esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Experimental Findings and Analysis of the Enhanced Network Model
#### 4.3.1 Analysis of Experimental Results Introducing SimAM Attention Mechanism
To further validate the effectiveness of embedding the SimAM module in the ShuffleNetv2 network model, we conducted comparative experiments with several mainstream attention mechanisms, and the results are presented in Table 3. It is evident that solely embedding a single attention mechanism into the network may disrupt the stability of the original network structure. Although there is not much impact on the number of parameters and the amount of model calculation, they all affect the detection accuracy to varying degrees. However, with the introduction of the SimAM attention mechanism, which is a parameter-free attention mechanism, the network allocates more attention to important neurons, enabling more detailed feature extraction and thus improving the accuracy of foreign object detection. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Precision (\%)** & **Recall (\%)** & **mAP (\%)** & **Parameters (M)** & **FLOPs (G)** \\ \hline YOLOv8n-shuffle & 94.8 & 88.0 & 93.5 & 1.60 & 4.7 \\ YOLOv8n-shuffle-SE & 89.5 & 79.6 & 88.1 & 1.64 & 4.8 \\ YOLOv8n-shuffle-CBAM & 86.0 & 80.1 & 87.5 & 1.65 & 4.7 \\ YOLOv8n-shuffle-CA & 94.7 & 87.5 & 93.2 & 1.64 & 4.7 \\ YOLOv8n-shuffle-ECA & 94.1 & 85.5 & 92.0 & 1.64 & 4.7 \\ YOLOv8n-shuffle-GAM & 94.1 & 84.3 & 91.1 & 1.75 & 4.8 \\ YOLOv8n-shuffle-NAMA & 95.2 & 85.4 & 92.3 & 1.64 & 4.7 \\
**YOLOv8n-shuffle-SimAM** & **95.3** & **90.0** & **93.8** & **1.60** & **4.7** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of experimental results with different attention mechanisms. Figure 9: Comparison of heat maps of different network models. (**a**) Original input image; (**b**) YOLOv8n heat map; (**c**) YOLOv8n-shuffle heat map; (**d**) YOLOv8n-SM heat map.
# Modeling Non-Equilibrium Dynamics and Saturable Absorption Induced by Free Electron Laser Radiation
## 4 Conclusions  
In this work we have shown the results of the application of a simplified three-channel model to the non-equilibrium dynamics of ultrathin aluminum films excited by FEL radiation at 33.3, 37 and 92 eV photon energy. The theoretical model has been developed and implemented into a Fortran 90 code using semi-classical rate equations coupled with the equation of propagation of the photon wave packets. X-ray transmission measurements for pulses of different photon energy and fluence were found to be in agreement with present simulations. Simulations were shown to be able to shed light on temporal dynamics (in the fs range) for nano-sized Al films strongly interacting with the photon pulse. Simulations of non-equilibrium dynamics were reported for three different maximum intensities of FEL pulses corresponding to typical fluences for a system far from saturation and near the saturation threshold. We remark that the three-channel model including a relaxed state produced a population inversion state, leading to negative temperatures which is a typical consequence of non-equilibrium dynamics induced by laser. We also expanded our non-linear model, including explicitly the two-photon absorption (TPA) cross-section and the effect of including electron heating for reproducing accurate transmission measurements. Within our simplified TPA model, we have obtained the highest entropy state in the limit of high fluence, corresponding to positive temperatures (no inversion). Through the series of numerical calculations using different models reported in this article, we have shown that for the case of short EUV/X-ray pulses, compared to the lifetime of the excited state, we need to employ a dynamical (time-dependent) model. Furthermore, electron heating effects must be taken into account for a deeper level of agreement with experimental data showing saturable absorption at high fluence, as shown by a direct comparison with accurate EUV absorption data. This work has been carried out in the framework of the TIMEX collaboration (time-resolved studies of matter under extreme and metastable conditions: [[http://gnxas.unicam.it/TIMEX](http://gnxas.unicam.it/TIMEX)]([http://gnxas.unicam.it/TIMEX](http://gnxas.unicam.it/TIMEX))). TIMEX was a project financed by the FERMI@Elettra FEL facility in Trieste in collaboration with the University of Camerino. [PERSON] gratefully acknowledges TIMEX research grants and the authors acknowledge the COST Action MP1306 EUSpec for support. Both authors equally contributed to this article. The authors declare no conflict of interest.
# Research on Lateral Deformation Control Criteria of Metro Shield Tunnels with Excessive Ellipticity
## 3 Finite-Element Analysis  
### Material Structural Modeling and Parameters  
This research utilizes general finite-element analysis software, Midas FEA NX 1.0, to develop the finite-element model of the shield tunnel's segment. Load simulations are performed on the three-dimensional finite-element model. Material specifications, bolt quantities and strengths, and reinforcement measures are chosen based on real-world scenarios. The concrete grade used for the shield tunnel is C50, employing the concrete plastic damage intrinsic model. The damage factor can determine the material's elastic modulus \(E\) once the concrete undergoes plastic damage.  
\[E=(1-d_{k})E_{0}\]  
where \(E_{0}\) is the initial modulus of elasticity of the material and \(d_{k}\) is the concrete damage factor, which can be expressed as the damage parameter of concrete in tension or compression, respectively.  
Stress-strain curves for concrete's uniaxial tensile and compressive conditions can be inferred from calculations in line with the concrete code [18]. The energy equivalent model determines the damage parameter \(d\)[19]. Two key plastic damage factors of C50 concrete are shown in Figure 6.
# Efficient Removal of Heavy Metal Ions in Wastewater by Using a Novel Alginate-EDTA Hybrid Aerogel
## Materials and Methods
### Adsorption-Desorption Experiments  
Adsorption experiment: Approximately 100 mg of Alg-EDTA was weighed and added to 50 mL 1.5 mM of metal ion solution, magnetically stirred for 6 h and filtered. The residual metal ions in the filtrate was measured using ICP-OES.  
Desorption and circulation experiments: Cd\({}^{2+}\)-loaded or Pb\({}^{2+}\)-loaded Alg-EDTA was immersed into 50 mL of 0.05 M HNO\({}_{3}\), magnetically stirred for 6 h, filtered, washed successively with deionized water, calcium hydroxide (Ca(OH)\({}_{2}\)), deionized water, and finally dried at 50 \({}^{\circ}\)C for 4 h, thereby obtaining the regenerated Alg-EDTA.
# A Novel Variable Step Size Incremental Conductance Method with an Adaptive Scaling Factor
## 2 Determination of the Optimal Scaling Factor Values
### Conventional VSS INC MPPT Algorithm  
Conventionally, the perturbation step size of the INC MPPT method is fixed. When a large perturbation step is used, the dynamic response will be quick, but there will exist a large steady-state oscillation. A small perturbation step has a favorable steady-state response, but the dynamic response will be slowed down. Therefore, how to obtain a balance between the dynamic response and the steady-state response becomes a major problem of the conventional INC MPPT method.  
To improve the performance, the fixed perturbation step size is replaced by the slope of power to voltage in VSS INC MPPT methods, as shown in Equation (4).  
\[V_{(n+1)}=V_{(n)}\pm M*\left|\frac{dP}{dV}\right| \tag{4}\]  
The scaling factor (\(M\)) which is a constant in scalar form is an important parameter of the VSS INC MPPT technique. However, the selection of the scaling factor \(M\) remains one difficulty for the implementation of the VSS methods. A large value of \(M\) is beneficial for the tracking speed; however, it will result in substantial oscillations around the MPP. In contrast, a small value of \(M\) can cause the MPP tracking speed becomes slow. For different conditions, the derived scaling factor is different,  
Figure 1: Equivalent circuit of solar cell.  
which limits the universality of the method. Therefore, a novel VSS INC MPPT method with an adaptive scaling factor is proposed in this study. The determination process of optimal scaling factors will be provided in the next subsections.
## 4 Source-Injection-Borehole Offset  
We consider the CSEM monitoring of the TU Delft campus reservoir with source offsets 1, 2, and 3 km from the injection well, which has zero offset. The source is an \(x\)-directed electric dipole. We carried out 1D numerical experiments to evaluate the source frequency and sensitivity of the EM components to the resistivity change. The results indicate that the vertical electric field is the most sensitive to the changes, and that a source frequency around 1 Hz offers an adequate depth of propagation and resolving power. The resistivity change has a disk shape with 300 m radius, centred at the injection well. The radius of the change is one-fifth the distance between the injector and the producer. The survey domain was discretized with a uniform grid spacing of 50 m along the polar and longitudinal axes, while the azimuthal direction was divided into 40 segments of 9\({}^{\circ}\).  
Figure 3 shows the \(z\)-component of the electric field for different source offsets. Changing the source offset from 1 to 3 km did not change the magnitude of the difference; however, it affected the amplitude of the electric fields reaching the resistivity change and, subsequently, the ability to monitor the 300 m resistivity change. At an offset of 1 or 2 km, the monitoring electric fields can cover the resistivity change and the area around it. A difference of moderate confidence was observed within and around the location of the resistivity change. As the time-lapse fields were scattered around the resistivity change, a difference of low confidence could be determined some distance away. A source offset of 3 km led to a difference of the back-scattered field in a relatively small area. This limits the freedom of measurement for the difference of the forward-scattered field, especially in the case of cold fronts with larger radii. Consequently, we set a source offset in the range of 1-2 km with 1 Hz frequency to acquire vertical electric fields for the monitoring of resistivity changes in the TU Delft campus reservoir model.
## 3 Results and Discussion
### SDS on CO\({}_{2}\)-Induced Oil Swelling
This adsorption-driven mechanism modifies the diffusion pathways of CO\({}_{2}\), ensuring deeper penetration and uniform distribution into the oil phase. As a result, the enhanced dissolution of CO\({}_{2}\) leads to significant oil swelling, reducing the viscosity of the oil and improving its mobility. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Injection Pressure**} & \multicolumn{2}{c}{**Distance [\(\mu\)m]**} \\  & **(FI: 0.2 to 0.9)** \\ \hline
5 psig CO\({}_{2}\) & Pure oil at 180 min & 2358.2 \\  & Pure oil at 360 min & 2395 \\  & SDS-oil at 180 min & 2399.9 \\  & SDS-oil at 360 min & 2320.7 \\ \hline
10 psig CO\({}_{2}\) & Pure oil at 180 min & 2220.9 \\  & Pure oil at 360 min & 2225.6 \\  & SDS-oil at 180 min & 2008.1 \\  & SDS-oil at 360 min & 895.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Distances between two datapoints corresponding to fluorescence intensity (FI) change from 0.2 to 0.9 on the exponential fit for pure oil and SDS-mixed oil at 5 and 10 psig CO\({}_{2}\) pressures, measured at 180 and 360 min. Figure 6: Illustrative image representing the reduced IFT between oil and CO\({}_{2}\) in presence of SDS.
## 2 Background
### Other Existing Methods  
The research in WSNs in the past years have focused on two main areas namely: (1) Network related issues such as, routing and MAC layer designs and (2) Application related issues. In this article we are concerned about the application related issues, specifically we tackle the problem of distributed inference as it relates to in-network data suppression and communication reduction in WSNs. In the literature, learning models have been used to tackle this class of problem. Theoretically, models allow the possibility of examining fundamental questions of inference through learning techniques under tight communication constraints in wireless sensor networks. Some of these approaches have been well studied using parametric models, with the assumption that data to build a suitable model is available, and the system designers have prior domain knowledge of the application setting. However, if few data is available and there is limited application-specific knowledge, the non-parametric model building is preferred. From this stand point, the focus of this article is applying a combination of well-known alternative non-parametric learning-theoretical models, where data is sparse and prior knowledge of the environment is limited. Some of these models, such as compressed sensing, least mean square estimation, and regression estimation models have been widely studied in the field of signal processing, machine learning and in statistics.  
One of the key research questions that arises in the literature is: are we able to apply these set tools for intelligent inference and data acquisition in WSNs? As discussed in [17], the classical limits of the algorithms for non-parametric learning are not always applicable in WSNs, in part because the classical models from which they are derived have abstracted away from the communication involved in the data acquisition process. We believe that this question can be addressed by understanding the fundamental limits learning models impose on energy and bandwidth. Thus we consider a solution that combines signal processing, data suppression and communication to the above problem.  
In the next sections of this article, we briefly cover some key elements as it relates to our proposed design framework. Our intention is to jointly explore these key areas in order to achieve a robust WSN deployment.
## 3 Main Parameter of AFPMBLDCM
### No-Load Back EMF  
When the AFPMBLDCM is running under no-load conditions, the permanent magnet rotates with the rotor. It creates a constantly changing magnetic field in the air gap. The windings generate an induced electromotive force by cutting magnetic field lines. The main air-gap flux \(\Phi_{\delta}\) of the AFPMBLDCM is distributed along the axial direction, and the effective length is distributed along the radial direction. Therefore, the air-gap magnetic density \(B_{\delta}(\theta)\) at the average radius was selected for equivalent calculations. As shown in Figure 4, the position of the conductor on magnetic steel is indicated by the radius \(c\) and polar angle \(\theta\).  
The distance between the inner and outer radii of the winding \(R_{i}\) and \(R_{o}\) can be any length d\(c\). When the winding conductor rotates at an angle d\(\theta\) at \(\Omega\) angular velocity, the average induced electromotive force \(E_{1}\) generated by a single winding conductor can be obtained as follows.  
\[E_{1}=\Omega\int_{R_{i}}^{R_{o}}B_{\delta}(\theta)cdc=\frac{1}{2}\Omega B_{ \delta av}(R_{o}^{2}-R_{i}^{2}) \tag{8}\]  
\[B_{\delta av}=\alpha_{i}B_{\delta} \tag{9}\]  
where \(\alpha_{i}\) is the polar arc coefficient. \(B_{\delta av}\) is the average air-gap flux density of the motor at one pole distance.  
When the winding coefficient is \(K_{x}\), the number of turns per phase is \(N_{x}\). When the number of parallel branches is 1, the induced electromotive force \(E_{x}\) of each phase winding can be written as  
\[E_{x}=N_{x}K_{x}\alpha_{i}\Omega B_{\delta}(R_{o}^{2}-R_{i}^{2}) \tag{10}\]  
If the magnetic pole material, shape, polar arc coefficient, inner diameter and outer diameter of the AFPMBLDCM are determined, the permanent magnet thickness \(h_{\text{m}}\) is also limited. The thickness of the permanent magnet can be calculated based on the principle of maximum magnetic energy product. According to design experience and the thickness of the permanent magnet, the length of the air gap can be calculated. The initial thickness of the permanent magnet described in this paper is 3.4 mm. The length of the air gap is 0.8 mm. In summary, the design parameters of the AFPMBLDCM can be obtained, as shown in Table 1.  
Figure 4: Inductive Electromotive Force Analysis Illustration.
# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
## 3 Satellite Measurement of Precipitation
### Scientific and Technological Challenges
#### 3.2.2 Observing Snow and Ice
An overlooked component of the precipitation system is freezing rain. GPM has recently made it possible to observe freezing rain features mostly occurring over northern hemispheric land in winter [189]. One more marginal contribution comes from falling hail. A few attempts have been made to detect hail events from PMW radiometers [190; 191; 192] and radars [193; 194]. However, hail detection algorithms do not always perform correctly in all hailstorm situations.
## 1 Introduction
The traditional process is not only expensive, but also it is  
Figure 1: Paddy rice production worldwide (data adapted from reports of FAO between 2006 and 2018 [4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16]); values for 2017 and 2018 are estimated and forecasted data according to the latest report of FAO [4]. hazardous to the environment because during the production of 1 ton of silica, approximately 0.23 ton carbon dioxide, 0.74 ton sodium sulfate and 20 tons of waste water are produced, and it violates the principle of sustainable development [50]. Therefore, an economically feasible and environmentally benign route as an alternative method is required to produce silica. In this respect, a combined energy application and biogenic silica production from RH and RS under controlled conversion conditions would be a promising approach regarding climate protection and zero waste production. Currently, open burning and land filling are the common strategies for RH and RS disposal, which have their own challenges including air pollution, greenhouse gas emission, and large landfill space occupancy because of their low density [24; 58]. Furthermore, uncontrolled open burning of these materials produces crystalline ash with high emissions [59], which is well recognized as a lung carcinogen as well as the health risk from silicosis caused by silica deposition in the lung tissue [60; 61]. To overcome these issues, researchers have been investigated feasible, economical, and environmentally friendly strategies to convert RH and RS into pure biogenic silica via combustion based on various scientific publications [53; 54; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73] and patents [74]. Several studies have been carried out to review the field of biogenic silica production and combustion of RH and RS as raw materials [24; 50; 51; 55; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89]. Most of these studies have focused on applications of the rice husk ash (RHA), rice straw ash (RSA) and pure biogenic silica [50; 75; 88; 89; 90] or the synthesis of advanced materials from RHA and RSA [50; 76; 77; 90], influence of RHA and RSA on concrete and cement properties [78; 87; 86; 87] or energy and power generation using RH and RS [79; 82; 85]. However, to the best of our knowledge, there is no review highlighting the combination of pre-treatment strategies as well as post-treatment of the ashes with combustion in order to produce high quality biogenic silica from RH and RS. This review provides a comprehensive literature survey to cover both treatment and combustion for high quality biogenic silica production. First, fuel properties of RH and RS are reviewed and compared with clean wood. Subsequently, combination of several pre-treatment strategies to increase the quality of biogenic silica including chemical pre-treatment and combustion of the raw materials as well as post-treatment of RHA and RSA are discussed.
# Process-Aware Enactment of Clinical Guidelines through Multimodal Interfaces
## 2 Background
### Clinical Guidelines  
Over the last year, there was an increasing interest from the medical community to investigate and develop evidence-based _clinical guidelines_ (CGs). CGs are based on the best available medical research evidence, and are represented in the form of recommended care pathways to support proper decision-making in patient care for specific medical conditions [17; 18; 30; 31]. Typically, a CG does not enforce mandatory requirements but is used as a reference framework for evaluating clinical practice.  
As shown in Figure 2, CGs are defined to capture domain-specific knowledge, and need to be complemented by additional \"knowledge layers\" that include doctors' basic medical knowledge (BMK), site-specific knowledge and patient-related information (such as medical history and current conditions) to obtain concrete care pathways that can be applied to a specific patient. Care pathways suggest the required clinical activities, together with their sequencing and timing, for the management of patient conditions. According to [14], the combination of care pathways and patient-related  
Figure 1: Classifying healthcare processes in six clinical macro steps.  
information enables the definition of an individual care pathway, which results in the actual patient treatment process.  
One strength of adopting CGs and care pathways in the management of patient care relies on their structuredness and process-oriented perspective, differently from traditional clinical decision-making that leads to loosely structured or unstructured working procedures. The research literature in the medical informatics community has deeply investigated the definition of models, languages and systems for the management of CGs and care pathways, focusing on the so-called \"computer-interpretable clinical guidelines\" (CIGs). The work [28] presents a recent survey classifying the existing frameworks for managing CIGs, which can be categorized as rule-based (e.g., Arden Syntax), logic-based (e.g., PROforma), network-based (e.g., EON) and workflow-based (e.g., GUIDE). Most of the available formalisms allow for representing CGs as task networks, where activities and decisions are related via scheduling and temporal constraints, often in a rigid flowchart-like structure. This has made the automated enactment of CGs using PAISs and process-oriented approaches as a relevant and timely challenge for the medical community [11; 28; 32].  
In this paper, we tackle this challenge by presenting the main findings of the TESTMED project, whose aim was to realize a clinical PAIS able to interpret CGs and orchestrate their execution among doctors and medical staff through mobile technologies and multimodal user interfaces.