esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation and User Scenario
### Evaluation
#### 4.4.4 Parameter Settings  
During the evaluation, a test dataset is used, consisting of tuples that include 30 responses related to the found articles, VR, and insights. The prepared responses include the following: (1) a user's preference question to recommend an item from the dataset, (2) a correct target answer/context related to the e-commerce domain, and (3) the answer generated by CORES in response to the question. The yoga mat dataset used in this experiment consists of 300 items within the e-commerce system. This dataset plays a crucial role in evaluating the efficiency of the proposed CORES system, as outlined in Table 3. CORES stands out from traditional methods by integrating context-aware AI, emotion-driven recommendations, and immersive VR shopping experiences. Unlike static ratings, CORES analyzes a wide range of factors, including users' preferences, emotional insights, interaction histories, and contextual attributes (e.g., _style_, _size_, _and color_), to provide personalized and dynamic recommendations. To manage new or unique items, CORES incorporates the following: (1) cold start handling that matches the attributes of new items to users' preferences using content-based filtering and (2) predicts emotional patterns based on similar items, ensuring accurate and reliable evaluations of previously unseen products.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Types of Questions** & **\#Questions** & **Examples of Requests** \\ \hline \multirow{3}{*}{Find items} & \multirow{3}{*}{10} & I want to buy a thicker mat for extra custioning, vibrant colors. \\  & & Please display three best items. Find the number of mixed styles. List \\  & & all thicker mats with low prices. \\ \multirow{3}{*}{VR visualization} & \multirow{3}{*}{10} & Provide details of more products. I want to show products in 3D \\  & & vision. \\ \multirow{3}{*}{Insight analysis} & \multirow{3}{*}{10} & Draw a bar chat for top purchased products. Are there any prices we \\  & & can increase, and by how much, without losing customers? \\ \hline \hline \end{tabular}
\end{table}
Table 2: Types of questions in CORES.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline \#items list & 300 \\ \#relevant items & 20 \\ \# non-relevant items & 270 \\ \#users & 100 \\ \# emotions for evaluation & 150,000 \\ \#positive emotions & 83,075 \\ \#negative emotions & 66,925 \\ Preferences \(W_{clear}\), \(W_{style}\), \(W_{e\text{co-friendless}}\) & 0.5, 0.3, 0.2 \\ \(\alpha\), \(\beta\) & 0.4, 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental setup.
# A Review of Deep Learning Applications for the Next Generation of Cognitive Networks
## 1 Introduction
Some examples are self-driving cars, robots, chatbots, text-to-speech gadgets, devices that automatically translate text and images [11], speech recognition systems, digital assistants such as Google Now and Amazon Alexa, automatic image classification systems; systems to answer natural-language questions [12], to play video games like chess, Jeopardy, GO, and poker [13] or dynamically adjust the difficulty [14]; and systems for adding sound automatically to silent movies, etc. For these reasons, DL tools are being adopted in many other domains such as health sciences for disease (cancer, dermatology problems, etc.) prediction. For example, in biological sciences, [PERSON] et al. [15] used a DL approach to forecast the survivability of a cancer cell line exposed to a medication in the biological sciences. [PERSON] et al. [16] employed DL in conjunction with a convolutional network architecture to predict DNA and RNA-binding protein specificities. [PERSON] et al. [17] employed a DL technique to predict tumor suppressor genes and oncogenes. Single-cell DNA methylation statuses have also been accurately predicted using DL techniques [18] in the genomic domain for the prediction of breeding values and phenotypes of traits of interest in many cultivars using as input environmental and genomic information [19; 20; 21; 22]. However, the application of DL in the telecommunications field is almost new; however, it is clear that what motivates its study is the need to build more efficient and autonomous network connectivity, that is, with less human intervention. In this paper, we review the applications of deep learning for next-generation cognitive networks to obtain a meta-picture of its performance and highlight how these tools can help solve the challenging problems of cognitive networks. We also provide the fundamentals of DL, the requirements for its appropriate use, general guidance on how to use the DL method effectively, the pros and cons of this technique, and the trends of DL applications.
## 4 Results and Discussion
### Performances of the BET Consensus Controller
#### 4.1.3 Utilization of Channels  
The channel usage was recorded as shown in Table 4 for the BET controller. The number of channels (NOC) was not constant during ET, and it depended on the value of ETF whether to stop or to allow agent communication among agents. Based on Table 4, the agent successfully reduced its channel utilization by 21.79%. Details of the calculations of the agent are presented below.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline Time (s) & 20.4 & 40.8 & 61.2 & 81.6 & 102 & 122.4 & 142.8 & 163.2 & 183.6 & 204 & 225.2 \\ \hline Iteration & 100 & 200 & 300 & 400 & 500 & 600 & 700 & 800 & 900 & 1000 & 1104 \\ \hline NOC & 248 & 181 & 177 & 255 & 436 & 500 & 500 & 500 & 500 & 500 & 520 \\ \hline \end{tabular}
\end{table}
Table 4: Number of channels per 100 iterations. NOC, number of channels.  
Figure 8: Agent movement to meet at rendezvous point.  
Total of iteration = 1104/2 = 552 iterations.  
Time per iteration = 225.424/1104 = 0.204 s.  
Each iteration is equivalent to 10 channels, which carried a total of 552 \(\times\) 10 = 5520 channels.
## 3 Flight Controller Design  
The tilt tri-rotor UAV is a novel aircraft with unique structure, resulting in a challenging control problem. The block diagram of the proposed flight control system is shown in Figure 3. The flight control system consists of two parts: the cascade controller and the control allocator. The cascade controller generates the virtual control quantities of the position and attitude control. The outer-loop is used for position control, and the inner-loop handles the attitude control of the tilt tri-rotor UAV. The control allocator, which maps the virtual control command to individual actuators, is used to deal with the problem of mismatch between the numbers of the virtual control quantities and the actual actuators.  
In the position control loop of the cascade controller, to ensure the trajectory tracking error converges to zero asymptotically, we choose the auxiliary dynamic system based SMC approach to design a saturated control input \(U_{p}=[U_{x},U_{y},U_{z}]^{T}\), which can limit the magnitude of the thrust and the reference signal of pitch and roll motion of the attitude loop. Then, we utilize the SMC approach with a disturbance observer for the attitude loop to address the model uncertainties and disturbances. Besides, an optimization algorithm based control allocation is developed to obtain high control precision.
## 3 Methodology
### Dataset Description and Analysis
#### 3.2.5 Data Enhancement and Scenario Testing  
To bolster the dataset and ensure it represents a wide range of daily operations, further variations were introduced:  
1. Scenario Variability * Occupancy Adjustments: Incremental increases of 10-40% and a cutback to 85% baseline to emulate extremes of usage; * Demographic Ratios: Shifts in the male-to-female ratio to gauge differential fixture requirements; * Temporal Patterns: Inclusion of seasonal and time-of-day fluctuations; * Event-Based Surges: Additional bursts in occupancy during conferences, meetings, or other peak activity windows.
2. Validation with Queueing Models * M/M/c and M/G/c queueing frameworks were applied to confirm that the synthetic data mirrored realistic operational patterns in queue lengths and waiting times.
3. Expert Review * Facility management practitioners reviewed these extended scenarios to validate that the adjustments stayed true to real-life building dynamics.  
Figure 4 illustrates the overall workflow for scenario generation and testing. This structured approach ensures the model's training extends beyond a single building context, making it more flexible for varied real-world conditions.  
Figure 3: Gender distribution across building floors.
## 3 Results
### Spectral Features Underlying the PLS R/NR Scale  
Visualization of the latent variables of the PLS model gives access to infrared wavenumbers retained to construct the R/NR spectral scale. Figure 4 displays the mean of the twelve latent variables of the PLS model together with the minimum to maximum space of variability of these latent variables represented by the shaded area. The spectral regions containing the most informative wavenumbers can be highlighted: especially 885-910 cm\({}^{-1}\) assigned to vibrations of phosphorylated proteins, 1125 cm\({}^{-1}\) vibration of carbohydrates, and Amide bands corresponding to vibrations of peptide bonds and informative of the secondary structure of the proteins (Amide III between 1200-1350 cm\({}^{-1}\), Amide II between 1500-1600 cm\({}^{-1}\) and Amide I between 1600-1700 cm\({}^{-1}\)). The vibration associated with the wavenumber at 1740 cm\({}^{-1}\) is likely to be assigned to the ester bond in lipids.  
Figure 4: Vibrational infrared features involved in the PLS modeling of the NAC response. The solid line corresponds to the mean of the first twelve PLS latent variables (LV) used in the model and the shaded area represents the minimum to maximum space of variability of these latent variables. The wavenumbers associated with the main spectral features are also indicated.  
Figure 3: 2D map of sensitivity (**A**) and specificity (**B**) for test set according to the values of percentage of pixels (\(x\) axis) and R/NR PLS score (\(y\) axis).
# Application of Deep Learning Techniques for the State of Charge Prediction of Lithium-Ion Batteries
## 4 Implementation and Results
### DataSet
The model from epoch 60 showed the best performance.
## 3 Results
### Validation of the Cost Optimization Method  
To contrast the results obtained by the PSO optimization algorithm, they are compared with the results obtained from an iterative heuristic optimization of the SAPV system design based on discrete increases and decreases in the installed PV power and battery storage capacity. The heuristic algorithm followed for the iterative optimization of the SAPV system is the one shown in Figure 11.  
Each candidate solution consists of:  
* Candidate 1: Discreet increase in PV power.
* Candidate 2: Discreet increase in battery capacity.
* Candidate 3: Discrete decrease in PV power.
* Candidate 4: Discreet decrease in battery capacity.  
The optimal improvement is chosen as the one that minimizes the cost of the installation among the four tested options. If this does not improve with respect to the system established in the previous optimization iteration, the algorithm concludes that it has reached the optimal solution.  
The solution found, the optimization time of the PSO optimization algorithm, and the discrete heuristic optimization procedure have been compared. In the first place, the solution reached in both methods is the same, showing that both converge equally for the same installed PV power and installed battery capacity (Figure 12).  
In the PSO algorithm, some points do not get to group with the rest at the optimal value in the established simulation time due to the lack of iterations. These values are typically the ones that were originally furthest from the optimal solution. The points that are not directly in the optimum will not affect the solution delivered by the method, since it is considered the best of each series.  
The simulation time is a difficult parameter to compare between the two methods, since while the PSO algorithm always performs the same number of operations given that the number of iterations and systems (particles) is predefined, the iterative heuristic method starts from an initial system that may be closer or further from the final solution, and the number of operations to perform will vary with it (Figure 13). The data collected for some of the simulations carried out in the previous sections are shown in Table 1.  
Figure 10: Evolution of the objective function for a particle and the global absolute.  
Figure 11: Iterative process of discrete optimization of the SAPV system.  
Figure 12: Convergence of the PSO method (red points) and heuristic method (rest of points for different initial values).
# Aging Impairs Unimanual and Bimanual Hand-Grip Force Control Capabilities
## 2 Materials and Methods
### Experimental Setup
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristics** & **Older Group** & **Younger Group** & \(\mathbf{p}\)**-Value** \\ \hline Sample size & 32 & 32 & - \\ Sex (female/male) & 26:6 & 26:6 & - \\ Age (years) & 64.6 \(\pm\) 3.4 & 23.3 \(\pm\) 2.4 & \(p\) \(<\) 0.001 * \\ Handedness & 34 right-handed & 34 right-handed & - \\ K-MMSE & 28 \(\pm\) 1.6 & 29.6 \(\pm\) 0.8 & \(p\) \(<\) 0.001 * \\ Weight (kg) & 60.2 \(\pm\) 9.3 & 59.8 \(\pm\) 10.5 & \(p\) \(=\) 0.871 \\ Body muscle mass (kg) & 21.9 \(\pm\) 4.1 & 24.4 \(\pm\) 6.0 * & \(p\) \(=\) 0.056 \\ Body fat mass (kg) & 19.8 \(\pm\) 5.7 & 15.5 \(\pm\) 3.6 * & \(p\) \(<\) 0.001 * \\ BMI (kg/m\({}^{2}\)) & 24.0 \(\pm\) 2.9 & 22.1 \(\pm\) 3.0 * & \(p\) \(=\) 0.017 * \\ \hline \multirow{3}{*}{Hand-grip MVC (kg)} & Unimanual dominant & 21 \(\pm\) 5.6 & 29.8 \(\pm\) 12.7 & \(p\) \(<\) 0.001 * \\  & Unimanual non-dominant & 19.3 \(\pm\) 5.6 & 26.4 \(\pm\) 11.3 & \(p\) \(=\) 0.002 * \\ \cline{1-1}  & Bimanual & 38.3 \(\pm\) 13.1 & 57.4 \(\pm\) 27.5 & \(p\) \(<\) 0.001 * \\ \hline \hline \end{tabular} Abbreviations. K-MMSE = Korean Mini-Mental State Exam; BMI = Body Mass Index; MVC = maximal voluntary contraction. Data are given as mean \(\pm\) standard deviation. * indicates missing body composition data (one male). * indicates \(p\) \(<\) 0.05.  
\end{table} 
Table 1: Demographic information for participants.
## 2 Materials and Methodology
### Classification of Features  
Feature extraction is one part of the problem-solving process, whereas the other part involves classification, which optimally discriminates the two given classes; it can be achieved using different available classification algorithms such as decision trees (DT), support vector machines (SVM), naive Bayes, logistic regression, and \(k\)-nearest neighbour.  
* DT is a classical classification supervised learning algorithm applied in different application domains such as medical diagnosis [18], signal processing [19], and intrusion detection [20]. It is a hierarchical classifier that builds a multi-label discrimination between classes to determine by determining their specific patterns, and it is very flexible in terms of handling both binary and multi-class classification problems [21].
* SVM is a supervised learning algorithm mostly used for binary classification [22]. It works by finding an optimal separating hyperplane (a decision boundary) that separate the two classes.
* Logistic regression (LR) is also used for binary classification problems [23]; it determines a relationship between categorical independent variables and dependent variables by evaluating probabilities using a logistic function. LR is computationally efficient and takes less time to train compared to SVM.
* The naive Bayes (NB) classifier finds the probability of each class using a Bayesian formula [24; 25]. It makes an assumption that all features of the samples in a particular class are independent of each other, and then discriminates the features by evaluating the posterior probability for each class, before allocating the feature to the class generating the maximum posterior probability.
* \(K\)-nearest neighbour (KNN) is a nonparametric classification algorithm that discriminates instances into their distinct classes according to the degree of likeness [24]. The input datasets are separated into \(K\) groups with during training where each instance is composed of features belonging to its group.  
These classifiers were trained one at a time to predict burns and healthy skin as shown in Figure 4. Each classifier was separately trained using two sets of features (FC1 and FC2). The broken lines from FC2 into the individual classifiers indicate that these features were not passed into any of the classifiers once these classifiers were actively classifying FC1 features. The FC2 features were classified after the classifiers completed their task on the FC1 features. The accuracy and training time of each classifier was then computed.
# An Approach to Industrial Automation Based on Low-Cost Embedded Platforms and Open Software
## 3 System Implementation
### Process Level
#### 3.2.3 Extended Kalman Filter
\begin{split}\mathbf{A}&=\begin{bmatrix}-\frac{a_{2 }}{A_{1}}\sqrt{\frac{g}{2}}\frac{u_{e_{2}}}{\sqrt{h_{e_{1}}}}&0\\ \frac{a_{2}}{A_{2}}\sqrt{\frac{g}{2}}\frac{u_{e_{2}}}{\sqrt{h_{e_{1}}}}&-\frac{a_{2}}{A_{2}} \sqrt{\frac{g}{2}}\frac{u_{e_{3}}}{\sqrt{h_{e_{2}}}}\end{bmatrix}\\ \mathbf{B}&=\begin{bmatrix}\frac{q_{0}}{A_{1}}&-\frac{a_{2}}{A_{1}}\sqrt{2 gh _{e_{1}}}&0\\ 0&\frac{a_{2}}{A_{2}}\sqrt{2 gh_{e_{1}}}&-\frac{a_{2}}{A_{2}}\sqrt{2 gh_{e_{2}}} \end{bmatrix}\\ \mathbf{C}&=\begin{bmatrix}1&0\\ 0&1\end{bmatrix};&\mathbf{D}&=\begin{bmatrix}0&0&0\\ 0&0&0\end{bmatrix}\end{split}  \tag{8}\]  
The parameters \(\mathbf{Q}\), \(\mathbf{R}\) and \(\mathbf{P}_{k}\) of the EKF are adjusted through recursive simulations in MATLAB, where \(\mathbf{P}_{k}=\mathbf{I}\in\mathbb{R}^{2\times 2}\) and \(\mathbf{Q}=1\times 10^{-8}\mathbf{I}\). The \(\mathbf{R}\) matrix setting considers two scenarios. The first scenario considers the transition during step-type reference changes, in which the correction component of the EKF is prioritized to track the measured signal. The value selected for this case is \(\mathbf{R}=1\times 10^{-8}\mathbf{I}\). The second scenario is presented when the tank levels enter a range of \(\pm 5\%\) error with respect to their references (steady state); in this case, the EKF estimation component is prioritized, selecting \(\mathbf{R}=1\times 10^{-2}\mathbf{I}\). The EKF delivers the \(\tilde{h}_{1}\) and \(\tilde{h}_{2}\) estimations of tank levels as outputs. Equations (9) and (10) present the residues obtained from the comparison of the estimated and measured values. \[r_{1}=h_{1}-\tilde{h}_{1} \tag{9}\]  
\[r_{2}=h_{2}-\tilde{h}_{2} \tag{10}\]  
Leak detection is performed by using thresholds. Experimentally, a threshold is determined as \(lim=-0.010\). When the residue is smaller than this limit, the existence of a leakage is concluded. The data set that pertains to a leakage is  
\[\mathit{leak}\left(\mathbf{r}\right)=\left\{\mathbf{r}=\left[ \begin{array}{ cc}r_{1}&r_{2}\end{array} \right]^{T}\left|\mathbf{r}\in r_{1}\lor r_{2}<lim\right\} \tag{11}\]
## 2 Materials and Methods
### Ecosystem Greenness Assessment
#### 2.4.1 Normalized Difference Vegetation Index (NDVI)  
The Normalized Difference Vegetation Index (NDVI) is derived from the difference between the reflectance of the near-infrared (NIR) band and the red band, normalized by their sum [37; 38]. This index effectively detects changes in vegetation cover and measures regional vegetation conditions, spatial heterogeneity, and spatiotemporal distributions [39]. In areas with dense vegetation, the contrast between the NIR and red bands is high, while it is minimal in regions with sparse or no vegetation.  
The NDVI was calculated as follows:  
\[NDVI=\frac{\rho_{n}-\rho_{r}}{\rho_{n}+\rho_{r}} \tag{17}\]  
Here, \(NDVI\) represents the Normalized Difference Vegetation Index, \(\rho_{n}\) represents reflectance in the NIR band, and \(\rho_{r}\) represents reflectance in the red band. Higher NDVI values indicate better vegetation cover.
## 2 Mathematical Representation
### Metamodeling of HNS Propagation  
As mentioned above, the present study developed a metamodel for predicting HNS propagation based on the CFD results. The well-known Kriging model was adopted for to make the metamodel [18; 19; 20]. Basically, the unknown function \(y(x)\) includes the regression model and the stochastic process and has two terms, as follows:  
\[y(x)=f(x)+Z(x) \tag{8}\]  
where \(f(x)\) represents the approximation function, and \(Z(x)\) indicates the localized deviation interpolating the n sampled values. Here, the mean value of the variance was assumed to be zero. The correlation between \(Z(x^{i})\) and \(Z(x^{i})\) is given by the covariance matrix:  
\[\text{Cov}[Z(x^{i}),\,Z(x^{i})]=\sigma^{2}\text{R}([R(x^{i},\,x^{i})]) \tag{9}\]  
where \(\mathbf{R}\) is the correlation matrix and \(\sigma^{2}\) is the process variance. \(R\) also indicates Gaussian correlation function expressed by  
\[R(x^{i}_{k},x^{j}_{k})=\exp\left[-\sum_{i,j=1}^{n}\theta_{k}\Big{|}x^{i}_{k}-x ^{j}_{k}\Big{|}^{2}\right] \tag{10}\]  
Consequently, the Kriging predictor \(\hat{y}(x)\) can be expressed at an arbitrary point \(x\) as follows:  
\[\hat{y}=\hat{\beta}+r^{T}(x)R^{-1}(y-f\hat{\beta}) \tag{11}\]  
where \(y\) represents the CFD simulation result with a column vector of length \(n,f\) is a unit column vector of length \(n_{s}\), and \(r^{T}\) refers to a column vector of length \(n\) between each point \(\{x^{1},\,...,\,x^{n}\}\) and arbitrary points \(x\). The column vector is expressed as follows:  
\[r^{T}(x)=\left[R(x,x^{1}),\,R(x,x^{1}),\,\ldots,\,R(x,x^{n})\right]^{T} \tag{12}\]  
In Equation (11), \(\hat{\beta}\) is obtained by \(\left(f^{T}R^{-1}f\right)^{-1}f^{T}R^{-1}y\) and the correlation parameterwhere \(\hat{\sigma}^{2}\) is determined by  
\[\hat{\sigma}^{2}=\left[\left(y-f\hat{\beta}\right)^{T}R^{-1}\left(y-f\hat{\beta} \right)\right]/n \tag{14}\]  
Indeed, \(\theta_{k}\) was iteratively determined for each variable based on the CFD simulation, requiring a large computational cost.