esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Evaluation of AM_A, AM_B, and Forging_C Mechanical Properties
#### 3.1.1 Tensile Strength Evaluation of AM Materials  
A tensile test was performed on AM_A and AM_B after the AM process using a universal testing machine (Instron 5988, Instron, Norwood, MA, USA). Figure 15 shows the shape and detailed dimensions of the tensile specimen subjected to the tensile tests. Tensile testing was performed at a rate of 1 mm/min. After five samples of each AM output were tested, the minimum and maximum values were discarded, and the average value of the remaining three was recorded.  
Figure 16 shows the fracture locations of the tensile specimens after the test was completed, whereas Table 14 outlines the tensile test results. The average tensile strength and elongation of AM_A were 1820 MPa and 8.44%, respectively, whereas those of AM_B were evaluated to be somewhat lower.  
Figure 16: Fracture locations of AM material tensile specimens: (**a**) AM_A; (**b**) AM_B.  
Figure 15: Tensile test specimen shape and dimensions. All dimensions in mm.  
After five samples of each AM output were subjected to tensile testing, the minimum and maximum values were discarded, and the average value of the remaining three was recorded. Table 15 outlines the bonding strength evaluation results for the two materials. The bonding strength was evaluated to be in the range of 575-647 MPa. A tensile strength of 569 MPa was achieved for the non-heat-treated S45C, whereas that achieved for the heat-treated and tempered S45C was 690 MPa [19]. Although a separate heat treatment process was not applied to the S45C base material, the interface was partially heated during the bonding specimen manufacturing via AM. Additionally, it was demonstrated that both types of laminated materials achieved a bonding strength of at least 80% of the base material value.
###### Abstract  
The emergence of biomedical sensor devices, wireless communication, and innovation in other technologies for healthcare applications result in the evolution of a new area of research that is termed as Wireless Body Area Networks (WBANs). WBAN originates from Wireless Sensor Networks (WSNs), which are used for implementing many healthcare systems integrated with networks and wireless devices to ensure remote healthcare monitoring. WBAN is a network of wearable devices implanted in or on the human body. The main aim of WBAN is to collect the human vital signs/physiological data (like ECG, body temperature, EMG, glucose level, etc.) round-the-clock from patients that demand secure, optimal and efficient routing techniques. The efficient, secure, and reliable designing of routing protocol is a difficult task in WBAN due to its diverse characteristic and restraints, such as energy consumption and temperature-rise of implanted sensors. The two significant constraints, overheating of nodes and energy efficiency must be taken into account while designing a reliable blockchain-enabled WBAN routing protocol. The purpose of this study is to achieve stability and efficiency in the routing of WBAN through managing temperature and energy limitations. Moreover, the blockchain provides security, transparency, and lightweight solution for the interoperability of physiological data with other medical personnel in the healthcare ecosystem. In this research work, the blockchain-based Adaptive Thermal-/Energy-Aware Routing (ATEAR) protocol for WBAN is proposed. Temperature rise, energy consumption, and throughput are the evaluation metrics considered to analyze the performance of ATEAR for data transmission. In contrast, transaction throughput, latency, and resource utilization are used to investigate the outcome of the blockchain system. Hyperledger Caliper, a benchmarking tool, is used to evaluate the performance of the blockchain system in terms of CPU utilization, memory, and memory utilization. The results show that by preserving residual energy and avoiding overheated nodes as forwarders, high throughput is achieved with the ultimate increase of the network lifetime. Castalia, a simulation tool, is used to evaluate the performance of the proposed protocol, and its comparison is made with Multipath Ring Routing Protocol (MRRP), thermal-aware routing algorithm (TARA), and Shortest-Hop (SHR). Evaluation results illustrate that the proposed protocol performs significantly better in balancing of temperature (to avoid damaging heat effect on the body tissues) and energy consumption (to prevent the replacement of battery and to increase the embedded sensor node life) with efficient data transmission achieving a high throughput value.  
internet of things; blockchain; implanted sensors; smart contract; thermal/energy-aware routing protocol 22 April 2020; Accepted: 22 June 2020; Published: 26 June 2020
# A Fully Integrated Low-Power Multi-Mode RF Receiver for BDS-3/GPS
## 4 Receiver Design
### CBPF
The capacitors in the \(C_{cal}\) array are incremented in binary and the control signal is 6 bits. So the error caused by the unit capacitor of \(C_{cal}\) is \((0.3-(-0.3))/(2^{6}-1)=0.95\%\). By adding a half-value fixed capacitor as shown in Figure 4, an accuracy of 0.475% is realized in the calibration capacitor array. And the \(3\sigma\) correction errors of \(\alpha\) and \(V_{off}\) are 0.5% and 0.8%, respectively. The three items constitute the RC calibration accuracy of \(\pm 1.8\%\). To verify the performance of the RC calibration, the receiver works at the worst corner where the resistors and capacitors are at the ff corner and \(-40\)\({}^{\circ}\)C, showing a maximum shift in the center frequency and bandwidth. Then the RC calibration process is enabled. The calibrated frequency response is compared with that under the typical corner, and the simulation results are shown  
Figure 4: Diagram of the RC calibration. in Figure 5. It proves that the RC algorithm realizes a perfect calibration of the center frequency and bandwidth. The CBPF has two operating modes: the narrowband mode for GPS and the broadband mode for BDS-3/GPS. The simulation results show that the bandwidths are, respectively, 2.046 MHz and 4.092 MHz, and there is no ripple in the band. IMRRs are greater than 50 dB and 45 dB, respectively. The simulated \(IP_{\mathrm{ldB}}\), referred to as the antenna input, is \(-43.54\) dBm with an NF of 32.5 dB, meeting the system requirements.
## 3 Results and Discussion
### Adsorption Isotherm Models
#### 3.3.2 Freundlich Model Isotherm for WPET-SiAMPy Composite Nanofibres  
The Freundlich isotherm is generally applicable to the adsorption that occurs on heterogeneous surfaces [49]. This isotherm gives an expression which defines the surface heterogeneity and the exponential distribution of active sites and their energies [50]. The Freundlich adsorption isotherm represents the relationship between the amount of metal adsorbed per unit mass of the adsorbent \(q_{e}\) and the concentration of the metal in solution at equilibrium. The Freundlich Equation can be described by the linearized form:  
\[\log q_{e}=\log k_{F}+\frac{1}{n}\log C_{e} \tag{7}\]  
where \(k_{F}\) and \(n\) are Freundlich constants. The values of \(k_{F}\) and \(n\) were graphically determined. A plot of \(\log q_{e}\) against \(\log C_{e}\) gives a straight line of slope \(n\) and the intercept is \(\log k_{F}\)[51].  
The results of the Freundlich isotherm of Cu\({}^{2+}\) ion adsorbed by WPET-SiAMPy nanofibres is presented in Figure 10. The Freundlich constants related to adsorption capacity \(K_{F}=0.18\) (L/mg), and adsorption intensity \(\frac{1}{n}=0.9572\) indicated the optimum adsorption characteristics of the WPET-SiAMPy composite nanofibres in Cu\({}^{2+}\) aqueous solution. This is due to the correlation of \(K_{F}\) that indicated that the high \(K_{F}\) coefficient increased with an increase in the initial concentration as a result of an increase in the activated surface characteristics after monolayer adsorption and \(\frac{1}{n}\) with high WPET-SiAMPy composite nanofibres' adsorption intensity. Thus, the correlation coefficient R\({}^{2}\) values for the WPET-SiAMPy nanofibres, which was 0.9952 for Cu\({}^{2+}\), indicated a favourable adsorption process.
## 2 State-of-the-Art  
### Diagnosis Tools  
As mentioned in the Introduction section, the ability and knowledge of the expert neurologist, together with the external clinical symptoms that the patient presents, are the classically used tools in the diagnosis of PD.  
One of the non-invasive methods commonly analyzed with precision for the evaluation and diagnosis of the status of PD is the analysis of initial walking patterns. Also known as gait cycles, these walking patterns measure the initial to final contact of the same foot when walking and were observed as a consequence of central pattern generators established by spinal circuits [10]. However, other parts of our brain, such as the basal ganglia, the cerebral cortex and the descending pathways to the spinal cord, also have a great influence on human locomotion.  
Currently, neuroimaging has been implemented as a powerful source of information for decision making (mainly relevant in the early diagnosis of brain disorders), being an important medical test for neurologists.  
Among the best known and most used technologies, it is worth mentioning structure MRI (sMRI), functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). In the field of PD, MRI has been used for the discrimination of PD and other Parkinsonian syndromes that present relevant structural modifications/changes [11]. MRI has also been used for the identification of possible patterns associated with volumetric alterations that may indicate the severity of the disease [12].  
Single-Photon Emission Computed Tomography (SPECT) is based on the Dopamine Transporter scan (DaTscan) method by using the radioligand Ioflupane I 123 (1231-FP-CIT). This technology has been successfully used to differentiate PD from other motor diseases non-associated with dopaminergic deficit [13; 14].  
There are studies in the literature that show how glucose hypo-metabolism occurs prior to localized atrophy of brain matter and that the conversion time is correlated with this hypo-metabolism. The use of FDG-PET images [15] has become an effective tool for this information.  
The numerous contributions presented in the literature analyzing the genetic factors underlying [PERSON]'s disease with dementia and with mild/moderate cognitive impairment are very promising and clearly demonstrate the importance of genetic for cognitive impairment in PD [16].  
As presented in the Introduction section, one of the reasons that characterize [PERSON]'s disease comes from the deterioration of dopamine-producing cells located in the substantia nigra of the brain [17], resulting in motor and non-motor symptoms (tremos at rest, motor retardation, bradykinesia, rigidity, etc). For the elderly, the identification and proper treatment of PD as early as possible is of great importance to be able to act medically and clinically in the prevention of the patient, with the intention of slowing down the progress of the disease as much as possible.
# Experimental Study on Multi-Cell Counting Using an Inertial Microfluidic Device
## 2 Materials and Methods
### Sample Flow Preparation
#### 2.3.1 Microparticle Selection and Ratios  
To represent the cellular components of blood, microparticles were selected to match the sizes of erythrocytes (RBCs) and monocytes (WBCs). These microparticles had diameters of approximately 8 \(\upmu\)m and 15 \(\upmu\)m, respectively [35]. Two different ratios of these microparticles were prepared to reflect normal (600:1) and diseased (400:1) conditions.  
These ratios were selected based on their clinical relevance, providing a model to study how changes in cell composition could affect separation in microfluidic devices. Table 1 provides a detailed overview of various RBC-to-WBC ratios and their associated medical conditions:
# Analysis of Dynamic Behavior of ParReEx Robot Used in Upper Limb Rehabilitation
## 7 Discussion  
Simulations of ParReEx robot dynamics were performed in three simulation cases: (1) without friction in the couplings, (2) with friction, and (3) with friction and flexible elements.  
The evolution of the RMS value of the resulting connecting forces in Couplings A and B for the three simulation scenarios is shown in Figure 40. The RMS value of a set of values (or a continuous-time waveform) is the square root of the arithmetic mean of the squares of the values, or the square of the function that defines the continuous waveform.  
The presence of friction in the coupling increases the reaction in Coupling A and decreases the reaction in Coupling B. This can be explained by the fact that charging Coupling A unloads Coupling B. The presence of a toothed belt drive in Coupling A causes additional loading of this driving joint. In this case, the bearing in Coupling B will be a driving one, i.e., it will also take up the axial forces that occur in the circular guide element.  
For the other couplings, friction increases the RMS values of the resulting connecting forces. Thus, Figure 41 shows the evolution of the RMS values for the resultant coupling forces in R1 and R2.  
Further, friction increased the actuation moment for flexion-extension (Torque 1) and radial-ulnar deviation (Torque 2), as Figure 42 illustrates.  
Figure 41: Evolution of the RMS values for the connection forces in joints R1 and R2.  
Figure 40: Evolution of the RMS values for the connection forces in Couplings A and B.  
Numerical simulation in ADAMS (Figure 21) showed that flexion-extension motion of the wrist joint required a maximum moment of 1.8086 Nm, and radial-ulnar deviation required a maximum moment of 5.5339 Nm. Experimentally, higher values were obtained for these parameters: 4.5 Nm for flexion-extension and 20 Nm for radial-ulnar deviation. As a remark on the experimentally determined values, it is found that these quantities do not start from zero, so in the initial starting position, the system is preloaded by the human subject's arm. The same happened in the numerical simulation in ADAMS, only in the simulation we considered the load produced by the patient's arm of 30 N as a constant load.  
We also found that the experimentally measured values for the actuation moments have similar variation. The differences between the experimentally measured values and those obtained by numerical simulation can be explained as follows:  
In the case of numerical simulation, we defined the motion law by the STEP function, and at the end of the stroke the angular velocity is reduced, which is not the case in the experimental case;  
The virtual prototype does not consider the presence of clearance in the kinematic joints. In the case of the experimental prototype, these clearances are inherent, so the components could not be assembled. The presence of these clearances produces jumps of moments at the end of the stroke, which is not the case with the virtual simulation.
# A One-Phase Tree-Structure Method to Mine High Temporal Fuzzy Utility Itemsets
## 5 Performance Evaluation  
Three real datasets, mushroom [35], retail [35], and foodmart [36], were used to compare the proposed _ATTFUIM_ and the previous _FHTFUIP_[18] algorithms. These two methods were run on a desktop computer with a Dual-Core Processor of 3.3 GHz and 16 GB of RAM. The programming language is JAVA. The quantitative value for each item in mushroom, retail, and foodmart datasets was assigned from 1 to 10 randomly. The profits for items were randomly set within 1 to 1000. In the experiments, the three datasets were transformed into three linguistic terms according to the pre-defined membership functions in Figure 1. The number of time periods is assigned 2.  
The running time and the memory consumption for the two methods in different thresholds were evaluated. The results are shown in Figures 6 and 7.  
Figure 6 shows the comparison results on the three different datasets under varying thresholds. Obviously, the proposed _ATTFUIM_ method has better performance in terms of computation time when compared to the _FHTFUIP_ method. The two algorithms derive the same _HTFUIs_, but _ATTFUIM_ needs less running time compared to the other one. Along with the threshold set at a lower value, _ATTFUIM_ has better performance than _FHTFUIP_. This is because the _HTFUIs_ derived by the _ATTFUIM_ method is processed by each node with the array-list structure information, but those derived from the _FHTFUIP_ method are mined by performing database rescans. For the three different datasets, when the threshold decreased, the proposed approach ran slower.  
Figure 7 shows the memory consumption for the two approaches on three different datasets under varying thresholds. Along with the threshold decreasing in the three different datasets, the memory space used for _ATTFUIM_ is more than that for _FHTFUIP_. This main reason is that each node in the proposed algorithm needs to keep the array-list data for mining information of the same linguistic term in different transactions. Besides, it can be observed that the proposed method required more memory consumption when the threshold decreased.  
Figure 6: The comparison of running time for the two methods with different thresholds.  
In summary, the proposed _ATTFUIM_ method with each node, including the array-list information, requires less running time when compared to the two-phase _FHTFUP_ method. Although the proposed method uses the array list to store mining information in each node, it does not need to rescan the database to determine _HTFUIs_ when compared with the _FHTFUP_ method. Thus, the computation time is significantly reduced.
## 2 Related works
### Coverage and Connectivity  
Coverage and connectivity are two important properties to WSN. Coverage describes how well sensors in the network can monitor a geographical region in question. Connectivity simply describes the connectivity properties of the underlying network topology and it is often desirable that the network is connected. If the network is partitioned, the sensed data cannot be known by sink and the sensor network is failed.  
Connectivity is a fundamental issue in wireless ad hoc environment. Many schemes have been addressed to conserve energy while maintaining the connectivity, which is also related to how to construct the minimum connected dominating set problem. Much research focused on designing energy-efficient distributed algorithms to construct a near optimal connected dominating set [27, 28].  
There has been a lot of research done to address the coverage problem in WSN. In [29], a centralized heuristic to select mutually exclusive sensor is designed to cover that independent the network region. In [30], a grid-based coverage algorithm is proposed in sensor network. A set of sensors can be deployed on the grid points to monitor the sensor field. The coverage is assumed to be full if the distance between the grid point and the sensor is less than the detection radius of the sensor. Otherwise, the coverage is assumed to be ineffective. If any grid point in a sensor field can be detected by at least one sensor, the field is completely covered. In [31], node self-scheduling algorithm is proposed. Each node autonomously and periodically makes decisions on whether to turn on or turn off itself only using local neighbor information. To preserve sensing coverage, a node decides to turn it off when it discovers that its neighbors (sponsors) can help it to monitor its whole working area. In [32], connected sensor coverage algorithm is proposed. The algorithm works by selecting a path (communication path) of sensors that connects an already selected sensor to a partially covered sensor.  
The selected path is then added to the already selected sensors at that stage. The algorithm terminates when the selected set of sensors completely cover the given query region.
## 4 Proposed Architecture
### Multi-Core and GPU Tightly Coupled Simulation
Cellular models can be divided based on their localities, and each simulation of sub cellular models is computed in one CPU core including communication between other sub-cellular models computing other CPU cores. Figure 4: Idea of using GPU in hybrid system simulation. Figure 6a illustrates DEVS flow of proposed heterogeneous architecture. DES models are allocated in CPU and CS models are allocated in GPU based on their characteristics of computation. In this process, in particular, the memory that is used to simulate CS is copied to GPU memory for computing in GPU. The DEVS simulator, which is adopted for this study, has a clear separation layer between an atomic model and coupled model to connect multiple models, and the corresponding coordinators as simulation kernels. We could successfully map the simulator evaluating continuous models into the GPU, with minimal modification of simulator framework. Figure 6b describes the concepts of abstract DEVS based simulators that proposed in this study. S:X means a simulator for atomic model X, and the atomic model represents the minimum model that cannot be divided into child atomic models. C:XY refers to a coordinator for coupled model XY, and the coupled model integrates multiple atomic models. The simulator updates the simulation clock for the atomic model, and it calls for the four atomic functions for the state transition. The coordinator manages simulation clock and calls four coupled DEVS functions for message routing and conflict resolution. Simulators for the continuous atomic model are simulated in the kernel when, after all, DES are simulated by the time global \(t_{N}\). A brief DEVS simulation cycle represented by a simulator and a coordinator is displayed in Algorithm 1. The state of the atomic model is changed as global \(t_{N}\) is imminent to the subsequent \(t_{N}\). Thus, we apply our approach of synchronizing all cellular models by finding out global \(t_{N}\), which updated the state of the last cellular model. After this global \(t_{N}\), all atomic models of CS, which, following differential equations, are simulated using multi-threads represented by a thread and a block index in GPUs. Figure 5: Proposed architecture in terms of model specification. Figure 6: (**a**) Flow of proposed architecture and (**b**) its abstract simulator. ```
fornumber of K cells fordiscrete-event simulation \[\text{coordinator.send}(t_{N})\] \[\text{simulator.reply}(next~{}t_{N})\] Figure 3.0 Figure 3.1 Figure 3.2 ifimminent Figure 3.3 endif Figure 3.4 Figure 3.5 endfor forcontinuous-time simulation by GPU kernel fori = 1 to K, i is calculated by thread, block index Figure 3.6 endfor endfor endfor
```  
**Algorithm 1** Proposed DEVS simulation cycle