esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of DSP-Based Enabling Technologies for Cloud Access Networks
## 3 CAN Network Elements
### Soft-ROADMs
#### 3.2.2 Soft-ROADM Experimental Demonstrations
For the before drop (BDR) case, the drop element is bypassed, and data is recovery is identical to that in the BAD add case. To allow acceptable BER levels for each Sub-B, the soft-ROADM add (drop) case employs the six (seven) highest frequency subcarriers out of 15 available. This is due to cross-channel interference (CCI) [21] occurring between orthogonal Sub-Bs with increasing interference levels at lower subcarrier frequencies. Figure 9a,b,d,e show the measured BAD and AAD BER curves for Sub-\(\lambda\) and Sub-B add operations respectively. The Sub-\(\lambda\) (Sub-B) add penalty for all channels is \(\leq\)1 dB (\(\leq\)1.4 dB) at an FEC limit of 1E-3. The add penalties can be attributed to: (i) Residual CCI, (ii) finite tap length-induced non-perfect filter orthogonality and out-of-band attenuation, and (iii) DD-induced intermixing products. Figure 9c shows the BER curves for the BDR and ADR operations on PB-I and PB-Q. The BDR case has reduced sensitivity compared to the before Sub-\(\lambda\) add case due to the extra subcarrier employed. The soft-ROADM has a similar drop penalty for both channels of \(\sim\)2 dB, which is mainly a result of signal distortions induced by the imperfect MZM-IM transfer function. In all cases, the Q channel performs slightly worse than the I channel due to the finite tap length-induced filter frequency response ripples being more prominent for the Q channels. Some examples of equalized and overlaid, subcarrier constellations at the receiver, are presented in Figure 9f-h for Sub-\(\lambda\) add (f), Sub-B add (g), and the drop operation (h). \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline add/drop data-carrying subcarriers per sub-band & 6/7 \\ \hline OFDM modulation format & 16-QAM \\ \hline DAC and ADC sample rate/bit resolution & 2 GHz /8 bits \\ \hline add/drop per channel raw bit rates & 0.3/0.35 Gb/s \\ \hline Samples per symbol \({}^{\star}\) IFFT/Cyclic prefix/Total & 32/8/40 Samples \\ \hline EML wavelength/modulation bandwidth & 1550 nm/10 GHz \\ \hline EML laser bias current/EAM bias voltage & 124 mA/\(-\)0.75 V \\ \hline EML/MZM driving voltage Sub-\(\lambda\) add operation & 2.3/1.0 Vpp \\ \hline EML/MZM driving voltage Sub-B add & 2.3/0.6 Vpp \\ \hline MZM DC bias Sub-\(\lambda\)/Sub-B add operation & 1.3/1.47 V \\ \hline MZM modulation bandwidth & 20 GHz \\ \hline PIN sensitivity \({}^{\#}\)/Bandwidth & \(-\)19 dBm/12.4 GHz \\ \hline Add/Drop optical launch power & 4.5/3.1 dBm \\ \hline Electrical signal amplitude at ADC & 300 mVpp \\ \hline EML/MZM driving voltage drop operation & 2.3/3 Vpp \\ \hline MZM DC bias drop operation & 1 Vpp \\ \hline \end{tabular} \({}^{\star}\) Before up-sampling and after down sampling; * Corresponding to 10 Gb/s NRZ data at a BER of 1.0 \(\times\) 10\({}^{-9}\).  
\end{table} 
Table 2: Transceiver and system parameters.
## 5 Experimental Results and Discussion
### Estimated State Transition Probabilities  
Estimates of transition probabilities describe the model and show which model parameters are most likely to be responsible for the error sequences seen in experiments. These estimates are derived by considering the measured error sequences that have already been provided. Therefore, the estimated state transition probabilities describe how a channel moves from one state to the next.  
In Tables 2-5, the most likely approximate state transition probabilities for Case 1 (laptop charger), Case 2 (computer monitor), Case 3 (laptop charger and computer monitor), and Case 4 (CFL bulb and hairdryer) are shown.  
Figure 11: Error sequence for 16-QAM (comprising a CFL bulb and a hairdryer).  
Figure 10: Error sequence for 16-QAM (comprising a laptop charger and a computer monitor).  
are the most likely and accurate models that produced the empirically obtained error sequences. Furthermore, it contributes to the validation that the learned models are the most credible and authentic. Analytical validation of the generated FMMs was performed using the log-likelihood ratio, the error-free run distribution, and the error probability.
###### Abstract  
This paper studied the path following problem for an underactuated vessel sailing in restricted waters with varying water depths. A novel high-gain extended state observer based adaptive sliding mode path following control scheme was proposed. The high-gain extended state observer based line-of-sight guidance law was designed according to vessel kinematics in the horizontal plane, which achieved accurate guidance in spite of time-varying sideslip angles. In the guidance system, a guidance angle was calculated to serve as a reference input for the yaw tracking control system. The sliding mode yaw tracking control system was designed, which can deal with model uncertainties and external disturbances. Since it is hard to obtain the exact model parameters in advance, an adaptive technique was adopted to estimate the unknown parameters, and an adaptive sliding mode control was designed to make the yaw tracking errors globally and asymptotically converge to zero in spite of unknown model parameters, model uncertainties, and external disturbances. Furthermore, the global uniformly asymptotically stability of the closed-loop system was proven based on the cascade system theory. Lastly, simulation experiments were conducted to validate the analysis results and to demonstrate the superiority of the proposed scheme.  
underactuated vessel; shallow water effect; path following; line-of-sight guidance; high-gain extended state observer; adaptive sliding mode control
# An Analytical Design of Simplified Decoupling Smith Predictors for Multivariable Processes
## 2 Materials and Methods
### Simplified Decoupling Smith Predictor Structure
Therefore, the problem of controller design for multivariable systems with multiple time delays is simplified to multiple single-loop Smith predictors and various simple methods can be utilized to design the control systems. The decoupling techniques are available for ideal, simplified, and inverted decoupling [4]. However, in this study, simplified decoupling was chosen because of its robustness and simple decoupling network (i.e., the diagonal elements of the decoupling matrix, \(\mathbf{D}(s)\), were set as unity) [4,5]. [PERSON] and [PERSON] proposed an extended method of simplified decoupling for general forms of a MIMO case where the decoupled apparent processes are given as follows [5]:  
\[d_{ji}=\frac{C_{ij}}{C_{ii}},i,j=1,2,\ldots,n;\,i\
eq j \tag{6}\]  
\[q_{ii}=\frac{g_{ii}}{\Lambda_{ii}}) \tag{7}\]  
where \(\mathbf{C}=(\mathrm{adj}\mathbf{G})^{T}\), for which \(\mathrm{adj}\mathbf{G}\) is the transpose of its cofactor matrix; and \(\Lambda_{ii}=[\mathbf{G}\otimes\left(\mathbf{G}^{-1}\right)^{T}]_{ii}\), for which \(\otimes\) is the Hadamard or Schur product of element-by-element multiplication. Using Equation (6), the decoupler elements for some typical processes were obtained and are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Process** & \multicolumn{2}{c}{**Decoupler Elements**} \\ \hline \(2\times 2\) & \(d_{21}=-\frac{g_{21}}{g_{22}}\) & \(d_{12}=-\frac{g_{12}}{g_{11}}\) \\ \hline \(3\times 3\) & \(d_{12}=-\frac{g_{33}g_{12}-g_{32}g_{13}}{g_{33}g_{11}-g_{31}g_{13}}\), & \(d_{  
The diagonal elements of the decoupled process are also expressed in Table 2. It is obvious that each element is complicated and incapable of serving in analysis and design problems. A CM method using Maclaurin series expansion was proposed by [PERSON] to reduce the decoupled transfer functions into some standard forms and solve the problems of decoupler realizability as well [5]. The greatest disadvantage of that method is the burden of algebraic computations when the order of MIMO systems increases. Therefore, in this work, a novel approach to deal with these issues of simplified decoupling was proposed by using the modified PSO algorithm [27; 28; 29].
# On the Design of a Network Digital Twin for the Radio Access Network in 5G and Beyond
## 2 NDT Architecture for the RAN
### Service Mapping Models
#### 2.2.3 Models Design  
The _service mapping models_ can be approached as analytical models, simulators, or ML-based models. The selection of one or another approach can depend on different aspects such as the availability of data (i.e., if there are not any available data, an ML model cannot be trained), the level of complexity of the selected approach or the required accuracy in the NDT. For instance, the traffic generation model in an NDT of the RAN can be approached as a ML model trained based on real data. However, if not enough data are available for the training of the ML approach, the model can also be designed as a simulator, whose parameters can be fine-tuned to adapt to real data collected from the RAN.
# A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment
## 3 Overview of Flood Sensor Development
### Flood In Situ Sensors
\begin{table}
\begin{tabular}{c c c c} \hline
**Sensor** & **Index** & **Advantages** & **Disadvantages** \\ \hline Camera Sensors & water levels, water quality, wave heights, fluctuation, flood extent & 1. High resolution & 1. Visibility is easily affected 2. Intuitive visual information & 2. Restricted installation location 3. Real-time & 3. Complicated data processing \\ \hline Bio Sensors & water quality & 1. Sensitivity to environmental changes & 1. Vulnerability to environmental interference 2. Complex data processing \\ \hline Microwave Sensors & water level, flow velocity, submerged objects, depth of standing water & 1. High-precision 2. Real-time 3. Remote monitoring 4. Not affected by the weather & 1. High costs 2. Vulnerable to interference \\ \hline Radar Sensors & water levels, flow velocity, wave height, underwater objects & 1. Remote monitoring 2. Highly adaptable & 1. High costs 2. High power consumption \\ \hline Ground LiDAR & water Levels, wave height, fluctuations, flood extent, flood depth, topography, deformation of geological structures & 1. Small-area economy 2. Portability 3. High-resolution, high measurement density, high-precision 4. Unaffected by light & 1. High costs 2. Long data processing time \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._decisions about flood hazard prediction and response [77; 78]; they can also be used to determine where to deploy sensors [79] and evaluate sensor functionality [80]. The IoT wireless sensor network in Colima-Villa de Alvarez, Mexico, used for hydrometeorological data collection and flood monitoring, has shown good robustness during extreme events [81]. To address sensor node failures during floods, UAV-integrated WSNs can serve as communication routers, collecting data from rainfall and water level sensors using INS and GPS systems [82].
## 4 Review of Existing Methods and Capabilities
### Safety Controls
#### 4.2.1 Run-Time Assurance  
A critical safety concern for autonomous systems is trust in decision making. As the ability of autonomy to make decisions, the state space of the system grows so large that it is impossible to verify and validate all possible decisions made by an autonomous system. As a result, a way to ensure that the system does not make decisions outside an acceptable region is required. The concept of run-time assurance has been investigated by the Air Force Research Lab [100; 101] and is supported by research by Barron Associates [102]. [PERSON] at NASA Armstrong has proposed an Expandable Variable-Autonomy Architecture (EVAA) system architecture that would enable confidence in UAS decision making by bounding the decision making to prevent a system from operating in unsafe manners [103]. Research continues to be performed in trust of autonomous systems, including in manned systems, such as autonomous ground collision avoidance systems on fighter aircraft [104; 105; 106; 107]. Extending the confidence in autonomous unmanned systems will be critical in the future.  
Ensuring that the system architecture enables safety, including implementing a run-time assurance concept as in the EVAA architecture, will be critical to ensure safe operations.
## 3 Improved Grey Wolf Particle Swarm Hybrid Optimization MSVR Localization Model Construction
### The IPSOGWO Model
#### 3.3.3 IPSOGWO-MSVR Positioning Model  
The whole process of localization of the IPSOGWO-MSVR algorithm proposed in this paper is as follows:  
Step 1 involves initializing algorithm parameters: setting the population size to 30, the number of iterations to 250, \(C\) to 0.1-100, \(\gamma\) to 0.01-100, \(\sigma\) to 0.01-1, social learning factors \(c_{1}(\max)\) and \(c_{2}(\max)\) to 2.05, and cognitive learning factors \(c_{1}(\min)\) and \(c_{2}(\min)\) to 1.1.  
Step 2 involves generating the initial population based on the improved Tent mapping, while simultaneously calculating the fitness values of the population individuals. The grey wolf ranks are determined by the three highest fitness values. The root-mean- square error is chosen as the fitness function for parameter optimization in this article:  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{K}\left(\sum_{j=1}^{N}\left(y_{i,j}- \widetilde{y_{i,j}}\right)^{2}\right)}{K}} \tag{28}\]  
where \(y\) and \(\widetilde{y}\) represent the actual coordinates and the coordinates predicted by the model, respectively.  
Step 3 involves calculating and updating A and C according to Formula (19) and updating the positions of the grey wolves according to Formula (25). The fitness values are then recalculated, and the grey wolf ranks are updated.  
Step 4 is the termination criterion. The algorithm terminates once it reaches the maximum number of iterations, returning the globally optimal parameter combination (\(C\), \(\gamma\), \(\sigma\)). Otherwise, steps 2-4 are repeated.  
Step 5 involves the MSVR conducting five-fold cross-validation training on the database according to the optimal parameter combination, outputting the optimal model, and predicting the target location based on the fingerprints during the online phase.  
The specific process is depicted in the following Figure 6.
# Research on High-Performance Fourier Transform Algorithms Based on the NPU
## 3 Background
### The Matrix Acceleration Technique in the Discrete Fourier Transform (DFT)  
The Fourier Transform is a mathematical procedure that extracts frequency components from input data samples. That transform is heavily used in signal processing algorithms, such as equalization, filtering, compression, and more.  
Figure 1: Huawei DaVinci architecture.  
For frequency domain analysis, the DFT is frequently used in engineering, and its Formula is shown as follows:  
\[X_{j}=\sum_{k=0}^{n-1}x_{k}\exp(-\frac{2\pi i}{n}jk)\;\;j=0,1,\ldots,n-1 \tag{1}\]  
The multiplication of an \(N\)-by-\(N\) DFT matrix and the discrete samples of the sequence of length \(N\) can be expressed in the form of Formula (2), where \(\omega=e^{\frac{-2\pi}{N}i}\). The complexity of the algorithm reaches \(O\big{(}N^{2}\big{)}\).  
\[\begin{bmatrix}1&1&1&1&\ldots&1\\ 1&\omega&\omega^{2}&\omega^{3}&\cdots&\omega^{N-1}\\ 1&\omega^{2}&\omega^{4}&\omega^{6}&\cdots&\omega^{2(N-1)}\\ 1&\omega^{3}&\omega^{6}&\omega^{9}&\cdots&\omega^{3(N-1)}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ 1&\omega^{N-1}&\omega^{2(N-1)}&\omega^{3(N-1)}&\cdots&\omega^{(N-1)(N-1)} \end{bmatrix}\begin{bmatrix}x[0]\\ x[1]\\ \cdots\\ x[N-2]\\ x[N-1]\end{bmatrix}=\begin{bmatrix}y[0]\\ y[1]\\ \cdots\\ y[N-2]\\ y[N-1]\end{bmatrix} \tag{2}\]  
From Formula (2), it can be seen that the DFT is the process of multiplying a DFT matrix of \(N\times N\) with an input vector of \(N\times 1\); this task is well suited to the powerful matrix processing capabilities of NPUs. When the input vector length is \(N\leq 16\), the DFT process can be executed as a single matrix multiplication instruction. When \(N>16\), the matrix multiplication can be decomposed into multiple low-order matrix multiplications using the block matrix principle, allowing the cube unit to handle the computation. Consequently, the computational complexity is significantly reduced to \(1/256\) of the original complexity. The direct calculation method still retains its relatively high-level calculation process, which means that as the order of matrices involved in matrix multiplication increases, the processing speed significantly decreases. However, that method holds engineering significance for lower-order inputs. This is due to the NPU's exceptional matrix computing capabilities and the finite nature of sample length \(N\) in typical engineering applications. Therefore, employing an NPU for direct DFT calculations using Formula (2) is a valuable engineering approach that effectively utilizes the computing power of the NPU cube unit.
## 4 Existing Standards
### IEEE 802.15.1 Bluetooth Low Energy  
Bluetooth Low Energy (BLE) [23] has been designed as a low power WSN standard to operate in the same 2.4 GHz range as classic Bluetooth but uses different channels and a lower data rate of 200 kb/s rather than the 1-3 Mbps achieved via classic Bluetooth. BLE does not support mesh networking and only supports one packet type. BLE uses DSSS as the spread spectrum method and supports three separate application profiles: Sporting, Consumer and Healthcare. BLE is not backward compatible with classic Bluetooth, but Bluetooth 4.0 dual mode capability supports a direct interface between the standards where the protocols share one physical radio and antenna. Due to the plethora of Bluetooth-enabled devices in the marketplace, BLE is likely to be adopted widely by device manufacturers as it is a \"least effort\" forward path to deliver low power functionality. Although the likely wide adoption of BLE as a low power WSN standard cannot be ignored, the standard does not support QoS and is not specifically designed for WBAN applications.
# Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?
## 5 Conclusions  
In this work, we studied the usefulness of training deep learning models with non-expert annotations for the segmentation of cardiac MR images. The need for medical experts was probed in a comparative study with non-physician sourced labels. Through framing the problem of relying on non-expert annotations as noisy data, we managed to obtain good performance on two public data sets, one of which was used to emulate an out-of-distribution data set. We found that training a deep neural network, regardless of its capacity (U-Net, Attention U-Net or ENet), with data labeled by a well-trained non-expert achieved comparable performance than on expert data. Moreover, the performance gap between the networks with non-expert and expert annotations on the out-of-distribution data set was less pronounced than the gap on the training data set. Future endeavors could focus on crowd sourcing large-scale medical data sets and tailoring approaches that take their noisiness into account.  
Conceptualization, P.-M.J. and A.L.; methodology, Y.S.; software, Y.S.; validation, P.-M.J. and A.L.; formal analysis, P.-M.J. and A.L.; investigation, Y.S. and P.-M.J.; resources, A.L.; data curation, Y.S.; writing--original draft, Y.S.; writing--review and editing, P.-M.J. and A.L.; supervision, P.-M.J. and A.L.; funding acquisition, A.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
Figure 4: Dice score on ACDC per disease. NOR: Normal, DCM: Dilated cardiomyopathy, HCM: hypertrophic cardiomyopathy, MINF: Myocardial infarction, RVD: Right ventricle disease.
## 4 Experimental Verification
### Analysis of Experimental Results  
#### 4.2.1 Self-Calibration Coordinates of Laser Tracer Stations  
The positioning precision of laser tracer is below 1 \(\upmu\)m [18]. The precision requirement of the laser tracer multi-station measurement system [19] built in this paper was below 1 \(\upmu\)m, \(\varepsilon=10^{-4}\). Through a series of experiments for the parameters, the coordinates of calibrated laser tracer stations were observed, and the optimal setting of the parameters was obtained as: \(\beta\) = 0.55 and \(\sigma\) = 0.4. In the measurement system, the initial value \(\mu_{i}\) was set as \(\|f_{i}(X_{0},Y_{0},Z_{0},d_{0})\|\), where \(X_{0}=-64.9938\), \(Y_{0}=-6.6256\), \(Z_{0}=122.5133\), and \(d_{0}=1128.5363\). Then \(\mu_{0}=0.75\) was calculated. According to Equation (17), these parameters follow the rules of \(\mu_{i}\) in L-M algorithm. According to the initial coordinate values \((X_{0},Y_{0},Z_{0})\) of the laser tracer, the initial distance \(d_{0}\) from laser tracer to the initial measured point, and the selected parameters in L-M algorithm, the coordinates \(P_{j}\Big{(}X_{j},Y_{j},Z_{j}\Big{)}\) of the laser tracer and the distance \(d_{j}\) from the laser tracer to the initial measured point were obtained by iteration (Table 1).
## 3 Momentum-Based Motion Detection  
The simplest method for detecting the user motion state is to use the received signal strength (RSS) analysis. RSS is a kind of time-series data. Since the RSS of an MN is related to the distance between an MN and its associated AP, the RSS value \(P_{r}\) at the time \(t\) is given by  
\[P_{r}\left[t\right]=P_{tx}-10\rho\log\left[d\right]+X_{dB} \tag{1}\]where \(d\) is the distance between the MN and the associated AP, \(P_{tx}\) is the transmitted signal power, \(\rho\) is the path loss exponent, and \(X_{dB}\) is a Gaussian random variable with zero mean and standard deviation \(\sigma_{dB}\) representing the shadow fading. According to Equation 1, the difference between two continuously measured RSS at distances \(d_{1}\) and \(d_{2}\) can be expressed as  
\[\Delta P_{r}\left[t\right]=P_{r}\left[t\right]-P_{r}\left[t-1\right]=-10\rho \log\left[\frac{d_{2}}{d_{1}}\right] \tag{2}\]  
Given the measured RSS interval and the direction and velocity of a user movement, the user motion can be identified based on Equation 2 as  
\[\text{user motion}=\left\{\begin{array}{ll}\text{stationary state},&\text{if } \Delta P_{r}\left[t\right]=0\\ \text{approaching state},&\text{if }\Delta P_{r}\left[t\right]>0\\ \text{leaving state},&\text{if }\Delta P_{r}\left[t\right]<0\end{array}\right. \tag{3}\]  
However, the measured RSS fluctuates constantly due to the fading effect. In the real world, the surrounding environment is always changing and hence RSS also changes continually. Even if the MN is stationary, the measured \(\Delta P_{r}\left[t\right]\) may not be zero. Therefore, an MN cannot easily detect user motion based only on \(\Delta P_{r}\left[t\right]\) without filtering fading noise.