esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Adsorption Studies
#### 3.3.4 Effects of Contact Time  
The study investigated how contact time influences extraction efficiency. This was performed by observing the efficiency over various time periods, maintaining constant conditions: a pH of 7.0, an initial concentration of 5 mg L\({}^{-1}\), an adsorbent weight of 55 mg, and a total sample volume of 10 mL. Extraction efficiencies greater than 96% were achieved within 10 min of interaction time (Figure S3). In the following experiments, a contact duration of 10 min was used to ensure the regularity of target drug absorption from aqueous samples. Similar observations were reported in the literature [17].
## 2 Experimental Section  
### Simulation of the Field Emission Model  
The field emission model was simulated using CST Studio Suite 2020 (CST). CST is an electromagnetic simulation software developed by the French Dassault Systemes. The trajectory and distribution of electrons under the action of the electric field in all space can be obtained by CST simulation [42]. Figure 1a shows the electron emission process based on CNTs cathode. A metal grid mesh with square grid holes was set between the anode and cathode. The width of the grid wire is represented by \(W\), and the distance between the wires is represented by \(Wd\), as depicted in Figure 1c. Electrons emitted from the cathode could traverse the grid holes and reach the anode plate, constituting the anode current. The ratio of anode current to cathode current was defined as electron transmittance. The formula for defining the aperture ratio is as follows:  
\[Aperture\ Ratio=\frac{Wd^{2}\times 100\%}{\left(Wd+W\right)^{2}} \tag{1}\]The initial parameter values of the specific structure in the CST model are presented in Table 1. According to the system parameter values in Table 1, the size of the cell, iteration number, relative accuracy, and other parameters can fully make the simulation results have a high simulation accuracy. The cathode-gate distance and gate-anode distance values of the current model can enlarge the area of the electron beam spot. The voltages and the distance between the grid wires are the key parameters that were varied in the simulation. Initially, the anode voltage was held constant at 7.0 kV, and the gate voltage was set to 3.0 kV to simulate and test the grid with various aperture ratios. The grid wire width was fixed at 20 \(\upmu\)m, while the distance between the wires was incrementally increased to achieve an aperture ratio ranging from 11% to 94%. In the simulation, we mainly investigated the current change for each electrode under different conditions, changes in electron transmittance, and the size of the electron beam spot.  
Figure 1: Measurement setup and the grids. (**a**) Electron emission process based on CNT cathode. (**b**) Experimental setup for electron emission measurement of CNT cathode. (**c**) Grid wire width and grid wire distance diagram. (**d**) Metal grids and their 50\(\times\) magnified images. (**e**) SEM of multi-walled CNT cathode. (**f**) Raman spectrum of the CNTs.
###### Abstract  
Purpose: The aim of this study was to present a new method to analyze the three-dimensional accuracy of complete-arch dental impressions and verify the reliability of the method. Additionally, the accuracies of conventional and intraoral digital impressions were compared using the new method. Methods: A master model was fabricated using 14 milled polyetheretherketone cylinders and a maxillary acrylic model. Each cylinder was positioned and named according to its corresponding tooth position. Twenty-five definitive stone casts were fabricated using conventional impressions of the master model. An intraoral scanner was used to scan the master model 25 times to fabricate 25 digital models. A coordinate measuring machine was used to physically probe each cylinder in the master model and definitive casts. An inspection software was used to probe cylinders of digital models. A three-dimensional part coordinate system was defined and used to compute the centroid coordinate of each cylinder. Intraclass correlation coefficient (ICC) was evaluated to examine the reliability of the new method. Independent two sample t-test was performed to compare the trueness and precision of conventional and intraoral digital impressions (\(\alpha\) = 0.05). Results: ICC results showed that, the new method had almost perfect reliability for the measurements of the master model, conventional and digital impression. Conventional impression showed more accurate absolute trueness and precision than intraoral digital impression for most of the tooth positions (\(p<0.05\)). Conclusions: The new method was reliable to analyze the three-dimensional deviation of complete-arch impressions. Conventional impression was still more accurate than digital intraoral impression for complete arches.  
d
# Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles
## 3 Results--Radiometric Comparison
### Temporal Stability  
Finally, an analysis of the temporal stability of SLSTR-A/B during the tandem phase was performed by monitoring changes in the sensor-to-sensor differences in days of gridded data during the tandem phase, as shown in Figure 6 for night-time observations of 280 K scene temperatures.  
The most obvious feature during this period is a step change in the observed differences apparent from August onwards. This step change is likely to be caused by two scheduled mission activities that took place shortly before this. Firstly, a planned change in the set-point temperature of the SLSTR-A detectors occurred in July; this was followed by outgassing of SLSTR-B shortly after. The resultant temperature change for both sensors is shown as solid lines in Figure 6 for reference. The spectral response functions (SRFs) of SLSTR TIR channels have been shown to be temperature sensitive [10], and so the change in the radiometric response is likely to be linked to the change in instrument temperature. Temperature sensitivity as the source of step change is further supported by the fact that channel S9 is the most temperature sensitive channel and shows the largest step change. After this initial step change, the channels S7 and S8 seem to stabilise fairly quickly, whereas S9 differences drift by almost 0.1 K as the temperature of SLSTR-B levels off.  
Figure 5: Differences between SLSTR-A and SLSTR-B for channels S8 and S9 in the oblique view plotted as functions of binned scene radiance, as in Figure 4, with the proposed stray light correction applied. Error bars indicate propagated instrument calibration uncertainties at \(k=3\), with no additional uncertainty corresponding to the straylight correction applied. The two dashed vertical lines indicate the temperatures of SLSTR’s blackbody calibration targets (data: 3 September 2018).  
Figure 6: Time series of differences between SLSTR-A and SLSTR-B TIR channels during S3A/B tandem phase. Data points represent radiometric differences observed for days of night-time gridded data at 280 K. The solid lines provide the temperature of SLSTR-A and B during this period.
## 3 Methodology  
A four-step methodology is employed, as depicted in Figure 1. In Step 1, both mobility and crash data are directionally and temporally linked to freeway segments using a GIS-based shapefile. Fifteen minute speed data are used as a mobility surrogate. In Step 2, the crashes are classified based on whether they are occurring during normally congested or uncongested periods. To differentiate between these conditions, a cut-off threshold is employed. In Step 3, for the crashes that occur during normally uncongested periods, it is determined whether non-recurring congestion was present at the time of the crash. Crashes occurring in non-recurring congestion where no or little bottleneck is normally activated are classified with a specific cut-off threshold. This threshold comes from a historic spatiotemporal congestion index that identifies recurrent congestion. Finally, in Step 4, the remaining crashes are classified as being during recurrent or non-recurrent conditions. First, crashes are classified as being recurrent congestion if they are within a recurrent congestion impact area whose bottleneck activates frequently. Crashes that cannot be classified this way are treated as special cases and classified with a supplemental methodology.
## 3 Results and Discussion
### Impact Resistance in PPE/HIPS/SBS Blends at 1:1:1 Ratio  
The PS content in the blends is essential for a notable performance in the impact resistance, according to the discussed results. For this reason, the PPE/HIPS/SBS411R-IM, PPE/HIPS/SBS411R-TSE, PPE/HIPS/SBS416R-IM, and PPE/HIPS/SBS416R-TSE blends with a weight ratio 1:1:1, i.e., PPE/HIPS/SBS (33.33 wt%/33.3 wt%/33.33 wt%) were evaluated. At this ratio, the PS content exceeded the rest of the components. Only PPE/HIPS/SBS blends with SBS radial copolymers were evaluated due to excellent impact resistance behavior.  
Figure 9 displays the Izod impact resistance of PPE/HIPS/SBS blends at 18, 24, and 33.33 wt% of SBS. It is important to note that PPE content was superior to the other components, in particular for the PPE/HIPS/SBS411R-18-TSE, PPE/HIPS/SBS411R-24-TSE, PPE/HIPS/SBS416R-18-TSE, and PPE/HIPS/SBS416R-24-TSE blends.  
The PPE/HIPS/SBS411R-TSE blend at a 1:1:1 ratio displays an impact resistance increase concerning the blend at 24 wt% SBS411R, from 16.75 kJ m\({}^{-2}\) to 27.83 kJ m\({}^{-2}\). However, the value is still lower than the PPE/HIPS/SBS411R-18-TSE (44.2 kJ m\({}^{-2}\)). Therefore, the limited ability of the TSE to distribute the highly viscous (highest M\({}_{w}\)) SBS411 continues to dominate even when the PS content increases.  
The PPE/HIPS/SBS416R-TSE (1:1:1) and PPE/HIPS/SBS416R-IM (1:1:1) blends exhibited excellent impact resistance with values close to 49.2 kJ m\({}^{-2}\) and 50 kJ m\({}^{-2}\), respectively. These results are attributed to the fact that S416R has a lower M\({}_{w}\) than S411R, and its distribution was more appropriate by IM.  
In summary, the methodology employed in melt blending was substantially influenced by the blend composition rather than the blending process (TSE or IM) with an appropriate distribution of components. In addition, for an appropriate copolymer distribution, the PS content is the critical factor to an improved impact resistance performance. As mentioned, the higher the disruption and distribution of the PB phase, the higher the PS content required to support it, which hampers increasing immiscibility with PPE, allowing the SBS to behave as an adequate toughener.
## 3 Encapsulation Protocol
### Ssh
#### 3.4.2 Machine Learning  
In addition to detecting passengers, ML is capable of classifying their protocols and applications. [PERSON] [43], [PERSON] [42], [PERSON] [44], [PERSON] [57] and [PERSON] [62] have all selected features to train from including packet size, forward/backward average IAT of the first few packets of flows. Without using IP, port number, and payload, [PERSON] [42] used C4.5, genetic programming (GP) and AdaBoost classifiers to classify SSH tunnel encrypted traffic. Experiments are conducted in public and private datasets using 39 packet header features (IP header length, checksum, etc.) and 22 flow-based features. In the best case, GP can achieve 98% accuracy, and it can also be classified by applications in tunnel traffic such as SCP, SFTP, Shell, X11 session, Telnet, etc. At its best, C4.5 can achieve 100% accuracy. An unsupervised clustering algorithm On-Line Density-Based Spatial Clustering (OLDBSC) was proposed by [PERSON] [44] to resolve the problems of large computation and high memory consumption. Instead of clustering the entire stream's features, the best priority feature algorithm is used to find an optimal feature set for a sub-stream and then to map the applications that have the highest probability to the application types using the best priority feature algorithm. This method is capable of identifying SSH tunnel traffic as well as classifying applications and identifying unknown application types in traffic. The accuracy rate in private datasets is as high as 99%.
## 3 Estimation and Reliability Calculation of Road Travel Time Distribution after Earthquake
### Impact Factors of Earthquake Damage of Path Travel Time
#### 3.2.4 Statistical Eigenvalue Calculation  
(1) Central moment calculation  
Average value of target travel time:  
\[\hat{\mu}_{T(\mathbb{C})}=\frac{\sum_{i\in\mathbb{C}}\omega_{i}T_{i}}{\sum_{i\in \mathbb{C}}\omega_{i}} \tag{25}\]  
where:  
\(\omega_{i}\) is the weighted value of travel time of the \(i\)-th trajectory observation.  
\(T_{i}\) is the travel time observation value converted by the \(i\)-th trajectory observation.  
The central moment is expressed as:  
\[\hat{\sigma}_{T(\mathbb{C})}^{2}=\frac{\sum_{i\in\mathbb{C}}\omega_{i}\Big{(}T _{i}-\hat{\mu}_{T(\mathbb{C})}\Big{)}^{2}}{\sum_{i\in\mathbb{C}}\omega_{i}} \tag{26}\]  
(2) Percentile calculation  
Step1: arranging all observed values Ti from small to large.  
Step2: find the [\(\mathit{np}\)/100 th] number ([*] is the upward rounding operation).  
Step3: the [\(\mathit{np}\)/100 th] number is the \(p\)-th quantile.  
(3) Estimation of probability density function  
In this paper, the latest Bayesian mean adaptive density estimation method (ADEBA) is used [31]. Compared with the previous adaptive probability density estimation method, two parameters \(\zeta\) and \(\zeta\) are added, which makes the change in bandwidth H in the kernel density method more consistent with objective laws. Therefore, the travel time probability density function of the target path can be expressed as:  
\[\hat{p}(t|\mathbb{C},\xi,\zeta)= \frac{1}{\sum_{i=1}^{\mathbb{C}}\omega_{i}}\sum_{i=1}^{\mathbb{N }_{c}}\frac{\omega_{i}}{h_{i}(\xi,\xi)}K\bigg{(}\frac{t-T_{i}}{h_{i}(\xi,\xi) }\bigg{)} \tag{27}\]  
where:  
\(K\) is the Gaussian kernel function.  
In addition, the bandwidth \(h_{i}\) is:  
\[h_{i}(\xi,\xi)= \frac{\xi}{(p_{0}(t_{i}))^{\xi}} \tag{28}\]  
where:  
\(\xi\) and \(\zeta\) are parameters, which are obtained by grid search.  
\(\omega_{i}\) is the weighted value of travel time of the \(i\)-th trajectory observation.  
\(P_{0}(t_{i})\) is a prior preliminary estimate of \(p(x)\).
## 4 Discussion
### Effects of Biopolymers on Macroscale
Over time, the E-value tends to steadily increase. The most pronounced improvement in the _TI_ value occurred at 0.5% XG content. As the content increased further, the rate of improvement gradually slowed. This behavior is attributed to the molecular structure and action mechanism of XG in soil. XG molecules feature a unique long-chain structure, which, at low concentrations, effectively fills the voids between soil particles, enhancing the bonding force and significantly improving the soil's toughness. The optimal effect occurs when the content is at 0.5%, where filling and bonding are most effective. However, as the XG content increases beyond 0.5%, the molecules may begin to entangle or locally aggregate, weakening the toughening effect. At concentrations exceeding 1.5%, this entanglement or aggregation becomes more pronounced, leading to a slight decrease in the _TI_ value. The effect of LBG on soil toughness was similar to that of XG. At the age of 0 days, this could be due to GG not fully interacting with the soil particles when first applied, and factors such as the soil's moisture distribution and interparticle void structure may have temporarily hindered GG's strengthening effect. At the age of 3 days, GG molecules gradually penetrated the soil particles and interacted with them through hydrogen bonds and other forces, thereby altering the soil's structural characteristics. Gum substances such as XG, LBG, and GG improve soil cohesion by strengthening the bonding force between particles. These colloidal substances fill soil voids and adhere to particle surfaces, forming a cemented structure that boosts cohesion. The molecular structure, intermolecular forces, and affinity for soil particles vary among gums, leading to different effects. GG, with its longer molecules, stronger water absorption, and superior adhesive properties, forms more stable cemented structures, resulting in the highest cohesion improvement. At lower dosages, gums effectively fill gaps between soil particles, enhancing bonding and cohesion. However, excessive gum concentrations can cause over-bonding, reducing friction between particles and potentially weakening cohesion. At a 2% concentration, XG and LBG show decreased cohesion, while GG maintains stable performance. XG, LBG, and GG, as natural polymers, improve cohesion between soil particles and enhance soil stability by forming cementitious structures. However, excessive concentrations of these colloids may reduce friction between particles, which negatively affects the soil's mechanical properties. GG, with its longer molecular chains and stronger water absorption, maintains superior performance even at higher concentrations, especially in improving both the internal friction angle and cohesion. Compared with other common stabilizers (such as chemical curing agents, microbial curing, etc. ), biopolymers have unique advantages in improving the mechanical properties of granite residual soil, such as excellent environmental protection, sustainability, and high strength enhancement efficiency shown in the soil stabilization process.
## 3 Components of VPN/Overlay Architecture
This is different from VLANs, where sub-interfaces share the same MAC address. Each MACVLAN sub-interface has a unique MAC and IP address directly integrated into the underlay network. Typically employed in virtualization, MACVLAN interfaces allow Containers or VMs to obtain DHCP addresses directly, easing integration into existing networks. MACVLAN offers four types, with the MACVLAN bridge being the most common, enabling local communication without external routing. External connectivity utilizes the underlay network, as illustrated by two Containers communicating via the MACVLAN bridge. * _MACsec_: MACsec operates at Layer 2, ensuring transparent protection (integrity and/or encryption) within the network. Unlike IPsec, which can pose performance challenges, MACsec is designed to run at line rate, typically in a hardware's ASIC, although it is not universally supported across hardware. The protected MACsec frame utilizes an Ethertype of 0x88e5. IPVLAN is akin to MACVLAN but with a key distinction: the endpoints share the same MAC address. Supporting both L2 and L3 modes, IPvlan offers flexibility in networking configurations. In L2 mode, each endpoint retains the same MAC address but receives a different IP address. Conversely, L3 mode facilitates packet routing between endpoints, enhancing scalability. While the Ethernet Header and SecTag are sent in plaintext, they are always integrity-protected by ICV. The default cryptographic algorithm is AES-GCM-128. Additionally, MACsec supports optional replay protection with a configurable replay window. Figure 1 illustrates the generic concept of stacking and encapsulation, fundamental to understanding the functioning of Overlay networks and VPNs. These solutions, backed by commercial products like Mikrotik and open-source ones like OpenWrt and Linux distributions, are both at the forefront of the field and serve as a foundation for new research, encompassing VPN protocols, Linux operating systems, and both proprietary and open-source hardware platforms, as referenced in [23].
###### Abstract  
The power amplifier (PA) and low-noise amplifier (LNA) are the most critical components of transceiver systems including radar, mobile communications, satellite communications, etc. While the PA is the key component of the transmitter (TX), the LNA is the key component of the receiver (RX) of the transceiver system. It is pointed out that traditional design approaches for both the LNA and PA face challenging drawbacks. When designing an LNA, the power gain and noise figure of the LNA are difficult to improve simultaneously. For PA design, it indicates that efficiency and linearity of the PA are also hard to improve simultaneously. This study aims to surmount this by proposing a novel independently biased 3-stack GaN high-electron-mobility transistor (HEMT) configuration for efficient design of both PA and LNA for next generation wireless communication systems. By employing an independently biased technique, the proposed configuration can offer superior performance at both small-signal (SS) for LNA design and large-signal (LS) for PA design compared with other typical circuit configurations. Simulation results show that by utilizing an adaptive bias control of each transistor of the proposed configuration, both power gain and noise figure can be improved simultaneously for the LNA design. Moreover, efficiency and linearity can be also improved at the same time for the PA design. Compared results with other typical configurations including a single-stage, conventional cascode, independently biased cascode, and conventional 3-stack reveals that the proposed configuration exhibits superior advantages at both SS and LS operation.  
GaN HEMT; independently biased; microwave engineering; RF circuit design; low-noise amplifier; power amplifier +
Footnote †: journal: Applied Physics Letters
# An Experimental Study on the Reflection Characteristics of Laser Echo Light Waves
## 2 Basic Principles  
### Light Intensity Flicker Characteristics  
The phase and amplitude of the light beam during transmission cause the light intensity at the receiving end to fluctuate randomly over time. The flicker index is widely used as an indicator to measure light intensity fluctuations, and is often used to evaluate the impact of atmospheric turbulence on the transmission of optical signals. In engineering, the flicker index is usually used to characterize light intensity fluctuations, and its expression is [14]  
\[\sigma_{I}^{2}=\frac{\left\langle I^{2}\right\rangle}{\left\langle I\right\rangle ^{2}}-1 \tag{1}\]  
where \(\left\langle\ \cdot\ \right\rangle\) is the ensemble average, that is, the statistical average over a long period, and \(I\) represents the light intensity.