esa-sceva/satcom-chunk-collection huggingface dataset:
# Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach +
## 3 Materials and Methods
### Filter Design
\begin{split} K_{k}&=P_{k}^{-}H_{k}^{T}(H_{k}P_{k}^{-}H_{k}^{T }+R_{k})^{-1},\\ \hat{q}_{k}&=\hat{q}_{k}^{-}+K_{k}(z_{k}-h(\hat{q}_{k}^{-})),\\ P_{k}&=(I-K_{k}H_{k})P_{k}^{-},\end{split}  \tag{13}\]  
where \(K_{k}\) is the Kalman gain, \(H_{k}\in\mathbb{R}^{M\times 6}\) is the Jacobian of the measurement function (9), i.e.,  
\[H_{k}=\frac{dh(\hat{q}_{k})}{d\hat{q}_{k}}= \begin{bmatrix}\frac{\partial d_{1, k}}{\partial\theta_{k}}&-\frac{\partial d_{1,k-1}}{\partial\theta_{k-1}}\\ \frac{\partial d_{2,k}}{\partial\theta_{k}}&-\frac{\partial d_{2,k-1}}{ \partial\theta_{k-1}}\\ \vdots&\vdots\\ \frac{\partial d_{M,k}}{\partial\theta_{k}}&-\frac{\partial d_{M,k-1}}{ \partial\theta_{k-1}}\end{bmatrix} . \tag{14}\]EKF Smoother  
The EKF is a filter, hence it is able to retrieve the state \(\hat{s}_{k}\) using the measurements up to time \(kT_{s}\), i.e., the first part of the problem defined in Section 3.2. For the second, we make use of the RTS smoother [41], which builds up a backward recursion for vehicle trajectory estimate, as detailed next:  
\[\hat{q}_{k+1}^{-} =A_{k}q_{k}, \tag{15}\] \[P_{k+1}^{-} =A_{k}P_{k}A_{k}^{T}+B_{k}Q_{k}B_{k}^{T},\] \[G_{k} =P_{k}A_{k}^{T}[P_{k+1}^{-}]^{-},\] \[\hat{q}_{k}^{s} =\hat{q}_{k}+G_{k}[\hat{q}_{k+1}^{s}-\hat{q}_{k+1}^{-}],\] \[P_{k}^{s} =P_{k}+G_{k}[P_{k+1}^{s}-P_{k+1}^{-}]G_{k}^{T},\]  
where \(G_{k}\) is the smoother gain and where we used the superscript \(s\) to denote the smoothed quantities. Furthermore, we have implemented two different types of smoother. As depicted in Figure 1, the forward filter is responsible for processing the RFID phase samples sequentially, and the backward filter augment the sample estimation reversely to compensate for the estimation error of the EKF. In the fixed-lag smoother (see Figure 1, bottom), for estimating the smoothed value at time step \(k\), only the measurements between \(k+1\) and \(k+n+1\) are used, where \(n\) is a fixed-lag (i.e., a fixed number of phase samples to be smoothed). Therefore, for each smoothed estimate \(\hat{q}_{k}^{s}\), an adaptive window of \(n\) RFID phase measurements is considered. Thus, the smoothed values for all time steps can be achieved using a sliding window of size \(n\) in near real-time [42]. Another approach instead considers all the values, thus letting \(n\) be the sequence of _all_ the available measurements (see Figure 1, top). Obviously this strategy is much more computationally demanding compared to fixed-lag smoother, and might not be affordable in real time. Figure 1: The process of the (**Top**) EKF-Smoother fixed-lag and (**Bottom**) EKF-Smoother Full-length algorithms.
## 3 Results and Discussion
### Corrosion Degree  
Aiming at quantifying the corrosion level, an indicator of corrosion degree, as detailed in Section 2, was used. The corrosion degree (CD) was obtained through the following equation:  
\[CD(\%)=\frac{m_{u}-m_{c}}{m_{u}}\times 100 \tag{4}\]  
where \(m_{u}\) is the mass of 30 uncorroded fibers and \(m_{s}\) is the mass of 30 corroded fibers.  
The results are summarized in Table 7.  
For intermediate corrosion levels, the variation of CD is consistent with the fact that for the same current intensity, the higher the content of steel, the lower the relative corrosion-induced mass loss. For the same steel content, the higher the specific surface, or the lower the diameter, is, the higher the relative mass loss.  
The CD for severe corrosion is not higher than CD for intermediate corrosion, except for F80 ZP, while it would be expected to be higher considering the visual aspect of the specimens, the longer period of impressed current, and even the lower flexural performance. The cause of these apparently contradictory results lies in the fact that for severe corrosion, it was difficult to integrate fibers at the most stressed regions, i.e., outer regions. So, fiber had to be collected from inner regions, where the corrosion process was less intense. This did not happen for F80 ZP, as the CD was lower. [PERSON] and [PERSON] (2016) found that for a corrosion degree above 2%, the fiber tends to fail rather by fiber rupture than by fiber pullout. This phenomenon will be more pronounced with the decrease in fiber diameter, which is in agreement with the fact that the CD in F40 RC is lower than in F40 ZP.
## 2 Principle
### Multispectral Image Reconstruction Method
#### 2.2.2 Improved Method to Reduce Computation Time
The reference pixel in Step 1 should be changed to other pixels which do not belong to the processed category. In our method, the optimal quadratic program is run once at each similar spatial region. This greatly decreases the running time. Although each pixel requires the follow-up calibration process, this is a linear computation which has much less complexity than the quadratic optimization program. Therefore, this reconstruction method using spatial similarity can greatly reduce the computation time. Further, the segment linear calibration compensates for the recovery bias and ensures the recovered spectral images are of a high quality.
## 4 Evaluation
### Statistical Significance Analysis  
To strengthen the reliability of the experimental findings, statistical significance testing was conducted using paired \(t\)-tests on the repeated experimental runs for each learner category (Fast, Moderate, and Slow). The goal was to determine whether the observed improvements in the key performance metrics--Time Spent on Exercises, Error Rate, and Success Rate--between Phase 1 (static fuzzy weights) and Phase 2 (reinforcement learning-optimized fuzzy weights) were statistically significant.  
For each metric, five repeated runs were conducted with randomized student session data, and the mean values were compared across the two phases using the paired \(t\)-test. The resulting \(p\)-values are reported in Table 5.  
These results confirm that the observed improvements between Phase 1 and Phase 2 are statistically significant across all learner categories and all three learning performance indicators. Therefore, the reinforcement learning-based fuzzy weight optimization contributes meaningfully to enhancing learning efficiency and student success.  
Figure 5: Success rate improvement before vs. after RL optimization.
## 3 OFDM Methodology
### FrFT Method  
The FrFT can also replace the FFT in the traditional OFDM system, the resulting system methodology is known as FrFT based OFDM system and mathematically it can be represented as:  
\[x_{n}=\sum_{n=0}^{N-1}X_{n}F_{\alpha}(m,n),0\leqslant m<N, \tag{5}\]  
where \(X_{n}\) is the signal data transmitter and \(F_{\alpha}(m,n)\) is the IFrFT transform as detailed explained in [54], where it can be represented as:  
\[\begin{split} F_{\alpha}(m,n)=\sqrt{\frac{\sin(\alpha)-j.\cos( \alpha)}{N}}\exp(\frac{j}{2}m^{2}\cot(\alpha(u)^{2})\exp(\frac{j}{2}n^{2}\cot( \alpha))\times\\ \exp(-j\frac{2\pi mn}{N}),\end{split} \tag{6}\]  
where \(\alpha=p.\frac{\pi}{2}(0<\alpha<\pi,\,p\) is the fractional of the transform, \(u\) is the sample space. When \(\alpha\leq\frac{\pi}{2}\) the transform will be a conventional OFDM.
## 3 Methods  
### Principle of DNN  
A normal DNN structure consists of an input layer, one or more hidden layers, and an output layer, as illustrated in Figure 2a. Each layer consists of nodes, which are connected to the nodes of the next layer by means of connection links. Figure 2b presents the process of moving through the input layer and hidden layers to produce the target values at the output layer. Equations (1) and (2) present the formula that uses the input variables to calculate the target value, where \(x_{1}\) and \(x_{2}\) are the input variables, \(n_{1}\) and \(n_{2}\) are the hidden layer node values, \(f\) is the activation function, \(\hat{y}\) is the target value, \(w\) is the weight, and \(b\) is the bias. The activation function uses the weighted sum of nodes as the input and converts it into a valid value. The Rectified Linear Unit (ReLU) is a typical nonlinear activation function used in regression analysis [57]. The ReLU outputs zero if the input value (\(z\)) is less than 0, and it outputs the same value as the input value if it is greater than 0 (see Equation (3)).  
\[n_{1}=f(x_{1}w_{11}+x_{2}w_{21}+b_{1}),\ n_{2}=f(x_{1}w_{12}+x_{2}w_{22}+b_{2}) \tag{1}\]  
\[\hat{y}=f(n_{1}w_{31}+n_{2}w_{32}+b_{3}) \tag{2}\]  
\[f=\begin{cases}z<0&f(z)=0\\ z\geq 0&f(z)=z\end{cases} \tag{3}\]  
To minimize the error between the predicted and observed values, the weights assigned to the nodes of all layers are modified by a back-propagation method [58] every time the model is trained. This method measures the error function slope using the gradient descent method and updates the weights of all neurons to minimize the error. Typical gradient descent methods include stochastic gradient descent [59], RMSprop [60], and Adam [61]. Equation (4) presents the formula that updates the weights. The weights are adjusted until the partial derivative of the error function is 0. In the equation below, \(i\) is the node number of the previous layer, \(j\) is the node number of the next layer,  
Figure 2: Description of DNN model: (**a**) structure of DNN model; (**b**) process for forecasting output through DNN model.  
is the weight at time \(t\), \(w_{ij}^{t+1}\) is the updated weight at time \(t+1\), \(\eta\) is the learning rate, and \(E\) is the error function.  
\[w_{ij}^{t+1}=\ w_{ij}^{t}-\eta\frac{\partial E}{\partial w_{ij}} \tag{4}\]
# Closed-Loop Cognitive-Driven Gain Control of Competing Sounds Using Auditory Attention Decoding
## 2 Methods
### Experiment Protocol
#### 2.1.2 Open-Loop AAD Phase  
In the open-loop AAD phase, the calibrated AAD algorithms were used to identify the attended and the unattended speaker without presenting feedback to the participants. The open-loop AAD phase consisted of one session lasting 10 min. During this session, participants were cued by an arrow on a screen every minute to switch attention between the competing speakers while recording the ongoing EEG responses. Afterwards, participants were asked to rate how much effort it took to follow the attended speaker, to ignore the unattended speaker, and to switch attention to the cued speaker on a scale from 0 to 10, with 0 being least effort and 10 being most effort. In addition, participants were asked to rate how well they understood the attended story on a scale from 0 to 10, with 0 being nothing understood and 10 being everything understood. Furthermore, the participants were asked to fill out a questionnaire consisting of multiple-choice questions about the stories uttered by the speakers. While participants were rating and answering the questionnaire, the decoding performance of several AAD algorithms (see Section 2.5.1)  
Figure 1: Experiment protocol used to calibrate and evaluate the cognitive-driven gain controller system. The experiment protocol consists of a calibration phase with four sessions, an open-loop AAD phase with one session and a closed-loop AAD phase with three sessions.  
was evaluated using the recorded EEG responses. The following AAD algorithms were considered:  
* LW-GLM: AAD algorithm using a generalized linear model (GLM) with a large correlation window (LW) of 15 s.
* LW-SSM: AAD algorithm using a state-space model (SSM) with a large correlation window (LW) of 15 s.
* SW-SSM: AAD algorithm using a state-space model with a small correlation window (SW) of 0.25 s. Using a small correlation window was motivated by the results in Reference [9], where it was shown that the state-space model is able to translate highly fluctuating coefficients of the spatio-temporal envelope estimators into reliable probabilistic attention measures.  
Note that, in this paper, an AAD algorithm using a generalized linear model with a small correlation window was not considered, since initial experiments showed highly fluctuating correlation coefficients with unreliable probabilistic attention measures.
# Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks
## 4 Experimental Results
### Simulated Data
#### 4.1.1 [PERSON]'s Score-Analysis  
Results employing [PERSON]'s method in the score-analysis phase are shown in Table 1, each row depicts an experiment. Every value is rounded down to two decimal places and represents an average among 5 trials.  
Figure 3: Transition networks of stationary first-order DBNs (\(m=1\)). The network (**a**) on the left represents the transition network of DBN \(A\) which generates normal subjects. Networks (**b**,**c**) represent DBN \(B\) and \(C\), respectively, which generate anomalous subjects. Dashed connections represent links which are removed with respect to the normal network (**a**), while red links symbolize added dependencies. Solid black edges are connections which are common with respect to (**a**).  
The outcomes demonstrate that datasets with solely 100 subjects (\(N=100\)) perform generally poorly, since these do not possess enough information about the data's underlying processes. Accuracy as well as F\({}_{1}\) scores tend to decline with the increase of outlier ratios, due to less normal data available for a correct modeling phase. The latter is observed by the decrease of TPR measurements. The computed thresholds converge to more stable values with the increase of data, hence outputting more reliable values for every performance measure.  
Discussing the impact of outlier ratios, [PERSON]'s method is recognized to be more effective in the presence of lower anomaly percentages due to the increasingly asymmetric score distribution when increasing the number of outliers, as already confirmed in [38]. Moreover, when \(P_{O}\) is high enough and the majority of outliers are generated by a common process, the score distribution of abnormal data becomes visible, causing poor performance in experiments with 20% of outliers. Such explains why, for the same \(P_{O}\), F\({}_{1}\) scores may decrease with the increase of subjects. The breakpoint of [PERSON]'s method [37] prevents favorable results when in the presence of abundance outlierness. However, FP tend to disappear, reflecting high precision measurements.  
Comparing experiments from both anomalous networks \(B\) and \(C\), accuracy is in general higher in experiments with \(C\), since the latter has fewer connections in common with the normal model \(A\), resulting in a more dissimilar structure. However, such is not always true, since asymmetric distributions perturb [PERSON]'s analysis.  
Control experiments performed using datasets solely comprised by normal subjects demonstrated favorable results with [PERSON]'s score-analysis, contrary to the GMM strategy, which divides the distribution in two classes creating an high number of FP.
## 4 Discussion
In Table 2, the dimensions of the developed mold are compared against the dimensions of a conventional mold that is required to produce the presented bi-injected parts. A schematic representation of these two models is illustrated in Figure 19, which shows a reduction in mold dimensions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Type** & \begin{tabular}{c} **Mold Height** \\ **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Mold Length** \\ **(mm)** \\ \end{tabular} &
\begin{tabular}{c} **Mold Width** \\ **(mm)** \\ \end{tabular} \\ \hline Conventional & 430 & 510 & 580 \\ \hline Hybrid Movement & 370 & 446 & 580 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mold dimensions for conventional and the developed hybrid movement mold. Figure 19: Schematic representation of both mold types (Yellow—designed hybrid movement mold; Blue—conventional mold): Front view of both molds, with the identification of type 1 cylinder positioning (**a**); Isometric view of both mold types (**b**). Figure 18: Conventional injection mold structure. Analyzing the values presented in Table 2, an increase of about 33% in mold volume was observed, significantly reducing the required mold height, while also reducing the required mold length. This mold height reduction is relatively important, as it influences the injection machine selection. With a more compact mold, a smaller machine can be selected (if the required clamping weight is met), effectively reducing operating costs while producing the parts within the requirements. Another advantage of the developed system is that the cylinders responsible for the mold movements can be installed outside of the mold (Figure 13), and do not require additional mold space to accommodate these components. The positioning of the type 1 cylinders is shown in Figure 19, with the complete system shown in the Figure 20.  
This methodology could be used in various applications, effectively reducing the required mold space for the injected parts obtention. As previously mentioned, this enables the selection of smaller machines with a lower operating cost. In the presented case, a machine with a clamping force of 250 t was used for the developed mold; however, if the conventional mold were to be used, due to the increased mold height, a machine with a clamping force of 350 t would be required to accommodate the mold. The use of this larger machine would cause an increase in the operating cost of about 22%. This value was calculated based on the medium operating cost per hour, associated with each of the machine types. These values are presented in A.U./h (arbitrary units per hour) in Table 3.  
This highlights the cost effectiveness of this approach; however, this comparison is appliable to the machining operating costs and unrelated to productivity gains.
## 4 Random Forests Theory
### Improved Random Forest Algorithm  
The RFs algorithm uses bootstrap sampling, which is a simple random sampling method with permutation. When extracting each training subset, about a third of the samples are not selected, and these data are called out-of-bag data. These data are of high research value and can be used as an alternative to cross-validation methods for datasets. Based on the traditional RFs algorithm, this study designs a weighted random forestsalgorithm based on precision prediction with perturbed OOB datasets and a few object datasets. In the training phase of the algorithm, the test dataset and the out-of-bag dataset with additional disturbance factors are used to predict the fault diagnosis accuracy of the decision tree on the tested object. The out-of-bag weight and test weight are calculated according to the above precision. In the decision-making stage, the voting weights of the decision tree are adjusted according to the out-of-bag weight and test weight. The detailed implementation process is described below. First, the test data, \(P\), and the training dataset, \(T\), are collected at the test sample rate \(X_{TP}\) (the ratio of test data to training data). The training dataset, \(T\), is from an ideal system simulation, and the relatively small test dataset, \(P\), is from the test object with uncertain perturbation factors. In the training phase, bootstrap sampling is used to separate the training data subset \(S\) and the out-of-bag data \(O\) from the training dataset, \(T\), at the out-of-bag data rate \(Xos\) (the ratio of the OOB data to the training subset data), and add a certain random Gaussian white noise to the OOB data \(O\). The fault diagnosis accuracy of the out-of-bag dataset and test dataset with disturbance factors reflects the fault classification ability of the decision tree. The decision trees with higher classification accuracy and better classification effect will have heavier weight. After training the \(k\)th decision tree with the training subset \(S_{k}\), use the out-of-bag dataset \(O_{k}\) with variance \(M\) gaussian white noise to predict the fault diagnosis accuracy of the kth decision tree. For the \(k\)th decision tree, the weights for OOB data are as follows:  
\[w_{O_{k}}=\frac{X_{O_{k}}^{corr}}{X_{O}},\ k=1,2,\ldots,K \tag{7}\]  
In this formula: \(X_{o}\) is the total number of samples, and \(X_{O_{k}}^{corr}\) is the number of samples correctly classified by the \(k\)-th decision tree. Use the test dataset \(P\) to predict the fault diagnosis accuracy of the \(k\)th decision tree. The ratio of the number of correct test samples to the number of predicted test samples is the weight of the predicted test. \[w_{P_{k}}=\frac{X_{P}^{corr}}{X_{P}},\ k=1,2,\ldots,K \tag{8}\]  
In the decision-making stage, the OOB data weights are combined with the predicted test weights to determine the final weights. When voting for fault diagnosis, the vote result of each decision tree is multiplied by its corresponding weight \(W_{k}\).
## 4 Experiment and Result Analysis
### Ablation Analysis
EINET, the F1 of Model 8 reduces by 0.91% (micro-average) and 1.11% (macro-average) in named entity recognition, and it reduces by 1.34% (micro-average) and 1.20% (macro-average) in relation extraction. This experiment further proves that the global semantic information and the local semantic information are effective. Model 9 is our baseline model. Compared with Model 9, the F1 of EINET is 2.37% (micro-average) higher and 2.26% (macro-average) higher in named entity recognition, 3.43% (micro average) higher and 3.04% (macro average) higher in relation extraction. The experimental results show that the performance gain of EINET in named entity recognition and relation extraction is remarkable, which also confirms that our proposed method of enriching explicit and implicit semantics is effective for joint entity and relation extraction.
## 2 Materials and Methods  
Based on the first principle, the adsorption and gas-sensing properties of SF\({}_{6}\)-N\({}_{2}\) mixture decomposition components (NO\({}_{2}\), SOF\({}_{4}\), SOF\({}_{2}\), SO\({}_{2}\)F\({}_{2}\)) on pristine and Ag\({}_{2}\)O-doped InN are investigated in this paper. The exchange-correlation function is described by a generalized gradient approximation (GGA) with the Perdew-Burke-Ernzerhof (PBE) functional [30; 31]. By constructing a 4 \(\times\) 4 \(\times\) 1 InN supercell, the adsorption properties and doping were investigated. The Monkhorst-Pack (MP) method was used to describe the Brillouin zone, and 7 \(\times\) 7 \(\times\) 1 k-point was set for all simulation calculations [32; 33]. The energy convergence accuracy, maximum stress, and max displacement were set as 1 \(\times\) 10\({}^{-5}\) Ha, 2 \(\times\) 10\({}^{-3}\) Ha/A, 5 \(\times\) 10\({}^{-3}\) A, respectively. A 15 A vacuum slab was adopted in order to prevent interactions between the adjacent layers.  
The formation energy (\(E_{form}\)) describes the stability of the doped structure, and the calculation formula is as follows:  
\[E_{form}=E_{Ag_{2}O\text{-}InN}-E_{Ag_{2}O}-E_{InN} \tag{1}\]  
where \(E_{Ag_{2}O\text{-}InN}\), \(E_{Ag_{2}O}\), and \(E_{InN}\) are the total energy of Ag\({}_{2}\)O-InN, isolated Ag\({}_{2}\)O atom and pristine InN monolayer.  
The adsorption capacity of the InN monolayer towards gas molecules is described by adsorption energy (\(E_{ads}\)). The \(E_{ads}\) is defined as:  
\[E_{ads}=E_{gas/sur}-E_{gas}-E_{suf} \tag{2}\]  
where \(E_{gas/sur}\) is the total energy of absorbed pristine or Ag\({}_{2}\)O-doped InN system, and \(E_{suf}\), \(E_{gas}\) represents the total energy of pristine or doped InN and isolated gas molecule, respectively.  
In addition, Mulliken analysis was considered to analyze the mechanism of charge transfer (\(Q_{t}\)) throughout the work. \(Q_{t}\) of each gas adsorption process can be calculated by the following equation:  
\[Q_{t}=Q_{gas/sur}-Q_{gas} \tag{3}\]  
\(Q_{gas/sur}\) and \(Q_{gas}\) denotes the charge transfer amount after adsorption carried by the gas and isolated gas, respectively. If \(Q_{t}\) is negative, that the substrate material loses electrons.  
\[E_{g}=\left\lfloor\frac{E_{LUMO}-E_{HOMO}}{\left\lfloor\frac{E_{HOMO}}{\left \lfloor\frac{E_{HOMOTo further analyze the energy gap (\(E_{g}\)) of molecular orbital, the highest occupied molecular orbit energy (\(E_{HOMO}\)) and the lowest occupied molecular orbit energy (\(E_{LUMO}\)) is investigated. \(E_{g}\) is defined as Equation (4).