esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Examples
### Performances of \(\hat{N}\)  
We verified that the estimator \(\hat{N}\) is asymptotically unbiased. In fact, Figure 3 shows that, for most of the SNRs taken into consideration obtained by changing \(S\) with \(N=1\), the estimator is practically unbiased even for small number of samples. For high SNRs the number of samples must be increased beyond \(2^{22}\) to get acceptable small squared bias; e.g., less than \(10^{-2}\).  
In Figure 4 the estimated variances, denoted with (sim), along with the corresponding asymptotic variances, denoted with (theor), and CRLBs, at different SNRs are shown. Note that the CRLB of \(\hat{N}\) (17) depends on \(N^{2}\) only and in the figures remains constant as, also in this case, the increase of the SNR is obtained by increasing \(S\) while the noise power remains constant with \(N=1\). Variances are reported on a logarithmic scale as \(10\log_{10}\) var versus the \(\log_{2}K\), where \(K\) is the number of observed samples.  
Results show that the estimated variance achieves the asymptotic performances for the whole range of SNRs into consideration. As for the case of the signal power estimation, it is not possible to predict the number of samples from which the asymptotic variance expresses the exact variance because the asymptotic analysis presented in Section 4 is based on the fundamental assumption that \(K\rightarrow\infty\).  
While the noise power estimator is asymptotically consistent in all cases, it is not efficient, since it cannot achieve the CRLB, in all cases under investigation. The difference between the asymptotic variance and the CRLB remains constant as the number of samples increases and depends on the current value of the signal power \(S\). In Figure 4b we note that the difference decreases as \(S\) increases; e.g., for a given \(K\) the asymptotic variance of \(\hat{N}\) with an SNR of \(3.0103\) dB is closer to the CRLB than the asymptotic variance of \(\hat{N}\) at \(0\) dB. On the other hand, in Figure 4c-f we see that the difference becomes lager and larger and asymptotic variances of the noise power estimator are quite faraway from the CRLB. In other words, though the estimator \(\hat{N}\) is consistent, it may require too many samples to get acceptable performances. Moreover, it is not asymptotically efficient since the asymptotic variance is not superimposed to the corresponding CRLB.
# A PINN-Based Nonlinear PMSM Electromagnetic Model Using Differential Inductance Theory
## 4 Examples of PINN Surrogate Modeling
### PMSM Torque Calculation Based on PINN Modeling
#### 4.4.2 Torque Calculation Under USE Conditions  
During the actual operation of the PMSM, due to a series of nonlinear factors such as inverter dead zone effect, the three-phase currents cannot be completely sinusoidal and \(i_{dq}\) contains harmonic components. To verify the validity of the PMSM nonlinear mathematical model and the PINN model in solving the torque under USE conditions, the harmonic currents are injected based on the second operating point. Table 4 shows the magnitude and phase of the injected harmonics of each order, and  
Figure 11 shows the three-phase current excitation and waveforms of \(i_{d}\) and \(i_{q}\) after harmonic injection.  
Figure 12 presents a comparison of the torque and flux linkage results obtained by the GLM, PINN, and OLAM methods against those from FEA. Among the three, GLM exhibits the largest deviation, with an EDS reaching 27.02. The PINN model predicts torque more accurately than OLAM--its outputs align closely with non-FEA sampling points, as anticipated from the SE condition analysis. Specifically, OLAM yields a maximum torque error of 1.28 and an EDS of 6.8, whereas PINN reduces these to 0.37 and 1.52, corresponding to just 22% and 29% of the OLAM values, respectively. Apart from the 18 th-order harmonic,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Harmonic Order** & **5** & **7** & **11** & **13** & **17** & **19** & **23** & **25** \\ \hline Amplitude (A) & 1.30 & 0.40 & 1.37 & 2.24 & 0.19 & 0.06 & 0.15 & 0.17 \\ Phase (rad) & 1.84 & 1.16 & 4.99 & 3.45 & 2.80 & 5.94 & 4.11 & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Injected current harmonics.  
Figure 10: Torque and armature flux linkage under the SE condition computed by the FEA, PINN, OLAM, and GLM methods: (**a**) torque waveform; (**b**) torque FFT; (**c**) d-axis flux linkage; (**d**) q-axis flux linkage (\(I_{\text{AMP}}=490\) A, \(\beta=62\) deg).  
PINN also outperforms OLAM in estimating both average torque and the amplitude of each harmonic order, as shown in Figure 12.
## 3 Data and Method
### Land Use/Cover Change Index  
LULC change has been identified as one of the most important drivers of change in ecosystems and their services [32; 33]. However, information about the consequences of LULC change for ecological situations and ecosystem services is largely absent or hard to find, and the LULC dynamic degree model used in previous research [21] can only evaluate the LULC change magnitude and does not indicate the change in ecological situation. To convert LULC statistics into measures of macro-ecological situation changes in a convenient manner, we graded 8 out of the 9 LULC types (excluding the build-up area) listed in Table 1 based on ecosystem services value of each LULC type and the extent to which the transitions between different LULC types influenced the delivery of ecosystem services (Table 2). Estimates were based on a mix of expert knowledge, literature sources [34; 35] and the natural situation of the TRHR--for the \"Chinese Water Tower,\" water regulation and supply are the most important services of the TRHR, followed by soil conservation, and then by the supply function, climate regulation and carbon mitigation [22]. A higher ecological level value indicates that the LULC change will impact the macro-ecological situation and ecosystem services more significantly, and _vice versa_.  
The LULC change index considers both the change of areas and the ecological levels. It is calculated as:  
\[LCCI=\sum_{k=1}^{n}\left[A_{k}\times\left(D_{ak}-D_{lk}\right)\right]/A\times 1 00\% \tag{1}\]  
where: \(LCCI\) is the LULC change index; \(k=1\), \(2\), \(3\), \(4\), \(5\), \(6\), \(7\), \(8\) represents the LULC types; \(A_{k}\) is the area of the LULC change; \(D_{ak}\) is the ecological level after change; \(D_{bk}\) is the ecological level before change; and \(A\) is the total area of the research region. A positive value means that the LULC changes in a positive way and the macro-ecological situation meliorates, and _vice versa_.
# Thermal Characterization of Phase Change Materials by Differential Scanning Calorimetry: A Review
## 2 Phase Change Materials and Base Concepts
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Application** & \multicolumn{2}{c}{**Classification**} & \multicolumn{2}{c}{**Ideal Properties**} \\ \hline  & & & **Technical properties** \\  & & & ✓ & Phase change temperature in the \\
**Thermal storage**: & & **Organic (Paraffin and non-Paraffin)** & & range of operating temperature \\ ✓ & Direct steam generation in & ✓ & Available & ✓ & High phase transition enthalpy \\  & concentrating solar power plant & ✓ & Inexpensive & \\ ✓ & Industrial waste heat recovery & ✓ & Stable & \\  & coupled with compressed air energy & ✓ & Compatible & \\  & storage system & ✓ & High latent heat & ✓ & High density \\ ✓ & Domestic applications & ✓ & Low thermal conductivity & ✓ & Small density change \\
**Thermal control**: & & **Inorganic (Salt and salt hydrate)** & & \\ ✓ & Automotive (interior air conditioning, & ✓ & Smaller density change & \\  & thermal management of batteries) & ✓ & Nonflammable & ✓ & No supercooling \\ ✓ & Thermal management of electronic & ✓ & Corrosive & \\  & devices (small available space) & ✓ & Unstable & ✓ & No corrosivity \\ ✓ & Textiles (human comfort in outdoor & & **Metallic (In and Al)** & ✓ & Non-flammable \\  & work and for sport) & ✓ & Stability & ✓ & Non-explosive \\ ✓ & Medical (warming and cooling of & ✓ & High thermal conductivity & ✓ & Non-toxic \\  & body parts) & ✓ & High melt temperature & ✓ & Low hygroscopicity \\ ✓ & Transportation (maintain goods at the & ✓ & High density & \\  & desired temperature) & ✓ & Low latent heat & \\ ✓ & Buildings (construction & ✓ & Low latent heat & ✓ & Non-harmful for environment \\  & materials, ventilation) & **Eutectic** & & and water resources \\  & & ✓ & Desired transition point & **Economic** \\  & ✓ & Characterization challenges & ✓ & Low cost \\  & & & ✓ & Available at large scale \\  & & ✓ & Available worldwide \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PCM applications and their targeted optimal properties [2; 13; 16; 17].
# Damping and Natural Period Evaluation of Tall RC Buildings Using Full-Scale Data in Korea
## 3 Empirical Formulae for Damping Ratio of RC Buildings for Wind Design
### Frequency- and Amplitude-Dependent Damping Ratio
#### 3.2.2 Architectural Institute of Japan (Ajl) 2000  
In Japan, the damping ratio for wind design is provided in a book \"Damping in structures\" [14] published by Architectural Institute of Japan (Ajl), as opposed to a design code. This book is called AIJ 2000 in this paper. AIJ 2000 provides empirical formulae for the damping ratio based on the measured damping ratios of 137 steel buildings, 43 SRC buildings, and 25 RC buildings.  
The damping formulae provided in AIJ 2000 are categorized by structure type as follows:  
\[\zeta=0.014f_{1}+470\Big{(}\frac{X}{H}\Big{)}-0.0018\text{ for RC buildings} \tag{10}\]  
\[\zeta=0.013f_{1}+400\Big{(}\frac{X}{H}\Big{)}-0.0029\text{ for steel buildings} \tag{11}\]  
where \(f_{1}\) is the first mode natural frequency, and \(x\) is the tip displacement. The empirical formulae for first mode natural frequency are given as follows:  
\[f_{1}=\frac{1}{0.015H}\text{ for RC buildings} \tag{12}\]  
\[f_{1}=\frac{1}{0.020H}\text{ for steel buildings} \tag{13}\]  
Because Equations (12) and (13) have been derived using the data of existing buildings, it is suggested that they be used in the range of 10.8 m \(<H<\) 129.8 m for RC buildings and 19.1 m \(<H<\) 282.3 m for steel buildings.
## 1 Introduction
[12] also analyzed the recent advancement in water quality indicators for eutrophication in global freshwater lakes. In the same vein, [PERSON] et al. (2024) [13] studied nutrient transport following water transfer in China. Several studies published previously [14; 15; 16; 17; 18; 19] have highlighted the Lobo water body's advanced eutrophication state. These studies were based on the analysis of biological and physicochemical parameters from point samples taken from the water body and did not allow an in-depth understanding of the eutrophication phenomenon, in particular, the nutrient transfer in the basin. To remedy this lack in this context, methodologies based on the combination of hydrological models and geographic information systems (GIS) are believed to be more effective. Hydrological models of watersheds are driven using mathematical formulas linked to physical processes and conceptual models. The basin hydrologic functioning includes an analogy and a concept. In the analogy, soils and groundwater are considered reservoirs whose rate of emptying depends on the filling. The physically based models are directly linked to physical processes [20; 21]. The principle of calculation is based on precise physical and empirical representations of natural processes. Empirical models [22] such as the USLE (Universal Soil Loss Equation) erosion model [23], derived from laboratory or field experiments. These models are global: this the case of the GR4 model which can represent the watershed in a single entity for global assessment [24]. However, global-scale models are not suitable for studying hydrological processes of watersheds, because they do not take into account the spatial variability of phenomena such as runoff or erosion directly related to land use. Thus, semi-distributed models such as the Soil and Water Assessment Tool (SWAT), come in addition to overcome this lack by now, considering topography, land cover, soil and soil management practices in the basin. This study aims to assess the nutrient loads from the amounts of fertilizers applied to different crops in the Lobo watershed in order to understand the spatio-temporal evolution of nutrient transport processes using the SWAT model.
###### Abstract  
PMUTs have been widely studied in recent years, particularly those based on the SOI (silicon-on-insulator) process, which have been partially commercialized and are extensively used in advanced applications such as ultrasonic ranging and spatial positioning. However, there has been little research on their high-temperature reliability, a critical area for their use in extreme environmental conditions. In this study, we investigate the high-temperature characteristics of air-coupled PMUTs based on SOI under various structural conditions, employing both finite element analysis (FEA) and experimental validation. We assess the performance of PMUTs at elevated temperatures by examining key parameters such as resonant frequency, the electromechanical coupling coefficient, mechanical amplitude, and warpage, all analyzed as functions of temperature. The experimental results show that temperature-induced drift becomes more significant as the back cavity size increases and the top silicon layer thickness decreases. These findings are consistent with the trends observed in the finite element analysis. Specifically, a PMUT with a back cavity diameter of 1000 \(\upmu\)m and a top silicon thickness of 4 \(\upmu\)m exhibits a temperature drift rate of up to 47.3% when the operating temperature rises from room temperature to 200 \({}^{\circ}\)C. Furthermore, at elevated temperatures, the maximum electromechanical coupling coefficient improves by 68.6%, and the mechanical amplitude increases by 66.1%. Heating experiments using a 3D profiler reveal that warpage increases from 0.3 \(\upmu\)m to 2.15 \(\upmu\)m as the temperature reaches 150 \({}^{\circ}\)C. These findings offer important theoretical insights into the temperature-induced drift behavior of PMUTs under high-temperature conditions. This study provides a comprehensive understanding of the performance variations of PMUTs, including changes in electromechanical coupling, mechanical amplitude, and structural warpage, which are critical for their reliable operation in extreme environments. The results presented here can serve as a foundation for the design and optimization of PMUTs in applications that require high-temperature stability, ensuring their enhanced reliability and performance in such demanding conditions.  
s +
Footnote †: journal: Applied sciences
## 2 Description of Multi-Agent Model
### Rescue Teams  
The essential parameters of rescue teams include search radius \(s_{r}\), maximum turning angle \(s_{a}\), speed of rescue \(s_{\textit{rubble}}\), scope of cooperation \(s_{c}\), move length \(s_{l}\), turning angle \(s_{\theta}\).  
Search radius \(s_{r}\) indicates the maximum extent of the search for survivors. Maximum turning angle \(s_{a}\) indicates the range of turning angle in each step, shown in Figure 2. Speed of rescue \(s_{\textit{rubble}}\) indicates speed of move rubbles by a rescue team.  
Scope of cooperation \(s_{c}\) indicates that the cooperation only occurs between rescue teams in this specified area.  
Figure 1: The model of multi-agent simulation.  
In rescue teams' searching process, shown in Figure 2, they move along the steps which are determined by move lengths \(s_{l}\) and turning angles \(s_{\theta}\). The move length and turning angle are drawn from particular probability distributions. The steps are terminated when a move length or boundary is reached, or when a buried site is detected. At the end of a step, rescue teams move to the buried site or selects next step to continue searching [29].  
Truncated Levy walks are adopted to simulate rescue teams' search behaviors in this paper [25]. The move lengths in Levy walks are drawn from generalized Levy probability density distribution:  
\[P(s_{l})={cs_{l}}^{-\mu},1<\mu\leq 3 \tag{1}\]  
where \(c\) is a constant, \(\mu\) denotes Levy index. In order to impose restrictions on move lengths, Truncated Levy walks are adopted in the simulation. Thus, the probability density function of move length is  
\[P(s_{l})=\left\{\begin{array}{ll}0,&s_{l}>l_{\max}\\ \frac{\mu-1}{l_{\min}^{-\mu}-l_{\max}^{-\mu}}\times{s_{l}}^{-\mu},&l_{\min} \leq s_{l}\leq l_{\max},1<\mu\leq 3\\ 0,&s_{l}<l_{\min}\end{array}\right. \tag{2}\]  
where \(l_{\min}\), \(l_{\max}\) denote the minimum and maximum move length, respectively. The buried sites within the search radius are possible to be detected by rescue teams; consequently, the move lengths are larger than \(l_{\min}\), corresponding to the search radius of rescue teams. The move lengths are truncated at the distance \(l_{\max}\), corresponding to limit scale of environment [25; 30]. The corresponding sampling functions are obtained directly applying the Inverse Transform Sampling.  
\[s_{l}=\left[(l_{\max}^{1-\mu}-l_{\min}^{1-\mu})\gamma+l_{\min}^{1-\mu}\right] ^{1/1-\mu},\quad\gamma\sim U(0,1) \tag{3}\]  
The turning angles in traditional Levy walks are drawn from uniform distribution \(U(0,\,2\pi)\).  
In this paper, turning angles \(s_{\theta}\) are drawn from uniform distribution \(U(0,\,s_{a})\), where \(s_{a}\) is used instead. The \(s_{a}\) shall be discussed later as a controllable parameter.  
The searching plan of rescue teams is shown in Figure 3.  
Figure 2: The movement of rescue teams.
# Enhanced Electrokinetic Remediation for the Removal of Heavy Metals from Contaminated Soils
## 3 Results
### Electrokinetic Treatment
The anolyte was deionized water. The electrolysis of water in the anode produced H\({}^{+}\) ions that formed an acid front that acidified the soil specimen. As a result, the soil pH decreased in sections 1-4 and remained at the initial soil pH in sections 5 and 6 (Figure 3). The pH profile in the soil with the combination of the citrate as a complexing agent in the interstitial fluid showed a decisive influence in the speciation of metals and their transport along the soil specimen. As presented in Figure 5, Zn was mobilized in sections 1-3 and accumulated in sections 4 and 5. The concentration of Zn in the last section 6 remained unchanged. This behavior can be explained with a closer analysis of the pH in the soil and the chemical speciation of the system Zn\({}^{2+}\)-citrate (Figure 6). Zn\({}^{2+}\) is the predominant species in the solution at a very acid pH \(<\) 1.5. In the presence of citrate, Zn\({}^{2+}\) forms six different complexes (negative, neutral and positive complexes). The relative predominance of the complexes depends on the medium pH. At pH = 2-3 the dominant species is the positive complex [ZnH\({}_{2}\)L]\({}^{+}\). L represents the ligand citrate. At pH = 3-4.5 the dominant species is the neutral complex [ZnHL]. At pH \(>\) 5, the dominant species is the negative complex [ZnL]\({}^{-}\). The pH in the soil sections 1-4 was below pH = 4, so Zn was mobilized by the combined effect of the H\({}^{+}\) attack and the stabilization in the solution of Zn\({}^{2+}\) with citrate forming positive or neutral complexes. These complexes were transported towards the cathode by electromigration and/or electroosmosis. In soil sections 5 and 6, the pH is about 5, and the predominant species was the negative complex [ZnL]\({}^{-}\). The negative complex will be transported towards the anode by electromigration. This explains why Zn is accumulated in section 5 of the soil specimen. Similar behavior was found for Cd, Cu, Co. These three metals form various neutral, positive and negative complexes with citrate [24], similar to the case explained for Zn. As a result, Cd was mobilized in sections 1-3 and 6 and accumulated in sections 4 and 5. Cu was removed from both ends of the soil specimen due to the formation of positive complexes on the anode side and negative complexes on the cathode side. Cu was accumulated in the center of the soil specimen (sections 3 and 4). In a similar way, cobalt was accumulated in sections 5 and 6. It is noteworthy the small but significant mobilization of Pb in the anode and cathode side and a slight accumulation in the center of the soil specimen. These results
## 4 Experiments
### Results
This demonstrates that simply increasing the dilation rate results in a stronger bias towards shapes in the model. (**a**) The visualization images were obtained by applying Grad-CAM to the original ResNeXt (texture-biased model). (**b**) The visualization images were obtained by applying Grad-CAM to ResNeXt with a dilation of three (shape-biased model). logo images. In particular, on the logo dataset, models with larger kernel sizes achieved higher accuracy than models with larger dilation. This can be attributed to the relatively weak shape features and the presence of texture in the logo images. Additionally, the gaps introduced by dilation could have affected the performance, making a larger kernel size more beneficial. However, for the sketch images, which were the most shape-dominant, the two pre-trained models with modified dilation factors outperformed the other models. This suggests that dilation-factor changes that expand the receptive field of a CNN and introduce gaps in the kernel size are more efficient for increasing the shape bias. As shown in Table 5, in the early stages of training, all variants with a shape bias surpassed the accuracy of the original architecture in two epochs. However, in the case of a kernel size of five, although the initial accuracy exceeded that of the original, the final accuracy fell below it. This may be attributed to the features extracted by the encoder of the model employing the kernel size of five as they are immediately comprehensible to the classifier, but fail to fully capture the image characteristics as effectively as the original encoder, resulting in accuracy inversion as the learning progresses. This phenomenon was observed across all shape-dominant datasets and warrants further investigations of the model with a kernel size of five. The experimental results are shown in Figure 3. Both increasing the kernel size and the dilation rate facilitated efficient learning with limited data. This was particularly evident in datasets where shape information was paramount, such as the sketch and cartoon datasets. These findings suggest that in transfer learning scenarios targeting shape-dominant datasets, employing models with a shape bias enables more efficient learning and enhances accuracy compared to traditional methods. As demonstrated in the above experiments, increasing the kernel size and dilation enhanced shape bias. Furthermore, the increase in dilation contributed more significantly to the enhancement of shape bias compared to the increase in kernel size. This is likely because the increase in dilation introduced non-convolutional pixels between those involved in the convolution operation, allowing the model to ignore fine, continuous textures. Additionally, the proposed shape/texture bias measurement method offered a more quantitative evaluation of the relationship between the model and the data compared to existing bias measurement techniques.