esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Influence of Different Process Parameters on MLA
#### 3.1.3 Molding Pressure  
Molding pressure is one of the critical factors affecting the surface quality of the lens. Figure 5d shows the height of the aspheric lens under different molding pressures at the molding temperature of 130 \({}^{\circ}\)C and holding temperature of 70 \({}^{\circ}\)C. When the embossing force is 300, 400, and 500 N, the thicknesses of the micro lens we measured are 1.05, 0.91, and 0.83 mm, and the diameters are 12.05, 13.22, and 14.51 mm, respectively. Furthermore, we can estimate that the average molding pressures on the sample surface are 2.7 \(\times\) 10\({}^{6}\), 3.0 \(\times\) 10\({}^{6}\), and 3.3 \(\times\) 10\({}^{6}\) Pa. This is positively related to the applied embossing force. As the molding pressure increases, the degree of replication of the lens increases; however, when the pressure is greater than a specific value, the lens's profile height hardly changes because PMMA has filled the entire cavity. If the molding pressure is too small, the aspheric lens cannot be filled, affecting image quality. However, if the molding pressure is too high, it will cause the mold to be easily worn and indirectly affect the surface quality.
# Monitoring Changes in the Enhanced Vegetation Index to Inform the Management of Forests
## 2 Materials and Methods
### Statistical Methods
#### 2.3.2 Modeling EVI Magnitude of Change  
To determine the relative contribution of climate and forest-related variables in explaining EVI magnitude of change we used boosted regression trees (BRT [64]). BRT is a statistical (machine) learning technique that is better suited than traditional statistical approaches to deal with non-linearities and interactions that are likely present in ecological  
Figure 2: Main methodological steps used in this study. After downloading MODIS EVI data, pixels were filtered to only keep pixels with good quality data, within forested areas. EVI time series were then created and processed with three BFAST algorithms: bfast, bfast01, and bfastclassify. The bfast algorithm decomposes a time series into a seasonal component, trend, and noise. Using piece-wise linear regression on the trend component, it detects one or more breaks (if any). Here, we use three main outputs provided by bfast: type of break, magnitude of break, and time of break with 95% confidence intervals (CIs) The bfast01 algorithm runs a seasonally adjusted regression model on the ts and only detects the major break (if any). The bfastclassify algorithm then uses bfast01’s output to classify trends into one of eight possible trend types (Figure A1). Only the magnitude of break values estimated by bfast was used in boosted regression trees (XGBoost models) to explore their relationship with predictor variables (dashed box at bottom, Equation (1) in main text). Satellite icon from flaticon.com (accessed on 29 April 2022). data [64]. We used the Extreme Gradient Boosting (XGBoost) library to run BRT in an efficient, flexible, and scalable manner [65]. The following model formulation was used with XGBoost:  
\[dY=f(X1,X2) \tag{1}\]  
where \(dY\) is a vector containing the magnitude and sign (positive or negative) of EVI breaks. \(X1\) represents a matrix of six climate-related predictors: winter DD5 with a 1-year lag, winter DD5 with a 3-year lag, summer CMI, summer CMI with a 1-year lag, summer CMI with a 2-year lag, and summer CMI with a 3-year lag. \(X2\) represents a matrix of three forest-related predictors: forest age, percent conifer trees, and forest protection status. Each row in the data frame corresponds to the magnitude of a distinct break (one or more breaks per time series pixel are possible) linked in time and space with predictor variables (based on pixel ID and year). A total of nine predictors were used in each model (Table 1). We ran one model for negative breaks and another for positive ones. XGBoost (version 1.7.3) was computed using the Python programming language (version 3.10). More details about the steps taken are given in Appendix A.3. Figure 2 summarizes the major steps described above with a high-level methodological flow chart.
# Evaluation of Local Security Event Management System vs. Standard Antivirus Software
## 6 Conclusions  
We have presented a detection system based on open-source tools, which is able to capture security critical events of the system. The proposed event-based analysis method, linked to MITRE Cyber Kill Chain, can stop the attack at the right moment, when reaching the state of \"Execution/Exploitation\", preventing that PowerShell or Cmd are called.  
Although standard antivirus programs are very effective and computationally efficient while detecting malware in general, they struggle with different obfuscation techniques. When obfuscation is applied to the malware, the proposed event-based strategy offers a second layer of analysis to improve detection rates compared to the set of non-detections and false positives of traditional antivirus systems alone.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**AV/Test** & **A** & **B** & **C** & **D** & **H** \\ \hline PandaDome 20.01.00 & ✗ & ✗ & ✗ & ✗ & ✗ \\ BitDefender 1.0.17.221 & ✗ & ✗ & ✓ & ✓ & ✓ \\ TrendMicro 16.0 & ✗ & ✗ & ✗ & ✗ & ✗ \\ Sophos 10.8.10.810 & ✗ & ✗ & ✗ & ✗ & ✗ \\ Karspersky 21.2.16.590 & ✓ & ✗ & ✓ & ✓ & ✓ \\ Avast 21.1.2443 & ✗ & ✗ & ✗ & ✗ & ✗ \\ AVG 20.10 & ✗ & ✗ & ✗ & ✗ & ✗ \\ Avira 15.0.2101.2070 & ✗ & ✗ & ✗ & ✗ & ✗ \\ Eset 14.0.22.0 & ✗ & ✗ & ✓ & ✓ & ✓ \\ BullGuard 21.0.387 & ✗ & ✗ & ✓ & ✓ & ✓ \\ EBDS & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 5: Execution of malicious code modified using XOR encryption.  
For the purpose of experimentally validating the methodology proposed in this article, a comparative study has been carried out against 10 traditional detection systems (antivirus), always using the same set of samples and with the same obfuscation method in each case. Antivirus software was tested in different Windows 10 virtual machines, allowing the software to use all the potential of heuristics and real-time detection modules; nevertheless, it did not perform well for samples with obfuscation. On the other hand, the proposed methodology was able to detect the detect the sequence of events that were escalating in the severity.  
As future work, Artificial Intelligence algorithms will be added to detect hazardous sequences of events, potentially being able to detect threats even in earlier stages of the attack. The current detection method was implemented by finding the state of \"Execution/Exploitation\", but without exploiting details about sequences of states.  
Conceptualization, A.P.-S.; methodology, A.P.-S. and R.P.; software, A.P.-S.; validation, A.P.-S. and R.P.; invitigation, A.P.-S. and R.P.; writing--original draft preparation, A.P.-S. and R.P.; writing--review and editing, A.P.-S. and R.P. All authors have read and agreed to the published version of the manuscript. This research no received external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Enhanced Path Planning by Repositioning the Starting Point +
## 5 Conclusions
### Broader Scientific and Technological Impact  
This research contributes substantially to the advancement of autonomous systems and sustainable technology applications. The geometric median-based optimization approach demonstrates how fundamental mathematical principles can be effectively applied to complex engineering challenges, providing a template for similar optimizations in other autonomous vehicle domains.  
The findings support growing evidence that intelligent path planning can dramatically enhance the viability and sustainability of drone operations across sectors, including precision agriculture, environmental monitoring, infrastructure assessment, and emergency response coordination. As drone technology continues evolving, optimization approaches like those presented here become increasingly vital for maximizing operational efficiency and environmental responsibility.  
The demonstrated improvements of 4-22% in flight distance efficiency, while appearing incremental, represent substantial gains when scaled across the expanding global drone fleet. These improvements could contribute to significant energy savings and extended operational capabilities that may accelerate drone adoption across multiple industries. This research establishes a foundation for future developments in autonomous vehicle optimization while addressing pressing needs for more sustainable and efficient robotic systems in an increasingly technology-dependent world.  
Conceptualization, G.G. and I.K.; methodology, G.G. and I.K.; software, G.G. and I.K.; validation, G.G. and I.K.; formal analysis, G.G. and I.K.; investigation, G.G. and I.K.; resources, G.G. and I.K.; data curation, G.G. and I.K.; writing--original draft preparation, G.G. and I.K.; writing--review and editing, G.G. and I.K.; visualization, G.G. and I.K.; supervision, I.K.; project administration, I.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The primary data utilized in this study are openly accessible in both Microsoft Excel (version 2507) and SQLite 3 formats through the \"Adjusting-the-Starting-Point\" GitHub repository, available at: [[https://github.com/ggasteratos/Adjusting-the-Starting-Point](https://github.com/ggasteratos/Adjusting-the-Starting-Point)]([https://github.com/ggasteratos/Adjusting-the-Starting-Point](https://github.com/ggasteratos/Adjusting-the-Starting-Point)) (accessed on 3 August 2025).  
The authors declare no conflicts of interest.
## 2 Novel Diabetic Retinopathy (DR) Detection Framework and Experiment
### Methodology
#### 2.2.2 DR Diagnosis Criteria  
DR is diagnosed according to certain criteria through the clinical, comprehensive observation of the eye, and it depends strongly on the experience of the medical practitioner. It can also be performed indirectly based on the imaging of the eye and the analysis of the images based on visual evidence; this is also dependent on the experience of the doctor.  
In the first method, a comprehensive dilated eye exam is performed, where drops are placed into the eyes to widen (dilate) the pupils and provide the doctor with a better view of the interior of the eyes. However, these drops can cause the vision of the patient to blur, which lasts until they wear off several hours later.  
In the second method, i.e., a fundoscopic exam, DR is characterized based on cotton wool spots, flame hemorrhages, dot-blot hemorrhages, and boat hemorrhages. As both methods are dependent on the experience of the doctor, and as the first method is uncomfortable for patients, it is essential to consider a new type of diagnosis--an automatic framework based on optical imaging. In this study, in order to utilize such optical images, pre-processing methods were adopted.
## 2 Design of the 3.7 kW WCS
### Design of the Input and Output Electrical Modules  
#### 2.1.1 Design of Rectifier  
The primary single-phase rectifier consists of four diodes in Figure 2. The diodes of the rectifier are determined by the input voltage \(U_{\text{ac}}\) and effective input current \(I_{RMS}\). \(I_{RMS}\) can be calculated by the following formula:  
\[I_{RMS}=\frac{P_{0}}{\eta U_{\text{ac}}\cos\theta}, \tag{1}\]  
where \(P_{0}\) is the maximum power of the WCS, \(\eta\) is the efficiency of the rectifier, and \(\cos\theta\) is the designed power factor of system. The Schottky diode as a reverse freewheeling diode is selected.  
Figure 1: Components of WCS.  
Figure 2: Circuit structure of WCS.
## 4 Discussion
### Limitations
#### 4.4.4 Temporal Limitations of Study Design  
The included studies featured a mix of longitudinal and cross-sectional designs. While these methodologies provide valuable insights, they are limited in establishing causal relationships. Cross-sectional studies, in particular, are unable to infer temporality, making it difficult to determine whether orthodontic treatment preceded or followed the development of TMDs. Longitudinal studies with extended follow-up periods are needed to better understand the temporal dynamics of TMDs progression and orthodontic interventions.
# Underwater Acoustic Target Tracking: A Review
## 3 Knowledge of Acoustic Target Tracking Instruments
In this section, we will introduce three frequently-used acoustic tracking instruments of underwater target tracking.
## 2 Experimental Overview
### Testing Procedures  
#### 2.2.1 Strength-Characteristic Testing  
Mechanical strength indicators are crucial testing criteria for mixed materials used as roadbed fill. This study primarily investigates the mechanical properties of phosphopysum-modified lime-ash stabilized soil through UCS, splitting tensile strength, and California Bearing Ratio (CBR) tests. Both UCS and splitting tensile strength specimens were cylindrical with dimensions of \(\Phi\) 50 mm \(\times\) H 50 mm. The compaction degree was set at 96%, and samples were prepared using a static pressure method with a one-time molding process. After maintaining pressure for 3 h, the specimens were demolded and then transferred to a standard curing chamber (temperature 20 \(\pm\) 2 \({}^{\circ}\)C, relative humidity \(>\) 95%) for curing. The curing periods for UCS specimens were 7 days, 14 days, 28 days, 60 days, and 90 days, while those for splitting tensile strength specimens were 28 days, 60 days, and 90 days. Each group included six parallel samples, totaling 840 samples. Prior to testing, the samples were soaked in water for 24 h the day before the end of their respective curing periods. The tests were conducted using a universal testing machine with a strain rate controlled at 1 mm/min, and the maximum pressure at failure was recorded. CBR test specimens measured \(\Phi\) 152 mm \(\times\) H 120 mm, with a compaction degree of 96%. Each group included three parallel samples, totaling 15 samples. After soaking in water for 96 h, a penetration  
Figure 3: Relationship between UCS and phosphoric gypsum dosage.  
Figure 2: Improve the compaction curve of lime-fly ash soil.  
rod was used to press into the specimens at a rate of 1 mm/min using a pavement material strength tester. The CBR value was determined as the ratio of the displayed pressure value when the penetration rod penetrated the sample by 2.5 mm to the standard crushed stone pressure.
# Response Prediction for Linear and Nonlinear Structures Based on Data-Driven Deep Learning
## 3 Construction of Dataset and Training of Models
### Clustering of Seismic Waves Based on TSkmeans
#### 3.2.2 Clustering of Seismic Records  
In order to better extract the hidden features in the seismic sequence and reduce the dependence of the ANN model on the training dataset, this study uses the time series \(k\)-means (TSkmeans) algorithm to conduct cluster analysis of seismic records with different characteristics. The aim of the TSkmeans algorithm is to divide the sequence sets \(X=\{X_{1},\,X_{2},\,\ldots,\,X_{nl}\}\) (each sequence \(X_{i}=\{x_{i1},\,x_{i2},\,\ldots,\,x_{im}\}\) contains \(m\) time steps) into \(k\) clusters based on \(k\) clustering centers \(C=\{C_{1},C_{2},\,\ldots,\,C_{k}\}\) (\(C_{i}=\{c_{i1},\,c_{i2},\,\ldots,\,c_{im}\}\)). The objective function of the TSkmeans algorithm is formulated as follows:  
\[P(U,C,W)=\sum\limits_{p=1}^{k}\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{m}u_{ip} w_{pj}(x_{ij}-c_{ij})^{2}+\frac{1}{2}\alpha\sum\limits_{p=1}^{k}\sum\limits_{j=1}^{m -1}\left(w_{pj}-w_{p(j+1)}\right)^{2} \tag{7}\]  
Subject to  
\[\left\{\begin{array}{l}\sum\limits_{p=1}^{k}u_{ip}=1,u_{ip}\in\{0,1\}\\ \sum\limits_{j=1}^{m}w_{pj}=1,0\leq w_{pj}\leq 1\end{array}\right. \tag{8}\]  
Figure 6: Spectral characteristics of selected seismic acceleration records.  
where \(U\) is an \(n\times k\) binary matrix, \(W=\{W_{1},W_{2},\ldots,W_{k}\}\) is the weight matrix, and \(\alpha\) is a balance parameter controlling the smoothness of the weights of time stamps, which can be expressed as:  
\[\alpha=\sum_{i=1}^{n}\sum_{j=1}^{m}\left(x_{ij}-\frac{\sum_{i=1}^{n}x_{ij}}{n} \right)^{2} \tag{9}\]  
The TSkmeans algorithm achieves the classification of time series by minimizing the objective function \(P(U,C,W)\). The main process is as follows:  
* Input: the sequence set \(X\), the number of clustering centers \(k\), the equilibrium parameters \(\alpha\), and the random initial values \(W\) and \(C\).
* Repeat: Step 1: Based on \(W\) and \(C\), \(U\) is solved by the following formula: \[u_{ip}=\left\{\begin{array}{ll}1,&if\ D_{pj}\geq D_{p^{\prime}j},p^{\prime} \
eq p,1\leq p^{\prime}\leq k\\ 0,&otherwise\end{array}\right.\] (10) where \(D_{pj}\) can be expressed as: \[D_{pj}=\sum_{j=1}^{m}w_{pj}(x_{ij}-c_{pj})^{2}\] (11) Step 2: Based on the initial \(W\) and \(U\) obtained in step 1, \(C\) is solved by the following formula: \[c_{pj}=\frac{\sum_{i=1}^{n}u_{ip}x_{ij}}{\sum_{i=1}^{n}u_{ip}}\] (12) Step 3: \(U\) and \(C\) are obtained based on steps 1 and 2, and \(W\) is solved by quadratic programming.
* Output: Determine convergence, output \(U\), \(C\), \(W\).  
Based on the TSkmeans algorithm, cluster analysis was performed on 60 original acceleration records according to the spectral characteristics, and the original seismic records and corresponding structural responses were divided into three cluster datasets by determining three cluster centers. The cluster centers are shown in Figure 7.  
Figure 7: Cluster centers of original acceleration records.
## 4 Experiments and Results
### Classification Performance  
The final classification performance metrics for both models were evaluated on the validation and test datasets. The teacher model achieved a validation accuracy of 99.3%, a precision of 98.8%, a recall of 99.1%, and an F1-score of 98.9%. The student model, leveraging knowledge distillation, attained a validation accuracy of 98.7%, a precision of 98.2%, a recall of 98.6%, and an F1-score of 98.4%. On the test dataset, the teacher model maintained slightly superior performance metrics compared to the student model. The confusion matrices for both datasets reveal high true positive and true negative rates, with minimal false positives and false negatives, underscoring the models' robustness in classification tasks.  
Table 1 presents the final classification metrics for both the teacher and student models across the validation and test datasets. The teacher model consistently outperformed the student model, albeit with a marginal difference, showcasing the effectiveness of knowledge distillation in maintaining high accuracy with a lightweight architecture. Figure 5 displays the confusion matrices for the teacher and student models on the validation dataset. Both models demonstrated high true positives referring to chickens correctly identified and higher true negatives stating the rates of non-chickens correctly identified, with negligible misclassifications.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **Dataset** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline EfficientNet-B0 & Validation & 99.3\% & 98.8\% & 99.1\% & 98.9\% \\  & Test & 99.0\% & 98.5\% & 98.8\% & 98.6\% \\ DeiT-Tiny & Validation & 98.7\% & 98.2\% & 98.6\% & 98.4\% \\  & Test & 98.5\% & 97.9\% & 98.3\% & 98.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Final classification metrics for teacher and student models on validation and test datasets.  
Figure 4: Training and validation loss and accuracy curves: (**a,b**) are the training and validation loss curves for teacher and student models, respectively. (**c,d**) are the training and validation accuracy curves for both models.
## 1 Introduction
Third, and equally significant, we propose a hierarchical evaluation system (model-dimension-indicator) through the P-R-A-L framework, which offers the following practical advantages:  
Figure 1: The P-R-A-L closed-loop resilience framework  
* Measurability and Technical Maturity: Indicators are designed with a focus on quantifiable metrics and rely on mature, widely available technologies for ease of implementation. * Reusability: Many indicators leverage existing security operations tools and data, reducing the need for bespoke solutions. * Flexibility: The framework allows for multiple technical approaches to measure the same indicator, accommodating diverse organizational contexts and resource constraints. * Alignment with Endogenous Security Principles: The design of the R (Destruction Resistance) and L (Evolutionary Learning) dimensions embeds endogenous security concepts, emphasizing intrinsic system capabilities and self-evolution to counter threats. The remainder of this paper is organized as follows. Section 2 reviews related work in cybersecurity resilience and dynamic risk modeling, highlighting the gaps addressed by our approach. Section 3 presents the overall RC model architecture, detailing its theoretical foundation and mathematical formulation. Section 4 elaborates on the P-R-A-L framework, providing operational definitions and indicator design principles. Section 5 presents an empirical validation of the framework through a case study on a real-world critical infrastructure system. Section 6 discusses the findings and future research directions. Finally, Section 7 concludes with a summary of key findings and contributions. Through this work, we aim to deliver a robust, actionable framework for enhancing cybersecurity resilience in an era of persistent and evolving threats.
## 2 On-Site Operation Experiments and Results
### On-Site Measurement Error Data of SEMs  
Measurement error data of 23 m under 13 testing conditions from August 2018 to December 2019 have been collected and are shown in Figure 3.  
Figure 3: Metering error of 23 electricity meters under all test conditions. (**a**) Error data under conditions of current \(I_{\text{max}}\) and power coefficient 1.0; (**b**) Error data under the remaining conditions. The black, red, and blue curves are the mean value curves, upper, and lower envelopment lines, respectively.  
Figure 2: Environmental data. (**a**) Temperature vs. time; (**b**) Humidity vs. time; (**c**) Irradiation vs. time.  
The error consistency was defined as the deviation between the measurement error and the mean value. \(3\sigma_{t}\) was employed to describe the measurement error consistency. The smaller the value of \(3\sigma_{t}\) was, the better the consistency of the measurement error of the smart electricity meters.  
\[\sigma_{t}=\frac{1}{n-1}\sqrt{\sum\limits_{i=1}^{n}{(\gamma_{t,i}-\mu_{t})^{2}}}, \tag{1}\]  
where \(\mu_{t}\) and \(\sigma_{t}\) are the mean and standard deviation of the measurement error of smart electricity meters, \(\mu_{t}=\frac{1}{n}\sum\limits_{i=1}^{n}{\gamma_{t,i}}\), and the measurement error values at time \(t\) for \(n\) SEMs are \(\gamma_{t,1}\), \(\gamma_{t,2}\),..., \(\gamma_{t,n}\).  
The mean and standard deviation sinusoidally fluctuate with calendar time. Although on-site operation data reflect the real influences of environmental conditions on measurement error, the environmental factors vary with calendar time, as shown in Figure 2. As a result, it is difficult to analyze the effects of the operation conditions on measurement error of SEMs.