esa-sceva/satcom-chunk-collection huggingface dataset:
# A Feasibility Study for a Hand-Held Acoustic Imaging Camera
## 7 Miniaturized Array Optimization: Results and Discussion
### Thirty-Two-Microphones, [0.5, 6.4] kHz, 21 \(\times\) 21 cm\({}^{2}\) Array  
This section compares the performance of the current acoustic device _Dual Cam_ against a new prototype with shortened dimensions but equivalent bandwidth. We simulate this current experimental condition:  
* \(L\) = 50 cm;
* \(N^{\circ}\) of microphones = 128 mic;
* \(K\) = 7 (FIR length);
* \(u\in[-1.5;1.5]\) ; \(v\in[-1.41;1.41]\);
* \(N^{\circ}\) of iterations \(\approx 10^{5}\);
* \(uMainLobe_{low}=-0.06\) ; \(uMainLobe_{high}=0.06\);
* \(vMainLobe_{low}=-0.06\) ; \(vMainLobe_{high}=0.06\);
* Bandwidth = [500, 6400] Hz.  
Simulations are conducted to analyse the key metrics of white noise gain, directivity patterns, and grating lobes. The results demonstrate the feasibility of achieving a compact form factor while maintaining wideband performance through optimization of design parameters.  
In our simulation, the third scenario expands the bandwidth to cover _Dual Cam_ 2.0 specifications for comparison with the current device:  
* \(L\) = 21 cm;
* \(N^{\circ}\) of microphones = 32 mic;
* \(K\) = 31 (FIR length);
* \(u\in[-1.5;1.5]\) ; \(v\in[-1.41;1.41]\);
* \(N^{\circ}\) of \(iterations\approx 10^{5}\);
* \(uMainLobe_{low}=-0.2\) ; \(uMainLobe_{high}=0.2\);
* \(vMainLobe_{low}=-0.2\) ; \(vMainLobe_{high}=0.2\);
* Bandwidth = [500, 6400] Hz.  
Compensating for the larger wavelength at 0.5 kHz required shrinking once again the array to 0.21 \(\times\) 0.21 m\({}^{2}\) to maintain the density with 32 microphones to give the area reduction. The cost convergence in Figure 19a follows a similar trend but more iterations are needed to escape poor local minima. The layout in Figure 19b retains an irregular structure with permutations tailored to the acoustic parameters. The current _Dual Cam_ working prototype (Figure 20) has a better WNG and directivity, especially at low frequencies (Figures 21 and 22); also, the main lobe of the BP and EBPP is sharper (Figures 23 and 24). Instead, the grating lobes at high frequencies are more or less the same. The directivity comparison (Figure 13a) with a 0.21 \(\times\) 0.21 m\({}^{2}\) prototype in the bandwidth [0.5, 6.4] kHz in Figure 21a indicates better sensitivity below 4 kHz. The beam patterns in Figure 14 verify directional selectivity and sidelobe suppression within the  
Figure 18: Simulation results for [2, 6.4] kHz optimization of a 32\(-\)element 0.21 m acoustic array. Beampatten comparison: BP (**left**) vs EBPP (**right**) at \(\approx\)6 kHz.  
This supports developing compact prototypes based on these optimized configurations. Ongoing research is focused on the physical implementation of miniaturized arrays guided by these modelling results.  
Figure 23: Current _Dual Cam_ prototype: (**a**) Directivity and (**b**) WNG over the full bandwidth [500, 6400] Hz on a 50 \(\times\) 50 cm\({}^{2}\) array.  
Figure 22: Current _Dual Cam_ prototype. Expected beam pattern power at different frequencies: (**a**) 500 Hz, (**b**) \(\approx\)2.5 kHz, (**c**) \(\approx\)6 kHz.
###### Abstract
Not only that, but it enhances their entertainment and sports activity by adding new technologies to conventional treatments such as videogames or virtual reality [12]. Different studies indicate that subjects with neck pain may present coordination alterations in the synergy between the activity of the deep and superficial cervical flexor musculature as well as a decrease in the activity of this deep musculature, accompanied by fatigability and a delayed activation of the same, which may result in a diminished capacity in the control of the deep flexor and extensor muscles [13]. Alterations in cervical movement and motor control appear more frequently in patients with neck pain than in healthy subjects [14]. On the other hand, a previous pilot study concluded that adding virtual reality to kinesthetic exercises did not further improve range of motion, speed, and accuracy of movement [15]. Other work has shown that virtual reality increases improvement in the rotational range of motion in both healthy subjects and patients with neck pain [16]. This opens the possibility of using VR, MR, or AR with the aim of improving these outcomes. Currently, there are systematic reviews and meta-analyses on the use of VR for musculoskeletal disorders in the cervical region and spine [17; 18]. These studies beforehand support the use of VR, but studies with better methodological quality should be conducted. Studies that measure range of motion as a motor variable currently exist [19; 20]. However, there are no studies with other sensorimotor variables such as joint position error or deep flexor strength. To the authors' knowledge, there are no international published studies applying MR in the cervical region and spine. Therefore, there is a need to study the future potential of mixed reality in the physical rehabilitation of people with musculoskeletal disorders, specifically in the cervical spine. For this purpose, the primary objective of this study was to explore the possibilities of a mixed-reality game, performing task-oriented cervical exercises compared with conventional therapeutic exercises in sensorimotor outcome measurements in asymptomatic subjects. Added to this, the study aimed to assess the usability and satisfaction of the program and the setup used for performing task-oriented cervical exercises in asymptomatic subjects.
## 3 The Inversion Strategy of FPAR
### Applicability Analysis  
A comparison of the FPAR inversion results and FPAR products in the Heihe research area was conducted to determine the effects of the differences in remote sensing data sources, applied models, and algorithms. This comparison also helps to prove the validity of the method and adjust the inversion strategy according to the characteristics of the FY-3C data. The two inverted FPAR results, MOD09-FPAR and FY-FPAR (which were obtained from MOD09 GA and FY-3C MERSI, respectively, by using the same PROSAIL model and LUT algorithm), and the MODIS FPAR product (referred to as MOD15-FPAR) were compared against each other relative to the Heihe FPAR product (known as HeiHe-FPAR), published by the Cold and Arid Region Science Data Center (Figure 3).  
As shown in Figure 3a,b, based on the same PROSAIL model and LUT algorithm, the difference in remote sensing data leads to inconsistent results. The FPAR from the FY-3C data was clearly high, especially in low-value areas. As shown in Figure 3a,c, based on the same MOD09 data, different algorithms lead to different results. Based on the correlation analysis, the FPAR results of Figure 3a-c correlated with the Heihe-FPAR of Figure 3d, and the scatter plots are shown in Figure 4.  
As shown in Figure 4a,c, based on the same MOD09 data, the correlation between MOD09-FPAR and Heihe-FPAR was higher than that of MOD15-FPAR and Heihe-FPAR (0.8922 \(>\) 0.7991). This shows that the applied method (PROSAIL model and LUT algorithm) is feasible.  
Figure 4: The scatter plots for the FPAR of the validation date. The horizontal axis is the Heihe-FPAR product; the vertical axes in (**a–c**) are the inversions of the FPARs from the MOD09 and FY-3C data and the MODIS FPAR product, respectively.  
Figure 3: Comparison of the FPAR inversion results and FPAR products in the Heihe research area; (**a,b**) are the inversion of FPARs from the MOD09 and FY-3C data, respectively; (**c,d**) are the Moderate Resolution Imaging Spectro Radiometer (MODIS) FPAR products and Heihe products, respectively.  
On the other hand, the degree of agreement between MOD09-FPAR and Heihe-FPAR (R = 0.8922) is significantly greater than that between FY-FPAR and Heihe-FPAR (R = 0.5746), as shown in Figure 4a,b. In other words, the correlation between FY-FPAR and Heihe-FPAR is relatively low because the difference in the band range between the FY-3C MERSI data and the MOD09 GA data (which is not a complete coincident), and the correlation of the reflectance of the different channels with blue, green, red, and near infrared, is about 0.6 (as shown in Table 4).  
Therefore, since the feasibility of the applied model and algorithm has already been proven, the inversion of FPAR from FY-3C data needs to be combined with the characteristics of the Fengyun satellite to optimize the inversion strategy.
# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 4 Smart City De-Anonymization and Re-Identification Use Cases
### Intelligent Transportation Systems  
The evolution of intelligent transport systems occurred in an accelerated, multifaceted way and often based on technological advances considered revolutionary for the urban mobility sector. Recently, the widespread use of ITS in the operation and management of urban mobility has become part of everyday life. Numerous tools are available today for different contexts and scales, with applications that directly impact both locally and across global society [142]. In case of re-identification, a victim/target privacy is heavily compromised in cases where access to vehicle systems provides \"attackers\" with near-complete information about where, when and for how long the victim/target visited a specific location. It can provide additional information about who the victim/target called. This privacy breach is a major security risk.  
Vehicle re-identification methods require sets of detectors mounted along the road. In this technique, a unique serial number for a device in the vehicle is detected at one location and then re-detected (re-identified) further down the road. Travel times and speed are calculated by comparing the time a specific device is detected by sensor pairs. This can be done using the MAC addresses of Bluetooth or other devices, or using the RFID serial numbers of electronic toll collection (ETC) transponders (also called \"toll tags\") [143].  
Furthermore, an increasing number of vehicles are equipped with satellite/GPS navigation systems (satellite navigation) that have two-way communication with a traffic data provider. The position readings from these vehicles are used to calculate vehicle speeds. Modern methods may not use dedicated hardware, but smartphone-based solutions using so-called Telematics 2.0 approaches [142].  
Finally, smartphones with multiple sensors can be used to track traffic speed and density. Accelerometer data from smartphones used by car drivers is monitored to find out traffic speed and road quality. Audio data and GPS tagging from smartphones allow the identification of traffic density and possible traffic jams. This was implemented in Bangalore, India, as part of an experimental Nericell research system [144].
## 2 Methods and Tools
### Malfunctioning Conditions Modelling  
In total, 4 malfunctioning conditions are investigated in this study, as listed in Section 1. The first one is the change in SOI, which can be caused due to the fuel injector wear and tear or engine detuning. In specific, engine detuning can occur after a large number of operating hours, as the Variable Injection Timing (VIT) of individual cylinders may require retuning [33]. In addition, detuning occurs due to the wear and tear of the injector pump or the camshaft lobe, the latter occurs to a large percentage of the world fleet, which still operates using camshaft-driven engines. The SOI angle variation for each cylinder are taken into account by using the \(\theta_{SOI}\) term in Equation (5), which is expected to vary by \(\pm 2.5^{\circ}\) with respect to the healthy engine condition SOI [48].  
The second malfunctioning condition considered is the change in RHR. In specific, this can occur due to a number of reasons including poor fuel quality, and worn or clogged fuel injectors [33; 34]. To change the RHR, this study considers the variation of the Weibe shape parameter \(w\) employed in Equation (4). This impacts the fuel burn rate, and as a results the combustion rate. Since the model is very sensitive to changes of the Weibe shape parameter, its range was considered \(\pm 0.5\) with respect to the healthy engine's parameter value.  
The third malfunctioning condition is a change in the scavenge air pressure. A decrease in the scavenge air pressure can occur due to scavenge air cooler fouling, turbocharger compressor fouling, air filter fouling, turbine wear or a combination of those. On the contrary, an increase in the scavenge air pressure can occur due to fouling in the turbine nozzle, or due to higher exhaust gas temperatures resulting from the incomplete combustion, late combustion of poor quality fuel [29]. This malfunction is implemented to the thermodynamic model as a change in the scavenge air pressure, which is an input parameter affecting the air flow rate though the scavenging ports that are utilised to calculate the cylinder inlet mass \(m_{i}\). This study considers changes in the scavenge air receiver pressure by \(\pm 0.2\) bar with respect value for the healthy engine condition [32].  
The fourth malfunctioning condition is the engine blowby, which occurs due to wear in the cylinder liner or piston rings [34; 36]. This was modelled by simulating an additional isentropic orifice, namely the blowby orifice, which connects the scavenge air receiver with the cylinder. The effective area profile of the blowby orifice was set as constant throughout the entire engine revolution, and its value was adjusted manually such that a range of typical blowby mass flowrates can be obtained [36; 49].  
The modelled malfunctioning conditions and their respective parameter changes are summarised in Table 2.
## 3 Proposed Effectiveness Evaluation Method of Safety Barrier
### System Failure Probability Analysis  
#### 3.2.1 Determine the Possibility of Each Basic Event in FT via IVIFNs  
The possibility of each basic event in FT is given by expert group members according to their own knowledge and experience, as well as the actual operation situation of the system. However, it is difficult for experts to determine IVIFNs directly by using a direct assignment method, so linguistic terms (see Table 3) are adopted. Experts select the linguistic terms from Table 3 to indicate the possibilities of events [46]. The selected linguistic terms are then converted into the corresponding IVIFNs to perform an aggregation operation using Equation (19).  
\[\widetilde{P}_{Xi}=IVIFWA\Big{(}\widetilde{P}_{Xi}^{E_{1}},\widetilde{P}_{Xi }^{E_{2}},\ldots,\widetilde{P}_{Xi}^{E_{K}}\Big{)} \tag{19}\]  
where \(\widetilde{P}_{Xi}\) is the possibility of the \(i\)th basic event, and \(\widetilde{P}_{Xi}^{E_{i}}\) is the possibility of the \(i\)th basic event given by the \(j\)th expert. Using Equation (20), the possibility is converted to a probability [47].  
\[P_{Xi}=\left\{\begin{array}{ll}\frac{1}{10^{6}},&for\;FP_{Xi}\
eq 0\\ 0,&for\;FP_{Xi}=0^{\prime},\;k=\left(\frac{1-FP_{Xi}}{FP_{Xi}}\right)^{\frac{ 1}{3}}\times 2.301\end{array}\right. \tag{20}\]  
where \(FP_{Xi}\) is the crisp value of \(\widetilde{P}_{Xi}\) after defuzzification through Equation (17), and \(P_{Xi}\) is the probability of the \(i\)th basic event.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Citerion** & **Classification** & **Score** \\ \hline \multirow{4}{*}{Work experience (Q1)} & \textgreater{}30 years & 4 \\  & 21–30 years & 3 \\  & 11–20 years & 2 \\  & \textless{}10 years & 1 \\  & Doctor’s & 4 \\  & Master’s & 3 \\ Education level (Q2) & Bachelor’s & 2 \\  & Technical school & 1 \\  & Completely related & 4 \\ Professional relevance (Q3) & Basically related & 3 \\  & Basically irrelevant & 2 \\  & Completely irrelevant & 1 \\  & Senior & 4 \\ Professional title (Q4) & Deputy senior & 3 \\  & Intermediate & 2 \\  & Primary & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Weighting scores of experts.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Linguistic Terms** & **Abbreviation** & **IVIFNs** \\ \hline Absolutely Low & AL & ([0, 0.2], [0.5, 0.8]) \\ Very Low & VL & ([0.1, 0.3], [0.4, 0.7]) \\ Low & L & ([0.2, 0.4], [0.3, 0.6]) \\ Medium Low & ML & ([0.3, 0.5], [0.2, 0.5]) \\ Medium & M & ([0.4, 0.6], [0.2, 0.4]) \\ Medium High & MH & ([0.5, 0.7], [0.1, 0.3]) \\ High & H & ([0.6, 0.8], [0. 0.2]) \\ Very High & VH & ([0.7, 0.9], [0. 0.1]) \\ Absolutely High & AH & ([0.8, 1.0], [0, 0]) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Linguistic scale and their corresponding IVIFNs [46].
## 2 Reliability Analysis with Censored Data
Complete data refers to records where the exact failure or event occurrence time is known without uncertainty. In other words, no censoring is present in the data, and the failure times are fully observed. Complete data provide precise information about when the failure event occurred, enabling accurate analysis of the reliability metrics and statistical modeling. On the other hand, censored data refers to observations where the exact failure or event occurrence time is either unknown or partially known. Censoring occurs in different forms:  
1. Left censoring occurs when the event of interest (failure) has occurred before the study started, and only the time since the event is known. In such cases, the data provide a lower bound for the failure time, but the exact time remains unknown. 2. Right censoring occurs when the event of interest (failure) has not occurred by the end of the observation period or study duration. The data indicate that the failure event will occur at some point in the future, but the exact time is unknown. Right censoring often occurs in reliability tests, where a specified number of units are tested until the end of the study period, and the unfailing units are right censored. 3. Interval censoring arises when the exact failure time is unknown, but it is known that the failure occurred within a specific time interval. This form of censoring provides information about a range of possible failure times. Understanding the type of censoring present in the data is crucial for appropriate data analysis and modeling, as different techniques are employed to handle each type. By properly accounting for censored data, a more accurate and comprehensive reliability analysis can be performed, facilitating informed decision making in maintenance and operational strategies. Censoring data are denoted by the \(\delta_{i}\) variable to indicate that the event is censored, that is (Equation (3)) [20] \[\delta_{i}=\left\{ \begin{array}{ll}1,&\text{for uncensored data}\\ 0,&\text{for censored data}\end{array} \right.\] (3)  
Censored data pose a challenge in statistical analysis, as they contain incomplete information and introduce uncertainty in the failure times. Specialized statistical methods are necessary to appropriately handle censored data, accounting for the missing information and capturing uncertainty. By employing these methods, more accurate predictions and informed decisions can be made based on the available data. Censored data are frequently encountered in reliability studies when analyzing the historical failure data of equipment. Therefore, a comprehensive understanding of censored data and their impact on reliability analysis is crucial to ensure the accuracy and reliability of the results. Researchers can obtain more robust and trustworthy findings by applying appropriate statistical techniques for censored data, enabling effective decision making in reliability and maintenance practices.
## Case Studies
### Case Study 1: Edmodo  
#### 5.2.1 Background  
Edmodo is one of the most popular educational technology platforms used by most students and teachers to gain access to and share information, resources, and feedback in a safe environment. Starting in 2008, it has now grown to cater to millions of users at all levels of education, from primary schools to universities around the world. The platform has the interface of a social network, and users are used to communicating online, which makes it user-friendly. Among its features are assignment submission, quizzes, polls, and a resource-sharing system, all designed to improve the student experience in the actual classroom and online.  
The choice of Edmodo for a case study of the Data-Driven Personalized Learning Model (DDPLM) is deliberate, considering its powerful data analytics feature and high usage. Edmodo produces a lot of interaction data, such as student submissions, time logs, discussion entries, and direct messages. These data give a holistic picture of student engagement and performance, which is the perfect environment for the implementation of advanced data-driven educational models like DDPLM.  
The platform's strength in data handling and user interaction makes it a good candidate for studying how personalized learning can be achieved in a semi-formal educational environment. The integration of DDPLM expands Edmodo's ability from mere management functions to the analysis of students' learning patterns, allowing customization of the contents and teaching method to suit a variety of learners.  
This case study focuses on how DDPLM uses Edmodo's existing infrastructure to create adaptive learning paths that correspond to each student's personal profile [45]. It is a thorough study of the interaction between DDPLM's analytical tools and Edmodo's user data, investigating improvements in learning results, engagement, and educational productivity. The ultimate purpose is to show the power of DDPLM in turning traditional learning settings into lively, information-driven environments that greatly enhance teaching and learning experiences.