esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Fault Diagnosis Method
### Proposed TfEn-Based Feature Extraction
\(q_{i,j}\) is the normalized probability density for the block \(W_{i,j}\), which quantifies the concentration of energy in each local time-frequency block. 5. According to the normalized probability density of all these blocks, the TfEn feature of the given time series \(x(t)\) can be extracted as follows: \[s(q)=-c\sum_{i=1}^{I}\sum_{j=1}^{J}q_{i,j}\ln q_{i,j},\] (11) where \(c\) is an arbitrary constant and it is generally set as one, \(s(q)\) is the TfEn feature of 1D time series, which jointly represent its complexity and irregularity from both time and frequency domains. 6. Repeating the above operations for all the \(S\) sequences decomposed by CEEMD, we will finally obtain the TfEn feature vector \(TfEns\) of size \(1\times S\): \[TfEns=\big{\{}s(q)_{1},s(q)_{2},\ldots,s(q)_{s^{\prime}},\ldots,s(q)_{S} \big{\}},\] (12) with the help of \(TfEns\), we obtain the multi-scale representations reflecting the complexity and dynamic change of nonlinear and non-stationary vibration signals. Besides that, it is worth noting that TfEn requires no state space reconstruction and a large amount of loop comparisons, so it has higher computational efficiency than the methods such as SampleEn and ApEn.
# Data and Knowledge-Driven Bridge Digital Twin Modeling for Smart Operation and Maintenance
###### Abstract  
The rapid expansion of civil infrastructure in China underscores the critical need for advanced solutions to ensure the structural health of aging bridges. This study introduces a novel data and knowledge-driven digital twin modeling (DK-DTM) framework designed to enhance the safe and efficient operation and maintenance (O&M) of bridges. Such a system should be capable of (1) monitoring structural dynamics in real time, (2) capturing spatiotemporal details and changes (e.g., defects and deformations), (3) analyzing structure deterioration patterns, (4) predicting structure failure risks, and (5) generating optimal maintenance and repair actions for ensuring structural safety. Previous studies have developed advanced sensing techniques and robust artificial intelligence algorithms for capturing and analyzing bridge health conditions. However, most existing techniques and algorithms heavily rely on high-quality data, which are difficult to obtain during bridge O&M. This raises the critical question of how to incorporate expert knowledge together with data-driven tools to establish a trustworthy DT for bridge O&M. This study presents the DK-DTM framework, which uniquely integrates multi-source data collection, spatiotemporal modeling, and expert knowledge reasoning. By combining these components, the framework supports smart structural health assessments of bridges, enabling comprehensive monitoring, prediction, and decision-making for efficient maintenance. The spatial and temporal models provide real-time data, while the expert knowledge model functions as an automated evaluation tool for structural health assessment. The results demonstrate that the proposed DK-DTM framework significantly enhances the accuracy and efficiency of O&M processes for aging bridges, addressing key gaps in existing digital twin methodologies.  
digital twin model; structural health assessment; expert knowledge +
Footnote †: journal: Applied sciences
# Valuing Resilience Benefits of Microgrids for an Interconnected Island Distribution System
## 3 Microgrid Case Study  
### Description of the Islands of Puerto Rico  
Within the multi-island case study, the main island of Puerto Rico, where the vast majority of the 2.5 million residents are located, has a 230 kV backbone ring and a meshed 115 kV networked transmission system. The grid system consists of a highly dispersed subtransmission system operating at 38 kV. To note, most industrial and large commercial customers are supplied at the subtransmission level, whereas most commercial and all residential customers are supplied at the distribution level. The utility operates \(\sim\)1400 distribution feeders, currently supplied at four different voltage levels: 13.2 kV, 8.32 kV, 7.2 kV, and 4.16 kV, which are about 25%, 15%, 3%, and 55% of the feeders, respectively. The two small islands, Vieques and Culebra, which are the subject of this paper, house about 10,000 residents combined. A submarine cable connects one of the 38 kV lines from the main island to one of the two smaller islands, and, in tandem, the second smaller island. These small islands each have a 38/4.16 kV distribution substation, and have three and two distribution feeders, respectively, as illustrated in Figure 3.
## 5 Conclusions  
In this study, we provided quantitative effects of the value variations of several widely used ET products (AWAP, CSIRO, GLDAS, MODIS, and TerraClimate) on water resources management and ecosystem assessment using a water balance equation and its transformations, the relationships between GPP, WUE and ET. There are obvious differences of ET value among the five ET products in the MRC at annual and monthly scales. This leads to huge differences in the estimations of annual runoff (\(-\)16.3 to 161.3 mm) and annual water storage (\(-\)61.5 to 116.1 mm) and also for the annual irrigation water per area requirements ranging from 49.1 mm to 154.6 mm. The ET products for forest are relatively consistent among each other, but those for grassland present a large varying range. There are different productivity predictions of the ecosystem with the same water use based on the relationships between the annual GPP and varying ET for both forest and grassland, however, the forest has a better capacity of dealing with water stress than the grassland according to the values and the change amplitudes of their WUE. The effects of the variations among the ET products on water resources and ecosystem management are remarkable and need to attract more attention.  
Funding: This work was funded by the National Natural Science Foundation of China (Project No: 41601036, 41571031), \"Light of West China\" Program of CAS, the International Postdoctoral Exchange Fellowship Program, the Australian Research Council (Project No: [PERSON]), and the Strategic Priority Research Program of the Chinese Academy of Sciences (Project No: Y82 [PERSON]).  
The data are available from the corresponding data sites and providers listed in Table 1. We thank the two referees as well as the editors for providing valuable comments.  
The authors declare no conflict of interest.
## 4 Analysis and Interpretation of Results
### Dependence of the Occurrence of Negative Prices on RES Generation
#### 4.2.2 France--Figure 3  
Analysis of the French market reveals a markedly different pattern of negative price incidence depending on the hourly combined generation from vintage and solar resources. In contrast to Germany, where a strong and systematic non-linearity was observed, the French case shows a more multidimensional and less predictable pattern of occurrence of negative prices, while the dependence itself is less clear-cut. In most generation bins in the range 0 to around 10,000 MW, the share of hours with negative prices remains very low (up to 0.5%), which shows the high capacity of the French system to absorb variable generation from RES in the low to medium ranges without market distortions. Only after the threshold of around 8500 MW is there a more marked increase, with peaks in the 12,000-17,000 MW bands, where the share of negative prices increases by up to 4-5%. The occurrence of negative prices occurs in several isolated power bands, sometimes without continuity or discontinuity, which may indicate that the occurrence of negative prices in France is not exclusively determined by the level of RES generation.  
The diversity of results in France is related to several specificities of its energy system:  
1. France has a strong presence of nuclear power plants, which generate most of its electricity. However, it is not easy to quickly adapt their output to current demand. If solar or wind generation is added, which cannot be accurately predicted, it can create a surplus of electricity in the short term.
2. Another factor is that France does not have such a high volume of immediate electricity exports, so excess generation often remains in the country.
3. Compared to other countries such as Germany, France has a smaller share of solar and wind resources. Therefore, the total amount of energy produced from these sources tends to be lower, which reduces the likelihood of surpluses.
4. France, on the other hand, has slightly better conditions for adapting consumption. For example, by using heat pumps, electric heating or automatic consumption control in public buildings.  
Negative electricity prices in France do not arise regularly or systematically. Rather, they are exceptional situations that occur, for example, on weekends when demand is lower or when electricity exports abroad are temporarily not working. They can also be caused by sudden surpluses in wind or solar generation in certain parts of the country,  
Figure 2: Share of negative price in % in Germany.  
such as Occitanie or Nouvelle-Aquitaine. This is also why negative prices sometimes occur at unexpected times, outside the main trends. The analysis shows that it is not possible to simply predict the occurrence of negative prices in France on the basis of the amount of electricity generated from renewable sources alone. It is much more likely to be a combination of several factors--such as overall consumption, the set-up of nuclear plants, problems in the transmission system, and the ability to export electricity out of the country.
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 4 Security and Resilient Architecture
### Contingency Response
#### 4.4.2 Resourcefulness  
The resourcefulness capability of a smart grid during cyber-physical events is a critical element to mitigate the damage, which refers to the time period from \(t_{2}\) (restoration preparation start) to \(t_{3}\) (recovery process start). Gathering and analysing useful grid information to judge which part of the infrastructure is the attack/failure surface and which part is suffering the most, prioritizing every instruction, and applying suitable mitigation methods are the main steps in this period. The state estimation process plays a very important role in supporting the resourcefulness capability.  
In [79], the author proposed a security assessment model CPINDEX, which relies on information flows in between assets, to develop a cyber-originated vulnerability ranking model for physical power system assets. First, they made use of information flows among system assets (files and processes) collected from IDS logs as the learning phase's input. Next, they generated the Dependency Graph (DG) to demonstrate interactions and dependency relationships (a source object, a sink object, and their security contexts) between files and processes. Then, they used Bayesian network formalism to store probabilistic dependencies in DG. At last, all variables will be added to the Conditional Probability Table (CPT) and associated with vertexes, which provide CPINDEX with the calculated probability values of the critical computing assets.  
In [27], the author proposed a two-step vulnerability ranking model. First, they developed a cybersecurity vulnerability ranking model to identify asset (power generators) vulnerabilities, and this model has 6 evaluating attributes: Discovery, Feasibility, Access, Detection, Threat, and Connection Speed. Secondly, for the physical vulnerability, they contributed a topology model, which uses the concept of vertex centrality, which is closeness centrality (shortest path matrix), to calculate coefficients to vertices changes in topology, which indicates the severity of state changes after the outrage happened. The closeness centrality algorithm is also capable of the \"N-X\" case.
## 4 Radiation Metrics
### Specific Absorption Rate (SAR)  
Specific absorption is the rate at which energy is absorbed by a mass within a given volume, considering its density. It is the time derivative of the increased absorbed energy by the mass. SAR is expressed in the SI unit of Watts per kilogram (W/kg), where  
\[SAR=\frac{P_{i}}{m_{i}}=C_{h}\frac{dT}{dt}\bigg{|}_{t=0} \tag{7}\]  
where \(P_{i}\) is the power of the incident wave, and \(m_{i}\) is the mass of the body. Unlike the power density, SAR calculation considers the sample's physical properties. SAR is also described by the electric field:  
\[SAR=\frac{\sigma|\overrightarrow{E}|^{2}}{\rho_{m}} \tag{8}\]  
where \(\sigma\) is the tissue conductivity, and \(\rho_{m}\) is the tissue mass density. Furthermore, SAR can also be described by the temperature increase:  
\[SAR=C_{h}\frac{dT}{dt}\bigg{|}_{t=0} \tag{9}\]  
Figure 7: Weissberger and ITU foliage loss in mmWave for different foliage depths.  
where \(C_{h}\) is the heat capacity in \(\left(\frac{J}{kgC^{d}}\right)\), \(T\) is the temperature, and t is the exposure duration.
## 6 Stage 1: TS Modeling  
### TS Data Pre-Processing  
As the first step, all raw TS data were passed to the pre-processing module, which generated a group of stationary segments. Herein, one TS sample is presented to indicate the necessity of conducting the differencing operation and procedure of the auto-stationarity check. First, the ACF and PACF plots of the TS example are plotted in Figure 8. If only SSN is performed, Figure 8a shows certain trends in the ACF plot, and its values diminish slowly, indicating non-stationarity. By performing differencing once after SSN, the performance in both the ACF and PACF plots significantly improved, where the ACF diminished rapidly within the shown dashed lines with less than 15 lags and the PACF cut off a certain number of lags, Figure 8b. This observation roughly meets the property of the stationary condition and indicates that the differencing order \(I=1\) is sufficient.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Classifier** & **Details** \\ \hline \multirow{2}{*}{SVM} & 1. Use kernel-SVM with Gaussian kernel. \\  & 2. Use L2 regularization parameter [22]\(C=100\). \\ \hline \multirow{2}{*}{NN} & 1. Build a two-layer network. \\  & 2. Neurons in first and second layers are 200 and 100, respectively \\  & 3. Use ReLU [37] as an activation function. \\ \hline \multirow{2}{*}{RF} & 1. Use Gini impurity [36] as a split criterion. \\  & 2. Use 250 trees as the estimator and then average their predictions. \\ \hline \multirow{2}{*}{LR} & 1. Add L2 regularization term. \\  & 2. Use L2 regularization parameter \(C=100\). \\ \hline \multirow{2}{*}{EVC} & 1. Ensemble of the above four individual classifiers with their optimal settings. \\  & 2. Use a hard-voting mechanism. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Implementation details of the classifiers.  
Subsequently, the stationarity of the TS with the ADF test was examined, where \(p\)-values of 0.78 and 0.01 were achieved for the cases of SSN and SSND, respectively. The former significantly exceeds 0.05, which accepts the null hypothesis (non-stationary), but the latter rejects the null hypothesis and accepts the alternative hypothesis (stationarity). After examining several plots of different segments, we concluded that stationarity was difficult to achieve for the collected data from sensors if only SSN was used. However, one-time differencing significantly resolved this issue, supporting the need of ARIMA\((p,1,q)\) models. Herein, if one-time differencing still cannot satisfy the stationary condition, the segment would be discarded. Finally, the label distribution among all segments is shown in Figure 9, which is somewhat imbalanced, e.g., UD segments are about twice those of AL. It addition, both training and test datasets shared the same distribution after pre-processing.  
Figure 8: Standardized (STD) value, ACF, and PACF of one sample TS segment with different pre-processing. (Dashed lines: approximate 95% confidence intervals of the white noise results; vertical lines: values beyond which autocorrelations are (statistically) significantly different from zero [38]).
## 4 Experiments Results and Analysis
### Analysis of Experimental Results
The experimental results illustrate that the introduction of pre-trained models in the process of NER of shipping enterprise credit can obtain rich word-level semantic representations, alleviate the problem of multiple meanings of words, and improve the recognition effect of shipping enterprise credits. In addition, the experimental results of comparing the BERT-BiLSTM-CRF model with the BERT-BiGRU-CRF model show that the simpler structure of BiGRU can achieve better recognition results for the shipping enterprise credit dataset. By adding the word vector, word length feature vector, and pos feature vector in the text to the BERT-BiGRU-CRF model, the F1 value of shipping enterprise credit NER reaches 92.84%; compared with the single BERT-BiGRU-CRF model recognition result, the F1 value \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline HMM & 60.23 & 61.25 & 60.71 \\ BiLSTM-CRF & 69.84 & 68.16 & 69.01 \\ SVM-BiLSTM-CRF & 70.66 & 69.38 & 69.98 \\ BiGRU-CRF & 71.03 & 68.56 & 69.35 \\ Word2 Vec-BiGRU-CRF & 73.35 & 72.74 & 72.99 \\ BERT-BiLSTM-CRF & 79.52 & 78.43 & 78.80 \\ BERT-BiGRU-CRF & 84.54 & 82.14 & 83.38 \\ BERT-MF-BiGRU-CRF & 92.84 & 90.46 & 91.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experiment results of NER based on different models (%). improves by 8.3%, accuracy improves by 8.3%, and recall improves by 8.37%. All three have been greatly improved, which can strongly demonstrate that for shipping enterprise credit data, the introduction of multiple features in the named entity recognition process can effectively improve the model entity recognition effect, especially for the entities with long entity names in shipping enterprise credit; the fusion of multiple features can better solve the complex nesting problem existing in shipping enterprise credit entities and improve the effect of entity recognition. Compared with other models, this paper proposes using the BERT-MF-BiGRU-CRF model for shipping enterprise credit entity recognition, which has the highest accuracy, recall, and F1 value. This proves that the introduction of pre-trained models in the NER process can make full use of the contextual information of the utterance to obtain rich word-level semantics, and on this basis, the fusion of multiple features in the input of the model can obtain rich text semantic features, which effectively solves the problem of complex name structures of shipping enterprise credit entities and improves the effect of shipping enterprise NER.
# Prediction of Manufacturing Quality of Holes Based on a BP Neural Network
## 1 Introduction
Based on the analysis of the above references, it can be seen that there were some studies on the prediction and analysis of the quality of hole-making, which mainly focused on the verticality of the deep holes, the roughness of the inner surface of the metal material, and the axial force of the composite material when drilling holes. The axial force of the composite material is an important factor that affects the defects of hole-making. These studies focus on predicting a single material and a single object. However, the aluminum alloy and CFRP stack material is the focus of this manuscript. Based on the BP neural network algorithm, the final drilling quality of the CFRP material is predicted and analyzed. Evaluation of the results can greatly reduce the probability of defective holes, as well as provide optimized solutions for efficient and high-quality hole-making of stack material.
# IoT Privacy and Security Challenges for Smart Home Environments
## 6 A Suitable Smart Home Architecture for Security
### Middleware Architectures and Security  
Middleware is a software layer that sits between the low-level layer of devices and the high-level application layer. It usually provides a common interface and a standard data exchange structure to abstract the complex and various lower-level details of the hardware. When the middleware receives a request from a higher-layer application, it converts the high-level standardized resources access request to the corresponding device-specific methods. When the device responds back to the application, the middleware processes the low-level methods and data transformations, and then sends the related abstract commands and data back to the application. The application does not need to know the underlying details of the different implementations of the hardware, it can just simply invoke the commands and functions provided by the middleware. Security and privacy protection should be considered at all levels of the middleware, from the lower hardware interaction level to the higher common interface level.  
VIRTUS Middleware [30] is a middleware solution based on the open eXtensible Messaging and Presence Protocol (XMPP) protocol. It adopts the Simple Authentication and Security Layer (SASL) protocol for authentication and the Transport Layer Security (TLS) for data security and privacy.  
Secure Middleware for Embedded Peer-to-Peer systems (SMEPP) [31] is a middleware focusing on providing peer-to-peer security communication between smart nodes. Before a device can communicate with others, it needs to join a group by providing a valid credential. There are three different security levels, but only level 1 and level 2 take up the security mechanisms. There is no security implementation under level 0. SMEPP implements pre-shared key cryptography under level 1 and public-key cryptography under level 2 for group admission. On the other hand, SMEPP adopts authentication under level 1 and authentication together with encryption approach under level 2 to protect data security.  
While middleware has been extensively used in corporate systems with desktop-class machines to manage complex heterogeneous networks, currently proposed IoT middleware solutions require substantial additional complex software layers and cryptographic routines to be implemented on devices which have neither the memory nor the computational power to host them. Apart from the performance problems, another concern for middleware architecture is that the coding defects in middleware inadvertently introduced by developers may potentially pose security threats to the IoT devices. So we reject middleware solutions as presently infeasible for many IoT-class devices.