esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The internet of things (IoT) is one of the most disrupting revolutions that is characterizing the technology ecosystem. In the near future, the IoT will have a significant impact on people's lives and on the design and developments of new paradigms and architectures coping with a completely new set of challenges and service categories. The IoT can be described as an ecosystem where a massive number of constrained devices (denoted as smart objects) will be deployed and connected to cooperate for multiple purposes, such a data collection, actuation, and interaction with people. In order to meet the specific requirements, IoT services may be deployed leveraging a hybrid architecture that will involve services deployed on the edge and the cloud. In this context, one of the challenges is to create an infrastructure of objects and microservices operating between both the edge and in the cloud that can be easily updated and extended with new features and functionalities without the need of updating or re-deploying smart objects. This work introduces a new concept for extending smart objects' support for cloud services, denoted as a sidecar object. A sidecar object serves the purpose of being deployed as additional component of a preexisting object without interfering with the mechanisms and behaviors that have already been implemented. In particular, the sidecar object implementation developed in this work focuses on the communication with existing IoT cloud services (namely, AWS IoT and Google Cloud IoT) to provide a transparent and seamless synchronization of data, states, and commands between the object on the edge and the cloud. The proposed sidecar object implementation has been extensively evaluated through a detailed set of tests, in order to analyze the performances and behaviors in real- world scenarios.  
Keywords:internet of things; edge computing; cloud computing; sidecar pattern +
Footnote †: journal:  
_Future Internet_**2019**, _11_, 145; doi:10.3390/611070145
## 4 Scaling Gene and Cell Therapy ATMPs from Research to the Clinic with AI
### Process Monitoring  
The personalized nature of autologous ATMPs results in inherent variability between patient cell samples used for manufacturing. This variability in starting material can propagate through the process, making achieving consistency between individualized batches challenging. Conventional bioprocess monitoring typically relies on manual and periodic offline sample analysis. AI enables improved real-time process oversight through advanced sensor instrumentation coupled with intelligent algorithms continuously analyzing data for patterns predictive of critical quality attributes.  
Multivariate AI models imply that machine learning algorithms can integrate data streams from arrays of in-line sensors and bioreactors probe analyzers, to identify interrelationships between bio-parameters and product quality. This higher-resolution view allows slight process deviations to be detected earlier and corrections to be made through automated feedback control, rather than periodic and manual sampling. Thus, AI process monitoring provides complete tracking and mapping of the multidimensional course of cell bioproduction. ML techniques can also incorporate spectroscopic [36], imaging, and other process data, to build predictive models of final product critical quality attributes such as identity, purity, and potency [37]. This real-time predictive capability facilitates standardized quality and reduced variability within and between batches, as well as reduced batch failures.  
In the exploration of AI's impact on ATMP production, we can observe distinct advantages and challenges, as outlined in Table 1. Enhanced process monitoring and the automation of manufacturing tasks are among the key benefits, leading to increased efficiency and reduced workload for biotechnologists. Moreover, AI's dynamic control and adaptability allow fine-tuning cell culture conditions, thus ensuring batch consistency, which is critical for patient-specific treatments. On the other hand, issues such as data security concerns and the requirement for specialized personnel present noteworthy obstacles, potentially hindering AI's integration.  
A further analysis is presented in Table 2 of the ATMP production process with and without AI integration. It is evident that AI integration significantly enhances process monitoring and manufacturing efficiency, offering a more sophisticated approach to data management, which traditional methods lack. The enhanced quality and consistency afforded by AI contribute to better control of product integrity; nonetheless, there are higher investment and costs required for implementing such advanced technologies. The need for personnel training and expertise remains an essential consideration to fully realize the potential of AI in this field.
## 2 Materials and Methods
### Sentinel-1 and-2 Data  
S1 and S2 are a part of the Copernicus Program satellite constellation (including Sentinel-1, -2, and -3) developed by the European Space Agency (ESA). The C-band SAR onboard S1 provides dual-polarization (i.e., VV and VH) observations and integrates four imaging modes with different resolutions and coverages [34]. This study used the S1A and S1B Level-1 Ground Range Detected (GRD) product from January 2017 to December 2019,  
Figure 1: Map of the US and study sites. DB: deciduous broadleaf forest, EN: evergreen needle-leaf forest.  
and the resolution of the product was 10 m. The backscatter coefficient of VV and VH of each study site was replaced by an average with a 15 \(\times\) 15 kernel to reduce the inherent speckle effects of radar imagery. For each site, two polarization combinations discussed in previous studies [1; 21], i.e., the ratio VH/VV and the differencing VV-VH, were extracted to analyze the effect of phenological estimation.  
In addition, S2 Level-1C data were used to compare the accuracy of S1 retrieval. The Level-1C products consist of 13 spectral bands at spatial resolutions from 10 m to 60 m, and B4 (centered at 665 nm) and B8 (centered at 842 nm) used in this study were at a resolution of 10 m [35]. A cloud mask band (QA60) was used to pick only cloud-free pixels. Scenes where more than 30% of the pixels within the subset were classified as cloudy were removed from the analysis.  
Then, the normalized difference vegetation index (NDVI), which reveals the difference between leaf spectral absorption and reflection, was calculated based on B4 and B8 reflectance using the following equation [36]:  
\[\mathrm{NDVI}=\frac{\mathrm{B8}-\mathrm{B4}}{\mathrm{B8}+\mathrm{B4}} \tag{2}\]  
Cloudy pixels (derived from the QA60) and water bodies (pixels with NDVI values less than 0) were removed to ensure only pixels of good quality were used for further analysis.
# Optimal Prediction of Individual Vessel Trajectories Based on Sparse Gaussian Processes
## 1 Introduction
* To optimize the performance of the SGP, hyperparameters such as the approximation method, number of inducing points, and kernel parameters are fine-tuned using a genetic algorithm (GA). This optimization process ensures that the SGP achieves enhanced prediction accuracy and efficiency by applying optimal hyperparameter values. * The SGP is capable of learning the distribution over a ship's trajectory derivatives (velocities) at each position. This capability enables trajectory prediction based solely on continuous positional data, without requiring additional input variables such as course or speed, thereby minimizing the model's input dimensionality while maintaining high prediction accuracy. * The robustness of the proposed SGP algorithm was ensured by withholding the test dataset during training and using it exclusively for final evaluation. Long-term prediction consistency was assessed by introducing slight variations in the input data and monitoring changes in prediction time. The model's performance was rigorously evaluated across multiple metrics. These evaluations demonstrate its accuracy, efficiency, and stability under different conditions. The subsequent sections of this article are structured in the following manner: Section 2 discusses the limitations and challenges of existing literature and how they are addressed by the proposed approach. Section 3 provides background information on the proposed method. Section 4 presents a technique for forecasting the trajectory of a vessel using the SGP algorithm. Section 5 contains comprehensive information regarding the model's development and the experiments conducted for simulation purposes. Section 6 discusses the implications of the results and the limitations of this study. Section 7 serves as the final part of the paper, providing a conclusion and proposing potential avenues for future research.
## 3 Simulation Experiment Results  
The radial basis function neural network is trained by MATLAB, and the horizontal ordinates of four control points in middle of the fifth order Bezier curve are obtained. After optimization, the coordinates of the six control points of the quintic Bezier curve in  
Figure 16: Trained radial basis function neural network.  
Equation (19) are known as \(P_{0}(1,1)\), \(P_{1}(2.6442,1)\), \(P_{2}(1.1935,1)\), \(P_{3}(6.8065,3.1)\), \(P_{4}(5.3558,3.1)\), and \(P_{5}(7.5,3.1)\) separately.  
By substituting six control points into Equation (19), the Bezier curve expression of the optimized path is obtained. Figure 17 shows the performance of Bezier curve path trained by radial basis function neural network. In Figure 17a, the path of the Bezier curve trained by the radial basis function neural network is shown as the solid line. The curvature of the optimized parking path is shown as the dotted line. The curvature curve of the whole path is a smooth and continuous curve, which meets the curvature continuous condition of the parking path. Compared with Figure 13, the curvature of the optimized path changes more gently in the parking process, without obvious mutation, so it is more advantageous to continuously change the front wheel steering angle of the vehicle and reduce the control difficulty.  
The curvature of a point on the arc is the reciprocal of the radius, so the curvature value \(\mid K\mid\) of arc corresponding to the minimum turning radius \(R_{\text{m}}\) is calculated as follows  
\[\mid K\mid=1/R_{\text{m}}=0.2381\text{ m}^{-1}\]  
The maximum curvature value of parallel parking path based on Bezier curve \(\mid K\mid_{\text{max}}\) is 0.2284 m\({}^{-1}\), which is less than the allowable maximum curvature value 0.2381 m\({}^{-1}\). So, the path curvature constraint is met.  
MATLAB platform is used to model and simulate parallel parking condition. Pure pursuit control algorithm is used to track planning path [10] and verify the safety of the path. During parallel parking, the right rear corner and the right front corner of the vehicle are most likely to collide with the left rear corner of the vehicle in front. As shown in Figure 17b, in the parking process of the tracking planning path, the two points that are most prone to collision do not collide avoiding collision with the vehicles in front. After parking, the rear of the vehicle has a certain distance from the vehicles in the rear, so it does not collide with the vehicles in the rear. Therefore, the planning path optimization meets the parking safety requirements.
# Content-Based Approach for Improving Bloom Filter Efficiency
## 2 Background and Related Work
This principle can be exploited to re-identify sensitive encoded values, irrespective of the specific encoding function being used. To address this issue, [PERSON] et al. [14] developed the hashing BF method as a solution. [PERSON] et al. [1] presented a comprehensive study to explore the potential utilization of BFs in the field of big data research. Their investigation primarily targeted the application of a specialized variant of the BF known as the fingerprint BF. It specifically serves the purpose of fingerprint-matching tasks. Additionally, the authors introduced the multidimensional BF, which exhibits a remarkable proficiency in handling arrays with dimensions extending beyond the conventional 2D and 3D domains. [PERSON] et al. [15] introduced an adaptive memory access bloom filter (BF) that specifically addresses the issue of false positives when dealing with repeated elementsacross different queries. Their approach aims to minimize false positive occurrences in such scenarios. The approach achieved an FPR of less than 5% for networking applications while using minimal memory. Another study conducted by [PERSON] and [PERSON] [16] looked at various types of BFs for different domains, specifically focusing on one-hashing BFs. [PERSON] et al. [17] introduced the concept of an elastic BF, which allows for both deletions and expansions without increasing the FPR or decreasing query speed. The approach utilizes elastic fingerprints, which can expand or shrink. To the best of our understanding, none of the previously mentioned studies have primarily focused on resolving the problem of the FPR. Therefore, this paper presents a new method utilizing content to reduce false positives, which will be discussed in Section 3. It is also worth mentioning that this study furthers the discourse and investigations presented in papers [7; 13], serving as a direct sequel to the concepts and research introduced there.
## 3 Results
### An Investigation of the Influence of Temperature on Damping Characteristics  
In the last stage, the influence of the temperature on the ability of the damper to dissipate vibration energy was specified. For this purpose, a test was conducted from the initial temperature until the steady-state value temperature was established at a given excitation. The tests were conducted for an amplitude of 60 mm and a frequency of 1 Hz (from a temperature of 27 \({}^{\circ}\)C), and a frequency of 0.5 Hz for an initial temperature of \(-30\)\({}^{\circ}\)C. The reduction in excitation parameters compared to previous tests was due to a reduction in the probability of shock absorber failure. In the test performed for shock absorber 2 at an amplitude of 80 mm and a frequency of 1 Hz, the valve failed at its initial stage (after forty cycles). This manifested itself in a sharp reduction in resistance forces. Figure 8 summarises the force-displacement characteristics obtained when the shock absorber was heated.  
The most rapid changes in resistance forces are observed for negative operating temperatures. Due to the rapid increase in viscosity of the oil at negative temperatures, the force obtained at \(-30\)\({}^{\circ}\)C is 2.5 times greater than for 20 \({}^{\circ}\)C. In comparison, a temperature change from 30 \({}^{\circ}\)C to 130 \({}^{\circ}\)C reduces the maximum resistance force by only about 11%.  
Based on the recorded force-displacement characteristics, the amount of energy dissipated to the environment \(\mathrm{E_{d}}\) during each duty cycle was calculated [16]:  
\[\mathrm{E_{d}}=\oint\mathrm{Fdx} \tag{5}\]  
The results are shown in Figure 9. For a frequency of 0.5 Hz, thermodynamic equilibrium (equilibrium between the work of the external force and the heat dissipated to the environment) was reached at 68 \({}^{\circ}\)C (dissipating approximately 106 J per cycle). For a frequency of 1 Hz, the values of 136 \({}^{\circ}\)C and 158 J were obtained, respectively. It can be concluded that the amount of dissipated energy is practically independent of the frequency of the displacement of the shock absorber piston. This is because it is primarily dependent on the rate of change of oil viscosity. The values of energy dissipation rates depending on the temperature are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline
**Temperature** [\({}^{\circ}\)**C**] & **Energy Dissipation Rates** [\(J^{\circ}\)**C] \\ \hline \(-\)20 & \(-\)2.14 \\ \(0\) & \(-\)0.564 \\ \(20\) & \(-\)0.325 \\ \(60\) & \(-\)0.192 \\ \(100\) & \(-\)0.122 \\ \(130\) & \(-\)0.091 \\ \hline \end{tabular}
\end{table}
Table 2: Temperature dependence of the energy dissipation rate.  
Figure 8: The influence of temperature on shock absorber force–displacement characteristics: (a) A = 60 mm, f = 1 Hz; (b) A = 60 mm, f = 0.5 Hz.  
Figure 9: Energy dissipated in one cycle.  
The amount of energy dissipated per cycle decreases non-linearly with the temperature. The fastest changes are again observed for low temperatures. For temperatures above 20 \({}^{\circ}\)C, a linear change at a rate of approximately \(-0.2\,J/^{\circ}\)C can be assumed.
# A Complete Pipeline for Heart Rate Extraction from Infant ECGs
## 2 Materials and Results
### The HR Pipeline
#### 2.3.4 Signal Quality Index Calculation
At each time point (i.e., heartbeat measurement), the percentage of local beats within the filter width that deviated by a multiplicative factor of 1.3 above or below the local median was calculated (i.e., the local median indicated the existence of a poor signal within the sliding window, and for a median HR of 100 bpm, the proportion of beats outside the range of 77-130 bpm was calculated). If the percentage of poor beats was \(\leq\)25%, then SQI = 1 (high signal quality) at that time point. Otherwise, SQI = 0 (low signal quality). The sliding window was then moved to the next time point. This multiplicative factor was similar to the value reported by [PERSON] et al. [59], which used outlier detections from the median of the five most recent \"correctly labelled\" HR values. To make the SQI more accurate, additional manipulations were used to account for the specific locations of the high-deviation beats. First, the regions of good SQIs were then extended (i.e., set to SQI = 1) according to whether the beats just beyond the boundary of the good SQI region were within the local median range. Second, continuous regions \(>\)3.5 s long of high-deviation beats (\(>\)1.3 beats from local median) were set to SQI = 0, as were any gaps in the heart rate longer than 2.5 s. Lastly, any remaining good regions \(<\)5 s long were set to SQI = 0 to leave regions of a reasonable size. These parameters could be tailored depending on the length of the useful heart rate region for a given research question, and how precise a heart rate was required to be. The Boolean SQI vector can then be applied to the heart rate by either setting areas of bad signal to 0 bpm, or by cutting those regions from the signal.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 2 Technology Background of Physical Layer  
The PHY layer of wireless communication systems is responsible for transmitting and receiving data over the air interface. The PHY is also a potential target of various security attacks, such as jamming, spoofing, eavesdropping, and relay attacks. These attacks can degrade the performance, reliability, and confidentiality of wireless communication. Therefore, it is important to design and implement security mechanisms at the PHY level, in addition to at the higher layers of the protocol stack.
## 2 Materials and Methods
### Statistical Analysis  
The Shapiro-Wilk test was employed to assess the normal distribution of the data. Means, standard deviations (SDs), and 95% confidence intervals (95% CIs) were calculated for K\({}_{\text{MEAN}}\), K\({}_{\text{BEST}}\), and R\({}_{\text{P}}\). The statistical analysis was conducted using STATA statistical software version 14.2 (Stata-Corp, LLC, College Station, TX, USA).  
Linear repeated measures mixed models were performed to examine the effects of a Superjump(r) training session on the subjects' stiffness (K\({}_{\text{MEAN}}\) and K\({}_{\text{BEST}}\)) and R\({}_{\text{P}}\) performances in relation to sex. In the model, subjects were considered as the random effect, whereas sex (female vs. male) and testing time (PRE vs. POST) were treated as fixed effects. The models were fitted using maximum residual likelihood to account for the small sample size. For assessing the significance of main effects and interactions, a repeated-measures analysis of variance (ANOVA) test was utilized to compute the degrees of freedom of a t-distribution as subjects were tested in both PRE and POST training sessions. The contrast method was subsequently employed to test whether the means of the dependent variables (i.e., stiffness, R\({}_{\text{P}}\)) for sex (female vs. male) and testing time (PRE and POST) were equivalent. The contrast method tests included ANOVA-style tests of the main effects, which were used to perform the comparison against the reference categories (i.e., PRE vs. POST; female vs. male). The main effect and interaction statistical significance were set at \(p<0.05\). In case significant main effects and interactions were found, a post hoc analysis was conducted using Bonferroni correction for multiple comparison adjustments across all terms. For the post hoc pair-wise comparisons, significance was set at \(p<0.008\) to provide a meaningful analysis for the comparisons in small groups. In order to provide an estimate of the measurement error of the linear repeated measures mixed models, standard errors (SEs) and intra class correlation coefficients (ICCs) were used. Additionally, [PERSON]'s effect sizes (ESs) were calculated to ascertain practical significance. An ES of 0.2 or less was considered trivial, an ES ranging from 0.3 to 0.6 was considered small, an ES less than 1.2 was considered moderate, and an ES greater than 1.2 was regarded as large [35].
# Clinical, Research, and Educational Applications of ChatGPT in Dentistry: A Narrative Review
## 4 Discussion
### Educational Enhancements
#### 4.4.2 The Teachers' Perspective  
A worldwide survey of educators by [PERSON] et al. showed a prevalent positive yet cautious view towards AI chatbot integration in dental curricula, underscoring the need for clear implementation guidelines [113]. GPTs can generate always-changing questions for exams, especially at the knowledge and comprehension levels, making them useful for large-scale exams provided that supervision on the output is always ensured [107].  
Moreover, ChatGPT may generate scenarios, questions, answers, and explanations in real time, building more engaging learning for students. This type of content can simulate a realistic conversation between students and virtual patients or clinical scenarios, offering the chance to exercise in a controlled environment. [PERSON] et al. included ChatGPT actively in an entire course, deeming it extremely useful also in summarizing general overarching principles, which are sometimes spread over more than one chapter, allowing students to reach the learning goals [109].  
Educators can use GPTs to check if tasks were fulfilled using GPTs, adjusting their teaching and assessments to benefit learners while addressing potential misuse [108; 112]. Moreover, GPTs can supplement humans in grading paper and essays [110; 111].