esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Microbial Community Composition
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**LCM**} & \multicolumn{2}{c}{**BBM**} & \multicolumn{2}{c}{**OSM**} & \multicolumn{2}{c}{**KC**} \\ \cline{3-10}  & & & **C** & **TR** & **C** & **TR** & **C** & **TR** & **C** & **TR** \\ \hline \multirow{4}{*}{Donor A} & \multirow{2}{*}{lumen} & PC & 8.3 & 22.4 \({}^{*}\) & 8.4 & 19.5 \({}^{*}\) & 10.3 & 22.4 \({}^{*}\) & 9.4 & 21.2 \({}^{*}\) \\  & & DC & 18.8 & 23.1 \({}^{*}\) & 21.9 & 20.3 & 21.5 & 24.8 & 21.2 & 23.3 \\ \cline{2-10}  & \multirow{2}{*}{mucus} & PC & 18.1 & 25.2 & 13.5 & 25.9 \({}^{*}\) & 16.9 & 20.6 & 15.0 & 21.9 \({}^{*}\) \\  & & DC & 15.6 & 31.5 & 25.0 & 26.0 & 25.4 & 25.1 & 26.2 & 28.9 \\ \hline \multirow{4}{*}{Donor B} & \multirow{2}{*}{lumen} & PC & 5.5 & 22.6 \({}^{*}\) & 7.1 & 21.5 \({}^{*}\) & 6.8 & 16.8 \({}^{*}\) & 3.5 & 20.4 \({}^{*}\) \\  & & DC & 15.6 & 21.7 \({}^{*}\) & 14.3 & 19.4 & 18.1 & 19.2 & 17.8 & 18.7 \\ \cline{2-10}  & \multirow{2}{*}{mucus} & PC & 15.8 & 20.3 & 14.7 & 17.6 & 11.8 & 19.1 \({}^{*}\) & 10.9 & 17.5 \({}^{*}\) \\  & & DC & 10.2 & 24.0 \({}^{*}\) & 11.2 & 22.5 \({}^{*}\) & 11.7 & 26.5 \({}^{*}\) & 13.7 & 24.3 \({}^{*}\) \\ \hline \multirow{4}{*}{Average of Donors} & \multirow{2}{*}{lumen} & PC & 6.9 & 22.5 \({}^{*}\) & 7.7 & 20.5 \({}^{*}\) & 8.6 & 19.6 \({}^{*}\) & 6.4 & 20.8 \({}^{*}\) \\  & & DC & 17.2 & 22.4 \({}^{*}\) & 18.1 & 19.9 & 19.8 & 22.0 & 19.5 & 21.0 \\ \cline{2-10}  & \multirow{2}{*}{mucus} & PC & 16.9 & 22.7 & 14.1 & 21.7 \({}^{*}\) & 14.4 & 19.9 \({}^{*}\) & 12.9 & 19.7 \({}^{*}\) \\  & & DC & 12.9 & 27.8 \({}^{*}\) & 18.1 & 24.3 & 18.5 & 25.8 & 19.9 & 26.6 \\ \hline \hline \end{tabular}  
* \(p<0.05\).  
\end{table} 
Table 1: Reciprocal Simpson diversity index in the lumen of the proximal colon at the end of the control and treatment periods. Figure 4: _Cont._  
Figure 3: Changes in microbial community composition (relative abundance) at the phylum level in the luminal PC and DC for (**A**) Donor A, luminal PC; (**B**) Donor B, luminal PC; (**C**) Donor A, luminal DC; (**D**) Donor B, luminal DC. BBM = berry blast muffin; C = control period; DC = distal colon; KC = Kodiak cake; LCM = lemon chia muffin; OSM = oat spice mookie; PC = proximal colon; TR = treatment period.
## 4 Results and Discussion
Based on the classification results so far obtained, we can identify the NN as the best classifier since it outperformed the DT, KSVM, and RF classifiers. Figures 4 and 5 report an example of the classification of two days of observations, i.e., day 11 and day 17 in Table 1, respectively. In Figure 4, the red dots, representing a misclassification of rain conditions, correspond to a low intensity of rain measured by the TBRG. On the other hand, the more intense rainy observations are well-classified by the  
Figure 3: For each classification algorithm (from left to right DT, KS, NN, RF) trained with 12 features (top: (**a**)) and 6 features (bottom: (**b**)) a triplet of boxplots is shown, reporting the specificity (Spe), recall (Rec), and F1-score (F1) metrics, respectively. (**a**) Boxplot of the metrics using 12 Features. (**b**) Boxplot of the metrics using 6 Features. NN. The purple dots point out a misclassification in non-rainy observations. As mentioned above, specificity could be not that accurate since the rain gauge is not close to the SRS. Indeed, the misclassified observations occurring around 12:00 seem to be related to a rain event that has not been detected by the TBRG. Figure 4: Day 11 classified by NN trained with 12 features (**a**) and with 6 features (**b**). The blue dots refer to not-rainy observations correctly classified, the purple ones are not-rainy observations classified as rain, the green ones are rainy observations correctly classified, and the red dots represent rainy observations classified as not rain. The cyan bars represent the TBRG measurements different from 0 mm/h, i.e., associated with the rain observations. (**a**) Day 11 Neural Network 12 Features. (**b**) Day 11 Neural Network 6 Features. In Figure 5, the NN classified correctly all the rain observations, thus having a recall of 100%. On the contrary, it misclassifies some non-rainy observations. It is worth noting that most of these points occur near the rain observations. Probably some of them refer to a rain condition not measured by the TBRG.
# Application and Assessment of Cooperative Localization in Three-Dimensional Vehicle Networks
## 4 Main Contributions and Novelties  
In this paper, we extend a previous conference proceedings publication [46] to include additional subcases, physical experiments, and identifying additional limitations to CL. We extend the centralized EKF-based CL algorithm presented in [13; 14] to include 3D vehicle models and assess its performance. Specifically, we utilize quaternions to represent vehicle orientation in order to avoid singularities. For measurements, we introduce quaternions corresponding to relative orientation represented by the rotation matrix and present linearized measurement equations, i.e., the output matrix. We then assess the performance of CL in 3D vehicle networks through a series of Monte Carlo-type simulations. We use a sequential update procedure [47] to assess the effect of simultaneous and redundant measurements on individual vehicles, as well as the overall network. Assuming that absolute orientation can be accurately measured by onboard sensors, we further assess the performance of CL when only relative position measurements are available and investigate the circumstances under which the method could be effective or ineffective. Finally, experiments are preformed to verify the algorithm.  
The main contributions and novelties of this publication are outlined as follows:  
1. The development and application of singularity-free EKF-based CL for networks of three-dimensional vehicles;
2. The first comprehensive assessment of EKF-based CL with only relative position measurements;
3. The identification of limitations of CL with respect to the number of simultaneous measurements, redundant measurements, and relative position-only measurements;
4. The experimental verification of the EKF CL algorithm's performance and limitations.  
The paper is organized as follows. The vehicle model is presented in Section 5. A multi-vehicle system with state and measurement equations and their linearization are presented in Section 6, followed by the CL algorithm in Section 7. Simulations and experiments are presented in Sections 8 and 9, respectively, followed by the concluding remarks in Section 10.
## 3 Theory
### Event-Triggered Consistency Theory of Multi-Microwave Sources under DoS Attacks
Assuming a single heating cycle in the microwave switching system as a Markov jump system, let \(0=T_{0}<T_{1}<\cdots T_{n-1}\leq t<Tn\), and \(\lambda>\lambda_{1}>0\), \(T_{n+1}^{on}-T_{n}^{on}\leq\bar{T}<T_{n+1}-T_{n}=T\), next we have \(\mathrm{N}(t)+\lambda\dot{\mathrm{N}}(t)\leq\mathrm{N}(t)+\lambda\dot{ \mathrm{N}}(t)+\varsigma^{T}(t)\times(N_{p}\otimes I_{N})\varsigma(t)\), and let \(e(t)=\delta\mathrm{P}(t)\), combining Equation (19), Equation (20) and introducing important conclusions from reference [26,28]:  
\[\mathrm{N}(t)\leq e^{-\lambda(t-t_{q})}N(\mathrm{P}(t_{q}))+\int_{t_{q}}^{t}e^ {-\lambda(t-s)}ds \tag{21}\]  
And then:  
\[ \begin{split}&\mathrm{N}(t)\leq e^{-\lambda(t-t_{q})}N(\mathrm{P}( t_{q}))+\int_{t_{q}}^{t}e^{-\lambda(t-s)}ds\leq e^{-\lambda(t-t_{q})}N( \mathrm{P}(T_{n}^{off}))+\int_{t_{q}}^{t}e^{-\lambda_{1}(t-s)}ds\\ &\leq\mu^{N_{p}(T_{n}^{off},t)}e^{-\lambda(t-T_{n}^{off})}N( \mathrm{P}(T_{n}^{off}))+\int_{T_{n}^{off}}^{t}\mu^{N_{p}(s,t)}e^{-\lambda_{1 }(t-s)}ds\\ &\leq\mu^{N_{p}(T_{n-1},t)}e^{-\lambda(t-T_{n}^{off})-\lambda_{1 }(T_{n}^{off}-T_{n}^{on})-\lambda(T_{n}^{on}-T_{n-1})}\times N(\mathrm{P}(T_{ n-1}))+\int_{T_{n-1}}^{t}e^{-\lambda_{1}(t-s)}ds+\int_{T_{0}}^{t}\mu^{N_{p}(s,t)}e^{- \lambda_{1}(t-s)}ds\\ &\leq\mu^{N_{p}(0,t)}e^{-\left[\frac{\lambda(T-T)+\lambda_{1}T}{T }\right]}t_{e^{-\left[\lambda-\frac{\lambda(T-T)+\lambda_{1}T}{T}\right]}\left( t_{n}-T_{n}^{off}\right)}\\ &\times e^{-\left[\lambda_{1}-\frac{\lambda(T-T)+\lambda_{1}T}{T }\right]}\left(T_{n}^{off}-T_{n}^{on}\right)-\left(\lambda-\frac{\lambda(T-T)+ \lambda_{1}T}{T}\right)\times e^{\left(T_{n}^{on}-T_{n-1}\right)}N(\mathrm{P }(t))+\int_{0}^{t}\mu^{N_{p}(s,t)}e^{-\lambda_{1}(t-s)}ds\\ &\leq\mu^{N_{p}(0,t)}e^{2|\lambda_{1}|T}e^{-\left[\lambda_{1}- \frac{\lambda(T-T)+\lambda_{1}T}{T}\right]}tN(\mathrm{P}(t))+\int_{0}^{t}\mu^ {N_{p}(s,t)}e^{-\lambda_{1}(t-s)}ds\end{split}  \tag{22}\]  
Taking expectations from both sides of Equation (22), combined with Equation (21):  
\[E(N(t))\leq e^{-\left[\frac{\lambda(T-T)+\lambda_{1}T}{T}+\bar{q}-\mu\bar{q} \right]t}e^{2|\lambda_{1}|T}N(P(t)) \tag{23}\]  
where \(\mu\geq 1\), when the DoS attacks satisfies \((\lambda(T-\bar{T})+\lambda_{1}\bar{T})/T\geq\mu\bar{q}-\bar{q}\), the closed-loop system Equation (11) can stably exist under DoS interference attacks [26,28].
## 5 Exploratory Analysis for Variable Pre-Selection  
### Non-Machine Learning Methods  
Exploratory analysis is often used to detect the relationships between the response variable and predictive variables for non-machine learning methods, such as LM, generalized linear models (GLM), and kriging with an external drift (KED). By applying such analysis, people intend to find data nature and structure [85]. Key issues may include the identification of (1) outliers, (2) homogeneity in variance of response variable, (3) data distribution of response variables including normality, (4) collinearity (i.e., the correlation among predictive variables), (5) relationships or response curves of response variable to predictive variables, (6) how strong the relationships are between response variable and predictive variables, (7) possible interactions among predictive variables, (8) independence of a response variable, whether temporally, spatially, or both, and (9) source of random errors, which may lead to a mixed-effect model or additional predictive variable(s) [71]. On the basis of the above analyses, certain actions can be taken to deal with relevant samples or variables. For instance, some predictive variables can be removed if their correlations with the response variable are low. However, it would be wise to let the variable selection process determine which variables should be removed because some variables may be important predictive variables even with low correlations. Highly correlated predictive variables, usually determined based on correlation coefficient (\(r\)) or variance inflation factor (VIF), can also be eliminated to reduce the collinearity, although caution should be taken for this exercise [9; 86]. Relevant issues about collinearity were also discussed [87]. Some predictive variables may need to be specified to their second or third orders if a non-linear relationship is detected. Moreover, some interactions may need to be considered and tested.
# Control of DC Motors to Guide Unmanned Underwater Vehicles
## 1 Introduction
[22], who used neural networks for self-tuning of piezoelectric actuators. [PERSON] et al. [23] combined adaptive control techniques and the theory of statistical learning--inspired by classical neural networks with several hidden levels, in which the architecture of the adaptive controller is divided into three different levels, each each of which implement an algorithm based on learning from data. Akin to these efforts to incorporate stochastic learning approaches into self-tuning adaptive controllers, this manuscript proposes utilization of deterministic learning approaches to augment self-tuning control of DC motors. [PERSON] et al. [24] illustrated the profound ability of deterministic self-awareness statements to control highly nonlinear systems such as the forced van der Pol circuit, while [PERSON] et al. [25] investigated application of deterministic artificial intelligence (D.A.I.) methods applied to spacecraft. In light of [PERSON] and [PERSON]'s advice for follow-up research, [PERSON] et al. [26] elaborated on autonomous trajectory generation schemes for D.A.I., while [PERSON] et al. [27] made the next step of progress by articulating optimal learning methods and minimizing the 2-norm of estimation errors. Their results revealed worthwhile performance improvement, applied to spacecraft attitude control. Funded by the U.S. Office of Naval Research, [28] applied the methodology to unmanned underwater vehicles as a follow-up to implementation of classical methods [29], culminating in the first book on deterministic artificial intelligence [30]. Using this established methodology, this manuscript applies the methods to control DC motors, in direct comparison with the aforementioned self-tuning methods of Astrom and Wittenmark, which were already compared to one another other in 2017 [11]. This manuscript will describe the detailed development of the deterministic artificial intelligence method and subsequent application of the method to identical systems of the comparison in [11]. The demanded trajectory was the square wave, similar to that used in [11], which was illustrated in 2020 by [PERSON] et al. [31], in order to remain challenging for nonlinear adaptive methods. This is displayed in Figure 1a, accompanied by a similar plot from [11] which establishes context for this manuscript. The research questions addressed include:  
1. Can deterministic artificial intelligence (D.A.I.) be successfully applied to DC motors as has recently been accomplished in spacecraft and unmanned underwater vehicles? 2. How does the control of DC motors with D.A.I. compare to the performance of indirect and direct self-tuning regulators (with variations of modeling to include plant cancellations and disparate pole excesses)? 3. Can implementation of D.A.I. aid target tracking of challenging square wave trajectories superior to the cited nonlinear adaptive control techniques (self-tuning regulators and model-reference adaptive controllers)?
## 3 Results
### Datasets  
We utilize a diverse set of datasets to evaluate the effectiveness of our approach across various domains:  
The PPA dataset we used consists of 576,289 nodes and 30,326,273 edges. Each node has 50 features. The data are derived from a protein-protein interaction graph containing proteins from 37 organisms. Each node is characterized by attributes such as protein type, composition count, and composition size. Edges signify confidence levels in specific protein interactions, including gene co-occurrence, fusion events, and co-expression.  
The DDI dataset contains 4267 nodes and 1,334,889 edges, with an average degree of 500.5. This dataset represents a drug-drug interaction network, where nodes represent drugs and edges indicate interaction relationships. Its goal is to predict interactions between drugs. Positive samples indicate the presence of interaction and negative samples indicate absence. It is aimed at aiding pharmaceutical research.  
The Collab dataset has 235,868 nodes and 1,285,465 edges. Each node has 128 features. It represents a collaboration network among authors. Nodes stand for authors and edges represent collaborations. Each node is characterized by 128-dimensional features obtained by averaging word embeddings from published papers. The task in this dataset involves predicting future author collaborations based on past interactions.  
The Citation2 dataset has 2,927,963 nodes and 30,561,187 edges. Each node has 128 features. It encompasses multiple citation networks from diverse academic fields. The goal is to predict missing citation relationships based on existing citations. The dataset includes citation networks from the Computer Science Department at New York University (from 2003 to 2013) and the Max Planck Institute for Mathematics in Germany (from 1993 to 2003), comprising all papers from each institution.  
The Jujube dataset contains 2118 nodes and 43,417 edges. Each node has 32 features. It is a proprietary dataset related to the sales links of Xinjiang jujube. It is sourced from statistical data on the jujube market from the Xinjiang Production and Construction Corps and local market research. In experimentation, the dataset will be divided into training and testing sets, with 247 nodes selected for validation and testing purposes.  
The metrics reported in Table 1 include Hits@100, Hits@20, and MRR (Mean Reciprocal Rank) for a comprehensive evaluation of the link prediction performance.
# Dosimetric Evaluation in Micro-CT Studies Used in Preclinical Molecular Imaging
## 2 Materials and Methods  
### The microCT Unit  
The unit consists of a 50 kV microfocus X-ray tube with a focal spot size of 35 \(\upmu\)m (Oxford Instruments XTF5011, X-ray Technologies Inc., Scotts Valley, CA, USA), a fixed tungsten (W) anode, and a 125 \(\upmu\)m beryllium exit window. The added filtration is a 0.5 mm aluminum (Al) plate. The source's maximum operation current is 1 mA, and it delivers a 22\({}^{\circ}\) cone radiation beam. The electron gun assembly is packaged inside a stainless-steel lead-lined tube that provides X-ray shielding to 0.25 mR/h at 5 cm [12]. A CsI(Tl) pixelated flat panel detector (Hamamatsu C7942) is used as the X-ray image sensor and is placed at a fixed distance from the source (425 mm). Both tube and detector rotate in synchrony around the subject; the X-ray source's distance to the isocenter is 290 mm. The described geometry allows two FOVs: transaxial (80 mm) and axial (65 mm). The equipment is fully shielded against X-rays by achieving an integrated and fully enclosed shield of 1 mm lead and lead-shielding glass. At a distance of 10 cm from the surface of the housing, the maximum exposure arising from the X-ray is below 1 \(\upmu\)Sv/h. Furthermore, the X-ray tube can only be activated when all the equipment's doors and covers are closed.  
The manufacturer presets the CT acquisition configuration by combining the following parameters: tube voltage, filament current, and X-ray emission time (i.e., number of projections) during the study. Consequently, the system allows combinations between two voltage values, 35 kV (named Low Voltage) and 45 kV (High Voltage), with currents of 0.2 mA (named Low Dose) or 0.4 mA (High Dose). The combination of voltage and current with the modality (number of projections) determines the \"quality\" of the image; the nominal number of projections (views) of the \(\upmu\)CT unit are 270 (named Standard), 420 (Good), 600 (Best), and 1000 (High Res). The microCT unit can also obtain conventional radiographic (2D) images with the same combination of these parameters.
## 2 Related Work  
During the past few years, there are many methods attempting to segment the salient object from the saliency map of an input image [13; 14; 15; 16; 17; 18; 19]. The quality and effectiveness of segmentation are decided by the quality of saliency map [20]. Based on the milestone work, some structural matrix decomposition-based methods transform a saliency detection problem into a feature subspace decomposition problem, which can improve detection results in terms of both speed and accuracy. Particularly, many studies conclude that low-rank matrix decomposition-based methods can obtain better saliency detection performance. These methods assume that an image can be represented as a combination of a highly redundant part (e.g., visually consistent background regions) and a sparse part (e.g., salient object foreground regions). Therefore, given the feature matrix of an input image, it can be decomposed into a low-rank matrix corresponding to the non-salient background and a sparse matrix corresponding to the salient foreground objects. [PERSON] et al. [21] employed sparse coding as a feature representation vector of image. [PERSON] et al. [22] designed multi-scale superpixel segmentation to construct the feature matrix and prior matrix. Although [PERSON] et al. [23] adopted learnt linear transformation of the feature space to integrate low-level features and high-level prior knowledge, the learnt transform matrix is correlated for training data set. Unfortunately, the sparsity assumption of the salient objects can't be guaranteed universally, especially when the salient objects with big size occupy most of the image, and then suffer from limited adaptability. Therefore, [PERSON] et al. [24] developed tree-structured sparsity-inducing regularization and Laplacian regularization to disentangle the salient objects and background precisely, and then obtained competitive results. But it may be difficult to suppress some small background regions with distinctive appearances because of the constructed index-tree is not precise enough. Subsequently, [PERSON] et al. [25] presented diversity-induced regularization based on Hilbert-Schmidt independence criterion, which make the background much cleaner in the saliency map and boost the saliency detection performance. But, they don't consider the low-rank characteristic for the foreground regions and background regions simultaneously, and ignore the spatial and pattern relations of image regions, which may lead to very noisy saliency map and influences on the final segmentation performance.  
To solve the problems mentioned above, the proposed DLRSD model considers the correlation between defective regions and defect-free regions, which is different from existing methods in essence. Besides, it uses the nuclear norm to depict the low-rank property of defect object rather than consider it as the sparse noises, which can produce more accurate and reliable saliency map that represents the defect foreground image.