esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Parameter Optimization for Spectral Broadening
The effect is even more dramatic when the full material dispersion is introduced by using virtual femtosecond laboratory software Lab2 [22]. The resulting _B integral_ (Figure 1, continuous line) shows a pronounced decrease even for crystals thinner than a few mm. To find the optimal experimental conditions for a specific crystal thickness and a given minimal beam waist, we varied the input pulse energies and the type of bulk materials (Figure 2). To get direct information on the achieved broadening we calculated the compressibility factor, which is the Fourier limit of the broadened pulse spectrum after propagating through the material divided by the initial Fourier limited pulse length (25 fs for all simulations). Figure 2a shows, that for a fixed crystal length an increase of the input pulse energy increases the achievable compressibility factor. This behavior can be understood from Equations (1) and (3). The self-focal length scales roughly inversely with the square root of the pulse power \(P\), while the pulse  
Figure 1: Amount of self-phase modulation (SPM) for 25 fs, 308 nm, 3 UV pulses broadened in a CaF\({}_{2}\) crystal. Calculated B-integral at increasing levels of theory for varied crystal length. The input beam waist is set to a value for which the self-focal length is 1.25 \(\times\) the crystal length. intensity is directly proportional to the power. Therefore, the compressibility factor can be estimated to roughly scale with \(\sqrt{P}\). **Figure 2.** Compressibility factor (ratio between input and output Fourier limit) for a 25 fs pulse. Results for varied input energies, pulses centered at 308 nm, and a CaF\({}_{2}\) crystal (**a**) and for different crystals with 266 nm pulses of 3 \(\upmu\)J energy (**b**). The input beam waist is set to a value for which the calculated self-focal length is \(1.25\times\) the crystal length. In Figure 2b the simulation results for different materials are shown. The \(n_{2}\) values of MgF\({}_{2}\) (\(1.5\times 10^{-16}\) cm\({}^{2}\)/W), fused silica (\(7.8\times 10^{-16}\) cm\({}^{2}\)/W) and Sapphire (\(6.0\times 10^{-16}\) cm\({}^{2}\)/W) at 266 nm were taken from the literature [19, 23]. The simulations show that a larger \(n_{2}\) generally leads to an increase of the compressibility factor. However, the differing dispersion of the materials can change this general trend (see crossing of MgF\({}_{2}\) and Sa curves in Figure 2b). The simulations show that due to the pronounced dispersion in the UV, it is favorable to use short crystals with tight focusing. This is unlike spectral broadening in gases, where meter-long propagations are needed due to the much lower \(n_{2}\) values. 1 mm thick crystals lead to compressibility factors between 2 to 2.5 for Fourier limited Gaussian pulses with energies of a few \(\upmu\)J. The required beam waist radius lies in the order of 100 \(\upmu\)m. These parameters are experimentally sensible.
## 3 Results and Discussion
### Model Interpretation
Using the results obtained in the present paper and considering the radius of nonlinear load influence around the well, the mentioned model could be supplemented, describing the recovery of permeability and production rate by implementing nonlinear loads in the case of the production of crude oil containing paraffins and asphaltenes. The potential application of the results obtained can be found in the field of oil production, especially to enhance oil recovery at wells with low formation pressure and paraffins presented in crude oil concentration. Those are usually the reasons for blockage of the pore space in the near-wellbore zone since there is a flocculation phenomenon observed near the well that degrades the permeability and decreases the well rate. The results obtained in the present study would be useful for understanding the effect of vibrations on rock permeability. In order to unblock the pore space, well tools may be developed that could help production engineers fight paraffin precipitation in near-wellbore rock.
# Optimal Design and Control of MMC STATCOM for Improving Power Quality Indicators
## 4 Modular Multilevel Converter-Based STATCOM (MMC STATCOM)  
In order to simplify the analysis of the proposed network, the upper and lower arms of the MMC STATCOM are proposed to be equivalent to one power unit, which includes two IGBTs shunted by opposite-connected diodes and a capacitor forming a bidirectional chopper. Figure 8 shows a simplified one-phase equivalent circuit of the proposed MMC STATCOM. Point P in the circuit refers to the positive DC-link, while point N refers to the negative DC-link. In addition, the following assumptions are provided for more analysis,  
1. The DC-bus voltage \(V_{d}\) is constant without a ripple.
2. There is a uniform distribution of the DC input current \(I_{d}\) among the three phases.
3. The switching losses in the main components of the circuit are ignored.  
Figure 8: The one phase equivalent circuit of the MMC static synchronous compensator (STATCOM).  
The terminal voltage \(\rm u_{a}\) and output current \(\rm i_{a}\) are assumed to be pure sinusoidal:  
\[u_{a}=U\sin(\omega t) \tag{7}\]  
\[i_{a}=I\sin(\omega t+\varphi_{i}) \tag{8}\]  
where \(U\) denotes the phase voltage peak value, \(I\) denotes the phase current peak value, \(\omega\) is the angular frequency and \(\varphi_{i}\) is the phase shift between voltage and current.  
For a symmetrical three-phase system, the phase current \(i_{a}\) is divided equally between the two legs. Thus, the resulting share from the upper and lower legs can be identified as follows [27]:  
\[i_{Pa}=\frac{1}{2}I[K_{I}+\sin(\omega t+\varphi_{i})] \tag{9}\]  
\[i_{Na}=\frac{1}{2}I[K_{I}-\sin(\omega t+\varphi_{i})] \tag{10}\]  
where \(K_{I}\) is the current coefficient that is expressed as  
\[K_{I}=\frac{2}{3}\frac{I_{d}}{I} \tag{11}\]  
The differential equations of the upper and lower arms of Figure 8 can be introduced as follows:  
\[C\frac{dV_{c}}{dt}=s_{1}i_{Pa} \tag{12}\]  
\[C\frac{dV_{c}}{dt}=s_{2}i_{Na} \tag{13}\]  
\[L\frac{dip_{a}}{dt}+Ri_{Pa}=\frac{V_{d}}{2}-U\sin(\omega t)-s_{1}V_{c} \tag{14}\]  
\[L\frac{di_{Na}}{dt}+Ri_{Na}=\frac{V_{d}}{2}+U\sin(\omega t)-s_{2}V_{c} \tag{15}\]  
where \(V_{c}\) is the voltage of the capacitor, \(s_{1}\) and \(s_{2}\) are the upper and lower leg switching functions, \(L\) denotes the AC inductance of the leg inductor and \(R\) is the equivalent resistance of the leg inductor.
## 3 Results and Discussion
### Discussion  
The developed low-cost control system enabled the use of stepless speed control with the accuracy as indicated in the analysis.  
The results showed that the use of an automated spindle rotation control provides a stable cutting-speed. In conventional systems, there is sometimes a need to reduce the cutting speed by 20%. By using an inverter for speed control of the electric motor, it was possible to achieve stepless control of the spindle rotation.  
As shown in Figures 16 and 17, machining within the full range of lathe speeds was done with only two gears (G1-D3 and G1-D2). The deviation increased with the diameter, as shown in the graph presented in Figure 17.  
The deviation in accuracy was only in excess of 4% at low electric motor speeds of 600 rpm, due to the 8-bit converter controller. The error value dropped below 4% to reach a value below 2% for a nominal speed of 1500 rpm and for speeds of 2400 rpm 1%.  
This level of error reduces the processing efficiency and significantly changes the cutting conditions, which may in turn have an impact on the surface condition and cutting-edge durability.  
Further improvements in the present system can be made by applying feedback loops to enable maintenance of the set speed based on the actual spindle speed.  
In addition, automatic measurement of the workpiece diameter or the distance of the tip of the cutting edge from the center of rotation is advisable for automatic selection of the speed for a previously established cutting-tool material and a workpiece with a preset optimum cutting speed.  
It should also be mentioned that further improvements to these machines can be introduced through modernization of the feed systems with a servo-drive type or by using a step motor; the servo-drive is especially convenient due to its low cost.  
The cost of the control system, without taking into account the cost of the computer was approximately $ 175 with power of 1.5 kW [36]. The ATMEGA microprocessor may also be used instead of a computer. Only the price of the inverter changes with a higher drive power. For example, the cost of LG/LS--7.5 KW 3F, SV075 IG5A-4 inverter is $ 465 [37]; far less expensive than CNC machines with infinitely variable speed controls.  
The stepless speed control enables optimum cutting speeds to be selected based on the material type of the edge and the material to be machined for each workpiece diameter. Reported literature and practice allow us to achieve the desired edge durability [30; 32; 38] and surface condition [27; 39; 40]. Achieving a constant cutting speed is particularly desirable in turning faces with a continuous change in the turning diameter. The developed configuration is adapted to the development of continuous on-line control of the machining speed, which requires the equipment to be equipped with a measurement system for the machining diameter.
## 3 Results
Based on the results obtained, the dimensionless thermal resistances \(\rho_{\text{pg}}\) and \(\rho_{\text{pp}}\) can be expressed as a function of all of the considered dimensionless controlling parameters through the following empirical correlations derived by a multiple regression method:  
\[\rho_{\text{pg}}=3.7\cdot\left(L^{-0.036}H^{0.0025}W^{0.07}K^{-0.045}-K^{-0.053 }\right) \tag{11}\]  
\[\rho_{\text{pp}}=0.016\cdot L^{4.26}H^{-0.155}W^{-2.33}K^{-0.66}+0.72\cdot L^{0.867}-0.5\cdot H^{0.086} \tag{12}\]  
The standard deviation of the errors of Equations (11) and (12) are 3.2% and 3.5%, respectively, with a percent range of error of \(\pm 5\%\) and a 95% level of confidence, as shown in Figures 13 and 14. Figure 8.0 Distributions of the dimensionless heat loss from the supply pipe \(q_{\text{s}}\) versus \(L\) using \(H\) as a parameter. Figure 8.1 Distributions of the dimensionless heat loss from the supply pipe \(q_{\text{s}}\) versus \(L\) using \(W\) as a parameter. Figure 8.2 Comparison between Equation (12) and the numerical data obtained for \(\rho_{\rm pp}\). Figure 8.3 Distributions of the dimensionless heat loss from the supply pipe \(q_{\rm s}\) versus \(L\) using \(K\) as a parameter. Figure 8.4 Comparison between Equation (11) and the numerical data obtained for \(\rho_{\rm pp}\). Notice that the results obtained by the employment of these correlations differ by less than 5% from the numerical solutions computed by [PERSON] and [PERSON] [9] for a buried pre-insulated double-pipe system, as reported in Table 4, which demonstrates that Equations (11) and (12) can safely be used to predict the heat losses from underground twin pipes in a wide range of application for district heating systems. Finally, the dimensionless optimal center-to-center pipe spacing \(L_{\text{opt}}\) can be correlated to the controlling parameters by the following empirical equation:  
Figure 8.5  
with a 3.1% standard deviation of errors, with a percent range of error of \(\pm 5\%\) and a 98% level of confidence, as shown in Figure 8.6
## 2 Materials and Methods
### Activity Measurement of AOB and NOB  
The activity of enriched AOB and NOB was measured through batch tests evaluating the effect of FA. These tests were conducted in 100 mL, incubated at 25 \(\pm\) 1 \({}^{\circ}\)C, and stirred at 100 rpm using a magnetic stirrer. Biomass was collected from the inoculum of PN1 and PN2, as well as from the suspended sludge on day 50. The sludge was washed three times with a phosphate buffer solution (0.14 g/L KH2 PO4 and 0.75 g/L K2 HPO4) to remove any remaining substrates, and then inoculated at a concentration of 1 g VSS/L. During the reaction, sufficient oxygen was supplied with an air pump to maintain a DO concentration above 0.5 mg/L. For measuring AOB activity, 50 mg-N/L of (NH4)2 SO4 was added to the medium, and for measuring NOB activity, 50 mg-N/L NaNO2 was added. The composition of mineral medium for both tests was the same, consisting of 482.4 mg/L NaHCO3 (as CaCO3), 70 mg/L KH2 PO4, 17.5 mg/L CaCl2-2H2O, 12.5 mg/L MgSO4, and 9.0 mg/L FeSO4.7H2O. The batch tests were performed in duplicate. AOB activity was determined by the consumption rate of NH4+, while the NO2- production rate was used to determine NOB activity.
## 3 IWOA Simulation Experiments and Result Analysis  
### IWOA Experimental Setup  
To analyze and test the performance of the Improved Whale Optimization Algorithm (IWOA), the following experimental setup was designed:  
1. Compare IWOA with other swarm intelligence algorithms to verify its optimization effectiveness.
2. Conduct a sensitivity analysis of the adaptive inertia weight of IWOA, determining the maximum and minimum inertia weights as 0.8 and 0.1, respectively.
3. Perform statistical analysis of the optimization performance of each algorithm.
4. Analyze the computational complexity of the IWOA algorithm. The compared algorithms included: the classic baseline algorithm Particle Swarm Optimization (PSO); and the modern swarm intelligence algorithms Sparrow Search Algorithm (SSA), Marine Predators Algorithm (MPA), Secretary Bird Optimization Algorithm (SBOA), and Pied Kingfisher Optimizer (PKO). The original WOA was also included for comparison to evaluate the performance improvement of the proposed enhancement strategy. The control parameters for all algorithms were set as shown in Table 2, with the parameters of the compared algorithms consistent with those in the original literature.  
All algorithms in this experiment were implemented using MATLAB R2020a (.m files) and run independently on a Windows 10 system with an Intel i5 processor and 16 GB of RAM.  
Eight benchmark test functions were selected to evaluate IWOA's convergence speed and optimization capability. The expressions, search ranges, optimal values, and types of the test functions are listed in Table 3. The population size for all algorithms was set to 30, with a total of 500 iterations, and each algorithm was independently run 50 times.
# Review on the Service Safety Assessment of Main Cable of Long Span Multi-Tower Suspension Bridge
## 1 Introduction
Meanwhile, coupled effects of temperature, electrolyte corrosion solution, and time-varying dynamic loads induce dynamic contact and slip characteristics between the main cable and saddle under effects of temperature and electrochemical corrosion, and thus affect the anti-sliding safety between main cable and the saddle. Therefore, load-bearing safety and anti-sliding safety are two key issues in service safety assessment of main cable of long-span multi-tower suspension bridge. Lots of scholars have carried out relevant researches on tribo-corrosion-fatigue of the main cable, contact and slip behaviors of the saddle, and service safety assessment of the main cable as follows.
# Image Dehazing Algorithm Based on Deep Learning Coupled Local and Global Features
## 4 Experimental Results and Analysis
### Running Time  
The above experimental results have verified the effectiveness of the method in this paper, and in order to further verify the performance of the method in this paper in terms of efficiency, the average running times of different algorithms were obtained by counting the above experimental times, as shown in Table 3. In the algorithm running efficiency experiments, we used the trained algorithm models on the uniform haze test set SOTS and the non-uniform haze test set NTIRE2020-Dhaze for forward inference, to input the foggy images into the model to infer the fog-free images, and calculate the average time of different algorithms to remove the haze, where the model with a similar number of algorithm parameters in [15] was chosen to be similar to this paper as dehazeformer-t for the calculation. As can be seen from the table, the running time of the method in this paper was significantly faster than that of the traditional defogging algorithms in [5,6], and the deep learning methods in [15]; compared with the deep learning defogging algorithms in [11,12], the method in this paper also had an advantage in running speed. Because the separable convolution was used to build the multi-head attention mechanism, the computation cost was greatly reduced and the running speed of the program was improved.
# The Influence of Effective Mass on the Striking Force of Lead Jab and Rear Cross Punches of Boxers
###### Abstract  
Background: Modern combat sports, including boxing, categorize participants by body mass to ensure fairness and safety. The effective mass, or the ability to maximize body mass during a punch, significantly impacts striking force. This study aims to explore the relationship between effective mass and striking force in lead jab and rear cross punches of boxers. Material and methods: Thirteen male boxers with an average body mass of 90.6 kg and average height of 184 cm participated. The study employed an AMTI MC12-2K force plate (AMTI, Watertown, MA, USA) and Noraxon Ultium EMG sensors (Noraxon, Scottsdale, AZ, USA) to measure impact force and acceleration. Each boxer performed five maximum-force strikes with both lead jab and rear cross techniques. Results: The rear cross punch generated significantly higher ground reaction force (1709.28 \(\pm\) 486.62 N) compared to the lead jab (1182.56 \(\pm\) 250.81 N). However, effective mass values were similar for both punches: lead jab (18.95 \(\pm\) 5.29 kg, 21.51% of body mass) and rear cross (18.50 \(\pm\) 5.56 kg, 21.04% of body mass). Higher body mass and longer training tenure positively correlated with higher effective mass. An inverse relationship was found between fist acceleration and effective mass. Conclusions: Effective mass plays a crucial role in punch biomechanics, with similar utilization between lead jab and rear cross punches despite the latter's higher force. Training focused on optimizing body mass utilization and refining punch techniques can enhance punch effectiveness.  
biomechanics; boxing; effective mass; combat sport +
Footnote †: journal: Applied Sciences
# Review of 3D Imaging by Coded Aperture Correlation Holography (COACH) +
## 2 System Architectures
### Coded Aperture Digital Holography
#### 2.1.1 Coded Aperture Correlation Holography Architectures
Therefore, for \(N\) independent complex PSH holograms and object holograms recorded under statistically independent CPMs, averaging over \(N\) reconstructions theoretically increases the SNR by \(\sqrt{N}\)[53]. On the other hand, the averaging procedure decreases the time resolution, since instead of 3 intensity patterns, \(3N\) intensity patterns are required to reduce the background noise. The results of averaging with 5, 10, 15 and 20 samples are shown in Figure 6c-f, respectively [52].
## 7 Conclusions  
(1) A measurement system for determining mass characteristics is developed based on the three-point measurement method and the constant torque measurement principle, and it can greatly improve the accuracy of the pico-satellite test, reduce the cost, and reduce the measurement time.  
(2) The centroid and moment of inertia of the standard workpiece are measured via a comparison between the simulation and the experiment. The cause of the deviations is analyzed, and the error of the measurement is compensated. The deviation of the centroid and moment of inertia are 0.041 mm and \(1.6\times 10^{-3}\) kgm\({}^{2}\), respectively. The measurement results can satisfy the measurement design requirements.  
(3) The measurement system was applied to measure the centroid and moment of inertia of the pico-satellite. The measurement results show that the centroid deviation is less than 1 mm, and the moment of inertia deviation is less than 1.5 \(\times\) 10\({}^{-2}\) kgm\({}^{2}\). The measurement results show that the precision of the measurement system can satisfy the measurement design requirements.  
**Author Contributions:** [PERSON] made substantial contributions to design of the work, designed the experiment, collected data and implemented analysis, and drafted the work. [PERSON] made substantial contributions to collected data and implemented analysis, and drafted the work. [PERSON] made substantial contributions to the conception of the work, and revised it critically for important intellectual content.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
The rapid growth of not just mobile devices but also Internet of Things (IoT) devices has introduced a new paradigm in mobile networks. This evolution and the continuous need to provide spectrum efficient, high data rates, low latency, and low energy consumption radio access networks have led to the emergence of fifth generation (5G) networks. Due to technical and economical limitations, the satellite air interface is expected to complement the 5G terrestrial air interface in the provision of 5G services including IoT communications. More importantly, it is on this premise that the 5G satellite air interface is expected to provide network access to IoT devices in rural and remote areas termed Internet of Remote Things (IoRT). While this remains an interesting solution, several radio resource management issues exist. One of them, spectrum management, in the 5G satellite as it affects IoRT communications, remains unclear. Hence, the aim of this paper is to investigate and recommend the spectrum management scheme that will be most suitable not only for Human-to-Human communications but also Machine-to-Machine communications in 5G satellite networks. In order to conduct this investigation, a new dynamic scheduling scheme that will be suitable for such a scenario is proposed in this paper. The investigation is conducted through simulations, using throughput, delay, spectral efficiency, and fairness index as the performance metrics.  
5G; satellite; IoRT; spectrum management; packet scheduling +
Footnote †: journal:  
_Future Internet_**2019**, _11_, 257; doi:10.3390/f11120257