esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Fault Diagnosis Method Based on 1 DCNN and INPSO-SVM
### Fault Identification Based on INPSO-SVM Algorithm
#### 3.3.1 An Improved New PSO Algorithm (INPSO)  
In order to overcome the problem of trapping into local minimums and facilitate the convergence speed of the basic PSO algorithm, a novel population initialization method based on Tent map and an enhanced particle position update approach based on Levy flights was developed. The modified method can reduce the population degradation and enrich the population diversity. Moreover, the premature convergence is avoided by searching the neighborhood of the particles that have fallen into local optimums.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 7 Computational and Intelligent Method-Driven Development of Epoxy Resins
### Summary  
Computational modeling and machine learning (ML) demonstrate complementary advantages in epoxy resin research. Computational modeling techniques, such as molecular dynamics (MD) and finite element analysis (FEA), elucidate material micromechanisms rooted in physical principles, including crosslinked network evolution. In contrast, ML enables rapid performance prediction (e.g., fracture toughness) through data-driven pattern recognition, bypassing explicit mechanistic modeling.
## 4 Security Analysis
### Error Detection and Verification  
In the proposed security protocols, since the challenge-response and encryption are carried out through the f() function of the secret sharing between the UE and the MME and the key definition method, the UE and MME can verify if the other is a legitimate entity. In addition, in the challenge-response process, the UE can detect errors through \(\text{ad}_{i}\
eq\text{ad}_{i}\), and the MME can detect errors through \(\text{r}_{i}^{0}\
eq\text{r}_{i}^{0}\) and \(\text{r}_{i}^{1}\
eq\text{r}_{i}^{1}\).
# Power-Split Hybrid Train Configuration Design Based on a Single-Row Star Row
## 3 Preliminary Configuration Scheme Selection  
The ratio of the electromechanical path power to the engine output for the power-splitting structure should be lowered; a numerical analysis was performed to decide between the configurations that were previously proposed. The range of the planetary row member characteristic parameters was [1.5, 3], whereas the drive system transmission ratio range was [0, 5]. Equations (4) and (6) show that the ratios of the electrical power to the engine power for the two motors in the input and output distribution configurations were opposite each other. The numerical simulation results obtained by taking the absolute value of the ratio from the numerical analyses are shown in Figure 4.  
The ratio of the electrical power of the electromechanical path to the mechanical power output of the engine for the power-split configuration should satisfy \(\eta\leq 1\) and be as small as possible. The acceleration time for a train (0-100 km/h) was used as the assessment index for evaluating the power performance of each scheme. Schemes (a) and (c) in Figure 4a have \(\eta>1\) when the transmission ratio range is [0, 5], so neither of them satisfies the requirements. When the transmission ratio is in the [0, 5] range, schemes (a) and (c) in Figure 4b have \(\eta>1\). The growth rate for scheme (d) is excessive, so it restricts the transmission ratio options and the scheme falls short of the criteria. In the search for solutions with better dynamics, the schemes with acceleration times longer than 90 s (schemes (d) and (e) in Figure 4) were eliminated after the performance was assessed for the remaining configurations.  
The calculation procedure begins with dividing the speed into N equal parts; next, Equations (3) and (5) determine the speed-torque relationship for the corresponding configuration, in addition to establishing the maximum acceleration as the objective function,  
Figure 4: (**a**) Ratio of the electrical power to the engine power for the input; (**b**) ratio of the electrical power to the engine power for the output split arrangement.  
designing the control variables, and calculating the acceleration time. Finally, all the calculated hybrid train configurations and their acceleration times are shown in Figure 5.  
Figure 5 shows that the large acceleration times of the input and output split schemes (e) and (f) force their elimination. The gap between the power performance values for output split scheme (b) and input split scheme (f) is the smallest, but because the transmission ratio of scheme (b) is \(>3\), there are more restrictions on the subsequent design of the planetary row gear set. Therefore, schemes (b) and (f) in the input split configuration were chosen for the structural design in this study.
# Stress Analysis in Damaged Pipeline with Composite Coating
## 4 Discussion  
One way to extend the life of thin-walled structures is to apply thin composite elements to defective areas, which take on part of the external load and reduce the stress concentration near the defects. The assessment of the strength of such complex structures remains an urgent task for a number of industries.  
An innovative insulating composite coating for underground pipeline sections has been considered, which can be applied without decommissioning of the pipeline. According to the developer, such a coating, despite its anticorrosion properties, should also prevent the growth of fatigue cracks and mechanically strengthen the pipe surface. Therefore, the motivation of our study was the desire to obtain estimates of the impact of such an insulative composite coating on the strength of defective and defect-free sections of pipeline.  
The problem of stress concentration near small surface cracks in a pipeline reinforced with a thin spiral-wound composite coating was investigated. The coating consisted of three  
Figure 5: The dependence of the J-integral on the depth of the defect.  
Figure 6: The dependence of the stress intensity factor on the depth of the defect.  
layers (epoxy-fiberglass-polyurethane). The spiral winding of an isotropic finite-width bandage at an angle to the pipe axis caused a weak anisotropy of the \"pipe-to-coating\" structure with respect to internal pressure or longitudinal loading.  
Initially, within the classical shell theory framework, the basic stress state of a defect-free construction, which depends on the geometry and component composition of the composite coating, was investigated. The obtained characteristics of the load of the coated pipe were used for analytical-numerical analysis of stress distribution near small surface defects such as cracks. The effect of the thin composite coating and the size of the defect on the magnitude of the energy flow to the crack apex, and on the values of the stress intensity coefficients in its vicinity when the pipeline is loaded by the pressure of the pumped product under different values of the repair pressure, was studied using the finite element method.  
In general, the use of the results obtained in this study will improve the safety of pipeline systems through the use of composite materials.
## 3 Results and Discussions
### Battery  
To operate a refrigeration plant for a day, 11.5 MWh of energy is required. This energy is to be stored in the battery bank. The battery bank is designed with an allowable depth of discharge of 0.8 and provides 600 V of battery bus voltage to the inverter. With these design criteria, the total battery capacity of 23.9 KA-h and 14.3 MWh is required to operate the system. However, in designing the battery system, since the refrigeration plant is in operation throughout the day, the only energy required to operate the system during no-sun hours is to be stored in the battery bank. That means, the refrigerator is in operation during the sunshine hours for which the energy is not stored. With this approach, the total battery capacity required for the system is 10.6 MWh and 17.9 KA-h.
# Incremental Construction for Scalable Component-Based Systems
## 5 System Construction in EX-MAN
### Weather Information System
In Figure 28, the internal construction of the two weather stations LWS1 and LWS2 are shown with request/response arrows to depict the flow of data and control through the system. To get weather data from two weather stations, a request (numbered 1) is made through the SEQ1 connector. This connector splits the received request into sub-requests (one for each composed component) to LWS1 and LWS2. Once the response of the first request to the first composed component LWS1 is received then the next sub-request is made to next composed component. This sequence is also shown with the help of numbered arrows. The request to the first LWS1 is received by PIPE2 connector. This connector is basically a sequencer with an added feature of passing data from the response of one component into the request of later components. PIPE2 sends the request to SC1 to establish the satellite link and pass the response of this request (successful or unsuccessful) from SC1 to guard connector G1. If the result from SC1 is successful then the G1 allows the request to go ahead to the WS1 component. PIPE1 in WS1 makes seven requests in sequence to the connected components from component GT1 to component HM1. For each request, the respective component returns the observed and computed data to PIPE2 as a response. After getting data from these seven components, PIPE1 passes the collected data from seven components as request to component DS1 to store the data locally. Finally, PIPE1 prepares a response for the received request from G1 which is then sent to PIPE2 and then to SEQ1 as the final response from component LWS1. Similarly, SEQ1 makes the request to the second component LWS2 and collects the response and then prepares a response numbered as 50 for the request numbered 1.
## Materials and Methods  
### Study Area  
The study area covers the whole country (Japan) comprising 47 prefectures. This research introduced prefectural region-wise modeling and mapping of land cover and plant ecological communities. Since the largest prefecture (Hokkaido, Japan) was split into two parts, this research dealt with 48 prefectural regions. The location map of the study area is shown in Figure 1.  
The geography of the country is bordered by the Pacific Ocean in the east and Sea of Japan in the west, with a mountain range running through the middle of it. The country is divided into four climatic zones: subtropical and warm-temperate in the south, cool-temperate in the central area, and arctic in the north. The latitudinal variations vary from seashore to alpine regions, constituting typical vegetation zones corresponding to temperature gradients, such as Evergreen Broadleaf Forests (_Camellia japonica, Cinnamounu japonica, Quercus glauca_, etc.) distributed in the lower belt of the subtropical and warm temperate zone, Deciduous Broadleaf Forests (_Fagus crenata_, _Quercus serrata_, etc.) and Evergreen Conifer Forests (_Pinus densiflora_, _Abies firima_, etc.) distributed in the cool temperate zone, Evergreen Conifer Forests (_Tsuga diversifolia_, _Tlujopsis dolabrata_, etc.) distributed in the subalpine zone, and Deciduous and Evergreen Shrubs (_Pinus punila_, _Quercus crispula_, etc.) distributed in the alpine zone. This study area was chosen for the implementation of the DGPE classification system because of the diversity of ecological communities incorporated by the country associated with wider variations of both altitudinal and latitudinal ranges.  
The community-level vegetation types were enumerated in the research by adopting the Dominant Genus-Physiognomy-Ecological (DGPE) system developed in the previous study [69] for satellite-based classification and mapping of plant ecological communities.  
Figure 1: Location map of the study area.  
Table 1 shows the list of DGPE communities enumerated countrywide. It also includes broad categories of land cover and agricultural types (water, barren, built-up, solar panels, upland field, paddy field, cultivated pastures, and orchard).  
The DGPE communities were confirmed through field observations in several prefectures. Photographs taken during field observations are provided in Supplementary Material S1 for identifying some of the typical plant ecological communities of the study area.
## 2 Materials and Methods
### Plans Delivery  
The linac Elekta Synergy, equipped with a multileaf collimator of 40 pairs of 1 cm wide leaves, was used for dose delivery. Each plan (square field or VMAT) was irradiated three times. Before irradiation, 3D volumetric imaging was acquired for set-up correction with a three translational degree-of-freedom robotic couch using the kV cone-beam CT (CBCT) XVI 5.1. For VMAT plans, before each irradiation, a Gafchromic(r) EBT3 film was inserted between two slices containing the isocenter point.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Plan**} & \multirow{2}{*}{**Arcs**} & \multirow{2}{*}{**Beam**} & \multirow{2}{*}{**Tot MU**} & \multirow{2}{*}{**Structures Used in Plan Optimization**} & \multirow{2}{*}{**Dose Prescription Tot (frac) Gy**} \\  & & & & & \\ \hline \multirow{3}{*}{Prostate} & \multirow{3}{*}{One 360\({}^{\circ}\) arc} & \multirow{3}{*}{10 MV} & \multirow{3}{*}{896.3} & PTV; CTV; Bladder; & \multirow{3}{*}{70.2 (2.7)} \\  & & & & & \\ \hline \multirow{3}{*}{Brain} & \multirow{3}{*}{One 360\({}^{\circ}\) arc} & \multirow{3}{*}{6 MV} & \multirow{3}{*}{324.1} & PTV; CTV; Brain, & \multirow{3}{*}{24 (2)} \\  & & & & Brainstem, Ocular Bulbs & \\ \hline \multirow{3}{*}{Head and neck} & \multirow{3}{*}{Four 360\({}^{\circ}\) arcs} & \multirow{3}{*}{6 MV} & \multirow{3}{*}{838.1} & PTV 70; PTV 60; PTV 54; & \multirow{3}{*}{70–60–54 Brainstem} \\  & & & & & \\ \hline \multirow{3}{*}{Lung} & \multirow{3}{*}{One 180\({}^{\circ}\) arc} & \multirow{3}{*}{6 MV} & \multirow{3}{*}{439.1} & PTV; CTV; Lungs; & \multirow{3}{*}{50 (2)} \\  & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used for planning the volumetric modulated treatment with the radiobiological cost functions of MONACO 5.11.  
Figure 2: The Alderson phantom with the applied contours used for planning.
## 3 Theoretical Background and Improvements
### Converting MODIS GPP Estimates to Crop Yields  
To compare with the NASS Quick Stats dataset, the GPP estimated by our method and the GPP derived from the MOD17 product (hereafter referred as to the MODIS-GPP algorithm) were converted to crop yields based on a common method used in agricultural studies [18, 56]:  
\[\mathrm{Yield}=\sum_{n>N_{0}}^{n-N_{1}}\mathrm{GPP_{crop,\;DOY\;n}}\times \frac{\mathrm{HI}}{(1+\mathrm{RS})}\times\frac{1}{1-\mathrm{MC}} \tag{11}\]  
Figure 5: A simple representation of MODIS GPP (Gross Primary Production) and MR (Maintenance Respiration) components of corn pixels in Olmsted County, Minnesota.  
where GPP\({}_{\text{crop, DOYn}}\) is the estimated GPP of crops (_i.e._, corn or soybeans) for a specific DOY n. The yield was integrated for an apparent growing season, which began in mid-May (DOY N\({}_{0}\)) and ended in late-October (DOY N\({}_{1}\)). The value for N\({}_{1}\), the growing season endpoint, was set as 297 in our study, and N\({}_{0}\) was defined as 129 for corn and 137 for soybeans, as in Equation (6). RS is the root to shoot ratio, HI is the harvest index, and MC is the grain moisture content. We chose MODIS GPP instead of NPP in this formulation because our preliminary tests as similar to other studies [18] revealed that yield estimates derived from MODIS NPP were relatively low.  
The crop-type specific parameters involved in our calculations are defined in Table 2. The root: shoot ratio, which depends on the growing stages and fertilization of crops, is considered to be constant for a specific crop type when harvested. [PERSON] [56] reviewed past studies and chose the root: shoot ratio of 0.18 for corn and 0.15 for soybeans. The harvest index (HI) varies little for each crop species, except under extreme stress conditions [56]. The HI of corn is 0.53, as measured by experiments in nine US locations [57]. The soybean HI is 0.42 based on reported regression equations [56, 58]. The moisture content is estimated as 11% for corn grain and 10% for soybeans [35]. The values of these parameters vary in different studies. [PERSON] _et al._[34] observed that regional yield predictions were insensitive to the variability in the radiation use efficiency and harvest index. [PERSON] _et al._[56] attributed most of the uncertainty to the variability in the harvest index and root:shoot ratio, which resulted in an overall variability of \(\sim\)10%. The uncertainty associated with variation in these defined parameters was considered to be of secondary importance in the context of other modeling uncertainties such as the RUE variations and the MODIS data.
## 3 Applications and Ongoing Developments  
As described above, the basic principle of ultrafast ultrasound imaging was developed in the 1970s. However, practical applications of ultrafast ultrasound imaging only arise from the early 2000s. Ultrafast ultrasound imaging was first used for visualizing the propagation of a shear wave induced by acoustic radiation force applied by an ultrasonic push pulse [16]. The measurement of shear wave propagation speed is useful for evaluation of the elastic properties of biological tissues. Shear wave imaging had a great impact on the field of medical ultrasonics. Owing to the extremely high temporal resolution of ultrafast ultrasound imaging, various applications have been developed for functional imaging, such as blood flow imaging [16; 17; 18; 19; 20], evaluation of cardiac function [21; 22; 23], and vascular viscoelastic properties [24; 25; 26].  
Ultrafast ultrasound imaging has not been used practically for very long due to its limited image quality and hardware limitations. However, it attracts significant attention because its extremely high temporal resolution is of great benefit for measurements of tissue dynamic properties. Ultrafast functional ultrasound imaging is now moving to 3D imaging. Various developments are ongoing for transducer fabrication, large-scale acquisition systems, beamforming in 3D space, and estimation of 3D tissue functional properties.  
We would like to thank talented authors, professional reviewers, and a dedicated editorial team of Applied Sciences. This issue would not be possible without their contributions. In addition, we would like to give special thanks to Ms. [PERSON] from MDPI Branch Office, Beijing.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Reverberation Time
Reverberation time is the time between the instant in which the direct sound ends and the instant in which the sound level has dropped by 60 dB. The reverberation of a space has positive and negative aspects: in fact, if a certain value of the reverberation time helps to reinforce the direct sound it therefore improves listening; on the other hand, an excessive value of the sound tail compromises its quality [1;2]. For good reception, both in the case of speech and in the case of music, it is necessary to contain the decaying tail of reflected sound from a source and ensure a good spatial distribution of the sound field inside the room so that all listeners can perceive and appreciate the sound regardless of the space in which they are located [21]. An optimal reverberation time represents the right compromise between achieving a sufficient sound level in all parts of the space and reducing blending and overlap with reflected sounds caused by excess reverberation [22]. So, the optimal value of the reverberation time depends on the use to which the space is intended (Table 1). In Table 1 the parameters are defined as follows:  
* T30 is a measure of reverberation time using a 30 dB interval with the evaluation starting after the sound level has dropped by 5 dB from steady state. Since the absorption of the materials varies with the frequency, the reverberation time must also be calculated at the various frequencies. The measurement of the reverberation time can be performed according to the ISO 3382-2 [23]. The standard specifies the methods for measuring the reverberation time in rooms. In addition, it describes the measurement process, the equipment required, the coverage required and the method of evaluating the data and submitting the test report. The standard proposes two methods for measuring the reverberation time: the interrupted noise method and the integrated impulse response method. Both methods return comparable values, but the second requires more sophisticated tools [24;25]. In the interrupted noise method, a loudspeaker source must be used, and the input signal must be derived from random or pseudorandom broadband electrical noise. If a pseudo causal noise is used, it must randomly stop without using a repeat sequence. The sound source should be as omnidirectional as possible. The number of microphone stations used is determined by the coverage requested. Given the intrinsic randomness of the sound source, it is necessary to average over several measurements performed at each station to obtain an acceptable repeatability. Therefore, a minimum of three measurements should be made at each position and the results averaged. The impulse response from a source position to a receiver position in an environment is a well-defined quantity that can be measured in many ways, using for example gunshots, frecrackers, or hand claps as signals. The pulse source must be able to produce a peak sound pressure level sufficient to ensure a decay curve that begins at least 45 dB greater than the background noise in the corresponding frequency band [26].
## 2 Materials and Methods
### Sampling, Measuring pH, and Water Activity (\(a_{w}\))  
Two g of fermented WPC was collected with a spatula from each of the discs formed immediately after inoculation and after 24, 48, and 72 h in a 15 mL Falcon tube. Samples were stored in a freezer at \(-\)18 \({}^{\circ}\)C. Two g of the fermented WPC was also added to 20 mL of demineralized water for pH measurement (Metrohm, Herisau, Switzerland) in a 50 mL Falcon tube. For _S. thermophilus_, 1:10 dilutions in a peptone salt solution (Maximum Recovery Diluent, Merck, Schaffhausen, Switzerland) were prepared from 2 g of fermented WPC and spread on M17 agar (Biolife, Milan, Italy). Countable dilutions containing 10-300 colonies per plate were used for enumeration at 24, 48, and 72 h to monitor changes in the number of CFU per gram of WPC. Quantification was not possible for _A. oryzae_ and _R. oligosporus_ strains, as these are filamentous fungi.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Temperature** & **CO\({}_{2}\) [ppm]** & **Humidity [\%]** \\ \hline
25 & 611 & 57.2 \\
30 & 611 & 46.1 \\
37 & 499 & 33.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measured CO\({}_{2}\) and relative humidity for the fermentation incubators.  
Water activity was measured using an a\({}_{\mathrm{w}}\)-meter (AW SPRINT TH500, Novasina, Lachen, Switzerland) on one of the discs for each of the three strains at the inoculation and at the end of the experiment after 72 h of incubation.