esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare--A Review
## 4 IOT and Healthcare
### Medical Equipment  
Wearable medical devices are the most attractive option today for consumers of all ages for tracking their own vital signs in real time. In addition to Fitbit, Apple Watch and Co., other wearable technologies have now been developed. Not only do they record data, but they also perform certain functions based on commands or recognized situations. One example is \"intelligent associations\". They are equipped with sensors that assess the size of the underlying wound to determine whether it is healing or not, or if there is an infection, and whether a topical solution may need to be administered [25].  
\"Networked contact lenses\" are also a form of wearables in health technology. In 2014, Google and Novartis began developing a connected contact lens that could monitor blood sugar levels by analyzing the patient's tear fluid. The data collected via the contact lenses is then sent to an insulin pump and the patient is informed whether their blood sugar level has reached a dangerous level and needs to be adjusted. This advance in non-invasive monitoring of diabetic patients could be life-changing for many. Because many patients suffer from having to pick themselves several times a day in order to measure their blood sugar level, such innovations give diabetes patients hope that non-invasive techniques are actively being sought and that they are on the verge of becoming a reality [30].
## 6 Experimental Results and Discussion  
Because our algorithm needed to be applied in the warehouse environment, some datasets were captured in a typical warehouse, and the results were summarized and analyzed in order to evaluate the efficiency and accuracy of our proposed stereo SLAM algorithm. Our results were mainly compared with ORB-SLAM2, which had balanced performance in all aspects. Open Source Computer Vision Library (OpenCV) was used in our algorithm to realize basic image processing. Both algorithms were run on a desktop PC with Intel Core I7-7700 CPU @ 3.6-4.2 GHz and 8 GB RAM. The desktop PC ran the Ubuntu 18.04 64 bit system.
# Machine Learning in Medical Triage: A Predictive Model for Emergency Department Disposition
## 1 Introduction  
The demand for emergency department (ED) services is escalating globally, leading to overcrowding and a subsequent decline in the quality of care [1; 2]. Delays in care, exacerbated by increased demand, have been linked to heightened morbidity and mortality rates [3]. The COVID-19 pandemic has further highlighted the critical nature of effective patient triage in EDs, as seen during the healthcare crisis in Italy's Lombardy region [4]. Efficient triage is crucial as it directly impacts patient outcomes and can be life-altering.  
The decision to admit patients is typically made after a clinical evaluation, involving various tests and procedures. In busy or underst staffed environments, a reliable decision-support system, informed by machine-learning algorithms trained on historical ED data, could significantly aid medical staff by predicting hospital admissions.  
[PERSON] et al. [5] emphasize the importance of resource availability in enhancing the performance of predictive models. The theory of the five Vs--velocity, volume, value, variety, and veracity--highlights essential data characteristics that must meet specific standards for different applications [6; 7]. Medical data, laden with sensitive information, poses high disclosure risks, driving the need for open data solutions. Open data offers immense potential by illuminating disease causes, effective treatments, and hospital admission management, which has garnered attention from various sectors over the past two decades [8]. Notably, large datasets such as Healthdata.gov and the MIMIC Critical Care Database are crucial for developing robust predictive models [8].  
This paper introduces an automated machine-learning (AutoML) approach using the MIMIC-IV-ED database to predict patient dispositions at triage, marking a significant contribution to medical informatics and emergency care. Our study extends beyond traditional AutoML applications by integrating a context-aware preprocessing methodology tailored for the idiosyncrasies of emergency medical data, which often includes irregular, incomplete, and anomalous data patterns.
# Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks
## 4 The Proposed Approach
### Model Training and Testing
#### 4.4.2 Finetuning on UFLV-DS Dataset
On the other hand, sufficient negative examples for each class were required to ensure that system is learning the class features accurately. Therefore, our approach tried to reduce the negative examples to a feasible extent by keeping the examples with more elaborate (negative) features (as detailed in Section 4.1). However, even after balancing the dataset, learning of the abnormal behavior seemed to be difficult. This was mainly due to the inherent problem described above. Thus, as expected, the model still seemed more inclined towards predicting everything as the majority class i.e., none. It is noteworthy that this problem underlines the main purpose of including the \"none\" class. It allowed us to monitor and improve the performance of the model against the specific negative examples of each of the abnormal behavior classes. Therefore, we used the cross-entropy loss function \(\mathcal{L}\) for the softmax defined as:  
\[\mathcal{L}(y,t)=-\sum_{c=1}^{C}t_{c}log(y_{c}) \tag{1}\]  
where C-dimensional vector \(y\) is a vector of real values between 0 and 1 representing the prediction of the network and \(t\) is a C-dimensional vector of ground truth values. In this equation, the importance of one class can be increased by introducing a weight within the loss function. Therefore, our updated loss function \(\mathcal{L}\) is given by:  
\[\mathcal{L}(y,t)=-\sum_{c=1}^{C}w_{c}.t_{c}.log(y_{c}) \tag{2}\]  
where \(w\) contains the weights associated with each class, \(y\) is the prediction and \(t\) is the ground truth. In this way, a class weight enables us to use different weights for each class. This means that a class weight of 1.0 can be used if no change in the weighting of the class is required. Whereas, by using a higher weight for a class, the loss function will be penalized for every mistake on that class more than a mistake on the other classes. In other words, by increasing their weight, the network prioritizes the learning of certain classes (abnormal behavior in this case) possibly at the expense of worsening the learning for the other class (i.e., none). However, the resulting bias can be considered acceptable because of the importance of detecting an abnormal event even at the expense of some false alarms. The current implementation estimated the class weights by using **compute_class_weight** in **scikit_learn** and used the parameter **class_weight** while training the model.
# Design of a Technique for Accelerating the WSN Convergence Process
## 3 Theoretical Principles Used in Research
### Ad Hoc On-Demand Distance Vector-Backup Routing (AODV-BR)  
The AODV protocol does not use alternative paths. Consequently, when a route is disconnected, the nodes of the broken route drop the data packets because there is no alternative route to the destination available until a new route is established. Therefore, mechanisms are being sought to eliminate this problem [64].  
Figure 2: Sending messages in the AODV protocol.  
Backup Routing is a mechanism for creating an alternative path that extends the functionality of the original AODV protocol. Alternative routes are created at the phase when the response to the route search (RREP) is returned. Once received, RREP slightly modifies the functionality of the AODV protocol. Nodes in the network use the nature of wireless communication when they intercept packets transmitted by neighboring nodes. They will use the packets captured in this way to create an alternative path. Since RREP messages are sent unicast but still using radio communication, neighboring nodes capture these messages, and if this packet is not directed to it, it records the neighbor from which it captured this message in its alternative routing table. This node will be closer to the destination node as the next hop. If a node captures multiple RREP messages for the same route, the node is in the radio range of multiple primary route nodes. In this case, it selects the best route in terms of capture time and stores it in its alternative routing table. After the primary path is established, nodes containing entries in the alternate routing table are an active part of the network. The alternative routes together with the primary route form a herringbone structure (Figure 4).  
After an outage, a re-search for the path is started by the source node S. This search is faster compared with the search using classic AODV, because the neighboring nodes keep the path to the destination D in the alternative routing table. Since the convergence time is shorter, there are also smaller packet losses [65; 66].  
Figure 4: Structure of AODV-BR network.  
Figure 3: Propagation of RREQ (**A**) and RREP route (**B**).
## 4 The Application of the Proposed Model
### Case Study
#### 4.1.2 Application of the Fuzzy VIKOR Method
\begin{table}
\begin{tabular}{c l c} \hline \hline
**Criteria** & **Sub-Criteria** & **Normalized By Cluster** & **Limiting** \\ \hline \multirow{4}{*}{Safety} & C1: Effectiveness of PPE selection for the site & 0.1441 & 0.0666 \\  & C2: Availability and accessibility of the selected PPE & 0.2520 & 0.1164 \\  & C3: System for inspecting the appropriate use of PPE by workers & 0.1855 & 0.0856 \\  & C4: Procedure to monitor the usage of PPE & 0.4184 & 0.1932 \\ \hline \multirow{4}{*}{Comfort} & C5: Flexibility of movement & 0.4064 & 0.1140 \\  & C6: Equipment manipulability & 0.1720 & 0.0483 \\  & C7: Proper usage instructions & 0.1372 & 0.0385 \\  & C8: Type suitability & 0.2845 & 0.0798 \\ \hline \multirow{4}{*}{Awareness} & C9: Encouragement from supervisors or safety officers & 0.1065 & 0.0181 \\  & C10: Rules and regulations for using PPE & 0.4725 & 0.0802 \\  & C11: Workers’ consciousness of the usage of the PPE & 0.2530 & 0.0429 \\  & C12: Training program of PPE for the workers & 0.1680 & 0.0285 \\ \hline \multirow{4}{*}{Other criteria} & C13: Employment stability & 0.2703 & 0.0237 \\ \cline{1-1}  & C14: Health conditions & 0.6059 & 0.0532 \\ \cline{1-1}  & C15: Cultural and language environment & 0.1238 & 0.0109 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Rank and weights of the identified criteria and sub-criteria. For example, for Alternative 1, the expert assessment results are illustrated in Table 9. Therefore, the number of experts that selected \"VH\", \"H\", \"F\", \"L\", and \"VL\" was 2 (8.7%), 6 (26.1%), 9 (39.1%), 5 (21.7%), and 1 (4.3%), respectively. The fuzzy numbers for each linguistic variable (\"VH\", \"H\", \"F\", \"L\" and \"VL\") were (0.75, 1.00, 1.00), (0.50, 0.75,1.00), (0.25, 0.50, 0.75), (0.00, 0.25,0.50), and (0.00, 0.00,0.25). According to Equation (11), the aggregated fuzzy number \(P(\theta)\) for C1 can be calculated as (0.294, 0.533, 0.761), where \(0.087\times 0.75+0.261\times 0.50+0.391\times 0.25+0.217\times 0.00+0.043 \times 0.00=0.294; 0.087\times 1.00\) + \(0.261\times 0.75+0.391\times 0.50+0.217\times 0.25+0.043\times 0.00=0.533; 0.087\times 1.00+0.261\times 1.00\) + \(0.391\times 0.75+0.217\times 0.50+0.043\times 0.25=0.761\). From the result of the aggregated fuzzy number \(P(\theta)\), it can be indicated that the resulting value for criterion C1 is a fuzzy number of (0.294, 0.533, 0.761). It represents that the most possible value of \(P(\theta)\) for criterion C1 is 0.533, the upper possible value is 0.761, and the lower possible value is 0.294. Similarly, the aggregated fuzzy number for each criterion was calculated, and the fuzzy decision matrix for each PPE plan alternative was illustrated in Table 10 with respect to different criteria.
## 3 Results and Discussion
### Prediction of Ultimate Damage Load
#### 3.2.1 Prediction of Artificial Neural Network of Acoustic Emission Single-Parameter Data
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rubber Content** & **Actual Value (kN)** & **Predictive Value (kN)** & **Prediction Error** \\ \hline
5\% & 16.24 & 16.44 & \(-13.23\%\) \\
10\% & 15.27 & 14.31 & \(-5.52\%\) \\
15\% & 11.32 & 12.64 & 7.28\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: ASL (7) single-parameter neural network prediction results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rubber Content** & **Actual Value (kN)** & **Predictive Value (kN)** & **Prediction Error** \\ \hline
5\% & 16.24 & 16.48 & 1.46\% \\
10\% & 15.27 & 14.39 & \(-5.78\%\) \\
15\% & 11.32 & 12.12 & 7.04\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Center frequency (9) single-parameter neural network prediction results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Rubber Content** & **Actual Value (kN)** & **Predictive Value (kN)** & **Prediction Error** \\ \hline
5\% & 16.24 & 16.48 & 1.46\% \\
10\% & 15.27 & 14.39 & \(-5.78\%\) \\
15\% & 11.32 & 12.12 & 7.04\% \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Peak frequency (10) single-parameter neural network prediction results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rubber Content** & **Actual Value (kN)** & **Predictive Value (kN)** & **Prediction Error** \\ \hline
5\% & 16.24 & 16.57 & 2.04\% \\
10\% & 15.27 & 15.05 & \(-1.47\%\) \\
15\% & 11.32 & 11.89 & 5.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Amplitude (5) single-parameter neural network prediction results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rubber Content** & **Actual Value (kN)** & **Predictive Value (kN)** & **Prediction Error** \\ \hline
5\% & 16.24 & 16.48 & 1.46\% \\
10\% & 15.27 & 14.39 & \(-5.78\%\) \\
15\% & 11.32 & 12.12 & 7.04\% \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Peak frequency (10) single-parameter neural network prediction results. Figure 15: Ten single-parameter neural network training performance results: (**a**) rise time; (**b**) count; (**c**) energy; (**d**) duration; (**e**) amplitude; (**f**) RMS; (**g**) ASL; (**h**) signal strength; (**i**) center frequency; (**j**) peak frequency. Figure 16 shows the absolute values of the prediction errors of the 10 AE single-parameter neural networks. The prediction errors of the two single-parameter neural networks with rise time (1) and amplitude (5) are relatively small. The predictions with center frequency (9) and peak frequency (10) are moderate, all within 8%. The remaining parameters of the neural network prediction results are poor. Therefore, rise time is the most suitable acoustic emission parameter for single-parameter neural network prediction of crumb rubber concrete failure load.
# Knitted Microwave Transmission Line for Wearable Electronics
###### Abstract  
This paper introduces a novel approach to fabricating textile microwave transmission lines through knitting techniques. These textile-based transmission lines, capable of transmitting high-frequency signals between wearable transceivers and antennas, offer significant potential for the development of advanced wearable electronics. By leveraging a single technological process, our proposed method enables the creation of flexible and wearable devices. To demonstrate the feasibility of this approach, we present the design and numerical modeling of a microstrip line operating within the gigahertz frequency range. A prototype structure was fabricated and experimentally characterized, revealing moderate attenuation of less than 5 dB for frequencies below 2.5 GHz. However, a major challenge in the field of wearable electronics is the real-time applicability of such devices. Our work aims to address this challenge by providing a flexible and scalable solution for integrating wireless communication capabilities into wearable systems. Future research will focus on further optimizing the design and fabrication processes to enhance performance and minimize signal loss, ultimately enabling the realization of practical and user-friendly wearable devices.  
e-tectiles; knitted fabric; wearable electronics; microwave elements 2024202024141079810798  
2024
## 2 Operation of the BB84 Protocol
### Parameter Estimation  
This is followed by a parameter estimation procedure, where [PERSON] selects a fraction of her signal slots and broadcasts which signal she sent for these slots. Accordingly, [PERSON] discloses his measurements on those signals and the respective outcome. Finally, they accept the decision if [PERSON] and [PERSON], from their corresponding announcements, conclude that all the signals provided their correct deterministic outcomes whenever their basis choice coincided. If this happens, they proceed with the protocol or abort.
## 5 Limitations and Future Research Directions  
Despite the growing body of evidence supporting the efficacy of endermologie, several limitations must be acknowledged, particularly regarding the heterogeneity of existing studies. The studies analyzed in this review varied considerably in terms of methodology, patient selection, treatment parameters, and outcome measures, which may impact the generalizability of the findings. One key source of heterogeneity was the patient selection criteria. Additionally, variations in the treatment protocols applied--such as differences in vacuum intensity, duration, frequency, and the number of sessions--introduced inconsistencies in the reported outcomes. While some studies demonstrated significant improvements in post-surgical recovery, others reported skin elasticity and cellulite reduction, raising questions about the standardization of treatment parameters. Moreover, the lack of large-scale, long-term clinical trials limits our ability to assess the durability of endermologie's effects. Additionally, there is an absence of comparative studies evaluating endermologie against other physical therapy modalities.  
From a practical perspective, several factors may influence the accessibility and applicability of endermologie. The cost of treatment remains a potential barrier, as sessions may not be covered by health insurance, limiting their availability to a wider patient population. Additionally, the procedure requires specialized equipment and trained personnel, which may affect its integration into standard medical and physiotherapy settings. Furthermore, certain contraindications, such as active skin infections, severe circulatory disorders, or deep vein thrombosis, must be considered when determining patient eligibility for the therapy.  
Given these limitations, future research should aim to address several critical gaps. First, long-term efficacy validation is needed through well-designed, randomized controlled trials (RCTs) with larger sample sizes and extended follow-up periods. Second, mechanisticexploration at the cellular and molecular levels should be prioritized to better understand how endermologie influences fibroblast activity, extracellular matrix remodeling, and adipose tissue metabolism. Finally, comparative studies evaluating endermologie alongside other therapeutic interventions will help clarify its unique benefits and relative efficacy in different clinical and aesthetic applications.  
Addressing these research gaps will not only enhance our understanding of endermologie's mechanisms and effectiveness but also support its broader adoption as a scientifically validated therapeutic tool in both medical and aesthetic practice.
# MT-FBERT: Malicious Traffic Detection Based on Efficient Federated Learning of BERT
## 4 Experiments
### Experiment Setup
#### 4.1.2 Comparison Methods  
To comprehensively evaluate MT-FBERT, we conducted comparisons with various baselines, including machine learning methods, deep learning methods, and pre-training methods.  
1. Classical machine learning-based method: FlowPrint [57] relies on statistical features such as packet size distribution, inter-arrival times, and directional flow patterns to generate discriminative fingerprints for different network traffic types.
2. Deep learning-based method: TFE-GNN [58] employs graph neural networks to perform supervised traffic analysis by processing both packet-level features and raw byte sequences.
3. Pretraining-based method: YaTC [36] captures traffic representations during pre-training and subsequently fine-tunes for specific tasks with limited labeled data.
# Nonlinear Maximum Power Point Tracking Control Method for Wind Turbines Considering Dynamics
## Design of the Improved MPPT Control Method  
The design of the improved MPPT control method is based on assumptions 9-10:  
* The equivalent aerodynamic torque remains unchanged during one discrete time step while discretizing Equation (10).  
Figure 4: Contour map of \(C_{\text{p}}(\lambda,\beta)\). \(C_{\text{p}}(\lambda,\beta)\) represents the power coefficient with different TSR \(\lambda\) and different blade-pitch angle \(\beta\). The red line shows the optimal blade-pitch angle under different tip speed ratios. Other lines show the contour map of \(C_{\text{p}}(\lambda,\beta)\).  
Figure 3: Power flow of the wind energy generation system.  
The blade-pitch actuator dynamic effects can be simulated by the first-order model with a time constant of 3 s.  
Figure 5 shows the control diagram of the improved MPPT control method considering the wind turbine dynamic response. There are five parts marked in three colors. The aerodynamics and mechanical aspects of wind turbines are established using the FAST code in part 1. Part 2-5 are realized in MATLAB/SIMULINK (MathWorks, Natick, MA, USA). FAST (National Renewable Energy Laboratory, Golden, CO, USA) interfaces with MATLAB/SIMULINK using \(\mathbb{S}\)-function. In part 2, aerodynamic torque is estimated using the UKF-based estimator. Wind speed and TSR are estimated using the Newton-Raphson method. In part 3, the feed-forward control is used to improve the dynamic response of wind turbines. The error between the estimated aerodynamic torque and the optimal steady torque is used as the feed-forward signal to control generator torque. To get better performance under various wind conditions, gain parameters in the feed-forward path are nonlinearly regulated by the estimated generator speed. In part 4, a DFIG model is built in MATLAB/SIMULINK for simulating the dynamic process of the DFIG system and achieving the setting value of the electromagnetic torque. Meanwhile, the estimated TSR is used as the reference signal for the optimal collective blade-pitch angle regulation under non-optimal TSR conditions in part 5, which can improve the wind power capture under a wider non-optimal TSR range.
# Prediction of Air Quality Combining Wavelet Transform, DCCA Correlation Analysis and LSTM Model
## 2 Materials and Methods  
### Materials  
This study utilized data from the China Graduate Student Mathematical Modeling Competition, which has a 2-year time span (April 2019 to July 2021), hourly resolution, and a 10-dimensional dataset containing SO2, NO2, PM10, PM2.5, CO, O3, temperature, barometric pressure, humidity, and wind speed, with a total of 252,616 valid values.