esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Generative AI for Detecting Pedophilia Crimes
## 3 Methodology
### Data Source  
Google Scholar (www.scholar.google.com) was selected as the primary data source for this review due to its extensive multidisciplinary academic literature, including peer-reviewed articles, conference papers, and technical reports relevant to AI applications and criminology.  
In order to maintain the relevance and timeliness of the research, only studies published between 2010 and early 2025 were included, as this period reflects the significant advances in generative AI and machine learning applications.  
In addition, the review focused exclusively on studies published in English, as this remains the dominant language in the scientific literature on artificial intelligence and criminology. Although this introduces a potential language bias, it was deemed necessary to maintain consistency in terminology and data interpretation.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Feature** & **ChatGPT (OpenAI)** & **Gemini (Google AI)** & **LLaMA (Meta AI)** \\ \hline \multirow{4}{*}{Core Architecture} & GPT with Reinforcement Learning from Human Feedback (RLHF) [12, 13]. & Transformer-based with Retrieval-Augmented Generation (RAG) and modifications for TPU efficiency [12, 13]. & Transformer-based with Root Mean Square Layer Normalization (RMSQLN) and Swish-Gated Linear Unit (SwiGLU) [13]. \\ \hline \multirow{4}{*}{Multimodality} & Primarily text-focused, with some multimodal capabilities noted as a relative weakness [12]. & Natively multimodal, designed to process text, images, audio, and video [12]. & Primarily text-focused, trained on text and code [13]. \\ \hline \multirow{4}{*}{Key Strengths} & Exels in conversational flow, creativity, and following instructions [12]. & Prioritizes factual accuracy [12] and high computational efficiency (speed and energy) [12]. & Shows high consistency in performance across different languages, suggesting easier transferability [13]. \\ \hline \multirow{4}{*}{Key Weaknesses} & Susceptible to “jailbreaking”, logical inaccuracies, and high computational costs [12]. & Exhibits inconsistent safety/censorship filters [13]. Being a more recent model, it has less real-world data exposure [12]. & Exhibits a strong “optimistic bias”. Larger models have impractical hardware requirements for local use [13]. \\ \hline \multirow{4}{*}{Context Window} & Able to handle prolonged interactions and maintain context [12]. & Able to handle extended conversations and decipher intricate prompts [12]. & Trained with a 2 K (LLaMA) or 4 K (LLaMA2) token context length [13]. \\ \hline \end{tabular}
\end{table}
Table 1: Comparative Summary of LLM Technical Attributes and Challenges. This table provides a side-by-side comparison of the key technical features of the primary LLMs discussed in this review (ChatGPT, Gemini, and LLaMA). It covers their core architecture, key strengths, known weaknesses, and other relevant attributes.
# Method and System for Heart Rate Estimation Using Linear Prediction Filtering +
## 2 Materials and Methods
### LPC--Linear Prediction Coefficients  
The cornerstone concept of LPC is to express an optimal one-step-ahead prediction \(\hat{x}[n]\) of a discrete signal \(x[n]\) from its past \(p\) samples [29; 30], whose difference equation is given by  
\[\hat{x}[n]=-\sum_{k=1}^{p}a_{k}x[n-k], \tag{1}\]  
in which \(a_{k}\) are the coefficients of LPC and \(p\) is the order of the predictor.  
The error of estimation \(e[n]\) is determined by  
\[e[n]=x[n]-\hat{x}[n]. \tag{2}\]  
From Equations (1) and (2) can be rearranged as  
\[e[n]=x[n]+\sum_{k=1}^{p}a_{k}x[n-k], \tag{3}\]  
whose linear prediction coefficients \(a_{k}\) minimize the mean square of error \(e[n]\). It can be found by applying Equation (4):  
\[\left[\begin{array}{c}a_{1}\\ a_{2}\\ \vdots\\ a_{p}\end{array}\right]=-\left[\begin{array}{cccc}r_{0}&r_{1}&\ldots&r_{p-1 }\\ r_{1}&r_{2}&\ldots&r_{p-2}\\ \vdots&\vdots&\ldots&\vdots\\ r_{p-1}&r_{p-2}&\ldots&r_{0}\end{array}\right]^{-1}\left[\begin{array}{c}r_ {1}\\ r_{2}\\ \vdots\\ r_{p}\end{array}\right], \tag{4}\]  
where correlation coefficients \(r_{k}\) are given as  
\[r_{k}=\sum_{i=0}^{N-1-k}x_{i}[n]x_{k+i}[n]. \tag{5}\]  
The error signal \(e[n]\) is responsive to sudden variations in the signal \(x[n]\) due to its high-pass behavior, as can be deduced by applying \(\mathcal{Z}\)-Transform to error \(e[n]\) (3) (\(H(z)=\frac{E(z)}{X(z)}=\frac{\mathcal{Z}\left\{e[n]\right\}}{\mathcal{Z} \left\{x[n]\right\}}\); notice that from Equation (2), one has \(\mathcal{Z}\left\{e[n]\right\}=X(z)+\sum_{k=1}^{p}a_{k}z^{-k}X(z)\), then Equation (6) holds).  
\[H(z)=\frac{E(z)}{X(z)}=1+\sum_{k=1}^{p}a_{k}z^{-k}. \tag{6}\]  
In the next section, this property is used to estimate the heartbeat.
# Covert Anti-Jamming Communication Based on Gaussian Coded Modulation
## 3 System Model
### Time-Frequency Modulation  
The real-valued codeword \(\mathbf{c}\) of dimension \(2N\) can be rewritten as a complex-valued codeword vector of dimension \(N\), i.e., \(\mathbf{x}\in\mathbb{C}^{N}\), where \(\mathbb{C}\) is a set of complex numbers. The real part of \(\mathbf{x}\) is the first half of the vector \(\mathbf{c}\) and the imaginary part of \(\mathbf{x}\) is the latter half of \(\mathbf{c}\). That is, for the real-valued codeword \(\mathbf{c}=\left[c_{1},c_{2},\ldots,c_{2N}\right]^{T}\), the corresponding complex expression is \(\mathbf{x}=\left[c_{1}+jc_{N+1},\ldots,c_{N}+jc_{2N}\right]^{T}\). Each element of \(\mathbf{x}\) is assigned to the time-frequency grid for transmission by OFDM. Let the number of OFDM subbands be \(N_{F}\) and the number of time slots be \(N_{T}\), where \(N=N_{F}N_{T}\). Figure 5a illustrates the proposed signal in the time-frequency domain, where \(N_{F}=N_{T}=16\). The signal is transmitted through all 16 subbands, whereas the frequency-hopping spread spectrum (FHSS) signal in Figure 5b uses only a single subband in a time slot. The energy of the proposed signal remains under the noise floor at the plane where the energy = 1, whereas the energy of the FHSS signal with the same power has peaks above the noise floor.  
The complex codeword vector \(\mathbf{x}\) is divided into \(N_{T}\) column vectors, i.e., \(\mathbf{x}_{k}\)\((k=1,2,\ldots,N_{T})\), where \(\mathbf{x}=\left[\begin{array}{cccc}\mathbf{x}_{1}^{T}&\mathbf{x}_{2}^{T}& \cdots&\mathbf{x}_{N_{T}}^{T}\end{array}\right]^{T}\). The \(k\)th OFDM symbol is determined by an IFFT of \(\mathbf{x}_{k}\). Here, we omit the cyclic prefix (CP) for simplicity, as channel estimation and the inter-symbol interference problem are beyond the scope of this paper. If one assumes perfect channel estimation, then the CP being jammed cannot be a problem as the CP is discarded on the receiver side. However, note that jamming attacks on the CP can distort the OFDM system efficiently [20; 21], thus degrading the performance of the system. Future research should study the effect of CP jamming.
## 4 Methodology
### Multi-Perspective Hypergraph Construction  
In the realm of location prediction, understanding the intricate interaction relationships between users and locations is paramount. These relationships are multifaceted, encompassing (1) user-location interactions, (2) transitional dynamics that reflect the relationships among different locations, and (3) geographical correlations that emerge from the physical proximity of these locations. To capture and model these complex dynamics effectively, previous studies have introduced methodologies that treat users and locations as discrete nodes within a graph framework, wherein the interconnections between them are represented as edges.  
Figure 3: Overall framework of the proposed Multi-Perspective Hypergraphs with Contrastive Learning (MPHCL) model for location prediction.  
However, traditional graph structures involve notable limitations [35], as they predominantly focus on pairwise relationships. This approach restricts the ability to capture higher-order interactions, i.e., those that involve multiple nodes concurrently, which are often crucial for understanding the underlying semantic context. For instance, the social dynamics surrounding a user's engagement with multiple locations or the interrelationships among a cluster of geographically proximate locations may involve intricate interactions that a conventional graph representation cannot adequately express.  
Given these constraints, we recognize the necessity of a more sophisticated framework that can accommodate the complexities of these relationships. Inspired by the highly flexible and versatile nature of hypergraphs [36], we propose a novel approach that entails the design of three distinct hypergraphs. These hypergraphs are not merely enhancements of traditional structures but, rather, represent a paradigm shift in how we conceptualize relational data within the location prediction sphere. Each hypergraph is meticulously crafted to encapsulate different aspects of user-location interactions, thereby facilitating a richer representation of the relational patterns.
# Origin and Function of Structural Diversity in the Plant Specialized Metabolome
## 4 Decoration and Conjugation in Specialized Metabolism
### Function of Decorations
In _Linum album_ hairy root culture, during the first days of the exponential phase, 6-methoxypodophyllotoxin glucoside is deglucosylated, whereas, after the exponential phase, when the total tissue weight remains stable, 6-methoxypodophyllotoxin is glucosylated again [73]. In Arabidopsis, kaempfer and quercetin 3-O-\(\beta\)-glucoside-7-O-\(\alpha\)-rhammoside accumulate in response to abiotic stress, whereas the disappearance of the stress induces enzyme-mediated cleavage of the 3-O-\(\beta\) linked glucose moiety of these flavonoid biglucosides, releasing 7-O-\(\alpha\)-rhammosides [144]. Exudation of fragments of the flax seed macromolecular polyester early during germination suggested the activity of hydrolytic enzymes, liberating large amounts of phenolics in the spermosphere [145]. Such observations indicate that metabolite modification, allowingrapid sequestration or release of active compounds, is a key element in the adaptive capacity of plants to changes in their environment [126]. Strong correlations between individual metabolites and antioxidant activity of plant extracts are rarely observed, even if multivariate models can show the predictive power of the metabolome for antioxidant activity and can indicate which groups of metabolites contribute most to this activity [142]. It would be interesting to investigate further if decorations by common glycosylation and/or acylations, conjugation and polyester formation, in combination with enzymatic hydrolysis, form a tool of the plant specialized metabolism for reversible storage and mass release of high concentrations of synergistically functioning metabolites.
# CAIT: A Predictive Tool for Supporting the Book Market Operation Using Social Networks
## 4 Results
### Performance Evaluation Methods: For Classification Part  
* K-Fold stratified cross-validation, this validation seeks to ensure that each \(k\) group is representative in all data strata. It is intended to ensure that each class is (roughly represented equally in each test fold) and thus avoid overtraining. In this specific case, our variable \(k=10\)
* _Accuracy_ (Equation (2)), which refers to how close a sample statistic is to a population parameter, being _TP_ (true positive value), _TN_ (true negative value), _FP_ (false positive value), _FN_ (false negative value).
* _Precision_ (Equation (3)) with which this algorithm hits each of the classes is also analyzed.
* _Recall_ (Equation (4)), represents the model's ability to correctly predict the positives out of actual positives.
* _F1-Score_ (Equation (5)), this gives a weighted average of the precision and recall metrics. It is the best metric for averaging out and balancing all the evaluation metrics as a whole.
* _Mean Absolute Error (MAE)_ is used, which is a measure of the difference between two continuous variables (Equation (6)). Where \(y_{i}\) is the prediction, \(\hat{y}\) and the true value. \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\] (2) \[Precision=\frac{TP}{TP+FP}\] (3) \[Recall=\frac{TP}{TP+FN}\] (4) \[F1\text{-}Score=\frac{Precision*Recall}{Precision+Recall}\] (5) \[MAE(y,\hat{y})=\frac{1}{n_{samples}}\sum_{i=0}^{n_{samples}}|y_{i}-\hat{y}_{i}|\] (6)  
Algorithm Comparison Analysis  
All the values of the metrics discussed above are combined into a single overall scorecard for each classifier for each of the segmentations.  
Figure 7: Distribution of the volume of books for each of the clustering segmentations. The results show a significant data imbalance between the different segmentations.  
As can be observed in Tables 1 and 2, all show that the XGBoost classifier algorithm outperforms the decision tree, KNN, and random forest. Observing Table 3, we could also select the KNN, but the XGBoost is chosen as the optimal one, given its flexibility as a parallelizable algorithm, and that in general in the three segmentations, the scores of this algorithm are higher than other classifiers. The selected classifier can provide very good consistency across all classes. Let us analyze the obtained results from the expert's segmentation (Table 2) concerning the quartiles segmentation (Table 1). We do not see a great difference with what can be interpreted, as the use of this type of segmentation does not improve or worsen the expert's segmentation. However, if we compare the expert's segmentation with the clustering segmentation (Table 3), we observe that the classification improves significantly since it fully adjusts to the patterns found in the data.
# Analysis of Behavioral Characteristics of Smartphone Addiction Using Data Mining
## 4 Data Analysis Based on Data Mining  
### Data Mining Model  
Data mining refers to the process of discovering useful correlations that are hidden among a large amount of data and extracting that information for future predictions or decision making. Smartphone usage data is an example of such massive data that can be mined for useful correlations.  
Figure 1: Statistics of the collected data with used apps. (**a**) Main screen. (**b**) Chart screen. (**c**) Data usage. (**d**) Number of screen turn-on events. (**e**) Time spent on an app. (**f**) Number of app launches. (**g**) Frequently used apps.  
Our research process is depicted in Figure 2; it involves combining self-reported data (via questionnaires), collected data from users' smartphones, data mining for analyzing the effect of smartphone addiction on users, and finally, learning achievement.  
A decision tree model was used as the data mining model. The purpose of this study was to identify the factors that influence the addiction, rather than determining whether it is the addiction. This factor analysis method is traditionally used in correlation multiple regression, and structural spinning model analyses. However, in recent years, decision tree models have been widely used. This is because decision trees internally use the evaluation criteria such as the information gain concept and the Chi-square test to find important variables. We analyze the relationship between several input variables and dependent variables using decision tree analysis. We also visualize the results as a tree model, so that we can easily understand which factors have a considerable effect on addiction and apply them to decision-making.
# Application of External Torque Observer and Virtual Force Sensor for a 6-DOF Robot
## Materials and Methods
### Hybrid Position/Force Control Architecture  
The architecture of the proposed position and force hybrid controller is shown in Figure 6. Position control was achieved by specifying a target position and executing Cartesian-space trajectory planning. The calculated trajectory points are coordinate values, so they need to be converted into motor angle commands using inverse kinematics. After obtaining the motor trajectory commands, motion is carried out using Cyclic Synchronous Position (CSP) control mode. The position control, velocity control, and torque control in Figure 6 are all internal loops provided by the motor driver. The actual position control error will be compensated in the next command cycle through error compensation mechanisms. The force control component compares the feedback torque from each axis motor with the estimated torque from the dynamic model and calculates the external torque. A virtual force sensor is established to calculate the estimated end force and target force, using the principles of the virtual force sensor. The force error is then converted into variations in each joint of the robotic arm for force control.  
Figure 5: Flowchart of the virtual force sensor algorithm.  
Figure 6: Architecture of hybrid position/force control.
## 3 Results  
### Chemical Composition of RGEO  
Steam distillation of the fresh plant material of \(R\). _graveolens_ gave a yellowish oil with an intense and penetrating odour with a yield of 0.29% (\(\
u/w\)). The extraction yields obtained for \(R\). _graveolens_ are comparable to those reported in the literature [17; 42; 43; 44; 45]. As previous reported by [PERSON] et al. [46], the total EO content of plants was affected by genetic background, environmental conditions, and soil composition.  
GC-MS analysis of the RGEO identifies thirty-seven compounds (Figure 1 and Table 2), representing 98.68% of the obtained oil. The main detected compounds are 2-Undecanone and 2-Nonanone at 76.19% and 7.83%, respectively, followed by 2-Undecanol at 1.85% and 2-Tridecanone at 1.42%. The abundance of 2-Undecanone in RGEO is in accord with the previous studies conducted on RGEOs from Egypt [47], Algeria [45], Iran [48], and Saudi Arabia [49]. However, the proportions and nature of the identified chemical compounds of the analysed EOs are not always the same compared with the previous studies. These differences may be due to genetic, distinct environmental and climatic conditions, geographic origins, and plant populations [49; 50].  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**No** & **Compounds** & **KI****a** & **\%** \\ \hline
1. & 3-Octanone & 908 & 0.06 \\
2. & beta-Thujene & 912 & tr. \\
3. & 4-Carene, (1S,3S,6R)-(\(-\)) & 919 & tr. \\
4. & Hydroperoxide, 1-ethylbutyl & 925 & 0.24 \\
5. & Hydroperoxide, 1-methylpentyl & 934 & 0.19 \\
6. & (2E)-2-Hexenyl benzoate & 942 & tr. \\
7. & 1-Cyclohexyl-2-propen-1-ol & 947 & tr. \\
8. & 2-Bornene & 959 & tr. \\
9. & para-Cyrene & 1005 & 0.58 \\
10. & beta-Terpinyl acetate & 1011 & 0.08 \\
11. & Eucalyptus & 1014 & tr. \\
12. & 4-Carene, (1S,3R,6R)-(\(-\)) & 1042 & 0.06 \\
13. & 2-Nonanone & 1076 & 7.83 \\
14. & 2-Decanone & 1190 & 0.75 \\
15. & Cyclopropanecarboxylic acid, nonyl ester & 1238 & 0.49 \\
16. & (S)-(+)-Carvone & 1248 & 0.18 \\
17. & 2-Undecanone & 1308 & 76.19 \\
18. & 2-Undecanol & 1315 & 1.85 \\
19. & 1-methyl-cycloundecanol & 1380 & 1.13 \\
20. & 2-Dodecanone & 1412 & 0.63 \\
21. & beta-Carvophyllene & 1439 & 0.55 \\
22. & 2-Acetoxyt tetradaceane & 1450 & 1.34 \\
23. & Germacrene-D & 1478 & 0.42 \\
24. & 2-Tridecanone & 1515 & 1.42 \\
25. & Hexa-hydro-farnesol & 1536 & 0.28 \\
26. & Elemol & 1568 & 0.48 \\
27. & 9-Methyl-10-methylenericylc [4.2.1.1(2,5)]decan-9-ol & 1598 & 0.81 \\
28. & 2,5-Octadecayionic acid, methyl ester & 1605 & 0.19 \\
29. & 4-(3,4-Methylenedioxyphenyl)-2-butanone & 1612 & 0.15 \\
30. & Ascaridole epoxide & 1615 & 0.24 \\
31. & Valeric acid, 2-tridecyl ester & 1658 & 0.59 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of RGEO cultivated in western Romania.  
Figure 1: Gas chromatogram of RGEO cultivated in western Romania.
# Deep-Learning-Based Framework for PET Image Reconstruction from Sinogram Domain
## 2 Methodology
### Framework Based on Conditional Generative Adversarial Network
#### 2.2.2 Objective Function  
The conditional restriction can make the results closer to the desired ones. Therefore, the input includes both random noise \(z\) and condition \(y\), and the objective of the cGAN can be represented as  
\[L_{\text{CGANs}}(D,G)=E_{x,y\sim p_{data(x,y)}}[\log D(x,y)]+E_{x\sim p_{data(x )},z\sim p_{z(z)}}[\log(1-D(x,G(z,y)))] \tag{4}\]  
As shown in Equation (4), generator \(G\) attempts to adjust parameters to minimize \(\log(1-D(x,G(z,y)))\), while \(D\) tries to maximize it; thus, they are two adversarial models. Training according to adversarial loss makes the generated images clear but does not ensure similarity with the established image. To enhance the accuracy of low-frequency information, the \(L1\) regularizer is introduced in the proposed model. Thus, the entire objective function can be described as  
\[G^{*}=arg\min_{G}\max_{D}L_{CGANs}(D,G)+\lambda L_{L1}(G) \tag{5}\]  
where \(L_{L1}(G)\) is the \(L1\)-norm, which is represented as  
\[L_{L1}(G)=E_{x,y\sim p_{data}(x,y),z\sim p_{z}(z)}[\|x-G(y,z)\|_{1}]. \tag{6}\]
# A Parametric Study of Wind Pressure Distribution on Facades Using Computational Fluid Dynamics
## 2 Description of the CFD Model
### Boundary Conditions  
The inlet condition was assigned a velocity of 24.9 m/s in the horizontal direction, which corresponds to a 1-in-50-year worst case wind speed (the basis for critical loading of structures in Dublin as per the Eurocode). Wind is stochastic and to generate this behavior in the inflow, the Spectral Synthesizer method was used [6]. This method necessitates setting parameters for turbulence intensity (chosen as 11.2%) and turbulence length scale (chosen as 1 m), values for which were chosen using guidance from [14]. Initial application of steady RANS was used by the Spectral Synthesizer to capture inflow turbulence through the instantaneous velocity field command. This took place before activating the LES turbulence model. The outlet was defined as a pressure-outlet. Operating conditions were at atmospheric pressure (101,325 Pa) and therefore had constant static pressure conditions [6]. Symmetry conditions were used for the top and bottom boundaries, as suggested by [6]. These boundaries act with slip wall conditions, unable to support shear stresses, and therefore they impart no change in velocity to fluid moving alongside. Finally, the edges of the building and facade were assigned no-slip conditions, meaning they could support a shear stress but cause adjacent viscous air to become stationary to a point when in contact with it.