esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design Optimization  
### Optimization Process  
Figure 6 shows the shape optimization process of the PMSM to minimize the demagnetization rate of the permanent magnets and maximize the average torque. First, the multi-objective functions, constraints, and optimal design variables were established. Next, an initial modeling and parametric analysis setting for the automatic change of design variables was performed using the FEA software Maxwell. Next, DOE was executed using an automated DOE procedure through co-simulation between Maxwell and the optimal design software PIAnO, which uses Maxwell's macro functions [9]. By creating VBSript files and batch files, load analysis and demagnetization analysis were automatically performed in Maxwell. Maxwell's analysis results were transferred to PIAnO, and the responses of multi-objective functions and constraints were automatically calculated. In addition, when the computational analysis for one experimental point was finished, the shape design parameters were automatically changed in Maxwell without the aid of CAD tools by receiving PIAnO's command. This process was automatically repeated as many times as the number of DOE.  
To increase the accuracy of the metamodels, it was necessary to obtain as many DOE results as possible. However, in most previous studies, DOE was performed manually and an insufficient amount of data was obtained. Furthermore, because demagnetization analyses are complex, they were not included in the DOE [1; 2; 3; 4; 5].  
In this study, the demagnetization analysis was included in the DOE process using the automated DOE procedure. From the obtained DOE results, the influence of design variables on the multi-objective functions and constraints was analyzed through a screening technique. Next, metamodels of multi-objective functions and constraints were established using the DOE results, and the best metamodels were selected through accuracy evaluation. Finally, metamodels were combined with an optimal algorithm to obtain optimal solutions.  
Figure 6: Optimization design process.
# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
## 3 Results
### Quantisation Noise  
In our enterprise dataset, we noted that the timestamps are given to 3 decimal places and saw evidence that this quantisation is a significant factor in our data. For example, consider when we partitioned the data into \"Batch By Category\" and \"Non-Batch By Category\". As the data were heavy-tailed and the majority of the data fell in the first bin of the histogram (Figure 23), we applied [PERSON]'s ladder of power and transformed the data. The transformed data (Figure 23) shows several peaks. Naively redrawing the density plot for the head of the service time, we note four distinct Gaussian distributions in Figure 24.  
Of course, our data contain four distinct values, quantised to 1 ms. However, the smoothed density plot, which works well for the larger values, represents these values as Gaussian distribution around these discrete values. Because a significant amount of our data is spread between values separated by 1 ms, quantisation noise is an important factor for our data.
## 3 Results and Discussion
### Stage III
#### 3.3.4. Loose Bulk and Tapped Density  
The 2 FI model facilitated the significant prediction (\(p\)-value \(<\) 0.05) of \(d_{L}\) and \(d_{T}\) of model MD powders. A satisfactory fit of the model to the data verified experimentally was noted, which was confirmed by the relatively high value of the determination coefficients (R\({}^{2}\) = 0.7691 for \(d_{L}\) and R\({}^{2}\) = 0.7726 for \(d_{T}\)) and the insignificant result of the lack of fit test (\(p\)-value \(>\) 0.05) (Table A2). Based on the estimated parameter values (Equations (6) and (7)) and response surface plots (Figures 10 and 11) it can be concluded that increasing IAT and FC had a positive effect on \(d_{L}\) and \(d_{T}\), while increasing IAH had negative effect on both parameters. The effect of IAH was larger than IAT and FC; however, as ANOVA showed, IAH and FC had a significant influence on \(d_{L}\) and \(d_{T}\), while the IAT term was not significant (\(p\)-value \(<\) 0.05).  
In general, the bulk density decreases with increasing temperature, which is related to the larger particle size of powders obtained at a higher temperature [34; 36; 37]. However, according to the PCA (Figure A4), the particle size was not correlated with \(d_{L}\).  
\[d_{L}=0.1335568+0.002916\cdot IAT-0.077194\cdot IAH+0.010992\cdot FC+0.000218 \cdot IAT\cdot IAH-0.000109\cdot\] \[IAT\cdot FC+0.001952\cdot IAH\cdot FC \tag{6}\]  
\[d_{T}=0.5520+0.0556\cdot IAT-0.1210\cdot IAH+0.1190\cdot FC+0.0506\cdot IAT \cdot IAH-0.0281\cdot IAT\cdot IAH+ \tag{7}\]  
Figure 11. Loose bulk density (\(d_{T}\)) response surface plots for paired factors: (**A**) inlet air temperature (IAT) and inlet air humidity (IAH); (**B**) inlet air temperature and feed solution concentration (FC); (**C**) feed solution concentration (FC) and inlet air humidity (IAH), when the third factor was adjusted to its midpoint.  
Figure 10. Loose bulk density (\(d_{L}\)) response surface plots for paired factors: (**A**) inlet air temperature (IAT) and inlet air humidity (IAH); (**B**) inlet air temperature and feed solution concentration (FC); (**C**) feed solution concentration (FC), and inlet air humidity (IAH) when the third factor was adjusted to its midpoint.
# KDM Security IBE Based on LWE beyond Affine Functions
## 5 Our Full KDM-IBE Scheme
### The Efficiency Analysis  
Our proposed KDM-IBE scheme, based on the LWE assumption, is comparable to other existing KDM-IBE [2; 10] schemes in terms of the public key size, ciphertext size, secret key size, and encryption and decryption times. Due to the different underlying hard assumptions, we could not compare the abovementioned parameters to [18]. Here, we present Table 2 comparing our scheme to other schemes in terms of the relevant parameters.  
The distinguishing feature of our full scheme is an additional step that converts affine functions to polynomial functions during the encryption and decryption processes. We utilized the randomized encoding of functions technology to perform the conversion (as described above).  
The complexity of randomized encoding is typically considered to be polynomial, denoted as \(O(poly(n))\), with the degree of the polynomial generally restricted to a constant or quadratic level [21]. This implies that the complexity of randomized encoding is typically \(O(n)\), or \(O(n^{2})\). Therefore, the actual running time of our encryption and decryption algorithm is \(O\big{(}n^{2}\big{)}+O(poly(n))=O\big{(}n^{2}\big{)}\) and \(O(nlogq)+O(poly(n))=O\big{(}n^{2}\big{)}\).  
It is worth noting that as the degree of polynomial functions increases, such as for degrees higher than 6, the complexity of randomized encoding exceeds \(O\big{(}n^{2}\big{)}\). This significantly affects the efficiency of our scheme. Therefore, as we aim to achieve arbitrary functions to be the challenge function ensemble, the efficiency of the scheme will be reduced.
# Research on Improving Satellite Positioning Precision Based on Multi-Frequency Navigation Signals
## 1 Introduction
proposed a method called physical augmentation factor of precision (PAFP) to equivalently improve the spatial layout [22]. The communication signal covers a wide range and has a wide signal frequency band. With the development of communication and positioning technology and as the variety of user needs continues to increase, the combination of communication and navigation has also become a hot research topic. This paper combines the characteristics of the CAPS system and the existing technology to carry out research on using multi-frequency navigation signals to improve positioning precision. Without increasing the number of satellites, by increasing the number of satellite downlink navigation signals, the pseudo-range equations of multi-frequency navigation signals are established to improve the positioning precision. The simulation experiments were carried out using the CAPS measured data and the observation data of the International GNSS Service (IGS) station in Fangshan, Beijing. According to the experimental results, the PDOP value and positioning precision are statistically analyzed to evaluate their improvement. This research provides theoretical support and data  
Figure 1: Schematic diagram of the principle of the CAPS system. reference for the research on the positioning system combining communication satellites and navigation and has certain research significance and application value.
# Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors
## 2 Optical Methods and Platforms of Remote Sensing of Plants
### Multi- and Hyperspectral Imaging
The registration of the waves in the SWIR light range requires detectors with InGaAs, which is a rare and expensive material [46]. It should be noted that hyperspectral cameras with an extremely broad spectral range (for simultaneous measurement of visible light, NIR, and SWIR) include matrices of both types [49]. Hyperspectral cameras (with small modifications) can be used for investigation in different spatial scales: from leaf to plant conapies and ecosystems [8]. Both the whole spectra of reflectance and specific spectral bands (which are the basis of the calculation of RIs) can be analyzed with hyperspectral imaging; both variants of analysis can be used for the detection of stress changes (e.g., revealing damage induced by diseases) [44]. Multispectral cameras are widely used alternatives for the hyperspectral cameras and can be applied for the remote sensing of single plants, their groups, canopies, and ecosystems [8]. These cameras are based on relatively simple technical solutions (e.g., simultaneously using several elementary cameras with different narrow-band spectral filters (Figure 3e) [44; 48]), can minimize spectral data, and allow to estimate specific RIs; as a result, these cameras are also effective and provide the high-throughput scanning of plants. However, the combination of several cameras into the integrated imaging system can cause problems of alignment of the measured images. The different optical properties of the lens and spectral filters in the used elementary cameras cause different optical paths and aberrations in the measured images [50]; the problems are more expressive at low distances from plants. Potentially, the matrices of cameras can also have some differences. An alternative variant of the multispectral system can be equipped by the wheel of spectral filters (Figure 3f); using the single camera solves the problem of the alignment of measured images. However, the limited velocity of the mechanical rotation of this wheel decreases the velocity of the multispectral imaging [51]. Finally, it should be noted that the multispectral cameras with several elementary cameras equipped by the narrow band spectral filters are the main type of multispectral cameras that can be localized on flying mobile platforms (copters, drones, airplanes, and satellites). Thus, the imaging on the basis of the measurements of reflected light at narrow spectral bands is a dynamically developing field of plant remote sensing. The hyper- and multispectral systems are actively used by investigators for imaging with a wide range of spatial and temporal scales.
## Monitoring and Analysis of the Roadway Forming Effect
### Discussion  
In pillarless mining by GSCRF, in view of the stress environment of GSCRF, it is necessary to effectively control the roadway surrounding rock, including immediate roof control, main roof control, gob side gangue control, and air leakage design. The surrounding rock control process follows the procedure below:  
1. Immediate roof can be pulled up Since the immediate roof is relatively broken and its strength is low, in order to effectively control the immediate roof during roadway forming and throughout the mining of the working face, it is necessary to give full play to the anchoring effect of the anchor cables so that the main roof moves together with the immediate roof as a whole and will not segregate from the main roof rock beam.
2. Broken immediate roof can be held As the immediate roof is broken, it is necessary to install auxiliary support devices such as anchor nets and anchor cable beams in the roadway to completely hold the broken immediate roof and prevent it from caving in and affecting the safety of the roadway.
3. Gangue can fill up the gob The immediate roof outside the roadway is not supported so that it will be successfully caved after the working face has been advanced; the gangue will form a wall and no overhanging wall will appear.
4. Gangue can be kept out of the roadway In order to prevent the gangue from falling directly into the roadway, a steel wire net must be put up along the gob side, and secured by dense props with sufficient strength.
5. The main roof sinks without impact The fallen immediate roof must be high enough for the main roof rock to touch the gangue slowly to prevent the main roof from sinking suddenly into the gob and producing impact. Compared with other methods for forming the mining roadway, the technical characteristics of GSCRF and these methods are listed in Table 4.  
Figure 21: Overall effect of GSCRF.
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
## 6 Results and Discussion
### Investigation of Traffic Offload to D2D Routes with and without Backbone Route  
This investigation measures the packet delivery ratio of each available route to understand the amount of traffic offloaded to D2D routes transmission in backbone routes. Traditionally, due to the lack of D2D routes, all traffic must be transmitted over the backbone route without traffic offload. The backbone route uses licensed channels without PUs' activities at all times, and so packet delivery is not interrupted by PUs'. The D2D communication uses white spaces (or underused licensed channels without PUs' activities). Figure 7 presents the packet delivery ratio of both D2D and backbone routes. The packet delivery ratio of routes 1, 2, and 3 reflects the amount of traffic offloaded from MC BS or the backbone route. Route 1 (\(fc_{s}\)-\(fc_{1}\)-\(fc_{4}\)-\(fc_{d}\)) has the highest priority since it has 450 s out of 600 s (or 75% of PUs' OFF time), route 2 (\(fc_{s}\)-\(fc_{2}\)-\(fc_{5}\)-\(fc_{d}\)) has 300 s (or 50%), and route 3 (\(fc_{s}\)-\(fc_{3}\)-\(fc_{6}\)-\(fc_{d}\)) has 150 s (or 25%).  
There are two main investigations. First, packet delivery over each of the three available D2D routes that contributes to traffic offload in the presence of a backbone route (\(fc_{s}\)-\(mcBS\)-\(fc_{d}\)) through MC BS (shown as lighter shade bars in Figure 7). Since the D2D routes have different amounts of white spaces, they have different packet delivery ratios. Route 1 has delivered 44.57% of the traffic, route 2 has delivered 23.4%, and route 3 has delivered 12.13%, and so the total amount of traffic delivered along the D2D routes is 80.10%. When PUs' activities reappear in all the three D2D routes, traffic must be transmitted over the backbone route, which has delivered 9.26% of the traffic. Hence, the overall packet delivery ratio is 89.36%. Secondly, packet delivery over each of the three available D2D routes in the absence of a backbone route (shown as darker shade bars in Figure 7).  
When PUs' activities reappear in all the three D2D routes, packet loss occurs since traffic cannot be transmitted over the backbone route, causing a lower overall packet delivery ratio. Route 1 has delivered 42.15% of the traffic, route 2 has delivered 21.3%, and route 3 has delivered 11.6%. Hence, the overall packet delivery ratio is 75.07%. However, the source node can still transfer traffic over the backbone route in the presence of abackbone route. Despite the presence of the backbone route, some packet loss is observed due to the random reappearance of PUs' activities and route switches.
## 2 Materials and Methods
### Methods  
Bidimensional fluorescence images of living diatoms and frustules were recorded by an Axiomat microscope Zeiss (German), using a TRITC-filter set (\(\lambda_{\text{exc}}\) = 540 nm, \(\lambda_{\text{emi}}\) > 590 nm) for detection of [Ru(bpy)\({}_{3}\)]\({}^{2+}\) 2[PF\({}_{6}\)]\({}^{-}\) and a combination of FITC-filter (\(\lambda_{\text{exc}}\) = 465 nm, \(\lambda_{\text{emi}}\) > 525 nm) and DAPI-filter (blue passband) for [Ir(ppy)\({}_{2}\)bpy]\({}^{+}\)[PF\({}_{6}\)], AlQ\({}_{3}\), and ZnQ\({}_{2}\). A confocal laser scanning microscope, Leica SP8 X, with upright configuration was used for photophysical and morphological characterization; 3D and \(\lambda\) scan images were performed using a UV/diode laser with 405 nm or 488 nm excitation wavelength and a HC PL APO CS2 63x/1.40 oil objective. Emission spectra were recorded in the 450-700 nm spectral range. This interval was divided into 36 detection steps with 5 nm step size. Fourier Transformed Infrared-Attenuated Total Reflectance (FTIR-ATR) spectra were acquired with a Perkin Elmer Spectrum Two Spectrophotometer equipped with A 2 \(\times\) 2 mm diamond crystal. Spectra were recorded in the range 4000-400 cm\({}^{-1}\) with a 2 cm\({}^{-1}\) resolution, using 0.25 cm\({}^{-1}\) acquisition interval and acquiring 32 scans for each sample. Raman spectra were collected using a LabRAM HR Horiba-Jobin Yvon spectrometer with 532 nm excitation laser under ambient conditions at low laser power (<1 mW) to avoid laser-induced damage. The measuring time was \(\sim\)200 s. The excitation laser beam was focused through a 50X optical microscope (spot size \(\sim\)1 mm and work distance 1 cm). The spectral resolution was \(\sim\)1 cm\({}^{-1}\).
## 3 Results and Discussion
### Verification of Numerical Simulation Method  
The schematic of the experimental system is shown in Figure 4, which is composed of the blower, air tank, pressure regulator, flow regulator, discharge valve, electric heater,  
Figure 3: Schematic mesh of numerical simulation model.  
experimental unit and measuring instrument. The inlet air is compressed by the blower. Measure the pressure through inclined pipe differential pressure gauge and pressure digital display gauge. K-thermocouple and multi-channel temperature measuring instruments are used to measure the temperature for the pipeline and the experimental unit. The range of temperature measurement is \(-200\)-\(260\)\({}^{\circ}\)C, and the accuracy of that is 0.4%. The flow is measured by an integrated ultrasonic mass flowmeter with a measuring range of 10-100 m\({}^{3}\)/h and an accuracy of 1%.  
Experimental measurement parameters such as temperature, temperature, pressure and rate of flow are related to the experimental results. In the experiments, the measurement errors of temperature, pressure and rate of flow are about 0.45%, 7.39%, and 7.39%, respectively. According to the error transfer principle, the experimental error is \(\sqrt{\left(0.45\%\right)^{2}+\left(7.39\%\right)^{2}+\left(1\%\right)^{2 }}=7.47\%\).  
Take the impingement hole diameter of 6, 7.2 and 8.4 mm, respectively, for the experiment. Due to the limitation of the experimental devices working range, the Re range in the experiment is 8 \(\times\) 10\({}^{3}\)-2 \(\times\) 10\({}^{4}\). Compare numerical simulation results of the average Nusselt number with experimental ones for the lower surface, and the results are shown in Figure 5. Under the same conditions, the numerical simulation results are consistent with the experimental ones. The heat transfer performance improves with the increase in the impingement hole diameter. The average error of the simulation results and the experimental results is about 15.83%. This includes the error between simulation results and real results, and the error between test results and real results. This is caused by the errors of experiments and simulation. The simulation method can be considered to be correct.
# Clustering of a Health Dataset Using Diagnosis Co-Occurrences
## 3 Results
### Analysis of the Clustering Characteristics  
This section aims to illustrate the quality of clustering by analyzing the contents of the clusters obtained. As illustrated in Table 3, the segmentation produced by the clustering enabled analysis of the structuration of the ED health profiles. The number of visits per patient was associated with the cluster size with a number between 1.17 and 1.34 for the first group (cluster 1 to 5) and between 1.04 and 1.18 for the second group (cluster 6 to 16). Given the general aspect of the population and the absence of stratification, each of the 16 clusters was characterized with a wide range of population with standard deviations between 20 and 30 years. However, some clusters have distinct sociodemographic and clinical characteristics that can be summarized with:  
* Cluster 1, as suggest the type of health problem it contains, is characterized by a majority of women (71%), a high rate of biological examinations at 64.6% (rank 2 nd), a relatively high use of the long triage circuit with a ratio of 1.17 (rank 4 th), and a high hospitalization rate of 21% (rank 6 th).
* Clusters 2, 4 and 8 have the highest mean age at 49.58 (3 rd), 57.86 (1 st) and 55.53 (2 nd) years, and are associated with the most care-intensive patients. Indeed, they are characterized by the highest rate of hospitalization, at 34% (3 rd), 37% (2 nd) and 44% (1 st) respectively, the highest use of the long triage circuit with ratios of 0.61 (2 nd), 0.24 (1 st) and 1.12 (3 rd) respectively, the highest probability of radiological examination at 55.3% (2 nd), 74.5% (1 st) and 39.7% (3 rd), the highest probability of biological examination, at 66.6% (2 nd), 82.2% (1 st) and 60.3% (4 th), and the highest mean number of medications (prescribed) at 2.08 (2 nd), 2.96 (1 st), and 2.46 (3 rd), respectively.
* Cluster 3 is characterized by the youngest population, with a mean age of 20.84 years, further analyses show that it contains 51.2% of visits with children of 5 years or less, and it is related to winter epidemics, with 23.4% of visits occurring in December and January (compared to the expected 16.6%).
* Clusters from the second group (6 to 16) are, except for cluster 10, characterized by a male predominance, varying between 52% and 64%, with clusters 6, 7 and 16 being the three most concerned with prevalence of men of 61%, 62% and 64% respectively. This is likely associated with the fact that most of these clusters are either directly related to trauma, for seven clusters: 6, 7, 8, 9, 12, 15 and 16, or indirectly for the other four clusters (clusters 10, 11, 13, 14).