esa-sceva/satcom-chunk-collection huggingface dataset:
Sensors_**2015**, _15_, 24977-24995; doi:10.3390/s151024977  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
Performance Evaluation of Wearable Sensor Systems: A Case Study in Moderate-Scale Deployment in Hospital Environment  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 3 and [PERSON] 1**  
1 Department of Electrical and Computer Engineering, National University of Singapore,  
Singapore 119613, Singapore; E-Mail: [PERSON]IL_ADDRESS]  
2 Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore 138632,  
Singapore; E-Mail: [EMAIL_ADDRESS]  
3 Department of Computing, Imperial College, London W120 NN, UK;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel./Fax: +65-65-161-076.  
Academic Editor: [PERSON]  
_Received: 18 August 2015 / Accepted: 21 September 2015 / Published: 25 September 2015_  
**Abstract:** A wearable sensor system enables continuous and remote health monitoring and is widely considered as the next generation of healthcare technology. The performance, the packet error rate (PER) in particular, of a wearable sensor system may deteriorate due to a number of factors, particularly the interference from the other wearable sensor systems in the vicinity. We systematically evaluate the performance of the wearable sensor system in terms of PER in the presence of such interference in this paper. The factors that affect the performance of the wearable sensor system, such as density, traffic load, and transmission power in a realistic moderate-scale deployment case in hospital are all considered. Simulation results show that with 20% duty cycle, only 68.5% of data transmission can achieve the targeted reliability requirement (PER is less than 0.05) even in the off-peak period in hospital. We then suggest some interference mitigation schemes based on the performance evaluation results in the case study.  
**Keywords:** wearable sensor system; body sensor network; inter-user interference; interference mitigation
# An Overview of the Public Health Challenges in Diagnosing and Controlling Human Foodborne Pathogens
## 1 Introduction
The widespread overuse of antibiotics and the resulting growth of multidrug-resistant pathogenic bacteria has led to a paradigm shift in our understanding of antibiotics. Clinical failures associated with antibiotic therapy can be attributed to delays in the detection of foodborne pathogens [60]. Therefore, early detection of pathogens is essential to avoid the emergence of multidrug-resistant bacteria. Increasingly, concerns have been raised about the prevalence of antibiotic resistance among foodborne bacterial infections and its impact on treatment outcomes [61]. As multidrug resistance has become widespread in bacteria, alternative infection-control measures, such as prebiotics, probiotics and bacteriophages, are required to combat the problem [62]. A good example is the use of bacteriophages as biocontrol agents in controlling foodborne pathogens and preventing contamination of food contact surfaces [63]. A bacteriophage, or phage as it is sometimes known, is a virus that infects and multiplies in bacterial cells only if there is a host [64; 65]. Due to their unique properties, phages are considered to be promising tools in the fight against bacterial diseases--as well as in the fight against many diseases--and have been widely used in modern medicine as a result [58]. Phage therapy is only one of the many applications for phages in industry. To ensure the safety of the consumer, every antibacterial treatment used in the food production industry must be carefully chosen before it can be used [58]. Thereare several advantages to using phages over chemical antibacterial agents, including the fact that they do not contribute to deterioration of the product's quality; that they may be applied to a wide range of substrates; and that resistance to viruses can be addressed much more simply, in comparison to commonly used antibacterial agents [66; 67; 68]. It has been found that phages can naturally be found in food, which indicates that this is one way for them to come into contact with humans [69]. In addition to being cost-effective and easy to use, phage-based techniques are establishing themselves as an alternative to traditional antibacterial treatments. Several studies have been conducted on the effectiveness of phages in eradicating foodborne pathogens [70; 71; 72; 73]. It is of great importance to note that since phage research has grown significantly in the last couple of decades, we also discuss in this review the most recent discoveries on how effectively phages can be used against the principal food-related microorganisms and how these viruses may impact the method of microbiological management employed in food production. There is an imperative need to find effective alternatives that will allow the food industry to meet the high standards of food safety in the production of food items, as we believe maintaining the safety of food items to be a significant and major global challenge.
## 3 Results and Discussion
### Synthesis of Asp-CFM Molecule  
In the first step, Fmoc aspartic anhydride (Fmoc-Asp-anh) was synthetized (Figure 1). A total of 135 mg (0.38 mmol) of Fmoc-Asp-OH (linker) was added to the flask with 500 \(\upmu\)L (5.30 mmol) of acetic anhydride. The flask was then heated in an oil bath to 85 \({}^{\circ}\)C. An additional 500 \(\upmu\)L of acetic anhydride (1 mL in total) was added. The mixture was heated for about 20 min until the linker was completely dissolved. The flask was then cooled rapidly to room temperature, and the product was filtered and washed with diethyl ether. The resulting white dusty precipitate was dried in vacuo.  
To the Eppendorf tube with 8.40 mg (25 \(\upmu\)mol) of Fmoc-Asp-anh, 10 mg (20.83 \(\upmu\)mol) of CFM dissolved in 200 \(\upmu\)L of DMF was added. The synthesis was carried out overnight at room temperature, and the progress of the reaction was checked by HPLC. After completion of the reaction, the solvent was removed under vacuum and the residue dissolved in  
Figure 1: HPLC chromatogram (at 280 nm) of the reaction mixture of Fmoc-Asp-anh synthesis (R\({}_{\mathrm{T}}\) = 17.11 min, gradient 1).  
acetonitrile. The crude product Fmoc-Asp-CFM, consisting of two structural (constitutional) isomers (Scheme 1, products of step two), was purified by HPLC (gradient two, Figure 2, R\({}_{\rm T}\) = 24.71 and R\({}_{\rm T}\) = 24.99 min) and lyophilized: yield 75% and purity over 98%. The presence of the product was confirmed by mass spectrometric analysis.  
MS for C\({}_{38}\)H\({}_{40}\)N\({}_{7}\)O\({}_{10}\)S\({}_{2}\)\({}^{+}\), TOF MS ES\({}^{+}\)\(m/z\): (calculated: 818.23); found: 818.36.  
In order to remove the Fmoc-protecting group, 5 mg (6.11 \(\mu\)mol) of Fmoc-Asp-CFM was used, dissolved in 50 \(\mu\)L of 2.5% piperidine in DMF (Scheme 1, step three). The reaction mixture was maintained for 30 min at room temperature. The final products (isomer Asp-CFM(\(\alpha\)), R\({}_{\rm T}\) = 18.67 min, and isomer Asp-CFM(\(\beta\)), R\({}_{\rm T}\) = 19.33 min) were isolated from the reaction mixture by HPLC (Figure 3, gradient two) and analyzed by MS. For further syntheses, the beta isomer obtained with a higher yield was used.  
Figure 3: HPLC chromatogram (at 270 nm) of deprotection reaction mixture of Fmoc-Asp-CFM derivative in 2.5% piperidine solution after 30 min; isomer Asp-CFM(\(\alpha\)) with R\({}_{\rm T}\) = 18.68 min and isomer Asp-CFM(\(\beta\)) with R\({}_{\rm T}\) = 19.33 min (gradient 2).  
Figure 2: HPLC chromatogram (at 270 nm) of the reaction mixture of Fmoc-Asp-CFM derivative synthesis (products of step 2: isomer \(\alpha\) (R\({}_{\rm T}\) = 24.71 min) and isomer \(\beta\) (R\({}_{\rm T}\) = 24.99 min) (gradient 2).  
MS for C\({}_{23}\)H\({}_{30}\)N\({}_{7}\)O\({}_{8}\)S\({}_{2}\)\({}^{+}\), TOF MS ES\({}^{+}\)\(m/z\): (calculated: 596.16); found: 596.29.
# Simulation Study of Thermal-Mechanical Coupling Fretting Wear of Ti-6 Al-4V Alloy
## 4 Results and Discussion
### Effect of Fretting Speed on Wear and Temperature Rise  
Fretting speed is also an important factor affecting wear characteristics, especially when there is a temperature effect. For that purpose, the effect of fretting speed on wear and temperature rise was explored. Three different fretting speeds of V, 10 V, and 20 V were applied in the model. Figure 11 shows the results of contact pressure and wear for different fretting speeds. The contact pressure is not identical for the three fretting speeds in Figure 11a, implying that fretting speed influences wear profile. However, it is difficult to obtain a clear relation between fretting speed and contact pressure because a small change in the wear profile results in a large change in contact pressure. Figure 11b describes the law between the wear depth and the fretting speed. The increase in the fretting speed increases the temperature of the contact surface, which results in an increase in wear depth with the increase in fretting speed. The increase in wear depth is less when the fretting speed changes from V to 10 V, but the increase in wear depth becomes greater when the fretting speed changes from 10 V to 20 V. The reason for this is that as velocity increases, so does the temperature. As shown in Figure 12, the surface temperature at a fretting speed of 20 V is considerably higher than the surface temperatures at the other two speeds.  
Figure 12 shows the temperature distribution of the contact surface at different fretting speeds. It can be found that the temperature of the contact area increases with the increase in the fretting speed, but the temperature away from the contact area is higher at the fretting speed of V. The temperature of the contact surface comes from frictional heat generation and heat conduction. For the area far from the contact center, the temperature comes only from heat conduction. When the total number of cycles is the same, the fretting time is inversely proportional to the fretting speed. Correspondingly, the fretting speed V has the most time for heat conduction, resulting in a higher temperature in that region.  
Figure 11: Variation pattern of contact pressure and wear depth with fretting speed. (**a**) Contact pressure; (**b**) Wear depth.  
Figure 12: Variation pattern of temperature distribution with fretting speed.
# Post-Quantum Authentication in the MQTT Protocol
## 2 Problem Statement
### Digital Signatures Used for Authentication  
The traditional MQTT interaction over a public MQTT channel, with authentication provided by digital signatures, can be seen in Figure 2. The client sends a ClientHello message to the broker with a random nonce, \(r_{c}\), for freshness. The broker responds with a BrokerHello message containing another random nonce, \(r_{b}\), and then sends its public key signed by an authority, \(\texttt{cert}[pk_{s}]\), verifying that the public key does indeed belong to the expected party. After receiving the signed public key, the client verifies the signature and then encapsulates a premaster secret, \(pms\), using the public key. This encapsulation, \(ct_{s}\), is then sent to the broker.  
When the broker receives \(ct_{s}\), it decapsulates \(ct_{s}\) with its secret key, \(sk_{s}\). Both parties use the premaster secret, \(pms\), to generate further keys that can later be used for symmetric encryption to provide confidentiality. Both parties then verify that the other party has indeed received all the messages that were sent to it, and that they have generated the same keys by sending hash message authentication codes (HMACs) back and forth in the ClientFinished and BrokerFinished messages. These messages allow both parties to check that the other has been able to calculate the same hash, based on the contents of all previous messages and the final generated key. This completes the authentication of the broker, meaning the clients can be sure that they are interacting with the intended broker. The client will then send the packets required to complete the MQTT handshake, and the protocol will continue as normal.
## 3 Materials and Methods
### Multivariate Adaptive Regression Splines (MARS)
The initial phase, known as the forward pass, involves integrating the predictor variables into the model and fine-tuning the positions of their corresponding knots. This process gives rise to the creation of double-sided basis functions, one for each predictor. In the context of an \(X\) matrix, composed of '\(n\)' samples and '\(p\)' predictors, '\(n\times p\)' pairs of basis functions emerge, as exhibited in Equations (2) and (3). These functions are associated with knots, denoted by \(x_{ij}\), where '\(i\)' ranges from 1 to '\(n\)' and '\(j\)' spans from 1 to '\(p\)'. Every single data point affiliated with a predictor variable is taken into account as a possible knot for that variable's pair of basis functions. However, the forward pass process often results in the formulation of a convoluted model that tends to overfit, hence reducing its predictive capability. To address this issue, the second phase, or the backward pass, is implemented. During this phase, the model is systematically simplified through sequentially eliminating the least contributing, and therefore redundant, basis functions. The backward pass continues until an optimized sub-model is achieved, characterized by its lowest Generalized Cross Validation (GCV). This enhancement substantially boosts the predictive prowess of the MARS model. The computation for determining the GCV follows the formula provided below:  
\[GCV(M)=\frac{\frac{1}{x}\sum_{i=1}^{n}\left(y_{i}-f_{M}(\hat{x}_{i})\right)^{ 2}}{\left(1-\frac{M+\lambda(M-1)/2}{n}\right)^{2}} \tag{5}\]Figure 1: Research methodology.
## 3 Integrated Simulation-Optimization Approach  
Hybrid models play an important role in most real-world systems. Multiple perspectives can be obtained, each of which can answer a different important question. For answering the range of questions that can be asked with respect to a system, a combined set of model types can be the answer. Hybrid methods can bring more comprehensive and efficient estimations of a reality by enhancing the synergies among different methods and giving the suitable output for decision-makers [41]. One of the main goals of the SO method as a hybrid model is to efficiently address both the optimization and the uncertainty. An overview of the application of SO methods in designing resilient supply chain networks is presented in [42]. There are many ways to combine simulation and optimization, and the appropriate design depends strongly on the properties of the problem. The guideline 3633.12 [43] by the German Association of Engineers (Verein Deutscher Ingenieure (VDI)) provides a classification for different combinations of simulation and optimization in terms of sequential and hierarchical combination, which has been in detail elaborated by the Arbeitsgemeinschaft Simulation (ASIM) in Germany [44]. A sequential combination assumes that either simulation or optimization is completed before the other one can be executed. Within a hierarchical combined approach that can be called several times during the overall execution. Moreover, the details of the main classifications of various SO combinations are described in [13]. According to their classifications, we consider an analytical model improvement approach, where simulation is used to improve the model results, either by refining its parameters or by extending them, e.g., by considering different scenarios. In this context, the SDSM, based on an SO concept for APLs presented in [45], provides a suitable methodology to determine the behavior of the parameters in our multi-period capacitated FLP model. A well-proven SO approach to solve this kind of problems is provided by simheuristic algorithms [46; 47].  
In particular, our approach consists of the following steps, which are shown in Figure 3: (i) for each district, we use the SDSM to generate an estimate of expected demand; (ii) for different scenarios, each scenario being defined by a different level of demand (e.g., lower than expected, as expected, or higher than expected), solve the associated CFLP model; and (iii) use a Monte Carlo simulation to evaluate the solutions obtained in the previous step when used in a stochastic environment. Here, we assumed that the demand per district is uncertain and follows a known probability distribution, with the aim of comparing total costs with the reliability.
## 4 Simulation Evaluation
### Comparison with Other Methods  
Here, we evaluate the superiority of SC-Sched by comparing it with the other two methods in the two simulation scenarios. Additionally, in the fixed sink scenario, we also evaluate the basic SC-Sched method (called SC-Sched(simple)), which does not have the functions of retransmissions for dealing with frame loss and parallel transmissions on different channels for shortening data collection time. Note that the parameters of ContikiMAC are adjusted so that it achieves a similar frame delivery ratio to SC-Sched while keeping its energy consumption is as low as possible (the duty cycle is 5%, the retransmission limit of data frames is 4, and the contention window size is set according to the node density).
## 3 Postulated Models Describing the Mechanism of Very High Cycle Crack Initiation and FGA-Formation
### Polygonization with Nucleation and Coalescence of Micro-Debondings  
Another approach to explain the formation of the FGA as well as the fatigue in the VHCF-regime was postulated by [PERSON] et al. [12]. According to [PERSON] et al., the fine-grained three-dimensional layer with the size of FGA is formed around the failure inducing non-metallic inclusion due to the long-term exposure to polygonization. Along the boundary layer between this fine-grained zone and the original microstructure, micro cracks are supposed to initiate continuously as a result of further cycles. After sufficiently long periods of cycles, these micro cracks then coalescence together and form a propagating crack. In contrast to the mechanisms described above the formation of the FGA is supposed to be the cause for the late crack initiation and the VHCF failure in general.  
[PERSON] et al. [116; 105] postulate a similar mechanism and conclude that the FGA-formation is a result of localized plastic deformation. Because of the cyclic stress, the dislocations are supposed to rearrange into dislocation cells and plastic recrystallization is expected to occur in the area of increased stress around the non-metallic inclusion. However, while forming the FGA, additional micro pores aresupposed to form because of dislocation annihilation acting as crack initiation sites. [PERSON] et al. [117] postulate the \"fragmentation of martensitic laths and formation of dislocation cells\" mechanism which is also based on the formation of dislocation cells as a result of annihilation of low angle boundaries and the breakdown of martensitic laths due to localized plastic strain around critical inclusions. Further the extension of fine grains with newly formed boundaries/interfaces and micro cracks are supposed to result in a widespread interface linking or coalescence forming interior small cracks during the process and thus building the FGA. [PERSON] and [PERSON] [104] postulate a very similar model that is also based on the rearrangement of dislocations into dislocation cells resulting in grain refinement. In contrast to the models described above, they postulate that micro crack initiation might also occur in front of the crack tip depending on the local microstructure resulting in the discontinuous formation of the fine-grained layer. For the mechanism of rearrangement of dislocations into dislocation cells [PERSON] et al. [118] is cited and it was observed by [PERSON] et al. [119] that those dislocation cells led to the formation of small sub grains or low angle boundaries in ferrite grains in VHCF regime.
## 2 Device Structure and Simulation Method  
Figure 1 shows the structures of MOSFET (Figure 1a), planar TFET (Figure 1b), DG TFET (Figure 1c), and EHBTFET (Figure 1d) used in this research. The design parameters used for the simulations are summarized in Table 1. In the modern integrated chip (IC) process, the grain size of the metal gate has a range of approximately 5 to 20 nm [39; 40]. The grain size decreases when the DC (direct current) power of the sputtering process increases [40]. On the other hand, the grain size increases when the process temperature increases during or after the deposition process [39; 41]. In this research, TiN (titanium nitride) was applied as the gate metal for the WFV effect analysis. In the case of TiN, it is also known that the grain size can be reduced by incorporating Cu or C when the TiN layer is deposited [42; 43]. In this simulation and analysis, each metal grain was assumed to be a cube with a side length of 10 nm within the general range of the modern IC process. With regard to the WF value distribution of TiN, much research is still on-going, in order to develop a better understanding of the WF variation in TiN (e.g., considering the work-function (WF) of the grain boundary [44] and increment of the high-WF grain portion with the increased process temperature [41]). In this study, it was assumed that 60% of TiN grains were crystallized in \(<\)200\(>\) with 4.6 eV WF and 40% were crystallized in \(<\)111\(>\) with 4.4 eV WF, which are generally accepted values [39]. The blue arrows in Figure 1 indicate the positions where the tunneling mainly occurs in the on-state of each structure. Unlike the planar TFET and DG TFET, where tunneling occurs at the boundary between the source and channel region, EHBTFET has a tunneling current between the main gate (MG) and the sub gate (SG), as shown by the arrow in Figure 1d.  
Figure 1: \(Cont.\)  
In order to compare and analyze the WFV effects of each structure, three-dimensional (3D) simulations were conducted using Synopsys SenataurusTM (Ver. K-2015.06-SP1, Synopsys, Mountain View, CA, USA) [45]. Fermi-Dirac statistics, doping concentration dependent mobility, Shockley-Read-Hall (SRH) recombination, and modified local density approximation (MLDA) models were used to calculate and extract the electrical characteristics of TFETs in the simulation. For an accurate calculation of band-to-band tunneling (BTBT), a dynamic non-local BTBT model was applied with theoretically calculated parameters [46] generally used in recent TFET research [47, 48, 49].
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
## 5 Open Issues and Challenges
### Achieving Security, Trust and Privacy  
In a smart health-care network, security is an important challenge, due to the connectivity of different IoT devices. It is challenging to implement complex security algorithms and protocols due to limited battery life and processing power on IoT devices [81]. In the future, most of the IoT device will be at risk of attacks. This can lead to different types of threats and attacks in terms of privacy and security. The following issues must be taken into account to design a successful 5G based smart health-care network.  
* A secure and straightforward communication must be delivered between smart health-care devices and cloud database centre for data authenticity and integrity.
* Well-defined approach must be provided for risk assessment, to detect upcoming and present attacks.
* Strong privacy policy must be provided for new user approval and trust.