esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Explainable AI Methods
#### 3.3.2 Mathematically Explained XAI  
This section introduces two widely adopted and useful techniques for performing the task of mathematical interpretability implemented in the presented work. The mathematical interpretability offers t-SNE and UMAP techniques to represent the high-dimensional graph into lower dimensional space without compromising on the clustering structure.  
Primarily, both the t-SNE and UMAP are meant for visualization; however, the main difference lies in the interpretation of the distance between the clusters. The t-SNE merely preserves the local structure in the data, whereas the UMAP can preserve both local and global structure in the data, which means that unlike the UMAP, the dissimilarity and the distance between clusters can not be interpreted with the t-SNE.  
* T-Distributed Stochastic Neighbor Embedding (t-SNE) The t-SNE [57] is a variation of the SNE technique that makes the visualization of high-dimensional data possible by associating with each datum a location in lower dimensional space of two or three dimensions. It has been developed to face two issues that affect SNE technique: 1. The optimization of the cost function, by using a variation of SNE cost function (symmetrized) and using a Student's t distribution for the computation of similarity between two datapoints in the lower-dimensional space. 2. The so-called \"crowding problem\", by using a heavy-tailored distribution in low-dimensional space.
* Uniform Manifold Approximation and Projection (UMAP) The UMAP [58] is a nonlinear technique for the dimensionality reduction. It is based on three assumptions: 1. Data are uniformly distributed on an existing manifold; 2. Topological structure of the manifold should be preserved; 3. Manifold is locally connected. The UMAP method can be divided into two main phases: learning a manifold structure in a high-dimensional space and finding the relative representation in the low-dimensional space. In the first phase, the initial step is to find the nearest neighbors for all datapoints, using the nearest-neighbor-descent algorithm. Then, UMAP constructs a graph by connecting the neighbors identified previously; it should be noticed that the data are uniformly distributed across the manifold, so the space between datapoints varies according to regions where data are denser or sparse. According to this assumption, it is possible to introduce the concept of _'edge weights'_: from each point, the distance with respect to the nearest neighbors is computed, so the edge weights between datapoints are computed, but there exists a problem of disagreeing edges.
# Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids
## 1 Introduction
### Contributions and Research Questions  
This paper was motivated by how AR-LSTM and S-MPC could collaborate in applying the EM of MGs. While there is a consensus that combining the two algorithms may yield benefits, little has been done to develop methods that involve the two algorithms working together. In addition, works have investigated how these controllers can collaborate with the algorithms working with different control designs and modes. No previous research has compared and combined S-MPC and AR-LSTM for the same optimal control problem formulation in EM for MGs.  
The second objective of this paper was to propose a novel method known as switched auto-regressive neural control (S-ANC), which merges S-MPC and AR-LSTM synergistically. The development and formulation of this new S-ANC algorithm were motivated by the conceptual and practical comparison of S-MPC and AR-LSTM. In contrast to comparable approaches, our method combines the S-MPC objective function and constraints with the AR-LSTM optimization and prediction function. This practice ensures interoperability between the two methods and enables the truncation of the S-MPC optimization problem, which can become highly complex, even for relatively simple MG structures. Finally, a flexible hybrid MG case is used to describe and evaluate this new algorithm.  
Consequently, the primary contribution of this paper is the introduction of S-ANC, a control algorithm that combines techniques from the communities of control theory and ML. This algorithm is evaluated, and a new standard framework is generated for EM of hybrid MGs. In addition, the proposed S-ANC algorithm applies to various applications and domains, such as complex industrial processes and energy markets. This study also combines control theory and ML by comparing and disentangling the key distinctions between S-MPC and AR-LSTM.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Control Method** & **Optimality** &
\begin{tabular}{c} **Computational** \\ **Time [s]** \\ \end{tabular} & **Multiple Models** & **Adaptability** & **Constraints** \\ \hline MPC [62; 63] & ✓ & \(>\)10\({}^{4}\) [High] & \(\times\) & Good & ✓ \\ \hline S-MPC [15; 16; 20; 64] & ✓ & \(\approx\)10\({}^{4}\) [Moderate] & ✓ & Outstanding & ✓ \\ \hline DLC [29] & ✓ & \(\approx\)10\({}^{4}\) [Moderate] & \(\times\) & Good & ✓ \\ \hline AR [32] & \(\times\) & \(\approx\)10\({}^{4}\) [Moderate] & \(\times\) & Poor & \(\times\) \\ \hline CNN [65; 66] & \(\times\) & \(<\)10\({}^{4}\) [Low] & \(\times\) & Poor & \(\times\) \\ \hline RNN-LSTM [67; 68] & ✓ & \(<\)10\({}^{4}\) [Low] & \(\times\) & Poor & \(\times\) \\ \hline S-ANC & ✓ & \(<\)10\({}^{4}\) [Low] & ✓ & Outstanding & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of Control Methods.
# Priority-Based Machine-To-Machine Overlay Network over LTE for a Smart City
## 4 Modelling PRACH for H2H and PM2M Traffic
### PM2M Overload Calculation  
The Bit Error Rate (BER) of the PM2M overlay depends on the interference caused by H2H traffic. We estimate the interference through the bit error rate BER experienced by PM2M traffic. To this end, we need to find the outage/overload probability of PM2M based on SINR requirements for PRACH, which can be expressed as:  
\[\begin{split} E_{b}/N_{0}&=\frac{\frac{N_{b}I_{1}} {R}}{\frac{(n)I_{1}+(N_{M}-1)I_{1}+\eta_{p,1}+\eta_{0}W}{W}}\\ &=N_{b}\frac{W/R}{n+N_{M}-1+\eta_{p,1}/I_{1}+\eta_{0}W/I_{1}}\end{split} \tag{11}\]  
where  
\(N_{b}\) is the number of preamble elements;  
\(W\) is the LTE bandwidth;  
\(R\) is the LTE PRACH preamble data rate;  
\(I_{1}\) is the received signal power;  
\(\eta_{p,1}\) is the outer cell interference power; \(\eta_{0}\) is the spectral density of white noise; and  
\(N_{M}\) is the number of PM2M preamble codes that are always active.  
The overload probability of PM2M data on each preamble can be derived analogously to Equations (2) and (5) as:  
\[\begin{split} M\_load_{n}&=P_{r}(n+\frac{\eta_{p,1 }}{I_{1}}>\frac{WN_{b}}{RT_{M}}-N_{M}-\frac{W\eta_{0}}{I_{1}})\\ &=erfc(\frac{\frac{WN_{b}}{R}\frac{1}{I_{M}}-\frac{W\eta_{0}}{I _{1}}-n-N_{M}+1-k_{m,1}}{\sqrt{k_{v,1}}})\end{split} \tag{12}\]  
Then, the total PM2M overload probability can be obtained as:  
\[\begin{split} P_{M\_load}&=\sum_{n=0}^{\infty}P_{n }M\_load_{n}\\ &=\sum_{n=2}^{n_{\text{max}}}\frac{(\lambda_{total})^{n}}{n!}e^{ -\lambda_{total}}M\_load_{n}\end{split} \tag{13}\]  
Using the PM2M overload probability, we could approximate the BER as half of the overload:  
\[BER_{PM2M}=0.5P_{M\_load} \tag{14}\]
## 4 Results  
The results and a discussion of the research are presented in this chapter, which details both software and hardware specifications. Table 1 presents a comparison of performance metrics (FID scores and average inception scores) in three different models (CLIP, SD and DB + LoRa) in three different datasets (single, multi and both). The FID scores range from0.06 to 0.12, with SD showing the best performance (lowest FID scores) in all datasets. For Inception scores (IS avg), DB + LoRa consistently achieves the highest values (2.36), followed by SD (2.33), while CLIP shows the lowest IS avg (1.57). Each model maintains consistent performance across different datasets, with only minor variations in FID scores.  
Table 2 presents a detailed evaluation of model performance using Inception Score Groups (G1-G10) in three different models (CLIP, SD, and DB+LoRa) and three dataset configurations (single, multi, and both).  
The scores show that CLIP consistently maintains lower values (around 1.51-1.57) across all groups, while SD and DB+LoRa achieve higher scores (ranging from 2.22-2.77). In particular, SD shows greater variability with peaks in G2-G4 (\(\approx\) 2.40) and G10 (2.77), while DB+LoRa demonstrates more stable performance between groups with slight improvements in G4 (2.41) and G9 (2.46).
## 3 Materials and Methods
### Classification  
Once the GA selects the best discriminative features, they are used to estimate the parameters of the classification model. Re-sampling statistical techniques are regularly used to avoid variability and uncertainty in evaluating the model performance. That is  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type of Feature**} & \multicolumn{4}{c}{**Image Channel**} & \multirow{2}{*}{**Total**} \\  & **R** & **G** & **B** & **Gray-Scale** & \\ \hline FOS Features & 4 & 4 & 4 & - & 12 \\ HOS features & 112 & 112 & 112 & - & 336 \\ HOG features & - & - & - & 81 & 81 \\ \hline
**Total** & 116 & 116 & 116 & 81 & 429 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Details of the original 429 features, before reduction by GA.  
done by estimating the parameters of the model multiple times from feature samples, using a technique such as the \(k\)-fold crossvalidation, where the best discriminative feature set is partitioned into \(k\) groups. Each group can be used as a test set while the other groups are used for training.  
In this article, we use the \(k\)-fold crossvalidation technique for both datasets. For the HRF dataset, a leave-one-out crossvalidation is applied, i.e., \(k=1\). That is good for small datasets, such as the HRF dataset, which has 45 images only. The HRF feature set is split such that, every time, 44 images are used for training and the remaining images are used for testing.  
On the other hand, there are 288 images in Dataset_2. Thus, a 9-fold crossvalidation is the better choice. The features of Dataset_2 are split into 9 equal parts, 8 used for training and 1 for testing. These values lead to accurate estimation with low bias and modest variance while every fold has the same sample number of images, namely 32. The process of random partitioning is iterated 45 times for the HRF dataset and 9 times for Dataset_2, with testing sets determined at run time. The results of this exercise are reported in the tables below.  
Determining the most applicable supervised ML algorithm is an overwhelming task. The most suitable option to find the proper algorithm that can achieve a satisfying performance is experimentation. After extensive analyses and trade-offs between the speed, accuracy, and complexity of several classifiers, we found that four supervised ML algorithms achieved a good performance in our problem: \(k\)-nearest neighbor (\(k\)NN) with Euclidean distance measure, naive Bayes with Gaussian distribution (NB-G), decision tree (DT), and linear discriminant analysis (LDA).
## 4 Materials and Methods
### Dataset Acquisition  
Three publicly available benchmark datasets from TCIA Repository [24] are used to train, validate and test _DETECT-LC_. The datasets are NSCLC Radiomics [25], NSCLC Radio-Genomics, NSCLC Radiomics-Genomics [26]. Several studies have been experimented with these datasets, which allows state of the art comparison with our model. In addition, it includes thoracic cavity binary segmentation masks, which enables the validation of our simple segmentation approach.  
**NSCLC Radiomics (Lung 1)** includes 422 non-small cell lung cancer (NSCLC) patients' pre-treatment CT scans. After the applied preprocessing, the dataset is set to be 395 volumes. On average, there are 123 slice/patient. Clinical data is provided together with the CT scans.  
**NSCLC Radiomics-Genomics (Lung 3)** holds pre-treatment CT scans, gene expression, and clinical data of 89 non-small cell lung cancer (NSCLC) patients. The included patients were treated with surgery.  
**NSCLC Radio-Genomics** is a cohort of 211 patients, where imaging data are also paired with gene mutation, RNA sequencing data from samples of surgically excised tumor tissue. Also, clinical data including survival outcomes are provided.  
For each dataset, the distribution of patients across the various histology groups and stages is shown in Table 1. In addition, the age distribution of subjects across stages I, II, IIIA and IIIB (gender differentiated) is illustrated in Figure 3. The violin plot shows no significant difference in terms of age between different stage groups (\(p=0.067\), Mann-Whitney U test).  
The datasets included carcinoma subtypes and stages that are out of the scope of the specified classes. Hence, their respective records were eliminated. The typical data used in our experiments are outlined in Table 1.  
Figure 2: _DETECT-LC_ multistage computational model for lung cancer pathology subtype or stage determination.
## 2 Experiments
### Experimental Apparatus and Main Procedures
#### 2.2.3 NMR (Nuclear Magnetic Resonance)  
Reservoir rocks and other porous media can be studied using NMR, an effective method for studying fluid behavior and interactions [28]. NMR provides insights into fluid distribution, pore size distribution, fluid-phase characterization, and wettability [29]. We used MnCl\({}_{2}\) solution for NMR spectroscopy instead of distilled water, because manganese chloride solution is a frequent paramagnetic shift reagent in NMR spectroscopy. Paramagnetic shift reagents are chemicals that interact with the NMR instrument's magnetic field, causing a change in the chemical shift of adjacent nuclei, resulting in a shift in the NMR [30]. The Mn\({}_{2}\)+ ion in manganese chloride has an unpaired electron, making it paramagnetic. When Mn\({}_{2}\)+ ions are given to a sample, they interact with the magnetic field of the NMR instrument, causing a paramagnetic shift in adjacent nuclei such as proton (1H) and carbon-13 (13C). This shift can help to improve the resolution and sensitivity of the NMR spectra by enhancing the NMR signals of these nuclei. Manganese chloride is a popular paramagnetic shift reagent in NMR spectroscopy because it is widely available, reasonably priced, and gives strong signal enhancement for a wide variety of nuclei [31]. The detailed experimental steps of NMR are as follows and shown in Figure 4:  
10. Sample Preparation: Similar to the core displacement method, rock core samples were extracted and prepared for experimentation. The cores were cleaned, dried, and saturated with kerosene. For NMR, we used unsaturated dry rocks and rocks  
Figure 3: Core displacement experiment scheme.  
Figure 2: Core displacement experiment flow chart.  
saturated with kerosene and deionized water, first to observe all rock information, and then to observe the difference they make.
* NMR Measurements: The saturated cores were placed within an NMR instrument, and NMR measurements were performed. NMR signals were generated by the hydrogen nuclei (protons) present in the fluids within the rock pores.
* Fluid Characterization: Information on fluid-phase characteristics, including fluid composition, saturation, and distribution, was provided by NMR. It helps in understanding how low-salinity water, nanofluids, and mixed hybrid solutions interact with the reservoir rock, and how they affect the wettability and displacement of oil.
* Time-Lapse Monitoring: NMR can also be used for time-lapse monitoring to observe fluid displacement and flow processes within rock cores. This helps in tracking changes in fluid saturation, flow patterns, and recovery efficiency during low-salinity water, nanofluid, and mixed hybrid solution flooding experiments.
## 2 Materials and Methods
### The Architecture of GBNSS  
The GBNSS model is a graph-based neural network (GNN) designed for biological network similarity search. It integrates both topological and biological information using graph convolutional networks (GCNs), an attention mechanism, and a tensor network module to effectively capture species-specific biological network features. The architecture is structured as follows and the architecture of GBNN is shown in Figure 2:  
* Input Layers: The model operates on pairs of biological networks, where the edge indices and feature matrices of both networks are provided as inputs. Each node is represented by a feature vector of a size derived from the topological features and biological features.
* Graph Convolution Layers: The model consists of three layers of graph convolutions, each responsible for extracting increasingly abstract representations from the input networks. These three GCN layers ensure that the model captures both local information (immediate neighbors) and global patterns (up to three-hop neighbors), which are essential for biological network similarity search.
* Attention Mechanism: The abstracted node features from the GCN layers are passed through an attention mechanism, which helps in identifying the most important nodes in each graph. This mechanism generates a set of pooled features for each graph, effectively compressing the node features into a global graph representation.
* Neural Tensor Network: The pooled features from both graphs are passed through a tensor network layer, which calculates a pairwise similarity score between the two sets of graph representations. The layer uses 16 tensor neurons, which allows it to combine the two feature sets and capture complex interactions between the two graphs.
* Fully Connected Layer: The output from the neural tensor network is passed through a fully connected layer. This layer reduces the feature dimension and prepares the features for the final scoring layer.  
Figure 2: The architecture of a graph neural network in GBNSS.  
* Output layers: The final layer is a single neuron (output = 1), which outputs a similarity score, representing the predicted similarity between the two biological networks.
# Optimal Design and Hybrid Control for the Electro-Hydraulic Dual-Shaking Table System
## 3 Control Scheme Design
### Model Inverse Controller Design
The purpose of adding the term \(z^{p}\) into Equation (25) is to ensure the designed inverse model physically realizable while ensuring the magnitude-frequency characteristic of the designed inverse model consistent with the true inverse model. Combining Equations (24) and (25), the close-loop system transfer function with feed-forward inverse controller compensation can be obtained  
\[G_{s}(z)=\hat{G}_{d}(z)\hat{G}_{d}^{-1}(z)=\frac{N_{-}(z^{-1})N_{-}(z)}{z^{p}N _{-}^{2}(1)} \tag{26}\]Implementing the factorization, the \(N_{-}(z)\) can be expressed as  
\[N_{-}(z)=\prod_{i=1}^{p}N_{i-}(z)=\prod_{i=1}^{p}(z+a_{i})\ |a_{i}|\geq 1 \tag{27}\]  
Substituting Equations (27) into (26), it can be obtained  
\[G_{s}(z)=z^{-p}\prod_{i=1}^{p}\frac{N_{i-}(z)N_{i-}(z^{-1})}{\left[N_{i-}(1) \right]^{2}}=z^{-p}\prod_{i=1}^{p}\frac{(z+a_{i})(z^{-1}+a_{i})}{\left[(1+a_{i })\right]^{2}} \tag{28}\]  
Substituting \(z=e^{i\omega T}\) into Equation (28), where \(T\) is the sampling time and \(T\) = 0.001 s. Then, the following results can be obtained  
\[\angle G_{s}(e^{i\omega T})=-pwT \tag{29}\]  
\[\left|G_{s}(e^{i\omega T})\right|=\prod_{i=1}^{p}\left|\frac{a_{i}^{2}+2a_{i} \text{cos}\left(\omega T\right)+1}{a_{i}^{2}+2a_{i}+1}\right| \tag{30}\]  
The sampling time \(T\) is 0.001 s, thus \(wT\) can be approximately equal to 0 in a relatively large frequency width range. As a consequence, it can be obtained that \(\left|G_{s}(e^{i\omega T})\right|\approx 1\) from Equation (30) and that \(\angle G_{s}(e^{i\omega T})\approx 0\) from Equation (17) when \(wT\) is close to zero. From the above and combining Equations (29) with (30), it can be concluded that  
\[G_{s}(z)=\hat{G}_{d}(z)\hat{G}_{d}^{-1}(z)\approx 1 \tag{31}\]  
The Equation (31) demonstrates that the designed \(\hat{G}_{d}^{-1}(z)\) is approximately the inverse model of the identified system model and can be used as the system feed-forward inverse controller.
## 2 Materials and Methods
### Statistical Analysis  
From each simulation condition, the peak ACL force for each bundle was extracted. The peak muscle force before the peak ACL force was extracted. A temporal relationship was assumed where it would take an unknown amount of time from muscle contraction until the force was fully transferred to the ACL. Kinematics at the time of peak ACL force were extracted.  
The inputs to the OpenSim model were the forces applied to the knee via moments and muscle forces. The forces in turn influence kinematics, which dictate the ACL load. Generalized linear models [25] were constructed to reflect this causality. The first model was the influence of kinematics on ACL forces. The second model was the influence of external moments on ACL forces. The third model combined external moments and muscle forces.  
Because the relationship between forces and ACL loads may be non-linear and rely on complex interactions, a generalized boosted regression (GBM) was also created [26]. GBM is a machine learning algorithm that uses [PERSON]'s Gradient Boost algorithm to iteratively build a regression model via decision trees [27]. The algorithm begins by initializing a constant value upon which the model will be built. Then, residuals are calculated using the constant as the predicted value, and regression trees are made to fit those residuals. The model is updated by adding the constant value to the new regression tree multiplied by a learning rate. This process of making new regression trees based on the residuals of previous models and updating that model is repeated for a user-defined number of iterations or until additional regression trees fail to improve the model. The number of decision trees used to train the model in this study was set to 350, and the size of each tree could have a depth no larger than three. To prevent overfitting, cross-validation was implemented in the model with only a fraction (70%) of the data used to create each new tree in the model.  
Fit between models was compared using the squared correlation coefficient (R2), and variable importance was compared using the adjusted partial correlation coefficient. As calculations of partial correlations are not possible for a GBM model, variable importance is calculated as the loss of root mean square when the variable is excluded. Due to the exploratory nature and large amounts of data available, alpha was set at 0.00001, and no \(p\)-value adjustments were performed.