esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparative Experiments Results and Discussion
As illustrated in Figure 11c,d, although all evaluated networks demonstrated basic localization competence for distributed landslide patterns, ours achieved superior shape fidelity and spatial consistency, producing segmentation results which most closely approximated ground-truth distributions. As shown in Figure 11c,d, although all evaluated networks demonstrated basic localization competence for distributed landslide patterns, ours achieved superior shape fidelity and spatial consistency, producing segmentation results which most closely approximate ground-truth distributions. As shown in Figure 11e, for images with complex interleaving of landslide and non-landslide areas, the transfer learning-based segmentation method demonstrated supe  
Figure 11: Visualization results of comparative experiments of different segmentation algorithms. (top) **Ours**; LAN-UKAN with FTTL. (left) (**a**) Concentrated landslide areas, (**b**) relatively concentrated landslide areas with fractures, (**c**) fragmented and dispersed landslide areas, (**d**) dispersed landslide areas, and (**e**) complex interleaving of landslide and non-landslide areas. rior performance in edge detail segmentation compared with traditional segmentation approaches. In particular, ours achieved the most outstanding performance among all methods, enabling clear and accurate delineation of landslide areas with precise boundary definition between landslide and non-landslide regions. This methodology significantly enhanced segmentation effectiveness, yielding optimal results. In conclusion, our model (LKN-UKAN with FTTL) outperformed both conventional segmentation architectures and existing transfer learning methods across various challenging scenarios, including edge refinement, fracture zone detection, and distributed landslide recognition. This empirical evidence substantiates the framework's practical value and transformative potential for landslide image analysis applications.
## 2 Dynamic Nuclear Polarization in Semiconductor Nano-and Microstructures
### Optically Induced DNP in Semiconductors
#### 2.1.2 Principle of DNP  
The process of DNP occurred by off-center irradiation of the resonance of paramagnetic centers, which were present at low concentration in the sample [9]. Its importance was shown to far exceed that of the other methods of nuclear polarization.  
Spin-temperature theory was initially suggested for the investigation of nuclear magnetism in solids and then evolved into an important tool for the explanation of NMR in solids. A first extension of the theory to extremely low temperatures, for systems of nuclear spins at normal concentrations, led to the understanding of nuclear magnetic cooperative ordering. A second extension of the spin-temperature theory, to dilute electronic spin systems both at high and low temperatures, led to the unveiling of several physical mechanisms contributing to dynamic polarization.  
The basic hypothesis in the spin-temperature theory is that the evolution of an isolated system of a big number of interacting spins leads to an equilibrium state characterized by a temperature. In fact, spin systems are never completely isolated. Therefore, the concept of spin temperature is meaningful only if the rate of achievement of internal equilibrium in the spin system is much faster than the spin-lattice relaxation rate. Variation of polarization and of dipolar inverse temperature as a function of irradiation frequency for a spin system in a high field according to spin-temperature theory is provided in Figure 3.  
Figure 3: Variation of polarization (**a**) and of dipolar inverse temperature (**b**) as a function of irradiation frequency for a spin system in high field according to spin-temperature theory. Full curves: complete saturation, broken curves—partial saturation. (From Reference [9] ©(1978) IOP Publishing. Reproduced with permission. All rights reserved).  
Figure 2: **(a)** Signal proportional to the derivative of the \({}^{29}\)Si nuclear magnetization obtained in a dc magnetic field 1 G after 21 h of irradiation with circularly polarized light at 77 K. (**b**) Signal proportional to the derivative of the equilibrium Si nuclear magnetization in a dc magnetic field 6 kG at 300 K. (Reprinted figure with permission from Reference [14] © (1968) by the American Physical Society).
# Experimental Study on the Condensation Heat Transfer on a Wettability-Interval Grooved Surface
## 2 Description of Experiment  
### Sample Preparation  
A pure copper sheet with a size of 50 mm \(\times\) 60 mm \(\times\) 1 mm was used as the substrate in the experiment. The plain copper sheet was polished with 2000# sandpaper first, followed by ultrasonic cleaning in ethanol solution for 20 min to remove the impurities and oxides on the surface. For the hydrophilic surface, the cleaned copper sheet was immersed in the 30 wt% hydrogen peroxide aqueous solution for 2 h. After the reaction, a thin layer of CuO film was formed on the copper surface [28]. Finally, the hydrophilic copper surface was obtained after being cleaned with deionized water, purging, and drying with nitrogen.  
As for the hydrophobic surface, the silane hydrophobic agent Soft99 (GLACO) was uniformly sprayed on the cleaned surface. After the solvent evaporated, the copper sheet was dried at 70 \({}^{\circ}\)C for 1 h to become hydrophobic. Using the above treatment, the plain hydrophobic surface was made.  
To prepare the hydrophilic grooved surface, the microgrooves with 0.2 mm width, 0.2 mm depth, and 0.5 mm interval were fabricated through CNC machining based on the pure copper sheet.  
To prepare the surface with wettability-interval microgrooves, the microgrooves with 0.2 mm width, 0.2 mm depth, and 0.5 mm interval were fabricated through CNC machining based on the hydrophobic copper sheet prepared in the previous step, and the preparation diagram is shown in Figure 1. Since the original copper substrate was hydrophilic, the machined channel was hydrophilic, and the ridge of the groove remained hydrophobic; thus, the copper surface with wettability-interval microgrooves was obtained and named the wettability-interval grooved surface.  
Figure 1: Diagram of preparation of surface with wettability-interval microgrooves. The part of the hydrophobic copper surface that has been grooved becomes hydrophilic and the remaining one is still hydrophobic.
## 3 Results and Discussion
### Shape of the Interaction Surface  
Comparison of the average CV values of different surface regions can also tell if a particular surface area is protruding or is recessed: The average CV value of a protruding surface area is smaller than the overall average of the surface atoms' CV value, while the recessed region's CV average is larger.  
Using the definition of interaction surface as above, the CV values of the interaction and full surface were found to be 0.454 (s.d. = 0.044) and 0.464 (s.d. = 0.029), resp. While this difference is quite small it is significant at the level \(p=0.001\) according to the Student t test. Thus, we can conclude that the interaction surface is likely to be protruding, even if only slightly.
## 4 Application of the Model  
In this section, we use the proposed model to perform a system-level EMC evaluation on vehicle 1. Vehicle 2 is the auxiliary vehicle for evaluation. For the provided example, there are two vehicles, and the detailed parameters of the vehicle-mounted radio stations and antennas, as well as system performance indicators, are shown in Tables 5 and 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No. 4. Ultra-Shortwave Radio Station (VHF4)** & **No. 5. Shortwave Radio Station (HF5)** \\ \hline Working state & Transmitting & Working state & Receiving \\ Working frequency (MHZ) & 80 & Working frequency (MHZ) & 11 \\ Transmission power (W) & 50 & Sensitivity (dBm) & \(-107\) \\ Bandwidth (MHZ) & 17 & Bandwidth (MHZ) & 3 \\ VSWR & 1.5 & VSWR & 1.5 \\ \hline Antenna gain (dB) & 1 & Antenna gain (dB) & 1 \\ Antenna polarization & Vertical polarization & Antenna polarization & Vertical polarization \\ \hline \multicolumn{3}{c}{**Vehicular Performance Indicator**} \\ \hline Vehicular height (m) & 3 & & \\ Antenna height (m) & 2.5 & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Vehicle 2 parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Modulation Types** & **Coherent Modulation** & **Noncoherent Modulation** \\ \hline
2 ASK & \(\frac{1}{2}\text{erfc}^{1}(\frac{\sqrt{SNR}}{2})\) & \(\frac{1}{2}e^{\frac{SNR}{4}}\) \\
2 FSK & \(\frac{1}{2}\text{erfc}^{1}(\sqrt{\frac{SNR}{2}})\) & \(\frac{1}{2}e^{\frac{SNR}{2}}\) \\
2 PSK & \(\frac{1}{2}\text{erfc}^{1}(\sqrt{SNR})\) & / \\
2 DPSK & \(erfc^{1}(\sqrt{SNR})\) & \(\frac{1}{2}e^{-SNR}\) \\ \hline \hline \end{tabular} \({}^{1}\)_erfc_\((x)=\frac{2}{\sqrt{\pi}}\int_{x}^{\infty}e^{-t^{2}}dt\).  
\end{table}
Table 4: Equations for calculating BER for different modulation types.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No. 1. Shortwave Radio Station (HF1)** & **No. 2. Ultra-Shortwave Radio Station (VHF2)** \\ \hline Working state & Transmitting & Working state & Transmitting \\ Working frequency (MHZ) & 15 & Working frequency (MHZ) & 45 \\ Transmission power (W) & 50 & Transmission power (W) & 50 \\ Bandwidth (MHZ) & 3 & Bandwidth (MHZ) & 10 \\ VSWR & 1.5 & VSWR & 1.5 \\ \hline Antenna gain (dB) & 1 & Antenna gain (dB) & 1 \\ Antenna polarization & Vertical polarization & Antenna polarization & Horizontal polarization \\ \hline \multicolumn{3}{c}{**No. 3. Ultra-Shortwave Radio Station (VHF3)**} & **Vehicular Performance Indicator** \\ \hline Working state & Receiving & Communication distance (km) & 15–30 \\ Working frequency (MHZ) & 53 & Antenna isolation (dB) & \(\geq\)20 \\ Sensitivity (dBm) & \(-116\) & Decrease in receiver sensitivity (dB) & \(\leq\)6 \\ Bandwidth (MHZ) & 15 & Vehicular height (m) & 3 \\ VSWR & 1.5 & Antenna height (m) & 2.5 \\ \hline Antenna gain (dB) & 1 & & \\ Antenna polarization & Vertical polarization & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Vehicle 1 parameters.
## 2 Results and Discussion
### Analysis and Quantification of Atropine and Scopolamine in Different Types of Infusions
Few studies have been performed regarding the presence of TAs in infusion waters. In a previous work, [PERSON] et al. [30] found concentrations of atropine ranging from 0.08 to 3.88 ng/mL and of scopolamine ranging from 0.48 to 1.13 ng/mL in 17 teas and herbal infusions, including chamomile, lemon balm, lemon verbena, green tea, and peppermint. Of these 17 samples, 14 exceeded the limits set by Commission Regulation (EU) 2021/1408 [29]. In the study of [PERSON] et al. [31], 33 teas and herbal infusions were tested and 4 were found positive for atropine and 5 for scopolamine. The samples included black tea, mint, and a mixture of herbs. Only one sample from this study exceeded the legislated limit of 0.2 ng/mL for liquid infusions, with 0.881 ng/mL for atropine and 1.517 ng/mL for scopolamine. This sample was composed of different herbs, such as liquorice, rhubarb, mallow, and fennel. The data found in this work and the aforementioned studies [30; 31] confirm the transfer of these toxins from the dry tea or herbal tea to the brew. Therefore, to assess the realistic exposure of the consumer, these toxins should be determined in infusion waters rather than in dried herbs.
# Fast Frequency Acquisition and Phase Locking of Nonplanar Ring Oscillators
## 5 Conclusions  
Nonplanar ring oscillators have a high frequency stability, a narrow linewidth, and low intensity noise. These devices are becoming the preferred laser sources in coherent space optical communications. They can also be used in coherent laser beam combinations. In these applications, phase locking is  
Figure 5: Frequency acquisition and phase locking process at a 164 MHz initial frequency difference.  
Figure 6: Measured frequency acquisition and phase locking time vs. the initial frequency difference. The dashed line is the linear fitting line.  
the key technique. Traditionally, laser frequency acquisition based on temperature control consumes quite a long time. In this work, we designed and achieved a frequency acquisition sub-loop based on PZT control. The acquisition sub-loop operated coordinately with the phase locking loop based on the loop filter. The frequency acquisition range reached 164 MHz, with an acquisition time of less than 500 ms. For a smaller initial frequency difference, acquisition times decreased. This technique significantly shortens the frequency acquisition time, which makes the system reach work status much more quickly. In addition, by improving the processing speed of the control circuit, acquisition time can be further shortened.  
This research is supported by the National Natural Science Foundation of China (Grant No. 61308041 and 61405030) and the PetroChina Innovation Foundation (Grant No. 2017D-5007-0603).  
[PERSON] and [PERSON] conceived and designed the experiments, [PERSON] and [PERSON] designed the feedback circuits; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] designed the nonplanar ring oscillators.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Catalyst Preparation  
The preparation technique of the CuO/CeO\({}_{2}\)-based monolith has been described in [49]. Briefly, a commercial honeycomb monolith (cordierite, 400 cpsi; Corning, NY 14831, USA), cut in the desired shape and dimension (cylinder; \(D=16\) mm; \(L=12\) mm), was washcoated with colloidal ceria (specific surface area (SSA) = 120 m\({}^{2}\)/g, Nyacol Nano Technologies Inc., Ashland, MA 01721, USA, CeO\({}_{2}\) particle size <20 nm) by a modified dip-coating procedure and then dried at 120 \({}^{\circ}\)C for 1 h and calcined in air at 450 \({}^{\circ}\)C for 2 h. Cycles of dip-coating followed by drying and calcination were repeated until the target ceria weight (corresponding to about 15 \(\upmu\)m layer) was reached. A slurry containing 100% colloidal ceria was chosen to washcoat the ceramic substrate on the basis of the results reported in [49], in order to reproduce a structured catalyst with a good mechanical resistance and a high dispersion of copper.  
Copper oxide was deposited onto ceria by wet impregnation using an aqueous solution of copper acetate monohydrate (Sigma-Aldrich srl, Milan, Italy); the sample was then dried at 120 \({}^{\circ}\)C for 1 h and calcined in air at 450 \({}^{\circ}\)C for 2 h again in order to decompose acetate into copper oxide. The procedure  
Figure 1: Block flow diagram (BFD) of the process. FP: Fuel Processor; PEM: proton exchange membrane. Red solid lines represent gases flowing from the FP going to rector containing fresh/regenerated catalyst; blue dashed lines represent air flowing to reactor containing catalysts after CO adsorption.  
was repeated until the desired copper oxide load (4 wt % with respect to the active layer), determined by sample weight after each cycle, was obtained.  
The final catalyst (CuO/CeO\({}_{2}\)) amount was about 450 mg/monolith. The ICP-MS analysis (performed with an Agilent 7500 CE (California USA) Instrument) revealed that the copper content (4.0 wt %) corresponded to the target load within the experimental error (\(\leq\)5%).
## 3 System Architecture and Design of Intelligent Reflecting Surface (IRS)
### Layers of IRS  
A typical IRS has three layers as shown in Figure 4. These layers can be explained as follows:  
* The first/outer layer: This layer consists of a large array of passive reconfigurable patches printed on a dielectric substrate to manipulate the incident signals [81].
* The second/intermediate layer: This layer consist of a copper plate to reduce signal energy leakages during reflection [81].
* The third/inner layer: This layer consists of a control circuit board with the ability to steer the reflection phase and amplitude in real time. The smart controller is typically a FPGA which regulates the reflection and configuration and also serves as a gateway between the BS and the destination [81].  
Figure 3: IRS-aided wireless transmission system: (**a**) In the presence of LoSILink; (**b**) In the absence of LoS link.
## 3 Results
### Wind Path Stability  
The air volume evaluated in the CFD model is 500 m long, from the SHAWT to the anemometer location, 275 m wide and 140 m high. This volume total is divided into 31,262,736 cells, but the mesh size changes from 8 m on the outside limits of the volume to 0.6 m on the objects; and on the wall of the wind turbine, the mesh size is 0.1 m. The boundary condition in the model is the velocity vector. Figure 8 shows the volume model.  
A dynamic analysis was performed for the nominal wind speed of 4.9 m/s and with a recurrent wind speed of 8 m/s, with the predominant wind direction of 83.3\({}^{\circ}\) (east to west) with 24% repeatability. Figure 9 shows the results of this analysis, a top view of the installation site is presented and a cut in the flow lines was performed at a height of 18 m.  
In addition, a second analysis with similar wind speeds was conducted, but with a wind direction of 75\({}^{\circ}\). This direction is the second direction with a greater repeatability with 17%, and it crosses the top of the buildings. Figure 10 shows the results of this second analysis.  
It can be concluded that buildings are not an obstacle to the free flow of wind, no turbulence is generated around the SHAWT, and furthermore, at a higher speed, the behavior of the wind is increasingly stable.  
Figure 8: (**a**) Total volume of the computational fluid dynamics (CFD) model and (**b**) longitudinal cut of the mesh sizes (air volume around the objects). The reference longitudinal axis of the cut is shown in (**a**).  
Figure 7: Wind turbine and anemometer installation area.
## 2 Materials and Methods
### Signal Acquisition and Processing
#### 2.3.1 Signal Amplification and Analog Filtering  
The load cell converted the weight signal of the object on the scale into a voltage signal. The load cell range used in this article was 5 kg, the voltage output range was 0-20 mv, and the 0-5 kg weight signal linearly corresponded to the 0-20 mv voltage signal. Due to the weak output voltage signal of the sensor and the existence of external interference [34; 35], it was necessary to process the output signal of the load sensor. This paper used an analog circuit to amplify, filter, and reduce the noise of the voltage signal. As shown in Figure 4, the analog circuit was divided into three main parts. The first part was a differential amplifier circuit, with the resistors R1 = R2 and R3 = R4 used to amplify the signal and suppress the in-phase signal. The second part was a second-order analog low-pass filter that mainly played the role of low-pass filtering [36; 37]. The third part was an RC-\(\tau\)-shaped filter; due to the resistance value and capacitance value being small in this part, it mainly played the role of noise reduction and current-limiting to protect the MCU.  
Figure 4: Signal amplification and filter circuit.  
Figure 3: Signal transmission direction; A/D, analogue to digital converter; USART, universal synchronous asynchronous receiver and transmitter; MCU, microcontroller unit; PC, personal computer.  
The magnification of the first part was:  
\[A_{1}=\frac{R_{3}}{R_{1}}(u_{i+}-u_{i-}) \tag{1}\]  
The magnification and transfer function of the second part were:  
\[A_{2}=\frac{R_{7}}{R_{8}} \tag{2}\]  
\[\mathrm{G}(s)=\frac{1}{C_{1}C_{2}R_{5}R_{6}s^{2}+[C_{2}R_{6}+C_{2}R_{5}+C_{1}R _{5}(1-A_{2})]s+1} \tag{3}\]  
Combined with Equations (1)-(3), the transfer function of the signal amplification and filter circuit was:  
\[\mathrm{G}(s)=\frac{A_{1}\times A_{2}}{C_{1}C_{2}R_{5}R_{6}s^{2}+[C_{2}R_{6}+C _{2}R_{5}+C_{1}R_{5}(1-A_{2})]s+1} \tag{4}\]  
In this equation, \(R_{5}\), \(R_{6}\), and \(R_{9}\) are resistances whose unit is \(\Omega\), and \(C_{1}\), \(C_{2}\), \(C_{3}\), and \(C_{4}\) are capacitances whose unit is F.
# Pairwise Likelihood Estimation of the 2 PL Model with Locally Dependent Item Responses
## 1 Introduction
### Pairwise Likelihood Estimation (PML)  
In this section, we review pairwise (maximum) likelihood (PML) estimation for IRT models [14; 15; 16; 17]. For item responses \(X_{i}\) with IRFs \(P_{i}(x,\theta;\gamma_{i})\) (\(i=1,\dots,I\)), marginal univariate probabilities are given by  
\[L_{1,X_{i}}(x;\mathbf{\gamma}_{i})=\mathsf{P}(X_{i}=x)=\int P_{i}(x,\theta;\gamma _{i})\phi(\theta)\,\mathrm{d}\theta, \tag{4}\]  
where \(\phi\) denotes the density of the standard normal distribution. PML estimation also relies on the evaluation of bivariate probabilities for an item pair \((X_{i},X_{j})\)  
\[L_{2,X_{i}X_{j}}(x,y;\gamma_{i},\gamma_{j})=\mathsf{P}(X_{i}=x,X_{j}=y)=\int P _{i}(x,\theta;\gamma_{i})P_{j}(y,\theta;\gamma_{j})\phi(\theta)\,\mathrm{d}\theta. \tag{5}\]  
In PML estimation, item parameters \(\mathbf{\gamma}=(\mathbf{\gamma}_{1},\dots,\mathbf{\gamma}_{I})\) are computed by maximizing the weighted sum of the likelihood contributions of univariate and bivariate frequencies. In more detail, the PML optimization function is given by  
\[l(\mathbf{\gamma})=\sum_{i=1}^{I}w_{1,X_{i}}\left(\sum_{x=0}^{1}n_{X_{i},x}\log L _{1,X_{i}}(x;\mathbf{\gamma}_{i})\right)+\sum_{i=1}^{I-1}\sum_{j=i}^{I}w_{2,X_{i}X _{j}}\left(\sum_{x=0}^{1}\sum_{y=0}^{1}n_{X_{i}X_{j},xy}\log L_{2,X_{i}X_{j}}( x,y;\gamma_{i},\mathbf{\gamma}_{j})\right), \tag{6}\]  
where \(n_{X_{i},x}\) is the univariate frequency with which item \(X_{i}\) takes the value \(x\). Moreover, \(n_{X_{i},X_{j},xy}\) denotes the bivariate frequency in the sample with which item \(X_{i}\) has the value \(x\in\{0,1\}\) and item \(X_{j}\) has the value \(y\in\{0,1\}\). The choice of the weights \(w_{1,X_{i}}\) and \(w_{2,X_{i}X_{j}}\) can be determined by the researcher's objectives (see also [18]). In the simulation study and the empirical example, we focus on PML estimation that involves all item pairs with the weights \(w_{1,X_{i}}=1/I\) and \(w_{2,X_{i}X_{j}}=2I^{-1}(I-1)^{-1}\). By doing so, the univariate and bivariate frequencies equally contribute to the analysis.