esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Formation Controller Design
### Convergence Analysis
* When \(t>T_{convergence}\), \(b_{j}=\infty\), the saturations are removed, such that \(\Delta=\hat{X}_{i}^{d}-\tilde{X}_{i}^{d}+k_{4X}(\widehat{\hat{X}}_{i}^{d}- \tilde{X}_{i}^{d})+k_{3X}(\hat{\hat{X}}_{i}^{d}-\hat{X}_{i})+k_{2X}(\hat{\hat{ X}}_{i}^{d}-\hat{X}_{i})=\kappa^{T}\hat{x}_{i}^{d}\), where \(\kappa=[0,k_{2X},k_{3X},k_{4X},1]^{T}\), its norm is \(\|\kappa\|=\kappa\). According to theorem 1, \(\|\hat{x}_{i}^{d}\|\leq\beta\) when \(t>T_{convergence}\). Then, we conclude that \(\|\delta_{i}\|=|\Delta_{i}|=\kappa\|\hat{x}_{i}^{d}\|\leq\kappa\beta<\infty\). Therefore, the perturbed term \(\delta\) in (36) is bounded and satisfying \(\|\delta_{i}\|\leq\delta_{\max}=\max\{\mathbf{d},\kappa\beta\}\), for all \(t\geq t_{0}\). The control gains are selected such that \(A_{c}\) is Hurwitz. Since \(A_{c}\) is Hurwitz, there exists a positive-definite matrix \(P\), which solves the Lyapunov equation \(A_{c}^{T}P+PA_{c}=-Q\), for a positive-definite matrix \(Q\). A Lyapunov function is selected as \(V=e_{X_{i}}^{T}Pe_{X_{i}}\). Then, its time derivative yields  
\[\dot{V}= e_{X_{i}}^{T}(A_{c}^{T}P+PA_{c})e_{X_{i}}+2e_{X_{i}}^{T}P\delta_{i} \leq-e_{X_{i}}^{T}Qe_{X_{i}}+2\lambda_{\max}(P)\|e_{X_{i}}\|\|\delta_{i}\|\] \[\leq-\lambda_{\min}(Q)\|e_{X_{i}}\|^{2}+2\lambda_{\max}(P)\|e_{X_ {i}}\|\|\delta_{i}\|=\|e_{X_{i}}\|(2\delta_{\max}\lambda_{\max}(P)-\lambda_{ \min}(Q)\|e_{X_{i}}\|)\]  
Then, \(\dot{V}<0\), if \(\|e_{X_{i}}\|>\frac{2\delta_{\max}\lambda_{\max}(P)}{\lambda_{\min}(Q)}\). When \(t>T_{convergence}\), according to [PERSON]'s invariance principle, for any initial condition \(x(t_{0})\), the state \(x\) will finally stay in the set \(S_{b}=\{e_{X_{i}}\|e_{X_{i}}\|\leq\frac{2\kappa\beta\lambda_{\max}(P)}{ \lambda_{\min}(Q)}\}\). The error \(e_{X_{i}}\) in (36) is ultimately bounded. The same result on \(Y\) can be obtained similarly. **Remark 4**. : _According to Theorem 2, the tracking error of quadrotor is proportional to the bound \(\beta\) of the observer error after finite time \(T_{convergence}\). If the observer error asymptotically converges to zero, then the formation tracking error will also converge to zero asymptotically._  
**Remark 5**. : _In practice, before the time instant \(T_{convergence}\), the bounds \(b_{j}\) are chosen small enough in order to prevent the unstable dynamics, since the observer error probably enormous, which is generated by great initial deviation._
# A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks
## 2 Sensor Node Energy Model
### Analytical Energy Model
#### 2.3.1 Micro-Controller Unit (MCU)
Figure 4a shows that when the FPU was disabled the time was estimated with a 10.5% error. This is due to the fact that CM primarily focuses on integer operations commonly used in embedded systems and neglects features like the FPU. Moreover, the STM32F401 RE is running the room occupancy estimation  
Figure 4: Processing-time estimation using the CM/MHz figure (**a**) when the FPU is disabled and (**b**) when it is enabled. algorithm [1], which uses a significant number of floating-point operations. In this work, The MCU's energy consumption is estimated using  
\[E_{MCU}=V_{WMCU}\left(I_{CoreMark}+I_{PPH}^{MCU}\right)t_{process}, \tag{2}\]  
where \(I_{CoreMark}\) is the MCU's current consumption when running CM, \(I_{PPH}^{MCU}\) is the current consumption of other peripherals (e.g., peripheral power domain, RF core, I2C, and timers), and \(V_{WMCU}\) is the operating voltage.
## 3 Adaptive Control of PEMFC Power Supply
### RBFNN Estimation  
The nonlinear estimator based on the RBFNN is developed to deal with the complicated perturbations. The control schematic diagram is shown in Figure 2, and \(x\) represents \(i_{L}\) or \(v_{o}\) when inductor current or load voltage is regulated. **Lemma 2**. : _RBFNN can model the unknown and continuous function f(X): \(R^{l}\to R\) with an ideal weight vector \(\omega^{*}\in R^{n}\), the basic function vector \(\Phi(X)=\left[ \begin{array}{cccc}\varphi_{1}(X)&\ldots&\varphi_{n}(X)\end{array}  \right]^{T}\). f(X) is described by RBFNN as:_  
\[f(X)=W\sideset{}{{}^{T}}{\Phi}(X)+d \tag{57}\]  
_where d is an approximation error bounded by \(|d|\leq\overline{d}\). \(W^{*}=\left[ \begin{array}{cccc}\omega_{1}^{*}&\ldots&\omega_{n}^{*}\end{array}  \right]^{T}\), \(\omega^{*}\) is denoted as the weigh to minimize the norm of approximation error for all \(X\in\Omega\subset R^{l}\), expressed by:_  
\[\omega_{j}*=\arg\limits_{\omega_{\Omega}\in\Omega}\left\{\sup\limits_{X\in \Omega}\left|f(X)-\hat{\omega}^{T}\varphi(X)\right|\right.\right\},\quad j=1,2,\ldots n \tag{58}\]  
_where \(\hat{W}\in R^{n}\) is the estimation of RBF optimal weight vector \(W^{*}\). The Gaussian function is chosen as the basic function,_  
\[\varphi_{j}(X)=\exp\left(-\frac{||X-\kappa_{j}||}{\theta_{j}^{2}}\right), \quad j=1,2,..,n \tag{59}\]  
_where \(\kappa_{j}\) is the position of the j-th neuron, \(\theta_{j}\) is the center width of the j-th neuron._  
The approximation of \(f(X)\) is given by:  
\[\hat{f}(X)=\hat{W}^{T}\Phi(X) \tag{60}\]  
and for the system \(\hat{f}(X)=\hat{\vartheta}(X,\hat{\omega})=\left[ \begin{array}{cccc}\frac{1 }{L}&\frac{1}{C}&\frac{1}{RC}\end{array} \right]^{T}\). Determine the inputs of RBFNN by:  
\[X_{i}=\left[ \begin{array}{cccc}i_{L}&\dot{i}_{L}\end{array} \right]^{T} \tag{61}\]  
\[X_{v}=\left[ \begin{array}{cccc}v_{o}&\dot{v}_{o}\end{array} \right]^{T} \tag{62}\]  
The estimations are formulated by:  
\[\hat{\vartheta}_{q}=\hat{W}_{q}{}^{T}\Phi_{q}(X) \tag{63}\]  
Figure 2: RBFNN estimation control schematic diagram. where \(\hat{W}_{q}=\left[ \begin{array}{cccc}\omega_{1,q}&\cdots&\omega_{n,q}\end{array}  \right]^{T}\) and \(\Phi_{q}(X)=\left[ \begin{array}{cccc}\varphi_{1,q}(X)&\cdots&\varphi_{n,q}(X) \end{array} \right]^{T}\), \(q=1,2,3\). Define Lyapunov function \(B_{3}\):  
\[B_{3}=A_{2}+\frac{1}{2\chi_{1}}\widetilde{W}_{a}{}^{T}W_{a}+\frac{1}{2\chi_{2 }}\widetilde{W}_{b}{}^{T}W_{b}+\frac{1}{2\chi_{3}}\widetilde{W}_{c}{}^{T}W_{c} \tag{64}\]  
where \(\chi_{r}\) (\(r=1,2,3\)) are positive constants, \(\widetilde{W}=W^{*}-\hat{W}\).
## 4 Statistical Evaluation of Results  
### Correlation between the Volume and Senderness Ratio and Mechanical Strength  
Multiple regression was used to obtain the following relations between the fiber variables (slanderness ratio, \(l/d\), and fiber dosage, \(V_{f}\)) and normalized mechanical strengths (normalized splitting-test tensile strength, \(n_{sp}\), and normalized flexural strength, \(n_{r}\)). These relationships are observed in empirical Equation (4), nonlinear, and (6), linear. No expression of the compressive strength is proposed as a function of the fiber variables because, as seen in the previous section, there was no statistically significant correlation.  
\[n_{sp}=1.006-0.040\times V_{f}(\%)-0.000426\times\frac{l}{d}+0.00535\times V_ {f}(\%)\times\frac{l}{d}\ R^{2}=0.787 \tag{4}\]  
\[f_{sp-calc}=n_{sp}\times f_{sp-pattern} \tag{5}\]  
\[n_{r}=1.022+0.193\times V_{f}(\%)+0.000407\times\frac{l}{d}\hskip 72.27 ptR^{2}=0.746 \tag{6}\]  
\[f_{r-calc}=n_{r}\times f_{r-pattern} \tag{7}\]  
The relationships between the measured strength parameters and results from Equations (5) and (7) and the 95% reliability intervals are shown in Figures 13 and 14. The correlation between the measured and calculated values was 0.787 and 0.746 for \(f_{sp}\) and \(f_{r}\), respectively. The correlation ratios for the slenderness ratio and fiber dosage exhibited a significant positive ratio for \(f_{cm}\) and \(f_{r}\) and a strong relation for \(f_{sp}\). Moreover,  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \multicolumn{3}{c}{**Variables**} & \multicolumn{3}{c}{**Variables**} & \multicolumn{3}{c}{**Variables**} & \multicolumn{1}{c}{**Q1**} & \multicolumn{1}{c}{**Q2**} & \multicolumn{1}{c}{**Q3**} & \multicolumn{1}{c}{**IV1**} & \multicolumn{1}{c}{**IV2**} & \multicolumn{1}{c}{**Q1**} & \multicolumn{1}{c}{**Q2**} & \multicolumn{1}{c}{**Q3**} \\ \hline
**IV1** & **IV2** & & & & & No fiber & - & & \\ \hline \multirow{4}{*}{\(l=40\) mm} & \(V_{f}\) = 0.00\% & - & & & & \(l=40\) mm & yes & \\  & \(V_{f}\) = 0.40\% & yes & & & \(l=50\) mm & no & medium & no \\  & \(V_{f}\) = 1.20\% & yes & & & \(l=60\) mm & yes & & \\  & \(V_{f}\) = 0.00\% & - & & & & No fiber & - & \\  & \(V_the typical error between the measured and calculated values was 16.97% and 19.52%, for \(f_{sp}\) and \(f_{r}\), respectively. Figure 15 denotes the regression graphs resulting from applying Equations (1)-(3) and the standardized prediction profile graphs. Here, increases of up to 30% and 10% were predicted for \(f_{cm}\), \(f_{sp}\) and \(f_{r}\), respectively, for the types of concrete and ranges of the parameters used in the present study.  
Figure 14: Measured and calculated values of \(f_{r}\).  
Figure 13: Measured and calculated values of \(f_{sp}\).
# A Distributed Real-Time Monitoring Scheme for Air Pressure Stream Data Based on Kafka
## 3 Design of Real-Time Monitoring Method  
### Hardware  
We constructed a low-power sensor network to efficiently and reliably transmit real-time air pressure data. The hardware devices used are shown in Figure 3.  
The Arduino Uno is based on the ATmega328 microcontroller and features 14 digital input/output pins, 6 analog inputs, and a USB connection interface; the hardware specifications are outlined in Table 1. We can execute the code by directly uploading sensor code to the EEPROM of the Arduino Uno without the need for any external software connection to a computer, which ensures that the device can independently gather real-time data and maintain high portability [31]. We constructed a low-cost, low-power, and scalable real-time monitoring platform tailored for air pressure data streams by integrating the communication functionality of wireless communication modules and the data acquisition capabilities of pressure sensor modules into the Arduino Uno microcontroller.  
The SIM700 wireless communication module supports multi-band NB-IoT, dual-band GPRS/EDGE, and LTE-FDD communication. Sensor devices need to gather and transmit data frequently to monitor in real time. Therefore, we have opted to use the GPRS network and transmit real-time data to Kafka via UDP communication to provide an efficient data transmission channel that facilitates prompt responses to the data sent by sensor devices.  
The MPX5700 AP sensor is a single-port silicon pressure sensor integrated into a six-pin SIP package [10]. It supports I2C digital output, allowing calibration based on known air pressure values, and can quickly and accurately measure pressure values in various environments, such as pipelines. Its parameters are listed in Table 2. In this scheme, the microcontroller and pressure sensor are connected via the I2C bus communication protocol to gather real-time air pressure data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Content** & **Specification** \\ \hline Microcontroller & ATmega328 \\ Working voltage & 5 V \\ CPU frequency & 16 MHz \\ EEPROM & 1 KB \\ Flash & 32 KB \\ SRAM & 2 KB \\ Dimensions & \(75\times 55\times 15\,\mathrm{mm}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of Arduino Uno.  
Figure 3: Hardware components used in the sensor network.
# Influence of Construction Joint and Bridge Geometry on Integral Abument Bridges
## 1 Introduction
### Objectives and Scope  
The present study investigates steel and PSC girder IAB superstructure and substructure response considering AB joint. Although there has been a great deal of numerical and experimental IAB research performed, most have focused on substructure response and bridge length effects; backfill height and AB (abutment to backwall) joint stiffness effects were not extensively investigated yet. Backfill height plays an important role in an IAB because it determines backfill pressure (load) and passive pressure (resistance) of an IAB. Moreover, AB joint significantly influences the IAB behavior and responses. The differential rotation in AB joint can relieve the moment in the superstructure and substructure, but the abutment rotation may increase pile moments. [PERSON] and [PERSON] [5] reported that the maximum and minimum differential AB joint rotations in four monitored bridges over 7 years were 0.141\({}^{\circ}\) and 0.011\({}^{\circ}\), respectively. Thus, these AB joint and bridge geometric parameters should be investigated to improve the current IAB design.  
A sophisticated 3D nonlinear finite element (FE) analysis model similar to those of [PERSON] et al. [36], [PERSON] et al. [37], and [PERSON] et al. [29] was developed and utilized in a numerical parametric study. This numerical model includes both soil-structure interaction and time-dependent effects and has been utilized in a parametric study. The study evaluates the effect of four dominant parameters (Table 1) that affect bridge response parameters: (1) girder material, (2) bridge length, (3) backfill height, and (4) construction joint below girder seats. This study also considered other factors influencing bridge response that are normally not within the control of the designer: (1) bridge construction timeline, (2) concrete thermal expansion coefficient, (3) backfill stiffness, and (4) pile-soil stiffness. These factors were considered as constant for the study. This four-parameter study includes 72 study cases. Each case was investigated for both rise and fall of AASHTO extreme temperature. Representative magnitudes within a range for each of the four parameters were selected and are presented in Table 1. To compare girder material and bridge geometry influence, the study evaluated four critical superstructure and substructure response parameters: (1) girder axial force, (2) girder bending moment, (3) pile moment, and (4) pile head displacement.
## 2 Materials and Methods
### Validation
#### 2.2.3 Validation of AMARA against Human Scorers, MARA and ABAMAR  
The new algorithm (AMARA) was validated against (i) manual artifact scoring by experts with experience in NIRS data processing; (ii) MARA; and (iii) ABAMAR. Four expert human scorers (S\(\vdash\)S\(\vdash\)S\(\vdash\)) identified manually the artifacts in the 24 recordings. These experts had a minimum of two years of experience in post-processing of large NIRS data sets, however with different applications. For MARA, the same MSD parameters were used as for AMARA. ABAMAR was implemented adopting the parameters given in [63] except for the motion detection threshold, which was set to 0.2-0.55 g/s in order to be better adapted to our measured data (g: earth's gravity). AMARA was applied to [O\(\alpha\)Hb] and [HHb] time series. [O\({}_{2}\)Hb] and [HHb] time series of the long light path (source-detector separation: 2.5 cm) was used.  
The [O\(\alpha\)Hb] and [HHb] time series were segmented into 30 min time periods and presented to the scorer (S\({}_{1}\) to S\(\dash\)) one after the other. The subsequent presentation of 30 min time periods helped to increase the accuracy of the scoring. In addition to the NIRS data, the corresponding acceleration signals for the \(x\)-, \(y\)- and \(z\)-axis were presented simultaneously to the scorer (plot not shown). Four signals (2 \(\times\) HHb and 2 \(\times\) O\(\alpha\)Hb) were evaluated in each night, because the sensor measured two brain regions. This led to a total of 96 signals with a mean duration of 8.5 h. A binary artifact signal was obtained from each scorer containing \"1\" for every time point scored as an artifact. An artifact was considered as a \"real\" artifact when at least three human scorers marked it as artifact. The following measures were calculated to compare the different algorithms: (i) the total number of artifacts (\(N_{Method}\)) identified in all recordings identified by each method; (ii) the total number of \"real\" artifacts (\(N_{real}\)); (iii) the average number of artifacts per signal calculated as the total amount of artefacts divided by the 24 measurements and the four signals per measurement (\(\overline{N}=N_{Method}\)/24/4); (iv) the mean length of an artifact (\(L\)); (v) the number of not identified \"real\" artifacts (\(N\)); (vi) the sensitivity of the method \(S=(N_{real}-N\))/\(N_{real}\), (vi) the mean number of artifacts within a \"real\" artifact (\(\overline{N}_{real}\)), and (vii) the number of false positives (_FP_), defined as the number of detected artifacts not occurring within a \"real\" artifact. To compare AMARA with MARA, all artifacts found by MARA were set to \"true\" and _FP_, _NI_, \(S\) and \(\overline{N}_{real}\) were compared. To compare specifically the movement detection between AMARA and ABAMAR, only the ABAMAR artifacts were set to \"true\" and again _FP_, _NI_, \(S\) and \(\overline{N}_{real}\) were compared. In addition to the artifacts found by AMARA, artifacts found only by the movement detection of AMARA were also taken into account (denoted by AMARAacc in Tables 2 and 3).
## 3 RS Applications in Ocean
### Ocean Surface Wind (OSW)  
OSW is an essential parameter for various applications, such as marine disaster monitoring, climate change modeling, water mass formations, and Numerical Weather Prediction (NWP) [76; 77; 78; 79]. Considering the limitations of the traditional methods for OSW estimation (e.g., anemometers and buoys) [76; 80], RS observations have emerged as cost-effective techniques [81]. Remotely sensed OSW information mainly relies on the relationship between the OSW and the sea surface roughness, which represents emissive and reflective properties of the ocean surface [79]. Five RS systems have been frequently applied to measure OSW: microwave radiometer, GNSS-R, SAR, scatterometer, and HF radar. The advantages and disadvantages of each system, summarized in Table 1, are discussed in more detail in the following subsections.  
Figure 1: Overview of the met-ocean applications of RS which are discussed in this review paper.
# Frechet-Derivative-Based Global Sensitivity Analysis and Its Physical Meanings in Structural Design
## 1 Introduction
One may immediately think of the following: What sensitivity information can we obtain if we measure how the output probability density function (PDF, rather than the failure probability) varies when the input PDF (not only with respect to the mean or variance) is changed? This thought appeared in [10], wherein a new GSI based on the concept of the Frechet derivative (short for Fre-GSI) was proposed. Nevertheless, there are still some issues with Fre-GSI, which need to be discussed. More importantly, how different GSIs serve the global sensitivity analysis and what intrinsic connections are between Fre-GSI and other GSIs have not been explored as yet. Although many GSIs are worth discussing, this study mainly focuses on variance-based GSI, moment-independent GSI, failure-probability-based GSI, and Fre-GSI. Firstly, the importance measure and direction indicator of Fre-GSI are given in this work. Some valuable properties of these two quantities are studied. Then, a unified description from a functional perspective is proposed to explain the differences as well as the similarities between the four GSIs. Some practical links between the Fre-GSI and the three classical GSIs are derived accordingly. Test examples are studied to verify the proposed links, and the specific advantages of the four GSIs, which serve for global sensitivity analysis, are also illustrated. The novelty of this paper lies in three aspects: (1) a unified description is proposed for understanding the similarities and differences between the four GSIs from a functional perspective; (2) practical links are derived between the Fre-GSI and the variance-based GSI, moment-independent GSI, and failure-probability-based GSI; (3) a complementary nature of the four GSIs is revealed throughout numerical and engineering applications.
## 3 System Model
### Optimal Position of the Relay  
To determine the optimal position of the relay, following three positions of the relay are analyzed.  
At position 'A' relay is placed near to the source and away from the destination, as shown in Figure 2. The distances from source to destination, source to relay, and relay to destination are 12 m, 4.2 m, and 8.1 m, respectively.  
Figure 1: System model.  
At position 'B', relay is placed at equally distance from source and destination, as shown in Figure 3. The distances from source to the destination, source to relay and relay to destination are 12 m, 6.2 m, and 6.2 m, respectively.  
At position 'C', relay is placed away from source and near to destination, as shown in Figure 4. The distances from source to destination, source to relay and relay to destination are 12 m, 8.1 m, and 4.2 m, respectively.
# Self-Reporting Technique-Based Clinical-Trial Service Platform for Real-Time Arrhythmia Detection
## 4 Discussion
### Consideration of the Algorithm Performance  
The ultimate goal of this study was to develop a clinical-trial service platform that not only collects the ECG and other health information from the participants in remote places, but also detects different types of arrhythmias. However, the arrhythmia-detection algorithm has not been completed. The algorithm aimed to identify eight types of arrhythmias, but its performance has only been evaluated on four types of the diseases until now. Furthermore, the evaluation was performed using an artificial ECG signal generated from the simulator, rather than human ECG. ECG signals can be easily contaminated by various noise sources, such as electrode displacements, electromyogram, and powerline interference. Thus, performance identified by the simulated signals might not be reproduced with the real ECG. In particular, this morphology-based algorithm might suffer from poor performance in detecting arrhythmias due to probably distorted ECG. It necessitates further evaluation and improvement with a large number of human subjects.  
Improvement of the algorithm is underway by Tribell Labs. Aside from their efforts, the SNUH-CTC is developing a new arrhythmia-detection algorithm based on the latest deep-learning and noise-removal techniques. As soon as development is completed, its performance will be evaluated with human subjects as well as the ECG simulator. Once high detection capabilities are confirmed, the new algorithm will be imported into the batch server of this platform. Thereafter, a new clinical trial can be performed to identify different types of arrhythmias throughout the complete system.