esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Intrinsic and Extrinsic Factors Associated with Hair Graying
### Race, Gender, and Family History  
The age at which premature hair graying is defined varies by race: 20 in Caucasians, 25 in Asians, and 30 in African Americans [18; 22; 62]. This is because the genetic background and natural hair color of different races vary, and the time at which the results of hair graying become apparent varies [73]. Although natural hair color and brightness vary by race, there is no evidence that graying occurs more frequently or intensely in certain races [19].  
In many races, men tend to develop gray hair earlier and more extensively than women of similar age [19]. Graying patterns also vary by gender [19; 78]. In men, hair graying tends to occur first and is more prominent in the temporal region (the sides of the head). In women, the frontal and parietal regions (top and crown) are affected more often and early. In a survey study conducted on 620 gray-haired Korean subjects, 33.9% of men and 55.2% of women responded that they dye their hair regularly [79]. This means that women are relatively more concerned about hair graying.  
Several studies have supported that hair graying is significantly affected by a hereditary component [66; 67; 73]. A cross-sectional survey of 100 young men less than 25 years old with premature hair graying showed that 39% of individuals had a family history, with 26% from the paternal side, 10% from the maternal side, and 3% from both parents [67]. It is noteworthy that a man's hair graying may be more influenced by his father's history than his mother's.
# DB-Net: Detecting Vehicle Smoke with Deep Block Networks
## 4 Method
### Coarse-to-Fine Training  
As mentioned in the last Section, the LaSSoV [25] has the largest number of images with bounding-box annotations, which can be regarded as rough polygon annotations with abundant background information. To fully take advantage of this dataset, we propose a coarse-to-fine training strategy that first pre-trains the DB-Net on LaSSoV, and we then finetune it on our PoVSSeg.  
Before training, we converted the semantic segmentation dataset and the bounding box-based detection dataset into the block-wise labeled dataset to fit the DB-Net structure. To do this, we divided the images into several blocks of the same size. According to DB-Net's structure, the block size was set to \(16\times 16\). For example, when the image size was \(960\times 540\), the image was divided into \(60\times 34\) blocks. The blocks that contained smoke were labeled as smoke blocks, while the rest were labeled as background blocks. The original PoVSSeg was annotated with high-quality polygons, and the detail was retained in the block-wise labeled PoVSSeg-block. The bounding-box annotated LaSSoV was also converted into a block-wise labeled dataset. Notably, the bounding box contained vehicle smoke and a part of the background, which results in some background blocks incorrectly labeled as vehicle smoke after converting to the block-wise annotated dataset. Therefore, only a coarsely annotated block-wise dataset, LaSSoV-block, can be gained after conversion. Figure 5 shows the converted block-wise annotated sample from PoVSSeg and LaSSoV.  
Figure 4: Overall structure of the DB-Net. The main branch is responsible for extracting features, while the aggregation branch is designed to fuse these features. The “\(\bigoplus\)” represent pixel-wise summation.  
Figure 5: Block-wise annotation converted from PoVSSeg and LaSSoV. Left: Fine-annotated sample. Right: Coarse-annotated sample.  
After the data conversion, the model was first trained on the LoSSoV-block training set for mining the information hidden in various backgrounds and learning the ambiguous knowledge about the smoke. It is difficult for the model to extract accurate features via coarse training due to error annotation in the LoSSoV-block dataset. Therefore, the PoVSSeg-block training set, which contains precise annotation, was utilized to fine-tune the model after coarse training. Fine-tuning is a crucial part of the training process for the model to acquire correct knowledge about the smoke. By training the model in this manner, we were able to take advantage of high-quality annotations in the PoVSSeg-block, as well as rich samples in the LoSSoV-block, resulting in a more effective model.
## 2 Materials and Methods
### Materials of 3D Printed Bite Blocks  
Initially, the 3D printed bite blocks were laid out with MED610, a biocompatible material widely used in medical treatment, surgical guides, orthopaedic procedures, and dental implants. However, patients with HNC often complained of pain and discomfort when 3D printed bite blocks, fabricated from the rigid MED610, were used for oral wounds, mucositis, or xerostomia during RT. Therefore, we fabricated the 3D printed bite blocks with silicone, an elastic but firm, strong, durable, and safe material. These 3D printed bite blocks were manufactured by the silicone 3D printer (S100, San Draw  
Figure 1: Clinical flow chart showing the steps in the head and neck RT process with the silicone 3D printed bite blocks. First, a primary CT simulation for the head and neck was performed with conventional oral corks; second, the obtained CT images were converted to uncompressed DICOM images; third, the DICOM images were translated into blueprints with the occlusal surface and tongue contoured, and the blueprints were checked by radiation oncologists; fourth, 3D printing with silicone proceeded according to the blueprints; fifth, secondary CT simulation with silicone 3D printed bite blocks was carried out; sixth, RT planning and dosimetry with silicone 3D printed bite blocks was reviewed before treatment; seventh, the patient positioning verification was checked by port film imaging. RT, radiotherapy; 3D, 3-dimensional; CT, computed tomography; DICOM, Digital Imaging and Communications in Medicine.  
Inc., Taichung, Taiwan), and the material of bite blocks was SIL 28 (San Draw Inc., Taiwan), which was one part of Room Temperature Vulcanization (RTV) silicone specially designed for 3D printing, has passed biocompatibility testing of ISO 10993-5 (Figure 2). The hardness (ASTM D2240) of SIL 28 was 28, Shore A, and the tensile strength was 270 psi. The tear strength was 5 kgf/cm, and the elongation was 300%. The silicone bite blocks had superior mechanical and physical properties, such as good elasticity, adequate rigidity, heat and cold resistance, and great biocompatibility. The hardness of the unique silicone 3D printed bite blocks was adjustable by varying their internal structures and silicone components. The silicone bite blocks lost their smell after being left to stand in air. Their dosimetry was examined and showed no interference in RT planning. All patients tolerated the blocks well in intraoral placement.  
Figure 2: Shape and clinical use of the silicone 3D-printing bite block. The silicone 3D printing bite block exhibited superior mechanical and physical properties, such as good elasticity, adequate rigidity, heat and cold resistance, and great biocompatibility. (**a**) The shapes of the occlusal surface and tongue depressor were integrated into one piece. (**b**) The patient tolerated the silicone 3D printed bite block well in the CT simulation and RT process.
# On-Demand Waveguide-Integrated Microlaser-on-Silicon
## 2 Results and Discussion  
### On-Demand Si-Waveguide-Integrated Microdisk Laser  
Figure 1a depicts a schematic of the proposed microdisk laser transferred onto a small Si-post and integrated with a strip-type Si WG. The III-V semiconductor microdisk includes an optical gain medium and represents a high-index active optical microcavity that supports resonant whispering gallery modes (WGMs). The Si-post and WG structures were rationally designed to achieve an efficient light coupling when the light source was integrated. Thereafter, the PDMS transfer-printing technique was used for a highly accurate and damage-free transfer of a microdisk onto a target Si-post, which readily enables the realization of on-demand microlaser-on-Si. Figure 1b depicts a tilted-view scanning electron microscopy (SEM) image of a fully fabricated WG-integrated laser device. The InGaAsP microdisk with a diameter of approximately 4.5 \(\upmu\)m and thickness of 260 nm was transferred onto a Si-post with a size of approximately 1.6 \(\upmu\)m. The multi-quantum  
well (MQW) structure with a central emission wavelength of approximately 1.5 \(\upmu\)m was introduced in the middle of the microdisk, where it served as an optical gain medium. When integrated, the Si-post supported the central part of the microdisk such that the boundary of the microdisk, where the WGMs are typically formed, could be air-suspended. This high-index contrast helps in strong field confinement, and thus enables the successful excitation of high-quality-factor (Q-factor) resonant modes.
# The Modification of pBFT Algorithm to Increase Network Operations Efficiency in Private Blockchains
## 3 Modification of the pBFT Algorithm
### Performance Evaluation of mpBFT
#### 3.3.2 NCC: Network Communication Cost  
The network communication cost can be written as Equation (2) based on the node communication process in the mpBFT process.  
Equation (2): network communication cost.  
\(\gamma\) = computational cost for frequency of use (\(\alpha\)1) and the institution's reliability (\(\beta\)1)  
\[\text{NCC}-\text{case1}:\ \left(\frac{N}{3}\right)\times\left(\frac{n}{3}\right)+\gamma \tag{2}\]  
\[\text{NCC}-\text{case2}:\ \left(\frac{N}{3}\right)+\left(\frac{N}{3}\right)+\gamma \tag{3}\]  
While pBFT requires \(2N^{2}\), mpBFT NCC(NCC-case1, NCC-case2) requires \(\frac{N^{2}}{3}+\gamma\) and \(\frac{2N}{3}+\gamma\). \(\gamma\) is computational cost for frequency of use (\(\alpha\)1) and the institution's reliability (\(\beta\)1).  
As shown in Table 3, as the number of nodes increases, a larger difference in network communication costs can be seen.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Node** & **pBFT** & **mpBFT** & **mpBFT** \\  & **Case1** & **Case2** \\ \hline
4 & 1.452 & 0.7981 & 0.7981 \\
5 & 1.783 & 0.8991 & 0.8991 \\
6 & 1.902 & 0.9919 & 0.9919 \\
7 & 2.411 & 1.1891 & 1.1891 \\
8 & 2.684 & 1.4523 & 1.4523 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fault Tolerance Comparison.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Node** & **pBFT** & **mpBFT** & **mpBFT** \\  & **Case1** & **Case2** \\ \hline
1 & 2.1 & 0.3 & 1.2 \\
2 & 7.9 & 0.9 & 2.1 \\
3 & 18.1 & 2.5 & 3.2 \\
4 & 32.1 & 2.9 & 4.5 \\
5 & 51.9 & 3.7 & 5.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Network Communication Cost Comparison.
## 2 Principle of DETL  
Currently, there are several ways to categorise DETL. With its rapid development, many classification methods cannot fully cover the types of lenses or adequately differentiate them individually. This paper classifies lenses according to their tuning principles and structures. Among them, the tuning principle is mainly divided into two categories: deformation-based and displacement-based zoom. The former achieves zoom by changing the lens shape through a dielectric elastomer actuator, while the latter accomplishes zoom by driving the axial or radial displacement of the lens position. The structure of the lens is divided into monolithic, composite, array, and metalenses, with a certain correspondence between them, as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tuning Principle** & **Subtype** & **Lens Construction** & **Type of DEA** \\ \hline Deformation-based & Direct Drive & Monolithic Lens & Planar DEA, Spherical DEA and Conical DEA \\ \hline  & & Lens Array & Planar DEA \\ \hline  & Indirect Drive & Monolithic Lens & Spherical DEA \\ \hline  & & Lens Array & Spherical DEA \\ \hline Displacement-based & Axial Displacement & Composite Lens & Planar DEA and Bending-Mode DEA \\ \hline  & Lateral & & \\  & Displacement & Lens Array & Planar DEA \\ \hline  & & Metalens & Planar DEA \\ \hline \hline \end{tabular}
\end{table}
Table 1: DETL classification (author’s original classification).  
Based on this categorisation, this chapter first introduces the working principle of DEA, followed by a detailed description of the principles and structures of various types of DETLs.
# Semantically Guided Enhanced Fusion for Intent Detection and Slot Filling
## 4 Experiments
### Dataset and Settings  
In the experimental setup, two benchmark datasets are utilized to evaluate the performance of IDSF tasks, which are ATIS [2] and SNIPS [34]. The ATIS dataset contains recordings of booking flights, whose training set contains 4478 utterances, development set contains 500 utterances, and test set contains 893 utterances. SNIPS was collected from the Snips personal voice assistant, whose training set contains 13,084 utterances, development set contains 700 utterances, and test set contains 700 utterances.  
Some hyperparameters are set initially as follows: the Adam optimizer with a learning rate of \(1\times 10^{-5}\), batch size of 32, training epochs of 30, and a cap on sentence length at 32 tokens. Moreover, to convert vector values to probabilities effectively and enhance computational speed, the ReLU activation function is integrated.
## 3 Results and Discussion
### Electrochemical Characterization
According to density functional theory (DFT) analysis, the work function of SWCNTs is higher than that of In\({}_{2}\)S\({}_{3}\), resulting in a downward band bending at the interface and a potential barrier for electron transfer. However, the study found that the barrier height is relatively low, allowing for efficient electron transfer across the heterojunction [15]. \[\text{In}_{2}\text{S}_{3}+\text{OH}^{-}\leftrightarrow\text{In}_{2}\text{S}_{3 }\text{OH}+\text{e}^{-} \tag{1}\]  
Figure 2g illustrates the Ragone plot (a graph between power density and energy density) of samples INS and INSC. For SC applications, the Ragone plot can analyze the functionalities and operational material efficiency [16]. Sample INSC exhibits a high energy density of 22.93 Whkg\({}^{-1}\) at a power density of 400 Wkg\({}^{-1}\). CV measurements  
Figure 2: (a) Comparative CV curves at a scan rate of 100 mVs\({}^{-1}\); (b) extracted specific capacity values at various scan rates; (c) CV curve at the various potential windows at a scan rate of 100 mVs\({}^{-1}\); (d) comparative GCD curves; (e) extracted specific capacity values at various current densities; (f) GCD curve at the various potential windows; (g) Ragone plot; (h) cyclic stability spectra; and (i) Nyquist plot of sample INS and INSC. at 5 mVs\({}^{-1}\) were performed to evaluate its cyclic stability (Figure 2h) for 3000 cycles. Interestingly, sample INSC (96.8%) displays outstanding cyclic retention compared to the INS sample (90%). Swelling and shrinkage are speculated to be two main factors that influence the stability of electrode materials. Thus, significant enhancement of cyclic stability is attributed to SWCNTs enabling physical reinforcement, leading to a robust morphology of the heterostructure. In addition, the insertion of SWCNTs into electrode materials significantly increases the stability behaviors of the electrodes by forming \(\pi-\pi\) bonds [17]. This increases the number of active sites and creates a more conductive channel, allowing greater electrode-electrolyte interaction. SWCNTs establish cross-linking with layers, which increases conductivity and, as a result, improves the electrochemical performance [18]. Electrochemical impedance spectroscopy (EIS) is used to examine the charge transfer and estimate the impedance parameters of the electrode material. Figure 2i shows that the EIS spectra of the fabricated SSC device range from 100 kHz to 0.01 Hz with a bias voltage of 0.1 V. The nearly identical shape of the EIS curves implies that the charge is channelized comparably in each material sample. A smaller semicircle, smaller intercept, and more vertical line toward the low-frequency side suggest a greater mass transfer limit, faradic charge transport, and reduced inter-facial charge resistance in the sample INSC. The estimated impedance parameters for both the electrode samples are tabulated in Table S2 in Supporting Information.
## 4 Laboratory Experiment  
### Experiment Setup  
Figure 6 shows the wired setup of laboratory experiment and parameters are listed in Table 3. We assumed spectrum superposing scenarios between the primary system and the secondary system with STPA-BAA. Two prototype systems transmit multicarrier signals and interfere with each other. Each signal output from the transmitter is divided and superposed to another system through a variable attenuator (ATT) and a phase shifter. Interference signal level is controlled by the variable attenuator. The phase shifter provides the angle of arrival (AoA) difference between the primary and secondary receivers. AoA difference is set to 20 degrees where desired and interference signals are isolated enough and BAA can effectively suppress interference signal. Additive white Gaussian noise (AWGN) is also added; the signal-to-noise power ratio (SNR) is set to 30 dB. We assumed an interference limited scenario in order to clearly evaluate interference suppression performance of BAA. SNR and SIR are determined by measuring each signal power (desired signal, interference, and noise) just before the receiver input. This wired setup brings a frequency flat channel. Although the practical effectiveness of the STPA-BAA should be evaluated in multipath fading channel, this experiment first verifies its performance from the implementation viewpoint, the quantization levels or the receiver dynamic range.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Number of Transmission antenna & 1 (Primary system) \\  & 1 (Secondary system) \\ Number of Reception antenna & 2 (Secondary system) \\ Modulation scheme & QPSK (quadrature phase shift keying) \\ FEC (forward error correction) scheme & Convolutional code \\ Constraint length & 7 \\ FEC Coding rate & 1/2 \\ FEC Decoder & Viterbi \\ Bit interleave & None \\ Channel & Frequency flat \\ SNR (signal-to-noise power ratio) & 30 dB \\ Angle of arrival difference & 20\({}^{\circ}\) \\ Number of sample for BAA, \(S\) & 512 (128 symbols) \\ CMA iteration number & 10 \\ High/Low-level subcarrier power ratio, \(G\) & 4, 8, 12, 16, 20, 24 dB \\ Number of high-level subcarrier, \(N_{\text{High}}\) & 1, 2, 3, 4, 5, 7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experiment parameters.  
Figure 6: Experiment block diagram. BER: bit error rate; ATT: attenuator.
# A Multi-Objective Optimization of Secure Pull Manufacturing Systems
## 1 Introduction
The technique was later modified by various researchers to enable encrypting and reducing the storage size of data, which resulted in faster and more secure data transmission over networks [12; 13]. DNA encryption can be defined as the practice of hiding data by modifying a DNA sequence. The design of BioGamal technique was first introduced in [14]. BioGamal is designed to increase information security through the use of ElGamal's digital signature technology, combined with DNA encryption and decryption techniques. Optimization is the major concern for the production to achieve the best possible result under the given conditions. In recent approaches, using a single-objective approach loses information about the balance between the two objectives. In contrast, the multiobjective approach gives decision-makers a deeper understanding of the balance between the two objectives. In this regard, a multiobjective approach to optimization is recommended. In a casual case including the advancement, the issue was consistently made up by augmenting or limiting an actual capacity, which could get the info esteems from inside a permitted set and, afterward, register the outcome for the capacity. Likewise, advancement hypotheses and procedures for different arrangements incorporate applied science. In addition, optimization needs to reach \"best worth\". That esteem, for the target work, should be controlled in various types of target capacities and spaces. Various papers are completed in the optimization process concerning the Kanban cards [15]; a simulation optimization technique is applied alongside a blend of simulation software packages and metaheuristic algorithms--for example, genetic and Guided Local Search, GLS [16],algorithms--which are more adaptable to take care of the issue. At last, the predominance of this plan is experiential. In this article, we propose a new secure optimized pull manufacturing system to increase the security of data transferred through Kanban cards, as well as minimize the time and cost of the manufacturing process. The BioGamal algorithm is designed to increase the security of the Kanban cards of a pull system by combining Gamal algorithm and DNA cryptography. Gamal is used as a digital signature to authenticate the data on the Kanban card, while DNA is used to encrypt and decrypt data. Then, the FPA multiobjective technique is applied with a different number of stages to optimize the cost and time of the overall secured pull manufacturing system. The article is organized as follows: Section 2 give a brief description of the Kanban system, pull control system, security system, and flower pollination algorithm. The proposed secure optimized pull manufacturing system is presented in Section 3. Section 4 examines the simulation model and the performance of the proposed system. Finally, Section 5 concludes the proposed work.
# An open-BIM Approach to IoT Integration with Incomplete As-Built Data
## 1 Introduction
### Uses of the BIM and IoT Technologies  
In the manufacturing and construction phase, sensor data and BIM technologies can be used to monitor the construction site schedule and improve the procurement process [27]. The use of Virtual and Augmented Reality (VR/AR), which simulate the reality using either virtual reality headsets or multi-projected environments or simply add digital elements to a live view (e.g., the game Pokemon Go), can support construction operations and prevent issues in the execution process (e.g., interference among systems and structural parts) [28]. Global Positioning System (GPS) technologies and Radio-Frequency IDentification (RFID) sensors are utilised to monitor the positioning of building components against the BIM model [29]. In construction logistics and management, IoT data can be employed to track and improve construction site operations [29] within the context of the lean construction, concerning the digitisation and automation of the construction supply chain [30]. In Health and Safety (H&S) management, VR and BIM data have been employed to improve the training process of workers, and their ability to recognise and assess risks [31]. In the same sector, Ref. [32] propose integrating wireless sensor networks and BIM technologies to monitor the safety status (presence of hazardous gas) of underground constructions sites.  
In FM, BIM and IoT data integration have been studied to enrich the condition monitoring of critical assets and real-time assessment of their performance [33]. VR/AR technologies enhance indoor navigation [34], which upgrades maintenance procedures. BIM and energy data integration improves energy management [35]. Ultrasonic sensors can be used with BIM for maintenance service optimisation [36]. Dynamic environmental data can be used to achieve higher user comfort and to adapt system behaviour [37].  
The number of applications is growing, and the topic has gained momentum, representing a leading research field, which lays a solid foundation for the identification, collation and curation of operational datasets and demonstrating the great potential of the DT applications in AECO.