esa-sceva/satcom-chunk-collection huggingface dataset:
# Clustering of a Health Dataset Using Diagnosis Co-Occurrences
## 4 Discussion
The hard nature of the constructed cluster, where each diagnosis belongs univocally to only one cluster, was chosen here to facilitate the interpretation of the cluster and of the evolution of ED visits. Fuzzy clusters, produced by exploratory factor analysis or by fuzzy c-means for example, where diagnoses belong to multiple clusters/factors using weighted links, seems more appropriate for disease analysis and the impact of comorbidities on risk of undesired health outcomes, as it can discover more complex patterns [19; 39]. Due to the novelty of the ED global population context, the clusters obtained can hardly be compared with those found in the literature, which focuses almost entirely on old, chronically ill patients [16; 17]. The only two comparable clusters are those of cluster 2 (General symptoms and mental disorders) and cluster 4 (General Symptoms and chronic conditions), which can, respectively, be assimilated to the mental health conditions grouping, and the cardio-metabolic conditions grouping found by [PERSON] et al. [17]. This can be explained by the fact that these clusters contain the oldest, most chronic populations, making them coherent with the population in the literature. As shown by the results, these populations are also the most care-intensive, accounting for 25% of the visits according to cluster 2 and 4 prevalence. Cluster 1 (Digestive disorders, pregnancy, menstruation), cluster 3 (Infectious diseases) and cluster 5 (Mental disorders and at-risk behaviors) represent three new categories of multimorbidity clusters that characterize the specific health problems of the ED encountered in populations other than the elderly and chronically ill. With 1.17 to 1.34 visits per patient, these clusters are the most representative of the health problems with readmission behaviors encountered in the ED. This contrasts with clusters 6 to 16, which are all trauma-related and much less likely to result in readmission patterns, with 1.05 to 1.18 visits per patient. This can be linked to the fact that these types of health problems are most often acute, and not chronic. Indeed, they require unscheduled care provided essentially by EDs, and are hard to prevent by healthcare consultations, since they are often associated with accidental or at-risk behaviors.
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
## 4 System Model and Route Selection
### Route Selection  
The D2D routes are preferred over the backbone route. For simplicity, D2D routes are called primary routes and the backbone route is called a secondary route. The secondary  
Figure 4: A D2D route from a \(fc\) source node \(fc_{s}\) to a \(fc\) destination node \(fc_{d}\).  
route is only selected whenever primary routes are unavailable due to the appearance of PUs' activities. The end-to-end delay of a primary route increases with the hop count of the route [47]. Based on the IEEE 802.15.4 standard [12; 37], the end-to-end delay of a route increases with the hardware and software processing delay, the queue size, and the appearance of PUs on a route [48; 49]. Hence, a threshold \(\alpha\), which is given to the source node by the MC BS or CC, is used to evaluate the end-to-end delay of D2D routes and monitored by the CC. As shown in Figure 5, a source node requests for a route to its destination node. The CC provides information about available routes through MC BS. The given routes are ranked by CC based on: (a) the presence of PUs' activities (or the number of white spaces) on each route; (b) the end-to-end delay of the route; and (c) the number of hops of a route. A route with a higher number of white spaces is ranked higher. Using this information, the source node ranks the routes again based on its own criteria. This is because the CC gives a higher priority to the backbone route since it does not have PUs' activities and provides lower end-to-end delay and number of hops. Meanwhile, the source node gives a higher priority to D2D routes and keep the backbone route as an alternative. In other words, the highest-ranked route given by the CC may not be selected by the source node. The source node makes route selection based on the presence of the random PUs' activities in those channels. For instance, the source node has selected route 1, which has the highest ranking; however, it switches to route 2 when the PUs' activities reappear in route 1. Therefore, the source node switches routes dynamically due to the random reappearance of PUs' activities.
## Cases Studies
### Comparisons of the Proposed Strategy and the Conventional Strategy  
To illustrate the superiorities of the proposed self-healing strategy, the benchmark scenario is studied to provide detailed comparison. Scheme 1 is the self-healing scheme obtained by the proposed strategy. Scheme 2 is the self-healing scheme obtained by the conventional strategy, in which the power quality constraint set and droop characteristic of DGs are not considered. The values of power-quality indexes of Scheme 2 are calculated. Concretely, the two schemes are minutely listed in the following Table 1.  
From Table 1, the following advantages of the proposed strategy can be summarized.  
(1) Total recovery capacity is larger. Specifically, total load removal of Scheme 1 is 9830.21 kW, while the value of Scheme 2 is 10,429.45 kW. That is, the total recovery capacity has risen nearly 20.31% from 2949.84 kW to 3549.08 kW, which means the proposed strategy can realize stronger recovery.  
(2) The obtained recovery scheme has better power- quality indexes and higher reliability.  
* On one hand, the _Cune-ul_ index of DG5 in Scheme 2 exceeds the negative sequence protection action threshold value of 10%. It may cause DG5 to quit operation and may further result in operation failure of the recovered island as power imbalance.
* On the other hand, _THD-ul_ indexes of all nodes in Scheme 1 are less than 4% as the maximum is 3.42%, while _THD-ul_ indexes of all nodes in Scheme 2 are exceeded as the minimum is 4.41%. The excessive THD may cause abnormal operation or may cause the equipment to quit working. That is, the reliability of Scheme 2 will not be guarantee.  
To sum up, the proposed strategy can realize the larger total recovery capacity and higher reliability by considering the DG frequency adjustment characteristic and the comprehensive set of power-quality constraints.
# 3D Numerical Simulation and Performance Analysis of CO2 Vortex Tubes
## 2 3D Numerical Simulation of Swirl Flow Pipe
### Grid Independence Study  
In this process, grid independence analysis was performed to ensure the authenticity, validity, and accuracy of the numerical simulation results. Therefore, 3D CFD analyses were carried out for swirl flow pipes with lengths of 125 mm and 175 mm using different mesh sizes.  
In order to eliminate the error caused by the grid number on the simulation results, the grid number of the swirl flow pipe with 125 mm length was divided into 539,859, 604,313, 680,638, and 931,576, the mesh was divided into unstructured meshes, as shown in Figure 3. The simulation results are shown in Table 1. The simulation results were compared with [PERSON]'s experimental data \(\Delta\)T = 20 K, and it was found that the temperature difference of the simulation results when the grid number is 539,859 or 604,313 is 15.9 K and 18.3 K, which are different from the experimental values in [PERSON]'s study. It can be concluded that the accuracy of the simulation results will be affected by the small number of grids in the swirl flow pipe model. When the number of grids is 680,638 and 931,576, \(\Delta\)T is 19.5 K, and 20.5 K, respectively, which are different from the experimental data by 0.5 K. Too large of a number of grids will lead to longer computation time and slower convergence of the residual values. Therefore, it is appropriate to choose a cell grid of 680,638 for the simulation of the swirl flow pipe with a length of 125 mm. Similarly, Table 2 shows that when the number of cells is 949,684, \(\Delta\)T is 22.7 K, which is only 0.7 K different from the experimental data (\(\Delta\)T = 22 K). Therefore, for the simulation of the swirl flow pipe with a length of 175 mm, the cell grid number of 949,684 provides approximate results.  
The cold airflow temperature is calculated as the difference between the temperature of the compressed gas when it enters the swirl flow pipe inlet and the temperature of the gas when it leaves the cold end.  
\[\Delta\mathrm{T_{c}}=\mathrm{T_{in}}-\mathrm{T_{c}}\]  
The hot airflow temperature difference is the difference between the temperature of the compressed gas as it enters the swirl flow pipe inlet and the temperature of the gas as it leaves the hot end.  
\[\Delta\mathrm{T_{h}}=\mathrm{T_{h}}-\mathrm{T_{in}}\]
# Comparison Among Different Rainfall Energy Harvesting Structures
## 7 Conclusions  
This work has presented the experimental comparison between different harvesting structures, subjected to stresses due to raindrops: the cantilever, the bridge and the floating circle.  
Harvesters were subjected to the same artificial rain conditions, using different raindrop heights: 0.5, 1 and 2 m. The intent was not to see the energy collected, but how they behave under the exact same stress conditions.  
The collected waveforms were significantly different. The ideal waveform to be used in rectifying circuits is a slightly damped sine wave, which was only approached by the cantilever structure. The cantilever harvester proved itself to be the most suitable harvesting structure, due to which it was the only structure considered in the simulations of electronic circuits.  
Based on this information, the comparison was made between different rectifying circuits: full-bridge, Greinacher doubler, the Cockcroft Walton Cascade Voltage doubler and the Karthaus Fisher Cascade Voltage doubler. The best performance for the rectifying circuit was achieved by the full bridge system, followed by the Greinacher doubler. The employment of several stages of energy storage is contrasted by the harvester's difficulty in extracting energy, such that each capacity can be seen as a bottleneck, rather than a resource.  
It was necessary to experimentally compare the two circuits that exhibited the best characteristics. The combination of internal impedance of the piezoelectric generator and impedances of the rectifying circuits highlighted that the full bridge had the best behavior.  
This work was financially supported by MIUR--Ministero dell'Istruzione dell'Universita e della Ricerca (Italian Ministry of Education, University and Research) and by LEAP (Laboratory of Electrical Applications) of the University of Palermo.  
The author declares no conflicts of interest.  
Figure 22: Transient voltage obtained for Greinacher rectifying circuit.
# Determination of Hydrogen's Thermophysical Properties Using a Statistical Thermodynamic Method
## 1 Introduction
The thermophysical properties of hydrogen can be determined by taking at most second-order derivatives of the Helmholtz free energy with respect to temperature or density based on the fundamental EOSs. Reading data from RREPROP is another typical method used to determine the thermophysical properties of hydrogen. Sometimes, the thermophysical property data in RREPROP are listed in a database for a finite number of states. In a limited range, spline interpolation is used to predict properties in arbitrary states. In this study, the thermophysical properties of hydrogen were obtained by developing computational programs based on the statistical thermodynamic model. The ortho-H\({}_{2}\) fraction in equilibrium hydrogen (equilibrium H\({}_{2}\)) and the para-ortho H\({}_{2}\) conversion heat were derived from the nuclear rotation partition functions of ortho-H\({}_{2}\) and para-H\({}_{2}\). The isobaric heat capacity and enthalpy were derived from [PERSON]'s [5; 19] fundamental EOSs. To compare different methods for determining hydrogen's thermophysical properties under an engineering background, a vapor-cooled shield (VCS)--a multilayer insulation (MLI) combined structure for liquid hydrogen storage--was investigated. The heat transfer model for the VCS-MLI was introduced and validated. The thermophysical properties were obtained via different methods, and the accuracy of the statistical thermodynamic method was verified using REFPROP data and other researchers' results. Then, three tests were conducted to quantitatively analyze the convenience of the statistical thermodynamic method by comparing the running speeds of programs with different methods. These methods include statistical thermodynamic calculation, reading REFPROP data, spline interpolation, etc. When the performance of a VCS-MLI structure integrated with para-ortho H\({}_{2}\) conversion was predicted, the statistical thermodynamic method can save about 92% of the time consumed by reading REFPROP data. The developed programs based on the statistical thermodynamic method can also be applied to determine the thermophysical properties of other fluids.
# A Hybrid Key Generator Model Based on Multiscale Prime Sieve and Quantum-Inspired Approaches
## 5 Quantum-Inspired Algorithms and Their Development  
[PERSON]'s equation provides a theoretical basis for most quantum cryptography techniques, from random number generation to secure key exchange (for more details, see Appendix A in addition to the previous section). Although actual implementations must take into account advanced quantum hardware (e.g., a quantum computer or a QRNG device), the concepts can also be used within classical simulations for the purpose of improving cryptographic security. There are five programs that generate a 2048-bit decimal string (i.e., 256 decimal digits) for each of the situations described above (see Appendix B for the code). Although random generators are classical pseudo-random number generators, we use them here as a baseline to prove how specific post-processing techniques inspired by quantum mechanics can increase entropy, reduce statistical bias, and approximate certain statistical characteristics observed in quantum randomness. This comparison does not aim to equate pseudo-random and quantum randomness, but rather to highlight conceptual parallels and evaluate the effectiveness of improved classical methods. The comparison serves to explore hybrid models within quantum-inspired cryptography and information theory:  
1. Key generation using the eigenvalues of the Schrodinger equation. We use a numerical method to solve the Schrodinger equation and generate random numbers based on its eigenvalues.
2. Emulated quantum random number generator (EQRNG). This emulates a quantum random number generator based on probabilistic measurements.
3. Computational key distribution (CQKD). This emulates the BB84 protocol for secure key exchange.
4. Quantum simulations for pseudo-random numbers. We use a quantum system with arbitrary potential to generate a sequence of keys.
5. Quantum hash function based on wave dynamics. We use a quantum Fourier transform (QFT) to generate a hash-like function.  
Each algorithm generates a 2048-bit key (256 decimal digits) based on different quantum principles:1. Eigenvalues of the Schrodinger equation (numerical quantum physics--this work),
2. QRNG (quantum random number generation),
3. QKD (BB84 protocol for secure key exchange),
4. Quantum simulation (extraction of quantum probability distributions),
5. Quantum hashing (hashing based on quantum waves).  
These keys can be used for advanced cryptography and security applications. We conducted a comparative analysis of the five quantum key generation methods based on performance metrics, including execution time, entropy (randomness evaluation based on NIST guidelines), mean value, and standard deviation. You can examine the detailed results in the table provided.  
Additionally, we generated the following graphical comparisons: 1. Execution Time Comparison which displays the computational efficiency of each method (see Figure 2); 2. Entropy Comparison to evaluate the randomness of the generated keys, that is crucial for
# Infrared and Visible Image Fusion with a Generative Adversarial Network and a Residual Network
## 4 Experiments and Results  
### Related Pre-Process and Settings  
#### 4.1.1 Data Augmentation  
In our experiments, the dataset consists of 41 pairs of infrared and visible images that are collected from [66]. These image pairs mostly have evident pixel intensity in infrared images and abundant detail information in visible images. These image pairs are divided into two parts: 31 pairs for training and 10 pairs for testing. If we only use these a few dozen original images for training, it is very hard to get a stable and compatible model. So we decide to enlarge the training set by cropping source image pairs into small image blocks with certain stride. Note that, no matter the original images or the image blocks cropped, the infrared image and visible image must appear in pairs. The size of the image block is set to \(128\times 128\) and the stride is set to \(11\) in the following experiments, in consideration of the practical effect and the ability of the GPU (Graphics Processing Unit). As a result, 46,209 pairs of images are combined together as the dataset for training. The testing set consists of 10 pairs of source images that are not involved in training.
# Mental Workload Classification Method Based on EEG Independent Component Features
## 2 Materials and Methods
### Experiments and Data Preprocess
#### 2.1.2 Data Processing  
The reference of data processing was reset as the average value of A1 and A2. The 30-channel EEG signals were filtered with 1 Hz high-pass filter and 30 Hz low-pass filter, respectively. FIR filter was used, and the filter order was 3300.  
In order to ensure the validity of the data analysis, the EEG signals of 10 participants (sub 1-sub 10, aged between 22 and 25 years, with two females and eight males) were selected to analyze according  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Task Title**} & \multirow{2}{*}{**Task Content**} & \multicolumn{2}{c}{**LMW**} \\  & & **MMW** & \\  & & **HMW** & \\ \hline \multirow{2}{*}{System Monitoring} & Monitor the scales of F1-F4 in Area 1 and response with & 1 & 12 & 24 \\  & mouse when the scales are not around the center. & & 1 & 12 & 24 \\ \hline \multirow{2}{*}{Tracking} & In Area 2, keep the target at the grid center by joystick in & 1 & 12 & 24 \\  & MANUAL mode and no action is required in AUTO mode. & & 1 & 12 & 24 \\ \hline \multirow{2}{*}{Scheduling} & Monitor scheduling bar in Area 3 and response to the & 1 & 12 & 24 \\  & activated communication with keyboard immediately. & & 1 & 12 & 24 \\ \hline \multirow{2}{*}{Resource Management} & Monitor oil volume in tanks and pump status in Area 4. & & & \\  & Click the corresponding oil pump with mouse when & 1 & 12 & 24 \\ \multicolumn{2}{c}{failure occurs.} & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Occurrence frequency of four tasks.  
Figure 2: Task interface of MATB-II.  
to the selection criterion of \"high performance\" and \"less artifacts\". The criterion of high performance meant that the accuracy rate of operating performance should be greater than 0.90. The accuracy rates of operating performance of two participants were less than 70, which did not achieve the standard of high performance, so their data were not analyzed. The criterion of less artifacts meant that the artifacts cannot exceed 5 min in the original EEG data. One participant had too many artifacts, which was not conducive to signal analysis, so this paper analyzed EEG signals of 10 participants.  
The above data processing was applied to preprocess all the EEG signals. The middle 600 s EEG data in each mental workload were chosen and divided into 300 data segments, namely, the length of the segment was 2 s. Firstly, the NASA-TLX scale was used to evaluate the experimental parameter rationality of MATB-II. Then the segments were labeled according to different mental load state of MATB-II. There were 900 data segments in three mental workloads. The training set and the testing set were divided into 7:3, that is, 630 training segments and 270 testing segments.
## 3 Methods
Additionally, CITs were performed at the crack edges, as well as in front of the crack tip, and thus, in the plastic zone, which can be estimated according to Reference [34]. These measurements were conducted at a fatigue crack, observed at a specimen of Crofer(r)22 H loaded for 4342 TMF-cycles (see Figure 4a). In addition to \(F_{max}=1000\) mN, a maximum indentation force of 100 mN was used to get a higher resolution of the material volume around the fatigue crack (see Reference [16]). Figure 3: Evolution of stress range during oop-TMF loading of P91 and Crofer®22 H [5]. The CITs were evaluated using the PhyBaLCHT procedure presented in detail in References [13; 15; 16]. During indentation, the indentation force \(F\) and indentation depth \(h\) are measured continuously, revealing an \(F\)-\(h\) hysteresis from the 2 nd cycle on (see Figure 4b). The half-width of the \(F\)-\(h\) hysteresis at mean loading is considered as a characteristic parameter to describe the deformation behavior and is called plastic indentation depth amplitude h\({}_{\text{a,p}}\). The h\({}_{\text{a,p}}\)-N relation plotted schematically in Figure 4c, shows a slope change from the 5 th cycle, which indicates saturation of macroplastic deformation, and hence, the deformation behavior in CITs is dominated by microplasticity in this regime. Consequently, the regime after the 5 th cycle, which can further be described by the power-law function h\({}_{\text{a,p II}}\) (see Equation (1)), is analyzed to characterize the material's cyclic properties [15]. \[\text{h}_{\text{a,p II}}=\text{a}_{\text{II}}\cdot\text{N}^{\text{e}_{\text{II}}} \tag{1}\]  
The slope of h\({}_{\text{a,p II}}\) indicates the amount of cyclic hardening during CIT and is described by the exponent e\({}_{\text{II}}\), called cyclic hardening exponentCHT [15]. A steeper slope results in a higher \(|\text{e}_{\text{II}}|\), which implies a larger amount of cyclic hardening during indentation testing. In addition to that and in correspondence to conventional microhardness measurement, the Martens hardness (_HM_) can be determined from the \(F\)-\(h\) relation of the 1 st cycle. To obtain statistically reliable values of h\({}_{\text{a,p}}\)-N curves, and _HM_, for each condition, mean values were determined using 40 indentation points, respectively. Note that for the measurements in the relatively small plastic zone in front of the crack tip, only 20 indentation points could be realized. For investigating the mechanical properties at the crack edges, 40 indentation points were utilized with \(F_{max}=1000\) mN, whereas a \(F_{max}=100\) mN enabled the possibility to perform 60 CITs along the crack edge. For analysis in the fatigue crack vicinity, CITs were performed along the fatigue crack (comp. Figure 4a) and on different planes prepared by grinding and polishing the sample. In Section 4, for each condition, the mean values and 90% confidence intervals, based on the overall number of indentation points, are shown.