esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting Deceptive Behaviours through Facial Cues from Videos: A Systematic Review
## 5 Discussion
### RQ4: Which Datasets for Automated Deception Detection Are Used?
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Dataset** & **Availability** & **Size** & **Source** \\ \hline [PERSON] and [PERSON], 2020 [23] & Real-life trial & Available upon request & 121 videos (61 deceptive and 60 truthful) & “The Innocence Project” website \\ \hline [PERSON] and [PERSON], 2023 [24] & [PERSON] et al.’s database & Not available & 102 participants & Spontaneous interview \\ \hline [PERSON] et al., 2021 [25] & [PERSON] et al.’s database & Available on request & 32 video clips of 16 individuals & The game show “the Moment of Truth.” \\ \hline [PERSON] et al., 2015 [26] & Deceit-detection test & Not available & 12 video-recordings & Storytelling of life moments \\ \hline [PERSON] et al., 2022 [27] & [PERSON] et al.’s dataset & Not available & 62 videos of Italian volunteer participants & Spontaneous interview \\ \hline [PERSON] and [PERSON], 2016 [28] & [PERSON] and [PERSON]’s dataset & Not available & 324 video clips & YouTube \\ \hline [PERSON] et al., 2019 [14] & Real-life trial & Available upon request & 121 videos (61 deceptive and 60 truthful). & “The Innocence Project” website \\ \hline [PERSON] et al., 2021 [29] & [PERSON] et al.’s database & Not available & 888 video clips from 102 participants & Spontaneous interview \\ \hline [PERSON] and [PERSON], 2014 [30] & [PERSON] and [PERSON]’s dataset & Not available & 324 video clips & YouTube \\ \hline [PERSON] et al., 2021 [31] & Low-stakes deceit (LSD) dataset & Not available & 624 recordings of 312 participants & Interviewees under a controlled environment \\ \cline{2-5}  & Real-life trial & Available upon request & 121 videos (61 deceptive and 60 truthful). & “The Innocence Project” website \\ \hline [PERSON] and [PERSON], 2020 [32] & Real-life trial & Available upon request & 121 videos (61 deceptive and 60 truthful). & “The Innocence Project” website \\ \hline [PERSON] et al., 2014 [33] & [PERSON] et al.’s dataset & Not available & 242 videotaped interviews of 121 interviewer-interviewee pairs & Spontaneous interview \\ \hline [PERSON] et al., 2020 [34] & Box of Lies corpus & Publicly available & 68 recorded clips (39 truths and 29 lies) & “Box of Lies” game \\ \hline \multirow{5}{*}{[PERSON] et al., 2020 [35]} & Real-life trial & Available upon request & 121 videos (61 deceptive and 60 truthful). & “The Innocence Project” website \\ \cline{2-5}  & Opinion dataset & Not available & Not available & Storytelling of movies \\ \cline{2-5}  & Crime dataset & Not available & Not available & Interviewees under a controlled environment \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Datasets used by the selected studies.
## 3 System Model and Problem Formulation
Since the number of tasks served by a MECS \(i\) during a time slot is \(\text{min}\{Q_{i}(t)+\lambda_{i}(t),C_{i}\}\), the load of the MECS during a time slot becomes \(\rho_{i}(t)=\text{min}\{Q_{i}(t)+\lambda_{i}(t),C_{i}\}/C_{i}\). Accordingly, the amount of energy consumed by the MEC system during a time slot is determined as  
\[\zeta(t)=\sum_{i\in\mathcal{M}}\eta_{i}(t)\zeta_{i}(t). \tag{8}\]  
Our goal is to design a controller that can find an optimal server status vector \(\eta_{i}(t)\) at the beginning of each time slot. In other words, we aim to minimize \(\zeta(t)\) while satisfying \(\delta(t)\leq\delta_{th}\). Formally, our problem is stated as follows:  
\[\eta^{*}(t)=\operatorname*{arg\,min}_{\eta(t)}\zeta(t)\] Figure 1:0 Figure 1:1  
The second constraint indicates that at least one MECS should be in an active state for each time slot. The formulated problem is a constrained combinatorial optimization problem. The exact solution to such a problem can be found using the ES (Exhaustive Search) technique. However, as the number of possible combinations increases exponentially with the number of MECSs in the system, the time required for ES to find a solution also increases exponentially with \(|\mathcal{M}|\). To address this scalability issue of ES, metaheuristic optimization techniques such as GA (Genetic Algorithm) have been used commonly. These techniques find approximate solutions to constrained combinatorial optimization problems through an iterative process. Therefore, as the number of iterations increases, the search space expands, which increases the probability that the solution found by GA will match the solution found by ES. However, increasing the number of iterations leads to greater computational overhead and search time. On the contrary, reducing the number of iterations to decrease search time significantly increases the probability that the solution found by GA is different from the optimal combination. To resolve these issues and determine solutions to the formulated problem more quickly and accurately than GA, we devise a controller by adopting deep-learning models.
# Laser Cooling beyond Rate Equations: Approaches from Quantum Thermodynamics
## 3 Results
For each Rabi frequency, we show results computed using the full Bloch-Redfield equation (solid black curve), a phenomenological Lindblad equation (dashed orange curve), and the Bloch-Redfield equation in the secular approximation without the principal value terms (dashed black curve). The Rabi frequencies \(\Omega\) are: (**a**) 0.01, (**b**) 0.1, (**c**) 0.5, and (**d**) 1.0. In Figure 4 we compare the cooling spectra predicted by the full Bloch-Redfield equation with those obtained from the exact numerical method [46]. The numerical method simulates the time-evolution of the open quantum system, using discrete timesteps. For these simulations, we have taken a timestep \(dt=0.05\), and computed the heat current, at a time \(t=30.0\), from the difference in the heat transfer at two times. The mean heat transfer is computed by evaluating the annotated reduced density matrix, \(\rho_{u,S}(t)\), and computing the finite difference approximation to the derivative in Equation (15) from the values of \(\operatorname{Tr}\rho_{u,S}(t)\) at \(u=0.05\) and \(u=0.0\). The numerical accuracy and convergence of these simulations involve two further parameters: a maximum number of timesteps retained in the influence functional, \(K\), and a cut-off parameter controlling the truncation of the singular-value decompositions. We take \(K=100\), and use a cut-off of \(10^{-7}\). We see from Figure 4 that the Bloch-Redfield equation is in excellent agreement with the numerical results. The non-Lorentzian behavior of the cooling profile on the high-energy side, predicted by the full Bloch-Redfield and secular equations, is present. There is a slight overestimate of the cooling power in the tails of the profiles, which we believe is because of the heat current has not yet reached its steady-state value at those small values of the cooling power.
# Radiometric Identification of Signals by Matched Whitening Transform
## 4 Results
### Class Confusion Matrices
This length generates 40 blocks that are used to get classification statistics in the form of confusion matrices. Table 3 shows the results @ SNR = 15 dB added Gaussian noise. This is above and beyond what is already in the data. All sources are identified correctly except for KRATOS RTSim which is identified as Teledyne Paradise. Even then, the 2.5% difference is well within the statistical variations of the run. The present correct classification numbers for each source show a large drop compared to Table 2 but the majority voting scheme still makes the correct decision, albeit at a reduced margin. For example, Agilent data are correctly associated with Agilent only 30% of the time but that is still higher than any others. Tables 4 and 5 repeat the process for SNR = 5 dB and 0 dB. Even though the rates and margins are lower, the majority vote scheme still picks the correct class. When margins are low, statistical variability plays a role in making correct source identification. Notice that the large margin of USRP in Table 2 helps it largely maintain correct identification even at 5 dB SNR in Table 4. To show how dire the situation is, Figure 10 shows the constellation in SNR = 5 dB noise. The lack of identifying features is evident throughout. Note that RTSim and Paradise are tied. This difficulty is of course reflected in Table 4 as well but correct identification is still possible. Four out of five sources are correctly identified and the fifth one is tied. Table 5 is the extreme case of SNR = 0 dB. EBEM and Paradise are still correctly identified. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Agilent** & **EBEM** & **Paradise** & **RTSim** & **USRP** \\ \hline Agilent & **22.5** & 20.0 & 7.5 & 15.0 & 17.5 \\ Viasat EBEM & 10.0 & **80.0** & 2.5 & 0 & 5.0 \\ Teledyne Paradise & 15.0 & 0 & **52.5** & 12.5 & 10.0 \\ KRATOS RTsim & 12.5 & 20.0 & **12.5** & **12.5** & 15.0 \\ USRP & 35.0 & 2.5 & 20.0 & 2.5 & **37.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Same as Table 3 @ SNR = 5 dB. Figure 10: Constellations in SNR = 5 dB noise. Constellation structures have practically disappeared, yet the source identification is still largely possible. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Agilent** & **Viasat EBEM** & **Paradise** & **RTSim** & **USRP** \\ \hline Agilent & **91.4** & 0 & 0 & 8.6 & 0 \\ EBEM & 0 & **100** & 0 & 0 & 0 \\ Teledyne Paradise & 0 & 0 & **77.1** & 0 & 22.9 \\ KRATOS RTsim & 0 & 0 & 0 & **100** & 0 \\ USRP & 0 & 0 & 0 & 0 & **100** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Five source cross classification rates.
###### Abstract  
The objective of this study was to elucidate the transmission pathways and spatial distribution of potential source areas for PM\({}_{2.5}\) pollution in Chenzhou City across diverse seasons in southern Hunan. Utilizing Sampling Kriging interpolation analysis, we modeled the temporal and spatial oscillations of PM\({}_{2.5}\) concentrations in Chenzhou City, complemented by HYSPLIT air mass backward trajectories. Furthermore, the model conducted cluster analysis to identify discernible patterns. Our findings unveiled marked seasonal variations in PM\({}_{2.5}\) concentrations within Chenzhou City. The pinnacle is discerned during winter (75.13 \(\mu\)g\(\cdot\)m\({}^{-3}\)), whereas the nadir is pronounced in summer (27.64 \(\mu\)g\(\cdot\)m\({}^{-3}\)). Notably, the PM\({}_{2.5}\)/PM\({}_{10}\) ratio surpasses 0.55 during both autumn and winter. Spatially, Chenzhou exhibits an annual average distribution of PM\({}_{2.5}\) pollution characterized by a gradient \"from north to south in the western and central sectors, tapering towards the east\". Pollution source analysis suggests that PM\({}_{2.5}\) pollution in Chenzhou City is predominantly ascribed to local emissions. Transmission pathway analysis reveals that the primary transmission corridors, spanning northwest Guangdong, southwestern Henan, Hubei, southern Anhui, and specific zones of southwestern Jiangxi, consistently align with external PM\({}_{2.5}\) pollution sources affecting Chenzhou City throughout the year. Noteworthy seasonal disparities emerge in the spatial distribution and contribution of potential source regions. During spring, autumn, and winter, the predominant contributing regions are primarily located in adjacent provinces. In contrast, during summer, regions with relatively elevated values predominantly streak across the central and western sectors of Jiangxi and the southeastern Hunan region. A comprehensive examination of the seasonal distribution patterns, potential transmission routes, and likely contributing sources of PM\({}_{2.5}\) in Chenzhou City can offer invaluable insights for regional atmospheric environmental governance. Furthermore, it underscores the paramount importance of collaborative regional strategies directed towards the prevention and control of PM\({}_{2.5}\) pollution.  
2024
# Investigation on Intelligent Early Warning of Rock Burst Disasters Using the PCA-PSO-ELM Model
## 2 Model Principles and Methods
### PSO Method  
The particle swarm optimization (PSO) method symbolizes a random search algorithm that simulates the bird foraging behavior [35; 36]. Supposing there is a swam composed of M particles in the D-dimensional search space, the spatial position of the i-th particle can be expressed as \(X_{i}=(x_{i1},x_{i2},\cdots,x_{iD})\). In the mean time, the particle movement speed can be signified as \(v_{i}=(v_{i1},v_{i2},\ldots,v_{iD})\), while the optimal position experienced by the particles refers to \(P_{i}=(p_{i1},\cdots,p_{iD})\) and the optimal position in history underwent by the swam can be denoted as \(P_{g}=\big{(}p_{g1},p_{g2}\cdots,p_{gD}\big{)}\). Thus, the updated formula for the velocity and position of the d-th dimension (\(1\leq d\leq D\)) of each generation of particle i can be simplified as follows.  
\[v_{id}^{(i+1)}=wv_{id}^{(i)}+c_{1}r_{1}\Big{(}p_{id}-x_{id}^{(i)}\Big{)}+c_{2 }r_{2}\Big{(}p_{gd}-x_{id}^{(i)}\Big{)} \tag{2}\]  
\[x_{id}^{(i+1)}=x_{id}^{(i)}+v_{id}^{(i+1)} \tag{3}\]In the formula, \(w\) represents the inertia weight; \(c_{1}\) and \(c_{2}\) indicate the learning factors, respectively; and \(r_{1}\) and \(r_{2}\) depict the two random numbers that vary within [0, 1]. The flowchart of the PSO algorithm is exhibited in Figure 1.
## 3 Experimental Results and Analysis
### Experiments  
The hardware and software configuration is described as follows: AMD Ryzen 9 3950X 16-Core Processor 3.50 GHz, 64 GB RAM, GeForce RTX 3090, Windows 10. The environment configuration is set as follows: CUDA Version 11.1, Python3.7.10, PyTorch1.8.0. The selected insulator data set is a public one with 6860 images [[https://github.com/heitorcelix/public](https://github.com/heitorcelix/public)]([https://github.com/heitorcelix/public](https://github.com/heitorcelix/public))insulator-datasets (accessed on 12 April 2021). The data set occupies a storage space of 3.3 GB. The data set consists of real images and synthetic images, and has been enhanced. This experiment uses 6174 pictures as the training set and 686 pictures as the testing set. An example of the image is shown in Figure 2. Since the format of the network data set is a coco format, the first step is to write a script to convert it to a VOC2012 format. In the experiments, although the insulator data set used is public, no literature related to this data set is available. Therefore, we have to compare the performance of the three networks, the proposed faster R-CNN with Resnet50+ECA-net, the original faster R-CNN, and faster R-CNN with Resnet50, on the public data set mentioned in this paper.
## 5 Experiments Results
### Comparison with Other Classification Methods  
To demonstrate the effectiveness of the proposed hybrid DenseNet, our model is compared with several widely used methods in active HSI classification and the state-of-the-art methods, including (1) the SVM with a radial basis function kernel (SVM-RBF) [31], (2) the 3D CNN established in [17] and (3) the spectral-spatial residual network (SSRN) proposed in [18]. For all methods, the input data is processed by PCA that reduces the spectral bands from 174 to 40. For SVM-RBF, a spectral-based classifier, we simply feed the PCA processed data to SVM-RBF pixel by pixel. For 3D CNN, SSRN and the hybrid DenseNet, we use \(7\times 7\times 40\) neighbors of each pixel as the input.  
The batch size is set to be 32 and the SGD optimizer is adopted. The learning rate is set to be 0.001 and we train each model for 50 epochs with 100% training samples provided by spectral library. Experiments are run for 5 times, and the mean classification results in percent are shown in Table 6. The classification maps of different methods are shown in Figure 10. As the table and figure shows, all methods could get a relatively good performance for those objects under illumination of SC laser, which demonstrates that the active HSI has the ability to distinguish different materials accurately.  
However, although SVM-RBF could get comparable or even higher performance compared to other deep-learning-based methods, its performance on classifying Class 6 (green plastic bottle cap) is poor, only 62.26%, which restricts its application in active HSI classification. Compared with 3D CNN, the proposed hybrid DenseNet has a deeper architecture, which gives it a stronger ability for feature extracting and learning, and thus obtains better performance. As for SSRN, it has deep network and could achieve better performance than 3D CNN. However, it firstly extracts spectral features then extracts spatial features; the extracted spectral features may be destroyed when extracting spatial features, which  
Figure 9: Hybrid DenseNet framework for active HSI classification.  
may influence its performance. Regarding the proposed hybrid DenseNet, it has deeper network than 3D CNN and extracts spectral features and spatial features parallelly which can preserve spectral and spatial features well. Although the classification results for some classes and metrics are not the highest, hybrid DenseNet achieves an acceptable and agreeable performance for all indexes, which proves its effectiveness on classifying active HSI data. In addition, for all methods, misclassification appears frequently at the areas where light intensity is weak, such as the edge of SC laser spot and shadow areas. The issue about illumination condition still needs further explorations and improvements.
## 2 Materials and Methods
### Tooth Color Assessment  
To ensure an equitable distribution of teeth for each experimental group, a spectrophotometer device (VITA Easyshade Advance 4.0, VITA Zahnfabrik, Germany) was used to measure initial color values before the experiment, thus calibrating the groups. An equal number of color tones were distributed to each group (three teeth, color A2; two teeth, color B1; two teeth, color B2; and three teeth, color A3 in the same experimental group) [24]. Teeth were placed against an absolute white background (standard calibration tile according to Commission Internationale de l'Eclairage [CIE] L*: 93.84, a*: 1.48, b*: 3.76), and the central area of the buccal surface was subjected to color measurement.  
Care was taken to ensure the device's tip made full contact with the flattest area of the tooth's buccal surface, limiting the measurement to the central area only. The same researcher performed all measurements, verifying the spectrophotometer's tip contact with the tooth before each measurement. The device was specially calibrated according to the manufacturer's instructions before measuring each new sample group, and the measurements were subsequently repeated three times for each tooth by the same operator ([PERSON]).  
The color evaluation was conducted following the CIE Lab* color space parameters. The lightness of the object is indicated by the L* coordinate, which scales from white (0) to absolute black (100). The a* coordinate signifies the chromaticity on a green (\(-\)) to red (\(+\)) axis, with values typically ranging from \(-\)70 for green to +70 for red. The b* coordinate measures chromaticity on a blue (\(-\)) to yellow (\(+\)) axis, where values range from \(-\)80, indicating blue, up to +100, signifying yellow.  
Following the baseline assessment (T0), the teeth were immersed in artificial saliva and placed in an incubator set at 37 \({}^{\circ}\)C to mimic the oral environment and aging procedures. This saliva was refreshed every seven days. Subsequent color evaluations were conducted after (T1) 7, (T2) 30, (T3) 90, and (T4) 180 days to benchmark against the initial L*a*b* values (baseline), enabling comparisons over time and assessments of color stability or change. The measurements were performed in accordance with the CIEDE2000 (\(\Delta\)E00) system. \(\Delta\)E00 was calculated using the following formula [25; 26]:  
\[\Delta E_{00}=\left[\left(\frac{\Delta L^{\prime}}{K_{L}S_{L}}\right)^{2}+ \left(\frac{\Delta C^{\prime}}{K_{C}S_{C}}\right)^{2}+\left(\frac{\Delta H^{ \prime}}{K_{H}S_{H}}\right)^{2}+R_{T}\left(\frac{\Delta C^{\prime}}{K_{C}S_{C} }\right)\left(\frac{\Delta H^{\prime}}{K_{H}S_{H}}\right)\right]^{1/2}\]
## 2 Materials and Methods
### Modification Scheme and Test Arrangements
#### 2.2.3 Disintegration Test  
The disintegration test measures the disintegration ratio and reveals the soil behavior when immersed in tap water, which is the main method to study the disintegration resistance of subgrade fillers. The soil samples were prepared by static compression due to the controllable wet density and compactness of the soil samples. This avoids the deficiency of inconsistent density between soil layers, usually caused by the proctor compaction method. The ring knife was placed in the compactor, and the precisely configured treated soil with the optimum moisture content was also put into it. After compaction, a specimen that had a diameter of 61.8 mm, a height of 20 mm and a volume of 60 cm\({}^{3}\) was obtained. Then, the specimens were sealed with plastic sheeting and placed in a constant 20\({}^{\circ}\) temperature box. Given the compactness of the subgrade filling in the actual project, the disintegration test of untreated and treated soils with the addition of 4%, 6% and 8% was carried out under 90% and 100% compactness.  
A self-made disintegration system (see Figures 4 and 5), which is mainly composed of an acrylic water tank (length 38 cm, width 26 cm, height 20 cm), metal net (length 25 cm, width 20 cm), wire, dynamometer, data collector and computer, was adopted. This disintegration device eliminates the inaccurate dynamic measurement of the measuring cylinder device recommended by \"Test Methods of Soils for Highway Engineering\" [40] and allows for different specimen sizes. The soil sample was placed in the center of the hanging basket net, about 20 cm from the bottom of the water tank. The force sensor is connected to the hanging metal net to measure the weight change of the soil sample. After debugging the sensor and data collector, the water tank was quickly filled with tap water, and the data started to be recorded at the frequency of 1 Hz when the soil sample was put into the water. The disintegration process could be observed and photographed until the soil sample is completely disintegrated. The disintegration ratio was calculated as follows:  
\[A_{t}=\frac{F_{0}-F_{t}}{F_{0}-F_{k}}\times 100\%, \tag{5}\]  
where \(A_{t}\) is the disintegration ratio of the sample at time \(t\), \(F_{0}\) is the instantaneous reading of the dynamometer when the soil sample is completely immersed in the water, \(F_{t}\) is the dynamometer reading at time \(t\) and \(F_{k}\) is the dynamometer reading for metal net and wire when no sample is in the water.