esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Current m-Health scenarios in the smart living era, as the interpretation of the smart city at each person's level, present several challenges associated with interoperability between different clinical devices and applications. The Continua Health Alliance establishes design guidelines to standardize application communication to guarantee interoperability among medical devices. In this paper, we describe the implementation of two IEEE agents for oxygen saturation level (SpO2) measurements and electrocardiogram (ECG) data acquisition, respectively, and a smartphone IEEE manager for validation. We developed both IEEE agents over the Bluetooth Health Device Profile following the Continua guidelines and they are part of a telemonitoring system. This system was evaluated in a sample composed of 10 volunteers (mean age 29.8 \(\pm\) 7.1 y/o; 5 females) under supervision of an expert cardiologist. The evaluation consisted of measuring the SpO2 and ECG signal sitting and at rest, before and after exercising for 15 min. Physiological measurements were assessed and compared against commercial devices, and our expert physician did not find any relevant differences in the ECG signal. Additionally, the system was assessed when acquiring and processing different heart rate data to prove that warnings were generated when the heart rate was under/above the thresholds for bradycardia and tachycardia, respectively.  
Bluetooth HDP; Continua Health Alliance guidelines; digital signal processors (DSPs); electrocardiography; ISO/IEEE standards; SpO2; telemedicine Article
# Estimation of the Measurement Accuracy of Wireless Passive Resonance Sensors
## 2 Signal-to-Noise and Interference Ratio by a High-Q Resonator as Cooperative Target
### Signal Power
Free space electromagnetic propagation was assumed with \(P_{t}=10\) mW, \(g_{t}=g_{r}=2\), \(\alpha_{m}=0\), \(H_{R}(\omega)=1\), i.e., an stimulation at resonance frequency, and \(\alpha\cdot t_{load}=\pi\), i.e., an excitation via \(Q\) oscillations. The curves are shown from a distance between the reader and transponder of \(R\geq\lambda\). The phase shift \(\varphi_{2}\) in Equation (10) results from the delay caused by the transmission from the reader to the transponder due to the stimulation time \(t_{load}\) and by the re-transmission from the transponder to the reader. For electromagnetic waves in the UHF and VHF range, \(\alpha_{m}\approx 0\). If the stimulation time \(t_{load}\) is in the order or larger than \(Q/f_{0}\), then the resonator is maximally stimulated and we can neglect the phase shift due to the interference between the forced oscillation  
\[P_{r}=P_{t}\cdot g_{t}\cdot g_{r}\cdot e^{-\alpha_{m}R}\bigg{(}\frac{\lambda}{ 4\pi R}\bigg{)}^{2}. \tag{2}\]4 Typical reception power \(P_{in}\) in the reader when reading passive resonant transponders. Electromagnetic propagation in free space was assumed (\(\alpha_{m}=0\)). The transmission power \(P_{t}\) is 10 mW, the antennas radiate into the half-space (\(g_{t}=g_{r}=2\)) and the stimulation was carried out at resonance frequency (\(H_{R}(\omega)=1\)) over the duration of \(Q\) oscillations (\(\alpha\cdot t_{load}=\pi\)). The curves are drawn from top to bottom for the frequencies 433 MHz (red line), 868 MHz (green line), 915 MHz (blue line), 2.45 GHz (magenta line) and 5.8 GHz (black line) starting from a distance of 1 \(\lambda\). and the natural oscillation in the resonator [49]. If further, the antenna is matched to the impedance of the resonator at resonant frequency, and the interrogation frequency is close to the natural frequency, i.e., \(H_{R}(\omega)\approx 1\), then Equation (11) simplifies to  
\[P_{r}=P_{t}\cdot g_{t}\cdot g_{r}\cdot e^{-\alpha_{m}R}\bigg{(}\frac{\lambda}{ 4\pi R}\bigg{)}^{2}. \tag{2}\]5
# Comprehensive Document Summarization with Refined Self-Matching Mechanism
## 5 Results
### Ablation Test  
In order to analyze the contribution of different components to the final performance, we performed ablation tests on the RSME. The experimental results are shown in Table 2. When the Gaussian bias component was removed, the performance declined by 0.4 on ROUGE-1, 0.2 on ROUGE-2. When we removed the self-matching component, the performance declined by a large margin on all three indicators by 0.7, 0.4 and 0.8 respectively. The deviation strongly reveals that global document awareness is crucial to the long document summarization task. The Gaussian bias was used to measure the distance between the predicted center and alignment, which can effectively model the localness and improve the representation of the local context. The improved performance of the two components on ROUGE was 1.1, 0.6, and 0.7, which reveals that combining them to the coarse-to-fine strategy was effective. When the pointer network component was removed, the performance decreased slightly by 0.2, 0.2 and 0.5 on the three indicators respectively, since pointer network can guide the sentence selection with respect to the relative importance gain of the previous selection. Pointer network was efficient in many tasks, and also embodied important value in our work.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Categories** & **Model** & **ROUGE-1** & **ROUGE-2** & **ROUGE-L** \\ \hline \multirow{3}{*}{**Abstractive**} & ConvS2S & 39.8 & 17.3 & 36.5 \\  & PGN + Cov & 39.5 & 17.3 & 36.4 \\  & Fastabs & 41.4 & 18.7 & 37.7 \\ \hline \multirow{3}{*}{**Extractive**} & LEAD-3 & 40.3 & 17.7 & 36.6 \\  & HSSAS \# & **42.3** & 17.8 & 37.6 \\ \cline{1-1}  & Refresh & 40.0 & 18.2 & 36.6 \\ \cline{1-1}  & BanditSum & 41.5 & 18.7 & 37.6 \\ \cline{1-1}  & SWAP-NET \# & 41.6 & 18.3 & **37.7** \\ \hline \multirow{3}{*}{**Extractive (ours)**} & RSME & 41.5 & **18.8** & **37.7** \\ \cline{1-1}  & BERT-RSME & **42.4 \({}^{*}\)** & **19.8 \({}^{*}\)** & **38.9*** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The full-length ROUGE F1 score on the non-anonymized CNN/Daily Mail dataset. Those marked with ‘#’ represent training and evaluation on the anonymous version. All of our ROUGE scores are reported by the official ROUGE script, with a 95% confidence interval of at most \(\pm\)0.24. The promotion is statistically significant with respect to those strong baselines. Results with \({}^{*}\) are statistical significantly.
# UAV Formation Trajectory Planning Algorithms: A Review
## 4 Intelligent Algorithm
### Machine Learning Algorithm
#### 4.2.2 Reinforce Learning  
The reinforcement learning (RL) algorithm is a new learning method which combines dynamic programming with supervised learning. The reinforcement learning algorithm keeps learning in the interaction with the environment, implements the \"reward-punishment\" mechanism, maximizes the reward index through the feedback evaluation, and realizes the optimal decision output in the UAV formation trajectory planning. Figure 23 shows a reinforcement learning (RL) model.  
Figure 23: A reinforcement learning model.  
Figure 22: A neural network model (connection: different combinations).  
[PERSON] et al. [122] proposed a strategy-based Deep-Sarsa algorithm, which combined traditional Sarsa and neural network to find the optimal trajectory of UAV formation and improved the poor trajectory planning ability of heuristic algorithm in dynamic environment.  
[PERSON] et al. [123] proposed a multi-agent reinforcement learning algorithm. The algorithm combines the Multi-Agent Deep Deterministic Policy Gradient Algorithm (MADDPG) and the Simultaneous Target Assignment and Trajectory Planning (STATP) method to solve the Multi-Agent Deep Deterministic Policy Gradient (MUTAPP) problem in dynamic environments.  
[PERSON] et al. [124] proposed a q-learning based decentralized multi-UAV cooperative reinforcement learning algorithm (DMUCRL). The algorithm enables UAVs to independently choose their cruising strategy and charging scheduling and at the same time share the learning results in the communication network according to the specified time, which solves the problem of efficient content coverage for multi-UAV trajectory planning.  
[PERSON] et al. [125] proposed a collaborative trajectory planning method for multiple UAVs based on attentional reinforcement learning. This method uses a neural network with an attention mechanism to generate a UAV cooperative reconnaissance strategy (AM) and uses a reinforcement algorithm to test a large amount of simulation data and optimize the attention network. It solves the problem in which it is difficult for traditional heuristic algorithms to extract empirical models from large sample terrain data in time.  
[PERSON] et al. [126] proposed a trajectory planning method based on the fusion of the Sparse Search Algorithm (SSA) and Biologically Inspired Neural Network (BINN). The algorithm uses SSA to find the node with the lowest comprehensive cost and then uses the b-spline curve to fit it; then, it uses the improved BINN method to replan the local trajectory, which solves the problem where the trajectory planning stability of the heuristic algorithm is poor in a dynamic environment, or the trajectory is probably not the optimal question.
# Density Peaks Clustering Algorithm Based on a Divergence Distance and Tissue--Like P System
###### Abstract  
Density Peaks Clustering (DPC) has recently received much attention in many fields by reason of its simplicity and efficiency. Nevertheless, empirical studies have shown that DPC has some shortfalls: (i) similarity measurement based on Euclidean distance is prone to misclassification. When dealing with clusters of non-uniform density, it is very difficult to identify true clustering centers in the decision graph; (ii) the clustering centers need to be manually selected; (iii) the chain reaction; an incorrectly assigned point will affect the clustering outcome. To settle the above limitations, we propose an improved density peaks clustering algorithm based on a divergence distance and tissue--like P system (TP-DSDPC in short). In the proposed algorithm, a novel distance measure is introduced to accurately estimate the local density and relative distance of each point. Then, clustering centers are automatically selected by the score value. A tissue--like P system carries out the entire algorithm process. In terms of the three evaluation metrics, the improved algorithm outperforms the other comparison algorithms using multiple synthetic and real-world datasets.  
density peaks clustering; divergence distance; clustering centers selection; tissue--like P system +
Footnote †: journal: 10 February 2023
## 2 State of the Art
### Definition of Petri Nets  
A Petri net is graphically represented by circles, referred to as places, and rectangles, referred to as transitions, which are interconnected through directed arcs. The dynamic behavior of the modeled system is described by an ordered sequence of transition firings. When a transition is fired, it consumes tokens from its input places and generates new tokens in its output places. The number of tokens required to fire a specific transition, as well as the location of the newly generated tokens, is determined by the directions and weights associated with the input and output arcs. The distribution of tokens across the places of the net is called the marking, which represents the state of the modeled system at a given moment. Let N denote the set of non-negative integers [20; 21]. Formally, a Petri net is a five-tuple \(PN=(P,T,F,W,\mathbf{M}_{0})\), where  
* \(P\) is a finite set of places;
* \(T\) is a finite set of transitions, disjoint from \(P\);
* \(F\subseteq(P\times T)\cup(T\times P)\) is the set of arcs;
* \(W:F\rightarrow\mathbb{N}^{+}\) is the weight function of the arcs;
* \(\mathbf{M}_{0}:P\rightarrow\mathbb{N}\) is the initial marking. The matrices associated with a Petri net are the following:
* Input matrix: \(\mathbf{I}:P\times T\rightarrow\mathbb{N}\) \[\mathbf{I}(p,t)=\begin{cases}W(p,t)&\text{if }(p,t)\in\ F\\ 0&\text{otherwise}\end{cases}\]* Output matrix: \(\mathbf{O}:P\times T\rightarrow\mathbb{N}\) \[\mathbf{O}(p,t)=\begin{cases}W(t,p)&\text{if }(t,p)\in\ F\\ 0&\text{otherwise}\end{cases}\]
* Incidence matrix: \(\mathbf{C}=\mathbf{O}-\mathbf{I}\) The transition function \(\delta:\mathbb{N}^{P}\times T\rightarrow\mathbb{N}^{P}\) defines the dynamics of the Petri net: \[\delta(\mathbf{M},t)=\mathbf{M}+\mathbf{C}(\cdot,t)\quad\text{if}\quad \mathbf{M}\geq\mathbf{I}(\cdot,t)\] where
* \(\mathbf{M}\) is the current marking;
* \(\mathbf{C}\) is the incidence matrix;
* \(\mathbf{I}\) is the input matrix;
* \(t\) is a transition. T-invariants are vectors \(\mathbf{x}\in\mathbb{N}^{T}\) such that \[\mathbf{C}\,\cdot\,\mathbf{x}=\mathbf{0}\] P-invariants are vectors \(\mathbf{y}\in\mathbb{N}^{P}\) such that \[\mathbf{y}^{\mathbf{T}}\cdot\mathbf{C}=\mathbf{0}\]  
The dynamics of a Petri net describe the evolution of the marking through the firing of enabled transitions. This process captures the system's behavior over time, reflecting changes in state as transitions occur.
## 2 Basic Principles and Methodologies
### The Coefficient of Heating Effect
It should be noted that the temperature phantoms in Ref 11 to Ref 14 [35; 36; 37; 38] are reproduced from measured hot-state temperature data provided by a real boiler plant, and their \(C_{\rm HE}\) values are less than 1.2, which belong to the relatively uniform temperature distributions and are suitable for most of the existing reconstruction algorithms. Figure 1: The root mean square error and its corresponding \(C_{\rm HE}\) value in the literature review. Ref 1 to Ref 15 in the figure refer sequentially to [4; 6; 11; 14; 17; 20; 28; 32; 33; 34; 35; 36; 37; 38; 39]. Improvements to the Reconstruction of High-Gradient Temperature Fields with Limited Measurement Points  
In high-gradient temperature fields with limited measurement points, the reconstruction of the AP is an ill-posed problem with sparse valid data. Therefore, two improvements to the reconstruction process have been primarily proposed. First, the hybrid kernel method can improve the reconstruction accuracy to a limited extent by adjusting the ratio of different basis functions in the hybrid kernel, thereby ensuring a more accurate RBF approximation and improving the overall reconstruction accuracy. Second, inspired by the CFD simulation in which refined meshes are employed for modelling complex parts to achieve enhanced results, local mesh refinement for hotspot regions is considered to simultaneously improve both overall reconstruction accuracy and hotspot tracking performance.
# Novel 3U Stand-Alone CubeSat Architecture for Autonomous Near Earth Asteroid Fly-By
## 2 Mission Description  
Several mission concepts have been proposed to explore NEAs (and small bodies in general). Usually, proposals consist of a mothership carrying several CubeSats on-board [6; 8], or stand-alone missions [9; 10]. These missions are based on either a rendezvous or a fly-by of the target. The former usually allows a more extensive exploration of the target, but it also requires a significantly larger \(\Delta V\), which complicates the design of a small and low-cost CubeSat. The latter has a significantly shorter scientific phase, but with a much smaller \(\Delta V\) and a simpler communication subsystem design.  
The mission concept presented in this paper is characterized by a NEA fly-by achieved after an Earth's escape, which is in line with the current space exploration trend. A single fly-by approach has been chosen against multiple ones (sometimes referred as 'NEAs tour') for two main reasons: first, exploring multiple targets with a 3U architecture appears not to be feasible, especially for the limited propellant mass which can be stored on-board; secondly, it is cost-effective due to the standardization related to CubeSats with the same design. Moreover, a fully autonomous and COTS 3U CubeSat configuration is considered, in order to minimize the launch mass and volume, as well as the cost. The mission concept can be described by a Mission Objective (MO) and a Mission Philosophy (MP):  
* MO: improve target Near Earth Asteroid dataset in terms of dimension, shape, rotational parameters, composition and ephemerides.
* MP: maximize the scientific return with limited cost, by COTS components, autonomous operations and Guidance, Navigation, and Control (GNC), commercial launcher, small size, and standard architecture. MO and MP translate in Mission Requirements (MR), summarized in Table 1.  
The mission phases are described in Table 2. The CubeSat is released in an injection orbit around the Earth and it is de-spinned. Then, propulsion system is turned on to inject the CubeSat in an Earth's escape trajectory, followed first by the interplanetary cruise and then by the close encounter phase (defined by the navigation switch from absolute to relative). Following the scientific phase, the acquired data are transmitted to the ground segment, and finally the spacecraft is injected (if necessary) in its final disposal trajectory. The duration of each phase varies depending on the selected NEA target. Table 2 reports typical values that can be expected from each phase.  
As injection orbit, a Super Synchronous Geostationary Transfer Orbit (SSGTO) has been selected, due to its high energy and commercial nature, which would further decrease the costs. SSGTO is characterized by apogee and perigee altitudes respectively of 295 km and 90,000 km.
###### Abstract
Encrypted traffic using known encryption protocols can be directly screened, and the encryption of unknown protocols was detected by the proposed Segmented Entropy Distribution Capsule Neural Network (SED-CapsNet). After that, TLS parameter validation is implemented to verify standard TLS encrypted traffic to ensure the sample authenticity. Finally, the consistency between DNS and HTTPS parameters is used to correlate DNStraffic samples with HTTPS traffic samples to guarantee the integrity of labeled samples. The system solves seven difficulties in one go and it has been released at: [[https://data.iptas.edu.cn/web/tbps](https://data.iptas.edu.cn/web/tbps)]([https://data.iptas.edu.cn/web/tbps](https://data.iptas.edu.cn/web/tbps)) (accessed on 15 April 2022). An anonymized dataset obtained by A3C has also been published at: [[https://data.iptas.edu.cn/web/tbps](https://data.iptas.edu.cn/web/tbps)]([https://data.iptas.edu.cn/web/tbps](https://data.iptas.edu.cn/web/tbps)) (accessed on 15 April 2022). Our main contributions can be summarized as follows:  
* We point out that the most serious bottleneck in the field of encrypted traffic classification currently lies in reliable and continuously updated datasets. Then we describe the problems existing in encrypted traffic labeled sample acquisition. To solve the core labeled sample acquisition problems, we break the research threshold of large-scale labeled dataset requirements by putting forward the A3C architecture. * We develop A3C into three system instances on Windows, Linux, and Android. We break through the limitation of the traditional traffic sample collection method, which needs to restrict the currently running application. We realize the correspondence between the process and traffic on different OSs to carry out the automated process-level isolated collection and labeled sample construction of the traffic in the mixed application scenario. * We propose a sample encryption validation method based on SED-CapsNet to detect the encryption of the collected data and realize the purification of samples. Our method performs much faster than the state-of-the-art HEDGE method and can still achieve high effectiveness (1/8 time cost with 83.12% F1-score). * We make public the A3C system and an anonymized dataset obtained through the system under an existing large-scale network, which break the current situation that the research on encrypted traffic classification requires a large-scale experimental environment and considerable labor cost. The rest of the paper is structured as follows. Section 2 analyzes the commonly used datasets, dataset construction methods, and classification methods of encrypted traffic. Section 3 briefly introduces the encrypted traffic classification and the encrypted traffic sample collection. Section 4 presents the whole picture of the A3C system. Then it describes the methods used in the online acquisition procedure. Section 5 describes how to validate the encrypted flow. In Section 6, the A3C system is evaluated through experiments in many aspects. Finally, we summarize our research and prospect the future.
# A Security and Privacy Scoring System for Contact Tracing Apps
## 1 Introduction
### Contribution  
We propose a contact tracing app scoring system based on consensus privacy principles for contact tracing and a vulnerability scoring system we developed. The rubric for the vulnerability scoring was defined by ourselves with some ideas based on those of the common vulnerability scoring system (CVSS). Attacks against contact tracing systems with either the goal of accessing private information or introducing false information into the system were created by us to score the security of contact tracing systems. We reviewed the state of the art to identify a taxonomy of contact tracing apps. Then, we applied the scoring creation methodology to a representative sample of the contact tracing approaches. Our final analysis shows that centralized Bluetooth with added location functionality has low privacy and security, while GPS logging scored high in security and medium in privacy. The decentralized Bluetooth method scored high in privacy and medium in security.
## 4 Methodology
### Intention Recognition  
The intention recognition module is responsible for interpreting user commands and determining the appropriate actions for [PERSON] to execute. This is accomplished using an RNN model developed with TensorFlow and Keras, trained on a labeled dataset consisting of phrases corresponding to various user intents. Each user phrase is first tokenized into numerical sequences, which are then transformed into feature vectors using an embedding layer. These feature vectors are processed by the RNN, which captures the sequential structure of language and classifies the user's intention.  
This system allows [PERSON] to respond effectively to a wide range of natural language commands, enhancing the fluidity of human-robot interactions. Common intents recognized by the system include commands for movement, object detection, reminders, and conversation initiation.  
Examples of typical user requests are as follows:  
* Movement: \"Move forward\", \"Rotate 90 degrees to the left.\"
* Object detection: \"What do you see around you?\", \"Is there a table in the room?\"
* Reminders: \"Remind me to call [PERSON] in 10 min.\", \"Set a reminder for my doctor's appointment.\"
* Conversation initiation: \"[PERSON], let's have a chat.\", \"Start a conversation mode.\"  
As observed, key phrases such as \"move\", \"turn\", \"remind\", \"object\", or \"conversation\" help the model classify user requests efficiently. The network has been trained to recognize and categorize these linguistic patterns, enabling it to deduce the appropriate action without the need for rigid command syntax. This flexibility improves the natural flow of interaction between the user and [PERSON], making the system more user-friendly and adaptable to different contexts.  
The dataset for intention recognition was built manually due to the absence of an existing dataset suitable for our methodology. It includes balanced samples across the main categories of movement, reminders, object detection, and conversational commands. Each category contains 80 sentences, designed to cover diverse phrasing and scenarios.  
The dataset was split into 75% for training and 25% for testing, ensuring robust evaluation. This approach ensures reproducibility and allows researchers to adapt or extend the dataset for similar studies.