esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Hilbert-Huang Transform for Vibration-Based Damage Detection of Utility Timber Poles
## 3 Improved Hilbert Huang Transform
### Mother Wavelet Selection  
Selection of proper mother wavelet is vital in WPT since the results obtained from WPT will get affected by the chosen mother wavelet. In this study, maximum energy to Shannon entropy criterion is utilised to select the proper mother wavelet. [PERSON] et al. (2009) and [PERSON] et al. (2010) used a similar criterion for rotating machinery vibration signal analysis using WPT and DWT. According to this criterion, the mother wavelet that produces the maximum energy to Shannon entropy ratio is selected as the most appropriate one. Energy of the signal is obtained from the following expressions:  
\[E_{j}^{i}=\sum_{k=1}^{n}\left(C_{j,k}^{i}\right)^{2} \tag{17}\]  
\[E_{j}=\sum_{i=1}^{2^{j}}E_{j}^{i} \tag{18}\]  
where \(E_{j}^{i}\) is the energy of \(i\)th wavelet packet component at \(j\)th decomposition level of the signal and \(E_{j}\) is the sum of all packet component's energy in \(j\)th level. \(E_{j}\) actually represents the energy of original raw signal. Shannon entropy (\(S_{entropy_{j}}\)) is defined as follows:  
\[p_{i}=\frac{E_{j}^{i}}{E_{j}} \tag{19}\]  
\[S_{entropy_{j}}=-\sum_{i=1}^{m}p_{i}\;log_{2}\;p_{i}\;if\;p_{i}=0\;then\;p_{i} log_{2}\;p_{i}=0 \tag{20}\]  
Maximum energy to Shannon entropy criterion, \(R(s)\) is calculated using Equation (21).  
\[R(s)=\frac{E_{j}}{S_{entropy_{j}}} \tag{21}\]
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 4 Preliminary Concepts  
This section provides a brief explanation of the notions of smart healthcare, enabling technologies, and QoS. It has been the objective of the research to explore further the present approaches of mixing various technologies to produce a high-quality SLR to describe the concepts and practices utilized in smart healthcare applications. According to this perspective, the goal of this research is to understand and examine the ways that current research is now producing, reporting, and applying the process of introducing and employing cutting-edge ICT technologies to provide smart healthcare services with a higher QoS. The top three technologies used in smart healthcare are IoT, cloud computing, and ML, as was discussed in the pre-SLR section. Further details on the applications of IoT, ML, and cloud computing are provided after an introduction to smart healthcare. The foundations of QoS, its metrics, and its application to smart healthcare are then investigated.
# A New Approach to Defining Uncertainties for MODIS Land Surface Temperature
## 2 Framework
### Uncertainty Budget
#### 2.5.1 Level-1  
Ideally, Level-1 products would provide per pixel uncertainty information that could be propagated to Level 2, but frequently this is not yet the case. In general, Level-1 uncertainties are from effects related to the instrument calibration. If available, a standard deviation of the measured values when viewing a calibration target would be the basis for the Level-1 uncertainty. Where this is not known, as is the case for MODIS, a constant noise equivalent differential temperature (NEDT) value per channel is used as a standard uncertainty estimate. With the necessary information on locally correlated effects generally absent for Level-1 data, an assumption is made that the error sources at Level 1 are not correlated. We accept this assumption in our MODIS based on, for example, the work of [49] which shows that Aqua bands 31 and 32 are stable with very low systematic errors. Indeed, for many operational sensors this information is not available, and so an assumption is generally made that locally correlated effects are removed through calibration models. The FIDUCEO project ([[http://www.fiduceo.eu/](http://www.fiduceo.eu/)]([http://www.fiduceo.eu/](http://www.fiduceo.eu/))) is attempting to address this, and future work could investigate applying such techniques to different sensors. In our study, locally correlated effects are resolved at Level 2.
## 4 Applications
### Measurement of Vocal Cord Vibration  
In order to expand the application of the fabric-based strain sensor, the vocal cord vibrations during speaking were measured--this is important for developing artificial throats [38]. Normal voice production relies on three organs, i.e., the lung, larynx, and sound teeth. When producing sound, the vocal folds close from the open state. As air expelled by the lung passes through the closed vocal folds, they vibrate. Meanwhile, the tension length of the vocal folds alters with changes in pitch. Finally, sound produced by the vibrations of the vocal folds is modified via sound teeth. In general, the main vocal organ is the larynx located in the throat. Therefore, the strain sensor was attached to the throat for measuring the vocal cord vibrations, as shown in the inset of Figure 7c.  
During the measurement, the speaker was requested to repeatedly vocalize two Chinese characters, \"Nan\" and \"Jing\", while keeping the speech rate as smooth as possible. Figure 7c shows the signals of the relative change in resistance for four repeated pronunciations. There are two local peak values for saying \"Nan Jing\" each time. The first local peak is higher, which may be due to the greater vibration caused by the Chinese character \"Nan\". When the Chinese character \"Jing\" is said, the vocal cords contract and the throat node vibrations are small, resulting in the second local peak.
## 3 Results and Discussion
### Structural Characteristics of ACM-Stabilized Soya Milk Emulsion  
TEM imaging of soya milk emulsion stabilized with ACM was investigated with concentrations of 30 mL and 50 mL (_w/w_) soya milk to 3 g and 5 g concentrations of ACM. Figure 1c,d depict the TEM images of 30 mL soya milk emulsion stabilized with 3 g African catfish. The images show the encapsulation of the soya milk dispersion by the ACM to form a cohesive aggregate structure. Figure 1e,f show the TEM images of 50 mL soya milk emulsion stabilized with 1 g African catfish with similar encapsulation. The particle size measured from TEM analysis for 30 mL (_w/w_) Soya milk emulsion stabilized with 3 g ACM was 112-125 nm while that for 50 mL (_w/w_) was between 173-180 nm. Figure 1c,d show TEM images of 30 mL soya milk emulsion stabilized with 3 g ACM (_w/w_). The images show that aggregates of thread-like filamentous fibers of ACM encapsulate the soya milk emulsion leading to the bridging and flocculation of the soya milk emulsion. The particle size of emulsions stabilized with a higher concentration of ACM was lower than the particle size of emulsion stabilized with a lower concentration of ACM this might be a result of constant time and homogenization speeds used in formulating the emulsions. The TEM images of 30 mL and 50 mL soya milk emulsion stabilized with ACM (Figure 1c-f) showed similar encapsulation. It can be inferred that the TEM images obtained for both emulsions showed that the soya milk dispersion was encapsulated by the ACM to form a cohesive aggregate raw structure. Thus, it can be postulated that encapsulation of the soya milk stabilized emulsion could be a result of the ACM potential to adhere to the soya milk emulsions' droplets which induces a kind of cohesive interaction and confers stability as suggested by [2].
## 5 Experimental Results
These results were obtained with the thermal noise of the input reference frequency at \(-153\) dBc/Hz. Additionally, this phase noise performance is sufficient to support 5G FR1 networks and IoT applications, highlighting the effectiveness of the WBPLL in these advanced communication systems. The proposed wideband PLL offers the advantage of a wide frequency tuning range within a small chip area. To achieve this compact size, the oscillator is designed based on a ring oscillator (RO) instead of an LC-based oscillator. Although RO has poorer out-band noise characteristics compared to LC oscillators, increasing the reference frequency and loop bandwidth can suppress the out-band noise from the RO effectively. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **This Work** & **JSSC’22**[23] & **JSSC’22**[24] & **ISSCC’20**[25] & **ISSCC’20**[26] & **ISSCC’20**[27] & **JSSC’20**[28] \\ \hline Technology & 28 nm & 40 nm & 65 nm & 65 nm & 65 nm & 40 nm & 40 nm \\ \hline Architecture & Digital SSPLL & IL-DPLL & MDLL & DPLL & DPLL & Analog PLL & DPLL \\ \hline Fractional-N support & Yes & Yes & Yes & No & Yes & No & Yes \\ \hline Fref (MHz) & 153.6 & 64 & 50 & 21 & 100 & 120–123 & 50 \\ \hline Frequency out (GHz) & 3.5 & 2.43 & 1.50 & 5.25 & 5.50 & 7.68 & 2.40 \\ \hline Frequency range & 4.2 & 0.9 & 1.2 & X & 1.5 & 0.192 & 1.45 \\ (GHz) & (1.9–6.1) & (1.8–2.7) & (0.8–2.0) & (5.25) & (4.5–6.0) & (7.68–7.872) & (1.67–3.12) \\ \hline Integrated phase & \(-33.42\) & \(-32.89\) & \(-38.97\) & \(-26.84\) & \(-36.01\) & \(-37.9\) & \(-32.35\) \\ noise (dBc) & (10 k to 10 M) & (10 k to 10 M) & (10 k to 10 M) & (1 k to 100 M) & (1 k to 30 M) & (1 k to 100 M) & (1 k to 100 M) \\ \hline Power consumption & \multirow{2}{*}{10.1} & \multirow{2}{*}{2.76} & \multirow{2}{*}{11.95} & \multirow{2}{*}{9.01} & \multirow{2}{*}{9.88} & \multirow{2}{*}{6.48} & \multirow{2}{*}{4.85} \\ (mW) & & & & & & \\ \hline Area (mm\({}^{2}\)) & 0.055 & 0.09 & 0.18 & 0.161 & 0.105 & 0.075 & 0.086 \\ \hline Normalized area & \multirow{2}{*}{0.055} & \multirow{2}{*}{0.063} & \multirow{2}{*}{0.077} & \multirow{2}{*}{0.069} & \multirow{2}{*}{0.045} & \multirow{2}{*}{0.052} & \multirow{2}{*}{0.060} \\ (mm\({}^{2}\)) & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparisons with prior-art RO-based frequency synthesizers.
## 2 Materials and Methods
### Sample Preparation  
The protocol for sample preparation using a 3D printer was as follows. After fabrication, all samples were ultrasonically cleaned for 5 min in a solution of 99% isopropyl alcohol (Green Pharmaceutical Co., Jincheon, Republic of Korea) using an ultrasonic cleaner for 3D-printing resins (Form Wash, Formlabs Co., Somerville, MA, USA). The cleaned samples were then subjected to postcuring using an ultraviolet curing machine (Arum UV Curing Machine, Arum Co., Daejeon, Republic of Korea) for 10 min to obtain the final cure. After postcuring, the bottom support was removed using a tungsten carbide burr without additional treatment of the bottom surface.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & \multirow{2}{*}{**Devices**} & \multicolumn{2}{c}{**Materials**} \\ \cline{3-4}
**Name** & & **Name** & **Composition** \\ \hline AL & Asiga Pro 4K DLP 3D & & Titanium dioxide, Trimethylloppane trimethacrylate, Diphenyl phosphine oxide, Silicon Oxide, Urethane Dimethacrylate, and pigments \\ \hline AO & Asiga Pro 4K DLP 3D & & Diurethane dimethacrylate, 2-Propenoic acid, 2-methyl-, (1-methyl ethylidene) bis (4,1-phenyleneoxy(1-methyl-2,1-ethanediy!)) ester, 2-HEMA, diphenyl (2,4,6-trimethylbenzoyl!) phosphine oxide, and additives \\ \hline PO & Phrozen sonic mighty 4K & & ODS C\& Permanent A2 & Same as the composition of AO group \\ \hline ML & DWX-51D & & Kuwotech PMMA A2 [6] & Polymethyl methacrylate \\ \hline \hline \end{tabular}  
* Asiga Co., Alexandria, NSW, Australia (light intensity = 7.5 mW/cm\({}^{2}\), layer thickness = 0.05 mm, curing time per layer = 4 s); 2; Phrozen Tech Co., Hsinchu city, Taiwan, China (light intensity = 3.2 mW/cm\({}^{2}\), layer thickness = 0.10 mm, curing time per layer = 4 s); 3 Roland DGC Co., Irvine, CA, USA; 4 Arum Dentistry Co., Daejeon, Republic of Korea; 5 ODS Co., Incheon, Republic of Korea; 6 Kruwotech Co., Kwangju, Republic of Korea.  
\end{table}
Table 1: Specifications of materials and devices utilized in this study.  
Figure 1: Merlon fracture test model as specified in ISO 18675: (**A**) top view, (**B**) cross-sectional view along AA, (**C**) cross-sectional view along BB, and (**D**) photographic image of the printed model with bottom support. The variable \(x\) represents the merlon wall thickness. (Unit: mm).  
A total of 120 specimens were fabricated according to the Merlon fracture test model, divided into four experimental groups (thirty samples per group) with three varying thicknesses. The specimens that were partially broken or deformed during the manufacturing process were discarded and reprinted to ensure an equal number of specimens in each experimental group. Based on ISO 18675, the thickness used in the Merlon fracture model ranges from 0.1 mm to 0.5 mm. However, models with thicknesses of 0.1 mm and 0.2 mm could not be successfully fabricated using either the 3D-printing or milling processes. Consequently, these thickness conditions were excluded from the manufacturing accuracy tests.
# Investigation of High-Sensitivity pH Sensor Based on Au-Gated AlGaN/GaN Heterostructure
## 3 Results and Discussion
### Au-Gated vs. SiO\({}_{2}\)-Gated Sensing Membrane  
Figure 9 presents the I\({}_{\rm DS}\)-V\({}_{\rm DS}\) characteristics of the Au-gated and SiO\({}_{2}\)-gated sensing membrane pH sensors with W/L = 40/40 um in the three pH standard buffered solutions at the different V\({}_{\rm Ref}\). The V\({}_{\rm Ref}\) ranges from \(-\)3 to 1 V (1 V/step). The drain-source voltage (V\({}_{\rm DS}\)) was swept from 0 to 5 V.  
The S\({}_{\rm A}\) of the Au-gated and SiO\({}_{2}\)-gated pH sensors are shown in Figure 10. The experimental results show that the S\({}_{\rm A}\) of the Au-gated and SiO\({}_{2}\)-gated sensors are 136.91 uA/pH and 35.00 uA/pH (V\({}_{\rm DS}\) = 3.5 V, V\({}_{\rm Ref}\) = 0 V), respectively. Notably, the S\({}_{\rm A}\) of the Au-gated sensor is 3.9 times that of the SiO\({}_{2}\)-gated sensor under these conditions, which reveals the influence of sensing membranes made of different materials on the sensitivity. The density of surface states on Au exceeds that of SiO\({}_{2}\), indicating that Au surfaces offer more surface-active sites for the adsorption of H\({}^{+}\) and OH\({}^{-}\). For Au, the adsorption energy between its surface and H\({}^{+}\)/OH\({}^{-}\) is typically more negative than that for SiO\({}_{2}\), indicating a more robust adsorption propensity.  
Table 2 details some previous reports on the current sensitivity of the AlGaN/GaN pH sensors. Compared with some previous reports, the newly developed Au-gated AlGaN/GaN heterostructure-based pH sensor of this work shows much higher current sensitivity (S\({}_{\rm A}\)) under similar W/L ratio, V\({}_{\rm DS}\), and V\({}_{\rm Ref}\) conditions. Further, it demonstrates that Au as a sensing membrane can achieve higher sensitivity.  
Figure 10: S\({}_{\rm A}\) of the Au-gated and SiO\({}_{2}\)-gated pH sensors.  
Figure 9: I\({}_{\rm DS}\)-V\({}_{\rm DS}\) of pH sensors with W/L = 40/40 μm in the three pH standard buffered solutions at different V\({}_{\rm Ref}\): (**a**) Au-gated sensing membrane; (**b**) SiO\({}_{2}\)-gated sensing membrane.
# A QoS Optimization Approach in Cognitive Body Area Networks for Healthcare Applications
## 3 System Model
### WBAN Network Topology  
IEEE 802.15.6 considers WBANs to operate in either a one-hop or a two-hop star topology, with the central node to be located at the center of the body, e.g., at waist level, as shown in Figure 1[22]. Two types of transmissions exist in the one-hop star topology: (A) transmissions from the sensor device to the gateway and (B) from the gateway to the sensor device. In the two-hop star topology, the sensor nodes connect to the gateway via their peer devices or multi-hopping is used to send data to the gateway [29]. In our system model, the two-hop star topology exists between sensor nodes carried by the patient and the gateway via the central node at the waist (Figure 1). We assume that the data sent by the central node is the collection of data from the other sensors. However, each central node carried by an individual person is connected to the gateway via the one-hop star topology, as shown in Figure 3, where N corresponds to the nodes and they communicate with the gateway. Since our research focuses on the spectrum optimization, the analysis of the individual sensor's data (two-hop star topology mentioned above) is not in the scope of this paper.  
The time division multiple access (TDMA) technique is used for medium access. Each node is assigned one time slot; hence, all nodes' data are combined into a superframe for transmission from the gateway to the backend network for further data processing and/or diagnosis purposes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Values** \\ \hline Frequency band & 2.4–2.483 GHz \\ Number of channels & 79 (according to ISM band) \\ Payload & 0–2040 bits \\ Channel bandwidth & 1 MHz \\ Data rates & 121.4 Kbps–971.4 Kbps \\ Modulation Scheme & DBPSK/DQPSK \\ \hline \hline \end{tabular}
\end{table}
Table 1: System features and requirements.  
Figure 2: Physical layout and WBANs deployment. The colors show the associations between the nodes and gateways.
## 4 Discussion
#### 4.2.2 Limitations
An ultrafast imaging sequence with a small number of transmit unfocused beams (such as five steered plane waves) might not provide optimal resolution for accurately segmenting the near and far surfaces of the temporal bone layer. To improve the performance of the GB method, a single-element synthetic aperture imaging sequence could be used first to estimate the position and geometry of the bone layer. Then, this information can be used for correcting the phase aberration when imaging with the ultrafast imaging sequence (multi-angle plane waves or diverging waves). Moreover, a transducer operating at an ultrasound frequency higher than 2.5 MHz may help to determine the position and geometry of thin temporal bones (close to 1 mm thickness). As with the PS method, the performance of the GB method applied to a real temporal bone with complex three-dimensional shape would improve with a two-dimensional matrix array transducer. With such a transducer, the three-dimensional position and geometry of the bone layer could be determined. This would enable accurate three-dimensional ray tracing and, therefore, optimal phase aberration correction.
# Quantum Circuit Synthesis Using Fuzzy-Logic-Assisted Genetic Algorithms
## 2 Background
### Quantum Circuit Compilation and Synthesis  
Quantum circuit _compilation_, or circuit _synthesis_, is the process of converting quantum operations into a circuit compatible with the target device. A finite set of gates, called a _basis gate set_, can be used to construct all possible quantum operations [20]. Quantum devices usually implement such a gate set, referred to as its _native basis gate set_. In this work, we specifically refer to the process of converting an input unitary matrix to a machine-compatible circuit as 'quantum circuit synthesis'. Since connectivity of qubits is required for applying certain gates [5], the structure of qubit connectivity, called the qubit _topology_, is an integral part of quantum compilation. It determines where gates can be applied and if any routing is necessary to move logical qubits to appropriate physical qubits [5; 21]. However, routing generally requires application of additional gates (e.g., CNOT or SWAP gates [22]), which can increase the circuit depth and introduce gate errors; see Figure 2. Since NISQ devices have short decoherence times and high noise sensitivity, it is crucial to reduce the circuit depth and number of gates. Therefore, optimizing compilers for these devices must attempt to minimize the circuit depth, as well as the number of gates. In addition, to minimize the routing overhead, compilers will also attempt to optimize the mapping between logical and physical qubits, attempting to place qubits with higher levels of interaction close to each other, such that fewer routing gates are required; see Figure 3.  
Compilation can be categorized into two types: _exact_, which produces a circuit equivalent to the original operation up to a global phase, and _approximate_, which approximates the original operation with some error. Exact synthesis techniques generally result in very high circuit depths and gate counts, and generating exact circuits of optimal depth can be computationally unfeasible for anything but the smallest qubit counts. Hence, many modern optimizing compilers trade-off approximation error for reduced circuit depth and gate count. The primary compiler provided by the IBM Qiskit framework [23] is an example of an exact compiler, while the BQSkit compiler [11] is an example of an approximate compiler (which can also perform exact compilation).  
Figure 3: Careful qubit mapping can reduce gates needed for routing.  
Figure 2: Routing necessary to make an input circuit compatible with qubit topology.
## 3 System Design
### Transmission Mechanism
#### 3.2.4 Protocol Performance Optimization and Quality Assurance  
To further enhance the SRT protocol's performance in chain transmission, our system integrates several protocol performance optimization and quality assurance strategies. Firstly, a timestamp synchronization mechanism is employed to ensure temporal consistency across multi-hop transmissions. Secondly, adaptive parameter tuning is implemented based on network path characteristics, dynamically optimizing key parameters such as latency and retransmission timeouts according to link hierarchy, geographical distance, and current network conditions. Lastly, a traffic shaping mechanism, utilizing a token bucket algorithm, smooths out burst traffic, preventing network congestion from impacting downstream nodes.  
In the context of P2P systems, quality management mechanisms are crucial, and node reputation management stands out as a significant approach. The ReputeStream system [7] introduces a reputation management mechanism based on a multi-layer architecture, which inspires the design of this system.