esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dataset and Feature Description
### Feature Description
#### 3.2.2 Pyramidal HOG  
Researchers have mainly relied on PHOG feature descriptors [44; 45] for object recognition. The spatial pyramid representation of HOG is computed here. It mainly sets up the local shape andretains the spatial information by segregating it into various levels. The spatial information was retained by using HOG descriptor in every level. In this work, Canny edge detector [46] was applied to obtain the contour of each region. The edge detector was mainly used to capture the local shapes. An arrangement of minute spatial grids was shaped by doubling the separations in each axis direction for every region. For each resolution level \(L=l\), the grid consists of \(\mathbb{2l}\) cells along each dimension. For example, at \(L=2\), the number of grids along \(X\)_axis_ will be 4. As a result, the total number of grids becomes \(4*4=16\). Further, a Sobel mask [47] of window size \(3\times 3\) was applied for extracting the orientation of the gradients along the edge contours. At this stage, the procedure of gradient binning was performed similar to HOG descriptor. The gradients corresponding to same cell are quantized and combined into \(N\) histogram bins. We increment the bin count if the gradient direction value lies in the range of that particular bin. Mainly, the orientation for binning was performed using either \([0^{\circ}\) to \(360^{\circ}]\) range or \([0^{\circ}\) to \(180^{\circ}]\) range where the contrast sign was neglected [36]. These bins were sorted and concatenated into a single sequence corresponding to the same level. For each level, a histogram will be obtained. Finally, all the histograms corresponding to each level were merged to get the final feature vector. In the present work, we have used the PHOG descriptor to capture facial features from the images (having three different dimensions of \(32\times 32,\ 48\times 48\) and \(64\times 64\)) of the JAFFE and RaFD datasets. The number of pyramids was kept up to level 3 (\(L=3\)) and number of histogram bins was 8 (\(N=8\)). The orientation of the gradient ranges between \(0^{\circ}\) to \(360^{\circ}\). The number of features obtained from PHOG can be formulated as: \(N*\sum\limits_{l\in L}4^{l}\). Putting \(L=3\) and \(N=8\), we obtain final feature vector of size 680.
# Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite
## 3 Improved D* Lite
### Optimization of Priority Queue  
The original D* Lite does not rank nodes in the priority queue \(U\). Thus, it has a time complexity of \(O(1)\) when it pushes a new node into the queue for further expansion. However, the time complexity is significantly increased to \(O(n)\) when it pops up a node with the smallest key from the priority queue to expand each time, which is a time-consuming operation for path planning. Therefore, the time complexity for path expansion is \(O(n^{2})\) if there are \(n\) nodes in the priority queue, which is a relatively low efficiency for a complex environment with thousands of nodes.  
To improve the path search efficiency and decrease the search time, we use the minimum binary heap [30,31] to optimize the priority queue. Figure 6 shows an example of using the minimum binary heap to store data. The node with the smallest key value is on the top of the heap and is stored on the first position of the linear array. All its predecessor nodes have larger key values and are stored on the second and third positions of the array, respectively.  
D* Lite has three types of operations on the priority queue: (1) pop up the node with the smallest key value; (2) search a specific node in the priority queue; (3) push a node into the priority queue. The characteristics of binary heap guarantee the stability of nodes in the priority. Table 2 is a comparison of the time complexity of the conventional priority queue and the minimum binary heap. We find that the time complexity of the pop up operation on the conventional priority queue is \(O(n)\). When we replace the conventional priority queue with the minimum binary heap, the time complexity of the pop up operation is sharply decreased to \(O(1)\). We find a similar result on the search operation. However, the  
Figure 5: Procedure of path planning and optimization.  
time complexity for the push operation of the minimum binary heap is larger than that of the conventional queue. On the whole, the time complexity of the minimum binary heap is much lower than that of the conventional priority queue.
## 4 Intravalley Relaxation and Fermi Degeneracy Formation
### Possible Scenario  
In this section, we discuss a possible scenario for the aforementioned intravalley relaxation process. When we focus on an initial few ps after the photo-excitation, the electron-electron (e-e) and electron-phonon (e-ph) scatterings would be the possible processes. As suggested by the experiment [7], however, the electron distribution soon after the photo-excitation is far from the Fermi-Dirac one. This means that the e-e scattering is not dominant in the early stage of relaxation. This fact would be validated due to the following reasons: One is the low carrier density in the experiment, which is estimated to be \(\sim\)0.003 electrons per unit cell. The other is the fine parabolicity of \(\Gamma\) valley band dispersion in GaAs. The e-e scattering process, which satisfies both energy and momentum conservation, is a rare event with the parabolic band dispersion. Thus, the relaxation process would be dominated by e-ph scattering. On the other hand, when the system reaches near the  
Figure 2: A schematic view of band structure of GaAs is shown. According to the distribution of photo-injected electrons, intravalley and intervalley scatterings become possible. For more details, see the text.  
Fermi degeneracy, the electron distribution goes to the Fermi-Dirac one. In this case, the e-e scattering should be considered. This effect is discussed in Section 4.5. Thus, the photo-excited electrons will relax toward the CBM by emitting their energy to the phonon system.
# Effective Multi-Frame Optical Detection Algorithm for GEO Space Objects
## 3 Methodology
### Candidate Extraction: PP-YOLOv2  
The main consideration of PP-YOLOv2 is to achieve a better balance of speed and accuracy to a certain extent as a variant of YOLO, rather than designing a novel framework. PP-YOLOv2 has shown excellent real-time detection performance in generic object detection due to its high computation speed. Therefore, we exploit PP-YOLOv2 model as single-frame object detector to extract potential candidate target. The overall architecture of PP-YOLOv2 is shown in Figure 6. It is a further optimization and upgrade based on PP-YOLOv1 [9]. It improves the accuracy of the model by combining more than 10 new tricks on the baseline for higher performance. The mAP tested on COCO2017 [26] test-dev data is improved from 45.9% to 49.5%.  
Figure 4: Flowchart of the proposed system.  
Figure 5: The bounding box generated from the given target coordinates.  
The backbone network is ResNet50-vd-dcn with deformable convolutional layers to increase the inference speed. One of the FPN variants named PAN is used as the detection neck to enhance the ability of pyramid representation. The detection head contains two convolutional layers: a 3 \(\times\) 3 convolution followed by 1 \(\times\) 1 convolution to obtain the final prediction. The number of predicted output channels is 3 \(\times\) (\(K+5\)), where \(K\) represents the number of categories. Three different anchor points are assigned to each position of the predicted feature map. For each anchor point, the first \(K\) channels represent the predicted probability of the \(K\) category, the subsequent 4 channels represent the position, and the last channel represents the target score. The classification and localization loss functions are cross entropy loss and L1 loss, respectively.  
Through employing 2D single-frame detector of PP-YOLOv2, the output data as candidate coordinates are a set of position coordinates as described below:  
\[\mathcal{P}=\Big{\{}P_{n}^{i}=(x_{n}^{i},y_{n}^{i})\Big{|}n\in[1,\dots,N_{i}],i \in[1,2,3,4,5]\Big{\}}, \tag{1}\]  
where \(N_{i}\) is defined as the number of detections in the \(i_{th}\) frame.
# Physiological Responses Related to Sitting Comfort Due to Changes in Seat Parameters
## 3 Results
### Physiological Responses  
The values of differences in physiological indices and the results of the statistical analysis in each part are summarized in Table 2. Regarding changes between the supine and standing positions, a one-sample \(t\)-test revealed that the values of \((\theta+\alpha)/\beta\) in the central and peripheral lobes were significantly increased by the supine position, along with a significant increase in RRI and HF content, and a significant decrease in LF/HF. In Part 1 (Reclining-Neutral), the RRI significantly increased, while the respiration rate significantly decreased. There were no significant differences in the indices related to EEGs due to reclining. In Part 2 (Ottoman-Reclining), the values of \((\theta+\alpha)/\beta\) in the central lobe were significantly decreased. Additionally, there was a significant increase in RRI and HF content, and a decrease in LF/HF due to the ottonan. In Part 3 (Slab-Neutral), there was a marginally significant increase in the values of \((\theta+\alpha)/\beta\) in the central and peripheral lobe due to the slab, but there were no significant differences in the indices related to ECGs and the respiration rate.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Reference (Supine-Standing)**} & \multicolumn{3}{c}{**Part 1 (Reclining-Neutral)**} \\ \cline{2-7}
**Index** & \multicolumn{2}{c}{**Mean of**} & \multicolumn{1}{c}{**Mean of**} & \multicolumn{1}{c}{**Mean of**} & \multicolumn{1}{c}{**\(p\)-Value**} \\  & **Difference (SD)** & **t** & **\(p\)-Value** & **Difference (SD)** & **t** & **\(p\)-Value** \\ \hline \((\theta+\alpha)/\beta\) of Cz & \(-0.04\) (0.47) & \(-0.61\) & 0.547 & 0.11 (0.85) & 0.98 & 0.331 \\ \((\theta+\alpha)/\beta\) of C3 & \(0.27\) (0.53) & 3.37 & 0.002 & 0.10 (0.49) & 1.53 & 0.131 \\ \((\theta+\alpha)/\beta\) of C4 & \(0.15\) (0.51) & 2.03 & 0.048 & 0.00 (0.79) & 0.02 & 0.983 \\ \((\theta+\alpha)/\beta\) of Pz & \(0.34\) (1.24) & 1.82 & 0.075 & 0.01 (0.90) & 0.11 & 0.916 \\ \((\theta+\alpha)/\beta\) of P3 & \(0.51\) (1.40) & 2.44 & 0.019 & 0.03 (1.03) & 0.19 & 0.851 \\ \((\theta+\alpha)/\beta\) of P4 & \(0.38\) (1.59) & 1.61 & 0.114 & \(-0.03\) (0.89) & \(-0.26\) & 0.797 \\ RRI (ms) & \(186\) (94) & \(13.21\) & \(<\)0.001 & 13 (27) & 3.93 & \(<\)0.001 \\ LF/HF & \(-2.26\) (2.89) & \(-5.25\) & \(<\)0.001 & \(-0.03\) (1.25) & \(-0.18\) & 0.862 \\ HF content & \(0.36\) (0.22) & \(10.80\) & \(<\)0.001 & \(-0.01\) (0.18) & \(-0.55\) & 0.583 \\ Respiration rates (Hz) & \(-0.005\) (0.042) & \(-0.86\) & 0.393 & \(-0.009\) (0.026) & \(-2.51\) & 0.015 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the differences in physiological indices in each part.  
Figure 9: Differences in relaxation value in each part. Asterisks indicate significances: ** for \(p<0.01\).
# A Semantic Segmentation Dataset and Real-Time Localization Model for Anti-UAV Applications
###### Abstract  
With the rapid development of the unmanned aerial vehicle (UAV) industry and applications, the integration of UAVs into daily life has increased significantly. However, this growing presence raises security concerns, leading to the emergence of anti-UAV technologies. Most existing anti-UAV systems rely on object detection techniques. Yet, these methods often struggle to detect small-sized UAVs accurately. Semantic segmentation, which predicts object locations at the pixel level, offers improved localization for such small targets. Due to the lack of existing datasets for anti-UAV semantic segmentation, we propose a new dataset comprising both infrared (IR) and visible light images. Our dataset includes a total of 605,045 paired UAV images and corresponding segmentation masks. To enhance object diversity and improve model robustness, the dataset integrates multiple existing sources. In addition to the dataset, we evaluate the performance of several baseline models on the semantic segmentation task. We also propose a lightweight model to demonstrate the feasibility of real-time UAV localization using semantic segmentation on VL and IR data.  
anti-UAV; UAV segmentation; semantic segmentation; dataset 2025  
S.-C. [PERSON] A Semantic Segmentation Dataset and Real-Time Localization Model for Anti-UAV Applications
## 5 In-Lab System Test
### Discussion  
The laboratory test confirmed the integrated and effective operation of the proposed monitoring and control platform under controlled wind conditions. In the first experiment, the wind speed was incrementally increased, resulting in a nonlinear growth in output power consistent with the aerodynamic characteristics of small-scale PMSG-based wind turbines. Although the system's performance was constrained using an indoor fan, the results demonstrated stable operation and proper MPPT behavior. The maximum power reached approximately 20 W, well below the nominal turbine capacity of 200 W under optimal outdoor wind conditions, highlighting the strong dependence of system performance on the available wind resource. This limitation is expected, as the airflow generated by the indoor axial fan lacks the uniformity, speed, and turbulence of real wind conditions. The reduced aerodynamic efficiency leads to lower mechanical input power, constraining electrical output despite correct system operation.  
Figure 12: Voltage and current plot shown in the monitoring and control app.  
In the second experiment, conducted at constant wind speed, a step change in the power reference was issued through the MATLAB(r) supervisory application, from 11 W to 5 W. The system's dynamic response to this change, captured in the measurements, exhibited a total delay of approximately 180 ms between the command issued from the app (t = 5.0841 s) and the actuation start by the Arduino(r) controller (t = 5.2441 s). This latency includes the communication path through the WebSocket protocol, transmission over the UART interface, and processing time within the microcontroller. According to the IEC 61400-25-5 standard [44], which defines requirements for communication performance in wind power systems, such latencies are acceptable provided they are compatible with the application's control dynamics. In small wind systems with relatively slow response times, a delay on the order of hundreds of milliseconds is well within acceptable limits for supervisory control and power regulation tasks. The test results confirm that the platform maintained full bidirectional communication integrity, with no packet loss or command failure observed throughout the experiments. These findings validate the robustness of the proposed architecture in both monitoring and real-time control scenarios.
## 2 Principles of the Proposed Method
### CD Estimation Principle Based on FrFT
#### 2.2.1 Chirp Signal and FrFT  
The frequency of the chirp signal changes linearly with time, and its mathematical expression in the time domain is as follows:  
\[E(t)=A\exp(j(\pi kt^{2}+2\pi f_{0}t+\varphi)) \tag{10}\]  
where \(A\) is the envelope of the signal, \(k\) is the chirp parameter, \(f_{0}\) is the initial frequency, and \(\varphi\) is the original phase value.  
If the chirp signal is transformed by FrFT, it is equivalent to rotating the time-frequency plane according to the characteristics of FrFT. The rotation angle is related to the order of transformation. At this time, a special order or rotation angle will be found. After transformation, it will be found that the energy of the signal can be concentrated to the greatest extent to form the energy peak, and its performance is such that the signal will not occupy a large range, as in the time domain or frequency domain, but only a small range in the transformed fractional domain.  
When performing FrFT, the set chirp signal (plus white Gaussian noise) is \"rotated at any angle\", which gathers most of the energy in a specific FrFT domain and appears in a form similar to the spike of the impulse function. Compared with the signal containing effective information, the energy of additional Gaussian white noise spreads evenly in the whole time-frequency plane and will never produce a spike similar to the impulse function. This property can be used to complete the detection and parameter estimation of the chirp signal.  
For the convenience of derivation, it is assumed that the initial frequency and initial phase of chirp signal are zero. According to the simple geometric transform analysis, the relationship between the chirp parameter of the chirp signal and the transform order is as follows:  
\[k=-\cot(p\cdot\frac{\pi}{2})\frac{d\omega}{dt} \tag{11}\]  
where \(d\omega\) and \(dt\) represent the sampling interval of the chirp signal in the frequency domain and the time domain, respectively. Therefore, as long as the order \(p\) is found, the chirp parameter of the chirp signal can be calculated by the formula. In order to find the order \(p\), it is necessary to define an energy concentration (EC) function as follows, and the extreme value of the EC function can be used to search for the best order.  
\[EC(p)=\int_{-\infty}^{+\infty}\big{|}X_{p}(u)\big{|}^{4}du \tag{12}\]  
To sum up, the basic idea of using FrFT to estimate the chirp parameter is as follows:  
1. The rotation angle or transform order is set as a variable, and then the pre-detected chirp signal is continuously processed by FrFT to form a two-dimensional distribution with the signal energy as the longitudinal axis on the time-frequency plane;
2. The two-dimensional search method is used to find the peak coordinate point through the EC function on the time-frequency plane: \[\{p_{0},u_{0}\}=\text{argmax}(EC(p))\] (13)
3. By substituting the obtained optimal order into Equation (11), the estimated value of the chirp parameter is calculated.
## 2 Materials and Methods
### Volatile Compounds Quantification by Gas Chromatography/Mass Spectrometry (GC/MS)
#### 2.3.3 Chromatographic Conditions  
The separation, identification, and quantification of volatile compounds in unfermented and fermented kiwi juice were performed using a GC 7820 A gas chromatograph (Agilent Technologies, Santa Clara, CA, USA) coupled with an Agilent MSD Series 5975 mass spectrometer. The GC-MS system was equipped with a ZB capillary column (Phenomenex; 60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m film thickness).  
For the separation of volatile compounds, the initial column temperature was set to 40 \({}^{\circ}\)C and held for 5 min. A temperature ramp followed, increasing at 3 \({}^{\circ}\)C/min up to 220 \({}^{\circ}\)C, where it was held for 10 min. The column flow rate was maintained at 1.2 mL/min, with hydrogen used as the carrier gas. Mass spectra were scanned at 70 eV within a mass range of m/z 10 to 1000.  
The volatile organic compounds (VOCs) were identified by comparing their mass spectra with those in the Wiley spectral library. When a pure volatile compound was available, its identification was further confirmed by comparing the corresponding retention times [10].  
The concentrations of VOCs in the samples, expressed as means \(\pm\) standard deviations from two independent experiments, each with two analytical replicates (Tables S1-S3), were determined using 3-octanol as the internal standard, based on the ratio between the peak area of each volatile compound and that of the internal standard [10].
## 5 Damage Detection and Localization
### Impact Damage
Figures 18-23 display the comparisons between the embedded and surface-mounted results for damage detection and localization at frequencies of 50 kHz  
Figure 17: C-scan results of the impact damage at (**a**) 15 J for the 2 mm panel, (**b**) 20 J for the 4 mm panel, and (**c**) 57 J for the 9 mm panel. Figure 21: Cont. Figure 19: Detections of impact damage for the 4 mm thick panel at **(a)** 50 kHz and **(b)** 250 kHz using embedded PZT transducers and at **(c)** 50 kHz and **(d)** 250 kHz using surface-mounted PZT transducers. Figure 20: Detections of impact damage for the 9 mm thick panel at **(a)** 50 kHz using embedded PZT transducers and at **(b)** 50 kHz and **(c)** 250 kHz using surface-mounted PZT transducers. Figure 21: Localization of impact damage for the 2 mm thick panel at **(a)** 50 kHz and **(b)** 250 kHz using embedded PZT transducers and at **(c)** 50 kHz and **(d)** 250 kHz using surface-mounted PZT transducers (where the \({}^{\prime\prime}\)\(\bigcirc\)” is the position for real damage and the \({}^{\prime\prime}\)\(\times\)” is the position for predicted damage). Figure 22: Localization of impact damage for the 4 mm thick panel at **(a)** 50 kHz and **(b)** 250 kHz using embedded PZT transducers and at **(c)** 50 kHz and **(d)** 250 kHz using surface-mounted PZT transducers (where the \({}^{\prime\prime}\)\(\bigcirc\)” is the position for real damage and the \({}^{\prime\prime}\)\(\times\)” is the position for predicted damage). Additionally, in Figures 21-23, the DAS algorithm is effective in accurately locating the damage position for the 2 mm and 4 mm thick panels at 50 kHz. However, the DAS algorithm is unable to accurately locate the impact damage for all three panel thicknesses at 250 kHz. Overall, the findings indicate that the A\({}_{0}\) mode at 50 kHz is more suitable for accurately locating the impact damage. This conclusion is consistent with previous experimental results utilizing surface-mounted PZT transducers [28]. It is worth noting that the DAS algorithm using embedded PZT transducers in the A\({}_{0}\) mode demonstrates greater sensitivity for impact damage detection compared to surface-mounted PZT transducers at 50 kHz.
# An Enhanced Privacy-Preserving Authentication Scheme for Vehicle Sensor Networks
## 6 Analysis
### Security Analysis
#### 6.2.2. Informal Security Analysis  
Confidentiality of Session Key  
In our proposed scheme, when an authentication, secure communication or key update procedure is performed, a session key is generated using two random numbers chosen by the participants. Then, the generated key is used to ensure a secure communication. Moreover, the random numbers used to generate each session key are different. Therefore, it is difficult for an adversary \(A\) to successfully guess the session key or derived it from the communicated messages.  
Anonymity  
In our proposed scheme, to preserve users' privacy, the original identity of every participant is converted into an alias via an XOR operation with a hash that takes a random number \(r_{i}\) as an input (i.e., \(AID_{i}=ID_{i}\bigoplus h(r_{i})\), \(AID_{i}=ID_{i}\bigoplus h(r_{i}P_{pub_{j}})\)). Therefore, an adversary \(A\) cannot determine a user's original identity without the random number \(r_{i}\) or the private key \(x_{j}\) even if \(T\) has been obtained because of the hardness of the ECCDH problem in \(G\).