esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analytical Procedures  
#### 2.3.1 Physico-Chemical, Chemical, and Biological Methods  
Dry matter in the initial wastes and in the composting samples was analyzed after samples drying for 12 h at 105 \({}^{\circ}\)C. Electrical conductivity (EC) and pH were assessed after a water-soluble extraction using the ratio 1:10 (\(w/v\)) with a conductivity/pH meter (Crison Instruments, S.A., Barcelona, Spain). After combustion at 550 \({}^{\circ}\)C for 24 h, organic matter (OM) was determined following the standard method CEN13039 [23]. Total organic carbon (TOC) and total nitrogen (TN) were assessed in an elemental analyzer (Truspec CN, Leco, St. Joseph, MO, USA) at 950 \({}^{\circ}\)C by dry combustion. Extractable organic carbon (Cex) was obtained after extraction with 0.1 M NaOH, while fulvic-acid-like carbon (Cfa) was obtained after precipitating the humic-acid-like carbon (Cha) of the NaOH extract at pH 2.0, following the procedure detailed by [PERSON] et al. [24]. All these fractions and that of water-soluble carbon (Cw), obtained in a water-soluble extract (1:20 \(w\):\(v\)), were assessed with an automatic carbon analyzer (TOCV-CSN, Shimadzu Corporation, Kyoto, Japan).  
The humification indices for each set were assessed using the formulas used by [PERSON] et al. [22]:  
* Humification ratio (HR) = 100\(\left(\frac{C_{Cex}}{T\mathrm{OC}}\right)\);
* Humification index (HI) = 100\(\left(\frac{C_{Cfa}}{T\mathrm{OC}}\right)\);
* Percentage of humic acids (Pha) = 100\(\left(\frac{C_{Cth}}{C_{C}}\right)\);
* Polymerization rate =\(\frac{C_{Cth}}{C_{A}}\).  
Cation exchange capacity (CEC) was assessed in the end-products with the BaCl\({}_{2}\)-triethanolamine method, as described in [PERSON] et al. [24]. In addition, microelements, P, and heavy metals were determined in these samples by means of ICP-OES (ICAP 6500 DUO, Agilent Technologies, Inc., Santa Clara, CA, USA), after digestion with HNO\({}_{3}\)/HCIO\({}_{4}\). Phytotoxicity was assessed with the methodology described by [PERSON] et al. [25] with _Lepidium sativum_ L. The stability test was conducted using the method described by [PERSON] et al. [26]. All the analytical determinations were conducted in triplicate.  
OM losses were calculated from the concentrations of OM losses calculated from the ash concentrations at the beginning (X\({}_{1}\)) and at the end (X\({}_{2}\)) of the process, according to the following equation [PERSON] et al. [24]:  
\[\mathrm{OM\ loss\ (\%)=100-100\left[(X_{1}\ (100-X_{2}))/(X_{2}\ (100-X_{1}))\right]} \tag{1}\]
## 4 Analysis and Verification of Rolling Bearing Fault Diagnosis Tests  
### Selection of Test Data  
The experimental data utilized in this study comprises the vibration signals from rolling bearingss obtained from Case Western Reserve University [41] ([[https://engineering.case.edu/bearingdatacenter](https://engineering.case.edu/bearingdatacenter)]([https://engineering.case.edu/bearingdatacenter](https://engineering.case.edu/bearingdatacenter))) (accessed on 9 April 2025), specifically focusing on the drive-end data of the deep groove ball bearing model SKF6205-2 RSJEM. Single-point faults were introduced into the test bearings through the application of electrical discharge machining, thereby simulating four distinct operational states of the rolling bearings. The primary objective of this experiment is to evaluate the efficacy of the IGTO method in optimizing VMD.  
The experimental data pertains to a rotational speed of 1772 revolutions per minute (r/min) with a sampling frequency of 12 kilohertz (kHz) under zero-load normal operating conditions. The dataset encompasses both normal and fault states, with a fault diameter of 0.5334 mm affecting the inner race, outer race, and rolling elements. The four operational states are categorized as follows: normal state (L0), outer race fault (L1), inner race fault (L2), and rolling element fault (L3).  
Figure 4: Flowchart of IGTO-VMD-PE.  
Figure 5: Fault diagnosis flowchart for adaptive parameter optimization.
Fraction calculus nowadays has practical applications in several branches of science and technology, including viscoelasticity, electrochemistry, neuro-dynamics, and heat transfer in heterogeneous media [5; 6; 7; 8; 9]. In electrical engineering is used for the modeling some kind of supercapacitors, transformers and non-linear circuits [10; 11; 12]. Additionally, it has been theorized the existence of dual port components with fractional order characteristic. This kind of components, usually called _Fractances_[13; 14; 15], exhibit an impedance that can be expressed as:  
\[Z(\omega)=\frac{1}{F(\omega)^{\alpha}}=\frac{1}{F\omega^{\alpha}e^{\beta\pi \pi/2}} \tag{3}\]  
where \(\omega\) is the angular frequency, \(\alpha\) a real parameter ranging from -1 to 1, and \(F\) is a constant value termed as fractance and its unit is [\(3\)s\({}^{-\alpha}\)]. Fractances can be considered as a generalization of the traditional components, representing a resistor for \(\alpha=0\), a traditional inductor for \(\alpha=-1\), and a traditional capacitor for \(\alpha=1\). However, higher interest towards these components arises for non-integer values of \(\alpha\), where the components produce non-traditional behaviors. It is worth to note that, according to (3), fractances have a magnitude with a constant slope of \(-\alpha 20\) dB/dec and a phase not depending by the frequency and equal to \(-\alpha\pi/2\). For this reason, the fractances are classified as Constant Phase Components (CPCs). By a theoretical point of view, fractances find several applications in the design of fractional order filters [16; 17; 18; 19] and in chaotic circuits and systems [20; 21; 22]. Anyway, one of the most practical limitations in the study of fractances is the difficulty to implement such a component in reality. Several studies in the literature try to implement fractances in different ways. Among the proposed solution, it has been found that the fractor's order is always a pre-defined value of the implementation. This requires changing the implementation any time it is necessary to change the fractor's order. In this paper, we propose a new methodology to implement a fractance with tunable fractor's order, that can be adopted in experimental studies without the need to re-implement the circuit when the fractor's order is changed. The paper is organized as follow: apart from this Introduction, in Section 2 the state of art of fractances' implementation is discussed. In Section 3, the new methodology for implementing fractances with tunable fractor's order is discussed and applied in a practical example. Finally, the Conclusions are presented.
## 3 Experimental TES System: Design, Construction, and Performance Evaluation of a Prototype
### Experimental Results and Discussion
#### 3.2.2 Impact of the Operating Conditions  
Different experiments have been performed to highlight the influence of the HTF flowrate \(\dot{m}_{HTF}\) and inlet temperature (\(T_{in}\)) during the storage and discharge phases. The studied parameters were storage efficiency \(\eta_{stored}\) and responsiveness \(\tau_{95\%}\). The latter is defined as the time required for the storage to reach 95% of the total charged or discharged energy. Specific results for storage phase are presented in Table 5.  
Table 5 highlights that the more the flow rate increases, the faster the charge is, allowing a higher efficiency by reducing the experimental duration and thus the thermal losses. However, this increase is not proportional, since, above a certain threshold, the limitation to the thermal transfer is no longer linked to the HTF, but to the kinetics of the melting of the PCM storage material. Indeed, the investigated conditions led to a 14% gain in charge duration by doubling the mass flowrate. The inlet temperature's increase was more efficient in reducing the storage duration: a 24% reduction was reached by a 5 \({}^{\circ}\)C increase, which is acceptable regarding the PCM thermal aging behavior presented in Section 2. The study of the tank in two positions, vertical and horizontal, showed similar results in both cases when suitable diffusers were associated with each of these configurations.  
Finally, it can be noted that after numerous hours of operation and cycling, no visible degradation was observed on the nodules, supporting suitable compatibility with the SA-PA mixture.  
To explore the TES system's performance, under a broader range of operating conditions than can be experimentally addressed, a numerical model has been developed.
## 4 Case Study
### Impact of Multiple Slices on User Satisfaction  
In Figure 4, user satisfaction with respect to Latency, Privacy, Mobility, and Computing Capacity is illustrated. Different types of DL slices were used, each tailored to specific computational and communication characteristics, as detailed in Table 1. The results demonstrate that with five slices, all considered KPIs achieve high satisfaction levels for all users. Conversely, with only one slice, satisfaction levels drop significantly, underscoring the effectiveness of the proposed multi-slice DLaaS framework.
# Combining Machine Learning and Social Network Analysis to Reveal the Organizational Structures
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{10}{c}{**Percentage of the Used Features**} \\ \hline  & & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** & **100\%** \\ \hline \multirow{3}{*}{**F-score macro average for the SVM classification of three levels of the hierarchy in the Enron dataset. The values which are bolded are the best results for a given minimum communication activity.**} \\ \cline{2-11}  & & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** & **100\%** \\ \hline \multirow{3}{*}{**F-score macro average for the Decision Tree classification of three levels of the hierarchy in the Enron dataset. The values which are bolded are the best results for a given minimum communication activity.**} \\ \cline{2-11}  & & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** & **100\%** \\ \hline \multirow{3}{*}{**F-score macro average for the Random Forest classification of three levels of the hierarchy in the Enron dataset. The values which are bolded are the best results for a given minimum communication activity.**} \\ \cline{2-11}  & & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** & **100\%** \\ \hline \multirow{3}{*}{**F-score macro average for the Decision Tree classification of three levels of the hierarchy in the Enron dataset. The values which are bolded are the best results for a given minimum communication activity.**} \\ \cline{2-11}  & **1 month** & 0.3853 & 0.4674 & 0.4857 & 0.4853 & 0.4830 & 0.4991 & 0.5236 & 0.5074 & **0.5613** \\ \cline{2-11}  & **2 months** & 0.3995 & 0.4273 & 0.4514 & 0.4843 & 0.4596 & 0.4765 & **0.5637** & 0.5293 & 0.5078 & 0.5159 \\ \cline{2-11}  & **3 months** & 0.3170 & 0.4570 & 0.4598 & 0.5245 & 0.5131 & 0.5164 & 0.5205 & 0.5380 & 0.5554 & **0.5568** \\ \cline{2-11}  & **4 months** & 0.3471 & 0.4716 & 0.4435 & 0.4603 & 0.5287 & 0.5305 & **0.5683** & 0.5337 & 0.5123 & 0.5641 \\ \cline{2-11}  & **5 months** & 0.3609 & 0.3393 & 0.5300 & 0.5099 & **0.5827** & 0.5434 & 0.4761 & 0.5199 & 0.5484 & 0.4946 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: F-score macro average for the Random Forest classification of three levels of the hierarchy in the Enron dataset. The values which are bolded are the best results for a given minimum communication activity.
## 3 Materials and Methods
### Sample Noise Detection Based on Confidence Learning
#### 3.1.3 Evaluation of Noise Detection Effect  
Accuracy, Precision, Recall, F1 Score [25], Relabel Accuracy, and Retention Rate are statistically determined in a noise detection. The formulas for the above estimates are as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{8}\]  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]  
\[Recall=\frac{TP}{TP+FN} \tag{10}\]  
\[F1=\frac{2\cdot TP}{2\cdot TP+FN+FP} \tag{11}\]  
\[Relabel\ Accuracy=\frac{TN_{rl}}{N_{rl}} \tag{12}\]  
\[Retention\ Rate=\frac{N_{R}}{N_{O}} \tag{13}\]  
where \(TP\) is the number of true noise samples recognized, \(FP\) is the number of non-noise samples incorrectly recognized as noise, \(FN\) is the number of unrecognized true noise samples, and \(TN\) is the number of non-noise samples correctly recognized. \(N_{rl}\) is the sample number to be relabeled, \(TN_{rl}\) is the sample number relabeled correctly; \(N_{O}\) is the sample number of the original dataset, and \(N_{R}\) is the sample number that has been retained after noise detection. It can be perceived from the formulas that Accuracy reflects the accuracy of overall prediction, Precision reflects the reliability of prediction results of noise  
Figure 3: Application method.  
samples, and Recall reflects the accuracy of noise samples in the original samples. F1 score reflects the comprehensive evaluation of the above three values on the noise detection effect and the higher the value, the better the noise detection effect and the larger the detection range. The Relabel Accuracy is the proportion of the correct revised samples in all the revised samples, reflecting the positive effect of relabeling. The higher the revision accuracy is, the more effective the relabeling is. The Retention Rate is the proportion of the total amount of optimized samples to the total amount of original samples. The larger the sample retention rate is, the fewer samples that are lost and the better the data retention.
## 4 Suitability Evaluation and Effect Analysis of Non-Conventional Lanes
### Chapter Summary  
This chapter systematically analyzed the suitability evaluation and effects of non-conventional lanes from five aspects: applicable conditions, traffic efficiency, traffic safety, environmental and social benefits, and comprehensive evaluation systems. Current evaluation approaches suffer from fragmented frameworks and methodological biases. Studies employ incompatible criteria--traffic efficiency focusing on delay [62; 63], safety-emphasizing conflicts [10; 17], and environmental studies concentrating on emissions [34]--preventing holistic assessment. Simulation-based studies consistently report more optimistic results than field implementations, with predicted benefits often 15-20% higher than achieved performance, highlighting the theory-practice gap requiring attention.  
Through the analysis in this chapter, non-conventional lanes demonstrate positive effects in multiple dimensions, but there are also some urgent problems to be solved. Particularly in establishing scientifically comprehensive evaluation systems, achieving balanced optimization of multiple objectives, and considering long-term and indirect benefits, in-depth research is still needed. With the development of intelligent transportation technology, the evaluation methods and systems for non-conventional lanes also need continuous innovation and improvement.
## 2 Overview of Pressurized Capsule Structure  
### Pressurized Capsule  
The pressurized capsule, also known as a capsule, is a component of a spacecraft that requires a specific internal pressure to meet the needs of astronauts or equipment such as payloads during operation. It is a special section of the spacecraft that must withstand and transmit both the aerodynamic loads generated by the launch rocket and a certain internal pressure load. The pressurized capsule can create a living environment close to the ground atmosphere for astronauts in space. Therefore, the pressurized capsule is an indispensable core module for manned spacecraft.  
Manned spacecraft mainly include three types: space station (orbital experiment module) [3], manned spaceship and space shuttle. According to whether the pressurized capsule returns to the ground completely, the pressurized capsule of manned spacecraft can be divided into the returnable type and the non-returnable type. Among them, the pressurizedcapsule of the space station (orbital experiment module) is the non-returnable type, and the pressurized capsule of the manned spaceship and space shuttle is the returnable type.  
There are many famous manned spacecraft pressurized capsules. In China, these include the reentry capsule and orbital capsule of the _Shenzhou_ manned spacecraft (as shown in Figure 1a), the experimental module of the _Tiangong_-1 space laboratory (as shown in Figure 1b), and the large column capsule, small column capsule, and node capsule of the _Tianthe_ core module (as shown in Figure 1c); other countries' include Russia's _Soyuz_ manned spacecraft reentry capsule and orbital capsule (as shown in Figure 1d), the United States' _Apollo_ crewed capsule (as shown in Figure 1e) and lunar module, the International Space Station Japanese Experiment Module (JEM) (as shown in Figure 1f), the Columbus experimental module [3] and so on.
# Research on the Influence of Dust Suppressants on the Coupling Behavior of Dust-Mist Particles
## 2 Experimental Materials and Basic Parameters  
### Experimental Materials  
A mineral dust was selected in this research, the main composition of which is hematite Fe\({}_{2}\)O\({}_{3}\), and the size distribution of the test dust is shown in Figure 1.  
The test dust has a particle size range of 0.1 \(\upmu\)m to 85.0 \(\upmu\)m, and the maximum value of the volume distribution is 12.059 \(\upmu\)m. The characteristic value of the cumulative particle size distribution is d10 = 1.521 \(\upmu\)m, d50 = 7.918 \(\upmu\)m, and d90 = 28.678 \(\upmu\)m.  
Several different types of dust suppressants were selected: Dodecyl Alcohol (DA), Alkylphenol Polyoxyethylene (OP-10), and Sodium Dodecyl Sulfate (SDS). These suppressants were chosen based on their wide application, dust suppression effectiveness, and chemical properties, as detailed in Table 1. Water was used as the reference material for comparison.  
Based on the pre-test results, when the mass concentration of the dust suppressant solutions exceeds 1.0%, there is no significant change in surface tension. This is because the suppressants have already reached their Critical Micelle Concentration (CMC) state, beyond which the surfactant molecules aggregate into micelles rather than adsorbing at the liquid-air interface. Additionally, when the surface tension drops below 1.0 \(\times\) 10\({}^{-5}\)%, it becomes similar to that of water, making such solutions unsuitable for comparative experiments with water. Furthermore, excessively high viscosity values hinder dust reduction through the spray and render the optimization of atomization effects ineffective.  
The mass concentrations of the dust suppressant solutions were set at 1.0 \(\times\) 10\({}^{-5}\)%, 1.0 \(\times\) 10\({}^{-3}\)%, 1.0 \(\times\) 10\({}^{-2}\)%, 1.0 \(\times\) 10\({}^{-1}\)%, and 1.0%. These ranges ensure stable spraying and allow for meaningful comparisons with water, facilitating a systematic exploration of the relationship between changes in surface tension, viscosity, and atomization effects.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dust Suppressants** & **Chemical Formula** & **Class** & **Functions** \\ \hline Dodecyl Alcohol & C\({}_{12}\)H\({}_{26}\)O & Anion & Lubricant and emulsifier \\ Sodium Dodecyl Sulfate & NaC\({}_{12}\)H\({}_{25}\)SO\({}_{4}\) & Anion & Wetting agent, foaming agent, and emulsifier \\ Alkylphenol & C\({}_{\mathrm{n}}\)H\({}_{2\mathrm{n-1}}\)(OCH\({}_{2}\)CH\({}_{2}\))\({}_{\mathrm{m}}\)OH & Non-ionic & Emulsifier, stabilizer, and wetting agent \\ \hline \hline \end{tabular}
\end{table}
Table 1: Three selected dust suppressants.  
Figure 1: Dust sample size distribution results.
# The DREAM Endstation at the Linac Coherent Light Source
## 4 Scientific Application
### Advanced Reaction Microscopy for Studying Extended Nanosystems  
As the study of extended quantum systems and nanoparticles becomes more and more feasible due to the rapid development of synthesis and sample delivery systems, an instrument such as DREAM will play a crucial role in studying light-nanoparticle interactions. As such, research into the properties of various nanoscopic systems has the potential to lead to breakthroughs in both fundamental science as well as in novel applications and technologies. On the fundamental level, research into nanoparticles, clusters, and other such complex assemblies allows for the investigation of scaling laws to understand how the physical response of a system evolves as the characteristic spatial scale changes from atomic/molecular to a macroscopic nature. Recent experiments involving the discovery of intermolecular Coulombic decay in endohedral fullerene [38], demonstrating the enhanced formation of H\({}^{3+}\) on nanoparticles [39], multi- and single-photon processes in fullerene-like systems using hard and soft X-ray photons [24; 40] were only possible through coincidence measurements of ions on a shot-by-shot basis. Such measurements will form the basis of studying gas-phase nanocatalysis and photon-induced enhancements of surface chemistry at LCLS-II using the DREAM endstation.
# Research on the Scope of Spectral Width Parameter of Frequency Domain Methods in Random Fatigue
## 4 Comparative Analysis
### Rainflow Amplitude Distribution
Especially for broadband random processes with spectral width parameter \(\varepsilon\) greater than 0.7, the Dirlik model has obvious advantages. 4. Zhao-Baker model: The fitting degree of the Zhao-Baker model is similar with the Dirlik model, the difference is that the broadband random process, the Zhao-Baker model will overestimate the part of the low-stress area. Since the area sum of the probability density curve is 1, overestimating the low-stress area will result in underestimating the high-stress area, which will underestimate the fatigue life of the structure and the results will be unsafe. 5. Nakagami distribution: Nakagami distribution cannot accurately describe the distribution of rain flow amplitude in the entire stress amplitude range when the spectrum width parameter is small and large. However, in the transition area between the narrow-band random process and the broadband random process, a better fitting effect can be achieved. This is the only distribution model in the four models that can better fit the transition area. The rain flow amplitude histograms in the figures are obtained by first performing time-frequency conversion on the simulated spectrum to obtain a time history curve, and then performing rain flow counting. Since the simulated spectral curves are the power spectral density curves and do not contain phase information, the time history curves obtained each time are different, and the results of the rain flow count will also be different. However, due to the same statistical characteristics before and after conversion, the trend of the rain flow amplitude histogram obtained by the rain flow counting method with the spectral width parameter value \(\varepsilon\) is approximately the same. However, it will still cause the above probability density curve and cumulative probability density curve to have certain limitations and errors. At the same time, the analysis of the above graphs shows that the application scope of different frequency-domain methods has certain human factors. The limits of the applicable range cannot be obtained accurately, so the specific life calculation should be combined with the S-N curve of the material and the Miner damage criterion to obtain the accuracy and applicability of different frequency-domain methods through the intuitive numerical relationship.