esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study
## 2 Reliability of Sensors in Cyber-Physicals Systems. A Brief State-of-the-Art Review
Scheduling policies formulated as a risk-sensitive Markov Decision Process were considered in an asymptotic approach to reliability [49]. Likewise, the Markov model for the reliability analysis of sensor nodes in wireless sensor networks was proposed in [3]. Similarly, the use of the stochastic optimization problem to maximize estimation reliability in water distribution networks was suggested in [50]. The quantification of the reliability of grid splitting under degraded communication conditions in the presence of cyber interdependencies was presented in [51]. Recently, a method based on the finite element model of a bridge integrated with structural vibration data for a self-governing structural health monitoring system was shown in [52]. A set of algorithms for estimating the reliability of the system under certain responsible time constraints was also recently suggested in [53]. Based on this review, it is evident that probabilistic-based approaches have been explored for addressing sensor reliability. Certainly, the evaluation of sensor reliability is a highly important task, especially for Internet of Things (IoT) applications, and new performance indices need to be proposed [54]. Some recommendations for the standardized evaluation of sensor reliability are given in [55]. A recent review of analyses of the state of the art time delays, network size, energy efficiency, scalability, and reliability of mobile wireless sensor networks can be found in [56]. Attention has also been focused on lowering latency and improving protocols [57; 58]. How can machine learning methods enable improvements in sensor reliability in cyber-physical systems? The answer to this question is not easy, because the conditioning and re-elaboration of machine learning methods for assessing sensor reliability is not straightforward. This is indeed a new research trend with some pioneering reports in the early of 1990s [59]. Bayesian approaches [60; 61], Fuzzy set theory [62; 63], Dempster-Shafer (D-R) evidence theory [64; 65; 66], and Grey Group Decision-Making [67], among other methods, have recently been explored to address the reliability of sensors using Artificial Intelligence. However, many issues related with sensor reliability in cyber-physical systems using Internet-of-Things data remain unsolved, such as efficient conditioning and pre-filtering of big sensor data, high computational cost and limited parametrization of machine learning methods.
## 2 Materials and Methods
### Optimization of the IDW Parameter with PSO  
The optimization we propose is to identify the value of the optimal \(\beta\), from (2) using PSO as the search algorithm. The PSO metaheuristic was chosen due to its simplicity with respect to coding,small computational cost and reduced number of parameters (by comparison with genetic algorithms, for example). PSO's documented success in optimization applications in a wide range of engineering problems [28; 29] was also a decisive factor in choosing PSO over other metaheuristics.  
The particles' positions in the PSO algorithm encode candidate solutions for the power parameter in Formula (2) of the IDW weights. They are randomly generated initially within the interval [1.0001, 5] (since \(\beta>1\), from the IDW definition).  
The fitness function used in the algorithm assigns to each particle the mean squared error (MSE) of the predictions made with IDW, using as weight the power parameter \(\beta\)encoded by the respective particle:  
\[\text{fitness}(\beta)=\frac{1}{n}\sum_{i=1}^{n}\left(p_{i}-m_{i}\right)^{2} \tag{15}\]  
where:  
* \(n\) is number of stations used in the IDW,
* \(p_{i}\) is the value predicted by IDW for the precipitation at station \(i\),
* \(m_{i}\) is the (known) measured value for the precipitation at station \(i\).  
As far as we know, the proposed approach is new in the literature.  
In the following we will refer to the improved algorithm as Optimized IDW (OIDW). Figure 4 presents the flowchart of the OIDW algorithm.
# AI-Powered Stroke Diagnosis System: Methodological Framework and Implementation
## 4 Methodology Development
### Technological Components
#### 4.1.2 Data Preprocessing and Normalization  
Structured clinical variables, including aggregated vital signs, laboratory measurements, demographic characteristics, and the binary stroke designation, are consolidated into a single dataset using the patient-stay identity. Absent numeric values are replaced with zero to indicate the lack of a recorded measurement. Categorical variables, including gender, ethnicity, and ICD-9 diagnosis codes, are converted via one-hot encoding, resulting in binary indicator columns. Each continuous variable is standardized to the \([0,1]\) range using min-max scaling to standardize all characteristics on a similar scale. Considering that less than 10% of cases in the eICU database pertain to stroke, we implement the SMOTE algorithm on the training subset to synthetically augment the minority class, thus ensuring our tree-based models do not neglect these infrequent yet clinically significant instances.  
Table 1 presents a succinct summary of all preprocessing, normalization, and model configuration specifics.
## Decision Fusion with Channel Errors in Distributed Decode-Then-Fuse Sensor Networks  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1 and [PERSON] 2**  
\({}^{1}\) School of Marine Science and Technology, Northwestern Polytechnical University,  
127 Youyi West Road, Xi'an 710072, China; E-Mails: [EMAIL_ADDRESS] (H.W.); [EMAIL_ADDRESS] (X.S.)  
\({}^{2}\) School of Electrical and Electronic Engineering, Nanyang Technological University,  
50 Nanyang Avenue, 639798, Singapore; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-29-8849-5836; Fax: +86-29-8849-2612.  
Academic Editor: [PERSON]  
_Received: 13 April 2015 / Accepted: 30 July 2015 / Published: 5 August 2015_  
**Abstract:** Decision fusion for distributed detection in sensor networks under non-ideal channels is investigated in this paper. Usually, the local decisions are transmitted to the fusion center (FC) and decoded, and a fusion rule is then applied to achieve a global decision. We propose an optimal likelihood ratio test (LRT)-based fusion rule to take the uncertainty of the decoded binary data due to modulation, reception mode and communication channel into account. The average bit error rate (BER) is employed to characterize such an uncertainty. Further, the detection performance is analyzed under both non-identical and identical local detection performance indices. In addition, the performance of the proposed method is compared with the existing optimal and suboptimal LRT fusion rules. The results show that the proposed fusion rule is more robust compared to these existing ones.  
**Keywords:** decision fusion; channel errors; average bit error rate; likelihood ratio test; decode-then-fuse; sensor networks
# Imaging the Permittivity of Thin Film Materials by Using Scanning Capacitance Microscopy
## 3 Results
From Figure 6b, it could be seen that the signal of SCM of MoO\({}_{3}\) film was slightly weaker than that of MoO\({}_{3}\) film-doped NaCl. The doped NaCl changed the thickness and capacitance of the film. If the value of \(h/\varepsilon_{r}\) of the film at every point could be obtained from the SCM image, the value of \(\varepsilon_{r}\) of the film at every point could be calculated with \(h=\overline{h}+\Delta h\). The 2D image of the relative permittivity \(\varepsilon_{r}\) could be obtained. From Figure 3b, every signal of SCM could be changed to the value of \(\frac{\text{d}\text{d}\text{d}\text{d}\text{d}\text{d}\text{d}\text{d}\text{d}}\) at the corresponding point. By using \(R_{0}=16.6\) nm, \(\theta_{0}=20^{\circ}\), \(z^{\prime}=10\) nm, \(k_{\text{strstrity}}^{dielectric}=0.043\) aF/nm, and Equation (20) above for data reconstruction, every value of \(\frac{\text{d}\text{d}\text{d}\text{d}\text{d}\text{d}\text{d}\text{d}}\) could be changed to the value of \(h/\varepsilon_{r}\) at the corresponding point. Then, the SCM image (shown in Figure 4) was reconstructed to the 2D image of \(h/\varepsilon_{r}\) (shown in Figure 6a). Figure 6b shows its profile for \(y=56.6\) um. It could be seen from Figure 6a,b that the value of \(h/\varepsilon_{r}\) of MoO\({}_{3}\) film was slightly larger than that of MoO\({}_{3}\) film-doped NaCl. From Figure 6a, every value of \(h/\varepsilon_{r}\) could be changed to the value of \(\varepsilon_{r}\) with above \(h=70\) nm \(+\)\(\Delta h\) at the corresponding point of Figure 3. Then, the 2D image of \(\varepsilon_{r}\) could be obtained (shown in Figure 7a). Figure 7b was its profile for \(y=56.6\) um  
Figure 5: (**a**) The thickness and (**b**) the SCM profiles for \(y=56.6\) μm. Figure 6: (**a**) The 2D image of \(h/\varepsilon_{r}\) and (**b**) its profile for \(y=56.6\) μm. It could be seen from Figure 7a,b that the value of \(\varepsilon_{r}\) of MoO\({}_{3}\) film was slightly smaller than that of MoO\({}_{3}\) film-doped NaCl. From Figure 7b, it could be seen that the average value of \(\varepsilon_{r}\) of MoO\({}_{3}\) film was about 8.0, and that of MoO\({}_{3}\) film-doped NaCl was about 9.5.
## 3 Results  
### Spatiotemporal Variations of Vegetation Growth  
We obtained the vegetation coverage levels of the upper and middle reaches of the YRB during the growing season from 2000 to 2019 (Table 3). The medium vegetation cover areas were the largest (33.54%), followed by medium-high vegetation cover areas (30.92%, Table 3). Overall, the spatial distribution of mean NDVI during the growing season declined trend from southeast to northwest (Figure 2a), which was consistent with the spatial pattern of precipitation (Figure S1a) but opposite to the spatial distribution of sunshine duration (Figure S1c). NDVI had no obvious relationship with the spatial pattern of temperature (Figure S1b). The NDVI of the forests and savannas is high, while the grasslands have a low NDVI. Although the upper reach is primarily occupied by grasslands (Figure 1d), rivers are widely distributed (Figure 1c), which is conducive to vegetation growth, showing a high NDVI value. Compared with other study areas, the southeast has more precipitation and has extensive forests, croplands, and savannas (Figure 1d), so the NDVI is relatively high.  
Figure 2: Spatial distribution of the mean NDVI (**a**) and inter-annual variation rate (slope) (**b**) during the growing season in the upper and middle reaches of the Yellow River basin from 2000 to 2019. Black dots indicate statistically significant correlations at the 95% confidence level. Same below.  
From 2000 to 2019, the mean NDVI during the growing season of the upper and middle reaches of the YRB exhibited a significant growth trend with a rate of 0.0056 per year (\(p<0.001\)) (Figure 2b). Similarly, the mean GPP and LAI also showed a positive and significant trend (\(p<0.001\)) at rates of 0.3718 per year and 0.0231 per year (Figure S2), respectively. The inter-annual variation of these vegetation indicators suggested that the vegetation coverage and productivity significantly increased in the study area over the two decades. In addition, we calculated the inter-annual variation rates of vegetation NDVI in each active season over the study period (Figure S3). The spatial distribution of inter-annual variation rates of NDVI in the early, peak, and late growing season (EGS, PGS, and LGS) was similar, with the rates equal to 0.0062 per year (\(p<0.001\)), 0.0059 per year (\(p<0.001\)), and 0.0046 per year (\(p<0.001\)), respectively. EGS showed the strongest trend of vegetation greening, followed by PGS, although PGS showed a higher vegetation degradation trend than the other two sub-seasons (Figure S3b). Only LGS had a lower vegetation variation trend than the 20-year growing season mean trend (0.0056 per year). For climate variables, the precipitation in the growing season in the upper and middle reaches of the YRB increased significantly over the 20-year period, at a rate of 2.467 mm/year (\(p<0.05\)) (Figure S1d). However, the interannual variation rate of sunshine duration decreased significantly, by -6.0620 h/year (\(p<0.05\)) (Figure S1f). We found that the temperature increased at a rate of 0.012 \({}^{\circ}\)C/year (\(p>0.05\)), which did not pass the significance test (Figure S1e).
## 3 Description of the Proposed VLC Prototype
### Hardware Design and Implementation
#### 3.1.2 Discussions on the Design of the VLC Receiver
The adaptive filter also consists of several selectable 4 th order Bessel low pass analogic filters that were obtained using a Sallen-Key configuration. In this case, the number of modulation frequencies in use determines the number of parallel filters (i.e., currently the VLC system works with three modulation frequencies). The Bessel filters have been considered due to their ability to prevent significant signal phase distortions within the bandpass. The cutoff frequency for the low-pass filters is individually established for each communication frequency. The microcontroller unit controls the selection of the active frequency band, which is able to make this decision based on the information extracted from the message header. Basically, the synchronization header of each message provides the VLC receiver with information regarding the frequency of the incoming data. Based on this fact and when considering the power spectral density distribution of the encoded data, the microcontroller selects the optimal configuration of the adaptive filters (i.e., the filter having the most appropriate output signal). In the current configuration, the VLC receiver is able to process the data transmitted using frequencies between 22-100 kHz, whereas frequencies up to 1 MHz are envisioned. In the next phase, the output of the filtering block is introduced in a Schmitt trigger circuit providing the digital data signal. The reconstructed signal is processed by the 180 MHz microcontroller, which extracts the data after demodulation and decoding. The signal is analyzed based on a pulse width measurement algorithm in order to decode the data. This implies the identification of the rising and of the falling edges as well as a time measurement between these two events. The microcontroller is able to decode messages transmitted using direct sequence spread spectrum (DSSS) modulation along with sequence inverse keying (SIK) coding, and on-off keying (OOK) modulated messages while using Manchester, respectively, Miller coding (delay modulation). Nevertheless, the microcontroller must know which decoding algorithm to use in order to properly extract the data. Hence, the information regarding the configuration of the data (i.e., modulation, coding technique, frequency, message length) is also provided in the message header (see Figure 3). Founded on this flexible configuration, the VLC receiver should be able to operate under both ideal and unfavorable conditions. Software Design and Implementation: Discussions on the Modulation Techniques, Coding Techniques, Data Rates and Communication Protocols
# Novel Insights on Plant Extracts to Prevent and Treat Recurrent Urinary Tract Infections
## 1 Introduction  
Urinary tract infections, abbreviated UTI, frequently occur in the urinary tract, affecting up to 150 million people/year worldwide [1, 2]. Recurrent UTI (rUTI) is more frequent in young females, but it has been estimated that up to 50% of women experience a UTI at least once in their lives [3, 4]. A notable proportion of patients who develop UTI have no discernible causes. However, UTI is mainly associated with patients with detected urinary tract abnormalities, suppressed immune systems, long-term catheter use, and recent urinary procedures. A lot of recent studies suggest a genetic predisposition to the recurrent symptomatic UTI. Numerous genes seem to contribute and have been strongly associated with UTI-prone patients [2, 5].  
rUTI is defined as two or more episodes of symptomatic lower UTI in the last six months or three or more episodes in the past 12 months, based on the European Union guidelines for rUTI in adults. Uropathogenic _E. coli_ (UPEC) is the most common bacterial agent for all types of UTI and is responsible for 70-90% of urinary infections, followed by _K. pneumoniae_ in uncomplicated UTI and _E._ spp. in complicated UTI [6]. _E. coli_ belongs to the _Enterobacteriaceae_ family of bacteria. Despite being one of the most well documented and studied bacterial species globally, it continues to pose a constant threat to human health, particularly in medical settings [7, 8].  
rate of UTI but induces adverse effects, including vaginal and oral candidiasis, and gastrointestinal symptoms, and the spread of antibiotic resistance. Following an antibiotic resistance index assessment, these infections have become increasingly difficult to treat since the mid-2000s [9]. This emphasizes the need for new treatment regimens against urinary infections with innovation by introducing alternative therapies based on herbal extracts, nanoparticles, and dietary supplements [10]. Plant remedies are a natural option for the long-term treatment of rUTI, especially in a synergistic antibiotic approach, as they have antimicrobial effects, can reduce different symptoms (e.g., anti-inflammatory, antipyretic, and analgesic effects), and help decrease adverse antibiotic effects. Moreover, they can be a prophylactic treatment to prevent rUTI for patients that have experienced urinary infection for years and in precision medicine.  
This review aims to present the current knowledge about promising plant extracts, including candidate plant-based products that can be applied in the management of the rUTI as prophylactic or treatment therapy, and future directions for research are highlighted.
## 6 Conclusions  
This study presented the IIFPFCM clustering algorithm, focusing on achieving fast convergence through independent combinations with both Euclidean distance and city block distance. Both proposed methods exhibited faster convergence speeds compared to the traditional IFCM and IPFCM clustering methods in SIDA. Furthermore, there was an issue of partitioning into large and small groups in the context of SID. The proposed methods also yielded superior performance results compared to the traditional IFCM clustering method for addressing this challenge. Additionally, traditional IFCM clustering is susceptible to outliers. This study also introduced the IIFPFCM algorithm, which tackles outliers by employing interval distance measurements. In other words, we enhanced the convergence speed of IFCM, reduced the computation time, and achieved a more effective handling of outliers. In our experiments, we compared three types of data points and introduced outlier data to each of these types. The results of these experiments demonstrated that IIFPFCME effectively enhances convergence speed and the partitioning of data into large and small groups. However, it is worth noting that IIFPFCMC effectively handles outlier data but is sensitive to the choice of initial values in this study. Additionally, the proposed algorithm is currently not affiliated with any commercial entities, and its development remains in the theoretical stage. Regarding the directions for further development of the presented algorithms, SIDA will be employed in many sectors, like healthcare, marketing, education, etc., since SIDA usually provides analysts with a large amount of data on behavior. Moreover, in robotics and autonomous systems, SIDA can aid in interpreting these data for navigation, obstacle avoidance, and decision-making processes. In agriculture, SIDA can aid in soil classification, crop monitoring, and yield prediction by handling the imprecision inherent in agricultural data. In the context of market research, SIDA can be employed to segment markets and understand consumer groups based on imprecise data like survey responses. In bioinformatics, SIDA can assist in grouping similar genetic profiles, which can be useful in understanding diseases and developing treatments.  
Conceptualization, S.-C.C. and J.-T.J.; methodology, S.-C.C. and J.-T.J.; programming, S.-C.C.; writing--original draft preparation, S.-C.C. and J.-T.J.; writing--review and editing, W.-C.C. and J.-T.J.; funding acquisition, J.-T.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Science Council Under Grant NSTC 110-2221-E-150-040.  
Not applicable.  
The data presented in this study are available in article.  
The authors declare no conflict of interest.
## 3 Psychophysical Experiments
### General Procedures  
At the beginning of experiments, subjects were prompted to sit comfortably on a chair. They were asked to wear headphones playing white noise (see Figure 5). The headphone was important to block out sounds from the vibrotactile display as well as the office environment. Before the experiment started, the objective and the function of the vest was explained. A total of 52 vibrotactile patterns encoding attitude angles except for the angles ranging from 0 to 5 degrees the angle of more than 90 degrees, which were presented in sequential order to familiarize the subjects with the meanings of the patterns. For each coding method, the subjects should receive training for 10 min before starting formal experiments.  
In the formal experiment, \(52\times 3\) trials were presented for each coding method via an experimental software. Thus for each subject, a total number of 1092 (\(52\times 3\times 7\)) trials were recorded throughout the whole experiment. At the end of each trial, subjects needed to report the flight attitude angles by clicking corresponding radio button in the experimental software. To avoid fatigue, participants could take a break between trials and sessions on request. In order to avoid biased responses, angles were presented in a pseudo-randomized order. The subjects needed to select the direction within the prescribed time (6 s). To reduce the practice and habituation error easily occurring in a psychological experiment [38], a random vibration pattern different from before was given to the subject when a judgement timed-out occurred. During the formal experiment, no feedback about correctness of the answers was given. The subject's selected angle and reaction time were recorded in a database. After the completion of each coding method, subjects were asked to complete a questionnaire and provide a ranking of their preferred vibrotactile coding method.  
To assess the impact of the coding method on the performance, we analyzed the data by employing a one-way within-subjects design ANOVA, using standard software (IBM SPSS statistics 20, IBM Corp., New York, NY, USA). The within-subjects factor is the coding methods. Simple main effects comparisons and post hoc analysis with Bonferroni correction were further performed to test the significant interaction effects. Wilcoxon signed rank tests and Friedman tests were conducted to compare the preferences among coding methods. We employed a one-way repeated measure multivariate analysis of variance (MANOVA) to test our objective measures.