esa-sceva/satcom-chunk-collection huggingface dataset:
# AI- and Deep Learning-Powered Driver Drowsiness Detection Method Using Facial Analysis
## 3 Proposed Methodology
### Eye and Face Detection (Feature Extraction)  
In the feature extraction stage, the focus was on extracting two critical features--the eyes and mouth--from the input photos, which was essential for recognizing eye states (open or closed) and detecting yawning. The duration of eye closure and the frequency of yawns acted as major markers of exhaustion; for instance, if eyes remained closed for over 3 s or if a driver yawned more than three times in 60 s, the system suggested weariness. These limits are based on common drowsiness-related actions but do not account for other environmental factors contributing to driver weariness.  
To perform this, Haar cascade classifiers were applied for object detection, evaluating the intensity distribution of picture pixels. The \"extract face landmarks\" tool was used to illustrate the identification of the mouth and eye regions, obtaining 68 landmark points matching to distinct facial features. These points, given as (x, y) coordinates, assisted in identify certain facial regions. Figure 6A depicts the face extraction process on an input image, demonstrating the facial and mouth region landmarks discovered by the usage of the \"extract landmarks\" function. The centroid of the Region of Interest (ROI), which included the eyes and mouth, was also selected to underline critical detecting aspects. Figure 6B displays the centroid of the ROI covering the mouth and left eye. By focusing on these specific traits, the impact of extraneous information on the training dataset was minimized, boosting the classification models' performance. Overall, the feature extraction stage assisted the training of the classification models for the precise detection of yawning and eye states, boosting efficacy and precision through the use of Haar cascades and face landmark extraction.
## 2 Calculated Methods  
Current work was calculated with the Gaussian software 09 [21]. For D35 and XY1 geometric optimizations (see Figure 1), we used density functional theory (DFT) [22], B3 LYP [23; 24; 25], and 6-31 (d) basis set. Absorption spectra and fluorescence were done with time-dependent DFT (TD-DFT) theory [26] using the Cam-b3 lyp functional [27], 6-31 (d) basis set. The solvent effect (solvent = acetonitrile) with the CPCM model was included in calculation [28]. The ground state geometry of dyes attached to TiO\({}_{2}\) clusters was optimized with the same functional, and 6-31 (d) basis set for N, S, C, H, O, and LANL2 DZ functional [29] for Ti. Electronic transitions of Dye/TiO\({}_{2}\) were calculated with TD-DFT//Cam-B3 LYP and the same setup of basis set as the optimization. The dimer of D35 and XY1 was optimized with the DFT//B3 LYP/6-31 (d), the absorption spectra were calculated with TD-DFT//Cam-B3 LYP/6-31 (d), and the intermolecular CT was estimated from Marcus theory [30]. The electron injection from dye to semiconductor was calculated with the Newns-Anderson model [31; 32]. A 3D cube representation of photon-induced charge using different density was performed to investigate the electronic density change in the smaller molecular solar cell [33].  
Figure 1: Chemical and optimized structures for D35 and XY1, respectively.
# Wire and Cable Quality Traceability System Based on Industrial Internet of Things and Blockchain
## 2 Key Technologies
### Blockchain Technology  
Blockchain technology is a distributed and decentralized database technology with the main features of data decentralization, transparency, security, and immutability [21]. It realizes secure storage, transparent verification, and immutability through encryption algorithms and consensus mechanisms. The blockchain consists of date blocks, each containing a certain number of transaction records, linked together by hash pointers to form a chain. The core concepts are decentralization and consensus mechanisms. Decentralization means that no central authority or third party controls the entire system, but rather it is maintained and managed by multiple nodes in the network. The consensus mechanism is used to ensure that agreement is reached among the nodes in the network to guarantee the accuracy and consistency of the data.
## 3 Results and Discussions
### Effect of Nanoparticles on Microstructural Properties  
#### 3.5.1 X-ray Diffraction (XRD)  
The XRD micrographs for untreated and treated soil samples after 14 days of curing are illustrated in Figure 5. The main minerals identified in the natural and treated soil samples at various 20 angles are quartz, kaolinite and illite. A minor reflection of goethite was also observed. Although no major changes occurred in the diffractograms of the treated soil in comparison to the natural soil, the apparent appearance of several new reflections for the treated soil were observed. These reflections are associated with the formation of cementing gels, including calcium silicate hydrate, calcium aluminate hydrate, magnesium silicate hydrate, and magnesium aluminate hydrate due to the reactions of the calcium and magnesium ions with the silicates and aluminates at the soil surface, as shown in Equations (4)-(7) [40].  
\[Ca^{2+}+OH^{-}+\textit{Soluble Silica} \rightarrow \textit{Calcium Silicate Hydrate}\ (C-S-H) \tag{4}\] \[Ca^{2+}+OH^{-}+\textit{Soluble Alumina} \rightarrow \textit{Calcium Aluminate Hydrate}\ (C-A-H)\] (5) \[Mg^{2+}+OH^{-}+\textit{Soluble Silica} \rightarrow \textit{Magnesium Silicate Hydrate}\ (M-S-H)\] (6) \[Mg^{2+}+OH^{-}+\textit{Soluble Alumina} \rightarrow \textit{Magnesium Aluminate Hydrate}\ (M-A-H) \tag{7}\]  
Hence, the formation of these new phases act as binders to improve the bonding between the soil particles, and consequently caused the increased of the UCS of the treated soil samples. The absence of these reflections in the natural soil indicated that the stabilization process was due to the hydration and pozzolanic reactions.
# A Cooperative Control Method for Excavation Support Robot with Desired Position/Posture
## 2 ESR System Modeling and Description
### Kinematic Model with AHC for ESR
The deflection angle \(\delta_{i}\) of the lateral AHC is expressed as:  
\[\delta_{i}=\arccos(\frac{B-x_{i}-x_{i+4}}{H_{i}})\ (i=1-4) \tag{22}\]  
The distance difference \(d_{x}\) between the two sides edge along the \(X\)-axis is written as:  
\[d_{x}=H_{2}\sin\delta_{2}+\left(\frac{L}{2}-n\right)(\cos\alpha_{L}-\cos \alpha_{R})-\frac{b}{2}(\sin\alpha_{L}-\sin\alpha_{R}) \tag{23}\]  
The top view in the kinematics model shows that the distance \(y_{L}\) from the coordinate origin \(O_{L}\) to the left side of the edge denotes:  
\[y_{L}=\frac{(b+d_{2}+d_{4})\cos\gamma_{L}}{2} \tag{24}\]  
Then, the distance \(y_{R}\) from the coordinate origin \(O_{R}\) to the right-side edge of denotes:  
\[y_{R}=\frac{(b+d_{6}+d_{8})\cos\gamma_{R}}{2} \tag{25}\]  
The distance from the core key position point to space edge side is written as follows:  
\[y_{i}=(d_{i}+b)\cos\gamma\ \ (i=1-8) \tag{26}\]  
Herein, when solving for the distance to the left of the edge, let \(\gamma=\gamma_{L}\) hold. When solving for the distance to the right of the edge, setting \(\gamma=\gamma_{R}\) holds.
## 3 Analysis and Discussion of the Results
### Validation of the CFD Simulation and Analysis of the Loss Behaviour
Figure 12a illustrates the power losses resulting from shock loss (\(P_{\text{loss,St}}\)) as a function of the mass flow-averaged relative velocity at the impeller inlet (\(\overline{w_{1}}\)). The control volume for calculating \(P_{\text{loss,St}}\) ranges from \(0\leq s\leq 0.25\). The parabolic curve with a minimum is clearly shown. It is also evident that a varying offset is superimposed on the shock losses for the different prewhirl body angles. It can be observed that the greatest shock losses occur at negative prewhirl angles. In their respective studies, [PERSON] et al. and [PERSON] et al. demonstrated that the velocities and entropy productions at the inlet of the impeller are dependent upon the flow rate and prewhirl angle [23; 25]. [PERSON] et al. ascribe the augmented entropy production at the blade leading edge to an area of low pressure on the pressure side of the blade [23]. Nevertheless, the effect of the angle of incidence at the leading edge of the blade is not considered. Figure 11b illustrates the leakage flow through the gap for the three investigated swirl angles. Figure 12b illustrates the leakage flow through the gap for the three investigated prewhirl body angles. The leakage flow was plotted depending on the root of the delivery head (\(\sqrt{H}\)). In an earlier publication, we developed a model for this purpose that employs the relationship \(Q_{\text{Sp}}\propto\sqrt{H}\)[26]. The linear relationship is observed well up to \(3.5\,\text{m}^{1/2}\). Due to partial load recirculation, a bending of the curves occurs at higher values. Furthermore, the leakage flow exhibits only slight variations across the prewhirl body angles. In general, the discrepancies between the experimental curves measured for the three prewhirl body angles and the proportionality approach for describing them can be considered to be minimal. Figure 13 illustrates the entropy production on the three blade sections, Span 0.1, 0.5 and 0.9, for the flow rate \(Q=100\frac{\text{m}^{3}}{\text{h}}\) at the three investigated prewhirl body angles, \(\Delta a_{\text{pb}}\in\{-30^{\circ};0^{\circ};+30^{\circ}\}\). Span 0.1 is near the hub, span 0.5 is at the centreline meridian streamline, and span 0.9 is near the shroud. According to Figure 11c, the highest losses occur at the blade inlet and outlet regions. Furthermore, the majority of losses occur on the suction side of the blades, particularly at a span of 0.9 when \(\Delta a_{\text{pb}}=-30^{\circ}\). Figure 12: (**a**) Power loss due to the shock loss (\(P_{\text{loss,St}}\)) over the mass flow-averaged relative velocity at the impeller inlet (\(\overline{w_{1}}\)) for the three prewhirl body angles \(\Delta\alpha_{\text{pb}}\in\{-30^{\circ};0^{\circ};+30^{\circ}\}\); (**b**) leakage flow (\(Q_{\text{Sp}}\)) in the gap plotted against the root of the head (\(\sqrt{H}\)) for the three prewhirl body angles \(\Delta\alpha_{\text{pb}}\in\{-30^{\circ};0^{\circ};+30^{\circ}\}\).
## 4 Characteristics of Different Time Distribution Systems
### Precision Time Protocol
#### 4.2.2 Accuracy  
Any asymmetry in propagation time and variations in transmission delay can degrade the accuracy of offset measurements between two clocks [84]. In practice, the path delay between a master and slave clock is rarely symmetrical.  
PTP clock synchronization accuracy relies on the precision of recorded timestamps. To enhance this accuracy, three key elements have been introduced: hardware timestamping, transparent clocks, and boundary clocks [84].  
PTP employs hardware timestamping instead of software timestamping, allowing devices dedicated to synchronization to achieve higher time resolution. Unlike NTP, PTP devices timestamp the time spent by synchronization messages within each device, accounting for device latency.  
Transparent clocks refer to network devices, such as switches, that facilitate compensation for variable packet delays. These devices provide \"residence time correction\" by adding the time spent within each transparent clock to the correction field of PTP messages, thereby improving delay measurement accuracy across the network [84].  
Figure 8: Basic PTP timing message exchange [80].  
Figure 7: Hierarchy topology of PTP.  
Boundary clocks are used to segment large networks, reducing the load on each PTP master by handling fewer clients. A boundary clock synchronizes its local clock to a PTP master using information from \"Sync\" messages and can act as a surrogate PTP master for downstream clients, as illustrated in Figure 8[84].  
With these design enhancements, PTP achieves synchronization accuracy and precision within the sub-microsecond range, requiring minimal network and computing resources [82]. In well-designed networks, sub-nanosecond time transfer accuracy is attainable.
## 3 Experimental Results and Analysis
### Convergence Speed Analysis  
The convergence curves for the six meta-heuristic algorithms on the 23 benchmark functions are depicted in Figure 3.  
Figure 3: _Cont_.  
Figure 2: Radar chart of sort of algorithm.  
Figure 3: _Cont_.  
As shown in Figure 3, the convergence speed of ISSA in the unimodal functions \(F_{1}\)-\(F_{7}\) is faster than that of the remaining five compared algorithms. In particular, ISSA can converge to the optimal value within 60 times in \(F_{1}\)-\(F_{4}\). This indicates that the introduction of the individual optimal idea of PSO in the position update of the investigator greatly accelerates the convergence speed of the algorithm. For the multimodal functions \(F_{8}\)-\(F_{13}\), the convergence speed of ISSA is also better than the remaining five comparison algorithms. Among the fixed-dimensional functions \(F_{14}\)-\(F_{23}\), except for \(F_{14}\), the convergence rate of ISSA is not weaker than that of PSO, Gold-SA, HHO, GTO, and SSA. In summary, the improved ISSA has a faster convergence speed.
# Towards Productive and Ergonomic Order Picking: Multi-Objective Modeling Approach
## 1 Introduction
### Goal and Structure  
In the present paper, we consider the overall manual man-to-goods order picking activity of small boxes in various dimensions and masses from a multi-shelf warehouse rack through time, energy expenditure and health-risk viewpoints. It is an environment that has not yet been covered by previous research in terms of a comprehensive approach from economic and ergonomics perspectives. For health risk modeling OWAS method is used as it has already been shown to be appropriate in previous research.  
We extend research work in the area of storage assignment models and contribute to the literature by:  
* Considering overall order picking activity of small boxes in various dimensions and masses from a multi-shelf warehouse rack through time, energy expenditure and health-risk viewpoints. The inclusion of boxes with different masses and dimensions and different stock structures in terms of the share of light and heavy boxes in the total stock is original and provides managerial insight into the effects of using distinctive objective optimization functions;
* Developing a solution to a storage assignment problem using a multi-objective model including time, energy expenditure and health risk based on binary integer linear programing with the possibility of a different weighting of individual optimization criteria;
* Comparing the results of the popular with managers time based-objective function with the newly developed multi-objective function including time, energy expenditure and health risk in terms of the occurrence of situations that may endanger the physical health of order picker;
* Considering the applicability of the developed one- and multi-objective functions.  
The paper structure is as follows: Section 2 defines the problem and develops the multi-objective model. The case study is described in Section 3, and the methodology for the experiment in Section 4. Section 5 is organized around results. The paper ends with a discussion in Section 6 and conclusions in Section 7.
# EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
## 3 Materials and Methods
### Image Segmentation and Postprocess  
For visual semantic segmentation, two well-established networks are utilized to identify and isolate relevant entities. DeepLabv3 [22] is employed due to its highly regarded performance in semantic segmentation, specifically for extracting feature semantics related to poles and traffic signs. LaneNet [23] is utilized for segmenting lane markings. The Cityscapes dataset [24] and CULane dataset [25] are utilized to construct the initial training dataset.  
Post-processing techniques, such as line fitting, are applied to refine the results of image semantic segmentation, particularly for lane markings and poles. Due to occlusions by vehicles, lane markings often experience a significant reduction in valid features, especially when dashed lines are occluded. Line fitting is an efficient and effective postprocess means for these kinds of cases. The segmented lane markings are projected to inverse perspective mapping, and lane counts are determined based on peaks along the x-axis. Lines are then fitted based on the collection of pixels within each lane, followed by transforming the fitted lane markings back to the original projective view. Similarly, line fitting is employed as a repair mechanism to address occlusions in the middle part of poles. Post-processing techniques are utilized to obtain rectangle bounding boxes for the traffic signs.  
Furthermore, it is important to note that the segmentation results of poles and traffic signs, due to their small size, often exhibit increased noise to maintain a high recall ratio. However, these occasional miss-detections are relevant to the registration process, as they directly impact the accuracy of object alignment and localization.  
Figure 3: Detailed Flowchart of EOR.
# Link Connectivity and Coverage of Underwater Cognitive Acoustic Networks under Spectrum Constraint
## 4 Analysis of Link Connectivity and Probability of Coverage
### Spectrum Availability  
Next, we analyze the spectrum availability of a pair of SUs. In UCANs, in order to avoid interference from SUs to PUs, a spectrum sensing process has to be conducted at SUs before transmissions are initiated [5; 30]. During the spectrum sensing process, we assume that if the SNR  
Figure 2: Maximum communication distance \(r_{\max}\) (km) with different spreading factor \(k\) and wind speed \(w\) (m/s) when \(s=1\), \(P_{s}=100\) dB, and \(\delta_{s}=20\) dB. (**a**) \(k=1\); (**b**) \(k=2\).  
(dB) at an SU is no less than a detection threshold \(\delta_{d}\) (dB), the SU must be silent (i.e., it cannot transmit data). In other words, an SU cannot have spectrum if the following condition is satisfied:  
\[SNR_{dB}=10\log P_{p}-10\log A(r,f)-10\log N(f)\geq\delta_{d}. \tag{7}\]  
Similar to the calculation of \(r_{\max}\), if we let LHS of Equation (7) be equal to RHS and combine with Equations (A2)-(A5) (defined in Appendixes A and B) and Equation (7), we can then obtain the _detection range_ of an SU, which is defined as the maximum distance that an SU can detect PUs, denoted by \(r_{d}\). Similar to \(r_{\max}\), we can obtain the numerical values of \(r_{d}\). In particular, Figure 3 shows a detection region of a pair of SUs (e.g., SU\({}_{1}\) and SU\({}_{2}\)). As shown in Figure 3, the detection region of each SU is a circle with a radius of _detection range_\(r_{d}\). We observe that a pair of SUs can have the spectrum if both of the following conditions are satisfied:  
1. No PUs in the detection region of SU\({}_{1}\);
2. No PUs in the detection region of SU\({}_{2}\).  
According to the fact that PUs follow HPPP, we can have the probability that two SUs can have the spectrum, denoted by \(p_{spe}\), as follows:  
\[p_{spe}=e^{-S\lambda_{p}}, \tag{8}\]  
where \(S\) is the area of the detection region of two SUs, depicted as the blue region as shown in Figure 3. We can see from Figure 3 that \(S\) is a piecewise function of the distance \(r\). Then, \(S\) can be expressed as:  
\[S=\left\{\begin{array}{ll}\pi r_{d}^{2}\,+\,(\pi-\theta_{0})r_{d}^{2}\,+\,r _{d}\sin(\frac{\theta_{0}}{2})r&r\leq 2r_{d}\\ 2\pi r_{d}^{2}&r>2r_{d}\end{array},\right. \tag{9}\]  
where \(\theta_{0}=2\arccos\frac{r}{2r_{d}}\).  
After combining Equation (9) with Equation (8), we can have \(p_{spe}\) as follows:  
\[p_{spe}=\left\{\begin{array}{ll}e^{\left(\pi r_{d}^{2}+(\pi-\theta_{0})r_{d} ^{2}+r_{d}\sin(\frac{\theta_{0}}{2})r\right)\lambda_{p}}&r\leq 2r_{d}\\ e^{2\pi r_{d}^{2}\lambda_{p}}&r>2r_{d}\end{array}.\right. \tag{10}\]  
Figure 3: Detection region of a pair of SUs.
## 4 Experimental Evaluation
### Threshold Awareness Results  
The following simulations evaluate and acquire the best threshold index number that reduces miss frame probability. Therefore, 700 data frames, i.e., TIBWB-OFDM blocks, were randomly interleaved with 300 equally size blocks of additive white Gaussian noise (AWGN) with independent uncorrelated samples. All preamble signals were identical, which reflects the same transmitted high correlation peak amplitude. A critical performance variable is the selection of the threshold cross-correlation level, used to indicate the beginning of a new frame. Hence, the used value should be as accurate as possible to achieve acceptable frame detection that subsequently influences BER system performance and error awareness.  
Figure 4 shows the probability of correct detection as a function of the setup threshold level. This value should be selected in the range of 25%-30% of the maximum of the autocorrelation \(P_{zc}\), in which the probability of correct detection is almost perfect. When the threshold parameter is near \(P_{zc}\), no data frames are detected because the signal suffers from fading (i.e., variable gain attenuation due to multipath effect), with the cross-correlation value hardly reaching the \(P_{zc}\) value. In this case, since almost all frames are marked as noise, the probability of right detection is 30%, corresponding to the fraction of sent noise frames. By settling the threshold too low, on the other hand, the opposite effect occurs, with almost all frames being detected as data. Here, coexisting noise peaks combined with those of the data sequences, both above the threshold, made it impossible to detect a single peak value, making the frame-recognition task very challenging to accomplish. However, in this scenario, a noteworthy difference was visible between the nonamplified ZC sequences and the remaining sequences. Since the threshold is defined as a percentage of \(P_{zc}\), for the 3 and 6 dB cases, the threshold was much higher, precluding any noise frame to be marked as data; thus, the probability of correct detection was above 30%.