esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 5 Metal Oxide Thin Films
### Bismuth Oxide Films  
The formation of Bi\({}_{2}\)O\({}_{3}\) on stainless steel took place by using bismuth(III) nitrate (cationic) and double distilled water (anionic) under 80 deposition cycles. The growth of the films was described by [PERSON] and co-workers [179], as highlighted in Equations (52)-(54).  
\[\mathrm{Bi(NO_{3})_{3}}\mathrm{-}\mathrm{5H_{2}O+N(CH_{2}CH_{2}OH)_{3}}\to \mathrm{[BiN(CH_{2}CH_{2}OH)_{3}]^{3+}+5H_{2}O+3(NO_{3}^{-})} \tag{52}\]  
\[\mathrm{[BiN(CH_{2}CH_{2}OH)_{3}]^{3+}\to Bi^{3+}+N(CH_{2}CH_{2}OH)_{3}} \tag{53}\]  
\[\mathrm{2 Bi^{3+}+6 OH^{-}\to 3H_{2}O+Bi_{2}O_{3}} \tag{54}\]  
XRD studies highlighted the cubic phase; two strong peaks (20 = 28\({}^{\circ}\) and 33\({}^{\circ}\)) appeared. Several peaks could be seen in the FTIR, including 844 cm\({}^{-1}\) (stretching vibration of Bi-O-Bi), 1058 cm\({}^{-1}\) (stretching vibration of Bi-O bond), 3440 cm\({}^{-1}\) (O-H stretching). TEM and SEM analyses revealed well-distributed needles with smaller particle sizes (5-10 nm) on the substrate. The interplanar spacing was 0.312 nm and contributed to the (201) lattice plane as indicated in the HR-TEM image (Figure 9). The findings showed that the best specific capacitance was 329.6 F/g when the scan rate was 5 mV/s (1M Na\({}_{2}\)SO\({}_{4}\) electrolyte), while the retention capacity was 72% after 3000 cycles.
# Chipless Radio Frequency Identification (RFID) Sensor for Angular Rotation Monitoring
###### Abstract  
A novel, chipless, Radio Frequency Identification (RFID) sensor is proposed for monitoring angular rotation. The rotation state is recovered by collecting the cross polar response of a tag, based on a periodic surface composed of a set of dipoles. The encoding mechanism allows the sensor to be very robust, even if it is applied on metallic objects, or in an environment with strong multipath. The proposed sensor does not require a large operational bandwidth. Instead, only a small set of reading frequencies are required. The number of reading frequencies required is dependent on the number of the employed dipoles. It is demonstrated that the rotation state of an object can be monitored within a span of 180 degrees, with up to a three-degree resolution, by employing a chipless RFID sensor comprising of four dipoles. The far field reading scheme and the absence of any electronics device allow the sensor to be employed in harsh environments.  
sensor; chipless RFID; rotation monitoring +
Footnote †: journal: _technologies_
## 2 Structural Reliability  
The theory of structural reliability pertains to the estimation of the failure probability of structural design-related limit-state functions by considering the uncertainties in both the load effects and strength.
# Modeling and Analysis of a Generic Internal Cargo Airdrop System for a Tandem Helicopter
## 3 Flight Dynamics Model  
### Basic Model Descriptions  
In Section 2, the interaction forces and moments between the cargo and the helicopter during the process of the cargo airdrop are obtained. They are represented by \(\mathbf{F}_{\rm L}\) and \(M_{\rm L}\), respectively. These can be incorporated into the tandem rotor helicopter flight dynamics model  
\[\left\{\begin{array}{c}m_{\rm B}\dot{\mathbf{V}}_{\rm B}+m_{\rm B}\mathbf{\omega}_{ \rm B}\times\mathbf{V}_{\rm B}=\mathbf{F}_{\rm B}\\ \dot{\mathbf{H}}_{\rm B}+\mathbf{\omega}_{\rm B}\times\mathbf{H}_{\rm B}=M_{\rm B}\\ \mathbf{\omega}_{\rm B}=T_{\rm Euler}\left[\dot{\theta}_{\rm B},\ \dot{\phi}_{\rm B},\ \dot{\psi}_{\rm B}\right]^{\rm T}\end{array}\right. \tag{17}\]  
where \(m_{\rm B}\) is the mass of helicopter, \(\mathbf{V}_{\rm B}\) the vector of the helicopter velocity, \(\mathbf{\omega}_{\rm B}\) the helicopter angular velocity, \(\mathbf{H}_{\rm B}\) the vector of the angular momentum of the helicopter in the helicopter body axis system (\(\alpha x_{\rm B}y_{\rm B}z_{\rm B}\)), and \(T_{\rm Euler}\) the Euler Transmission matrix.  
In the above formula, the resultant force vector (\(\mathbf{F}_{\text{B}}\)) and resultant moment vector (\(\mathbf{M}_{\text{B}}\)) acting on the helicopter can be written as:  
\[\left\{\begin{array}{l}\mathbf{F}_{\text{B}}=\mathbf{F}_{\text{RF}}+\mathbf{F}_{\text{RR}} +\mathbf{F}_{\text{FUS}}+\mathbf{F}_{\text{G}}+\mathbf{F}_{\text{L}}\\ \mathbf{M}_{\text{B}}=\mathbf{M}_{\text{RF}}+\mathbf{M}_{\text{RR}}+\mathbf{M}_{\text{FUS}}+ \mathbf{M}_{\text{L}}\end{array}\right. \tag{18}\]  
where the aerodynamic force and moment vectors of the front and rear rotors (\(\mathbf{F}_{\text{RF}}\), \(\mathbf{F}_{\text{RR}}\), \(\mathbf{M}_{\text{RF}}\), \(\mathbf{M}_{\text{RR}}\)) are estimated using the Rotor Momentum Theory [39]. The aerodynamic force and moment vectors of the fuselage (\(\mathbf{F}_{\text{FUS}}\), \(\mathbf{M}_{\text{FUS}}\)) are estimated by linear interpolation using the wind tunnel test data [40; 41]. The test was conducted using a rotor-off fuselage at the conditions of different fuselage angle of attack and sideslip angle, both with range from \(-90\) to \(90\) degrees. Because the Reynolds number in the test was not available, the data were normalized by fuselage dynamic pressure in order to be widely used in different flight conditions.
## 3 Results
### Comparative Assessment With MSP
#### 3.3.2 Bilateral Variation in Antegonion  
Considering the significant difference in the anterior mandibular landmark, Me, with MSP in transverse measurement, we conducted a further statistical analysis of the bilateral variation for Ag (posterior mandibular landmark). There was a statistically significant difference (\(p<0.05\)) between the right and left sides for both FH1 and FH2 groups. Despite similar patterns (right \(>\) left) of transverse measurements at Ag among all four groups, FH3 and FH4 showed no statistically significant difference.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Group** & **Mean \(\pm\) SD** & \(p\)**-Value** \\ \hline U6 to FH (mm) & FH1 & \(1.18\pm 1.05\) & 1.000 \\  & FH2 & \(1.18\pm 1.06\) & \\  & FH3 & \(1.17\pm 1.17\) & \\  & FH4 & \(1.16\pm 1.18\) & \\ Ag to FH (mm) & FH1 & \(1.40\pm 1.04\) & 0.991 \\  & FH2 & \(1.40\pm 1.05\) & \\  & FH3 & \(1.47\pm 1.37\) & \\  & FH4 & \(1.47\pm 1.37\) & \\ J to MSP (mm) & FH1 & \(1.89\pm 1.44\) & 0.750 \\  & FH2 & \(1.90\pm 1.45\) & \\  & FH3 & \(1.64\pm 1.34\) & \\  & FH4 & \(1.65\pm 1.33\) & \\ Ag to MSP (mm) & FH1 & \(3.05\pm 2.86\) & 0.978 \\  & FH2 & \(3.08\pm 2.89\) & \\  & FH3 & \(2.87\pm 2.72\) & \\  & FH4 & \(2.86\pm 2.73\) & \\ Me to MSP (mm) & FH1 & \(0.56\pm 0.27\)\({}^{\text{A}}\) & \textless{}0.001 \\  & FH2 & \(0.55\pm 0.28\)\({}^{\text{A}}\) & \\  & FH3 & \(1.37\pm 1.23\)\({}^{\text{B}}\) & \\  & FH4 & \(1.33\pm 1.16\)\({}^{\text{B}}\) & \\ Occlusal plane cant (\({}^{\circ}\)) & FH1 & \(1.25\pm 1.13\) & 1.000 \\  & FH2 & \(1.25\pm 1.13\) & \\  & FH3 & \(1.25\pm 1.26\) & \\  & FH4 & \(1.24\pm 1.27\) & \\ Mandibular gonal cant (\({}^{\circ}\)) & FH1 & \(0.88\pm 0.66\) & 0.989 \\  & FH2 & \(0.89\pm 0.66\) & \\  & FH3 & \(0.93\pm 0.89\) & \\  & FH4 & \(0.93\pm 0.88\) & \\ Ramal inclination (\({}^{\circ}\)) & FH1 & \(2.08\pm 1.73\) & 0.915 \\  & FH2 & \(2.08\pm 1.72\) & \\  & FH3 & \(2.31\pm 1.98\) & \\  & FH4 & \(2.28\pm 1.97\) & \\ \hline \hline \end{tabular} \(p\)-values were calculated using one-way ANOVA with Tukey’s HSD. Means followed by a common upper-case letter are not significantly different at \(p<0.001(\text{A}<\text{B})\).  
\end{table}
Table 4: Comparison of each variable according to each FH plane.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & & **FH1** & **FH2** & **FH3** & **FH4** & \(p\)**-Value** \\ \hline \multirow{3}{*}{Me to MSP (mm)} & \(\leq\)2.0 & 38 (100) & 38 (100) & 29 (76.3) & 31 (81.6) & 0.001 \\  & 2.0 to 4.0 & 0 (0) & 0 (0) & 7 (18.4) & 4 (10.5) & \\ \cline{1-1}  & \(\geq\)4.0 & 0 (0) & 0 (0) & 2 (5.3) & 3 (7.9) & \\ \hline \hline \end{tabular} Data are presented as numbers (%). The \(p\)-value was calculated using linear-by-linear association.  
\end{table}
Table 5: Frequency change of the amount of mention (Me) deviation according to each FH plane.
# Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks
## 3 Basic System Design
### Network Architecture  
The surface/platter of a magnetic disk storage device consisting of tracks and sectors provides an interesting approach that may be applied to a large scale WSN. This assumption led to the Disk Based Data Centric Storage (DBDCS) architecture, as shown in Figure 3a, dividing the rectangular field into a matrix of storage cells (referred to as a sector) where row and column represent track \(T_{i}\) and sector \(S_{j}\), respectively. The physical deployment is mapped to a \(m\)\(x\)\(n\) matrix, where \(m\) is the number of tracks and \(n\) is the number sectors for each track. Hence, the nodes in the network are divided into _S(mxn)_ sectors, each comprising a Sector Head (SH) and sector members that communicate via one hop to the SH (see Figure 3c), where \(SH_{i}\in[1..S]\). Each node is configured to be aware of the deployment layout by knowing: (1) each SH is assigned with the sector number as a virtual address and node id; and (2) all member nodes know their own node id and number of tracks (\(m\)) and sectors (\(n\)) of the network field. As shown in Figure 3b, the intra-sector communication (_i.e._, communication from sector members to \(SH\) or _vice-versa_) is constrained to one hop while inter-sector transmission is multi-hop. For simplification, the sensor nodes inside each sector are not shown explicitly in Figure 3b. Instead, an aggregated link (see Figure 3c) is shown to represent the total traffic from member nodes to head node.
## 4 Proposed Secure MP-FrFT-OFDM Cryptosystem  
Due to their effective use of network resources and bandwidth, ability to accommodate a range of mobility scenarios, and ability to deliver high data rates, OFDM systems have demonstrated widespread success in many wireless communication applications. Thus, it is anticipated that OFDM will continue to be a crucial enabling technology in present and future systems, including 5 Gs [42]. In order to deal with inter-channel interference (ICI) and inter-symbol interference (ISI) problems and permit simultaneous data transmission via band-limited channels, OFDM was first presented in the middle of the 1960s [48]. Wide frequency selective channels are, in theory, divided into a number of small, flat fading sub-bands by OFDM. Despite the fact that OFDM sub-bands are made orthogonal and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test** & \(p\)**-Value** & **Result** \\ \hline Monobit frequency & 0.5961 & Success \\ \hline Block frequency & 0.3673 & Success \\ \hline Runs test & 0.7286 & Success \\ \hline Longest run of ones & 0.8837 & Success \\ \hline Binary matrix rank test & 0.2735 & Success \\ \hline Discrete Fourier transform & 0.1942 & Success \\ \hline Non-overlapping template & 0.3061 & Success \\ \hline Overlapping templates & 0.7398 & Success \\ \hline Universal statistical & 0.8394 & Success \\ \hline Linear complexity & 0.7193 & Success \\ \hline Serial test & 0.5037 & Success \\ \hline Approximate entropy & 0.6695 & Success \\ \hline Cumulative sums (forward) & 0.8359 & Success \\ \hline Cumulative sums (reverse) & 0.3891 & Success \\ \hline Random excursions & 0.7291 & Success \\ \hline \hline \end{tabular}
\end{table}
Table 1: The randomness tests results for the proposed MCC-MF sine map based on NIST SP800-22 tests.  
Figure 3: (**a**) BD of the conventional non-modular FSCM. (**b**) LE of the conventional non-modular FSCM. (**c**) BD of the proposed MCC-MF sine map. (**d**) LE of the proposed MCC-MF sine map.  
independent of one another, a guard band known as the cyclic prefix (CP) is necessary to lessen the impacts of ISI and ICI. Instead of employing an empty guard space, the idea of a CP is based on adding a cyclic extension to the symbol itself. In the suggested encryption scheme, authenticated biometric features are utilized as the biometric secret key generation with the proposed MCC-MF sine map chaotic secret key generation in order to design a DCBS generator for the MPFrFT OFDM image encryption.
## 3 Results  
The measured stiffnesses of the eight custom PD-AFOs were in the range of 0.15-0.23 N*m/deg and 0.10-0.27 N*m/deg in plantarflexion and dorsiflexion, respectively.  
Patients walked faster while wearing the PD-AFO with respect to the shed condition (normalized speed AFO = 53.8 \(\pm\) 14.0 %height/s; normalized speed SHOD = 50.8 \(\pm\) 15.0 %height/s; \(p\) = 0.015). No difference in walking speed was observed with respect to the Codivilla Spring (normalized speed CODIVILA = 53.6 \(\pm\) 15.3 %height/s; \(p\) > 0.05).  
Figure 2a shows the mean temporal profiles (\(\pm\)1 SD) of the calf-shell plantarflexion/dorsiflexion angle (deg) during the normalized stance phase duration. Similarly to what is generally observed for ankle motion, plantarflexion of the PD-AFO at early stance is followed by dorsiflexion during midstance and by late-stance plantarflexion. This motion generates a resisting torque due to the PD-AFO stiffnesses \(\mathrm{k_{p}}\) and \(\mathrm{k_{d}}\) (Figure 2b).  
Figure 3 reports the temporal profiles of the work done by each of the eight custom PD-AFOs during the normalized stance phase duration. In all PD-AFOs, energy was absorbed during the load-acceptance phase (1-15% stance) and at late stance (50-90% stance). Energy was released up to midstance (15-50% stance) and during push-off (80-100% stance). At early stance, the maximum energy absorbed and released by the PD-AFOs was 0.011 \(\pm\) 0.005 J/kg at 12 \(\pm\) 2% (\(\mathrm{t_{1}}\) in Figure 3) of the stance phase. At late stance, the maximum energy was 0.008 \(\pm\) 0.007 J/kg at 88 \(\pm\) 9% (\(\mathrm{t_{2}}\) in Figure 3) of the stance phase. The average maximum energy released across the whole stance phase was 0.013 \(\pm\) 0.005 J/kg.  
Figure 4 reports the inter-participant mean ankle power during the normalized stance phase duration in the PD-AFO and shed conditions. Although the difference was not statistically significant (\(p\) = 0.06), a trend for reduced maximum normalized ankle power at push-off was observed. This was 1.9 W/kg and 2.0 W/kg, respectively, in the PD-AFO and shed conditions.  
Figure 2: (a) Mean temporal profiles (\(\pm\)1 SD) of the calf-shell plantarflexion/dorsiflexion angle [deg] during normalized stance phase (%). From heel-strike to t1, the calf-shell rotates posteriorly (i.e., the foot plate plantarflexes with respect to the calf-shell); from t1 to t2, the calf-shell rotates anteriorly (i.e., the foot plate dorsiflexes with respect to the calf-shell); from t2 to toe-off, the calf-shell rotates posteriorly back to the neutral position. (b) Temporal profile of the mean (\(\pm\)1 SD) AFO resisting moment (N*m) over normalized stance phase (%). The resisting moment in gait was estimated by using the moment/angle relationship following the experimental measurements of AFO stiffness.
## 2 Materials and Methods
### The FREDoC Framework
For example, the fuzzy set medium high is a triangular number given by the triplet (0.07, 0.15, 0.18). The central number, 0.15, is the relevance value at which the membership degree to the fuzzy set is at its maximum and equal to 1. The first triangular fuzzy set, low, is a right function triangular fuzzy number; its central value is 0.01. For relevance values less than or equal to 0.01, the degree of membership in the low fuzzy set is always equal to 1. Similarly, the last fuzzy set, very high, is a triangular left function fuzzy number. Its central value is 0.18. For relevance values greater than or equal to 0.18, the degree of membership in the very high fuzzy set is always equal to 1. The fuzzy partition in Figure 1 respects the Ruspini constraint; in fact, the sum of the membership degrees of a value of relevance to all five fuzzy sets is always equal to 1. In Table 2 are shown the values of relevance of each emotional category to a document (r), the normalized relevance (r), and the final fuzzy relevance assigned to the emotional category. \begin{table}
\begin{tabular}{c c c c} \hline
**Emotional Category** & **r** & **r\({}^{\prime}\)** & **Fuzzy Relevance** \\ \hline Joy & 0.05 & 0.05 & Medium low (Ml) \\ Trust & 0.10 & 0.09 & Medium (M) \\ Surprise & 0.13 & 0.13 & Medium high (Mh) \\ Expectation & 0.21 & 0.20 & High (H) \\ Fear & 0.05 & 0.05 & Medium low (Ml) \\ Sadness & 0.05 & 0.05 & Medium low (Ml) \\ Disgust & 0.02 & 0.02 & Low (L) \\ Anger & 0.05 & 0.04 & Medium low (Ml) \\ Optimism & 0.09 & 0.09 & Medium (M) \\ Love & 0.01 & 0.01 & Low (L) \\ Aggression & 0.18 & 0.17 & High (H) \\ Submission & 0.01 & 0.01 & Low (L) \\ Disapproval & 0.05 & 0.05 & Medium low (Ml) \\ Contempt & 0.01 & 0.01 & Low (L) \\ Remorse & 0.01 & 0.01 & Low (L) \\ Awe & 0.02 & 0.02 & Low (L) \\ \hline \end{tabular}
\end{table} 
Table 2: Example of classification of a document in FREDoC. Figure 1: Example of fuzzy partition of the relevance universe of discourse [0, 1]. By setting a threshold for fuzzy relevance, below which the incidence of the emotional category in the document is considered not significant, FREDoC will classify the document by assigning to it only the emotional categories with fuzzy relevance greater than or equal to the threshold, together with their respective fuzzy relevance. In the example in Table 2, setting as the threshold the fuzzy relevance medium high, the document will be classified as Surprise, with fuzzy relevance medium high, Expectation, with fuzzy relevance high, and Aggression, with fuzzy relevance high.
## 3 Results
### Transfer Learning to Diagnose COVID-19 from Chest X-ray
For the same image, the classification score of corresponding adversarial image \(x^{\prime}\) was investigated if both \(x\) and \(x^{\prime}\) were correctly classified. Figure 8 shows that, for the same image, the FGSM attack resulted in a degradation of the probability score for the image to belong to a particular class. As shown in Figure 8 for a \(\varepsilon\) of 0.009, the probability for a COVID-19-positive image belonging to COVID-19-positive decreases to 0.91 from that of 1.00 for the VGG16 network. Additionally, for the Inception-v3 model, the probability also decreases, to 0.93 from that of 1.00. It is obvious that if the \(\varepsilon\) is further increased, the probability will decrease and result in misclassification. Moreover, the decreased probability value is also dangerous because medical imaging requires high-precision performance. Figure 6c,i shows that \(\varepsilon\) of 0.009 can generate adversarial images where perturbations are not recognizable in the human eye; Table 5 depicts that \(\varepsilon\) of 0.008 can cause an average correct class probability drop of 0.24 for the VGG16 model. Thus, the confidence of the classifier to predict the correct class of a sample is reduced, causing the model to be less reliable. The Inception-v3 model was found to be robust to FGSM attack for this task. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(\epsilon\)} & \multicolumn{2}{c}{**VGG16 with Adversarial Image**} & \multicolumn{2}{c}{**Inception-v3 with Adversarial Image**} \\ \cline{2-7}  & **Total Correct** & **Total Incorrect** & **ACC (\%)** & **Total Correct** & **Total Incorrect** & **ACC (\%)** \\ \hline
0.0001 & 52 & 2 & 96.29 & 54 & 0 & 100 \\
0.0003 & 52 & 2 & 96.29 & 53 & 1 & 98.15 \\
0.0005 & 52 & 2 & 96.29 & 52 & 2 & 96.30 \\
0.0007 & 52 & 2 & 96.29 & 51 & 3 & 94.44 \\
0.0009 & 51 & 3 & 94.44 & 50 & 4 & 92.59 \\
0.001 & 51 & 3 & 94.44 & 49 & 5 & 90.74 \\
0.003 & 40 & 14 & 74.07 & 36 & 18 & 66.67 \\
0.005 & 16 & 38 & 29.63 & 31 & 23 & 57.41 \\
0.007 & 10 & 44 & 18.52 & 30 & 24 & 55.56 \\
0.009 & 9 & 45 & 16.67 & 30 & 24 & 55.56 \\
0.01 & 4 & 50 & 7.41 & 30 & 24 & 55.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Diagnostic performance drop for different \(\varepsilon\) of FGSM attack in chest X-ray images. Figure 7: Performance for different \(\varepsilon\) of FGSM attack in chest X-ray images.
# Advancements in the Intelligent Detection of Driver Fatigue and Distraction: A Comprehensive Review
###### Abstract  
Detecting the factors affecting drivers' safe driving and taking early warning measures can effectively reduce the probability of automobile safety accidents and improve vehicle driving safety. Considering the two factors of driver fatigue and distraction state, their influences on driver behavior are elaborated from both experimental data and an accident library analysis. Starting from three modes and six types, intelligent detection methods for driver fatigue and distraction detection from the past five years are reviewed in detail. Considering its wide range of applications, the research on machine vision detection based on facial features in the past five years is analyzed, and the methods are carefully classified and compared according to their innovation points. Further, three safety warning and response schemes are proposed in light of the development of autonomous driving and intelligent cockpit technology. Finally, the paper summarizes the current state of research in the field, presents five conclusions, and discusses future trends.  
fatigue driving; distracted driving; machine vision; multimodal detection; safety warning and response +
Footnote †: journal: journal: Medical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, China  
development of automotive intelligence. Deep learning [14], a subset of machine learning composed of multiple neural network layers, utilizes large datasets to autonomously learn features and patterns, providing a robust framework for understanding and interpreting drivers' behaviors and states. Physiological electrical signals, important manifestations of the body's internal electrical activity, primarily include brain waves, heart signals, muscle activity, and eye movements, which can indirectly reflect a person's fatigue or distraction state [15]. By integrating intelligent sensors (such as visual and physiological signal sensors) with deep learning and other technologies, Intelligent Detection Methods have been formed.  
Over the past five years, the confluence of cognitive science, vehicular technology, and artificial intelligence has catalyzed unprecedented advancements in the comprehension and mitigation of driver fatigue and distraction. Despite these remarkable technological strides, traffic incidents stemming from impaired driving states continue to pose a significant public safety challenge, highlighting the critical need for more sophisticated detection methods. This comprehensive review undertakes a meticulous exploration of the cutting-edge intelligent detection techniques developed in the past five years, categorizing and conducting a comparative analysis based on signal modalities and types of feature extraction. This endeavor aims to bridge the prevailing gaps in both academic discourse and practical implementations within this domain.