esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Driver Face Feature Fatigue Detection Algorithm Research
## 4 Experiment and Analysis
### Fatigue Testing Experiments
#### 4.4.1 MAX-MIN  
Threshold Setting  
In this experiment, 12 videos of YawDD drivers were chosen, including both male and female drivers, as well as drivers with and without spectacles. The 12 videos of drivers acquired in the driving environment were used as the basis for defining the _MAX-MIN_ threshold. These videos featured a lot of awake and exhausted phases.  
Furthermore, the parameters undetected and false detections were established for optimization in order to reduce the _MAX-MIN_ threshold value's error rate. False detection is the opposite of undetected, which indicates that the system wrongly believes the driver is fatigued even if they are not. Undetected refers to a situation when the driver is exhausted, but the system is unable to detect it.  
As demonstrated in Figure 10, the threshold value of _EAR/EAR-MAX_ is 0.5, which corresponds to the lowest false detection rate and failure rate of driver tiredness detection. The lowest rates of false detection and non-detection of driver weariness occur when _MAR/MAR-MIN_ is 3 or 4. The mouth opens and shuts while driving much less frequently than the eyes do. As a result, in the actual experiment, the mouth was more susceptible to influence than the eyes. As a result, eye detection likewise has a much higher proportion of false positives than mouth detection. The threshold value of _EAR/EAR-MAX_ is set lower, and the threshold value of _MAR/MAR-MIN_ is set higher in order to reduce false detections and missed detections in eye detection. When considered together, the threshold-setting scheme is as follows:  
Figure 10: Wrong and missed detection experiments for different ratios: (**a**) shows the wrong and missed detection images under the different _EAR_ and _EAR-MAX_, and (**b**) shows the wrong and missed detection images under different _MAR_ and _MAR-MIN_.  
Figure 9: Face localization, key point detection, and detection of the eyes and mouth.  
(1) The driver is in a fatigued driving state if the ratio of _EAR/EAR-MAX_ is less than 0.5.  
(2) The motorist is deemed to be operating their vehicle while fatigued if the _MAR/MAR-MIN_ score is higher than 3.2.  
Precision, recall, and F-score evaluation measures were utilized to gauge the _MAX-MIN_ algorithm's superiority and confirm the scheme's precision. The findings demonstrate that the _MAX-MIN_ algorithm used in this paper's driver fatigue state detection has a detection precision of 98.8%, a recall rate of 90.2%, and an F-score of 94.3%.
## 2 Materials and Methods
### Laser Irradiation  
A custom-built laser irradiation setup utilizing an ultra-short titanium-sapphire laser (FEMTOPOWER(tm) Compact(tm) PRO HE, FEMTOLASERS Produktions GmbH, Vienna, Austria) was employed to irradiate the surface of dental samples. The system delivered linearly polarized 30/s laser pulses at a central wavelength of 800 nm, with a pulse repetition rate of 1 kHz and a pulse energy of 0.8 mJ per pulse. A protected aluminum 90\({}^{\circ}\) off-axis parabolic mirror (MPD149-P01, Thorlabs Inc., Newton, NJ, USA) with a focal length of 100 mm was used to focus the _fs_-laser beam onto the samples.  
To ensure precise beam alignment, a CMOS (Complementary Metal-Oxide-Semiconductor) camera beam profiler with a resolution of 2448 \(\times\) 2048 pixels (CMOS BC207 VIS(M), Thorlabs Inc., Newton, NJ, USA) was utilized to diagnose the beam profile near the focal point. This setup ensured near-perfect alignment of the parabolic mirror, minimized beam aberration at the point of focus, and produced a focal spot with a 22 \(\upmu\)m diameter, as measured using the CMOS camera at \(1/e^{2}\) of the maximum intensity, with an \(M^{2}\) value of 1.3 for all experiments.  
The sample blocks were positioned normal to the _fs_-laser beam and mounted on a linear translation stage, with the X-axis position controlled by a high-precision motorized stage (OptoSigma Corp., Santa Ana, CA, USA) offering a minimum incremental motion of 0.1 \(\upmu\)m. No self-phase modulation or filamentation was observed in front of the focal point at the irradiation site during the experiments.  
The samples were subjected to _fs_-laser irradiation at an average power of 76 mW, corresponding to laser fluence of 20 J/cm\({}^{2}\). The laser parameters employed in this study are shown in Table 1.  
Each sample was subjected to five separate _fs_-laser irradiation exposures under the given sample translational speeds (also known as sample speed) and laser pulse numbers. The resultant linear irradiation exposure areas/sections on the sample surface are referred to in the text as tracks.
## 2 Materials and Methods  
### Zeolitites  
The natural K-rich, Na-poor zeolite used in this study is a byproduct from a quarry located near Sorano village (Italy, Grosseto province) (Figure 1, 42\({}^{\circ}\)41'20.65\" N; 11\({}^{\circ}\)44'26.29\" E) that is mainly exploited to obtain blocks and bricks for construction and gardening. It is a low-cost, natural and eco-friendly granular by-product of the quarrying activity, part of a large zeolitized pyroclastic deposit whose total zeolitic content is on average 70% (chabazite, 68.5%; Phillips, 1.8%; Analcine, 0.6%; [10]). The total CEC of the zeolitite is 2170 mmol kg\({}^{-1}\), of which 1460 are due to Ca\({}^{2+}\), 600 to K\({}^{+}\), 70 to Na\({}^{+}\) and 40 to Mg\({}^{2+}\). The dry bulk density and water retention vary with grain size from 870 kg m\({}^{-3}\) and 48.4% for \(\mathcal{O}<\)3 mm to 560 kg m\({}^{-3}\) and 34.2% for \(\mathcal{O}\) 3-6 mm, respectively.  
The grain size distribution of the employed zeolites are reported in Figure 2. Part of the NZ was subjected to an enrichment process, which allowed the saturation of the zeolitite with NH\({}_{4}\)\({}^{+}\), creating the NEZ. The enrichment process was carried out by static mode in a prototype tank where the zeolitite is mechanically mixed with pig slurry (average NH\({}_{4}\)\({}^{+}\) content of 2000 mg L\({}^{-1}\)) at specific solid/liquid ratio, stirring and resting times, gaining between 400 and 800 mmol of NH\({}_{4}\)\({}^{+}\) per kg of material [23]. N and C pools of this material are reported in Table 1.  
Figure 1: Location of the exploited zeolitite quarry and ZeoLIFE experimental field.
## 5 Characterization of Isolated-Aperture Surfaces
### [PERSON]'s Principle  
[PERSON]'s principle was originally derived for optics [37] and [PERSON] developed the EM version of it in [38]. [PERSON]'s principle relates scattering properties of the three radiation problems shown in Figure 14, with a given EM source located in the region \(z<0\):  
1. in free space;
2. there is an infinitely large planar zero-thickness PEC screen having an aperture with arbitrary shape, which is located at \(z=0\) plane;
3. there is a zero-thickness plate made of PMC located at \(z=0\) plane, whose shape and position is exactly same as the aperture in the last problem.  
Figure 12: Surface susceptibilities of the unit cell shown in Figure 11 at 10 GHz: (a) \(\overline{\chi}_{ee}\), (b) \(\overline{\chi}_{mm}\).  
Figure 13: Comparison between simulated and computed transmission coefficients of the unit cell shown in Figure 11 with \(\theta=45^{\circ}\) at 10 GHz: (a) magnitude, (b) phase.  
As discussed in Section 2, the screen and plate are complementary structures that can cover the whole \(z=0\) plane without overlapping. Suppose that the EM fields in the region \(z>0\) of the three cases are denoted as \((\vec{E}_{i},\vec{H}_{i})\), \((\vec{E}_{e},\vec{H}_{e})\) and \((\vec{E}_{m},\vec{H}_{m})\) respectively. [PERSON]'s principle demonstrates that [38]  
\[\vec{E}_{i}=\vec{E}_{e}+\vec{E}_{m} \tag{33a}\] \[\vec{H}_{i}=\vec{H}_{e}+\vec{H}_{m}. \tag{33b}\]  
Thus, the combined transmitted EM fields of the complementary surfaces are equal to the ones of the free-space problem shown in Figure 14a. Since there is no scatterers in Figure 14a, the transmitted fields at \(z=0\) plane are exactly same as the incident fields.  
Now consider the zero-thickness PEC metascreen shown in Figure 15a, where the source is replaced with a plane wave propagating towards the region \(z>0\). Its corresponding complementary metafilm made of PMC is shown in Figure 15b. According to [PERSON]'s principle, along \(z=0\) plane the summation of the transmitted field in these two cases are equal to the incident field. Since both of these two surfaces are assumed to be periodic and infinitely larger at \(z=0\) plane, the field relations can be characterized by scattering coefficients \(\Gamma\) and \(T\). Denoting the transmission coefficients as \(T_{e}^{\text{screen}}\) and \(T_{m}^{\text{film}}\) respectively, it can be readily demonstrated that  
\[T_{e}^{\text{screen}}+T_{m}^{\text{film}}=1. \tag{34}\]  
Through [PERSON]'s principle, relations between the scattering coefficients of PEC metascreen and its complementary PMC metafilm are established.  
Figure 14: [PERSON]’s principle: (**a**) free space, (**b**) perfect electric conductor (PEC) screen, and (**c**) perfect magnetic conductor (PMC) plate.  
Figure 15: Scattering problem of metascreen and metafilm (**a**) PEC metascreen, (**b**) complementary PMC metafilm.
# Study on Surface Damage Induced by High Heavy Layer Movement and Mining-Induced Earthquakes
## 2 Methodology
### Model Construction and Simulation Scheme for Numerical Simulation
#### 2.3.1 Model Construction and Parameter Selection for Numerical Simulation  
Using the 7202 working face as the research context, a corresponding model was constructed along the strike direction based on its geological data and coal seam columnar information. To simplify the modeling process and reduce computational complexity, the influence of working face support was not considered. Rock layer with similar mechanical properties or relatively small thicknesses were grouped together, resulting in 12 distinct groups. In the simulation, rock blocks were modeled using the Mohr-Coulomb model, while joints were simulated using the Coulomb slip model with surface contact. Detailed parameter settings can be found in Table 2.
# Topology Optimization Based Material Design for 3D Domains Using MATLAB
## 3 Model Order Reduction in Material Design Optimization
### Approximate Reanalysis  
In the case of the approximate reanalysis, the reduced basis matrix \(\Phi\) is created at every iteration from scratch. Only the first reduced basis vector \(\Phi_{1}\) and the corresponding stiffness matrix are preserved throughout all iterations of the STO procedure. Thus, the first column of the reduced basis matrix \(\Phi\) (first reduced basis vector) is computed following the expression of Equation (18), and the stiffness matrix \(K_{0}\) is saved.  
\[\Phi_{1}=U_{1}=K_{0}^{-1}\cdot F \tag{18}\]  
Then in every iteration utilizing the reduced basis vector \(\Phi_{1}\) and the stiffness matrix \(K_{0}\), the reduced basis matrix \(\Phi\) is created from the following expression of Equation (19):  
\[\Phi_{i}=K_{0}^{-1}\cdot(F-\Delta K\cdot\Phi_{i-1})\quad i=2\ldots i_{max} \tag{19}\]  
where \(\Delta K\) denotes the difference between matrix \(K_{0}\) and the stiffness matrix of the current iteration; \(i_{max}\) denotes the maximum number of reduced basis vectors to be created. It is worth noticing that the inverse of matrix \(K_{0}\) is computed once at the first iteration of the process and is then used in (19) without the need to be computed again. The reduced basis matrix \(\Phi\) is constructed in every iteration, while it is defined using Equation (19). In addition, the first reduced basis vector \(\Phi_{1}\) needs to be updated during the optimization procedure. Similarly to the other two techniques, the time for updating the reduced basis matrix is controlled by the value of the residual force but can also be set to be performed after a fixed number of STO problem solution iterations or by taking into account the change of the objective function and the design variables. A more detailed review of the approximate reanalysis approach can be found in Ref. [22].
## Materials and Methods
### System Identification and Modelling of Civc/civd  
Although the system under study (the finger/blood circulation) is inherently a non-linear system, the essential perturbation behaviour can often be approximated well by simple linearized Transfer Function (TF) models [20; 23; 24; 25; 26; 27; 28; 29].  
For the purposes of the present paper, the following linear, single-input, single-output (SISO) discrete-time transfer function (DTF) is considered [20],  
\[\begin{array}{l}\text{y(k)}=\frac{\text{b}_{1}z^{-1}+\ldots+\text{b}_{m}z^{- m}}{1+\text{a}_{1}z^{-1}+\ldots+\text{a}_{m}z^{-n}}\text{u(k-\delta)}+(\text{k})= \frac{\text{B}(z^{-1})}{\text{A}(z^{-1})}\text{u(k)}+(\text{k})\\ y(k)=\frac{\text{b}_{1}z^{-1}+\ldots+\text{b}_{m}z^{-m}}{1+\text{a}_{1}z^{-1} +\ldots+\text{a}_{m}z^{-n}}\text{u($k-\delta$)}+\xi(k)=\frac{\text{B}(z^{-1}) }{\text{A}(z^{-1})}\text{u($k$)}+\xi(k)\end{array} \tag{1}\]  
where \(y(k)\) is the finger skin temperature (\({}^{\circ}\)C) and \(u(k)\) is the temperature of the Peltier elements (\({}^{\circ}\)C), while \(A(z^{-1})\) and \(B(z^{-1})\) are appropriately defined polynomials in the backshift operator \(z^{-1}\), i.e., \(z^{-i}y(k)=y(k-i)\) and \(\xi(k)\) is additive noise, a serially uncorrelated sequence of random variables with variance \(\sigma^{2}\) that accounts for measurement noise, modelling errors and effects of unmeasured inputs to the process (assumed to be a zero mean). For convenience, any pure time delay of \(\delta>1\) samples can be accounted for by setting the \(\delta-1\) leading parameters of the \(B(z^{-1})\) polynomial to zero, i.e., \(b_{1}\), \(\ldots\), \(b_{\delta-1}=0\).  
A step-input was applied, as explained earlier, by suddenly decreasing the temperature of the Peltier elements from 20 to 2 \({}^{\circ}\)C. In practice, the transition of the input to the new steady state is taking some time (transition time). However, for convenience, in this paper the step-input was idealised and normalised by assuming that it starts from zero and changes instantaneously at a time equal to zero,  
\[u(t)=\left\{\begin{array}{l}0\text{ \ for }t<0\\ 1\text{ \ for }t\geq 0\end{array}\right. \tag{2}\]  
Figure 3: The different set temperature of the Peltier elements for (**A**) experimental phase (I) and (**B**) phase (II) of the second set of experiments.  
The _Simplified Refined Instrumental Variable_ (SRIV) algorithm was utilised in the identification and estimation of the models (model parameters and model structure) [30; 31].  
Firstly, the appropriate model structure was identified, i.e., the most appropriate values for the triad [\(n\), \(m\), \(\delta\)] (see Equation (1)), where \(n\) and \(m\) are the number of poles and zeros, respectively. Two main statistical measures were employed to determine the most appropriate values of this triad. Namely, the coefficient of determination \(R_{2}^{T}\), based on the response error; and _YIC_ (Young's Information Criterion), which provides a combined measure of model fit and parametric efficiency, with large negative values indicating a model that explains the output data well and yet avoids over-parameterisation [31; 32].
# A Predictive Model to Correlate Amino Acids and Aromatic Compounds in Calabrian Honeys
## 2 Materials and Methods  
### Reagents  
Amino acid standards were acquired from Sigma-Aldrich (Steinheim, Germany). The L-amino acid kit comprised p.a. standards with a purity of 98%. The kit encompassed the following amino acids: aspartic acid (Asp), asparagine (Asn), glutamic acid (Glu), glutamine (Gln), alanine (Ala), arginine (Arg), glycine (Gly), leucine (Leu), histidine (His), hydroxyproline (Hyp), isoleucine (Ile), lysine (Lys), methionine (Met), phenylalanine (Phe), proline (Pro), serine (Ser), threonine (Thr), tryptophan (Trp), valine (Val), and cystine (Cys). Sodium chloride (NaCl) was also procured from Sigma-Aldrich. For the chromatographic analysis, high-grade solvents were employed: acetonitrile (ACN), methanol (MeOH), and water (H\({}_{2}\)O) of MS grade. Furthermore, formic acid, ammonium acetate, and ammonium formate (utilized as eluent additives for LC-MS) were sourced from Honeywell Fluka (Harvey St., Muskegon, MI, USA).
###### Abstract  
When observing the Earth's radiation signal with a geostationary orbiting (GEO) mechanically scanned microwave radiometer, it is necessary to correct the antenna beam pointing (ABP) in real time for the deviation caused by thermal distortions of antenna reflectors with the help of the on-board Image Navigation and Registration (INR) system during scanning of the Earth. The traditional ABP determination and beam-pointing error (BPE) analysis method is based on the electromechanical coupling principle, which usurps time and computing resources and thus cannot meet the requirement for frequent real-time on-board INR operations needed by the GEO microwave radiometer. For this reason, matrix optics (MO), which is widely used in characterizing the optical path of the visible/infrared sensor, is extended to this study so that it can be applied to model the equivalent optical path of the microwave antenna with a much more complicated configuration. Based on the extended MO method, the ideal ABP determination model and the model for determining the actual ABP affected by reflector thermal distortions are deduced for China's future GEO radiometer, and an MO-based BPE computing method, which establishes a direct connection between the reflector thermal distortion errors (TDEs) and the thermally induced BPE, is defined. To verify the overall performance of the extended MO method for rapid ABP determination, the outputs from the ideal ABP determination model were compared to calculations from GRASP 10.3 software. The experimental results show that the MO-based ABP determination model can achieve the same results as GRASP software with a significant advantage in computational efficiency (e.g., at the lowest frequency band of 54 GHz, our MO-based model yielded a 4,730,000 times faster computation time than the GRASP software). After validating the correctness of the extended MO method, the impacts of the reflector TDEs on the BPE were quantified on a case-by-case basis with the help of the defined BPE computing method, and those TDEs that had a significant impact on the BPE were therefore identified. The methods and results presented in this study are expected to set the basis for the further development of on-board INR systems to be used in China's future GEO microwave radiometer and benefit the ABP determination and BEP analysis of other antenna configurations to a certain extent.  
geostationary orbiting (GEO); microwave radiometer; antenna beam pointing (ABP); ABP determination; beam-pointing error (BPE); image navigation and registration (INR); matrix optics (MO); thermal distortion error (TDE)
# A Survey of Security Vulnerability Analysis, Discovery, Detection, and Mitigation on IoT Devices
## 2 Background
### Attack Surface
#### 2.3.1 Attack Surface on the Hardware Layer  
Attack surface on the hardware layer is different from the traditional security field. It mainly includes three aspects: unsafe debugging interface, unprotected flash chip, and leakage of sensitive hardware information.  
1. **Unsafe debugging interface.** When the IoT device is manufactured, the debug interface such as UART is left on the circuit board to facilitate the repairing. If it is no authentication or weak  
Figure 3: Attack surface of IoT device.  
authentication, attackers can obtain high authority shell to modify or replace the firmware by the interface. The unsafe debugging interface is the first item on an IoT security check-list.
2. **Unprotected flash chip.** Because the flash chip is often used to store firmware, it has become the focus of attention. If the chip is not read-write protected, security researchers can read the firmware for analysis or write modified firmware to bypass authentication of interface access.
3. **Leakage of sensitive hardware information.** The hardware circuit layout is not well sealed. Leakage of hardware information such as sounds and power consumption causes a side-channel attack [28; 29; 30; 31], which attackers can acquire important information such as encryption keys.
## 3 Results  
### Model Performance Evaluation Based on Typical Vegetation Indices  
We utilized the spectral reflectance of seven bands from Landsat 9 remote sensing images extracted for four typical periods, and five commonly used vegetation indices for cultivated land quality assessment were selected, namely NDVI, EVI, SAVI, RVI, and DVI. The calculation formulas for these indices are provided in Table 2. The data from the four periods--April, May, August, and October 2022--were used to calculate the five vegetation indices, resulting in a total of 20 model input variables, which served as the input for our model. We compared the performance of our proposed BO-Stacking-TabNet model against other commonly used machine learning models in the field of remote sensing-based cultivated land quality assessment. All models were optimized using Bayesian optimization to achieve the best hyperparameters. Table 4 presents the final performance results. As seen in Table 4, the BO-Stacking-TabNet model outperformed all others in terms of Accuracy, Precision, Recall, and F1 score. The original TabNet model, without any improvements, also demonstrated strong performance, ranking second in terms of Accuracy, Recall, and F1 score. The Random Forest model showed excellent performance in Precision, ranking second in that category, and its Accuracy was relatively good. However, its performance was mediocre in terms of Recall and F1 score. XGBoost exhibited relatively good and stable performance across Accuracy, Precision, Recall, and F1 score. CatBoost, althoughslightly inferior to XGBoost in these four metrics, still showed relatively strong performance. LightGBM, on the other hand, only performed well in terms of Accuracy, ranking poorly in the other three metrics. Gradient Boosting Trees (GBT) performed well in Accuracy, slightly better than Random Forest, but had average performance in the other three metrics. Overall, only the TabNet and BO-Stacking-TabNet models showed consistently superior performance across all metrics, which demonstrates TabNet's excellent capabilities in the evaluation of cultivated land quality based on vegetation indices. Additionally, the BO-Stacking-TabNet model enhanced the original TabNet model's performance, with an increase in Accuracy by 0.7%, Precision by 19.6%, Recall by 0.03%, and F1 score by 1.16%. This confirms the effectiveness of our improved BO-Stacking-TabNet model in enhancing the performance of the TabNet model.