esa-sceva/satcom-chunk-collection huggingface dataset:
# Multivariate Network Layout Using Force-Directed Method with Attribute Constraints
## 3 Methods
### Attribute Community Layout  
We utilize the force-directed method with constraints to calculate the location of the attribute community. The forces received by each community include _spring force_, _repulsive force_, _collision force_ and _central force_. The simulated annealing method is used to obtain the community location coordinates. In the following, we introduce the problem illustration.  
We define the entire multivariate network as \(G=(V,E)\), where \(V=\{v_{1},v_{2},...,v_{n}\}\) represents \(n\) nodes in the network, and \(E=\{e_{1},e_{2},...,e_{m}\}\) represents \(m\) edges in the network, where each edge \(e_{k}=(v_{i},v_{j})\) connects two different nodes in the network. On this basis, we obtain the attribute community structure of the multivariate network \(G_{C}=(V_{C},E_{C})\), where \(V_{C}=\{v_{c_{1}},v_{c_{2}},...,v_{c_{n}}\}\) represents the locations of \(c_{n}\) attribute community centers, and \(E_{C}=\{e_{c_{1}},e_{c_{2}},...,e_{c_{m}}\}\) represents the connection relationships between communities. Each edge \(e_{c_{k}}=\{v_{c_{i}},v_{c_{j}}\}\) connects two different attribute communities. Note that \(V_{C}\) and \(E_{C}\) do not exist in the original multivariate network. For each attribute community center \(v_{c_{i}}\) in \(V_{C}\), its coordinates are randomly initialized \(v_{c_{i}}=(x_{c_{i}},y_{c_{i}})\). If there is an edge \(e_{k}\) connection between the nodes of two attribute communities, then there is an edge \(e_{c_{k}}\) between the two communities.
# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 6 Open Research Issues and Challenges
### Sensor-to-UAV Data Transfer  
Uploading a certain amount of data to an in-flight UAV while using low energy is still challenging [57]. All existing routing protocols consider sensor data-collection missions in one-dimensional WSNs. However, a UAV routing path is three-dimensional. To detect the target node, UAV path planning and optimal mobility need to be investigated further.
# Adaptive Deployable Thin Spherical Shell Reflectors
## 1 Introduction  
The surface figure accuracy of space reflectors must be maintained to a fraction of the wavelength in spite of the manufacturing and deployment errors, gravity gradient and thermal loads. The thermoelastic loads due to thermal gradients often constitute the main contributor to the shape distortion, especially in LEO (Low Earth Orbit), because of the frequent eclipses, which induce unsteady thermal loads [1]. The jitter induced by the attitude control wheels are the main disturbance to the pointing stability.
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 2 Classification of Whole-Genome Alignment Algorithms
### Anchor-Based Methods
#### 2.2.3 Blastz  
BLASTZ is a bioinformatics tool used for aligning DNA sequences, particularly large genomic sequences or whole genomes, making it suitable for comparing entire genomes of different species or strains. It is widely used for identifying conserved regions, homologous genes, and evolutionary relationships among genomes. Many research projects have analyzed similarities between the human and the mouse genome. Many programs have been implemented to align the mouse and human chromosomes, such as Blast2 sequences [34] and Pattern Hunter [35]. The high percentage of similarity between the two genomes has led researchers to implement a new algorithm that is able to detect chromosomal synteny and orthologous regions. The first use of the algorithm was to assess the conservation of synteny between human and mouse chromosomes. The experimental results showed that human Chromosome 20 is partially homologous to mouse Chromosome 2. In addition, only 3.3% of human Chromosome 20 nucleotides align outside of the mouse chromosome 2.  
Overall, BLASTZ is a powerful tool for whole-genome sequence alignment, providing insights into genome evolution and functional conservation across different organisms. BLASTZ can also be integrated into larger bioinformatics pipelines or used through web-based interfaces. Additionally, it is related to other alignment tools, and its chaining strategy is like that used in the Axt tools, such as AxtBest.  
The BLASTZ method employs a local alignment algorithm, emphasizing the identification of conserved regions within sequences. It uses a strategy called \"chaining\" to link multiple short alignments into longer, contiguous alignments, which helps identify regions of similarity even in the presence of genomic rearrangements [31].  
Figure 5: Mauve algorithm pseudo-code.  
First, the algorithm started by detecting similar regions between the two genomes that occur in the same order and orientation. Via this approach, it is possible to detect exact orthologous regions [36].  
Second, BLASTZ aligns the other regions to detect small matching subsequences (seeds) and extend them while limiting the gap length to \(\mathbf{k}\) (Figure 6). This step is based on looking for identical words of eight or nineteen consecutive nucleotides in each sequence. To increase sensitivity, the algorithm allows transitions between the nucleotides. BLASTZ has a complexity of \(O(k\times n\times m)\), where \(k\) is the number of hits found and \(n\) and \(m\) are the lengths of the two sequences being compared.
# IoT and UAV Integration in 5G Hybrid Terrestrial-Satellite Networks
## 5 IoT, UAVs, and Satellites in the Emerging 5G Technology  
### IoT and 5G  
Considering the foreseen 5G framework, the International Telecommunication Union (ITU) grouped different applications with the same needs in terms of performance requirements and offereduser QoE in three different use cases [44]. One of them is the massive Machine-Type Communication (mMTC), which is characterized by a very large number of connected devices located in small areas and typically transmitting a low volume of non-delay-sensitive data. Most IoT applications fall into this category. However, the plethora of IoT application scenarios is huge and poses different challenges, which in some cases, cannot be solved by simply enhancing the terrestrial infrastructure. For example, in some rural and remote areas, both the CAPEX and OPEX costs of building (or enhancing) and managing a terrestrial communication infrastructure are not justified by the economic returns. Solutions to extend the Internet connectivity to smart farming and smart villages, among other 5G application scenarios [45], need to go beyond the terrestrial infrastructure.
# A Hybrid Mapping Method with Position and Stiffness for Manipulator Teleoperation
## 5 Results
### Performance Evaluation for Compliant Teleoperation in Contact Space  
In order to validate the effectiveness of stiffness mapping method for compliant teleoperation, the human robot interaction experiments with low and high stiffness are designed. In the low stiffness experiment, the operator A uses the palm open gesture to generate low stiffness at his arm endpoint to teleoperate the low stiffness of slave manipulator, and the operator B pushes the manipulator with small force by his fingertip, shown in Figure 15(a). Similarly, in the high stiffness experiment, the operator A uses the palm close gesture to generate high stiffness at his arm endpoint to teleoperate the high stiffness of slave manipulator, and the operator B should exert large force through his palm to push the manipulator, shown in Figure 15(b).  
Figure 16: Experimental setup of human robot interaction.  
Figure 14: Cartesian trajectory of manipulator.  
Figure 15: Actual joint trajectory with virtual impedance mapping.  
The experimental results of torque and stiffness are shown in Figure 17. In the initial stage, the interaction force is small and the stiffness of the joint is low. When the interaction force increases, the joint stiffness increases. In the experiments, the operator A can intuitively teleoperate the stiffness of the slave manipulator in order to naturally interact with the operator B.
## 2 Improved Adaptive VMD Algorithm  
### VMD Decomposition Principle  
VMD is a completely non-recursive and adaptive signal processing method recently proposed by [PERSON]. It can determine the frequency center and bandwidth of each component by iteratively searching for the optimal solution of the variational model, so that the effective separation of signal frequency can be adaptively realized. VMD is a variational problem-solving method based on the three concepts of classical Wiener filtering, Hilbert transform, and frequency mixing. To estimate the bandwidth of each modal function \(u_{k}\), the following steps construct a variational problem:  
1. Obtaining a corresponding unilateral spectrum by performing a Hilbert transform on each \(u_{k}\).
2. Moving each \(u_{k}\) spectrum to a respective estimated center frequency by an exponential hybrid modulation method.
3. The signal is demodulated according to the Gaussian smoothness and the gradient squared criterion to estimate the bandwidth of each \(u_{k}\).
# Search Patterns Based on Trajectories Extracted from the Response of Second-Order Systems
## 1 Introduction
Through its different behaviors, such as oscillatory, underdamped, or overdamped, a second-order system can exhibit distinct temporal responses [30]. Such behaviors can be observed as search trajectories under the perspective of metaheuristic schemes. Therefore, with second-order systems, it is possible to produce oscillatory movements within a certain region or build complex search patterns around different points or sections of the search space. In this paper, a set of new search patterns are introduced to explore the search space efficiently. They emulate the response of a second-order system. The proposed set of search patterns have been integrated as a complete search strategy, called Second-Order Algorithm (SOA), to obtain the global solution of complex optimization problems. To analyze the performance of the proposed scheme, it has been compared in a set of representative opti  
Figure 1: Visual taxonomy of the nature-inspired metaheuristic schemes. mization problems, including multimodal, unimodal, and hybrid benchmark formulations. The competitive results demonstrate the promising results of the proposed search patterns. The main contributions of this research can be stated as follows:  
1. A new physics-based optimization algorithm, namely SOA, is introduced. It uses search patterns obtained from the response of second-order systems. 2. New search patterns are proposed as an alternative to those known in the literature. 3. The statistical significance, convergence speed and exploitation-exploration ratio of SOA are evaluated against other popular metaheuristic algorithms. 4. SOA outperforms other competitor algorithms on two sets of optimization problems. The remainder of this paper is structured as follows: A brief introduction of the second-order systems is given in Section 2; in Section 3, the most important search patterns in metaheuristic methods are discussed; in Section 4, the proposed search patterns are defined; in Section 5, the measurement of exploration-exploitation is described; in Section 6, the proposed scheme is introduced; Section 7 presents the numerical results; in Section 8, the main characteristics of the proposed approach are discussed; in Section 9, finally, the conclusions are drawn.
## 2 Materials and Method
### Diffraction Pattern Encoder  
This work inputs the diffraction pattern as a one-dimensional vector of length 140 into the model, aggregates the information into a 384-channel one-dimensional vector through two layers of 1D-resnet [44], and uses this vector as the embedding vector of the diffraction pattern.  
The core advantage of 1D convolutional networks in XRD pattern encoding stems from their deep alignment with the characteristics of diffraction signals. As a one-dimensional sequence of angle intensity, the key information in XRD patterns is embedded in the local peak shape structures: 1D-CNN can accurately capture the position, intensity, and half-width of diffraction peaks through the local perception characteristics of convolutional kernels. Hierarchical convolutional structures can abstract peak shape combination patterns step by step, achieving efficient mapping from atomic-level diffraction features to macroscopic phase recognition.
# Thermal Conductivity and Rheology of Graphene Oxide Nanofluids and a Modified Prediction Model
## 3 Results and Discussion
### Thermal Conductivity of Graphene Oxide Nanofluids  
#### 3.3.1 Effect of Temperature  
In this section, the effect of temperature on the thermal conductivity of graphene oxide nanofluids is investigated. A laser thermal conductometer is used to measure the thermal conductivity of nanofluids, which is based on the hot wire method. Figure 5 shows the relationship between the relative conductivity and the temperature under different nanoparticle concentration. It can be seen that the thermal conductivity of nanofluids increases with the increment of temperature.  
Figure 4: Relationship between viscosity and shear rate of nanofluids (**a**–**e**) GO–water 0.002%, 0.004%, 0.006%, 0.008% and 0.010% and (**f**) relationship between temperature and dynamic viscosity.  
What is more, it is noticed that with the temperature being increased, the gradient of thermal conductivity also shows an increasing trend. On the one hand, with the increase in temperature, the increased Brownian motion of nanoparticles could cause the increased collision probability between nanoparticles in the fluid, which accompanies the enhancement of thermal conductivity. On the other hand, with the temperature increased, collision between the base liquid molecules and nanoparticles, the vibration frequency and thermal motion velocity of the base liquid molecules, would also increase; all these parameters can enhance thermal conductivity [18]. The maximum thermal conductivity is obtained at 50 \({}^{\circ}\)C and 0.010% mass concentration is 0.767 W/mK.
## 3 Dental Age Assessment  
### Traditional Approach  
The analysis of dental maturity is another age-assessment technique, as teeth mineralization is less affected by nutritional or endocrine abnormalities compared to the skeleton.  
Teeth are a valuable source of age information, particularly in severe accidents where other body parts may not be usable due to burns or severe damage, but teeth are still present. Additionally, several studies have suggested that dental age evaluation is more closely related to physiological age than skeletal estimation [77; 78].  
Dental age estimation is substantially different between childhood and adulthood: in childhood, the atlas approach is one of the simplest traditional methods, which permits a comparison between the morphological stages of dental development of the subject with a reference standard age-matched orthopantomography. A popular atlas method has been proposed by [PERSON] et al., including a table of reference radiographs of children aged from 4 months until 21 years [79].  
[PERSON] et al. also created an atlas system and provided various tables, each of which was tailored for a particular sex, while [PERSON] et al. changed the [PERSON] et al. system, adding the third module and producing more detailed tables [1].  
The method of [PERSON] et al. (1973) [80; 81] is based on a scoring system that analyzes the first seven teeth of the left lower quadrant and labels the development stage of each tooth from A to H. Lastly, a global sex-specific maturity score is obtained by summing up the single scores and then converting them into chronological age. This latter method showed a good correlation with the GR estimation [82] and with the chronological age of the Indian population [83].  
Several studies had shown that [PERSON]'s method was affected by an overestimation bias, thus, [PERSON] et al. revisited and tested it on the Belgian Caucasian population, creating new tables with scores expressed in years [84].  
Teeth are a good age mark even in adulthood, as the permanent tooth is not a static element; on the contrary, the dentin and the dental pulp undergo pathological and physiological modifications, and every tooth can be analyzed to assess age. However, canines are  
particularly useful because they are often still found in older people, in which the loss of various dental elements constitutes a common limitation [78].  
The morphological techniques are based on the evaluation of regressive variations, such as the occlusal attrition, the coronal secondary dentine opposition, the apical resorption, the loss of periodontal attachment, and many others [77]. Radiological methods based on measurements of tooth, pulp, and root lengths and the calculation of many width ratios, performed on periapical radiographs, have been developed [85].  
No matter the methods used and the accuracy of human experts, traditional dental age estimations show standard deviations of about 10-12 years because of the large spread of morphological variations that exist in nature [77; 86] and the high intra- and inter-rater variation, both of which represent a considerable limitation [77].
# Edible Packaging: A Technological Update for the Sustainable Future of the Food Industry
## 3 Results  
### Edible Packaging  
Edible food packaging may not interact with the product or can be active or intelligent packaging [7]. In active packaging, active components such as vitamins, antioxidants, and antimicrobials are added; these components are controlled in their release into the product and can increase food durability and other characteristic features like sensory features or product safety [3; 6]. Intelligent packaging has some sensors inserted within the films and coatings that can induce responses or changes that alter the freshness, weight, safety, and/or quality of packaged food products. These sensors are usually colorimetric indicators. Anthocyanin and curcumin are some examples of natural pigments that can be used as pH indicators. These sensors are mainly used for the preservation of seafood and meat products [7].  
The materials used to package food are made from compounds that humans can eat without posing any health risks. By simply altering their thicknesses with only a small intervention in the material composition and structure, they can convert these materials into various types of films and coatings. Thickness is a very important physical property for making edible films, and the optimal thickness is considered to be less than 0.3 mm [8].  
A typical edible film should be non-toxic; have good sensory qualities, microbial stability, and good mechanical properties; be safe for human consumption and non-pollutive; and low-cost. Edible food packaging materials, such as the conventional types, have to be tested to verify whether they are safe for ingestion based on certain quality requirements. One critical factor to assess the safety of edible packaging is its microbiological quality.
## 3 Our System Model and Primer
### Architecture  
The telecommunications service provider infrastructure that provides wireless connectivity to mobile users consists of the flying base station, the stationary terrestrial base station, and the backend core network. Figure 1 shows these entities and the wireless links (dashed arrows) between the user, flying base station, and stationary terrestrial base station. While the user and the base stations communicate locally within one or two channel links, the base stations on the networking edge connect to the core network server through wired communications via routers and switches, as they are geographically far apart. Onecore network serves multiple base stations covering different local cells and geographical regions. Our focus is on the wireless channels between the user, the flying base station, and the stationary base station; the wired communications and processing between the stationary base station and the backend are beyond our scope.  
Because the flying base station is not directly connected to the wired connections and cannot serve as the bridge gateway, the flying base station serves as a relay to the stationary base station. Both the downlink and uplink communications are conducted wirelessly to support the aerial mobility of the flying base stations. While the stationary base station possesses MIMO and full-duplex capabilities, we consider both scenarios: when the flying base station has full-duplex capability and when it does not, which impacts its connectivity provision performances.  
We consider a scenario where the flying base station is added to the stationary base station's existing communication channel to the user (the flying base station cannot operate by itself) and provides another channel option. The flying base station is, thus, _beneficial_ if it provides better communication channels and connectivity than the stationary base station.
# Four-Channel Buck-Type LED Driver with Automatic Current Sharing and Soft Switching
## 4 Results and Discussion  
### Experimental Results  
In the following, the waveforms were measured at rated load. From Figure 24, it can be seen that the voltages across the energy-transferring capacitors \(C_{1}\), \(C_{2}\) and \(C_{3}\), called \(V_{\text{C1}}\), \(V_{\text{C2}}\) and \(V_{\text{C3}}\), are all stabilized at different certain voltage values. Figure 25 shows the gate driving signals for \(S_{1}\) and \(S_{2}\), named \(v_{gs1}\) (=\(v_{gs3}\)) and \(v_{gs2}\) (=\(v_{gs4}\)), respectively. In addition, two blanking times of 700 us were inserted between \(v_{gs1}\) and \(v_{gs2}\). The corresponding oscillation in the negative half of \(i_{Lr1}\) (=\(i_{\text{C1}}\)) and \(i_{Lr2}\) (=\(i_{\text{C2}}\)) was caused by the resonance of the parasitic capacitance of the switch and the inductance in the circuit. In Figure 26, it can be seen that the DC values of the inductor currents \(i_{Lq1}\) (=\(i_{L1}\)), \(i_{Lq2}\) (=\(i_{L2}\)), \(i_{Lq3}\) (=\(i_{L3}\)) and \(i_{Lq4}\) (=\(i_{L4}\)) are approximately the same, and this is consistent with the current sharing among the four phases, so the load current can be evenly distributed. Figure 27 shows the currents flowing through the LED strings \(LS_{1}\), \(LS_{2}\), \(LS_{3}\) and \(LS_{4}\), named \(I_{01}\), \(I_{02}\), \(I_{03}\) and \(I_{04}\); and it can be seen that they are almost identical. Figures 28-31 display the soft-switching waveforms for the switches \(S_{1}\) to \(S_{4}\). In these figures, it can be seen that all the switches experience ZVS turn-on. Additionally, in these figures it can be seen that the resonant waveforms of the switches \(S_{1}\) and \(S_{3}\) during the turn-off period are due to the parasitic capacitances of \(S_{1}\) and \(S_{3}\) resonating with inductances. Figure 32 shows the curve of efficiency versus load current. In this figure, for the proposed circuit the maximum efficiency occurring at 50% load is 91.78% and the efficiency at 50% load is 87%. In addition, the proposed circuit has better efficiency performance than the circuit shown in [23] does. This is because the proposed circuit has ZVS soft switching only above 50% load. Note that Figure 33 shows a photo of the experimental setup.  
Figure 26: Measured waveforms at rated load: (1) \(i_{Lq1}\) (=\(i_{L1}\)); (2) \(i_{Lq2}\) (=\(i_{L2}\)); (3) \(i_{Lq3}\) (=\(i_{L3}\)); (4) \(i_{Lq4}\) (=\(i_{L4}\)).  
Figure 31: Measured waveforms at rated load: (1) \(v_{g4}\); (2) \(v_{ds4}\).  
Figure 32: Curves of efficiency versus load current without and with ZVS.  
The current sharing error percentages of LED strings under different loads were calculated as shown in Table 4, where \(n\) is \(1\) to \(4\). In this table, the absolute error percentage above \(50\%\) load is within \(2\%\). The higher the current load is, the less the absolute error percentage. As for \(25\%\) load, the absolute error percentage is within \(4\%\) due to the load current being low.