esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of OCT in the Gastrointestinal Tract
## 3 OCT Applications in the GI Tract
### Small Intestine  
The length, tortuous anatomy and folds in the small intestine pose particularly difficult challenges for OCT imaging in the GI tract. Nonetheless, several papers show successful utility of OCT. [PERSON] et al. demonstrated OCT's ability to differentiate celiac disease based on villous atrophy [97; 98], while [PERSON] et al. used OCT to reveal the submucosal mass of a duodenal neuroendocrine tumor (NET) [99]. Additionally, [PERSON] et al. used ultrahigh-speed endoscopic OCT to study the structural differences of [PERSON]'s disease in the terminal ileum (TI), showing that TI with [PERSON]'s lieitis exhibited irregular mucosal patterns and enlarged villous internal structures [100].
## 6 Conclusions  
Line scanning is a major hyperspectral imaging measurement method that is well-suited for inspection of food and agricultural products, especially for food items moving along the processing and production lines. Driven by interest from both academic and industrial areas, line-scan hyperspectral imaging techniques have been evolving rapidly over the past 15 years in the food and agricultural field. The techniques have been intensively researched and developed with use of different physical principles and wavelength ranges. Various custom-designed line-scan hyperspectral imaging systems have been developed around the world, and successfully used to measure physical, chemical, and biological properties of a broad range of food and agricultural products for the purposes of surface, subsurface, and internal evaluations. As the technologies mature, integrated line-scan hyperspectral imaging systems have become commercially available to provide all-in-one inspection solutions, helping to further expand the scope of practical food applications. Existing and emerging techniques in this area were reviewed to reflect current practices and future trends of line-scan hyperspectral imaging for food safety and quality applications. Advances in both high-performance hardware components and fast data analysis methods and software will drive the future development of line-scan hyperspectral imaging techniques.  
This research was partially supported by a grant from the Next-Generation BioGreen 21 Program (No. PJ01125602), Rural Development Administration, Republic of Korea.  
**Author Contributions:** [PERSON], [PERSON], and [PERSON] conducted pioneering work on this subject and led the choice of the topic for the review; [PERSON] and [PERSON] further developed the techniques and performed the literature review; [PERSON] and [PERSON] wrote the paper.  
Figure 11: Line-scan hyperspectral/multispectral reflectance imaging for online poultry carcass inspection: (**a**) a photograph of the system on a commercial chicken processing line operating at a speed of 140 birds per minute; and (**b**) compiled line-scan images of one wholesome chicken and one systemically diseased chicken at selected wavelengths. Reproduced with permission from [32], Copyright ASABE, 2008.
## Appendix A
\begin{split}& cV(\zeta)^{(3\alpha-1)/2\alpha}-2c_{\Delta}V( \zeta)^{1/2}\Big{|}\Delta\Big{|}\geq 0\\ & cV(\zeta)^{(3\beta-1)/2\beta}-2c_{\Delta}V(\zeta)^{1/2}\Big{|} \Delta\Big{|}\geq 0\end{split} \] (A21)  
one can deduce that:  
\[ \begin{split}& V(\zeta)\geq\Big{(}\frac{2c_{\Delta}|\Delta|}{c} \Big{)}^{2\alpha/(2\alpha-1)}\\ & V(\zeta)\geq\Big{(}\frac{2c_{\Delta}|\Delta|}{c}\Big{)}^{2 \beta/(2\beta-1)}\end{split} \] (A22)  
Because of \(|\Delta|\leq\varepsilon d_{1}\), (A22) can be rewritten as follows:  
\[ \begin{split}& V(\zeta)\geq\Big{(}\frac{2c_{\Delta}d_{1}}{c} \Big{)}^{2\alpha/(2\alpha-1)}\triangleq V_{1}\\ & V(\zeta)\geq\Big{(}\frac{2c_{\Delta}d_{1}}{c}\Big{)}^{2\beta/ (2\beta-1)}\triangleq V_{2}\end{split} \] (A23)  
When \(2c_{\Delta}\Delta/c\geq 1\), we can obtain the following from (A19):  
\[\dot{V}(\zeta)\leqslant-cV(\zeta)^{\frac{3\delta-1}{2\beta}}\] (A24)  
Then, the convergence time (\(T_{1}\)) from \(V_{0}\triangleq V(\zeta(t_{0}))\) to \(V_{1}\) is:  
\[ \begin{split}& T_{1}\leqslant\frac{1}{\varepsilon}\cdot\frac{2 \beta}{\beta-1}\Bigg{(}V_{1}^{-\frac{\delta-1}{2\beta}}-V_{0}^{-\frac{\delta-1}{ 2\beta}}\Bigg{)}\leqslant\\ &\frac{1}{\varepsilon}\cdot\frac{2\beta}{\beta-1}V_{1}^{-\frac{ \delta-1}{2\beta}}=T_{\max,1}\end{split} \] (A25)  
In other words, \(\zeta\) is the finite-time (\(T_{1}\)) convergent to the neighborhood (\(E_{1}\)) of the origin, where:  
\[\mathbf{E}_{1}=\Big{\{}\zeta\in\mathbb{R}^{2}\Big{|}V(\zeta)\leq V_{1}\Big{\}}\] (A26)  
and \(T_{1}\leq T_{\max,1}\). When \(2c_{\Delta}\Delta/c\leq 1\), we can obtain the following from (A20):  
\[\dot{V}(\zeta)\leqslant\left\{ \begin{array}{c}-cV(\zeta)^{\frac{3\delta-1}{2 \beta}},V(\eta)\geqslant 1\\ -cV(\zeta)^{\frac{3\delta-1}{2\beta}},1\geqslant V(\eta)\geqslant V_{2}\end{array} \right.\] (A27)  
Then, the convergence time (\(T_{2}\)) from \(V_{0}\triangleq V(\zeta(t_{0}))\) to \(V_{2}\) is:  
\[ \begin{array}{c}T_{2}\leqslant\frac{1}{c}\cdot\frac{2\beta}{\beta-1}\left(1- V_{0}^{-\frac{\delta-1}{2\beta}}\right)+\frac{1}{c}\cdot\frac{2\alpha}{1- \alpha}\left(1-V_{2}^{\frac{1+\alpha}{2\beta}}\right)\leqslant\\ \frac{1}{c}\cdot\left(\frac{2\alpha}{1-\alpha}+\frac{2\beta}{\beta-1}\right)=T _{\max,2}\end{array} \] (A28)  
In other words, \(\zeta\) is the finite-time (\(T_{2}\)) convergent to the neighborhood (\(E_{2}\)) of the origin, where:  
\[E_{2}=\left\{\zeta\in\mathbb{R}^{2}\Big{|}V(\zeta)\leq V_{2}\right\}\] (A29)  
and \(T_{2}\leq T_{\max,2}\). Hence, \(\zeta\) is the fixed-time convergent to a neighborhood of the origin. The convergence time is presented in (A25) and (A28), and the convergence domain is presented in (A26) and (A29). By \(\zeta_{i}\triangleq e_{i}(\varepsilon t)/\varepsilon^{2-i},i=1,2\), the observation error of \(\left|\sum\limits_{j=1}^{N}a_{ij}(t_{goi}-t_{goj})-Z_{1}\right|\) and \(|w_{ri}-Z_{2i}|\) will converge to a neighborhood of the origin in a fixed time. This completes the proof of Theorem 1.
# A Brief Survey of Fixed-Parameter Parallelism
## 6 Common \(Fpt\) Methods
### Kernelization  
The notion of kernelization plays a central role in parameterized complexity theory. In general, a kernelization algorithm takes an arbitrary instance \((I,k)\) of a problem that is parameterized by \(k\), as input, and produces as output an equivalent instance \((I^{\prime},k^{\prime})\) such that \(|I^{\prime}|\leq g(k^{\prime})\) for some function \(g\) that is independent of the input size (depends only on the parameter \(k\)). The instance \((I^{\prime},k)\) is often called a problem kernel.  
When a kernelization algorithm exists for a parameterized problem \(X\) we say \(X\) is kernelizable. The importance of kernelization in this field also stems from the fact that a problem is fixed-parameter tractable if and only if it is kernelizable [26]. Of course the interest is in achieving a smallest possible kernel, and it is natural to further classify \(FPT\) problems based on whether kernels of polynomial-size can be obtained (in polynomial-time). For example, while Vertex Cover admits a kernel of size bounded by \(2k\), the Connected Vertex Cover problem is unlikely to have a polynomial kernel unless \(NP\subseteq coNP/Poly\)[27].  
A kernelization algorithm is usually based on identifying certain structures in the input instance and then dealing with them, to reduce the instance in size, successively until the instance no longer has such favorable structures to reduce. After that, the instance should be of bounded size in terms of the parameter.
## 3 Results and Discussion  
### Breakdown Results  
Table 1 presents the evolution of the breakdown voltage of kraft paper impregnated with three types of insulating oils, two natural esters (NE 1204 and NE 1215) and one mineral oil (MO), subjected to thermal aging at 120 \({}^{\circ}\)C for various durations: S0 (fresh oil), S1 (10 days), S2 (20 days), and S3 (30 days).  
In general, a progressive decrease in breakdown voltage as aging time increases was observed, regardless of the insulating oil used (Figure 2). This decline reflects the thermal degradation of the cellulosic paper, leading to a gradual loss of its dielectric strength. However, the rate of degradation varies significantly depending on the impregnation fluid. Paper impregnated with mineral oil shows the greatest reduction in breakdown voltage, dropping from 16.9 kV at S0 to 14.1 kV at S3, a decrease of approximately 16.6%. In comparison, the reduction is considerably less severe with natural esters: NE 1215 decreases from 18.4 kV to 17.0 kV (\(-\)7.6%), while NE 1204 drops from 18.5 kV to 16.5 kV (\(-\)10.8%). This result confirms the superior thermal stability of natural esters compared to mineral oil. This enhanced performance may be attributed to several factors: the greater ability of natural esters to absorb and retain moisture, their antioxidant properties, and their stronger chemical affinity with cellulose, which collectively help to limit the degradation mechanisms of the paper. Interestingly, even in the initial state (S0), papers impregnated with natural esters exhibit higher breakdown voltages than those with mineral oil, suggesting better initial impregnation and increased compatibility with cellulosic insulation. When comparing the two natural esters tested, NE 1215 demonstrated better performance after 30 days of aging, with a final breakdown voltage of 17.0 kV compared to 16.5 kV for NE 1204. This difference may be attributed to variations in chemical composition or oxidative resistance between the two ester types, although further chemical analysis is needed to confirm this assumption.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Oil Type**} \\ \hline
**Aging Stage** & **NE 1204** & **NE 1215** & **MO** \\ \hline S0 (fresh oil) & 18.5 & 18.4 & 16.9 \\ S1 (10 days) & 17.6 & 17.7 & 16.2 \\ S2 (20 days) & 17.2 & 17.5 & 15.6 \\ S3 (30 days) & 16.5 & 17 & 14.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Breakdown voltage evolution of paper impregnated with different insulating oils (in kV).  
Figure 2: Histograms of breakdown voltages (S0 to S3) for the oil/paper insulation samples.
## 4 Conclusions  
In this study, the effects of trace transition alloy elements and two kinds of homogenization on the mechanical properties and the recrystallized microstructures of Al-4.5 Zn-1.5 Mg alloys were explored through the microstructure observation and the measurement of mechanical properties. The results are summarized as follows:  
1. The grain refinement effect of the as-cast, homogenized and T6 recrystallized grains of the alloy containing 0.05 Sc was more obvious than that of the alloy containing 0.1 Zr. Through the homogenization heat treatment, most of the as-cast dendrite structures of Al-4.5 Zn-1.5 Mg alloys containing Zr and Sc had been eliminated.
2. After the homogenization heat treatment, the Al-4.5 Zn-1.5 Mg alloys containing Zr and Sc precipitated high-temperature thermally stable Al\({}_{3}\)Zr and Al\({}_{3}\)Sc grain phases dispersed in the aluminum matrix. The dispersed grain phases had the effect of suppressing the recrystallization and grain growth. Although the Al\({}_{3}\)Zr grains were finer than the Al\({}_{3}\)Sc grains, the dispersed Al\({}_{3}\)Sc grain phases of the Sc alloy still had a better ability to suppress T6 recrystallization and grain growth than the Al\({}_{3}\)Zr dispersed grain phase of the Zr alloy.  
Figure 9: Fracture surfaces observation by SEM of T6 tempered: (**a**) 1-Hom. alloy A(0.1 Zr); (**b**) 1-Hom. alloy B(0.05 Sc); (**c**) 2-Hom. alloy A(0.1 Zr); (**d**) 2-Hom. alloy B(0.05 Sc).  
Compared with the one-stage homogenization, the two-stage homogenization made the Al\({}_{3}\)Zr and Al\({}_{3}\)Sc dispersed grains finer and denser. Therefore, the mechanical strength of the T6 alloys through the two-stage homogenization heat treatment was better than that through the one-stage homogenization heat treatment. Through the two different kinds of homogenization, the size difference between the Al\({}_{3}\)Zr dispersed grain phases of the Al-4.5 Zn-1.5 Mg alloys was larger than that between the Sc alloys. As shown in the collected data, through the two-stage homogenization heat treatment, the Zr alloy improved the mechanical strength more efficiently than the Sc alloys.
4. In the T6 state, the Al-4.5 Zn-1.5 Mg alloy containing a trace of scandium (0.05 Sc) through the two-stage homogenization heat treatment had the lowest recrystallization amount and the highest tensile strength.  
Conceptualization, S.-L.L.; Data curation, T.-A.P.; Formal analysis, T.-A.P. and S.-L.L.; Funding acquisition, H.-Y.B.; Investigation, Y.-C.C. and J.-W.Z.; Methodology, J.-W.Z.; Project administration, S.-L.L.; Resources, M.-C.C.; Software, M.-C.C.; Writing-original draft, Y.-C.C. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the financial support received from the Ministry of Science and Technology (R.O.C) under Contract No. 109-2221-E-008-040-. Thanks to Prof. [PERSON] and [PERSON] of Instrumentation Center, National Taiwan University for FEG-SEM experiments.  
The authors declare no conflict of interest.
## 1 Introduction
### External Load--Monitoring Distance during Game Play  
GNSS--Global Navigation Satellite Systems tracking (named in common as GPS tracking) of individual athletes during football game play can provide information about the physical demands and external load [13; 14]. In particular, data describing total distance (TD--total distance covered during a match), sprint distance (SD--distance covered with a speed of a minimum 4.75 m/s for at least 1 s. A sprint effort ends when speed has decreased below 75% of the sprint threshold) and high intensity running (HIR--distance covered with speed over 5 m/s) during game play in AF players. Although GPS tracking is a good tool for the assessment of match loads, simultaneously neither GPS nor HR monitoring is adequate for monitoring the training effort. [PERSON] et al. (2022) indicate the following parameters as the most important in assessing the load in team sports: accelerations and decelerations, impacts, high intensity efforts, aerobic and anaerobic components--speed zones, total distance and relative distance per minute [15].  
An understanding of the relationship of pain and loads on single lower extremity and upper extremities, including the hands, for mobilizing the crutches swiftly and accurately, is lacking [10]. An understanding of the internal and external loads would help to improve the training process, team management, design prevention and rehabilitation programs. However, to achieve the above setting, further study directions are needed.
# A Low-Power WLAN Communication Scheme for IoT WLAN Devices Using Wake-Up Receivers
## 3 Target Scenarios for IoT Device in IEEE 802.11 ba  
The use cases of IEEE 802.11 ba include various proposed scenarios of IEEE 802.11 ba, including various IoT applications. In [22], scenarios including warehouses, cattle farms, and wearable devices were proposed. In those scenarios, there are a number of sensors that are controlled by a mobile device, which may request the sensed data from all of the sensors, as in Figure 5. For example, in smart-home applications in Figure 6, light sensors, temperature sensors, and door sensors transmit sensed data to a mobile device when the user wants it, and after the user is notified about the information, they may transmit control information to the smart home device to turn off the light, alarm, etc. In those IoT applications, the mobile device needs to acquire data from many sensors.  
The conventional transmission method for this scenario in IEEE 802.11 ba is based on unicast transmission, as shown in Figure 7. With this method, the triggering frame that requests the sensed data from the STA may be implemented in the application layer. When the mobile data needs to collect the sensors' data, the AP transmits the wake-up frame to the STA, and it then waits for the predefined wake-up delay in the association procedure. After the wake-up delay, the AP starts to transmit the triggering frame of the sensed data, which is denoted as a single-user (SU) trigger, to request the sensed data. The STA that wakes up from the wake-up frame and receives the single-user trigger sends the acknowledgment frame for the single-user trigger and transmits the data in the SU physical layer protocol data unit (PPDU) after a subsequent contention. For the successful transmission of data frames, the AP transmits the acknowledgment of the data frame, and starts the transmission procedure for the next STA.  
Figure 4: Uplink multiuser OFDMA procedure in accordance with IEEE 802.11 ax. TF = trigger frame; Multi-STA BA = multi-station blockack; UL MU PPDU = uplink multi-user physical layer protocol data unit.  
However, in the conventional transmission procedure in this massive uplink scenario, the transmission delay is increased as the number of STAs increases because of the repetition of the entire procedure for each STA. Therefore, the conventional method based on unicast transmission may not be sufficient to meet the required delay.
## 2 Materials and Methods
### Development of CAD and FE Model  
A 52 kg male participant with a below-knee (BK) amputation on his right leg voluntarily took part in this study. The participant, a traumatic amputee, had been using a PTB socket with a SACH foot for daily activities for 13 years. Recruitment was limited to one subject due to the challenges in enlisting trans-tibial amputees for socket testing. The study received approval from the ethical committee of the University of Engineering and Technology Peshawar (protocol code: 371/Met/UET). Informed consent was obtained prior to participation.  
Computed tomography (CT) scans were performed from the hip joint to the stump end with a scanning interval of 6 mm. Scans taken with the participant in a supine position and the knee extended horizontally were processed to delineate bone and soft tissue boundaries, which were then converted into 3D Stereolithography (STL) models using Slicer 5.0 software. For the liner design, the outer surfaces of the soft tissue CAD model were extracted and extruded by 3 mm, both inward and outward, using Autodesk Meshmixer. This inward compression aimed to account for the bony characteristics of the stump and optimize tissue conformity.  
The internal profile of the liner was designed to mimic the anatomical shape of the residual limb. The socket CAD model was created by offsetting the outer surface of the stump by 3 mm to accommodate the liner and providing a uniform thickness of 4 mm. These dimensions were chosen based on established design standards and biomechanical considerations [43].  
\[\textit{Socket Thickness}\ (\mathrm{mm})=\textit{Patient weight}\ (\mathrm{kg})/\ 20 \tag{1}\]  
The STL files for the bone, soft tissues, liner, and socket were processed in Geomagic Studio to create surface bodies using Non-Uniform Rational Basis Splines (NURBS) patches. These surface models were then converted into a solid model using Rhinoceros 6.0 software. Finally, the models were imported into ANSYS Workbench 19.0 for meshing and FE analysis. The finite element mesh consisted of 29,361 nodes and 15,622 tetrahedral elements, each  
Figure 1: Methodology adopted for development and evaluation of TSB prosthesis.  
with a 5 mm size. Material properties for the bone, soft tissues, liner, and socket were assumed to be linearly elastic, isotropic, and homogeneous, as outlined in Table 1[30; 43; 44].
## 1 Introduction
### Advancements over Prior FMCW Experiments with GDD Technology
This eliminates the need for mechanically aligned components like the Wire Grid Polarizer (WGP), simplifying the system's overall architecture. Additionally, the compact and robust nature of the waveguide splitter improves stability, making it more suitable for advanced imaging configurations, including future FPA implementations. Range Extraction vs. Imaging  
The primary limitation of the previous method was its scope, which was confined to range extraction. While the system could determine distances, it was incapable of constructing detailed 3D surface profiles or imaging. This limitation stemmed from both the optical design and the manual nature of the scanning process. In contrast, the present work represents a breakthrough by enabling 3D imaging with FMCWs. The integration of automated raster scanning, coupled with improved signal acquisition and processing, facilitates the creation of detailed 3D representations of objects. This advancement was made possible through the incorporation of a waveguide splitter and a more sophisticated scanning mechanism. Automation and Efficiency  
The earlier setup required manual adjustments for scanning, which was time-intensive and prone to human error. In this work, the system is automated for raster scanning, ensuring precise and efficient control over beam positioning. Additionally, signal acquisition and processing are fully integrated, further streamlining the imaging process and improving reproducibility. In summary, the current work builds upon previous efforts by addressing the limitations of the Wire Grid Polarizer, transitioning to a waveguide splitter for an enhanced SNR and enhanced stability, adopting a refined quasi-optical design, and expanding the system's capabilities from simple range extraction to automated 3D imaging. These advancements collectively mark a significant evolution in MMW imaging using the FMCW radar principle with GDD technology.
# Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM
## 2 Algorithm Description
### Track Anomaly Detection Based on MOD-Bi-LSTM
#### 2.1.3 Track Outliers Detection  
1. Model training: The data is divided into training set and test set. The Bi-LSTM model uses the information of the current time, pre-t time and post-t time. The track features were extracted by forward and backward LSTM, respectively. In the feature extraction process, each track point is represented by a 5-dimensional vector: \(x_{t}\)= {longitude, latitude, height, heading, speed}. The \(x_{0}\) represents the input information of the track point at the current time. The \(x_{-t}\) and \(x_{t}\) represent the track point information at the time t before and after \(x_{0}\), respectively. The input of the model is the sequences of track point vectors, and the features between the sequences are extracted automatically by Bi-LSTM. In the training process, the Bi-LSTM model can automatically learn the relationship between the track sequences.
2. Flight prediction: The test set is added into the trained Bi-LSTM neural network model to predict the flight time series. Various evaluation indexes are used to evaluate the algorithm performance, and the prediction results are compared with the LSTM and Bi-LSTM algorithm without MOD preprocessing.
3. Aircraft abnormal behavior detection: the predicted sequence value and the real sequence value are differentiated to obtain the difference sequence, and the difference sequence is solved by the SVDD algorithm to obtain the threshold of abnormal detection. In the test phase, whether the flight state of the aircraft is abnormal is judged by comparing the differentials with the threshold.  
Figure 1: Flowchart of MOD-Bi-LSTM anomaly detection algorithm.