esa-sceva/satcom-chunk-collection huggingface dataset:
# Ventricular Fibrillation Prediction and Detection: A Comprehensive Review of Modern Techniques
## 2 Modern ECG Signal Processing Techniques for Detection and Prediction of Ventricular Fibrillation
### Neural Networks and Deep Learning Algorithms
#### 2.2.1 Convolutional Neural Networks (CNNs)
The training dataset was fed into the network, and the network weights were updated using the RMSprop optimizer with binary cross-entropy as the loss function. After each epoch, the network was tested using an internal validation dataset. The network hyperparameters, batch, and step size were also tuned during this process, and the network with the lowest loss value was selected once the loss value in the validation set stopped decreasing for the next 12 epochs. In [28], the authors use machine learning and deep learning models to automate diagnostics by capturing unseen patterns in datasets. Thus, the proposed method focuseson data augmentation using the augmentation technique that increases the size of the dataset by generating different images. The system proposed by the authors is a novel CNN model tuned with hyperparameters to obtain an optimal combination of parameters that minimizes the loss function and produces a better result. The results reported in [28] show that the hyper-adjusted model outperforms other machine learning models and the basic CNN model for the accurate classification of normal and five other different types of arrhythmias. The paper [29] presents a feature learning strategy that uses the improved variational mode decomposition technique to detect ventricular cardiac arrhythmias on ECG signals. A deep learning neural network and bat-rider optimization algorithm (BROA) as an optimized classifier are used to extract the features. To test the proposed algorithm, the ECG signals with arrhythmias from the MIT-BIH database are used.
# TMD Damping for Structures with Uncertain Modal Parameters
## 3 Effects of TMD Damping  
To show how TMD damping impacts the displacement response of the primary structure and the relative motion of the TMD mass, computations are performed for a structure with nominal modal parameters (Figure 1), for a structure with \(f_{1}=1.05f_{1,nom}\) and \(m_{1}=m_{1,nom}\) (Figure 2), and for the typical TMD mass ratio of 1%. The values of \(\gamma\) are selected to be greater and smaller than those given in (15) to clearly demonstrate the impact of the substantially reduced and increased TMD viscous coefficient \(\gamma c_{2}\) on \(X_{1}/X_{1,static}\) and the TMD relative motion amplitude \(X_{d}=max(abs(x_{1}-x_{2}))\). To normalize the y-axis of the TMD relative motion plot as well, \(X_{d}\) is divided by the maximum of \(X_{d}\) due to nominal TMD damping.  
Figure 1 clearly demonstrates that--in the case of precisely known modal parameters--any TMD damping other than the optimum solution (9) increases the primary structure displacement amplitude \(X_{1}\). Increased TMD damping (\(\gamma=1/0.75\approx 1.33\) and \(\gamma=2\)) tends to lock the TMD mass to the primary structure modal mass, whereby the system structure/TMD behaves like a single degree-of-freedom system with increased \(max(X_{1})\). Decreased TMD damping does not sufficiently dampen the two resonances of  
the system structure/TMD, whereby \(max(X_{1})\) is greater than optimal as well. In addition, the displacement response due to the TMD tuning, according to [13], is included in Figure 1. This tuning leads to a more sound TMD relative motion response with a slope of zero at excitation frequency equal \(f_{1,nom}\) but leads to a greater \(max(X_{1})\). In contrast to the case of nominal structural parameters, where nominal TMD damping minimizes \(max(X_{1})\), Figure 2 shows that increased TMD damping reduces \(max(X_{1})\) if the actual modal properties differ from the nominal modal parameters used for TMD design. For the considered case, \(f_{1}=1.05\)\(f_{1,nom}\) and \(m_{1}=m_{1,nom}\), the damping increase factor \(\gamma=1/0.75\approx 1.33\) leads to a smaller \(max(X_{1})\) than that obtained with nominal TMD damping. Hence, increased TMD damping does not reduce the primary structure response if the actual modal parameters agree with those used for TMD design, but increased TMD damping may lead to a smaller primary structure response if the actual modal properties differ from those used for TMD design and if \(\gamma\) is appropriately selected. The goal of this paper is to provide such damping increase factors, depending on the difference between the actual modal parameters and the nominal modal parameters used for TMD design.  
Figure 1: Normalized responses of (**a**) primary structure displacement and (**b**) TMD relative motion for different TMD damping increase factors and for \(f_{1}=f_{1,nom}\) and \(m_{1}=m_{1,nom}\).  
Figure 2: Normalized responses of (**a**) primary structure displacement and (**b**) TMD relative motion for different TMD damping increase factors and for \(f_{1}=1.05f_{1,nom}\) and \(m_{1}=m_{1,nom}\).
## 3 Results and Discussion
### Mechanical Characteristics of BSs  
To investigate the mechanical properties of the proposed structures, ANSYS, a commercial finite element software, was utilized to perform simulations under different loading conditions, such as compression, torsion, and bending. The 3D printer used in our study is the LianTai 3D Lite600, which uses SLA (Stereolithography Apparatus) technology for printing. It has a print platform size of 800 \(\times\) 800 \(\times\) 600 mm and a layer thickness range of 0.05-0.25 mm. Additionally, it has a high positioning accuracy of \(\pm\)0.008 mm per layer and excellent printing precision, with a tolerance of \(\pm\)0.1 mm for pr  
Figure 1: Designed models of bio-inspired structures inspired by the starfish and leaf beetle. The leaf beetle is presented in panel (**a**), while panel (**b**) provides an enlarged view of its wing surface. Various details of the structure are highlighted in panels (**c–e**), which illustrate the portion indicated by arrows in the previous panel. Panels (**f–i**) provide zoomed-in views of specific regions within panel (**e**), showcasing different configurations of the thin-walled tubes used in the structures. Specifically, panel (**g**) displays a quadrangular configuration, whereas panel (**i**) demonstrates a hexagonal configuration. Finally, panel (**j**) depicts the starfish, while panel (**k**) illustrates the various shapes of the thin-walled tubes utilized in the designs.  
100 mm for larger prints. As R4600 resin material was used to manufacture the actual samples for 3D printing, its material properties were used in all simulations. Material properties of R4600 resin material are listed in Table 1, as provided by the manufacturer. Additively manufactured tubes are illustrated in Figure 2.  
Lightweight numbers (LWN) were used to evaluate the lightweight efficiency of each BS, which can be expressed as follows:  
\[\text{LWN}=\frac{\text{Maxload}}{\text{Weight}} \tag{5}\]  
Further, LWN-C refers to compression, LWN-T is torsion, and LWN-B refers to bending.
# Classification of Hyperspectral Images Based on Supervised Sparse Embedded Preserving Projection
## 2 Relation Work
### Sparsity Preserving Projection  
Sparse representation can use a small amount of data to represent the main information of an image. Sparsity preserving projection (SPP) is an unsupervised dimensionality reduction algorithm based on the sparse representation theory. Unlike traditional graph construction methods, the algorithm constructs a graph representing the relationships between data samples through sparse representation. This is a global sparse graph construction method, and thus there is no need to artificially select neighbor parameter values. The core idea of SPP was to construct the sparse reconstructive weight matrix and to look for an optimal projection vector \(V\) that minimizes the projection error between the original sample on the \(V\) and the sparse reconstruction sample on the \(V\).  
Assuming that the sample set \(X=\{\,x_{1},x_{2},\ldots,x_{N}\}\) and \(N\) make up the total number of samples, the objective function of the original sparse reconstruction weight was:  
\[\min_{s_{i}}\lVert s_{i}\rVert_{1}\quad s.t.\quad x_{i}=Xs;\quad 1=1^{T}.s_{i}. \tag{4}\]  
Due to the noise of the sample, the constraints were not necessarily satisfied, so we could relax the constraints to get the following equation:  
\[\min_{a_{i}}\lVert s_{i}\rVert_{1}\quad s.t.\quad\lVert x_{i}-Xs_{i}\rVert \leq\xi;\quad 1=1^{T}.s_{i}, \tag{5}\]  
where \(\xi\) represents the allowable error value for sparse reconstruction. By solving the above equation, it was possible to obtain the sparse representation coefficient matrix \(S=[s_{1},s_{2},\ldots,s_{n}]\), where \(s_{i}\) was the sparse representation coefficient of \(x_{i}\).  
In the process of dimensionality reduction, the purpose of SPP was to keep the sparse representation relationship constant between samples, and thus the objective function of the SPP could be written as:  
\[J\left(\,V\right)=\min_{V}\sum_{i=1}^{N}\left\lVert V^{T}x_{i}-V^{T}Xs_{i} \right\rVert^{2}, \tag{6}\]  
where \(V\) was the projection matrix. After a simple algebraic operation, the objective function of SPP could be recorded as:  
\[\begin{array}{ll}J\left(\,V\right)&=\min_{V}\sum_{i=1}^{N}\left\lVert V^{T} x_{i}-V^{T}Xs_{i}\right\rVert^{2}\\ &=\min_{V}V^{T}X(I-S_{\beta})X^{T}V\end{array} \tag{7}\]  
In order to prevent degeneracy, an extra constraint was added: \(V^{T}XX^{T}V=I\). Thus, the objective function of the SPP was expressed as:  
\[J\left(\,V\right)=\min_{V}\frac{V^{T}X(I-S-S^{T}+S^{T}S)X^{T}V}{V^{T}XX^{T}V}. \tag{8}\]  
To obtain more stable numerical solutions, we transformed the above-mentioned minimization problem into the following maximization problem:  
\[J\left(\,V\right)=\max_{V}\frac{V^{T}XS_{\beta}X^{T}V}{V^{T}XX^{T}V}, \tag{9}\]where \(S_{\beta}=S+S^{T}-S^{T}S\), the optimization problem in Equation (9) could be solved using the generalized eigenvalue vectors \(XS_{\beta}X^{T}V=\lambda XX^{T}V\), and the best projection matrix was obtained as \(V=[v_{1},v_{2},\ldots,v_{d}]\).
# Automated Anomaly Detection and Causal Analysis for Civil Aviation Using QAR Data
## 3 Preliminary Preparation for Data and Causal Analysis
### Problem Statement  
The training data are defined as \(X_{train}=\{x_{1},x_{2},\ldots,x_{L},Y\}\), including the manual labels, where \(L\) is the number of attributes in the input data. For each timestamp \(t\), \(x^{t}\in R^{L}\) is an \(L\)-dimensional vector consisting of \(L\) numerical parameter values. \(x^{t}\) is defined as \(x^{t}=\{x^{t}_{1},x^{t}_{2},\ldots,x^{t}_{L}\}\), in which \(x^{t}_{i}\) is the value of the \(i\)-th parameter at timestamp \(t\).  
The aim of the work is to train a machine learning model \(M\) based on \(X_{train}\) to detect different anomalies from QAR data. Specifically, given the test data \(X_{test}=\{x_{1},x_{2},\ldots,x_{L}\}\) input to \(M\), it will produce an output vector \(y\in R^{L}\), where \(y_{t}\in\{0,1,\ldots,k\}\) indicates the type of anomaly.
###### Abstract  
The commercial category of virgin olive oil is currently assigned on the basis of chemical-physical and sensory parameters following official methods. Considering the limited number of samples that can be analysed daily by a sensory panel, an instrumental screening tool could be supportive by reducing the assessors' workload and improving their performance. The present work aims to in-house validate a screening strategy consisting of two sequential binary partial least squares-discriminant analysis (PLS-DA) models that was suggested to be successful in a proof-of-concept study. This approach is based on the volatile fraction fingerprint obtained by HS-SPME-GC-MS from more than 300 virgin olive oils from two crop seasons graded by six different sensory panels into extra virgin, virgin or lampante categories. Uncertainty ranges were set for the binary classification models according to sensitivity and specificity by means of receiver operating characteristics (ROC) curves, aiming to identify boundary samples. Thereby, performing the screening approach, only the virgin olive oils classified as uncertain (23.3%) would be assessed by a sensory panel, while the rest would be directly classified into a given commercial category (78.9% of correct classification). The sensory panel's workload would be reduced to less than one-third of the samples. A highly reliable classification of samples would be achieved (84.0%) by combining the proposed screening tool with the reference method (panel test) for the assessment of uncertain samples.  
**Keywords:** virgin olive oil; sensory quality; volatile compounds; HS-SPME-GC-MS; chemometrics; panel test; validation; untargeted
## 2 Materials and Methods
### Participants  
Based on previous similar outcomes [4; 17], a priori analysis suggested that a minimum sample size of 11 participants was needed to observe a 0.04 m\(\cdot\)s\({}^{-1}\) change in movement velocity, with an alpha = 0.05 and statistical power = 0.80 [18]. A drop-out rate of 10% was also considered. The inclusion criteria for participation were male individuals aged 18 years or over, consistently engaging in resistance training in the last six months and performing the squat and bench press in their training routines, able to complete the experimental procedures, and having no clinical conditions that could risk their health during the performance of exercises. All subjects were informed about the experimental procedures and provided informed consent to participate in the study. The University of Beira Interior Ethics Committee approved the study (CE-UBI-PJ-2021-018), which follows the recommendations of the Declaration of Helsinki. Of the 16 participants who provided consent, 2 were excluded for not meeting the inclusion criteria, and therefore, 14 were randomly divided into the two sequences, specifically S + BP (n = 7) or BP + S (n = 7). Throughout the data collection process, two participants of each sequence were excluded (lost to follow-up or dropped out without a specific reason). Thus, 10 male sports science students remained for the final analysis (Figure 2). Table 1 presents the characteristics of the participants at baseline.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Mean \(\pm\) SD** \\ \hline Age (years) & 20.9 \(\pm\) 0.7 \\ Height (cm) & 175.7 \(\pm\) 9.7 \\ Body mass (kg) & 73.9 \(\pm\) 7.3 \\
1 RM bench press (kg) & 77.0 \(\pm\) 18.3 \\ Bench press relative strength & 1.0 \(\pm\) 0.2 \\
1 RM squat (kg) & 91.0 \(\pm\) 16.3 \\ Squat relative strength & 1.2 \(\pm\) 0.3 \\ \hline \hline \end{tabular}  
* RM = one-repetition maximum load.  
\end{table}
Table 1: Participants’ characteristics at baseline (n = 10).  
Figure 1: Study design. 1 RM = 1-repetition maximum. BP + S = bench press followed by squat; S + BP = squat followed by bench press. Each stage corresponds to one session.
# Reinforcement Learning Approach for Adaptive C-V2X Resource Management
## 3 Proposed Reinforcement Learning Approach for Adaptive Resource Management
### Impact of MCS Index
The corresponding reward function can be attained as:  
\[R_{t}=PRR_{t}-PRR_{t-1}, \tag{8}\]  
in the initial time step, the initial Q-value is set to equal to current \(PRR_{0}\) based on the initial MCS index value \(I_{MCS_{0}}\). As the learning step runs, the appropriate action was selected according to the \(\varepsilon\)-greedy exploration mechanism,  
\[\pi_{t}=\left\{ \begin{aligned} & choose\;random\;action\;in\;A,\;if\; \xi_{t}<\varepsilon\\ & arg\;\underset{A}{\max}Q(S,A)_{t},\;otherwise\end{aligned} \right., \tag{9}\]  
where \(\xi_{t}\) is a random value between 0 and 1, and \(\varepsilon\in[0\,1]\) is the predefined epsilon value. Algorithm 1 explains the detailed procedure of our proposed RL approach. ```
1:Initialize:
2: initialize\(T\), \(I_{MCS_{0}}\), \(\alpha\), \(\gamma\), \(\varepsilon\)
3: calculate \(PRR_{0}\) based on \(I_{MCS_{0}}\)
4:while\(t\leq T\)do
5: for each: LTE event
6: calculate the current Q-value based \(PRR_{0}\)
7: calculate the current rewards based on Equation (8)
8: explore the next action that satisfy the condition in Equation (9)
9: calculate the current value function target based on Equation (6)
10: calculate the current \(\Delta Q_{t}\) based on Equation (7)
11: update the Q-value based on Equation (5)
12: calculate the new \(PRR_{t}\)
13:endwhile
```  
**Algorithm 1** Pseudocode for proposed RL approach
## 2 The 3D A2A MIMO Channel Model
### Specular and Diffuse Components
#### 2.2.2 Powers of the Rays
Conversely, a smaller value of \(\alpha_{R}\) leads to a wider scattering lobe. \[Z_{v}=\frac{1}{\eta}\sqrt{\eta-(\sin\theta_{i,n})^{2}} \tag{48}\]4 Polar representation of the normalized scattering radiation pattern. In summary, the characteristics of the channel are determined by the phases and powers of MPCs, including the LoS, specular, and diffuse components. As discussed in Section 2.1, the phase of the LoS component from \(A_{p}^{t}\) to \(A_{q}^{r}\) depends on the initial distance of the LoS ray, the relative locations of \(A_{p}^{t}\) and \(A_{q}^{r}\) in the Tx and Rx arrays, and the Doppler shifts caused by the UAV flight and vibrations. Apart from the above-mentioned factors, the phases of the specular and diffuse components are also influenced by the phase shift of the reflection coefficient, i.e., \(\varepsilon_{n}=\arg(\Gamma_{n})\). Regarding the power of the MPCs, all the MPCs experience path losses. Additionally, the energy of a wave reflected from a rough surface undergoes attenuation since a portion of the energy scatters in various directions. The attenuation of power due to reflection is quantified using (51), which depends on a combination of factors, including the carrier frequency, incident angle of the ray, and electrical properties and roughness of the reflecting materials. The proportion of scattering power is calculated using (56). Furthermore, the power of the \(n\)th diffuse component relative to the specular ray is characterized using the directive model, as demonstrated in (57).
## 9 Results and Discussion  
### Number of Containers and SLA  
One of the key motivations behind designing the hybrid model was to minimize the number of containers while simultaneously reducing SLA violations. The results, shown in Figure 7, demonstrate that the hybrid model successfully achieved this objective across all workloads. Its aggressive reuse of available containers resulted in spawning 20% fewer containers than OpenWhisk, even in the matrix multiplication (MM) case. OpenWhisk was the second-best performer in terms of reducing the average number of containers spawned, but this came at the cost of the highest SLA violation rate compared to all other baselines. While Hot Concurrency maintained an acceptable SLA, it did so by keeping a large pool of idle instances ready to handle bursts of traffic, which led to a high number of active containers. By combining accurate predictions with a proactive, container-aware scheduling mechanism, the hybrid model achieves a clear balance between container efficiency and SLA guarantees.  
Figure 7: (a) SLA violation evaluation results. (b) Number of spawned containers evaluation results.
## 2 Materials and Methods
### GC/MS Analysis  
To analyze the constituents of RoEO, a comprehensive GC/MS method was employed. The analysis aimed to identify and quantify the various components by comparing their mass spectra with reference spectra from the NIST Library 2020 [50].  
The instrumentation used for this analysis included a Thermo Scientific Focus GC (Norristown, PA, USA) equipped with an AI/AS 3000 autosampler and coupled with a DSQ II mass detector. The GC system was fitted with a TraceGOLD TG-624 column, which measured 60 m in length, 0.25 mm in internal diameter, and had a film thickness of 1.4 \(\upmu\)m.  
For the analysis, an injection volume of 1 \(\upmu\)L was used. The flow rate was maintained at 1.4 mL per minute, and a split ratio of 1:50 was applied, using helium as the carrier gas. The oven temperature program was set to begin at 90 \({}^{\circ}\)C. Subsequently, the temperature was increased at a rate of 3 \({}^{\circ}\)C per minute until it reached 220 \({}^{\circ}\)C, where it was maintained for an additional five minutes.  
The MS conditions were carefully controlled to ensure accurate detection and analysis of RoEO's constituents. The MS transfer line temperature was maintained at 240 \({}^{\circ}\)C, while the ion source temperature was set at 230 \({}^{\circ}\)C. Electron impact ionization (EI) was employed at 70 eV, with the spectra recorded in full scan mode over a mass range of 50 to 450 \(m/z\).  
Throughout the analysis, performed in triplicate, all constituents' retention times (RTs) were meticulously recorded. The resulting mass spectra were then scrutinized and compared to reference spectra from the NIST Library 2020 to ensure the accurate identification and quantification of each component [50; 51].  
This detailed GC/MS method allowed for a comprehensive profiling of RoEO, providing valuable insights into its chemical composition and facilitating further applications and research in various fields.
## 5 Discussion
### Limitations  
This study has limitations that may affect the interpretation of results. Although zero-shot prompting was used, commercial models received a brief initial instruction to establish context, potentially giving them an advantage over open-source models that received no prompt. Additionally, platform-specific variability in commercial LLMs could lead to non-deterministic responses. Another limitation was the exclusion of questions containing images [50]. Since not all the evaluated LLMs are multimodal, only text-based questions were used to ensure a fair comparison. Including image-based questions could have unfairly advantaged multimodal models, as unimodal LLMs would not be capable of analyzing visual content. Another limitation is that the study focused exclusively on the first phase of the Revalida exam, which consists of objective questions. It did not include the discursive section or the second phase of the exam, both of which are essential components for fully assessing medical knowledge and certifying physicians in Brazil.  
In the qualitative analysis, one limitation was that the participants were aware that the explanations being evaluated corresponded only to questions that GPT-4o had answered correctly, based on the official answer key. This prior knowledge may have influenced the physicians' assessments, potentially introducing bias into the evaluation of the explanations. Nonetheless, this was an intentional strategy, as the goal of the study was to evaluate the model's reasoning, not the participants' medical knowledge.  
Another limitation of this study lies in the reliance on GPT-4o-generated justifications from past Revalida exams (2017-2023) as the source of domain knowledge. Although only correct answers were retained, no formal expert validation was performed for the justifications used in the knowledge base. This leaves open the possibility that subtle inaccuracies or context-specific reasoning patterns from justifications may have influenced model adaptation. Lastly, the fine-tuning process was limited to a single open-source model (LLaMA 3.1), and further investigation is needed to assess whether similar gains can be achieved across different open-source models.