esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Our Contribution  
The aim of this paper is to investigate the feasibility of the SpO2 and ECG medical devices, following the Continua guidelines [16], for increasing the adherence of patients to a cardiac rehabilitation treatment. Cardiac rehabilitation patients have cardiovascular problems. Vital signs such as heart rate (HR) and oxygen saturation are used as predictors of recovery after surgery or in patients with coronary artery disease (CAD) who underwent rehabilitation [17; 18]. When they are being evaluated or undergo physical training, it is essential to monitor their blood pressure and heart rate (HR) both at rest and the increase during the exercise they are doing, measuring baseline blood pressure and heart rate and peak effort. In addition, SpO2 is measured because if it drops below 90%, training must be stopped. Therefore, the measure of SpO2 and HR is mandatory for any patient undergoing cardiac rehabilitation treatment or high-risk patients [19]. In the next sections, we describe our system architecture consisting of two modules. The hardware module includes a description of the signal acquisition, processing, and transmission, corresponding with the IEEE agent-node that transmits personal health data. The software module includes a description of an Android application that receives processed information, corresponding with the IEEE manager-node that receives personal health data. Finally, results of first clinical trials, a discussion, and conclusions are presented.  
Figure 1: Organization and dependencies of the protocols established by the Continua Health Alliance, over the Open Systems Interconnection (OSI) model.
## 7 Path Tracking  
The endogenous dynamic loop calculated in Section 5 allows us to define the new input as follows:  
\[\vartheta_{x}=-\left(\frac{k_{2}^{x}s^{2}+k_{1}^{x}s+k_{0}^{x}}{s\left(s+k_{ 3}^{x}\right)}\right)\left(x-x^{*}\right)+\dddot{x}^{*} \tag{28}\]  
Figure 9: Example of a proposed trajectory.  
\[\vartheta_{y}=-\left(\frac{k_{2}^{y}s^{2}+k_{1}^{y}s+k_{0}^{y}}{s\left(s+k_{3}^{y} \right)}\right)\left(y-y^{*}\right)+\tilde{y}^{*} \tag{29}\]  
where \(k_{i}^{x},k_{i}^{y}\in\mathbb{R}\), \(i=0,\;1,\;2\;k_{i}^{x},k_{i}^{y}\) are chosen so that the polynomials of Equations (30) and (31) are Hurwitz polynomials:  
\[P_{x}(s)=s^{4}+k_{3}^{x}s^{3}+k_{2}^{x}s^{2}+k_{1}^{x}s+k_{0}^{x}=0 \tag{30}\]  
\[P_{y}(s)=s^{4}+k_{3}^{y}s^{3}+k_{2}^{y}s^{2}+k_{1}^{y}s+k_{0}^{y}=0 \tag{31}\]  
ensuring an asymptotic pursuit of the desired trajectory \((x^{*},y^{*})\).  
In Sections 5-7, we assume that we have values of the coordinates of the point \(P\). In a visual servo, the only sensor used is the visual sensor. In order to determine the values of the coordinates of this point, we will use a perspective camera embedded in the robot.  
When moving the robot, two cases are possible: The first is that the object does not leave the field of view of the camera during the servoing. Estimating the robot's situation is then possible by using images from the camera and developing a pose estimation algorithm. The visual servo control diagram is given in Figure 10. The second possible case is when the object leaves the field of view of the camera. In this case, we cannot estimate the situation (pose) of the robot from the acquired images and we will therefore consider a blind movement of the robot thanks to an open-loop control described by Figure 11. Once the object returns to the field of view of the camera, the command developed in the first case is then reconsidered.
# The Use of Radio and Telemedicine by TMAS Centers in
## 3 Results
### Study Characteristics  
The major characteristics of examined studies based on study type, number of patients, year published, and observations are presented in Table 3. There was no evidence of studies reporting the telemedical assistance of seafarers with case studies before 2015. One study published in 1980 highlighted the transformation of medical messages from the casualty officer of the Royal Naval Hospital Plymouth via Portishead Radio [11]. This study analyzed the range of medicines prescribed for seafarers' medical problems. Several diseases have been linked to diet-associated factors, such as alcohol and obesity. Because the full free text was not available, it was not included in the final survey.  
\begin{table}
\begin{tabular}{c c c} \hline
**TMAS Centre** & **Location and Country** & **Means of Communication** \\ \hline Med solutions international & New York, USA & Email \\ \hline RMN & Bergen, Norway & Telephone \\ \hline CCMM & Toulouse, France & Radiotelegraphy, Telephone \\ \hline UCMTM & Gdynia, Poland & Telephone and Email \\ \hline RMD & Esbjerg, Denmark & Radio \\ \hline C.I.R.M. & Rome, Italy & Telephone, Email, and Web applications \\ \hline RMN: Radio Medicio Norway; CCMM: Centre De Consultation Medicale Maritime; UCM: University Center of Maritime and Tropical Medicine; RMD: Radio Medical Denmark; and C.I.R.M: Centro Internazionale Radio Medicio. \\ \hline \end{tabular}
\end{table}
Table 2: List of TMAS centers associated with the present study.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Study Type** & **Sample** & **Year** & **Medical Advice** & **Observations** & **Ref** \\ \hline Descriptive & 551 & 2016 & \begin{tabular}{c} Management of \\ cardiac symptoms \\ \end{tabular} & \begin{tabular}{c} Pre-employment medical \\ examinations improved \\ preventive measures. \\ \end{tabular} & [21] \\ \hline Observational & 169 & 2017 & \begin{tabular}{c} Emergency helicopter \\ evacuations (helivacs) \\ between the two ferries \\ \end{tabular} & \begin{tabular}{c} Every two weeks, one person was \\ airlifted. The majority of Halifax \\ was heart-related, with more \\ cardiac cases airlifted \\ than ambulances \\ \end{tabular} & [17] \\ \hline Case study & 5 & 2017 & \begin{tabular}{c} Diagnoses and follows up \\ on medical conditions \\ were carried out \\ \end{tabular} & \begin{tabular}{c} In the development of telemedical \\ technologies, the participants \\ demonstrate a continual interest \\ in teleconsultations \\ with photographs \\ \end{tabular} & [23] \\ \hline Observational & 225 & 2019 & \
\begin{tabular}{c} Ensure permanent access \\ to medical advice \\ for seafarers \\ \end{tabular} &
\begin{tabular}{c} Providing medical assistance for \\ various medical problems to \\ seafarers requires close \\ multidisciplinary cooperation \\ between medical officers. \\ \end{tabular} & [18] \\ \hline \end{tabular}
\end{table}
Table 3: Study characteristics.
## 3 Modelling and Optimization
### Weighting Factor and Normalization  
There are two ways to choose the weighting factors related to the control objective [28]. The first one is offline and the second one uses mathematical dependencies in the control objective to reach the selected result. Here, \(W_{1}\) is taken as 0.8 and the \(W_{2}\) is varied between 1.9 to 18, because of only two terms in Equations (9) and (19), respectively. This can be different for different working conditions. In this work, a normalization method that evaluates the maximum variations of every state variable is used as an extra optimization norm in CF, as given in Equation (20). The modified MPC minimizes the normalized and weighted CF for the switching-state selections.  
\[\text{CF}=\sqrt{\left(  
Figure 5: Leakage current path: (**a**) through \(S_{5}\) (\(R_{1}\)), and (**b**) through \(S_{6}\) (\(R_{2}\)).
## 5 Lempel-Ziv Compression on a Distributed System
### Sliding Window Compression on a Distributed System  
We simply apply in parallel sliding window compression to blocks of length \(kw\). It follows that the algorithm requires O\((kw)\) time with \(n/kw\) processors and the multiplicative approximation factor is \((k+m-1))/k\) with respect to any parsing. In fact, the number of factors of an optimal (greedy) factorization on a block is at least \(kw/m\) while the number of factors of the factorization produced by the scheme is at most \((k-1)w/m+w\). As shown in Figure 2, the boundary might cut a factor (sequence of plus signs) and the length \(w\) of the initial full size window of the block (sequence of w's) is the upper bound to the factors produced by the scheme in it. Yet, the factor cut by the boundary might be followed by another factor (sequence of x's) which covers the remaining part of the initial window. If this second factor has a suffix to the right of the window, this suffix must be a factor of the sliding dictionary defined by it (dotted line) and the multiplicative approximation factor follows.  
We obtain an approximation scheme which is suitable for a small scale system but due to its adaptiveness it works on a large scale parallel system when the file size is large. From a practical point of view, we can apply something like the gzip procedure to a small number of input data blocks achieving a satisfying degree of compression effectiveness and obtaining the expected speed-up on a real parallel machine. Making the order of magnitude of the block length greater than the one of the window length largely beats the worst case bound on realistic data. The window length is usually several thousands kilobytes. The compression tools of the Zip family, as the Unix command \"gzip\" for example, use a window size of at least 32 K. It follows that the block length in our parallel implementation should be about 300 K and the file size should be about one third of the number of processors in megabytes. An approach using a tree architecture slightly improves compression effectiveness [11]. However, the  
Figure 2: The making of the surplus factors.  
scalability of a parallel implementation of sliding window compression on a distributed system with low communication cost seems to guarantee robustness only on very large files. We show in the next subsection that LZW compression is scalable and robust on arbitrary files if implemented on a tree architecture [12].
# A Compact Low-Power LoRa IoT Sensor Node with Extended Dynamic Range for Channel Measurements
## 2 Hardware Design  
To give a full overview of the hardware design, a block diagram of the node is presented in Figure 1. The largest blocks in this diagram depict an 8-bit, low-power microcontroller--a C8051F342 (by Silicon Labs, Austin, TX, USA)-- and the earlier mentioned RN2483 LoRa transceiver module. These components provide the core functionality of the microcontroller section and the RF-section, respectively.  
Figure 1: Block diagram of the wireless sensor node.
# Topology-Embedded Temporal Attention for Fine-Grained Skeleton-Based Action Recognition
## 5 Conclusions  
In this work, we propose a novel topology-embedded temporal attention module (TE-TAM) to improve the performance of GCNs for fine-grained skeleton-based action recognition. GCN-based models with TE-TAMs achieve dynamic attention learning by embedding the informative and flexible topology relationship constructed by modeling local areas in spatial and temporal dimensions. In addition, experiments on FSD-30 proved the importance of simultaneous modeling of local area dependencies between adjacent  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Top-1 Acc (\%)\({}^{1}\)** \\ \hline STGCN [3] & 60.8 \\ STGCN+TE-TAMs & **61.2\({}^{\textbf{[0.4]}}\)** \\ \hline AGCN [4] & 58.9 \\ AGCN+TE-TAMs & **60.2\({}^{\textbf{[1.3]}}\)** \\ \hline CTRGCN [5] & 61.3 \\ CTRGCN+TE-TAMs & **63.1\({}^{\textbf{[1.8]}}\)** \\ \hline \hline \end{tabular}  
* Top-1 Acc(\(\%\))\({}^{1}\): Top-1 Acc on \(TestSet_{1}\).  
\end{table}
Table 4: Comparison of accuracy for three GCN-based models with and without TE-TAM modules.  
Figure 6: Training process. Training loss, Top-1 Accuracy, and Top-5 Accuracy of AGCN, STGCN, and CTRGCN with and without TE-TAM.  
bone points in spatial and temporal dimensions for attention learning and the effectiveness/robustness of the TE-TAM. Finally, we should acknowledge the weaknesses of the proposed TE-TAM: (1) the topology modeling method in our experiments introduced much more parameters but showed a relatively weak performance; (2) the embedding function chosen in this work was relatively simple and rough. Thus, the TE-TAM may have not yet been fully functioning. We will address the problem in our future work.
## 6 Case Study Validation
### Computational Accuracy Analysis  
Figures 13-15 illustrate the error variation trends of the internal forces at critical cross-sections and lateral deformations of vertical structural members calculated via the proposed method and conventional methods relative to the dynamic time history results, plotted against the dimensionless boundary distance ratio \(R\), under three real earthquake records. Owing to the consistent internal forces between Sections A and B, as well as between Sections C and D, and the identical deformation patterns of the left and right sidewalls, the graphical representations are simplified. For each seismic scenario, only the internal force results for Section A and Section D, along with the deformation results for the left sidewall, are displayed. Table 1 systematically summarizes the internal forces and deformation results for key sections obtained via different computational methods from the three earthquake records.  
The proposed method has significant advantages in terms of computational accuracy over the conventional IRDM. Under various seismic excitations, the errors of the new method are generally controlled within 5%, with a maximum error not exceeding 10%, which is markedly superior to the conventional method's maximum error of \(-32.64\%\). As the boundary distance ratio \(R\) varies, the errors of the new method initially decrease but then increase, ultimately converging with the conventional method's error curves toward the same asymptote. This behavior arises because at smaller \(R\) values, the artificial boundaries not only balance non-free-field displacements induced by soil-structure kinematic incompatibility, but also provide sufficient constraints to the computational model, whereas as \(R\) increases, the constraining effects of artificial boundaries gradually diminish. When \(R\) becomes sufficiently large, the boundary type exerts a negligible influence on the results, leading to convergence between different boundary configurations. Furthermore, the proposed method demonstrates enhanced computational accuracy under boundary distance ratios of \(R=0.5\) and \(1.0\), exhibiting significantly lower errors than conventional methods while effectively reducing the computational scale. This dual advantage of precision and efficiency renders the method particularly suitable for seismic response analysis of large-scale, complex underground structures, aligning with the stringent requirements of modern seismic design standards.
# FMS Scheduling under Availability Constraint with Supervisor Based on Timed Petri Nets
## 4 Flexible Manufacturing System Description  
This paper covers a special class of FMS, where the flexibility designs a machine that can perform different types of jobs. The time that a machine needs to change the operating mode is negligible. The considered FMS is defined as a set of multiple job types where the processing order of all jobs through the machine set is the same.  
The following assumptions are considered:  
* \(n\) jobs of different type are initially placed in the initial buffer;
* \(m\) machines are available; the initial and the final buffer will be modeled respectively by \(j=0\) and \(j=m+1\) as two additional virtual machines;
* Jobs are executed in the same order as defined in the first machine;
* Each machine is subject to a preventive maintenance action expressed by an unavailability period; this action is assumed to be preprogrammed;
* Each machine performs only one job at a time;
* Processing time depends on the job type;
* Job preemption is forbidden.  
As mentioned, our objective is to determine the optimal sequence of jobs where the total completion time is subject to optimization. In the second part, we focus on the implementation of this optimal solution in order to synthetize the manufacturing system supervisor coupled to the initial timed Petri net.  
In studies involving TPN models, it is often the case that there are computing risks involved, in the case of an important number of machines, so it is necessary to decompose the entire TPN. The main objective is to guarantee an easier solving process.  
Figure 2: Illustration of Timed Petri Net (TPN) parameters in the production process.
## 4 Simulation and Analysis
### The Impact of Spot Position on Position Detection Accuracy  
From the analysis in Section 2, when \(\omega\), \(R_{SN}\), and \(N\) are constant, the position detection accuracy is also related to the position of the Gaussian spot incident on the QD. Assume that \(\omega\) is 2 mm and \(N\) is 1024. The spot positions are controlled to change in the range of \(-\)0.22 mm\(\sim\)+0.22 mm with a step size of 0.01 mm. Figure 9a,c show the relationship between the position detection accuracy and the spot center position when the SNR is 29.69 dB and the SNR is 31.74 dB, respectively. These relative error curves are Figures 9b and 9d, respectively. From Figure 9a,c, it can be seen that the position of the spot has little effect on the position detection accuracy. Under the same condition, the position detection accuracy can be improved obviously by increasing the SNR of the system.  
When the spot position is changed, the average relative error of the simulation value relative to the calculated value of the formula is shown in Table 4. As can be seen from Table 4, when the SNR is 29.68 dB, the AREs of signal 1, signal 2, and signal 3 are 2.56%, 2.26%, and 2.46%, respectively. When the SNR is 31.74 dB, the AREs of signal 1, signal 2, and signal 3 are 2.52%, 3.07%, and 2.75%, respectively. This indicates that the simulation value of the position detection accuracy is close to the mathematical model.  
Figure 9: **(a)** The influence of the spot position on position detection accuracy when the SNR is 29.68 dB. **(b)** The relative error curve when the SNR is 29.68 dB. **(c)** The influence of the spot position on position detection accuracy when the SNR is 31.74 dB. **(d)** The relative error curve when the SNR is 31.74 dB.
## 4 Performance Evaluation
### Regression Models  
The configuration environment is presented in Section 3.1. In this section, we will analyze the different regression models and compare them to the actual results. When quantifying the strongest beam power, we use the root mean square error (RMSE) to determine the quantization regression accuracy, and so the unit is dB. The RMSE can be described by the following formula:  
\[\text{RMSE}=\sqrt{\frac{1}{m}\sum_{i=1}^{m}(\boldsymbol{\hat{y}}_{i}- \boldsymbol{y}_{i})^{2}}, \tag{10}\]  
where \(\boldsymbol{\hat{y}}_{i}\) is the predicted beam power; \(\boldsymbol{y}_{i}\) is the predicted value; and m is the number of beams.  
Table 3 presents the prediction results of random forest regression, linear regression, gradient boosting regression, and support vector regression. The results show that random forest regression has the best results because it can handle very high-dimensional data without feature selection and, at the same time, the model has a strong generalization ability [20]. Random forest regression also has a fast training speed, and so this method can be widely used in industrial production.
# TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data
## 3 Background
### Generative Adversarial Networks  
The method of anomaly detection based on the GAN [8], as shown in Figure 2, is to learn the normal behavior in the data through the idea of adversarial training and learn the patterns of the normal data from the training data with the discriminator and generator of GAN. If the test data are consistent or similar to the generated data, it indicates normal data, otherwise they are abnormal data. The generator \(G\) generates fake samples, and then the discriminator \(D\) is used to distinguish true samples from fake samples. During the training process, the generator \(G\) and the discriminator \(D\) perform adversarial training, and finally, the generator \(G\) can generate fake samples, which can deceive the discriminator.
# A Methodology for Controlling Bias and Fairness in Synthetic Data Generation
## 3 Methodology for Data Generation
For such a purpose, the latent dataset has to be converted into a per-feature finite set of ordinal values by applying an appropriate data binning. Since each feature \(K\) ranges among \(q_{K}\) values, \(q_{K}-1\) thresholds, named latent variable cutoffs, have to be identified. This discretization process has some implications on the bias among the feature values. More precisely, latent variables, described by Gaussian probability distributions, have an implicit ordering on their samples, which, in turn, results in an ordering among the categorical values of their binned features. Although the ordering among the categorical values of each feature can be arbitrarily imposed (e.g., gender, civil/marital status and living area), the impact of the dependencies of the other latent variables is determined by the position of its bin, that is, by its corresponding cutoffs. Indeed, since the latent variable of each node is derived as the weighted sum of the internal Gaussian distribution and the latent variables of connected nodes, large edge weights increase the probabilities of large values, and, hence, the frequencies of outer bins. Notably, the partitioning of the probability density function of a latent random variable \(X_{k}\) in \(q_{K}\) areas with the same size results in a balanced dataset with respect to the values of this feature. Conversely, a dataset having features with unbalanced marginal frequencies can be generated by specifying appropriate cutoffs associated with uneven probability distribution areas. In detail, the proposed methodology leverages the fact that each latent random variable part of the \(\mathcal{N}(0,\Sigma)\) Gaussian is itself a Gaussian distribution with mean equal to zero. The cutoffs are then computed such that the probability associated with each bin matches the requested marginal probability. Once these cutoffs have been determined, the latent dataset is converted into the feature domain dataset \(\mathcal{D}\) by bucketing each latent random variable accordingly. Figure 2 shows an example of a latent variable converted into a balanced ordinal random variable with three possible categorical values. Note that, if latent variables \(X_{i}\) are initialized with a non-Gaussian distribution, then dependencies among the various features are not described any more by \(\mathcal{N}(\bar{0},\Sigma)\). This is because, in general, the algebraic composition of non-Gaussian random variables has an arbitrary probability distribution. In this case, the latent dataset can be derived by sampling this arbitrary distribution via numerical techniques. Figure 2: Probability density function of a latent random variable to be converted into a three-value discrete random variable. The two cutoffs, namely \(c_{1}\) and \(c_{2}\), split area under the curve into three regions associated with three feature outcomes with the same probability.