esa-sceva/satcom-chunk-collection huggingface dataset:
# ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning
## 3 Methodology
### Module 3: Model Update Strategy  
This module performs selective updates to the model in response to concept drift, as identified by the previous modules. It replaces only a subset of base detectors, avoiding full retraining and preserving valuable historical knowledge. This incremental update strategy enables the model to adapt over time while maintaining robustness under non-stationary conditions.  
Given the replacement ratio \(replacement\_ratio\in[r_{\min},r_{\max}]\) computed by the distribution-based drift detection module (Module 1), the number of base detectors to be updated is determined as follows:  
\[K=\lfloor replacement\_ratio\cdot N\rfloor\]  
where \(N\) is the total number of base detectors in the model.  
Then \(\mathcal{M}_{j-1}\) denotes the current model, consisting of \(N\) base detectors. These detectors are ranked in ascending order of \(p\)-values from Module 2, resulting in an ordered list:  
\[ranked\_list=[d_{(1)},d_{(2)},\ldots,d_{(N)}]\]  
The subset selected for replacement is defined as follows:  
\[\mathcal{D}_{\text{replace}}=\{d_{(1)},d_{(2)},\ldots,d_{(K)}\}\]  
A new iForest consisting of \(K\) base detectors is trained on the current data window \(T_{j}\). These new detectors replace \(\mathcal{D}_{\text{replace}}\), while the remaining detectors are preserved. The updated model at time \(j\) is expressed as follows:  
\[\mathcal{M}_{j}=(\mathcal{M}_{j-1}\setminus\mathcal{D}_{\text{replace}}) \cup\mathcal{D}_{\text{new}}\]  
where \(\mathcal{D}_{\text{new}}\) contains the \(K\) base detectors newly trained on \(T_{j}\), which replace the drift-affected detectors in \(\mathcal{D}_{\text{replace}}\).  
This update strategy offers stability of the model. Full retraining of all base detectors may lead to catastrophic forgetting and unnecessary computational burden. By contrast, the partial update strategy preserves a portion of prior knowledge while allowing drift-sensitive components to adapt. It also ensures efficiency adaptation by avoiding full model retraining and limiting updates to a smaller subset of base detectors. By adjusting the update strength in proportion to drift intensity and preserving unaffected detectors, the model adapts efficiently without overfitting to transient changes.  
The procedure is outlined in Algorithm 3.  
```
1:Input: Model \(\mathcal{M}_{j-1}\) with \(N\) base detectors;  ranked detector list \(ranked\_list=[d_{(1)},d_{(2)},\ldots,d_{(N)}]\); current window \(T_{j}\)
2:Output: Updated model \(\mathcal{M}_{j}\)
3:\(K\leftarrow[\,replacement\_ratio\cdot N\,]\) {Number of detectors to replace}
4:\(\mathcal{D}_{\text{replace}}\leftarrow\{d_{(1)},\ldots,d_{(K)}\}\) {Top-\(K\) drift-affected detectors}
5:\(\mathcal{D}_{\text{new}}\leftarrow\) Train \(K\) new base detectors on \(T_{j}\)
6:\(\mathcal{M}_{j}\leftarrow(\mathcal{M}_{j-1}\setminus\mathcal{D}_{\text{ replace}})\cup\mathcal{D}_{\text{new}}\)
7:return\(\mathcal{M}_{j}\)
```  
**Algorithm 3** Model Update Strategy (Module 3)
# Automated Trucks' Impact on pavement Fatigue Damage
## 3 Results
### Alternative Payement Structures
#### 3.3.4 Overview of Fatigue Damage Index Variations across Evaluated Scenarios  
Table 2 provides an overview of the variations in the DI values across different scenarios. In this table, the comparisons of DI values are presented by percentage changes in the fatigue damage index, which serve as indicators, offering insights into the potential trends of increased or decreased fatigue damage within specific scenarios. Since the direct interpretation and utilisation of fatigue damage alteration magnitudes may be limited, our analysis focuses on discerning trends in these alterations. In other words, rather than solely focusing on absolute fatigue damage index values, our study emphasises the comparative patterns observed across diverse scenarios. By examining the relative changes in fatigue damage, the present study aimed to elucidate the influence of various parameters, including wandering patterns, lane widths, market penetration rate, and pavement layer thicknesses and stiffnesses. Therefore, the magnitudes of alterations may not be interpreted and employed directly; instead, the observed trends are more informative.  
According to the results presented in Table 2, the scenarios reveal significant variations in DI depending on the lane width (3 m vs. 3.5 m), market penetration rates (ranging from 20% to 100%), and wander modes (zero vs. uniform). For instance, a segregated scenario with a 3.5 m lane width and zero wander at a 100% market penetration rate shows a substantial increase in the DI by 278.57%, indicating aggravated fatigue damage. Conversely, the same scenario with uniform wander demonstrates mitigator fatigue damage with a decrease in DI by 7.05%.  
Figure 11: Changes in fatigue DI values by changing layers’ structure by applying 100% ATs with (a) zero-wander and (b) uniform-wander modes.  
Furthermore, Table 2 highlights the effectiveness of various pavement alternative structures in reducing the DI. Increasing the HMA layer's thickness from 10 cm to 20 cm shows a notable reduction in the DI, particularly in zero-wander scenarios with higher AT penetration rates. For example, a segregated scenario with 100% penetration and zero wander reduces the DI by 42.92% with the increased HMA layer thickness. Similarly, increasing the base layer's stiffness from 300 MPa to 600 MPa consistently reduces the DI across all evaluated scenarios, with reductions exceeding 75%.  
Overall, Table 2 illustrates the critical impact of traffic integration, lane width, and pavement layer modifications on fatigue damage. It underscores the importance of strategic pavement design and traffic management to mitigate fatigue damage and prolong pavement life.
###### Abstract
In addition, how to realize a better balance among the braking performance (BP), regenerative braking loss efficiency (RBLE), and battery capacity loss rate (BCLR) is also studied inadequately. The objective of the regenerative braking control strategy for an R-EEV is to guarantee the comprehensive performance of the R-EEV during operation, and this comprehensive performance includes braking performance and regenerative braking efficiency. Meanwhile, the strategy ensures RCC is always within the controllable range and battery state of charge (SoC) is close to the pre-set value, which not only protects the battery pack, but also gets the ideal braking comprehensive performance. In this paper, a revised regenerative braking control strategy (RRBCS) based on the optimal slip ratio (OSR) with the rate and shape of battery RCC considerations is introduced. In addition, a multi-objective parameters optimization method (MOOM) for RRBCS is proposed to match the BP, RBLE, and BCLR in the power system of R-EEV. The major contribution and innovations of this work are mainly reflected in the following three aspects: (1) It adds the analysis of RCC's impact on battery health, and the adverse effects of RCC on battery health are reduced by the designed \"conservative braking mode\" in RRBCS. (2) The conflict among BP, RBLE, and BCLR are revealed and analyzed through the proposed MOOM, and the control parameters' optimal solution of RBCS is obtained based on the global optimization algorithm instead of deriving from the developer's experience to get a better balance among three objectives. (3) The sensitivity analysis of the optimal solution on various tire-road adhesion conditions is carried out to validate the effectiveness and feasibility of the proposed MOOM. The rest of this paper is organized as follows: In Section 2, the RRBCS is given and revised allocation logic of the regenerative braking work-point is introduced. Section 3 describes the MOO problem, and the non-dominated sorting genetic algorithm (NSGA-II) is selected to solve the MOO problem and simulation model is built. In addition, the simulation test and non-inferior Pareto solution sets are given. In Section 4, a series of simulation tests on various tire-road adhesion conditions are carried out to validate the effectiveness and feasibility of the proposed MOOM. This is followed by the conclusion in the final section.
###### Abstract  
There has been considerable research in recent years on the additive manufacturing (AM) of carbon fiber reinforced polymer (CFRP) parts based on the process of fused deposition modeling (FDM). The currently-applied steps within the manufacturing pipeline, such as slicing and path planning, consider only the planar case of filament deposition and mostly make no use of the possibility to place single pre-impregnated (prepreg) filaments. Classical methods such as tape-laying and laminating struggle with highly curved and complex geometries and require the costly production of molds, whereas when using AM, these geometries can be realized more easily and molds can be created using the same process. In this paper, a set of algorithms is presented that aims to resolve these problems. Criteria are formulated which enable the goal oriented development and evaluation of the presented methods and represent metrics for future methods. The developed algorithms enable the use of both continuous and discontinuous fiber patches in a much wider range of applications in designing and manufacturing of CFRPs. This opens up new possibilities in this promising field. The developed metrics and infrastructure further constitute progress in the field of multi-axis non-planar path planning for slicing algorithms in general and the conducted evaluation proves the formal applicability of the developed algorithms.  
c 20221258199 2022125819 2  
of industrial robots. These methods can achieve good mechanical properties but struggle with highly curved parts or complex geometries as buckling or problems in draping can be observed. The use of FDM allows the guided placement of single lines of the pre-impregnated filament, eliminates the need for expensive molds to be manufactured and enables the use of more complex parts with higher curvatures.  
As well as exhibiting the drawbacks of planar algorithms in general [10], planar algorithms struggle to achieve good mechanical properties and high compliance with load cases [8]. This leads to the use of multi-axis printers being proposed for manufacturing CFRP parts with FDM [11]. A major limiting factor is the lack of software to enable the path planning for printing [8].  
This paper aims to start the process of offering a toolbox of algorithms for creating new ways to construct and create fiber reinforcements on free-form surfaces. The structure of the paper is as follows: In the next sections prior works will be discussed, and our method will be presented. Section 2 gives an overview and explanation of the employed methods and developed evaluation criteria. In Section 3, the proposed algorithms are described and explained. In Sections 4 and 5, the evaluation of the algorithms as well as the discussion of the results and the conclusion can be found.
###### Abstract  
Identifying dust aerosols from passive satellite images is of great interest for many applications. In this study, we developed five different machine-learning (ML) based algorithms, including Logistic Regression, K Nearest Neighbor, Random Forest (RF), Feed Forward Neural Network (FFNN), and Convolutional Neural Network (CNN), to identify dust aerosols in the daytime satellite images from the Visible Infrared Imaging Radiometer Suite (VIIRS) under cloud-free conditions on a global scale. In order to train the ML algorithms, we collocated the state-of-the-art dust detection product from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) with the VIIRS observations along the CALIOP track. The 16 VIIRS M-band observations with the center wavelength ranging from deep blue to thermal infrared, together with solar-viewing geometries and pixel time and locations, are used as the predictor variables. Four different sets of training input data are constructed based on different combinations of VIIRS pixel and predictor variables. The validation and comparison results based on the collocated CALIOP data indicate that the FFNN method based on all available predictor variables is the best performing one among all methods. It has an averaged dust detection accuracy of about 81%, 89%, and 85% over land, ocean and whole globe, respectively, compared with collocated CALIOP. When applied to off-track VIIRS pixels, the FFNN method retrieves geographical distributions of dust that are in good agreement with on-track results as well as CALIOP statistics. For further evaluation, we compared our results based on the ML algorithms to NOAA's Aerosol Detection Product (ADP), which is a product that classifies dust, smoke, and shu using physical-based methods. The comparison reveals both similarity and differences. Overall, this study demonstrates the great potential of ML methods for dust detection and proves that these methods can be trained on the CALIOP track and then applied to the whole granule of VIIRS granule.  
C CALIOP; VIIRS; machine learning; deep learning; dust detection
## 5 Comparative Evaluations
### Evaluation Results for Inaudibility  
Figure 11 shows the evaluation results, namely, PESQ and LSD with respect to the bit rate, for the sound quality test of the LSB, DSS, MC, and proposed methods. As we can see, the LSB method has the best sound quality among the four methods. The DSS method has the PESQ of less than 3 and the LSD of more than 1 dB. The PESQ for the MC method is greater than 3 to 16 bits, and the LSD is less than 1 dB. The PESQ for the proposed method is greater than 3 until 8 bits, and the LSD is 1 dB.
## 1 Introduction
A node in the open list will be transferred to the closed list when the distance from the start node is less than the best path so far [14]. In consideration of low search efficiency of Dijkstra path planning algorithm, an improved algorithm extended from Dijkstra algorithm, i.e., A-Star (A*) algorithm, was introduced by [15]. In contrast to the Dijkstra algorithm, A-Star algorithm transfers a node in the open list to the closed list by introducing the node potential that is the underestimate of the remaining distance to the destination node. A-Star algorithm has higher efficiency mainly because the node potential gives the direction to the destination node. A-Star algorithm forms the backbone of vehicle navigation systems, which has been used for robots, road vehicles, unmanned aerial vehicles, humans, etc. [2; 14; 16; 17; 18]. A traditional A-Star path planning algorithm generates an optimal path only by minimizing the path length in a grid map. Improvements for the A-Star algorithm have been done all the time. [PERSON] proposed a Hyperstar algorithm based on A-Star approach for risk averse vehicle navigation [14]. [PERSON] et al. improved the A-Star to decrease the computer calculation cost [19]. [PERSON] et al. presented an enhanced A-Star algorithm capable of generating smooth and feasible paths considering orientation restrictions [2]. These improvements for the A-Star algorithm have solved several specific problems and widen the application areas. However, for the path planning of vessels, the A-Star and the A-Star based methods still need to be improved to generate safer and more efficient paths. This study presents an improved A-Star based global path planning method to satisfy the requirements of the economy (path length), the obstacle avoidance, the traffic separation rule, the berthing and the manoeuvrability restriction considering the water current effects at the same time. The remainder of this article is organized as follows. Risks of the obstacles, the traffic separation, the berthing, manoeuvrability restriction are modelled in Section 2. In Section 3, an improved A-Star path planning algorithm considering risk models is proposed. In Section 4, four simulation case studies are carried out to verify the performance of the proposed methods for the normal path planning,berthing path planning and combined path planning, respectively. In the Section 5, the conclusions and future directions are presented.
# An Alternative Globalization Barometer for Investigating the Trend of Globalization
## 3 Results
### Model Results  
Figure 6 shows prediction results for globalization trajectory using the Random Forest model. Both _de facto_ and _de jure_ globalization are projected to exhibit a steady upward trend in the next five periods after 2020.  
The projected _de facto_ globalization for 2021 to 2025 is as follows: A significant rebound is expected for 2021, describing a \"V\" curve. After the rebound, _de facto_ globalization is highly likely to follow a steady upward trajectory for the remainder of the 5 years; _de facto_ economic globalization in the same 5-year period would experience a large-scale rebound; _de facto_ social globalization would decrease in 2021, followed by a steady, gradual recovery; while _de facto_ political globalization has the potential to experience a large-scale rebound in 2021, followed by a steadily increasing trend.  
The _de jure_ globalization trend in general may remain relatively stable. For the next 5 years, _de jure_ globalization is projected to maintain steady yet slow levels of upward momentum. _De jure_ economic globalization keeps pace with overall globalization, i.e., a rebound to form a \"V\" curve, tapering into a slow increase; _de jure_ social globalization  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Dimension**} & \multicolumn{3}{c}{**5-Fold Cross-Validation**} & \multicolumn{3}{c}{**Out-of-Bag**} \\ \cline{2-7}  & **R\({}^{2}\)** & **RMSE** & **MAE** & **R\({}^{2}\)** & **RMSE** & **MAE** \\ \hline _De facto_ globalization & 0.9502 & 0.0129 & 0.0628 & 0.9508 & 0.0126 & 0.0620 \\ _De facto_ economic globalization & 0.9750 & 0.0248 & 0.0630 & 0.9818 & 0.0190 & 0.0551 \\ _De facto_ social globalization & 0.8731 & 0.0608 & 0.0697 & 0.8486 & 0.0490 & 0.0649 \\ _De facto_ political globalization & 0.9400 & 0.0469 & 0.1344 & 0.9390 & 0.0462 & 0.1335 \\ _De jure_ globalization & 0.9864 & 0.0038 & 0.0366 & 0.9865 & 0.0037 & 0.0357 \\ _De jure_ economic globalization & 0.9896 & 0.0088 & 0.0576 & 0.9899 & 0.0085 & 0.0560 \\ _De jure_ social globalization & 0.9885 & 0.0035 & 0.0332 & 0.9903 & 0.0029 & 0.0310 \\ _De jure_ political globalization & 0.9459 & 0.0257 & 0.0668 & 0.9547 & 0.0228 & 0.0624 \\ \hline \end{tabular}
\end{table}
Table 2: Model evaluation results.  
maintains a slowly upward pace for this period; while _de jure_ political globalization is expected to maintain its previous level for 2021, and increases after 2022.
# Role-Based Access Control Model for Inter-System Cross-Domain in Multi-Domain Environment
## 6 Model Evaluation  
### Model Characteristics  
For support role name repetition, it is achieved by splitting roles into abstract and specific roles. Domain administrators create specific roles for systems in the domain, then assign permissions to specific roles based on responsibilities of the associated abstract roles. Different permissions are allowed for specific roles with the same name in different domains, so it will not cause role naming conflicts.  
For platform-domain isolation management, RBAC-IC has designed three types of personnel: platform administrators, domain administrators and ordinary users. Platform administrators can create abstract roles, but abstract roles do not assign permissions and authorize users. Therefore, platform administrators cannot control the database of the domain server. Domain administrators can create specific roles that are valid only within their domain, and assign permissions to them and authorize them to ordinary users. In this way, privacy disclosure to platform administrators is avoided.  
For inter-domain isolation management, RBAC-IC regards specific roles as a five-tuple, in which if domain and system element are different, the effective system and domain of the specific role are different. This ensures that systems and data in different domains are isolated from each other.  
For fine-grained cross-domain sharing, domain administrators create special specific roles for cross-domain access, and users can cross-domain access after authorizing these specific roles. These specific roles are authorized by the domain administrator of the domain where they are located, and the authorizing rights of the roles are not assigned to other domain administrators, to achieve fine-grained authorization. In addition, domain administrators can also set valid time for these specific roles, and these specific roles will automatically become invalid after the expiration.  
Table 6 shows the comparison between RBAC-IC and other schemes in the above four characteristics.
## 1 Background  
Infrastructure's vulnerability is a topic of rising interest in the scientific literature due to its strategic importance [1; 2; 3; 4]. In particular, [PERSON]. [5] proposed to divide the costs connected with infrastructure assets into direct and indirect losses. Direct costs may be defined as the losses that the owner of the infrastructure must incur to recover from the event. For example, direct costs are connected with the materials and labor necessary to rehabilitate or replace parts of the infrastructure. Indirect costs are more difficult to define. For example, they may consist of the losses that the users incur due to increased travel time or vehicle operations, but also the losses to the surrounding region (e.g., time delays inducing interruption of goods and services). Another definition was proposed by [6], dividing the losses into direct (e.g., repair of infrastructure in terms of replacement of damaged contents and components and indirect (e.g., business disruptions, costs for relocations, and losses due to interruption of business) losses.  
Among the various components of road infrastructure, bridges are generally the most vulnerable elements to damage, and their damage and/or collapse may induce important losses to social and economic activities, as well as to transportation networks and to the economies of entire regions [7; 8]. The study of these critical assets has been the object of several contributions, such as [9; 10; 11; 12; 13; 14; 15]. In particular, [PERSON]. [16; 17] considered several post-disaster assessments [16; 17].  
In this background, several studies proposed to consider the seismic resilience of infrastructures [18; 19; 20]. A method for the assessment of the seismic resilience of road infrastructure (SRRI) is herein proposed. The basic hypothesis is that other sources of losses may be neglected in relation to those due to the damage or collapse of bridges. In this regard, the losses and the repair time that are necessary in order to calculate the seismic resilience are calculated by the implementation of the framework presented in [21] and the formulations presented in [7; 8]. Indirect losses are calculated by considering two typologies of indirect costs (i.e., prolongation time and connectivity losses), as proposed by [5]. A case study is herein performed to validate the SRRI methodology.  
The main novelties consist of (1) developing a new SRRI methodology that may assess the resilience of entire road infrastructures instead of their individual components, (2) proposing a probabilistic-based approach (based on the PBEE methodology) instead of a deterministic estimation of losses, (3) a new formulation that considers interdependencies and needs to be considered in the assessment of indirect losses, and (4) presenting a case study that validates the framework and may be taken as a first attempt to implement the framework.