esa-sceva/satcom-chunk-collection huggingface dataset:
# Preparation and Application of ZIF-8 Thin Layers
## 3 Results
### Electrochemical Thin Layer Deposition and Effect of Synthesis Conditions
#### 3.1.3 Synthesis Duration  
An extended synthesis time affects the total amount of zinc dissolved, leading to extended crystal growth. The synthesis time for the deposition of a thinner layer must not fall below the critical limit needed for the incubation time [37]. That means that the nucleation period for the formation of crystal seeds can take more time than is provided for the reaction. Thus, not only the crystal size but also the number of crystals can be drastically reduced or fully inhibited. Variation of the synthesis time led either to a thin incomplete layer with small separate particles with <1 \(\upmu\)m diameter for a synthesis duration of 5 min, or to well-defined particles with 1.0 \(\upmu\)m to 1.5 \(\upmu\)m diameter, creating a homogeneous complete coating for a synthesis time of 30 min, or spherical crystals with a size of 2.0 \(\upmu\)m to 2.5 \(\upmu\)m in diameter with a synthesis time of 60 min (see Figure 4G-I, respectively). The resulting coating for the longest synthesis time of 60 min showed a weak adhesion because of a high thickness. With an extended synthesis time, the particle size increases as well. Therefore, the optimal synthesis time must be as short as possible to create small particles, whileovercoming the incubation time of nucleation and creating a complete and homogeneous coating. For ZIF-8 synthesis, this corresponds to 30 min.
## 4 Results
### Experimental Comparison with Existing Techniques
#### 4.2.2 Experiments on the Boston University (BU) Dataset  
Although the Boston University (BU) dataset contains images with uniform and varying lighting, our 'PA_B-J48' performs better than EM-Stat, TA, AM-Mouth, Ali, Lee, and Frid in terms of Se, P, F, TP-Rate, and ROC-Area, as can be seen in Table 3. The performance accuracy of existing approaches reduces significantly compared to the DrivFace dataset due to the varying lighting conditions of this dataset. However, the performance of 'PA_B-J48' remains stable in this dataset due to the adaptive nature of the Boosting framework.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **EM-Stat** & **TA** & **AM-Mouth** & **Ali** & **[PERSON]** & **Frid** & **PA_B-J48** \\ \hline A (\%) & 90.78 & 89.56 & 89.73 & 92.00 & 88.52 & 91.30 & **94.43** \\ \hline Se (\%) & 91.00 & 89.00 & 89.00 & 92.00 & 88.00 & 91.00 & **94.00** \\ \hline P (\%) & 89.00 & 80.00 & 0 & 91.00 & 82.00 & 91.00 & **94.00** \\ \hline F & 0.89 & 0.84 & 0 & 0.91 & 0.84 & 0.91 & **0.94** \\ \hline TP & 0.91 & 0.89 & 0.90 & 0.92 & 0.88 & 0.91 & **0.94** \\ \hline FP & 0.61 & **0.89** & **0.89** & 0.38 & 0.86 & 0.41 & 0.32 \\ \hline ROC & 0.86 & 0.64 & 0.64 & 0.86 & 0.58 & 0.71 & **0.91** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the existing approaches, EM-Stat, TA, AM-Mouth, Ali, Lee, and Frid with the proposed approach, PA_B-J48, in terms of accuracy (A), sensitivity/recall (Se), Precision (P), F-Measure (F), TP Rate, FP Rate, ROC Area for DrivFace dataset.
# Handover Scheme in LEO Satellite Networks Based on QoE for Streaming Media Services
## 4 Performance Evaluation
### Simulation Parameters  
We simulate the OneWeb constellation in the simulation software to generate the ephemeris data required by our proposed algorithm. The altitude of this LEO satellite is 1200 km, and the rest of the parameters are shown in Table 3. The weather parameters are predefined into five levels, which are ranked by their degradation impact on the communication channel from severe (Level 5) to optimal (Level 1). Specifically, Level 5 is rainy conditions (most severe), and Level 1 is clear sky (optimal conditions). Each user is assigned a unique weather array containing indices \([1,5]\) that define their weather level variation sequence. Whenever a user attempts to access a new satellite, the system advances to the next index in their weather array, simulating time-varying weather conditions. Detailed definitions of all five weather levels are provided in the Appendix A. The simulation time slot is set to 1 s, and the simulation time length is set to 1 h. Considering the randomness of the user when using the streaming service, we divide the user's video playback time into several segments with several minutes between each segment. Our simulations initially revealed that under low-load conditions, both our proposed method and baseline approaches achieved near-maximum MOS scores, making performance comparisons indistinguishable. To create meaningful evaluation conditions, we strategically configure a high-load environment by reducing the satellite downlink power to 24 dBW.  
For performance comparison, there are few schemes to analyze the QoE streaming media service in an LEO communication system. Therefore, two traditional handover schemes, i.e., balanced power and channel load, and the longest visible time are selected for comparison.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of users & 25 \\ \hline Satellite downlink transmit power \(P_{tot}\) & 24 dBW \\ \hline Satellite operating frequency band \(f\) & 30 GHz \\ \hline Minimum elevation angle for satellite & 10\({}^{\circ}\) \\ service & 10 \\ \hline Number of channels available for & 10 \\ satellite downlink \(N\) & 29 dB \\ \hline Receiving antenna gain & 5 dB \\ \hline Bandwidth \(B\) & 500 MHz \\ \hline Subscriber service bitrate \(R_{v}\) & 6 Mbps\textasci{}1 + 4 Mbps\textasci{}8 + 2 Mbps\textasci{}13 + 1 Mbps\textasci{}3 \\ \hline Buffer capacity limit \(H_{buff}\) & 60 s\textasci{}\textasci{}R_{v}\) \\ \hline Buffer handover threshold \(H_{Thre}\) & 30 s\textasci{}R_{v}\) \\ \hline Playback threshold time \(T_{b}\) of the initial & 5 s \\ buffering & \\ \hline Power occupation threshold \(H_{oc}\) & 10 s\textasci{}\textasci{}R_{v}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Some parameters of the simulation system.
## 3 Results and Analysis
### Vehicular Positioning
Figure 17: Carrier-to-Noise Ratio (C/N\({}_{0}\)) values of the R10 and 66 units during the vehicular test (22 Sep18). Figure 18: _Cont._  
The two iterations of the rectangular vehicular trajectory (Lap 1 and 2) allows one to assess height profile repeatability as a function of horizontal coordinates. This is done in Figure 19 for the R10 and the two GPSMap 66 units, all in differential code and carrier phase mode. Predictably, the R10 height repeatability is within 10 cm except where obstructions along the road result in losses of phase lock. Repeatability of the GPSMap 66 units is lower, with the RMS difference being slightly over 1 m for the green unit. These results confirm independently the validity of the mean and RMS values shown in Figure 18.  
Galileo only results: Although the number of satellite available above the horizon was only five and in a sub-optimal geometry, enough Galileo satellites were available to calculate an independent solution for part of the time as shown in Figure 20. Despite the poor geometry and signal shading, the differential code/carrier solution was available some 70% of the time during the 25-minperiod. The single point position is noisier than the GPS one due to higher DOP values and lower measurement redundancy. Figure 19: Height repeatability of units between two laps of the same trajectory during the vehicular test (22 Sep18). Figure 18: GPS single point and differential (code/carrier phase) agreement of the GPSMap 66 units with the reference trajectory during vehicular test (22 Sep18). P10 Vehicular test: The P10 was tested in the same manner as above but at a different time on the same trajectory, again with two laps. The GPS C/N\({}_{0}\) values, shown in Figure 21, exhibit the same attenuation pattern as seen for the GPSMap 66 test, although the average C/N\({}_{0}\) is lower by 4 dB-Hz, a highly significant and detrimental effect on carrier phase tracking. The comparison of the P10 and R10 reference trajectory is shown in Figure 22 During the first lap, the P10 maintained phase lock and the results are very good and similar to those of the GPSMap 66 in the previous test. During the second lap, phase lock was lost and did not recover, likely due to Android 8 operating system limitations as mentioned earlier. Hence the second half of Figure 22 shows performance in differential code mode only. Galileo coverage was available and the P10 was able to successfully track signals during various periods but only in E1 code mode. The geometry of the SVs tracked was not sufficient to calculate reliable positions. Figure 21: GPS Carrier-to-Noise Ratio (C/N\({}_{0}\)) values of the P10 during vehicular test (28 Jun18). Figure 18.0 Galileo single point and differential (code/carrier phase) performance of the 66 unit during vehicular test (22 Sep18).
## 2 Materials and Methods
### TOOCAN Dataset  
To assess the influence of mesoscale convective systems (MCS) on extreme rain events, we used the Tracking Of Organized Convection Algorithm through a 3D segmentationN (TOOCAN) database, developed by [45]. TOOCAN has been used to detect and track MCSs using infrared images from a geostationary satellite.  
This algorithm considers MCS as a system made up of a convective core where heavy rainfall typically occurs at a scale of 10 to 100 km, associated with a stratiform anvil with lighter precipitation, as well as non-precipitating cirriform cloudiness at a typical scale of 100 to 1000 km [46]. The MCSs are further distinguished by a life cycle that may be divided into three phases: commencement, maturity, and a declining phase in which the proportion of the stratiform anvils to the convective core changes with time [47].  
This algorithm works in a time sequence of infrared images to identify and track MCSs in a single 3D segmentation step, with spatial and temporal resolution. To detect the convective nuclei within a 235 K cold cloud shield, TOOCAN uses an iterative process of detection and propagation of convective seeds to segment individual convective systems.
# Detailed Design and Aerodynamic Performance Analysis of a Radial-Inflow Turbine
## 2 Design Process  
The design process will be accomplished based on the predefined parameters such as total inlet temperature, total inlet pressure, mass flow rate, total-to-static pressure ratio, and specific speed (or number of rotor revolution). Equation (1) provides a relation between specific speed and rotor rotational speed. In this equation, \(Q_{5}\) is the volume flow rate at rotor exit and \(\Delta H_{id}\) is the isentropic expansion from the stage inlet total conditions to rotor exit static conditions. The value of these parameters for the RI turbine installed on the ACM at the moment are presented at Table 1.  
\[n_{s}=\frac{\omega\sqrt{Q_{5}}}{\left(\Delta H_{id}\right)^{0.75}} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Unit** & **Value** \\ \hline Total inlet temperature & K & 364 \\ Total inlet pressure & kPa & 541.822 \\ mass flow rate & Kg/sec & 0.217 \\ total-to-static pressure ratio & - & 4.85 \\ number of rotor revolution & - & 75,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input parameters to design the RI turbine for the ACM.  
[PERSON] [7] provided the relations presented in Equations (2) and (3) to compute other basic design parameters such as total-to-static velocity ratio, \(\
u_{ts}\), and total-to-static efficiency, \(\eta_{ts}\). The present program is able to take both of these parameters from the designer and compute them by Equations (2) and (3).  
\[\
u_{ts}=0.737n_{s}^{0.2} \tag{2}\]  
\[\eta_{ts}=0.87-1.07{\left(n_{s}-0.55\right)}^{2}-0.5{\left(n_{s}-0.55\right)}^ {3} \tag{3}\]  
As a general rule, the preferred range of values for the specific speed is 0.45 to 0.75. However, the other design constraints such as Mach number, speed, mechanical integrity and size can impose the specific speed. In this work, the specific speed was determined by rotational speed due to mechanical integrity and sizing adaptation between the designed turbine and the previous turbine.  
Figure 1 illustrates the flowchart of the design process. In preliminary design, a mean-line analysis method is used to compute the performance of the turbine designed by the preliminary design code based on the input value. In an iterating process, the final dimensions of the RI turbine are defined to achieve the design input parameters. The turbine rotor is finalized in the preliminary design method, and then exported into the detailed design code. In this step, the distribution of the blade thickness and angle at camber surface are computed and the suction and pressure surfaces of rotor blades are generated to be employed in a more accurate turbine analysis, such as quasi-3D method and CFD. In the final step of the design process, ANSYS-CFX software is applied to simulate flow in the designed turbine. This workflow enables the opportunity to modify the turbine model and therefore improve turbine performance.
## 3 Results and Discussion
### Experimental
#### 3.1.5 The Effect of Nozzle Temperature Variation on Anisotropy
Figure 22 shows the SEM images comparing the two build orientations--LBO and TBO--for samples made of ABS at different nozzle temperatures. The effects of temperature changes on the mechanical properties of the printed ABS samples were investigated and the results of the ultimate tensile strength (UTS) strain at break (\(\Delta\)), elastic modulus (E), and their associated root square error percentages (SQRT error mean) of ABS polymer in two build orientations--longitudinal (LBO) and transverse (TBO)--were mentioned in Table 6.  
Figure 21: (**a**) Tensile strength, (**b**) elongation and (**c**) Young’s modulus values for ABS samples when the nozzle temperatures change from 220 \({}^{\circ}\)C to 280 \({}^{\circ}\)C for the two build orientations—LBO and TBO. Figure 22: SEM images of the 3D-printed ABS samples. (**a–d**) represent LBO and (**e–h**) represent the TBO at 220 \({}^{\circ}\)C, 240 \({}^{\circ}\)C, 260 \({}^{\circ}\)C, and 280 \({}^{\circ}\)C nozzle temperatures, respectively. Yellow marks show the unevenly fractured surfaces whereas the red marks represent the unwanted formed air voids. According to Figure 23, for parts printed at 260 \({}^{\circ}\)C in both build orientations, the mechanical anisotropy reaches its minimum. This indicates that at this temperature, optimal mechanical properties can be anticipated. Beyond this temperature, the anisotropy increased significantly for both build orientations. This insight can assist in selecting the appropriate polymer when mechanical anisotropy plays a crucial role in the design of the 3D-printed sample.
# A VHF Circularly Polarized Turnstile Antenna with Balun for Meteor Radar
## 3 Feeding Network of the Proposed Antenna  
Generally, Wilkinson power divider and baluns are all based on quarter wavelength sections of transmission lines, which indicates that they need to be longer (and have a higher loss) to operate at lower frequencies. This limits the practical low end of the frequency band to around 500 MHz to 1 GHz for microstrip baluns. On low dielectric substrates, the parts  
Figure 5: The simulated far-field results of the designed turnstile antenna. (**a**) 3D radiation plot of the proposed antenna; (**b**) Results of coplanar polarization and cross polarization in two orthogonal planes; (**c**) Results of gain and axi ratio.  
Figure 6: (**a**) The schematic diagram of the designed feeding network of the proposed antenna. (**b**) The designed flux coupled balun.  
will be larger (and usually have a lower loss), while on higher dielectric substrates, they will be smaller (and have a higher loss). In addition, crowded and complicated microstrip lines will cause distributed capacitances and inductances unexpectedly within a small size. As a result, the microstrip power divider and balun are no longer acceptable for VHF antenna fabrication.  
To ensure that the input power of all units of the antenna is the same, a T-junction, with a 2:1 power ratio is employed in the feeding network as shown in Figure 5. Figure 6 presents the T-junction. The connector is designed for a special purpose. Its appearance is a normal N-type, but it can connect with two 75 \(\Omega\) radio frequency cables. The impedance of the parallel 75 \(\Omega\) cable can be treated as \(Z_{75\text{p}}=75/2=37.5\)\(\Omega\). The length of the two 75 \(\Omega\) cables is a quarter wavelength (reference frequency \(f_{0}=39\) MHz) for a quarter wavelength impedance transformer. The mathematical symbols utilized in this part are defined in Table 2.  
The impedance of the quarter wavelength impedance transformer can be calculated using the following equation:  
\[Z_{trans}=\sqrt{Z_{0}Z_{L}} \tag{1}\]  
It is clear that:  
\[Z_{trans}=\sqrt{Z_{0}Z_{L}}=\sqrt{50\times 25}=35.35\Omega\
eq Z_{75\text{p}}=75 /2=37.5\)\(\Omega \tag{2}\]  
To address the issue of mismatch, fine-tuning of the T-match will solve this problem.  
For VHF operation frequencies, a flux-coupled balun transformer is used to reduce the impact from the common mode current. Ideally, a flux-coupled transformer could be used whenever balun functionality is required. It is well understood, relatively simple to build, provides an arbitrary impedance ratio than can be easily tuned, and provides both DC and ground isolation [15]. Figure 6 presents the designed flux-coupled balun. With the help of the flux-coupled balun, balanced feeding can be achieved.  
As for the 90\({}^{\circ}\) phase shifter, the demand can be satisfied with a quarter wavelength radio frequency coaxial cable.
# Targeting the Early Endosome-to-Golgi Transport of Shiga Toxins as a Therapeutic Strategy
## 4 Small-Molecule Inhibitors Targeting Early Endosome-to-Golgi Transport of STx, STx1, and STx2
### Compound 134  
[PERSON] et al. performed a high-throughput screen of 14,400 compounds with the goal of identifying compounds that protect against STx cytotoxicity in cells [17]. This screen identified one compound, #134 (Figure 5), which inhibits the endosome-to-Golgi transport of STxB/STx1B and protects against STx toxicity when used at a concentration of 50 \(\upmu\)M in Vero cells [17]. A second compound, #75, which inhibits toxin transport to perinuclear endosomes at a concentration of 25 \(\upmu\)M was also identified [17]. These compounds were primarily developed as tools to study retrograde trafficking, and their therapeutic potential was not explored. Indeed, both compounds impact Golgi morphology [17], which may limit their translational potential.  
Of note, the above screen subsequently led to the identification of a third compound, named Golgicide A, which also blocks the endosome-to-Golgi transport of STxB/STx1B [68]. However, Golgicide A is an inhibitor of the ARF guanine exchange factor GBF1 and has pleiotropic effects on membrane trafficking [68], which makes it unsuitable for clinical development.  
Figure 4: Role of late endosome–lysosome fusion in STx2 transport. (**A**). Under control conditions when late endosomes can undergo fusion with lysosomes, STx2B traffics from early endosomes to the Golgi. (**B**). When late endosome–lysosome fusion is inhibited, STx2B fails to exit early endosomes to traffic to the Golgi and, instead, is degraded in late endosomes. The underlying molecular mechanisms are unclear, but available evidence suggests that late endosome–lysosome fusion plays a critical role in the recruitment of retromer to early endosomes.
## 2 Field Experiment
### Measurement Plan
#### 2.3.1 LVDT Displacement Transducer Setting  
Figure 4a shows the set locations of the LVDT--displacement transducers. CDP-type [18] LVDT displacement transducers were exploited with a capacity of 25-100 mm, due to their stability, nonlinearity of 0.1% Rated Output, sensitivity of 100-500 (\(\times 10^{-6}\) strain/mm), and accuracy of \(\pm 0.1\%\). They were set at the end of the right and left end shears/post, attached to the bottom chord by a rigid magnet, demonstrated in Figure 4b,c, denoted as R1, R2, R3, R4, L1, and L2 to achieve a good deflection profile. The deflection is attributed to the composite actions between the panels' chords and the respective members. Additionally, the transducers exclusively determined the displacement measurements under static loading conditions. To achieve the profile along both spans at intervals of approximately 5.0 m, the deflection angle from a point normal to the bearing on the base plate was determined from Equation (2). The latter was diagrammatically summarized in Figure 4d. LVDT displacement transducers were set on the abutment from a point normal to the bearing on the base plate presented in Figure 4b,c. They were then connected to a data logger DC-204R for recording voltage output emanating from the LVDT Transducer. It, in this way, acted as a link to the PC, controlled by DC-7630 Tokyo Sokki as the system interface with multichannel capabilities.  
\[\theta=\sin^{-1}\left\{\frac{V_{0}-V_{1}}{r}\right\} \tag{2}\]  
where \(\theta\)--Deflection angle, \(V_{0}\)--Initial transducer reading, \(V_{1}\)--Transducer reading after loading, \(r\)--Distance between LVDT displacement transducer shaft to a point on the base plate and normal to the bearing.
# Human Activity Recognition Using CSI Information with Nexmon
## 4 The Experimental Setup and CSI Data Collection  
This section covers the preparation of the experimental setup including the hardware configuration for the Raspberry Pi 3B+, 4B, and Asus RT-AC86U. It also includes a detailed explanation of challenges and workarounds. This section discusses the system's architecture and propagation model of the LOS scenario and the human activities' CSI data collection.  
For the experimental setup, the data collection was done with a dual-band router Fritzbox (2.4 GHz and 5 GHz), Pi 3B+ and Pi 4B, and Asus RT-AC86U.