esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of Nanostructures within InGaN-Based Multiquantum-Well Light-Emitting Devices
## 1 Introduction  
III-N compound semiconductors have been used in a wide range of applications because of their broad applications in the fabrication of light-emitting diodes (LEDs), laser diodes (LDs), solar cells, and photodetectors, which emit over a broad spectral range from infrared to ultraviolet [1; 2; 3]. Much interest has been focused on InGaN/GaN multiquantum wells (MQWs) because they can act as the active layer in high-brightness III-N LEDs and blue and green LDs [3; 4]. For conventional LEDs, the quantum-confined Stark effect (QCSE) due to the large strain-induced polarization field reduces the overlap integral between the electron and hole wave functions, thereby reducing radiative recombination rates and related radiative efficiency. Some methods have been suggested to address the reduced overlap integral between the electron and hole wave functions, including polarization band engineering [5], developing InGaN LEDs on GaN templates with nonpolar or semipolar crystal orientations [6; 7]; thus, the improvement of internal quantum efficiency can be achieved. A significant improvement of device efficiency has recently made rapid progress in LED development [8; 9].  
For lighting applications, InGaN/GaN MQW devices must deal with the requirements of high current injection. However, their performance decreases significantly as the operating current density increases. Several mechanisms have been suggested to explain the efficiency droop, including Auger recombination [10; 11; 12; 13], carrier delocalization from In-rich low-defect-density regions at high carrier densities [14; 15], and carrier leakage at high forward currents [16]. It was reported that Auger recombination plays an important role in the efficiency droop of MQW LEDs at high current density, a dilute-As GaNAs material with negligible interband Auger process was proposed recently as a possible material to solve the problem [10; 11; 12; 13]. In addition, the performance of such devices is alsolimited by the loss of electrons overflowing from the active layer to the nonradiative recombination centers under high current density [17]. In this study, we introduced a multiquantum-barrier (MQB) nanostructure instead of a conventional GaN barrier into the InGaN-based MQWs to improve the performance of the devices. Temperature-dependent electroluminescence (EL) measurements were conducted over a temperature range from 20 to 380 K and an injection current level from 10 to 100 mA. A higher EL spectra intensity and an extended energy scale of potential fluctuations were obtained for the sample with MQB nanostructures. High-resolution X-ray diffraction (HRXRD) spectra were measured and analyzed using a theoretical model to further characterize the nanocrystallites in the samples. Moreover, the external quantum efficiency of the samples was compared. The results indicate that the operation of InGaN/GaN MQW light-emitting devices is improved because of the nanostructures within InGaN/GaN MQW barrier layers.
## 3 Results and Discussions
### Membrane Fabrication Condition Optimization
within 1 h, the salinity reduction after 60 min of running sPES membranes showed the best performance, which was 33%. Starting from 20 h, the salinity reduction performance decreased substantially. As shown in Figure 5d, when the solvent evaporation time was within 1 h, the normalized specific energy consumption of sPES exhibited the best performance, which was 0.045 kWh/m\({}^{3}\) per meq/L of salt removed. Moreover, starting from 20 h, the normalized specific energy consumption performance increased substantially. Based on the results shown in Figure 4, it can be concluded that for PES membranes, when the solvent evaporation time was 3 h, the general performance of the PES membrane was the best. For sPES membranes, as shown in Figure 5, when the solvent evaporation time was within 1 h, the general performance of the sPES membrane was the best. Therefore, PES membranes and sPES membranes with the best performance were chosen for future analysis, which meant PES membranes with a solvent evaporation time of 3 h and sPES membranes with a solvent evaporation time of 1 h were selected for subsequent evaluations.
## 2 Method  
### Process Framework  
We employed a combined global and local path design approach to generate an overall decision-making planning path. The design process is illustrated in Figure 1. Firstly, environmental modeling preparation is conducted, including extracting ocean depth data and creating a three-dimensional depths map. Based on the characteristics and parameters of the USV, the minimum safest depth is determined, and the minimum safest depth contour lines are delineated to ensure navigation safety. Secondly, during the path planning stage, the streamline method simulates fluid motion within the flow field for path planning. Global path searching is performed in complex environments to avoid local optima. GeoNetworking-mode is introduced to alert against hazardous zones in locally complex sea areas and the improved A* algorithm is employed for detailed planning. This combined approach enhances the precision of path planning and ensures navigation safety and efficiency.
## 4 Results
### Comparison of the Accuracy of Regression Analysis Models
Under this criterion, the distance between each model and REF directly indicates the precision of its predictions and evaluation metrics; shorter distances signify superior model performance. Through a systematic comparative analysis, the MPA-XGBoost model was identified as significantly outperforming other candidates, demonstrating a notably closer proximity to REF. This finding clearly underscores the model's exceptional performance in terms of prediction accuracy and evaluation standards. Specifically, compared with traditional machine learning methods and other enhanced algorithms, the MPA-XGBoost model achieves a substantial advancement in regression analysis capabilities. It not only captures complex variable relationships with greater accuracy but also effectively minimizes prediction errors, thereby markedly improving the reliability and stability of its results.
# Optimizing Perishable Product Supply Chain Network Using Hybrid Metaheuristic Algorithms
## 1 Introduction
The second phase involved modeling the inventory control problem using a queue system based on the established locations and routes. The model was solved using an improved GA. [PERSON] et al. [31] introduced the three dimensions of sustainable development into the supply chain network and examined the LIRP for PPSCN. They constructed a two-phase optimization model and combined GA and particle swarm optimization (PSO) algorithms to solve it. Lastly, [PERSON] et al. [5] addressed the challenges of sustainable PPSCN in emerging markets. They developed a comprehensive model that integrates location, inventory, and routing factors, aiming to minimize economic costs and carbon emissions while maximizing product freshness. As mentioned previously, the existing research on LIRP for PPSCN encompasses various problem types such as deterministic, stochastic, single-period, multi-period, single-product, multi-product, and others. These studies have examined the impact of perishable products on inventory and transportation processes, including damage, specific shelf life, and occurrences during these processes. Some scholars have focused on one or two of these elements. Table 1 provides a comparison of our work and the related literature. The main innovations and contributions of this study that differentiate it from existing ones are the following:  
* Formulating a novel LIRP model that addresses the design of a multi-period, multi-product PPSCN while simultaneously considering damage in the inventory and transportation processes, as well as specific shelf life;
* Developing two hybrid metaheuristic algorithms: hybrid GA and VNS (HGA-VNS) and hybrid MPGA and VNS (HMP-GA-VNS) to search for optimal solutions;
* Designing a new chromosome representation for the unique structure of the problem;
* Employing the Taguchi experimental design method and conducting means and analysis of variance to assess the effectiveness of the developed hybrid metaheuristic algorithms;
* Conducting sensitivity analysis to validate the proposed model. The paper is organized as follows: Section 2 presents the problem and the mathematical model. Section 3 provides an overview of the proposed solution methods. Section 4 includes the Taguchi experimental design method, computational results, sensitivity analyses, and managerial insights. Lastly, in Section 5, conclusions, limitations, and future works are provided.
# Ontology Modeling Using Fractal and Fuzzy Concepts to Optimize Metadata Management
## 2 Literature Review
### Fractal Concepts for Data Modeling and Management  
Fractal concepts describe complex geometric shapes that exhibit self-similarity at various observation scales [32]. Unlike traditional Euclidean geometry, fractals often have non-integer dimensions, reflecting their complex and infinitely recursive nature. Common examples from nature, such as coastlines, snowflakes, and tree branches, intuitively demonstrate this repetitive pattern phenomenon. This property provides powerful metaphors and mechanisms for solving data modeling and management challenges, especially in large hierarchical systems [33]. The principle of self-similarity implies that small parts of a structure are similar to the whole, which allows for the recursive representation of nested hierarchies and repeating patterns.  
Fractal-based approaches inherently support scalability and adaptability. The need for a structural redesign is minimized, as the same generative patterns can be reapplied to integrate new layers or components. This contrasts with rigid tree-based models that require restructuring for each additional detail. Furthermore, the idea that complex systems can be generated from simple recursive rules enhances the understandability andmaintainability of the data models. The core concepts of recursive extensibility, pattern reuse, and structural consistency strongly support their use in frameworks such as FFOM.
## 3 Results  
### Analyzing the Shear Wave's Characteristics in the Time Domain  
To study the shear wave's temporal characteristics, we examined its magnitude and shape throughout time by visualizing the axial velocities at three different time points. The resulting shear wave propagation of the experimentally measured SWE acquisitions with and without compounding are compared in Figure 3. Immediately, we observe a different shear wave propagation pattern: the shear wave front, represented by the downward axial velocities, is split into two for the single plane wave images whereas one uniform wave front is present for the compounded images. Furthermore, the wave front is also broader along the lateral direction when including compounding. Next to these differences in shear wave shape, we also observe a lower shear wave magnitude (maximal axial velocity amplitude at a certain time point can be up to 3 mm/s smaller) for the compounded images.  
The imaged and biomechanical shear wave propagation for the simulations are depicted in Figure 4. For the biomechanical simulation (first row in Figure 4), we observe again a split in shear wave front during wave propagation, which is well captured in the virtual single plane wave images (second row in Figure 4), but less visible in the virtual compounded images (third row in Figure 4). Furthermore, the shear wave front is apparently broader in the imaging simulations compared to the biomechanical simulation. Additionally, the simulated axial velocity patterns of the virtual images show a clear decrease in tissue velocity magnitude (\(\sim\)23.0% for single PWI and \(\sim\)69.4% for compounded PWI at the top of the phantom compared to the biomechanics simulation).  
Figure 4: Comparison of the biomechanical shear wave propagation (upper panels) and the virtually imaged shear wave propagation without and with compounding (middle and lower panels respectively) at time points 1.13 ms, 1.55 ms and 2.00 ms (assuming \(t_{0}=0\) s corresponds with the start of the pushing sequence). The white dotted lines represent shear wave propagation paths at 15% and 40% tissue depth with respect to the ventricular thickness.  
Figure 3: Comparison of shear wave propagation measured on the ventricular phantom at time points 1.12 ms, 1.55 ms and 1.99 ms (assuming \(t_{0}=0\) s corresponds with the start of the pushing sequence and an ultrasound system’s electronic dead time of 0 s) for single and compounded Plane Wave Imaging (PWI). The white dotted lines represent shear wave propagation paths at 15% and 40% tissue depth with respect to the ventricular thickness.
# Indoor WiFi-Beacon Dataset Construction Using Autonomous Low-Cost Robot for 3D Location Estimation
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Checkpoint**} & \multicolumn{3}{c}{**Sample Mean of RSSI, dBm at Different Heights**} \\ \cline{2-4}  & **Height 0.3 m** & **Height 0.9 m** & **Height 1.5 m** \\ \hline \(14\) & \(-58.17\) & \(-54.95\) & \(-49\) \\ \hline \(44\) & \(-69.6\) & \(-62\) & \(-57.13\) \\ \hline \(77\)  
For all of the data, more than 65% of the checkpoints had the maximum power at the height of 1.5 m, and the remaining checkpoints had weak RSSI values, with very little difference between the three heights. Figure 14 shows the percentage of checkpoints that had the maximum RSSI value at 1.5 m.  
The variation in the data values was attributed to factors in the real indoor environment in which the data were collected. Such factors are not typically found in a laboratory environment. For example, the real indoor environment had obstacles such as human movements, doors, and windows, in addition to the effect of the building material in different sections of the scanned building. In general, the shortest distance between the transmitter and receiver results in the maximum power and clearly demonstrates the effect of the height of the handheld device on the signal strength, whereas as the handheld \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Checkpoint** \\ **Number** \\ \end{tabular} } & \multicolumn{2}{c}{**Sample Mean of RSSI, dBm at Different Heights**} \\ \cline{2-4}  & **Height 0.3 m** & **Height 0.9 m** & **Height 1.5 m** \\ \hline
1 & \(-\)86.5 & \(-\)83.5 & \(-\)79.9 \\ \hline
5 & \(-\)87 & \(-\)82.37 & \(-\)81.4 \\ \hline
13 & \(-\)86.12 & \(-\)77.72 & \(-\)73.8 \\ \hline
20 & \(-\)87.8182 & \(-\)78 & \(-\)74.12 \\ \hline
25 & \(-\)81.57 & \(-\)80.08 & \(-\)79.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between the three different heights at different checkpoints from AP (4). Figure 13: RSSI values from AP (4) at different heights. device moves away from the AP and approaches a new one, the signal strength of the first AP as perceived by the device is reduced in general and the effect of the height of the transceiver/handheld device becomes negligible. The use of the robot in capturing the signal strength has its strengths, since due to the systematic approach of using the robot, many sources of errors related to a human operator are eliminated. However, there may still be slight differences between the data collected by the robot compared to similar data collected from a human's handheld device due to different polarization, nature and speed of movement, and variation in height and location, amongst other factors. It is worth noting that the Wi-Fi infrastructure that was the subject of this study is static, with the locations of APs and their channel chosen to reduce interference and generally fixed. In addition, there is a very limited number of routers with minimum interference between them (in the range of 2-3 dBM).
## 3 Results and Discussion
### Regression Models and Response Surfaces
In the present study, the optimal proportions for a three-component extractant mixture (ethanol, propanediol, and water) were ascertained to be those in which the quantities were more equal. By applying the DOE method for ternary mixtures and using utility profiles, an extraction model was developed with optimized conditions: ethanol 32.8%, water 39.2%, and propanediol 27.8% (Figure 4). The method of mixture designs and triangular response surfaces was shown to be highly effective for determining the optimal extractant mixture for isoflavenes extraction from soybean seeds. \begin{table}
\begin{tabular}{c c c c c} \hline
**Source** & **SS** & **MS** & **F-Value** & **\(p\)-Value** \\ \hline  & & Genistin & & \\ \hline Model & 0.30003 & 0.15088 & 361.6285 & 0.00000 \\ Total error & 0.00985 & 0.00041 & & \\ Lack of fit & 0.00780 & 0.00024 & 0.52370 & 0.60276 \\ Pure error & 0.00205 & 0.00045 & & \\ Cor total & 0.30988 & 0.05585 & & \\ Model fit; R\({}^{2}\) = 0.9535 Accuracy: Adj. R\({}^{2}\) = 0.9682 & & & \\ \hline  & & Malonyldaidzin & & \\ \hline Model & 0.05620 & 0.15088 & 361.6285 & 0.00000 \\ Total error & 0.00500 & 0.00041 & & \\ Lack of fit & 0.00048 & 0.00024 & 0.52370 & 0.60276 \\ Pure error & 0.00452 & 0.00045 & & \\ Cor total & 1.06120 & 0.05585 & & \\ Model fit R\({}^{2}\) = 0.9925 Accuracy: Adj. R\({}^{2}\) = 0.9953 & & & \\ \hline  & & Malonylglycitin & & \\ \hline Model & 0.040246 & 0.008049 & 33.70776 & 0.000000 \\ Total error & 0.003343 & 0.000239 & & \\ Lack of fit & 0.001927 & 0.000482 & 3.40081 & 0.052986 \\ Pure error & 0.001416 & 0.000142 & & \\ Cor total & 0.043589 & 0.002294 & & \\ Model fit R\({}^{2}\) = 0.8959 Accuracy: Adj. R\({}^{2}\) = 0.9233 & & & \\ \hline  & & Malonylgenistin & & \\ \hline Model & 1.57859 & 0.26309 & 144.0319 & 0.00000 \\ Total error & 0.02374 & 0.00182 & & \\ Lack of fit & 0.01812 & 0.00604 & 10.7555 & 0.00179 \\ Pure error & 0.00561 & 0.00056 & & \\ Cor total & 1.60232 & 0.08433 & & \\ Model fit R\({}^{2}\) = 0.9783 Accuracy: Adj. R\({}^{2}\) = 0.9852 & & & \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._Figure 3: The response contour plot of the impact of the solvent composition on the extraction rate of daidzin, genistin, malonyldaidzin, malonylglycitin, and malonylgenistin. The concentration of extracted isoflavenes is expressed in mg/g DM (dry matter).
## 8 Fourth, Fifth and Sixth Generations
#### Using FSO Links Only in 5G Communication
[251], introduce a new work using optical links to provide a robust outdoor backhaul and fronthaul for 4G and 5G small cells. The distance of links varies from 20 to 200 m. The work was conducted in Berlin, Germany, for 5 months, where the visibility was never below 180 m and 99% of the time was above 1 km. Results confirm that rate-adaptive transmission boosts the availability of the communication system in the presence of fog or sunlight. In real-time operation, the prototype achieves 800, 500, and 225 Mb/s over 20, 100, and 215 m link distance, respectively, with 2 ms latency at 95% load using low-cost optoelectronic components and a 1 Gbps baseband chipset. This project confirms that optical wireless communication systems can be a low-cost wireless backhaul solution for small radio cells such as WiFi, LTE, and 5G. To achieve 5G connectivity, high data rate and low latency are needed, which can be reached by using low-cost laser diodes instead of LEDs.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### General IoT Applications  
#### 3.4.1 MEGAN  
MEGAN [14] is a sensor node that serves a variety of IoT applications. The low-power, advanced RISC-based ATmega324 PA microcontroller was utilized. The microcontroller contains 32 kB flash, 2.5 kB static RAM, and 1 kB of EEPROM. A clocking frequency of 8 MHz was utilized by the authors but can be increased to 16 MHz. The mode offers the advantage of choosing a communication module for a particular application. In their demonstration, the authors utilized ZigBee (XBee series 1) and Bluetooth (HC-05) modules for communication. However, according to the authors, the node can support other communication technologies such as general packet radio service (GPRS), Wi-Fi, and the GSM. Besides, the node consists of 32 general purpose input/output (GPIO) pins that enable several sensors and actuators to be connected to it.  
A 3.7 V, 800 mAh Li-ion battery is utilized to power the node. The battery is recharged using a recharging circuit incorporated in the sensor more. The current consumption of the node in the sleep and active states is 10.1 mA and 7.95 mA, respectively. The consumption in the active state when utilizing the Bluetooth module is 16.44 mA, while it is 59.65 mA when the Zigbee module is selected.
# Grapevine Branch Recognition and Pruning Point Localization Technology Based on Image Processing
## 3 Results
### Results of the Thinning Algorithm
#### 3.3.2 Comparison of Results from Different Thinning Algorithms  
We chose three refinement algorithms, Zhang-Suen thinning algorithm, Hilditch thinning algorithm and Stentiford thinning algorithm, to test the same dataset and the evaluation metrics obtained are shown in Table 6.  
From the data in Table 6, we can see that the Hilditch algorithm has the longest average running time of 3.01 s and the Zhang-Suen algorithm has the shortest average running time of 1.29 s. the Zhang-Suen algorithm has the highest average thinning rate of 95.44%, which means that the number of foreground pixels after thinning is the lowest. The Zhang-Suen algorithm has the highest average sensitivity of 90.87%. The Zhang-Suen algorithm has the highest degree of change in thinning before and after running the image, which is 99.62%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & \(\bar{X}\) **(s)** & \(\bar{R}\) & \(\bar{S}\) & \(\overline{\Delta T}\) \\ \hline Zhang-Suen & 1.29 & 95.44\% & 90.87\% & 99.62\% \\ Hilditch & 3.01 & 94.84\% & 89.75\% & 99.51\% \\ Stentiford & 1.37 & 93.51\% & 89.29 & 98.15\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Thinning algorithmic evaluation indicators.  
It can be concluded from the data in the table that the Zhang-Suen algorithm is the best in each evaluation index.  
It can be seen through the result graphs of the three thinning algorithms in Figure 12 that the Zhang-Suen thinning algorithm performs the best, the Hilditch algorithm's thinning results are not in the center of the image but at the edge position and the Stentiford algorithm's thinning at the intersection of the objects is lower. However, for this paper, in the pruning point location, the requirement of branches and trunk of the thinning map cannot have redundant small branches; otherwise, interference in judging the location of pruning point occurs easily, so in this paper the Zhang-Suen thinning algorithm was modified, as in Formula (7), to obtain the total thinning process as shown in Figure 13.