esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Analysis of vibration signals enables the identification of modal parameters, including natural frequencies, modal shapes, and damping ratios, which are crucial for structural health monitoring (SHM). This study employed power spectral density (PSD) and continuous wavelet transform (CWT) to examine a 3-DoF structure under various excitation scenarios (ambient, transient, and harmonic). PSD provided insights into the frequency domain, while CWT revealed detailed variations in the time-frequency domain, enabling the selection of robust features for damage identification and localization applications.  
The main findings highlight that energy density features derived from CWT, such as scalogram images \((SC_{i})\), their statistical metrics, spectral energy \((ES_{i})\), and total energy per sensor \((TES_{i})\), show notable sensitivity in distinguishing healthy from damaged states compared to PSD peaks. The sensor located at the base of the structure (S1) was particularly effective in identifying this difference. The experimental natural frequencies showed slight variations between healthy (AV case) and damaged conditions (AVD, ID, and IH cases), especially at the second natural frequency.  
Future research should continue to identify relevant features for precise damage localization and quantification based on sensor positioning, analyzing signal processing techniques in the time-frequency domain under various damage scenarios and structural components.  
Conceptualization, L.E.P.B. and M.C.D.S.; methodology, L.E.P.B.; software, L.E.P.B. and M.C.D.S.; validation, M.C.D.S. and D.G.; writing--review and editing, M.C.D.S. and D.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SHM & Structural Health Monitoring \\ PSD & Power Spectral Density \\ CWT & Continuous Wavelet Transform \\ OMA & Operational Modal Analysis \\ EMA & Experimental Modal Analysis \\ \end{tabular}
# Probing Dust and Water in Martian Atmosphere with Far-Infrared Frequency Spacecraft Occultation
###### Abstract  
Airborne dust plays an active role in determining the thermal structure and chemical composition of the present-day atmosphere of Mars and possibly the planet's climate evolution over time through radiative-convective and cloud microphysics processes. Thus, accurate measurements of the distribution and variability of dust are required. Observations from the Mars Global Surveyor/Thermal Emission Spectrometer Mars Mars Reconnaissance Orbiter/Mars Climate Sounder and Mars Express/Fourier Transform Spectrometer and the Curiosity Rover have limited capability to measure dust. We show that spacecraft occultation of the Martian atmosphere at far-infrared frequencies between 1 and 10 THz can provide the needed global and temporal data on atmospheric dust by providing co-located measurements of temperature and dust opacity from the top of the atmosphere all the way down to the surface. In addition, spacecraft occultation by a small-satellite constellation could provide global measurements of the development of dust storms.  
Mars; radio occultation; dust storms 2023
# Blow-Up Dynamics and Synchronization in Tri-Trophic Food Chain Models
## 4 Generalized Synchronization of the UR Model and HP Model Using the OPCL Coupling Method  
In this section, we use the OPCL coupling technique to synchronize the UR model in Equation (5) and the HP model in Equation (6). We considered the system in Equation (5) to be the driver system \(x(t)=F(x(t))\), \(x(t)\in\mathbb{R}^{n}\) and the system in Equation (6) to be the response system \(y(t)=G(y(t))\), \(y(t)\in\mathbb{R}^{n}\). We compute the Jacobian of the system in Equation (6) and obtain  
\[J(y)=\begin{pmatrix}1-2 my_{1}-\frac{Dwy_{2}}{(D+y_{1})^{2}}&\frac{-wy_{1}}{y_{ 1}+D}&0\\ \frac{D_{1}w_{1}y_{2}}{(D_{1}+y_{1})^{2}}&-a_{2}+\frac{w_{1}y_{1}}{y_{1}+D_{1} }-\frac{D_{2}w^{\prime}y_{3}}{(y_{2}+D_{2})^{2}}&-\frac{w^{\prime}y_{2}}{y_{2} +D_{2}}\\ 0&\frac{w_{3}D_{3}y_{3}}{(y_{2}+D_{3})^{2}}&-n+\frac{w_{3}y_{2}}{y_{2}+D_{3}} \end{pmatrix}. \tag{7}\]  
From the Jacobian, we can write the constant H-matrix as  
\[H=\begin{pmatrix}h_{1}&h_{2}&0\\ h_{3}&h_{4}&h_{5}\\ 0&h_{6}&h_{7}\end{pmatrix}. \tag{8}\]  
We required the constant H-matrix to satisfy the Routh-Hurwitz criterion and thus chose the parameters \(h_{1}=-2,h_{2}=0,h_{3}=0,h_{4}=-1,h_{5}=0,h_{6}=0\), and \(h_{7}=-3\). We next chose a transformation matrix \(\alpha\) with arbitrary constant elements to enable us to achieve a desired goal dynamic. For example, let  
\[\alpha=\begin{pmatrix}-2&0&1\\ -2&0&0\\ 2&-1&1\end{pmatrix}. \tag{9}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols** & **Description** \\ \hline \(a_{1}\) & intrinsic growth rate of prey \\ \(b_{1}\) & measure of competition among prey \\ \(a_{2}\) & intrinsic death rate of \(x_{2}\) in the absence of food \(x_{1}\) only \\ \(D\), \(D_{1}\) & measure of the level of protection offered to the prey by the environment \\ \(D_{2}\) & value of \(x_{2}\) at which its per capita removal rate becomes \(w_{2}/2\) \\ \(D_{3}\) & Loss of \(x_{3}\) due to lack of favorite food \(x_{2}\) \\ \(c\) & growth rate of \(x_{3}\) via sexual reproduction \\ \(w,w_{1}^{\prime}s\) & maximum value that per capital rate can attain \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._We achieved the goal dynamics  
\[\begin{pmatrix}g_{1}\\ g_{2}\\ g_{3}\end{pmatrix}=\begin{pmatrix}-2&0&1\\ -2&0&0\\ 2&-1&1\end{pmatrix}\begin{pmatrix}x_{1}\\ x_{2}\\ x_{3}\end{pmatrix}=\begin{pmatrix}-2x_{1}+x_{3}\\ -2x_{1}\\ 2x_{1}-x_{2}+x_{3}\end{pmatrix}, \tag{10}\]  
In addition, the coupling term is given by  
\[D=\begin{pmatrix}-2x_{1}+x_{3}\\ -2x_{1}\\ 2x_{1}-x_{2}+x_{3}\end{pmatrix}-\begin{pmatrix}lg_{1}-mg_{1}^{2}-\dfrac{wg_{1} g_{2}}{g_{1}+D}\\ -a_{2}g_{2}+\dfrac{wg_{1}g_{2}}{g_{1}+D_{1}}-\dfrac{w^{\prime}g_{2}g_{3}}{g_{ 2}+D_{2}}\\ -ng_{3}+\dfrac{w3g_{2}g_{3}^{3}}{g_{2}+D_{3}}\end{pmatrix}+\begin{pmatrix}d_{1 1}&d_{12}&d_{13}\\ d_{21}&d_{22}&d_{23}\\ d_{31}&d_{32}&d_{33}\end{pmatrix}\begin{pmatrix}y_{1}+2x_{1}-x_{3}\\ y_{2}+2x_{1}\\ y_{3}-2x_{1}+x_{2}-x_{3}\end{pmatrix}, \tag{11}\]  
where  
\[d_{11} =h_{1}-l+2 mg_{1}+\dfrac{Dwg_{2}}{(g_{1}+D)^{2}},\] \[d_{12} =h_{2}+\dfrac{wg_{1}}{g_{1}+D},\] \[d_{13} =h_{3},\] \[d_{21} =h_{4}-\dfrac{D_{1}w_{1}g
# Recurrent Neural Network Based Short-Term Load Forecast with Spline Bases and Real-Time Adaptation
## 2 Methodology  
The proposed RNN-based STLF procedure, with selected bases through a semi-parametric model and a real-time load forecast adjustment scheme, is shown in Figure 1. In the first stage, forecasts of daily load patterns up to the next seven days are obtained using the apparent temperatures predicted by the Taiwan Central Weather Bureau (TCWB).  
In the second stage, based on past-forecasted results, real-time adapted forecasting load sequences are generated through the interpolation method using real-time adaptation and an exponentially weighted moving average (EWMA). Figure 1 presents the data flow of the proposed RNN-based RNN\({}_{adp}\) model.
# Semantic Matching Based on Semantic Segmentation and Neighborhood Consensus
## 4 Experiments
### Ablation Study  
To verify the effectiveness of each module in the network, namely the 4D convolution and the feature extractor based on semantic segmentation, we designed different algorithm variants shown in Table 2, where \(\checkmark\) means that the module is included, while \(\times\) indicates that the module is not included. Comparing the first and second rows, PCK is significantly improved after adding 4D convolution. When comparing the second and third rows, PCK is further improved after the semantic segmentation information is added to the feature extractor. It demonstrates that the feature based on semantic segmentation and the 4D convolution both have positive effects on semantic matching.  
We visualized the keypoint estimation as shown in Figure 6 to analyze the importance of each module from a qualitative perspective. The second to fourth columns are obtained by the variant algorithms of the first to third rows of Table 2, respectively. The colored line segments in the images connect real keypoints and estimated keypoints. They indicate  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Semantic Segment.**} & \multirow{2}{*}{**4D Convolution**} & \multicolumn{3}{c}{**PF-PASCAL**} \\ \cline{3-5}  & & \(\mathbf{\alpha}\) = 0.05 & \(\mathbf{\alpha}\) = 0.10 & \(\mathbf{\alpha}\) = 0.15 \\ \hline \(\times\) & \(\times\) & 0.306 & 0.499 & 0.612 \\ \(\times\) & \(\checkmark\) & 0.409 & 0.740 & 0.865 \\ \(\checkmark\) & \(\checkmark\) & 0.555 & 0.842 & 0.932 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation analysis. \(\checkmark\) indicates that the corresponding component is included, and \(\times\) indicates that the component has been removed. The best PCK results are in bold.  
Figure 6: Keypoint estimation visualization. \(A\) represents the source image, and \(B_{1}\), \(B_{2}\), and \(B_{3}\) are target images. The dots in these images are the ground-truth keypoints, the boxes are the transferring results of keypoints from source to target images according to semantic matching. The color line segments are the keypoint estimation errors. \(B_{1}\), \(B_{2}\), and \(B_{3}\) correspond to different variant algorithms from the first row to the third row of Table 2.  
semantic matching errors since the keypoint estimation is based on keypoint transferring according to semantic matching. The longer the line segment, the greater the error. Figure 6 shows that by adding the 4D convolution and the information of semantic segmentation, the colored line segments gradually become shorter, demonstrating the gradual reduction of keypoint estimation errors and the improvement of semantic matching accuracy.
# Explainable AI for Alzheimer Detection: A Review of Current Methods and Applications
## 2 Techniques in Alzheimer's Disease Detection
### XAI Methods Applied to Alzheimer's Detection
#### 2.4.1 SHAP (Shapley Additive exPlanations)  
A popular XAI technique called SHAP, which is not dependent on any specific model, explains individual predictions by allocating their cause to the contribution of each feature. SHAP has been utilized in AD detection to pinpoint particular characteristics that are most important in predicting the beginning or course of the disease on MRI images as in [14; 25; 27; 67]. SHAP is a popular XAI technique that provides model-agnostic explanations by attributing individual predictions to the contributions of different features. While SHAP helps in interpreting DL models for AD detection, offering some insights into which features influence predictions, it still functions as a black box in many cases as in [5; 43]. For example, applying SHAP to a model trained on brain MRI data reveals feature importance scores, but it does not always provide a clear, intuitive understanding of how these features interact or contribute to the overall decision-making process [25]. According to [25] the Shapley value for a feature value is its effect on the batch, weighted and summed over all conceivable feature value combinations:  
\[\phi_{j}(\text{val})=\sum_{\{S\subseteq\{1,\ldots,p\}\setminus\{j\}\}}\frac{|S |!(p-|S|-1)!}{p!}(\text{val}(S\cup\{j\})-\text{val}(S)) \tag{5}\]  
where \(S\) is a subset of features utilized in the model, \(p\) is the number of features, and \(x\) is the vector of feature values for the example that requires explanation. \(\text{Val}(S)\) is the predicted value of the features in the set \(S\) that are prioritized over features that are not in the set \(S\):  
\[\text{val}_{x}(S)=\int\hat{f}(x_{1},\ldots,x_{p})\,\text{dP}_{x_{S}=S}-\mathbb{ E}_{X}(\hat{f}(X)) \tag{6}\]  
The sole attribution method that meets the requirements of efficiency, symmetry, illusory, and summation--all of which are useful in determining equitable compensation--is Shapley's value. Consequently, the contribution of a variable (or several variables) to the difference between the value predicted by the model and the average of all individual projections is its Shapley value for a particular individual.
## 6 Discussion
### How Volcano Monitoring Benefits from PlanetScope Imagery
Sentinel-2 and Landsat-8 provide a hybrid solution here, combining IR bands with good geometric resolution, allowing hotspots to be located spatially (e.g., NHI) [16]. By integrating these data with PlanetScope, the spectral limitations of PlanetScope can be circumvented. Another major advantage of PlanetScope data is the daily coverage of the entire earth's landmass. Due to the constellation with more than 150 Doves, this high temporal resolution can be achieved [44]. Especially in volcano monitoring, this is a drastic increase compared to the otherwise used optical sensor systems such as Sentinel-2 and Landsat. Due to the highly dynamic nature of volcanic hazards, a repetition rate of several days prevents the detection of fast-moving changes [50; 26]. Individual lava flows of different eruptive phases separated in time can be distinguished [22; 24; 61; 66]. However, differentiating lava flows within a single eruptive phase is difficult. Based on the high temporal resolution of PlanetScope data, it is possible to analyze individual lava flows and their dynamics during an eruptive phase. Figure 13: Lava areas derived from PlanetScope and Sentinel-2 data for the Karangetang II erup-tion event. Both lava flows were classified under the same conditions using the OBIA change detection methodology based on _dNDVI_ and NHI. A pre- (9 May 2019) and post-scene (4 December 2020) were used for classification and modelling using Sentinel-2. The results with the 10 m Sentinel-2 imagery are also satisfactory. However, marginal areas to the N of the surface are recorded much more coarsely. In the S of the lava field larger differences to the PlanetScope derived lava area were detected. Due to the lower resolution, the GLCM differences could possibly not detect detailed differences between the objects, which is why the lava areas appears somewhat coarser here. phase, especially in conjunction with the developed OBIA change detection methodology (Section 4.3). The daily coverage of PlanetScope allows an almost daily (depending on cloud cover) mapping of the lava flows, which in turn provides information about the eruption activity. Hence, especially in near-real-time monitoring of volcanic activity, useful information can be generated [13].
# Equivalent Linearization Methods for a Control System with Clutching Inerter Damper
## 5 ELM3 Based on SLM
### Iterative Procedure  
The values of the equivalent linearization parameters \(b_{eq}\) and \(c_{eq}\) can be determined by the following iterative procedure.  
**Step1:** Calculate \(b_{eq}\) using Equation (56) and set \(c_{eq}\) to 0 at the first iteration.  
**Step2:** Calculate \(\sigma_{x}^{2}\) and \(\sigma_{x}^{2}\) in Equations (60) and (61).  
**Step3:** Substitute \(\sigma_{x}^{2}\), \(\sigma_{x}^{2}\) obtained in Step 2 into Equation (58), then a new \(c_{eq}\) is obtained.  
**Step4:** Repeat Step 2 with \(b_{eq}\) obtained from Equation (56) and \(c_{eq}\) obtained from Step 3.  
**Step5:** Repeat Step 3 and Step 4 several times until the value of \(c_{eq}\) is accurate enough.
# Experimental Study on Vertical Shear Behaviors of an Immersion Joint with Steel Shear Keys
## 2 Experimental Design
### Immersion Joint Model
#### 2.1.2 Vertical Steel Shear Keys  
As shown in Figure 2, four sets of vertical steel shear keys are divided into two types, VSKA and VSKB. Each set consists of three steel keys. The size of VSKA is 60 mm \(\times\) 274 mm \(\times\) 190 mm, whereas the size of VSKB is 120 mm \(\times\) 380 mm \(\times\) 190 mm. All shear keys are box-type structures installed on the embedded anchor plates. Two sets of VSKA are installed on the two sidewalls of the element, and two sets of VSKB are installed on the two middle walls. The top and the bottom shear keys are fixed on element 1, and the middle shear keys are fixed on element 2. Details of VSKA and VSKB are depicted in Figure 3.  
Figure 3: _Cont._  
Figure 2: Prefabricated elements. (**a**) Element 1; (**b**) element 2.  
The shear capacity of the vertical steel shear key is assumed to be provided by the bolts. Hence, the shear capacity of the vertical shear key can be approximately calculated with Equation (1).  
\[SC_{VSK}=\sum SC_{B}, \tag{1}\]  
where \(SC_{VSK}\) and \(SC_{B}\) represent the shear capacity of the vertical shear key and that of the bolt, respectively. Based on Equation (1), the design shear capacities of one VSKA and one VSKB are 120 and 320 kN, respectively. Additionally, the design shear capacity of the immersion joint in the vertical direction is assumed to be the sum of the shear capacities of the four sets of steel shear keys, which is 880 kN.  
As shown in Figure 2, in each set, two shear keys are installed on element 1, whereas the other shear key is installed on element 2. The connection is formed when two elements contact each other. As shown in Figure 1, a rubber bearing is installed between every two keys in each set.
## 3 Results  
Table 1 presents a quantitative evaluation of the performance of our developed models, namely, model \(\mathcal{F}_{\eta+\rho_{t}}\) trained on \(\mathbb{D}_{\eta}\) and \(\mathbb{D}_{\rho_{1}}\) and model \(\mathcal{F}_{\eta+\xi+\rho}\) trained on \(\mathbb{D}_{\eta+\xi}\) and \(\mathbb{D}_{\rho}\). These models were trained using both synthesized image-mask pairs and unannotated real images extracted from video clips of wheat fields. The performance of the baseline model \(\mathcal{S}\) from [PERSON] et al. [7] is also reported in Table 1. The results demonstrate that the  
developed models, \(\mathcal{F}_{\eta+\rho_{1}}\) and \(\mathcal{F}_{\eta+\zeta+\rho}\), consistently outperformed the baseline model \(\mathcal{S}\). These models rely on computationally annotated synthetic images and real unannotated images extracted from the video frames of wheat fields.  
Figure 6 presents a qualitative evaluation of the performance of model \(\mathcal{F}_{\eta+\zeta+\rho}\), identified as the best-performing model, and model \(\mathcal{S}\) on several different domains. In general, model \(\mathcal{F}_{\eta+\zeta+\rho}\) demonstrates superior performance compared to model \(\mathcal{S}\). We also reported a case, as shown in the middle column, where model \(\mathcal{S}\) performs better.  
Table 2 presents the performance of the proposed models across various domains of the GWHD dataset. The results indicate that the proposed models generally outperform model \(\mathcal{S}\) in most domains, exhibiting a lower variance and greater stability.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Evaluation Method** & **Dice** & **IoU** \\ \hline \(\mathcal{S}\)[7] & Internal test set \(\Psi\) & 0.709 & 0.565 \\ \(\mathcal{F}_{\eta+\rho_{1}}\) & Internal test set \(\Psi\) & 0.773 & 0.638 \\ \(\mathcal{F}_{\eta+\zeta+\rho}\) & Internal test set \(\Psi\) & 0.807 & 0.686 \\ \hline \(S\)[7] & External test set \(\Gamma\) & 0.367 & 0.274 \\ \(\mathcal{F}_{\eta+\rho_{1}}\) & External test set \(\Gamma\) & 0.551 & 0.427 \\ \(\mathcal{F}_{\eta+\zeta+\rho}\) & External test set \(\Gamma\) & 0.648 & 0.526 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The performance of the trained models was evaluated on our internal and external test sets using the IoU and Dice scores. Model \(\mathcal{F}_{\eta+\rho_{1}}\) was trained on \(\mathbb{D}_{\eta}\) and \(\mathbb{D}_{\rho_{1}}\) and model \(\mathcal{F}_{\eta+\zeta+\rho}\) is the result of fine-tuning model \(\mathcal{F}_{\eta+\rho_{1}}\) on datasets \(\mathbb{D}_{\eta+\zeta}\) and \(\mathbb{D}_{\rho}\). We also compared the performance of these two models with the model developed in [7]. All of these models rely exclusively on synthesized image–mask pairs and/or unannotated images.  
Figure 6: Showcasing the prediction performance of model \(\mathcal{F}_{\eta+\zeta+\rho}\) (highlighted in a red box in the upper row) in comparison with the results obtained by model \(S\)[7] (highlighted in a blue box in the lower row) on samples from the Global Wheat Head Detection dataset [23].
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 1 Introduction
Additionally, due to the high spatial heterogeneity and rugged terrain, the geographical rules vary from region to region, and it is advisable to try to construct an optimal rule for each region. Moreover, uncertainties and errors are inevitable for various auxiliary data, and how to make best use of them and avoid any negative impacts caused by such uncertainties needs to be considered. Pixel-based and object-oriented classification are two different approaches for land cover mapping. The object-oriented classification method was found to be able to avoid the \"Salt and Pepper Noise\" often existing in pixel-based classification results [25], taking fully into consideration spectral, shape and texture features [26] as well as topographical features, and produced more accurate land coverproducts in highly heterogeneous areas. Most importantly, it will be the optimal classification method for the next generation of global land cover products [5; 14]. This study should be treated as a reference for other object-oriented land cover mapping projects over large areas or even the globe in the future. Currently, many artificial intelligence algorithms have been applied in the field of land cover mapping, such as Support Vector Machine [27; 28], decision tree [29], neural network [30] and genetic algorithms [31]. In these algorithms, decision tree is a \"white box\" model, and this helps to understand the relationship between the inputs and outputs. Meanwhile, it provides a convenient way to represent the geographical rules used in classification. Therefore, the object-oriented classification approach and the decision tree algorithm were chosen for land cover mapping in mountainous areas. Since 2010, the Chinese government has launched the Strategic Priority Research Program \"Climate Change: Carbon Budget and Relevant Issues\" (hereinafter referred to as \"Carbon Special Program\") [32] and \"National Ecosystem Survey and Assessment of China (2000-2010)\" Project (hereinafter referred to as \"Ecological Decade Project\") [33], aiming to answer relevant scientific questions about the terrestrial carbon budget and to assess the quality of eco-environments in China. Southwestern China, where more than 90% of land is mountains and hills, is one of the core regions in the Carbon Special Program and Ecological Decade Project due to its enormous carbon storage capacities and a fragile ecological environment. The specific objectives of this research were to (1) develop an operable object-oriented land cover hierarchical classification method based on the multi-source and multi-temporal satellite images for the largely mountainous area; (2) provide an interactive quality control approach based on a variety of geo-knowledge to further improve the quality of land cover products over mountainous areas; and (3) generate a new 30 m-resolution land cover product in Southwestern China (CLC-SW2010) based on the proposed approach according to a pre-defined time schedule.