esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of Sea Fog Environment Polarization Transmission Characteristics
## 3 Polarization Transmission of Marine Fog Simulation Based on RT3
### Model Validation of Polarization Transmission  
This research also demonstrates the accuracy of setting RT3 under cloudy and wet conditions. The particles are dispersed evenly. For calculation purposes, the simplified two-layer atmosphere is viewed as a single layer. The scattering coefficient of air molecules varies with the wavelength of the incoming light, and the model parameters are allocated accordingly. Settings were input according to Table 2, and then simulation results were processed and evaluated.  
The test findings are shown in this publication as a curve for ease of viewing. The test results are shown in Figure 2. The test particles follow a gamma distribution, with an effective radius of 0.20 m on average.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parametric** & **Value** \\ \hline Wavelength of incident light & 450 nm \\ Optical thickness & 1 \\ Surface albedo & 0.99 \\ The scattering coefficient & \(k=1.44*10^{-6}*\lambda\) \\ The surface temperature & 300 K \\ Angle of incidence & \(\theta_{0}=\arccos(0.2)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Validation parameter values for simulation model.  
Figure 1: Flowchart of the modeling of sea fog polarization based on RT3.  
Figure 2 demonstrates that the findings for the \(I\) and \(V\) components predicted using the sea fog polarization transmission model presented in this work are mostly similar to published results. When the optical thickness is small, there is some discrepancy between the findings shown in this article and those in the references, but as the optical thickness increases, the general trend of the results presented in this research is consistent with those in the literature.  
From the fitting of the two curves above, it is evident that the simulation results in this paper are essentially consistent with the results in the literature [16], indicating that the sea fog polarization transmission model based on RT3 described in this paper is accurate for calculating the radiative transmission of scattering media.
## 4 Discussion  
This study investigated the effects of different pyometric training protocols on body composition in professional boxers. Following eight weeks of training using Plyometric Stair Jump (PSJ) and Plyometric Reaction Box Jump (PRJ) protocols, significant reductions in both body weight and body fat percentage were observed in both groups. These findings suggest that pyometric training contributes not only to performance enhancement but also to improvements in body composition. The results are consistent with the existing literature, supporting the effectiveness of pyometric exercises, particularly in reducing body fat and achieving an optimal body composition. This section aims to interpret the current findings in light of previous research, discuss the possible physiological mechanisms underlying the results, and consider their practical implications. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Group**} & \multirow{2}{*}{\(\Delta\)**X**} & \multirow{2}{*}{**SD**} & \multicolumn{3}{c}{**95\% CI**} & \multirow{2}{*}{\(t\)} & \multirow{2}{*}{\(p\)} & \multirow{2}{*}{**Cohen’s d**} \\ \cline{5-6}  & & & & & & **Lower** & & & \\ \hline One-repetition & G1 & 24.360 & 4.451 & 27.544 & 21.175 & 17.304 & \textless{}0.001 & 1.895 \\ \cline{2-6} maximum bench press test (BP 1 RM) & G2 & 10.600 & 3.044 & 12.777 & 8.422 & 11.011 & \textless{}0.001 & 0.955 \\ \hline Maximum punching velocity & G1 & 2.570 & 0.603 & 3.001 & 2.138 & 13.463 & \textless{}0.001 & 0.973 \\ \cline{2-6} (PVmax)—Rear arm (RA) & G2 & 2.086 & 0.932 & 2.753 & 1.418 & 7.072 & \textless{}0.001 & 0.836 \\ \hline Maximum punching velocity & G1 & 2.810 & 0.533 & 3.191 & 2.428 & 16.663 & \textless{}0.001 & 1.031 \\ \cline{2-6} (PVmax)—Lead arm (LA) & G2 & 2.306 & 0.958 & 2.991 & 1.620 & 7.611 & \textless{}0.001 & 0.871 \\ \hline Yo-Yo Intermittent Recovery & G1 & 0.705 & 109.163 & 783.090 & 626.909 & 20.423 & \textless{}0.001 & 1.843 \\ \cline{2-6} Tests—Distance & G2 & 0.482 & 110.835 & 561.286 & 402.713 & 13.752 & \textless{}0.001 & 1.028 \\ \hline Yo-Yo Intermittent Recovery & G1 & 5.920 & 0.919 & 6.580 & 5.263 & 20.359 & \textless{}0.001 & 1.628 \\ \cline{2-6} Tests—VO2 max & G2 & 4.0470 & 0.931 & 4.713 & 3.380 & 13.732 & \textless{}0.001 & 1.365 \\ \hline Maximal heart rate & G1 & 25.700 & 4.877 & 29.189 & 22.210 & 16.663 & \textless{}0.001 & 1.619 \\ \cline{2-6} (HR max) & G2 & 16.700 & 8.794 & 22.991 & 10.408 & 6.005 & \textless{}0.001 & 1.431 \\ \hline \hline \end{tabular}  
* Group 1—Plyometric Stair Jump group, Group 2—Plyometric Reaction Box Jump group, \(\Delta\)X—difference in means (FT-IT), SD—standard deviation, \(t\)—Student’s T value, \(p\)—level of probability, CI—confidence interval, Cohen’s d—effect size value.  
\end{table} 
Table 8: Statistical analysis of motor and functional fitness parameters—paired Student’s test between final and initial tests.
## 3 Results and Discussion
### Chemical Compatibility
#### 3.4.2 Chemical Compatibility after 100 Thermal Cycles  
Figure 7 displays the XRD patterns of Sample S8 before and after 100 thermal cycles. The peaks of the thermally cycled sample show one-to-one correspondence to the peaks of the sample before thermal cycling, which verifies the chemical compatibility conclusion drawn above. This also suggests the thermal stability of the composite material under the conditions of this set of experiments; see Section 3.6 for more discussion.
###### Abstract  
Active seat suspensions can be used to reduce the harmful vertical vibration of a vehicle's seat by applying an external force using a closed loop controller. Many of the controllers found in the literature are difficult to implement practically, because they are based on using unavailable or difficult and costly measurements. This paper presents both simulation and experimental studies of five novel, simple, and cost-effective control strategies to be used for an active seat suspension in order to improve ride comfort at low frequencies below 20 Hz. These strategies use available and measurable feedforward (preview) information states from the vehicle secondary suspension, as well as feedback states from the seat suspension, together with gains optimised to minimise the occupant vibration. The gains were optimised using a genetic algorithm (GA), with a fitness function based on the seat effective amplitude transmissibility (SEAT) factor. Constraints on the control force and the seat suspension stroke were also included in the optimisation algorithm. Simulation and laboratory experimental tests were carried out to assess the performance of the proposed controllers according to the ISO 2631-1 standard, in both the frequency and time domains with a range of different road profiles. The experimental tests were performed using a multi-axis simulation table (MAST) and a physical active seat suspension configured as a hardware-in-loop (HIL) simulation with a virtual linear quarter vehicle model (QvM). The results demonstrate that the proposed controllers substantially attenuate the vertical vibration at the driver's seat compared with both a passive and a proportional-integral-derivative (PID) active seat suspension and thus improve ride comfort together with reducing vibration-linked health risks. Moreover, experimental results show that employing both feedforward information and feedback vehicle body and seat acceleration signals in the controller provides isolation performance gains of up to 19.5 dB over the human body sensitivity frequency range and improves the ride comfort in terms of the SEAT factor and the weighted root mean square (RMS) seat acceleration by at least 25% when compared with a passive system, irrespective of vehicle forward speed.  
active seat suspension; preview controller; HIL; genetic algorithm +
Footnote †: journal: Applied Soft. 2018
###### Abstract  
A novel two-stage n-PSK partitioning carrier phase recovery (CPR) scheme for circular multilevel quadrature amplitude modulation (C-mQAM) constellations is presented. The first stage of the algorithm provides an initial rough estimation of the received constellation, which is utilized in the second stage for CPR. The performance of the proposed algorithm is studied through extensive simulations at the forward error correction bit error rate targets of \(3.8\times 10^{-3}\) and \(1\times 10^{-2}\) and is compared with different CPR algorithms. A significant improvement in the combined linewidth symbol duration product (\(\Delta\
u T_{s}\)) tolerance is achieved compared to the single-stage n-PSK partitioning scheme. Superior performance in the \(\Delta\
u T_{s}\) tolerance compared to the blind phase search algorithm is also reported. The relative improvements with respect to other CPR schemes are also validated experimentally for a 28-Gbaud C-16 QAM back-to-back transmission system. The computational complexity of the proposed CPR scheme is studied, and reduction factors of \(24.5\mid 30.1\) and \(59.1\mid 63.3\) are achieved for C-16 QAM and C-64 QAM, respectively, compared to single-stage BPS in the form of _multipliers_\(\mid\)_adders_.  
c Article Two-Stage n-PSK Partitioning Carrier Phase Recovery Scheme for Circular mQAM Coherent Optical Systems
# An Efficient Approach to Manage Natural Noises in Recommender Systems
## 3 The Proposed Approach of Managing Natural Noises
### Detection of Natural Noise
#### 3.1.2 The Subcategories of Users and Items
Positive users are the opposite of negative users. They are generous and tend to provide items with high ratings. Average users like to offer medium ratings to items. Similarly, Figure 0(b) illustrates the three distances from item \(i\) to the three kinds of items given in Table 2. We classify items into three subcategories: No-Preferred, Av-Preferred, and Preferred. The rules of classifying items are as follows:  
* If \(d(\omega_{i},\omega_{No-Preferred-Extreme})\) is the minimum value among \(d(\omega_{i},\omega_{No-Preferred-Extreme})\), \(d(\omega_{i},\omega_{Ap-Preferred-Extreme})\) and \(d(\omega_{i},\omega_{Preferred-Extreme})\), the item \(i\) is regarded as a No-Preferred item;
* If \(d(\omega_{i},\omega_{Ap-Preferred-Extreme})\) is the minimum value among \(d(\omega_{i},\omega_{No-Preferred-Extreme})\), \(d(\omega_{i},\omega_{Ap-Preferred-Extreme})\) and \(d(\omega_{i},\omega_{Preferred-Extreme})\), the item \(i\) is regarded as a Av-Preferred item; \begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{Special virtual users} & Negative-Extreme & \(x_{u}=1,y_{u}=0,z_{u}=0\) & Only provides low ratings for items \\  & Average-Extreme & \(x_{u}=0,y_{u}=1,z_{u}=0\) & Only provides medium ratings for items \\  & Positive-Extreme & \(x_{u}=0,y_{u}=0,z_{u}=1\) & Only provides high ratings for items \\ \hline \multirow{2}{*}{Special virtual items} & No-Preferred-Extreme & \(x_{i}=1,y_{i}=0,z_{i}=0\) & Only get low ratings from users \\  & Av-Preferred-Extreme & \(x_{i}=0,y_{i}=1,z_{i}=0\) & Only get medium ratings from users \\ \cline{1-1}  & Preferred-Extreme & \(x_{i}=0,y_{i}=0,z_{i}=1\) & Only get high ratings from users \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The special virtual users and items, which both include three different types. * If \(d(\omega_{i},\omega_{Preferred-Extreme})\) is the minimum value among \(d(\omega_{i},\omega_{No-Preferred-Extreme})\), \(d(\omega_{i},\omega_{No-Preferred-Extreme})\) and \(d(\omega_{i},\omega_{Preferred-Extreme})\), the item \(i\) is regarded as a Preferred item. Here, No-Preferred items are more likely to get low ratings from users. On the contrary, most users will offer high ratings to Preferred items. In the case of Av-Preferred items, they tend to get medium ratings. Based on above rules, the subcategories of users and items are summarized in Table 3.  
**Remark 1**. : _If there are two or more than two minimum values, this user (or this item) is neglected, and we do not classify it as any subcategory._
## 2 Materials and Methods  
### Data  
The data used in the two-leaf light use efficiency model (TL-LUE) model to simulate GPP in China over the period 1981-2012 were as follows: (i) grid-based meteorological data, including \(R_{\mathrm{g}}\), \(R_{\mathrm{d}}\), air temperature (\(T_{\mathrm{a}}\)), minimum temperature (\(T_{\mathrm{amin}}\)), maximum air temperature (\(T_{\mathrm{amax}}\)), and vapor pressure deficit (VPD) at the daily scale; (ii) 8-day remotely sensed leaf area index (LAI); and (iii) remotely sensed land-cover data.
# Generating Low-Earth Orbit Satellite Attitude Maneuver Profiles Using Deep Neural Networks  
[PERSON]  
Division of KOMPSAT-7 Program, Korea Aerospace Research Institute, Daejeon 34133, Republic of Korea; [EMAIL_ADDRESS]; Tel.: +82-42-860-2974
# Electromagnetic Radiation Shielding Using Carbon Nanotube and Nanoparticle Composites
## 5 Potential Applications and Associated Challenges of Shielding Materials
### Applications
Each layer can be designed to conduct heat away from the laser spot and to produce debris to partly block the laser for a short time. Shielding materials to combine with CNT fabric may include reactive borides, boron nitride nanotubes, radiation-sensitive coatings, and metal-organic frameworks. Blocking High Power Microwaves (HPMs). CNT fabric composites are highly effective for attenuating and reflecting microwaves and can carry a structural load. HPMs damage electronic components and can be blocked by designing CNT hybrid fabric with sufficient thickness. Boundary effects and layering can also help to reduce the thickness of the material to block HPMs. Blocking Space Radiation from Solar Flares. A problem that threatens thousands of satellites surrounding earth is solar storms. Solar storms (flares and coronal mass ejections) release streams of charged particles such as electrons and protons at high speeds. X-rays and ultraviolet light can also accompany solar flares. These eruptions from the sun can interact with Earth's magnetic field and produce a geomagnetic storm, which is a major disturbance of Earth's magnetosphere or the space environment surrounding earth. The geomagnetic storms also cause electrical flows within the Earth's ionosphere. Operation of the space internet constellation is affected by the geomagnetic storms. Radiation shielding in small satellites cannot completely protect the satellite from the powerful geomagnetic storms. Particles from the storm can penetrate satellite hardware and damage electronic components, including circuits, solar panels, and sensitive instruments. During a geomagnetic storm, the Earth's atmosphere can also heat up and expand, increasing the density of the thermosphere. This increased density increases the drag on satellites in a low earth orbit, potentially causing them to fall to earth sooner than expected. Lightweight CNT-based hybrid fabrics can be designed to increase the shielding of radiation from solar storms. Multifunctional applications. CNT nanoparticle composite materials can be designed to be multifunctional or multi-use. Examples are using a CNT fabric with nanoparticles as a thermal insulation material and a radiation-shielding material for simultaneous electronics cooling and shielding applications. Another example is using CNT nanoparticle composite materials to increase electrical conductivity and provide radiation shielding of structural composite materials that use epoxy or polyimide matrices. Another application is using WS\({}_{2}\) nanoparticles that act as a dry lubricant in CNT fabrics for simultaneous lunar dust shielding and cosmic radiation shielding. Other applications are possible based on the wide selection of combinations of nanoparticles available. Improved modeling of CNT nanoparticle composites is needed to fully understand the effects of the nanoparticle size, shape, concentration, distribution, and interaction with other particles and nanotubes.
# Hybrid NHPSO-JTVAC-SVM Model to Predict Production Lead Time
## 3 Lead-Time Prediction Based on NHPSO-JIVAC-SVM
### Data and Preparation
#### 3.1.4 Performance Metrics  
To measure prediction accuracy, this study applied certain widely used regression prediction performance metrics: root-mean-square error (RMSE), mean absolute error (MAE), and MAPE, as shown in Table 4. Here, \(N\) is the sample size, \(y_{i}\) is the actual value, and \(\hat{y}_{i}\) is the predicted value.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metrics** & **Calculation** \\ \hline RMSE \((y,\hat{y})\) & \(\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2}}\) \\ MAE \((y,\hat{y})\) & \(\frac{1}{N}\sum\limits_{i=1}^{N}|y_{i}-\hat{y}_{i}|\) \\ MAPE \((y,\hat{y})\) & \(\frac{100\%}{N}\sum\limits_{i=1}^{N}\left|\frac{y_{i}-\hat{y}_{i}}{y_{i}}\right|\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Regression prediction performance metrics.  
Figure 6: Flow chart of the prediction models.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**NHPSO-JTVAC** [] & **Parameter Setting** \\ \hline Number of particles & 20 \\ \hline Number of search dimensions & 3 \\ \hline Range of SVM parameters & \(C[10^{0},\,10^{3}]\) \\  & \(\epsilon[10^{-3},\,10^{0}]\) \\  & \(\sigma[10^{-2},\,10^{1}]\) \\ \hline \hline \(c^{Iter}\) & Changing from \(c^{1}=c_{i}=\)0.5 to \\  & \(c^{Iter_{min}}=c_{f}=0\) \\ \hline Maximum number of generations & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter setting of the NHPSO-JTVAC algorithm.  
Lower values of MAPE, MAE, and RMSE indicate higher accuracy of the model, meaning that the prediction results are more convincing. According to the MAPE metric, which has been widely applied to evaluate industrial and business data, when MAPE < 10%, it can be considered as highly accurate forecasting; when 10% < MAPE < 20%, it can be considered as good forecasting; when 20% < MAPE < 50%, we can see it as reasonable forecasting; when MAPE > 50%, the interpretation is inaccurate forecasting [42].
## 3 Results
### Interventions and Outcome Measures  
The studies analyzed encompassed a wide range of interventions and outcome measures aimed at assessing PIP joint stiffness. Among surgical procedures, total PIP arthroplasty with different types of implants, open reduction and stabilization of fractures, percutaneous A1 pulley release for the treatment of flexion contractures, and vascularized joint transfer from the foot for PIP joint reconstruction were identified. Complementary orthopedic and rehabilitation treatments included the use of extension and progressive mobilization orthoses, specific splints for correcting swan-neck deformity and trigger finger, and neuromuscular taping for pediatric PIP strain recovery. Experimental interventions covered biomechanical and joint stability studies on fractures and dorsal dislocations, PIP arthrodesis simulations with thermoplastic splints at different angles, and dynamic external fixation for stabilizing unstable fractures. Additionally, systematic reviews and comparative analyses facilitated the evaluation of therapeutic strategies, contrasting conservative and surgical approaches while reviewing post-traumatic complications.  
To assess intervention effectiveness, the studies employed a variety of outcome measures. The range of motion (ROM) was the primary metric used, enabling the evaluation of flexion and extension improvements before and after treatment, with particular attention to differences between active and passive ROM. Joint stability was also assessed through displacement thresholds in dorsal dislocations, while grip strength was measured at various PIP fusion angles. Pain reduction and patient satisfaction were relevant parameters, considering post-treatment pain relief and satisfaction levels after surgical and orthotic interventions in relation to residual mobility. Furthermore, postoperative complications and secondary outcomes were analyzed, including complication rates, persistent joint stiffness, and the need for reintervention in cases where conservative treatment was unsuccessful. Overall, the reviewed studies implemented a variety of therapeutic approaches and evaluation methodologies, with clinical trials and biomechanical studies providing the most precise measurements. However, while systematic reviews offered a comprehensive perspective, their interpretation was constrained by the methodological heterogeneity of the included studies.
## 3 Results
### Phase-3: Finite Element Modeling and Validation  
The meshed FE model of the tensile specimen gage with strand width to inter-strand spacing ratio of 1:1 contained 2,045,737 triangular elements and 3,424,882 nodes. The stress distribution in the model is presented in Figure 6. The average stress in the 0\({}^{\circ}\) and 90\({}^{\circ}\) oriented strands was 16.57 N/mm\({}^{2}\) and 1.36 N/mm\({}^{2}\), respectively. The higher stress in the 0\({}^{\circ}\) oriented strands is on account of them being aligned along the direction of loading. The average reaction force determined by averaging the force on all the nodes at the fixed end was 113 N, corresponding to a strain of 0.59 acting across a cross-sectional area of 5.09 mm\({}^{2}\), resulting in an effective elastic modulus of 37.51 N/mm\({}^{2}\) (Equation (1)).  
For the validation study, the effective modulus estimated from the meshed FE model was compared to the average experimental elastic modulus of biopltted specimens (n = 3) of the same porous geometry. The experimental stress versus strain plots and the three tensile measures are presented in Figure 7a. Stress undulations were observed after the yield point, similar to the trend seen in bulk specimens of 0\({}^{\circ}\) configuration. The difference between the FE-estimated and experimental elastic moduli (37.51 N/mm\({}^{2}\) and 34.63 N/mm\({}^{2}\), respectively) was 8.3%. A comparison of experimentally-determined tensile properties of non-porous bulk specimens of 0\({}^{\circ}\) and 90\({}^{\circ}\) configurations and porous specimens with 1:1 ratio of strand width to inter-strand spacing is presented in Figure 7b. On average, the tensile properties of the porous specimens were 44% and 13% less than those of 0\({}^{\circ}\) and 90\({}^{\circ}\) configurations, respectively.  
Figure 5: Plots of stress versus strain for three bulk specimens of (**a**) 0\({}^{\circ}\) configuration and (**b**) 90\({}^{\circ}\) configuration after Phase-2 tensile testing, represented by lines with different types of line style.