esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Regulatory Barriers to Implementing an EU-Wide Grid Digital Twin
### Economic Regulatory Barriers and Incentives Misalignment
Determination periods typically span three to five years, while the benefits of digital twins, such as asset lifetime extension, operational savings, or improved planning, often emerge over a longer timeframe [16]. Some societal benefits, including enhanced decarbonization and increased system resilience, may also fall outside the scope of current tariff-setting methodologies [24]. To address this, regulators require tools that can incorporate forward-looking, system-wide, and external benefits into their cost-benefit analyses. The revised TEN-E regulation has taken a step in this direction by expanding eligibility for EU funding to smart grid projects of common interest, including digital infrastructure that demonstrates measurable benefits such as capacity optimization or CO\({}_{2}\) reduction [33]. Lastly, regulatory sandboxes and innovation roll-out mechanisms are being explored to test digital solutions under relaxed conditions, allowing regulators to observe their impact before formally integrating them into the mainstream framework [23]. Countries like the Netherlands and France have introduced such sandboxes, and there is increasing interest in expanding them at the EU level to de-risk early deployments of digital twins and generate the evidence base needed for broader regulatory reform [24]. In summary, economic regulation across the EU continues to favor tangible, traditional infrastructure investments over digital innovations, creating a disincentive for deploying digital twins [16]. Overcoming this requires modernizing incentive frameworks through TOTEX-based models, formal innovation allowances, regulatory sandboxes, and cost-allocation mechanisms that reflect the shared and long-term value of digitalization [24]. Encouragingly, EU regulators such as ACER and CEER have acknowledged these gaps and are exploring pathways to align economic regulation with the digital transition [23]. With coherent adjustments, the regulatory environment can shift from an obstacle to an enabler of the European digital twin grid.
## 5 Experimental Analysis
### Analysis of Results  
This section presents and discusses the results of experiments performed by running STREED-Net and the models presented in Section 5.1 on the three case studies. Moreover, three different evaluation metrics are used in this study to compare the results obtained:Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Absolute Percentage Error (APE), which are defined as follows:  
\[RMSE=\sqrt{\frac{\sum_{n=1}^{N}\sum_{m=1}^{M}[(t_{n,m}-t_{n,m})^{2}+(\hat{\omega} _{n,m}-\omega_{n,m})^{2}]}{N\times M}} \tag{29}\]  
\[MAPE=100\cdot\frac{\sum_{n=1}^{N}\sum_{m=1}^{M}\biggl{|}\frac{(\hat{t}_{n,m}-t_ {n,m})+(\hat{\omega}_{n,m}-\omega_{n,m})}{(\hat{t}_{n,m}-t_{n,m})}\biggr{|}}{N \times M} \tag{30}\]  
\[APE=100\cdot\sum_{n=1}^{N}\sum_{m=1}^{M}\biggl{|}\frac{(\hat{t}_{n,m}-t_{n,m}) +(\hat{\omega}_{n,m}-\omega_{n,m})}{(\hat{t}_{n,m}-t_{n,m})}\biggr{|} \tag{31}\]  
where \(\hat{t}_{n,m}\) and \(\hat{\omega}_{n,m}\) are, respectively, the predicted Inflow and Outflow for region \((n,m)\) at time \(t^{\prime}\) and \(N\times M\) is the total number of regions in the city.  
Finally, it is worth noting that to account for and reduce the inherent stochasticity of learning-based models, each experiment was repeated ten times (replicas) using a different random seed in each replica. Mean and standard deviation are reported for each metric to provide a robust indication of the overall behavior of the compared methods.
###### Abstract
Their implementation as part of stereotypes definition at the profile level enables the evaluation of stereotyped model constructions and the detection of inconsistencies. In addition to the creation of profiles, the use of SysML and the adoption of a systems modeling approach also allows for the linking of the simulation models with other models of the referent system (e.g., specification or design models), checking consistency between models to support the collaboration between design and analysis experts. The design and validation of the simulation model using SysML have been studied in previous works, such as [9]. This work also addresses the subsequent model transformation to executable models using languages like Modelica by applying the SysML4 Modelica profile [10]. A similar transformation mechanism is proposed in [11], also based on the application of an SysML profile, to enable an automated transformation. Other authors have explored the use of SysML activity diagrams to describe workflows for connecting simulation and design models and facilitating collaboration between system architects and analysis experts [12; 13]. In the manufacturing domain, there is a lack of developments adopting this approach, and it is hard to find works focused on geometric deviations and their propagation in multi-stage systems. In [14], SysML is used for the preliminary design of a multidomain simulation system that integrates the analysis of productivity and geometric quality to study how different control strategies influence performance measures. However, all the previously mentioned works use SysML as a modeling language only to define the descriptive models facilitating communication between engineers. The present paper explores the SysML's capability to define DSMLs as SysML profiles, including domain-specific semantics to develop simulation software systems. Specifically, this research proposes a language with which to define simulation models for manufacturing systems, focused on a joint analysis of productivity and geometric quality performance. The proposed SysML profile (geometric deviation propagation Simulation--SysML4 GDPSim) integrates the discrete behavioral aspects of a manufacturing system [15] and topologically and technologically related surfaces (TTRS) [16] concepts to model the geometrical quality characteristics of the products. The content and structure of this document is summarized as follows. Section 2 briefly discusses the complexity and main characteristics of the supersystem to be analyzed (including the interactions between the product, the process, and the resources), the simulation systems for their analysis, and the deviated geometry modeling to support quality analysis. Section 3 is dedicated to the proposed SysML profile, presenting the metamodel description, the implementation of the profile, and its application during library model definition. Section 4 presents a case study to exemplify the application of the proposal and validate its suitability in the context of manufacturing simulation. Finally, Section 5 presents a final discussion and Section 6 summarizes some conclusions and future works.
# LGNMNet-RF: Micro-Expression Detection Using Motion History Images
## 5 Results and Discussion
### Ablation Study
#### 5.2.1 Labelling
The ME detection experiment with \(k=37\) shows that the best performance was for the input of the MHI feature, with \(T_{sEXT}=0.165\) s at a 0.3604 F1-score. MHI with the LGNMNet architecture with Softmax obtained lower F1-scores. The optical flow input features with the Random Forest classifier achieved a slightly higher F1-score of 0.3842, which is 0.0238 higher than the MHI results. This may be due to the \(k\) parameter used to generate the MHI, which might not be the optimum value for the SAMM-LV dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Input** & \(k\) & **Classifier** & \(T_{sEXT}\) **[s]** & \(T_{fEXT}\) **[frames]** & **TP** & **FP** & **FN** & **F1** \\ \hline \multirow{2}{*}{OF} & \multirow{2}{*}{Softmax Random Forest} & 0.000 & 0 & 12 & 88 & 45 & 0.1529 \\  & & Random Forest & 0.000 & 0 & 17 & 82 & 40 & 0.2179 \\ \hline \multirow{6}{*}{MHI} & \multirow{6}{*}{Softmax 6} & 0.000 & 0 & 4 & 17 & 53 & 0.1026 \\  & & 0.033 & 1 & 3 & 9 & 54 & 0.0870 \\  & & 0.067 & 2 & 9 & 37 & 48 & 0.1784 \\  & & Softmax & 0.100 & 3 & 11 & 49 & 46 & 0.1880 \\  & & 0.133 & 4 & 7 & 74 & 50 & 0.1014 \\  & & 0.167 & 5 & 9 & 74 & 48 & 0.1374 \\  & & 0.200 & 6 & 9 & 35 & 48 & 0.1782 \\ \hline \multirow{6}{*}{MHI} & \multirow{6}{*}{Random Forest} & 0.000 & 0 & 14 & 36 & 43 & 0.2617 \\  & & 0.033 & 1 & 16 & 118 & 41 & 0.1675 \\  & & 0.067 & 2 & 15 & 64 & 42 & 0.2206 \\  & & 0.100 & 3 & 11 & 40 & 46 & 0.2036 \\  & & 0.133 & 4 & 15 & 37 & 42 & 0.2752 \\  & & 0.167 & 5 & 16 & 33 & 41 & 0.3019 \\  & & 0.200 & 6 & 15 & 52 & 42 & 0.2419 \\ \hline \multirow{2}{*}{OF} & Softmax Random Forest & 0.000 & 0 & 74 & 284 & 226 & 0.2249 \\  & & Random Forest & 0.000 & 0 & 72 & 278 & 228 & 0.2215 \\ \hline \multirow{6}{*}{MHI} & \multirow{6}{*}{Softmax 18} & 0.000 & 0 & 112 & 152 & 188 & 0.3972 \\  & & 0.100 & 3 & 111 & 158 & 189 & 0.3902 \\  & & 0.200 & 6 & 119 & 167 & 181 & 0.4061 \\  & & 0.300 & 9 & 107 & 127 & 193 & 0.4007 \\  & & 0.400 & 12 & 105 & 90 & 195 & 0.4242 \\  & & 0.500 & 15 & 111 & 158 & 189 & 0.3902 \\  & & 0.600 & 18 & 108 & 135 & 192 & 0.3978 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: LGNMNet-RF ME F1-scores \(\uparrow\) with \(k=6\) and MaE F1-scores with \(k=18\) on CAS(ME)\({}^{2}\). Lastly, in the MaE detection experiment with \(k=174\) for the SAMM-LV dataset, the MHI input feature with LGNMNet and LGNMNet-RF obtained similar results, but the LGNMNet-RF had the highest F1-score of 0.4444 at \(T_{sEXT}=0.600\) s. The optical flow input features with the Random Forest classifier are also slightly higher than the MHI input features, at an F1-score of 0.4778. Optical flow input features obtained slightly better results for ME and MaE detection in the SAMM-LV dataset but obtained a low F1-score in the CAS(ME)\({}^{2}\) dataset. On the other hand, even though the MHI input features obtained a slightly lower F1-score than the optical flow features in SAMM-LV datasets, MHI input features obtained high F1-scores in the CAS(ME)\({}^{2}\) dataset.
# Design of a Technique for Accelerating the WSN Convergence Process
## 6 Evaluation
### Scenario 1--Simple Outage  
The mechanism was first tested on a simulation with a simple AODV life cycle (Figure 17), namely on the critical node intermediateNodeA and the node that sends the ping request to the sender. The BFD default interval was set to 1 second and the accelerated interval to 100 milliseconds.  
Simulation result without BFD mechanism:  
---------------------------------  
sent: 177 received: 176 loss rate (\%): 0.564972  
round-trip min/avg/max (ms): 1.90644/3.85699/336.706  
stddev (ms): 25.2391 variance: 0.000637012  
---------------------------------  
The result of the simulation with the BFD mechanism on the nodes sender and intermediateNodeA:  
---------------------------------  
sent: 177 received: 177 loss rate (\%): 0  
---------------------------------  
Figure 16: BFD state machine.  
round-trip min/avg/max (ms): 1.90644/2.03401/13.4117 stddev (ms): 1.14656 variance: 1.31461e-06 ------------  
The results show that the implemented BFD mechanism managed to save the previously \"lost\" ping and also significantly reduce the maximum packet travel time. This maximum time is assigned from the detailed listing of the event log to the first broadcast ping that had to wait for the connection to be established. After the outage, it was found that previously, the lost ping had to wait for a shorter time, namely 11.94927 ms.
# Calculation Method for Settlement Deformation of Existing Tunnel Induced by Underpass Construction
However, the calculation results of settlement and deformation caused by tunnel underpassing are still empirical, and the theoretical method needs to be further expanded and verified. This study adopts the energy technique to determine the deformation of existing tunnels when a new tunnel underpasses it. To validate the approach, field experiments and computer simulations are conducted while taking soil loss into consideration. On this basis, the effects of new tunnel depth, new tunnel diameter, and strata loss rate on the settlement of the existing tunnel are analyzed.
## 3 Networked Mobile Robot System Design Issues
### Coordination Control  
Coordination between robots means that they cooperate to achieve a given common goal. The usage of multiple robots has several advantages over single robot systems: cooperating robots have the potential to achieve a given task faster than a single robot by working in parallel [65]. Complex and high level tasks cannot be accomplished by a single robot even if they have high sensing and actuation capabilities. Moreover, the overall performance of the solution for a single robot system cannot be improved, while for a network of robot unit's coordination and cooperation enhance the efficiency of the system performance in terms of time, energy and data fusion [55, 66].  
In the literature, multi robot systems are categorized according to their coordination level, as fully coordinated, weakly coordinated and not coordinated. Coordinated robot networks are classified into centralized and decentralized [67]. Many control tasks are based upon the partitioning of the mission into different subtasks, which are then assigned to individual robots by a central unit or robot leader [68, 69]. However, these systems do not handle the problem of distributing resources among robots. In centralized methods, a robot works as a 'leader' and group members send their acquired information to the related unit. The leader, in turn, plans optimal actions for each of the group members. In this case, robot units act only according to the leader's commands. Although in such cases coordination control of robots can be perfect and all the important information can be used by the team members, these methods are computationally hard and have a heavy communication burden [70, 71]. An increase in complexity is proportional to the number of robots and this makes the usage of these systems difficult in real-time application. Moreover, these systems are not robust in cases of single point failures, and if there is a problem with the leader's abilities such as communication errors or physical crash, the task cannot be completed by any of the remaining robots.  
On the other hand, in decentralized systems each robot unit is completely autonomous in the decision-making process [72, 73, 74] and executes a coordination protocol, while taking independent decisions. Since these systems are generally more robust to communication and group member failures, accomplishment of the task is not affected by a single point of failure, which gives the opportunity to use this architecture in real world applications [75]. However, disadvantages exist in distributed systems as well, such as integration of local task accomplishments towards a global aim. Moreover, all problems cannot be broken down into small tasks that will be assigned to separate robots.
## 2 Materials and Methods
### Evaluation of Performance  
In this study, a time series data set was divided into two subsets for training and testing the models; the first 70% of the data set was used to train and the remaining (30%) was used to test the models. To evaluate the performance of the PSO-GA-BPNN model and other prediction models, some comparison standards were employed as follows.  
\[\text{APE}=\frac{\left|y-\hat{y}\right|}{y}\times 100\% \tag{14}\]  
\[\text{MAPE}=\frac{1}{n}\sum_{t=1}^{n}\frac{\left|y-\hat{y}\right|}{y}\times 1 00\% \tag{15}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{t=1}^{n}\left(y-\hat{y}\right)^{2}}{n}} \tag{16}\]  
\[R^{2}=\frac{\left(\sum_{t=1}^{n}\left(y-\overline{y}\right)\left(\hat{y}- \overline{\hat{y}}\right)\right)^{2}}{\sum_{t=1}^{n}\left(y-\overline{y} \right)^{2}\sum_{i=1}^{n}\left(\hat{y}-\overline{\hat{y}}\right)^{2}} \tag{17}\]  
where APE is the absolute percentage error, MAPE is the mean absolute percentage error, RMSE is the root mean square error. In Equations (14)-(17), \(y\) is the measured quality parameter in period \(t\), while \(\hat{y}\) is the predicted quality parameter, \(n\) is the total number of periods. \(R^{2}\) is the coefficient of determination, \(\overline{y}\) is the average of measured quality parameter in period \(t\), \(\overline{\hat{y}}\) is the average of the predicted quality parameter in these equations.  
The APE is able to show how far each predicted value deviated from the measured value even when the value of error is very small. \(APE_{max}\) also shows the point at which the worst prediction effect occurs. Average prediction accuracy can be seen in MAPE, which helps to show the performance of the prediction model. RMSE is recognized as one of the most important indicators for evaluating the performance of prediction models; the smaller the value of RMSE, the better the prediction of the  
Figure 2: Flow chart of PSO-GA-BP neural network prediction model.  
model. While the value of \(R^{2}\) (between 0 and 1) represents whether the measured value is related to the predicted value, it represents the degree of fitting between the measured value and the predicted value. The closer the value of \(R^{2}\) is to 1, then the better the fitting result is. Normally, MAPE and RMSE are always used to evaluate the accuracy, while APE and \(R^{2}\) are more suitable for evaluating the robustness of the model.
## 3 Data-Driven Solutions Applied for the Detection of NTLs
### Unsupervised Methods
#### 3.2.2 Regression Models  
These algorithms are used to forecast time series. Two types of regression are defined: (i) Autoregressive Moving Average (ARMA) and (ii) Integrated Autoregressive Moving Average (ARIMA). These techniques require comparing the energy consumed by users with the predicted energy. The greater the difference, the higher the probability that the customer is committing fraud [4; 16; 37; 43]. The multiple linear regression model has allowed for the efficient detection of thefts, even in areas with difficult access to energy meters. The disadvantage of this application in some cases is that it requires records from smart meters [37], which can be limiting for the use of this technique.
# SPDepth: Enhancing Self-Supervised Indoor Monocular Depth Estimation via Self-Propagation
## 3 Methods
### Overall Loss Functions  
For the proposed depth estimation network SPDepth, the complete loss function is formulated as follows:  
\[L=L_{ph}+\lambda_{1}L_{sm}+\lambda_{2}L_{spp} \tag{7}\]  
where \(L_{ph}\) is the patch-based photometric loss, \(L_{sm}\) is the depth smoothness loss term and \(L_{spp}\) denotes the plane fitting loss. All the loss terms are defined in the same way as P\({}^{2}\)Net [1]. Moreover, \(\lambda_{1}\) is set to 0.001 and \(\lambda_{2}\) is set to 0.05 in the experiments by following the baseline [1].  
To be specific, let \(I_{t}\) denote the target image, \(I_{s}\) denote the source image and the patch-based photometric loss \(L_{ph}\) is written as  
\[L_{L1}=\left\|I_{t}\big{[}P_{i}^{t}\big{]}-I_{s}\big{[}P_{i}^{t\to s} \big{]}\right\|_{1} \tag{8}\]  
\[L_{SSIM}=SSIM(I_{t}[P_{i}^{t}],I_{s}[P_{i}^{t\to s}]) \tag{9}\]  
\[L_{ph}=\alpha L_{SSIM}+(1-\alpha)L_{L1} \tag{10}\]  
Figure 4: Zeros are padded on both sides of the input to form windows at the edges. The number of zeros for each dimension equals the window radius. Both key feature windows and depth windows are partitioned in this way.  
where \(\alpha\) is set to 0.85, SSIM denotes structural similarity and \(P\) represents the 3 \(\times\) 3 patches defined as follows:  
\[P=\{(x_{i}+x_{k},y_{i}+y_{k}),x_{k}\in\{-N,0,N\},y_{k}\in\{-N,0,N\},1\leq i\leq n,i\in\mathbb{N}\} \tag{11}\]  
where \((x_{i},y_{i})\) is the coordinate of the extracted key point, \(n\) denotes the number of key points and \(N\) is set to 2.  
The depth smoothness loss \(L_{sm}\) is calculated as  
\[L_{sm}=\abs{\partial_{x}d_{t}^{*}}e^{-\abs{\partial_{x}t_{t}}}+\abs{\partial_ {y}d_{t}^{*}}e^{-\abs{\partial_{y}t_{t}}} \tag{12}\]  
where \(d_{t}^{*}=d_{t}/\overline{d}_{t}\) denotes the normalized depth.  
The plane fitting loss \(L_{spp}\) is formulated as follows:  
\[L_{spp}=\sum_{m=1}^{M}\sum_{n=1}^{N}\abs{D(p_{n})-D^{\prime}(p_{n})} \tag{13}\]  
where \(M\) is the number of extracted plane super-pixels, \(N\) is the number of pixels within each super-pixel. \(D(p_{n})\) is used for back-projecting to the 3D space to fit the plane parameters, \(D^{\prime}(p_{n})\) is the depth retrieved in terms of plane parameters and camera intrinsic parameters.
## 5 Conclusions and Future Work  
This paper introduces an innovative end-to-end model, BiLSTM-BGAT-GCN, aimed at addressing the task of ASTE. By integrating GCN and GAT, and introducing an improved biaffine attention mechanism, this model significantly optimizes the capability to process the relationships between aspect and opinion terms within texts. It has enabled a deeper understanding of the complex relationships between aspect and opinion terms in texts, while also increasing the accuracy and efficiency of sentiment triplet extraction. Moreover, by incorporating multiple linguistic features, the model has further enhanced its comprehensive understanding of texts, making sentiment triplet extraction even more precise. This innovation not only provides an effective technical approach to solving the ASTE task but also offers a new perspective for sentiment analysis research in the field of natural language processing. Experimental results demonstrate that the proposed BiLSTM-BGAT-GCN model achieves outstanding performance across multiple standard datasets, showing significant improvements, especially in capturing complex textual relationships and enhancing sentiment analysis accuracy, compared to existing methods. Future research will explore the further optimization of model parameters and structures to adapt to a more diverse range of text types and sentiment analysis tasks. Additionally, considering the richness of multimodal data, combining textual information with other modalities such as images and videos to more comprehensively understand and analyze sentiments is also a direction worth further investigation.  
Conceptualization, methodology, validation, Y.P. and J.-XZ.; writing--original draft preparation, Y.P.; writing--review and editing, J.-X.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 62103093, the National Key Research and Development Program of China under Grant 2022 YFB3305905, the Xingliao Talent Program of Liaoning Province of China under Grant XLYC2203130, the Natural Science Foundation of Liaoning Province of China under Grant 2023- MS-087, and the Fundamental Research Funds for the Central Universities of China under Grants N2108003.  
Data are contained within the article.  
The authors declare no conflicts of interest.