esa-sceva/satcom-chunk-collection huggingface dataset:
# Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study
## 2 Data-Gathering Frameworks Depending on Sink Types
### Ground Mobile Sink-Based Frameworks
#### 2.3.1 Circular-Pathway Mobile Sink-Based WSN Data Collection (CPDC)  
[PERSON] et al., proposed a mobile sink-based WSN data-collection mechanism [61], in which the geographical area is divided into different sectors. Depending on the fitness value, each sector contains a CH whose primary responsibility is to perform the data-collection task inside its corresponding sector and relay the data to the mobile sink after the primary calculation. The sectors have the same geographical area. The fitness of being a CH depends on the distance and energy level of a node and its distance to the data sending nodes. A node willing to send data to the CH can decide to send data via a multi-hop or single-hop data transmission, depending on which is the most optimal method.  
In terms of the energy-consumption policy, the CHs are connected in a greedy manner. The CH node that is nearest to the sink node is selected as the gateway node to disseminate the data. This mechanism follows a predefined trajectory for a mobile agent. The sensor nodes are equipped with a GPS mechanism. Each sector has a fixed angle value, which can be derived as follows:  
\[A=\frac{2\pi}{N_{CHs}} \tag{2}\]  
where the number of CHs is expressed by \(N_{CHs}\), and \(A\) denotes measurement of the angle. The main reason for using the mobile sink is to reduce long-distance communication, which consumes a significant amount of energy. To establish communication among the clusters, a greedy policy is applied to conserve the energy of the WSN node. At the beginning of the process, the sink node queries all the CHs in the network. After collecting all the replies, it elects the gateway node. All the CHs are willing to transmit data sent to the CHs designated by the sink, which are geographically closer to the gateway node. The robot maintains a fixed speed during the data-collection mission. Figure 4 shows the working mechanism of the CPDC protocol.  
Advantages: The sink maintains a constant velocity. This mechanism enables the nodes to calculate the position of the sink after a certain amount of time. As a result, the sink does not need to broadcast its position repeatedly to the sensor nodes.  
Limitations: The CHs are connected in a greedy manner with each other, but no solution is provided for an isolated cluster member. This mechanism assumes that every node has location information about other nodes, which is not a good assumption, as the sensor nodes are inexpensive and do not have a large buffer memory. For a large and dense deployment, this information will be an extra burden on the sensor node's memory.  
Figure 4: Working mechanism of the CPDC protocol.
# Relief Aircraft Dispatch Strategies Based on Different Levels of Information Sharing Systems
## 6 Numerical Experiments
### Artificial Designed Demand  
From past relief activities, all data on transportation injury constituted the number of total rescued injuries in all relief activities or on some days. Most of the relief data are confidential and unavailable to the public. It is, thus, difficult to access the injury information. Hence, in this study, we used more general artificial data.  
We update the injured every time interval, and each time the inputs in each shelter are determined by the demand/capacity (D/C) and the Gini coefficient, as shown in Figure 8. D/C is the proportion of the total number of injured (demand) in all shelters and the capacity of all the aircraft that can satisfy the total demands of all shelters for each time input. The Gini coefficient indicates the degree of imbalance among shelters, which can determine the proportion of each shelter. The demand of each shelter for each time input can be determined based on the total demand of all shelters and the proportion of each shelter. The Gini coefficient is defined as follows:  
In Figure 8, there are four shelters. \(I_{1}\), \(I_{2}\), \(I_{3}\), and \(I_{4}\) (in ascending order) that indicate the number of injured in each shelter. The Gini coefficient can be calculated as follows:  
\[P_{1}=I_{1},\ P_{2}=I_{1}+I_{2},\ P_{3}=I_{1}+I_{2}+I_{3},\ P_{4}=I_{1}+I_{2}+I _{3}+I_{4} \tag{6}\]  
\[S_{1}=\frac{0+P_{1}}{2}*L+\frac{P_{1}+P_{2}}{2}*L+\frac{P_{2}+P_{3}}{2}*L+ \frac{P_{3}+P_{4}}{2}*L \tag{7}\]  
\[S_{2}=\frac\[Gini=\frac{S_{2}-S_{1}}{S_{2}} \tag{9}\]  
A larger Gini coefficient indicates a significant unbalanced demand distribution among these shelters. By setting different D/C and Gini coefficients, we can simulate different general situations to test our model.
# Special Issue on Materials Development by Additive Manufacturing Techniques
### Metals  
Nowadays mainly steels, titanium, aluminum, and nickel alloys are successfully processed by AM and used in disparate applications [9]. However, not all alloys belonging to these families can be successfully processed by the most common metal-AM techniques, such as laser powder bed fusion (LPBF), electron beam melting (EBM), and directed energy deposition (DED).  
Steel has been by far the first alloy class to be processed and has, therefore, been used in several industries, such as the automotive and aerospace ones [10]. Among AM processable steels, the most studied ones are stainless steel, such as 316L and 304L, precipitation hardening (PH) steels, such as 17-4 PH, tool steels, such as H13 and M2, and maraging steels, such as 18 Ni-300. Large efforts have been made in previous works in order to understand the microstructure of steels' built components and their correlation with the building parameters together with the effect that they have on mechanical properties [11]. [PERSON] et al. summarized the main data and results obtained on DED 316L samples in a review published in the present special issue [11].  
Titanium alloys are the most-used alloys in AM thanks to the wide range of applications they have in the biomedical and aerospace fields. Many of these applications can take advantage of the possibility to produce complex and customized parts. Furthermore, the vacuum EBM process of Ti6 Al4V Gd23 alloy allows the control of the interstitial content and the consequent respect of the standards.  
Aluminum alloys also had large success in the AM field mainly thanks to the strong interest of aerospace companies that need the production of complex, lightweight components [12]. Currently, however, mainly Al-Si alloys, with a near eutectic composition, are processable by AM while most of the Al high-strength alloys strongly suffer from solidification cracking during AM processing. There is, therefore, a limited amount of aluminum alloys processable by AM and, recently, universities, research centers, and companies are investing in the development of new compositions specifically designed for AM, such as the Scalmalloy(r) or the A20 XTM [3].  
Nickel alloys, such as In625, In718, and HastelloyX, have been widely used for the AM production of parts that need high-creep and corrosion resistance, such as engine turbine blades, turbochargers, heat exchangers, and petrochemical equipment [13]. The strong interest in this alloy class has pushed the research in the understanding of the microstructure-properties correlation of these materials. Recently other alloys belonging to the Ni family have been successfully processed by AM. As an example, in this special issue, results about the Monel Ni-Cu alloy are reported [14]. This alloy, which has been recently processed by LPBF, showed good processability within specific parameters. High-mechanical properties were measured thanks to fine microstructure and high residual stresses [14].
## 3 Experimental Results
### Spectral Comparative Analysis of Pothole Surface from Original and Calibrated Bands  
Upon the successful sampling of spectral radiance data from both original and calibrated spectral bands, the sampled data were plotted on linear graph to determine the difference between pothole radiance properties obtained from of original bands and those which were sampled from calibrated spectral bands. Figure 7 shows the spectral radiance properties of potholes derived from original and calibrated bands.  
Figure 6: Pothole features demarcated on calibrated spectral channels: (**a**) blue, (**b**) green, (**c**) red, (**d**) NIR and (**e**) red edge channels.  
Figure 7: Spectral radiance comparison between original and calibrated bands; (**a**) blue, (**b**) green, (**c**) red, (**d**) NIR and (**e**) red edge.  
Generally, calibrated spectral bands exhibited low spectral radiance values when compared with uncalibrated spectral bands.
## 3 Background
### Stable Marriage Problems with Weighted Preferences  
A _stable marriage problem with weighted preferences_ (SMW) [8] is a classical SM, where every man/woman gives also a numerical preference value for every member of the other sex, which represents how much he/she prefers such a person. Such preference values are natural numbers and higher preference values denote a more preferred item. Given a man, \(m\), and a woman, \(w\), the _preferencevalue_ for man \(m\) (resp., woman \(w\)) of woman \(w\) (resp., man \(m\)) will be denoted by \(p(m,w)\) (resp., \(p(w,m)\)).  
**Example 1**: _Let \(\{w_{1},w_{2}\}\) and \(\{m_{1},m_{2}\}\) be, respectively, the set of women and men. An instance of an SMW is the following: \(\{m_{1}:w_{1}^{[9]}>w_{2}^{[1]}\) (i.e., man \(m_{1}\) prefers woman \(w_{1}\) to woman \(w_{2}\), and he prefers \(w_{1}\) with a value of nine and \(w_{2}\) with a value of one), \(m_{2}:w_{1}^{[3]}>w_{2}^{[2]}\), \(w_{1}:m_{2}^{[2]}>m_{1}^{[1]}\), \(w_{2}:m_{1}^{[3]}>m_{2}^{[1]}\}\). The numbers written into the round brackets identify the preference values. \(\Box\)_  
We will introduce new notions of stability and optimality for SMWs that have not been considered in [8], and we will show how to find them by adapting the classical Gale-Shapley algorithm for SMs.
# Genetic Variation Affects the Anti-Melanogenic Efficacy of _Platycodon grandiflora_ Flowers
###### Abstract  
This study investigated the utilization of by-products from _Platycodon grandiflora_ and the role of genetic diversity in their anti-melanogenic properties, focusing on the purple-flower (PF) and white-flower (WF) varieties. Our results show that the WF variety exhibited significantly higher anti-melanogenic activity, attributed to higher concentrations of key saponins such as platycodin D3 and platycodin D. These saponins demonstrated strong tyrosinase inhibitory effects as confirmed by molecular docking analysis. Further, the WF variety showed increased expression of genes involved in saponin biosynthesis, highlighting the role of genetic diversity in determining phytochemical composition and pharmacological efficacy. The superior anti-melanogenic activity of WF suggests its potential as a valuable ingredient in the cosmetic industry for skin-whitening products. Our findings emphasize the importance of utilizing by-products and selecting specific genotypes to enhance the quality and efficacy of plant-derived products. Future research should explore the full spectrum of bioactive compounds in _P. grandiflora_, investigate sustainable extraction methods, and conduct clinical trials to validate the safety and effectiveness of these compounds in cosmetic and therapeutic applications.  
Keywords:anti-melanogenic property; genetic diversity; flower color; _Platycodon grandiflora_
# Antennas for Licensed Shared Access in 5G Communications with LTE Mid- and High-Band Coverage
## 1 Introduction
Both antennas cover all the LSA bands, as well as the LTE mid and high bands. These antennas occupy a small area and they do not require any passive or active lumped components for input impedance matching. In this paper, we describe the design and working principle of all three antennas. The wideband antenna, which is more compact than the dual-band antenna and planar antenna, was prototyped and tested experimentally. This paper is organized as follows. First, the design and working principle of the planar antenna (Ant#1) are described in Section 2. Second, the compact dual-band antenna (Ant#2) is presented in Section 3. Third, a wideband antenna (Ant#3), which is even more compact than the previous two antennas, is described in Section 4. Ant#3 is fabricated and the measured results are discussed in Section 5. A comparison of all three antennas is also given in this section. Finally, Section 6 concludes the paper.
# Collaborative Indoor Positioning by Localization Comparison at an Encounter Position
## 3 Proposed Method
### Localization Comparison  
After an encounter is detected, the localization accuracies of two devices are compared using a machine learning model. Generally, models require large amounts of training data. However, actual walking data are difficult to obtain comprehensively. Therefore, we synthesize walking data and perform the baseline method to generate training data.  
Data generation is described in Section 3.3.1. Furthermore, feature extraction and the machine learning model for localization comparison are described in Sections 3.3.2 and 3.3.4, respectively.  
Figure 4: Relationship between the RSSI and distance. The value label is an average RSSI over 20 trials at each distance.  
Figure 3: The flow of the proposed encounter communication.
## 3 Results and Discussion
### XPS Analysis  
To determine the chemical state of the ZnO nanoparticle surfaces, XPS measurements were performed. From Figure 3, one can find that the energy differences between the Zn 2p\({}_{3/2}\) and Zn 2p\({}_{1/2}\) peaks of the two samples are 23.09 and 23.11 eV, respectively. These values are within the range of the XPS standard reference value of ZnO [36]. The Zn 2p XPS peak is sharp, and its corresponding binding energy indicates the presence of Zn\({}^{2+}\) lattice ions [37].  
Figure 4 shows that O 1s core level spectra are quite asymmetric, with a broad shoulder by the side of the higher binding energy, indicative of oxygen-rich states (-OH, H\({}_{2}\)O, O\({}_{2}\), etc.) on the surface [38]. The O 1s spectra of ZnO NPs can be described as the superposition of two Gaussian peaks. The main peak, at about 530.1 \(\pm\) 0.1 eV, is attributed to oxygen in the ZnO crystal lattice (O\({}_{  
Figure 1: XRD spectra of different ZnO nanoparticles (NPs): (**a**) Sample A; (**b**) Sample B.  
Figure 2: SEM micrographs of different ZnO NPs: (**a**) Sample A; (**b**) Sample B.  
second peak, at a higher binding energy (O\({}_{\rm H}\)) of 532.0 \(\pm\) 0.1 eV, is related to the presence of hydroxyl groups (-OH) [37].  
The XPS data of Zn 2p and O 1s of the vacuum-treated and pristine ZnO NPs are shown in Table 2. As the atomic ratio of Zn to O\({}_{\rm L}\) at the surface of the samples is much higher than 1.0, this suggests that there are many oxygen vacancies at the surface of the ZnO NPs. An oxygen vacancy acts as an active site, which can easily combine and form stable structures with other active species [37]. This accounts for the high fraction of hydroxyl groups adsorbed on the ZnO surface. In addition, the atomic ratios of Zn to O\({}_{\rm L}\) and O\({}_{\rm L}\) to O\({}_{\rm H}\) on the surface of Sample B are both higher than those of Sample A. Therefore, the concentration of oxygen vacancies in ZnO increases when pristine ZnO is treated. One possible reason for this is that a large number of hydroxyl groups are desorbed from the ZnO surface and then pumped out of the vacuum chamber.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ZnO Sample**} & \multicolumn{2}{c}{**Binding Energy of Oxygen (eV)**} & \multicolumn{2}{c}{**The Atomic Ratio of O\({}_{\rm L}\) to O\({}_{\rm H}\)**} & \multicolumn{2}{c}{**The Atomic Ratio of Zn to O\({}_{\rm L}\)**} \\ \cline{2-5}  & **O\({}_{\rm L}\)** & **O\({}_{\rm H}\)** & \\ \hline Sample A & 530.288 & 531.955 & 2.02 & 1.39 \\ Sample B & 530.240 & 531.903 & 2.56 & 1.51 \\ \hline \hline \end{tabular}
\end{table}
Table 2: XPS data of oxygen and zinc elements at the surface of different ZnO NPs.  
Figure 4: O 1s XPS spectra of different ZnO NPs: (**a**) Sample A; (**b**) Sample B.  
Figure 3: Zn 2p XPS spectra of different ZnO NPs.
## 4 Case Simulation and Result Analysis
### Harmonic Signal Detection Experiment
Comparing Figure 15 with Figures 16-18, it can be seen that the classical stochastic resonance detection system and the unsaturated stochastic resonance detection system do not have a stochastic resonance phenomenon under strong noise interference, and only the accelerated stochastic resonance detection system realizes the conversion of the noise energy to the signal energy, suppresses the noise, and succeeds in having stochastic resonance. The results of this experiment also show that the steeper internal potential well can effectively promote the oscillatory jump of particles between potential wells, which is consistent with the previous derivation. Figure 14: ASR detection system output signal waveform and spectrum at \(D=1\). (**a**) Time domain waveform. (**b**) Spectral amplitude. Figure 16: SR detection system output signal waveform and spectrum at \(D=5\). **(a)** Time domain waveform. **(b)** Spectral amplitude. Figure 15: Input Noise signal waveform and spectrum at \(D=5\). **(a)** Time domain waveform. **(b)** Spectral amplitude. The detection accuracies of the three stochastic resonance detection methods were analyzed to determine the frequency of each harmonic component based on the spectrum, as shown in Table 1.  
Figure 17: UBSR detection system output signal waveform and spectrum at \(D=5\). (**a**) Time domain waveform. (**b**) Spectral amplitude. Figure 18: ASR detection system output signal waveform and spectrum at \(D=5\). (**a**) Time domain waveform. (**b**) Spectral amplitude. As can be seen from Table 1, all three stochastic resonance detection systems are able to realize the difference-free detection of harmonic frequencies under the interference of weak and strong noises, which depends on the fact that the stochastic resonance system driven by the periodic signal will not affect the frequency of the driving signal but instead clamps the random noise to oscillate the output at the driving frequency, which is a unique advantage of the stochastic resonance detection principle relative to the traditional detection methods.
## 5 Experimental Results and Analysis
### Ablation Study
At the same time, an experimental evaluation of the TDEDA module highlighted that the accuracy of the designed TDEDA model was better than that of DSA and DCA on the Weibo dataset, increasing by 2.1% and 1.0%, respectively. On the Twitter dataset, the accuracy increased by 2.4% and 1.2%, respectively.
# Research on Robotic Peg-in-Hole Assembly Method Based on Variable Admittance
## 3 Results and Discussion
### Comparison of Achievements and Analysis of Advantages  
The proposed method in this study achieves convergence within an average of 50 training steps for the pin-in-hole assembly task. When compared to fixed admittance control, the application of the trained model reduces the assembly time by 9.6%. Furthermore,  
\begin{table}
\begin{tabular}{c c c} \hline  & **Fixed Admittance** & **Variable Admittance** \\  & **Assembly** & **Assembly** \\ \hline Fx\_max/N & 1.8 & 1.0 \\ Fy\_max/N & 6.1 & 4.5 \\ Fz\_max/N & 2.0 & 1.6 \\ Tx\_max/N\(\cdot\)m & 0.51 & 0.42 \\ Average assembly time/s & 25.2 & 23.0 \\ Standard Deviation of time/s & 0.53 & 0.66 \\ Coefficient of Variation & 2.10\% & 2.87\% \\ \hline \end{tabular}
\end{table}
Table 3: Adjustment stage force and Assembly Time.  
the implementation of this method results in a significant decrease in interactive forces: a 45% reduction in the \(X\)-direction, a 26% reduction in the \(Y\)-direction, and a 20% reduction in the \(Z\)-direction. These improvements in force management contribute to a more effective and efficient assembly process.  
As shown in Figure 14, the convergence speed of different authors' training assembly tasks using reinforcement learning is presented. In this study, the training of assembly tasks relies on an underlying force controller. Compared with the methods that train assembly tasks directly without relying on an underlying force controller [19; 20], the training speed in this study is approximately 20 times faster. Additionally, compared with other methods that also use reinforcement learning to train an underlying force controller to optimize assembly tasks [27; 28; 29], this study achieves a slight improvement in training speed.  
As shown in Figure 15, the maximum force reduction achieved before and after reinforcement learning training in different studies is presented for the assembly task. Due to the unknown critical parameters of the systems in the relevant studies, it is challenging to directly apply their methods to this study. However, a comparison of the degree of force reduction before and after training can still be made. Compared with studies relying on underlying force controllers [29; 30], this study achieved a higher percentage reduction in force. Meanwhile, compared with studies not relying on underlying force controllers [20; 31], this study still demonstrated better control over the force.  
Figure 14: Convergence episodes from different studies.  
Figure 15: Maximum reduction in force from different studies.  
The above analysis demonstrates that the method proposed in this study can accelerate the training speed of peg-in-hole assembly, while also ensuring a better assembly effect and effectively suppressing the maximum force during the assembly process.
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 2 Background
### FPGAs in Wearable Medical Devices
#### 2.3.1 FPGA Architecture  
Although the architecture varies across vendors, the basic logic block generally includes lookup tables (LUTs) and flip-flops (FFs). These components form logic elements  
Figure 1: Typical processes in wearable medical devices.  
(LEs) or logic cells (LCs), which in turn form configurable logic blocks (CLBs) or adaptive logic modules (ALMs), depending on the vendor's terminology. Additionally, the architecture includes configurable I/O blocks and programmable interconnects [21]. LUTs are the fundamental units within an FPGA, performing combinational logic by storing precomputed results for various input combinations, while FFs are used for sequential architectures. These logic elements are housed within LEs or LCs, while multiple LEs or LCs are organized into slices, which serve as subdivisions within a larger block. Finally, these slices are grouped together within a CLB or ALM, the primary building block of an FPGA [22]. CLBs or ALMs contain the necessary components to implement the logic functions specified by the user, forming a hierarchical structure which allows for highly flexible and configurable digital circuit implementations. These architectures also feature clock circuitry to distribute clock signals to each logic block.