esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Compact Microstrip Decoupled Array
## 4 Conclusions  
The measured results are shown in Figure 18. The difference between the measured value of the \(S\) parameter (2.52 GHz) and the simulation result (2.45 GHz) is 0.07 GHz. There are two reasons for the discrepancy between the measured results and the simulated results. One of these reasons is the imbalance in the power distribution of the feed network. When measured, it is difficult to make the output current of the two ports exactly the same. Another reason is the fluctuation of the relative permittivity of the fabricated substrate, leading to a deviation in the measured and simulated center frequency. These factors can contribute to the differences observed in the performance of the antenna. However, it is worth noting that the miniaturization of the antenna did not hardly affect its radiation patterns. The measured results are in good agreement with the simulated results in terms of electrical performance.  
The miniaturization of mono-pulse antennas through the design of miniaturized radiating units and reduced spacing between dual-element antenna arrays exhibits promising prospects for various applications. This approach enables the maintenance of unchanged electrical characteristics after miniaturization by decoupling the operation. It effectively reduces the size of sum and difference antennas while ensuring good electrical performance. Moreover, this method can be extended to two-dimensional mono-pulse antennas and miniaturized antenna arrays.  
Conceptualization, Z.W. and D.Y.; methodology, Z.W.; software, K.X.; validation, Z.W., D.Y. and K.X.; formal analysis, K.X.; investigation, D.Y.; resources, Z.W.; data curation, Z.W.; writing--original draft preparation, D.Y.; writing--review and editing, K.X.; visualization, Z.W.; supervision, Z.W.; project administration, K.X.; funding acquisition, D.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Antenna Unit Size** & **Array Type** & **Array Spacing** &
\begin{tabular}{c} **10 dB Bandwidth** \\ **(GHz)** \\ \end{tabular} & **Gain (dBi)** & **Isolation (dB)** \\ \hline
[21] & \(0.98\,\lambda\times 0.7\,\lambda\) & \(2\times 2\) & \(0.48\,\lambda\) & 7.9–9.59 & 8.3 & \(>\)18 \\ \hline
[22] & \(0.22\,\lambda\times 0.18\,\lambda\times 0.76\,\lambda\) & \(5\times 6\) & \(0.22\,\lambda
# Comparison of Stress Concentration Factors Obtained by Different Methods
## 4 Stress Concentration Induced by a Change in Cross-Sectional Area  
In technical engineering practice, one of the most common forms of stress concentration are the locations of sudden changes in the shapes and especially the sizes of the cross-sectional areas. Such concentrators mainly concern bars, shafts and beams with tensile, torsional and bending stresses and often providing functional movement. When designing them, it is necessary to take into account not only the method of stressing but also to ensure that it is functional and can be assembled into a system. The design of such structural elements and their frequent cross-sectional changes are thus subject to many design constraints arising from the location of bearings, pulleys, gears and other structural elements. From the point of view of the magnitude of stress concentration, the most unfavorable condition is that of sharp transitions between cross-sections. The corresponding stress in such a notch is then greater the more significant the change in cross-section [12; 13].  
The design of the transition method between shaft sections with different diameters depends on whether an additional structural element is applied at a given location or whether the geometry of the critical region can be varied within a certain range. Historically, the sharp transition was initially replaced by a constant radius rounded transition. In order to reduce stress concentration to the smallest possible level, this radius is chosen to be as large as possible [13; 14; 15]. However, further experimental and numerical analyses have shown that more significant peak stress reduction can be achieved by complex rounding with variable radii. Such optimization of the transition curve can then lead to elliptical, parabolic or other more complex shapes [6; 16]. However, this approach requires case-by-case optimization. The technological handling of more complex transitions can also pose problems. Last but not least, such a solution must also satisfy the intended design layout. For example, the transition radius must be smaller than the radius of curvature of the edge of, e.g., the inner ring of the bearing. However, the size of the radius may already be so small that it may not be sufficient for the necessary peak stress reduction. In such a case, a more optimization, design and technology-intensive solution is required, using, e.g., relief grooves [6; 17].
# Fault Diagnosis for a Class of Robotic Systems with Application to 2-DOF Helicopter
## 2 Preliminaries
### Fault Diagnosis  
Information redundancy is used to determine if a fault occurs in a system. A form of redundancy is achieved by duplicating and tripling measuring elements, actuators and in some cases, components of a system, which is called physical redundancy. Redundancy can also be analytical, when information on the signals being measured is obtained through the use of models and/or output observers.  
Generally, the models of a system are used to estimate the value of the output. The difference between the measurement of the output and the estimate is used to construct a signal that ideally depends only on the fault (in case it is present), this signal is called residual, which is close to zero in case there are no faults present in the system. Alternatively, residual can be also defined using the output estimation error of an output observer, see ref. [29] and Figure 1.  
Different methods depend on the different types of models that can be obtained. Sometimes, instead of using the model directly, an observer is used (model plus correction factor for the estimation error). In this case, there is an additional degree of freedom in the selection of the observer's gain that can be used to determine the radius of convergence of the estimation error to zero in the absence of failures. The gain of the observer can be used to discriminate between faults, to emphasize the effect of one residual over the others, as can be seen in ref. [29].  
The way in which the gain is selected can, in many cases, allow us not only to achieve detection, but also to isolate faults. Thus, in order to achieve the fault diagnosis, it would only be necessary to identify the fault.  
A different situation occurs when the model is not initially available. In this case, there is a wide range of proposals in the literature, as can be seen in ref. [4]. In general, model-free schemes operate in two steps: first, a model or a residual generator is estimated by using input and output data that has no failures; second, once this residual generator (or model) is available, it is used to detect and/or isolate the faults. It should be mentioned that these techniques presuppose a model (of the system) in the form of a difference or differential equation.  
Note that even though these last methods emphasize that they do not need a system model (at least initially), they eventually end up inferring the model and implementing it. The latter is, however, transparent to the user.
## 2 Literature Review and Hypotheses Development
### Speech Features
\begin{table}
\begin{tabular}{c c} \hline
**Speech Features** & **Definition** \\ \hline Fundamental frequency (F0) & The rate of vocal fold vibration. \\ \hline Pitch period & The fundamental period of the signal. \\ \hline Pitch period entropy (PPE) & The impaired control of F0 during sustained phonation. \\ \hline Spectral slope & The observed tendency to have low energy during \\  & high frequencies. \\ \hline Spectral spread & The total bandwidth of a speech signal using spectral centroid. \\ \hline Spectral centroid & A measure used to evaluate the brightness of a speech. \\ \hline Spectral entropy & Observed to assess silence and voice region of speech. \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of common speech features indicative of emotion. In the context of voice user interface evaluation, a study by [PERSON] and [PERSON] [42] analyzed speech amplitude, pitch, and duration to assess participants' speech behaviour patterns during voice user interface usage. More precisely, speech patterns were observed during responses following errors produced by iPhone's Siri. As stressed by the authors, few studies have investigated users' speech behaviour patterns while using a voice user interface. As seen in [PERSON] and [PERSON]'s study [42], as well as various HCI and non-HCI studies, speech features were informative of affective states. With this said, this leads us to our first replication hypothesis:  
**Hypothesis 1 (H1)**. : _There is a relationship between the amplitude of targeted speech features and the emotional intensity of users during voice user interface interactions._
## 3 Method
### HMM of Cognitive States  
We facilitate the Hidden Markov Model (HMM) on top of a human cognitive model to capture the cognitive states of a production line worker who suffers from working stress. The HMM is deemed a double stochastic model, which converts the serial correlation of observable data into a probability transition of latent states [23]. The states of a system are hidden to the observer. However, by learning through advanced algorithms, determining which state the system is most likely when an output is observed is possible. The model is maximized when the states are indirectly visible but should be estimated through visible process outputs. Each state has a known probability distribution over the possible outputs. That is, the sequences of outputs generated by an HMM state transition reveal information on incumbent states.  
An HMM can be formally characterized by five elements. \(N\) is the number of states in the model. The state values in the state variable \(q_{t}\) are denoted as \(S=\{S_{1},S_{2},\cdots,S_{N}\}\). \(M\) is the number of distinct observation symbols for the outputs of an HMM. The symbols are denoted as \(V=\{V_{1},V_{2},\cdots,V_{N}\}\). A transition probability matrix (\(A=\left\{a_{ij}\right\}\)) indicates the likelihood of transitioning from one state to another, where \(a_{ij}=\mathrm{Prob}(q_{t+1}=S_{j}|q_{t}=S_{i}),1\leq i,j\leq N\) represents the probability value of state \(q_{t}\) of going to state \(S_{j}\) at time \(t+1\), given that at time \(t\), the state is \(S_{i}\). An output probability matrix (\(B=\left\{b_{j}(k)\right\}\)) indicates the likelihood for a certain measure value to come from a given state, where \(b_{j}(k)=\mathrm{Prob}(V_{k}(t)|q_{t}=S_{j}),1\leq j\leq N,1\leq k\leq M\) presents the probability of the \(k\) th observation symbol because the state is state \(S_{j}\) at time \(t\). Lastly, an initial state distribution \(\pi_{1}=\{\pi_{i1}\}\) indicates the likelihood for a new input sequence to start in a given state at the initial time \(t=1\), where \(\pi_{i1}=\mathrm{Prob}(q_{1}=S_{i}),1\leq i\leq N\). The sum of all probability distributions must be equal to 1, while the sum of all elements in a row in the \(A\) matrix must also be equal to 1.  
The dynamic feature of the finite state machine is highlighted for heterogeneous human behavior. This study estimates an operator's mental state through an HMM, which is a Markov process with unobserved (hidden) states. During the training stage, we estimate the state transition matrix according to the measured molding sequencing, personal traits, and annotative mind states. During the operation stage, we estimate the state of workers' attention solely based on measured molding sequencing. We will formulate a contingent safety rule, which forces a suspected worker to rest when she or he is unable to surpass the safety standard under continuous operation.
## 1 Introduction
The reflectance-based remote-sensing method of LAI estimation can more correctly represent the ecosystem behavior as it also includes the LAI of non-vascular plants. The biochemical composition of vegetation and its structure directly influence vegetation's absorption and reabsorption properties. The radiative transfer models (RTMs) of vegetation are tools to explain the light interaction with leaves and vegetation canopies. The interaction of radiation with leaves' internal structure is responsible for important physiological processes in the plant, such as photosynthesis and transpiration. Thus, the light-leaf interaction determines the exchange of energy and matter between vegetation and the atmosphere. Therefore, the RTMs are used to connect the vegetation properties with remote-sensing observations and vegetation monitoring. However, the application of RTMs is challenging because of the unavailability of all the required parameters to feed into the RTMs. By inverting a coupled RTM-photosynthesis model to hyperspectral proximal sensing (reflectance and SIF), the properties of vegetation can be analyzed and can be further used in RTMs to retrieve the photosynthetic properties and energy balance of vegetation [20; 21; 22]. Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) is one of the RTMs that incorporates different models to understand radiation within vegetation canopies [23]. Even after certain limitations in SCOPE, such as not having soil water balance and consideration of leaves clumping, the model has proven to be robust for the study of several vegetations and their functioning [23; 24; 25]. The implementation of the SCOPE model for the purpose of understanding certain characteristics of peatland vegetation has not been undertaken before. Therefore, the objectives of this work are (1) to decipher the potential of SCOPE inversion in the simulation of LAI for peatland vegetation. (2) to observe the potential of SCOPE for SIF estimation of the peatland ecosystem when the high-resolution hyperspectral sensors for fluorescence spectrum retrieval are not available.
## 2 Literature Review
### Blockchain in Wireless Sensor Network (WSN)  
In WSN, different issues exist like data storage, computational complexity and node failure. In [21], an incentive mechanism for WSN is proposed. The WSN nodes that store the data are awarded with incentives upon reliable storage of data. More the data are stored, more the incentive amount is given. Two blockchains are used in the proposed scenario: data storage and access control. Instead of using PoW consensus mechanism, the authors used Provable Data Possession (PDP). PoW has the major limitation of high resource consumption, which is tackled using PDP. With blockchain technology, storage complexity is also optimized. In WSNs, nodes are resource constraint entities, which have less computational power. By keeping this issue in mind, the authors proposed a novel rolling blockchain mechanism in [22] that overcomes data storage and computational complexity problems. Due to the resource constraint nature of WSN nodes, PoW is not advisable to be used in WSN. From simulation results, authors concluded that if the network is denser, then there is less chance of node failure and vice versa. The excessive node failure leads to network breakdown.  
In [23], the authors presented a framework for Device-to-Device (D2D) cellular network. The main objective of the proposed framework is to authenticate Channel State Information (CSI). In a data intensive system, D2D communication is not suitable for large number of mobile users. The proposed algorithm improved the spectra efficiency without using any consensus mechanism. However, this system is only designed for non-cooperative mobile users.
## 4 Experiment Results and Analysis
### Runtime Analysis  
The runtime of RC_Cuts, ATT_Cuts, and the proposed method are shown in Table 3 over images of MSRA10K (typical image resolution of \(400\times 300\)) using a PC with 3.60 GHz CPU and 4 GB of RAM. The RC_Cuts method here was the fastest (about 1.24 s per image), followed by the proposed method (about 1.33 s) and ATT_Cuts (about 1.86 s) method.
## 5 Conclusions  
This paper proposes the T5 NN-ESTD framework to address the limitations in long-term time-series forecasting. Building on iTransformer, the model jointly trains short-term and long-term prediction branches. For long-term prediction, T5 NN-ESTD replaces the MLP decoder with an LSTM-based decoder to improve long-term sequence stability. Afterward, the dimension-alignment strategy is designed to unify outputs by replicating short-term predictions. In addition, T5 NN-ESTD introduces an ensemble mechanism that leverages short-term forecasts to dynamically correct long-term bias. Experimental results demonstrate that T5 NN-ESTD effectively reduces error accumulation over extended horizons, outperforming existing methods in accuracy and robustness. The framework highlights the potential of combining transformer architectures with recurrent mechanisms and bias-correction strategies for multi-horizon forecasting.  
Conceptualization, J.W.; Methodology, W.Z.; Investigation, F.C.; Resources, L.W.; Writing--original draft, R.P.; Writing--review and editing, C.Y. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Natural Science Foundation of Shanghai (Grant No. 24 ZR1425500), the National Natural Science Foundation of China (Grant No. 62002216), and the Shanghai Sailing Program (Grant No. 20 YF1414400).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.  
Figure 6: Performance of meteorology forecasting.
## 1 Introduction
### Contributions and Paper Organization  
We focus on AoI minimization optimization in a highly dynamic vehicle platooning. The considered platooning system comprises various autonomous vehicles in which CVs are responsible for communicating between the vehicle pairs and broadcasting the CAM messages to FVs by updating the communication link to the RSU. The key objectives are listed as follows:* To model MEC-Aided Platooning to enhance autonomous driving in ITS. A computation offloading method is proffered to handle the latency and resource management challenges in platooning.
* To formulate a multi-objective optimization problem for enabling platoons by minimizing the AoI and maximizing the communication reliability in vehicular networks.
* To design a MARL framework over DDPG algorithms to train triple cascaded critics, and a global and a local critic to facilitate spectrum access to the CVs.
* To accomplish the Cascaded Multi-Agent DDPG algorithm to address the overestimation bias issue and better control the estimation bias.
* To demonstrate with experiments the performance of the proposed framework by indicating three-times better convergence, ensuring the mean Age of Information to be less than 10 ms and the transmission probability of CAM message verification to be almost 99% for diverse platoon criteria.  
The remainder of the paper is organized as follows. Section 2 discusses the system model of the proposed framework. The mathematical modeling of the optimization problem is formulated in Section 3. Section 3 describes the proposed MARL algorithm in the MEC-Assisted Platooning system with convergence analysis in Section 4. Numerical experiments and results are demonstrated in Section 4, and Section 5 draws a conclusion to the paper. The primary notations and their explanations are listed in Table 1.
# A Modified Residual-Based RAIM Algorithm for Multiple Outliers Based on a Robust MM Estimation
## 4 An RB RAIM Based on a Robust MM Estimation  
### Robust Principle and an RB RAIM Detector Based on a Robust Estimation  
The least squares method has been criticized for its dramatic lack of robustness and outliers having an arbitrarily large effect on the residuals by LS estimation. In this connection, [PERSON] introduced the notion of the breakdown point \(\varepsilon^{\star}\), which is the smallest percentage of contaminated data that can cause the estimator to take on arbitrarily large aberrant values. In the case of least squares, \(\varepsilon^{\star}=0\).  
To reduce the effect of outliers on parameters estimations, robust estimation methods have been widely used; M estimation is the most popular as it is based on the generalized maximum likelihood estimation theory and is closely connected to traditional LS procedures.  
Mitigation of multiple outliers for GNSS positioning has been reported using M estimation. The authors confirmed superior performance of the scheme over a classical LS solution for the scenarios with multiple outliers. The M estimator was first proposed by [PERSON] where the LS cost function of squared residuals was replaced by a symmetric convex cost function \(\rho\):  
\[\text{minimize}\sum_{i=1}^{n}w_{i}\rho\left(\frac{r_{i}}{\delta}\right) \tag{15}\]  
where \(r_{i}\) is the residual of each observation and \(\delta\) is the scale factor of the residuals (an estimator of the spread of the random errors). A robust alternative is subtracting the median instead of the mean and then taking the median of the absolute values, which yields the median of absolute deviation (MAD) and the normalized MAD (MADN) as:  
\[\hat{\sigma}=MADN(r)=\frac{median\left(\left|r-median(r)\right|\right)}{0.6745} \tag{16}\]  
The normalized residual of the \(i\)th pseudo-range could be formed as \(v_{i}=\frac{r_{i}}{\delta}\).  
The LS estimation makes full use of all pseudo-ranges that include the possible existence of outliers; therefore, the LS residuals are directly affected by outliers. For a robust estimation, the weight of outliers may be reduced in the process of iteration to weaken the influence of outliers on the residuals. The flow chart of the RB RAIM detector based on a robust estimator is shown in Figure 1.  
The robust M estimator can be derived as:  
\[\hat{\mathbf{x}}_{R}=\left(\mathbf{H}^{\text{T}}\mathbf{P}\mathbf{H}\right)^{ -1}\mathbf{H}^{\text{T}}\mathbf{P}\mathbf{y} \tag{17}\]  
where \(\mathbf{P}\) is a diagonal equivalent weight matrix and the \(i\)th diagonal element \(\overline{p}_{ii}\) of \(\mathbf{P}\) is computed as:  
\[\overline{p}_{ii}=p_{i}w_{i} \tag{18}\]  
where \(w_{i}\) is the \(i\)th diagonal element of \(\mathbf{W}\). The weight matrix \(\mathbf{W}\) is replaced by \(\mathbf{P}\) in a robust M estimation.