of data from numerous bases, respectively, hold dismissed data that increases potential or overwhelms important data. In comparison, information sparsity is established in VANETs.
## 5 Discussion  
### The Relationship Between Fractal Dimension, Porosity, and Permeability  
The complexity of the reservoir's pore structure and the heterogeneity of the reservoir are closely related to the fractal dimension of the reservoir, which also has a certain impact on the reservoir's physical properties. Generally, the larger the fractal dimension D of the reservoir, the stronger the heterogeneity, resulting in lower connectivity, more complex pore-throat structure, and poorer reservoir properties. To specifically study the relationship between fractal dimension, porosity, and permeability, a scatter plot of porosity, permeability, and fractal dimension for the reservoir in the study area was drawn (Figure 7). This study found a certain degree of negative relationship between porosity, permeability, and fractal dimension. As the fractal dimension increased, both porosity and permeability decreased, reflecting that, as the reservoir's pore structure becomes more complex and heterogeneity increases, the connectivity of the reservoir decreases; its pore-flow capacity weakens; and its physical properties worsen.