esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Computational Experiments
### Sensitivity Analysis  
In this section, a sensitivity analysis is conducted considering the government's investment to evaluate its impact on the recycling rate. Do note that the recycling rate is defined as:  
\[\text{Recycling rate}=\frac{\text{Total amountof construction waste recycled}}{\text{Total amount of construction waste generated}}\]  
Figure 5 shows the results of the sensitivity analysis, where the value of the investment ranges from 800 to 2500 million CNY with a step size of 50 million CNY. Each marker represents the observed recycling rate percentage, corresponding to specific investment levels. When the investment is set at 800 million CNY, no generated construction waste is recycled. The recycling rate exhibits a positive trajectory in conjunction with increasing investment. The maximum recycling rate observed reaches 92.72% (indicated by the red dashed line in Figure 5), which is achievable beyond an investment level of 22.5 billion CNY. A red marker indicates that when the amount of investment equates to the disposal of construction waste to landfills, the recycling rate is 31.90%.  
Figure 4: The comparison of the recycled amount of construction waste disposed of by recycling centers solved by models [M1] and [M3].  
Figure 5: Sensitivity analysis of the government’s investment in the establishment of recycling centers.
# Machine Learning Based Energy-Efficient Design Approach for Interconnects in Circuits and Systems
## 2 Low Power Approach for Interconnect Design  
### Off-Chip Interconnect Design Flow  
Designing a transmission line for off-chip interconnect is a multi-dimensional process. The design procedure of a transmission line includes both the frequency domain and the time domain due to the its frequency-dependent characteristics. While the scattering parameter (S-parameter) is typically employed to evaluate design properties in the frequency domain, the eye diagram with eye height (EH) and eye width (EW) is generally used to determine the signal quality in the time domain. Additionally, the characteristic impedance (\(Z_{0}\)), which is a significant parameter, is required to design carefully to avoid reflection loss along the interconnect.
# GNSS Code Multipath Mitigation by Cascading Measurement Monitoring Techniques
## 7 Conclusions  
Different multipath monitoring techniques were investigated under three major groups as the multipath error estimation and correction, stochastic weighting, and detection and exclusion (or de-weighting) of distorted measurements. While exclusion or de-weighting is challenging due to its effect on geometry, for a dual-frequency receiver, a new Geometry-Free (GF)-based detection metric was investigated given its capability to be combined with a preceding CMC-based error correction to potentially reduce the number of excluded (or de-weighted) measurements in position solutions. Three geometry-based algorithms, namely measurement subset testing, consecutive exclusion and iterative change of measurement weights, were presented to reduce the effect of multiple simultaneous multipath below a tolerable PDOP threshold. Analysis of GPS data showed that while the monitoring techniques can improve position performance under different multipath scenarios, neither technique is 100% effective and each has its own limitations. Hence, the complementary combination of all approaches was investigated in two cascading methods. Comparing 3D positioning RMS values, the proposed combined approaches showed more than 38% improvement over a conventional C/N\({}_{0}\)-weighted LS solution (and also higher than those of the other techniques) for static, pedestrian and vehicular tests. However, the proposed combined method did not show improvement in a city core area with a limited number of satellites in view and higher dynamics.  
[PERSON] conceived, conducted and wrote the paper with continuous advice from [PERSON], [PERSON] and [PERSON].  
This research was funded by a University of Calgary Eyes High Doctoral Recruitment Scholarship held by [PERSON] and an iCORE/Alberta Innovates grant held by [PERSON].  
The authors declare no conflict of interest.  
Figure 17: Positioning RMS errors and 3D improvement percentages for (**a**) campus multipath canyon and (**b**) downtown area—GPS L1 positioning.
## 3 Inverse Problem  
The inverse problem we are interested in consists of determining the couple \((\mu_{a},\mu_{s})\) from the set of true data \(y_{i}\) such that  
\[F_{i}(\mu_{a},\mu_{s})=y_{i}\qquad 1\leq i\leq s \tag{3}\]  
where we denote by \(F_{i}\) the forward operator which is assumed to be Frechet differentiable, and \(y_{i}\) the approximate measured data. In this study, we restrict our attention to the reconstruction of the absorption coefficient, and we assume that the distribution of the scattering coefficient is known. Then, the objective function can be written as follows:  
\[J(\mu_{a})=\frac{1}{2}\sum_{i=1}^{s}(F_{i}(\mu_{a})-y_{i})^{2} \tag{4}\]  
Then, this problem can be stated in terms of an optimization problem  
\[\mu_{a}^{*}=argminJ(\mu_{a}) \tag{5}\]  
Since the inverse problem is ill-posed, it requires regularization. A Total Variation regularization is applied [21]. By adding a regularization term, the cost function is formulated as  
\[J_{R}(\mu_{a};\lambda,\mu_{a}^{0})=\frac{1}{2}\sum_{i=1}^{s}(F_{i}(\mu_{a})-y _{i})^{2}+\lambda R(\mu_{a}) \tag{6}\]  
where \(R(\mu_{a})=\|\mu_{a}-\mu_{a}^{0}\|^{2}\) is the regularization operator that enforces smoothness conditions in the solution, and \(\lambda\) is the regularization parameter. \(\mu_{a}^{0}\) denoted the initial guess error.The forward operator \(F_{i}\) is linearized around some initial guess \(\mu_{a}^{0}\).  
\[F_{i}(\mu_{a})=F_{i}(\mu_{a}^{0})+F_{i}^{\prime}(\mu_{a}^{0})(\mu_{a}-\mu_{a} ^{0})+W(\mu_{a}^{0},i) \tag{7}\]  
where \(F_{i}^{\prime}\) is the Frechet derivative of the forward operator \(F_{i}\), and \(W\) denotes the Taylor remainder for the linearization around \(\mu_{a}^{0}\).  
The gradient of the objective functional can be written as follows:  
\[\
abla J_{R}(\mu_{a};\lambda,\mu_{a}^{0})=\sum_{i=1}^{s}F_{i}^{\prime}(\mu_{a })^{*}(F_{i}(\mu_{a})-y_{i})+\lambda R^{\prime}(\mu_{a}) \tag{8}\]  
where \(R^{\prime}(\mu_{a})\) is the Frechet derivative of regularization operator with respect to \(\mu_{a}\).
# Innovative Data-Driven Machine Learning Approaches for Predicting Sandstone True Triaxial Strength
## 4 Data Description  
Fourteen different sandstone true triaxial datasets were collected from existing studies, encompassing uniaxial compressive strength (UCS, \(\sigma_{c}\)) and the maximum principal stresses (\(\sigma_{1}\)), intermediate principal stresses (\(\sigma_{2}\)), and minimum principal stresses (\(\sigma_{3}\)) at failure. The characteristics of these datasets, along with their references, are presented in Table 1. To facilitate model training and evaluation, the collected data were randomly divided into a training set (70%) and testing set (30%). Figure 4 illustrates the correlations between the collected variables and the distributions of the training and testing sets. It can be observed from the figure that the distributions of the training and testing sets are highly consistent, and the correlations between variables within each dataset are also similar.  
Figure 5 illustrates the distribution of \(\theta\) obtained from the collected true triaxial experimental data. It is evident from the figure that most experiments do not cover the entire range of \(\theta\), which fails to reflect the variation in the true triaxial strength of rocks as the stress conditions transition from CBM to TBM. When utilizing such data for model  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Number of Data**} & \multicolumn{2}{c}{\(\sigma_{1}\) **(MPa)**} & \multicolumn{2}{c}{\(\sigma_{2}\) **(MPa)**} & \multicolumn{2}{c}{\(\sigma_{3}\) **(MPa)**} & \multirow{2}{*}{**References**} \\  & & **Min** & **Max** & **Min** & **Max** & **Min** & **Max** & **Max** \\ \hline
1 & 29 & 92.1 & 492.1 & 10 & 413 & 10 & 100  
training, it becomes challenging for the model to accurately assess the unexplored range of \(\theta\). Hence, during the model training process, it was essential to uniformly input experimental data from different sandstones into the model to ensure the accurate prediction of the true triaxial strength of rocks across the entire range of \(\theta\). Additionally, the data obtained for sandstone No. 10 cover a relatively complete range of \(\theta\) compared to other sandstones. Moreover, the trend of the true triaxial strength of the rock when the stress condition changes from CBM to TBM for similar \(I_{1}\) states is also presented more clearly. Therefore, for subsequent analyses of the deviatoric plane and failure envelope shape, sandstone No. 10 was selected as a comparative reference.  
Figure 5: The \(\theta\) distributions of true triaxial data for different sandstones.
## 3 Results and Discussion
### FDS Analysis of Oil/Paper Insulation  
Figures 3-6 present the results of Frequency Domain Spectroscopy (FDS) measurements. They show the evolution of dielectric properties, including real permittivity (\(\epsilon^{\prime}\)), loss tangent (tan \(\delta\)), imaginary permittivity (\(\epsilon^{\prime\prime}\)), and frequency-dependent conductivity, as a function of thermal aging and following dielectric breakdown, for paper samples impregnated with the three insulating oils under study. The real part of permittivity (\(\epsilon^{\prime}\)) reflects the material's ability to store electrical energy, while the imaginary part (\(\epsilon^{\prime\prime}\)) is associated with dielectric losses due to polarization and conduction mechanisms. The dielectric loss tangent (tan \(\delta\)) quantifies the energy dissipated as heat, and conductivity is influenced by the presence of degradation by-products, such as acids and water molecules, resulting from thermal and electrical stress. These parameters are key indicators for assessing the insulation condition of oil-paper systems.  
The FDS measurements presented in Figures 3-6 were crucial for monitoring the evolution of the dielectric properties of the oil-impregnated paper across the three aging stages:  
* Real Permittivity (\(\epsilon^{\prime}\)): A progressive increase in this parameter was observed, especially for mineral oil from stage S1. This rise indicates an enhanced ability of the material to polarize, likely due to an increase in polar groups and residual moisture as a result of aging. In contrast, the variation of \(\epsilon^{\prime}\) for the natural esters remains lower and more gradual, reflecting better preservation of the cellulose structure.
* Dielectric Loss Tangent (tan \(\delta\)): The evolution of tan \(\delta\), particularly in the low-frequency range (1 mHz-10 Hz), emerges as a key indicator of aging. For Polaris GX,  
Figure 4: Dielectric spectra of the imaginary part of the permittivity for fresh and aged paper samples impregnated with different oils, after breakdown.  
Figure 3: Dielectric spectra of the real part of the permittivity for fresh and aged paper samples impregnated with different oils, after breakdown.  
a sharp rise begins from S2, suggesting the accumulation of conductive degradation products and moisture. NE 1204 shows a more moderate and gradual increase in the various analyzed parameters, whereas NE 1215 maintains stable tan \(\delta\) values up to S2, with only a slight rise at S3, indicating very limited aging effects.
* Imaginary Permittivity (\(\epsilon^{\prime\prime}\)) and Frequency-Dependent Conductivity: Both \(\epsilon^{\prime\prime}\) and conductivity increase significantly for mineral oil starting from S2, highlighting greater dielectric losses due to Joule heating and a decline in insulating performance. In contrast, NE 1204 exhibits moderate changes, while NE 1215 consistently shows the lowest values up to S3.  
The results indicate that natural esters, particularly NE 1215, significantly mitigate the aging-related deterioration of the dielectric properties of oil-impregnated paper.
# Smartphone-Based NFC Potentiostat for Wireless Electrochemical Sensing
## 3 Results  
### NFC Potentiostat Operation  
The main components of an NFC potentiostat are shown in Figure 2a, containing a loop antenna, NFC microchip (SIC4341) and connector for electrode interface designed on a printed circuit board with a credit card size. The details of the integrated circuit shown in Figure 2b include (1) an analog part to connect between radio signals and controller, (2) digital controller and memory unit to control the operation of integrated circuits to respond to data transmission that operates at 13.56 MHz in the high frequency (HF) radio band and  
Figure 1: Smartphone screenshots showing the SIC4341-POTEN Android application: (**a**) initial screen, (**b**) electrochemical parameters settings, (**c**) cyclic voltammogram and (**d**) resulting data.  
is compatible with ISO14443A, and (3) the chemical sensor processing to measure signal responses from sensors.  
The operational steps of the NFC potentiostat are shown in the Supplementary Information Video S1. When an NFC smartphone approaches the NFC antenna, the NFC microchip is woken up by the radio frequency (RF) power from the induced magnetic field. If the incoming power is sufficiently high, indicated by its internal status, the NFC microchip starts initializing the system, activates the microchip peripheral devices and waits for a command from the NFC smartphone for further operations. The NFC microchip (SIC4341) consists of an internal potentiostat that is capable of supporting current measurement from chemical sensors at a maximum input current of \(\pm\)20 uA. The biasing voltage between working electrode and reference electrode can be between \(-\)800 mV and 800 mV, generated from the differential voltage of dual digital-to-analog converters (DACs) having full scale voltage of 1.28 V with a step of 5 mV. The NFC smartphone application controls the biasing voltage across the sensor by transmitting a command to adjust the DAC voltage. The amount of current from the sensor in the physical world is processed by the 10-bit analog-to-digital converter with a digital signal processor, and finally converted into digital data stored in internal memory, ready to be transmitted back to the NFC smartphone. The application on the NFC smartphone can periodically transmit commands to set the DACs, following biasing profiles such as cyclic voltammetry. The NFC smartphone can read the conversion result after each period to reconstruct a voltammetry waveform in the application graphic interface. As the timing can be set by the application, for example, for cyclic voltammetry, the sweep rate can be set on the application from 5 mV/s up to 1000 mV/s. The application provides a graphic user interface (GUI) to set the three electrode terminals freely and flexibly to support various electrode arrangements.
# Advanced Construction of the Dynamic Matrix in Numerically Efficient Fuzzy MPC Algorithms
## 2 Efficient Fuzzy MPC Algorithms with the Advanced Construction of the Dynamic Matrix
### Optimization Problem in the Numerical and Analytical Versions of the Algorithms  
Now, assume that future control values are decomposed as follows:  
\[u_{k+i|k}^{m}=\tilde{u}_{k+i|k}^{m}+u_{k+i|k-1}^{m}\, \tag{38}\]where \(\tilde{u}^{m}_{k+i|k}\) can be interpreted as the corrections of the control signal \(u^{m}_{k+i|k-1}\) obtained in the last \((k-1)\)st time step. Thus, future control increments are described by the following, similar formula:  
\[\Delta u^{m}_{k+i|k}=\Delta\tilde{u}^{m}_{k+i|k}+\Delta u^{m}_{k+i|k-1}\,. \tag{39}\]  
Note that the values \(u^{m}_{k+i|k-1}\) and \(\Delta u^{m}_{k+i|k-1}\) are known, as they were calculated in the last time step, and their influence on the output variables is contained in the free response (22), described in Section 2.1. Therefore, now the dynamic matrix (33) will be used to predict the influence of control corrections on the control plant outputs, and after using the free response (22) and the dynamic matrix (33), one obtains the following prediction:  
\[\mathbf{y}=\mathbf{\tilde{y}}+\mathbf{A}_{k}\cdot\Delta\mathbf{\tilde{u}}. \tag{40}\]  
where:  
\[\Delta\mathbf{\tilde{u}}=\left[\begin{array}{c}\Delta\mathbf{\tilde{u}}^{1}\\ \vdots\\ \Delta\mathbf{\tilde{u}}^{n_{u}}\end{array}\right]\!,\,\Delta\mathbf{\tilde{u}}^{m}= \left[\begin{array}{c}\Delta\tilde{u}^{m}_{k+1|k}\\ \vdots\\ \Delta\tilde{u}^{m}_{k+s-1|k}\end{array}\right]\,. \tag{41}\]  
Note that the prediction depends linearly on corrections \(\Delta\mathbf{\tilde{u}}\).
# Reliability Assessment of Steel-Lined and Prestressed FRC Slabs against Projectile Impact
## 6 Discussion of Results
This figure also shows that an FRC slab without prestressing and steel lining, subjected to the projectile strike velocity of 118.7 m/s, should have a steel fiber proportion of about 1.2% to attain the desired reliability index of 3.0. The effect of prestressing is also obvious from this figure that it makes a noticeable improvement in reliability. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Specimen** & **Specimen ID** & \begin{tabular}{c} \(V_{P}\) \\ **(m/s)** \\ \end{tabular}  & \begin{tabular}{c} \(V_{0}\) \\ **(m/s)** \\ \end{tabular}  & \begin{tabular}{c} \(V_{0}/V_{P}\)** \\ **(m/s)** \\ \end{tabular}  & \ \begin{tabular}{c} **Damage** \\ \end{tabular}  & \begin{tabular}{c} \(P_{f}\) \\ \end{tabular}  & \(\beta\) \\ \hline  
P & \(6.2  
Figure 7 shows how steel lining improves the safety of RC slabs. This figure shows that steel lining can improve the reliability of both prestressed and non-prestressed slabs substantially. When there was no steel lining (and no steel fibers as well), the reliability index of both prestressed and non-prestressed slabs was far below the desired reliability index of 3.0 when impacted by the projectile of 118.7 m/s impact velocity. With the use of 1.8 mm thick steel lining, the reliability improves to 3.33 and 3.68 for non-prestressed and prestressed slabs, respectively. Thus, the steel lining on the back face makes the slab reliability almost double when the slab has no lining. Additionally, steel makes them as reliable as desired. The effect of prestressing is again obvious from this figure that it makes a noticeable improvement in reliability. Figure 5: Effect of \(V_{0}/V_{p}\) on reliability indices of the specimens. Figure 6: Effect of steel fibers on the reliability of PFRC specimens.
# Adaptive Learning in AI Agents for the Metaverse:
### Research Objectives and Key Questions  
#### 1.3.1 Research Objectives  
This study aims to thoroughly investigate the mechanisms and advantages of adaptive learning in AI agents within the Metaverse, emphasizing its transformative potential in dynamic and complex virtual environments. The central goal is to establish a comprehensive understanding of how these agents can be developed and deployed to enhance user interactions, system efficiency, and scalability. To achieve this, the study is structured around the following objectives:  
1. Identify Adaptive Capabilities This research seeks to determine the specific adaptive learning capabilities required by AI agents in the Metaverse. It focuses on understanding how these agents process and respond to continuous streams of user data and environmental signals in real-time, enabling nuanced, context-aware interactions;
2. Assess Impact on Performance The study aims to evaluate the influence of adaptive learning on the operational efficiency and effectiveness of AI agents. By analyzing various adaptive behavior models, the research examines their impact on key performance metrics, such as responsiveness, predictive accuracy, decision-making, and user engagement;
3. Analyze Scalability in Virtual Environments The research explores how adaptive learning mechanisms can be scaled to meet the expanding complexity and diversity of the Metaverse. It assesses the scalability of these systems as user bases grow, emphasizing the adaptability of AI agents in handling diverse virtual scenarios;
4. Develop the ALMAA Framework The study introduces the ALMAA framework, which outlines a systematic approach for integrating adaptive learning into AI agents. The framework provides practical guidelines to ensure robustness, efficiency, and continuous evolution in response to the changing demands of virtual ecosystems;
5. Address Challenges and Limitations By identifying the potential challenges and limitations in implementing adaptive learning, the study offers strategies and solutions to overcome these barriers. It aims to ensure the successful deployment of AI agents in diverse and complex virtual environments.  
3.2 Research Questions Building on the objectives outlined above, this study seeks to address the following central research question: How can adaptive learning frameworks enhance AI agents' effectiveness in dynamic and complex virtual environments like the Metaverse? To answer this, the study further explores the following sub-questions:  
1. What adaptive capabilities are critical for AI scalability and contextual decision-making in the Metaverse?
2. How does the ALMAA framework align theoretical principles with real-world platform operations?
3. What challenges and opportunities arise when implementing adaptive AI systems in diverse virtual ecosystems? These objectives and research questions collectively guide the exploration of adaptive learning frameworks in AI agents, bridging the gap between theoretical constructs and practical applications in the Metaverse.
## 2 Literature Survey  
In PMIPv6 [3], a localized shim protocol is suggested to enable multi-homing. This approach is a mixture of the PMIPv6 and Shim protocols, and also addresses the multi-homing with mobility support. The Shim protocol is used locally in the PMIPv6 domain between the multi-homed MN and LMA in this scheme. A robust multi-homing framework is given for the delivery of the flow scheme to the Shim locator. Results from the simulation show that in conjunction with the localized Shim protocol, the PMIPv6 can effectively sustain both moving session and multi-homing.  
The locator identifier separation protocol (LISP) [4] and shim6 [5] supply a technique for allowing terminal devices to have multi-homing. However, [PERSON] [6] are analyzing a complete understanding of the benefits that can be achieved by using multi-homing and a method to achieve them. Shim6 and LISP, however, require a huge involvement of inherited terminals, even as explained in [6] which is suitable for reliable entities such as improved routers, and small devices such as smart phones.  
Even if a host wants to utilize the additional needs to be set on the multi-homed node; these additional responsibilities are represented by a connection manager (CM). The important job this CM does is to provide an interface between the network connection and network application. Nevertheless, the functions of the CM have many drawbacks, such as user-side restriction and automatic managed network operation. There are a few other frameworks in PMIPv6 for managing multi-homing based on the MN functions.