esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### General Pattern and Correlation
The distributions of tick counts from the sampling areas within CSP and CRSP show slight differences in both mean (triangles) and median (orange lines), and a \(t\)-test revealed these differences were not significant (\(t\) = \(-\)0.586, \(p\) = 0.562). However, the areas classified as hotspots--sites with tick counts \(\geq\) 20--in CRSP generally had higher tick observations than the hotspots in CSP. For example, the maximum questing ticks collected from a single site in CRSP was 70, followed by a site where 46 ticks were collected. The maximum number of ticks collected from a single site in CSP was 31. When the data from all parks on all dates are visualized with temperature and relative humidity, the numbers of questing black-legged ticks show nonlinear patterns that are consistent with the previous studies. While higher temperature generally leads to more questing ticks, the relationship is nonlinear (Figure 5). Specifically, the optimal temperature for tick question behavior is approximately 20 \({}^{\circ}\)C in suburban New York. When the temperature is lower than 20 \({}^{\circ}\)C, a higher temperature promotes questing, while a temperature higher than 20 discourages question. At the same time, questing activities seem indifferent to very high (\(\geq\)25 \({}^{\circ}\)C) or very low (\(\leq\)0 \({}^{\circ}\)C) temperatures. The case of relative humidity is even more complex than the temperature. While the linear trend suggests higher humidity leads to more questing activities, their relationship also indicates nonlinearity (Figure 6). Opposite to the temperature, the fitted curve shows a worse humidity level around 42%, and a lower or higher humidity level can increase ticks' questing activities. However, this pattern is not supported at all individual parks as the nonlinear relationship only holds at CRSP. At CSP, the trend is clearly an upward linear one, which is consistent with the existing theories. The other two parks, RPP and FINS, have no obvious trend because the temperature at RPP is very low and the land cover at FINS has little vegetation. Unlike the temperature and relative humidity, other environmental factors display few generalizable patterns due to the significant variations at local scales. Of course, the absence of patterns and the nonlinear relationships are not surprising as the black-legged ticks' questing activities interact with local factors in intricate ways. On the one hand, the patterns revealed from our data, particularly the temperature, are in accordance with the published results from other researchers, which largely verifies our data collection method. On the other hand, the complexity of the patterns also requires more advanced statistical methods to examine the relationships among those factors in the presence of nonlinearity.
# A Dual-Band 8-Antenna Array Design for 5G/WiFi 5 Metal-Frame Smartphone Applications
## 5 Conclusions  
In conclusion, a dual-band, 8-port MIMO antenna for 5G/WiFi 5 smartphones was successfully designed, fabricated, and tested in this paper. The antenna exhibited favorable performance, with acceptable ECCs for the MIMO system within the operating bands (3.4-3.6 GHz, 4.4-5.0 GHz, and 5.15-5.85 GHz) and isolation exceeding 16.8 dB. The antenna efficiencies surpassed 41.5% in the target frequency bands. The calculated ECCs from measured electric field results were below 0.18 in the operating bands. Overall, the proposed MIMO antenna array demonstrated significant promise for 5G massive MIMO mobile communication systems.  
Conceptualization, H.L.; methodology, H.L. and S.X.; investigation, L.H. and S.X.; writing\(-\)original draft preparation, H.L.; writing\(-\)review and editing, Q.C. and G.L.; supervision and funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Bandwidth (GHz)**} & \multirow{2}{*}{**Isolation (dB)**} & \multirow{2}{*}{**ECC**} & \multicolumn{2}{c}{**Total Efficiency (\%)**} & \multirow{2}{*}{**MIMO Order**} & \multirow{2}{*}{**Metal Frame**} \\  & & & & & & \\ \hline
[1] & 3.3–4.2, 4.4–5.0, 5.15–5.925 (\(-6\) dB) & \textgreater{}11 & \textless{}0.1 & 40–71 & 8 & yes \\ \hline
[4] & 3.4–3.6, 4.8–5.1 (\(-6\) dB) & \textgreater{}10 & \textless{}0.08 & 41–72, 40–85 & 8 & yes \\ \hline
[5] & 3.3–4.2, 4.8–5.0 (\(-6\) dB) & \textgreater{}10 & \textless{}0.12 & 53.8–76.5, 40–85 & 8 & yes \\ \hline
[13] & 3.3–6 (\(-10\) dB) & \textgreater{}18 & \textless{}0.08 & 69.85–90 & 8 & yes \\ \hline
[20] & 3.3–3.84, 4.61–4.91 (\(-10\) dB) & \textgreater{}15 & \textless{}0.02 & 76–85, 66–82 & 4 & no \\ \hline
[21] & 3.4–3.8, 4.8–5.0 (\(-6\) dB) & \textgreater{}15.5 & \textless{}0.07 & 42–83, 40–85 & 8 & no \\ \hline
[22] & 3.4–3.8, 5.15–5.925 (\(-6\) dB) & \textgreater{}11 & \textless{}0.1 & 52.4–71.7, 48.9–75.4 & 10 & no \\ \hline Proposed work & 3.4–3.6, 4.4–5.0, 5.15–5.85 (\(-6\) dB) & \textgreater{}16.5 & \textless{}0.18 & 72–82.4, 41.5–75.6 & 8 & yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between the proposed 5G MIMO antenna and other relevant designs.  
Figure 13: (**a**) The simulated reflection coefficients of the single-hand mode. (**b**) The simulated transmission coefficients of the single-hand mode. (**c**) The simulated transmission coefficient of the double-hands mode. (**d**) The simulated transmission coefficients of the single-hand mode.  
**Funding:** This work was partly supported by the National Natural Science Foundation of China under grant 61671330, the Science and Technology Department of Zhejiang Province under grant LGG19F010009, and Wenzhou Municipal Science and Technology Program under grants C20170005 and 2018 ZG019.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Methodology
### Topology  
The topology is divided into input and output sections. The input section consists of a single-phase half-bridge, and the output section consists of a three-phase full-bridge. Figure 5 shows a simplified topology of STPC. Here, the source is assumed to be a single-phase Pole-mounted Transformer (P.TR), while the LV Grid represents a three-phase LVDS.  
The power semiconductor devices used in an STPC are SiC-MOSFETs. Although Si-IGBTs could be used, SiC devices are more efficient and support higher switching frequencies, which allows for a reduction in the LC-filter size. In addition, the recent increase in SiC device production has made them more economically viable than before. The STPC topology can be divided into a single-phase converter as the input and a three-phase inverter as the output. There is a DC-link in the middle, which acts as a smoothing circuit. A simplified circuit representation of this is illustrated in Figure 5. While not identical, it is conceptually similar to the Back-to-Back Converter used in wind power generation. Since it can be implemented using power semicon  
Figure 4: Method of three\(-\)phase voltage shift.  
Figure 5: Simplified topology of an STPC.  
from this advantage. Table 4 presents the parameter settings applied to the topology of the STPC.  
The power semiconductors used in the topology are calculated using the following equation. Since an STPC consists of a combination of 10 kVA modular units, the input power is calculated as 10 kVA. Additionally, the input voltage is calculated to be 220 V for a single-phase system, and the output voltage is calculated as 380 V/\(\sqrt{2}\). The margin is assumed to be 2.  
\[\frac{p_{input}}{v_{input}\times pf}\times\sqrt{2}\times margin=\frac{10\; \mathrm{kVA}}{220\;\mathrm{V}\times 0.9}\times\sqrt{2}\times 2=142.8\to 150[A] \tag{1}\]  
\[\frac{p_{output}}{v_{output}\times 3}\times\sqrt{2}\times margin=\frac{10\; \mathrm{kVA}}{380\;\mathrm{V}/\sqrt{3}\times 3}\times\sqrt{2}\times 2=42.97\to 50[A] \tag{2}\]  
In a PDS, the RMS voltage is commonly used, but power semiconductor devices must be rated based on peak voltage; hence, \(\sqrt{2}\) is applied. A design margin is also included to operate below rated limits, reducing thermal stress and extending component lifespan. Based on the calculation, it was determined that the input converter uses a 150A SiC-MOSFET (Infineon, Neubiberg, Germany), and the output inverter uses a 50A SiC-MOSFET (Infineon, Neubiberg, Germany).
# An Accurate Probabilistic Model for TVWS Identification
## 5 Experimental Results and Discussion
### Variability of the Sensed Signals  
The reason to take simultaneous samples of the noise \(W(p)\) and the composite signal \(X(p)\) at each time instant \(t_{i}\), responds to the variations experienced in the UHF-TV spectrum by the above-mentioned signals. In Figure 19 we can observe the pdf of the average values for \(W(p)\) and \(X(p)\). To obtain this pdf, from trajectory T2, 1,166,928 samples were collected. The mean values of the signals were obtained, and a CI corresponding to 95% was calculated and plotted. Figure 19 also shows that the noise mean  
Figure 17: Location of the represented points.  
Figure 18: Spectrum detected and analyzed with the proposed model: (**a**) Central south part of the city, (**b**) South-east, (**c**) Middle East, and (**d**) Eastern limit of the city of Windsor. Note that as far of the center the sample is taken, less PUs are detected.  
\(\overline{\mathbf{W}}(p)\) has a value of \(-108.9725\) and can have a variation of \(1.05\) dB. The mean of the composite signal \(\overline{\mathbf{X}}(p)\) has a value of \(-103.9889\) with a CI of \(6.72\) dB.  
Around \(\overline{\mathbf{X}}(p)\), represented with a red dashed line, the mean of the composite signal values, could vary in \(6.72\) dB. Now, it means that sometimes the respective pdf of the noise and the composite signal may be closer or separated from each other. This variation is confirmed by the plots represented in Figure 13. In order to avoid any error produced by the variation mentioned above, it is recommended to obtain separate readings for signal and noise. This is more applicable when the M-SSS is used and the samples are taken in different geographical locations every \(t_{i}\).
## 3 Materials and Methods
### Tracer Tests  
When performing the tracing test with sodium chloride, the measuring points were equipped with Eltratec logger, which enables the continuous recording of pressure changes, temperature, and electrical conductivity. The probes were installed in wells and water-courses or springs. For the uranium follow-up test, we used two Cyclops probes to measure fluorescence (Cyclops-7 Loggers, obtained from the American manufacturer Precision Measurement Engineering, Inc., Vista, CA, USA) and one ISCO autosampler, which was used only in the test set of tests. The measurement record for the Cyclops probes was set to a 10 min interval, and the ISCO autosampler was set to take samples every 6 h (four samples per day), which was reduced to every 12 h (two samples per day) in the second half of the test. At other measuring points, samples were taken manually in 250 mL dark bottles and sent to the Karst Research Institute ZRC SAZU, where they were analyzed with a PERKIN ELMER LS 45 luminescent spectrometer (Waltham, MA, USA).  
Three series of tracer tests were conducted in the Urbas landslide and its surrounding area: the first in spring 2020, the second in fall 2020, and the final series in spring 2022. The initial tracer test on 17 April 2020 involved dissolving 50 kg of table salt (NaCl) in approximately 200 L of water and injecting the solution near the Ui-1 spring. To monitor the test, probes were installed in observation wells and springs to continuously record electrical conductivity (Table 1). In addition to continuous readings, periodic manual control measurements were performed. The second tracer test was conducted on 14 May 2020. For this test, we injected 2 kg of uranine on the scree slope at location I-1 (Figure 1), just above the landslide scarp. The tracer was diluted with approximately 100 L of water and poured directly into the gravel material. The third tracer test took place on 1 October 2020. We injected uranine below the Urbas spring (Ui-1) at 1:00 p.m. For this test, two sites were equipped with Cyclops automatic fluorometers: borehole PP-8 and the sampling point at the stream (Figure 1). Additionally, samples from each site were collected to determine laboratory fluorescence (Table 1). Concurrently, a fourth tracer test was conducted on 1 October 2020, around 12:00 p.m. A saline solution was injected into borehole PP-12A (Figure 1). Salt was selected as the tracer due to the proximity of the Urbas spring, which supplies drinking water under an allocated water right arrangement. Electrical conductivity measurements were recorded at four monitoring sites: springs Ui-1, Ui-4, and Ui-6, as well as borehole PP-4 (Figure 1). In total, 100 kg of salt was injected into borehole PP-12A and diluted with about 200 L of water. The final series of tests was conducted on 16 May 2022, when we injected 1 kg of uranine solution into the scree slope at injection point I-2 (Figure 1).
## 4 Conclusions  
Concluding the above results, the relativistic intense laser pulse enables us to form a relativistic dense and thin electron layer when interacting with the ultrathin target. Optimum foil thickness for layer production is much smaller than targets for fast ion generation and the optimal parameter \(\varepsilon_{0}=\pi n_{f}/\,n_{\mathrm{cr}}\lambda\) depends not only on intensity, but also on duration of laser pulse. For a laser pulse of length of about 10 laser periods, the thickness \(\varepsilon_{0}\)\(\approx\) 0.01 can be optimal. At the instant of termination of a laser pulse the electron energy in a layer does not come back to its initial value in an optimum range of thickness. Therefore, the plasma screening for an optimum target ceases to be necessary. At the same time, final magnitude of energy of a layer can be less than maximum possible magnitude reached during the pulse action. Then, by means of the plasma screening (DREAM scheme), it is possible to fix the  
Figure 7: The dependence of the energy conversion coefficient of the main laser pulse to the hard quanta on the laser intensity at 0.6 nm target width.  
maximum value. Because energy of a layer's maximum is considerably extended in the spatial area, there is therefore no necessity for high accuracy for the screen positioning. Maximum energy in the case of the circular polarization is higher, and electron layer can propagate for a longer distance compared to the linear one. The spatial domain of maximum energy for the circular polarization is wider, thus the requirements for the location of secondary plasma mirror are lower. Reflection of a counter-propagating laser pulse off the relativistic mirror, produced by the main pulse in DREAM scheme, enables the production of KeV coherent radiation with an efficiency of about 0.1% in respect to the energy of the main laser pulse. Peak brightness of a source of the hard quanta gained in DREAM scheme exceeds that of known sources of hard radiation, and average brightness exceeds that of X-ray tubes and laser-electron generators, but it is less than that of a synchrotron X-ray source.  
[PERSON] acknowledges the provided computation resources of JSC at project HBUIS.
## 4 A Generalized Framework for Tactile Internet in the beyond 5G Era  
Based on the abovementioned literature research, the generalized architecture of a TI system, consists primarily of a master domain, a network domain, and a controlled domain. Concretely, the master domain typically consists of a human operator (tactile user) and the human system interface (HSI), which oversees converting human input into tactile data using appropriate tactile encoding techniques. The HSI generates tactile data, which is sent to the controlled domain via the network domain. The controlled domain or environment contains a remotely controlled robot or tele-operator, and the master domain directly controls the controlled domain using various command signals (e.g., velocity, position). The controlled domain then sends feedback signals to the master domain (e.g., force/position, surface texture). The master domain receives audio/visual feedback signals from the controlled domain in addition to haptic feedback signals. As shown in the Figure 13, this communication domain could be made up of an internet/core network, a radio access network (RAN), and a tactile support engine. In addition, the underlying communication channel must meet several requirements, including ultra-responsive and ultra-reliable connectivity [59]. The main functions of a beyond 5G (B5G) core network to support a TI system are the handling edge-cloud interactions and access, application-aware quality of service (QoS) provisioning and security [60].
## 3 Results and Discussion
### SEM-EDX and HR-TEM Analysis
SEM scale bar = 1 \(\upmu\)m. (**c**) Energy dispersive X-ray (EDX) spectra of _L. plantarum_ TA4 exposed to Ag\({}^{+}\). (**d**) The particles size distribution of biosynthesized AgNPs. (**e**) High-resolution transmission electron microscope (HR-TEM) micrographs of biosynthesized AgNPs at scale bar = 20 nm. (**f**) HR-TEM micrographs of biosynthesized AgNPs at scale bar = 10 nm. the attachment of these organic molecules that encapsulated the NPs led to the increased NPs sizes. Moreover, the measurement of hydrodynamic diameter was calculated from the core of the NPs to the water shell that surrounds the NPs [44; 48], while TEM analysis only measures the core of the NPs. Lastly, it was speculated that the significant increase in size might be attributed to the agglomeration of AgNPs in the suspension. [PERSON] et al. [49] reported that the size of NPs was increased up to four times when suspended in PBS due to the agglomeration of NPs. The resulting agglomeration state was affected by the pH and ionic strength of the aqueous suspensions, which subsequently led to the accumulation of NPs by weak van der Waals forces and the formation of large-size NPs in the DLS [49]. Nevertheless, the agglomeration of NPs is a reversible condition; it can be disrupted by the ultrasonication process. In our study, the suspension of AgNPs was not subjected to the ultrasonication process before the DLS analysis, which may have led to a significant increase in size through the agglomeration of AgNPs.
# Two-Parameter Probabilistic Model and Experimental Research on Micron Particle Deposition
## 3 Results and Discussion
### Particle Migration and Deposition Experiment
#### 3.2.2 Test Samples  
For the silica (SiO\({}_{2}\)) particles used in the experiment, the sample parameters are shown in Table 1.  
The sample pretreatment is as follows:  
1. Cleaning and pretreatment: The experimental samples of silica (SiO\({}_{2}\)) particles are first dissolved with acetone on the surface of the experimental samples, then washed with deionized water many times to remove the surface stains of the experimental samples, and finally dehydrated with absolute ethanol.
2. Drying pretreatment: The silica (SiO\({}_{2}\)) particle experimental sample is stored in a dark and dry area. Before the experiment, according to the experimental needs, take an appropriate amount of the sample and put it into the drying oven to heat it to 110 \({}^{\circ}\)C and dry it for 12 h, and then put it into the dryer for later use, so as to reduce the humidity of the experimental sample and reduce the influence of capillary force between the particles of the experimental sample on the experimental results during the experiment.
3. Electrostatic pretreatment: In the storage area where the silica (SiO\({}_{2}\)) particle experimental sample is located, a suspended ionizing fan is used to remove the electrostatic neutralization in the environmental space, reducing the electrostatic carryover of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **Name** & **Size** & **Composition** & **Purity** & **Density** & **Product Batch Number** \\ \hline
1 & Spherical silica & 10 μm & SiO\({}_{2}\) & 9.99\% & 2.32 g/cm\({}^{3}\) & KG-24 \\
2 & Spherical silica & 40 μm & SiO\({}_{2}\) & 9.99\% & 2.32 g/cm\({}^{3}\) & KG-22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample parameters.  
the experimental samples and reducing the influence of Coulomb force between the experimental sample particles on the experimental results during the experiment.  
The LS-909 laser particle size analyzer from Omic was utilized to verify the particle size of silicon dioxide (SiO\({}_{2}\)) particles in the experimental samples, using the dry circulation injection method. The tested median particle size was found to be consistent with the data provided by the sample manufacturer.
## 4 Experiments and Results
### TRIDEC Drilling Support Components
#### 4.1.3 Knowledge Editor Component  
The knowledge editor is a component that enables a user to create, update, and delete detected events as well as any proposed counter-actions and recommendations. Moreover, the component provides comfortable views on certain critical events in a time-oriented fashion. In certain cases, particular know-how of human experts is required to directly edit model parameters such as thresholds, weights, data type prototypes, or cluster centers.  
Data transmission is a big challenge because of the amount of data with regards to real-time transmission. For data transmission, a standard encoding such as WITSML [38] is used. Such standards are based on XML. For instance, one rig currently provides more than 700 real-time channels sampled with a frequency of 1 Hertz. Figure 8 shows a portion of such data together with the associated information on some states of the drilling rig. The transmission of such a large amount of data from one rig is accomplished by the use of DSL or satellite communication channels. Continuously receiving such data streams concurrently from numerous rigs may exceed the capacity of such channels.  
Figure 8: Sensor data time series (four **upper** charts) and state of the drilling (**bottom** chart) for one rig sampled at 0.1 Hz. The top chart shows the block position (red) and drill string rotation speed (green). The second chart shows the torque applied to the drill string (yellow) and the hook load (green). The third chart shows the pump pressure measured at the standpipe (orange) and the mud flow rate (blue). The fourth chart shows the measured depth of the bit (green) and the measured depth of the borehole (blue); also, the main operations trip-in, drilling and trip-out can be identified by the bit depth in this chart. The bottom chart shows the 10 possible operational state labels that may occur at a rig; the predominant light and dark blue encoded states indicate drilling.