esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Use of Structured Prior Models for Bayesian Compressive Sensing of Modulated Signals
## Appendix B Sampling Algorithm  
Given the observed model (2) and the considered hierarchical prior models either in (5)-(7) or (8), it is clear that the presence of heteregenous correlation coming from the likelihood (namely from \(\mathbf{H}\mathbf{\Phi}^{-1}\)) together with the anisotropic structure of the prior scale matrix \(\mathbf{\Sigma}\) depending on unknown parameters, bring some difficulty in the sampling of \(\mathbf{x}\) and the selection variable \(\mathbf{z}\) in the spike-and-slab models. Therefore, it is desirable to separate these two sources of correlation in order to facilitate sampling. Since the likelihood is Gaussian, we propose in this paper to use the data augmentation Gibbs sampler proposed in [58] in order to dissociate the likelihood and the prior covariance matrices. More specifically, an auxiliary variable is added to the model so that  
\[\mathbf{u}|\mathbf{x},\mathbf{o}\sim\mathcal{N}\Big{(}\mathbf{\Gamma}\mathbf{ \Phi}^{-1}\mathbf{x},\tau\mathbf{\Gamma}\Big{)} \tag{13}\]  
where \(\mu>0\) such that \(\mathbf{\Gamma}=\frac{1}{\mu}\mathbf{Id}-\mathbf{H}^{\top}\mathbf{H}\) is positive definite.  
In the augmented space, the joint distribution of \((\mathbf{x},\mathbf{\Theta},\tau,\mathbf{y},\mathbf{u})\) writes  
\[\mathrm{p}(\mathbf{x},\tau,\mathbf{\Theta},\mathbf{u},\mathbf{y})\] \[=\mathrm{p}(\mathbf{y}|\mathbf{x},\tau)\mathrm{p}(\mathbf{u}| \mathbf{x},\tau)\mathrm{p}(\mathbf{x},\mathbf{\Theta}|\tau)\mathrm{p}(\tau)\] \[=\exp\Big{(}-\frac{1}{2\tau\mu}\big{(}\|\mathbf{x}\|^{2}-2\mu \mathbf{x}^{\dagger}\mathbf{\Phi}(\mathbf{H}^{\top}\mathbf{y}+\mathbf{u}) \big{)}+\mu\|\mathbf{y}\|^{2}\] \[+\mu\mathbf{u}^{\top}\mathbf{\Gamma}^{-1}\mathbf{u}\big{)}\ \Big{)}\mathrm{p}(\mathbf{x},\mathbf{\Theta})\tau^{-\frac{N+M}{2}-1} \tag{14}\]  
In the following, we give the expressions of the resulting conditional densities for the different variables of the problem.
## 6 Self-Healing Flexible Sensors  
Self-healing implies the sensor can repair itself after damages incurred during use from torsion forces, cuts, cracks, fractures, curling, bending, friction forces, scratching and other damages. The ability to self-heal improves the service lifespan of the sensor as well as enhance its performance by reducing losses in sensitivity due to damages (scratches, cracks, cuts, etc.). The ability of a sensor to recover itself from damage reduces service costs and reduces the use of materials in producing more sensors. Sensors are incorporated in virtually all aspects of our lives, as shown in Figures 1 and 2 (e.g., in food packaging and health monitoring), and the production of sensors is predicted to continue rising. Therefore, the service lifespan of these sensors should ideally be long to avoid contamination of the environment due to increased sensor-waste disposal. Self-healing can be achieved in a number of ways. One of the ways is whereby a repair material is embedded within the sensor such that in the event of a crack, the repair material is released due to the expansion of the crack. The repair material would either simply fill the gap itself or react with a catalyst and polymerise to fill the gap. In either case, this method is usually irreversible. The second method to achieve self-healing is whereby the bonds within the polymer can rebind after a crack spontaneously or under the action of an external stimulus such as heat or light. When a material can change shape or self-heal after being 3D printed, it is called a 4D printed object, with the fourth dimension being time. There exist two main ways of fabricating self-healing sensors. One way involves the use of a self-healing polymer as the flexible material with a conductive material layer. Another way involves premixing the self-healing polymer with the conductive nanomaterial to produce a self-healing nanocomposite that can then be used to fabricate the sensor via a particular method (e.g., moulding and 3D printing). In a study by [PERSON] et al., a self-healing CaCu\({}_{3}\)Ti\({}_{4}\)O\({}_{12}\) sheet with SWCNTs on the top and bottom surfaces was used to fabricate a capacitive sensor [37]. The sensor was subject to a crack and was able to recover its original capacitive value and tensile property after subjection to heat at 150 \({}^{\circ}\)C for half an hour [37]. Similarly, a self-healing ammonia gas sensor that could self-heal within 30 min was prepared by incorporating MWCNTs within polyelectrolyte multilayer matrix [90]. In another report, SWCNTs were incorporated into a polymer matrix with self-healing properties and heat sensitivity in producing a temperature sensor [84]. The sensor can recover from damages such as cracks upon heating. The polymer matrix shifts upon heating which moves the conductive network thereby recreating the electrical connection. Other than biocompatibility, flexibility and good transparency, PDMS also has self-healing properties. Two self-healing PDMS sheets were used to cover a silver nanoparticle based conductive layer in fabricating a sensor for human-machine interaction. The sensor
# A Posteriori Detection of Numerical Locking in \(hpq\)-Adaptive Finite Element Analysis
## 1 Introduction
### Novelty of the Paper  
The novelty of this work consists in the new algorithms for a posteriori detection and assessment of the numerical locking phenomena. This refers to shear, membrane, or shear-membrane locking. With these new algorithms, one is able to detect the presence of the phenomenon and assess its strength so that the adequate numerical means can be used to remove the phenomenon. The proposed numerical means of the removal consist in introduction of the new adaptation step, called the modification one, into the existing three-step, model- and \(hpq\)-adaptive finite element procedure for analysis of complex structures. The new step employs the mentioned detection and assessment algorithms and performs modification of the initial mesh through \(p\)-enrichment. The numerical cost of this new step is low as the detection and assessment is performed on the initial, usually coarse mesh.  
The applied adaptive method [38; 45; 46], the new algorithms are incorporated in, takes advantage of the hierarchical models proposed in [31; 47], hierarchical \(hp\)-approximations elaborated in [41; 42], a posteriori error estimation from [30; 43; 44], and error-controlled adaptive procedure given in [40].
## 5 Simulation Model and Results
### Energy Consumption at Various Nodes  
In case of conventional S&W ARQ, it is obvious that most of the energy is consumed by the source node, compared to destination and _CNodes_ (as \(P_{Tx}>P_{Rx}>>P_{Hdle}\)). In a long run, this method can quickly discharge a sensor node which is active most of the time, collecting and transferring data to a base station. In this regard, the cooperative protocols have the inherent advantage of distributing the energy consumption across the network by temporarily transferring the source's burden to the _CNodes_, thus allowing the source to conserve energy for future use. However, this approach of temporarily transferring the burden to _CNodes_ by asking them to share their energy resources may cause a _CNode_ to lose a significant amount of energy, thus affecting its own applications. The C-HARQ proves to be very efficient in this aspect as it asks the _CNodes_ to carry the burden of delivering only the smaller FEC fragments while maintaining the source responsible for transmission of larger data packets. To  
Figure 6: **(a)** Throughput efficiency _vs._ the number of nodes in the cooperative region (\(nCN\)); **(b)** Energy efficiency _vs._\(nCN\).  
illustrate this better, Figure 7 presents the energy consumption distribution in the network for CARQ and C-HARQ protocols, operating at their optimum settings. It gives an estimate of the average energy consumed per data bit by different nodes in successfully delivering the data from source to destination. The percentage value shown for a node denotes its percentage contribution to the total energy consumption. For the case of _CNodes_, the combined total energy consumption of all the _CNodes_ is plotted (\(nCN=5\) in this case).  
For the CARQ protocol, operating at its optimum throughput setting (_i.e._, at payload length of 1,000 bits), the total energy consumed per data bit by all the _CNodes_ is found to be greater than twice the energy consumed by the source; indicating that an individual _CNode_ lose quite a significant amount of energy in providing cooperation or delivering other's data. On the other hand, C-HARQ (at 1,000 bits) not only consumes less energy but also reduces the burden of the _CNodes_ by transferring some of the burden back to the source. When operating at its optimum payload length (6,000 bits), C-HARQ not only delivers a throughput efficiency of approximately four times the optimum throughput efficiency of CARQ (Figure 4a), at the expense of same or rather slightly less energy per bit, but also maintains the source responsible for major part of the energy consumption (by further transferring back the burden of _CNodes_ to source). Thus, it can be said that C-HARQ provides an optimum performance between S&W ARQ and CARQ in terms of energy distribution. In other words, the use of error correction codes in C-HARQ helps a cooperative protocol to enhance its throughput while using the _CNodes_ to deliver only the smaller FEC packets decreases the energy sharing burden of _CNodes_.
# Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
###### Abstract
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**City Level** & **Concept** & **Examples/Potential Examples * & **Ref.** \\ \hline
**High-carbon city** & A city with high greenhouse gas (GHG) emissions. & Beijing, Chicago, Shanghai, New York, Seoul, Tokyo, Hong Kong, Los Angeles & [11; 12; 13] \\ \hline
**Low-carbon city** & A city with reduced greenhouse gas emissions. & Copenhagen, Oslo, Vancouver, Barcelona, Sydney & [9; 10; 11] \\ \hline
**Nearly zero-carbon city** & A city approaching carbon neutrality. & Melbourne, Reykjavik, Stockholm, Freiburg & [14; 15] \\ \hline
**Zero-carbon city (ZS) or zero-energy city** & A city with net-zero greenhouse gas emissions. & Canberra *, Oslo *, Bulawayo *, Chongming *, Denver *, Cape Town * & [11; 12] \\ \hline
**Negative-carbon city** & A city that absorbs more carbon than it emits. & Curitiba, Brazil (for its extensive urban green spaces) & [16; 17; 18] \\ \hline
**100\% renewable energy city** & A city that has to target a 100\% renewable energy supply. & Reykjavik, Canberra *, Paris *, San Francisco *, Minneapolis & [9; 10; 11] \\ \hline
**Smart city (SC)** & A city uses digital technologies such as AI, digital twins, and the Internet of Things (IoTs) for resource management efficiency. & Singapore, London, Dubai, Masdar City, Brisbane, New administrative capital, Taipei, GIFT City, Amsterdam, Manchester & [19; 20; 21] \\ \hline
**Smart and zero-carbon city (ZSC)** & A city combining smart technologies and zero-carbon goals. & NEOM *, Copenhagen *, Melbourne * & [22; 23; 24] \\ \hline
**Smart and positive-carbon city** & A city combining smart technologies and carbon absorption such as tree planting and carbon sequestration including bio-sequestration. & NEOM *, Stockholm *, Canberra, Dar es Salaam * & [25; 26; 27; 28; 29; 30] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Different concepts of smart and sustainable city development. generation [9; 10; 11]. Carbon valorization is another crucial aspect of this transformation, which uses captured CO\({}_{2}\) as a feedstock to produce industrial goods such as plastics, fertilizers, and alcohol. City electricity decarbonization strategies show that transitioning urban energy systems will require new green technologies and large-scale investment in renewable energy and electrical infrastructure to achieve sustainable, net-zero cities [16; 17; 18].
## 3 Results
### Performance Comparisions  
The energy consumption associated with each of the \"base\" process schemes (Cases 1 to 4) is presented below in Table 3. In all cases, the \"base\" operating parameters are used (see Table 1) and the values of all variable operating parameters are optimized to minimize energy consumption.  
The results show that the 15 bara cases offer reduced power consumption in all of the \"base\" process schemes. They also show that a 3-stage NH\({}_{3}\) process has the potential to outperform a 2-stage expander-based process and that a 2-stage open CO\({}_{2}\) cycle can also offer low energy consumption. Based on these findings, Cases 4b, 5a, 5b, 6a, and 6b were developed. The performance of the optimized versions of these schemes is presented below in Table 4.  
Table 4 shows that Case 6b offers lowest overall energy consumption when compared at 25 \({}^{\circ}\)C cooling temperature and that the use of expanders in the open CO\({}_{2}\) refrigeration cycle offers a significant benefit over the use of valves. In the case of the NH\({}_{3}\) refrigeration process, the benefit of using expanders is not as pronounced.  
Based the results shown on Tables 3 and 4, three cases were selected for study in the next part of the work: Case 6b, because it offers the lowest overall power; Case 6a because it offers a more conventional alternative to Case 6b; and Case 5a to provide a comparison with a NH\({}_{3}\)-based process. Case 5b was not  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case** & **Case 4b** & **Case 5a** & **Case 5b** & **Case 6a** & **Case 6b** \\ \hline Refrigerant & CO\({}_{2}\)-open & NH\({}_{3}\) & NH\({}_{3}\) & CO\({}_{2}\)-open & CO\({}_{2}\)-open \\ Cycle & 2-stage & 3-stage & 3-stage & 3-stage & 3-stage \\ Cooling method & expanders & valves & expanders & valves & expanders \\ Cooling stages & n/a & 2-stage & 2-stage & n/a & n/a \\ Pressure (bara) & 15 & 15 & 15 & 15 & 15 \\ Energy (kWh/ton) & 89.5 & 91.8 & 90.8 & 93.0 & 88.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of optimized power consumption for the new process schemes.  
selected because the addition of expanders offered little reduction in energy consumption compared to Case 5a. The detailed modelling results for the cases shown in Tables 3 and 4 are presented in Appendix A.
## 5 Evaluation Scenario Using the Synthetic Maritime Traffic Generation System  
The scenario for evaluating MASS using S-MTGS has the following advantages compared to the scenario using the existing method. First, it is possible to conveniently create more diverse scenarios even under the same conditions. For example, generating head-on ships using existing methods requires work each time for each scenario; using S-MTGS, head-on ships can be generated randomly or by normal distribution under the same conditions. Figure 7 shows a scenario for an encountered vessel using the existing method, and Figure 8 shows an example of scenario creation for an encountered vessel using S-MTGS.  
Second, it has the advantage of being able to create dynamic scenarios based on the ship's position. For example, when creating a more complex scenario with a waypoint, the scenario using the existing method has no choice but to create the scenario based on the ship's planned route tracking position. If the MASS avoids collision and deviates from the planned path tracking position, the second scenario ship may not affect the evaluation of the MASS. Figure 9 shows a multi-ship scenario using the ex  
Figure 7: One ship scenario: Head-on situation scenario by existing method.  
scenario ship is created on the orange line, which is the planned route, showing that it has less impact on the evaluation of the MASS.  
On the other hand, when using S-MTGS, scenario ships are dynamically generated based on the position of the MASS, allowing for the evaluation of different collision avoidance systems under the same criteria. Figure 10 shows a scenario where multiple ships are created using S-MTGS; it can be seen that the second ship is created on the green line, which is a collision avoidance path, according to the position of the MASS.  
Figure 8: One-ship scenario: head-on situation scenario generated by S-MTGS.  
Figure 9: Multi-ship scenario: head-on situation generated by existing method.
## 1 Introduction
### The Digital Twin  
The concept of the digital twin was envisioned by [27] and was first documented in 2002 [28]; it refers to a virtual model that accurately reflects a physical system or object. A digital twin can be used for various purposes, including design optimization, quality control, monitoring, decision-making, and policy formulation [29]. Its utilization has been reported in the realm of ocean and coastal fields [30; 31; 32; 33], where the digital twin is regarded as a potent tool for a further understanding of problems that may not be easily discovered in reality. However, there are currently few digital twin applications for coastal research. Some of these applications use deep learning methods for classification, reconstruction, and other functions [24; 34; 35; 36; 37; 38]. Therefore, there is a need for more integrated research in the traditionally interdisciplinary field of coastal studies.  
Consequently, to the best of our knowledge, there is still a need for an accurate, intelligent, and intuitive tool to assess the risk in a specific tidal flat at the highest possible spatial and temporal resolution. This study aims to address this gap through the following steps: (1) evaluating the performance of different UAVs for coastal monitoring, with specific emphases on UAVs equipped with different sensors or modules; (2) building a high-resolution 3D coastal terrain efficiently from the most suitable UAV; (3) employing a highly accurate ocean model to generate the time series of tidal level; and (4) developing a digital twin of the actual coast to visualize and assess coastal risks by integrating rapidly monitored terrain data with the numerical ocean model based on a 3D engine. This innovative framework promises to offer accurate tidal level predictions, providing valuable insights for mitigating the risks associated with rising seawater levels. It can not only showcase the current state of the tidal flat but also review conditions over a period, including reproducing the past and predicting the future. Ultimately, this paper contributes to the broader discourse on effective coastal management and the integration of cutting-edge technologies in addressing emerging environmental challenges.
# Bridge Health Monitoring Using Proper Orthogonal Decomposition and Transfer Learning
## 5 Results and Discussion
### Scenario 1: DI Known, DL Unknown  
Figure 4 shows confusion matrices used to compare predicted labels from transferred knowledge using the JDA-kernel to \"true\" values, which represent the DLs (see Figure 1b), for target domains \(M_{1},\ M_{2},M_{3},\ M_{5}\) at \(DI=80\%\). Confusion matrices are used to compare predicted labels obtained using the TL algorithm to true labels for the damage states with  
Figure 3: TL process for training, cross validation, and hyperparameter tuning.  
axes labels for the matrices corresponding to damage states labels from \(\mathcal{Y}_{t}\ \in\{1,\ 2,\ldots,20\}\). It is observed that DL labels obtained using the JDA-kernel for the target domain at most locations agree very well with the assigned labels for all models. Prediction error increased in DLs 2, 4, 7, and 9. These errors were attributed to average POMs being of similar magnitudes for the examined DLs and, as a result, the classifier could not effectively distinguish between them. Coupled POD JDA-kernel DL detection performance for each model is summarized in Table 2, which shows the accuracy of the algorithm for all models and for a DI of 80%. Overall, the algorithm was shown to perform well for identifying DLs for this DI.  
Prediction results for DIs of 40, 60, 80, and 100% are shown in Figure 5. \(M_{1}\) was arbitrarily selected as the target model for all cases as M1 results were representative of other models. As expected, JDA-kernel accuracy increases in higher DIs, with similar DL prediction patterns being observed, as for the confusion matrices shown in Figure 5. Table 3  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Target Model** & \
\begin{tabular}{c} **Accuracy, Cross-Validation** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy, Entire Data (\%)** \\ \end{tabular} \\ \hline \(M_{1}\) & 90 & 100 \\ \(M_{2}\) & 88 & 89 \\ \(M_{3}\) & 87 & 85 \\ \(M_{5}\) & 87 & 94 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coupled POD JDA–kernel Accuracy, DI = 80%.  
summarizes JDA-kernel accuracy results in \(M_{1}\) at different DIs. The table details improved accuracy for higher levels of DI. Similar patterns were observed for the other models.
## 3 Development of Thermal Condition Monitoring System  
### Design Concept Relevance to DERs Integrated Grid  
The emerging power grid control is experiencing innovations in intelligence in order to deal with increasingly diversified challenges, involving the environment, security, and infrastructure. The application of smart grid is realized with proactive usage of technology advancement in the areas of monitoring, regulation, and controlling through OT to address the challenges [44]. Resilient communication links serve as essential characteristics in the integration of DERs grid for independent and uninterrupted operations. In achieving such an objective, the proposed concept resolves to utilize ZigBee communication assisted link for condition monitoring and data acquisition including alert notifications. It integrates the WSNs and DERs grid to achieve network efficiency. The ZigBee communication link uses spread-spectrum communication techniques to complement smart grid technology in homes and industrial automation [10; 11; 45; 46; 47].  
On the contrary, the traditional methods are saddled with signal transmission latency and high capital installation and maintenance costs. The proposed concept comes with composite characteristics such as temperature condition monitoring, data acquisition, wireless data transmission spectrum, and data storage with abnormal condition alerts or notifications. The alerts could easily be configured for control solutions. Nevertheless, the concept is envisaged to be less costly in terms of installation and maintenance. Moreover, control solutions could be proposed to involve many activation functions. Additionally, it would be ideal for rural and remote areas deployment [10; 11; 12; 48; 50; 12]. It is significant to note that current carrying capacity and maximum permissible temperature are two variables that influence the design and operation of distributed grid control configurations. The current carrying capacity determines the thermal capacity according to multiple factors such as the temperature limit, safety resistance clearance of critical components, and the system voltage limit. The thermal effect is influenced by the heat loss radiative force. It correlates with the difference in temperatures between the equipment in operation and the cooling medium. The maximum current carrying capacity and operating temperature provide relevant information of the DERs grid failure prognosis through real-time condition monitoring [49; 51; 50]. A literature review of numerous communication links support ZigBee/GSM WSNs as an active link for monitoring and coordinating thermal effects [9; 24; 44; 45; 52; 53].