esa-sceva/satcom-chunk-collection huggingface dataset:
# Unexpected Ferromagnetism--A Review
## 2 Examples of \(d^{0}\) Ferromagnetism
### Carbon Nanostructures
#### 2.3.4 Carbon Nanotubes  
Carbon nanotubes have been widely used in nanotechnologies due to their unique properties [72; 80]. Magnetic carbon nanotubes could open a whole new range of applications. Studies on the possible magnetization of carbon nanotubes when in contact with a magnetic material were conducted. Results indicated that spin-polarized charge transfer occurs when a carbon nanotube is in contact with a ferromagnetic material, leading to the magnetization of the carbon nanotube [81]. Room temperature ferromagnetism was also observed in hydrogenated carbon nanotubes. The mechanism proposed here was based on the introduction of hydrogen in a disordered nanotube structure, thus facilitating the hydrogen uptake and inducing a ferromagnetic moment. Moreover, it was observed that the ferromagnetic behavior was dependent on temperature and nanotube wall thickness [82]. A recent study proposed a nitrogen plasma treatment protocol for the treatment of carbon nanotubes with the purpose of ferromagnetism enhancement. The results suggested that the main reason for the ferromagnetic behavior is granted to the bonding configuration between pyridine and amine N [83]. Room temperature ferromagnetism was also observed in single-walled carbon nanotubes. Recent experiments on this subject excluded the possibility of impurity-induced magnetism but rather attributed it to carbon vacancies [84].
## 2 Materials and Methods
### Gilder Deployment and Data Collection
#### 2.2.4 Ocean Modelling  
The continental slope off Norway is a transport passage for the warm-saline Norwegian Atlantic Current (NwAC) towards the Arctic Ocean. Here, the current is primarily guided by the continental slope. Hence, mesoscale or sub-mesoscale eddies are consid  
Figure 2: Extended Lofoten–Vesteralen (LoVe) region (insert) and tracks of surface and subsurface platforms deployed in the GLIDER project, from March to September 2018 (Orange: Seaglider®, Green: Wave Glider, Purple: Sailbuoy).  
ered the primary mechanism in lateral heat transport. Indeed, observations and models indicate strong flow field variability in this region [45; 54]. The complex flow field and geometry of coastal and fjord systems entails not only a high spatial resolution of the ocean circulation model but also associated high-resolution observations and forcing fields, e.g., from numerical weather prediction models. Data assimilation (DA) using near-real-time observations is much needed to improve predictive skills in such models. Accordingly, to better understand the physics of the ocean along the coastal zone of the LoVe region, meteorological and oceanographic data collected by the three gliders were assimilated into a regional version of the ROMS model (NorShelf-2.4 km). The NorShelf-2.4 km model already assimilates data from diverse sources, e.g., sea surface temperature, high-frequency radars, hydrography provided by CTD sections from occasional research cruises, FerryBox data, and ARGO floats. This model runs operationally at the Norwegian Meteorological Institute (MET Norway) using four-dimensional variational data assimilation (4D-Var [55]). ROMS provides several formulations of 4D-Var; NorShelf-2.4 km uses the physical space analysis system (PSAS). Within this setup, the assimilation window was two days, which was chosen based on previous 4D-Var experiments on the coast of Norway with a 2.4 km resolution [56]. To benefit from the improved hydrography and circulation that the DA in NorShelf-2.4 km provides, a very high-resolution unstructured model was nested as a grid into the NorShelf-2.4 km [57]. This allowed for the introduction of a large number of rivers (more than 7000 large and small rivers) and positioned them correctly on the coastline. Here, we adopted a 3D version of the Finite-Volume Community Ocean Model (FVCOM [58]) and imposed a variable spatial resolution from 30 m along the coast and straits to 2.4 km at the boundaries. Hence, by imposing high-quality boundary conditions, an enhanced picture of the ambient hydrodynamical processes was achieved. In order to assess the skill of NorShelf-2.4 km compared with MET Norway's high-resolution NorKyst-800m forecast model, which does not employ data assimilation, we compared T-S diagrams.
# Anomaly Detection in Microservice-Based Systems
## 2 Data and Materials
### Dataset Labeling  
The pre-processing phase starts by labeling the records as anomalous and normal, adding in the metrics CSV the IsError column, represented by a boolean (true is anomalous, false is normal). After labeling, all types of application anomalies were added in a single CSV representing application anomalies. In the case of application anomalies, a column with the identifier of the respective record was also added.  
The application-level anomaly dataset contains 3430 records, of which 1715 represent normal and 1715 represent abnormal records. Figure 2 plots the data, two features simultaneously. The diagonal plots show the univariate distribution data in each column. The service-level anomaly dataset contains 2074 records, of which 1043 represent normal and 1031 represent abnormal records. Figure 3 displays the feature plots for the service-level anomaly dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Time** & **99 th Percentile** & **50 th Percentile** & **Mean** & **2 xx 1** & **4 xx/5 xx 2** & **IsError** \\ \hline
2023-02-27 & 0.01390 & 0.00257 & 0.00234 & 164 & 0 & True \\
18:46:00 & 0.00906 & 0.00264 & 0.00257 & 167 & 0 & True \\
2023-02-27 & 0.05550 & 0.00253 & 0.00318 & 173 & 0 & True \\
18:47:00 & 0.00953 & 0.00280 & 0.00304 & 187 & 0 & False \\
2023-02-23 & 0.01610 & 0.00276 & 0.00309 & 262 & 0 & False \\
18:38:00 & 0.00989 & 0.00273 & 0.00303 & 264 & 0 & False \\ \hline \end{tabular} 1
[FOOTNOTE:1]Footnote 1: footnotetext: Represents the number of responses with the HTTP response code in the 200s of the services. 2  
\end{table}
Table 1: An excerpt from the dataset created for training and validation of the system for the application-level anomalies.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Time** & **99 th Percentile** & **50 th Percentile** & **Mean** & **2 xx 1** & **4 xx/5 xx 2** & **IsError** \\ \hline
2023-02-22 & 4.920 & 2.030 & 2.070 & 24.4 & 0 & True \\
2023-02-23 & 0.498 & 0.375 & 0.388 & 24.4 & 0 & True \\
15:44:00 & 0.498 & 0.375 & 0.388 & 24.4 & 0 & True \\
2023-02-22 & 2.470 & 1.200 & 1.050 & 20.0 & 0 & True \\
2023-03-21 & 0.00495 & 0.00250 & 0.000578 & 604.0 & 0 & False \\
14:08:00 & 0.00495 & 0.00250 & 0.000578 & 604.0 & 0 & False \\
2023-03-21 & 2023-03-21 & 0.00324 & 0.317 & 898.0 & 6.67 & False \\
13:54:30 & 4.740 & 0.00324 & 0.317 & 898.0 & 6.67 & False \\
2023-02-27 & 2023-02-27 & 2.200 & 2.610 & 31.1 & 0 & False \\
19:08:00 & 7.380 & 2.200 & 2.610 & 31.1 & 0 & False \\ \hline \end{tabular} 1
[FOOTNOTE:1]Footnote 1: footnotetext: Represents the number of responses with the HTTP response code in the 200s of the services. 2  
\end{table}
Table 2: An excerpt from the dataset created for training and validation of the system for the service-level anomalies.  
Figure 2: Representation of the pairwise relationships of the application-level anomaly dataset.
# Free Vibration Analysis of Arches with Interval-Uncertain Parameters
## 2 Model of the Arch and Procedure of Deterministic Free-Vibration Equations
### Establishment of Free-Vibration Equations
Using the DQ method, the arch is discretized according to sample points \(\vartheta_{i}\), and the propositions of the original Equation (5) can be rewritten as:  
\[\left\{ \begin{array}{c}\frac{EA}{R^{2}}\sum\limits_{j=1}^{H}\xi_{ij}^{(2)}U_ {j}-\frac{GA}{R^{2}\kappa_{0}}U_{i}-\left(\frac{EA}{R^{2}}+\frac{GA}{R^{2} \kappa_{0}}\right)\sum\limits_{j=1}^{H}\xi_{ij}^{(1)}V_{j}-\frac{GA}{R\kappa_ {0}}\Phi_{i}=-\omega^{2}\rho AU_{i}\\ \left(\frac{EA}{R^{2}}+\frac{GA}{R^{2}\kappa_{0}}\right)\sum\limits_{j=1}^{H} \xi_{ij}^{(1)}U_{j}+\frac{GA}{R^{2}\kappa_{0}}\sum\limits_{j=1}^{H}\xi_{ij}^{ (2)}V_{j}-\frac{EA}{R^{2}}V_{i}+\frac{GA}{R\kappa_{0}}\sum\limits_{j=1}^{H} \xi_{ij}^{(1)}\[PERSON]_{j}=-\omega^{2}\rho AV_{i}\\ -\frac{GA}{R\kappa_{0}}U_{i}-\frac{GA}{R\kappa_{0}}\sum\limits_{j=1}^{H}\xi_ {ij}^{(1)}V_{j}+\frac{EI}{R^{2}}\sum\limits_{j=1}^{H}\xi_{ij}^{(2)}\[PERSON]_{j}- \frac{GA}{\kappa_{0}}\[PERSON]_{i}=-\omega^{2}\rho I\Phi_{i}\end{array} \right. \tag{12}\]  
where \(U_{i}\), \(V_{i}\), and \(\Phi_{i}\) represent the tangential, radial, and rotational vibration amplitude values, respectively, at \(\theta_{i}\), \(i=1,2,\ldots.H\). The set of equations can be reduced to  
\[[K]\left\{\delta\right\}+\omega^{2}[M]\left\{\delta\right\}=0 \tag{13}\]  
in which \([K]\) and \([M]\) are the global stiffness matrix and mass matrix, respectively, and \(\left\{\delta\right\}=\left\{U_{1}\ V_{1}\ \Phi_{1}\ldots U_{H}\ V_{H}\ \Phi_{H}\right\}\) is the generalized displacement vector. Relation (6) can be transformed into:  
\[U_{i}=0,\,V_{i}=0,\,\Phi_{i}=0,\,i=1,\,H \tag{14}\]  
Considering the boundary condition, Equation (13) can be written as  
\[ \begin{bmatrix}[K_{bb}]&[K_{bd}]\\ [K_{db}]&[K_{dd}]\end{bmatrix} \left\{ \begin{array}{c}\left\{\delta_{b}\right\} \\ \left\{\delta_{d}\right\}\end{array} \right\}+\omega^{2} \begin{bmatrix}[0]&[0]\\ [0]&[M_{dd}]\end{bmatrix} \left\{ \begin{array}{c}\left\{\delta_{b}\right\} \\ \left\{\delta_{d}\right\}\end{array} \right\}=0 \tag{15}\]  
The subscripts '\(b\)' and '\(d\)' in the matrix denote the components related to the boundary and internal domain, respectively, in Equation (15). The preservation of only the internal domain displacement vector is employed to streamline computation and enhance efficiency. Now Equation (15) can be expressed as follows:  
\[\left([K_{dd}]-[K_{db}][K_{bb}]^{-1}[K_{bd}]\right)\{\delta_{d}\}+\omega^{2}[M_{ dd}]\{\delta_{d}\}=0 \tag{16}\]  
Letting \(\left[\widetilde{K}\right]=[K_{dd}]-[K_{db}][K_{bb}]^{-1}[K_{bd}]\), \(\left[\widetilde{M}\right]=[M_{dd}]\), Equation (16) can be written as:  
\[\left[\widetilde{K}\right]\{\delta_{d}\}+\omega^{2}\left[\widetilde{M}\right] \{\delta_{d}\}=0 \tag{17}\]  
The natural frequencies can be obtained by the eigenvalue decomposition of Equation (17).
## 6 Simulation and Experiment (Python and ROS Simulation)
### Experiments and Analysis in Python
The comparison and analysis with the classic RRT algorithm, RRT* algorithm, and P_RRT* algorithm verified the effectiveness of the improved hybrid algorithm in this paper. Experiment 3. The reliability of the algorithm was evaluated by changing the step length of the RRT algorithm in the hybrid algorithm. Each step was carried out 200 times. The experimental results are shown in Table 4, and the trajectory of different step lengths is shown in Figure 11. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Step**} & \multirow{2}{*}{**Average Search Time**} & \multicolumn{2}{c}{**Average Number of**} & \multirow{2}{*}{**Average Path Length**} & \multirow{2}{*}{**Search Success Rate**} \\  & & & **Sampling Nodes** & \\ \hline
0.6 & 6.2 & 13.8 & 19.4 & 100\% \\
0.8 & 5.9 & 12.6 & 19.1 & 100\% \\
1.0 & 3.2 & 11.9 & 19.1 & 100\% \\
1.2 & 5.6 & 12.0 & 19.1 & 100\% \\
1.4 & 6.7 & 12.7 & 19.2 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of hybrid algorithms with different step sizes. Figure 10: Comparison of paths generated by the different algorithms under the same conditions, such as the number of obstacles and step lengths. As shown in Table 4, the search time was the smallest when the step size was 1.0 (42.8-52.2% shorter than at the other step sizes). The search time at the left end with a step size of 1.0 gradually decreased, while the search time at the right end had an increasing trend due to the excessive step size. The search time of the path did not decrease with increasing step size, and when the step size was 1.0, the search path was the shortest and the time was the shortest. In summary, when the step size was 1.0, the algorithm achieved the optimal effect. Therefore, the selection of the step size is still very important for the hybrid algorithm, and the experiment verified the reliability of the algorithm. Figure 11 shows the trajectory diagrams of different step lengths fitted with Bezier curves. The effect is best when the step length is 1.0 in the figure: The trajectory is smooth, and the path is the shortest.
## 5 Novel Hydraulic Fracturing Method  
Based on the results of the above numerical simulation, a new variable-viscosity and variable-density fracturing method was proposed for tight oil and gas reservoirs. The morphology of proppants in fractures was optimized by precisely controlling the viscosity of the fracturing fluid and proppant density. The method includes the following two steps:  
* Pumping high-viscosity fracturing fluid and low-density proppant  
First, high-viscosity fracturing fluid and low-density proppant are pumped into the fracture. The low-density proppant is carried to the far side of the fracture, increasing the distance of proppant placement and the effective fracture stimulation area. Simultaneously, the accumulation of proppant at the fracture inlet is reduced, static pressure at the fracture inlet is reduced, and the risk of proppant clogging is decreased.  
* Pumping low-viscosity fracturing fluid and high-density proppant  
Figure 9: Proppant placement under different pumping sequences: (a) pumping high-density proppant first and then low-density proppant; (b) pumping low-density proppant first and then high-density proppant.  
Then, low-viscosity fracturing fluid and high-density proppant are pumped into the fracture while ensuring that the viscosity ratio of the fracturing fluid is between 2 and 5. The high-density large-diameter proppant settles and accumulates at the fracture inlet, filling the \"sand-free zone\" formed in the initial stage, and forming fractures with high conductivity in the near-well zone. At the same time, high-density proppants continue to accumulate on low-density proppant dunes, increasing the height of the proppant dunes and forming an effective proppant placement, thus effectively improving the reservoir.  
According to statistics, the method has been successfully applied to more than 10 oil wells of the Bohai Bay Basin in eastern China, and the average daily oil production per well increased by 7.4 t, greatly improving the performance of fracturing.
## 2 Materials and Methods
### Raman Spectroscopic Method  
Raman spectra of 2 mL mixed liquor samples were directly acquired after collection and without pre-treatment using a fiber coupled Raman probe (RPB Raman probe, In-Photonics) routed to a modular spectrometer (Ocean Optics QE65 Pro), and an 785 nm excitation laser (RGBLase LLc, Fremont, CA, USA) with 500 mW output. The Raman probe used was a non-immersible anodized aluminum probe with a stainless steel tip and focused light with a working distance of 7.5 mm. Thermo-electric cooling was applied in the spectrometer with a detector set point of \(-\)10 \({}^{\circ}\)C. Each spectrum was obtained in the Raman shift range from 2677.68 to \(-\)62.34 cm\({}^{-1}\), with a 3.69 cm\({}^{-1}\)/pixel linear dispersion, corresponding to 1044 data points. Raman spectroscopy analysis was directly performed at-line on the mixed liquor samples, without any pre-treatment, in order to mimic online measurements. One scan was performed for each sample. A 5-s integration time was applied during spectral acquisitions on cycles A, B and C, and an integration time of 200 ms was used on samples from cycle D to avoid signal oversaturation. The acquired spectra correspond to the first basic measurements in a lab-scale photo-BNR. In real systems, overlaying fluorescence is an expected interference and this aspect will be focused on a future work.
## 3 Results and Discussion  
### Color Parameters  
The color of food products has a great influence on its acceptability to the consumer [36]. The orange color of carrots depends mainly on carotenes, which are influenced by variety, maturity, and growing conditions [22]. The effect of heat treatments on carrot root color was observed in all parameters of the CIEL*a*b* model (Table 1). The L* parameter, representing brightness, ranged from 40.74 to 53.35 across the analyzed carrot samples. The highest L* value was recorded for the raw sample. Among the processed samples, the highest L* value (43.17) was recorded for the carrot root steamed in the combi oven. Boiling and sous-vide also had the strongest effect on the L* value, reducing it by about 24% compared to that of the control sample. The a* values were positive, indicating a color shift towards red, and ranged from 8.12 to 24.80. The lowest values were observed in the carrot roots subjected to sous-vide and steaming in combi oven processes, while the highest values were found in the raw sample. The b* values, reflecting color shifts between blue and yellow, ranged from 22.00 for sous-vide carrot samples to 29.28 for raw carrot samples. All heat treatments resulted in a decrease in color saturation (C*), with the lowest value for the sous-vide process, indicating a paler, more grayish color. The lowest changes in C* were caused by the baking process, which resulted in a decrease of approximately 29% compared to that of the control sample. The total color differences \(\Delta\)E of the processed carrot samples compared to those of the raw carrot sample were in the range of 17.40-22.09. The values indicate a very distinct (\(\Delta\)E > 3) color deviation, which will be noticeable to a consumer. Differences in the color of carrot roots can also be seen in Figure 1. Clear differences can be seen in the inner and outer parts of the carrot root cross-section. The decrease in color parameters occurring in carrot root slices as an effect of heat treatments has also been observed, e.g., by [PERSON] and [PERSON] [37] during spout-fluidized bed drying at 60-90 \({}^{\circ}\)C; by [PERSON] et al. [38] during cooking treatments (steaming, boiling, stir-frying, and frying); and by [PERSON] [39] during frying at 130-190 \({}^{\circ}\)C. Furthermore, [PERSON] et al. [22] reported the same values of \(\Delta\)E in carrot samples subjected to different cooking methods, i.e., sous-vide, cook-vide, and traditional cooking. They also observed that the \(\Delta\)E value increases during cooking at lower temperatures over extended durations, which may explain the greater differences observed between the sous-vide and baking methods in the present study. [PERSON] et al. [4] hypothesized that the observed color changes could be due to the release of \(\alpha\)- and \(\beta\)-carotenes under low-temperature, and vacuum conditions.
## 6 Study Analysis and Results
### Precision and Percent Deviation  
The precision and percent deviation from crash history are calculated for various upper tail segment groups (20, 40, 60, 80, and 100) for the proposed method and the performance is compared with that of the EB and the PSI methods. A higher percentage of precision and lower value of percent deviation indicates better performance.  
\[Precision=\frac{TP}{TP+FP}*100\% \tag{3}\]  
\[Percent\ Deviation=(1-TP/M)*100\% \tag{4}\]  
where \(TP\), true positive, is the number of sites correctly identified as safety improvement sites by the evaluating method; \(FP\), false positive, is the number of sites falsely identified as safety improvement sites by the evaluating method (not listed in crash history); and \(M\) is the number of upper tail segments that are considered.  
Figure 4 shows precision across various upper tail groups (20, 40, 60, 80, and 100). Precision is also calculated for the EB method, and the PSI method to see how the performance of the proposed method compares to that of the other two methods. The precision percentage based on crash density analysis is shown in Figure 4a. The figure shows that the precision value is less for the proposed method when compared with the EB and the PSI methods. The value for the proposed method varies from 30% to 68%, versus 62% to 76% for the EB method and 85% to 90% for the PSI methods. On the other hand, the precision percentage based on crash frequency shown in Figure 4b indicates that the precision value varies from 60% to 78% for the proposed method versus 81.6% to 86% for the EB method and 83.3% to 88% for the PSI method. This confirms that both the EB and PSI methods were more consistent with the crash history compared with the proposed method. Further, the proposed method exhibited better performance and higher consistency with crash history when analyzed using crash frequency over crash density.  
Figure 4: Precision comparison using proposed method, EB method, and PSI method versus crash history: (**a**) crash density-based analysis; (**b**) crash frequency-based analysis.  
Figure 5 shows the percent deviation from the crash history for the proposed method, the EB method, and the PSI method. A quick examination of Figure 5 clearly shows higher deviation for the proposed method compared to the EB and the PSI methods both for crash density and crash frequency analyses. The other trend clearly exhibited in this figure is that the percent deviation for the proposed method decreases with the increase in the number of upper tail segments. Specifically, the percent deviation of 70% for density-based analysis and 40% for frequency-based analysis were observed for the 20 upper tail segments. However, the corresponding percent deviation for the 100 upper tails is 32% and 22% for density-based and frequency-based analyses, respectively. The EB and the PSI methods, while generally showing less deviation and better performance, exhibited less variation in percent deviation with the increase in upper tail segments.
# A Lunar-Orbiting Satellite Constellation for Wireless Energy Supply
## 7 Power Budget
### Scenario 3  
In the last scenario considered, where satellites orbit in an Earth-Moon DRO, the power transmitted is higher than the other two scenarios as shown in Table 4 because the average altitude is about 800 km. Figure 17 makes it possible to appreciate how the transmission time increases. In the 10-satellite constellation case shown in Figure 18, it is possible to note the aforementioned power peaks of 20.68 kW because of the simultaneous transmission of two satellites. In the sub-case with \(\alpha_{max}=80^{\circ}\), we can figure out how three satellites transmit together for a few minutes to obtain power peaks of three times the base load (31.02 kW). Concluding in the third scenario, the best three-satellite constellation case could provide 155 kWh of energy in one day, while the best 10-satellite constellation case could provide 519 kWh of energy in one day.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Scenario 3: Earth-Moon Distant Retrograde Orbit**} & \\ \hline \(\alpha_{max}\) & & 50\({}^{\circ}\) & & 60\({}^{\circ}\) & & 70\({}^{\circ}\) & & 80\({}^{\circ}\) \\ \# sat & 3 & 10 & 3 & 10 & 3 & 10 & 3 & 10 \\ \(P\) (kW) & 3.13 & 10.56 & 4.05 & 13.55 & 5.15 & 17.20 & 6.46 & 21.61 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Power converted in scenario 3.  
Figure 17: _Cont._