esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Understanding the Function of Aegerolysins
## 2 Aegerolysins
### Fungal Aegerolysins from Ascomycota--Dothideomycetes  
Aegerolysins from _Alternaria gaisen_  
The cosmopolitan genus _Alternaria_ (_Pleosporales_) contains many species of economic importance, including saprophytes, phytopathogens, and zoopathogens known to produce a variety of mycotoxins and allergens [36]. _Alternaria gaisen_ Nagano ex Hara, 1920 (black spot of Japanese pear), occurs on the Japanese pear, _Pyrus pyrifolia_, especially the cultivar Nijisseiki, but also other cultivars of both Japanese and Chinese pears [37].  
The accuracy and effectiveness of crop protection measures depends on correctly identifying of the pest of concern from the outset. However, the relatively invariant internal transcribed spacer sequence of structural ribosomal RNA that have been described and selected for fungal barcoding, for _Alternaria_ species and strains did not allow discrimination of isolates of different _A. alternata_, _A. arborescens_, _A. brasscicola_, _A. gaisen_, _A. mali_, _A. tenuissima_, and _A. yalinificiens_. When the partial genomic sequences of the putative aegerolysin **L152** were analyzed, the lineages were resolved, and they reflected the diversity previously suggested by the morphological evaluation of the sporulation patterns [36]. The presence of putative aegerolysin **L152** homologs in both phytopathogenic and non-phytopathogenic species argues against their involvement in pathogenesis. In addition, the putative aegerolysin gene is not located on any of the conditionally disposable chromosomes, so it is likely to be present in all small-spored _Alternaria_ spp. and not only in the toxin producers [36]. Expression of **L152** in _A. gaisen_ was strongly upregulated after light exposure, a stimulus that also triggers conidibophores to develop [36].  
The only aegerolysin of _A. geisen_ strain BMP2338, **L152**, also has a neighboring partner protein gene: **L152B**.
# Noninvasive Magnetic-Marking-Based Flow Metering with Optically Pumped Magnetometers
## 2 Materials and Methods
### Determination of \(\Delta t\)  
The voltage \(U(t)\) applied to the RF coil is a sinusoid  
\[U(t)=A(t)\sin(\omega_{L}t), \tag{3}\]  
where \(\omega_{L}\) is the Larmor frequency and \(A(t)\) is the modulated amplitude with a sinusoidal shape. To feed the RF coil, only the positive halfwave was used. Let the time of excitation \(t_{E}\) be defined as \(A(t_{E})=\max(A(t))\). Then the detection time \(t_{D}\) is the corresponding point in time where the magnetic signal detected by the OPM is at its local minimum. An illustration of how the envelope RF voltage affects the magnetic signal recorded by the OPM is shown in Figure 3. To determine \(t_{E}\) and \(t_{D}\) from the RF voltage and magnetic field data, parabolas were fitted to the raw data:  
\[f(t)=y(t-t_{E,D})^{2}+\text{Offset}. \tag{4}\]  
The horizontal shift of the function \(t_{E}\) and \(t_{D}\) were used to calculate \(\Delta t\) and determined as fit parameters. Thus, \(\Delta t\) is the time difference between \(t_{E}\) and \(t_{D}\):  
\[\Delta t=t_{D}-t_{E}. \tag{5}\]  
This method of data acquisition implies that we measure the average flow of water inside the tube. For demonstration purposes the AM frequency was set to 100 mHz.  
Figure 3: Schematic representation of the RF-Pulse and the OPM signal used in the experiment. The center of the RF pulse envelope (blue) defines the time \(t_{E}\). The time where the OPM signal (red) reaches its minimum defines \(t_{D}\). The quantity of interest is the delay between \(t_{E}\) and \(t_{D}\): \(\Delta t=t_{D}-t_{E}\).
## 3 PolyCISAMR Algorithm
### Overview of CISAMR Algorithm
Figure 13: Transforming a structured mesh composed of four-node quadrilateral elements into a conforming mesh using the CISAMR algorithm (a regular node is shown in “yellow” whereas a hanging node is shown in “red”): (**a**) \(h\)-adaptive refinement in the vicinity of material interfaces; (**b**) \(r\)-adaptivity of the nodes of elements cut by the interface; and (**c**) sub-triangulating the elements deformed during the \(r\)-adaptivity process and elements with hanging nodes. * If \(d_{1}<0.5h_{1}\) and \(d_{2}\geq 0.5h_{2}\): move the node to the closer intersection point at distance \(d_{1}\) and ignore the presence of the intersection point at distance \(d_{2}\). * If \(d_{1}<0.5h_{1}\) and \(d_{2}<0.5h_{2}\) and \(d_{1}<d_{2}\): move the node to the closer intersection point at distance \(d_{1}\) and discard the farther intersection point at distance \(d_{2}\). * Finally, all background elements that were deformed during the \(r\)-adaptivity process, along with elements with hanging nodes created due to \(h\)-adaptive refinement of their neighboring elements, are subdivided into conforming three-node triangular (T3) elements. The final conforming mesh created after applying this step is depicted in Figure 13c. Note that all remaining nonconforming elements after the completion of the \(r\)-adaptivity phase are diagonally cut by the interface. To minimize the aspect ratio of the resulting T3 sub-elements, the following rules are applied while subdividing Q4 elements (cf. Figure 15):
* **Case 1:** If a Q4 element does not intersect the interface along the diagonal emanating from its smallest angle \(\theta_{\text{min}}\), T3 sub-triangles are created by cutting that element along the diagonal corresponding to its largest angle. * **Case 2:** Otherwise, there are two possible scenarios: 1. If \(\theta_{\text{min}}>60^{\circ}\): subdivide the element by cutting along \(\theta_{\text{min}}\) to ensure that the aspect ratios of the resulting T3 elements are acceptable. 2. If \(\theta_{\text{min}}<60^{\circ}\): subdivide the element by cutting along both diagonals, resulting in the creation of four conforming sub-triangles. It is worth mentioning that the CIAMR algorithm guarantees an upper bound of three on-element aspect ratios for problems with smooth interfaces, which might increase to four for domain boundaries and interfaces with sharp corners (e.g., the polycrystalline microstructures discussed next). More algorithmic details are provided in [44], along with the pseudocode of the original CISAMR algorithm for creating hybrid Q4-T3 meshes. Figure 14: Different case scenarios of relocating a mesh node during the \(r\)-adaptivity phase of CISAMR. Figure 15: Single and double diagonal rules for sub-triangulating a Q4 element cut by the material interface.
# STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction
## 2 Related Work
### RefSR  
The reference-based super-resolution (RefSR) technique utilizes a high-resolution (HR) reference image similar to a low-resolution (LR) image to enhance the quality of image reconstruction. Compared to the traditional single-image super-resolution (SISR) method [37], RefSR is able to recover image details better, and performs excellently, especially in the scenes with similar contents [38; 39]. The basic principle of RefSR is to supplement the missing content in LR images by extracting high-frequency information and details from the reference image, which usually involves the steps of feature extraction, alignment, and fusion to achieve more accurate pixel-level reconstruction. Representative models include the following: VDSR [7], which performs super-resolution reconstruction via multilayer convolutional neural networks (CNNs), and laid the foundation for the development of RefSR, although it is mainly used for SISR; SRGAN [12], which introduces generative adversarial networks (GANs) [40] to generate more realistic images, and whose adversarial training mechanism and perceptual loss can be adapted to RefSR; and DRCN [23], which improves the super-resolution effect through recursive structure and deep feature extraction, which is suitable to be used in conjunction with reference images. Recently, RefSR-GAN [41; 42], as a specially designed RefSR model, combines the adversarial training of GAN and the reference image feature extraction, aiming to improve the reconstruction quality in remote sensing images. RefSR has a wide range of potential applications in the field of remote sensing image reconstruction.
# Large Deformation Modeling of Wing-Like Structures Based on Support Vector Regression
###### Abstract  
Large flexible aircrafts produce large deformation during flight, leading to obvious geometric nonlinearities. Large deformation modeling is essential for modern aircraft design. Calculation of large deformation based on a full-order model often carries an unbearable computing burden. The reduced-order model (ROM) can be efficient in calculation but requires lots of test datasets. This study investigates support vector regression (SVR) to build a regression model to calculate the static large deformation of wing-like structures. The correlation coefficient (R) and root mean square error (RMSE) are used to evaluate the performance of the regression models. In contrast to the ROM that has been proposed, the regression model based on SVR requires far fewer training cases, with almost the same accuracy in this research. Meanwhile, comparison with another prediction model built based on random forest regression (RFR) has also been reported. The results reveal that the SVR algorithm has better accuracy on this issue. Finally, ground test results of a real large flexible wing model show that the regression model proposed here reaches a good agreement with measurement data under applied load. This work illustrates that the machine learning algorithm is an efficient and accurate way to predict large deformation of aircrafts.  
Keywords:large deformation; geometric nonlinearity; support vector regression; reduced-order models +
Footnote †: journal: Applied Sciences
## 4 Contrast Analyses between Different Rotation Modes
However, as the cylinder rotates into the backward motion region, the water droplets cannot impinge on the back surface of the cylinder because the linear speed of the cylinder is higher than the wind speed. The front surface can also capture the water droplets in the backward motion region. Therefore, the ice layer converges on the front surface of the cylinder and becomes thicker than the layer under the low TSR condition. It can also be seen from Figure 6 that the coverage area of the ice layer reduced with an increase in the TSR. The reason for this result is that the range of the inflow angle, which is the included angle between the relative wind speed and the linear velocity of the cylinder, decreases with an increase in the TSR. According to Figure 20, when the linear velocity of the cylinder is constant, the magnitude and direction of the relative wind speed changes at different positions in the circle. In contrast, when the cylinder rotates around the horizontal axis, the magnitude and the direction of the relative wind speed do not change, as shown in Figure 18. Based on the relative positions of the cylinder and the incoming flow, as shown in Figure 20, the ranges of the inflow angle under high TSRs were analyzed and are shown in Figure 21.  
It is assumed that the vector of the linear velocity of the cylinder coincides with the x-axis, and the range of the inflow angle decreases with an increase in the TSR. Under the low rotational speed condition, the linear velocity of the cylinder is lower than the wind speed. The direction of the relative wind speed approximately coincides with the direction of the wind speed. In this condition, the range of the inflow angle is \(360^{\circ}\). Therefore, in one circle of the cylinder trajectory, the whole cylinder surface can be in contact with the incoming flow, and ice accretion occurs on the whole cylinder surface. When the TSR is higher than 1, the range of the inflow angle gradually decreases, and the relative wind  
Figure 16:0 Schematic diagram of the relative positions of the cylinder and the incoming flow. speed only acts on the front surface of the cylinder. The range of the inflow angle narrows towards the linear velocity of the cylinder when the TSRs are 1, 1.5, 2.5 and 3.5. The ranges of the inflow angle are \(-90\)-\(90^{\circ}\), \(-42\)-\(42^{\circ}\), \(-24\)-\(24^{\circ}\) and \(-17\)-\(17^{\circ}\), respectively. Therefore, the decrease in the inflow angle is the key factor leading to the reduction of the icing region on the blade surface in the high TSR condition. Hence, the icing area ratio on the cylinder surface is approximately 50% in the high TSR condition.
## 3 Data and Methods  
### Data Sources  
As SAR imagery is available from 2017 onwards for the GBM delta coastline within Bangladesh, only images from this period were utilized for the analysis. To identify monsoonal flood events, area-averaged daily accumulated rainfall data from the Global Precipitation Measurement (GPM) mission were collected for the coastal region of Bangladesh for the years 2017 to 2024. The annual rainfall data were analyzed to identify peak rainfall events, and the event associated with the most extensive inundation was selected for flood mapping, as summarized in Table 1. For both monsoonal rainfall and cyclone-induced flooding, SAR images were acquired corresponding to the peak rainfall day and landfall dates of major cyclonic events, which are also mentioned in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Monsoon Rainfall Events**} & \multicolumn{4}{c}{**Cyclone Events**} \\ \cline{2-7}  & \multicolumn{3}{c}{**Area**} & & & & **Sustained** & \\ \multirow{2}{*}{**Year**} & **Peak** & **Awaraged** & \multirow{2}{*}{**SAR Image**} & **SAR Image** & **Name of** & **Findall Date** & **Winds** & \\  & **Rainfall** & **Mainfall** & **Acquisition Date** & **Cyclone** & **Landfall Date** & **G-min** & **SARI Image Acquisition Date** \\  & **Date** & **Magnitude** & **(mm/Day)** & & & & **Sustained** & \\ \hline
2017 & 24 July & 110 mm & 24 July to 29 July & Mora & 30 May & 110 km/h & 30 May to 5 June \\
2018 & 10 June & 112 mm & 11 June to 18 June & Titi & 10 October & 165 km/h & 9 October to 16 October \\
2019 & 5 June & 40 mm & 1 June to 8 June & Fani & 3 May & 185 km/h & 26 April to 3 May \\
2020 & 22 October & 110 mm & 22 October to 29 October & Amphan & 20 May & 240 km/h & 19 May to 26 May \\
2021 & 27 July & 98 mm & 27 July to 01 August & Yaas & 26 May & 140 km/h & 21 May to 28 May \\
2022 & 9 June & 50 mm & 9 June to 14 June & Sitrang & 24 October & 85 km/h & 24 October to 26 October \\
2023 & 7 August & 90 mm & 8 August to 0 August & Midhhi & 17 November & 95 km/h & 14 November to 19 November \\
2024 & 1 August & 110 mm & 2 August to 4 August & Remai & 26 May & 110 km/h & 24 May to 29 May \\ \hline \hline \end{tabular}
\end{table}
Table 1: Peak rainfall and cyclone events.
## 2 ERT Background  
ERT mainly uses two current poles (C1, C2) and two potential poles (P1, P2) to collect measurements, as shown in Figure 1. The low frequency alternating current accesses the geological formation through current poles. Under different geometrical positions, potential poles and current poles are separately used to measure the potential difference and current. Since the calculation involves the distribution of the conductive medium in the formation, there are a large number of potential difference solutions and boundary conditions that are necessary to solve through forward modeling, as shown in Equation (1). The resistivity calculated by forward modeling is called apparent resistivity, which is usually not the true resistivity of the underground electrical formations. The true resistivity can be obtained by calculating the apparent resistivity through a suitable inverse computation [18].  
\[\varrho_{a}=\frac{V_{P1}-V_{p2}}{I}\times\frac{2\pi}{\frac{1}{r1}-\frac{1}{r2}- \frac{1}{r3}+\frac{1}{r4}}=K\frac{\Delta V}{I} \tag{1}\]  
where \(K\) is the geometrical arrangement parameter of the electrode; \(\Delta V\) is the measured potential difference; and \(I\) is the circulating current intensity.  
Apparent resistivity means that when taking measurements in real geological formations, the measured resistivity may transform by changing the electrode spacing and position as the resistivity of the formation may be heterogeneous. For example, if we were to lay dozens of electrode rods equally on the Earth's surface in advance, then constantly change the spacing and strafing right to measure by the current poles and potential poles aforementioned, it can obtain a pseudo-section as shown in Figure 1. Finally, the resistivity distribution of the representative geological formation should be obtained through inverse analysis. Inverse analysis generally adopts an optimization method because the resistivity value calculated by inverse computation is close to that of the true resistivity value [19]. The software of inverse analysis makes an initial value guess for the inverse model through the measured pseudo-section, and after presetting the initial inverse model and minimizing the difference between the model's reacting value and the measured data value through multiple operations. The result of the inverse at present is the resistivity distribution of the real geological formation. This study adopted the EarthImager inverse software developed by AGI to perform numerical simulations and calculations [20].  
The simulated testing method in this study adopted dipole-dipole electrode arrangement. The electrode sequence of the dipole-dipole array was C2, C1, P1, P2, respectively. The two current poles form a dipole, and the two potential poles form another dipole, where C1C2 = P1P2 = a and C1P1 = na. When the parameter n is gradually increased, the resistivity of the formation changing from shallow to deep can be obtained. The high sensitivity of this arrangement is concentrated between the paired current poles and the potential poles, so horizontal formation changes are more suitable, while the vertical direction changes poorly.
# The Effect of L-dopa on Postural Stability in Parkinson's Disease Patients
## 1 Introduction
Thanks to the L-dopa stimulation strategy, it is possible to shorten the _off_ time, extend the _on_ states and reduce the severity of symptoms in the _off_ period, which reduces the severity/duration of chorea dyskinesia and minimizes the symptoms of nocturnal disability [16]. Although L-dopa is highly effective, it is not ideal. It is associated with many problems such as fluctuations, dyskinesias, psychotic, autonomic or behavioral disorders [17]. Researchers are now learning how to combine it with other drugs such as dopamine agonists, or how it can be safely used after deep brain stimulation [18]. The main therapeutic goal in [PERSON]'s disease is to improve motor efficiency by optimally prolonging the _on_ the period without the severity of chorea dyskinesia and non-cardiac symptoms. Posture- and gait-related disorders are one of the first changes signaling the disease. Therefore, physiotherapeutic procedures should firstly be aimed at improvement in this area. These symptoms predispose to the occurrence of falls, dangerous due to the risk of injuries and trauma, as a result of which the patient may be devoid of his/her ability to move. In relation to the-above, the aspect of the postural stability testing associated with L-dopa pharmacotherapy is of great importance. Early recognition of balance disturbances combined with targeted physiotherapy can reduce the negative effects and thus, improve the efficiency of patients. The study aim was to assess the effects L-dopa have on postural stability among patients with advanced idiopathic Parkinson's disease.
## 1 Introduction  
High peak-to-average power ratio (PAPR) and wideband signals in 5G systems demand power amplifiers (PAs) with improved efficiency [1, 2]. In user equipment (UE), envelope tracking (ET) has been widely adopted to enhance PA efficiency while maintaining linearity [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14].  
While efficiency improvements in base station PAs have been achieved using Doherty amplifiers [15, 16], research on the ET technique for base stations remains limited. As shown in Table 1, unlike UE, base station PAs require high output power and supply voltage operation. Considering the peak power and efficiency of the PA, the supply modulator (SM) must be capable of delivering peak output power exceeding 100 W to meet these demands. To address this, this work proposes a hybrid supply modulator (HSM) with a high-voltage linear amplifier (LA) based on Laterally Diffused Metal-Oxide Semiconductor (LDMOS) transistors. Integrated LDMOS-based SM reduces PCB-induced parasitic impedance, enabling wideband and high-voltage operation. LDMOS transistors provide high drain-to-source breakdown voltages (10-48 V) but have gate-to-source breakdown voltage limits below 5 V and significantly larger parasitic capacitance than CMOS transistors, which can limit speed and stability. Therefore, ensuring fast charging and discharging is critical for wideband ET.  
This work presents a rail-to-rail Class-AB LA that satisfies the high voltage, fast transient, and wideband requirements of HSM for efficient 5G base station transmission. To enhance efficiency, the LA uses separate low-voltage (LV) and high-voltage (HV) domains. In Section 2, the output power range requirements and the topology of the proposed HSM for the base station PA are discussed. The design of the proposed wideband LA is also described in detail in the same section. In Section 3, the simulation results and performance evaluation of the proposed circuit are presented. Finally, Section 4 concludes this work.
## 7 Applications of HAR in Healthcare
### Monitoring of Physical Rehabilitation Performance  
Physical therapists can use HAR to monitor the performance of their patients and curate personalised rehabilitation plans for them. HAR systems can help them have a holistic view of the performance of their patients and note areas of weakness with respect to the patient's everyday life. This means that they can curate a rehabilitation plan tailored to each patient and also effectively monitor the progress of each patient. Physical therapy usually involves constantly re-assessing the biomechanical abilities of the patient, and this takes up a considerable amount of time in the recovery process [120]. With a HAR-based rehabilitation system, the time for recovery would be reduced, with the time spent on constant biomechanical assessment being eliminated or greatly reduced.  
Patients carrying out rehabilitation in a familiar environment such as their home have shown to improve their performance after being discharged [121]. However, physical therapists may not be available to monitor their progress at home, and family members lack the knowledge to properly monitor and guide them [122]. A HAR-based monitoring system would allow the patients to carry out their rehabilitation at home and still receive the needed guidance from their physical therapists.  
It is important to note that due to the fatal nature of mistakes in healthcare, these suggested application areas are to complement the healthcare professionals and not to replace their expertise.