esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Vibration Control Structure on Suspended Ceiling Using Pulley Mechanism
## 3 Component Details of the Pulley-Damper System
### Dynamic Test of the Viscous Damper  
Figure 8 explains an assembly of the rotational non-linear viscous damper using silicon oil, which was specially developed to enhance the potential damping performance of the pulley-damper system. The component consists of two rotary dampers of 60 mNm-rated torque, two ball bearings, and other fixtures. Wire-cable should be wrapped around the spool several times to allow for a large stroke of the amplified wire-cable movement. As the velocity-force relationship can be expressed by Equation (11), and to quantify the velocity dependence, the sinusoidal excitation test and the frequency range between 0.1 Hz and 1.0 Hz were conducted to understand the variable of the equation:  
\[F=CV^{\alpha} \tag{11}\]  
where \(F\) is the damping force, \(C\) is the coefficient of the fluid inside the damper, \(V\) is the velocity of the damper, and \(\alpha\) is the exponent (0.0 \(<\alpha\leq\) 1.0).  
Figure 9 presents the test specimen for the sinusoidal wave excitation of the proposed rotational viscous damper. The damper is connected to actuator by the wire-cable to convert the horizontal movement of actuator to the rational motion in damper. Besides, the counterweight was set at the other side of wire end to provide initial wire tension to avoid the cable being slack during the excitations.  
Figure 8: (**a**) Overview of the damper attached to the supporting beam in the specimen; (**b**) details of the damper components.  
Figure 9: Test setup for characterizing the dynamic behavior of viscous damper under sinusoidal wave excitation.  
The hysteresis loop of the force-displacement relationship and force-velocity relationship under the sinusoidal frequency test of the non-linear viscous damper is presented in Figure 10. The damping force gradually grows up as the input frequency increased. While the energy absorption of the viscous damper is relatively limited at approximately 25 N, the velocity power coefficient of \(\alpha=0.9\) and the damping coefficient \(C=0.06\) N-(s/mm)\(\alpha\) was decided based on the SRSS (square root of the sum of squares) of all the test results and modeled in the following simulation analysis.
###### Abstract
Furthermore, the research shows that the prediction accuracies of grass N decrease with increasing phenology when using high spatial multispectral commercial sensors: rapid-eye [18] and world-view 2 [9]. Conversely, Sentinel-2 (MSI) performed better than rapid-eye in predicting grass N in the African Savanna biome, albeit with moderate and poor accuracy [19]. Notably, research shows that red edge is strongly correlated to N. Therefore, Sentinel-2, a freely available multispectral sensor, has great potential for predicting grass N because of its strategically positioned red-edge bands. Furthermore, research shows that including red-edge-based vegetation indices improves the estimation of grass N [9]. The estimations of foliar nitrogen have the potential to provide insight into animal feeding patterns and distribution [16; 19]. Remote sensing sensors with red-edge bands can benefit the accuracy of mapping vegetation biochemical concentrations because this spectrum is the point of maximum slope in healthy vegetation [16]. Sentinel-2 MSI (S2) has become a highly sought-after instrument for vegetation mapping because of free access to datasets and its superior spectral quality [19; 20], especially the inclusion of multiple red-edge bands, which are better suited for chlorophyll estimations [16]. The launch of Sentinel-2 with the inclusion of red edge present an opportunity to validate N models of coarser remote sensing sensors against those with high resolution [9]. Nonetheless, with random forest (RF) algorithm, both Landsat 8 and Sentinel-2 achieved comparable superior accuracies in forest mapping [19; 20; 21]. The objective of this study was not to test the performance of machine learning algorithms in modeling grass N even though studies show that RF is more robust than other modeling techniques and yields better estimates of biochemical traits using remote sensing data [9; 22]. Machine learning techniques are often used for vegetation mapping via classification [23]; however, the application of machine learning in grassland modeling could also be beneficial for predicting the impacts of climatic changes. Using band optimization from multiple sensors and machine learning, grass biochemical properties were estimated with an accuracy of up to 82% in alpine grasslands [22]. However, and to the best of our knowledge, this is one of the first attempts of mapping grass N in this biome, i.e., Mesic mountainous grassland using remote sensing tools. Therefore, this study sought to: (1) identify the optimal spectral bands for predicting grass N in vegetatively homogenous Mesic mountain grasslands; (2) model and establish grass N prediction accuracies based solely on bands, vegetation indicators, and a combination of the two; and (3) rank the spectral bands and vegetation indices variables according to the order of importance.
## 2 Project Overview  
The Zhuyuan-Bailonggang sewage connecting pipe project spans 19.8 km and incorporates both pipe jacking and shield tunnelling sections. The project commences at the Zhuyuan Sewage Treatment Plant and terminates at the Bailonggang Sewage Treatment Plant. The 17\(\#\) shaft is strategically situated within the greenbelt between Renmintang Road South and the Suitang River. It serves as a critical function during construction by acting as the reception point for both the shield tunnelling and jacking machines and supports drainage operations during the operational phase. However, due to spatial constraints at the site, conventional construction methods are inadequate, necessitating the adoption of vertical shaft sinking machine (VSM) technology.  
Surface settlement monitoring points (DB-1-1 to DB-1-7 and DB-2-1 to DB-2-5) are arranged on both sides of the shaft (DB represents the monitoring point of surface settlement measurement), and four inclinometer points (CX-1 to CX-4) are set up around the shaft to monitor the horizontal displacement at various elevation levels of the surrounding soil during the sinking process (CX represents the monitoring point of horizontal displacement). The layout of the monitoring points is shown in Figure 2.  
The 17\(\#\) shaft is a prefabricated lining structure designed for shield tunnelling, with a total depth of 39 m. Its cross-section is circular, featuring an inner diameter of 12 m, an outer diameter of 12.8 m, and a lining thickness of 0.4 m, along with a structural length of 1.5 m. The excavation depth reaches approximately 40.14 m. Stratigraphically, the geological profile from top to bottom consists of fill soil, silty soil, and clay layers, as illustrated in Figure 3.  
After the prefabricated shaft is lowered to an elevation of 40.14 m (with an elevation of 0 corresponding to ground level), a cast-in-situ shaft wall is constructed at its upper section. Once the sinking process is completed, the shaft wall is connected to the foundation ring beam. The ring beam has a cross-sectional dimension of 2.5 m \(\times\) 2.6 m, with inner and outer diameters of 13 m and 18.2 m, respectively. Beneath the ring beam, 12 bored piles, each with a diameter of 0.8 m and a length of 50 m, are installed to bear the equipment load during the sinking stage as well as the self-weight of the shaft.  
Figure 1: Research process.  
Additionally, high-pressure jet grouting piles, 4 m thick, are placed below the ring beam for the foundation reinforcement.
## 3 Numerical Calculation Method in Different States
### Numerical Calculation Method in the Stick State  
Unlike the slip state, a definite relationship between the friction force \(f\) and the normal force \(N\) cannot be established in the stick state. Static friction acts at the tip of the blade as a constraining force to balance the external forces acting on the wiper blade and keep the tip stationary. The holonomic constraint that the displacement of the tip cannot vary must be satisfied. A constant \(\alpha\) is introduced to indicate the stationary position of the tip. In the stick state, the distance \(\Phi\) between the tip and \(\alpha\) should always be 0. This condition can be expressed as follows:  
\[\Phi=v_{3}-\alpha=0, \tag{46}\]where the \(y\)-coordinate position of the tip \(v_{3}\) is given by Equation (6). In the stick state, \(v_{3}\) remains constant at \(\alpha\). Equation (46) is an independent equation describing the relationship between \(\theta\) and \(\varphi\). The dynamic behavior of the wiper blade in the stick state becomes resolvable by associating Equation (46) with Equations (14)-(16). However, because Equation (46) is not a differential equation, a set of algebraic and differential equations (ADEs) consisting of Equations (14)-(16) and (46) must be solved.  
To solve this set of ADEs, [PERSON]'s stabilization method [20] is employed to modify the ADEs into a set of differential equations. The second-order differential equation:  
\[\ddot{\Phi}+\beta_{1}\dot{\Phi}+\beta_{2}\Phi=0 \tag{47}\]  
is introduced instead of Equation (46). The parameters \(\beta_{1}\) and \(\beta_{2}\) are both constants. The solution \(\Phi\) of Equation (47) can be maintained close to 0 if \(\beta_{1}\) and \(\beta_{2}\) in Equation (47) have suitable values. Equation (47) can be written in dimensionless form as follows:  
\[\begin{split}&\ddot{\theta}\bigg{[}\frac{l_{1}\cos(\theta+\theta_{ 0})}{\cos\theta_{0}}\bigg{]}+\ddot{\varphi}\bigg{[}\frac{l_{2}\cos(\varphi+ \theta_{0})}{\cos\theta_{0}}\bigg{]}\\ =&\ddot{\theta}\bigg{[}\frac{l_{1}\sin(\theta+ \theta_{0})}{\cos\theta_{0}}\bigg{]}+\ddot{\varphi}\bigg{[}\frac{l_{2}\sin( \varphi+\theta_{0})}{\cos\theta_{0}}\bigg{]}-\beta_{1}\dot{\Phi}-\beta_{2} \Phi-\omega^{2}\cos(\omega t).\end{split} \tag{48}\]  
Combining the differential equations and rewriting them in matrix form yields  
\[\begin{split} B\begin{bmatrix}\ddot{\theta}\\ \ddot{\varphi}\\ N\\ f\end{bmatrix}=\mathbf{Q},\end{split} \tag{49}\]  
where the matrices \(\mathbf{B}\) and \(\mathbf{Q}\) are  
\[\mathbf{B}=\begin{bmatrix}b_{11}&b_{12}&b_{13}&b_{14}\\ b_{21}&b_{22}&b_{23}&b_{24}\\ b_{31}&b_{32}&b_{33}&b_{34}\\ b_{41}&b_{42}&0&0\end{bmatrix}, \tag{50}\]  
\[\mathbf{Q}=\begin{bmatrix}Q_{1}\\ Q_{2}\\ Q_{3}\\ Q_{4}\end{bmatrix}. \tag{51}\]  
The elements of \(\mathbf{B}\) and \(\mathbf{Q}\) are given in Appendix B.  
The differential equations for \(\theta\) and \(\varphi\) are then obtained from Equation (49) by multiplying the inverse of matrix \(\mathbf{B}\) from the left side.
# 3D Point Cloud Completion Method Based on Building Contour Constraint Diffusion Probability Model
## 2 Related Works
### Deep Learning-Based Point Cloud Completion Methods
#### 2.2.1 Voxel-Based Method  
The point cloud complementation of voxelized point clouds can be accomplished using CNN. [PERSON] et al. [20] introduced 3D ShapeNets, a method that utilizes Convolutional Deep Belief Networks to transform 3D geometric shapes into binary variable probability distributions on 3D voxel grids. The authors of [21] employed MRF model combined with both geometric and multi-view RGB data for point cloud completion, the authors of [22] created a complementary point cloud by combining the overall structural information and local geometric information of the input point cloud, and the authors of [23] fused 3D shape information through a volumetric deep neural network to complement the point cloud. GRNet [24] regularizes disordered point clouds into 3D meshes to better access the geometric structure and contextual information of the point clouds. SPU-Net [25] achieves the simultaneous acquisition of contextual information within local regions and between local regions by combining self-attention modules with GCN. It then utilizes a hierarchically learnable folding strategy to generate point clouds with a learnable two-dimensional grid. However, these methods usually require voxelization of the point cloud followed by a convolution operation to complement the point cloud, resulting in high computational cost.
###### Abstract  
The accurate tracking of every production step and related outcome in a supply chain is a stringent requirement in safety-critical sectors such as civil aviation. In such a framework, trusted traceability and accountability can be reliably and securely managed by means of blockchain-based solutions. Unfortunately, blockchain cannot guarantee the provenance and accuracy of the stored information. To overcome such a limitation, this paper proposes a secure solution to strongly rely on the tracking information of the physical assets in the supply chain. The proposed solution exploits Hardware Security Modules (HSMs) to provide required cryptographic primitives through a Near-Field Communication (NFC) connection. In our approach, each transfer of the assets is authenticated, verified, and recorded in the blockchain through the HSM. Transaction entries are signed, thus providing a guarantee of ownership and authenticity. The proposed infrastructure has been subject of an exhaustive security analysis and proved resilient against counterfeiting attempts, stakeholder repudications, and misleading information.  
supply chain; Industry 4.0; blockchain; Hardware Security Module (HSM); Near-Field Communication (NFC); information tracking +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# A Trusted Routing Scheme Using Blockchain and Reinforcement Learning for Wireless Sensor Networks
## 4 Experimental Analysis and Evaluation
### Simulation Results  
From Figure 6, we can see that in the routing environment with 25% malicious nodes, the delay performance of our BC algorithm reduces the average packet delay by around 48% when compared to traditional BP algorithm. The performance is similar to that of QL-BP algorithm while the average packet delay reduces about 54%. The packet delay of TB-BP algorithm reduces around 32%. Our RLBC algorithm performs best and it reduces around 78% delay when compared to BP algorithm, 52% compared to the QL-BP algorithm, and 67% compared to the TB-BP algorithm.  
We also implemented the comparative experiments in the routing environment with 50% malicious routing nodes, and the average packet delay of each algorithm was shown in Figure 7. As we can see, under the influence of a large number of malicious nodes, both algorithms of our system still maintained good performance. Our RLBC algorithm reduced average packet delay by  
Figure 6: Average delay of packets with 25% malicious nodes.  
around 81% while the BC algorithm reduced it by around 55% and its performance exceeded the QL-BP algorithm, which it only reduced by around 32%, while the TB-BP algorithm only reduced by around 37%. This was because in QL-BP algorithm, the queue length information released between the routing nodes is not trusted. The malicious nodes can increase the parameter \(q_{t}(x,a_{k})\) in Algorithm 2 by issuing a false lower queue length, thereby greatly increasing the probability that the data packet is sent to the malicious node and affecting the normal routing scheduling work. The experimental results show that our RLBC algorithm is not susceptible to the influence of malicious nodes in terms of average packet delay, and the effectiveness proved that it is feasible to use it to improve the performance of routing algorithm.
## 5 Simulation Examples
### Evaluation of the Results  
In cases without uncertainties, the algorithm consistently converges to the optimal solution. These results were compared against centralized methods, specifically the Lambda-iteration method and the Lagrange multipliers method, and they are identical at the defined time step. This result confirms the convergence of the distributed method to the optimal solution and the statement established in **Theorem 1**.  
However, in uncertain scenarios, continuous dynamic changes at each step negatively impact speed and convergence to the optimal solution. Factors like traffic delays and gradient calculation noise significantly extend the convergence time. This often results in the algorithm remaining approximately 3-4 kW below the required value due to fluctuations in the learning gain or continuous changes in power from renewable sources. An additional feedback link could be introduced to evaluate the difference and adjust the required load on the network accordingly to address this limitation. This approach could eliminate this issue, ensuring accurate convergence to the optimal solution even in scenarios with multiple uncertainties on a shorter time horizon.  
However, the algorithm remains stable even with the complete loss of information that could otherwise destabilize it, and even in the face of multiple uncertainties.  
Figure 16: Example 6 results for the \(E_{b_{i}}(t)\) and time delay on the 1 st generator with more uncertainties.
## 3 Results and Discussion
### Multivariate Data Analysis
#### 3.2.4 Comparisons Between Predictive Models
Based on different subsets of serum cytokines, it was possible to develop good predictive models (AUCs between 0.768 and 0.810) for IMV need and mortality in the ICU, highlighting the relevance of cytokines in disease progression among critically ill patients. When comparing Groups A vs. B and B vs. C, models based on cytokine sets consistently demonstrated superior predictive performances compared to those based solely on routine blood analyses. However, this difference was statistically significant only in the A vs. B comparison. In contrast, models relying on routine blood analyses showed significantly lower predictive performance in both comparisons when measured against hybrid models, as demonstrated in the previous sections. This is in accordance with a retrospective cohort study that highlighted the limited predictive capability of models based exclusively on routine blood analyses [73]. Indeed, in the present study, hybrid models showed good performance. Even in cases where hybrid models did not outperform the best models, based either on cytokines or routine blood analyses alone, the differences were not statistically significant. When comparing Groups A plus B to Group C, models based solely on cytokines demonstrated the poorest performance, likely due to the high heterogeneity within the control group. This group included patients both with and without IMV and those who died requiring IMV. Hence, the variability in cytokine levels across these subgroups may have diluted the predictive power of the models relying solely on these biomarkers. This further highlights the value of hybrid models which integrate multiple data types to better capture the complex physiological and clinical variations among patients at the ICU, ultimately leading to more accurate predictions. Hence, our findings underscore the need for further comprehensive modelling approaches based on different biomarker sets, particularly in heterogeneous populations. While our analysis provides a valuable framework for predictive modeling, particularly in the ICU context, where these kinds of approaches are more restricted, some limitations must be addressed. Our goal was to develop predictive models based on a small number of variables to ensure their practical applicability in ICUs and to complement existing scoring systems. Despite the promising results, it is relevant to highlight the small dimension of the studied population (24 patients, equally distributed among three groups) and the fact that only male patients were included, limiting the generalizability of our findings to broader populations. Therefore, future studies involving larger and more diverse ICU cohorts are essential, not only for improving generalizability but also for performing external
## 5 Conclusions  
An experiment and a correction method for the responsivity temperature dependence in the SWIR channels of the PSAC are given in this article. An instrument-level thermal vacuum experiment and the detector-level experiment were carried out, respectively, and the curve of the relative responsivity change with the PST was obtained. Additionally, the consistency of the results obtained by the two experimental methods was assessed. The two types of experiments show that the detector responsivity of the 2250 nm band with about 35 \({}^{\circ}\)C PST difference between laboratory-based radiometric tests (PST\(\approx\)\(-\)30 \({}^{\circ}\)C) and on-orbit operation (PST \(\approx\)\(-\)65 \({}^{\circ}\)C) was reduced by approximately 1.3% and 1.5%, respectively. The temperature dependence in the SWIR channel was corrected to ensure the on-orbit applicability of the laboratory-based absolute radiometric calibration results and the atmospheric correction accuracy of the PSAC. The good consistency between the two experimental results indicates the effectiveness of the detector-level test for this type of detector.  
Compared with the instrument-level test in harsh conditions, the detector-level test reveals the key impacts that the substitution test system may have on the test results and verifies the feasibility of using the substitution test when obtaining RTDC parameters. In addition, in practical applications, there is a need to consider possible influences in the detector-level test, such as the differences in characteristic parameters of the detector between the test and the detectors actually used in the instrument and the differences in the experimental system. Finally, as an exercise, the potential low-cost detector-level experimental approach explored in this paper provides an experimental reference for studies involving other remote sensors used for similar issues.  
Conceptualization, H.D., Z.L. and F.T.; Methodology, H.D. and F.T.; Software, X.L., F.T., M.L., L.Y. and Z.S.; Validation, H.D., P.Z. and M.S.; Formal analysis, H.D.; Investigation, H.D.; Resources, Z.L., Q.C., P.Z., M.S. and J.H.; Data curation, H.D., C.L. and X.Z.; Writing--original draft, H.D.; Writing--review & editing, H.D., Z.L. and F.T.; Visualization, H.D. and L.Y.; Supervision, Z.L.; Project administration, Z.L., and J.H.; Funding acquisition, Z.L., and J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 42275144), the HFIPS Director's Fund (Grant No. YZJJ202201-TS), and the K. C. Wong Education Foundation (Grant No. GJTD-2018-15).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
Conflicts of Interest: The authors declare no conflict of interest.  
Figure 20: On-orbit application of G(T). The DN values at the nadir of the 2250 nm channel increased by approximately 1.37% with G(T) correction.