esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Working Principle
synchronous motor vector control technology to design a motor controller, dynamically adjusting the PI parameters of the speed loop and maintaining a stable control effect and high control accuracy of the system. The control system flow chart is illustrated in Figure 4. The permanent magnet synchronous motor engaged in the helicopter traction system, subject to the transmission system and the helicopter's load torque, extracting a designated speed from the aforementioned traction transmission dynamics module. This speed is simultaneously input into the control system with the anticipated speed provided by the manipulation console for the helicopter traction system. Graduated feedback control of the speed of the motor is then executed via a fuzzy PI controller. Moreover, the input current of the synchronous motor is regulated through the vector control module, thereby altering its drive torque and subsequently adjusting the motor speed. The essence of vector control technology is the equivalent representation of AC motor mathematical models as DC motor models for control. This technique achieves control through regulation of the stator current, thereby controlling motor torque; through coordinate transformation, decoupling control of flux linkage and torque is realized. In this article, the combined space vector pulse width modulation (SVPWM) technology adopts a vector control strategy with \(\mathrm{i}_{\mathrm{d}}=0\) and constructs a dual closed-loop permanent magnet synchronous motor vector control module with a speed loop and current loop, as shown in Figure 5.  
The structure of the fuzzy PI controller is shown in Figure 6. The fuzzy PI controller obtains real-time adjustments of parameters P and I through fuzzy inference based on the input speed difference (e) and the rate of change (ec) of the speed difference \(\Delta\mathrm{Kp}\) and \(\Delta\mathrm{K}_{\mathrm{i}}\), which are finally substituted into Equation (1) to obtain the adjusted PI parameters. In the application of fuzzy control for PMSM control, the majority assumes the transformation of the vector control rotational speed loop's PI control into a fuzzy PI control. This approach  
Figure 4: Flow chart of the traction control system. Figure 5: Vector control system structure. dynamically adjusts the PI parameters of the rotational speed loop to arrive at appropriate values for P and I, providing a degree of robustness. \[\left\{ \begin{aligned} & K_{p}=K_{p}^{\prime}+\Delta K_{p}\\ & K_{i}=K_{i}^{\prime}+\Delta K_{i}\end{aligned} \right. \tag{1}\]
## 1 Introduction  
Nanotechnologies are among the most important topics in today's investigations; among them, adsorptive nanomaterials have shown their tremendous potential, and different compositions and configurations of these nanomaterials are being investigated for different applications [1; 2; 3; 4; 5; 6]. These techniques have been extensively used in separation science, i.e., metal recovery from pregnant and waste solutions, the obtaining of precious and strategic metals, and the treatment of effluents which include, e.g., toxic metals.  
Included in the above, carbon nanomaterials in single-walled, multiwalled, and functionalized configurations are gaining considerable importance due to their application in the recovery and separation of metals from aqueous phases [7; 8; 9; 10; 11; 12].  
Chromium is a toxic element [13], especially in its VI oxidation state, but solutions containing chromium(III) are also considered to be hazardous in nature due to the real potential of oxidation to the VI state; thus, its elimination from different aqueous solutions should be considered a major priority. The recovery and separation of chromium(III) from aqueous solutions with different materials and nanomaterials have been reported in the literature, with about 60 papers dealing with the matter in 2019 [14]; however, the majority of this information is related to the removal of Cr\({}^{3+}\) from acidic to neutral aqueous pH values, and only scarce information is available on the use of carbon nanotubes as adsorbents for Cr(III) separation and/or speciation in an alkaline medium [15; 16].  
In the present work, the Cr(III) adsorption results are presented from alkaline aqueous solutions using multiwalled carbon nanotubes. This alkaline medium is representative of the solutions which can be encountered or dumped in nuclear tank wastes. Different experimental variables affecting Cr(III) uptake were investigated, together with the fit of experimental data to various models. Finally, Cr(III) desorption is accomplished by the use of acidic solutions.
## 6 Discussion
### Speech Is Universally Desired, but Social Companionship Is Contentious  
It has long been of interest to the robotics research community for a person to communicate with a robot through a language interface and vice versa [9]. Speech is a mainstream way people interact with technology. Popularized in the 1970s [58], interactive voice response systems are used to route customer service phone calls [59]. Voice User Interfaces [60] are synonymous with virtual personal assistants, like Amazon's Alexa or Apple's Siri [61], and seem to come with smart consumer electronic products. Thus, unsurprisingly and overwhelmingly, 86.7% (79 of 91) of our OA participants wanted to communicate with the caregiving robot through speech (see Section 5.2.1).  
Early research in Socially Assistive Robots (SAR) [62] focused on how a robot can engage and verbally direct a person in an assistive context, such as stroke rehabilitation [63]. Human-Robot Interactions (HRI) with SARs evolved into interactions that were necessarily social, such as SARs as exercise coaches [63], teachers or teaching assistants [64], and home companions [65], and more recently into addressing loneliness [66], depression [67], and mental well-being [68]. LLMs have enabled HRI researchers to implement robots capable of speech-to-text and text-to-speech; the benefit is that robots are more likely to correctly parse what a user says and then respond appropriately with more human-like variation, although there are limitations [69]. Thus, this new class of LLM-enabled SARs can act as conversational companions to provide social and emotional support in OAs' everyday lives [70].  
It is debatable whether robots should be social companions. There are strong feelings in both directions, and we heard both sides from our OA study participants (see Section 5.2.1). On one hand, 56% of participants wanted to talk with the robot beyond giving commands. If OAs' conversations with the robot were more social in nature, such as storytelling [71], there is an opportunity to decrease loneliness, which is a significant predictor of OAs' poor health [72]. Thus, social conversation with the robot might help an OA age-in-place for a longer duration. On the other hand, 28.6% (5 JP, 21 US) limited verbal communication to only commanding the robot. 3 participants were particularly vocal about the inappropriateness of a caregiving robot also being a social companion (see Section 5.2.2). PU21 and PU37 commented that a robot is only a machine meant for improving their lives and disposable or replaceable when no longer serving its purpose, which relates to the dynamic of a master-servant relationship--an oppression aggression [73]. PJ21 objected to giving a robot artificial intelligence, which relates to rebellion aggression [73].
# An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services
## 6 Discussion  
The objective of the experiments was to find the best continuous authentication model on (1) single signal vs fusion signal based authentication, and (2) single user vs group of user-based authentication, using conventional classifiers and contemporary deep learning algorithms. In Table 10 the summarised results presented from the experiments. From Table 10 it is observed that in single authentication mode, among the classifiers, the Naive Bayes model has provided the best result for ECG-only authentication reaching 100% precision and 99.92% accuracy and zero false-positive cases out of 2533 test samples. The Naive Bayes based model demonstrated resiliency against a large number of intrusion attacks during the scalability test. When 10,213 samples were tested in the second experiment, this model experienced zero false-positive cases and reached an EER of 0.024. During the PPG-only authentication experiments, none of the classifiers reached up to a good benchmark, although LSTM provided a comparatively better result [62]. In the case of ECG and PPG signals based on RMS type fusion, the deep learning LSTM model illustrated good precision and low EER compared to CNN based model, even though we chose to change signal sample length from two heartbeats to 16 heartbeats. It will ensure a multimodal and reliable authentication scheme.  
When multiple user groups of three and five are created to verify the group authentication model, overall authentication performance is reduced compared to the ECG-only model when using the classifiers. LSTM deep learning net provided satisfactory accuracy and precision. However, many valid login attempts are declined. Therefore, further research is required to improve the sensitivity and accuracy of group authentication. Based on the benchmark performance defined earlier, our proposed models can achieve high accuracy, especially in single and multiuser authentication, using Naive Bayes, LSTM and CNN, as illustrated in Table 10.  
From our literature review, only a few notable works have reported ECG and PPG together as biometric traits for multimodal authentication systems. We present the comparison in Table 11.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline KNN Fine (3 Usr) & 90.3\% & 37.0\% & 92.6\% & 91.5\% & 0.07 \\ \hline Ensemble Bagged Trees (3 Usr) & 93.5\% & 50.7\% & 95.6\% & 94.6\% & 0.04 \\ \hline Naive Bayes (3 Usr) & 90.3\% & 19.7\% & 82.8\% & 86.2\% & 0.17 \\ \hline SVM (3 Usr) & 97.6\% & 27.4\% & 88.1\% & 92.4\% & 0.12 \\ \hline GAM (3 Usr) & 98.4\% & 35.3\% & 91.7\% & 94.8\% & 0.08 \\ \hline CNN (16 HB 3 Usr) & 88.7\% & 73.4\% & 97.5\% & 93.1\% & 2.45 \\ \hline LSTM (16 HB 3 Usr) & 30.2\% & 21.3\% & 89.8\% & 45.6\% & 10.23 \\ \hline CNN (16 HB 5 Usr) & 76.4\% & 91.9\% & 97.1\% & 86.4\% & 2.88 \\ \hline LSTM (16 HB 5 Usr) & 83.1\% & 73.3\% & 95.5\% & 89.5\% & 4.88 \\ \hline \hline \end{tabular}
\end{table}
Table 9: ECG-PPG Fusion Result Details—Multiuser Authentication.
_Article_  
An Automatic HEp-2 Specim Analysis System  
Based on an Active Contours Model and an  
SVM Classification  
[PERSON] *\(\copyright\), [PERSON] and [PERSON]  
Department of Physics and Chemistry, University of Palermo, 90128 Palermo, Italy;  
[EMAIL_ADDRESS] (V.T.); [EMAIL_ADDRESS] (G.R.)  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +39-091-238-99050 check for  
**Featurered Application: In this paper we describe a complete system (fluorescence intensity classification, image preprocessing, cell segmentation, cell classification, and image staining patterns classification) to support the autoimmune diagnostics in HEp-2 image analysis. The system has been tested on a heterogeneous public database; heterogeneity comes from the geographical origins of indirect immunofluorescence (IIF) images and acquisition instrumentation adopted. This system is able to recognize six different patterns--homogenous, speckled, nucleolar, centromere, nuclear dots, and nuclear membrane.**  
**Abstract:** The antinuclear antibody (ANA) test is widely used for screening, diagnosing, and monitoring of autoimmune diseases. The most common methods to determine ANA are indirect immunofluorescence (IIF), performed by human epithelial type 2 (HEp-2) cells, as substrate antigen. The evaluation of ANA consist an analysis of fluorescence intensity and staining patterns. This paper presents a complete and fully automatic system able to characterize IIF images. The fluorescence intensity classification was obtained by performing an image preprocessing phase and implementing a Support Vector Machines (SVM) classifier. The cells identification problem has been addressed by developing a flexible segmentation methods, based on the Hough transform for ellipses, and on an active contours model. In order to classify the HEp-2 cells, six SVM and one \(k\)-nearest neighbors (KNN)classifiers were developed. The system was tested on a public database consisting of 2080 IIF images. Unlike almost all work presented on this topic, the proposed system automatically addresses all phases of the HEp-2 image analysis process. All results have been evaluated by comparing them with some of the most representative state-of-the-art work, demonstrating the goodness of the system in the characterization of HEp-2 images.  
**Keywords:** IIF images; Hough transform; active contours model; cell segmentation; SVM; KNN; ROC curve
# Integration of Multi-Branch GCNs Enhancing Aspect Sentiment Triplet Extraction
## 3 Framework of MBGCN
### Biaffine Attention  
Biaffine attention has been proven to have the ability to capture the relationship among the different words or word pairs [23; 52]. Thus, in this paper, we also apply it to predict the relation probability of word pairs in a sentence. To present the process of biaffine attention, the hidden states \(h_{\xi}\) and \(h_{\tau}\) of \(w_{\xi}\) and \(w_{\tau}\) in \(\mathbf{X}\) are extracted from \(h^{l}\). With the aforementioned \(\mathbf{MLP_{a}}\) and \(\mathbf{MLP_{\alpha}}\), the aspect-specific feature \(h_{\xi}^{a}\) (Equation (9)) and opinion-specific feature \(h_{\tau}^{\sigma}\) (Equation (10)) are obtained, which are adopted into the processing of biaffine attention directly.  
\[h_{\xi}^{a} =\mathbf{MLP_{a}}(h_{\xi}), \tag{9}\] \[h_{\tau}^{\sigma} =\mathbf{MLP_{\alpha}}(h_{\tau}). \tag{10}\]  
and the transformation of biaffine attention can be formulated as  
\[g_{\xi,\tau}=h_{\xi}^{a\top}\mathbf{U}_{\alpha}h_{\tau}^{\sigma}+\mathbf{U}_{ a}(h_{\xi}^{a}\oplus h_{\tau}^{\sigma})+\mathbf{b}, \tag{11}\]  
\[R_{\xi,\tau,\delta}=\frac{\mathbf{exp}(g_{\xi,\tau,\xi})}{\sum_{\xi=1}^{\Xi} \mathbf{exp}(g_{\xi,\tau,\xi})}, \tag{12}\]  
where \(\mathbf{U}_{\alpha}\), \(\mathbf{U}_{a}\), and \(\mathbf{b}\) are the trainable weights and biases, and \(\oplus\) denotes the operation of concatenation. The relations between \(w_{\xi}\) and \(w_{\tau}\) are modeled as \(R_{\xi,\tau}\in\mathbb{R}^{1\times\Xi}\). \(\Xi\) is the number of relation types. Furthermore, we use \(R^{\text{ln}}\) to represent the relations obtained in this manner in the following sections.  
With the aspect-oriented and opinion-oriented processing of biaffine attention, the probability of relation \(R^{\text{ln}}\) between the word pairs in a sentence can be modeled effectively. Furthermore, this relation will be integrated with the other four types of linguistic features via GCNs adequately.
## 3 Results and Discussions  
This section discusses the results from the evaluation of green chemistry metrics for biodiesel production under the synergistic effect of microwave and ultrasound using ethanol and methanol as solvents. The most common method of biodiesel production is by transesterification (alcoholysis) of oils (triglycerides) with methanol in the presence of a catalyst [24; 25] which is shown in Figure 1[26]. The stoichiometric reaction requires 1 mole of triglyceride and 3 moles of an alcohol solvent. However, excess alcohol is used to drive the reversible reaction forward in order to increase the yields of the alkyl esters and to assist phase separation from the glycerol.  
According to the twelve principles of green chemistry, a green solvent should meet numerous criteria such as low toxicity, non-flammability, non-mutagenicity, non-volatility and widespread availability among others. Moreover, these green solvents have to be cheap and easy to handle and recycle [27]. Here, we considered ethanol as a solvent due to its renewable characteristics. Producing biodiesel from low quality feedstock such as waste cooking oils is a challenging task due to the presence of high free fatty acid (FFAs) content. In addition, use of homogeneous catalysts can cause serious separation issues [28]. However, use of heterogeneous catalysts requires high temperatures and possibly high pressures with extended reaction times. Production of heterogeneous catalysts is also not a simple task. Cheap materials such as egg shells and other carbon-based catalysts can be used for this purpose [29] but this is not the focus of this research. For these reasons, we have considered the process intensification effects of microwave and ultrasound irradiations with the homogeneous catalyst-mediated transesterification reaction.
# Intrusion Detection for in-Vehicle Communication Networks: An Unsupervised Kohonen SOM Approach
## 4 Theoretical Background
### Unsupervised SOM Neural Network
The distance to the BMU at epoch \(t\) is here denoted with the subscript \(c\):  
\[d_{c}(t)\ =\ \min_{j}\ d_{j}(t). \tag{2}\]  
Once BMU is found, the neuron and its spatial neighbors are updated by the following:  
\[W_{j}(t+1)\ =\ W_{j}(t)+h_{jc}(t)\Big{[}X_{i}(t)-W_{j}(t)\Big{]} \tag{3}\]  
where \(h_{jc}(t)\) is the _neighborhood function_. The rate of change at different neurons around the BMU depends on the mathematical form of the neighborhood function. This function has a very central role in SOM networks since it preserves the topological properties of the input data. A variety of neighborhood functions can be used, but the most applied in SOM neural networks is the _Gaussian neighborhood function_:  
\[h_{jc}(t)\ =\ \alpha(t)\cdot\exp\left(\frac{\|r_{j}-r_{c}{}^{2}\|}{2\sigma(t) ^{2}}\right) \tag{4}\]  
where \(\alpha(t)\) is the _learning rate function_ which is a function monotonically decreasing at each iteration \(t\) and \(r_{j}\) is the position of neuron \(j\). The online training algorithm of the SOM can be implemented using a stepwise recursive procedure. The pseudo-code is detailed in Algorithm 1. Figure 1: Kohonen Self-Organizing Map (SOM) neural network. ```
Input: Input layer consisting of CAN message data vectors \(X_{i}\)with \(i\)=1,...,\(n\), Output: Output Kohonen layer \(Rx\) containing final codebook vectors \(W_{i}\)associated to neurons \(R_{j}\) with \(j\)=1,...,\(m\), Results: Assign CAN message data vectors \(X_{i}\) to the winning neuron BMU on the Kohonen map set(\(n\), \(m\), \(s\)) // set number of messages, number of neurons, number of epochs \(W_{i}\leftarrow\emptyset\) for\(j=1,\ldots,\)\(m\)do \(W_{j}\leftarrow\) random(\(X_{i}\)) // random codebook vectors initialization endfor \(\mathrm{set}(\alpha)\) // set initial learning rate for\(t=1:s\)do for\(i=1:n\)do \(X_{i}\leftarrow\) random(\(X_{i}\)) //random selection of a CAN message data vector for\(j=1:m\)do \(d_{j}=\left\|X_{i}-W_{j}\right\|\) //compute Euclidean distance endfor \(d_{c}=\min\limits_{j}d_{j}\) //compute the winning neuron BMU for\(j=1:m\)do \(W_{j}=W_{j}+h_{jc}(\alpha)\big{[}X_{i}-W_{j}\big{]}\) // update neurons endfor endfor \(\mathrm{update}(\alpha)\) endfor
```  
**Algorithm 1** Kohonen SOM algorithm. The batch algorithm is a variant of the traditional online SOM algorithm. Neurons' codebook vectors are adjusted only after all the input data vectors \(Xi\) in the input layer are assigned to their winning neuron's BMU in the Kohonen network. Codebook vectors of BMU and neighbors' neurons are updated as follows:  
\[W_{j}(t+1)\ =\ \frac{\sum_{i=1}^{n}h_{jc(i)}(t)X_{i}}{\sum_{i=1}^{n}h_{jc(i)}(t)} \tag{5}\]  
where _c(i)_ is the index of the winning neuron's BMU for the input data vector \(X_{i}\) and \(n\) is the number of input data vectors, at iteration \(t\).
## 4 Implementation
### Experiment Environment
#### 4.1.2 Experimental Environment  
The experiments have been conducted to evaluate the various performance metrics at the edge side and the server side, respectively. Table 1 shows the hardware specification of devices used for our experiments.
## 2 Methods  
### Considered Experimental Facility  
The CIRCE-HERO facility (Figure 1) was designed with the purpose of providing support for the development of pool type LMFBRs [20]. It consists of a large vessel, the pool, 8.5 m high and with a diameter of 1.2 m filled with LBE (Lead Bismuth Eutectic); inside the pool, an internal loop is allocated. This internal loop consists of several components. The most important are: the Fuel Pin Simulator (FPS), the Fitting Volume (FV) with the Argon Injection System, the Separator and the HERO steam generator. Figure 1 reports the distribution of the main components allocated inside the pool. For a more detailed description of the facility, the reader is referred to [20].  
This structure allows the development of natural circulation conditions through the internal loop; circulation may also be enhanced with the injection in the hot leg (Riser) of Argon, which is subsequently separated and collected in the dome at the top of the pool (Separator).  
The experimental campaign carried out at ENEA Brasimone RC [21] aimed at reproducing conditions compatible with a postulated PLOFA. In particular, the loss of flow was simulated by reducing the injected Argon mass flow rate and, consequently the LBE flow rate in the hot leg; heat generation in the FPS and water mass flow rate in the steam generator were also changed in accordance with the objective of the experimental campaign.  
Table 1 resumes the experimental conditions for the considered test case, highlighting the imposed operating conditions before and after the PLOFA test. This test consists in a progressive decrease of the injected Argon (see also Figure 5 in Section 3 for a detailed trend) in association with a dramatic decrease of the supplied power in about 20 s; the water mass flow rate was reduced too in the same time interval. As a consequence, while at the beginning the fluid flows in the CIRCE loop by mean of mixed/aided circulation, in the final part of the transient a buoyancy-driven flow is instead obtained.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Header** & **Before** & **After** \\ \hline Injected Argon Mass Flow Rate [N/s] & 2.73 & 0 \\ HERO Water Mass flow rate [kg/s] & 0.26 & 0.079 \\ Water Inlet Temperature [\({}^{\circ}\)C] & 336 & 336 \\ Power at the FPS [kW] & 356 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Considered operating conditions before and after the postulated transient.  
Figure 1: CIRCE-HERO facility.