esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Depth Learning in Stacked AutoEncoders
## 1 Introduction
However, determining the optimal depth and width of the network is not straightforward. These two hyperparameters are usually set empirically [17], or using some search strategies [18]. Moreover, they rely on the supervised information to compare the performance of the different settings/topologies. Thus, when the supervision information is not available, the problem is even more challenging. As such, AutoEncoders which learn, in an unsupervised way, abstract and complex feature representation of the input through the combination of the successive layers [13] face the challenging problem of determining the optimal topology in an unsupervised way. In fact, learning the topology of a stacked AE plays an essential role in its performance as it controls the level of abstraction and complexity of the learned feature map. Moreover, overly deep stacked AE are prone to the vanishing gradient problem and overfitting. Furthermore, setting the appropriate depth empirically by repeating the experiment requires the availability of labeled data, and thus can be applied for unsupervised data mapping using AutoEncoders. The main contribution of this paper is to automatically learn the optimal number of hidden layers of a stacked AutoEncoder. Therefore, a novel cost function will be designed and optimized apart from the AutoEncoder loss function. The optimization of the proposed cost function is intended to learn relevance weights for each hidden layer. Based on the learned weights, the irrelevant hidden layers will be pruned. This yields the optimal stacked AutoEncoder depth. Moreover, pruning irrelevant layers is an acclaimed technique in reducing overfitting [19]. Hence, it can increase the overall model learning stability [19]. More specifically, the hidden layers' relevance weights will be learned simultaneously, allowing the interpretation of the role of each hidden layer. In other words, the proposed approach is intended to learn the optimum AE depth and thus alleviate the typical issues faced by overly deep stacked AutoEncoders. As such, this research has the following objectives:  
1. Learn the stacked AE depth while training the model in an unsupervised way;
2. Design a novel objective function to learn the layers' relevance weights;
3. Design an algorithm to prune irrelevant layers.
it is specifically tailored to run on low-power client devices, thereby demonstrating its compatibility with power-constrained systems.  
The main purpose of this research was to collect essential measurements related to the analog characteristics of MRAM devices. These measurements included inter-cell variation, intra-cell variation, medians, and ranges. Additionally, the research investigated specific metrics of the MRAM TAPUF design, such as uniqueness, reliability, and randomness. By analyzing these measurements and metrics, our study aims to offer a comprehensive understanding of the performance and characteristics of the MRAM TAPUF.  
The main contributions of this paper are described as follows.  
* This paper proposes a novel PUF architecture that utilizes two MRAM devices;
* We utilized the unstable CRPs to enhance reliability and security;
* To address the scaling of CRPs, a technique called \"differential read\" was employed using MRAM cells from two devices. This approach effectively enhances the scalability of CRPs, allowing for improved performance and reliability in practical applications;
* The PUF was implemented on low-power hardware, and the experimental results showed that the PUF had excellent BER, inter-chip Hamming distance, and entropy density.  
The rest of this paper is organized into several sections. Section 2 initiates the discussion by describing the MRAM technology, PUFs, TAPUFs, prior work in the field, and the novel contributions of this study. In Section 3, we delve into the design of the MRAM TAPUF, explaining the enrollment process and the generation of bits on both the server and client sides. Section 4 is dedicated to the implementation of the design in hardware. Moving on to Section 5, the focus shifts to the electrical characterization of the MRAM devices and TAPUF, including evaluating the PUF's merits, such as uniqueness, reliability, and randomness. In Section 6, we present an overview of the advantages of our design, along with a comparative analysis of existing work. Finally, Section 7 concludes the paper by addressing future research directions, identifying areas that require further investigation, and proposing potential avenues for future studies and experiments.
# Research on the Influence of Backlash on Mesh Stiffness and the Nonlinear Dynamics of Spur Gears
## 3 Influence of Backlash on Dynamics  
### Single-Degree-of-Fredom(SDOF) Model and Dynamic Differential Equation  
The SDOF model is shown in Figure 8. Two gears are combined with supporting shafts, which are regarded as rigid bodies with large support stiffness. The influences of friction and radial support are ignored. The pinion and gear are represented by their base circle radii of \(r_{bp}\) and \(r_{bp}\), respectively. Then, the equations of motion can be expressed as follows:  
\[\left\{\begin{array}{rcl}I_{p}\ddot{\theta_{p}}+c_{m}\Big{(}r_{bp}\dot{ \theta_{p}}-r_{bg}\dot{\theta_{g}}-e(t)\Big{)}r_{bp}+r_{bp}K(t)h\Big{(}r_{bp} \theta_{p}-r_{bg}\theta_{g}-e(t)\Big{)}&=&T_{P}\\ I_{g}\ddot{\theta_{g}}+c_{m}\Big{(}r_{bp}\dot{\theta_{p}}-r_{bg}\dot{\theta_{g} }-e(t)\Big{)}r_{bg}+r_{bg}K(t)h\Big{(}r_{bp}\theta_{p}-r_{bg}\theta_{g}-e(t) \Big{)}&=&-T_{g}\end{array}\right. \tag{27}\]  
where \(I_{p}\) and \(I_{g}\) represent the polar mass moments of inertia, respectively. \(m_{p}\) and \(m_{g}\) represent the masses of pinion and gear. \(K(t)\) represents the time-varying mesh stiffness. \(c_{m}~{}=~{}2\zeta\sqrt{k_{m}/\left(1/m_{p}+1/m_{g}\right)}\) represents viscous damping, and \(\zeta\) represents the damping ratio. \(e(t)\) represents the static transmission error. \(T_{P}\) and \(T_{g}\) represent the input and output torques acting on the pinion and gear. To simplify the calculation, the fluctuations of input and output torque are neglected. The backlash function '\(h\)' can be expressed as  
\[h(r_{bp}\theta_{p}-r_{bg}\theta_{g}-e(t))~{}=~{}\left\{\begin{array}{rcl}r_{ bp}\theta_{p}-r_{bg}\theta_{g}-e(t)-b,~{}r_{bg}\theta_{g}-e(t)>b\\ 0,~{}|r_{bp}\theta_{p}-r_{bg}\theta_{g}-e(t)|\leq b\\ r_{bp}\theta_{p}-r_{bg}\theta_{g}-e(t)+b,~{}r_{bp}\theta_{p}-r_{bg}\theta_{g}- e(t)<-b\end{array}\right. \tag{28}\]  
By employing dynamic transmission error \(x(t)~{}=~{}r_{bp}\theta_{p}-r_{bg}\theta_{g}-e(t)\), Equation (27) can be re-formed as  
\[m_{e}\ddot{x(t)}+c_{m}x(\dot{t})+K(t)h(x(t))~{}=~{}F_{m}+F_{e}(t) \tag{29}\]  
with \(m_{e}~{}=~{}\frac{I_{g}I_{p}}{\left(I_{g}\dot{r}_{bp}^{2}+I_{p}r_{bg}^{2} \right)}\), \(F_{m}~{}=~{}m_{e}\Big{(}\frac{T_{pn}r_{bp}}{I_{p}}+\frac{T_{pn}r_{bg}}{I_{g}} \Big{)}\), \(F_{e}(t)~{}=~{}-m_{e}e(\ddot{t})\).
# A Design for an Internet Router with a Digital Optical Data Plane
## 9 Conclusions  
We show that the transition to native optical IP packet processing is inevitable, driven by the need for increased transmission capacity and avoiding format conversion, even if it might not be motivated by power reduction compared to electronic implementations. We have demonstrated the potential for developing an Internet router with an optical data plane by showing the feasibility of each of its component steps. We previously validated implementations of forwarding lookup via header matching and hop count decrement using 10 Gb/s RZ optical packets. We show the design and feasibility of an optical Internet checksum based on existing optical full-adders, as well as optical packet multiplexing of Internet traffic based on forward-shift SDLs, both using simulations. We also show the extension of optical processing from binary to multi-bit encodings, in order to support even higher optical transmission formats. The composition of these components is a true optical Internet router, and its implementation depends more on advancement in optical integration and regeneration than on any inherent impediment of efficiently optically processing IP packets.  
The authors would like to acknowledge the feedback provided by [PERSON] at USC, [PERSON] and her students at Cornell University, [PERSON] and his students at Colorado State University, and [PERSON] and his students at Penn State, all of whom endured early versions of presentations resulting in this work. This work was partly supported by the National Science Foundation's (NSF) Center for Integrated Access Networks (CIAN) Grant No. 0812072 and NSF Optical Turing Machine Grant No. 1344221. \"Tetris\" is a registered trademark of The Tetris Company, LLC, Honolulu, HI, USA.  
[PERSON] and [PERSON] conceived and developed the overall concept and architecture, with advice from [PERSON] on the capabilities of digital optical components. [PERSON] additionally developed the circuits for hop count decrement and Internet checksum and authored this manuscript (based on a previous conference publication he also authored). [PERSON] developed packet simulations to analyze performance (Section 6). [PERSON] also coordinated the optical implementations and analyses of optical header matching and optical hop count decrement.  
The authors declare no conflict of interest.
# Earthquake-Tolerant Energy-Aware Algorithm for WDM Backbone Network
## 2 Materials and Methods
### Energy Contribution and Seismic Measurements
#### 2.1.2 Richter and Gutenberg's Law  
Seismic activity is not distributed evenly across the surface of the Earth; rather, there are seismic zones that cover specific areas where seismic activity is notably higher. Figure 3 depicts the entire map of Greece, with each seismic zone distinguished by a different color, as provided by the Earthquake Planning and Protection Organization of the Ministry of Transport, Infrastructure, and Networks [36]. The zone with low seismic activity is marked in blue, the zone with medium seismic activity in green, and the zone with high seismic activity in orange. Since the regions of Greece encompass all these seismic zones, an alternative approach must be employed to calculate the probability of seismic activity in each region [6].  
In general, most quantitative seismic measures are based on the Gutenberg-Richter (G-R) size distribution law. In seismology, the Gutenberg-Richter (G-R) law is used to express the relationship between the magnitude and the total number of earthquakes in any region and time period where earthquakes of at least that magnitude occur. It is defined mathematically as follows [37]:  
\[log_{10}N(M)_{t}=a_{t}-bM \tag{2}\]  
where \(N(M)_{t}\) is the cumulative number of events with a size greater than or equal to \(M\), in a period of \(t\) years, \(a_{t}\) is the duration, in years, of the research, and \(b\) is the slope of the above equation. The values of \(a_{t}\) and \(b\) can be estimated from an empirical distribution of earthquake frequencies, using the method of least squares (Equations (7)-(9)). The same equation can be written as:  
\[log_{10}N(M)=a-bM \tag{3}\]  
Figure 3: New seismic hazard zoning map of Greece [6].  
where \(N(M)\) is the cumulative number of events of size greater than or equal to \(M\) in a year.  
\[a=a_{t}-log_{10}t \tag{4}\]  
\[T_{m}=\frac{10^{bM}}{10^{a}} \tag{5}\]  
\[P_{t^{\prime}}=1-e^{\frac{b^{\prime}t^{\prime}}{1m}} \tag{6}\]  
\[a_{t}=\frac{\sum_{i=1}^{n}x_{i}^{2}\sum_{i=1}^{n}y_{i}-\sum_{i=1}^{n}x_{i} \sum_{i-1}^{n}x_{i}y_{i}}{D} \tag{7}\]  
\[b=\frac{n\times\sum_{i=1}^{n}x_{i}y_{i}-\sum_{i=1}^{n}x_{i}\sum_{i-1}^{n}y_{i}} {D} \tag{8}\]  
In Equation (4), \(a\) is the annual G-R constant. Equation (5) expresses the average return (repetition) period (arp) for earthquakes of magnitude \(M\) or greater, while Equation (6) calculates the probability of an earthquake of magnitude \(M\) or greater in the considered area for the next \(t\) years, making the standard assumption that earthquakes follow a random (Poisson) distribution. In Equations (7)-(9), \(x\) represents the magnitude of the earthquake in a region, starting from a minimum cutoff magnitude (e.g., 4.0) on the Richter scale, while \(y\) represents the logarithmic value of the cumulative number of earthquakes with this minimum magnitude. Finally, \(n\) is the total number of available earthquakes above the cutoff magnitude, in the considered area [6].
# The Analysis and Verification of Unbiased Estimator for Multilateral Positioning
## 3 Multilateral Positioning Model  
For the multilateral positioning algorithm on a two-dimensional plane shown in Figure 1, the coordinates of anchor nodes are known as \(S_{1}(x_{1},y_{1})\), \(S_{2}(x_{2},y_{2}).\ldots.S_{n}(x_{n},y_{n})\). Assuming that the coordinate of the target node is T\((x,y)\), the true distance between the target node and anchor node \(S_{i}\) is \(d_{n}\), the following equation can be obtained:  
\[\left\{\begin{aligned} (x_{1}-x_{0})^{2}+(y_{1}-y_{0})^{2}& ={d_{1}}^{2}\\ (x_{2}-x_{0})^{2}+(y_{2}-y_{0})^{2}&={d_{2}}^{2}\\ \ldots\\ (x_{n}-x_{0})^{2}+(y_{n}-y_{0})^{2}&={d_{n}}^{2} \end{aligned}\right. \tag{1}\]  
Equation (1) can be transformed to a linear one by subtracting the last equation from other equations, and after deformation, it can be expressed as a linear one:  
\[A=BX \tag{2}\]  
where the coefficient matrix \(A\) is:  
\[A=\left[\begin{array}{c}2x_{1}-x_{n}\,2(y_{1}-y_{n})\\ 2x_{2}-x_{n}\,2(y_{2}-y_{n})\\ \ldots\\ 2x_{n-1}-x_{n}\,2(y_{n-1}-y_{n})\end{array}\right] \tag{3}\]  
and \(B\) is:  
\[B=\left[\begin{array}{c}{x_{1}}^{2}-{x_{n}}^{2}+{y_{1}}^{2}-{y_{n}}^{2}+{d_ {n}}^{2}-{d_{1}}^{2}\\ {x_{2}}^{2}-{x_{n}}^{2}+{y_{2}}^{2}-{y_{n}}^{2}+{d_{n}}^{2}-{d_{2}}^{2}\\ \ldots\\ {x_{1}}^{2}-{x_{n}}^{2}+{y_{1}}^{2}-{y_{n}}^{2}+{d_{n}}^{2}-{d_{n-1}}^{2} \end{array}\right] \tag{4}\]  
The estimated coordinate values of the target node can be calculated by least square method:  
\[X=\left(A^{T}A\right)^{-1}A^{T}B \tag{5}\]  
and if ranging technologies such as UWB or LoRa is utilized, the ranging information between the target node and anchor node can be obtained. Suppose the ranging distances are denoted as \(\hat{d_{i}}\), by substituting \(d_{i}\) as \(\hat{d_{i}}\) in Equation (5), we can get an estimator of the target node.
# Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios
## 5 Results Validation
### Validation of Optimization Results for Strain Sensors  
In the validation process of the optimization of positions and orientations of the strain sensors, two cases of the monitored load are considered: the applied force value and maximal von Mises stress in the whole structure. The range of the applied force for each load case location is between 0 and 100 N. Taking into account 100 N of force, the maximal values of equivalent stress in the whole structure are 3.2011 MPa, 3.6225 MPa, 3.5078 MPa, 4.8768 MPa, 4.7679 MPa, 3.6656 MPa, and 3.6778 MPa, respectively, for LC1-LC7. The locations of maximal stress for load cases LC1-LC7 are identical to the maximal values in the equivalent strain maps in Figure 12.  
Statistical comparison of the accuracy of the force prediction models was prepared analogously to the comparison of the temperature prediction models and is presented in Table 3. All the statistical measures are lower for optimal sensor positions than random sensor positions. Moreover, the mean value and standard deviation is lower by an order of magnitude. The histograms are presented  
The second case of predictions from the strain measurements concerned maximal equivalent von Mises stress in the entire structure. The statistical measurements of RMSE for all the training trials for both random and optimal sensor positions are listed in Table 4. Once again, all the measurements are lower for the optimal solution. The histograms of RMSE are compared in Figure 29. For optimal sensor placement, the RMSE range is narrower, and the bars are closer to zero than in the case of random sensor placement.
# Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics
## 3 Inter-Comparisons with Other Techniques
### Validations of GNSS Water Vapor with Other Techniques as Reference
Correlations with GNSS IWV were larger for ground-based instruments (\(R^{2}>0.97\)) while slightly smaller for satellite instruments (\(R^{2}>0.88\)). Sun-photometers and satellite retrievals were shown to need low cloud fractions to perform properly, being this more important than proximity between GNSS station and satellite pixel center or sun-photometer station. All instruments showed worse performance with respect to GNSS as cloud fraction increases, which in the case of satellite instruments can be explained for the so-called screening effect. As cloudy skies typically have larger IWV, the instruments show stronger dry bias for high IWV. Radiosonde and AIRS measure both in daytime and nighttime, and both showed a wet bias during nighttime, being reduced (radiosonde) or changed to dry bias (AIRS) during daytime. In any case, inter-comparisons are reported to be site- and instrument-dependent, as [PERSON] et al. [65] indicated. Not only IWV has been tested, but also profiles obtained through tomography. [PERSON] et al. [56] validated GNSS water vapor profiles with collocated MWR and radiosonde observations, using MWR as a reference in Ulleungdo (Korea) for a few days in July 2009. It was found that GNSS tomography could even improve radiosonde's RMSE values, but in some cases GNSS profiles were degraded, which was associated with the fact that only two GNSS stations were used in the tomography. Under severe weather, GNSS showed very good agreement with MWR, in comparison with radiosonde. This work concluded that another important issue for improving GNSS tomography was the satellite geometry, which is expected to improve with multi-GNSS approaches. The different validations show that GNSS has great potential as a tool for IWV monitoring. It is important to put an effort into homogenizing the series, especially when calculating trends and, when performing comparisons, taking into account possible height differences and other deficiencies in the instruments considered. These efforts help reduce spurious biases and variability. The GNSS technique has the advantage of being relatively cheap, weather-independent and with high temporal resolution, and for these reasons this technique is preferred to others in many cases.
## 2 Sequential Modelling Strategy
### Spacecraft Dynamics with Structural Considerations  
#### 2.4.1 Reference Frames  
All dynamics are defined in relation to an inertial reference frame \(\mathcal{N}\) with origin \(N\) (located at Earth's centre) and basis vectors \(\hat{\mathbf{n}}_{1},\hat{\mathbf{n}}_{2},\hat{\mathbf{n}}_{3}\). The target spacecraft is assumed to orbit the Earth in a circular orbit and is characterised by its body-fixed reference frame \(\mathcal{B}\) with origin \(B\) and basis vectors \(\hat{\mathbf{b}}_{1},\hat{\mathbf{b}}_{2},\hat{\mathbf{b}}_{3}\). Additionally, in the case of co-moving solar arrays, a reference frame \(\mathcal{H}\) is defined with origin \(H\) (located at the solar array hinge) and basis vectors \(\hat{\mathbf{h}}_{1},\hat{\mathbf{h}}_{2},\hat{\mathbf{h}}_{3}\). The basis vector \(\hat{\mathbf{h}}_{1}\) aligns in the anti-parallel direction to the solar array centre of gravity (\(S_{c}\)), \(\hat{\mathbf{h}}_{2}\) defines the rotation axis of the hinge (with angle \(\theta_{s}\)) and \(\hat{\mathbf{h}}_{3}\) completes the basis. Each body has its own centre of gravity (CG), as illustrated in Figure 4.
# The Integration of AI and Metaverse in Education: A Systematic Literature Review
## 1 Introduction
### Contribution  
Our study involved a systematic review following PRISMA methodology aiming to analyze both the significance and impact of integrating AI within the metaverse in education. This study pays special attention to technical aspects. We evaluated 474 research publications based on predetermined criteria to address the following research questions:  
1. What disciplines are covered by the AI-driven metaverse in education?
2. How does the integration of AI in the metaverse affect student engagement, motivation, and participation in educational activities?
3. What AI techniques are utilized in the metaverse for education?
4. What are the challenges of utilizing AI in the metaverse for education?  
Our systematic review was motivated by several key factors. First, knowledge regarding the interdisciplinary scope of the AI-based metaverse in education is limited, warranting an exploration of the associated fields and disciplines. Understanding the disciplines influenced by the integration of AI within the metaverse will also provide valuable insights to stakeholders in the education and technology sectors. Second, our study aims to explore how the AI-based metaverse, particularly the AI-based virtual learning environment, influences students' engagement, motivation, participation, and performance. Given that AI has shown promise in terms of personalizing learning pathways and promoting active participation, it is essential to evaluate its effectiveness in the context of the metaverse. Third, since AI encompasses abundant technologies, from natural language processing to computer vision, it is vital to identify the AI techniques used in the metaverse for education. This will help us to understand the AI methods and techniques that are most applicable and beneficial in this context. Finally, while the potential benefits of AI in the metaverse are considerable, significant challenges, including technical, ethical, and pedagogical challenges, remain. Recognizing these challenges is crucial for future research to ensure the accurate integration of AI in the metaverse for the purpose of augmenting education. Therefore, this systematic review will provide a comprehensive overview of the disciplines, impacts, techniques, and challenges associated with the integration of AI in the metaverse for education.  
The remainder of the paper is organized as follows. Section 2 presents a review of the literature on the metaverse and AI for education, examining systematic review studies specifically. Then, we detail the different phases of our research methodology in Section 3. In Section 4, we address the four research questions by conducting a comprehensive analysis of the eligible papers. Section 5 comprises the discussion and includes a critical evaluation of key findings and recommendations according to the challenges identified. Finally, we conclude the paper in Section 7.
## 5 Methodology for Anomaly Detection and Mitigation
### Anomaly Detection  
The anomalies shown before differ in their source, characteristics and area to which they belong. Indicators to identify the anomaly can be assumed either in the data-lake as part of the communication or directly on a specific robot or attached system. As the parameters of the anomaly can be assumed to be very different depending on the source and area of the anomaly, and also the necessary parameters to solve the anomaly depend on the goal of mitigation (e.g., optimize operation or mitigate a cyber-attack), different anomaly detection methods are needed. This chapter presents anomaly detection methods developed as part of the use-case to address the aforementioned anomalies.