esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In the proposed solar cell, the top surface of the SiNWs is coated with a graphene layer and the plasmonic Au NPs are distributed on the silicon surface. An FDTD method is used to calculate the optical power absorption and short-circuit current density of a SiNW solar cell. The open-circuit voltage, fill factor, and power conversion efficiency are calculated using numerical analysis. The maximum obtained optical power efficiency is 19.0% for \(J_{sc}=41.7\) mA/cm\({}^{2}\), \(V_{oc}=0.63\) V, and \(FF=0.73\). In addition, the optimum Au NP radius is 50 nm and the excess carrier concentration value is 1 \(\times\) 10\({}^{-15}\) cm\({}^{-3}\). The best performance of the introduced solar cell is obtained when Au NPs are used as a plasmonic material, rather than Ag, Al, or Cu materials. Moreover, the optimum NW length is simulated, \(L=5\) um, and the distance between two NWs, \(P=550\) nm, is also calculated. In addition, optical power distribution inside the NWs, on the silicon substrate, and in the interface between substrate and oxide layer shows that the graphene layer and Au NPs behave as nanoantennas by retransmitting the trapped light from the surface into the active layer of the solar cell.  
This research received no external funding.  
Not applicable.  
Note applicable.  
There is no conflict of interest.
## 4 Case Study Description
### Derived Cell Sequences  
The average time interval of a GPS trace of one visitor was 17.8 h during which an average distance of 5.4 km was covered by the visitor. These traces are accordingly translated into a set of cells by the method described in Section 3.1. Accordingly the traces are translated in a sequence of cells. Figure 2 illustrates the number of hits per cell. Most hits are located near the three largest music stages, which are located in cells 1, 2 and 8.  
The average number of cells in each sequence is 120.7. Two example cell sequences are visualized in Figure 4. As one can see, the consecutive cells in a sequence are not necessarily adjoining cells. The reason for this artefact in the cell sequences is that the temporal resolution of the GPS traces is relatively low (5-10 min), which is sparser than the maximum cell radius. Therefore, as the aim is to predict the cell sequence behavior of the visitors, the cell sequence prediction models should be allowed to skip adjoining cells as well.  
An analysis of the average transition probabilities for a specific cell shows that in general adjoining cells have a relative higher transition probability than cells further afield. At the same time, in some cases the transition probabilities for all adjoining cells are almost similar (see Figure 5a), while in other cases, one adjoining cell is favored over the other cells (see Figure 5a). The best model is expected to recreate the transition probabilities depicted above.  
Figure 4: Two examples of cell sequences, where (**a**) displays a trajectory that only visited one side of the terrain, (**b**) presents a trajectory that covered the entire terrain.
# Mdp1 _sciences_
## 3 Results & Discussion
### Realistic Forecasts: Exploratory Results  
After these conceptual discussions we now turn to actual forecasts obtained for the target site (T2). Figure 5 shows the true wind speed time series, as well as the forecast wind speed obtained with one method assimilating exogenous data (KF1), and the one method in this study using only on-site (local) data (KF4), both for a prediction horizon of two steps into the future. As evidenced by the figure, the local method shows the usual characteristics  
Figure 3: Site characteristics. (**a**) Relative locations of meteorological towers used in this work. (**b**) Wind roses from all towers.  
associated with autoregressive techniques, a lagging forecast signal, emulating the true wind speed time series, but always trying to catch up. Clearly, such a forecast is of very limited use for detecting future changes, and for acting on them. The non-local method (KF1), on the other hand, can be seen to actually predict the ramps, both upwards and and downwards.  
Figure 4: Fraction of true positives observed (**a**,**b**) and the respective detection efficiencies (**c**,**d**), for different combinations of the correlation coefficient \(\rho\) and the standard deviation ratio \(\kappa\). Left column (**a**,**c**): Negative correlation coefficient. Right column (**b**,**d**): Zero correlation. Circles: Numerical simulation. Solid lines: Theory.  
Figure 5: Example time series of the true wind speed, and the forecast wind speed, using one method with exogenous data (KF1) and the one without (KF4); see Table 2.  
In order to illustrate how these findings translate into the relevant variables of the FBC method, the two-step ahead wind speed changes (\(y\)) and the curtailment margin (\(P_{m}\)), we refer to Figure 6, where results obtained with method KF1 are shown as an example. It can be seen that three out of five incompliance events (taken to be events with \(y<-a=-1\) m/s) are correctly identified by the forecast. The curtailment margin (\(P_{m}=\frac{1}{2}(x+y)+a\)) can be seen to be positive at all times, and the forecast margin \(\hat{P}_{m}\) is close to the true value at most times, particularly at the detected incompliance events. Therefore, this qualitative inspection suggests that incompliance events can indeed by mitigated, using the FBC method, based on a realistic forecast method.
# Aero-Engine Remaining Useful Life Estimation Based on CAE-TCN Neural Networks
## 2 Methodology
### Proposed RUL Estimation Model  
As a commonly used time series prediction model, LSTM realizes the learning of time data by maintaining hidden variables with time steps. Precisely because of this, the model with LSTM could only learn iteratively along the time dimension. Thus, the model cannot be effectively parallelized when processing long time series data. In order to solve this  
Figure 3: TCN.  
problem, a novel RUL estimation model is proposed. It realizes the prediction of time series by replacing LSTM with CNN. It can improve the application efficiency while ensuring the prediction ability. The architecture of the proposed RUI estimation model is illustrated in Figure 4.  
There are two parts in the model, CAE and TCN. Firstly, CAE is used to realize learning of the degradation trend of the engine. Multi-dimensional time series are flattened as a single dimensional degenerate sequence by the encoder. Then, TCN is applied to achieve the RUL estimation. In the prediction phase, the multi-dimensional data is used as trained CAE input to realize degenerate coding. After encoding, the obtained degenerate sequence is used to predict the RUL through TCN. Data slicing and data stacking can lead to massive loss of information. CAE is used to keep the spatial information unchanged instead of data stacking, and extracts the information in a gentle way in the convolution layer. The feature extraction is realized by multiple convolution kernel, and then the dimension is reduced slowly combined with MaxPooling layers. In addition, the batch normalization layer is added to reduce overfitting. As shown in Figure 4, a thick cube is extracted first, and then a longer cuboid is extracted. This design is intended to aid CAE in better coding. This structure can effectively extract degraded features, while ensuring the ability of temporal features extraction. Considering the input of CAE, a sliding window is used. To simplify the calculation, the window width is the same as the dimension of the input data.
## 4 Results and Discussion
### Experiment of 2D-3D Registration
#### 4.1.4 MP-IDSSC Experiment
Figure 7: Image matching experiment results of image P1: (**a–f**) results of Trails 1-6. Green lines indicate correct matches when considering a threshold value of 2 pixels. The feature points are unevenly distributed in the vertical direction. In order to further illustrate the effectiveness of structural semantic constraints, we designed Trial 3. In Trial 3, there is only the constraint of inverse depth coordinates, and there is no constraint of structural semantics. The results show that the vertical error is also high, but compared with Trials 4-5, the experimental results still have advantages. This is because the SuperGlue and GMS methods only restrict the spatial position relationship of feature points in the image plane using a relative graph structure and may not be able to find an accurate matching relationship. For the most accurate correspondence to reduce false matching caused by similar multiple feature points, Trial 3 used the inverse depth coordinates of feature points and the 3D-2D projection relationship as constraints. Comparing Trials 1 and 4, the SuperPoint method is better than the D2-Net method when the MP-IDSSC is used as the feature matching method. Although D2-Net performs better than SuperPoint in the cross-modality image environment, the positioning accuracy of the D2-Net feature point is lower. After eliminating the feature points with large projection errors using IDSSC constraints, the remaining feature points participating in the camera pose estimation are fewer, and the RMS accuracy of the feature points is still low. In Trial 6, the results of matching and pose estimation are obviously wrong, which indicates that the traditional artificial feature extraction and RANSAC matching methods are not effective for images with large differences in appearance. Figure 8: Results of image matching experiment result of image P2: (**a–f**) results of Trails 1-6. Green lines indicate correct matches when considering a threshold value of 2 pixels. The feature points are unevenly distributed in the vertical direction. Considering the above experimental results, this method can optimize the initial vision sensor pose effectively, utilizes the constraints of inverse depth and structure semantic information to estimate the camera pose more accurately, and reduces the \(Z\)-coordinate error caused by the uneven distribution of feature points in the vertical direction. In addition, the proposed method is more accurate, has stronger adaptability than existing algorithms, and performs effectively in challenging conditions.
# Security Performance Analysis of LEO Satellite Constellation Networks under DDoS Attack
## 4 Result and Discussion
### Two-Satellite-Key-Node Attack  
Figure 4 shows the change in the transmission path of key satellite nodes No.119 and No.67 under the OSFpv2 protocol after the DDoS attack. The yellow * indicate the attacked satellite-key-node. The blue solid line represents the attack on the key node by the botnet host, the green solid line represents the original transmission path of the two satellites, the green dotted line represents the path change after being attacked, and the arrowheads represent the path direction (same as other Figures). Under normal transmission, the number of packets received by satellite No.66 is 3579. After attacking the key nodes of both satellites at the same time, the applied transmission link changes rapidly, while the number of packets received by satellite No.66 is 3483, which results in a low packet loss rate. As shown in Figure 5, under the Bellman-Ford protocol, after the attack, the number of packets received by satellite No.66 is 3539, which only has a certain impact. The average end-to-end delay under a two-satellite-key-node attack scenario is shown in Figure 6. It can be seen that only attacking two key satellite nodes of the whole local satellite network under the OSFpv2 protocol has little impact on its network security performance.  
Figure 4: Two-satellite-key-node attack scenario under OSPFv2 protocol.  
Figure 5: Two-satellite-key-node attack scenario under Bellman-Ford protocol.
## 6 The role of extracellular factors involved in lipid metabolism that participate in virus entry
### Receptors on the cell surface necessary for virus infection  
The HCV entry pathway requires interaction between virion components, in particular the envelope glycoproteins E1 and E2, and several molecules on the cell surface. These include the low-density lipoprotein receptor (LDL-R) [124], glycosaminoglycans [125], CD81 [126], SR-BI [127], Claudin-1 (CLDN-1) [128] and occludin [129]. In the context of lipid transport, two surface receptors, LDL-R and SR-BI, are worthy of note.  
LDL-R sequesters cholesterol-containing lipoproteins from the circulation for uptake of cholesterol into cells [130]. The receptor primarily binds LDL but it also has high affinity for VLDL particles containing multiple copies of apoE. Lipoprotein particles complexed to LDL-R are internalized by endocytosis via clathrin-coated pits and then transported to endosomes. The low pH environment in endosomes induces detachment of LDL-R from internalized lipoprotein particles, which are thereafter broken down by lysosomes to release cholesterol into the cell. HCV entry can be blocked by antibodies against LDL-R and a soluble peptide that encompasses the LDL-binding domain of the receptor [124,131]. Moreover, a recent report has demonstrated that reducing the abundance of LDL-R lowers infectivity of cell culture-derived virions, supporting the notion that interaction with the receptor contributes to productive infection [132]. The virion component that interacts with LDL-R is likely to be a cell-derived lipoprotein since antibodies against apoB and apoE disrupt infection [118,124,132,133].  
SR-BI is a scavenger receptor that plays a key role in promoting the exchange of cholesterol between cells and lipoprotein particles [134]. The primary ligand for SR-BI is high-density lipoprotein (HDL) from which it selectively transfers cholesterol ester by a non-endocytic mechanism that, in contrast to cholesterol import by LDL-R, does not require degradation of HDL-associated apolipoproteins. SR-BI was initially identified as a potential receptor for HCV entry since it was capable of binding to the soluble portion of E2 [127] although direct interaction between SR-BI and the viral glycoproteins on infectious particles has not been demonstrated. Antibodies against SR-BI and reduced expression of the receptor inhibit HCV infection [135-138], providing confirmatory evidence for its contribution to virus entry.
## 2 Materials and Methods
### Study Selection and Eligibility Criteria  
The initial search identified 348 potentially relevant articles. After removing duplicates, manual screening was performed according to the title and abstract. Those that were not related to the theme were excluded. Then, the final articles were thoroughly analyzed by two authors ([PERSON] and [PERSON]). The different phases of the systematic review were described using the PRISMA statement [12], which maps the number of records identified, included, and excluded and the reasons for exclusions. Studies were included or excluded using criteria defined using the PICOS (Population, Intervention, Comparison, and Outcome) principles [13]. Literature searches incorporated the following inclusion criteria: (i) male or female volleyball players of any age; (ii) studies that used wearable devices (e.g., accelerometers) to assess and/or monitor physical performance (e.g., jump height, dominant and non-dominant hand actions) in training and/or competition; (iii) experimental and observational studies. Exclusion criteria were: (i) studies with the older population or non-athletes; (ii) samples with a physical, chronic, neurological disability or a clinical diagnosis of attention deficit hyperactivity disorder. Following these criteria, 15 original research articles were evaluated in full text for eligibility. For the qualitative analysis, 9 articles were included. Theses, dissertations, and abstracts or conference proceedings were also excluded. Studies were in English and were published in a peer-reviewed journal. A detailed flowchart, including a systematic literature search, screening, eligibility, and enrollment, is shown in Figure 1.
## 5 Case Study: Practical Implementation of DT for HEMS  
The case study and simulation presented in this section aim to validate the proposed framework's effectiveness in addressing the primary scientific objectives of this research. Specifically, in this section, the proposed framework is validated by addressing the objectives outlined in Section 3.3 and their corresponding use cases. The following objectives are validated:  
* Realism and Immersion (Obj. 1). Use Case: Navigation and Interaction in a Digital Twin Environment. The simulation includes a visually and spatially accurate 3D model, allowing users to navigate and interact with the Digital Twin environment, improving realism and user experience.  
Figure 5: Gateway Middleware Framework.  
Figure 6: Architecture of IoT-enabled middleware and applications.  
* Functional Segmentation and Data Integration (Obj. 2 & 5): Use Case: Zone-Specific Energy Monitoring. The Digital Twin uses spatial segmentation of the 3D model to integrate energy and environmental data for targeted monitoring, improving precision and usability.
* Space-Specific Energy Tracking (Obj. 3):Use Case: Energy Analysis Across Zones. The framework tracks patterns unique to specific spaces (e.g., living room vs. kitchen), providing actionable insights tailored to individual zones.
* Environmental Monitoring and Thermal Comfort (Obj. 4): Use Case: Monitoring and Adjusting Comfort Levels. By leveraging material and spatial segmentation, the simulation monitors temperature and humidity across different zones, ensuring thermal comfort and energy efficiency.
* Precision in Smart Home Automation (Obj. 6): Use Case: Automated Room-Specific Adjustments. Automation features are tailored to the unique material and spatial characteristics of each room, enabling precise control over smart home devices.  
Overall, the case study and simulation demonstrate the successful validation of the proposed DT framework in fulfilling the key scientific objectives of this research. These include enhancing realism and user engagement through immersive 3D modeling, enabling precise energy and environmental monitoring with spatial and functional segmentation, and facilitating predictive analytics and automation for efficient and user-centric energy management. These outcomes highlight the framework's ability to bridge physical and digital domains, optimize energy consumption, and support sustainable practices in residential energy systems, thereby addressing the scalability, usability, and efficiency challenges outlined in this study.
## 3 Engineering Example  
### Single-Factor Analysis  
#### 3.1.1 Foundation Soil Conditions  
With the exception of Beiwangkou Town near Baiyangdian Duancun Town-Tongkou Town, Xiong County, the foundation soil conditions in Xiongan New Area are generally good, the soil mass is relatively uniform [18], there is only a small range of soft soil developed (Figure 5a), and the thickness is small (generally no more than 2 m). The stratum has a limited bearing capacity (95-115 kPa) in the 0-5 m depth range, making it unsuitable as the natural foundation-bearing layer for multi-story buildings. In most other places, the stratum below 100 m has a bearing capability of more than 120 kPa. It is appropriate for all types of engineering construction after treatment and may satisfy the criteria of pile foundation or pile end-bearing layer of multi-story structures (Figure 5b-f). The compressibility of soil in most areas of the new area is in the medium-low compression zone, and the soil is in the medium-high compression zone in the depth range of 0-5 m east of the first line of Zhanggang Township-Xiong County-Zhaobeikou Township-Yucun Township and west of the first line of Zhaili Township-Anzhou Township-Laohetou Township, as well as in the depth range of 30-50 m east of the first line of Nangzhang Township, southwest of Xiaoli Township, and Qiaogang Township-Zhugezhuang Township-Xiongzhou Township-Xinglonggong Township. The maximum weighted average compressive modulus is 27.17 MPa and 16.37 MPa, respectively (Figure 5g-k).
## 4 4-Chain Based Routing
### Protocol Operations
#### 4.2.2 Election of Chain Heads  
In this phase, the chain head is selected on the basis of \(W\). All the nodes compute this factor by using the Equation (35). In this mechanism, the network compares the value of \(W\) of all the nodes in the chain. The node with the highest \(W\) factor in the chain is selected as a primary chain head. Each node \(i\) calculates its distance with the parent node and then compares it with the distance to the sink. If the later distance is shorter, the node \(i\) acts as a secondary chain head and sends the collected data to the sink, instead of transferring it to the parent node.  
\[W_{i}=E_{i}/S_{i,s} \tag{35}\]  
where, \(E_{i}\) is the residual energy of _ith_ node and \(S_{i,s}\) is the distance between node \(i\) and _sink_.
## 5 Conclusions  
The adopted novel method for developing motion equations enables adding single TMDs or MTMDs to completely different degrees of freedom of the primary system. The system of equations allows for easy MTMD tuning for complex vibrations modes, with MTMDs located in local maxima of these vibrations' modes, while still analyzing the SDOF system with attached MTMDs. Locating MTMDs at the tip of the chimney provided a possibility to compare the results with studies on MTMD tuning. The next stage of the research was to optimize MTMD parameters in a complex structure, but still analyze as an SDOF system. Of course, the thesis of better effectiveness of MTMD over a single TMD was confirmed.  
Based on conducted analysis, it was observed that the \({k_{k}}^{t}\), \({\zeta_{k}}^{t}\) free optimization was of completely different nature in the case of optimizations based on the \({\mathfrak{H}_{2}}\) and \({\mathfrak{H}_{\infty}}\) norms, especially for the \({\zeta_{k}}^{t}\) parameters, where the \({\zeta_{k}}^{t}-{\zeta_{k-1}^{t}}\) increments showed totally different forms for both \(J1\) and \(J2\) objective functions.  
Conceptualization, P.W. and R.G.; formal analysis, P.W.; investigation, P.W.; methodology, P.W. and R.G.; resources, P.W.; software, P.W.; supervision, P.W.; validation, R.G.; visualization, R.G.; writing original draft, P.W.; writing review and editing, R.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science Financing Subsidy Lublin University of Technology (FN16/ILT/2020).  
Not applicable.  
Data Availability StatementData available on request due to restrictions e.g., privacy or ethical.  
The authors declare no conflict of interest.  
Figure 23: Module graph of a FRF in node 11,110 and 11,111 for optimal parameters and a different number of TMDs: (**a**) for the \(J1\) objective function; (**b**) for the \(J2\) objective function.