esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Setup Using the Explosive Driven Shock Tube
### Control of the Projectile Flight Trajectory
#### 2.2.2. Projectile Flight Trajectory and Dispersion of Impacts Using the EDST  
In this phase, the setup of Figure 2 previously explained in Section 2.1 is used. The flight trajectory of 5 and 7 mm diameter projectiles propelled by 10, 20, and 30 g C4 explosive charges is tracked. It should be highlighted that a reproducible oriented path of the projectiles allows for a better extraction of their velocities, arrival times, and consequently \(\Delta t\). For a better visualization of the passage of the blast wave front and the projectile in the field of view (FOV), images are processed using the particle image velocimetry (PIV) visualization technique [30]. This optical measurement technique is able, not only to provide quantitative data for the multiphase and transient characteristics of flows, but also to determine the spatiotemporal evolution of the shock wave and the projectile in all different flight phases.  
A typical result from the high-speed camera HSC (Video S2) for the flight trajectory of a 7 mm diameter projectile propelled from the explosion of 10 g C4 at the entrance of the EDST is shown in Figure 6. The projectile is masked by a black circle. This allows to clearly distinguish its position in each frame. Images 1 and 2 in Figure 6 show the first and the last appearance of an almost plane blast wave front in the FOV, respectively. Image 3 represents the interference of reflected blast waves from the surrounding of the experimental setup. At an advanced time step after the passage of the blast wave, the projectile appears for the first time in the FOV. After that, as shown in images 4 to 6, an oriented trajectory can be tracked as visualized by the black dashed line from the exit of the tube till the end of the FOV. This straight path is maintained until impact of the projectile on the target. The images depict the magnitude of the displacement D (in pixel), which represents the distortion of the air density due to the passage of the shock wave. D is shown in colors where red represents the highest values. The main features of the flow field around the supersonic freely flying projectile, such as the spherical and the detached shock wave in its front, have been captured with a good spatial resolution. The position of the impact point is recorded after each detonation. It is found that in more than 92% of the performed experiments, the projectiles impact the target within an area of 165 mm \(\times\) 165 mm as shown in Figure 7. The results are shown as a function of the different charge masses (10, 20, and 30 g C4) and projectile diameters (5 and 7 mm).  
Figure 5: Representation of the dispersion of projectile impacts on the target using varying spheres (5, 7, and 8 mm) attached either to only a detonator or to a 10 g C4 spherical explosive charge.  
As a first conclusion, the experimental results reveal the capability of the different projectiles to maintain an oriented path from the moment of detonation until impact on the target. The different impacts lie within the cross-sectional area of the EDST.
## 3 Results and Discussion
### Cathepsins Activity  
Increasing the temperature of the marinating process from 2 to 7-12 \({}^{\circ}\)C increased cathepsin D activity by only 1-4% (Table 1). In the case of cathepsin B, activity increased by 23-40% (most at 7 \({}^{\circ}\)C), while the highest increase in cathepsin L activity of 28-43% occurred in marinades ripened at the lowest temperature. Refrigeration temperature strongly decreases protease activity, but fish cathepsin activity remains at around 20% [25]. Even the application of negative temperatures (superchilling) does not inhibit cathepsin activity. Cooling close to 0 \({}^{\circ}\)C was found to contribute to an increase in cathepsin L activity in the sarcoplasm, but also a decrease in activity in the cellular structure, because of the tight tangle of more muscle fibers [26]. [PERSON] [4] noted that a decrease in temperature from 12 to 5 \({}^{\circ}\)C reduced the total proteolytic activity (GPA) in marinades, and the proportion of cathepsin D in GPA, particularly up to day 7. The cathepsins tested are endopeptidases, with cathepsin B (B1) also showing dipeptidase activity, which may be related to its higher activity in meat and brine under higher marinating temperatures. In optimal conditions, cathepsin D releases very short tetrapeptides and, together with cathepsin B, acts on myofibrillar proteins, while cathepsin L and, to a lesser extent, cathepsin B are specific to collagen, which has a greater effect on meat texture than myofibrillar proteins.  
In contrast, the effect of temperature on the activity of catheps D and L in the brine was different from that in the meat. At 12 \({}^{\circ}\)C, cathepsin D activity was 67% lower compared to 2 \({}^{\circ}\)C (Table 2). Cathepsin B activity increased from 1194 to 1463 U when the temperature was increased by 10 \({}^{\circ}\)C, while cathepsin L had the highest activity at 7 \({}^{\circ}\)C. Cathepsins are present in the brine because of their diffusion from the muscle tissue, which increases using frozen-thawed raw material and a high concentration of salt [5].
# A Study of Cislunar-Based Small Satellite Constellations with Sustainable Autonomy
## 5 Visual Simulations and Analysis (Constellation Scenarios)
### Earth-Based Constellation Simulation
#### 5.1.3 Effect Observed on Small Satellites Due to SRP (Earth's Side)  
This issue has been taken greater care of due to its critical significance and importance at this juncture of congested operations in lower Earth orbits. Two parameters are considered--1. SRP force and 2. SRP Torque--to observe their differences in critical (high influence of SRP) and normal conditions (minimum SRP). Figure 9 shows the variations in each of them.
## 5 Dataset Manipulation
### Data Scaling  
In the field of machine learning, it is common practice to pre-process the data by scaling the features to eliminate redundancy, improve stability, and aid convergence of algorithms. In this study, all the features used underwent scaling before being utilized. This step is essential to ensure that the features are on the same relevance level and can be compared effectively. In the literature, various techniques exist for feature scaling, but two main methods are commonly employed in machine learning. One of these methods is _normalization_, also known as Min-Max scaling. It involves shifting and re-scaling the values of the features so that they fall within the range of 0 to 1. This scaling technique helps to ensure that all features have equal importance and are comparable across the same scale.  
\[X^{\prime}=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{17}\]  
In the Equation (17), the symbol \(X\) represents the current value of the feature, \(X_{max}\) and \(X_{min}\) denote the maximum and minimum values of the feature column in the training set, respectively, and \(X^{\prime}\) represents the final value of the scaled feature. Normalization is applied when the data distribution is not assumed to follow a Gaussian distribution. Another commonly used scaling technique is _standardization_, which involves centering the values around the mean and dividing them by the standard deviation. This technique reshapes each value within its column using a common scale without losing any information or altering the differences in the ranges. Standardization is particularly useful when dealing with features that exhibit a Gaussian distribution.  
\[X^{\prime}=\frac{X-\mu}{\sigma} \tag{18}\]  
In the Equation (18), the symbol \(X\) denotes the current value of the feature, \(\mu\) and \(\sigma\) represent the mean and standard deviation of the feature column in the training set, respectively, and \(X^{\prime}\) represents the final value of the scaled feature. Standardization does not impose any specific range on the values, unlike normalization. It is particularly useful when dealing with data that follows a Gaussian distribution. The decision to use either normalization or standardization depends on the specific problem and dataset at hand. There is no definitive rule for choosing between the two techniques. It is generally recommended to try both approaches and compare their performance on the given task. In this work, both normalization and standardization were applied to evaluate their suitability for the case under examination. Ultimately, normalization was chosen because some features in the datasets had NaN (Not a Number) values after standardization. By opting for normalization, it was possible to retain as much information as possible from the original datasets and avoid discarding any features during the analysis.
## 3 Methodology
### Test Workbench  
Figure 5 shows the experimental laboratory workbench used for the measurement and estimation of the speed, torque, and efficiency. It is essentially composed of one three-phase IM (manufacturer WEG, model W22 Plus, nominal power 5 HP, nominal rotation of 1725 rpm) (1), the function of which is to provide torque to the set; one support with two bearings (2), one torque transducer (3), and one continuous current motor (manufacturer Varimot, model 132S, nominal power 1.48 HP, nominal rotation 1800 rpm) (4), which generates a braking torque in the shaft through a continuous voltage applied via a voltage regulator.  
The reference values for the speed and torque were collected by direct measurement using a digital tachometer and a torque transducer, respectively. These values were compared to the estimated speed and torque values to validate the proposed method.  
To measure the torque, an HBM T40B-200 torque transducer with flange coupling, nominal torque capacity of 200 Nm, 0.05% full-scale accuracy, and torque measurement capability at rotations up to 20,000 rpm was used. The speed measured directly at the motor shaft was obtained using a Minipa optical tachometer model MDT-2238B, which can operate in two ways: by contact and by photodetection. Due to the unfeasibility of using the contact mode, the optical reading mode was selected. In the photo mode, the measurement can be performed in the range of 2.5 to 100,000 rpm at a detection distance between 50 and 500 mm.  
Figure 6 presents the scheme of the workbench with controllable torque with all the components involved in the process, from the activation of the IM to the control of the load applied in the shaft by the DC motor, with data collection performed by a personal computer (PC) via a Universal Serial Bus (USB) connection plugged into the DAQ. The bench components are: (1) variable transformer, (2) rectifier, (3) tachometer, (4) direct current generator, (5) torque transducer, (6) IM, (7) current transformers (CTs), (8) power transformers (PTs), (9) AC speed driver, (10) signal conditioning, (11) acquisition system, (12) notebook, (13) DAQ USB-6215, and (14) resistor bank.  
For the shaft torque estimation using the air gap method, the voltages \(V_{ab}\) and \(V_{ca}\) were measured using a grain-oriented transformer, and the currents \(I_{a}\) and \(I_{b}\) were obtained using ACS/12 ELCTR-20A Hall effect sensors manufactured by Alegro. The signal from these sensors go through signal conditioners connected to an NI DAQ 6215 acquisition system manufactured by National Instruments, and the data are stored in a PC for analysis. In the experiments, the acquisitions in the workbench are performed simultaneously with the sound acquisition system developed in this study.  
Figure 5: Workbench for torque tests in IM.
## 4 Results and Discussion
### ESC Voltage and Helium Pressure Control  
To verify that the ESC voltage caused a large number of particles to be generated on the wafer surface, we sprayed powder on the surface of the edge ring around the ESC to simulate worst-case particle deposition conditions. In the experimental process, the ESC voltage was set to four conditions of 2500 V, 2000 V, 1700 V, and 1500 V to reduce the chucking force of the wafer to analyze and verify the number of particle contamination, as shown in Figure 8. The experimental results are shown in Figure 9. As the setting of the ESC voltage output parameter decreased, the particle contamination on the wafer also reduced. Therefore, the electric field, which varies with the ESC voltage output, is closely related to the number of suspended particles. Therefore, not only the electric field generated by the radio frequency discharge will cause the particles to be suspended. In the case of the ESC output voltage, particles also caused a suspension mechanism. On the other hand, in order to verify whether the number of particles is also related to the helium pressure of the ESC voltage, we performed experiments at the same ESC voltage of 2500 V using helium pressures of 0, 15, and 30 Torr. From the particle map results in Figure 10, it is shown that the size of the helium pressure does not affect the number of particles. Therefore, using a lower ESC voltage (i.e., lower chucking force) during wafer processing can reduce particle deposition on the wafer.  
Figure 8: (1) ESC voltages (2500, 2000, 1700, and 1500 V) and (2) helium pressures for the ESC (30, 25, 20, and 15 Torr).  
Figure 7: Particle distribution on the wafer in each chamber part when the ESC voltage was 2500 V.
## 2 Proposed Synchronization Algorithm Based on LPA
### Comparison between EL-Based and LPA-Based Synchronization Algorithms  
It can be seen from the comparison between the two timing error formulas of Equations (15) and (32) that the proposed timing error detector omits the derivative operation. The modified synchronization algorithm with LPA is constructed in Figure 1b. Compared with the EL synchronization algorithm shown in Figure 1a, the EL MF banks are saved, and the amount of MFs is reduced by 2/3. Based on the TPT, FPT with \(L^{\prime}=2\), and SSP with \(p^{\prime}\)-value phase state partition (\(p^{\prime}=4\)), the trellis state number \(N_{state}\) is decreased from 512 to \(p^{\prime}M^{L^{\prime}-1}=16\), and the specific complexity comparison of the above two synchronizers for the three multi-\(l\) CPM schemes is provided in Table 1. Note that EL-based synchronizer requires 3 MF banks for on-time-, early-, and late-MF paths with \(3N_{h}M^{L^{\prime}}=96\) MFs. Here, \(L^{\prime}=2\) brings lower performance loss compared with \(L^{\prime}=1\)[16]. Thus, the \(L^{\prime}=2\) is also set for LPA-based synchronization. Table 1 shows that under the same 16-state trellis, the LPA-based estimator from Equation (32) requires no subtractor and only 1/3 MFs of the EL-based method due to the reduction in the derivative in Equation (15). Note that the LPA-based estimator of the timing error signal, with \(MN_{state}=64\) branches of the trellis, has 64 multipliers more than the EL-based estimator. To avoid those multipliers usage, we propose the simplified LPA (SLPA) estimator from Equation (33) with the sign of the estimated \(\hat{b}_{n}\). It is much simpler than the other two estimators shown in Table 1. With a more complex trellis, the SLPA-based estimator can save more MFs and multipliers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & **Number of States** & **Number of MFs** & **Multiplier** & **Subtractor** \\ \hline \(h=\{\frac{4}{6},\frac{5}{16}\}\) with LPA & 16 & 32 & 64 & 0 \\ \(h=\{\frac{5}{16},\frac{6}{16}\}\) with LPA & 16 & 32 & 64 & 0 \\ \(h=\{\frac{9}{16},\frac{10}{16}\}\) with LPA & 16 & 32 & 64 & 0 \\ \(h=\{\frac{4}{16},\frac{5}{16}\}\) with SLPA & 16 & 32 & 0 & 0 \\ \(h=\{\frac{4}{16},\frac{5}{16}\}\) with EL in [26;32] & 16 & 96 & 0 & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Complexity comparison of the LPA-based, SLPA-based, and EL-based estimators.
# On the Use of LoRa Technology for Logic Selectivity in MV Distribution Networks
## 4 Enabling Technologies for Logic Selectivity
Due to its intrinsic characteristics, therefore, LoRa technology could be used to achieve logic selectivity since: (i) it overcomes the problems that arise from wired communications; (ii) it does not require third-party network infrastructure as required for GSM/3G/4G/LTE-M devices, but only the creation of a wireless link between substations in a Machine2 Machine (M2M) connection framework; (iii) it reaches transmission ranges considerably greater than those guaranteed by WLAN networks with embedded battery-saving systems that can operate autonomously for more than 10 years; and (iv) it is suitable for providing automation and intelligence to the existing power grid infrastructure. Therefore, the authors' goal is to verify that LoRa technology can be effectively used as a communication system and the technology enabling logic selectivity as an alternative to the technologies previously proposed and analyzed in the literature.
## 3 Results and Discussions
### Hysteretic Curves and Skeleton Curves
#### 3.1.1 Number of Layers  
Varying from the ordinary beam or column members, the width of the wall pier studied is much larger than that in the other two directions, so the stress and deformation vary with the positions of the pier. The force-displacement curve of the pier top reflects the global performance of the pier. However, the characteristics of its local position are closely related to the vertical mesh as a result of the nonlinear deformation being concentrated at the key position of the maximum moment. In the cantilever pier model, nonlinearity occurs in the lowest row of elements. To further acquaint the effect of the division of layers and meshing in the multi-layer shell element on the mechanical properties of wall piers, the reaction of the pier bottom corresponding to the horizontal lateral loading point in Figure 4, that is, the reactions at Node8 and Node18, are drawn into hysteretic curves and skeleton curves with the pier top displacements, as shown in Figure 6.  
It can be discovered from the curves at Node8 and Node18 that the loading in the positive and negative directions is basically symmetrical and the bottom of the wall pier is in the elastic stage before cracking and there are minor differences in the slope of each hysteretic curve. With the increase in horizontal loading displacement, the stiffness of the pier begins to degrade and the hysteretic area increases, but the shape is still a bow with an obvious pinch effect. The bearing capacities of models decrease rapidly after reaching the peak load in the light of the skeleton curves at Node8. The platform stages of the skeleton curves at Node18 are more obvious compared with those of Node8, which states that the pier is destroyed when the displacement grows steadily to a certain stage after its yielding.  
Figure 5: Lateral force–displacement hysteretic curves at pier top: (**a**) Layer divisions; (**b**) Mesh divisions.
# PS-CARA: Context-Aware Resource Allocation Scheme for Mobile Public Safety Networks
## 6 Simulation Results and Discussions
### Users Throughput for PS-CARA Scheme  
The performance of the proposed PS-CARA scheme is compared with respect to the average (50%) and edge throughput (5%). The simulation results shown in Table 4 clearly demonstrate the effect of adding the mPC to the conventional MBS system. The simulation results clearly demonstrate that by deploying mPC, the average and edge throughput increases by around 10.3% and 32.8%, respectively. Moreover, we also notice that by deploying a greater number of mPC per MBS, the average and edge throughput of the system increases. The average and edge throughput increases by deploying a greater number of mPC because it reduces the call blocking probability because of having additional resources for the emergency users. However, there is a chance of an increase in co-channel interference, but that is countered by using the PS-CARA scheme which has the capability of reducing the interference because of introducing power control.
###### Abstract  
In recent years, Global Navigation Satellite System (GNSS) radio occultation (RO) has become a critical observation system for global operational numerical weather prediction. Constellation Observing System for Meteorology, Ionosphere, Climate (COSMIC) 2 (COSMIC-2) has been a backbone RO mission for NOAA. NOAA also began to purchase RO data from commercial sources in 2020. To ensure the consistent quality of RO data from different sources, NOAA Center for Satellite Applications and Research (STAR) has developed capabilities to process all available RO data from different missions. This paper describes the STAR RO processing systems which convert the pseudo-range and carrier phase observations to excess phases and bending angles (BAs). We compared our COSMIC-2 data products with those processed by the University Corporation for Atmospheric Research (UCAR) COSMIC Data Analysis and Archive Center (CDAAC). We processed more than twelve thousand COSMIC-2 occultation profiles. Our results show that the excess phase difference between UCAR and STAR is within a few centimeters at high altitudes, although the difference increases towards the lower atmosphere. The BA profiles derived from the excess phase are consistent with UCAR. The mean relative BA differences at impact height from 10 to 30 km are less than 0.1% for GLObal NAVigation Satellite System (GLONASS) L2C signals and Global Positioning System (GPS) L2C and L2P signals. The standard deviations are 1.15%, 1.15%, and 1.32% for GLONASS L2C signal and for GPS L2C and L2P signals, respectively. The BA profiles agree with those derived from European Center for Medium-range Weather Forecast (ECMWF) reanalysis version 5 (ERA5). The Signal-to-Noise-Ratio (SNR) plays an essential role in the processing. The STAR BA profiles with higher L1 SNRs (L1 at 80 km) tend to yield more consistent results than those from UCAR, with a negligible difference and a smaller deviation than lower SNR profiles. Profiles with lower SNR values tend to show a more significant standard deviation towards the surface during the open-loop stage in the lower troposphere than those of higher SNR. We also found that the different COSMIC-2 clock solutions could contribute to the significant relative BA difference at high altitudes; however, it has little effect on the lower troposphere comparisons given larger BA values.  
radio occultation; bending angle; excess phase; precise orbit determination; COSMIC-2 2
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
###### Abstract  
The exorbitant increase in the computational complexity of modern video coding standards, such as High Efficiency Video Coding (HEVC), is a compelling challenge for resource-constrained consumer electronic devices. For instance, the brute force evaluation of all possible combinations of available coding modes and quadtree-based coding structure in HEVC to determine the optimum set of coding parameters for a given content demand a substantial amount of computational and energy resources. Thus, the resource requirements for real time operation of HEVC has become a contributing factor towards the Quality of Experience (QoE) of the end users of emerging multimedia and future internet applications. In this context, this paper proposes a content-adaptive Coding Unit (CU) size selection algorithm for HEVC intra-prediction. The proposed algorithm builds content-specific weighted Support Vector Machine (SVM) models in real time during the encoding process, to provide an early estimate of CU size for a given content, avoiding the brute force evaluation of all possible coding mode combinations in HEVC. The experimental results demonstrate an average encoding time reduction of 52.38%, with an average Bjontegaard Delta Bit Rate (BDBR) increase of 1.19% compared to the HM16.1 reference encoder. Furthermore, the perceptual visual quality assessments conducted through Video Quality Metric (VQM) show minimal visual quality impact on the reconstructed videos of the proposed algorithm compared to state-of-the-art approaches.  
HEVC; video coding; encoding complexity; Support Vector Machines; intra-prediction; Quality of Experience; VQM +
Footnote †: journal:  
_Future Internet_