esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Integrated System Platform  
The proposed model uses electromagnetic radio waves to command and detect the current status of nanorobots inside the body. Therefore, the cell phone is applied for medical nanorobotics platform [7, 64, 65]. This occurs as the cell phone emits a magnetic signature to the passive CMOS sensors embedded in the nanorobot, which enables sending and receiving data through electromagnetic fields [66]. From the last set of events recorded in pattern arrays, information can be reflected back by wave resonance [64].  
The nanorobot model includes embedded IC (integrated circuit) nanoelectronics [67], and the architecture involves the use of satellites and mobile phones for data transmission and coupling energy [68, 69]. The nanorobot is programmed for sensing and to detect concentration of alpha-NAGA in the bloodstream [7, 16, 70]. The nanorobot architecture uses an RFID (radio frequency identification device) CMOS transponder system for _in vivo_ positioning [70], adopting well established communication protocols, which allow track information about the nanorobot position [5, 7].  
The ability to manufacture nanorobots should result from current trends and new methodologies in fabrication, computation, transducers and nanomanipulation. Depending on the case, different gradients on temperature, concentration of chemicals in the bloodstream, and electromagnetic signature are some of relevant aspects when monitoring _in vivo_ biochemical parameters. CMOS VLSI (very-large-scale integration) design using deep ultraviolet lithography provides high precision and a commercial way for manufacturing early nanodevices and nanoelectronics systems. Innovative CMOSFET (complementary metal oxide semiconductor field effect transistor) and some hybrid techniques should successfully drive the pathway for the assembly processes needed to manufacture nanorobots, where the joint use of nanophotonics and CNTs can even accelerate further the actual levels of resolution ranging from 248 nm to 157 nm devices [71]. To validate designs and to achieve a successful implementation, the use of VHDL (very high speed integrated circuit hardware description language) has become the most common methodology utilized in the integrated circuit manufacturing industry [72].
## 3 Approach
### Material Analysis and Spectral Band Selection
We conducted a detailed comparison of 1095 nm and 1580 nm wavelengths, including the 1345 nm wavelength as an intermediate reference point to help validate our observations about moisture-based discrimination. Figure 9 compares three wavelengths for the head, arms, and clothing of a human and a mannequin. It can be seen that the relative intensity difference between the 1095 nm wavelength and the 1580 nm wavelength is reversed due to the moisture difference. Similarly, as shown in Figure 9,e, the arm areas of the human and mannequin display a phenomenon similar to that observed in the head, due to the difference between skin and plastic. In contrast, Figure 9,f show the clothing areas, where both the human and mannequin are wearing similar materials, indicating no relative difference between wavelengths. Figure 8: Region-based intensity analysis of the human head in the short-wave infrared (SWIR) range. Key wavelengths at 1095 nm (blue) and 1580 nm (green), serving as discriminators, and an intermediate wavelength at 1345 nm (yellow) are highlighted with bold colored lines. Additionally, we conducted experiments on various objects to differentiate between real and artificial fruits and vegetables. Using a Crevis HG-A130 SW camera with a 1550 nm bandpass filter, we captured images that leveraged insights from our previous experiments. At this wavelength, moisture absorption causes real fruits/vegetables to appear darker due to minimal reflection, whereas artificial fruits/vegetables appear brighter due to higher reflectance. Figure 10 provides example images of both real and artificial fruits/vegetables; while conventional visible-spectrum images make it challenging to distinguish between them, SWIR images reveal a clear distinction between real and artificial objects.
# Modeling Volatility Characteristics of Epileptic EEGs using GARCH Models
## 6 Results
### Half-life and Confidence Intervals
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Seizure Channels**} & \multicolumn{2}{c}{**Non-seizure Channels**} \\ \hline  & **RAST3** & **RAST4** & **RMST4** & **Half Life** \\ \hline \multirow{4}{*}{**Segment**} & **HL** & **HL** & **HL** & **Median** \\  & **95\% CI** & **95\% CI** & **95\% CI** & **95\% CI** & **Median** \\  & **Overlapping** & **Overlapping** & **Overlapping** & **Overlapping** & **Min** \\  & **Interval Count** & **Interval Count** & **Interval Count** & **Max** \\ \hline \multirow{4}{*}{1} & 1.64 & 1.78 & 6.36 & 14.24 \\  & (1.34,2.05) & (1.57,2.04) & (4.23,12.16) & 6 \\  & 0 & 0 & 29 & 312.3 \\  & 1.83 & 1.49 & & 12.27 \\
2 & (1.45,2.41) & (1.29,1.75) & N/A & 4.03 \\  & 1 & 0 & & 165.01 \\  & 5.25 & 1.93 & 6.49 & 18.42 \\
3 & (4.41,6.44) & (1.7,2.21) & (4.23,13.17) & 3.24 \\  & 21 & 0 & 27 & 452.16 \\  & 1.9 & 1.79 & 6.33 & 19.56 \\
4 & (1.62,2.28) & (1.57,2.06) & (3.53,23.46) & 4.44 \\  & 0 & 0 & 38 & 152.9 \\  & 1.27 & 1.5 & 6.42 & 8.27 \\
5 & (1.02,1.61) & (1.32,1.72) & (3.15,99.28) & 2.16 \\  & 0 & 0 & 50 & 413.78 \\  & 1.61 & 1.98 & 268.82 & 29.05 \\
6 & (1.35,1.97) & (1.75,2.26) & (134.36,148185.87) & 4.24 \\  & 0 & 1 & 14 & 139.58 \\ \hline \multicolumn{4}{c}{**(A)** ) Pre-seizure data.} \\ \hline  & & & & **Non-seizure Channels** & **Non-seizure Channels** \\ \hline \multirow{4}{*}{**Segment**} & **RAST3** & **RAST4** & **RMST4** & **Half Life** \\ \cline{2-5}  & **HL** & **HL** & **HL** & **Median** \\  & **95\% CI** & **95\% CI** & **95\% CI** & **Min** \\  & **Overlapping** & **Overlapping** & **Overlapping** & **Max** \\  & **Interval Count** & **Interval Count** & **Interval Count** & **Max** \\ \hline \multirow{4}{*}{1} & 1.13 & 1.47 & 6.49 & 9.33 \\  & (0.90,1.47) & (1.22,1.80) & (4.06,14.82) & 1.16 \\  & 1 & 1 & 42 & 283.79 \\  & 1.72 & 1.88 & 70.74 & 11.43 \\
2 & (1.30,2.39) & (1.53,2.37) & (36.74,850.50) & 4.89 \\  & 0 & 0 & 23 & 166.48 \\  & 1.75 & 1.71 & 13.65 & 18.72 \\
3 & (1.22,2.76) & (1.38,2.21) & (7.88,46.53) & 3.06 \\  & 1 & 1 & 55 & 237.25 \\  & 1.84 & 1.36 & 39.8 & 13.06 \\
4 & (1.44,2.48) & (1.16,1.61) & (21.74,219.32) & 6.13 \\  & 0 & 0 & 39 & 153.46 \\  & 5.62 & 6.18 & 16.56 & 6.61 \\
5 & (4.54,7.31) & (5.14,7.72) & (10.63,36.42) & 2.58 \\  & 30 & 30 & 38 & 54.82 \\  & 1.67 & 1.55 & 8.98 & 10.64 \\
6 & (1.33,2.19) & (1.32,1.84) & (5.37,24.85) & 4.67 \\  & 0 & 0 & 50 & 49.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Half-Life and 95% CIs (Asymptotic Method) Pre-seizure data.
## 4 Numerical Illustration
### Models Comparison  
In this subsection, we compare the fit of the ZIP regression model and the NBI and PLN regression models with varying dispersion based on DEV, AIC, and SBC, which are classic hypothesis/specification criteria.  
The DEV is defined as  
\[DEV=-2\hat{I}\big{(}\hat{\mathbf{\theta}}\big{)}, \tag{38}\]  
where \(\hat{I}\) is the maximum of the log-likelihood, and \(\hat{\theta}\) is the estimated parameter vector of the model. Furthermore, the AIC and the SBC are given by  
\[AIC=DEV+2\times df \tag{39}\]  
and  
\[SBC=DEV+\log(n)\times df\, \tag{40}\]  
where \(df\) are the degrees of freedom, and \(n\) is the number of observations in the sample.  
Figure 1: Normalized quantiles for the ZIP regression model and the NBI and PLN regression models with varying dispersion.  
The resulting DEV, AIC, and SBC values for the competing models are presented in Table 3. We observe that the PLN regression model provides the best fit with respect to all three criteria.
# Multi-Authority Revocable Access Control Method Based on CP-ABE in NDN
## 5 Analysis of Performance
### Flexibility Analysis  
Table 1 compares the CP-ABE schemes previously studied by relevant experts from the four aspects of access structure, type of authorization center, ability to resist collusion attacks, and authority revocation.  
As can be seen from Table 1, this scheme is more flexible than other solutions. Because it adopts the LSSS access structure, the scheme supports fine-grained access control, supports multiple authority, can resist collusion attacks, and supports user and attribute revocation. These features make the solution proposed in this chapter more suitable for practical application requirements.
# Message Passing Detectors for UAV-Based Uplink Grant-Free NOMA Systems
## 3 Adaptive UAV-BS Detector Based on the HMP Rule
### Computation of the HMP for GAMP-BG-MC Algorithm  
The probability model in (6) is implicit in the mix of linear and non-linear functional relationships, i.e., the product and summation operations among the variables as well as the updating of the Gaussian variance, while the single or combined MP rules are unavailable to calculate all the messages directly. Fortunately, the HMP rule proposed in [15] can tackle the message computation problems with the mix of linear and nonlinear models. The idea of the HMP rule [15] is that the FG groups all factor nodes into one set, \(\mathcal{A}_{\text{Hybrid}}\), while clustering all edges, \(\mathcal{E}\), into two sets, \(\mathcal{E}_{\text{BP}}\) and \(\mathcal{E}_{\text{MF}}\), depicted as solid and dashed lines in Figure 2, respectively. For \(\mathcal{E}_{\text{BP}}\) edges, the messages use the equations (Equations (5) and (6) in [15]) to calculate the messages. Consequently, Equations (7)-(9) in [15] are used to calculate the messages on the \(\mathcal{E}_{\text{MF}}\). For the convenience of MP algorithm design, the factor graph is divided into four parts, as shown in Figure 2. Part I is the GAMP algorithm to calculate the likelihood function and Part II denotes the MC part for each UE in the time domain. Then, the transition probability automatic update is Part III, and Part IV is used to update the Gaussian variance. Then, the message calculation procedure will be derived separately.
# Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Celephu, Bhutan
###### Abstract  
Gelephu, located in the Himalayan region, has undergone significant development activities due to its suitable topography and geographic location. This has led to rapid urbanization in recent years. Assessing land use land cover (LULC) dynamics and Normalized Difference Vegetation Index (NDVI) can provide important information about urbanization trends and changes in vegetation health, respectively. The use of Geographic Information Systems (GIS) and Remote Sensing (RS) techniques based on various satellite products offers a unique opportunity to analyze these changes at a local scale. Exploring Bhutan's mandate to maintain 60% forest cover and analyzing LULC transitions and vegetation changes using Sentinel-2 satellite imagery at 10 m resolution can provide important insights into potential future impacts. To examine these, we first performed LULC mapping for Celephu for 2016 and 2023 using a Random Forest (RF) classifier and identified LULC changes. Second, the study assessed the dynamics of vegetation change within the study area by analysing the NDVI for the same period. Furthermore, the study also characterized the resulting LULC change for Celephu Thromde, a sub-administrative municipal entity, as a result of the notable intensity of the infrastructure development activities. The current study used a framework to collect Sentinel-2 satellite data, which was then used for pre-and post-processing to create LULC and NDVI maps. The classification model achieved high accuracy, with an area under the curve (AUC) of up to 0.89. The corresponding LULC and NDVI statistics were analysed to determine the current status of the LULC and vegetation indices, respectively. The LULC change analysis reveals urban growth of 5.65% and 15.05% for Celephu and Celephu Thromde, respectively. The NDVI assessment shows significant deterioration in vegetation health with a 75.11% loss of healthy vegetation in Celephu between 2016 and 2023. The results serve as a basis for strategy adaption required to examine the environmental protection and sustainable development management, and the policy interventions to minimize and balance the ecosystem, taking into account urban landscape.  
Sentinel-2; semi-automatic classification; random forest; LULC; NDVI; Bhutan 2024  
[PERSON], K.: 1 Civil Engineering Department, College of Science and Technology, Royal University of Bhutan,  
Phuentsholing 21101, Bhutan  
1 Civil Engineering Department, College of Science and Technology, Royal University of Bhutan,  
Phuentsholing 21101, Bhutan  
2 Department of Civil Engineering, University of South Africa, Pretoria 1709, South Africa; [EMAIL_ADDRESS]  
M.: 2024
# Network Theory and Switching Behaviors: A User Guide for Analyzing Electronic Records Databases
## 1 Introduction
### The Statistical Analysis of Drug-Switching Behavior  
In the previous section, we described the relevance of using databases for investigating drug-switching behaviors. However, the analyses commonly employed in previous studies are usually characterized by some limitations. Indeed, studies are often limited to a comparison between the group of patients who have switched drugs with the group who have not switched in a given interval of time [50; 51; 52]. Then, the groups can be compared by employing an adherence index such as the Medical Possession Ratio (MPR). When the health records database provides information about only a few different types of drugs, the commonly employed approach is very effective. However, when the number of different types of drugs increases along with the dimension of the number of rows of the database, these methods can miss the real complexity of the switching behaviors of patients.  
Among the wide range of mathematical tools usefully employed within healthcare, graph theory [53; 54] could be an ideal tool to investigate switching behavior by means of database analysis (as previously suggested in the health domain [12] and in other fields, such as marketing research [55]). This paper aims to offer a simple and straightforward introduction to basic concepts of graph theory in order to exploit its potentialities as a tool to analyze switching behavior. The ideal reader is a healthcare researcher that can find here a ready-to-use guide to carry out the analysis of drug-switching behavior.  
In the following sections, first, the elementary concepts of graph theory relevant for this paper will be introduced. Then, after a description of the basic characteristics of illustrative electronic health records databases, an example of the application of graph theory for analyzing switching behavior will be displayed (including a Python script for obtaining the adjacency matrix necessary for network analysis).
# Efficacy of Maximally Expansion with Clear Aligner in the Mixed Dentition: A Systematic Review
###### Abstract  
The purpose of this systematic review is to analyze the dentoalveolar changes resulting from the use of clear aligners in the treatment of transverse maxillary deficiency among growing children in the mixed dentin stage. An electronic literature search was carried out using the following databases: PubMed, Scopus, Cochrane Library, Embase, and Web of Science. The review protocol was prospectively registered in the PROSPERO database. Eligible studies included children aged 6 to 12 years in the mixed dentition period, presenting with erupted maxillary first molars and a transverse deficiency of the maxilla, and undergoing treatment with Invisalign(r) First aligners. The review encompassed various study types including retrospective and prospective designs, randomized controlled trials, preliminary studies, and case series. Two independent reviewers conducted the data extraction process. The quality of evidence was assessed using the GRADE approach. Except for studies by [PERSON] et al., the risk of bias in selected articles was visually summarized in a traffic light plot using the robvis tool, following the ROBINS-I methodology. For the studies by [PERSON] et al., a separate visual representation was created using robvis with the RoB2 evaluation framework, prepared by the authors [PERSON] and C.A. In total, 14 studies were included in the final synthesis, selected from 265 records retrieved through electronic searches and an additional 36 identified via manual screening. Several parameters were considered in order to assess dentoalveolar expansion: intercanine width, intercanine transpalatal width, intercanine dentoalveolar width, first and second interdeciduous molar width, first and second interpremolar width, first and second interdeciduous molar transpalatal width, first interpremolar width, first interpremolar mesh, distal, and transpalatal width, molar inclination, arch depth, and arch perimeter, and intermalor dentoalveolar width. An improvement was recorded in all parameters. The studies comparing treatment with Invisalign(r) First clear aligners and rapid maxillary expander highlighted that these both determined statistically significant differences compared to the natural growth group. Treatment with Invisalign(r) First in mixed dentition proved to be very effective for dentoalveolar expansion of the maxillary arch, with good control of the crown angulation of the upper first molar and an increase in the palatal area similar to RME, compared to pre-treatment or to the natural growth group. It could represent an effective and comfortable alternative to the traditional rapid maxillary expander treatment. However, further high-quality studies are required to support our current observations and verifying the stability of treatment outcomes on a long-term basis.  
**Keywords:** palatal expansion technique; orthodontic appliances; removable; dentition; mixed; dentistry
# Low-Temperature Diffusion of Au and Ag Nanolayers for Cu Bonding
## 3 Results and Discussion
Figure 9 shows the FE-SEM and TEM results of the Cu bonding interface using an Ag nanolayer. The use of the Ag nanolayer also led to a pattern very similar to that of the Au nanolayer. However, in contrast to when the Au nanolayer was used, for the Ag nanolayer, oxygen is not observed solely at the Cu region of the interface; instead, it seems to be widely dispersed throughout the bonding interface. This suggests an oxidation process occurring in the Ag nanolayer. Despite the presence of oxygen inflow prior to bonding, it was verified that oxidation of the Ag nanolayer occurred. There is a strong probability that both Ag and AgO diffusion took place during the Ag-Cu interaction. When evaporated Au and Ag diffused inward during the bonding and annealing process, the interface was filled with Cu and O at the surface before bonding. The bonding strength of the bonded specimen was evaluated by shear strength measurements. A specimen of 2 mm \(\times\) 2 mm in size was used, and the average value was calculated from 20 shear strength measurements. The bottom part of the diced chip was fixed with a zig, and the tip was moved at a speed of 600 \(\mu\)m/s to measure the shear strength when separated by pushing the top part. As shown in Figure 10, the average shear strength of Cu bonding using an Au and an Ag nanolayer was 5.39 MPa and 6.55 MPa, respectively. For Cu bonding using the Au nanolayers, the average value was slightly lower, and the standard deviation was slightly broader than that of Cu bonding using Ag nanolayers. The relatively low shear strength values in this study can be attributed to two factors: firstly, the low bonding pressure utilized, and secondly, the presence of the non-uniform presence of a metal layer at the bonding interface. To enhance the shear strength of the bonding interface, it is essential to explore a thinner metal passivation layer. Figure 6: Sample preparation for TEM. **(a)** diced chips after bonding; **(b)** tilt view of one diced chip; **(c)** chip pretreated by ion beam milling. Also, the annealing process significantly contributes to improving bonding strength and Cu diffusion. Our current annealing temperature is set at 200 \({}^{\circ}\)C, which is the maximum temperature intended for the low-temperature Cu bonding system. Therefore, we may explore the possibility of extending the annealing temperature for further optimization. Figure 8: (**a**) FE-SEM and (**b**) TEM analysis results of a bonded wafer using an Au passivation nanolayer and (**c**) EDS scanning results. Figure 7: SAT image of a bonded wafer. (**a**) Au; (**b**) Ag [16] nanolayer. Figure 9: (**a**) FE-SEM and (**b**) TEM analysis results of a bonded wafer using an Ag passivation nanolayer and (**c**) EDS scanning results.
## 2 Methodology for Calculating Transient Magnetic Field and Induced Voltage  
### Lightning Current Responses in Photovoltaic (PV) Bracket System  
A PV bracket system is typically constructed by a series of tilted, vertical and horizontal conductor branches as shown in Figure 1. During a lightning stroke, the lightning current will inject into the PV bracket system from the attachment point and be distributed on all the branches. To calculate the lightning current responses, the PV bracket system is converted into an equivalent circuit consisting of a large number of resistances, inductances and capacitances. A current source \(i_{\text{s}}\) with specified lightning current parameter is applied to the node (corresponding to the attachment point) in the equivalent circuit to simulate the lightning stroke to the PV bracket system [16; 17], as shown in Figure 2. \(Z\) denotes the surge impedance of the lightning discharge channel and its value ranges from several hundred ohns to kilboms, depending on the amplitude of the lightning current [18]. The lightning current responses on all the branches can be obtained by performing the transient calculation for the equivalent circuit. The associated calculation procedure has been reported in detail in [10; 12]. In terms of the lightning current response on each branch, the transient magnetic field can be calculated in the PV bracket system.  
Figure 1: Photovoltaic (PV) bracket system.  
Figure 2: Circuit model of PV bracket system.