esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulation and Results
### Numerical Simulation Results
#### 3.2.2 Tool Temperature  
The distribution of the tool temperature field during the cutting process is shown in Figure 7 below. The heat-resistant temperature of the HSS tool is about 873 K, so the upper limit of the cloud temperature is set to the same value to directly observe the area on the tool that exceeds the heat-resistant temperature. It is evident that the highest temperature zone is always concentrated at the tool's tip and exceeds the material's heat-resistant temperature. The area constantly expands, and the parts of the experiment where thermal wear occurs are included, as seen in Figure 2. In contrast, the maximum temperature at the middle of the cutting edge is about 570 K, which is lower than the heat-resistant temperature.  
Figure 6: Chip deformation mode comparison of simulation and test.  
A Python script is developed to extract the maximum value of tool temperature at each moment, and then the tool's maximum temperature-displacement curve is shown in Figure 8. From 0 to 30 mm (corresponding to the AB segment in Figure 3), the tool temperature increases sharply to 1550 K. After 36 mm, the continuously curling chips accumulate on the front tool face, resulting in increased friction between the tool and the chips. So, the temperature continued to rise slowly. After 107 mm, the maximum tool temperature is stable at 1920 K.  
The above thermal-structural coupling analysis results reveal that the temperature distribution of the tool is highly consistent with the tool failure mode in the different areas in the test. The failure mode in the middle of the cutting edge at lower temperatures is dominated by mechanical wear. In contrast, thermal wear dominates the failure mode of the tool's tip above the heat-resistant temperature.
# Calculation and Verification of the Real-Time Working Characteristics of a Viscous Coupling
## 2 The Calculation of Shear Torque  
The shear torque transmitted by a VC is [6]:  
\[T_{q}=\frac{n\pi\rho_{s}v_{s}\Delta\omega\big{(}r_{2}^{4}-r_{1}^{4}\big{)}}{ \lambda} \tag{1}\]  
where \(v_{s}\)--the kinematic viscosity of the silicone oil; \(\rho_{s}\)--the density of the silicone oil; \(\lambda\)--the clearance between the plates; \(n\)--the plate number; \(r_{1}\)--the inner radius of the outer plates; \(r_{2}\)--the outer radius of the inner plates; and \(\Delta\omega\)--the speed difference between the input and output shafts.  
According to the equation above, the shear torque is affected by many factors: \(v_{s}\) and \(\rho_{s}\) represent physical properties of the silicone oil, and both are closely related to the temperature; \(\lambda\), \(n\), \(r_{1}\), and \(r_{2}\) are structural parameters of the VC; and \(\Delta\omega\) determines the shear rate of the oil. Considering that the speed difference of the VLSD and the shear rate are both small, we suppose that the viscosity of silicone oil does not change with shear rate [6]. Consequently, the shear torque is mainly affected by temperature.  
The approximate expression for the relationship between oil viscosity and temperature is [11; 12; 13]:  
\[\
u=\
u_{25}\hat{((T_{e}/298)}^{A}) \tag{2}\]  
where \(T_{e}\)--the absolute temperature of the silicone oil at this moment; \(\
u_{25}\)--the absolute viscosity of the silicone oil at 25 \({}^{\circ}\)C; \(A\)--constant, the value of which is related to the viscosity of the silicone oil (Figure 1).  
It can be seen in Equation (2) that the viscosity of the silicone oil decreases with an increase in the oil's temperature. According to Equation (1), when the viscosity of the silicone oil decreases, the shear torque decreases.
## 1 Introduction  
### Motivation  
Recording surgeries has a potential demand for educational and archiving purposes. Deeper analyses with video examples encourage better understanding and avoid future mistakes. Affordable, small form-factor cameras make recordings more casual and multi-view. Aiming for solid capturing of patients under surgery, mounting multi-view cameras within a surgical light (aka. shadowless lamp, hereafter referred to as a multi-camera shadowless lamp: _McSL_) is a reasonable choice. A shadowless lamp has multiple light sources rregated at a point. At least one of the light sources lights an area; hence, fewer shadows appear, even with interferences by surgeons' heads, hands, and tools. The analogy applies to viewpoints. At least one of the cameras prevents uncollected data capture.  
Once the surgery is recorded with a McSL, separated videos must be reassembled into an easy-to-refer-to format. [PERSON] et al. proposed a video analysis approach that can select the most non-occluded view over frames [1]. Reconstructing the surgery in 3D would be another option, given that a 3D scene allows viewers to navigate the scene, avoiding occlusions. However, one McSL video tends to be long (in several tens of minutes to hours), making frame-to-3D model conversion time consuming. Moreover, a major challenge lies in frame alignment because of two reasons. First, the geometrical relationship among the cameras is changed because each camera independently moves every time when the lighting is reconfigured (i.e., every time surgeons touch the surgical light). Second, surgery videos may contain highly reflective tissues and tools, uniform blue covers, and significantly occluding substances. Therefore, finding feature correspondences over cameras becomes difficult and 3D point localization becomes fragile. Manual calibration is always accessible [1], but the video post-processing becomes further cumbersome.  
We address the above issues with a video player that allows a user to access a McSL video hierarchically. First, the user highlights important frames in a McSL video and selects shorter videos around the frames. Then, our system performs feature matching and frame alignment algorithms that utilize the shorter video frames to find feature correspondences robustly. Therefore, only the highlighted frames are converted on-demand into a 3D mesh or multi-layer scene representation, which renders the frame in 3D and thus in an autostereoscopic display.
# Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks
## 4 MROR System and Network Model
### Network Model  
It is assumed that the nodes gain location awareness via an on-board GPS or by the implementation of a localization algorithm [22]. Unlike in [11; 12], wherein the used mobility model was not mentioned, in this work, in order to reflect the concurrent influence of mobility and radio channels on the lifetime of a route, we utilized the smooth mobility model [23]. This model was chosen because it allows flexible, small, equal-length time steps (\(\Delta t\)) for smooth movement description. In addition to the fact that this model complies with the physical law of smooth motion, it has nice steady-state properties of a uniform nodal distribution and a stable moving speed, which fits the operation of deployed sensor networks.  
The CR nodes are equipped with a half-duplex transceiver with CR capabilities. Based on a free-space path loss model, transmit power for both the PU and the SU is modeled to decay with distance. For each receiver-transmitter pair, the model considers both the signal-to-noise ratio (SNR) and receiver noise variance (RNV) based on the probabilistic wireless network simulator [24]. Thus, reception or detection is achievable only when the power limit from neighbors (_i.e._, SUs) and primary users (PUs) is above a threshold, \(\lambda_{su}\) and \(\lambda_{pu}\), respectively.  
The sensing mechanism for PU detection is the energy detection in a non-fading environment. When an SU performs spectrum sensing, the received signal, \(S\), is [25]:  
\[S_{rcc}^{su}=\begin{cases}n(t),&\text{if }H_{0}\\ n(t)+S^{p}(t),&\text{if }H_{1}\end{cases} \tag{1}\]  
where \(H_{0}\) represents the PU hypothetical idle state, and the active state is represented by \(H_{1}\). A zero-mean additive white Gaussian noise (AWGN), \(n(t)\), is added to the PU signal waveform, \(S^{p}(t)\). Thus, the probability of detecting PU activity, \(P_{d}\), and the probability for the occurrence of a false alarm, \(P_{f}\), can be derived as [25]:  
\[P_{d} =\Pr\left\{Y>\lambda_{pu}\left|H_{1}\right.\right\} \tag{2}\] \[P_{f} =\Pr\left\{Y>\lambda_{pu}\left|H_{0}\right.\right\} \tag{3}\]  
where \(\lambda_{pu}\) represents the decision threshold for the decision statistic, \(Y\), obtained from the energy detection algorithm. Hence, since the number of missed opportunities increases the false alarms, it implies that the probability of PU interference is increased by a low value of \(P_{d}\), while a high value of \(P_{d}\) will result in low spectrum utilization.
## 2 Design and Implementation
### Dual-Band Radio  
To improve the robustness of the wireless link in the face of strong interference, the iTDS-2 incorporates a dual-band radio, which automatically switches the communication band between 27 MHz and 432 MHz. The wireless link quality is detected by the SR-Rx in the iTDS-2 and a CC1110 transceiver (Texas Instrument, Dallas, TX, USA) in the TDS-UI, for 27 MHz and 432 MHz, respectively. The SR-Rx detects the interference at 27 MHz when the strength of the interference is higher than \(-\)73 dBm, the sensitivity of the SR-Rx. The CC1110 detects interference at 432 MHz when the packet error rate (PER) is above 0.3%, which corresponds to the bit error rate (BER) of \(10^{-5}\) for 296 bits in a packet [41]. When CC1110 detects the interference, the 27 MHz Tx in the TDS-UI sends an 8-bit Ack packet to the iTDS-2 for three times. When the SR-Rx in the iTDS-2 detects the 27 MHz carrier, the FPGA stores the incoming  
Figure 4: Digitally assisted SR-Rx: (**a**) block diagram and (**b**) timing diagram.  
8-bit data stream, continuously updates the data in a first-in first-out (FIFO) buffer, and compares it with a predefined Ack sequence (10100110) to determine whether the input is an Ack packet or a mere interference. If the FPGA recognizes an Ack packet, it changes the communication frequency from 432 MHz to 27 MHz, and vice versa if an Ack packet is not detected in the presence of 27 MHz interference.  
Figure 5 describes how the dual-band data link between the iTDS-2 and the TDS-UI works. For 13.2 ms time interval between data transmissions, the SR-Rx monitors interference at 27 MHz and receives information about interference at 432 MHz via the 27 MHz Ack link. The recovered data packet by the SR-Rx is fed into the FPGA to switch the communication band between 27 MHz and 432 MHz. The left side of the Figure 5b shows a transition from 27 MHz to 432 MHz and the right side shows a transition from 432 MHz to 27 MHz. The dual-band Tx transmits the magnetic sensor data within 1.4 ms via the selected communication band. Note that we assume at least one of the communication bands has a good wireless link quality. The dual-band Tx will continuously switch the band if both communication bands have poor wireless link quality below the threshold.
# Modeling Shadow with Voxel-Based Trees for Sentinel-2 Reflectance Simulation in Tropical Rainforest
## 2 Methodology
### Sentinel-2 Image  
The Bottom Of Atmosphere (BOA) reflectance provided by the Level-2 product from Sentinel-2 was used as a constraint to use when determining the optimal canopy shape for estimating shadow fraction. The red to near infrared (NIR) band is sensitive to illumination conditions [26]. The NIR band is more affected by multiple scattering than the red band, and CSRS does not consider multiple scattering. Therefore, in this study, the reflectance of Band 4 (red, center wavelength of 665 nm, spatial resolution of 10 m) of Sentinel-2 was simulated. The images used to calculate the relative reflectance were determined after investigating the SZA, which causes significant differences in reflectance due to canopy shape.  
Figure 2 shows the satellite overpass times for Sentinel-2A and B in the target region for 2019, and the SZA and Sun Azimuth Angle (SAA) at the time of satellite overpass. Satellite images were obtained using Google Earth engine, and metadata were referenced to plot the time of overpass, SZA and SAA. Sentinel-2A and Sentinel-2B acquired 38 and 37 images, respectively.  
Figure 1: Location of the study area: (**a**) Overall target forest (red rectangular shows the target area of 400 m \(\times\) 200 m). (**b**,**c**) Photographs taken by UAV (30 September 2018).
## 3 Experimental Setup and Results
there is no periodic power fading, demonstrating a better performance of anti-chromatic dispersion transmission. The reduction in IRR is mainly due to the power reduction caused by optical fiber loss. Then, we fix the LO signal at 5.5 GHz and change the RF frequency from 11.5 GHz to 12 GHz; the IRRs versus different IF frequencies are obtained as shown in Figure 7b. Similarly, the IRRs are all above 55 dB. In the experiment, to further study the performance of the proposed image-rejection sub-harmonic downconverter, the error vector magnitude (EVM) is measured. A vector signal generated from the vector signal sources (Rohde & Schwarz, SWM200) is used to drive the upper modulator. The modulator uses a 16-quadrature amplitude modulation (16 QAM) format. The symbol rate and carrier frequency of the vector signal are 50 MBaud and 11.7 GHz, respectively. The LO signal is fixed at 5.5 GHz. The downconverted IF signals are demodulated in the ESA. The electrical spectra and constellation diagrams are shown in Figure 8a-c, respectively. It is clearly seen from Figure 8a that the IF vector signal generated from the image signal is highly suppressed, which indicates a good wideband image rejection performance of the proposed downconverter. Meanwhile, the constellation diagrams are shown in Figure 8b,c. From Figure 8b,c, it can be seen that the useful signal has a better-demodulated result with an EVM of 4.6% while the demodulated result of the image-induced IF vector signal is poor, with an EVM of 31.57%, demonstrating that our proposed image-free downconverter exhibits strong image suppression capability for broadband signals. Figure 8: Measured (**a**) electrical spectra; (**b,c**) constellation diagrams of the generated 700 MHz IF signal downconverted from (**b**) the useful RF signal and (**c**) the image signal. Figure 7: The measured IRRs versus (**a**) different RF signals and (**b**) IF signals.
# Zero Drift Infrared Radiation Thermometer Using Chopper Stabilised Pre-Amplifier
## 2 Materials and Methods
### Comparison of IRTs Measuring Steady-State Temperature  
Comparisons were undertaken to determine the capabilities of the IRTs to maintain a nominal zero value when the IRTs were blanked by a cover at ambient temperature and a steady-state value when the photodiodes were exposed to a source at an arbitrary steady-state temperature. The IRTs were exposed to radiant sources with no optical configuration, other than the intrinsic window of the photodiodes. This, therefore, ensured a fair and direct comparison between the IRTs without the usual uncertainties associated with optical alignment, including aligning both IRTs to the target source at the same time. The IRTs were positioned at the same arbitrary distance of 150 mm from the radiant source and adjacent to the aperture centreline. The IRTs, therefore, measured the same radiant source concurrently and from adjacent positions, thereby ensuring that no difference arose from systematic errors associated with the experimental configuration. The TIA output voltages were recorded on a pair of Keysight Technologies 34465A digital multi-meters (DMMs) (Keysight Technologies Malaysia, Penang, Malaysia) with identical configurations to measure and store data. Measurements were recorded at 1 min intervals for three consecutive durations of 100 h each. The data sets were concatenated to provide contiguous IRT output voltage measurements over a total duration of 300 h. The configuration of the blackbody source and photodiodes is illustrated in Figure 1.  
The output voltages measured from the IRTs under the zero signal conditions were interpreted as resulting from the total of the offset voltage and noise of the op-amps only. The source was changed from providing a zero signal condition to both of the IRTs receiving blackbody radiation from an Ametek Land Instruments Landcal P1200B blackbody source (Ametek Land Instruments, Dronfield, UK), set at an arbitrary and constant temperature of 700 \({}^{\circ}\)C. The noise power on the output voltage was represented by the variance around the mean in both zero signal and steady-state signal tests.  
It was not critical that identical conditions were achieved for each IRT because small differences between the mean output voltages would not influence the variances. The excess kurtosis values of the measurements were evaluated to indicate whether the distributions had extensive or restricted tails and to indicate how much extremal values affected the variances.
# SwitchFuzz: Switch Short-Term Goals in Directed Grey-Box Fuzzing
## 6 Results
### Crash Exposure Capability
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Project**} & \multirow{2}{*}{**Tatget**} & \multirow{2}{*}{**Tool**} & \multicolumn{4}{c}{**nT**} & \multicolumn{4}{c}{**1T**} \\ \cline{3-8}  & & & **Runs** & **TTE(s)** & **Factor** & **Runs** & **TTE(s)** & **Factor** \\ \hline \multirow{2}{*}{MJS 1.21} & \multirow{2}{*}{mjs.c:13732} & SwitchFuzz & 6 & 12,520 & - & 4 & 20,221 & - \\ \cline{2-8}  & AFLGo & 4 & 14,234 & 1.14 & 1 & 21,175 & 1.05 \\ \hline \multirow{2}{*}{cb-multios[11]} & \multirow{2}{*}{service.c:65} & SwitchFuzz & 8 & 21 & - & 8 & 17 & - \\ \cline{2-8}  & AFLGo & 8 & 28 & 1.33 & 8 & 24 & 1.41 \\ \hline \multirow{2}{*}{libming 0.4.8} & \multirow{2}{*}{decompile.c:398} & SwitchFuzz & 7 & 6798 & - & 8 & 9365 & - \\ \cline{2-8}  & AFLGo & 6 & 8144 & 1.2 & 5 & 16,896 & 1.8 \\ \hline \multirow{2}{*}{libming 0.4.8} & \multirow{2}{*}{decompile.c:349} & SwitchFuzz & 8 & 13,073 & - & 5 & 16,503 & - \\ \cline{2-8}  & AFLGo & 7 & 21,004 & 1.61 & 3 & 19,083 & 1.16 \\ \hline \multirow{2}{*}{ysam 1.3.0} & \multirow{2}{*}{nasm-parse.c:1349} & SwitchFuzz & 8 & 9543 & - & 7 & 16,235 & - \\ \cline{2-8}  & AFLGo & 8 & 14,007 & 1.47 & 4 & 22,305 & 1.37 \\ \hline \multirow{2}{*}{ysam 1.3.0} & \multirow{2}{*}{nasm-eval.c:142} & SwitchFuzz & 6 & 22,374 & - & 7 & 22,572 & - \\ \cline{2-8}  & AFLGo & 4 & 22,766 & 1.02 & 2 & 26,542 & 1.18 \\ \hline \multirow{2}{*}{ysam 1.3.0} & \multirow{2}{*}{intrum.c:415} & SwitchFuzz & 7 & 14,698 & - & 7 & 17,915 & - \\ \cline{2-8}  & AFLGo & 5 & 21,634 & 1.47 & 3 & 24,040 & 1.34 \\ \hline \multirow{2}{*}{jasper[12] 1.900.1} & \multirow{2}{*}{mif\_cod.c:491} & SwitchFuzz & 8 & 83 & - & 8 & 114 & - \\ \cline{2-8}  & AFLGo & 8 & 103 & 1.24 & 8 & 140 & 1.22 \\ \hline \multirow{2}{*}{libtiff 0.4.7} & \multirow{2}{*}{tiff2 ps.c:2479} & SwitchFuzz & 8 & 1353 & - & 8 & 1512 & - \\ \cline{2-8}  & AFLGo & 8 & 1999 & 1.3 & 8 & 3376 & 2.23 \\ \hline \multirow{2}{*}{libxml 20902} & \multirow{2}{*}{valid.c:1181} & SwitchFuzz & 8 & 255 & - & 8 & 420 & - \\ \cline{2-8}  & AFLGo & 8 & 518 & 2.03 & 8 & 705 & 1.68 \\ \hline \multirow{2}{*}{libxml 20902} & \multirow{2}{*}{Valid.c:1195} & SwitchFuzz & 7 & 15,092 & - & 5 & 21,516 & - \\ \cline{2-8}  & AFLGo & 4 & 16,995 & 1.13 & 3 & 24,178 & 1.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of AFLGO vs. Switchfuzz in terms of crash recurrence capability on 11 targets in 7 real-world programs.
## 3 Results
### Classification Performance
These results are presented considering all variables (\(d=26\)) and only the selected variables (\(\hat{d}=14\)) when using the bootstrap resampling method. Comparing the figures of merit obtained using 26 and 14 features, it is observed that models using 14 features show a slight improvement in the classification performance  
Figure 6: Mean \(\pm\) std of the sensitivity (left panels) and AUC (right panels) considering 5 test subset partitions and different IR values, classifiers, and a hybrid approach (by combining undersampling and oversampling strategies) with: (**a,b**) SMOTEN; (**c,d**) TVAE; and (**e,f**) CTGAN. for most of the models. Mainly, this improvement can be seen when considering TVAE and the hybrid technique (average AUC value of 0.66 with all variables versus 0.70 with FS). However, the best performance is obtained when CTGAN and the hybrid strategy are considered, also outperforming the results provided by the undersampling approach.
# Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks
## 5 Conclusions  
Seizures cause physical and mental health disorders, and early detection of epileptic episodes and timely intervention can reduce patient suffering. Many studies have employed machine learning and deep learning methods for seizure detection. In this study, we propose a novel miniaturized capsule network framework to classify epileptic EEG signals.  
Its innovation lies in the compact capsule network architecture, which preserves the spatial characteristics of subtle epileptic EEG signals while reducing the number of network layers and simplifying the overall design. This study evaluates the efficacy of using the proposed micro-capsule network framework, identifying epileptic EEG signals in small-sample datasets. Experimental results demonstrate that the compact capsule network significantly enhances epilepsy detection, achieving recognition rates of 99.94% (AB vs. E), 99.49% (C vs. E), 99.62% (D vs. E), and 99.06% (A vs. C vs. E). Comparative analysis with existing literature confirms the method's effectiveness in automated seizure detection. These findings could extend to real-time seizure detection systems, mitigating the duration and discomfort of seizures while reducing clinicians' workloads. Further development of a lightweight capsule network framework is required to enhance method practicality, specifically addressing real-time processing constraints for single-channel epilepsy EEG signals under limited sample conditions, non-invasive dry or semi-dry electrodes are used to collect EEG signals, thereby expediting clinical translation of detection algorithms.  
Writing--original draft, writing--review and editing, B.W.; data curation, writing--original draft, [PERSON] ([PERSON]); software, writing--original draft, H.Z.; writing--review and editing, [PERSON] ([PERSON]); funding acquisition, writing--review, C.W. All authors have read and agreed to the published version of the manuscript.  
This work is financially supported by the STI 2030-Major Projects of Ministry of Science and Technology of the People's Republic of China (2021 ZD0201600 and 2021 ZD0201604) and the National Major Scientific Instruments and Equipments Development Project of the National Natural Science Foundation of China (82327810).  
Not applicable.  
The datasets analyzed for this study can be found in the Bonn University EEG dataset by visiting the following link: [[https://github.com/RYH2077/EEG-Epilepsy-Datasets](https://github.com/RYH2077/EEG-Epilepsy-Datasets)]([https://github.com/RYH2077/EEG-Epilepsy-Datasets](https://github.com/RYH2077/EEG-Epilepsy-Datasets)) (accessed on 25 August 2023).  
The authors are grateful to the Bonn University seizure dataset and anonymous reviewers for their valuable comments.  
The authors of this research paper declare that they have no conflicts of interest with any person or organization for carrying out this research work.