esa-sceva/satcom-chunk-collection huggingface dataset:
# Model Predictive Control and Its Role in Biomedical Therapeutic Automation: A Brief Review
## 3 MPC in Biomedical Applications  
Over the last several decades, control and system identification techniques have been applied in biological and biotechnological systems. As interest in the field of disease control grew in the 1960s-70s, researchers had a strong desire to develop mathematical frameworks for supporting medical operations and developing novel healing protocols [1]. Although control systems have been intertwined with medical applications for decades, their impact on medical devices and applications became apparent in the literature only recently [61]. Nowadays, ever more researchers are combining the principles of biomedical control engineering with the insights of molecular life sciences to solve modern problems through the measurement and modelling of biological systems. These researchers are developing biological-based/inspired technologies that will be useful in a variety of different industries, including manufacturing, defence, agriculture, and environmental and human health. From the point of view of the variety of issues being addressed and related real-time applications, the field of biological systems can still be considered to be in its infancy.  
Hence, there is a vast scope to develop closed-loop controls for biomedical systems in a way that they can be translated into workable and scalable technologies. It is still a challenge in the design of control and sensor systems to translate meaningful clinical health results of the patient body into quantitative control parameters. In this scenario, the MPC's receding horizon control concepts are particularly suited for effective medication delivery control. Hence, MPCs are being used in medical applications, as these controllers have been useful in overcoming signal disturbances and generating robust performances [1; 62]. The latest advances in MPC designs ensure robust and offset-free control definitions to address inherent vulnerabilities in the system models. The following subsections present MPC biomedical applications in therapies addressing type 1 diabetes (including artificial pancreas), anaesthesia, cancer (including hyperthermia and oncolytic viral treatments), fibromyalgiaalgia, and HIV.
# Predicting Hospital Admissions to Reduce Crowding in the Emergency Departments
## 4 Results
### Model Performance
#### 4.2.2 Pediatrics Dataset  
This subset included all the observations of patients from 0 to 17 years of age inclusive, which added up to 687,288 observations of 381,470 different patients. This model provided an accuracy of 0.9577, with an AUC of 0.8703 (95% CI 0.8667-0.8739). Note that this model has higher accuracy but lower AUC than the complete dataset; this is an indicator of this model being very good at identifying discharges but not so good at identifying admissions, which is probably a consequence _of_ a very imbalanced set in terms of the ratio of admissions/discharges. The importance of the variables in this model differed from that observed in the complete dataset as well, with symptoms and triage still occupying the first and second places (69.13% and 25.97%, respectively), but with days of age in the third position (3.02%) right in front of age in years (1.79%).
## 4 Discussion
It is likely that the 13 Hz sampling frequency was not high enough to capture the vibration during outdoor cycling. A total of 110 features were extracted from acceleration signals for physical activity classification. To reduce and optimize the number of features, two different feature selection schemes were used in this study. While the first scheme used different consecutive methods to reduce the number of features, the second scheme used forward SPS where features were added one-by-one. The first feature selection scheme enabled the reduction of the feature set from 110 features to 13 features without decreasing the classifier performance. It is possible that the feature set with 13 features was overfit for the conditions used in this study and would perform worse in other conditions. Compared to the feature sets of the first feature selection scheme, the SFS method used in the second scheme had higher performance with most window length and sampling frequency combinations. This difference was very noticeable when using the sampling frequency of 13 Hz. The number of features used in SFS feature sets were between 9 and 14 (Table 3). The large differences in average F1-scores shown in Figure 7 between SFS feature set and other feature sets while using sampling rates of 25 Hz and 13 Hz were mostly affected by outdoor cycling. Unlike other feature sets, the SFS feature set had a high F1-score while classifying outdoor cycling with all sampling frequency and window length combinations. The highest average classification F1 score was achieved with a parameter combination with SFS feature set (3 s window length, 50 Hz sampling frequency, 12 features), which also had the best performance while classifying static, low intensity, walking and outdoor cycling activity types (Table 4). It was predictable that the SFS method would provide better results, since the SFS method chose the best features to maximize the classification sensitivity separately for each window length and sampling frequency combination, while, with the first scheme, features were selected based on one sampling frequency and window length combination. The SFS method proved to be a simple comparison method for more comprehensive feature selection and showed that the effect of features depends on different classifier parameters, of which sampling frequency and window length were tested in this study. Despite the recent advances in deep learning based activity recognition, which reduces the dependency on hand-crafted feature sets and thus could outperform more traditional machine learning methods, it is still far from being used in online mobile systems due to excessive computational power it requires [33]. Thus, the methods and results of this study provide useful information to other researchers for designing and implementing state-of-the-art physical activity recognition for real-time wearable systems.
# Affine Formation Maneuver Control for Multi-Agent Based on Optimal Flight System
## 4 Formation Control with Virtual Leader
### Phase I  
With the available information regarding obstacles, it becomes feasible to pre-define the desired path and its corresponding configuration. The purpose of this part is to generate the nominal configurations during each time interval, which can provide the desired path for the leaders of the formation and the desired tracking velocity. The positions of agents are randomly selected from the box \([-3\) m, \(3\) m\(]\times[-3\) m, \(3\) m\(]\), as Figure 7b shows. Their velocities of leaders are set as constant \(v_{d}=45\) m/s and directions in intervals \([-\pi/2,\pi/2]\). Agents 1, 2, and 3 are assigned as leaders illustrated in red through the whole simulation and the four change forms of configuration are calculated, scaling, rotation, shear, and translation. It is shown that the affine-based leader/follower consensus protocol can form the continuous configuration change for the formation. The nominal configuration is established prior to 6 s. At the moment \(t=6\) s and \(t=13\) s the formation is expected to undergo scaling in the y-direction, with shrinking and enlargement occurring, respectively. Starting from \(t=19\) s and \(t=24\) s, the formation initiates rotation. Additionally, there is shearing at \(t=28\) s and \(t=36\) s, while the formation translates during other time intervals within the given range.  
One can see that the velocity for followers would have suddenly changed to meet the requirement in each time interval of the different configuration changes. The scale of change could not be controlled effectively in any case.  
Figure 6: Flow chart of proposed algorithm experimental process. The leader is red, virtual leader red-dotted, followers blue.
_Article_  
**Flexibility Measurement Affecting the Reduction**  
**Pattern of Back Muscle Activation during**  
**Trunk Flexion**  
[PERSON] 1,2,*(), [PERSON] 1, [PERSON] 1, [PERSON] 1 and [PERSON] 3  
1 Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei 24301, Taiwan; [EMAIL_ADDRESS] (Y.-M.H.); [EMAIL_ADDRESS] (Y.-C.C.); [EMAIL_ADDRESS] (T.-C.W.)  
2 Department of Industrial Design, Chang Gung University, Touyuan 33302, Taiwan  
3 Department of Electrical Engineering, National Central University, Touyuan 32001, Taiwan; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]  
Received: 10 July 2020; Accepted: 27 August 2020; Published: 28 August 2020  
**Abstract:** Numerous studies have been conducted on lower back injury caused by deeper stooped posture, which is associated with the back muscle flexion-relaxation phenomenon (FRP). Individual flexibility also affects FRP; individuals with high flexibility have the benefit of delayed FRP occurrence. This study attempted to determine the most efficient measurement of flexibility for evaluating the occurrence and degree of FRP when participants flexed their trunk forward. We recruited 40 male university students who were grouped on the basis of three flexibility measurements (toe-touch test, TTT; sit-and-reach test, SRT; modified Schober's test, MST) into three levels (high, middle and low). Muscle activation (thoracic and lumbar erector spinae, TES and LES, respectively; hamstring, HMS) and lumbosacral angle (LSA) were recorded when the trunk flexed forward from 0\({}^{\circ}\) (upright) to 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\) and 90\({}^{\circ}\). The results indicated that trunk angle had a significant effect on three muscle activation levels and LSA. The effects of muscles and LSA varied depending on flexibility measurement. TTT significantly discriminated LES electromyography findings between high and low flexibility groups, whereas MST and SRT distinguished between high and non-high flexibility groups. The TTT values positively correlated with the time of LES FRP occurrence, showing that the higher the TTT, the slower the occurrence of FRP. This is beneficial in delaying or avoiding excessive loading on the passive tissue of the lumbar spine when performing a deeper trunk flexion.  
**Keywords:** flexibility; flexion-relaxation phenomenon (FRP); electromyography; lumbosacral angle
# Wind Pressure Coefficient on a Multi-Storey Building with External Shading Louvers
## 3 CFD Simulation
### Reference Case
#### 3.1.6 Calculation of Wind Pressure Coefficient  
Wind pressure coefficient \(C_{\text{P}}\) is calculated from the pressure difference between static pressure \(P\) on the building surfaces and static pressure \(P_{\text{ref}}\) at the reference point, as provided in Equation (5):  
\[C_{\text{P}}=\frac{P-P_{\text{ref}}}{\frac{1}{2}\rho\text{I}_{\text{ref}}{}^{2}} \tag{5}\]  
The reference point was set on the lateral boundary at the reference height \(h_{0}\) and had the same distance from the inlet to the windward facade. \(\rho\) is the air density and taken as 1.225 kg/m\({}^{3}\).
# End-to-End Underwater Acoustic Communication Based on Autoencoder with Dense Convolution
## 2 FBMC System Model  
As shown in Figure 1, the FBMC transmitter first performs symbol mapping on the input bit stream, and the mapped complex symbols are rearranged by offset quadrature amplitude modulation (OQAM) modulation, interleaving the real parts and imaginary parts by half a symbol period. After inserting pilots, the \(M\) symbols are modulated onto \(M\) subcarriers through an inverse fast fourier transform (IFFT) transform and a polyphase network consisting of prototype filters.  
The discrete-time signal output after FBMC modulation can be expressed as [4]  
\[x(l)=\sum_{m=0}^{M-1}\sum_{n=0}^{N-1}a_{m,n}g_{m,n}(l) \tag{1}\]  
where \(M\) is the number of subcarriers, \(N\) is the number of FBMC symbols, \(a_{m,n}\) represents the real-valued transmitted symbols at the TF point \((m,n)\) and  
\[g_{m,n}(l)=g\bigg{(}l-n\frac{M}{2}\bigg{)}e^{j\frac{(m+n)\pi}{2}}e^{j\frac{2\pi m }{M}} \tag{2}\]  
is the prototype filter impulse response at this location, also known as the subcarrier basis function. \(e^{j\frac{(m+n)\pi}{2}}\) is the phase rotation factor. The prototype filter is designed to maintain orthogonality in the real domain, and the inner product of the subcarrier basis functions at \((m,n)\) and \((p,q)\) satisfies  
\[\Re\big{\{}\big{<}g_{m,n}\big{|}g_{p,q}\big{>}\big{\}}=\Re\bigg{\{}\sum_{l}g_{m,n}(l)g_{p,q}^{*}(l)\bigg{\}}=\delta_{m,p}\delta_{n,q} \tag{3}\]  
where \(\Re\{\cdot\}\) represents taking the real part. \(\delta_{m,p}\) is the Kronecker delta that satisfies \(\delta_{m,p}=1\) when \(m=p\). Therefore, only when \((m,n)=(p,q)\), \(\delta_{m,p}\delta_{n,q}=1\); otherwise, \(\delta_{m,p}\delta_{n,q}=0\). Assuming that the channel is frequency flat and invariant in a filter duration, the received data can be expressed as  
\[y_{p,q}=H_{p,q}a_{p,q}+\underbrace{j\sum_{(m,n\
eq p,q)}H_{m,n}a_{m,n}\big{<}g _{m,n}\big{|}g_{p,q}\big{>}}_{l_{p,q}}+\eta_{p,q} \tag{4}\]  
where \(H_{p,q}\) is the channel frequency response (CFR) and \(l_{p,q}\) and \(\eta_{p,q}\) are the Gaussian white noise and inherent imaginary interference at \((p,q)\). Due to the excellent TF focus characteristics of the prototype filter, the interference at the TF point \((p,q)\) mainly comes from the first-order neighborhood defined as \(\Omega_{p,q}^{1}=\{(p\pm 1,q\pm 1),(p\pm 1,q),(p,q\pm 1)\}\).  
Figure 1: System model of traditional FBMC communications.  
Given that the CFR remains constant over adjacent TF points, Equation (4) can be simplified as [22]  
\[y_{p,q}\approx H_{p,q}c_{p,q}+\eta_{p,q} \tag{5}\]  
where  
\[c_{p,q}=\big{(}a_{p,q}+jb_{p,q}\big{)} \tag{6}\]  
\(jb_{p,q}\) is the imaginary part of the interference term, which can be approximated as \(jb_{p,q}=j\sum\limits_{(m,n)\in\Omega_{p,q}}a_{m,n}\big{\langle}g_{m,n}\big{|} g_{p,q}\big{\rangle}\). If the transmitted data at TF point \((p,q)\) and its first-order neighbors are known, \(c_{p,q}\) can be used as a pseudo pilot to estimate the channel frequency response through the following equation.  
\[\hat{H}_{p,q}=\frac{y_{p,q}}{c_{p,q}}\approx H_{p,q}+\frac{\eta_{p,q}}{c_{p,q}} \tag{7}\]
# Resource Management and Secure Data Exchange for Mobile Sensors Using Ethereum Blockchain
###### Abstract  
A typical Wireless Sensor Network (WSN) defines the usage of static sensors; however, the growing focus on smart cities has led to a rise in the adoption of mobile sensors to meet the varied demands of Internet of Things (IoT) applications. This results in significantly increasing dependencies towards secure storage and effective resource management. One way to address this issue is to harness the immutability property of the Ethereum blockchain. However, the existing challenges in IoT communication using blockchain are noted to eventually lead to symmetry issues in the network dynamics of Ethereum. The key issues related to this symmetry are scalability, resource disparities, and centralization risk, which offer sub-optimal opportunities for nodes to gain benefits, influence, or participate in the processes in the blockchain network. Therefore, this paper presents a novel blockchain-based computation model for optimizing resource utilization and offering secure data exchange during active communication among mobile sensors. An empirical method of trust computation was carried out to identify the degree of legitimacy of mobile sensor participation in the network. Finally, a novel cost model has been presented for cost estimation and to enhance the users' quality of experience. With the aid of a simulation study, the benchmarked outcome of the study exhibited that the proposed scheme achieved a 40% reduced validation time, 28% reduced latency, 23% improved throughput, 38% minimized overhead, 27% reduced cost, and 38% reduced processing time, in contrast to the existing blockchain-based solutions reported in the literature. This outcome prominently exhibits fairer symmetry in the network dynamics of Ethereum presented in the proposed system.  
IoT blockchain applications; mobile sensors; secure sensor communication; Ethereum blockchain; resource management; data privacy; IoT security +
Footnote †: journal:  
_Article_
# First Retrieval of Sea Surface Currents Using L-Band SAR in Satellite Formation
## 4 Data Processing
### Image Registration
To enhance the clarity of the presentation, given the large scene scale and the high fringe density, the interferometric phase diagram (Figure 7b) is limited to the region outlined by the red box in Figure 7a. The interference fringe patterns in other regions of the image exhibit analogous interferometric patterns. The figure shows that the interferometric fringes are clear and sharp, indicating a good registration effect.
## 1 Introduction
Their proposal leverages multicast Domain Name Service (DNS) architecture by tagging multicast sessions with geographical and spatial information based on the channel content or the service provider location. Internet Group Management Protocol (IGMP) spoofing [8] tracks the active multicast sessions only in a LAN domain where we propose to extend that into the routing domain. The rest of the paper is organized as follows. In Section 2, we describe the requirements of collectively watching real-time contents to handle emerging traffic in the Future Internet (FI). The proximity discovery system using multicast is explained in detail in Section 3 while Section 4 presents the results of a survey and an analysis of network traffic in support of the proposed system. Section 5 discusses the opportunities created by the _ProxyTV_ concept in some areas for the FI and Section 6 concludes the paper.
## 2 Setting the Stage: Fundamental Mathematics and Knowledge
### Computation of the PM Process with the NAF Algorithm
#### 2.3.3 Primary Loop  
With NAF representation at hand, the algorithm proceeds into the primary loop. For each bit in the NAF representation (initiating from the leftmost bit),  
* the algorithm performs a \"differential addition\" operation on points \((W_{1}:Z_{1})\) and \((W_{2}:Z_{2})\). This operation, denoted as \(dADD((W_{1}:Z_{1}),(W_{2}:Z_{2}))\), modifies these points as required;
* in cases where the current bit within the NAF representation is \"1\", an additional \"differential addition\" operation is executed, involving points \((W_{2}:Z_{2})\) and \((W_{1}:Z_{1})\);
* The algorithm activates an inner loop that handles the windowing approach in order to optimize the procedure. Operation optimization using \"double-and-add\" operations is part of this method. The inner loop spans previous bits of the NAF representation, covering up to \(w\) bits in reverse order. Within this loop,points \((W_{1}:Z_{1})\) and \((W_{2}:Z_{2})\) are updated through the \"double-and-add\" operation, as represented by \(dbL((W_{1}:Z_{1}),\,(W_{2}:Z_{2}))\);
* if the bit corresponding to the current iteration within the inner loop is '1', an additional \"differential addition\" operation occurs for points \((W_{2}:Z_{2})\) and \((W_{1}:Z_{1})\).
## 2 Network Slicing Concepts
### 5G Network Slicing  
5G must enable network operators to ensure the same network can fulfil the heterogeneous demands of diverse types of applications. To efficiently achieve those requirements and determining how network resources are assigned, a service provider must integrate technologies like Software-Defined Networking (SDN), Network Functions Virtualization (NFV), and Machine Learning (ML) with a hierarchical transport architecture.  
According to the 5G definition the architecture has three main components [18; 19]:  
* **Radio Access Network (RAN)**: It covers everything related to the air interface between the user element and the base station. The RAN interfaces and interconnections is specified within 3G PPP Architecture Working Group.
* **Mobile Core (MC)**: Its central role is to act as a gateway for user traffic to and from the internet. The mobile core is composed of a set of network functions (NF) responsible for managing user mobility, access authentication, access authorization, location service management, registration and establish per-user tunnels between base stations for each different traffic type.
* **Backhaul Network** is the network that interconnects the RAN with the MC. It is not part of the 5G specification, so it is up to each network operator to decide how to implement it. It requires functionalities such as QoS management, synchronization and a stack of protocols like IP/MPLS or segment routing.  
Network slicing capabilities need to be available across all components of the 5G cellular network (RAN, MC, Backhaul network) in order to ensure a differentiated treatment of the packets in the network. Thus, the 5G working groups have specified a standard set of network slices, denoted Standardized Slice Type (SST), to determine how resources should be assigned at the RAN and MC [20].  
5G specifies two mechanisms for network slicing. The first one is based on QoS techniques, by applying a dynamic allocation of available network resources to different classes of traffic, and it is denoted as soft network slicing. The second one takes advantage of the software-based, cloud-based architecture of 5G, as well as component disaggregation, and achieves slicing through 5G component virtualization and replication. This second approach is denoted hard network slicing.
# Secured Real-Time Machine Communication Protocol
## 2 Existing Protocols
### Network Layer Protocols  
EtherNet/IP (Ethernet Industrial Protocol) is an application layer protocol that uses standard Ethernet and TCP/IP for industrial automation applications. EtherNet/IP facilitates easy integration with existing network infrastructure by using standard Ethernet [15]. It supports both real-time and non-real-time communication, providing flexibility for various industrial applications [15]. However, bandwidth limitations for high-frequency real-time data can affect performance in some scenarios [16], and the need for robust network infrastructure to avoid collisions and delays increases the complexity and cost of deployment [17].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Protocol** & **Year Developed** & **TCP/IP Layer** & **Advantages** & **Disadvantages** \\ \hline Modbus TCP & 1999 & Application & Simple, and widely supported & Limited security, non-ideal for large data transfer \\ \hline OPC UA & 2006 & Application & \begin{tabular}{c} Enhanced security and \\ open standards \\ \end{tabular} & \begin{tabular}{c} Complex implementation, high \\ computational cost \\ \end{tabular} \\ \hline PROFINET & 2003 & Transport & Support real-time and high speed & proprietary and higher cost \\ \hline EtherCAT & 2003 & Data Link & low cost, reliable and high efficiency & \begin{tabular}{c} non-ideal for real-time, potential \\ interoperability issues \\ \end{tabular} \\ \hline CAN Bus & 1983 & Data Link & \
\begin{tabular}{c} Highly reliable, efficient for \\ short messages \\ \end{tabular} &
\begin{tabular}{c} Limited data transfer rate, not \\ suitable for large-scale networks \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Advantages and disadvantages of existing industrial communication protocols.