esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Evaluation of Ultrasonic Detection of Solid Occlusions in CSP Molten Salt Piping
## 1 Introduction
This research underscores the potential of ultrasonic methods in monitoring the health of materials in CSP systems [21]. Detecting and managing solid inclusions in real time within CSP systems presents a significant challenge. Traditional temperature-based control mechanisms, while effective in monitoring bulk thermal conditions, cannot provide precise information on the extent or location of solidification [16]. This limitation often leads to excessive and energy-intensive activation of heating systems, which reduces overall efficiency. Ultrasound offers a promising alternative, due to its ability to detect phase changes, density shifts, and variations in viscosity within fluid mixtures [22]. By analyzing the interaction between ultrasonic waves and solid inclusions, it is possible to gather detailed information about the inclusions' spatial distribution and their impact on the system. This work focused on the numerical evaluation of ultrasonic techniques for detecting solid inclusions in CSP molten salt systems, a critical challenge for maintaining optimal operation in concentrated solar power (CSP) plants. This study employed finite element method (FEM) simulations in the time domain, to investigate the interaction of ultrasonic signals with solidified inclusions, enabling a detailed understanding of wave propagation and attenuation in the presence of these inclusions. Unlike previous studies, which primarily focused on basic detection techniques or limited material systems, this work integrated a more advanced approach by incorporating statistical analysis to interpret the ultrasonic signals. This allows for real-time detection and assessment of the internal state of the molten salt, offering significant advantages, in terms of operational monitoring and efficiency. The key innovation of this study lies in its ability to combine numerical simulations with probabilistic modeling, providing a more accurate and reliable framework for assessing the degree of solidification and the spatial distribution of inclusions. In contrast to traditional methods, which are often limited to temperature-based controls or qualitative visual inspections, the proposed ultrasonic-based monitoring approach offers a non-invasive, continuous, and high-resolution alternative for detecting and characterizing solid inclusions. The implications of this research extend beyond CSP plants, showcasing the versatility and transformative potential of ultrasonic technology in industrial fluids management [10,23].
# XPS on Co\({}_{0.95}\)R\({}_{0.05}\)Fe\({}_{2}\)O\({}_{4}\) Nanoparticles with R = Gd or Ho
## 3 Results and Discussions

Table 2: Site occupancies of cations for Co\({}_{0.95}\)R\({}_{0.05}\)Fe\({}_{2}\)O\({}_{4}\) samples, and calculated and experimental saturation magnetization moments measured at 4.2 K.  
The hysteresis loops were measured at 300 K, in external magnetic fields between \(-4\) and 4 T, and are shown in Figure 11. Small values of the coercive field were found at 300 K for all samples--see Table 3. We calculated the anisotropy constant (\(K\)) using the relation:  
\[K\cong\mu_{0}H_{\mathrm{C}}M_{S}\]  
where \(\mu_{0}\) is the vacuum magnetic permeability, \(Hc\) is the coercive field, and \(M_{s}\) is the saturation magnetization. The obtained values are presented in Table 3. Using the calculated \(K\), we determined the blocking temperature, \(T_{B}\), using the formula:  
\[T_{B}=\frac{KV}{k_{B}ln\left(\frac{\tau_{m}}{\tau_{0}}\right)}\]  
where we denoted \(k_{B}\) as the Boltzmann constant, \(T_{B}\) the blocking temperature of the samples, \(\tau_{m}\) the characteristic measuring time (on the order of hundreds of seconds for a vibrating sample magnetometer), \(\tau_{0}\) the attempt time (\(10^{-9}\) s usually considered), while \(V\) is the volume of a single nanoparticle. Usually, in laboratory measurements, the value of \(ln\left(\frac{\tau_{m}}{\tau_{0}}\right)\), is considered 25 [68]. The \(T_{B}\) calculated values in approximation of spherical nanoparticles are shown in Table 3.  
Figure 11: Hysteresis loops for Co\({}_{0.0095}\)R\({}_{0.05}\)Fe\({}_{2}\)O\({}_{4}\) nanoparticles at 300 K. Inset: zoom around 0 T.  
Figure 10: The temperature dependences of ZFC and FC curves. The calculated \(K\) values are comparable with a value of 13.8 kJ/m\({}^{3}\) reported by [PERSON] and [PERSON] [69]. The \(T_{B}\) values are smaller than the data obtained from ZFC-FC measurements, which are around room temperature. The differences could be attributed to the approximation of spherical nanoparticle shapes for volume calculations. [PERSON] et al. [70] demonstrated that the presence or absence of various types of intergrain exchange interactions is governed by the value of \(R=M\)r/\(M\)s (where \(M\)r is the remanent magnetization and \(M\)s is the saturation magnetization), which ranges from 0 to 1. When \(R\) < 0.5, particles interact predominantly via magnetostatic interactions. At \(R\) = 0.5, in line with the Stoner-Wohlfarth model, the system consists of non-interacting, randomly oriented particles. For \(R\) > 0.5, exchange coupling between particles becomes significant. In our samples, the \(M_{\rm r}\)/\(M\)s ratio at room temperature is below 0.5, indicating the predominance of magnetostatic interactions. Given the green synthesis route of preparation and the magnetic properties of these compounds, our goal is to use them for applications in cancer therapy, preparing magnetoelectric core-shell structures, where the aim is to locally generate electric charges in the body, controllable by relatively low external magnetic in order to affect only the cancer cells.
# Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications
## 2 System Model
### Power Capture Effect  
Due to the characteristics of wireless communications, a specific signal can be decoded when multiple signals are received at different powers, causing the capture effect. This phenomenon occurs when the strongest signal power received from a specific device is sufficiently large. In other words, the capture effect occurs when the signal-to-interference ratio (SIR) (in [16], the signal-to-interference-plus-noise-ratio (SINR) is used. However, we use SIR to model the capture effect as [15] for mathematical tractability) of the strongest signal is larger than a specific threshold [15; 16]. If \(P_{i}\) denotes the signal power received from device \(i\) at the eNB, the probability that the signal from device \(i\) is captured, i.e., \(\mathbb{P}_{pc,i}\), is defined as follows:  
\[\mathbb{P}_{pc,i}=\mathbb{P}\left(P_{i}>\beta\sum_{j\
eq i}P_{j}\right), \tag{1}\]  
where \(\beta\) is _the capture-threshold_.
# A Study of Forest Phenology Prediction Based on GRU Models
## 3 Results and Analysis  
### Extracting GEI  
GEI data was extracted from the ROI using Equation (2), and the start of the forest growing season (SOS), the peak growing season and onset of senescence (COS), and the end of the growing season (EOS) were obtained as the 98 th, 262 nd, and 332 nd day of the year, respectively. The GEI data accurately reflects the forest phenology growth and senescence process, which corresponds to three stages of vegetation phenology: the first stage is the rapid growth period, where GEI data shows a rapid upward trend; the second stage is the mature period, where GEI data remains relatively stable with only small fluctuations; and the third stage is the senescence period, where GEI data decreases sharply to its lowest value (Figure 5).  
Figure 5: Cont.  
The results showed that the GEI data were influenced by environmental factors and diurnal differences, but there were obvious seasonal variations. The forest was in a dormant period, and the GEI data, except for the impact of snowfall, had a relatively flat overall change trend. Under the influence of rising temperature and increasing precipitation, the forest gradually began to germinate. GEI data rose and vegetation began to enter the forest growing season, the time point at which the forest reached its peak and the GEI value reached a high value until the 98 th day of the year, which lasted about 62 days. The peak growing season and onset of senescence lasted until 19 September (262 days). Thereafter, the forest activity gradually weakened with the decrease of temperature, and the GEI data also decreased. On the 332 nd day of the year, the end of the growing season arrived. The forest was all withered and the GEI data returned to the low value again.
## 2 Materials and Methods
### Synthesis of WPU  
The WPUs were synthesized based on the modified acetone process [43], where butanone was used as a solvent (Scheme 1). IPDI and polyols were charged in the reactor, along with DBTDL catalyst under N\({}_{2}\) atmosphere. The reaction was proceeded at 75 \({}^{\circ}\)C until the theoretical NCO content of the prepolymers was reached, as determined by the di-n-butylamine titration method. Subsequently, N-MDEA was added and the reaction was proceeded for 1 h to attain the backbone hydrophilicity at 30 \({}^{\circ}\)C. Afterwards, CO was added, and the reaction was continued for 2 h at 78 \({}^{\circ}\)C. Finally, BDO was added to the reaction mixture, followed by the continuation of the reaction for 2 h at 78 \({}^{\circ}\)C. Subsequently, the reaction mixture was cooled to 25 \({}^{\circ}\)C and butanone (20 wt%) was added to reduce its viscosity. The neutralization reaction proceeded at the same temperature for 30 min. Following this, distilled water was added to the reactor, and contents were stirred at 600 rpm for 60 min and 300 rpm for another 60 min. Finally, butanone was removed by using a rotary evaporator at 50 \({}^{\circ}\)C under vacuum to obtain the WPU material. The composition and properties of WPUs are listed in Table 1, and the reaction schematic is illustrated in Scheme 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & **PCL-OH** & **CO** & **MDEA** & **Hard** & **CO (wt\%)** & **T-Peel Strength** & **Zeta** \\  & **(Molar)** & **(Molar)** & **(wt\%)** & **Segment (wt\%)** & **CO (wt\%)** & **(N/25 mm)** & **Potential** \\ \hline CO\({}_{0\%}\)–WPU & 1 & 0 & 3.8 & 37.5 & 0 & 17.2 & 39 \\ CO\({}_{2.50\%}\)–WPU & 1 & 0.095 & 3.3 & 38.9 & 2.5 & 23.4 & 45 \\ CO\({}_{5.04\%}\)–WPU & 1 & 0.190 & 3.2 & 40.3 & 5.0 & 29.5 & 50 \\ CO\({}_{7.40\%}\)–WPU & 1 & 0.285 & 3.2 & 41.6 & 7.4 & 36.8 & 54 \\ CO\({}_{9.66\%}\)–WPU & 1 & 0.380 & 3.1 & 42.8 & 9.7 & 26.8 & 55 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Feeding composition and properties of WPUs.
# Self-adaptive Artificial Bee Colony with a Candidate Strategy Pool
## 4 Experiments
### Comparison for CEC2013 Functions  
In this section, we used the CEC2013 testing suite in experiment 2, to further illustrate the effectiveness of our algorithm SAABC-CS in handling complicated functions and high-dimensional issues. Since the CEC2013 test functions are more complicated than the 22 fundamental scalar functions, it was challenging to find the global best solution for CEC2013. We compared our algorithm SAABC-CS with the original ABC [12] and four additional cutting-edge ABC algorithms (ABCNG [33]), KFABC [19], SABC-GB [15], and MGABC [30]). We measured the outcomes on D = 30, 50, and 100, to examine the comprehensive performance of these algorithms in several dimensions. For the fairness of the experiments, the methods were evaluated using the same parameters, SN = 100, limit = 1000, and MaxFEs = 10,000-D. The final statistical outcomes of the six related algorithms are encapsulated in Tables 2-4 based on 30 independent runs.  
For D = 30, the results of the above ABC variations are reported in Table 2. For 21, 17, 25, 20, and 25 of the 28 test functions, our algorithm SAABC-CS outperformed or equaled the ABC, ABCNG, KFABC, SABC-GB, and MGABC in terms of outcomes. Additionally, SAABC-CS achieved the best results on 17 functions of F1-F5, F7, F8, F10, F12, F13, F15, F18, F20, F23-F25, and F28 when it was compared with the other algorithms. The statistical results of all algorithms on CEC2013 with the dimension D = 50 are shown in Table 3. SAABC-CS outperformed or equaled the ABC, ABCNG, KFABC, SABC-GB, and MGABC on 21, 17, 25, 20, and 25 out of 28 functions, respectively. The outcomes of all algorithms with the criterion D = 100 are displayed in Table 4. On 24, 20, 25, 19, and 25 out of 28 functions, SAABC-CS outperformed or equaled the ABC, ABCNG, KFABC, SABC-GB, and MGABC, with comparable or superior performance. Moreover, on F1-F9, F12-F13, F15-F20, F23-F25, and F28, SAABC-CS always achieved the best value out of all algorithms. As a result, SAABC-CS had the highest overall performance of the six algorithms that were examined.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Function** & **ABC** & **ABCNG** & **KFABC** & **SABC-GB** & **MGABC** & **SAABC-CS** \\ \hline F12 & Mean & \(2.198673\times 10^{3}\) + & \(1.517617\times 10^{3}\) + & \(2  
To be more intuitive, we used the Friedman test [34] to rank all algorithms across all test problems, with the results shown in Table 5 and Figure 7. It is worth noting that the Friedman test uses the post hoc technique [35; 36; 37; 38], and the lower the ranking, the better the overall performance of the algorithm. From the data, we can see the average rank of SAABC-CS was always the first for 30, 50, and 100 dimensions. Therefore, SAABC-CS's effectiveness was always superior to the others on all dimensions.  
We can further demonstrate the efficacy of our approach in finding the optimal for the complicated functions, based on the comparison findings mentioned above for the CEC2013 functions.
# Prototype Network for Few-Shot Hazard Assessment of Vehicle Lane Changes in Risk Field
###### Abstract  
It is well known that road traffic safety is one of the crucial topics in the field of automotive safety in assisted driving. In the face of complex traffic scenarios, there are still a large number of unsolved problems in the identification of vehicle risk levels. Given the difficulty of obtaining sufficient data for model training in many vehicle risk scenarios, it is important to correctly identify the vehicle risk posed through few-shot testing. The main idea of this method is to learn the class prototype of each risk state of a vehicle through encoding and mapping the vehicle's characteristic parameters in the risk field and identifying the risk posed by few-shot vehicle lane changes through the application of the training of a prototype network. The latest results improve the efficiency of identifying risk in few-shot lane changes, which can provide practical guidance for risk identification and decision analysis in safe lane-change trigger scenarios based on the expected functional safety and provide theoretical support for the development of L3+ autonomous vehicles.  
prototype network; few-shot; vehicle lane change risk; vehicle hazard field
## 3 A Generalized Approach for Multiple Event Types  
In general, sensor nodes may have multiple sensing devices to monitor multiple attributes of the physical environment in which they are deployed. Each of these sensing devices will generate its own _Event_ and _Non-Event_ packets. Consequently, simply distinguishing _Event_ and _Non-Event_ packets may not be enough when the WSN is in a congested state. Different sensed data will have different value to the sink and it is important to ensure that more of the valuable data is collected by the sink when the network is congested. In this section, we extend _PCC_ by introducing a _Pricing System_which modifies the packet dropping policy based on different priorities of different _Event_ packets to achieve a specified balance between the aggregate \"value\" of the collected data and coverage fidelity. The key features of the proposed _Pricing System_ are the following:  
* The sink acts as the information consumer and sets a price that it is willing to pay for each different types of _Event_ packets. Higher prices indicates the sink prefers the sensor network to collect this corresponding category of _Event_ packet at the cost of more transmission resource. The ratio of different prices determines the balance between the priority and coverage. If all prices are equal, the _Pricing system_ degrades to _PCC_. If one of the prices is \(\infty\), the sink is willing to only accept the corresponding category of _Event_ packets and consequently the wireless sensor network would block all other types of _Event_ packets.
* The sensors operate as the information providers and when congested selectively drop packets according to the value that the sink places on the information in each packet (determined by the price set by the sink). When the buffer utilization is high, the sensor tends to keep packets with the lower accumulated survival probability \(P_{i}\) and higher price. The detailed algorithm is introduced in Section 3.1.
* The prices can dynamically vary according to the changes in the physical environment and the network condition. When the sink modifies the prices, the new prices are broadcast to the entire network and each sensor node uses the new prices to adjust the dropping policy during congestion.  
The _Pricing System_ gives more flexibility to the network administrators. It is easy to add or delete a category of information by adding a new price or setting the price to zero, as long as the hardware can sense the corresponding type of information. Adjusting the ranking of different types of _Event_ packets  
Figure 11: Influence of \(Q_{min}\) and \(Q_{max}\).  
can also be done through changing the prices of the data collected by the sensors. In addition, the prices configured by the network administrators is able to accurately control the dropping probabilities, and thus control the ratio of received packets in the sink. Based on the structure of _PCC_, we describe the algorithm for the _Pricing System_ in Section 3.1. and evaluate the performance improvement in Section 3.2..
## 1 Introduction  
The computational modelling of evolving discontinuities has witnessed considerable advances in recent times. Using the partition of unity property of finite element shape functions [1], it is possible to simulate discontinuities in a finite element solution across surfaces which are not aligned with the under lying mesh. Such methods are known as the extended finite element method [2; 3] and the generalised finite element method [4]. The approach has obvious application to the modelling of failure in solids, but it can also be applied for a variety of other applications.  
While the technology for modelling evolving discontinuities is now maturing, the implementation of such techniques can be tedious and requires a significant investment of time. For these reasons, application of extended finite element techniques is still largely the domain of those in a position to develop software rather than of the broader group, that is, users of computational technology. A limited number of libraries which support the extended finite element method are available (see for example [5]), however these libraries follow the traditional paradigm in which a user is required to program by hand the innermost parts of a solver. We present here our efforts towards the automated solution of partial differential equations which involve evolving discontinuities in the solution. The approach relies on automated code generation from high-level input. Progress has been made in automating large parts of the finite element modelling process for conventional formulations [6; 7], as well as discontinuous Galerkin methods [8] and \(H(\mathrm{div})\) and \(H(\mathrm{curl})\) conforming elements [9]. We will extend and generalise some of these ideas to the modelling of discontinuities. Namely, we will use a _form compiler_ to generate low-level computer code from a high-level input language. This permits a high degree of mathematical expressiveness, and by generating low-level code, there is the potential to generate more efficient code than that can be reasonably produced by hand [6; 10]. Furthermore, the approach detaches the model (differential equation) of interest from underlying implementation aspects, such as the discontinuity surface representation.  
The rest of this work is structured as follows. In Section 2. we introduce briefly aspects of automated computational modelling for continuous problems and present a simple example. This is followed by a concise overview of modelling discontinuities using the extended finite element method. We then present our approach to automating the solution of problems which involve discontinuities, and this is followed by a collection of examples.  
The approach described in this work is manifest in computer code which is available under the GNU Public License (GPL) and the GNU Lesser Public License (LGPL). We build on a number of tools which are part of the FEniCS Project [11], and which are freely available at www.fenics.org. The specific extensions and examples presented in this work are archived at [12].
# Purification of Produced Water by Solvents to Enhance Oil Recovery and Reuse Separated Droplets
## 4 Discussion
### Miscellanea  
To conclude, Figure 8 depicts a variety of solvents that cannot be classified into the chemical families previously analysed. Undoubtedly, the most notable is the exceptionally high performance of tetrahydrofuran, with virtually 100% recovery at all dosages. It is a small, cyclic molecule with an appropriate degree of ionization, thereby combining various advantages of xylene, cyclohexane, and the chlorinated hydrocarbons. In this respect, it is the most advantageous solvent of all. It has low viscosity and low density, which favours the flotation of the recovered oil. Additionally, with a boiling point of 66 \({}^{\circ}\)C, it can be easily distilled and reused in the process. It is not especially toxic, but it has the drawback of potentially forming explosive epoxides when distilled to dryness. This situation must be avoided. Supplementary Document S1 presents data on the toxicological properties of tetrahydrofuran as well as of crude oil for reference.  
Regarding N,N-dimethyl formamide, diethanolamine, and acetonitrile, molecules with different nitrogenous groups do not exhibit sufficient oil recovery capacity to be recommended for use. The behaviour of dimethyl sulfoxide is striking as a very small and polarized molecule that stabilizes the oil emulsion as its dosage increases. This behaviour can be likened to that of glycerin, as previously discussed, as it can be interpreted as the  
Figure 6: Percentage of residual oil removed from produced water versus various volumes of esters.  
Figure 7: Butyl acrylate.  
effect of dimethyl sulfoxide's affinity for water, its solvation, and probably its ability to increase the zeta potential of the oil droplets in the emulsion.