esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Energy Efficiency of the MC\(-\)ODBR\(-\)DCSK System  
In this section, the energy efficiency of the MC\(-\)ODBR\(-\)DCSK system in a Gaussian channel is analyzed.  
The proposed system boasts improved energy efficiency when compared to the DCSK system. In the DCSK system, a new chaotic reference is generated for every transmitted bit, whereas in our case, one reference is shared with \(M\) modulated bits. As a result, the transmitted bit energy \(E_{b1}\) for a conventional DCSK system is:  
\[E_{b1}=E_{ref}+E_{data} \tag{22}\]  
We discuss the energies required to transmit data and reference signals, denoted as \(E_{data}\) and \(E_{ref}\), respectively. Without a loss of generality, the data and the reference energies are equal:  
\[E_{ref}=E_{data}=\sum_{j=0}^{\beta-1}x_{j}^{2} \tag{23}\]  
In the DCSK system, the transmitted energy \(E_{b1}\) for a specific bit \(i\) can be expressed as:  
\[E_{b1}=2E_{data}=2\sum_{j=0}^{\beta-1}x_{j}^{2} \tag{24}\]  
The MC\(-\)ODBR\(-\)DCSK system utilizes two reference energies, which are shared with \(2M\) transmitted bits, then the energy of one given bit is the sum of its data carrier energy and a part of the reference energy:  
\[E_{b2}=\frac{1}{M}E_{ref}+E_{data} \tag{25}\]  
In our system, the energies on the (\(2M+2\)) subcarriers are equal:  
\[E_{data}=E_{ref} \tag{26}\]Therefore, the bit energy expression function of \(E_{t2}\) in the MC\(-\)ODBR\(-\)DCSK system is:  
\[E_{t2}=\frac{M+1}{M}E_{data} \tag{27}\]  
To study the energy efficiency, we compute the transmitted Data\(-\)energy\(-\)to \(-\)Bit\(-\)energy Ratio:  
\[\mathrm{DBR}=\frac{E_{data}}{E_{b}} \tag{28}\]  
For the DBR of MC\(-\)ODBR\(-\)DCSK system is:  
\[\mathrm{DBR}_{\mathrm{MC}-\mathrm{ODBR}-\mathrm{DCSK}}=\frac{M}{M+1} \tag{29}\]  
In a conventional DCSK system (i.e., for \(M\) = 1), the DBR is:  
\[\mathrm{DBR}_{\mathrm{DCSK}}=\frac{1}{2} \tag{30}\]  
In [18], the DBR of MC\(-\)DCSK system is:  
\[\mathrm{DBR}_{\mathrm{MC}-\mathrm{DCSK}}=\frac{M-1}{M} \tag{31}\]  
This indicates that half of the total transmission energy of the DCSK system is used to transmit reference signals. Through comparison, it can be concluded that the energy utilization rate of the MC\(-\)ODBR\(-\)DCSK system is significantly higher than that of the traditional DCSK and MC\(-\)DCSK system.  
As shown in Figure 7, it is evident that the energy efficiency of the MC\(-\)ODBR\(-\)DCSK system is higher than that of the MC\(-\)DCSK and DCSK systems. For \(M\) = 1 where we have one new reference for every bit, the MC\(-\)ODBR\(-\)DCSK system is equivalent to a DCSK system with DBR = 1/2. For \(M\) = 2, the MC\(-\)DCSK system is equivalent to a DCSK system with DBR = 1/2, and the DBR of the MC\(-\)ODBR\(-\)DCSK system is 2/3. For the same bit energy \(E_{b}\) in the MC\(-\)ODBR\(-\)DCSK system we can see, for example, that for \(M\) > 20, the reference energy accounts for less than 5% of the total bit energy \(E_{b}\) for each bit of the \(M\) data stream. This means that the energy used to transmit the reference is shared with \(M\) bits.
# A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications
## 2 Network Based Systems
### Range Based
#### 2.1.2 Angle  
Angle based localization methods use the angle of arrival of a signal to compute the position of the receiver. The working principle is similar to time based methods but instead of using the distances to the anchor nodes the angles are used. There are typically two main methods of obtaining the AoA of a signal [17]:  
* Use an array of sensors (for ultrasound systems) whose locations relative to the node center are known and use the difference in the ToA of the signal at each sensor to compute the AoA of the anchor node. In the case of using radio signals the array of sensors is replaced by an antenna array.
* Use two or more directional antennas pointing to different directions and with overlapping main beams. Then compute the AoA as a function of the ratio of the RSS of the individual antennas.  
Once the AoA of multiple anchor nodes is estimated, the computation of the position is done using triangulation. The basic idea is shown in Figure 3. If the position of the vertices of a triangle are known, it is possible to compute the position of any node inside the triangle knowing the angle at which the interior point sees the vertices [15]. There are many different ways of solving the triangulation problem, in [16] the most common methods are reviewed and a new method that does not take into account the ordering of the anchor nodes is presented.  
The main disadvantage of AoA based methods is the increase in the cost of the system due to the additional hardware, as these systems need arrays of sensors or antennas. Furthermore, the computation of accurate angle estimations is expensive in terms of computational cost and is negatively affected by low Signal to Noise Ratio (SNR) and small errors in the estimations of the RSS or the ToA [72]. Therefore, the scalability of the system is limited by the increase of the cost. In general, AoA systems are not commonly employed for indoor localization due to the additional hardware and computational power required.
# Multimodal Fall Detection Using Spatial-Temporal Attention and Bi-LSTM-Based Feature Fusion
## 5 Experimental Evaluation
### Ablation Study
#### 5.2.1 Ablation Study with UP-Fall Dataset  
Table 2 presents the ablation study of the proposed model using the UP-FALL multi-modal dataset. The models are evaluated with both skeleton data in Stream-1 and sensor data in Stream-2. The study demonstrates the performance of three configurations: Stream-1 only, Stream-2 only, and a multi-stream combination using both skeleton and sensor features. In the Stream-1 column, \"Yes\" or \"No\" indicates whether Stream-1 is used, and if used, the number of GSTCN modules is provided in the third column. Similarly, Stream-2 follows the same strategy, with \"Yes\" or \"No\" indicating if Stream-2 is used, and the model name specified if applicable. The results for Stream-2 (sensor data only) are shown first. Three models were evaluated: CNN, CNN with Bi-LSTM, and Bi-LSTM with Channel Attention (CA). For the CNN model, the accuracy was 97.78%, with the precision of 93.79%, recall of 92.92%, and F1-score of 93.02%. The combination of CNN and Bi-LSTM achieved 99.04% accuracy, with a precision of 96.92%, recall of 97.24%, and F1-score of 96.91%. The Bi-LSTM-CA model produced the highest accuracy of 99.07%, with a precision of 96.63%, recall of 97.21%, and F1-score of 96.75%. For the Stream-1 skeleton dataset with various GSTCN modules, the highest accuracy achieved was 91.86% with 6 GSTCN modules. The combination of 6 GSTCN modules in Stream-1 and Bi-LSTM-CA in Stream-2 resulted in the best performance with 99.09% accuracy, 97.06% precision, 97.18% recall, and 96.99% F1-score. Additionally, results with 9 GSTCN modules in the multimodal stream are also included.
# Existing and Emerging Breast Cancer Detection Technologies and Its Challenges: A Review
## 2 Existing Breast Cancer Detection Techniques & Technologies
### Microwave Imaging-Based Technology
#### 2.2.1 Microwave Tomography Technique  
Microwave Tomography (MT) is an emerging biomedical imaging technique to determine the dielectric characteristics of the tissue under investigation using the inverse scattering approach [73; 74]. MT is extensively used in medical for non-invasive biological imaging. 500 MHz and 30 GHz are the lowest and highest frequencies utilized in clinical MT, respectively, [75; 76; 77; 78]. It can be divided into three main components; sensing system, interfacing, and image reconstruction algorithm [79; 80]. This gives a dielectric contrast to it. Through inversion scattering, it generates a chart of permittivity and conductivity. The breast is lowered into a cylinder-shaped antenna system that completely covers the breast in a microwave tomography breast cancer investigation system, as illustrated in Figure 9. Microwave measurements are then conducted using as many antennas as possible, functioning as both a transmitter and a receiver. The wave field gets extremely intricate as microwaves penetrate tissue and are dispersed and reflected. The vast amount of data created by the big wave field is examined using a radical image reconstruction technique that creates a picture of the whole-body part's internal dielectric characteristics (the tissue under examination). The dielectric characteristics of malignant tissue, such as permittivity, conductivity, and electrical parameters, are critical in this detection methodology. These features were found to be vastly different from those of normal breast tissue.  
Figure 8: A schematic diagram showing the components of the microwave imaging system [57].
## 3 Data Processing and Kinematic Analysis
### Object Tracking and Management
#### 3.4.1 Interaction  
In the interaction step, the initial state and covariance of the model filter are probabilistically mixed with the state and covariance of the previous time step.  
\[\hat{x}_{j,k-1}^{*}=\sum\limits_{i=1}^{r}\mu_{(i|j),k-1}\hat{x}_{j,k-1},\hat{ P}_{j,k-1}^{*}=\sum\limits_{i=1}^{r}\mu_{(i|j),k-1}\left[P_{j,k-1}+\left(\hat{x}_{ j,k-1}-\hat{x}_{j,k-1}^{*}\right)\left(\hat{x}_{j,k-1}-\hat{x}_{j,k-1}^{*} \right)^{T}\right]. \tag{5}\]  
The conditional mode probabilities denoted by \(\mu_{(i|j),k-1}\) indicate that the system's transition from mode \(i\) (previous time step) to mode \(j\) (current time step), and can be calculated as:  
\[\mu_{(i|j),k-1}=\frac{P_{ij}\mu_{i,k-1}}{\sum\limits_{i=1}^{r}\overline{\mu}_ {i,k-1}}. \tag{6}\]  
The conditional mode probabilities are dependent on the priori mode probabilities of the current frame denoted by \(\mu_{k}^{-}=\left(\mu_{1,k}^{-},\dots,\ \mu_{r,k}^{-}\right)^{T}\). As seen in Equation (6), the conditional mode probabilities of the current frame can be expressed using the prediction previous frame's probabilities, and elements of the matrix from Equation (4).
## 2 Preliminary Experiments
### Algorithm for On-Machine Dimension Error Inspection  
For a stable cutting process, cutting force is mainly influenced by the depth of cut, the spindle speed and federate, while different cutting forces cause different cutting vibrations. Tool wear influences the dimension and geometry of a cutter. A worn turning cutter gives a smaller depth of cut, which will cause errors in the machined diameter of the work piece. In addition, different depths of cut cause different cutting forces which influence the cutting vibration. Therefore, it is possible to use cutting vibration to predict tool wear and, furthermore, to estimate the machining error caused by tool wear.  
In the experiments, five depths of cut (0.08, 0.09, 0.1, 0.11, 0.12 mm) were chosen for T03 and T04. While turning, the cutting vibrations were measured and their root-mean-square (r.m.s.) values were calculated. Figures 9 and 10 show the correlation between the cutting vibration and the depth of cut. As we can see from the two figures, the cutting vibration was about proportional to the depth of cut. The curves of T03 and T04 are very similar. When the depth of cut increased by 0.01 mm, the cutting vibration increased by about 0.05 g. To ensure the phenomenon shown in Figures 9 and 10 is true and repeatable, 14 experiments were conducted, and all the results were very similar. It implies that the correlation between cutting vibration and depth of can be used to develop the algorithm for on-machine dimension error inspection. The inspection algorithm is designed as follows:  
1. On-line extract the vibration signals (10-s length) of the first three cuts to compute the average r.m.s. value as the reference value.
2. Calculate the maximum allowable error based on the tolerance error given by the customer.
3. Convert the maximum allowable error into allowable vibration based on the curves shown in Figures 9 and 10.
4. On-line extract the vibration signals of the new cut. If the r.m.s. value of the cutting vibration of the new cut is greater than the allowable vibration, the machined work piece is a No-Go part. Otherwise, it is a qualified part.  
Figure 10: Cutting vibration _vs._ depth of cut for T04.  
Figure 9: Cutting vibration _vs._ depth of cut for T03.  
Figure 11 shows the flowchart of the on-machine dimension error inspection.
# Detection of Defects Using Spatial Variances of Guided-Wave Modes in Testing of Pipes
## 2 Background Theory
### Wave Formation
#### 2.1.3 Coherent Noise  
The spatial arrival of the flexural noises created by the wave mode conversion and the backward leakage due to the imperfect excitation is shown in Figure 1h,i, respectively. Their corresponding time domain signals are shown in Figure 2b,c. As can be seen, for the flexural signal, the reception point phases are highly non-linear, which is completely different from the case of the defect signal.  
Backward cancellation is not always perfect, due to the fixed ring spacing and the variance along the received waves from each transducer. In the temporal domain, these waves might still be detected, but when one looks at the phases between the two rings, it is observable that the two rings would have the exact opposite phases. Thus, it would be easier to detect signals received from the opposite direction in the spatial domain.
# Optimization of Coverage and Capacity Using Smart Antennae
## 5 Conclusions  
In contrast to traditional omni-directional antennas that can only adjust signal coverage by varying base station power, the intelligent directional antennas used in this study offer the capability to not only adjust base station power, but also manipulate the five different antenna directions. This allows the user equipment (UE) within the antenna coverage area to receive stronger antenna gain signals. Additionally, the algorithm proposed in this research can determine the appropriate power settings for each base station and determine which antenna directions should be turned on or off within the entire network system. As a result, this approach can save energy consumption related to base station transmission power while simultaneously reducing UE interference.  
Through experimentation, it was observed that coverage rates increased as the number of base stations (BS) increased and decreased as the test area expanded. Consequently, in practical deployments, it is recommended to increase the number of BS to counteract the coverage rate decrease caused by larger test areas.  
In conclusion, the adoption of intelligent directional antennas with the proposed algorithm offers a promising solution to address signal interference and improve network coverage. By optimizing power settings and antenna configurations, the network can achieve higher coverage rates and better performance.  
Validation, L.-L.H.; Investigation, P.-Y.S.; Data curation, Y.-S.C.; Writing--original draft, M.-C.H.; Visualization, Y.-T.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data are not publicly available due to privacy reasons.  
The authors declare no conflict of interest.
###### Abstract  
Ratcheting failure of materials and structures subjected to low cycle fatigue in the presence of significant mean stress is of great interest to researchers. In this experimental and numerical study, the response of 316L stainless steel samples was observed in symmetric strain control uniaxial test followed by post-stabilized monotonic test, uniaxial and biaxial ratcheting tests, in order to determine the Chaboche model parameters and to evaluate ratcheting prediction using finite element analysis. The critical elastic limit was initially obtained from incremental uniaxial cyclic tests. The Chaboche parameters were subsequently extracted from experimental hysteresis and post-stabilized monotonic stress plastic-strain curves using two optimization technics, namely, the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The two optimization methods were compared for efficiency, in terms of time and accuracy. The PSO method presented higher efficient results and was subsequently used to derive the parameters from hysteresis and post-stabilized monotonic curves. Different values (by definition) of elastic limit were also used. The Finite Element commercial software ANSYS was utilized with the Chaboche model to predict the uniaxial and biaxial ratcheting behavior of 316L stainless steel pipe. The comparison between experimental and the numerical simulation demonstrates that adopting post-stabilized monotonic curve rather than hysteresis curve and with accurate elastic limit obtained from incremental loading test improves ratcheting prediction significantly.  
Keywords:316L stainless steel; ratcheting; calibration; Chaboche model; elastic limit; hysteresis curve; monotonic curve +
Footnote †: journal: Applied Sciences
# An Approach to Data Analysis in 5G Networks
## 1 Introduction  
5G networks expect to provide a secure, reliable and high-performance environment with minimal disruptions in the provisioning of advanced network services, regardless the device location or when the service is required [1]. This new network generation will be able to deliver ultra-high capacity, low latency and better Quality of Service (QoS) compared with current Long Term Evolution (LTE) networks [2]. In order to provide these capabilities, 5G proposes the combination of advanced technologies such as Software Defined Networking (SDN) [3, 4], Network Function Virtualization (NFV) [5, 6], Cloud Computing [7], Self-organized Networks (SON) [8, 9], Artificial Intelligence, Big Data [10, 11, 12], Device to Device Communications (D2D), among others [13, 14, 15, 16, 17]. In particular, 5G will be able to face unexpected changes or network problems through the identification of specific situations and taking into account the user needs and the Service level Agreements (SLAs).  
Nowadays, the main telecommunication operators and community research are working in strategies to facilitate the decision-making process when specific events or situations compromises the health in 5G Networks [18, 19]. Meanwhile, the concept of situational awareness (SA) and incident management models applied to 5G Networks are also an emerge topic [20, 21]. In this context, Self-Organized Network Management in Virtualized and Software Defined Networks Project (SELFNET) [22] combines SDN, NFV and SON concepts to provide a smart autonomic management framework, analysing and resolving network problems and improving the QoS and Quality ofExperience (QoE) of end users. In order to facilitate the decision-making process, SELFNET proposes an analysis phase to diagnosis and predict possible problems in 5G Networks.  
This paper presents the design of SELFNET Analyzer Module, which main objective is to diagnose the network state and infer data from monitored low level and aggregated metrics in order to facilitate proactive responses. The contributions of this proposal include: (i) the description of diagnosis and prediction capabilities in 5G environments and how it is being applied in current research and projects; (ii) the introduction of SELFNET Analyzer Architecture, its design principles and requirements and (iii) the definition of data specification, as well examples, to obtain the initial parameters to diagnostic purpose. This document is organized into eight sections, being the first of them the present introduction. Section 2 describes 5G requirements, related works, the main characteristics and analytic capabilities of SELFNET Project. Section 3 outlines the design principles, requirements and the architecture of Analyzer Module as a whole. Then, Section 4 shows this module as a black, emphasizing the data inputs and outputs. Section 5 formally defines how the data must be specified. Section 6 illustrates examples of the data specification and their workflows. Section 7 discusses the main contributions of this proposal. Finally, conclusion and future work are presented in Section 8.
## 1 Introduction  
Cardiovascular diseases are the main causes of morbidity and mortality worldwide with the lifetime risk of more than 60% [1]. Therefore, both in vivo and ex vivo research is performed to find new strategies to protect the circulatory system [2]. However, in vivo experiments, performed mainly in animal models, may not directly reflect the changes in the human body, while clinical studies could raise ethical issues. On the contrary, ex vivo experiments have less limitations as they may be performed on tissues or on primary cells [3; 4]. In addition, recent biomedical research is directing the delivery of cells with biomaterials for the therapeutic advancement and regeneration of tissues [5; 6].  
Basically, the systemic circulation can be regarded as a network of blood vessels, namely arteries and veins, connected to a pump (the heart), that transports oxygen and nutrients within the body [7]. The pulsatile character of blood flow in the arterial system causes a strong and recurrent stretch of arteries' walls that may result in its damage, especially in the case of pre-existing alterations such as atherosclerosis [8]. However, wall stress and wall strength cannot be directly measured in vivo [9]. Moreover, vessels' wall compliance is of the utmost importance for the clinical outcome, as graft angulation presents a risk factor for shear stress-induced, platelet-mediated thrombosis [10]. Besides, it has recently been shown that longitudinal compliance exceeds circumferential compliance in an ex vivo porcine model [11].  
Various models have recently been developed to understand vascular biology, Among those are ex vivo tissue culture systems described to evaluate vascular responses in relation to various biomechanical and biochemical factors [12; 13]. So far, few efforts have been taken to exploit the potential of ex vivo vessel bioreactors towards a high-fidelity ex vivo replication of the hemodynamic factors affecting arterial wall behavior in an artificial environment. Ex vivo vessel culture systems for replication of the hemodynamic factors enable investigation of flow and pressure conditions [2; 14]. They may also offer the possibility to indicate the effects caused by wall shear stress [15; 16], as well as generated by the pure pulsatile pressure [17; 18]. However, most of the models published so far deal with the laminar flow of \"artificial arteries\" [19; 20] or graft preservation solutions [21].  
The aim of the study was to design and construct a dedicated ex vivo computer controlled circulatory bioengineering reactor to monitor vascular tissue responses in real time and under various physiologic conditions. This artificial circulatory model (ACM) aimed to combine the advantages of various models described so far and allows a unique insight into vascular adaptation to a combination of stimuli. The device was next tested with human common iliac artery and silicon tubes to verify its usefulness in analyzing mechanical parameters of tested interposition grafts.