esa-sceva/satcom-chunk-collection huggingface dataset:
# Phosphine-Free-Synthesized ZnSe/ZnS Core/Shell Quantum Dots for White Light-Emitting Diodes
## 4 Conclusions  
In this study, eco-friendly, cadmium-free, blue-emitting ZnSe C QDs were successfully synthesized without phosphine precursors. The blue-emitting ZnSe C QDs were synthesized using a zinc-precursor injection method in a phosphine-free organic solvent at high temperature. The surfaces of the as-synthesized ZnSe C QDs were passivated with ZnS to reduce surface defects and increase confinement of the exciton for a higher PL QY. The ZnS shell was also formed on the ZnSe core without phosphine precursors. The C/S structure of the ZnSe/ZnS QDs was confirmed by XRD. The main XRD peaks of the ZnSe at 27.2\({}^{\circ}\), 45.3\({}^{\circ}\), and 53.5\({}^{\circ}\) were shifted to higher angles due to the ZnS shell, which caused lattice compression and confirmed the formation of the C/S structure. After the formation of the ZnS shell, the PL QY of the ZnSe increased from 19.4% to 37.4% and the ZnSe/ZnS C/S QDs emitted deep-blue light peaking at 425 nm. By utilizing the ZnSe/ZnS C/S QDs, nUV LED-pumped blue LEDs and warm white LEDs were fabricated. The white LEDs fabricated by coating the blue-emitting ZnSe/ZnS C/S QDs and the yellow-emitting ZCIS/ZnS C/S QDs on the nUV LED chip showed warm white light. These results indicate that the ZnSe/ZnS C/S QDs have good potential for white LED-based lighting applications.  
Conceptualization, H.S.J.; methodology, N.B. and J.J.; formal analysis, N.B., J.J., H.K. and H.S.J.; investigation, N.B., J.J., H.K., J.Y.B. and B.-K.J.; resources, J.Y.B. and H.S.J.; data curation, N.B. and H.S.J.; writing--original draft preparation, N.B. and H.S.J.; writing--review and editing, N.B., H.K. and H.S.J.; supervision, B.-K.J., H.S.J.; funding acquisition, H.K. and H.S.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Future Key technology program (2E31181) by the Korea Institute of Science, the Materials Innovation Project (NRF-2020M3H4A3081791) funded by the National Research Foundation of Korea, and the National Research Foundation of Korea (NRF) grant funded by the Korea government (NRF- 2019R1A2C2088940).  
Not applicable.  
Figure 7: (**a**) EL spectra and (**b**) CIE color coordinates of the blue-emitting ZnSe/ZnS and yellow-emitting ZCIS/ZnS QD-converted warm white LEDs under an applied current of 60 mA. The inset shows photographs of the ZnSe/ZnS and ZCIS/ZnS QD-converted warm white LEDs (left) without and (right) with applied current.
## 3 Spherical Robotic Positioning Device  
A schematic diagram of the proposed robotic positioning system is presented in Figure 2a. The configuration of the mechanism and arrangement of joints are, respectively, a serial manipulator with 6-DOF and an RRPRR chain (R: revolute joint and P: prismatic joint). The rotational axes of the first, second, and third revolute joints are aligned to coincide with the origin O, and each joint is connected with spherical frames. Although the final stimulator posture is determined based on the combined motion of each joint, the configuration of each joint was designed according to a main role during device operation. The main roles of the first and second revolute joints, which take as the input values (\(\theta_{1}\), \(\theta_{2}\)), is generating large movements of the end-effector with the initial length (\(r_{d}\)) of the prismatic joint. It enables the robotic positioning device to move at the outer spherical space of radius \(r_{d}\), which is represented as a yellow spherical space in Figure 2b and it is hereinafter called a safety space. In other words, the stimulator can be transferred rapidly along a large path connecting two arbitrary points on the safety space using only two revolute joints. It is an efficient movement compared to the movement of serial manipulators using all actuators. Moreover, in this movement, the safety for patient is guaranteed because all parts of the device including a stimulator are moved at the outer of the safety space. The roles of the third, fourth, and fifth revolute joints (\(\theta_{3}\), \(\theta_{4}\), \(\theta_{5}\)) is controlling the stimulator orientation because their axes are designed to intersect at the point Q and are mutually orthogonal. The prismatic joint (\(d\)) adjusts the depth of stimulation. The movement of joints except the first and second actuators locates the stimulator at the target position exactly and slowly in the inner of the safety space, as shown in Figure 2b. For these reasons, this design configuration improves the efficiency of device movement around the head and safety for patients. With this proposed configuration, the device can move in highly dexterous manner, and can avoid singularity generated by kinematic characteristics. The desired payload was set to be 2 kg considering  
Figure 1: Developed system for non-invasive brain stimulation.  
the commercial TMS simulator (TAMAS, REMED Co., Republic of Korea). \(r_{d}\) is set to be 260 mm considering the average size of the human head and the stimulator thickness [5].  
Figure 2: **(a)** Schematic diagram of the developed positioning device and **(b)** motions of device according to movements of each joint.
# A Survey of Multi-Focus Image Fusion Methods
## 2 Fusion Methods and Analysis
### Multi-Focus Fusion Methods Based on Boundary Segmentation  
This paper proposes a new classification method named the boundary segmentation method. According to the spatial characteristics of the source image, this method mainly generates a weight map for each source image by processing the region of pixels. It then calculates the fused image by the weighted average method or maximum method. This method has high operation efficiency, and the fused image can retain the image information of each local section. However, due to the improper boundary segmentation, many algorithms based on boundary segmentation often lost the edge, contour, and other image details. Therefore, strengthening the extraction of image boundary can effectively improve the quality of fusion. In this paper, the boundary segmentation method is further divided into block-based fusion method, region-based fusion method, and pixel-based fusion method.
## 2 Materials and Methods
### Accuracy Metrics  
Classification accuracy metrics have been a focus of research for many years [37; 38; 39; 40], always based on confusion matrix (Tables 3 and 4). Nevertheless, remote sensing experts consistently utilize specific metrics, such as those listed in Table 5 (Overall Accuracy (OA), Producer Accuracy (PA), User Accuracy (UA)) [38]. With the increasing adoption of machine learning algorithms, many publications now report metrics that may be more familiar to machine learning practitioners, who may lack expertise in remote sensing.  
In the medical field, image processing commonly leads to classification metrics that assess whether a patient has a particular disease. This represents a classic binary classification scenario: a positive test result indicates a sick patient (True Positive, TP), a positive test result indicates a healthy patient (False Positive, FP), a negative test result indicates a healthy patient (True Negative, TN), and a negative test result indicates a sick patient (False Negative, FN). Common evaluation metrics for such tests include accuracy, sensitivity, and specificity. In medical testing, accuracy and sensitivity metrics are critical, especially concerning TN cases. However, this emphasis differs in remote sensing, where TN instances are not as significant.  
This distinction can be illustrated with an example. Table 3 presents a confusion matrix typical of the binary classification found in medical tests, where OA equals ACC. However,  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Predicted Class**} \\ \hline
**True Class** & \
\begin{tabular}{c} A \\ **a11** \\ a21 \\ a22 \\ a31 \\ \end{tabular} &
\begin{tabular}{c} B \\ a12 \\ a32 \\ a33 \\ \end{tabular} \\ \hline OA = (a11 + a22 + a33)/(a11 + a12 + a13 + a21 + a22 + a31 + a32 + a33); ACC = (a11 + a22 + a23 + a32 + a33); (a11 + a12 + a13 + a21 + a22 + a23 + a31 + a32 + a33). & \\ \end{tabular}
\end{table}
Table 4: Many-class confusion/error matrix.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metrics** &
\begin{tabular}{c} **Formula** \\ \end{tabular} \\ \hline OA (overall\_accuracy) & sum (TP)/(TP + TN + FP + FN) \\ PA (producer\_accuracy) & TP/(TP + FN) \\ UA (user\_accuracy) & TP/(TP + FP) \\ DME (omission errors) & FN/(TP + FN) \\ CME (errors\_of\_commission) & FP/(TP + FP) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy metrics used in remote sensing.  
in Table 4, which contains only three classes, the scenario changes. Here, ACC is notably greater than OA due to a substantial presence of TN (olive color), TP (red), FP (orange), and FN (red).  
Table 5 lists the metrics used in remote sensing, Table 6 presents metrics relevant to machine learning. It is important to note that while the names of these metrics may vary, their underlying concepts remain the same. For instance, Sensitivity, also referred to as the True Positive Rate (TPR), is equivalent to PA (Producer Accuracy), and Precision or Positive Predictive Value (PPV) is synonymous with UA (User Accuracy), as summarized in Table 7.
# Subperiosteal Anchorage in Orthodontics: A Narrative Review
## 1 Introduction  
Anchorage in orthodontics is described as the resistance to undesirable dental movement and the desire to have complete control over the anchorage is no doubt universal among orthodontists [1]. Many conventional tools used to improve orthodontic anchorage are not ideal because they rely on structures that are either conceivably mobile or depend on patient compliance. Nowadays, to obtain an absolute anchorage (e.g., skeletal anchorage), different types of intraoral implants have been developed and used in the palatal plate [2]. Unfortunately, traditional endosseous implants that are osseointegrated in the alveolar bone are excessively long for some intraoral sites such as the palate [3]; implants fitted in the palate for absolute anchorage of orthodontic devices must be shorter due to the reduced thickness of the palatal bone (2-8 mm, decreasing from anterior to posterior sites and from midline to medial and lateral areas) and to prevent the invasion of nasal cavities and the subsequent loss of osseointegration [4, 5]. Short implants and miniscrews are commonly used in the palatal plate by orthodontists but one potential disadvantage of placement is the surgical risk of perforating the nasal floor or causing root injury [6, 7]. If there is not sufficient bone mass to permit the positioning of a midpalatal short implant, then subperiosteal implants would be an alternative option because there is no restriction in terms of dental eruption stages and therefore no bone drilling is required [8].  
The successful use of a subperiosteal thin disk, the Onplant Orthodontic System(tm) (Nobel Biocare, Gothenburg, Sweden) (henceforth onplant), as an orthodontic anchorage in an experimental in vivo study in animals was first described by [PERSON] and [PERSON] 1995 [9]. The onplant is a relatively thin (75 \(\upmu\)m) titanium disk manufactured in two different sizes (7.7 or 10 mm in diameter) and less than 3 mm in height (Figure 1). The device has an internal hole into which a cover screw can be placed during the healing process; then the screw can be replaced by anchorage of the orthodontic device. Onplants used to be textured on a bone-interfacing surface and coated with a thin layer of hydroxyapatite; due to this bioactive surface the onplant is joined to the bone by a mechanical bond and osseointegration after a healing period of 3-4 months [10].  
Even though the onplants are no longer available on the market, clinical and biological advantages of subperiosteal implants should be considered in the development of new types of absolute anchorage to provide clinicians with a safer technique compared to palatal short implants or orthodontic miniscrews. Therefore, the purpose of this narrative review is to offer the reader an overview of this type of skeletal anchorage that, although no longer available, represents a starting point for its use in orthodontics, especially in the most critical patients in whom the use of miniscrews is not recommended.
# Grading Algorithm for Orah Sorting Line Based on Improved ShuffleNet V2
###### Abstract  
This study proposes a grading algorithm for Orah sorting lines based on machine vision and deep learning. The original ShuffleNet V2 network was modified by replacing the ReLU activation function with the Mish activation function to alleviate the neuron death problem. The ECA attention module was incorporated to enhance the extraction of Orah appearance features, and transfer learning was applied to improve model performance. As a result, the ShuffleNet_wogan model was developed. Based on the operational principles of the sorting line, a time-sequential grading algorithm was designed to improve grading accuracy, along with a multi-sampling diameter algorithm for simultaneous Orah diameter measurement. Experimental results show that the ShuffleNet_wogan model achieved an accuracy of 91.12%, a 3.92% improvement compared to the original ShuffleNet V2 network. The average prediction time for processing 10 input images was 51.44 ms. The sorting line achieved a grading speed of 10 Orahs per second, with an appearance grading accuracy of 92.5% and a diameter measurement compliance rate of 98.3%. The proposed algorithm is characterized by high speed and accuracy, enabling efficient Orah sorting.  
Orah grading; appearance inspection; deep learning; computer vision Article
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Material** & \(\omega\) **(cm\({}^{-1}\))** & \(v_{\rm TA}\) **(ms\({}^{-1}\))** & \(v_{\rm LA}\) **(ms\({}^{-1}\))** & \(v_{\rm s}\) **(ms\({}^{-1}\))** & \(\lambda\) **(Å)** & \(\kappa_{\rm L}\) **(Wm\({}^{-1}\)K\({}^{-1}\))** \\ \hline Rb\({}_{8}\)Al\({}_{8}\)Si\({}_{128}\) & 31.1 & 3010 & 5179 & 3161 & 6.35 & 1.05 \\ Cs\({}_{8}\)Al\({}_{8}\)Si\({}_{128}\) & 41.1 & 2744 & 5134 & 3065 & 6.35 & 1.01 \\ Cs\({}_{8}\)Na\({}_{16}\)Si\({}_{136}\) & 45.5 & 2728 & 4895 & 3038 & 6.34 & 1.15(1.9 \({}^{*}\)) \\ Cs\({}_{8}\)Si\({}_{136}\) & 43.7 & 2983 & 5373 & 3322 & 6.31 & 1.10 \\ Rb\({}_{8}\)Si\({}_{136}\) & 37.9 & 3028 & 5496 & 3375 & 6.31 & 1.12 \\ \hline \end{tabular}  
\({}^{*}\)Reference [32].  
\end{table} 
Table 2: Local density approximation (LDA)-performed rattling mode \(\omega\), group velocity of transverse acoustic (TA) phonons \(v_{\rm TA}\), group velocity of longitudinal acoustic (LA) phonons \(v_{\rm LA}\), sound velocity \(v_{\rm s}\), phonon mean free path \(\lambda\) and lattice thermal conductivity \(\kappa_{\rm L}\) for filled ternary clathrate (Rb,Cs)\({}_{8}\)Al\({}_{8}\)Si\({}_{128}\), Cs\({}_{8}\)Na\({}_{16}\)Si\({}_{136}\) and (Cs,Rb)\({}_{8}\)Si\({}_{136}\). Another feature regarding the guest resonant vibration is the rattler's impact on sound velocity. It is determined by our first-principles calculation (Figure 3) that little suppression of \(v_{\rm s}\) concerning acoustic phonons is found by means of framework substitution upon Al, which replaces the Si atoms on the 8\(a\) Wyckoff sites. Meanwhile, no appreciable difference is shown with the group velocities of LA phonons as well as the sound velocity in Cs\({}_{8}\)Al\({}_{8}\)Si\({}_{128}\) and Rb\({}_{8}\)Al\({}_{8}\)Si\({}_{128}\) (see Figure 3). The above table demonstrates our predicted group velocity for different acoustic phonon modes along with the macroscopic velocity (\(v_{\rm s}\)) in a detailed manner. From Table 2, our predicted lattice thermal conductivity of Cs\({}_{8}\)Na\({}_{16}\)Si\({}_{136}\) yields 1.15 W m\({}^{-1}\) K\({}^{-1}\), which stays lower than that of the experimental work (1.9 W m\({}^{-1}\) K\({}^{-1}\)). This is indicative of the fact that the phonon mean free path is underestimated. This induced underestimation might be due to an inappropriate usage of the neighboring guest-guest distance (6 th column of Table 2). As discussed in some reports [34; 35; 36], the dominant \"three-phonon\" processes were proposed here to govern the phonon-rattling interaction due to \"avoided-crossing\", leading to a reasonable evaluation on the mean free path. Therefore, it seems necessary to recompute the phonon lifetime of acoustic phonons near the avoided crossing point and to obtain a more reliable estimation on \(\kappa_{\rm L}\).
# Microencapsulation of Natural Food Antimicrobials: Methods and Applications
## 2 Encapsulation Methods
### Supercritical Antisolvent Precipitation (SAS)  
Supercritical fluids have the unique characteristics of acting like liquids and gases above their critical point and exhibit properties such as low viscosity, low density, high mass transfer and high solvating power. Due to its attractive properties as a clean, cost effective, chemically stable and nontoxic solvent, supercritical carbon dioxide (SC-CO\({}_{2}\)) has been regarded as an important option for the microencapsulation of bioactive agents for the food industry. In addition, it is easily retrievable after the process and ensures the formation of more homogeneous particles than those produced with organic solvents. SC-CO\({}_{2}\) needs relatively a mild temperature and pressure (temperature: 304.1 \({}^{\circ}\)K and pressure: 7.38 MPa) [61]. Different functions of supercritical fluids as solvents (rapid expansion of supercritical solutions, RESS), antisolvents (supercritical antisolvent precipitation, SAS) and cosolvents (particles from gas-saturated solutions, PGSS) led to the development of various particle formation processes [62].  
In the SAS technique, being the most popular precipitation technique and similar to spray drying, the application of SC-CO\({}_{2}\) as an antisolvent precipitates CO\({}_{2}\) insoluble polymers; consequently, bioactive substances are encapsulated within the polymer matrix of the particles (Figure 3). The solubility of the solutes in the mixture is reduced by the antsolvent activity of the high-pressure CO\({}_{2}\). For this technique and its variations, polymers should be dissolved in a solvent or solvent mixture (called a cosolvent) miscible in CO\({}_{2}\) before spraying feed formulation into SC-CO\({}_{2}\)[61;63]. Several morphologies, such as crystals, nanoparticles and microparticles, can be formed via the SAS precipitation pro cess [64]. Some advantages that make supercritical or gas-compressed techniques attractive for the production of protein-loaded polymeric micro/nanoparticles include process flexibility, high loading yield, efficient protein encapsulation and enhanced physicochemical properties [65]. The SC-CO\({}_{2}\) process used for encapsulation of lysozyme within PLGA microparticles improved protein stability and preserved its activity up to 90%, which was confirmed by _Microoccus lysodeikticus_ assays [32].
## 4 Factors Affecting the UHPC-NC Interface Adhesion
### Freeze-Thaw Cycles  
UHPC is almost unaffected by the freeze-thaw cycle in UHPC-NC specimens; however, its interface transition zone has high porosity and multiple tiny cracks, making it the weakest part of the combined member. In [64,72,78,94], the effect of the freeze-thaw cycles on the interface bonding strength of UHPC-NC was investigated. The interface bonding strength decreased as the number of freeze-thaw cycles increased. [PERSON] [95] investigated the effect of different factors on the interface bonding performance of the specimens  
Figure 20: Interface permeability test.  
under freeze-thaw cycles, reporting that the steel fiber content and interface roughness considerably affected the interface bonding split strength, whereas the casting direction exerted only a minor effect. [PERSON] [27] investigated the effect of the freeze-thaw cycles on the interface bonding strength of UHPC-NC and NC-NC using the constraint ring method. The interface bonding strength of UHPC-NC considerably decreased to less than that of the NC-NC. Furthermore, [PERSON] [96] conducted 0, 300, 600, and 1000 freeze-thaw cycles on combined specimens of NC-NC, NC high-strength mortar (HSM), UHPC-NC, and UHPC-UHPC to compare and analyze the interface bonding strengths through slant shear tests. As shown in Figure 21, the interface bonding strength decreases to different degrees. Moreover, UHPC-NC has higher bonding strength and freeze-thaw resistance than NC-NC.
# Plant Electrical Signal Classification Based on Waveform Similarity
## 2 Related Works
### Time Series Waveform Recognition and Classification
#### 2.2.2 Recognition of Waveform in Time Series  
The accuracy of detecting ECG QRS wave in MIT-BIH databases can reach 99.95% [61]. Meanwhile, noise depression and real time are two important performance indices in QRS detection. As discussed in Section 2.2.1, the filter methods in Section 2.2.1 can be used to remove noises in ECG. Then, the common detection method is setting proper thresholds after various transformations, the QRS waveform often can be detected above the thresholds.  
For the variance of plant action potential, we still cannot recognize APs accurately by detection methods alone.
# On Smartphone Power Consumption in Acoustic Environment Monitoring Applications
## 5 Limitations of the Study  
The most obvious limitation of the study is its relatively small sample size: we analyzed the power consumption of only 18 Android devices. Although the selected subset of smartphones represents devices from different manufacturers and includes various types of Android OSs, it is nonetheless very small in comparison to the number of Android smartphone models available on the market (as of October 2017, the Google Play database of supported Android devices contained more than 15,000 models). Thus, some conclusions, especially those reached in Section 3.4, should be used with caution. In addition, we focused mainly on devices in the low-end and mid-range categories. Future research on the topic should include a wider selection of test devices, and should evaluate more high-end smartphone models.
# Joint Balanced Routing and Energy Harvesting Strategy for Maximizing Network Lifetime in WSNs
## 2 Related Work  
### Routing Based on Forwarding Factors  
Many types of load-balanced transmission strategies have been proposed, including routing based on forwarding factors, hybrid power transmission, power control, etc. In the routing based on forwarding factors [16; 24; 25; 26; 27], routing paths are usually selected in accordance with several forwarding factors, such as the residual battery capacity, the distance to the sink, the degree of the nodes, and so forth.  
The authors in [24] studied the ant colony optimization(ACO)-based path selection schemes by considering several factors, including the battery level of forwarding nodes, the distance to the sink, the travelled distance, and so on. Based on the one-dimensional queue network, the authors in [25] proposed an opportunistic routing protocol by considering forwarding factors with the distance to the sink and the remaining battery capacity of the sensors. Based on the multiple forwarding factors, a position-aware routing scheme [26] is proposed to consider the factors of the residual energy, node degree, and distance to the sink to balance the node energy and improve the network lifetime. A mixed transmission scheme is proposed in [27] that each node trades off between the multi-hop and single-hop transmission to extend the network lifetime by considering the forwarding factors including the link reliability, the degree of nodes and the remaining battery capacity. In [16], two hybrid multi-hop/single-hop transmission strategies (power efficiency and power utilization strategies) with adaptive routing are proposed to extend the network lifetime of wireless sensor networks (WSNs). The power efficiency strategy aims to minimize the overall power consumption and the power utilization strategy endeavors to minimize the maximum power consumption among hotspots. In addition, the adaptive routing method is a late-remedy method to consider the channel status in each operation round.