esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Privacy-Preserving Collection of Health Data from a User's Wearable Device
## 2 Related Work
For interested readers, a survey on privacy and security issues when using body implantable medical devices is presented in [23]. For practical implementation, privacy preservation is essential in healthcare and medical systems. To preserve privacy, researchers have proposed several techniques such as encryption, anonymization, and data perturbation (through DP and LDP) that protect the health data from intruders. However, data perturbation techniques have emerged as the future of privacy preservation because of the simplicity associated with their mechanisms [24]. Thus, a number of conducted researches have focused on the use of data perturbation techniques for e-health data privacy preservation. [PERSON] et al. [25] perturbed clinical data using differential privacy to efficiently and accurately train a privacy-preserving deep learning model. The authors integrated differential privacy with a cryptographic mechanism to enhance privacy. [PERSON] et al. [26] proposed a lightweight framework for privacy-preserving data mining of cancer patients' data by using the Laplace noise of differential privacy. In [27; 28], differential mechanisms are integrated with encryption techniques to preserve the privacy of genomic data. In [29], [PERSON] et al. combined differential privacy with Boneh-Goh-Nissim cryptosystem and [PERSON]'s secret sharing for privacy-preserving aggregation of data from different health devices. [PERSON] et al. [30] combined differential privacy with machine learning to perform k-means clustering of differentially private health data recorded from medical IoT devices. Apart from healthcare and medical systems, data perturbation techniques have found use in other applications, such as private heavy hitter identification, which employs locally differentially-private algorithms to find the top k items with the highest frequency along with the estimated frequency for each such item [5; 31; 32]. LDP has also been used for marginal distribution estimation, which computes the joint distribution of multiple variables [33; 34] in a privacy preserving manner. Other applications that have integrated LDP for privacy preservation include: IoT acquired data analysis [6; 35; 36; 37; 38], deep learning [39; 40], and data mining [41]. In the literature, the focus is mostly on the privacy of the data already in storage. However, for those that aggregate data periodically, they consider the entire data set which can be inefficient and inaccurate because of the increased error. We intend to address the challenge in this work.
## 3 Materials and Methods
### Basic Parameters and Characterization of Rough Fractures  
#### 3.2.1. Roughness of the Specimens with Rough Fractures  
The unevenness of the fracture surface is usually characterized by fracture roughness. It is necessary to use high-precision equipment to measure the roughness of each rock fracture sample accurately. As shown in Figure 2, the fracture surface data of rough sandstone were obtained using a high-resolution multi-functional handheld three-dimensional scanner with a scanning accuracy of 0.05 mm and a scanning distance of 0.24 mm. The 3D scanner was used to scan and visualize the fracture surface of the rough sandstone. The morphology of the fracture surface of the rough sandstone was obtained, and the morphology of the sandstone fracture surface is shown in Figure 3.  
The joint roughness coefficient (JRC) proposed by [PERSON] and [PERSON] [41] is a dimensionless factor ranging from 0 to 20, which represents the roughness of a rough fracture surface [42]. The fracture surface should be first divided into equally-spaced profiles with 5 mm spacing parallel to the direction of fluid seepage, as shown in Figure 4. The fracture surface was divided into nine equally spaced two-dimensional profiles. Secondly, Equation (9a,b) was used to calculate the JRC values of the nine two-dimensional profiles. Finally, the JRC values were obtained by taking the average JRC values of the nine profiles. The JRC values of each two-dimensional profile was calculated according to the following empirical equation [43]:  
\[\text{JRC}=32.2+32.47\text{lg}z_{2} \tag{9a}\] \[Z_{2}=\left[\frac{1}{M}\sum\left(\frac{z_{i-1}-z_{i}}{x_{i-1}-x_{i}}\right)^{ 2}\right]^{1/2} \tag{9b}\]  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Density (kg/cm\({}^{3}\))** & **Compressive Strength (MPa)** & **Tensile Strength (MPa)** & **Elasticity Modulus (MPa)** & **Poisson’s Ratio** \\ \hline
2.32 & 32.12 & 2.85 & 8.9 & 0.31 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical and mechanical parameters of sandstone specimens.  
Figure 3: Visualized fracture surface of sandstone sample.  
Figure 2: Scanner.  
where \(z_{2}\) is a dimensionless parameter, which is defined as the root mean square deviation of the two-dimensional profile. \(x_{i}\) and \(z_{i}\) denote the coordinates of a profile. \(M\) represents the number of sample points along the fracture length. The estimated JRC values of eight sandstone specimens were calculated using Equation (9a), and their sizes were 11.70, 13.20, 14.35, 14.86, 17.12, 17.23, 18.97, and 19.65, respectively. The fracture samples with JRC values greater than 10 were closer to the fractures in natural rock mass [44].
## 5 GNSS Positioning Performance of Xiaomi Mi 8
### Positioning Strategies  
We developed a positioning software implementing the proposed algorithm based on an Android platform. It processes raw dual-frequency GNSS observations from Xiaomi Mi 8 with a time differenced Kalman filter in a single-frequency model. The detailed positioning strategies are summarized in Table 3: the receiver position and receiver clock offsets for each GNSS system are the parameters estimated. The ionospheric and tropospheric delays are corrected by the ionospheric delay correction model of each GNSS system and Saastamoinen model, and the satellite cutoff elevation angle is 10\({}^{\circ}\). The observations are weighted according to C/N0. Meanwhile, considering the differences in the quality of the broadcast ephemeris, a signal-in-space range error (SISRE) value-based weighting model is also tested [36].
# Secure Image Transmission Using Multilevel Chaotic Encryption and Video Steganography
## 5 Results and Testing  
This section discloses the findings from implementing the suggested MCC/AES/Stego system, which combines LSB steganography for data hiding in video frames, AES encryption for security, and multilevel chaotic maps for key generation. It includes the analyses and statistical tests conducted in Section VI.A.
# A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression
## 3 Materials and Methods: A Novel Proposed EEG Analog Front-End Circuit System
### EEG Sub-Circuit Modeling the Second High-Pass Filter before the Analog to Digital Conversion  
In this section, a Butterworth filter is implemented in order to exploit its characteristic of not allowing ripples in the passband while also helping us create a further gain of approximately 1.5 V/V in the wave bands of interest [57]. The two important parameters that characterize the Butterworth filter are \(\mathrm{A_{F}}\) gain and \(\mathrm{f_{i}}\) cut-off \(-3\) dB frequency:  
\[\mathrm{A_{F}}=1+\frac{\mathrm{R_{\mathrm{f42}}}}{\mathrm{R_{\mathrm{f41}}}} \tag{6}\]  
\[\mathrm{f_{i}}=\frac{1}{2\pi\mathrm{R_{\mathrm{f43}}}\mathrm{C_{\mathrm{f41}}}} \tag{7}\]  
Utilizing (6) to our sub-circuit design, the \(\mathrm{A_{F}}\) gain can be calculated as follows:  
\[\mathrm{A_{F}}=1+\frac{\mathrm{R_{\mathrm{f42}}}}{\mathrm{R_{\mathrm{f41}}}} \approx 1.588\]  
Utilizing (7) to our sub-circuit design, the 3 dB cut-off frequency \(\mathrm{f_{i}}\) can be calculated as follows:  
\[\mathrm{f_{i}}=\frac{1}{2\pi*3.3\ \mathrm{M}\Omega*100\ \mathrm{nF}}\approx 482.287\ \mathrm{\mu Hz}\]  
Thus, in the proposed circuit, the values are as follows:  
\[\mathrm{A_{F}}\approx 1.59\ \mathrm{and}\ \mathrm{f_{i}}\approx 0.48\ \mathrm{Hz}\]  
If \"f\" is the frequency of the input signal the magnitude of the voltage gain is as follows:  
\[\left|\frac{\mathrm{V_{out}}}{\mathrm{V_{\mathrm{rcbrf}}}}\right|=\frac{ \mathrm{A_{F}}*\frac{\mathrm{f}}{\mathrm{f_{i}}}}{\sqrt{\left(1+\left(\frac{ \mathrm{f}}{\mathrm{f_{i}}}\right)^{2}\right)}}=\frac{3.2926*\mathrm{f}}{ \sqrt{\left(1+\left(\frac{\mathrm{f}}{0.48}\right)^{2}\right)}}\approx\frac{3.29*\mathrm{f}}{\sqrt{\left(1+\left(\frac{\mathrm{f}}{0.48}\right)^{2}\right)}}\]  
In Figure 15 is depicted the EEG sub-circuit modeling the second high-pass filter before the Analog to Digital Conversion.
## 3 Results and Discussions
### Analysis of Volatiles of CLEO and Y-CNCs by SPME GC/MS
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**RT** & **Component** & **Yi/CNC** & **Yic** & **Yi-CNC** & **Yze** & **Yi-CNC** & **Yi-CNC** & **Yi-C** \\ \hline
15.55 & \(\alpha\)-Phelaudrene & 8.3351 \(\pm\) 0.8783.a* & 0.3717 \(\pm\) 0.0375 b & 6.9914 \(\pm\) 0.4999 a & 0.2566 \(\pm\) 0.0314 b & 8.5200 \(\pm\) 7.6635 a & 0.4488 \(\pm\) 0.0164 b \\
25.99 & \(\alpha\)-Phelaudrene & 10.6402 \(\pm\) 0.4542 b,cd & 13.7195 \(\pm\) 1.0082 b & 9.6020 \(\pm\) 1.7646 c d & 14.2596 \(\pm\) 0.4535 b & 7.4907 \(\pm\) 1.9764 d & 19.4343 \(\pm\) 0.5172 a \\
21.09 & \(\alpha\)-Plineau & 21.1401 \(\pm\) 1.3580 b & 12.0268 \(\pm\) 1.0208 b & 11.1601 \(\pm\) 0.4326 c & 17.8274 \(\pm\) 2.4968 b & 8.4679 \(\pm\) 3.9565 c & 20.9283 \(\pm\) 1.0090 a \\
30.27 & \(\alpha\)-Cabtene & 2.4888 \(\pm\) 0.5711 a & 2.3684 \(\pm\) 2.7628 d & 3.3363 \(\pm\) 0.7677 d & 9.0678 \(\pm\) 0.0004 a & 3.5411 \(\pm\) 2.2543 a & 0.6522 \(\pm\) 0.0774 d \\
33.28 & \(\beta\)-Cabtene & 2.08869 \(\pm\) 3.5884 a & 4.5292 \(\pm\) 4.4187 b & 27.7329 \(\pm\) 4.3901 a & 50.9254 \(\pm\) 0.4197 b & 25.9371 \(\pm\) 5.4584 & 6.7019 \(\pm\) 2.5573 b \\
35.56 & \(\beta\)-Cabtene & 10.3576 \(\pm\) 1.8838 a & 4.5208 \(\pm\) 0.6884 d & 1.2392 \(\pm\) 6.6910 d & 10.7452 \(\pm\) 0.0300 d & 15.1747 \(\pm\) 5.0197 a & 0.6523 \(\pm\) 0.0234 b \\
41.32 & \(\alpha\)-Cabtene & 2.0839 \(\pm\) 2.6888 a & 4.7062 \(\pm\) 1.0000 d & 40.9821 \(\pm\) 0.0498 a & 0.4548 \(\pm\) 0.0332 b & 7.1458 \(\pm\) 0.1097 a & 4.6699 \(\pm\) 0.0126 b \\
41.82 & \(\alpha\)-Cabtene & 65.0349 \(\pm\) 2.6888 a & 3.7642 \(\pm\) 1.0000 d & 3.7555 \(\pm\) 4.6087 b & 4.5488 \(\pm\) 0.0332 b & 7.2748 \(\pm\) 1.0197 a & 4.6699 \(\pm\) 0.0126 b \\
57.14 & \(\alpha\)-Cabtene & 64.7366 \(\pm\) 5.45487 a & 63.9563 \(\pm\) 5.7683 c & 34.1938 \(\pm\) 1.6337 b & 54.5912 \(\pm\) 1.9925 & 64.3032 \(\pm\) 6.9273 c & 9.9939 \(\pm\) 6.8278 c \\
61.36 & \(\alpha\)-Cabtene & 45.9767 \(\pm\) 5.9077 d & 8.1146 \(\pm\) 1.2309 b & 52.5298 \(\pm\) 1.0732 b & 16.7300 \(\pm\) 2.7939 b & 57.4688 \(\pm\) 1.0160 a & 2.5435 \(\pm\) 1.5184 b \\
61.82 & \(\alpha\)-Phelaudrene & 151.4517 \(\pm\) 0.2127 b & 33.5715 \(\pm\) 4.0509 c & 155.1897 \(\pm\) 17.9855 b & 54.6175 \(\pm\) 9.2841 c & 206.4238 \(\pm\) 3.07993 a & 67.7942 \(\pm\) 5.7487 c \\ \hline \end{tabular} \({}^{*}\) Different letters indicate significant differences (\(p<0.05\)) on each row by post the Tukey test.  
\end{table} 
Table 3: The relative concentration (\(\rm{\mu}\) octanol/mg) of tentatively identified VOCs present in the yeast films determined by SPME CC/MS.
## 4 Numerical Simulation
The fault estimation results are obtained by selecting parameter \(p=\{0.6,0.8,1.0,1.2,1.4\}\), respectively. Then, the results are shown in Figure 4.  
Obviously, when the difference between the estimated fault and the actual fault is large, the smaller parameter \(p\) has a faster response speed, but when the difference becomes small, the larger parameter \(p\) has more stable estimation effect. For this reason, according to Equation (15), the adaptive parameter estimation is adopted, which has the faster and more stable tracking effect at the same time. From Figure 5, we can know that the nonlinear observer with the adaptive parameters combines the stability of the large parameters and the rapidity of the small parameters, which can realize a more reasonable system state estimation. Compared with the PI observer Equation (4), its adjustment time is reduced from 1.8 s to 0.8 s, its estimation error for the step fault is reduced from 0.047 to 0.023, and its estimation r  
Figure 3: Fault estimation results of proportional integral (PI) observer. fault is reduced from 0.01 to 0.007. Thus it is significantly better than that of the PI observer in Equation (4). When Gaussian noise with mean value of 0 and variance of 0.05 is added to the system state, the system fault can still be effectively estimated, and the result is shown in Figure 6.  
Further, Figure 6 shows that the fault diagnosis algorithm based on adaptive nonlinear PI observer can still achieve better fault estimation effect in the case of system error. Compared with the fault diagnosis algorithm based on linear PI observer, the estimation error is significantly reduced, and the fault estimation result is more robust than that of the fault diagnosis algorithm based on nonlinear PI observer with fixed factor. Figure 4: Fault estimation of nonlinear observers with different parameters. Figure 5: Fault estimation of nonlinear observer with adaptive parameters (without noise).
## 3 Topological Classification
### FCS + UC Hybridization  
The UC boasts several advantages over batteries, including a greater power density, exceptional cold start operation, a longer theoretical lifespan, efficient charging and discharging, and minimal loss [54; 55; 56; 57; 58]. One of the UC's key features is its quick discharge speed, which dramatically improves a vehicle's power during acceleration [59]. As a result, the pairing of a fuel cell system with an ultracapacitor, known as FCS + UC hybridization, has garnered attention. The connection process of this hybridization is similar to that of the hybrid integration of the FCS and battery, with comparable advantages and disadvantages among the connection methods. Although UCs possess several advantages over batteries, they also have several drawbacks. The energy density of the UC is lower compared to batteries, which restricts its storage capacity. Additionally, its safety voltage is also lower than that of batteries [60; 61]. Furthermore, the high discharge rate and rapid charging make it challenging to control [62]. Controlling the current during charging and discharging, as well as prolonging the discharge time, are critical for the widespread implementation and use of this technology.
## 2 Methods
### Evaluation
#### 2.3.2 Analyses  
Three different analyses were performed: a performance analysis, a sensitivity analysis, and a repeatability analysis. The performance and sensitivity analyses used leave-one-out cross-validation (LOOCV) to maximally use the available data. The repeatability analysis was performed on a specially acquired dataset (Section 2.1).  
The performance analysis results are presented by comparing the results of the dynamic viewpoint method with classification results using (a) a single viewpoint, (b) by adding a randomly selected additional viewpoint (instead of selecting one using the aforementioned method), and (c) the combination of all six viewpoints. Additionally, the upper bound is indicated for all analyses. The upper bound is the best result that could have been achieved with two viewpoints and is calculated by assuming that the next viewpoint is the viewpoint with the lowest sensing time, \(t_{av}\), with a correct classification (if any). Since it is not always possible to yield a correct maturity classification based on the data of two viewpoints, the results were compared to the upper bound instead of a perfect classification.  
Sensitivity analyses were performed for pepper prices, \(c_{pepper}\), and robot costs, \(c_{robot}\) (influences cycle times), by changing the pepper prices between EUR 0.0 and EUR 1.00 per piece and the robot costs between EUR \(1\times 10^{-4}\) and EUR \(10\times 10^{-4}\) per second.  
The repeatability analysis was conducted to assess the algorithm's dependence on the pepper's orientation on the plant (Section 2.1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Actual**} \\ \cline{3-4}  & & **Mature** & **Immuture** \\ \hline \multirow{2}{*}{**Predicted**} & **Mature** & TP & FP \\  & **Immuture** & FN & TN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion table defining the true positive (TP), false positive (FP), false negative (FN), and true negative (TN).
light-emitting diodes (LEDs), organic light-emitting diodes (OLEDs)--to new opportunities in the biomedical domain, namely bioimaging, biosensing, cell tracking, photodynamic therapy, optical thermometry, information storage, and water remediation [5; 7; 8; 9; 10]. The scientific challenges associated with the development of new luminescent systems can be illustrated by the following interconnections: _Chemical Composition \(\leftrightarrow\) Structure and Morphology \(\leftrightarrow\) Luminescent Properties_.  
Regarding the chemical composition, the question is how to design the targeted stoichiometry: inorganic, organic, and hybrid systems are considered. The main strategies used to elaborate upon the luminescent systems are as follows: (i) the top-down approach, which covers physical and mechanical methods, and (ii) the bottom-up approach, which includes chemical methods, namely wet-chemical synthesis, co-precipitation, sol-gel, combustion, etc. Generally, though not universally, it is accepted that emission energy is dictated by intrinsic traps and/or the traps intentionally introduced into the host lattice by suitable emitters/dopants, such as rare earth ions that present intraconfigurational 4f-4f transitions or transitional metal ions that generate intraconfigurational 3d-3d transitions. The origin of the traps is still debatable, and herein, a deep understanding of the influence of synthesis parameters on the final stoichiometry is beneficial. Furthermore, another issue is the effect induced by the host lattice's defects or distortions and this marks the second challenge related to the system's crystallinity. Regarding the morphological characteristics, it is generally accepted that the synthetic method and parameters allow for the engineering of morphology, in terms of size (micro- or nano-scale) and shape, and offer good control over uniformity and agglomeration. All of these factors strongly influence the luminescence intensity by tuning the excitation irradiance and emission wavelength, as well as the lifetime and quantum efficiencies.  
In conclusion, this Special Issue focuses on the recent progress made in the field of luminescent materials and considers new designs, synthetic routes, and investigation methods to evaluate the applicative potential in lighting, displays, sensing, optical information storage, biomedicine, and so on. This Issue aims to encourage more scientists to discover the field of luminescence and I hope that these studies will represent outstanding pieces in this amazing Lego-puzzle-like domain. Moreover, due to the wide range of application prospects, the scientists involved will develop new applications and accelerate the development of new luminescent systems. Finally, I would like to express my appreciation to all of the authors and reviewers for their contributions.  
This research received no external funding.
creation of highly personalized medical devices that can be manufactured closer to the point of care. In the realm of robotic prosthetics, 3D printing allows for the creation of devices precisely matched to the anatomical requirements of each patient, thereby enhancing both functionality and comfort [2]. Compared to traditional prosthetic manufacturing, which often requires significant customization expenses, 3D printing provides an adaptable and cost-effective production method that lowers the financial barrier for patients and healthcare providers alike, especially in resource-limited settings.  
Traditional robotic prosthetics have been characterized by costly materials and complex manufacturing processes, limiting their accessibility to a broad segment of the population. In contrast, the integration of 3D printing has notably reduced production costs, enabling the development of affordable, functional, and aesthetically customized prostheses [3]. This technology not only offers increased design flexibility--facilitating the creation of modular prostheses that can be easily adapted and updated as patient needs evolve--but also has the potential to significantly reduce healthcare system expenditures over time by enabling local and scalable prosthetic production.  
This paper aims to review recent advancements in the field of robotic prosthetics produced through 3D printing, as well as to address current challenges and future prospects. In this review, a comprehensive overview of the current state of this technology is presented, emphasizing its impact on improving the quality of life for amputees and identifying areas where further research can optimize both functionality and accessibility.
## 4 Parametric Study of SHSS Evaluation Methods
### Effect of Nodal Averaging  
This feature averages the stress at nodes which may increase or decrease the stress results depending on the stress distribution (monotonic or non-monotonic). It is usually more effective where stresses are highly non-linear, but one must be careful while averaging nodal values at the weld-toe and should exclude the weld element and the element behind the weld-toe, because it decrease the stresses due to the larger weld area. After applying this feature in this study, the stress values were a bit reduced, but their impact on SHSS results was insignificant in SSE and Haibach methods; particularly, the LSE 515 method showed consistency in results for both the deck and the rib-toe (Figure 9a,b). Similarly, [PERSON] and [PERSON]'s method did not have much influence of nodal averaging in the case of the rib-toe, whereas the deck-toe showed significant decrease in SHSS results in 2-CH20/8 elements, which was evident in the 2-CH8 element (Figure 9a). The ASME TTSL method showed a considerable decrease in SHSS results in the deck and the rib-toe for 1/2-CH20E and 1-CH20 elements, respectively. In general, parabolic solid or shell elements (CH20/20E/CQS) were not affected by nodal averaging, whereas linear solid or shell elements (CH8/8E/CQ4) showed a decrease in SHSS results. The reduction in SHSS results was more pronounced in the 2-CH8 element (Figure 7a-d). Therefore, it can be concluded that SHSS evaluation methods are not sensitive to nodal averaging in parabolic elements (CH20/20E/CQS).