esa-sceva/satcom-chunk-collection huggingface dataset:
# Gain-Scheduled Disturbance Observer-Based Saturated Controllers for Non-Linear First-Order System +
## 3 Illustrative Example
### Offset Impact  
As can be seen in Figure 3, the actual input of the process depends on the addition of the input disturbance \(d_{i}\) and the offset of the controller \(u_{p}\) (which is part of the total output of the non-linear GS controller (18)). The signal \(u_{p}\) could be formally replaced by the total \"lumped disturbance\" \(d_{i}+u_{p}\). The question is whether the reconstruction and compensation of such a lumped disturbance provide the same performance as before.  
The transients shown in Figure 8 (down) correspond to GS controllers without using the separate value \(u_{p}\) in (18). They illustrate the advantages of treating the controller offset and disturbance compensation separately for accelerated responses. The omission of the \(u_{p}\) value is particularly evident at lower setpoints and for slower controllers, i.e., when the controller gains based on local and ultralocal models differ significantly, as mentioned in Remark 2. This finding also affects other approaches used for non-linear process, such as the ADRC mentioned earlier.  
In contrast to the zero disturbance values \(d_{f}\) reconstructed in the steady states of the upper responses, the lumped values \(d_{i}\) shown below are a replacement for the omitted values of \(u_{p}\). The differences in Figure 8 above and below also indicate that the rate of convergence of the reconstructed disturbances \(d_{f}\) to the external disturbances \(d_{i}\) will depend on the liquid level. Correction \(u_{p}\) in the GS controllers should be performed after each change in the setpoint. For disturbances with a constant setpoint, the change in \(u_{p}\) is usually not necessary. Therefore, a more detailed qualitative and quantitative analysis of the omission of \(u_{p}\) for disturbance responses (discussed in [5]) is not performed.
## 4 The Proposed TransTLA Method
### Long Short-Term Memory Network Layer  
To extract temporal features from both the source and target domain datasets, we employ LSTM networks. The core innovation of LSTM lies in its internal gating units: the  
Figure 3: The structure of the residual module.  
input gate, the forget gate, and the output gate. These gates work in concert to dynamically regulate the flow of information, allowing the network to learn both long-term and short-term dependencies. The input gate determines how much new input information to incorporate into the neuron's state at each time step, the forget gate decides which outdated information to discard from the state, and finally, the output gate determines how much of the current neuron's state to include in the next time step's output. This design makes LSTM particularly effective in sequence prediction tasks, especially in scenarios that require remembering long-term information. The structure of the LSTM network unit is shown in Figure 4, where \(C_{t-1}\) is the state of the neuron from the previous moment, \(h_{t-1}\) is the output of the neuron from the previous moment, and \(x_{t}\) is the current moment's time series data input. The gating mechanism provides the network with powerful memory and forgetting capabilities, which is crucial for the temporal feature extraction of the source and target domain datasets. However, LSTM can be more resource-intensive and slower to train, particularly when dealing with large batches or extended sequences.  
The LSTM network, fed by the TCN's feature outputs, operates with a set of parameters that govern its state transitions. Let \(X_{LSTM}=\{x_{1},x_{2},\cdots,x_{n}\}\) denote the input features, \(h_{t}\) the hidden state, \(C_{t}\) the cell state, and \(Y_{LSTM}=\{y_{1},y_{2},\cdots y_{n}\}\) the output sequence. The state updates of the LSTM network parameters can be represented as follows:  
\[f_{t}=\sigma\big{(}W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\big{)} \tag{4}\]  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{5}\]  
\[\widetilde{C}_{t}=\tanh(W_{\text{C}}\cdot[h_{t-1},x_{t}]+b_{\text{C}}) \tag{6}\]  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{7}\]  
\[C_{t}=f_{t}\odot C_{t-1}+i_{t}\odot\widetilde{C}_{t} \tag{8}\]  
\[h_{t}=o_{t}\odot\tanh(C_{t}) \tag{9}\]  
\[y_{t}=\sigma\big{(}Vh_{t}+b_{y}\big{)} \tag{10}\]  
where \(\sigma\) is the sigmoid function; \(\tanh\) is the hyperbolic tangent function; \(W_{f}\), \(W_{i}\), \(W_{c}\), \(W_{o}\), and \(V\) are the weight matrices for the forget gate, input gate, candidate memory cell, output gate, and sequence prediction output, respectively; and \(b_{f}\), \(b_{i}\), \(b_{C}\), \(b_{o}\), and \(b_{y}\) are the bias terms for the forget gate, input gate, candidate memory cell, output gate, and sequence  
Figure 4: The structure of the LSTM network.  
prediction output, respectively. \(\odot\) denotes the Hadamard product. By stacking two layers of LSTM networks, we extract deeper temporal features within the dataset.
# Biologically Inspired Visual System Architecture for Object Recognition in Autonomous Systems
## 4 The Proposed Architecture
### Attention Mechanism  
In order to improve its efficiency, the algorithm does not process the entire image at once, but rather processes a bounded number of small areas, using the attention mechanism illustrated in Figure 4. The attention of the algorithm is determined according to three biologically inspired attention generators. The first mechanism is the saliency detection mechanism. Saliency stands for the quality by which an object stands out relative to its neighborhood. Our eyes do not move smoothly across the scene, instead they make short and rapid movements between areas containing more meaningful features. Saliency detection is considered to be a key attentional mechanism that facilitates learning and survival by enabling organisms to focus their limited perceptual and cognitive resources on the most pertinent subset of the available sensory data [36]. In our model, we used the FRCNN natural structure and its Region Proposal Network (RPN) module, which is based on the activity feature map created by the convolutional layers, as a source for saliency objects proposal.  
A motion detection algorithm was used to draw attention to moving objects. Visual motion is known to be processed by neurons in the primary visual cortex that are sensitive to spatial orientation and speed and are considered a pre-attentive mechanism, which help focus attention on moving objects. A simple motion detection algorithm is used, which conducts two main operations: foreground-background separation and adjacent frames subtraction. The foreground-background separation is based on pixel value statistics [37]. The frames subtraction was performed on five adjacent frames in order to detect local changes caused by moving objects. The results of the two algorithms were merged into single object proposal mechanism.  
Finally, a random attention generator was employed to focus attention on random locations in the scene, in order to complement the other two search mechanisms. This random generator imitates the saccades movement of the human eyes that make rapid random jittering movements while scanning a visual scene, even when we are fixated on one point. The random attention generator proposes bounding boxes for exploration that are between the saliency and the motion detector bounding boxes.  
Figure 3: Top-level schematic illustration of the pathways in the Visual Associative Predictive (VAP) model.  
The output of the attention mechanism is a list of bounding boxes object proposals that is passed forward for further processing, as can be seen in Figure 7.
# AI-Enhanced Prediction of Peak Rate of Torque Development from Accelerometer Signals
## 2 Materials and Methods
### Deep Learning  
We utilized an artificial neural network (ANN) approach to predict the RTD signal from the ACC signal. To ensure robust model performance, the dataset was split into distinct training (70%) and testing (30%) sets, allowing for the rigorous evaluation of the predictive capabilities of the model. The architecture of the neural network model, as outlined in Table 1, was designed to capture the intricate dynamics between the ACC and RTD signals. It comprised multiple dense layers equipped with rectified linear unit (ReLU) activation functions, known for their ability to introduce non-linearity into the model, thereby capturing complex patterns in the data. The model culminated in a final linear activation output layer, facilitating the prediction of RTD values based on the input ACC signals.  
To optimize model training, we employed the mean squared error loss function and stochastic gradient descent optimizer, ensuring efficient convergence towards optimal model parameters. The training process involved iteratively updating the model parameters using batches of data, with a batch size of 20 and 1000 epochs to iteratively refine the model's predictive performance. Following model training, the prediction model was applied to the testing set, comprising a subset of the dataset (\(n=58\)), to assess its generalization ability to unseen data. To enhance the smoothness and clarity of the predicted signals, a low-pass, 4 th-order Butterworth filter with a 50 Hz cut-off frequency and zero lag was applied. This filtering process helped to mitigate noise and artifacts, ensuring the reliability of the predicted RTD signals. The implementation of the ANNs was carried out in a Python environment using TensorFlow and Keras libraries. The computational resources utilized for model training included a Google Collaboratory Notebook with a CPU-based backend boasting 12 GB of RAM, ensuring sufficient computational power for complex model computations. Further analyses were then performed using the signals.
## 4 Fingerprinting-Based Positioning with Spatial Side Information
### Pattern-Matching Methods
#### 4.4.1 Pattern-Matching Method 1-SingFingIn-PosOut  
The pattern-matching method 1-SingFingIn-PosOut consists of the traditional fingerprinting method based on associating a vector of fingerprints with a position, without spatial side information on the fingerprints. It maps a vector of fingerprints collected at a single, pointwise, position to the final position estimate. This method is depicted in Figure 10b.  
Figure 10: Fingerprinting pattern-matching methods. (**a**) 2D sensor array. (**b**) Method 1-SingFingIn-PosOut. (**c**) Method 2-SingFingIn-AggPosOut. (**d**) Method 3-MultFingIn-PosOut. (**e**) Method 4-MultFingIn-ClassOut.  
The method consists of calculating a position estimate using only a single sub-tile--for example, the central sub-tile. In the training phase, an FFNN with \(N_{F}\) inputs, with one for each fingerprint, is trained as a regression model. The input to the NN is a vector with elements that form a set \(\mathcal{F}_{0}\) of \(N_{F}\) fingerprints. These fingerprints are the ones measured in a selected reference sub-tile of a fixed tile. The training aims to map the input fingerprints to a reference ground truth position associated with the tile or to a reference position in the selected sub-tile. The final goal is to tune the parameters in the NN to produce as the output a position estimate for the selected reference position for a given set of input fingerprints.  
In the testing phase, the FFNN takes as the input a vector of fingerprints from a sub-tile in a moving tile. The selected sub-tile is consistent with that used in the training phase. The FFNN produces as its output a position estimate consistent with the reference position used in the training phase, with the coordinates \(\hat{x}_{\mathcal{F}_{0}}\) and \(\hat{y}_{\mathcal{F}_{0}}\).  
Regarding the selection of a reference sub-tile, we suggested above to use the central sub-tile. In cases with an even number of sub-tiles per side, the central sub-tile can be defined as one of the four closest to the center of the tile. Alternatively, the center of the tile, or any other part of the tile, can be used as the training target position. Regardless of the point of the tile selected as the target training position, it is important to use the same point in the tile for the reference ground truth position when calculating the positioning error to avoid introducing a systematic error.
## 3 Improved Multi Mobile Agent Data Fusion Scheme
### Multi Mobile Agent Framework
#### 3.1.1 Physical Architecture  
The physical layer can contain a variety of sensor nodes. They can be fixed or mobile, wireless or wired, and use data centric or IP centric routing protocols. Based on sensor function, we use the following categories:  
* Sensor nodes (SN): smart sensors, Personal Digital Assistants (PDAs), laptops, Tablet PCs, mobile robots, radio frequency interrupt links etc. which communicate wirelessly.
* Cluster head nodes (CHN): based on the needs of the application, any node may become a cluster head node.
* Regional management stations (RMS): powerful workstations communicating wirelessly with sensor nodes, and directly connected to the wired network that manages the subnet of the sensor network. It is an application defined by a geographic region, like the base station in a conventional wireless network.
# An Actor-Based Formal Model and Runtime Environment for Resource-Bounded IoT Services +
## 5 The Proximity Model
### Properties and Propositions
\begin{split}\langle(\textit{receive})_{s}\lvert C\lvert\Theta \rvert\{\langle\theta,\sigma,td,\pi\rangle,P\}\lvert m,\mu\rvert t\rangle \rightarrow\\ \langle[\textit{aggr}(d,\theta)]_{s}\lvert C\lvert\Theta \rvert P\lvert\mu\rvert t\rangle\end{split}  \tag{19}\]  
where \(m=\langle s\Leftarrow d\rangle\) is a contribution message, where \(\theta\) is the current state of the request \(\rho_{s}\). If the aggregation condition is met and \(td\geq t+d_{\xi}\), the server notifies a service update to the client:  
\[ \begin{split}\langle[\textit{aggr}(d,\theta)]_{s}\lvert C\lvert \Theta\rvert\{\langle\theta,\sigma,td,\pi\rangle,P\}\lvert\mu\rvert t\rangle \rightarrow\\ \langle(\textit{receive})_{s}\lvert C\lvert\Theta\rvert\{ \langle\theta^{\prime},\sigma,td,\pi\rangle,P\}\lvert m,\mu\rvert t\rangle \end{split}  \tag{20}\]  
where \(m=\langle\sigma\Leftarrow\theta^{\prime}\rangle\), \(\theta\) is the current state of the request \(\rho_{s}\), and \(\theta^{\prime}\) is the new state of the request. If the aggregation condition is not met, the server updates the request's state:  
\[ \begin{split}\langle[\textit{aggr}(d,\theta)]_{s}\lvert C \lvert\Theta\rvert\{\langle\theta,\sigma,td,\pi\rangle,P\}\lvert\mu\rvert t \rangle\rightarrow\\ \langle(\textit{receive})_{s}\lvert C\lvert\Theta\rvert\{ \langle\theta^{\prime},\sigma,td,\pi\rangle,P\}\lvert\mu\rvert t\rangle \end{split}  \tag{21}\]  
If there is a delay which can be tolerated to a group of sensing requests at a contributor \(c\), one sampling instance is carried out for all of them. Proof. : According to Axiom 6, if the delay in sampling can be tolerated to a group of sampling requests at a contributor \(c\), \(c\) can adjust their sampling rates within a predefined delay so that they all can share the same sampling instance. In this case, there must be a set of contributors \(C_{\rho_{i}}\in C\) that have conditionally committed to collectively serve a set of client tuples \(Z_{\rho_{i}}\), which is a superset of \(\rho_{i}\). According to Axiom 3, if a contributor \(c\in C_{\rho_{i}}\) conditionally commits to serve its part \(\xi_{c}\in\rho_{i}\), this means \(c\) has indicated its ability to serve \(\xi_{c}\) to the server, and \(xi_{c}\) can be accommodated in \(c\)'s sensing schedule. * \(c\) can determine the lowest sensing rate that would satisfy all sensing requests;
* All requests have compatible delay tolerances with those of requests already being served.
# A New Path Tracking Method Based on Multilayer Model Predictive Control
## 1 Introduction  
An autonomous vehicle system usually consists of mapping, positioning, perception, and navigation. Path tracking is a core system of the autonomous navigation system. Its task is to control the vehicles devices to track the reference path by minimizing errors [1; 2; 3; 4]. Based on our previous work [5], this paper still focuses on the path tracking of articulated vehicles. Of course, the control method proposed in this paper can also be applied in the path tracking of mobile devices such as front-wheel steering vehicles and differential robots.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Deterministic Models
#### 3.4.12. PHT3D  
PHT3D is a 3D multicomponent code for reactive transport in saturated porous media [699]. The reactive transport equation is solved with MT3 DMS using the sequential operator splitting technique [700, 701, 702, 703, 704] with a slight modification described by Walter et al. [705]. All concentration changes of aqueous components and immobile species resulting from reactive processes are calculated using PHREEQC. PHT3D has been integrated into the PMWIN package and Visual Modflow since version 4.1. PHT3D has been successfully used in various fields: controlled groundwater recharge, water quality in catchment areas of water abstraction points, fate of oxidisable organic compounds, fate of brominated/chlorinated compounds, dispersion-controlled transport, reactive transport under different density conditions, and the transport of pesticides. This tool has been used by both experts and beginners. Recent examples can be found in references [706, 707, 708, 709, 710, 711]. Ng et al. [711] investigated the fate of a historical hydrocarbon plume at the Bemidji site, where the secondary effects were the subject of simulations. In addition to the anaerobic biodegradation of BTEX, the outgassing of carbon, the input of dissolved inorganic carbon (DIC), pH buffering, and the immobilisation of Fe(II) were also included in the calculations.
###### Abstract  
The thermoelectric generator (TEG) stands out among many energy harvesters due to its simple structure, small size, rich thermal energy, and the absence of pollution and noise. However, previous studies have rarely probed into the influence of TEG internal resistances on extracting maximum power from TEGs, and the tracking of efficiency is limited. By analyzing the relationship between the tracking efficiency and the TEG internal resistances, a time exponential rate perturbation and observation (P&O) technology is proposed to achieve maximum power point tracking (MPPT) for a wide resistance range of the TEG. Using the time exponential rate P&O, the MPPT circuit observed the power change by comparing the positive-channel metal-oxide semiconductor (PMOS) on-time and perturbs the power by adjusting the negative-channel metal-oxide semiconductor (NMOS) on-time exponentially. The MPPT circuit was implemented in a 110 nm complementary metal-oxide semiconductor (CMOS) process. The tracking efficiency maintained a high level from 98.9 to 99.5%. The applicable range of the TEG resistance was from 1 to 12 \(\Omega\), which reflects an enhancement of at least 2.2 times.  
maximum power point tracking (MPPT); thermoelectric generator (TEG); time exponential rate perturbation and observation (P&O); power modulator  
2021  
16 May 2021  
19 May 2021  
**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
**Copyright:** 2021 by the authors. License MDPT, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Results and Discussion
### Effect of Molecular Distillation Parameters on Tocopherol Content  
Figure 3A shows that tocopherol content significantly decreased with increasing temperature, in agreement with the results of previous studies [23; 30]. Specifically, the total tocopherol content declined from 1063.2 to 322.3 mg/kg as the temperature increased from 120 to 200 \({}^{\circ}\)C. The above behavior was mainly ascribed to the increased evaporation of heavy components at high temperatures. Tocopherol is unstable at the high temperature and easily degrades, causing the decrease of tocopherol content. In the initial stage of distillation, the evaporation rate of light components is higher than that of heavy components, therefore the tocopherol content in distillates increased gradually. In the initial stage of distillation, the evaporation rate of light components exceeded that of heavy components, and the distillate therefore became enriched with tocopherols as the temperature increased [31]. However, when the temperature reached a certain value, noticeable evaporation of heavy components was observed, and no significant change of tocopherol content was therefore detected.  
Figure 3B shows that as scraper speed increased from 180 to 260 rpm, tocopherol content decreased from 1104.9 to 318.9 mg/kg, which was explained as follows. At low scraper speeds, no uniform liquid film was formed on the evaporation surface. As the scraper speed increased, the liquid film became thinner, and the surface renewal speed increased, which increased the tocopherol content of the distillate and greatly reduced the tocopherol content of the residue.  
Figure 3C shows that tocopherol content increased with increasing feed flow rate. This behavior was ascribed to the concomitant decrease of oil residence time, which, in turn, decreased the tocopherol distillation rate and increased the tocopherol content of distillation residues.  
Figure 2: Effect of (**A**) evaporation temperature, (**B**) scraper speed, and (**C**) feed flow rate on the peroxide (PV) and \(p\)-anisidine values of soybean oil. Note: The lowercase letters of a-d indicate that there are significant differences in peroxide value under different treatment conditions; the capital letters of A–E indicate that there were significant differences in \(p\)-anisidine value under different treatment conditions.  
Figure 3: Effect of (**A**) evaporation temperature, (**B**) scraper speed, and (**C**) feed flow rate on the topoherol contents of soybean oil. Note: Different letters indicate significant differences between the same groups (\(p<0.05\)).