esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The possibility and reliability of using established constitutive constants determined from low strain rate experimental testing as input codes in DEFORM-3D to generate flow stress at high strain rates were investigated. The following conclusions were drawn from the study:  
1. DEFORM-3D simulation could be used to conduct uniaxial compression test simulations at high strain rates by using established constitutive parameters from low strain rate uniaxial compression testing experiments. The results showed that the relationship between the DEFORM-3D-generated peak flow stress and all strain rates was linear, with a correlation of at least 0.98.
2. The peak flow stress values predicted by DEFORM-3D simulation were close to those obtained from experimental testing, but a difference of up to 20% may be expected. This difference can be minimized by improving the constitutive models from which the constitutive constants are derived.  
Conceptualization, M.B., J.O. and D.K.; methodology, M.B., J.O. and D.K.; software, M.B., J.O. and D.K.; validation, M.B., J.O. and D.K.; investigation, M.B., J.O. and D.K.; writing--original draft preparation, M.B.; writing--review and editing, M.B., J.O. and D.K.; project administration, M.B.; funding acquisition, M.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported through the AESA-RISE Fellowship Programme [ARPDF 18-03], the African Materials Science and Engineering Network (the Carnegie-IAS RISE network), and the DST-NRF Centre of Excellence in Strong Materials. AESA-RISE is an independent funding scheme of the African Academy of Sciences (AAS) implemented with the support of the Carnegie Corporation of New York. At the AAS, AESA-RISE is implemented through AESA, the academy's agenda and programmatic platform, created in collaboration with the African Union Development Agency (AUDA-NEPAD). The views expressed in this publication are those of the author(s) and not necessarily those of the AAS, AUDA-NEPAD, or the Carnegie Corporation.  
Not applicable.  
The raw data and supplementary data associated with this study will be made available on request to readers.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 1 Introduction
Due to increased international challenges regarding large outdoor fires and the built environment, the International Association of Fire Safety Sciences (IAFSS) established an international work group on this issue after discussions at the IAFSS 2017 conference in Lund, Sweden [29]. The work group objectives and goals were presented by [PERSON] et al. [30]. Such initiatives clearly show that the fire disaster risk needs to be better managed. It should also be mentioned that, when it comes to vulnerability, natural disasters caused by, e.g., earthquakes, flooding, extreme heat waves, drought, forest fires, and coastal erosion, show many similarities when it comes to the societal vulnerability impacts. This was highlighted in the European Commission project named \"Methods for the Improvement of Vulnerability Assessment in Europe (MOVE)\" [31]. Most risk parameters they defined were hazard independent, and could therefore also be used in a multi-hazard vulnerability assessment. Capacities in anticipation, coping and recovering were analyzed. Working with stakeholders was a positive experience since most of them recognized the importance of assessing vulnerability, and were open to consider it in their daily risk management activities. To reduce the fire disaster risk requires an interdisciplinary and multi-faceted approach, which may be summarized in five \"P\"s suggested by [PERSON] and [PERSON] [12]. People come into the picture both as an ignition source (directly via, e.g., cigarettes or indirectly via, e.g., power lines) and as potential victims of an ignited fire. The problem is partly solved if human ignition is prevented, especially in windy and dry conditions. However, lightening is also a major ignition source especially in wildfires. Planning may have been done without considering potential extreme fire events. Especially when planning has failed, the protection of homes from fires is critical. The fifth \"P\" is the prediction of extreme wind conditions in real time and communicating such information to fire brigades, homeowners and the public. The high fire disaster frequency experienced in the recent years and the climate changes expected during the next decades represent alarming signals for the future. There is therefore an urgent need for innovative and interdisciplinary research to (a) understand the complexity and (b) to proactively manage the increasing fire disaster risk. This implies increased capacity in dynamic risk anticipation. The motivation for the present paper is to communicate an innovative and interdisciplinary research agenda to approach to these challenges. The present paper briefly outlines fire as a threat in Section 2, with emphasis on dry fuel fire dynamics. Parameters influencing the fire disaster risk in different time scales and possibilities for mitigating these are presented in Section 3. A research agenda framework is presented in Section 4 and the suggested approach is discussed in Section 5, where comments are made on the likelihood of success of such an approach.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 4 Biomedical Applications
### Targeted Drug Delivery (TDD)  
As a direct consequence of their magnetic properties, MNPs can be employed as carriers for precise drug delivery, as they may be directed to certain tissues with the aid of an external magnetic field. This method enhances the bioavailability of medications and enables their targeted administration to affected tissue, while minimizing systemic effects. The focused administration of medication improves its therapeutic efficacy and minimizes the likelihood of adverse reactions [124; 132; 133; 134].  
TDD efficiency is highly influenced by the applied magnetic field, NP magnetism, size, shape, and surface coating, but also by extracellular and intracellular barriers [135]. Beside drugs, other molecules that can be loaded in MNPs involves nucleic acids, cells, proteins, or antibodies [135; 136].  
SPIONs coated with paclitaxel-chitosan and folate-polyethyleneglycol proved to be an efficient strategy for targeting fibrosarcoma. The nanosystem induced cancer cells' apoptosis and decreased tumor size [137].  
An external magnetic field combined with ultrasound led to the development of a TDD system composed of magnetic nanoparticle coated liposomes and microbubbles as a therapy for atherosclerosis plaques. This strategy was based on MNPs' capacity to penetrate and migrate into inaccessible tissue during MHT. Nevertheless, the ultrasound field can increase the therapeutic compounds delivery and thus the targeting of the desired tissue [138].  
Such approaches have been used in MNP research in theranostics, a new and interesting topic that recently gained a lot of attention [139; 140; 141; 142; 143; 144].  
MNPs loaded with nanoparticles containing growth factors were used both as delivery and labeling systems with a great potential in regenerative medicine [145].  
Multifunctional magnetic nanoparticles, also known as smart drug delivery systems, can update already developed cancer therapies [143]. Such examples can be found in Table 1.
## 2 User-Side Energy Storage Economy Analysis  
### Cost Model  
**Investment cost.** Considering the actual application, the initial investment cost of energy storage is composed of power cost and capacity cost [19]. The specific mathematical model is described as follows:  
\[C_{sys}=c_{e}S_{N}+c_{p}P_{N} \tag{1}\]where \(C_{sys}\) is investment cost of energy storage (\(\gamma\)). \(c_{e}\) is unit capacity cost of energy storage (\(\gamma\)/kW). \(S_{N}\) is energy storage capacity (kWh). \(c_{p}\) is unit power cost of energy storage (\(\gamma\)/kW), and \(P_{N}\) is energy storage rated power (kW).  
**Operation and maintenance cost.** The operation and maintenance cost of energy storage includes the energy storage system operating cost and the maintenance cost to ensure a good storage state, which is shown as follows:  
\[C_{om}=c_{om}P_{N} \tag{2}\]  
where \(C_{om}\) is annual operation and maintenance cost of energy storage (\(\gamma\)), and \(c_{om}\) is annual operation and maintenance cost coefficient of unit power of energy storage (\(\gamma\)/kW).  
**Total cost.** According to the determined social discount rate and inflation rate, operation and maintenance cost over life cycle can be converted to present value at the initial stage of equipment purchase. Adding the investment cost of energy storage, the total cost of energy storage in the full life cycle period is described as follows:  
\[C_{all}=C_{sys}+\sum_{t_{s}=1}^{N_{s}}C_{om}(\frac{1+ir}{1+id})^{t_{s}} \tag{3}\]  
where \(C_{all}\) is total cost of energy storage in full life cycle period (\(\gamma\)), \(N_{s}\) is lifespan of energy storage (year), \(t_{s}\) is used time of energy storage (year), _ir_ and _id_ are inflation rate and discount rate (%), respectively.
###### Abstract  
The increasing interconnection of machines in industrial production on one hand, and the improved capabilities to store, retrieve, and analyze large amounts of data on the other, offer promising perspectives for maintaining production machines. Recently, predictive maintenance has gained increasing attention in the context of equipment maintenance systems. As opposed to other approaches, predictive maintenance relies on _machine behavior models_, which offer several advantages. In this highly interdisciplinary field, there is a lack of a literature review of relevant research fields and realization techniques. To obtain a comprehensive overview on the state of the art, large data sets of relevant literature need to be considered and, best case, be automatically partitioned into relevant research fields. A proper methodology to obtain such an overview is the _bibliometric analysis_ method. In the presented work, we apply a bibliometric analysis to the field of equipment maintenance systems. To be more precise, we analyzed clusters of identified literature with the goal to obtain deeper insight into the related research fields. Moreover, cluster metrics reveal the importance of a single paper and an investigation of the temporal cluster development indicates the evolution of research topics. In this context, we introduce a new measure to compare results from different time periods in an appropriate way. In turn, among others, this simplifies the analysis of topics, with a vast amount of subtopics. Altogether, the obtained results particularly provide a comprehensive overview of established techniques and emerging trends for equipment maintenance systems.  
biblometric analysis; predictive maintenance; research front graph; article density; trend analysis +
Footnote †: journal: Applied Sciences
## 6 Validation of Lateral Pressure Estimation
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Net Normal Stress** & \(E_{sat}\) & \(H_{vast}\) & \(H_{last}\) & \(H_{issast}\) & \(\alpha\) & \(\beta\) & \(\lambda_{v}\) & \(\eta_{v}\) & \(\lambda_{h}\) & \(\eta_{h}\) \\ (**kPa**) & (**kPa**) & (**kPa**) & (**kPa**) & (**kPa**) & & & & & & & \\ \hline
0 & 12 & 12 & 20 & 34 & 102.502 & 0.747 & 5050.221 & 1.648 & 4518.053 & 1.673 \\
12.5 & 134 & 24 & 31 & 55 & 19.224 & 0.904 & 3694.508 & 1.646 & 3750.679 & 1.710 \\
25 & 261 & 47 & 52 & 97 & 13.227 & 0.873 & 3479.687 & 1.567 & 3232.893 & 1.502 \\
50 & 470 & 61 & 72 & 124 & 11.047 & 0.866 & 4396.977 & 1.694 & 4553.960 & 1.712 \\
100 & 901 & 88 & 152 & 175 & 10.640 & 0.864 & 4229.299 & 1.585 & 3393.036 & 1.676 \\ \hline \hline \end{tabular} Note: \(H_{\text{isoort}}\): the isotropic elastic modulus with respect to matric suction, \(H_{\text{isoort}}=(1+\upmu)/(1-\upmu)\cdot\text{d}s/\text{d}e_{\text{ij}}\); \(\lambda_{v}\) and \(\eta_{v}\): the fitting parameters for estimating \(H_{\text{isoort}}\); \(\lambda_{h}\) and \(\eta_{h}\): the fitting parameters for estimating \(H_{\text{isoort}}\).  
\end{table} 
Table 6: Summary of the calculating parameters used in the estimation. Figure 12: Comparison of the measured lateral pressure and the estimated lateral pressure by (**a**) the proposed method and (**b**) method without considering the anisotropic swelling behavior. MAE performed best [34]. The calculation results of those statistical indices are summarized in Table 7.  
As depicted in Table 7, it can be observed that the proposed method that considers the swelling anisotropy performed reasonably well in terms of determining the lateral pressure of expansive soil, and it has a superior predictive ability with higher \(R^{2}\) and RWI values and lower MAAPE, RMSE, and MAE values compared to the isotropic model. In conclusion, validation with high-quality test measurements affirmed the ability of the anisotropic constitutive model and estimation techniques to predict lateral pressures. Significant improvement over isotropic assumptions justifies the consideration of intrinsic anisotropy for more realistic expansive soil modeling and design. The results demonstrate the effectiveness of the proposed framework in capturing true stress-strain-suction behavior. With further refinement, the approach holds promise as a practical tool for facilitating the wider usage of expansive soils in geotechnical applications through proper earth pressure assessments.
## 3 Results
### Validation of the Models  
#### 3.1.1 TOSQAN ISP-47 Test Simulation  
As shown in Figure 2, the TOSQAN test facility [35] is a cylindrical vessel with an inner diameter of 1.5 m and a height of 4.8 m and includes a sump at the bottom to remove condensate.  
Figure 2: (**a**) Schematic diagram of the TOSQAN test facility; (**b**) geometry modeling; (**c**) mesh generation for test simulation.  
The temperature of the outer wall of the pressure vessel is controlled by the oil jackets, and in particular, the temperature of the wall in the middle is lowered to induce condensation of water vapor. A vertical pipe is installed at the center of the vessel to inject water vapor and non-condensable gas (here, air and helium).  
The experiment was conducted in a series of phases, and the injection conditions were varied in each phase by changing the injection rate and the injection material. Figure 3 shows the injection rates of the gas species in the TOSQAN ISP-47 test.  
For the analysis of the experiment, a grid was generated as shown in Figure 2c, and the wall was composed of three patches considering the condensation wall (upper wall: 122 \({}^{\circ}\)C; condensation wall: 101.3 \({}^{\circ}\)C; lower wall: 123.5 \({}^{\circ}\)C).  
Figure 4 shows the temperature distribution within the pressure vessel over time and the iso-concentration surfaces of water vapor and helium obtained from the analysis. Figure 5 shows the calculated pressure compared with the experimental data and the result calculated with NEPTUNE_CFD by [PERSON] et al. (2015). It can be seen that the calculated result is similar to the result of [PERSON] et al. and the experimental data. Figure 6 compares the water vapor concentration distribution of the calculated results with the experimental results. It is thought that the wall condensation analysis module of the 3D code simulates flow phenomena including water vapor condensation well.  
Figure 4: Change in temperature distribution in the TOSQAN test vessel over time.  
Figure 3: TOSQAN ISP-47 test conditions ([PERSON] et al., 2015).
## 2 Data and Methods
### Methods
#### 2.3.2 Spatial Change Trend Analysis  
The coefficient of variation (CV) can measure the dispersion degree of data. In this study, we calculated CV of each pixel's SWUE from 2007 to 2015 to characterize the spatial distribution and variations of ecosystem surface SWUE in northeast China. The formulas are as follows:  
\[\text{CV}= \frac{\text{SD}}{\text{SWUE}},\text{ SD }=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}\left(SWUE_{i}- \overline{SWUE}\right)^{2}},\text{ }\overline{\text{SWUE}}=\frac{1}{n}\sum \limits_{n=1}^{n}\text{SWUE}_{i}. \tag{2}\]  
Spatial trends of SWUE were explored using a linear regression model with time as the independent variable and SWUE as the dependent variable [34]. The slope of the fitted regression line at each pixel was calculated using Equation (3).  
\[\text{slope}=\frac{\text{n}\times\sum\limits_{i=1}^{n}i\times SWUE_{i}-\sum \limits_{i=1}^{n}i\sum\limits_{i=1}^{n}SWUE_{i}}{n\times\sum\limits_{i=1}^{n}i^ {2}-\left(\sum\limits_{i=1}^{n}i\right)^{2}}, \tag{3}\]  
where \(n\) represents the number of years, \(\text{SWUE}_{i}\) is the summed SWUE in growing season for year \(i\), \(\overline{\text{SWUE}}\) represents average SWUE during \(n\) years, and SD is the standard deviation. As the CV becomes smaller, the value of SWUE becomes more stable. The \(F\)-statistic was used to determine the significance of the linear regression model. Based on the slope value and significant levels, the change trends were classified into five grades: extremely significant increase (slope > 0, 0 < \(p\)\(\leq\) 0.05), significant increase (slope > 0, 0.05 < \(p\)\(\leq\) 0.1), no significant change (\(p\) > 0.1), significant decrease (slope < 0, 0.05 < \(p\)\(\leq\) 0.1), and extremely significant decrease (slope < 0, 0 < \(p\)\(\leq\) 0.05).
## 2 Methods: Modelling Approach
### Case Study Methods
[35], where the water-leaving signal is phytoplankton-dominated (e.g., in the Benguela system), it is quite reasonable to expect that some PFT information may be derived from the bulk radiometric signal. However, the challenge for the ocean colour community is determining the PFT signal in low biomass oceanic conditions, for example in the Southern Ocean. Phytoplankton dynamics in the Southern Ocean are particularly important for their role in uptake of anthropogenic CO\({}_{2}\) (around half of all oceanic uptake), and hence carbon sequestration [3; 4]. Variability in phytoplankton ecology is directly linked to mineral and nutrient cycles: assemblages of large diatoms drive primary productivity and carbon export, while assemblages of small phytoplankton play a significant role in nutrient recycling although the net productivity is very low [73]. The third Southern Ocean Seasonal Cycle Experiment (SOSCEx III) undertaken on the SANAE 55 cruise (austral winter 2015) provides the phytoplankton size distribution and Chl \(a\) data for this experiment [74]. Assemblage \(D_{eff}\) were calculated from Coulter Counter measurements, and Chl \(a\) determined by fluorometric analysis [5]. The additional \(a_{gd}(\lambda)\) and \(b_{bmap}(\lambda)\) components were estimated guided by observations in [75; 76] respectively, noting that these are simply used to approximate the bulk \(R_{rs}\) and do not influence any of the other results, as they are discussed in terms of likely variability rather than absolute magnitudes. EAP phytoplankton IOPs with generalised Chl \(a\)-carotenoid eukaryotic refractive indices were calculated according to the measured \(D_{eff}\) and Chl \(a\) concentrations, and were combined with these estimates and run through Hydrolight to produce the modelled \(R_{rs}\). Given that the refractive indices used to model the EAP IOPs for this example are from the generalised Chl \(a\)-carotenoid group suitable for diatom and dinoflagellate species, the likelihood of encountering _Phaeocystis_ sp. in the Southern Ocean must be addressed. Given the oceanographic context, as the \(D_{eff}\) of 16 \(\upmu\)m is reached, it can reasonably be assumed that the assemblage comprises both diatoms and _Phaeocystis_. The main accessory pigment in _Phaeocystis_ is 19-hexanoyloxyfucoxanthin, a derivative of fucoxanthin, a dominant light harvesting pigment in diatoms, and so it may be reasonable to model the intracellular absorption properties of individual cells with the generalised eukaryote refractive indices, but this species forms large floating colonies which result in quite different optical effects, and this cannot currently be addressed with the model. Thus, while the likely presence of _Phaeocystis_ is acknowledged, it is not explicitly catered for in the modelling. This does not affect the observations on identifying changes in \(D_{eff}\) in the discussion below.
# Photoacoustic Imaging with a Finite-Size Circular Integrating Detector
## 4 Test and Discussion
### Validation with a Digital Breast Phantom Based on Clinical MRI Data  
To bridge the gap between idealized simulations and real-world PAI challenges, we further validated our method using an open-source breast phantom derived from clinical MRI data (Washington University School of Medicine) [36]. This breast phantom, constructed based on clinical data, accurately reflects the distribution of breast tissues, including fibroglandular and adipose tissues, providing a more representative testing scenario than that offered by traditional simulations. Figure 13 shows the y-z cross-section of the digital breast phantom at x = 4 \(\times\) 10\({}^{-2}\) m, realistically simulating the photoacoustic response of breast tissue.  
As shown in Figure 14, we present the unprocessed blurred image, the image after Wiener deconvolution, and the image processed using the proposed method, along with their magnified views. In the unprocessed blurred image (Figure 14a), noise and blur effects are quite prominent, and significant detail is lost. The magnified view shows an unnaturally smooth transition between adipose tissue and fibroglandular tissue, which affects the diagnostic quality of the image.  
The image after Wiener deconvolution (Figure 14b) shows improvement in noise suppression, but magnification still reveals subtle blurring, especially at the interface between the adipose and fibroglandular tissues. The blur effect remains noticeable, and some regions suffer from detail loss, with no fundamental improvement in image quality.  
In contrast, the image processed using the proposed method (Figure 14c) demonstrates significant improvements. The magnified view shows sharper boundaries for the fibroglandular tissue, with more precise restoration of tissue structures. The transition between adipose and fibroglandular tissues is smoother and more natural, with clearer boundaries between different tissue types. Noise and artifacts are effectively suppressed, leading to a substantial improvements in overall image quality.  
To further quantify the performance of the proposed method, as shown in Table 4, the unprocessed image exhibits a low PSNR (16.81 dB) and SSIM (0.4941), indicating significant noise and blur, along with poor structural fidelity. After applying Wiener deconvolution, the PSNR and SSIM values improve to 22.61 dB and 0.5164, respectively. Although there is an improvement, some blur remains, particularly in detail recovery. In contrast, the proposed method achieves PSNR and SSIM values of 36.77 dB and 0.9912, respectively, showing a significant improvement in quality, especially in the recovery of fibroglandular tissue details and overall tissue structure.  
Figure 13: Tissues in the digital breast phantom.  
As seen from the results above, the proposed method achieves a good balance between deblurring and structural fidelity, validating the effectiveness of the algorithm in real breast photoacoustic imaging.