esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This study presents the use of PPG to achieve continuous non-invasive blood pressure measurement. The MIMIC-III waveform database base was used and its data processed, ultimately yielding a sample size of 1669 patients comprising 890 h of data. Models were developed using machine learning to determine discrete blood pressure values (SBP, DBP, and MAP), as well as the waveform shape. For the discrete BP prediction, three regression ANNs were the most successful, and the SBP, DBP, and MAP models have errors of 5.26 \(\pm\) 6.53 mmHg, 2.96 \(\pm\) 3.31 mmHg, and 3.27 \(\pm\) 3.55 mmHg, respectively.  
In all three models, over 85% of predictions have an error less than 10 mmHg, meeting the AAMII/ESH/ISO standard. For waveform prediction, a regression ANN with a custom loss layer yielded a strong correlation of 0.864.  
This study demonstrates a non-invasive blood pressure measurement technique that could improve patient management in a variety of clinical scenarios. Further investigations into the models' performance in different populations is required, and improvements may be achieved by training on a wider population outside of MIMIC-III's ICU patients.  
Conceptualization, B.T., A.F. and D.M.R.; methodology, B.T., A.F., A.P. and D.M.R.; software, B.T. and A.F.; validation, B.T. and A.F.; formal analysis, B.T. and A.F.; investigation, B.T. and A.F.; resources, B.T. and A.F.; data curation, B.T. and A.F.; writing--original draft preparation, B.T. and A.F.; writing--review and editing, B.T., A.F., A.P. and D.M.R.; visualization, B.T. and A.F.; supervision, A.P. and D.M.R. All authors have read and agreed to the published version of the manuscript.  
This research was only funded by the School of Electrical & Information Engineering at the University of the Witwatersrand, Johannesburg; and received no external funding.  
The secondary use of the data for this study was approved by the Human Research Ethics Committee (Medical) of the University of the Witwatersrand, Johannesburg (M210633; 2021/07/20).  
Not applicable.  
The dataset analysed in this study is the MIMIC-III Waveform Database [16; 17; 18], which is freely available under the Open Data Commons Open Database Licence [19].  
We would like to acknowledge Professor [PERSON] at the School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, who provided us with access to the school's high-performance cluster, which we used for all the intensive computations through the study.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ABP & Arterial Blood Pressure \\ ANN & Artificial Neural Network \\ CNN & Convolutional Neural Network \\ DBP & Diastolic Blood Pressure \\ LSTM & Long Short Term Memory \\ MAP & Mean Arterial Pressure \\ PPG & Photoplethysmography \\ SBP & Systolic Blood Pressure \\ \end{tabular}
# Dempster-Shafer Fusion Based on a Deep Boltzmann Machine for Blood Pressure Estimation
## 3 DBM-Based DS Fusion
### Statistical Analysis  
To utilize the cumulative distribution function (CDF) of the artificial feature, the normality of each CDF was clearly investigated. The Kolmogorov-Smirnov (KS) test [21] was thus conducted to evaluate the normality of each CDF. We suppose that \(\mathbb{F}^{*}\), a CDF of an artificial feature vector \([\theta_{1}^{*},\theta_{2}^{*},...,\theta_{B}^{*}]\), is obtained from the sampling distribution, \(\mathbb{F}\), where \(B\) is the number of replications. The KS test result is expressed as the probability of measuring the agreement between the hypothesis and the distribution of the artificial features [5]. The CDFs of the artificial features are confirmed as Gaussian distributions through the test results shown in Figure 2, where the CDF plots almost fit the normal distribution. As B increases, the CDF approaches the normal distribution more closely. The \(\mathbb{H}_{0}\) supposes that the CDF of an artificial feature follows a roughly Gaussian distribution, whereas the \(\mathbb{H}_{1}\) supposes that the distribution of an artificial feature does not approximate a Gaussian distribution [22]. In addition, we verified that the KS test results of all artificial features were zero. Thus, we did not reject the \(\mathbb{H}_{0}\) at  
Figure 1: Upper boxes: Procedures of BP (blood pressure) measurement, signal processing, oscillometric wave (OMW) signal, feature extraction, and artificial features generation. Right boxes (6 to 11) are DBMs (deep Boltzmann machine). Other boxes: DS (Dempster–Shafer) fusion to decrease uncertainty, where \(\mathbf{y}(1)\) is the output vector of the first classifier, and \(D\) represents the number of classifiers. DS fusion is further explained in Section 3.  
the \(\alpha\)(=0.05) significance level. We could thus assume that the artificial features had similar statistical properties as their original features.
## 2 Related Work
### Panic Behavior Detection  
In contrast to density-based methods, some researchers have explored the role of individual behaviors in identifying crowd panic. [PERSON] et al. incorporated trajectory analysis to track individual movements and detect deviations from normal crowd behavior [19]. Their approach successfully identified individuals who began moving erratically or in the opposite direction to the crowd flow, which is a key indicator of panic. However, trajectory analysis is often limited by the challenge of occlusions and the inability to track individuals in very dense crowds, where individuals may become difficult to distinguish due to visual clutter [20].  
Building upon this, [PERSON] et al. proposed a multi-agent model for crowd panic detection that combined trajectory data with crowd density maps [21]. Their model can track individual panic behavior by examining trajectory irregularities, such as increased speed or erratic movement patterns. Although this approach showed promise in simulated environments, it faced challenges when implemented in real-world scenarios in which the sensor data can be noisy or incomplete.  
In recent years, the integration of semantic understanding has emerged as a promising approach for panic detection. [PERSON] et al. utilized AI-driven natural language processing (NLP) techniques to identify distress signals through spoken language during crowd events [22]. This method combines video analysis with speech-to-text algorithms to detect panic-related communication, significantly enhancing the predictive performance of panic behaviors. However, this approach requires additional computational resources to processing and analyze speech, which may not always be feasible in real-time scenarios.  
Despite these advancements, existing methods often fail to integrate trajectory and semantic features, leading to high false alarm rates and limited adaptability in dynamic crowd scenarios, primarily due to the reliance on sparse data sources or isolated analyses of crowd density or individual behaviors. Related achievements in panic behavior detection are shown in Table 1. It is worth noting that approaches focusing solely on crowd density fail to account for sudden behavioral shifts or irregular trajectories that often accompany panic scenarios. Similarly, trajectory-based methods may not perform well in environments with occlusions or dense crowds, where tracking individual movements becomes impractical.  
Our work aims to overcome these limitations by adopting a multi-dimensional approach that combines crowd density maps, trajectory analysis, and semantic recognition to improve the real-time detection of panic behaviors. Integrating these different data sources can provide a more comprehensive and accurate understanding of individual and collective panic responses.
# Empowering WBANs: Enhanced Energy Efficiency Through Cluster-Based Routing and Swarm Optimization
## 2 Related Work
Conventional approaches overlook the dynamic topologies and situations in large-scale or real-time WBANs; however, this methodology offers a more flexible, scalable, and economical solution by combining these strategies. When variables like node mobility, fluctuating connection, or energy depletion cause network architectures to alter often, we say that the network has a dynamic topology. Regarding WBANs, ambient factors, sensor node energy consumption disparities, and physical motions like walking, sitting, or running all shape dynamic topologies. Due to their static or semi-static assumptions, many prior techniques have neglected dynamic topologies. Consider HEESR, which prioritizes data for routing optimization but could not adjust to mobile nodes. Despite improving cluster head selection, DECR uses static approaches that may have been too slow for modern networks. Because MTMAC and other protocols cannot adapt to topological changes caused by mobility, energy consumption and delays increase. Since WBANs have dynamic mobility, the DHCO protocol was designed for static WSNs. This approach overcomes these restrictions using algorithms like ABBSOA and MSSOA, which can alter their clustering and routing decisionsin real time to match network conditions. It maintains an effective exploration-exploitation balance even in highly dynamic WBAN situations with shifting node placements and energy levels. It maintains a firm balance between the two stages. This research improves the efficiency of wireless body area networks (WBANs) by optimizing their clustering and routing choices using the adaptive binary bird swarm optimization algorithm (ABBSOA) and the modified snake swarm optimization algorithm (MSSOA). While MSSOA employs snake-inspired pathfinding to guarantee optimum energy usage during data transmission, ABBSOA adjusts binary decision-making to choose the most energy-efficient sensor states and routing pathways. The hybrid method guaran
# Coexistence Scheme for Uncoordinated LTE and WiFi Networks Using Experience Replay Based Q-Learning
## 2 Related Work
### Enhancements  
We reviewed different coexistence schemes that are proposed to achieve fair and efficient coexistence of LTE and WiFi networks. However, most of the proposed solutions consider coordinated LTE and WiFi networks in which the traffic status of the technologies is exchangeable via a collaboration protocol. There are also some papers that propose WiFi traffic demand estimation techniques used in uncoordinated coexistence schemes. However, WiFi traffic load estimation is mostly done based on wrong assumptions such as the number of active APs, or COT. Therefore, this paper proposes an efficient coexistence scheme for uncoordinated LTE and WiFi which requires modifications only on the LTE eNB side. To estimate the traffic demand of WiFi, the coexistence scheme uses the saturation sensing model proposed in our previous work [8]. Moreover, we have reviewed many coexistence schemes that use Q-learning to determine optimal configuration. However, Q-learning is not an efficient solution for problems with dynamic environments. For this reason, we propose coexistence schemes that utilizes the experience replay technique to cope with the dynamic nature of traffic loads of the co-located technologies in coexistence problems. In general, this paper proposes a coexistence scheme that (i) only requires modifications on the LTE eNBs, i.e., no modifications are required on the legacy WiFi nodes (ii) does not require to decode WiFi traffic, (iii) does not require any coordination signaling protocol between the technologies, and (iv) is a suitable coexistence solution for co-located networks with dynamic traffic loads as it uses an experience replay technique for the optimal configuration selection.
## 2 Fundamental Concepts
### Artificial Neural Networks
Since backpropagation has a high time complexity, it is advisable to start with a smaller number of hidden neurons and a few hidden layers for training. Note that _Big-O_ quota is a mathematical notation that describesthe limiting behavior of a function when the argument tends towards a particular value or infinity. Even with a finalized optimization, ANNs can run into other problems, such as underfitting and overfitting. These two errors are more commonly seen in the construction of neural networks. They are the trend error and the variance error. The bias error arises because the network tries to describe a generalized behavior for its data, which does not suit the noise sufficiently and dictates a simplified trend. The network is tied to noise in the variance error and generates an excessively complex model. Combining a high trend error with a low variance error generates underfitting, where the developed model is straightforward, not fitting the points and, consequently, not correctly describing the natural phenomenon. When the opposite happens--that is, the model has a low tendency and high variance--overfitting is generated, where the network adapts excessively to the training data and loses the ability to generalize to points outside of the training set [17]. The neural network can be evaluated by its ability to fit the training data and predict data outside this set. The available data are generally divided into training, validation and testing to improve the model results. During the training stage, the outcomes calculated by the network are compared with the target (supervised learning); then, the weights are adjusted to approximate them. Then, in the validation step, the model undergoes a fine adjustment in the parameters, avoiding rigidity to the training data, thus reducing the chances of overfitting. Finally, the prediction is performed with the data that were separated for the test step, and then the result is compared with the absolute values [20]. Underfitting represents when the model performs poorly on training and test data. In contrast, overfitting indicates that the model acted well on the training data but then struggled on the unrecognized inputs; this should not be the case, as both data groups came from the identical distribution. One of the ways to evaluate the model's performance is the study of the learning curves. An analysis of the model with the training and test data can diagnose the possibilities of overfitting and underfitting [21]. Many techniques have been explored to accelerate its performance, considering that it may fall into local minima. One of the potential treatments to escape from local minima is by operating a minimum learning rate, which slows down the learning process. In [22], the authors present a new strategy based on the use of the bi-hyperbolic function, which offers greater flexibility and a faster evaluation time. On the other hand, we applied the genetic algorithm to the training of our neural networks.
## 3 Geopolymer Concrete (GPC)
### Mechanical Properties of Hardened Geopolymer Concrete
#### 3.2.1 Compressive Strength
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Additives** & **Remarks** \\ \hline [PERSON], [PERSON] et al. [67] & Superplasticizer & A high superplasticiser dose increases CS. \\ \hline [PERSON] et al. [68] & Ground granulated BFS & Compressive strength improves when GBFS dosage is increased. \\ \hline [PERSON] et al. [69] & PC mortar & GP composites were developed that have a more uniform and dense structure than concrete. \\ \hline [PERSON], [PERSON] et al. [70] & Ground granulated BFS, palm oil fuel ash & A 67 MPa CS was achieved by combining 30\% POFA with 70\% GGBB in FA-GP concrete. \\ \hline [PERSON], [PERSON] et al. [71] & Chitosan biopolymer & N-carboxymethyl chitosan’s addition greatly enhanced strength and led to a slight boost in compressive strength. \\ \hline [PERSON], [PERSON] et al. [72] & Ground granulated BFS & The CS of GP mixtures can be increased through the addition of slag to the raw material, with a slag/FA dosage ratio of 0.8, resulting in the highest strength. \\ \hline [PERSON], U et al. [73] & Sulfate of calcium and sodium, calcium chloride, and sucrose & The final setting time is significantly prolonged by the presence of sugar. As a rule, admixtures boost CS quality. \\ \hline [PERSON], [PERSON] et al. [74] & GBFS, GCS & Partial replacement with GCS yielded a higher CS than partial replacement with GBFS. \\ \hline [PERSON], [PERSON] [75] & ground granulated BFS & 48 MPa CSs were achieved with an M ratio of 0.96 SiO\({}_{2}\)/Na\({}_{2}\)O and a raw material composition of 70\% GGBPS and 30\% FA. \\ \hline [PERSON], [PERSON] et al. [76] & Red mud & There is a decline in CS after 120 days. Safe aggregation of metals where they cannot exceed safe limits. \\ \hline [PERSON], [PERSON] et al. [77] & Incinerated rice husk ash & Compressive and bond strength were enhanced when rice husk ash was added at an optimum dosage of 7\%. \\ \hline [PERSON] et al. [78] & Waste glass & Supplemental silicon causes a rise in CS concentration. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effect of different additives on geopolymer concrete compressive strength.
# DNN-Based FES Control for Gait Rehabilitation of Hemiplegic Patients
## 2 Methods
### Measurement and Processing of EMG Data  
To provide a reference EMG for the paretic side, EMG data were collected from six healthy male subjects aged between 20 and 30 years. The EMG data were collected during treadmill walking through off-the-shelf wireless surface EMG electrodes (Delsys Inc., USA). The EMG was measured from the soleus and tibialis anterior (TA) with walking speeds ranging from 1 to 5 km/h. Since the soleus and TA are the prime movers of ankle motion (plantarflexion and dorsiflexion), the EMG patterns of the two muscles can be used to describe the muscle activities for ankle motion during gait.  
The acquired EMG data were rectified using a 6 th order high-pass filter (HPF) with a cutoff frequency of 40 Hz. Subsequently, the rectified EMG signals were divided into gait cycles with 1000 data points so that the signals could be ensemble-averaged over gait cycles. To determine the gait cycle and gait phase, wireless FSRs were attached to the soles of two feet to gather ON/OFF information at the heels and toes based on a predetermined threshold. Measurements from immovable contact points during gaits, such as the toes and the first metatarsal heads, were used to provide stable contact information.  
The EMG signal was then normalized with respect to the peak value of the rectified EMG at a walking speed of 5 km/h. Figure 2 shows the ensemble average of rectified EMG from the soleus and tibialis anterior with varying walking speeds on the treadmill.  
The EMG magnitude of the upper 15% was considered as muscle activation, and the muscle activation during gait could be characterized as follows [36]:  
1. The activation timing of the soleus after the heel strike was unchanged regardless of the changes in walking speed.
2. The activation of soleus ends at about the timing of toe off.
3. The co-contraction of Soleus and TA occurs only for a short time at low walking speeds (1-2 km/h).
4. The activation timings of the soleus and TA nearly coincide with the timings of heel and toe contacts in higher walking speeds (3-5 km/h).  
Based on the muscle activation model, the EMG envelopes were functionalized using trigonometric functions by using a curve fitting tool in MATLAB software (MathWorks, Natick, MA, USA). Functionalized EMG envelopes are shown in Figure 3. The minimum  
Figure 2: EMG of (**a**) soleus and (**b**) TA during treadmill gait at various walking speeds (km/h).  
and maximum amplitudes of the functionalized EMG envelopes were chosen as feature points. Figure 3a,b show the feature points in the percentile gait cycle, where the soleus and TA had three and five feature points, respectively: S_1, S_2, and S_3 for the soleus and T_1, T_2, T_3, T_4, and T_5 for the TA. Figure 3c,d show the changes in the functionalized EMG envelopes depending on the changes in the walking speed. The observations from the gait experiments showed that the positions of all the feature points in the percentile gait cycle varied depending on the walking speed, except for T_3, as shown in Figure 3d.
# Detection of Arrhythmias Using Heart Rate Signals from Smartwatches
## 3 Materials and Methods
### Model Configuration
A sample is classified according to the majority vote of its neighbors, and among its \(k\) nearest neighbors, it is assigned to the most common class. Due to the need to store the complete training set, \(k\)NN can be computationally expensive, even in small data sets. All algorithms were implemented on the Google Colaboratory platform, with Python version 3.10.12 and using typical libraries such as Torch, Pandas, Numpy, and SkLearn. The optimization of parameters depended on the type of algorithm, as each one has different indicators. Table 3 shows the optimal parameters used with each algorithm. \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Model**} & \multicolumn{1}{c}{**Best Parameters**} \\ \hline MLP & activation: relu, alpha: 0.01, hidden\_layer\_sizes: (150, 150), \\  & learning\_rate: constant, solver: adam, epoch: 50 \\ SVM & C: 100, gamma: scale, kernel: rbf \\ Logistic Regression & C: 10, max\_iter: 100, penalty: 11, solver: saga \\ Random Forest & bootstrap: True, max\_depth: 10, min\_samples\_leaf: 1, \\  & min\_samples\_split: 2, n\_estimators: 100 \\ Gradient Boosting & learning\_rate: 0.1, max\_depth: 7, min\_samples\_leaf: 2, \\  & min\_samples\_split: 2, n\_estimators: 200, subsample: 0.8 \\ Decision Tree & criterion: gini, max\_depth: None, max\_features: None, \\  & min\_samples\_leaf: 1, min\_samples\_split: 2, splitter: random \\ \(k\)NN & metric: manhattan, n\_neighbors: 3, weights: distance \\ XGB & closample\_bytree: 0.8, learning\_rate: 0.1, max\_depth: 5, \\  & n\_estimators: 100, subsample: 0.8 \\ LGBM & closample\_bytree: 0.9, learning\_rate: 0.2, max\_depth: 5, \\  & n\_estimators: 100, subsample: 0.8 \\ CatBoost & depth: 7, iterations: 200, learning\_rate: 0.1, subsample: 0.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Optimal parameters for each algorithm. The parameters used in machine learning algorithms are well known. Only the deep learning algorithm, multi-layer perceptron (MLP), uses hidden layers. In our case, this was defined by the parameter hidden_layer_sizes(150,150), which refers to the use of two hidden layers, each with 150 neurons.
## 3 Results
### Impact of CNCORS on BDS/GPS Joint POD
#### 3.1.3. SLR Assessment  
The results of the SLR assessment are shown in Table 6. The accuracy of IGSO satellites in strategy 1 is significantly better than that in strategy 2, with the C08 and C10 satellites improving by 29.2% and 14.5%, respectively. The accuracy of the MEO satellites in strategy 1 is 5.7% higher than that in strategy 2. A comparison of the aforementioned improvement of the overlapping arcs reveals that the SLR test results are in line with the trend of the overlapping arc precision improvement. For GEO satellites, the precision of the overlapped arcs in the radial direction is better than 2.3 cm, but the SLR test results show that a sub-meter level error exists. This outcome is attributed to the even distribution of the ground stations for GEO satellites, through which the internal precision can reach a relatively high level. However, the accuracy is lower than that of IGSO and MEO satellites owing to the stationary orbit of the GEO satellites. We can thus infer that the distribution of ground stations significantly influences the orbit accuracy. Improving the accuracy of the GEO satellite orbit determination under certain tracking conditions of ground stations requires further study.