esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Signal Acquisition and Processing  
The signal transmission is shown in Figure 3. The load sensor was connected to the amplification and filtering circuit, and after signal amplification, filtering, and noise reduction, it was directly connected to the analogue to digital converter (ADC) channel of the MCU (STM32f103, ARM). The MCU scanned every 200 ms and saved a weight value for each scan. The chicken identification number was read by the RFID electronic module and passed to the MCU by the USART. The body weight sequence was digitally filtered after feeding to obtain the body weight [33]. The feed amount and drop amount were weighed statically. To study the effect of the amplification and filtering circuit, this project transmitted the weight data during feeding to the personal computer (PC) synchronously, to obtain the dynamic weighing sequence of body weight when feeding.
# Evaluation of Hydrogen Generation with Hybrid Renewable Energy Sources
## 1 Introduction
### Literature Survey
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Production Process** & **Feedstock and Energy Source** & **Pros and Cons** & **Examples** & **CO2 Emissions/H2 Generation** \\ \hline GREY & Feedback: natural gas, gasified coal & Pros: lowest cost, abundant & Canada produces approximately 3 & 9 kg CO2e/kg H2 \\  & & Cons: highest carbon & million tons of grey & \\  & without carbon capture and sequestration (CCS) & & intensity & hydrogen per year, primarily for industrial use & \\ \hline BLUE Produced from fossil fuels by steam methane & Feedback: natural gas, coal, crude bitumen & Pros: low-cost, abundant, low CI, pyrolysis offers scale and siting flexibility & Alberta’s Quest project & 2 to 3 kg CO2e/kg H2. \\  & & & pyrolysis offers scale and siting flexibility & \\  & other processes with carbon capture and sequestration (CCS). & & Cons: SMR pathway & \\  & & & sitting is constrained by CCUS, and the feedstock is not renewable & \\ \hline GREEN Produced from water by electrolysis using renewable electricity, such as hydroelectricity, wind, or solar. & Feedstock: water Energy source: renewable electricity & Pros: lowest carbon intensity, scalable & Air Liquide’s 20 MW & 0.8 (wind) to 3.4 (solar) electrolyzer plant in ScO2e/kg H2 \\  & & & Cons: highest cost, opportunity & Becancour, projects developing in BC to support hydrogen \\  & & & cost-competitive with electrification demand & fuelling network \\ \hline NUCLEAR Produced from water by electrolysis or high temperatures from nuclear energy & Feedback: water Energy source: uranium/nuclear electricity & Pros: low carbon intensity & Feasibility study planned in Bruce Cons: limited availability and siting constraints & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Common hydrogen feedstocks, production pathways, and CO2 emissions [16; 17]. Figure 1: Comparison of hydrogen’s current role in Canada’s energy systems (**A**) and its possible role in future net-zero energy systems (**B**) as a fuel [16; 17]. The most widely used technologies contributing to advances in hydrogen production are methane reforming, electrolysis, waste gasification, and coal gasification. Table 2 shows a comparison between hydrogen generation techniques and their efficiencies with the merits and disadvantages of every technology [18].
# Research on Dynamic Modeling and Vibration Characterization of Integrated Bearings
## 2 Integrated Bearing Dynamic Performance Analysis
### Influence of Structural Parameters on Vibration in Integrated Bearings
#### 2.2.2 Influence of Outer Groove Curvature Radius Coefficients on Vibration Value  
When the inner groove curvature radius coefficient is set at 0.529 and the cage pocket clearance is 0.051 mm, the relationship between the outer raceway curvature radius coefficient and bearing vibration in integrated bearings is presented in Figure 11. As illustrated  
Figure 10: Trend of vibration with variation in the curvature radius coefficient of the inner rolling path. (**a**) Trends in vibration velocity variation. (**b**) Trends in vibration acceleration variation.  
Figure 9: Frequency diagram of vibration acceleration of integrated bearing.  
in the figure, with an increase in the outer raceway curvature radius coefficient, both the vibration acceleration level and vibration velocity of the integrated bearings initially decrease and then increase. The optimal vibration characteristics are observed when the outer raceway curvature radius coefficient is in the range of 0.52 to 0.53.
## 4 Results Validations  
In this section, the proposed antenna results that were evaluated numerically using CST MWS are compared to measurements. The proposed antenna performances in terms of S\({}_{11}\) and gain spectra are evaluated up to 6 GHz. Therefore, the proposed antenna is fabricated and tested experimentally. The antenna is fabricated using a wet chemical etching process, where the fabricated prototype is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**LDR/\(\Omega\)** & **Gain/dBi** & **BER@QAM-8** & **BER@QAM-16** & **BER@QAM-32** & **BER@QAM-64** \\ \hline
0 & 11.6 & 0.0894 & 0.0921 & 0.1164 & 0.1313 \\ \hline
100 & 10.1 & 0.1034 & 0.1171 & 0.1234 & 0.1431 \\ \hline
200 & 8.6 & 0.1091 & 0.1198 & 0.1391 & 0.1595 \\ \hline
300 & 6.2 & 0.1178 & 0.1281 & 0.1498 & 0.1618 \\ \hline
400 & 5.5 & 0.1412 & 0.1521 & 0.1609 & 0.1732 \\ \hline
500 & 4.1 & 0.1569 & 0.1692 & 0.1760 & 0.1860 \\ \hline
600 & 3.2 & 0.1678 & 0.1773 & 0.1802 & 0.1971 \\ \hline
700 & 2 & 0.1756 & 0.1851 & 0.1977 & 0.2056 \\ \hline \hline \end{tabular}
\end{table}
Table 2: BER evaluation at different LDR resistance values.  
Figure 7: Fabricated prototype structure.  
To give the complete meaning of portable applications of such antenna, the antenna performances are tested in two different scenarios.
## 2 Research on the Collaborative Search Efficiency Evaluation Model of Multi-Sonar System
### Multi-Sensor Detection Information Fusion Model
(4) Calculation of the overall detection probability  
By combining the detection probabilities of each sensor with the comprehensive weights, the overall detection probability can be determined. Assuming that the detection probability of the \(i\)-th sensor is \(Pd_{i}\), the overall detection probability is \(Pd=\sum_{i=1}^{n}Pd_{i}W_{i}\).
# Laser Transverse Modes with Ray-Wave Duality: A Review
## 2 Ideal Spherical Cavity
### Parametric Equations for Geometric Rays  
By using the propagation matrix for a degenerate cavity with \(\Delta f_{T}/\Delta f_{L}=P/Q\), the characteristic equations for periodic ray trajectories can be verified as [18]  
\[x_{s}(z)=W[\cos(\theta_{s}+\phi_{x})\mp(z/z_{R})\sin(\theta_{s}+\phi_{x})] \tag{10}\]  
where \(\theta_{s}=(P/Q)2\pi s\), \(s=0,1,2\cdots Q-1\) is the running index for the different rays, \(z_{R}=(R_{c}/2)\sin[2\pi(\Delta f_{T}/\Delta f_{L})]\) is the Rayleigh range, \(W\) is the amplitude of the transverse waist, \(\phi_{x}\) is associated with the initial condition, and \(-\) and \(+\) in the symbol of \(\mp\) represent the leftward and rightward tracings, respectively. Let the amplitude \(W=\sqrt{N_{x}}w_{o}\), Equation (10) can be expressed as  
\[x_{s}(z)=\sqrt{N_{x}}\,w(z)\cos[\theta_{s}+\phi_{x}\pm\theta_{G}(z)] \tag{11}\]  
where \(w(z)=w_{0}\sqrt{1+\left(z/z_{R}\right)^{2}}\) is the Gaussian beam parameter with the waist \(w_{o}\), \(\theta_{G}(z)=\tan^{-1}(z/z_{R})\) is the Gouy phase, and \(N_{x}\) is the transverse order. Defining the dimensionless variable \(\vec{x}=\sqrt{2}x/w(z)\), the ray equation in Equation (11) can be given by \(\widetilde{x}_{s}(z)=\text{Re}[\sqrt{2}\,u_{s}^{\pm}(z)]\) with  
\[u_{s}^{\pm}(z)=\sqrt{N_{x}}\,\exp\{-i\left[\theta_{s}+\phi_{x}\pm\theta_{G}(z) \right]\} \tag{12}\]  
In the spherical cavity, the properties for the \(x\)- and \(y\)-direction are the same. Therefore, the trajectory equation for the \(y\)-direction can be expressed as \(\widetilde{y}_{s}(z)=\text{Re}[\sqrt{2}\,v_{s}^{\pm}(z)]\) with  
\[v_{s}^{\pm}(z)=\sqrt{N_{y}}\,\exp\{-i\left[\theta_{s}+\phi_{y}\pm\theta_{G}(z) \right]\} \tag{13}\]  
where \(\widetilde{y}=\sqrt{2}y/w(z)\), \(\phi_{y}\) is associated with the initial position, and \(N_{y}\) is the transverse order. If one of the \(N_{x}\) and \(N_{y}\) is zero, the ray traces form a planar periodic orbit. For convenience, we set \(N_{x}=0\) to discuss the feature of planar periodic orbits. Figure 2a shows the planar periodic orbits in the \(yz\) plane for \(P/Q=1/4\), \(2/7\), and \(3/11\) with \(\phi_{y}=0\). The planar periodic orbits can be seen to be the reciprocating paths. It can be confirmed that if the phase factor \(\phi_{y}\) is the integer multiple of \(\pi/Q\), the planar periodic tracing is a reciprocating type. On the other hand, when \(\phi_{y}\) is not the integer multiple of \(\pi/Q\), the planar tracing is a generic type, as an example seen in Figure 2b for \(\phi_{y}=\pi/(2Q)\). The total numbers of rays for the reciprocating and generic periodic orbits can be easily found to be \(Q\) and \(2Q\), respectively. In addition to planar orbits, when both of \(N_{x}\) and \(N_{y}\) arenot zero and \(\phi_{x}\
eq\phi_{y}\), the ray orbits are generally nonplanar, as shown in Figure 3 for \(P/Q=1/4\), \(2/7\), and \(3/11\) with \(N_{x}=N_{y}\) and \(\phi_{x}-\phi_{y}=\pi/2\). The total number of rays in the nonplanar orbits can be seen to be \(Q\).
# Distribution System State Estimation Using Model-Optimized Neural Networks
## 3 Neural Network-Based State Estimation
### Neural Network Theory
\[\frac{\partial C}{\partial W_{i,j}^{(r)}}=\frac{\partial C}{\partial a_{i}^{( r)}}\cdot a_{j}^{(r-1)} \tag{10}\]  
\[W_{i,j}^{(r),*}=W_{i,j}^{(r)}-\alpha\frac{\partial C}{\partial a_{i}^{(r)}} \cdot a_{j}^{(r-1)} \tag{11}\]  
Thus, we arrive at the final point which is Equation (11), where the change to the weight value, \(W_{i,j}^{(r)}\), is a decrement by a fraction of the gradient dictated by the learning rate, \(\alpha\), as shown in [33; 34; 35]. The differentiation while including specific activation functions is left to the reader for personal exploration. Here, the activation function is simply a pass through, that is, \(y=h(x)=x\). As mentioned before, this paper proposes the use of machine learning, specifically model optimized neural networks, to solve the state estimation problem. Using neural networks eliminates the need to formulate and compute the measurement function, \(h(x)\), the gain matrix, \(G\), which also stems from the measurement Jacobian, \(H\), and the covariance matrix, \(R\). All of the information that is contained within those quantities can be assumed to be summarily modeled within the neural network's weight values, thus making neural networks a worthy candidate for a state estimator. In particular, the model will _grow into_\(h(x)\), which is mathematically shown in Equation (12). \[x=LSTM(z,\,\epsilon) \tag{12}\]
# A Real-Time Data Delivery for Mobile Sinks Group on Mobile Cyber-Physical Systems
## 3 Performance Evaluation  
### Analysis  
We analyze the energy consumption of the flooding based scheme and the proposed protocol to define our routing protocol. To analyze the energy-efficiency of the proposed scheme, we focus on the worst-case communication overhead of each protocol. We consider a square area \(A\) in which \(N\) sensor nodes are uniformly distributed. There is a mobile sink group which has \(k\) multiple mobile sinks. The group moves at an average speed, while receiving \(d\) data packets from a source. The communication overhead to flood an area is proportional to the number of sensor nodes in the area, and that to send a message along a path by greedy geographical forwarding is proportional to the number of sensor nodes in the path. In this analysis, the mobile sink group has a radius \(R\). There are \(n=N/A^{*}\pi^{*}R^{2}\) sensor nodes in the group region.  
A sink group is assumed to update its location \(m\) times, and receive \(d/m\) data packets between two consecutive location updates. A radius of the expected group region is \(2R\). The overhead for group information calculation and advertisement is \(5n\)+\(kR\)+\(\sqrt{2A}\), where \(n\) is the number of sensor nodes in the group region, \(kR\) is the update cost from each sink to the leader sink, and \(\sqrt{2A}\) is the update cost from the leader sink to the location server in the sensor field. The communication overhead for location update is \(m(5n\)+\(kR\)+\(\sqrt{2A}))\).  
To deliver a data packet, we have two communication modes: unicasting from the source the entry point of the sink group and delivery mode within the group area. The length of the unicasting is \((D-R)\), where \(D\) is the average length between a source and the center of the group area and \(R\) is the radius of the area. Thus, the energy consumption of the unicasting is \((D-R)\)/\(r\). For delivery in the group area, the energy consumption is based on the number of sensor nodes in the area: \(N\pi R^{2}/A\). It is exponential to the radius of the area R. Our protocol divides the delivery before and after the branch. There are \(b=\lceil 2R/r\rceil\) branch points on the straight line \(2R\). After the branch, energy consumption is \(2 bR\), where the constant 2 is for the two branches in the opposite directions. Totally, the energy consumption is presented as follows,  
\[CO_{Floading} = m((5(N\pi R^{2}/A)+kR+\sqrt{2A})+\] \[d((D-R)/r+N\pi R^{2}/A),\] \[CO_{Proposal} = m((5(N\pi R^{2}/A)+kR+\sqrt{2A})+\] \[d((D-R)/r+\lceil 2R/r\rceil+(\lceil 2R/r\rceil)^{2})\]  
As a result, the energy consumption of the flooding based scheme depends on the density and the radius of the area, whereas that of the proposed scheme is affected only by the radius of the area.  
Figure 5: Mobility support for sinks out of the region.
# Adaptive Local Context and Syntactic Feature Modeling for Aspect-Based Sentiment Analysis
## 3 Methodology
### Aspect Term Extraction Structure
#### 3.1.4 Label Constraint Module  
To make full use of the information, the global-local information is modeled with the help of a bidirectional long short-term memory network (Bi-LSTM). The dependencies of the labels are then constrained using conditional random fields (CRF), which are automatically learned by the CRF layer from the training data during the training process. The illegal case of the label \"I\" preceding label \"B\" is avoided. When the model predicts the aspect label of the current word, it is important to understand the semantics of the previous word as well as to pay attention to the information of the latter word. After feeding the global-local contextual word vectors to the BiLSTM module and semantic modeling of each word, we obtain the final sequence \(S=\{s_{1},s_{2},\ldots,s_{n}\}\). For the real label sequence \(Y=\{y_{1},y_{2},\ldots,y_{n}\}\), the output graph of CRF is connected by undirected edges to predict the probability of the correct label for each word based on the state features and transfer features. The conditional probability \(p(Y|S)\) formula is shown below.  
\[p(Y\mid S)=\frac{\prod_{i=1}^{n}\psi_{i}(y_{i-1},y_{i},S)}{\sum_{y^{\prime}\in \mathfrak{P}(S)}\prod_{i}^{n}\psi_{i}\left(y^{\prime}_{i-1},y^{\prime}_{i},S \right)} \tag{6}\]  
where \(\mathfrak{P}(S)\) denotes all possible labeled sequences of the observed sequence and the conditional probability \(p(Y|S)\) is the score of the labeled sequence on a given observed sequence.
# Compact Quad-Port MIMO Antenna with Ultra-Wideband and High Isolation
## 2 Design of UWB Antenna Unit  
Traditional microstrip antennas typically come with drawbacks such as narrow frequency bands and parameter variations depending on the dielectric substrate and are therefore generally less used in UWB antenna designs. A planar monopole antenna can perfectly avoid these drawbacks, with the advantage of a wide working band and insensitive parameter variation, and with the advantages that come with a regular microstrip antenna: low profile, small size, lightweight, low cost, easy integration, etc. Therefore, the design work in this paper starts with a rectangular monopole antenna. In this section, the UWB monopole antenna is analyzed and discussed from design principles, structural evolution, critical parameter analysis, and simulation analysis.
# Effect of Carotid Stenosis Severity on Patterns of Brain Activity in Patients after Cardiac Surgery
## 3 Results
#### 3.2.2 EEG Data  
A repeated-measures ANOVA with a between-subjects factor of GROUP (three levels: CABG+left-sided CEA/CABG+right-sided CEA/isolated CABG) and within-subjects factors of EXAMINATION TIME (two levels: before/after surgery), AREA (five levels: frontal, central, parietal, occipital and temporal), and LATERALITY (two levels: left/right hemisphere) was conducted. The significant factors and interactions associated with the GROUP factor are found in EEG resting state with eyes closed in the theta1 frequency range.  
There was a significant factor in EXAMINATION TIME--F\({}_{1.108}\) = 46.6, \(p\)\(\leq\) 0.0001. It was found that the theta1 power increased after surgery at 7-10 days of the postoperative period as compared with the preoperative level both in the CABG patients and in the two CABG+CEA groups. This effect was more pronounced in CABG+right-sided CEA patients (\(p\) = 0.0001); they differed also from the isolated CABG group at 7-10 days after surgery (\(p\) = 0.026) (Figure 4).  
The interaction of factors GROUP \(\times\) LATERALITY (F\({}_{2,108}\) = 3.22, \(p\) = 0.04) was also significant. The left-sided CEA+CABG patients demonstrated the fewest lateral differences of theta1 power. The isolated CABG and CABG+right-sided CEA patients had higher theta1 power values in the left hemisphere as compared to the right one. This effect was more pronounced in CABG+right-sided CEA patients (\(p\) = 0.0004 and \(p\) = 0.00008, respectively) (Figure 4).  
Another significant interaction of factors GROUP \(\times\) EXAMINATION TIME \(\times\) AREA \(\times\) LATERALITY (F\({}_{8,432}\) = 2.15, \(p\) = 0.048) was revealed. The theta1 power differences between the patients who underwent isolated CABG and right-sided CEA+CABG were found. Before surgery, the right-sided CEA+CABG patients had higher theta1 power values than isolated CABG patients only in the frontal cortical regions in both hemispheres (\(p\) = 0.001 and \(p\) = 0.047, respectively). After surgery, the between-group differences were more pronounced in the left hemisphere. The right-sided CEA+CABG patients had higher theta1 power values than isolated CABG patients in all cortical regions, except occipital. In the right hemisphere, the between-group differences were only in the frontal, central and temporal regions, as seen in Figure 5.  
Figure 4: The postoperative theta1 rhythm power changes in the patients undergoing isolated CABG and simultaneous intervention (CABG+CEA): grey columns—the CABG patients, dark columns—the CABG+CEA patients; error bars denote SE, *\(-\)\(p\) < 0.05 Newman–Keuls multiple comparison test for the postoperative indicators in comparison to preoperative levels, *\(-\)\(p\) < 0.05 Newman–[PERSON] multiple comparison test for the postoperative indicators in CABG+CEA group as compared to CABG group.
## 2 Localization Solution
### System Design, Core Innovations, and Benefits  
Unlike traditional localization methods that rely on trilateration and require precise synchronization signals (such as infrared light), the system presented in this work operates on a fundamentally different principle. By eliminating the need for absolute ToF measurements and synchronization mechanisms, the system's design is significantly simplified while simultaneously enhancing accuracy and reliability. This approach differs from conventional beacon systems demonstrated in [20; 21], which typically use ultrasound for ToF measurements in combination with synchronization signals.  
The core innovation of this approach lies in its ability to determine position using only the time differences between received ultrasound signals. This method eliminates the need for synchronization between beacons and the robot. As a result, the solution offers substantial advantages in terms of reduced system complexity, lower implementation costs, and improved robustness against timing errors.  
The proposed system is specifically tailored for real-time applications in dynamic indoor environments, such as hospitals, museums, and other public spaces with foot traffic. Thanks to ultrasound technology, the system maintains robustness in changing environments and supports multiple robots simultaneously, ensuring consistent performance where it is essential. Moreover, the installation cost is economical, as multiple robots can share the same ultrasound pulses.  
A standout feature of our system is its relatively low computational footprint, especially compared to SLAM or vision-based localization methods. The simplicity of our approach could allow for real-time processing on a high-performance microcontroller, avoiding the need for more powerful and energy-intensive processors running a full OS. This not only reduces overall system costs but also significantly lowers power consumption.  
The system comprises \(n\) multiple beacons and \(n\) receivers, optimally placed to cover the operational area. The network of ultrasound beacons emit signals at precise intervals and in a specific order, while receivers mounted on the mobile robot detect and process these signals for position calculation.  
The system's ability to function without requiring precise timing synchronization allows for improved flexibility across diverse indoor settings. It can adjust more readily to different spatial configurations and is less susceptible to environmental factors that might disrupt precise timing signals. Furthermore, the simplification of the hardware requirements makes the system more cost-effective to implement and maintain, without sacrificing accuracy.
## 3 Results and Discussion
### Method Development
#### 3.1.1 Pyrolysis and Vaporization Conditions  
The pyrolysis and vaporization curve for an aqueous solution of NaF (20 uL of the 100 ug/L solution) are given in Figure 1. Interestingly, changing the pyrolysis temperature, integrated absorbance increased for about 15% within the range from 300 \({}^{\circ}\)C to 580 \({}^{\circ}\)C, then a narrow range between 580 \({}^{\circ}\)C and 640 \({}^{\circ}\)C with a plateau was observed. After that a typical dropdown of the integrated absorbance was noticed and a complete analyte loss at 1300 \({}^{\circ}\)C.  
A pyrolysis temperature of 600 \({}^{\circ}\)C was chosen as an optimum for all further experiments. Comparing to previous reports, where 500 \({}^{\circ}\)C [14; 30; 31; 36] and 550 \({}^{\circ}\)C [2; 27; 28; 29] were applied, selected conditions assure more efficient removal of volatile matrix components. Indeed, even higher pyrolysis temperature was reported (700 \({}^{\circ}\)C [33]) with the mixture of zirconium and calcium as chemical modifiers. Despite reaching a higher pyrolysis temperature, it is worth to notice possible interferences of CaF on the GaF, thus this option was withdrawn from further consideration.  
Changing the vaporization temperature, the appearance of gallium monofluoride in gaseous phase can be registered above 1000 \({}^{\circ}\)C. Then the integrated absorbance increases rapidly between 1000 \({}^{\circ}\)C-1400 \({}^{\circ}\)C reaching the plateau within 1400 \({}^{\circ}\)C-1600 \({}^{\circ}\)C. The further increase of vaporization temperature leads to a consistent absorbance decrease that refers to an analyte loss caused most probably by thermal decomposition of GaF, as it occurs in the case of CaF [21]. More detailed measurements were carried out within the plateau range to establish conditions assuring best sensitivity accompanied with the best precision of measurements, expressed as lowest relative standard deviation. The optimum temperature for GaF vaporization turned out to be 1540 \({}^{\circ}\)C and as it is consistent with those previously reported [27; 31; 36], it was used in all further investigations.