esa-sceva/satcom-chunk-collection huggingface dataset:
# Preliminary Study on the Damping Effect of a Lateral Damping Buffer under a Debris Flow Load
## 5 Design Procedure  
According to the mechanisms and parametric study of the lateral damping buffer, the design process of such a buffer in resisting the debris flows is proposed, as shown in Figure 13.  
Figure 12: The influence of the mass ratio: (**a**) Maximum acceleration; (**b**) r.m.s acceleration.  
Figure 13: The design process of the lateral damping buffer in resisting debris flows.  
The buffer is mainly made of boards, springs, particles and boxes. The specific steps to determine the necessary parameters for engineering design are described below.  
1. The size of the boards should be determined based on the specific engineering information of the target area, especially the statistical characterizations of the vulnerable debris flows. The length of the boards should be equal to the length of target buildings facing debris flows, and the height should be determined by the impact height of debris flows. For example, the height of the board can be twice of the height of the impact height;
2. The stiffness of the springs should be determined by numerical simulation and the actual circumstance, considering the debris flow load, spring deformation limit and disaster reduction effect synthetically. Generally, a relative \"soft\" spring is preferred according to the parametric study. However, its stiffness is also constrained by the workability under the impact load of debris flows;
3. The mass of particles and boxes should be determined by the mass ratio, which is the mass of particles and boxes to the mass of the primary structure. For the determination of sizes, refer to the previous study on filling ratio in particle damping technology [36; 37].  
In addition, the failure criterion is related to the regulations in the code. For example, in the \"Code for Seismic Design of Buildings\" [38] in China, the maximum inter-story drift under major earthquake is regulated as \(1/50\), and this value can be used to be a kind of failure criterion.
## 2 Materials and Methods
### Case-Study
#### 2.4.4 Modelling  
Model Construction  
The final data set was characterized by the repeated observation of 12 spectral bands (variables) averaged for each of the 107 different vine blocks (objects) at 25 potentially available dates over four months (13 May to 20 August), in 2019. This data set was meaningfully arranged in a three-way array \(\underline{\mathbf{X}}\) (\(I\times J\times K\)).  
As discussed previously, the number of images per block varied according to each block's geographical location and possible cloud (or other) effects. Thus, \(\underline{\mathbf{X}}\) was incomplete and there was a need for interpolation to obtain a continuous data cube. \(J\), defined as the number of dates selected to represent the time-series, can be determined by optimising the parameters \(N\) and \(P\). For this model, \(J\) was determined by using ranges of \(N\) and \(P\) described in Section 2.3.1. Once \(J\) was determined, interpolation was performed (again following the method in Section 2.3.1) to have a consistent time step.  
At the end of the image processing step the data were presented as:  
* a cube \(\underline{\mathbf{X}}\) (\(107,J,12\)) where the first dimension corresponds to the vineyard blocks (\(I\)), the second dimension corresponds to time (\(J\)), which is optimised during modelling, and the third dimension of the three-way array \(\underline{\mathbf{X}}\) corresponds to spectral bands (\(K\)) averaged for each field,
* a vector \(\mathbf{y}\) (\(107\)), corresponding to the estimated percentage yield loss by the winegrowers from the 107 blocks.  
Figure 6: Percentage of yield losses observed by winegrowers on 107 blocks in southern France.  
Model Validation  
The calibration and validation subsets were generated using the method outlined in Section 2.3.2. The joint analysis of RMSEC (calibration set) and RMSECV (cross-validation) as proposed by [PERSON] et al. [30] was used to determine the optimal number of latent variables in the regression model.  
Model Evaluation  
The N-PLS was applied as an approach to detect and characterize the vineyard response (estimated by the percentage of losses) to extreme heat stress by using 12 available reflectance spectral bands from a time-series of Sentinel-2 imagery. As defined in Section 2.3.2, the prediction performance of the N-PLS model was quantified by the standard determination coefficient R\({}^{2}\), the bias and the standard error parameters. The regression b-coefficients resulting from the application of the methodology were plotted (i) against time as a function of wavelengths and (ii) against spectral bands as a function of time identify the time and the wavelengths that best highlighted this phenomenon. To further illustrate this, a 3D view of the \(b\)-coefficients vs. time vs. spectral bands was also generated. Figure 7 summarizes the implementation and the workflow scheme of the N-PLS adapted to the case study.
## 2 Methods
### Feature Selection  
To evaluate the relevance of individual variables and reduce dimensionality, two feature selection strategies were tested: the mutual information-based selection, which scores features based on their statistical dependency with the classification label, and the Minimum Redundancy Maximum Relevance (MRMR) algorithm, which aims to optimise both relevance and feature diversity.  
In particular, the first function, given the feature matrix and corresponding class labels, returns a vector of non-negative values, each representing an estimate of the mutual information between a single feature and the label variable. By keeping track of the corresponding features, we obtained a ranked list based on their estimated information gain.  
The MRMR is a mutual information-based method that selects features by optimising both relevance and redundancy. It is designed to identify a subset of features that not only exhibit high statistical dependency with the target variable (maximum relevance), but also minimal redundancy among themselves. Although MRMR inherently considers feature redundancy, we also computed a correlation matrix across selected features to qualitatively verify the absence of highly correlated pairs.
# Design and Implementation of a System-on-Chip for Self-Calibration of an Angular Position Sensor
###### Abstract  
In this study, a novel signal processing algorithm and hardware processing circuit for the self-calibration of angular position sensors is proposed. To calibrate error components commonly found in angular position sensors, a parameter identification algorithm based on the least mean square error demodulation is developed. A processor to run programs and a coprocessor based on the above algorithm are used and designed to form a System-on-Chip, which can calibrate signals as well as implement parameter configuration and control algorithm applications. In order to verify the theoretical validity of the design, analysis and simulation verification of the scheme are carried out, and the maximum absolute error value in the algorithm simulation is reduced to 0.003%. The circuit's Register-Transfer Level simulation shows that the maximum absolute value of the angular error is reduced to 0.03%. Simulation results verify the calibration performance with and without quantization and rounding error, respectively. The entire system is prototyped on a Field Programmable Gate Array and tested on a Capacitive Angular Position Sensor. The proposed scheme can reduce the absolute value of angular error to 4.36%, compared to 7.68% from the experimental results of a different calibration scheme.  
Keywords:self-calibration; least mean square demodulation; angular position sensor; System-on-Chip +
Footnote †: journal: Applied Sciences
## 6 Fully-Distributed Clock Synchronization Algorithms
### Fully-Distributed Clock Synchronization Algorithms under Exponential Delays  
Compared to the fully-distributed clock synchronization algorithms that assume Gaussian network delays, fewer works have been proposed to account for exponential network delays in a fully-distributed manner. Considering only the estimation of clock offset, [PERSON] _et al._[57] pioneered a fully-distributed algorithm under exponential delays based on a factor-graph representation of the network and a max-product message update algorithm. From the two-way message exchange model in Equations (47) and (48), since the MLE for the clock offset difference \(\theta_{j}-\theta_{i}\) is given by \(S_{ij}=\left[U_{ij,(1)}-V_{ij,(1)}\right]/2\), where \(U_{ij,(1)}\) and \(V_{ij,(1)}\) denote the first order statistics of \(U_{ij,n}\stackrel{{\triangle}}{{=}}c_{j}(t_{n}^{2})-c_{i}(t_{n}^ {1})\) and \(V_{ij,n}\stackrel{{\triangle}}{{=}}c_{i}(t_{n}^{4})-c_{j}(t_{n}^ {3})\), respectively, the modified system model is expressed as:  
\[S_{ij}=\theta_{j}-\theta_{i}+Z_{ij} \tag{58}\]  
where \(Z_{ij}\stackrel{{\triangle}}{{=}}\left(X_{ij,(1)}-Y_{ij,(1)} \right)/2\) is a Laplace random variable, since \(X_{ij}\) and \(Y_{ij}\) are both assumed to be exponentially distributed. Thus, the likelihood function \(p(S_{ij}|\theta_{i},\theta_{j})\) is formulated based on the model in Equation (58). The factor graph representation of the network is identical to the one employed in [46]. In terms of the message update rule, the message transmitted from variable node \(\theta_{i}\) to factor node \(\gamma_{ij}\) is given by:  
\[m_{\theta_{i}\rightarrow\gamma_{i,j}}^{(l)}(\theta_{i})=p(\theta_{i})\prod_{k \in\beta_{i}\setminus j}m_{\gamma_{i,k}\rightarrow\theta_{i}}^{(l-1)}(\theta _{i}) \tag{59}\]  
Moreover, the message from a factor node \(\gamma_{i,j}\) to a variable node \(\theta_{i}\) is updated by the \"max\" operator:  
\[m_{\gamma_{i,j}\rightarrow\theta_{i}}(\theta_{i})=\max_{\theta_{j}}[m_{\theta _{j}\rightarrow\gamma_{i,j}}(\theta_{j})p(S_{ij}|\theta_{i},\theta_{j})] \tag{60}\]  
and the belief update is expressed as:  
\[b_{i}^{(l)}=p_{i}(\theta_{i})\prod_{j\in\beta_{i}}m_{\gamma_{i,j}\rightarrow \theta_{i}}(\theta_{i}) \tag{61}\]  
Under exponential random delays, [58] proposed a fully-distributed synchronization algorithm for joint estimation of clock offset and skew. The joint maximum likelihood estimation of the clock offset and skew is first formulated as a linear programming (LP) problem and solved in a centralized way. With the help of auxiliary replica variables, the original LP problem is re-formulated as an equivalent optimization problem with the structure more amenable for decomposition, which is further decoupled into a series of subtasks. By applying the classic alternating direction method of multipliers (ADMM), these subtasks are solved in a distributed way while still guaranteeing the convergence to the centralized solution.
## 4 Results and Discussion
### Effect of Working Fluids on Heat Pipe Thermal Resistance  
Figure 9 shows the thermal resistance and temperature difference (\(\rm T_{E}-T_{C}\)) of the tested MF-AHPAs with different working fluids at an inclination angle of 45\({}^{\circ}\). The thermal resistance of the acetone MF-AHPA reduced continuously and significantly as the heat load was increased up to 140 W. Between 140-230 W, the reduction of thermal resistance was insignificant and appeared to reach a minimum value. However, the thermal resistance curves of the cyclopentane MF-AHPA and n-hexane MF-AHPA are U-shaped, and different from the acetone one, meaning an occurrence of partial dry out after reaching minimum thermal resistances (MTRs). At high heat flux, the cyclopentane MF-AHPA and n-hexane MF-AHPA significantly suffered from local dry-out, resulting in a decreased heat transfer coefficient in the evaporation section [23]. Local dry-out could reduce an evaporation heat transfer coefficient by more than 25%. However, this didn't occur in the acetone heat pipe. Acetone, with the highest La number of 50,205, has a relatively high surface tension. Liquid acetone could spread quickly in the evaporation region due to strong capillarity, managing unwanted local dry-out well, and maintaining the desired performance.  
The MTRs of the acetone, cyclopentane, and n-hexane heat pipes are 0.048 K/W, 0.076 K/W, and 0.140 K/W, respectively. The MTR of the acetone MF-AHPA was 34% and 63% lower than these of n-hexane and cyclopentane ones, respectively. This illustrates that the working fluid plays a critical role in determining the thermal resistance of the MF-AHPAs. Acetone is the best working fluid among the selected three ones, which achieves the smallest thermal resistance at all of the working loads in this study. This should be because of its favourable thermophysical properties including low viscosity, high thermal conductivity, and a high latent heat of vaporisation.
# Emerging Non-Canonical Functions and Regulation by p53: p53 and Stemess
## 4 Therapeutic Strategies to Restore _wt_p53 Function in CSCs
### RING Finger E3 Ligases and CSCs Maintenance
Independent of p53, Mdm2 is associated with inhibition of lineage specific genes via the polycomb [271]. In patients with newly-diagnosed glioblastoma, temozolomide (TMZ) is the standard of treatment; however, a challenge exists in overcoming TMZ drug resistance mainly due to O\({}^{6}\)-methylguanine DNA methyltransferase (MGMT). Removal of the methyl group from TMZ induces chemoresistance in glioma stem cells and is prevalent in a significant fraction of glioblastoma cases. [PERSON] et al. (2011) [272] show that the mitogen-activated protein/extracellular signal-regulated kinase (MEK)-extracellular signal-regulated kinase (ERK)-Mdm2-p53 pathway regulates MGMT expression using patient-derived stem-like glioblastoma cells. Mdm2 was found to be upregulated via the MEK-ERK signaling pathway, thus inhibiting p53 function and maintaining the expression of MGMT. This study was pivotal in demonstrating that MEK inhibition could render resistant CSCs sensitive to TMZ. Dual treatment with a MEK inhibitor and TMZ can effectively control glioma CSCs from initiating tumors. Additionally, [PERSON] et al. (2010) [176] reported HSC/progenitor survival to be dependent on Mdm2 reduction of p53 activity induced by reactive oxygen species (ROS). In response to ROS, p53 trans-activates genes that regulate proliferation, differentiation, senescence and apoptosis [273]. It has been reported that p53 can elevate ROS by activating ROS-inducing genes Pig1, 8 and 12 [274]. In contrast, p53 has also been reported to protect stem cells from ROS damage via upregulation of GOX, p53-induced genes (PIGs) and downregulation of nitric oxide synthase 2 (NOS2) and cyclooxygenase 2 (COX2) [176]. While implementing a p53 mutation capable of inducing cell cycle arrest without apoptosis, \(\mathrm{Mdm2^{-/-}}\) p53 515C (encoding p53R172P) mice were generated resulting in normal HSC counts in fetal livers and depleted counts in postnatal bone marrow. These mice are also observed with increased ROS levels and p53R171P expression, activation of p16 lnk4a and cell death. The experimental study signifies the importance of the p53-Mdm2 pathway in hematopoiesis. In the model, basal ROS levels accumulate in hematopoietic tissues during development. The absence of Mdm2 in combination with basal oxidative stress permits p53 activity to induce ROS production via transactivation of PIGs, creating a positive feedback loop. HSCs and progenitors become depleted via the induction of cell cycle arrest, senescence and cell death. Sensitization of bone marrow HSCs to the ROS-p53 pathway can be exploited in leukemia, because leukemia stem cells can propagate from HSCs. During normal hematopoiesis, stress signals activate p53, and Mdm2 is required for p53 level regulation to enable stem cell survival [275].
# Monitoring Forest Diversity under Moso Bamboo Invasion: A Random Forest Approach
## 4 Discussion
### Sustainable Forest Management  
Moso bamboo is widely distributed across East and Southeast Asia [61]. Its robust invasiveness stems from the lateral spread of its rhizome system underground, which facilitates rapid invasion into neighboring broadleaf or coniferous forests [62; 63]. Research has consistently demonstrated that bamboo invasions can detrimtally impact the structure, function, and stability of the invaded ecosystem [3; 64; 6]. The need to monitor Moso bamboo invasion has become increasingly urgent given its potential adverse effects on ecosystems, especially with the promotion of its cultivation. Our study introduces a monitoring methodology that offers practical insights that are crucial for the development of sustainable forest management strategies and the conservation of biodiversity.
## 3 Characterization of damage along linear infrastructures  
Landslide-induced damage can be considered as a movement indicator to assess the spatial and temporal evolution of the infrastructure vulnerability. Displacements of the road surface induced by ground instability over time are cause of cracks occurrence on structural and non-structural elements.  
A detailed approach for the surveying and classification of damage plays a key role in the strategy to better delineate mass-movement boundaries and state of activity, by categorizing its detectable impacts on the ground, as well as to improve knowledge of the instability, to avoid repeated occurrences and to plan mitigation measures.  
Several studies investigate and suggest damage classification of infrastructures affected by landslides [26; 47; 48]; ref [49] propose a qualitative approach in landslide risk analysis and distinguish the following types of damage:  
* Negligible damage: No crack and deformation or isolated hairline cracks have been observed on pavement surface;
* aesthetic (or light) damage: Limited small cracks, local roughness, and distortions occurred to road surface. The functionality of roads is not compromised and damage can be repaired, rapidly and at low cost;
* Functional (moderate) damage: Diffuse cracking, severe deformations of pavement, possible partial collapse of superstructure. The functionality of the infrastructure is partially compromised and damage takes time and large resources to be fixed;
* structural (or severe) damage: Structural failures, dangerous deformation, and total collapse of road superstructure occur. Transportation routes are severely or completely damaged and require extensive work to be rebuilt.  
Classification of damage degree along linear infrastructure can be obtained through a detailed field survey; subsequently, this information has to be implemented in a GIS environment, thus providing a map of damaged sections with different level of severity.
## 3 Proposed Methods  
The flow chart of the proposed obstacle detection method, OD-LSF, is shown in Figure 1. It is mainly divided into Image preprocessing, OD-PNV, OD-SPH, Fusion and a Stixels Generation Algorithm based on Segmentation and Optimization (SGA-SO). Image preprocessing mainly includes stereo matching, superpixel segmentation and point cloud preprocessing. OD-LSF is the main body of this paper, which is mainly composed of four parts; namely, the OD-PNV, OD-SPH, fusion, and Stixels Generation Algorithm based on Segmentation and Optimization (SGA-SO). The OD-PNV method determines the probability that the superpixel belongs to an obstacle by calculating the local plane normal vector according to the fact that the point cloud distribution in the obstacle area tends to be vertical and the point cloud distribution in the free space tends to be horizontal. The superpixels are then classified according to the probability threshold. The larger the distance between the superpixel point cloud and the road surface, the higher the probability that it belongs to the obstacle area. Therefore, the OD-SPH method first determines the road surface by plane fitting the superpixels that are classified as free space by the OD-PNV method, then calculates the height of all the superpixel point clouds from the road surface. Finally, the obtained height information is used to estimate the probability that the superpixel belongs to the obstacle area. The fusion part fuses the probabilities obtained by the OD-PNV and OD-SPH methods, and finally classifies the superpixels according to the fused probabilities. On the basis of the above classification, the SGA-SO algorithm efficiently describes the traffic scene in the form of stixel.  
Figure 1: Flow chart of OD-LSF.
## 4 Numerical Investigation  
### Single Degree of Freedom (SDOF) Structural Configuration  
In order to quantitatively capture the performance gains of the proposed system on both the transient and steady-state components of the vibration response, four alternatives, namely passive (TMD), semi-active (STMD), active (ATMD) and semi-active hybrid (SHMD)-equipped (low-damped) SDOF structures, are investigated. For the simulations, the mass and stiffness of the SDOF structure is selected such that the resulting mass ratio of the structural mass to the mass of the damper is 1% and the frequency of the system is approximately 1 rad/s, typical for high-rise structural applications. The resulting mass, stiffness and damping matrices used in the simulations are:  
\[\begin{array}{c}M=\left(\begin{array}{cc}1000&0\\ 0&10\end{array}\right)\quad\text{Kg}\quad\quad K=\left(\begin{array}{cc}10 09.9&-9.9\\ -9.9&9.9\end{array}\right)\quad\text{Ns/m}\\ C=\left(\begin{array}{cc}51.22&-1.22\\ -1.22&1.22\end{array}\right)\quad\text{Ns/m}\quad C_{w}=\left(\begin{array}{ cc}50.04&-0.04\\ -0.04&0.04\end{array}\right)\quad\text{Ns/m}\end{array} \tag{13}\]  
In Equation (13), \(C\) is the damping matrix used for the case of TMD control, and \(C_{w}\) is the damping matrix used for the case of STMD, ATMD and SHMD control. It is evident that for the case of passive control, a damping ratio of 6.1% and stiffness tuning ratio of 0.9 derived using [PERSON]'s expressions found in [19] are used for optimal passive behaviour and maximum rms reduction at steady state. For the remaining three control cases, a minimal damping ratio of 0.2% is used in order to capture the inherent damping of the connection of the damper and the structural mass.