esa-sceva/satcom-chunk-collection huggingface dataset:
# Wearable Sensors for Vital Signs Measurement: A Survey
## 2 Classification of Wearable Medical Sensors
### Classification Method of Wearable Medical Sensors  
With the rapid development of science and technology, the application of sensors is becoming more intelligent. In the medical field, wearable medical sensors can convert chemical cost and concentration into electrical signals by using the principles of biological electrodes, physical changes and chemical reactions, to finally achieve data acquisition. Additionally, these sensors can selectively identify biochemical substances to achieve the effect of non-electrical parameters. Human data can be obtained from wearable medical sensors through implanted sensors, \"temporary\" insertion sensors, external sensors, and external sensors in contact with body fluids. The most important characteristics of implantable sensors are their small size, light weight, low power consumption, compatibility with the body, and stability, in that they will not decay over time. For example, the micro piezoelectric thin-film vibration sensor in blood vessels is helpful for cardiac pacing. \"Temporary\" insertion sensors are inserted into the body through an incision, such as catheterization. Such sensors, for example the silicon micromechanical pressure sensor to help shrink, is generally used for several minutes to several hours. An external sensor is mainly used to monitor the physiological parameters of the human body by connecting the electrode part to the outside of the skin. Sensors in contact with body fluids are usually used in vitro. For example, disposable blood pressure sensors are mainly used in surgery and intensive care, so they can continuously monitor the patient's blood pressure.  
In addition, unlike mechanical sensors, wearable sensors usually use photoelectric effects, biological impedance principles, or thermal sensing elements to measure vital signs and bioelectric activities. Biochemical sensors combine chemical sensitive layers and transducers to convert chemical or biological signal molecules into electrical signals, thus quantifying biological indicators, such as glucose, alcohol, and electrolytes. In clinical practice, physiological signals of concern include electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), electrooculogram (EOG), phonocardiogram (PCG), photoplethysmography (PPG), and ballistocardiograph (BCG); electrodermal activity (EDA) can often be combined with inertial measurement unit signals that characterize the patient's posture and body movement [10; 11; 12; 13; 14; 15; 16; 17]. Table 1 presents the monitoring characteristics, relevant technical parameters, and a summary of research into relevant fields of clinical application of wearable medical sensors.  
Furthermore, depending on the different functions of the wearable medical sensors, they can be divided into wearable medical sensors for detecting vascular infarction, respiratory intensity, body temperature, blood oxygen concentration, and sleep. Their specific classification is shown in Figure 1. The application analysis is emphasized according to its different effects.
# Aerodynamic Analysis and Noise-Reducing Design of an Outside Rear View Mirror
## 3 Simulation Results
### Result Comparisons and Discussions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Vehicle Speed (kph)**} & \multicolumn{4}{c}{**Vorticity: Measure Points**} & \multirow{2}{*}{**(A \(-\) B)/A (\%)**} \\ \cline{3-4}  & & **A: Gutter (1/s)** & & **B: Edge (1/s)** & & **A \(-\) B** \\ \hline \multirow{3}{*}{**Reference Model**} & 60 & 689 & 241 & 448 & 65.0 \\  & 80 & 664 & 242 & 422 & 63.6 \\  & **110** & 906 & 326 & 579 & 64.0 \\ \hline \multirow{3}{*}{**Outer Duct Model**} & 60 & 629 & 274 & 355 & 56.4 \\  & **80** & 472 & 207 & 264 & 56.1 \\  & **110** & 784 & 343 & 440 & 56.3 \\ \hline \multirow{3}{*}{**Inner Duct Model**} & 60 & 251 & 287 & \(-\)37 & \(-\)14.3 \\  & **80** & 347 & 375 & \(-\)28 & \(-\)8.1 \\ \cline{1-1}  & **110** & 454 & 553 & \(-\)99 & \(-\)21.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation results of vorticities according to vehicle driving speed between the gutter and the outside edge of the three OSRVM models. Figure 7: Comparisons of the average velocity and the increasing rate of the three OSRVM models for the three driving speeds. model dramatically decreased, enhancing the homogenous airflow and noise radiation around the mirror housing.
# Structural-Missing Tensor Completion for Robust DOA Estimation with Sensor Failure +
## 5 Simulation
### Comparison of the Estimation Accuracy with Different Number of Damaged Sensors  
In Figure 4, we compare the estimation accuracy of the proposed structural-missing tensor completion algorithm with those of the matrix completion method [23] and the CPD method [37], where the number of impaired sensors varies from 1 to 23. The SNRs of sources are equally set to 15 dB, and the number of snapshots is fixed to \(T=300\).  
It is clear that the proposed algorithm is superior to the compared methods in all simulated sensor failure scenarios. The improvement comes from the utilization of multi-spatial signal characteristics, and more importantly, the effective recovery of structural-missing elements in the formulated tensor signal. It is also worth mentioning that as the number of impaired sensors increases (30% sensor failure with 15 impaired sensors), the performance of all methods degrade, while the proposed algorithm presents a moderate reduction in estimation accuracy. The proposed method fails to estimate the DOA when the number of impaired sensors reaches 23 (47% of the sensors are impaired), which can be regarded as the boundary for the proposed algorithm. However, in practice, it is unusual  
Figure 3: DOA estimation with different array geometries.  
to have a very high percentage of impaired sensors. The above findings demonstrate the high robustness of the proposed algorithm against serious sensor failure conditions.
## 3 Results
### Time-Efficiency  
In order to evaluate the time efficiency of MI-PLIF surgeries performed by the assistance of C-arm fluoroscopy or iCT, the total operation length and the duration of pedicle screw insertion was assessed. The median time length for pedicle screw placement and the total operation (in hours:minutes:seconds) was 02:01:41 (1:47:59-2:41:03) and 03:46:31 (3:22:35-04:29:20) with iCT, and 1:20:10 (01:02:45-01:45:52) and 2:16:00 (1:52:02-03:01:06). With C-arm fluoroscopy, respectively. In general, pedicle screw insertion last approximately 42 min longer on average during surgeries performed with the assistance of iCT, compared to C-arm fluoroscopy, and the total operation lasted circa 90 min longer on average with iCT. Statistical analysis confirmed a significant difference in time efficiency between MI-PLIF surgeries assisted by iCT and C-arm fluoroscopy (Figure 8).
# A Partitioned IRS-Aided Transmit SM Scheme for Wireless Communication
## 2 System Model
### Maximum Likelihood Detection  
A joint detection of antenna index \(t\) and symbol \(x\) based on the ML detector is utilized for our PIRS-TSM scheme, written as follows:  
\[(\hat{t},\hat{x})=\underset{t,x}{\text{argmin}}|y-Lx|^{2}, \tag{3}\]  
where \(\hat{t}\), \(\hat{x}\) represent the estimated antenna index and symbol, respectively.
## 4 Graph Theoretic Resiliency Framework
### Preliminaries of the Resiliency Estimation Framework
The following vital observation can be noted:  
A resilient SCN must follow the resiliency curve shown in Figure 4. However, a highly resilient SCN should preferably have a shorter recovery phase (i.e., \(\min\xi_{rec}\)) and a longer degradation phase (i.e., \(\max\xi_{\text{deg}}\)). It is worth noting that a higher slope for the recovery phase and lower slope for the degradation phase are desirable.
# Identification of Wiener Box-Jenkins Model for Anesthesia Using Particle Swarm Optimization
## 4 Simulation Results
### Data Collection  
To generate the required input-output data, the patient model (1) and (2) is simulated under closed-loop PID control as shown in the block diagram of Figure 3. In the figure, the set point is the desired level of neuromuscular blockade (NMB), \(r(t)\) is the actual level of NMB measured by the supra-maximal train-of-four (TOF) stimulation of the ulnar nerve, \(e(t)\) is the error or difference between the desired and actual NMB, and the atracurium infusion rate is denoted by \(u(t)\)[2].  
The transfer function of the PID controller used is expressed as  
\[u(t)=K\bigg{(}e(t)+\frac{1}{T_{i}}\int_{0}^{t}e(\tau)d\tau+T_{d}\frac{de(t)}{ dt}\bigg{)}, \tag{19}\]  
where the parameters \(K\), \(T_{i}\), and \(T_{d}\) are the controller gain, integral time, and derivative time, respectively. In practice, the derivative term is filtered using a first-order filter  
\[G_{f}(s)=\frac{T_{d}s}{\frac{T_{d}}{N}s+1}, \tag{20}\]  
where the derivative gain \(N\) is usually set to 10. The settings for both the PID controller gains and the parameters for patient number 17 in the MPP dataset [37] are listed in Table 2.  
One hundred Monte Carlo simulation runs of the MPP body model response to muscle relaxants are conducted using a sample period of 0.1 min. The length of each run is 300 min, in which in the first 150 min, the set-point is 10% corresponding to the maintenance phase. It should be noted that before the start of the maintenance phase, an initial induction of a standard 500 [ug/kg] drug bolus is used. After the injection of the initial bolus, the PID controller starts to operate after 20 min. The output of the PID controller is normalized with respect to the patient weight (70 kg) and bounded by a saturation block representing infusion limits [37]. The remaining 150 min of each run represent the _recovery_ phase where the set-point is set to 99%.  
An independent sequence of binary random numbers (value \(\pm\) 1) are filtered using the following first-order filter:  
\[G(z)=\frac{0.05}{1-0.5z^{-1}},\]  
and added to the set-point in the whole 300 min period. The reason for using the filter is to avoid large control signals due to the derivative action. The atracurium infusion rate, \(u(t)\), in [ug/kg], and the level of neuromuscular blockade (NMB), and the reference signal are shown in Figure 4, in one sample run.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Population size, \(N_{p}\) & 50 \\ \(\theta_{min}\) & \(-2\) \\ \(\theta_{max}\) & \(+2\) \\ Inertia coefficient, \(w\) & 1.0 \\ Damping ratio of inertia coefficient, \(\zeta_{damp}\) & 0.9 \\ Personal acceleration coefficient, \(c_{1}\) & 2.0 \\ Social acceleration coefficient, \(c_{2}\) & 2.0 \\ Maximum iterations & 00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the PSO algorithm.
## 4 Experimental Result Analysis
### Load-Bearing Capacity of Full-Scale Specimens  
The push-out tests provided the failure modes, load-displacement curves, and shear capacities of the studs for the three full-scale specimens (Z-40, Z-32, and Z-16). Table 3 presents the shear capacities of the studs for each specimen. The results indicated that group stud connectors exhibited a significant degree of ductile failure during loading, with the studs undergoing substantial plastic deformation before ultimately shearing off. This failure mode is in agreement with the classical shear failure behavior of studs.  
Figure 10: Comparison of strain distribution patterns at different slip levels. (**a**) Specim Z-40; (**b**) Specim Z-32; (**c**) Specim Z-16; (**d**) Specim Z-13.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen** & **Z-40** & **Z-32** & **Z-16** \\ \hline Final load/kN & 13,757 & 11,261 & 5789 \\ Final deformation/mm & 3.90 & 3.50 & 3.34 \\ Shear capacity of single stud/kN & 85.98 & 87.98 & 90.46 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Push-out test results of group studs.  
The test data indicated that the ultimate loads for the Z-40, Z-32, and Z-16 specimens were 13,757 kN, 11,261 kN, and 5789 kN, respectively. The shear capacities per stud were 85.98 kN, 87.98 kN, and 90.46 kN, respectively. Although the reduction in the number of studs led to a decrease in the overall load-bearing capacity of the specimens, the shear capacity of individual studs increased slightly. This suggests that the group stud effect became more evident as the number of studs increased. Compared to the theoretically calculated value of 118.1 kN, the shear capacities of the full-scale specimens were reduced by 23.4% to 27.2%, primarily due to the uneven distribution of shear forces among the studs caused by the group stud effect. In denser stud arrangements, such as in the Z-40 specimen, the studs on the outer edges bore significantly more load than those in the center.
## 2 Literary Review and State of the Art
### Literary Review
In this context [42], based on the reference model, the study proposes an architecture that enables vertical and horizontal integration of the value chain, exemplified through a use case in the automated production of personalized juices, the main challenge being the need to ensure interoperability and seamless communication between the different systems in the value chain, especially when dealing with historical and real-time data; thus, a service-oriented architecture (SOA) that uses microservices and Asset Brokering Manager (ABM) to maintain a data flow between the flat and the cloud was employed. On the other hand, in the Moroccan industry [43], the researchers discover challenges such as integration with legacy systems, data management and privacy, cybersecurity risks and the acquisition of specialized talent, especially in the context of an emerging country like Morocco. In this context, the researchers present a simplified methodology to deploy Industry 4.0 technologies; that is, they show a structured framework that guides companies, especially in emerging countries, in the effective adoption of industrial digitization, optimizing processes, predictive maintenance and performance management. Starting with the digitization of assets and improving interoperability between systems by implementing SCADA and real-time data analytics, they incorporated predictive maintenance and performance management technologies to optimize operational efficiency, used Hadoop (Cloudera) as a data storage platform to handle large volumes of information and used MQTT/DSS for data management and transfer. Its validation was through proof of concept (PoC), where technology solutions were implemented and tested in a controlled environment before full deployment. While RAMI 4.0 provides a robust framework for digital transformation in manufacturing, its implementation can be complex due to the diverse nature of industrial systems and rapidly evolving technology. The success of the framework depends on continuous adaptation and the development of complementary methodologies to address industry-specific needs. As industries continue to embrace digitization, the referential model will play a crucial role in guiding the integration of emerging technologies and ensuring the interoperability and efficiency of industrial systems.
## 2 Results and Discussion
### Optimization of the Extraction Procedure
#### 2.2.3 Desorption Condition  
Desorption time and desorption temperature are two important variables that affect the accuracy and precision of the HS-SPME-GC analysis, especially when using a handmade fiber. Therefore, the effect of desorption condition was investigated by applying 0.5, 1, 2, 2.5, 3, and 4 min as the desorption time, and 150, 200, 250, 260, 270, 280, 290, 300, 310, and 320 \({}^{\circ}\)C as the desorption temperature. The results are represented in Figure 5. In accordance with the results, the best time for complete desorption of the analyte from the fiber was 2 min, while 300 \({}^{\circ}\)C was obtained as the best desorption temperature. This desorption temperature seems a bit high for MTBE as a volatile molecule (boiling point: 55.2 \({}^{\circ}\)C). It can be due to the deep penetration of analyte into the sorbent pores and cavities with stronger interactions, which require higher temperature and longer times to be desorbed.
# Proof-of-Concept of a Millimeter-Wave Integrated Heterogeneous Network for 5G Cellular
## 1 Introduction
The rest of this paper is organized as follows. Section 2 introduces the system architecture and the components of the mmWave-integrated HetNet. Section 3 discusses state-of-the-art mmWave antenna technologies for backhaul and access. Section 4 presents our PoC of the mmWave-integrated HetNet. Finally, Section 5 presents our conclusions.
# Polycaprolactone Blended Cosmetic Microneedle for Transdermal Hispidin Delivery System
## 3 Results and Discussion
### Anti-Inflammatory Test  
To determine whether hispidin, PLA, and PCL may have effects on macrophage cell viability, RAW264.7 macrophage cells were treated with hispidin at various concentrations (0, 1, 2.5, 5, 10, and 20 \(\upmu\)M) and with PCL and PLA (10 to 10,000 ppm) for 24 h. Hispidin, PLA, and PCL showed no effects on macrophage cell viability (Figure 5a-c). To examine whether hispidin has anti-inflammatory activities, the nitric oxide (NO) levels in RAW264.7 macrophage cell supernatants were examined. The results showed that treatment with hispidin, PCL, and PLA could dose-dependently decrease the production of NO in LPS-treated RAW264.7 macrophage cells (Figure 5d). Furthermore, the expression levels of iNOS and COX-2 were tested according to RNA levels. Hispidin at 50 \(\upmu\)M decreased iNOS RNA levels (Figure 5e). Furthermore, hispidin treatment at 50 \(\upmu\)M significantly downregulated the LPS-induced iNOS protein expression (Figure 5f).  
Figure 3: Cell viability test with hispidin, PCL, PLA, and their mixture in B16F10 melanoma cells. (**a**) B16F10 cells were treated with various concentrations (1–20 \(\upmu\)M) of hispidin, (**b**) PCL (10–10,000 ppm), and (**c**) PLA (10–10,000 ppm) for 24 h. Cell survival was measured by 3-(4,5-dimethylthiazol-2-yl)/2,5-diphenyltetrazolzolium bromide (MTT) assay. (**d**) B16F10 cells were pretreated with hispidin (1–20 \(\upmu\)M) mixed with PLA/PCL at identical concentrations (10,000 ppm). * _p_\(<\) 0.05, **_p_\(<\) 0.01 versus control.  
Figure 4: Effects of hispidin and mixtures of substance on melanogenesis using B16F10 melanoma cells. (**a**) B16F10 cells were treated with various concentrations (1–20 \(\upmu\)M) of hispidin. (**b**) B16F10 cells were pretreated with hispidin (1–20 \(\upmu\)M) mixed with PLA/PCL at identical concentrations (10,000 ppm). \({}^{**}\)\(p<0.01\) versus control.  
Figure 5: PLA, PCL, and hispidin inflammation in RAW264.7 cells. (**a**) Cells were treated with different concentrations of PLA, (**b**) PCL, and (**c**) hispidin with PLA/PCL, and incubated for 24 h. Cell viability was measured by MTT assay. (**d**) RAW264.7 cells were treated with indicated treatments. Levels of nitric oxide (NO) production were measured using Griess reagent. (**e,f**) The RNA and protein expression levels of inducible nitric oxide synthase (iNOS) and cytochrome c oxidase subunit II (COX-2) were detected by RT-PCR and Western blot testing, and the results were quantified. \({}^{*}\)\(p<0.05\), \({}^{**}\)\(p<0.01\), \({}^{***}\)\(p<0.001\) versus control.
# A Novel Genetic Algorithm for the Synthetical Sensor-Weapon-Target Assignment Problem
## 4 Experimental Studies
### Experiments on Comparison Algorithms  
In these experiments, the performances of the comparison algorithms are analyzed. As the optimal S-WTA scheme is unknown for each instance, we use the fitness of individual as the comparative metric. Therefore, we propose that the greater the fitness of an individual, the better the corresponding S-WTA scheme.  
As the proposed GA-SWTA and ds-GA are performed for 30 independent runs for each instance, we demonstrate the results from 30 runs. Although the MRBCH and Swt-opt are deterministic algorithms, their results are also shown from 30 runs to facilitate the analysis. Furthermore, for instance 21 to instance 25, the results of Swt-opt are not available within the acceptable time (15,000 s). The experimental results are presented in Figure 11.  
As shown in Figure 11, GA-SWTA performs better than other comparison algorithms in most instances. For the instance No. 1, ds-GA and GA-SWTA have the same performance. For the instance No. 3, all the algorithms can get their best result. For the instance No. 19, the performance of the Swt-opt outperforms other algorithms. For the instance No. 4 to the instance No. 25, GA-SWTA performs remarkably better than ds-GA. However, for the very large-scale instances, No. 24 and No. 25, MRBCH has the best performance.  
Based on the experimental results presented above, it can be concluded that our proposed algorithm can effectively solve the S-WTA problem. Although the MRBCH and the Swt-opt outperforms GA-SWTA in a few cases, GA-SWTA can still provide high-quality solutions for all instances. In addition, if the scale of the problem is small, the MRBCH, ds-GA, and proposed GA-SWTA have the similar performance; as the solution space of the small-scale instances is small, these algorithms can easily search most areas of the solution space. Moreover, if the scale of the problem is very large, MRBCH can achieve better results in solving this problem. This is because the used evolutionary operators are originally designed for small-scale WTA problems that lose efficiency when solving the large-scale S-WTA problem. Additionally, the Swt-opt has the best performance for the instance No. 3 and No. 19. However, for most instances, its performance is worse than other algorithms. Because the Swt-opt is designed to solve the special instance described in the work by the authors of [19], if more than one sensor or weapon can be assigned to one target, the objective cannot be calculated appropriately. Furthermore, the performance of ds-GA is worse than GA-SWTA for the vast majority of instances, which means that our proposed novel framework of GA is more suitable to solve the S-WTA problem.
# A Neural Parametric Singing Synthesizer Modeling Timbre and Expression from Natural Songs +
## 2 Proposed System
### Timbre Model
#### 2.3.1 Multistream Architecture  
Most parametric vocoders separate the speech signal into several components. In our case, we use three feature streams; a harmonic spectral envelope, an aperiodicity envelope and a voiced/unvoiced decision (continuous pitch is predicted by the pitch model and given as a control input). These components are largely independent, but their coherence is important (e.g., synthesizing a harmonic component corresponding to a voiced frame as unvoiced will generally cause artifacts, and vice versa). Rather than jointly modeling all data streams with a single model, we decided to model these components using independent networks. This approach gives us more fine-grained control over each stream's architecture, and also avoids the possibility of streams with lower perceptual importance interfering with streams of higher perceptual importance. For instance, the harmonic component is by far the most important, therefore we would not want any other jointly modeled stream potentially reducing model capacity dedicated to this component.  
To encourage predictions to be coherent, we concatenate the predictions of one network to the input of another, as depicted in Figure 4. In our current system, the aperiodic component depends on the harmonic component, and the voiced/unvoiced decision depends on both harmonic and aperiodic components. All the networks are similar, but have slightly different hyperparameters (see Table A1 in Appendix C for details). The voiced/unvoiced decision network has a Bernoulli output distribution rather than a mixture density (see Section 2.2.1). While we found this approach to generally work well, we did not exhaustively investigate the many other alternative approaches.