esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Setup and Measurement Procedures  
The experiments were carried out in a wind tunnel (12.5 m long, 2.7 m wide, and 1.8 m high) at the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway, which was equipped with a 220 kW centrifugal blower capable of generating wind speeds of up to 25 m/s. Aerodynamic drag was measured using a force platform (model 9286 AA, Kistler Instrument Corp., Winterthur, Switzerland). Video films recorded in the sagittal plane with a Sony HDR-HC7 high-resolution camcorder (Sony Corp., Tokyo, Japan) allowed determination of the skier's height at the shoulder relative to the ground utilizing two-dimensional kinematics software (Avi AD Measure v2.4, Intelligent Solutions and Consulting s.p., Kranjska Gora, Slovenia). In addition, another Sony HDR-HC7 camcorder filmed the skier from behind (Figure 1) and click-based interactive segmentation [18] of the images extracted from this film was employed to obtain the skier's \"frontal\" cross-sectional area (S). To obtain his reference cross-sectional area (S\({}_{\mathrm{r}}\)), each skier was subjected to an iDXA scan (Lunar iDXA, GE Healthcare, Madison, WI) prior to the measurements in the wind tunnel and these images were processed using the same interactive segmentation.  
During the measurements in the wind tunnel, each skier wore the entire gear--racing suit, ski boots, gloves, helmet, goggles, and poles-- that he wears when competing. Prior to these measurements, the difference between the kinesthetic sensation of anterior-posterior balance in the wind tunnel and on-snow was explained and demonstrated to the skiers.  
Figure 1: A skier in the wind tunnel filmed from behind (**left**) and the segmentation mask (**right**) used to determine his cross-sectional area.  
Thereafter, each participant was required to adapt his skiing posture to the conditions in the wind tunnel.  
A force plate, to which ski bindings were attached and adjusted individually with respect to the distance between the legs, was used to make the measurements. In random order, the skier adopted (in response to a visual signal) one of three different standard positions (high, middle, tuck), with exposure five times in each position to winds of 40, 60, and 80 km/h. These positions correspond to those assumed during giant slalom competitions, as determined by recording two subjects during such a competition. The high position is adopted during weight transfer, the medium during turning, and the tuck position at the highest speeds with less extensive turning.  
To compensate for the potential recording of forces due to the skier's body weight, the force plate signal was adjusted to zero in the absence of any wind prior to measurement at each different speed and for each individual skier. The aerodynamic drag (F\({}_{\mathrm{d}}\)) was determined from the component of force in alignment with the direction of the wind, utilizing the average of 5 s of continuous values recorded after a stable signal was achieved.
# Hair Regeneration Effects of _Lespedeza bicolor_ Extract In Vivo and In Vitro
## 1 Introduction  
Alopecia, also known as hair loss, is a dermatological disorder with a global distribution. Alopecia has many causes, including genetic factors, androgenic alopecia, stress, nutritional deficiencies, aging, and chronic inflammatory dermatological diseases. The hair growth cycle moves through several phases, including degeneration (catagen phase), rest (telogen phase), and regeneration (anagen phase) in adult hair follicles (HF) for extended periods [1; 2; 3; 4]. Because HFs are composed of dermal papilla cells (DPCs) and skin sheaths of human hair, they have an essential function of controlling hair growth throughout pathogenesis of specific conditions, including hair loss, as well as during the normal hair cycle [5]. The size and number of DPCs are closely related to the hair cycle, and the number of DPCs is relatively high in the regeneration phase [6]. Hair loss is often caused by a chronic scalp inflammation reaction and, as a result, the anagen phase of HF is shortened [7], and all or part of the hair of the head or body may be lost [8]. Currently, androgen receptor antagonists, androgen-independent hair growth stimulants, and 5\(\upsigma\)-reductase inhibitors, among others, are used as therapeutic agents for preventing hair loss and promoting hair growth [9]. However, due to unpredictable effects and side effects such as decreased sexual desire, their use may be restricted or made only temporarily available [10; 11; 12; 13]. Therefore, research into new drugs to promote hair growth is necessary. _Lespedeza bicolor_ is a legume that grows wild throughout Asia and has recently been cultivated for ornamental and medicinal purposes [14]. _Lespedeza bicolor_ has been used in folk medicine to treat nephritis, azotemia, hyperpigmentation, diuresis, diabetes, and inflammation [14; 15; 16; 17; 18]. Additionally, _L bicolor_ ethanol extract possesses antioxidant, anti-inflammatory, estrogenic, antityrosinase,antifungal, and antimicrobial activities [14; 17; 19]. _Lespedeza bicolor_ contains isoflav such as genistein and daidzein, flavonoids such as catechchin, quercetin, rutin, luteolin, and naringin, and flavonoid-based metabolites such as protocatechuic acid (PCA) [15]. PCA is one of the most effective components of many natural products and is the main metabolite of anthocyanins. PCA has been reported to have various pharmacological functions such as antioxidant, anti-inflammatory, antiviral, antiaging, and antiosteoporosis effects, as well as being involved in stem cell proliferation and differentiation [20; 21; 22]. In this study, the effects of LBE on DPC proliferation and cell cycle were investigated. Additionally, hair regeneration promotion effects by LBE were observed in C57 BL/6 mice.
## 3 Synthetic Routes to SnS
Mechanochemically synthesised SnS has been used for the creation of sodium-ion batteries with the potential for scalability, with phase pure SnS being produced within 2 h. A capacity of 300 mAh g\({}^{-1}\) over 60 cycles was obtained for SnS milled for 2 h with the addition of a sodium alginate binder [65]. A comparable sodium-ion battery made by wet chemical synthesis had a discharge capacity of 370-520 mA h g\({}^{-1}\)[20]. SnS synthesised by mechanochemistry has also found use in solar cells, with SnS agglomerates of 50-200 nm formed of smaller 10s of nm nanoparticles being produced. A power conversion efficiency of 1.71% was achieved using a Sn\({}_{1.05}\)S absorber layer, with a short-circuit current density (J\({}_{\text{SC}}\)) of 18.14 mA cm\({}^{-2}\), an open-circuit voltage (V\({}_{\text{OC}}\)) of 0.215 V, and a fill factor (FF) of 0.438. All of these parameters were higher for Sn\({}_{1.05}\)S than either Sn\({}_{0.95}\)S or Sn\({}_{1.00}\)Sm which was ascribed to improved microstructure crystallinity but also could potentially be due to suppressed Sn\({}_{2}\)S\({}_{3}\) impurity formation [66]. Synthesis of SnS by high energy milling has the advantages of being scalable, requiring only Sn and S powder, no additional heating, solvents, or other reaction conditions, and producing no unwanted by-products [65]. Another novel SnS synthesis technique involves the heating of solid-state tin dithiocarbamate in an inert atmosphere until decomposition to SnS occurs directly. This method has been shown to allow doping of the bulk SnS with Pb. It was found that when \(x\) = 0.5 for Pb\({}_{1-x}\)Sn\({}_{x}\)S (0 \(\leq x\leq\) 1), a transition from the cubic lead sulfide to orthorhombic SnS occurs as determined by XRD. This material allows a subsequent liquid-phase exfoliation into Sn(Pb)S nanosheets with improved optical properties, although this may be inefficient for pure bulk SnS powder synthesis due to the requirement of the dithiocarbamate synthesis step [67]. Tin sulfide (SnS) nanocrystals have attracted great attention in the field of photovoltaics, photodetectors, and electrochemical energy storage. Precise control of shape, size, composition, and phase crystallinity is essential, which can be achieved by controlling the synthetic conditions very carefully. An excellent and comprehensive review on the synthesis of semiconductor nanocrystals and colloidal quantum dots in organic solvents with particular emphasis on earth-abundant, nontoxic, and heavy metal free compounds has been written by [PERSON] et al. [68].
# Analysis and Experimental Test of Electrical Characteristics on Bonding Wire
## 2 Bonding Wire Model  
Figure 1 shows the package structure of a ball grid array (BGA) composed of a BGA solder ball, PCB board, lead frame, chip, gold bonding wires, epoxy resin encapsulation and so on. In addition, bonding wire can be used as signal transmission line, power, and grounding. In low frequency signal, the bonding wire is equivalent to a line without resistance, capacitor, or inductive transmission. However, in high frequency, the electrical properties of bonding wire have an important influence on the performance of the whole structure and even the circuit. On the one hand, the influence of the parasitic parameters of the bonding wire on the circuit cannot be ignored. At the same time, the inductance, resistance and capacitance (RLC) parameters of bonding wire are constantly changing because of the skin effect with frequency. On the other hand, the cumulative effect of many bonding wires greatly influences the signal transmission capacity, especially in high frequency system. Once the poor WB transmission performance affects the signal transmission and cause large signal loss, it may easily lead to signal crosstalk and other serious signal integrity problems.
# Investigations into Frost Flower Physical Characteristics and the C-Band Scattering Response
## Materials and Methods
### Radar Measurements  
A C-band polarimetric scatterometer (center frequency = 5.5 GHz, bandwidth = 500 MHz, ProSensing, Inc., Amherst, MA, USA) measured the microwave scattering response of the sea ice throughout the experiment. The antenna is a dual-polarized reflector with a beam width of approximately 5.5\({}^{\circ}\) in both the E-plane and H-plane. It was mounted on a scaffolding tower platform directly above the LiDAR, which is 5.1 m above the seawater surface. Measurements were conducted with 30\({}^{\circ}\) swaths in azimuth and elevation incidence angles were set at discrete increments of 5\({}^{\circ}\) from 20\({}^{\circ}\) to 55\({}^{\circ}\) from nadir. The estimated number of independent samples ranged from \(n=11\) at 20\({}^{\circ}\) to \(n=33\) at 55\({}^{\circ}\). The returned power was measured and the covariance matrix was calculated to allow the VV, HV, and HH normalized radar cross sections (NRCS) and polarimetric parameters to be obtained. When processing the scatterometer data on a per scan basis, we carefully ensured that the peak scattering was clearly identified (i.e., ensuring that a sufficient signal-to-noise ratio was maintained), which is discussed in Reference [17]. The data collected from a trihedral corner reflector were utilized for measurement calibration and an internal delay line calibration monitored system drift during the experiment via periodic sky calibrations. The manufacturer-tested sensitivity of the instrument is \(-40\) dB m\({}^{2}\)/m\({}^{2}\) at a range of 15 m. In the processing of our time-series scatterometer data, we applied a moving average filter (\(n=4\)) to better present the trend of the measured NRCS data. This system has been used in numerous field campaigns and its utility and processing were discussed in References [17; 34; 35]. Although all polarimetric data were collected, this manuscript focuses on the NRCS and their connections to the physical phenomenon observed in the SERF. Therefore, details on the polarimetric discriminants will be preserved for future work.
## 4 Analytical Framework for Heterogeneous Service Provisioning
### Problem Overview and Healthcare Service Requirements
#### 4.1.4 Machine Learning Integration  
To enhance adaptability and predictive capabilities in dynamic healthcare environments, we integrate ML techniques into the proposed rate-adaptive and latency-aware framework. These ML modules enable proactive resource management, anticipate changes in network load, and intelligently adjust service parameters in real time. The integration focuses on lightweight and scalable models suitable for deployment in resource-constrained IoT infrastructures. In particular, we utilize Long Short-Term Memory (LSTM) networks for traffic prediction, gradient-boosted decision trees (GBDT) for delay estimation, and Deep Q-Networks (DQN) for RL-based rate control. These models were chosen for their balance between prediction accuracy and computational feasibility at the mist and edge layers, benefiting from recent advances in TinyML and embedded inference.
## 3 Experiments for the SDOF System  
Figure 7 shows the simple model that was set up to prove the validity of the proposed PIPO controller given by Equation (5), whereas Figure 8 shows the wire connection diagram for the experiment. The displacement of the floor was measured using the laser displacement sensor, (M3L/20), from MEL, and the base movement was made by the linear servo motor, (LM-H3P2A-07P-BSS0), Mitsubishi Electric Co., Tokyo, Japan, whereas the PIPO control algorithm was implemented using dSPACE MicroLabBox.  
Figure 6: The time history of displacement.  
Figure 7: Experimental testbed.  
Through the free vibration experiment, the fundamental frequency of the model was found to be 3.2 Hz. The PIPO controller is implemented as a Simulink block diagram, as shown in Figure 9. The voltage signal output from the laser sensor is converted into actual displacement by multiplying the gain corresponding to the sensor characteristic. A moving average filter is used to remove sensor noise, and the displacement is used as an input to the PIPO controller. The outputted control displacement from PIPO controller is input to the servo motor. The proportional-integral-derivative (PID) controller is used for accurate displacement-tracking control of a linear servo motor [28]. For real-time displacement tracking, the servo motor driver's mode was set to speed mode. For details, refer to reference [25]. The gain values of the PIPO controller and the PID were experimentally set. When a control signal is suddenly input to the servo motor, the motor is shocked, so a switch was created in the program so that the control signal was input smoothly. Figure 10 shows the free vibration response of the uncontrolled system and the controlled system, whereas Figure 10 shows how quickly vibrations are suppressed when the PIPO controller is turned on. Using logarithmic decrement, the damping factor of the uncontrolled system is calculated as 0.033, and the damping factor of the controlled system is 0.168. Figures 10 and 11, the response curves obtained through experimentation, are very similar to the theoretical response curve shown in Figures 5 and 6. Therefore, it can be confirmed that the performance of the PIPO controller predicted by the theory in the previous section is valid. As shown in Figures 9-11, the effectiveness of the proposed PIPO controller has been demonstrated and proved experimentally. The reason that the very small residual vibration remains in the control state is that the input voltage is less than the minimum input voltage of the servo motor; that is, it corresponds to the dead band of the servo motor. Therefore, the phenomenon occurred due to the characteristics of the linear servo motor, not the PIPO controller.  
Figure 8: Wiring diagram.  
Figure 9: Simulink block diagram for the PIPO controller.
## 3 Parameter Optimization of the Current Controller and PLL
### Optimized Design of PLL Parameters
From Figure 19, it can conclude that the system stability is more sensitive to \(k_{\text{p-PLL}}\) than \(k_{\text{i-PLL}}\), with a smaller degree of change in \(k_{\text{p-PLL}}\) potentially having a larger influence on the system stability, while the impact of a change in \(k_{\text{i-PLL}}\) is smaller. Figure 14:1 PLL linearization model. Figure 14:2 Relationship curve between \(\phi_{pm\text{-PLL}}\) and g.  
Figure 14:3 Parameter range of PLL. Figure 14:4 Using stability evaluation methods to guide the selection of PLL parameters. Based on the analysis from Figure 19, the selection of the over-large \(k_{\text{p-PLL}}\) is negative to the system stability, while \(k_{\text{i-PLL}}\) has less influence on the system's stability. A smaller \(k_{\text{p-PLL}}\) is needed to ensure system stability. It can be seen from Equation (19) that \(k_{\text{i-PLL}}\) is positively correlated with \(\omega_{n}\), and \(\omega_{n}\) will affect the response speed of the system [33], so a larger \(k_{\text{i-PLL}}\) is needed to ensure the dynamic characteristics of the system needs. Therefore, it can be concluded that to enlarge the value of \(\phi\), \(k_{\text{p-PLL}}\) should be a small value and \(k_{\text{i-PLL}}\) should be a large value in the feasible range of Figure 14:5 Thus, the PLL parameters in this article are selected as \(K_{P}=50\), \(g=2\) and \(K_{I}=5000\). For the VSC with the parameters in Table 1, Figure 20 shows the Nyquist curves of the system before and after using the selected PLL parameters. Nyquist curves in Figure 20a encloses \((-1,j0)\), which means the system is unstable. After updating the parameters of PLL, Nyquist curves do not enclose \((-1,j0)\), and the system restores to a stable state.
# DMLU-Net: A Hybrid Neural Network for Water Body Extraction from Remote Sensing Images
## 3 Experiments
### Discussion
#### 3.4.2 Temperature Parameter T in DLMKA Module  
During training, we experimented with different temperature values (T = 0.1, 0.2, 0.3, 0.5, and 1.0). The results demonstrated that a hierarchical temperature configuration further enhanced model performance: the optimal setup used T = 0.3 at the encoder output, T = 0.2 in the bottleneck layer, and T = 0.1 in the decoder. This coarse-to-fine temperature strategy enabled progressive refinement, shifting from multi-scale aggregation to key-scale focus, thereby improving the modeling of large-scale water body structures and fine edge details.
## 4 Results and Discussion  
In this work, we first study the influence of the number of satellites and the number of stations on the detection results. Based on these results, the influence of the satellite distribution on the detection rate is analyzed. Finally, the detection rates at different frequency bands of different GNSS systems are compared.  
All the GNSS observation data used in the experiment are taken from the IGS data center. The sampling period is 30 s, and each sampling point is counted as an event. The solar radio emission power value at 1415 MHz provided by RSTN is compared with our detected samples. The emission power values of different magnitudes are separately counted. Accordingly, the detection rate, which is used to represent the detection performance, is defined as the ratio between the number of predicted SRB samples and the number of real samples (from RSTN) in each emission power interval. We define the minimum emission power corresponding to the detection rate of 80% as the detection threshold in the experiments.  
Table 4 presents the detailed information of each station used in the experiments, including ID, latitude, longitude and country.
## 4 Results and Discussion
### Consensus on Benefits
#### 4.2.5 Reduce Design Errors  
Five papers reported that AR application for communication helped to reduce design errors. Within the construction context, AR applications have been developed to combine virtual object information with real elements. This visualization of construction information results in better coordination and an easier understanding among project stakeholders. [PERSON] et al. (2013) stated that using AR visualization was expected to help reduce design and construction errors in advance and to reduce the time taken to select an optimized construction method and structure element [44]. Reducing design errors prior to the start of construction helps save cost and change orders to resolve those errors. In addition, the time needed to answer requests for information (RFIs) during the construction process is expected to be reduced when using AR to find design errors. AR for communication using visualization is a benefit that demonstrated a consensus among many papers.
## Materials and Methods
### Computational Parameters  
The CASTEP module (MS2016) based on DFT [26] was used to calculate the band structure and PDOS of pure anatase TiO\({}_{2}\) and RE/TiO\({}_{2}\). For RE/TiO\({}_{2}\), a supercell of 2 \(\times\) 2 \(\times\) 1 48-atoms anatase TiO\({}_{2}\) was used, and one Ti atom near the center of the supercell was replaced by one rare-earth atom. The general gradient approximation (GGA) for the exchange-correlation term was employed, and the Perdew-Burke-Ernzerhof (PBE) scheme was adopted for structure optimization. Convergence of energy change per atom is at 5 \(\times\) 10\({}^{-6}\) eV; stress is at 0.02 GPa; residual force is at 0.01 eV/A; and displacement of atoms is at 0.0005 A. The self-consistent field (SCF) tolerance was adopted with the convergence of 1.0 \(\times\) 10\({}^{-7}\) eV/atom. The FFT grid of the basis was 50 \(\times\) 50 \(\times\) 64. The kinetic energy cutoff for the plane-waves basis was set to 400 eV. For the computation, 3 \(\times\) 3 \(\times\) 3 k-points used for sampling the Brillouin zone, and the ultrasoft pseudopotential were applied in the above calculation. The electronic configurations considered are 3s\({}^{2}\)3p\({}^{6}\)3d\({}^{2}\)4s\({}^{2}\) (Ti), 2s\({}^{2}\)2p\({}^{4}\) (O), 5s\({}^{2}\)5p\({}^{6}\)5d\({}^{1}\)6s\({}^{2}\) (La), 4f\({}^{1}\)5s\({}^{2}\)5p\({}^{6}\)5d\({}^{1}\)6s\({}^{2}\) (Ce), 4f\({}^{7}\)5s\({}^{2}\)5p\({}^{6}\)5d\({}^{1}\)6s\({}^{2}\) (Gd) and 4f\({}^{4}\) 5s\({}^{2}\)5p\({}^{6}\)6s\({}^{2}\) (Yb) for anatase TiO\({}_{2}\) (unit cell) and RE/TiO\({}_{2}\) (2 \(\times\) 2 \(\times\) 1 supercell) (Figure 1).
## 4 Results
### Performance on the ISLES 2022 Dataset
These measurements are translated into differences in the contrast of the lesion with the rest of the tissues, with DWI being the representation with higher contrast. \begin{table}
\begin{tabular}{c c c c} \hline
**Plane** & **Metric** & **DWI** & **ADC** \\ \hline \multirow{4}{*}{Axial} & IoU & 0.6961 \(\pm\) 0.3414 & 0.5505 \(\pm\) 0.3666 \\  & F1-Score & 0.7517 \(\pm\) 0.3470 & 0.6192 \(\pm\) 0.3884 \\  & HD & 9.90 \(\pm\) 17.56 & 16.73 \(\pm\) 23.36 \\  & N. S. & 121 \(\pm\) 9 & 253 \(\pm\) 13 \\ \hline \multirow{4}{*}{Coronal} & IoU & 0.5833 \(\pm\) 0.4000 & 0.4954 \(\pm\) 0.3918 \\  & F1-Score & 0.6318 \(\pm\) 0.4187 & 0.5535 \(\pm\) 0.4193 \\  & HD & 10.73 \(\pm\) 19.32 & 18.08 \(\pm\) 26.32 \\  & N. S. & 188 \(\pm\) 19 & 364 \(\pm\) 27 \\ \hline \multirow{4}{*}{Sagittal} & IoU & 0.5636 \(\pm\) 0.3926 & 0.4554 \(\pm\) 0.3740 \\  & F1-Score & 0.6176 \(\pm\) 0.4147 & 0.5211 \(\pm\) 0.4103 \\ \cline{1-1}  & HD & 14.02 \(\pm\) 23.21 & 23.73 \(\pm\) 32.06 \\ \cline{1-1}  & N. S. & 133 \(\pm\) 25 & 318 \(\pm\) 8 \\ \hline \end{tabular}
\end{table} 
Table 6: Performance of the proposed model in different MRI modalities and planes using the ISLES 2022 dataset. Figure 11: Violin plot of the proposed model’s results on DWI and ADC images using the axial view, where the dot localizes the median, and the white line represents the mean: (**a**) F1-Scores by mask size category using DWI and configuration A (FL = 0.7, CDL = 0.3), (**b**) F1-Scores by mask size category using DWI and configuration B (FL = 0.9, CDL = 0.1), (**c**) F1-Scores by mask size category using ADC and configuration A (FL = 0.7, GDL = 0.3), (**d**) F1-Scores by mask size category using ADC and configuration B (FL = 0.9, CDL = 0.1). the model using (\(\lambda_{GDL}=0.3\), \(\lambda_{FL}=0.7\)) fails to segment some isolated pixels. In contrast, the (\(\lambda_{GDL}=0.1\), \(\lambda_{FL}=0.9\)) configuration covers a larger segmentation area, giving even more false positives in subfigure (k).