esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Standard of Evaluation
#### 4.3.3 Figures of the Results of Some Ablation Experiments  
In Figure 13, the first two lines are inshore scenes. The remaining two lines are offshore scenes. The red detection box is the ship target not detected by models. As shown in the figure, the detection accuracy of \(Auto-T-YOLO_{32\times 32}\) is higher than that of \(Auto-T-YOLO_{64\times 64}\) in rows 1 and 2. The red ground represents the ship target that the model failed to detect. Therefore, it can be seen that the \(Recall\) of \(Auto-T-YOLO_{32\times 32}\) in the inshore scene is also higher than that of \(Auto-T-YOLO_{64\times 64}\). This demonstrates how we use the MHSA mechanism to extract target semantic information from global to local, allowing critical aspects to be highlighted while reducing the impact of redundant information. In the preattention stage, setting the threshold to Auto--T-YOLO (32 \(\times\) 32) is also more appropriate for remote sensing image features and considerably enhances accuracy. When comparing the images of YOLO-MHSA and Auto-T-YOLO in rows 1 and 2, it can be seen that for some small-size ship targets that are reasonably close, the MHSA mechanism will incorrectly classify the two ship targets as one because it focuses on global information. Therefore, we can demonstrate the need to include a preattention stage. Following automatic classification, different feature extraction and fusion models can be employed to target features of varying sizes to improve accuracy and efficiency. Although the overall accuracy for the offshore images in rows 3 and 4 is already strong, there are still numerous missed targets in the YOLOv4-MHSA model. This also demonstrates that the MHSA mechanism's detection accuracy is not optimal for small-size objects. In the next step, this is where we need to work on making improvements. However, based on the visual ship detection findings, it is concluded that Auto-T-YOLO is the best in terms of detection results.  
Figure 13: Images of ablation experiments result.
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 4 Proposed Approach
### Cluster Head Election Strategy  
In the traditional K-means clustering algorithm, the election of CHs is inappropriate. To improve the performance and lifetime of FANETs, it is important to select the appropriate cluster head of each cluster. In a cluster, an appropriate cluster head should meet the following criteria: (1) in order to improve the success rate of information exchange between cluster head and cluster members and the utilization of network bandwidth resources, we should choose the node with high adaptive node degree; (2) in order to improve the lifetime of FANETs and avoid data transmission failure due to cluster head death, we should choose the node that has more energy left; (3) to ensure that the topology of the network does not change too much after each round of cluster maintenance, the distance between the node \(i\) and its cluster head should not be far.  
To elect the CH satisfying the above conditions, in this subsection, we will focus on the optimization of the CH election, where the adaptive node degree, node remaining energy, and the Euclidean distance between nodes are jointly considered.
## 5 Results and Discussions
### Temperature Profile  
Figure 4 displays the effect of the Eckert number and radiation parameters on the temperature profile. Growth in both parameters offers enhancement in the temperature profile. Since friction between fluid particles rises due to escalation in the Eckert number and heat flux also rises due to incoming radiations, this increases the temperature profile. Figure 5 shows the temperature profile with a variation of the Brownian motion parameter and Biot number, that is, temperature profile augmentations by incrementing the Brownian motion parameter and Biot number. Since the escalation of random movement of particles spreads the hot particles in the plate's vicinity, this leads to temperature profile growth.  
Figure 3: Variation of porosity parameter and inertia coefficient on velocity profile using \(N=25\), \(\beta=3\), \(M=0.1\), and \(E_{1}=0.01.(\mathbf{a})\)\(F_{r}=0.1\), \((\mathbf{b})\)\(\lambda=0.1\).
## 4 Analysis of Economical Structural Framing
### Fabrication Cost Analysis  
Finally, the person-day input required to produce one cubic meter of PMR units was calculated (Figure 8). As of 2022, the unit labor cost for intermediate engineers in Korea, where the case study was conducted, is 175 USD per person-day (Statistics Korea). If one person-day is required per square meter, the construction cost is increased by 12%. Additional labor needed for framing may hinder the application of prefabrication.  
Type A required a relatively low level of 0.35 person-day per cubic meter. Assuming a total construction cost of 1500 USD, the construction cost increased by 4%. Type B required a person-day input of 0.32, which was slightly lesser than that of Type A. The material input for the system channel of Type B was 61% of that of Type A; however, no significant differences were observed between their labor costs. The decrease in material input alone did not considerably affect the person-day input in the manufacturing stage because of parallel activities, such as the preparation of the lower pedestal of the frames to construct a 1-unit module and matching the horizontality of the modules.  
Figure 8: Manufacturing cost analysis (person-day input for manufacturing phase).  
Figure 7: Material cost analysis (system channel input for manufacturing).  
The highest personal-day input was required in the production stage for Type C and Type D. The material input for constructing Type C frames was relatively low; however, a decrease in material input did not lead to a decrease in the person-day input of production. In Type C, structural frames had to be constructed for fixing ducts, and using the basic system channels and both methods simultaneously was expected to be disadvantageous in terms of fabrication effort. In Type D, the most labor cost was invested in the fabrication stage, which is believed to have been affected by the increase in system channel material input.  
In terms of fabrication effort, Type E was the most advantageous. The fabrication effort required for Type E was expected to be relatively high because the shape of each multi-trade prefabrication module was slightly changed. However, the analysis results indicated that it required the lowest person-day input: 0.20. Because there was no work on the upper member and most of the work was done on the lower pedestal, the high convenience of fabrication increased the fabrication efficiency.
## 3 Results and Discussion
### Liquid-Liquid Equilibrium (LLE) Data
The results showed that \(D_{1}\) increases with lactic acid concentration in the aqueous phase while the trend is reverse for \(S\) (Table 3 and Figure 4a). In relation to each other, \(D_{1}\) is inversely proportional to \(S\) (Figure 4b). This suggests that the extraction of lactic acid using AE will involve a balance or a compromise between the size of an extraction unit (which depends on amount of solvent) and the number of extraction units needed for the process. Lactic acid is chemically similar to propionic acid with respect to number of carbon atoms in its molecule. However, in terms of extractability with AE, lactic acid is similar to acetic acid (Figure 4b). This could be due to the presence of hydroxyl functional group in the lactic acid molecule, which makes it more hydrophilic compared to propionic acid. Among the many solvents that have been considered for lactic acid extraction are those that are listed in Table 4. For the majority of the solvents, a diluent is necessary to improve \(D_{1}\) and consequently \(E(\%)\). Nevertheless, even for solvents that are utilized diluents, extraction efficiencies were not guaranteed as evident by \(E(\%)\) values as low as 16.7%. The highest efficiency obtained was 96.3%, which uses an alamine 336-octanol solvent system for lactic acid extraction. The solvent considered in this study did not utilize any diluent and in terms of \(E(\%)\), and is similar to tri-\(n\)-butyl phosphate with no diluent. The main difference, however, is the biodegradability and non-toxicity (to microbes) of AE which makes it a good candidate solvent for extractive lactic acid fermentation. According to [PERSON] et al. [1], due to the toxicity of most solvents listed in Table 4, their application for extractive lactic acid fermentation will require the utilization of immobilized cells or enzyme systems. This can possibly be avoided by using AE as the extraction solvent. Figure 4: (**a**) Distribution coefficient, \(D_{1}\), and separation factor, \(S\), against the mass fraction of lactic acid in the aqueous phase at \(T\) = 308.15 K and \(P\) = 0.1 MPa (atmospheric pressure); (**b**) \(S\) as a function of \(D_{1}\). Data for acetic, propionic, and butyric acids were calculated from [PERSON] et al. [28]. The relatively low extraction efficiency of AE could possibly have an effect on the economic feasibility of the separation process. There are however, other factors such as surfactant recycling, number of extraction stages, and fermentation mode (i.e., batch, semi-batch, and continuous) that need to be considered to evaluate the overall economic viability of the process. In a similar study by [PERSON] [52], the economic assessment of the extraction of butyric acid using AE as solvent resulted in a payback period of 2.5 years, indicating the economic attractiveness of the process. For the case of lactic acid, further evaluation is needed to estimate the economic feasibility of the extraction process.
# Evaluation of Accuracy of Traffic Flow Generation in SUMO
## 2 Simulation
### Trip Generation  
To simulate accurate traffic demand, we should know where people live and where they work. These data were all collected from public information. According to the Chinese government demographic census [19], the population in the Jianghan Zone is 683,500 (2010), and of those, the working population is calculated from working age (1960) and the unemployment rate in Wuhan [20]. According to the educational system in China, school-age is divided into primary school age (6-12) middle school age (13-15) and high school age (16-18). The number of residents in each community can be found on different estate websites, which also provides the number of households and the vacancy rate. For example, for the Wankecheng district located between Jiangzhen Street and the third ring road in the Jianghan area, it can be found at the LIANJIA website ([[https://wh.lianjia.com/](https://wh.lianjia.com/)]([https://wh.lianjia.com/](https://wh.lianjia.com/)), accessed on 12 March 2021) that the building numbers are 11 and the total number of apartments is 2906. Other information, like opening times and students' count of each school, numbers of staff in each workplace and also bus line interval time and stations are gathered through individual websites. Some of the data sources are listed in Table 1.  
Figure 1: Manually adjustment according to actual road structure.  
The geographical population distribution information is written in a statistic file in the form defined by SUMO. Along with the road network file, the statistic file is generated as trip files by ActivityGen. The statistic file contains general information such as inhabitants, households, car rate, etc., population and work position distribution, school information, distribution parameters, etc. Based on the information in the statistic file, ActivityGen allocates traffic demand on the road network to form a trip file. The trip file describes when each vehicle goes from which starting point to which destination point.  
In addition to commuting demand, traffic demand data also include shopping demand, entertainment demand, visiting friends or relatives, etc. These types of traffic demands are often non-directional in time and space. Therefore, these traffic demands are described as random trips in simulation. The incoming traffic and outgoing traffic of the selected road network are also described in statistic files.  
As the busiest zone in the city of Wuhan, the Jianghan Zone has a population of 683,500 within 33.43 square kilometers, which makes the population density 20,445 people/km\({}^{2}\). This huge population density makes the difficulty of travel obvious. Fortunately, the construction of public transportation is conducted pretty well, and the public transportation-sharing rate has raised close to 60% [21]. Due to the public information provided by Wuhan Transportation Committee [22], the traffic demand calculated from geographic population distribution data is verified by the total travel times of the whole city.
# Enhanced Effective Filtering Approach (eEFA) for Improving HSR Network Performance in Smart Grids
## Proposed eEFA Approach
### Discovering Passive QuadBox Rings
#### 3.2.1 Unidirectional Communication  
For unidirectional communications, eEFA uses the same process for discovering passive QuadBox rings as EFA. In other words, eEFA uses the locking message sent by the destination DANH to discover and lock passive QuadBox rings, as shown in Figure 3.
# Automatic Creation of Heuristic-Based Truck Movement Paths for Construction Equipment Control
## 3 Construction Equipment (Truck) Movement Path Creation  
### Heuristic Investigation for Generating Temporary Work Roads  
The location of a temporary road for trucks during a construction phase is an important factor, as distance can affect the overall cost of the process. However, despite these important factors, only a limited number of studies can be found in relation to this problem.  
Figure 2: Site sub-division, soil distribution, and 2 nd cell decomposition (c.f. [PERSON] et al., 2021).  
In addition, Korea's specifications for temporary roads are somewhat different for each design standard, and only basic contents related to road installation are presented. For this reason, most of the time, temporary roads are planned and installed by experienced workers at the start of construction. Thus, for this study, 18 construction managers and experienced workers working at 9 companies (Figure 3) were surveyed to analyze their know-how and heuristics regarding the installation of temporary roads. Here, the important factors when deciding temporary roads are summarized in Table 1. Next, five important factors were selected and weight values for each of these factors were set based on the AHP (analytic hierarchy process) technique. These values can be seen in Table 2, where the weight values were 42%, 12%, 14%, 17%, and 15% for transport distance, slope, cut soil, civil complaints, and distance to structure, respectively. Here, the consistency index (CI) was 7.7%.
# Real-Time Simulator for Dynamic Systems on FPGA
## 4 Development of the Prototype and Results
### Simulations in RTSDS-FPGA  
For the second set of results, simulations were performed with the embedded RTSDS-FPGA. For this implementation, bench tests were performed using the equipment shown in Figure 22, which included the ML605 kit, an MSO9104A oscilloscope (Agilent Instruments) for observing the data, and an RC circuit to extract the analog data from the PWM signal generated by the platform. The analog values obtained by the oscilloscope (Figure 22) were processed for better viewing by reducing the number of signals sampled.  
Figure 23 details the circuit used as a DAC, consisting of a digital-to-analog converter integrated with a low-pass filter. This circuit is responsible for converting the digital signals generated by the FPGA into analog signals suitable for analysis and measurement. The DAC setup includes resistors and capacitors selected to ensure appropriate response and minimize noise, allowing the analog signals to reflect the simulated values accurately. This circuit was essential for obtaining the output data analyzed and presented in the results.  
Figure 22: Test bench (Oscilloscope, ML605 and RC circuit).  
The observed noise in the output signal during the practical implementation of the system is primarily attributed to the digital-to-analog converter (DAC) used in the setup. The DAC was implemented using an RC circuit in conjunction with a PWM signal generated by the simulator circuit within the FPGA. While this method is functional, it inherently introduces noise due to the limitations of PWM and the filtering provided by the RC circuit. This noise can affect the fidelity of the output signal, particularly in applications that require precise signal differentiation. However, it is essential to note that the internal processing within the FPGA remains highly accurate, as demonstrated by the low error rates in Tables 2 and 3. The noise issue is primarily tied to the quality of the DAC, and using a higher-quality DAC could significantly mitigate this problem, ensuring that the high precision of the internal simulation is reflected more accurately in the output signal.
# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 1 Introduction
However, the primary motivation for employing a listener-centric approach in the current study is that it has the potential to allow preferences to emerge in parallel with the optimisation of intelligibility, making it possible to elucidate the relationship between the two. The objective of the present study is to better understand listeners' spectral reallocation preferences. We first assess the extent to which listeners are capable of using spectral energy modifications to improve speech intelligibility in the presence of masking noise. This issue is examined by comparing the distribution of preferences with keyword recognition scores, to find the preference values that optimise intelligibility. A second issue is whether listeners solely optimise intelligibility, or if they exhibit additional preferences that are independent of message comprehensibility. To address this question, the space of possible adjustments available to listeners was designed in such a way that intelligibility was likely to be constant across at least part of the adjustment range; in addition, a quiet condition was included to promote ceiling performance. This design enables the observation of changes in preferences, even when intelligibility is constant. A further question is whether preferences differ in the presence or absence of masking noise. We hypothesise that listeners' preferred adjustment values are more likely to be aimed at the maintenance of intelligibility in the presence of high levels of noise. Conversely, in quiet or low noise conditions, preferences may reflect other factors. To address our research questions, we conducted two experiments aimed at exploring spectral preferences. We chose to examine spectral preferences due to their established effectiveness in enhancing intelligibility. In the first experiment (Expt. 1; Section 3), we examined modifications across the entire speech bandwidth, while in the second experiment (Expt. 2; Section 4), we investigated bandwidth-limited modifications. In Expt. 1, participants had the choice to directly adjust the spectral tilt of the speech or indirectly modify it by adjusting the magnitude of one of three spectral bands. In the latter experiment, participants were presented with a single band of speech and given control over its spectral extent through three forms of bandwidth modification, or by changing the spectral location of the band. In each experiment, listeners had the ability to adjust the speech in real time for one of four features (eight features in all across the two experiments), thereby altering the speech spectrum. Immediately following the adjustment, participants carried out a speech-in-noise recognition task at the chosen setting.
## 2 Material and Methods
### Eddy Covariance Measurements  
Flux densities of CO\({}_{2}\), momentum, as well as the friction velocities over the rice and wheat canopy were measured by the EC technique from 1 July 2013 to 30 June 2014 from pre-transplanting/sowing to harvesting in both the crops and also during the fallow periods after rice and wheat. A sonic anemometer (CSAT3, Campbell Scientific, Logan, UT, USA) measured the three-dimensional wind and the sonic, or virtual, temperature along the three non-orthogonal sonic axes. The fluctuations in CO\({}_{2}\) and H\({}_{2}\)O density were measured using an LI-7200 (LI-COR, Lincoln, NE, USA) close-path infrared gas analyzer. The data from CSAT3 and LI-7200 were sampled at 10 Hz using a CR3000 (Campbell Scientific, Logan, UT, USA) data logger. The Edire software processed the data in real time and computed the CO\({}_{2}\) flux and frictional velocity along with the covariance, standard deviations, and means on a half-hourly basis. NEE is usually calculated as the sum of eddy CO\({}_{2}\) flux (Fc) and CO\({}_{2}\) storage change (Fs) within the airspace below the flux-measuring height [(24)]. In this study, the Fs term was neglected for the NEE calculation as the canopy height was relatively low at less than 1 m. In the current study, we use the term NEE, which indicates CO\({}_{2}\) flux only.
## 3 Experimental Setup
### PAUT Guided Wave Method  
Guided wave modes were generated on Al and CFRP plates by means of a commercially available phased array probe and wedges. The procedure of plate wave generation and parametric evaluation are described in detail in [8,18]. A 1.5 MHz phased array ultrasonic probe with 16 elements and related 60 degrees longitudinal wave wedge was used for guided wave generation and flaw detection. Figure 4 shows the setup used for inspecting the artificial defects (drilled holes) in CFRP sample. In CFRP sample, guided waves were generated in direction of the fibers.
## 3 Experimental Verification
### Mode-Tuning of LAMB Wave  
Although Lamb wave signals are complicated because of their multimodal characteristics, two fundamental Lamb modes S0 and A0 can exist and propagate in low frequency range with weak dispersion. S0 mode Lamb wave was selected as an incident wave because this mode is more sensitive to the through-thickness crack than the A0 mode [1].  
A mode-tuning experiment [32] was conducted to determine the optimal excitation frequency of the S0 mode. A pair of PZT transducers with a distance of 170 mm was attached to the upper surface of the plate. The tone burst was applied to one of the PZT transducers to generate Lamb wave, and the other was used to receive Lamb wave signals. The excitation frequency of the tone burst was swept from 100 to 500 kHz in steps of 10 kHz. At each excitation frequency, the amplitudes of the S0 and A0 modes were recorded. Figure 3 shows the mode-tuning result. It can be seen that the amplitudes of S0 and A0 modes fluctuate periodically versus the excitation frequency. The amplitude of the S0 mode reached its maximum at an excitation frequency of 280 kHz, while the amplitude of the A0 mode was relatively smaller. Therefore, to obtain a sufficiently strong nonlinear effect and to restrain the A0 mode, the S0-dominated Lamb wave was excited at a frequency of 280 kHz in the experiment. At this frequency, the length of the small crack was much smaller than the wavelength of the incident wave (about 20 mm). The small crack could thus be considered as micro-damage [24].  
Figure 3: Lamb wave mode-tuning curve for a 2 mm-thick aluminum plate excited by a 1 mm-thick PZT transducer.