esa-sceva/satcom-chunk-collection huggingface dataset:
# Opportunities and Challenges to Develop Digital Twins for Subsea Pipelines
## 5 Maintenance Planning Based on the Digital Twin  
In recent years, the digital twins have been implemented in different industrial sectors, and in the design, production, manufacturing, and maintenance of the subsea assets. Among these application areas, maintenance is one of the most researched applications, as the execution of maintenance tasks may have a great influence on the business.  
Different maintenance strategies might be used in the decision making, namely reactive maintenance, preventive maintenance, condition-based maintenance, predictive maintenance, and prescriptive maintenance [73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84]. Predictive maintenance combines conditioning monitoring integrating with an ML-based decision support system and can enhance economic efficiency and availability [4]. It has the capability to determine when to perform the maintenance based on the real conditions of the subsea pipelines. Once in place, these capabilities could reduce the unplanned maintenance downtime events and thus optimize the OPEX.  
Figure 7 shows a digital twin system for predictive maintenance. One of the components is a computational model of the asset which is normally a finite element (FE) model. The computational model of the subsea pipeline is updated based on different field sensor data such as the motion sensor/accelerometer, the subsea strain gauge, the acoustic doppler current profiler (ADCP), the wave radar, the subsea pressure sensor, and the subsea temperature sensor (see Figure 8). Both the data-driven and FE-based models can be used to predict the remaining fatigue life (RFL) of the pipelines based on the stress range data, and are further used in the decision-making process. Knowledge of the RFL can enable efficient maintenance planning and avoid unpredicted shutdowns.  
Another component of the digital twin is the IoT/sensor system installed in the physical asset. IoT brings together low-cost sensors, cloud computing, and BDA in subsea pipeline systems where robustness, reliability, and security are highly desired. The third component is the data analytics to find the insight between the measured sensor data and apply a machine-learning algorithm to find the RFL based on the measured strain gauge data. The hydrodynamic load can be measured in real-time using the field sensor system and fed into the digital model. Wireless data loggers are connected to the IoT-based systems which transmit the data to store in the cloud for online analytics, visualization, and reporting. The data stored in the cloud is accessible onshore or onboard for data analytics.  
Figure 9 illustrates the predictive maintenance model based on sensor data analytics and ML algorithms. The predictive maintenance schedule is estimated by using a system of artificial neural network (ANN). The memory blocks of a long short-term memory (LSTM) are used for the layers of a recurrent neural network (RNN).  
Figure 8: A field sensor system and the data-driven model [4].  
Figure 7: A digital twin system for maintenance [4].
## 3 Results
### Drowsiness Detection Classifier
However, it achieved a low frame rate of 6.87 fps on a Quadro P4000, which may limit its practicality in real-time applications. In contrast, the proposed method maintained a higher average frame rate of 16 fps, making it more suitable for real-time drowsiness detection on resource-constrained devices. Furthermore, the ensemble with InceptionV3 [25] and U-Net + Decision Network [19], although showing higher accuracies, likely have larger model sizes. Traditional machine learning methods such as HOG + SVM [23] and improved HOG with Naive Bayes [24] offer competitive accuracy while being more lightweight. The choice of method depends on the specific requirements of the application, including real-time processing capabilities, computational resources, and desired accuracy level. Moreover, the practical implications of deploying these models in real-world scenarios extend beyond computational efficiency. The robustness of the model under varying lighting conditions, the diversity of subjects in the dataset, and the ability of the model to generalize across different demographics are critical factors that influence the effectiveness of drowsiness detection systems. Future research could focus on enhancing dataset diversity, exploring hybrid models that combine the strengths of DBNs and deci \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy (\%)**} & **Model Size** & **Average CPU** & **Average RAM** & **Average GPU** & **Memory** \\  & & **(MB)** & **Usage (\%)** & **Usage (MB)** & **Usage (\%)** & **Usage (MB)** \\ \hline Ensemble & & & & & & & \\ with InceptionV3 [25] & 97.10 & - & - & - & - & - \\ Improved & & & & & & & \\ HOG + Naive & 85.62 & - & - & - & - & - \\ Bayes [24] & & & & & & & \\ KCF + & & & & & & \\ Dlib [16] & 92.00 & - & - & - & - & - \\ CNN + Facial & & & & & & \\ Landmark & 83.33 & 0.075 & - & - & - & - \\ Detection [15] & & & & & & \\ HOG + & 90.00 & - & - & - & - & - \\ SVM [23] & & & & & & & \\ DBN + HMM & 84.82 & 37.82 & 90 & 10.29 & 55.67 & 3234.51 \\ U-Net + & & & & & & \\ Decision & 96.72 & - & - & - & - & - \\ Network [19] & & & & & & \\ Proposed & & & & & & \\ Method & 83.96 & 5.05 & 85 & 7.16 & 40.41 & 2348.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison with similar papers based on accuracy, model size, and computational efficiency. sion tree-based methods such as XGBoost, and testing the models in more dynamic and challenging environments.
## 5 Discussions
### Economic Benefits  
The initial investment on construction of the CLT buildings may be a crucial factor that affects the development and adoption of this material. In comparison with conventional building materials, the initial cost of CLT system may not be an obstacle. Existing data from the US and Europe demonstrates that the initial construction cost of 10 to 18 story CLT buildings is approximately the same as that of conventional buildings built in concrete, bricks and steel. [PERSON] showed that in the Pacific Northwest, the cost of a CLT design option can be as cost-effective as reinforced concrete. The costs of a 26-meter-high CLT building (8 story) and 38-meter-high CLT building (14 story) are approximately 32.5 US dollars and 31 US dollars per square foot, which is slightly higher than the cost of the same buildings in RC [65]. Mahlum Architects et al. also indicated that the use of CLT as the construction material for a 10-story building may result in a slightly lower cost than the concrete alternative (4%) [66]. [PERSON] et al. highlighted that equivalent or better performance than current code and existing concrete and steel structures is expected from tall CLT buildings. The cost of tall CLT buildings is comparable or less expensive than concrete and steel options in the US [66]. Tall residential buildings are often believed to be high energy consumers, mainly due to the large amount of materials required for the structure during construction [9]. However, the total energy consumed by the building during its lifespan is not limited to the amount of material used, but also includes the energy consumed during the operational stage. This simulation uses 150 mm thick CLT panels for the external walls, which satisfies the local energy saving codes. That means that the CLT external walls are 50 mm  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Heating** & **Cooling** \\ \hline Harbin & 11.97\% & \(-\)3.30\% \\ Beijing & 22.11\% & \(-\)2.97\% \\ Shanghai & 30.94\% & \(-\)2.23\% \\ Shenzhen & No Heating & \(-\)0.63\% \\ Kunming & 23.30\% & \(-\)10.97\% \\ \hline \hline \end{tabular}
\end{table}
Table 15: Heating and cooling energy saving ratio comparisons between RC and CLT buildings.  
thinner than the RC walls. This additional interior floor area is attractive to developers and is another positive factor to encourage the development of CLT system.
## 5 Practical Application
The user is then supplied with the result of the diagnosis using the interface for the prediction output. Figure 17, below, shows the design of a mobile application prototype in which users can interact with the early glaucoma diagnostic platform. Users can take an image of an eye for diagnostic using the application connected to the fundus camera, as shown in Figure 17a, then the user can submit the image to the proposed cloud platform and have information about the diagnostic result sent back to the mobile application, as shown in Figure 17b. Our proposed model may not be used for general subjects because it was only trained on a particular dataset. Additionally, due to the constraints of the dataset used in our research, we did not apply the model to clinical trials with real patients. We could do clinical studies by working with doctors or university hospitals once another group of demographic subjects (for instance, in Indonesia and Republic of Korea) is gathered, but that is currently outside the scope of our current study.
# Dynamic Response and Geogrid Strain Analysis of GRS Retaining Wall
## 2 Model Tests  
### Test Model  
The structural form of the test model is a modular geogrid reinforced retaining wall. In order to make the model test results truly reflect the mechanical behavior of real GRS retaining walls under traffic loads, the similarity relationship of the test model was determined according to the dimensions of the test setup. The relationship between the variables through the method of dimensional analysis is derived with geometric similarity ratio \(\mathrm{C_{1}}=4\), filler volumetric weight similarity ratio \(\mathrm{C_{\gamma}}=1\), cohesion similarity ratio \(\mathrm{C_{C}}=1\), friction angle similarity ratio \(\mathrm{C_{\varphi}}=1\), geogrid tensile modulus similarity ratio \(\mathrm{C_{E}}=2\).  
The test model box is a homemade 3.0 m \(\times\) 1.6 m \(\times\) 2.0 m (\(L\)\(\times\)\(W\)\(\times\)\(H\)) large model box, as shown in Figure 2. The model box frame is made of 0.6 cm thick channel steel welded together and welded with channel steel of equal thickness in both longitudinal and transverse directions to prevent deformation of the box sides during loading. The model box is installed with double-layer 1 cm thick tempered glass on one side to facilitate test observation, and a retaining wall facing panel is installed on the front, with a retaining wall height of \(H=1.8\) m.  
Referring to the specification (Technical Specifications for Construction of Highway Subgrades [36]), every 15 cm of fill was compacted, the quality and height of each layer of fill were strictly controlled and the compaction of fill was not less than 95%. When filling, 20 kg weights were first used to manually compact 3 times, then an electric plate compactor was used to tamp and level. In order to prevent the position and direction of the test element from changing due to tamping during the filling process, the soil layer in which the measuring element was buried was tamped first, and then the measuring element was buried at each monitoring point. After backfilling the soil in the recess, the area was compacted twice. In order to make the geogrid connect firmly with the panel, the wall was installed with a reverse wrapped panel, reinforcement spacing \(s=0.3\) m (2 panel heights) and the buried length of each layer of geogrid was \(L=2.0\) m. The test filling process is shown in Figure 3, the geogrid and monitoring instrument arrangement is shown in Figure 4.  
Figure 2: Preparation: (**a**) test site layout; (**b**) sand; (**c**) geogrid; (**d**) panel.
## 6 Conclusions  
In this paper, a numerical study of the blasting excavation process is carried out using the FEDM-based MultiFracS software. Combined with the drainage channel project of Guanggu 1 st Road to Gaoxin 4 th Road, the numerical model considering the near-field fracture process is studied, and the influence of different millisecond delay and blasting excavation construction technologies on the fracture and the blasting vibration velocity of the surrounding rock around the blast hole is investigated. According to the numerical simulation results, the following conclusions can be drawn:  
1. The influence of the millisecond delay on the peak blasting vibration velocity is not a single positive correlation or negative correlation, but when the millisecond delay is greater than 40 ms, the delay time no longer affects the peak blasting vibration velocity. When the millisecond delay is 20-30 ms, the peak blasting vibration velocity is the smallest, and it meets the requirements of the project that the blasting vibration velocity is less than 2 cm/s.
2. Through the numerical simulation on the near-field surrounding rock fracture of the drainage channel project from Guanggu 1 st Road to Gaoxin 4 th Road, the optimal blasting excavation construction technology of grade III surrounding rock is the double side drift method. It has a better control effect on the deformation and crushing of the surrounding rock of the tunnel.  
Figure 16: Stress and time curve of monitoring element under different blasting excavation construction technologies.  
Not only can the MultiFracS software simulate the whole process of a complex fracture, fragmentation, and movement of rock and soil during blasting and excavation, but it can provide information on the evolution of the displacement field and stress field in the process of blasting, which provides a powerful simulation tool for blasting and excavation engineering.  
Conceptualization, W.K. and X.W.; methodology, C.Y.; software, C.Y.; validation, W.K. and X.W.; formal analysis, C.Q.; investigation, W.K. and X.W.; resources, X.W.; data curation, W.K., X.W. and C.Q.; writing--original draft preparation, W.K. and X.W.; writing--review and editing, C.Y. and C.Q.; visualization, W.K. and X.W.; supervision, C.Y.; project administration, C.Y.; funding acquisition, C.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under grant number 11872340; the GHfund A (20220201, ghfund20202019662); the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (CUGGC09).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Numerical Model Development
### Numerical Procedures  
Numerical modeling of the COS hydrolysis reactor was performed via reactor geometry. A 2-dimensional axi-symmetry model was used for efficient analysis using commercially available ANSYS Fluent 18.0 heat and fluid analysis software. The user-defined function mode of the ANSYS Fluent 18.0 was used to calculate kinetic data, which were used in the relevant governing equations via the source term. The elements for numerical modeling, numbered 54, 173-129, and 814, and the coupled governing equations were obtained by trial and error calculations using the finite volume method and the Semi-Implicit Method for Pressure Linked Equations (SIMPLE) algorithm. For actual analysis, convergence condition was taken to be less than \(10^{-5}\) of relative uncertainty level.
## 1 Introduction
### Resources Scheduling and Forecasts  
The problem of scheduling resources in a microgrid for a given time window in the future, also called unit commitment, accounting of the uncertainty that is related to IRES and its impact in the distribution network has been recognized [10] and efforts are being done in order to deal with it.  
A scheduling strategy requires forecasts as an input, and different forecasting methods have been used in the field of energy management for MGs. Some studies directly consider solar generation predictions without considering weather forecasts, while others also receive the information from forecast services [11,12], or compute it from local power measurements by applying time-seriesforecasting techniques, such as auto-regressive [13], persistence [14] or ANN [11]. In all of these cases, the weather conditions are implicit in the information, but no meteorological variable is directly considered. In other cases, the forecasts of PV power output are directly obtained from weather forecasts, by applying models to transform the meteorological data into output power estimations. These models can be based on real PV panels [15] or theoretical equations of PV generation [16]. Overall, they assume that PV production is fundamentally dependent on irradiance and temperature. Some approaches compute solar irradiance from other parameters, such as the clear sky index [17], sky cover [18], or sky clearness [19], rather than forecasting it directly.  
The scheduling horizons of the forecasts vary from 72 h to 15 min., depending on the EMS strategy used, however the 24 h horizon is the most common. The time resolution of those forecasts vary from 3 h to 15 min, with one hour being the most common resolution [20].  
Regarding the uncertainty, normal distribution is the most frequent option for describing these errors. Some adjustments thta are based on the expectation and the standard deviation are commonly referenced, but they lack numerical values (e.g., [17]). Other studies assume that forecast errors are well described by a uniform distribution. A thorough summary of the different forecasting techniques used for energy management of MGs can be found in Table 1 of the review performed by [PERSON] et al. [20]. As distributed generation becomes more widespread, local energy markets, where the exchange of electricity occurs in a peer-to-peer scheme, are something that might reshape the objectives of an energy management system in the near future [21; 22]. New technologies, such as blockchain, have been proposed as solutions to ensure security and trazability of the energy exchange in this type of markets [23]. This also remarks the importance of local-scale forecasting as the core a distributed energy-exchange scheme as well as for the resource-scheduling performed by an optimization-based energy management system.
### Objectives and Contributions  
The aim of this research is to develop a mathematical model of the radar system's operational process and to analytically evaluate its probabilistic and temporal characteristics, specifically in the context of formalizing the algorithm for tracking an airborne target's trajectory under external influences.  
The main objectives are as follows:  
* To establish a formalized algorithm for aerial target trajectory tracking;
* To develop a mathematical model of radar system performance under external interference conditions;
* To analyze the probabilistic and temporal characteristics of the interaction channel between the radar and an object emitting active interference.  
To achieve the stated goal, the following tasks must be completed:  
* Investigate the target tracking algorithm;
* Develop a mathematical model of radar system operation under interference conditions;
* Study the probabilistic and temporal characteristics of the interaction channel between the radar system and an object generating active interference.
## 2 Background
### Intent Message  
[PERSON] defined an Intent message as \"a trigger event for an activity or service to be performed along with the required data which supports that requested action\" [19]. Intent messages, when exchanged, can be used by malicious applications to escalate privileges. The inter-component communication mechanizing [20] of Android relies chiefly on Intent messages. Intents are generally used by Android to aid data delivery via asynchronous messages. An Intent object is significant in Android permission because it holds data that the Android system uses to define recipient components needed to operate. This is significant because it enhances and eases service implementation in passing data and making it available to applications. This allows the user to initiate an action in a different application by calling a simple operation that the user would like to perform. Examples of such activities include but are not limited to taking pictures or viewing maps [21]. Intent plays a significant role as a communication mechanism in intra-application and inter-application message exchange.
## 3 Experimental Setup
### Key Transmission Performance Metrics
#### 3.2.2 Computational Complexity and Processing Overhead  
* Offline Training vs. Online Inference
* The proposed masked autoencoder (MAE) involves a training phase (offline) and an inference (deployment) phase (online).
* Training Complexity:
* This is typically performed offline on a GPU-equipped workstation. The training process can indeed be computationally demanding. However, once training converges, the resulting model parameters are frozen and used for real-time inference.
* Inference Complexity:
* The inference side typically involves fewer matrix multiplications and can run on relatively modest hardware, such as embedded GPUs or even advanced DSP/FPGA solutions, depending on implementation details.
* Hence, while there is a significant one-time overhead for training, the real-time overhead for the user is predominantly in the decoder that reconstructs masked patches. This step can be optimized. Comparison with Conventional Li-Fi
* Traditional Li-Fi systems might use simpler digital signal processors for equalization or channel estimation, leading to relatively low-latency operations.
* However, those systems transmit the entire waveform, whereas our approach physically omits a large fraction of the samples and recovers them with deep learning. This trade-off can yield a net advantage in throughput if the MAE's inference overhead is kept manageable.
* To provide a rough computational comparison:
* If we consider a typical Li-Fi link employing FFT-based OFDM or DMT, we have an IFFT/FFT block plus straightforward channel equalization.
* Our method adds the overhead of a 1-D convolution and transformer-based reconstruction at the receiver. The precise complexity depends on hyperparameters such as the number of patches, embedding dimension, and number of attention heads.
* In future work, we plan to include scalability metrics (e.g., how many MAC operations or FLOPs per symbol) to give a more quantitative sense of overhead. Early estimates suggest that for moderate patch sizes and a well-optimized network, real-time operation at tens of MHz sampling rates can be feasible with standard GPU or FPGA acceleration.
* Possible Reduction in Complexity
* Techniques like model pruning, quantization, or lightweight transformers can reduce complexity further.
* Our approach can also be integrated with partial layering or smaller embedding sizes for faster inference, at some potential trade-off in reconstruction fidelity.
## 5 Proposed Hardware Architecture
### Overview of the Architecture  
The proposed architecture is composed of several components, including a memory unit (MU) for storing intermediate and final results, routing networks (RN) for transferring data, a read-only memory (ROM) for reading BEC parameters, an arithmetic logic unit (ALU) for performing computations, and a finite state machine (FSM) for generating control signals. To achieve pipelining, registers are placed at the input of the ALU. The design is based on the parameters recommended by the National Institute of Standards and Technology (NIST). Detailed descriptions of each component are provided in their respective Sections 5.2-5.6.