esa-sceva/satcom-chunk-collection huggingface dataset:
# Strategic Development of Piezoelectric Nanogenerator and Biomedical Applications
## 4 Biomedical Applications
### Bio Implant  
PENGs have also been explored for implantable applications owing to the biodegradable properties of piezoelectric materials. Considering this advantage, [PERSON] et al. [129] developed a PENG to enhance the repair of nerve tissues. They fabricated a device based on KNN, a self-powered in vivo electric stimulator, based on KNN, poly(L-lactic acid) (PLLA), and the proposed Poly(3-hydroxybutyrate-co\({}_{3}\)-hydroxyvalerate) (PHBV). Poly(lactic acid) (PLA) or Poly(\(\epsilon\)-caprolactone) (PCL) was used for encapsulation, as shown in Figure 11B(a,I,II). The fabricated device was biodegradable and able to harvest energy from ultrasounds. They investigated the functionality of the device in different media, such as water and cell culture, to confirm its biodegradable nature. Furthermore, the fabricated device was implanted in a lab rat (Figure 11B(a)) for 3 weeks. They observed no signs of organ damage in rats and concluded that the fabricated devices were safe for in vitro and in vivo applications. The fabricated device successfully delivered electrical stimuli and promoted nerve repair and monitoring without the use of rectifiers (Figure 11B). [PERSON] et al. [130] introduced a self-powered cardiac pacemaker based on the polymer PVDF. PVDF is a piezoelectric polymer that is safe and bio-compatible. Batteries have a limited life span, after which they require replacement. We can overcome this limitation with the use of self-powered devices. In the abovementioned study, the author fabricated a PVDF/ZnO/rGO-based PENG using the electrospinning method to power a pacemaker. the pacemaker was tested on an animal model. After the implantation of a self-powered pacemaker on the lateral wall of the left ventricle of a dog animal model, the PENG harvested 0.487\(\upmu\)J of energy with every heartbeat. This could represent a great step toward the fabrication of smart implant devices, which shows the future potential of self-powered implant devices. [PERSON] et al. [131] fabricated an electric stimulator for the acceleration of wood repair. The fabricated device was based on PVDF. They fabricated a piezoelectric hydrogel component made from carbonized polydopamine/polydopamine/polyacrylamideamide paired with an electrospun PVDF membrane. The hydrogel exhibited good mechanical strength, which supports activities such as walking, stretching, etc. The fabricated film encouraged the growth of cells via electric stimulation produced by body movements; in addition, it provided bacterial protection to the underlying wound.
###### Abstract  
Purpose: Given the growth in the use of wearable measuring technology, this study aimed to investigate the frequency of writing about wearable monitoring devices in the field of sports sciences and sports-related health professions during the years affected by the COVID-19 pandemic (2020 to 2022). The goal was to observe the number of studies right before the quarantine and during the first years of pandemic. Methodology: A systematic literature analysis was performed in the Web of Science Core Collection (WoS CC) and Scopus databases in March 2023. The filters used in the search were the following: original scientific papers in the English language and open access. The research field was sports sciences in the past three years (2020-2022) in the Wos CC, and health professions and medicine in Scopus. Results: The initial search resulted in 54 studies in the WoS, 16 of which were included in a detailed qualitative analysis, and 297 studies in Scopus with 19 of them analyzed (35 altogether). The keywords used were \"fitness watch\" (sport watch, smartwatch), \"smart shoes\", \"smart clothing\", \"smart ring\", \"smart belt\", and \"smart glasses\". In the past three years, there has been a steady increase in the number of studies using smart monitoring devices to measure their data (nine in 2020, nine in 2021, and seventeen in 2022). Results showed that the most used device is a smartwatch, while the most carried out studies were about physical activity and daily activities of living. Furthermore, there are more studies about measuring devices being used as testing equipment than about device performance in general. Conclusions: This study summarizes various research conducted in the field of sports with the use of wearable measuring devices to determine the frequency of use of such devices in sport studies.  
wearable monitoring technology; sensor systems; sport; physical activity; activities of daily life
# A Deep Learning Technique for Optical Inspection of Color Contact Lenses
## 2 Materials and Methods
### CNN-Based Contact Lens Discrimination  
Prior to discussing the CNN-based AOI system, this subsection reviews previous studies on CNN-based contact lens recognition (use of deep learning) as follows.  
* ContlensNet [11]: ContlensNet is a CNN trained with a large capacity of iris images and can be divided into three classes based on the iris images: normal (no lenses worn), textured (or colored) contact lenses worn, and soft (or clear) contact lenses worn.
* DCLNet [12]: DCLNet is a CNN designed via the customization of a support vector machine (SVM) in Densenet121 and detects contact lenses from iris images captured from heterogeneous sensors.
* DensePAD [13]: DensePAD is designed based on DenseNet with a depth of 22 to classify input iris images as real or iris spoofing with textured contact lenses.
* GHCLNet [14]: GHCLNet is a CNN based on ResNet-50 and can be classified into three categories: no, textured, and soft lens, without preprocessing of the iris image.
* In [15; 16; 17; 18; 19], a CNN was used to detect contact lenses in real time from iris images. Further, in [20], various CNNs were applied to detect defective colored contact lenses and the results were analyzed.  
Among these previous studies, [11; 12; 13; 14; 15; 16; 17; 18; 19] focused on determining whether contact lenses were worn based on iris images, whereas [20] focused on determining whether colored contact lenses were defective. However, most contact-lens-related studies using CNNs are related to contact lens discrimination for iris recognition systems, and few studies on CNN-based lens defect detection have been conducted. Therefore, this study proposes a contact lens defect detection process using a CNN to determine defects occurring during the lens production process.
## 5 Numerical Examples
### Experimental Runs Results
#### 5.2.3 Experiment 3  
The last experiment compared the different policies adopted to solve the problem. The no maintenance policy (NoMaint) implemented the _do-nothing_ action in each pipeline state except for the last state, where the pipeline would have to be replaced. The predefined policy was implemented by determining the maintenance action for a given state. This policy was labeled as DefPolicy. The effectiveness of implementing only inhibitors throughout the life of a pipeline was tested by setting the action to be _inhibit_ for given states and _do-nothing_ for all remaining others. Similarly, the pigging maintenance action was assessed by setting the action to _pigging_ for some states and _do-nothing_ for others.  
For the CSPOMDP, the different policies have been implemented on a medium-size model similar to the one described in experiment 2 (Section 5.2.2). Figure 6 shows the resulting distribution for each policy's discounted total cost. The box plot for each policy can be compared to assess the performance of each policy and determine the best alternative for  
Figure 4: Example of CSPOMDP model run results (It shows the total discounted reward distribution’s minimum and maximum values, 25 and 75 percentiles, and outliers for the 100 replications using the two solvers).  
Figure 5: Example of DSPOMDP model run results (It shows the total discounted reward distribution’s minimum and maximum values, 25 and 75 percentiles, and outliers for the 100 replications of the two DSPOMDP [7] using the two solvers).  
implementation. The cost of each policy can also be estimated from these plots. Accordingly, the DefPolicy was the best policy, followed by Pigging, POMCP, ARDESPOT, and Inhibitor, while NoMaint was the worst of the alternatives.  
The DS model run was also performed for the policies described above. Figure 7 shows that ARDESPOT and POMCP were the better policies, and no significant differences could be made among DeffPolicy, Pigging, and Inhibitor policies. However, NoMaint was observed to be the worst of all. Overall, the total cost was higher for this set of policy runs.  
Figure 6: Example of CSPOMDP model run results (POMCP and ARDESPOT—the two solver policies; NoMaint—only _do-nothing_ action; DefPolicy—implements predefined maintenance actions; Inhibitor—_Inhibitor—_Inhibitor_ and _do-nothing_ actions; Pigging—_Pigging_ and _do-nothing_ actions).  
Figure 7: Example of first DSPOMDP model run results (POMCP and ARDESPOT—the two solver policies; NoMaint—only _do-nothing_ action; DefPolicy—implements predefined maintenance actions; Inhibitor—_Inhibitor_—_Inhibitor_ and _do-nothing_ actions; Pigging—_Pigging_ and _do-nothing_ actions).
## 6 Numerical Simulations
### Control Parameter Setting Strategy  
In this subsection, some setting strategies are presented for the adjustment of the path following system parameters. The navigation module's parameters \(\kappa\), \(\gamma\), \(\Delta_{\min}\) and \(\Delta_{\max}\) and the control module's parameters \(k_{1}\), \(k_{ue}\), \(k_{re}\), \(\gamma_{u}\), \(\kappa_{u}\), \(\gamma_{r}\), \(\kappa_{r}\), \(K_{eu}\), \(\xi_{u}\), \(K_{er}\), \(\xi_{r}\), \(A\), \(B\) and \(D\) should be carefully tuned to optimize control performance. On the whole, there is no fixed principle for the adjustment of all parameters. However, according to the design principle of the path-tracking strategy and the practical experience of scholars, we can qualitatively sum up some rules.  
1. For the navigation system, larger values of gain \(\gamma\) and adaptive gain \(\kappa\) mean that the sideslip angle can converge at a faster rate. Meanwhile, \(\Delta_{\min}\) and \(\Delta_{\max}\) can also affect the convergence rate. A fast convergence rate means that the sideslip angle can be compensated better, but it certainly adds to the possibility of oscillation or divergence in the navigation system.
2. Larger gains \(k_{ue}\) and \(k_{re}\) do not affect the amplitude of generated control signals in (41) and (47). They are adjusted to get the desired following performance without consideration of the actuator saturation. Nevertheless, larger \(k_{ue}\) and \(k_{re}\) may cause unnecessary chattering of the control signals.
3. Large values of adaptive gains \(\gamma_{r}\) and \(\gamma_{u}\) in (44) and (50) increase the learning speed of the neural network minimum parameter learning method. This means that USV can obtain more accurate following performance. However, too fast a learning speed will affect the stability of the control system. \(\kappa_{r}\) and \(\kappa_{u}\) are used to optimize (44) and (50), respectively.
4. \(k_{1}\), \(A\), \(B\) and \(D\) can affect the response speed of the control system. Faster system response enables USV to reach the reference path in a shorter time, but it also raises the possibility of system instability.
5. \(K_{er}\), \(\xi_{r}\), \(K_{eu}\) and \(\xi_{u}\) are employed to adjust the auxiliary dynamic system. Only a suitable set of parameters can be used to achieve the desired results.  
In summary, the various models of the path-following system interact with each other. When adjusting the navigation or control parameters, both the control effect and the stability of the system need to be taken into consideration. Enough patience and full consideration are needed to solve this problem.
## 3 Results
### Accuracy of Prediction Models  
Both ROC and AUC metrics were used for evaluating the prediction model's performance. ROC analysis is used for evaluating the accuracy of the prediction model that classifies subjects into hypertension or non-hypertension, whereas AUC summarized the prediction accuracy. In addition, 5-fold cross-validation was also performed to obtain the estimates and confidence intervals for the AUC.  
The AUC (95%CI) value in the women is 0.827 (0.794-0.860) The AUC value for men is slightly lower than total women with the AUC (95% CI) value of 0.706 (0.652-0.760) as shown in Table 8. The ROC curves for LASSO using 5-fold cross-validation were shown in Figure 4.
## 4 Conclusions  
In this study, we performed FEA to improve the safety and rigidity of an indoor bicycle handle frame equipped with a monitor. The optimal design was obtained using simple analysis, and SVR was performed additionally.  
1. The load applied to the bicycle handle frame was divided into a total of four cases to perform the structural analysis. Among the four load conditions, cases with a standard safety rate below 2.0 were selected, and those two load cases were considered for optimal design.
2. Five design variables that affected the minimization of effective stress were assigned, and a total of 32 analyses were performed using a five-factor, two-level factor arrangement method. The results of the sub-analysis were derived, and their consequent sensitivity analysis and correlation with the maximum effective stress, which was the objective function, were confirmed through the Pareto chart. All five design variables were found to be significant. Therefore, 125 datasets, at five factors and three levels, were obtained and compared using regression equation and prediction equation with SVR.  
Figure 10: Data normalization results.  
Using the SVR of the radial basis function kernel method, the learning model with the highest accuracy was derived by obtaining the optimal values of \(C\), \(\varepsilon\), and \(\gamma\), which represented penalty values for the error interval. Herein, the mean square error was used for comparing the accuracy, and the best results were obtained when \(C=100\), \(\varepsilon=0.001\), and \(\gamma=1\).
4. Upon comparing the SVR prediction model with the existing regression equation through \(R^{2}\) values, the accuracy of the SVR model was found to be higher. Accordingly, an optimal design was conducted to secure the safety and rigidity of the monitor handle using the SVM prediction model. The design variable values wherein the effective stress from the model was minimized were \(x_{1}=120^{\circ}\), \(x_{2}=90^{\circ}\), \(x_{3}=30\) mm, \(x_{4}=0\) mm, and \(x_{5}=4.8\) mm. Consequently, the effective stress was reduced by up to 80% compared with the initial model. FEA was conducted by redesigning the model with the obtained values of the design variables, and the results obtained were similar to the predicted values. Thus, the reliability of this study was confirmed.  
Conceptualization, S.H.; methodology, S.H. and E.N.; software, E.N.; validation, E.N.; investigation, E.N.; writing--original draft preparation, E.N.; writing--review and editing, S.H.; visualization, E.N.; supervision, S.H.; project administration, S.H. All authors have read and agreed to the published version of the manuscript.  
This research was supported by \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021 RIS-004).  
Not applicable.  
The authors declare no conflict of interest.
# An Efficient Imaging Method for Medium-Earth-Orbit Multichannel SAR-GMTI Systems
## 3 Proposed Imaging Method
### Secondary Range Compression  
It can be seen from (23) that there are still quadratic and cubic terms of range frequency after range compression. If these terms are not compensated, defocus will occur in the range direction. Therefore, the operation of SRC is implemented, and the filter is constructed as follows  
\[H_{SRC}(f_{r},f_{a})=\exp\Biggl{\{}-\frac{j\pi f_{r}^{2}}{F_{ref}(f_{a})}-\frac {j\pi f_{r}^{3}}{M_{ref}(f_{a})}\Biggr{\}}, \tag{27}\]  
with  
\[\frac{1}{F_{ref}(f_{a})}=\frac{3\lambda l_{10}l_{30}}{8f_{c}2l_{20}^{3}}{f_{a}}^ {2}+\frac{\lambda}{4f_{c}2l_{20}}{f_{a}}^{2}+\frac{3\lambda^{2}l_{30}}{16f_{c}2l _{20}^{3}}{f_{a}}^{3}, \tag{28}\]  
\[\frac{1}{M_{ref}(f_{a})}{=}-\frac{3\lambda l_{10}l_{30}}{8f_{c}3l_{20}^{3}}{f_{ a}}^{2}-\frac{\lambda}{4f_{c}3l_{20}}{f_{a}}^{2}-\frac{\lambda^{2}l_{30}}{4f_{c}3l_{20} ^{3}}{f_{c}3l_{20}^{3}}{f_{a}}^{3}, \tag{29}\]  
where \(l_{10},l_{20}\) and \(l_{30}\) define the coefficients of the linear, quadratic, and cubic terms of range history of the static target located at the center of the illuminated scene.  
Comparing (25) with (28) and (26) with (29), respectively, one can see that there is a residual phase error after SRC by (27), and it can be expressed as  
\[PE_{r}=\Biggl{|}\frac{\pi f_{r}^{2}}{F_{1}(f_{a})}+\frac{\pi f_{r}^{3}}{M_{1}(f _{a})}-\frac{\pi f_{r}^{2}}{F_{ref}(f_{a})}-\frac{\pi f_{r}^{3}}{M_{ref}(f_{a}) }\Biggr{|}. \tag{30}\]  
This phase error is very small (about \(10\)\({}^{-10}\)) and much less than \(\pi/4\), and thus it can be neglected. Therefore, the signal after SRC is given by \[\begin{array}{ll}S_{1}(f_{r},f_{a})&=W_{r}(f_{r})W_{a}(f_{a})\exp\biggl{\{}j \biggl{(}-\frac{4\pi R_{0}}{\lambda}+\frac{\pi l_{1}^{2}}{\lambda L_{2}^{2}}+ \frac{\pi l_{1}^{3}l_{13}}{2\lambda L_{2}^{3}}\biggr{)}\biggr{\}}\exp\{-j\pi f_{ r}\tau_{1}(f_{a})\}\\ &\times\exp\biggl{\{}j\biggl{(}\frac{\pi l_{1}}{l_{2}}(f_{a}+M\cdot PRF)+ \frac{3\pi l_{1}^{2}l_{13}}{4l_{2}^{3}}(f_{a}+M\cdot PRF)\biggr{)}\biggr{\}}\\ &\times\exp\biggl{\{}j\biggl{(}\frac{\pi\lambda}{4l_{2}}(f_{a}+M\cdot PRF)^{2} +\frac{3\pi l_{1}l_{13}}{8l_{2}^{3}}(f_{a}+M\cdot PRF)^{2}+\frac{\pi l_{3} \lambda^{2}}{16l_{2}^{3}}(f_{a}+M\cdot PRF)^{3}\biggr{)}\biggr{\}}.\end{array} \tag{31}\]
## 3 Distributed Sensing  
In terms of distributed sensing, one paper is presented by [PERSON] et al. [8]. In their research article, the real-time train tracking from distributed acoustic sensing data is reported by presenting an algorithm that extracts the positions of moving trains for a given point in time from Distributed Acoustic Sensing (DAS) signals.
## 2 Micro- and Nano-Plastics in Aquatic Ecosystems Can Be Taken-Up by Fish and Reside in Their Brain  
Since microplastics (MPs) and nanoplastics (NPs) ingestion by aquatic organisms is reported by laboratory and field studies with a regard to commercial species too [36; 37; 38], an unavoidable concern is whether MPs and NPs, one ingested by fish, can be absorbed by the gastrointestinal (GI) tract and can pass into other body districts. However, this field of knowledge just started to be investigated recently. A proof of the MPs and NPs movement from intestine to other body compartments is represented by the evidence that, once these plastics are ingested, they can be found in different organs, outside the GI tract [39]. Oncethe gastrointestinal barrier is bypassed, also the brain may be at risk of exposure when micro and nanoplastics are able to pass the blood brain barrier (BBB). According to [PERSON]'s study on _Oreochromis niloticus_[40], MPs reach the fish brain along with blood circulation. This agrees with [PERSON] [41] and his team, hypothesizing that since nanoplastics can pass into red blood cells, as shown by [PERSON]'s in vitro study [42], they can reach the brain through the blood. Indeed, nanoplastic particles have been identified in the brain of fish after waterborne [43] or food-mediated exposure [35], indicating that they are capable of crossing a highly selective permeability barrier as the blood brain one [44]. [PERSON] et al. [35] demonstrated that, once ingested, 53 and 180 nm sized polystyrene plastics can arrive into the brain of Crucian carp (_Carassius carcassius_). [PERSON] et al. [45] demonstrated that 0.3, 5, and even 70\(-\)90 \(\upmu\) polystyrene MPs could be accumulated in the brain, indicating their tissue translocations within red tilapia (\(O\). _niloticus_). In their reviews, [PERSON] [46] and [PERSON] [47] agree that about 10 \(\upmu\)m or smaller microplastics might penetrate organs and cross the blood-brain barrier reaching the brain (Table 1). Furthermore, micro and nanoplastics could reach the brain before the BBB is established and this could result in an uptake of NPs by the brain at early stages of fish life. Indeed, [PERSON] et al. [41] demonstrated that 20 nm diameter polystyrene nanoplastics (PS-NPs) injected to yolk sac can reach the brain and bioaccumulate there. Even when zebrafish (_Danio rerio_) embryos are just exposed to NPs, these ones can accumulate in the yolk sac and migrate to brain [48]. However, [PERSON] [41] and his team also hypothesized that the presence of PNPs in the brain may be due to the alteration of the hemodynamic physiology induced by these plastics. These and other pathways traveled by MPs and NPs to the brain add up: NPs can reach the head of young zebrafish that have just undergone an in-water exposure [49] and MPs can cross the gills, reach the fish head, and accumulate there [50]. Therefore, it is necessary to investigate the effects micro and nanoplastics have on the brain.
# A Review of the High-Mix, Low-Volume Manufacturing Industry
## 9 Discussion
### Manual Production in HMLV  
Question 6: \"How is manual production optimised for HMLV?\"  
Few studies address ergonomics and productivity in the HMLV environment. The assembly complexity assessment model has been developed to measure the complexity of the assembly tasks to provide the foundation of work allocation to match workers' suitability.  
Assembly assistance systems, in the form of digital work instruction, have also been developed to improve the operators' abilities of the operators through the concept of Operator 4.0. Operator 4.0 includes assistance systems with adaptive real-time support, performance monitoring and data collection to aid the operator in manual assembly. Such assistance systems hope to reduce the cognitive load of the operators to cope with the high variety of information exchange while working in an HMLV environment.
# GPS Multipath Analysis Using Fresnel Zones
###### Abstract  
GNSS (Global Navigation Satellite Systems) multipath has been subject to scientific research for decades and although numerous methods and techniques have already been developed to mitigate this effect, it is still one of the accuracy-limiting factors in many GNSS applications. Since multipath is highly dependent on the individual antenna environment, there is still a need for new methods and further investigations to increase the understanding of this systematic effect. In this paper, the concept of Fresnel zones is applied to two different aspects of multipath. First, Fresnel zones are determined for the line-of-sight transmission between satellite and receiver. By comparing the boundary of the Fresnel zones to an obstruction adaptive elevation mask, potentially diffracted signals can be identified and excluded from the position estimation process. Both the percentage of epochs with fixed ambiguities and the positioning accuracy can be increased by the proposed method. Second, Fresnel zones are used to analyze the multipath induced by a horizontal and spatially-limited reflector. The comparison of simulated and real signal-to-noise (SNR) observations reveals a relationship between the percentage of the overlap of the Fresnel zone and reflector and the occurrence of multipath. It is found that an overlap of 50% is sufficient to induce multipath effects. This is of special interest, since this does not confirm theoretical assumptions of the multipath theory.  
GPS; Fresnel zones; multipath; signal diffraction; obstruction adaptive elevation masks +
Footnote †: journal: Sensors