esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Identification of Signal App Traffic
#### 4.2.1 Accessing/Opening the Signal App  
The opening of the Signal app activity represents the start of app traces on the Android device. Initially, it was difficult to classify the Signal-only traffic from the shared network traffic packets. Therefore, an extensive number of trace files were captured and monitored during the opening of the Signal app. The process of the above activity is as follows.  
* Open the Signal client app on User A device;
* Wait for 3 to 4 s without interacting with the app;
* Close the app.  
We observed that a standard DNS query was sent to access the Signal server from the client end, i.e., textsecure-service.whispersystems.org. In response, a list of eight servers with IP addresses was sent back to the client to establish a connection. In most cases, only the first two servers in the list were employed to establish communications. Further, it was noticed that the Signal app employed the two random ports with the server 443 port for authentication. An authentication process is shown in Figure 5. A TCP connection was established in three steps, SYN, SYN ACK, and ACK between client and server. After that, a TLS handshake occurred by using TLSv1.2. Certificates, and session keys were exchanged between the client and server. Next, the client exchanged the key change cipher specs and sent an encrypted handshake message to the server. In response, the server sent a new session ticket, changed the cipher specifications, and sent an encrypted handshake message to the client device using TLS.  
After the negotiation of session keys between the client and the server, the following packet patterns with payloads were observed between the server and client ends.  
* Signal client app (IP: 192.168.0.5) sent packets of (449, 372, 127) bytes with a payload of size (383, 306, 61), to the Signal servers;
* In response, the server (IP: 54.175.47.110) sent packets of (261, 261, 137) bytes to the Signal client with a payload of (195, 195, 71).  
The above activity was performed multiple times to confirm the patterns of opening the Signal app as shown in Figure 6. Hence, we concluded that the above patterns of bytes transmission indicate the opening or accessing of the Signal application.
## 4 Results
### Performance Analysis
In this case, considering the complexity of setting up the BB model, as well as its non-transparency, it is worthwhile to utilise the developed WB model for estimating BC measurements. The following performance metrics will focus on the WB model development applied on the Jordan data. In addition to these three metrics, we present the WB model results as a regression plot and an error histogram (Figure 5). The regression plot displays the relationship between the outputs of the proxy (y-axis) and the real measurement data (x-axis) (Figure 5a). The error histogram shows the histogram of the difference between estimated proxy values and real measurement values (Figure 5b). From both figures, it can be observed that the results are adequate: most of estimated BC values lie close to the ground truth line in the regression plot (with R\({}^{2}\) is at 0.77), whereas the error histogram demonstrates that the highest probability (i.e., histogram peak) lies at zero. The latter means that the difference between the BC estimations and the BC measurements are mostly close to zero. These figures conclude that the developed WB proxy is adequate for field deployment under measurement conditions similar to those in urban areas of Jordan.
# Singular Phenomenon Analysis of Wind-Driven Circulation System Based on Galerkin Low-Order Model
## 3 Governing Equations of Ocean Circulation
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dimensional Parameter** & **Symbol** & **Value** \\ \hline Characteristic horizontal length & \(L\) & \(5\times 10^{4}\) m \\ Ocean layer thickness & \(D\) & \(10^{3}\) m \\ Rossby number of the coordinate origin & \(\beta_{0}\) & \(1.6\times 10^{-11}\) m\({}^{-1}\)s\({}^{-1}\) \\ Characteristic horizontal velocity & \(U\) & \(1.0\) m \(\cdot\) s\({}^{-1}\) \\ Coriolis parameter & \(f_{0}\) & \(10^{-4}\) s\({}^{-1}\) \\ Ideal zonal wind stress & \(\tau_{0}\) & \(0.8\) Pa \\ Normalized gravitational acceleration & \(g^{\prime}\) & \(0.025\) m \(\cdot\) s\({}^{-2}\) \\ Density & \(\rho_{0}\) & \(10^{3}\) kg \(\cdot\) m\({}^{-3}\) \\ Vertical momentum exchange coefficient & \(A_{V}\) & \(0.01\) m\({}^{-2}\) s\({}^{-1}\) \\ Horizontal momentum exchange coefficient & \(A_{H}\) & \(100\)\(\sim\)2500 m\({}^{-2}\) \(\cdot\) s\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dimensional parameters. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dimensionless Parameters** & **Symbol** & **Values** \\ \hline Planetary vorticity gradient strength & \(\beta\) & \(0.04\) \\ Rotating Froude number & \(F\) & \(1.0\) \\ Reynolds number & Re & \(20\)–\(500\) \\ Bottom friction coefficient & \(\mu\) & \(0.05\) \\ Wind stress intensity & \(\alpha_{QG}\) & \(0.04\) \\ Relative inertial boundary layer thickness & \(\delta_{I}\) & \(5.0\) \\ Relative horizontal viscous boundary layer thickness & \(\delta_{M}\) & \(0.3684\)\(\sim\)1.0772 \\ Relative vertical viscous boundary layer thickness & \(\delta_{s}\) & \(0.625\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dimensionless parameters. Also, for Equation (5), the system with respect to the central axis \(y=1/2\) is antisymmetric without the wind stress term, and the relationship \(\Re_{QG}\) can be expressed as,  
\[ \begin{split}\Re_{QG}(\Psi(x,1-y))=-\Psi(x,y)\\ \Re_{QG}(u(x,1-y))=u(x,y),\quad\Re_{QG}(v(x,1-y))=-v(x,y)\end{split}  \tag{10}\]  
where the selection of \(w_{f}\) will satisfy the antisymmetric relationship.
## 4 System Architecture  
Figure 6 presents the system architecture of the proposed VRET, which is a refined version of the previous VRET system [11]. The exposure therapy (ET) scenario module (with five levels) provides the architecture for scene construction. The VR environment includes the development engine and the VR platform. The data collection module collects biodata and system parameter data during the experiments. These data are analyzed in the data analytic module. External components such as the database management vault and the VR-user interfaces are connected to the system core modules.  
Figure 5: The driving phobia VRET experiment flow.  
Figure 6: The driving phobia VRET system framework.  
The hardware of the VRET system includes the VR Head-mounted Display headset (HTC, VIVE Pro, Taipei, Taiwan), the steering wheel with joystick and pedals (Guillemot Co., Thrustmeter T300 RS GT, Carentoir, France), and biological signal sensors (Thought Technology Ltd., ProComp Infiniti, Montreal, QC, Canada). The software components include the development engine (Unity Technologies, Unity; San Francisco (HQ), CA, USA) and the VR platform (Valve Corp., Steam; Bellevue, WA, USA). For data storage and management, SQLite (SQLite.org) is used as DBMS and BioGraph Infinite software is used to record and visualize the biodata. For the software and hardware configuration and description, please refer to [11].
# Overload Mitigation of Inertial Grid-Forming Inverters Under Frequency Excursions
## 3 Overloading Mitigation Strategies  
As discussed in the previous section, removing the primary frequency regulation power of an inertial GFM inverter is not enough to prevent overload during frequency excursion. The inertial contribution must also be reduced to protect the inverter. From the perspective of active power controller, three different approaches are proposed: (1) parallel PI, (2) angle limiter, and (3) external frequency support.  
This section describes the structure of these overload mitigation schemes and analyzes their behavior under overloading conditions. The method in which operational limits \([P_{min},P_{max}]\) are calculated is out of the scope of this work. In this context, various methods have been proposed in the literature to obtain operational limits using converter voltage and current constraints [34].
## 2 Materials and Methods
### Single HM and NP Toxicity Tests at 1 h and 24 h  
To determine the toxicity of HMs and NPs to _E. acidiculatus_, we conducted preliminary toxicity range-finding tests using different concentrations of HMs and NPs. These tests aimed to establish the concentration range for the final assays. Each test included 5-7 different concentrations, ranging from the lowest concentration with no observed effect on cell viability to the highest concentration, resulting in 100% cell death. We prepared 0.1 M stock solutions for HMs and 1 M stock solutions for NPs by dissolving each chemical in a salt basal medium (SMB) with a pH of 7. Daily working solutions were then prepared by diluting the stock solutions to nominal concentrations as follows: 10-100 mg Cd/L, 0-2 mg Cu/L, 50-250 mg Zn/L, 10-150 mg ZnO/L, 1-10 mg CuO/L, 500-8000 mg TiO\({}_{2}\)/L, and 1000-15,000 mg SiO\({}_{2}\)/L. We conducted ecotoxicity assays at 1 h and 24 h using specific 3-well depression glass slides. One hundred cells from an exponentially growing culture were inoculated into each well, resulting in a final volume of 1 mL of ciliate culture medium containing the specified chemical concentration. The setup was covered to prevent evaporation and incubated for 1 h and 24 h in a humid chamber at 18 \(\pm\) 1 \({}^{\circ}\)C in the dark, without feeding the ciliates during the test period. After the 1 h and 24 h incubation periods, _Euplotes_ cells were examined under a stereoscopic microscope (20-40\(\times\) magnification) to determine mortality and survival at different test concentrations. The ciliates were considered dead if they were missing due to a cell burst or if they remained stationary at the bottom of the well, unresponsive to gentle mechanical stimulation with the tip of the micropipette. The control wells contained the same number of ciliates but lacked HMs and NP solutions. Replicates (\(n=3\)) were averaged for each concentration treatment. Cytotoxicity assessment endpoints included median lethal concentration (LC) LC\({}_{20}\) (concentration causing 20% mortality) and LC\({}_{50}\) (concentration causing 50% mortality) at 1 h and 24 h. Mean mortality was determined using a regression equation. Due to the number of exposed organisms, each analysis required a larger amount of working solution, resulting in minimal absorption of HMs and NPs due to bioaccumulation. To minimize chemical adsorption/release levels, we used 3-well depression glass slides, aiming to reduce these levels compared to conventional toxicity test setups.
## 5 Results
### Configuration I SVM Classifiers Performance  
Configuration I SVM classifiers' performance is illustrated in this section. Table 1 shows a comparison among four SVM classifiers constructed with each handcrafted descriptor set independently with the fusion of all handcrafted features obtained from the multiple domains. The table demonstrates that among individually handcrafted descriptors, the SVM classifiers learned with the statistical features obtained from the spatial domain achieved the highest accuracy of 85.7%, 92.8%, 91.0%, and 90.2% for linear, quadratic, cubic, and gaussian SVMs, respectively. In contrast to features extracted using DWT, GLCM, and GW, features based on statistics yielded far fewer relevant, non-redundant, easy to interpret, and distinctive features [64], therefore statistical features obtained higher results. However, these accuracies were boosted to reach 87.8%,94.2%, 95.1%, and 91.3% using the same classifiers when trained with the combined handcrafted descriptors. The studies [33,34] showed that combining multiple handcrafted features can lead to the enhancement of the performance of classification. Thus, these results verify that fusing handcrafted features from multiple domains can improve the results and is superior to using features from a single domain.
## Theory for the Local Wavenumber Approach
### Depth Reconstruction  
The average wavenumber \(\overline{k_{mm}}\) at each point can be obtained by the local wavenumber approach. Hence, the phase velocity \(C_{p}(x_{m},y_{n})\) can be calculated as below:  
\[C_{p}(x_{m},y_{n})=\frac{2\pi f}{\overline{k_{mm}}} \tag{13}\]  
The relationship between the phase velocity and the plate thickness can be gained from the phase velocity dispersion curve shown in Figure 1a. Consequently, the plate thickness \(d(x_{m},y_{n})\) of the target area can be reconstructed according to this relationship. Then, the depth distribution can be written as:  
\[h(x_{m},y_{n})=D-d(x_{m},y_{n}) \tag{14}\]  
where \(D\) is the thickness of the pristine plate. In order to evaluate the depth, the maximum depth error \(E_{depth}\) is defined as below:  
\[E_{depth}=\frac{|h_{\max}-h_{0}|}{h_{0}} \tag{15}\]  
Here, \(h_{\max}\) denotes the maximum value of the reconstructed depth and \(h_{0}\) is the actual value.
## 3 Emergency Braking Control Strategy Design  
The braking control system mainly contains controller selecting rules, braking control strategies, braking actuators, and vehicle dynamics. The schematic diagram of it is shown in Figure 4.  
The first thing that should be clarified is when the emergency braking control strategy needs to be triggered. Without any doubt, when the brake pedal travel exceeds a certain value, for example 95%, as shown in Equation (12), this means the driver wants the vehicle to stop as soon as possible. However, when driving on low adhesion grounds, it is impossible for the vehicle to achieve a deceleration exceeding the ability that the track-ground condition can provide. Thus, when the slip ratio goes higher than a threshold, the emergency braking control strategy needs to be triggered. It is shown in Figure 3 that a \(\mu-\lambda\) curve rises fast with the increase of the slip ratio from zero and turns to a flat growth as \(\lambda\) approaches 0.15, then when the slip ration goes over 0.2, the adhesion coefficient starts to fall with the growth of \(\lambda\), although the falling speed is related to a certain value of \(\lambda\) and what the track-ground condition is. Hence, the threshold of the slip ratio can be set as 0.15.  
The emergency braking control strategy proposed in this article consists of a sliding mode slip controller and a rule-based braking force allocation method. When the emergency braking control strategy is triggered, the sliding mode slip controller calculates the total braking torque demanded converted to the drive wheel according to the current vehicle status, and then, the braking torque allocation method divides the total demanded braking torque into three parts separately for the mechanical brake, the hydraulic retarder, and the PMSM according to the rules and vehicle status.
# Plasma Surface Modification of PDMS-Glass Microfluidic Chips for Oil Recovery Studies
## 3 Results and Discussions
### Surface Modification
#### 3.1.2 Glass  
Photos of water droplets on glass before and after treatment are shown in Figure 7.  
The dependence of the wetting angles during the advance and recede of a water drop on the glass surface is shown in Figure 8. It can be seen that already after 1 min of treatment, the wetting angle of the advancing liquid drops from 80\({}^{\circ}\) to 10\({}^{\circ}\), and the receding one from 55\({}^{\circ}\) to <1\({}^{\circ}\). For 2 min of treatment, the advancing angle drops to 5-6\({}^{\circ}\) and does not change any more during further processing for 5 min. The experiment showed that, using plasma treatment, a superhydrophobic surface can be obtained for both angles in less than 30 s. We explain this by the fact that the number of hydrophilic groups increases rapidly during plasma treatment, which does not contradict the known data from XPS-data analysis [36].  
Glass is mainly silicon dioxide, in which silanol-bound water, silanol, siloxane, geminal \(-\)OH and vicinal groups can be found on the surface. Siloxane groups predominate on the untreated glass surface. We explain the rapid hydrophilization of glass compared with PDMS by the fact that there are no hydrophobic groups on glass, unlike PDMS, and  
Figure 8: Dependence of the contact angle of wetting the glass surface with water on the time of treatment with electric plasma in the manufactured laboratory instrument.  
Figure 7: An advancing water drop on the glass surface before (**a**) and after (**b**) treatment in electric plasma for 60 sec.  
therefore the effect of the appearance of even a small amount of such groups can be much more pronounced.
# Dark Fermentation of Fruit Juice Effluents: Effects of Substrate Concentration
###### Abstract  
This study explores the impact of initial substrate concentrations on biomass growth, hydrogen production, and acid generation during acidogenic fermentation of a synthetic fruit juice wastewater. Four substrate concentrations, within the range 0.30-2.12 C mol-L\({}^{-1}\), were tested using a mixed acidogenic bacterial culture. Batch reactors were employed to conduct the study, and the results were analyzed to determine inhibition effects. The highest hydrogen production (6.1 L H\({}_{2}\)-substrate C mol\({}^{-1}\)) and hydrogen percentage in the gas phase (57%) were achieved at a substrate concentration of 0.30 C mol-L\({}^{-1}\). Higher substrate concentrations reduced the hydrogen production due to substrate and product inhibition events. The maximum H\({}_{2}\) potential production was 4.15 Nm\({}^{3}\)/m\({}^{3}\) reactor at a substrate concentration of 0.91 C mol-L\({}^{-1}\). Biomass growth and VFA production followed exponential trends at low substrate concentrations, 0.30 and 0.091 C mol-L\({}^{-1}\), while high concentrations resulted in linear trends due to inhibition effects caused by the substrate. The main acids produced were lactic at low concentrations, and acetic when dealing with high concentrations. The highest final acid concentrations were obtained with the highest initial substrate concentration, but their yields were significantly lower due to the substrate and product inhibitions experienced by the biomass.  
acidogenic fermentation; hydrogen production; volatile fatty acids; inhibition +
Footnote †: journal: applied sciences
# Design of Surface Plasmon Nanolaser Based on MoS2
## 3 Simulation Analysis
### Discussion on Threshold
#### 3.3.2 Discussion on the Influence of Geometric Structure on Threshold  
The threshold with different geometries is shown in Figure 8. The structures in Figure 8a,b are consistent with those shown in Figure 4a,b. As shown in Figure 8a, the threshold decreases as the height of the upper air gap increases. With the increase of the height of the lower air gap, the threshold decreases first and then increases, and the minimum threshold is 0.11 \(\mathrm{\SIUnitSymbolMicro m}^{-1}\). As shown in Figure 8b, with the increase of the radius of the CdS nanowire and the Ag nanowire, the threshold shows a decreasing trend. When the radius is large, the threshold caused by the radius change is smaller. Comparing Figure 8a,b, we can see the influence of the height of the air gap on the mode localization is much smaller than that of the CdS nanowire and the Ag nanowire. Therefore, in order to obtain a lower  
Figure 7: The threshold with different materials.  
threshold and smaller impact on other properties of the nanolaser, the radius of the CdS nanowire and the Ag nanowire should be changed preferentially.  
In summary, in order to optimize the overall performance of the laser and to balance the contradiction between \(\mathrm{A_{eff}}/\mathrm{A_{0}}\) and \(\alpha_{\mathrm{eff}}\), the radius of the CdS nanowires and Ag nanowires are 90 nm and 80 nm respectively, the spacer layer is 6 nm, and the air gaps are all 10 nm, which means the threshold of the laser is lower while the normalized mode area is smaller. This structure can achieve deep subwavelength confinement and a low-lasing threshold.
# Analysis of Non-Minimum Phase System for AC/DC Battery
## 2 Circuit Operation  
Figure 2 shows the converter operation modes that will be discussed in detail in this section. The analyzed converter has been design to operate in continuous conduction mode (CCM), which means the ripple current is significantly smaller than the average current thus, the peak current decreases in comparison with the operation in discontinuous conduction  
Figure 1: AC/DC single stage boost/buck power factor correction (PFC) converter.  
mode (DCM). In CCM, it has only two operating states; when a controlled switch is off the corresponding diode is forward biased. In the converter analysis, capacitors, inductors, diodes and MOSFETs are assumed ideal, neither equivalent series resistance (ESR) nor other parasitic effects are considered. The input voltage waveform is a rectified sine wave which obtained by a passive full bridge rectifier of the grid input sine voltage.  
Transferring the power from the input port to the output port is done by two different modes, and it depends on the needed input and output voltages as follows:  
**Boost mode**: Figure 1(a) shows a simplified model for the converter operation in boost mode. In this operation mode, the input voltage is lower than the output voltage and the two main switching elements are the switch \(Q_{1}\) and diode D1. Meanwhile, the switch \(Q_{2}\) is being on and the diode D2 is being off for the whole period. Inductor \(L_{1}\) is the main energy transfer element for the boost mode. As mentioned, the converter operates in CCM with two different states. The first state (State -I), when switch \(Q_{1}\) is on and diode D1 is off (reverse biased). At the same period, the intermediate capacitor \(C\) starts discharging. In the second CCM state (State -II), when the switch \(Q_{1}\) is off and diode D1 is on (forward biased), Inductor \(L_{1}\) starts to discharge its energy to the output load and charge the two capacitors: intermediate \(C\) and output capacitor \(C_{o}\).  
**Buck mode**: Figure 1(b) shows the simplified model for the converter operation in buck mode. In this operation mode, the input voltage is higher than the output voltage. The two main converter switching elements are switch \(Q_{2}\) and diode D2. Meanwhile, the switch \(Q_{1}\) is being off and the diode D1 is being on for the whole period. Inductor \(L_{1}\) is the main energy transfer element for the buck mode as well. In State-I, when the switch \(Q_{2}\) is on and diode D2 is off, inductor \(L_{1}\) starts charging by the input inductor current \(i_{L1}\). At the same time, inductor \(L_{2}\) and the intermediate capacitor \(C\) are discharging to the output load through switch \(Q_{2}\). The load current \(i_{o}(t)\) is the sum of the input inductor current \(i_{L1}\) and the second inductor current \(i_{L2}\). In State-II, when the switch \(Q_{2}\) is off and diode D2 is on, inductor \(L_{1}\) starts to discharge its energy to the output load. At same time, inductor \(L_{2}\) and intermediate capacitors \(C\) are charging.