esa-sceva/satcom-chunk-collection huggingface dataset:
# New Insight on the Formation of 2-Aminoacetophenone in White Wines
## 3 Results
### Influence of Copper, Iron and Manganese  
A white blend was spiked with salts of Cu(II), Fe(II), Fe(III) and Mn(II) in order to investigate the effect of these metal ions on the formation of 2-AAP. The additions were made with individual metal ions and with various mixtures of metal ions in order to reach their high concentration range found in wine [14]. In that respect, the targeted final concentration was 1 mg/L, 5 mg/L and 4 mg/L for Cu, Fe and Mn, respectively. Figure 5 shows that addition of Fe(II), Fe(III) and Mn(II) increased 2-AAP concentration in comparison to the control wine, with the highest content found in the wines spiked with Mn(II) alone. On the other hand, the addition of Cu(II) led to a significant decrease in 2-AAP. Interestingly, when Cu(II) was added in a mixture with other metal ions, the level of 2-AAP was systematically reduced (Figure 5). On the contrary, the addition of Mn(II) tended to increase the 2-AAP concentration in all the treatments, excluding when it was added with a mixture of Fe(II) and Fe(III). Finally, the addition of all the metal ions together led to a decrease in 2-AAP concentration after artificial ageing in comparison to the control treatment (Figure 5).  
Figure 4: Treatment of wine with different antioxidants (blue—no metal ion addition, orange—metal ions addition; C—control, GSH—glutathione, IYD—inactivated yeast derivates, TA1—commercial ellagitannin). Metal ions were added to reach 1 mg/L, 5 mg/L and 4 mg/L of Cu, Fe and Mn, respectively. Different letters above bars indicate significance at \(p\leq 0.05\) (Fischer’s LSD). All quoted uncertainty is the standard deviation of three replicates per treatment.
## 3 Materials and Methods
### Core Tasks and Two-Part Evaluations  
The study's experimental design encompassed three main tasks, each requiring two outputs from the agent: a numeric score (1-10) and a concise textual explanation. This approach ensures that an agent's performance can be evaluated not only on quantitative measures (e.g., alignment or clarity) but also on the reasoning reflected in its justifications.  
**Task 1 (Alignment Evaluation)** has agents receive an existing exam question and evaluate its alignment with course objectives. They provide numeric ratings (e.g., concept coverage, specificity/relevance, and accuracy/correctness) and a textual rationale. Comparisons with ground-truth scores reveal where the agent might over- or under-grade alignment.  
**Task 2 (Question Improvement)** asks agents to take a \"suboptimal\" exam question and propose an improved version, assigning scores for criteria such as alignment, clarity, fairness, or difficulty. This tests the agent's ability to refine prompts while adhering to curricular standards and is a practical scenario for real-world educational revision.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Course Name** & **Course Category** & **Question Category** & **Question Design** & **Count** \\ \hline \multirow{4}{*}{Data Science} & \multirow{4}{*}{Application-Oriented} & Application-Based & Multi-Step & 126 \\  & & Single-Step & 189 \\  & & Creation and Evaluation & Multi-Step & 126 \\  & & Knowledge-Based & Single-Step & 189 \\ \hline \multirow{4}{*}{Integrated Systems} & \multirow{4}{*}{Theory-Oriented} & Application-Based & Multi-Step & 174 \\  & & Single-Step & 261 \\  & & Creation and Evaluation & Multi-Step & 174 \\  & & Knowledge-Based & Single-Step & 261 \\  & & & Multi-Step & 174 \\  & & Single-Step & 261 \\ \hline \multirow{4}{*}{Operating Systems} & \multirow{4}{*}{Theory-Oriented} & Application-Based & Multi-Step & 232 \\  & & Single-Step & 348 \\  & & Creation and Evaluation & Multi-Step & 234 \\  & & Knowledge-Based & Single-Step & 351 \\ \hline \multirow{4}{*}{Web Design} & \multirow{4}{*}{Application-Oriented} & Application-Based & Multi-Step & 276 \\  & & Single-Step & 414 \\ \cline{1-1}  & & Multi-Step & 276 \\ \cline{1-1}  & & Single-Step & 414 \\ \cline{1-1}  & & Multi-Step & 276 \\ \cline{1-1}  & & Single-Step & 414 \\ \hline \end{tabular}
\end{table}
Table 1: Number of original exam questions across question categories, designs, and course characteristics.  
**Task 3 (Question Generation)** instructs agents to create a new question at a given Bloom's level and self-score it on alignment, clarity/fairness, difficulty, and originality. This tests the agent's capacity to synthesize novel items that remain pedagogically sound and balanced in complexity.
# A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging
## 3 Explanation Methods for Deep Learning Cancer Detection Models
### Post-Hoc
#### 3.1.5 Lung Cancer  
The authors of [128] described the goal of their paper \"To explain predictions of a deep residual convolutional network for characterization of lung nodule by analyzing heat maps.\" The authors used occlusion to systematically block regions of a nodule and map drops in malignancy risk score to generate clinical attribution heatmaps on 103 nodules from Lung Image Database Consortium image collection and Image Database Resource Initiative (LIDC-IDRI) dataset, which were analyzed by a thoracic radiologist. They report an accuracy of 85 percent for classification and qualitatively access their explanation results.  
The authors of [129] applied XAI visualization to gain an insight into the features learned by a DCNN trained to classify estrogen receptor status (ER+ vs. ER\(-\)) based on dynamic contrast-enhanced magnetic resonance imaging (DCEMRI) of the breast. The authors developed a network that utilized \"transfer-trained dual-domain DCNN architecture derived from the AlexNet model trained on ImageNet data that received the spatial (across the volume) and dynamic (across the acquisition sequence) components of each DCE-MRI ROI as input\". The authors implemented integrated gradients as a post-hoc explanation technique. They observed that the CNN learned relevant features from the spatial and dynamic domains. They note there were differences in the contributing features from the two domains.
## 4 Case Study and Applications
### A Classification Problem: Binary Prediction of Smoker Status Using Bio-Signals
#### 4.3.1 Statistical Study of Covariates  
A statistical study of the covariates on the dataset was conducted using Python libraries, specifically the function _chi2_contingency_ from the scipy library [28] to perform the chi-squared test (Table 6) and the statsmodels library [29] to calculate the odds ratio and the relative risk. As the majority of the characteristics in the dataset are continuous, they were converted to categorical variables, with four categories created for each continuous variable, as follows:  
* _Low_: Individuals whose scores fall between the 0 th and 25 th percentiles.
* _Medium low_: Individuals falling between the 25 th percentile and 50 th percentiles are classified within this category.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of neurons in the input layer & 68 \\ Number of neurons in the hidden layer & 129 \\ Activation function & Sigmoid function \\ Inertia constant \(W\) & 1.0 \\ Cognitive learning coefficient \(c_{1}\) & 3.5 \\ Social learning coefficient \(c_{2}\) & 1.8 \\ Interval of particle positions & \([-1,1]\) \\ Particle velocity ranges & \([-0.1,0.1]\) (0.1\(\times\)max position) \\ Fitness function & Binary entropy \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of the trained neural networks for Smokers classification.  
Figure 9: Convergence curves for each implemented variant: **(a)** DSPSO and **(b)** DAPSO.  
* _Medium high:_ Those falling between the 50 th and 75 th percentiles are classified here.
* _High:_ Those falling between the 75 th and 100 th percentiles are classified here.  
In this case, a standard confidence level of 95% will be employed, which corresponds to a significance level of \(alpha=0.05\). Since the \(p\)-value is very close to 0 and much smaller than \(alpha=0.05\), the null hypothesis that the variables are independent in all cases will be rejected.
## 5 Conclusions  
This study investigated the hole dimensional tolerances and surface finish in AA2024, AA6061, and AA5083 under dry conditions during a multi-spindle simultaneous drilling process. The holes produced in these alloys were compared under the same drilling parameters and using the same drilling tools results from drilling tests. It was concluded from the investigations that AA2024 outperformed in hole quality by producing less circularity error, cylindricity error, and perpendicularity error, irrespective of the drilling parameters. Only in the case of hole size, the holes drilled in AA6061 were less deviated from the nominal size. This was followed by AA2024 and then AA5083. SEM examination revealed surface damage in the form of metal debris adhesion, smeared material, side flow, and feed marks. The worst surface finish and the most oversized and irregular patterns formed around the top and bottom of the hole edges generated in the drilling of AA5083 due to its low machinability, low hardness value, low silicon contents, and large coefficient of thermal expansion. The spindle speed was found to be more influential than feed and affected the hole size with a percentage contribution of 9.71% for hole size and 11.35% cylindricity error. The spindle speed and feed showed an insignificant effect on the circularity error and perpendicularity error; however, in general, a non-linear trend was observed in affecting the hole quality. These variations were expected due to the minute cutting debris potentially adhered to the surface that affected the CMM measurement. Furthermore, the machine drill vibration and temperature variations were not considered in this study and need further investigation during the multi-spindle drilling process.  
Conceptualization, M.A., and M.T.-R.; methodology, M.A., M.T.-R., and K.G.; validation, M.A., M.T.-R., K.G., A.V., U.K., and W.K.; investigation, M.A., K.G., U.K., and W.K.; writing--original draft preparation, M.A.; writing--review and editing, M.T.-R., K.G., U.K., and A.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request.  
The first author would like to thank Edith Cowan University for the awarded (ECU-HDR) higher degree research scholarship.  
The authors declare no conflict of interest.
Figure A2: Hole images of AA6061.  
Figure A4: Top- and bottom-hole edges of AA2024.  
Figure A5: Top- and bottom-hole edges AA6061.
# Multipath Data Storage by Third-Order Nonlinear Optical Properties in Zinc Oxide Nanostructures
###### Abstract  
Simultaneous activation of multiphotonic absorption and nonlinear optical refraction were employed for developing a multipath load balancing technique assisted by light and zinc oxide nanostructures. Data storage as the real and imaginary part of the third-order nonlinear optical behavior of different regions in bidimensional systems was proposed. By recording the third-order nonlinear optical response exhibited by the nanomaterials, a straightforward XOR encryption system controlled by laser pulses was developed. Identification of nonlinear optical signals in propagation through the samples was monitored by a standard optical Kerr gate configuration. Vectorial two-wave mixing experiments were carried out for measuring the third-order optical nonlinearities of the samples explored by nanosecond pulses at 532 nm wavelength. A spray pyrolysis processing route was employed for the preparation of the samples in thin film form. Third-order nonlinear optical characteristics were analyzed taking into account the modification in the ZnO-based nanocomposites by single-pulsed irradiation close to the ablation threshold effect. The combination of nonlinear optics and nanostructured films can be considered for developing ultrafast smart objects with immediate applications for signal processing functions driven by multiphotonic phenomena.  
nonlinear optics; two-wave mixing; encryption; ZnO; nanostructures; thin solid films Article
# A New Lightweight Watchdog-Based Algorithm for Detecting Sybil Nodes in Mobile WSNs
## 3 Problem Statement and Attack Model Assumptions  
In what follows we describe the network assumptions and attack models employed. The subject network consists of two sets of nodes, normal sensor nodes (SN) which perform sensing, routing, and data aggregation, and watchdog nodes (WD) which are responsible for network monitoring and detecting Sybil IDs. It is further assumed that, in order to conceal their presence, the watchdog nodes do not send any messages while overhearing the transmissions of their neighbors.  
Each node has a unique ID and is not aware of its geographical location. All nodes (normal and watchdog) have the same wireless range and move according to Random-Way-Point mobility model [7] during the network lifetime. Regarding the attack models, we have considered the \"Direct, Simultaneous and Fabricated IDs\" Sybil attack models as described in [6]. The subject network is insecure due to the presence of some malicious nodes (MN) that fabricate some IDs, representing Sybil IDs. the malicious nodes broadcast Hello Packets using these fake IDs. This is intended to disrupt routing operation in the network.
# 3D Point Cloud Generation Based on Multi-Sensor Fusion
## 3 A Method of 3D Point Cloud Generation by Multi-Sensor Fusion
### Research on Track Mobile Laser Measurement System (TMLS)
#### 3.1.2 System Software Design  
We developed a program related to the processing of point cloud data in tunnel scanning based on TMLS using the Visual Studio 2017 platform and C++ language, which mainly included integrated system control, data acquisition, data post-processing and other modules. The diagram of the program design structure is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Index**} & \multicolumn{1}{c}{**Parameter**} & \multicolumn{1}{c}{**Remark**} \\ \hline \multirow{3}{*}{Initial alignment} & Horizontal attitude & 0.02\({}^{\circ}\) & \multirow{3}{*}{horizontal attitude angle \(<80^{\circ}\)} \\  & 0.05\({}^{\circ}\) (max) & & \\ \cline{2-3}  & Course & 0.06\({}^{\circ}\) & & static start \(<5\) min \\  & 0.10\({}^{\circ}\) & & dynamic start \(<10\) min \\ \hline Navigation & attitude And Course hold & 0.01\({}^{\circ}\)/h & pure inertial navigation \\ \hline \multirow{3}{*}{Positioning accuracy} & 0.8 mm/h & pure inertial navigation \\  & 0.2\%L (L is voyage) & & combined odometer \\ \hline \multicolumn{3}{c}{Maximum frequency of data transmission} & 2000 Hz \\ \hline \multirow{3}{*}{Other indicators} & Operating temperature & \(-\)40–65 \({}^{\circ}\)C \\ \cline{2-3}  & Input voltage & 18–36 VDC \\ \cline{2-3}  & Input current & \(<\)0.6 A \\ \cline{2-3}  & Power consumption & \(<\)14 W \\ \cline{2-3}  & Electrical interface & RS422/CAN/NET \\ \cline{2-3}  & Dimensions & 166 mm \(\times\) 151 mm \(\times\) 126 mm \\ \cline{2-3}  & Weight & \(<\)4 kg \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main technical parameters of a FSINS3X inertial navigation system.  
Figure 6: FSINS3X.  
Figure 8: Program design structure diagram.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Index** & **Parameter** \\ \hline \multirow{3}{*}{Attitude parameters} & Dynamic accuracy & 0.1\({}^{\circ}\) \\ \cline{2-3}  & Static accuracy & 0.01\({}^{\circ}\) \\ \cline{2-3}  & Resolution & 0.01\({}^{\circ}\) \\ \cline{2-3}  & Tilt range & \(\pm\)90\({}^{\circ}\) \\ \hline \multirow{3}{*}{Other indicators} & Maximum output frequency & 100 Hz \\ \cline{2-3}  & Voltage & 9-35 V DC \\ \cline{2-3}  & Working current & 30 mA \\ \cline{2-3}  & Operating temperature & \(-\)40–85 \({}^{\circ}\)C \\ \cline{2-3}  & Electrical interface & RS232 \\ \cline{2-3}  & Weight & \(<\)360 g \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main technical parameters of a BW-VG527 inclinometer.  
Figure 7: BW-VG527 inclinometer.  
The program can decode the original file and generate the point cloud data in the Las format, generate trajectory data and generate three-dimensional point cloud coordinates. The main interface is shown in Figure 9. The main functions include system control, data collection, ZFS format conversion to LAS format, trajectory data solution, generation of point cloud data in relative coordinate system and point cloud data in an absolute coordinate system.
# Comparative Study of Different Approaches for Islanding Detection of Distributed Generation Systems
## 2 Literature Review
### Over/Under Voltage and/or Frequency:  
This method of detection of the islanding problem is the simplest of the available methods where the voltage and/or frequency is continuously measured and when the voltage or frequency is measured to be outside the predetermined acceptable limits, the connection between the DG and the power grid is disrupted. In the case that the power rating of the DG is less than the power rating of the load, the mismatch of the power will be compensated by the grid. However, when the islanding occurs, there will not be any power supply from the grid, so in this case the voltage and frequency will deviate. That is, in this case, when there is islanding effect, the voltage at the Point of Common Coupling (PCC) should increase until active power P (DG) = Real Power of the load P (load). The Non-Detection Zone (NDZ) refers to the zone of within which the islanding condition is not detected by the islanding detection method.  
The frequency should also change such that the reactive power Q (DG) is equal to reactive power of the load Q (load). This happens due to the fact that the DG inverter will seek a frequency where the current-voltage phase angle of the load equals that of the DG inverter. This is because:  
\[P(load)=(V2\ (PCC))/R, \tag{3}\]  
\[\text{Q (load)}\ =\ \text{V2}\ (PCC)\ (1\ /\ (2\times\text{pi}\times\text{f} \times\text{L})\ -\ 2\times\text{pi}\times\text{f}\times\text{C}). \tag{4}\]This deviation can then be effectively measured to find the islanding case. Furthermore, the detection time of the over/under voltage is found to be 0.03 and 0.05 s and that of the over/under frequency is found to be 0.02 in both cases [56; 57]. The main drawback of the over/under voltage and over/under frequency anti-islanding method is its vast NDZ. This range is given by:  
For active power,  
\[\left(V\ /\ Vmax\right)^{2}\ -\ 1\ \leq\ \Delta P\ /\ P\ \leq\left(V\ /\ Vmin\right)^{2}, \tag{5}\]  
For reactive power,  
\[\text{Qf}\left(\ 1-\ \left(\text{f/fmin}\right)^{2}\right)\ \leq\ \Delta\text{Q}\ /\ \text{Q}\ \leq\ \text{Qf}\left(\ 1-\ \left(\text{f/fmax}\right)^{2}\right). \tag{6}\]  
These two ranges act as the limiting value of the voltage and frequency for the detection for the islanding condition in the net metering.
###### Abstract  
Accurate initial conditions are crucial for improving numerical weather prediction (NWP). Variational data assimilation relies on a static background error covariance matrix (**B**), yet its variance estimation is often inaccurate, affecting assimilation and forecast performance. This study introduces DRL-AST, a deep reinforcement learning-based adaptive variance rescaling strategy that dynamically adjusts the variances of **B** to optimize forecast skill through improved assimilation performance. By formulating variance rescaling as a Markov Decision Process and employing an actor-critic framework with Proximal Policy Optimization, DRL-AST autonomously selects spatio-temporal rescaling factors, enhancing assimilation and forecast accuracy without additional computational cost. As a new paradigm for adaptive variance tuning, DRL-AST demonstrates competitive improvements in forecast skill in experiments with the Lorenz-96 model by generating initial states that better conform to model dynamical consistency. Given its adaptability and efficiency, DRL-AST holds great potential for application in high-dimensional NWP models, where deep learning-based dimensionality reduction and reinforcement learning techniques could further enhance its feasibility and effectiveness in complex assimilation frameworks.  
variational data assimilation; spatio-temporal optimization; deep reinforcement learning; adaptive variance rescaling; numerical weather prediction
## 3 Results
### Concurrent Validity  
In order to examine the concurrent validity of the PRIA-RS, the athletes completed the CR-IWQ, the SCQ-RARC, and the AQ-RARC questionnaire, all of which have a high content and concurrent validity and acceptable reliability indexes [30; 31; 32]. The concurrent validity assessment indicated that the PRIA-RS questionnaire and its factors showed a statistically significant correlation with the other three constructs. In particular, the PRIA-RS questionnaire showed a statistically significant and high correlation with the SCQ-RARC (r = 0.366, \(p<0.05\)). The PRIA-RS factor \"Insecurity, Self-confidence, Individual perception\" correlated statistically significantly with the SCQ-RARC factors \"Confidence due to Rehabilitation\" (r = 0.307, \(p<0.05\)) and \"General Confidence\" (r = 0.245, \(p<0.05\)). The PRIA-RS factor \"Fear of Re-injury\" also showed a statistically significant correlation with the SCQ-RARC factors \"Confidence due to Rehabilitation\" (r = 0.276, \(p<0.05\)) and \"General Confidence\" (r = 0.231, \(p<0.05\)). Only the PRIA-RS factor \"Fear of Re-injury\" showed a statistically significant negative correlation with the CR-IWQ factor \"Re-injury worry due to opponent's ability\" (r = -0.249, \(p<0.05\)). The PRIA-RS questionnaire (r = 0.369, \(p<0.05\)), factor \"Insecurity, Self-confidence, Individual perception\" (r = 0.346, \(p<0.05\)), and factor \"Fear of Re-injury\" (r = 0.262, \(p<0.05\)) indicated a statistically significant high correlation with the AQ-RARC factor \"Functional attention\" (Table 4).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Factor Loadings**} \\ \hline
**PRIA-RS** & **1 st 1a** & **2 nd b** & **Communalities** \\ \hline Item 1 & 0.644 & 0.423 \\ \hline Item 2 & 0.743 & 0.649 \\ \hline Item 4 & 0.550 & 0.313 \\ \hline Item 5 & 0.880 & 0.832 \\ \hline Item 7 & 0.930 & 0.880 \\ \hline Item 9 & 0.578 & 0.334 \\ \hline Item 10 & 0.783 & 0.617 \\ \hline Item 3 & 0.713 & 0.508 \\ \hline Item 6 & 0.652 & 0.469 \\ \hline Item 8 & 0.568 & 0.425 \\ \hline Eigenvalues & 4.413 & 1.939 \\ \hline \% explained variance & 44.13 & 19.39 \\ \hline \% cumulative variance & & **63.52** \\ \hline \hline \end{tabular} a \"Insecurity, Self-confidence, Individual perception\", b \"Fear of Re-injury\".  
\end{table}
Table 3: Exploratory Factor Analysis: factor loadings, communalities, eigenvalues, and percentage of explained variance of the Psychological Readiness of Injured Athletic to Return to Sport (n = 113).