esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
In this paper, we described the implementation of two IEEE agents for SpO2 and ECG data acquisition, respectively, and an IEEE manager for validation. We developed both IEEE agents over Bluetooth HDP following the Continua guidelines. The proposed architecture is modular, meeting the wireless standards and allowing for real-time processing. Unlike application frameworks that require structural changes and create complex interdependencies, our model is service-oriented. This solution maintains a reliable, resilient, integrated, interoperable, and cost-effective system for personalized healthcare, while incorporating ubiquity. The modular design adopted is intended to provide a future-proofed system, whose functionality may be upgraded by modifying the hardware or software. The system features include automatic or on-demand data recording mode and warning state detection, such as abnormal oxygen levels, bradycardia, or tachycardia. Nevertheless, because of the modular nature of our implementation and the flexibility of the standard, new algorithms such as ventricular extrasystole detection algorithms could be implemented in the IEEE agents, and the warning events generated by such new algorithms could be sent over the same protocol implementation.  
Our expert physician checked the reliability of SpO2 and ECG signals captured by the system, and reported that both SpO2 values and ECG tracings provided by our system are reliable and acquired data are feasible for clinical use as this type of information is currently required in routine clinical practice. In this sense, we hypothesize that this system could be especially useful in cardiac rehabilitation programs, where a hard control of the health of the patient is needed to improve adherence. Actually, it has been shown that cardiac rehabilitation can reduce the morphortality by practically 50% in patients with heart disease. Nevertheless, participation and adherence of patients to cardiac rehabilitation and its associated treatment remains low, especially among the elderly, women, and low socioeconomic profile patients [28]. In this scenario, the use of smartphone-based applications has proved an improvement in patient adherence to the treatment; for instance, [PERSON] et al. [29] showed that the use of smartphone-based rehabilitation led to a significantly higher uptake (94% vs. 68%, \(p<0.05\)) and completion (80% vs. 47%, \(p<0.05\)) of the program. Using devices and applications based on standards, such as the one reported in this paper, would facilitate the use and integration of different measuring and mobile devices.
## 4 Experimentation
### Experimental Setup
In this study, we used the RapidMiner Studio 9.6 version [45] for simulating the proposed approaches. Workflows related to generating feature weightage for different feature scoring functions are hosted at MyExperiment.org, which is a collaborative repository to share workflows and other associated files [46; 47; 48]. Table 5 shows the parameters selected against each classification model. To evaluate the efficacy of the proposed approaches, we used several evaluation metrics such as accuracy, precision, recall also known as sensitivity, specificity, F1-measure, and Area under Receiver Operating Characteristics Curve (AUC). The evaluation metrics are computed through the confusion matrix such as:  
True Positive (TP): denotes positive instances predicted as positive. True Negative (TN): denotes negative instances predicted as negative. False Positive (FP): denotes negative instances predicted as positive. False Negative (FN): denotes positive instances predicted as negative. Based on the aforementioned definitions, the quality metrics of interest are calculated as follows:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+ \text{FN}} \tag{3}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{4}\]  
\[\text{Recall}/\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{5}\]  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{6}\]  
\[\text{F1}-\text{measure}=2\times\frac{(\text{Recall}*\text{Precision})}{(\text{ Recall}+\text{Precision})} \tag{7}\]  
The evaluation results are reported on 5 fold cross-validation in which the original dataset is horizontally partitioned into 5 partitions. In each iteration, four of the partitions are used for scoring features and obtaining a final feature subset. The remaining fifth partition is used for building classification models. In this manner, the reported result values for each classification model are averaged over different testing partitions. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Parameters** \\ \hline Naive Bayes (NB) & N/A \\ \hline Logistic Regression (LG) & N/A \\ \hline \multirow{3}{*}{Deep Learning (DL)} & Layers: 4 \\  & Hidden Layer size: 50 each \\  & Activation: Rectifier, Softmax \\ \hline \multirow{3}{*}{Decision Tree (CART)} & Impurity measure: Gini index \\  & Maximal depth: 4 \\ \hline \multirow{3}{*}{Random Forest (RF)} & Number of trees: 20 \\  & Maximal depth: 7 \\ \hline \multirow{3}{*}{Gradient Boosted Trees (GBT)} & Number of trees 20 \\  & Maximal depth: 7 \\ \cline{1-1}  & Learning rate: 0.100 \\ \hline \multirow{3}{*}{Support Vector Machine (SVM)} & Gamma: 0 \\ \cline{1-1}  & C: 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification models parameters.
# Highly Compact 4 \(\times\) 4 Flower-Shaped MIMO Antenna for Wideband Communications
## 1 Introduction
The model is fabricated on FR4 dielectric material for cost consideration with good impedance and radiation behaviors. In [16], fractal circular radiators are used to make a 4-port MIMO antenna operating in the UWB frequency range (2.67-12 GHz). Although the model achieved a good impedance performance in terms of return loss and insertion loss, it suffers from a larger substrate size (90 \(\times\) 90 mm\({}^{2}\)), which cannot be embedded in a portable device. Additionally, the isolation between radiators was not quite enough due to the absence of an isolation structure role. In [17], a quad-port semi-elliptical MIMO antenna is designed and fabricated, then tested for UWB applications that adopt two coupling mitigation mechanisms. The antenna succeeded in achieving the desired impedance, radiation, and diversity characteristics with an overall size of 40 \(\times\) 43 \(\times\) 1 mm\({}^{3}\). A four-port flexible MIMO antenna operating in the UWB frequency range (2.9-10.86 GHz) is utilized for IoT applications in [18]. The radiators are fed by a coplanar waveguide and cross-shaped isolation stubs are used to reduce the mutual coupling between radiators (\(<\)\(-\)22 dB). Furthermore, the model is tested on different parts of the human body (arm, back, leg) to investigate the effect of the human body on the desired performance. In [19], a four-port orthogonally arranged semicircular-shaped MIMO antenna is investigated for UWB applications. The isolation structures extended from the partial ground planes are utilized to enhance the isolation between radiators (\(<\)\(-\)17 dB), which has a direct impact on the MIMO diversity parameters. A very compact MIMO antenna was designed, simulated, fabricated, and tested in this paper. Decoupling structures are placed at the front and rear sides of the substrate, and long ground slots are introduced vertically on the partial grounds to introduce good isolation between the symmetrical ports. The suggested MIMO antenna covers the desired band for wideband communications with improved isolation greater than 20 dB over the achieved band. Moreover, the achieved radiation and diversity characteristics from the suggested antenna make it a good candidate for wideband MIMO communication systems.
# Long-Range Wireless Radio Technologies: A Survey
###### Abstract  
Wireless networks are now a part of the everyday life of many people and are used for many applications. Recently, new technologies that enable low-power and long-range communications have emerged. These technologies, in opposition to more traditional communication technologies rather defined as \"short range\", allow kilometer-wide wireless communications. Long-range technologies are used to form Low-Power Wide-Area Networks (LPWAN). Many LPWAN technologies are available, and they offer different performances, business models etc., answering different applications' needs. This makes it hard to find the right tool for a specific use case. In this article, we present a survey about the long-range technologies available presently as well as the technical characteristics they offer. Then we propose a discussion about the energy consumption of each alternative and which one may be most adapted depending on the use case requirements and expectations, as well as guidelines to choose the best suited technology.  
long-range; wireless; IoT; LPWAN; mobile; cellular; LoRa; Sigfox; LTE-M; NB-IoT +
Footnote †: journal: Future Internet
# Anchor-Based Method for Inter-Domain Mobility Management in Software-Defined Networking
## 2 Related Work
However, this method is limited to reducing inter-domain transfer delay and does not address optimal data routing after a domain change. To address the aforementioned issues, we propose a method (RI-SDN) that reduces inter-domain transfer delay by optimizing routes after inter-domain movement. This method is inspired by the SDN principle. First, the proposed method selects an anchor domain (i.e., a domain that determines the position of the mobile node and data flows between it and its correspondent) such that the anchor is closest to the correspondent node. Then, we determine paths based on the anchor, and parameters such as bandwith and the number of traversed domains.
## 3 Current Applications
### Myopia  
Myopia is a type of refractive error whose spherical equivalent is equal or higher than 0.50 diopters (D). It is classified as high when the spherical equivalent is equal or higher than 5.0 D or when the axial length is higher than 26 mm [58]. Myopia is associated with various ocular complications such as myopic macular degeneration, retinal detachment, cataract, and open-angle glaucoma [59]. The prevalence of myopia is expected to increase in the next years, and a meta-analysis estimated that by 2050, half of the world's population will be affected by myopia that will be high in 10% of subjects [60]. The increase in myopia prevalence is believed to be caused, in addition to genetic factors, by modern lifestyle characterized by reduced outdoor time for children and increased activities involving near vision, such as the use of digital devices [61; 62; 63]. The mechanism of action of PBM therapy in slowing myopia progression is not entirely clear. [PERSON] and co-authors highlighted how light affects dopamine metabolism in the retina [64]. However, it seems that depending on the wavelength, blue, red, and UV lights act differently. In particular, red light is that one most associated with slowing myopia [56; 65]. The most recent meta-analysis published in December 2023 demonstrated statistically significant results of PBM therapy in controlling axial length and spherical error refraction compared to the control group [66]. These data are consistent with those provided by the previous meta-analyses [67; 68]. Data from a secondary analysis of a multicenter randomized controlled trial [69] highlighted the correlation between PBM therapy and increased choroidal thickness at the macular level [70]. In the trial was used a desktop light therapy device (Eyerising [Suzhou Xuanjia Optoelectronics Technology, Suzhou, China]), which consists of semiconductor laser diodes which deliver low-level red light with a wavelength of 650 \(\pm\) 10 nm at an illuminance level of approximately 1600 lux through the pupil to the fundus. Additionally, it has been observed that changes in choroidal thickness at the macular level in the first 3 monthsare predictive of positive response at 1 year [71]. The efficacy of PBM therapy is dose-dependent and closely linked to patient compliance [69]. Despite PBM therapy being proven to be safe and free from complications even in the 2-year study conducted by [PERSON] and co-authors, it is essential to extend the follow-up to highlight any potential long-term adverse effects. In the same study, a rebound effect was documented at the end of 1 year of PBM therapy [72].
# Plenty of Room at the Bottom: Nanotechnology as Solution to an Old Issue in Enhanced Oil Recovery
## 2 Nanofluids
### Effect of Nanoparticles on Reservoir and Fluid Properties
#### 2.2.1 Aggregation and Retention in Porous Media  
Porous media are generally composed of a complex and random structure of channels and throats. Four different mechanisms lead to formation damage [57; 58]: (1) nanoparticle adsorption onto the rock surface, (2) mechanical entrapment (i.e., nanoparticles larger than the pore throat), (3) sedimentation or gravity settling, and (4) log-jamming (i.e., nanoparticles smaller than the pore throat accumulating and eventually leading to the blockage of the channel) (Figure 3).  
The retention of nanoparticles will alter some reservoir properties. For instance, nanoparticles modify the absolute permeability. This can be expressed as a function of the initial absolute permeability and a flow efficiency factor of the cross-sectional area open to flow [32]. Besides, the retention process also modifies the phases' relative permeabilities, a product of the wettability alteration. The latter comes to a limit when all the surface of the rock are covered by the nanoparticles reaching the maximum wettability alteration in the porous media. From that moment on, further retention of nanoparticles will affect the porosity and absolute permeability [59]. Related to this phenomenon is the aggregation or creation of larger clusters with similar nanoparticles (i.e., homoaggregation) or other colloidal/suspended particles (i.e., heteroaggregation). This increment in the size, and therefore in its critical velocity, can lead to sedimentation in the channels or throats. Moreover, these clusters can subsequently capture free nanoparticles, leading to a secondary aggregation process, increasing their size until a critical \"diameter\" is reached and the cluster collapses and is divided into several smaller pieces. Both the adsorption of nanoparticles and the heteroaggregation processes depend on a number of factors, namely: type of rock formation, pH, and the ionic strength or the salinity. Nevertheless, not all collisions of nanoparticles will form clusters, since this could be hampered by, for instance, steric or electrostatic hindering [60].
## 5 Experimental Studies and Tests
### Properties of the Data sets  
The tests were conducted in two stages: complexity and accuracy tests. In the first version of the analysis, these tests were implemented for:  
* 3109 features, 4312 instances, and 73 positions in the Acc. dataset;
* 2529 features, 3039 instances, and 30 positions in the CSE dataset;
* 1692 features, 2124 instances, and 184 positions in the Mar. dataset;
* 634 features, 982 instances, and 87 positions in the MI dataset;
* 154 features, 120 instances, and 8 positions in the Coo. dataset.  
In the second version of the analysis, these tests were implemented for:  
* 2081 features, 4312 instances, and 73 positions in the Acc. dataset;
* 1781 features, 3039 instances, and 30 positions in the CSE dataset;
* 1127 features, 2124 instances, and 184 positions in the Mar. dataset;
* 475 features, 982 instances, and 87 positions in the MI dataset;
* 97 features, 120 instances, and 8 positions in the Coo. dataset.  
Figure 4 shows the distributions of the numbers of cells in the datasets. The number of cells was calculated by multiplying the number of instances and features. There were two observations: the number of cells before and after conducting feature selection with FP-Growth. It was observed that changes were obtained separately for all datasets in proportion to the original cell numbers. It can be stated that the tests were implemented for datasets ranging from big data to standard-sized data.  
Figure 4: The number of cells in the datasets before and after use of the FP-Growth algorithm.
## 4 Proposed ms-SDRGN Solution For SAG-MCS  
Due to the multi-scale observation space and complicated SAG-MCS task, we propose a heuristic DRL method named _Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN)_. As illustrated in Figure 3, we first utilize a Multi-scale Convolutional Encoder to integrate local and global observed information for better feature extraction from observation space. Based on the concept of DRGN [37], we use graph attention mechanism (GAT [26]) to aggregate neighboring information through ad-hoc connections, and adopt gated recurrent unit (GRU [27]) as a memory unit for better long-term performance. In addition, we utilize a maximum-entropy method to learn stochastic policies via a configurable action entropy objective, and control each UAV agent in a distributed manner. Furthermore, a customized heuristic reward function is proposed for decentralized training.
## 2 Materials and Methods
### Data Processing  
The acquired signals were processed offline using MATLAB(r) (version 2020a). Initially, pressure signals were converted from voltage to millimetres of mercury (mmHg), as explained above. Then, both PPG and pressure signals were resampled to 256 Hz using linear interpolation, and filtered using a second order, low pass Butterworth filter, with a cutoff frequency of 20 Hz.  
Only red PPG signals were used for further analysis in this study. These were segmented into 5 min portions, with a stride of 30 s between consecutive segments. Then, interbeat intervals (IBIs) were identified using D2 Max [21] and corrected according to their length, as explained in [22]. The \(a\) fiducial point was used to extract PRV trends and time-domain, frequency-domain and nonlinear indices were obtained from them. From the time-domain, the average duration of IBIs (AVNN), their standard deviation (SDNN), the root-mean-square value of the successive differences (RMSSD), the number of successive differences with a duration longer than 50 ms (NN50) and the proportion of successive differences longer than 50 ms (pNN50) were extracted.  
PRV trends were interpolated using cubic spline interpolation and resampled to 4 Hz, and a fast Fourier transform was used to obtain frequency spectra. From these, frequency-domain indices were obtained. The absolute power of the very low frequency (frequencies below 0.04 Hz, VLF), low frequency (frequencies between 0.04 and 0.15 Hz, LF), and high frequency (frequencies between 0.15 and 0.4 Hz, HF) bands, as well as the total power of the spectra between 0.0033 and 0.4 Hz (TP), were obtained. Relative powers of the LF and HF band (nLF and nHF, respectively) and the ratio between LF and HF (LF/HF) were also measured. In addition, the centroids of the LF and HF bands, and from the whole spectra were obtained, and their \(x\)- and \(y\)-coordinates were extracted (cLF\({}_{\rm x}\), cLF\({}_{\rm y}\), cHF\({}_{\rm x}\), cHF\({}_{\rm y}\), cTP\({}_{\rm x}\), cTP\({}_{\rm y}\)). Finally, the spectral entropy (SpEn) was extracted from the obtained frequency-domain representation of PRV trends.  
Non-linear indices were also extracted. From the 1-lag Poincare plot, the area of the ellipse (S) and its major and minor axes (SD2 and SD1, respectively), the ratio between axes (SD1/SD2) and a measurement of the compaction of the points in the ellipse (COM) were obtained. From detrended fluctuation analysis, \(\alpha_{1}\) (A1) and \(\alpha_{2}\) (A2) were obtained. Finally, entropy-related indices were also measured. These were the basic-scale entropy (BSE), sign-series entropy (SSE), approximate entropy (ApEn), sample entropy (SampEn) and multi-scale entropy using SampEn as base function (MSE).
# MT-FBERT: Malicious Traffic Detection Based on Efficient Federated Learning of BERT
###### Abstract  
The rising frequency of network intrusions has significantly impacted critical infrastructures, leading to an increased focus on the detection of malicious network traffic in recent years. However, traditional port-based and classical machine learning-based malicious network traffic detection methods suffer from a dependence on expert experience and limited generalizability. In this paper, we propose a malicious traffic detection method based on an efficient federated learning framework of Bidirectional Encoder Representations from Transformers (BERT), called MT-FBERT. It offers two major advantages over most existing approaches. First, MT-FBERT pretrains BERT using two pre-training tasks along with an overall pre-training loss on large-scale unlabeled network traffic, allowing the model to automatically learn generalized traffic representations, which do not require human experience to extract the behavior features or label the malicious samples. Second, MT-FBERT finetunes BERT for malicious network traffic detection through an efficient federated learning framework, which both protects the data privacy of critical infrastructures and reduces resource consumption by dynamically identifying and updating only the most significant neurons in the global model. Evaluation experiments on public datasets demonstrated that MT-FBERT outperforms state-of-the-art baselines in malicious network traffic detection.  
malicious traffic detection; deep learning; federated learning; pre-training; BERT 202517323  
1
## 4 Results and Discussions
### Effect of Falling Angle under Different Submergence Conditions
#### 4.2.3 Cases of Submarine Landslides  
The falling angles (\(\theta\)) refer to the angle between the sliding direction of the slider along the slope and the positive x direction, and are set as \(20^{\circ},30^{\circ},40^{\circ},50^{\circ},60^{\circ}\) and \(70^{\circ}\), respectively, and the submergence conditions of landslides are submarine landslides (the falling height is \(-0.4\) m). When the falling angles are \(30^{\circ},50^{\circ}\) and \(70^{\circ}\), the temporal evolution of water elevation in the G\({}_{1}\) and G\({}_{2}\) point is shown in Figure 13. The front part of the wave train at G\({}_{1}\) decays significantly and the rear part decays first and then slightly increases as the falling angle increases. Specifically, the first peak, the second peak and the first trough in the front of the wave train can be identified at \(30^{\circ}\). When the falling angle is \(50^{\circ}\), the first peak is almost gone and the second peak and the first trough are reduced in amplitude. The two peaks and the first trough are already very small at \(70^{\circ}\). Compared to the  
Figure 12: The temporal evolution of the water elevation in G\({}_{1}\) and G\({}_{2}\) with the partial subaerial landslide: (**a**) \(\theta=30^{\circ}\); (**b**) \(\theta=50^{\circ}\); and (**c**) \(\theta=70^{\circ}\).  
\(\mathrm{G_{1}}\), the front and rear part of the wave list at \(\mathrm{G_{2}}\) are both attenuated significantly as the falling angle increases, which suggests that, for underwater landslides near the continental shelf, an increased falling angle would reduce the tsunami hazard because the slide stays on the continental shelf earlier and the slide continuing to slide at a small angle would cause greater harm.