esa-sceva/satcom-chunk-collection huggingface dataset:
# OTFS-Based Handover Triggering in UAV Networks
## 4 OTFS Modulation/Demodulation  
Without loss of generalization, in this paper, we will consider OTFS modulation/demodulation as given in Figure 2[12], as it is the primary DDMC scheme. In this section, the mathematical foundation of the OTFS modulation/demodulation will be given in detail while considering the time-varying DD CIR given (1) and (2). For comprehensive explanations, we will explain the two forms of OTFS modulation/demodulation. The first one is the original form of the OTFS introduced in [12], where OTFS is considered as a pre- and post-coded OFDM using inverse symplectic Fourier transform (ISSFT)/Wigner transform followed by Heisenberg/SFFT for modulation/demodulation, respectively. The second one is a more compact form based on the discrete representation of OTFS using matrix-vector notation, where the inverse Zak transform (IZT) is used for modulation and the Zak transform (ZT) for demodulation [13; 14].
# Sensor-Level Mosaic of Multistrip KOMPSAT-3 Level 1R Products
## 3 Experimental Results
### Piecewise Discrepancy Compensation  
After removing the matching outliers, we can estimate the discrepancy compensation of the projected image by averaging the image coordinates differences between the matching pairs. However, the discrepancy varies for each image line. As shown in Figure 7, averaging single image line discrepancies may produce inaccurate compensation values because there are no redundant matching pairs in an image line. Therefore, we estimated the local discrepancy compensation in the line and sample directions by averaging discrepancies in a block of image lines such as 500 image lines. In addition, we interpolated the averaged differences using a spline curve along the image line to ensure the continuity between compensated image blocks.  
Figure 9 shows the estimated local discrepancy for the line and sample directions for every 500 image lines after the spline interpolation. In other words, the red line was derived by averaging the red dots in Figure 8 for every 500 image lines and interpolating them in the spline curve. Figure 9a,b shows the line and sample compensations for Strip #1, and Figure 9c,d are for Strip #3. The rewards for sample coordinates ranging from 30 to 44 pixels are much larger than those for line coordinates.  
Figure 8: Discrepancy between the image coordinates in the matching pair—(**a**) line difference between Strips #1 and #2; (**b**) sample difference between Strips #1 and #2; (**c**) line difference between Strips #2 and #3; (**d**) sample difference between Strips #2 and #3.  
The piecewise image compensation produced the final strip mosaic in Figure 10. Note that the color balancing was not carried out in the study. Some examples showing geometric consistency at the strip boundary even over the building areas are presented in Figure 11.  
Figure 9: Estimated discrepancy compensation—**(a)** line compensation for Strip #1; **(b)** sample compensation for Strip #1; **(c)** line compensation for Strip #3; **(d)** compensation for Strip #3.  
Figure 11: _Cont._  
Figure 10: Final strip mosaic.
# PFSegIris: Precise and Fast Segmentation Algorithm for Multi-Source Heterogeneous Iris
## 3 Experiments and Analysis
### Experimental Results and Analysis
#### 3.4.2 Cross-Dataset Evaluation of PFSegIris  
To further verify that the iris segmentation algorithm designed in the paper has universality and fast segmentation performance for multi-source heterogeneous iris images, the model trained on a mixed iris dataset in Section 3.4.1 was tested directly on the four datasets JLU-7.0, CASIA-Iris-Interval-v4, UBIRIS.V2, and Mmu2, respectively. Figure 11 shows the cross-dataset segmentation results of PFSegIris on the four datasets JLU-7.0, CASIA-Iris-Interval-v4, UBIRIS.V2, and Mmu2, respectively. Table 4 shows the cross-dataset evaluation indexes on the four datasets.  
Conclusions can be obtained as follows after the analysis of Figure 11 and Table 4:  
1. The test results on four cross-datasets were still good, indicating that PFSegIris had learned the real iris features, had a certain migration ability and generalization ability, and had universality for multi-source heterogeneous iris image segmentation.
2. Among the four heterogeneous datasets, the effect of the CASIA-Iris-Interval-v4 was remarkably better than that of the other three datasets. The reason was that the images from CASIA-Iris-Interval-v4 were of high quality in near-infrared light, with large iris regions and almost no noise interference. Iris images from UBIRIS.V2 had more noise and therefore showed less generalization ability.
3. Fast segmentation was still realized on four cross-dataset iris images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **mIoU/\%** & **mPA/\%** & **F1-Score/\%** & **Average Time/s** \\ \hline JLU-7.0 & 97.06 & 98.56 & 97.68 & 0.11 \\ CASIA-Interval & 98.27 & 99.40 & 99.13 & 0.10 \\ UBIRIS.V2 & 96.69 & 97.30 & 97.29 & 0.09 \\ Mmu2 & 96.82 & 97.62 & 97.47 & 0.09 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The cross-dataset evaluation indexes of PFSegIris on the JLU-7.0, CASIA-Iris-Interval-v4, UBIRIS.V2, and Mmu2 iris datasets, respectively.  
Figure 11: The cross-dataset segmentation results of PFSegIris on the JLU-7.0, CASIA-Iris-Interval-v4, UBIRIS.V2, and Mmu2 iris datasets, respectively: (**a**) original images; (**b**) ground truth; (**c**) segmentation results; (**d**) merged images.
## 2 Multi-Objective Optimization Modeling in Material Cutting Stage of CNC Lathe
### Test Conditions and Data Acquisition  
The dry turning of 45 steel was carried out by using a ZAK4085 DI CNC lathe manufactured by Shanghai KeRong (Shanghai, China). The maximum turning diameter of the lathe body is 400 mm, the maximum speed is 2400 r/min, and the maximum power of the spindle is 7.5 kW. The machining tool used was a carbide tool made by Zhuzhou Diamond (Zhuzhou, China), namely, model WNMG080408. The diameter of the piece of 45 steel \(\phi d\) was 40 mm, and its length was 500 mm. A TP612 three-phase multi-function ammeter was used to measure the active power at the bus of the machine tool, and a program was written in the Python3.9 language to record the active power at a fixed interval of 0.5 s. The final test platform is shown in Figure 2, and the power curve of a complete machining process is shown in Figure 3.  
Machined surface roughness values were acquired by employing a MarSurf LD130 (Manufacturer: Mahr, Eislingen, Germany) from Germany, and the instrument used is shown in Figure 4. This instrument uses a rocker system with a tip to contact the measured workpiece. During the rocker's movement, the swing angle is mapped to the diffraction grille at the end via a diode light to generate an interference pattern. Finally, the data acquisition system draws the contour curve of the measured workpiece and calculates its surface roughness value.  
Figure 3: Power curve during machining.  
Figure 2: Test platform for active power measurement.  
According to the tool manufacturer's recommendations and the bearing capacity of the CNC lathe employed, the cutting speed \(v_{c}\), feed rate \(f\), and depth of cut \(a_{p}\) were set to three levels, as shown in Table 1. The Taguchi method was used to design nine tests, and the results of each turning test are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factory** & & **Level** \\ \hline \(v_{c}\) (m/min) & 40 & 50 & 60 \\ \(f\) (mm/r) & 0.2 & 0.3 & 0.4 \\ \(a_{p}\) (mm) & 0.3 & 0.5 & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Turning parameter levels.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Order** & \(v_{c}\) & \(f\) & \(a_{p}\) & \(P_{c}\) & MRR & SEC & \(Ra\) \\
**Number** & **(m/min)** & **(mm/r)** & **(mm)** & **(W)** & **(mm\({}^{3}\)/s)** & **(j/mm\({}^{3}\))** & **(\(\mu\)m)** \\ \hline
1 & 40 & 0.2 & 0.3 & 152.3 & 40.00 & 3.81 & 3.18 \\
2 & 40 & 0.3 & 0.5 & 325.2 & 100.00 & 3.25 & 4.25 \\
3 & 40 & 0.4 & 0.7 & 533.3 & 186.67 & 2.87 & 6.19 \\
4 & 50 & 0.2 & 0.5 & 268.8 & 83.33 & 3.23 & 2.07 \\
5 & 50 & 0.3 & 0.7 & 471.4 & 175.00 & 2.69 & 2.84 \\
6 & 50 & 0.4 & 0.3 & 278.5 & 100.00 & 2.79 & 4.59 \\
7 & 60 & 0.2 & 0.7 & 386.6 & 140.00 & 2.76 & 1.34 \\
8 & 60 & 0.3 & 0.3 & 238.3 & 90.00 & 2.65 & 1.87 \\
9 & 60 & 0.4 & 0.5 & 504.2 & 200.00 & 2.52 & 3.61 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Turning test data.  
Figure 4: Surface-roughness-measuring instrument.
## 1 Introduction
### Connected Vehicles  
Via advancements in wireless inter-vehicle communication, connected vehicles (CVs) have the potential to revolutionize transportation systems. CVs establish communication not only among themselves but also with external environments. Three main groups categorize this communication: vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-everything (V2X) [14]. CVs serve as the everything system for the Internet of Vehicles (IoV), a mobile system integral to the next generation of Intelligent Transportation Systems. Inter-vehicle communication, specifically vehicle-to-vehicle (V2V), enables the sharing of collected information within a vehicle with nearby vehicles. This includes data from onboard sensors, control systems, and computers. Remarkably, safety (ITS), such as collision detection, blind-spot warning, and information sharing, can be enabled without the need for additional road infrastructure. CVs operate as decentralized wireless ad hoc networks, supported by on-board units (OBUs) embedded computers in every vehicle [15; 16]. These OBUs gather vehicle data and GPS location information, enabling communication between vehicles (V2V) and between vehicles and Roadside Units (RSUs). The success of modern smart vehicles depends on the integration of three key components: sensor fusion for seamless integration of car sensors; information systems that merge high-definition mapping with precise navigation, artificial intelligence, and robust signal processing; and technologies enabling communication and collaboration among vehicles. Show Figure 1.
## 3 Active Control Design  
### Active Feedback Linearization  
The aim of feedback linearization is to make a nonlinear system linear using active control. The principle is to define a control vector which allows to completely neutralize the nonlinear phenomena. The feedback linearization method using the Lie algebra [8,9] can be simplified when the system dynamics is defined by second-order differential equations as explained by [PERSON] et al. [11]. The dynamic equation of the system studied is defined including the controller by  
\[\mathbf{M}\dot{\mathbf{X}}+\mathbf{C}\dot{\mathbf{X}}+(\mathbf{K}+\mathbf{K}_{ \mu})\mathbf{X}=\mathbf{F}_{\mathbf{n}\mathbf{l}}(\mathbf{X})+\mathbf{B}_{u} \mathbf{u} \tag{9}\]  
where \(\mathbf{u}\) and \(\mathbf{B}_{u}\) are the control vector and the matrix of the distribution of the control on the different degrees-of-freedom respectively. In the case of complete Input-Output linearization using \(n\) input, \(n\) output and \(n\) degrees-of-freedom, the matrix \(\mathbf{B}_{u}\) is square and the control vector \(\mathbf{u}\) applied to the nonlinear system can be chosen so that  
\[\mathbf{u}=\mathbf{B}_{u}^{-1}\big{(}\bar{\mathbf{u}}-\mathbf{F}_{\mathbf{n} \mathbf{l}}(\mathbf{X})\big{)} \tag{10}\]  
where \(\bar{\mathbf{u}}\) is the artificial command applied to the linear system. Equation (9) can then be written using Equation (10) in a linear form  
\[\mathbf{M}\ddot{\mathbf{X}}+\mathbf{C}\dot{\mathbf{X}}+(\mathbf{K}+\mathbf{K} _{\mu})\mathbf{X}=\bar{\mathbf{u}} \tag{11}\]  
Therefore, from the knowledge of the nonlinearities, this choice of control vector cancels fully the nonlinear force.
# Reviews on Corrugated Diaphrags in Miniature Fiber-Optic Pressure Sensors
###### Abstract  
Corrugated diaphragms (CDs) have been widely used in many fields because of their higher pressure sensitivity and wider linear range compared to flat diaphragms (FDs) in the same circumstances. Especially in the application of miniature fiber-optic pressure sensors, the introduction of the corrugated structure gives the sensor high sensitivity, large dynamic range, good linearity, small hysteresis, good stability, and so on. Research on CD-based miniature fiber-optic pressure sensors has gradually attracted more attention in recent years. In this paper, the principles of operation of a miniature fiber-optic pressure sensor are briefly introduced, then the mechanical properties of FD and CD, as well as their influences on the performance of the sensor, are analyzed in detail. The application status of CDs in miniature fiber-optic pressure sensors is reviewed, and our conclusions and the prospects for the application of CDs in miniature fiber-optic pressure sensors are given finally.  
corrugated diaphragm; fiber-optic; Fabry-Perot; pressure sensor; acoustic sensor; MEMS 2019
## 5 Results
### Results Obtained by Using the Combined System  
As the last step, we evaluated the recognition of noisy children's speech by combining the data augmentation scene described in Section 2 to the two time-scale modification methods described in Section 3. The evaluation was computed in a similar manner to that in Section 5.1 by using the four noise types and the four SNR categories and by including the three different data augmentation scenarios (\"same\", \"different\", and \"all\"). In each of the three scenarios, the recognizer trained in the corresponding data augmentation scenario was tested with noisy children's speech, which was time-scale-processed either using \(F_{0}\) modification or speaking rate modification and using both of them. The WER results obtained are reported in Table 6 by referring to data augmentation, \(F_{0}\) modification, and speaking rate modification by DA, \(F_{0}\)M, and SRM, respectively. From this table, the following main observations can be made. First, the system that combined all the three studied components (i.e., DA + \(F_{0}\)M + SRM) performed considerably better than the baseline in all the cases studied. In addition, combining _both_ of the two time-scale modification methods with DA yielded the best combined system in all cases. Second, by comparing the best combined systems (i.e., DA + \(F_{0}\)M + SRM) between the three noise augmentation scenarios, it can be seen that the WER values were best in the \"all\" scenario for all the noise types and SNR categories.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Noise Type**} & \multicolumn{1}{c}{**SNR (dB)**} & \multicolumn{1}{c}{**Baseline**} & \multicolumn{1}{c}{**With Speaking Rate Modification**} \\ \hline \multirow{4}{*}{Babble} & 0 dB & 53.35 & 40.32 \\ \cline{2-4}  & 5 dB & 36.43 & 28.64 \\ \cline{2-4}  & 10 dB & 30.29 & 25.73 \\ \cline{2-4}  & 15 dB & 26.46 & 23.32 \\ \hline \multirow{4}{*}{White} & 0 dB & 43.22 & 38.65 \\ \cline{2-4}  & 5 dB & 30.15 & 26.53 \\ \cline{2-4}  & 10 dB & 26.11 & 23.12 \\ \cline{2-4}  & 15 dB & 24.30 & 22.76 \\ \hline \multirow{4}{*}{Factory} & 0 dB & 65.51 & 51.34 \\ \cline{2-4}  & 5 dB & 42.47 & 26.37 \\ \cline{2-4}  & 10 dB & 30.90 & 25.65 \\ \cline{2-4}  & 15 dB & 26.30 & 24.87 \\ \hline \multirow{4}{*}{Volvo} & 0 dB & 21.09 & 18.65 \\ \cline{2-4}  & 5 dB & 20.20 & 17.43 \\ \cline{1-1} \cline{2-4}  & 10 dB & 19.53 & 17.17 \\ \cline{1-1} \cline{2-4}  & 15 dB & 18.92 & 16.34 \\ \hline \hline \end{tabular}
\end{table}
Table 5: WERs obtained in recognizing noisy children’s speech of four noise types in four SNR categories using speaking rate modification. The baseline system is the same as in Table 3.  
Figure 6: WERs as a function of the speaking rate modification factor \(\alpha\) for each of the four noise types and four SNR categories using the validation set. Note that the smaller the value of \(\alpha\) (i.e., towards the right end of the x-axis), the larger the strength of the speaking rate modification.
# Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs
## 4 Traffic Sign Detection
### Traffic Sign Proposal Extraction
This example shows that the number of the maximally stable extremal regions from the synthesized SGW map was obviously less than that of the grayscale image; however, these regions contained the traffic signs areas. Image (f) of Figure 7 shows the regions left after being filtered by the rules defined above. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Parameters** & **Minimum** & **Maximum** \\ \hline \multirow{4}{*}{GTSDB} & Height (H) & 16 & 128 \\  & Width (W) & 16 & 128 \\  & MSERs Area/Bounding Box Area (M\({}_{B}\)) & 0.4 & 0.8 \\  & Aspect ratio (A\({}_{r}\)) & 0.5 & 2.1 \\ \hline \multirow{4}{*}{CTSD} & Height (H) & 26 & 560 \\  & Width (W) & 26 & 580 \\ \cline{1-1}  & MSERs Area/Bounding Box Area (M\({}_{B}\)) & 0.4 & 0.8 \\ \cline{1-1}  & Aspect ratio (A\({}_{r}\)) & 0.4 & 2.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Filter rules defined in our method.
# Borehole Trajectory Optimization Design Based on the SAC Algorithm with a Self-Attention Mechanism
## 2 Materials and Methods
### SAC Algorithm Network Architecture and Objective Function  
SAC adopts the Actor-Critic structure, and the strategy network (Actor) represents the probability distribution from learning state to action. The value network (Critic) is used to evaluate the Q-value of the state-action pair [24]. It is an offline reinforcement learning, and its network architecture is shown in Figure 1:  
Critic network loss function:  
\[J_{Q}(\theta)=\mathbb{E}_{(s,\alpha,\tau,s^{\prime})\sim D}\Big{[}\big{(}Q_{ \theta}(s,a)-(r+\gamma V_{\overline{\varphi}}(s^{\prime}))\big{)}^{2}\Big{]} \tag{3}\]  
Actor network loss function:  
\[J_{\pi}(\phi)=\mathbb{E}_{s\sim D,\alpha\sim\pi_{\phi}}[\alpha\log\pi_{\phi}(a |s)-Q_{\theta}(s,a)] \tag{4}\]  
SAC automatically adjusts the policy entropy by adaptively updating \(a\).  
In Figure 1, the SAC network consists of parallel Actor and Critic modules. The Actor uses observed state sequences as input, passes through embedding and dense layers, and produces a probability distribution over actions. The Critic includes two Q-value estimators and a value estimator, which evaluate the quality of state-action pairs. The color-coded blocks denote network layers: blue for input embedding, gray for intermediate processing, and orange for output heads. Arrows represent the direction of data flow from state input to action/value outputs. SAC algorithm network structure diagram. The figure illustrates the core architecture of the Soft Actor-Critic (SAC) model used in this study. The left pathway represents the policy network (Actor), which receives state sequences and outputs action probabilities. The right pathway shows two Q-value networks and a value network (Critic). The blocks marked in different colors correspond to input embedding (blue), hidden layers (gray), and output layers (orange/red), while arrows indicate the data flow.
## 2 Materials and Methods
To evaluate the performance of asphalt mixtures, a selection of laboratory tests were performed and the results were examined. These tests included the following:  
* Air voids content according to EN 12697-8 [34];
* Moisture susceptibility (ITSR) according to EN 12697-12 [35] and the water + one freezing cycle according to AASHTO T283 (modified procedure used at CTU in Prague [36]);
* Determination of stiffness modulus according to EN 12697-26 [37], Annex C--IT-CY test method;
* Resistance to crack propagation according to EN 12697-44 [38] with a modified procedure: loading rate of 2.5 mm/min, diameter of test specimens of 100 mm, and compaction of test specimens by Marshall compactor--like that used for IT-CY stiffness test method (Figure 6);
* Complex modulus according to EN 12697-26 [37], Annex B--4 PB-PR method. \begin{table}
\begin{tabular}{l l} \hline
**Mixture’s Label** & **Description** \\ \hline Ref & Reference mixture containing 30\% RA but no recycling agents or fibers. \\
0.1\% J300 & +0.1\% (by weight of the total mixture) of jute fiber with 20 mm-long fibers. \\
0.2\% J300 & +0.2\% (by weight of the total mixture) of jute fiber with 20 mm-long fibers. \\
0.2\% J300 10 mm & +0.2\% (by weight of the total mixture) of jute fiber with 10 mm-long fibers. \\
0.3\% J300 & +0.3\% (by weight of the total mixture) of jute fiber with 20 mm-long fibers. \\
0.2\% J300 NaOH & +0.2\% (by weight of the total mixture) of NaOH-treated jute fiber. \\ \hline
6\%SR & +6\% (by mass of aged binder) of SR recycling agent. \\ J300 6\%SR & +0.2\% (by weight of the total mixture) of jute fiber and 6\% (by mass of aged binder) of SR recycling agent added to fibers (ii). \\  & agent added to fibers (ii). \\ NaOH 6\%SR & +0.2\% (by weight of the total mixture) of NaOH-treated jute fiber and 6\% (by mass of aged binder) of SR recycling agent added to fibers (ii). \\ \hline
6\%RF & +6\% (by mass of aged binder) of RF recycling agent. \\
6\%RF\_RA & +0.2\% (by weight of the total mixture) of jute fiber and 6\% (by mass of aged binder) of RF recycling agent. This recycling agent was added directly to the RA (i). \\
6\%RF\_fibers & +0.2\% (by weight of the total mixture) of jute fiber and 6\% (by mass of aged binder) of RF recycling agent. This recycling agent was added to the fibers (ii). \\ NaOH 6\%RF & +0.2\% (by weight of the total mixture) of NaOH-treated jute fiber and 6\% (by mass of aged binder) of RF recycling agent. This recycling agent was added to the fibers (ii). \\ \hline \end{tabular}
\end{table} 
Table 1: Description of tested mixtures. Figure 5: Jute fibers’ processing, from left to right: 20 mm-long elements of yarn, separate fibers being coated with bitumen and coated jute fibers.
# Securing the Future of Web-Enabled IoT: A Critical Analysis of Web of Things Security
## 5 Security Analysis of WoT
### WoT Threats
#### 5.2.1 T1: Confidentiality Threats  
Confidentiality threats arise when attackers gain unauthorized access to sensitive information. This can occur through vulnerabilities in things, APIs, and thing descriptions. Common vulnerabilities include inadequate encryption and weak access controls, making it easier for attackers to intercept data communications or access stored information. Attacks such as eavesdropping and Man-in-the-Middle (MitM) attacks can exploit these weaknesses, leading to the interception of data communications or the accessing of stored information. Misuses stemming from this threat can lead to privacy breaches and operational disruptions where attackers may steal sensitive information or manipulate device functionalities for malicious purposes [4; 8; 44; 45].