esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Capacity Rating Procedure
### New Unit Design Changes
The loss of life characteristics of the 50 MVA unit when Profile A is applied is defined by (9), which quantifies the trend line of calculating (6) as a function of stray losses (\(p_{s}\)) of the existing design. Equation (10) defines the percentage overall loss of life reduction as a function of the original design's \(p_{S}\). \[F_{EQA}(p_{S})=25{p_{S}}^{6}-111{p_{S}}^{5}-190{p_{S}}^{4}-155{p_{S}}^{3}-66{p_ {S}}^{2}-10{p_{S}}+33 \tag{9}\]  
\[R(p_{S})=\frac{F_{EQA(1,0)}-F_{EQA(p_{S})}}{F_{EQA(1,0)}}\times 100\% \tag{10}\]  
Increasing winding conductor size is a means of reducing \(l^{2}R\) losses. Temperature of the conductor increases as more current flows through it. The conductor heat is emitted through the insulation and into the transformer oil through conduction. Increasing the cross-sectional area of the conductor reduces the total resistance of the winding and allows the same current to flow through the conductor at a lower temperature. The loss of life characteristics of the 50 MVA unit when Profile A is applied is defined by (11), which quantifies the trend line of calculating (6) as a function of the original design's winding resistance (\(p_{I}\)). Equation (12) defines the overall aging reduction as a function of the original design's \(p_{I}\). \[F_{EQA}(p_{I})=-0.86{p_{I}}^{5}+5.4{p_{I}}^{4}-13{p_{I}}^{3}-15{p_{I}}^{2}-0.6{ p_{I}}+33 \tag{11}\]\[R(p_{I})=\frac{F_{EQA(1,0)}-F_{EQA(p_{I})}}{F_{EQA(1,0)}}\times 100\% \tag{12}\]  
Increasing the winding diameter on new units is accomplished by sizing conductors based upon a higher ampacity. Unfortunately, increasing the winding on units being rewound may be impossible due to space limitations within the unit. Older units designed with higher safety margins might be easily upgraded with larger windings. Newer units designed for efficiency may not possess the additional space. Transformer design attributes that have little effect on mitigating excessive aging due to a flattened profile include, increased volume of oil, increased tank volume, and decreased core losses.
## 4 Example Scenario: Smart Electric Car
### The Scenario  
In the scenario we propose, a smart electric-car device (either the connected car itself or a context-aware mobile device placed inside the car) makes use of local sensors to detect that the battery level is low. It is then able to access the Sensor Web to receive information on recent charging service times from other vehicles, real-time occupancy levels and energy prices of different nearby recharging stations, weather and road conditions, and so forth. With this information an on-board smart application could recommend the most suitable charging station. Furthermore, once the driver decides to recharge in a particular area or station, in turn this information can be published and shared through the Semantic Sensor Web, thus allowing other services to function optimally, such as smart grid management systems that would be able to keep track of, and respond to, accurate real-time information of local electricity demand (e.g., a smart grid management application could use the charge demands from all the electric cars running in a given area in order to plan the optimal distribution of the electric energy supply in view of this foreseeable demand) [32]. For this smart interaction scenario to be possible, the in-car smart device has to be able to follow the appropriate mechanisms to discover, access, and publish Sensor Web Information (implemented in the USN-Enabler).  
We are considering here smart devices also as producers of human-generated observations, which is still a challenging endeavor [33]. With this in mind, looking back at Figure 4 we can think of the user of the smart device providing a \"human observation\" through interaction with the smart device's Human Machine Interaction (HMI) system. In our example scenario, after receiving an alert from the battery level sensor, the driver interacts with the in-car HMI system to decide to stop at a particular charging station. When the precise _StopToRecharge_ information is made available in the in-car HMI,the HMI system generates the semantically annotated (_i.e._, O&M-OWL) human generated observation. It is also the HMI system that sends the semantically annotated observation through the USN-Gateway and publishes it on the Telco USN-Platform using the USN-Enabler _InsertObservation_ operation (the interested reader may see [34] for details on the particular procedure and the HMI technology we use). It is the smart device HMI system that acts as the procedure to acquire human-generated observations, and, accordingly, as can be seen in Figure 4, the _procedure_ attribute in the O&M-OWL representation is the in-car HMI system, which should be described also in the SensorML model of the smart car.
## 2 Materials and Methods
### Datasets
#### 2.5.2 Breast Cancer  
This study includes two different breast cancer datasets. The first is the BC-MLR dataset, which was obtained from the UCI (University of Chicago, Chicago, IL, USA) Machine Learning Repository (BC-MLR dataset: [[https://archive.ics.uci.edu/dataset/14](https://archive.ics.uci.edu/dataset/14)]([https://archive.ics.uci.edu/dataset/14](https://archive.ics.uci.edu/dataset/14)) /breast+cancer, accessed on 15 December 2024). This dataset contains 286 instances with nine attributes that capture both patient-specific characteristics and tumor-related details.  
For example, the dataset provides information on the patient's age at diagnosis (age) and menopausal status at that time (menopause). Tumour-specific characteristics include the maximum diameter of the tumor (tumour size) and the degree of malignancy (deg-malig).  
In addition, the dataset covers cancer spread, noting the number of lymph nodes involved (inv-nodes) and whether the tumor has invaded the lymph node capsule (node-caps). Information about the side of the breast involved (breast) and the location of the tumor (breast-quad) is also included. The characteristic irradiat indicates whether the patient received radiotherapy. The target variable Class indicates whether the patient had a cancer recurrence after treatment.  
The second dataset, known as BCNB, is the Early Breast Cancer Core-Needle Biopsy WSI dataset (BCNB dataset: [[https://paperswithcode.com/dataset/bcdalnmp](https://paperswithcode.com/dataset/bcdalnmp)]([https://paperswithcode.com/dataset/bcdalnmp](https://paperswithcode.com/dataset/bcdalnmp)), accessed on 15 December 2024). This dataset contains data from 1058 patients and includes comprehensive details of patient demographics and tumor characteristics. It records patient age at diagnosis (age) and tumor-related characteristics, such as tumour size and tumour type. Molecular markers including estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression are also recorded. In addition, HER2 expression provides immunohistochemistry test results to predict cancer response to treatment.  
Additional features of the BCNB dataset include histological grading, which assesses how closely tumor cells resemble normal breast cells, and information on surgical interventions (surgical), specifically the type of lymphadenectomy performed. The dataset also includes the Ki67 score, which measures cancer cell proliferation, and the molecular subtype, a classification based on common tumor characteristics and hormone receptor status. Finally, lymph node involvement is detailed by features such as the number of metastatic lymph nodes and ALN status, which indicates whether the lymph nodes under the arm are involved.  
Since we are aiming to work with both datasets for synthetic data generation, we focus on common features between the datasets: age, tumor-size, and inv-nodes.
## 3 Results  
### Investigation of Morphological Changes during Biotrophic Mycoparsitism  
During the biotrophic mycoparasitism, there are some degrees of morphological alteration in _Fusarium_ host cells affected by _S. mycoparsitica_. The results of the TEM analysis (Figure 1) depicted interesting dynamic changes in the host cell structure. In fact, _S. mycoparsitica_ (1) formed the approsserium (Ap) to adhere tightly to the host cell surface and produced infective-constricted hypha to penetrate cell wall. Subsequently (Figure 1A), the mycoparsite (2) colonized the host cell and formed haustorm (H) to expend the cytoplasm, and (3) degraded host cell organelles and compartments into irregular shapes of the remains and cytoplasmic droplets. In Figure 1B, (4) a nearly empty host cell which has a host nucleus with folding of the cell and loss of integrity and rigidity of the cell wall can be observed. In Figure 1C, (5) an advanced degradation of the host cell wall appeared on the cellular side in proximity to a mucosa-like substance produced by the mycoparsite (highlighted in the yellow arrow), whereas the cell wall opposite to the mycoparsite was still undamaged.  
In addition to the host cell morpho-structural alteration, two _S. mycoparsistica_ transcripts were associated with the early stages of biotrophic myocarditisism. These two transcripts encoding biotrophy-associated secreted (BAS) proteins, Sm68082_c0_gl_i1 and Sm68082_c0_gl_i2, were upregulated on the interactive _S. mycoparsistica_ at 1.5 days incubation time; the respective transcripts per million (TPM) values were 0.89 and 1.93 for the non-interactive transcriptome and the interactive transcriptome, respectively. Each transcript for BAS proteins showed the following identities: 47.1% (E-value 3.8 \(\times\) 10\({}^{-18}\) and Score 202) and 47.4% (E-value 1.6 \(\times\) 10\({}^{-17}\) and Score 190). This was different from the closest match to the biotrophy-associated secreted protein 2 in _Magnaporthe oryzae_; it is accumulated in the biotrophic interfacial complex (BIC), into and around the host cells. Further, a total of 81 transcripts of _S. mycoparsistica_ during biotrophic myocarditis were annotated to ABC transporters located in the plasma or organelle membrane. Figure 2 schematically represents the predicted localization of the ABC transporters and BAS proteins in _S. mycoparsistica_ whose functional transcripts were blasted against the UniProt Knowledgebase (UniProtKB, which is available at [[https://www.uniprot.org/](https://www.uniprot.org/)]([https://www.uniprot.org/](https://www.uniprot.org/))).  
Figure 1: Morphogenesis of biotrophic myocarditisism of _S. mycoparsistica_ on a _Fusarium_ host. The micrographs of TEM (magnification \(\times\) 12.0k) were derived from the samples (co-culture of _S. mycopansitica_ (3 days pre-inoculated) with _Fusarium_ isolate for 3 days on PGA plate **(A)**; co-culture of _S. mycoparsistica_ with _Fusarium_ isolate for 8 days on PGA slide culture **(B,C)**). Ap, appressorium; CW, cell wall; Cy, cytoplasm; Ehy, empty hypha; ER, endoplasmic reticulum; H, haustormium; M, mitochondrion; N, nucleus; PM, cell membrane; V, vacuole.
# Health Monitoring of Civil Infrastructures by Subspace System Identification Method:
## 4 Comparison between SSI and Other System Identification Algorithms  
The SSI-DATA is a direct method and it does not need any data pre-processing to calculate correlation functions or spectrum analysis. On the other hand, the subspace identification algorithm uses QR factorization, SVD decomposition, and LS robust numerical techniques in the analysis process [119]. Several comparative studies are presented below in order to evaluate the advantages and drawbacks of the subspace identification algorithm when compared to other time and frequency domain damage identification methods in SHM. Table 5 presents a comparison between SSI method and other system identification algorithms. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
## 3 The Proposed Energy-Balanced Data Collection (EBDC) Mechanism
### Initialization Phase  
Assume that the mobile sink is moving along the track \(k_{i}\). Each sensor, say \(s_{a}\), will send its reading to the closest sensor, say \(s_{b}\), in the track \(k_{i}\) in a multi-hop manner. Afterward, sensor \(s_{b}\) subsequently relays the reading to the mobile sink when the mobile sink passes through its transmission range. As a result, for any track \(k_{j}\), all sensors can equally share the data-relaying workloads. To evaluate the workload of each sensor in any track \(k_{j}\), this phase initially evaluates the number of sensors located in any track \(k_{j}\).  
As shown in Figure 3, let \(r_{i}\) denote the distance between the outside boundary of track \(k_{i}\) and the center of \(M\). Recall that the thickness of each track is a constant value \(2r\). The \(r_{i}\) can be represented by notations \(i\) and \(r\), as shown in Equation (12):  
\[r_{i}=i\times 2r \tag{12}\]  
Let \(O_{i}\) and \(a_{i}\) denote the area sizes of a circle with radius \(r_{i}\) and track \(k_{i}\), respectively. As shown in Figure 3, the value of \(a_{i}\) can be derived by Equation (13):  
\[a_{i}=O_{i}-O_{i-1}=\pi\left(r_{i}^{2}-r_{i-1}^{2}\right) \tag{13}\]  
Let \(S_{i}\) denote the set of sensors located in track \(k_{i}\), and let \(|S_{i}|\) denote the number of sensors belonging to set \(S_{i}\). For a given WSN with network density \(d\), Equation (14) evaluates the value of \(|S_{i}|\) where \(n\) denotes the total number of tracks:  
\[\left|S_{i}\right|=d\times a_{i},\text{ where }1\leq i\leq n \tag{14}\]  
In this phase, the number of sensors \(|S_{i}|\) in each track \(k_{i}\) is evaluated. After completing the executions of this phase, the mobile sink performs the _Energy Estimation Phase_. The following subsection describes the details of _Energy Estimation Phase_.
###### Abstract  
Displacement predictions are essential to landslide early warning systems establishment. Most existing prediction methods are focused on finding an individual model that provides a better result. However, the limitation of generalization that is inherent in all models makes it difficult for an individual model to predict different cases accurately. In this study, a novel coupled method was proposed, combining the long short-term memory (LSTM) neural networks and support vector regression (SVR) algorithm with optimal weight. The Shengjibao landslide in the Three Gorges Reservoir area was taken as a case study. At first, the moving average method was used to decompose the cumulative displacement into two components: trend and periodic terms. Single-factor models based on LSTM neural networks and SVR algorithms were used to predict the trend terms of displacement, respectively. Multi-factors LSTM and SVR models were used to predict the periodic terms of displacement. Precipitation, reservoir water level, and previous displacement are considered as the candidate factors for inputs in the models. Additionally, ensemble models based on the SVR algorithm are used to predict the optimal weight to combine the results of the LSTM and SVR models. The results show that the LSTM models display better performance than SVR models; the ensemble model with optimal weight outperforms other models. The prediction accuracy can be further improved by also considering results from multiple models.  
Shengjibao Landslide; displacement prediction; Three Gorges Reservoir area; long short-term memory neural networks; support vector regression; ensemble model +
Footnote †: journal: Applied Sciences  
1
## 3 Numerical Application
### Sobol Sensitivity Analysis  
Before applying the optimization procedure, a Sobol analysis is conducted, as it was done in [9]. The aim of this analysis is to explain the contribution of each parameter to the Amax variations. Three Sobol indices are derived from \(5\times 10^{5}\) images: the first-order index which indicates the contribution of a sole varying parameter; the second-order index for the combined contribution of two parameters; and the total index that represents the contribution of a parameter while taking into account all the interactions it has with the others. The range of variation of each parameter and the sensitivity results are displayed in Figure 3. For readability, only indices of value greater than \(5\times 10^{-2}\) are displayed.  
Every parameter appears to have a non negligible contribution to the variation of Amax, as shown by the total-order indices. None of these parameters can then be discarded from the optimization problem. The total contribution of each parameter seems not to be driven by its individual variations but by its cross-variations with the other parameters, as it is highlighted by the small values of first-order indices. Moreover, the sum of the total-order indices is not equal to one, which suggests that the parameters are correlated with one another.  
It should be pointed out that these results are case-dependent and can be very different with another application (other materials, defects, probes, etc.). That the total index of some parameters is higher than others is directly related to this specific inspection case. For example, \(D_{1}\) contributes less than \(C_{55}\) simply because the inspected defect is localized at the centre of the weld, where the bead length has little influence.  
Figure 3: Upper and lower bounds of the variation range of each parameter (**a**). Sobol indices of first, second and total-order (**b**). Only indices of values larger than \(5\times 10^{-2}\) are shown.
# Optimizing Assembly in Wiring Boxes Using API Technology for Digital Twin
## 2 The State of the Art
As the digital twin concept evolves, the integration of artificial intelligence and machine learning is being explored to optimize manufacturing processes and predictive maintenance, which could be relevant for fuse assembly in intelligent production environments [16]. These studies highlight the importance of digitization and automation within Industry 4.0, emphasizing their positive impact on the efficiency and sustainability of production processes. This paper builds on existing research in automation within wiring harness production, with a particular focus on the challenges associated with the manual insertion of fuses and relays. While previous studies have explored various aspects of automation, such as robotic manipulators and collaborative robots for wiring assembly, the specific task of fuse insertion has remained predominantly manual due to the precision and care required in handling sensitive components. In this work, we propose an innovative solution, utilizing a robotic arm integrated with API technology for digital twin to optimize the assembly process in automotive fuse boxes. Integrating a digital twin model enables real-time monitoring, simulation, and adjustments, enhancing the accuracy and efficiency of the insertion process. Our results demonstrate that the system can significantly reduce manual errors and improve the overall production quality, providing a viable solution for increasing automation in this critical aspect of automotive manufacturing.
## 4 Experimental Results and Discussion
### Vulnerability Score Metric
Next, Table 8 presents the security assessment results for a scenario where threats originate from the space segment and propagate across the entire network. Figure 9 employs a radar chart to visually analyze these results, applying max normalization to effectively illustrate the relative variations among the security metrics. According to the analysis, the total risk of \(tv_{123\text{-}\text{{MEO}}}\) in the no-patch scenario was the highest, at 30.5. This indicates that without the implementation of security measures, \(tv_{123\text{-}\text{{MEO}}}\) is highly susceptible to severe security threats. In contrast, when both patches were applied, the total risk decreased to 21.3, demonstrating that applying both patches is an effective strategy for mitigating threats. For \(tv_{123\text{-}\text{{LEO}}}\), the total risk was evaluated at 24.8 in the no-patch scenario but decreased to 22.5 after applying the OS patch. When the protocol patch and both patches were applied, the total risk decreased to 21.0 and 19.2, respectively, demonstrating that patch applications contribute to enhancing the security of the satellite network. Meanwhile, with the CFR strategy, the total risk for both scenarios was measured at 28.2 and 22.5, respectively, suggesting that blocking major attack paths can partially mitigate security threats. Additionally, increases in the shortest path and average path lengths were observed after the application of patches. This indicates that attackers would need to traverse longer paths to reach target systems, thereby validating the effectiveness of the implemented security measures. For instance, in the case of \(tv_{123\text{-}\text{{MEO}}}\), the shortest path length was 3, and the average path length was 2.80 in the no-patch scenario. After applying both patches, both metrics increased to 4.00. This demonstrates that the strategy of applying both patches effectively blocks or reroutes specific attack paths, thereby reducing the likelihood of the attack success. In conclusion, by increasing the shortest path length and significantly reducing the total risk, both patches can be evaluated as the most effective security strategy. Figure 9: Radar chart of vulnerability scores. (**a**) Legend for the radar chart. (**b**) Targeting the space segment with \(tv_{13}\). (**c**) Targeting the space segment with \(tv_{23}\). (**d**) Originating in the space segment with \(tv_{123\text{-}\text{{MEO}}}\). (**e**) Originating in the space segment with \(tv_{123\text{-}\text{{LEO}}}\).