esa-sceva/satcom-chunk-collection huggingface dataset:
# A Goal-Oriented Evaluation Methodology for Privacy-Preserving Process Mining
## 4 Case Study
### PPPM Analysis and the Derivation of Answers to Questions
#### 4.5.5 PPPM Analysis of TLKC (Q1.2.2)  
In this section, we present the quantitative results of applying the _TLKC_ PPPM model on our real-life event logs under two different aspects as result and time utility qualities. Here, the parameters \(k\) and \(l\) regulate the risk of attribute disclosure. All the other parameters are settled out as _weak_ for time performance consideration according to the authors original publication. In particular, for all experiments, when we included the timestamp event attribute, the trials were taking a very long amount of time, so we discarded it.  
Result Utility Quality  
The Figure 12 heatmap shows the applied _TLKC_ model for the PM quality metrics with respect to five different \(k\) and \(l\) parameter values behavior on our real-life event logs. For all the results, the baseline metric values without any anonymization were included in Table 3 and employed for the comparative interpretations.  
This model did not produce any result for the BPIC13 event log due to its possible algorithmic nature since it uses background knowledge specific to the data. Also for the CoSeLog event log, _TLKC_ is not applicable for \(k>16\) values. The CoSeLog and Road Traffic Fines event logs also had high overfitting with respect to the perfect precision metric value. The Sepsis event log showed the optimum quality metrics results for this model.  
Time Utility Quality  
Figure 13 includes the runtime (s) performance metric results of the applied _TLKC_ PPPM model model on our real-life event logs. During the experimentation with the _TLKC_ algorithm, a proportional increase in model runtime was observed with increasing privacy levels as a consequence of specific \(k\) and \(l\) parameter modifications, attributable to increased model complexity besides instance count size. However, for the Sepsis event, the log runtime values were very steady for all configurations byproduct of the model's behavior.  
Figure 12: Discovered TLKC anonymization model quality results.  
Figure 13: Discovered TLKC anonymization runtime (s) metric results.
## 3 Results
### Characterization of Physical Properties
#### 3.1.2 Atomic Force Microscopy  
Surface, Topography and Roughness  
Tapping scans were performed with a (INNOVA VESCO, Berlin, Germany) atomic force microscope (AFM) on a surface area of 5 \(\times\) 5 \(\upmu\)m\({}^{2}\), respectively, with a scan rate of 0.3 Hz and a resolution of 512 pixels, using an RTESPA tip. From the surface profile images of the layers obtained from the TiO\({}_{2}\) and Ag target, with and without oxygen addition, it can be observed that the morphology characteristics of the substrates are kept, as shown in Figure 3. Thus, surface texturing corresponding to the initial topography can be seen, forming structures with a typical size of \(\sim\)500 nm.  
Figure 3: AFM images of the surface topography of the samples deposited on the transparent polymer foils with TiO\({}_{2}\) and Ag in a sputtering process with Ar (**a**) and Ar+O\({}_{2}\) (**b**) mixture.  
Figure 2: XRD patterns of foils coated with three versions of thin films: Ag+SiO\({}_{2}\), Ag+TiO\({}_{2}\) and Ag+TiO\({}_{2}\) +O\({}_{2}\) (with 2.5% O\({}_{2}\) content in total gas flow).
# MicroForest: Lightweight Bottleneck Prediction for Manufacturing Processes on Edge Devices
## 3 Background and Motivation
### Decision Tree and Random Forest Structure  
A decision tree is a hierarchical model that recursively partitions the feature space via binary tests. Each node is associated with a feature \(f\) and a threshold \(\theta\), defining two child subsets from given a dataset \(D\):  
\[D_{L}=\{\,x\in D\mid f(x)\leq\theta\} \tag{1}\]  
\[D_{R}=\{\,x\in D\mid f(x)>\theta\} \tag{2}\]  
The optimal split \((f^{*},\theta^{*})\) at each node maximizes information gain \(IG\):  
\[(f^{*},\theta^{*})=\arg\max_{(f,\theta)}\ IG(D,f,\theta) \tag{3}\]Note that the information gain \(IG\) is defined as  
\[IG(D,f,\theta)=H(D)-\frac{|D_{L}|}{|D|}H(D_{L})-\frac{|D_{R}|}{|D|}H(D_{R}) \tag{4}\]  
where \(H(D)\) denotes the entropy of \(D\).  
Leaf nodes assign class labels based on the majority of samples reaching that leaf.  
A random forest is an ensemble of many decision trees, each trained on a bootstrap sample of the data and considering a random subset of features. At inference time, classification is performed by majority voting across all trees. This combination of bagging and randomized feature sampling improves generalization compared to a single decision tree. However, they exhibit two inherent limitations in resource-constrained environments. First, their model size and memory footprint are substantial: storing hundreds of decision trees consumes significant memory space. Second, their inference latency is high: predicting with a new data requires traversing and aggregating outputs from every tree in the ensemble, resulting in longer response times compared to single models (e.g., a single decision tree or a linear classifier). Consequently, random forests are often unsuitable for edge or embedded systems, where both memory and computational resources are limited.
# Energy Optimisation of Industrial Limestone Grinding Using ANN
## 6 Experimental Conditions  
The data used to build the neural network models were collected from a factorial experiment conducted at the grinding plant of KW Czatkowice in November and December 2023, covering five work shifts. As part of the experiment, four key controllable parameters were selected:  
* Screw conveyor motor speed;
* Temperature after the mill;
* Airflow before the main fan;
* Pressure drop across the mill.  
Each parameter was tested at three different levels. Each experiment lasted for 3 h, with a 1-h stabilization period between tests to allow process parameters to settle. During the experiments, automatic control loops in the SCADA system were disabled, which made it possible to capture the boundary conditions of the process. Detailed experimental conditions are shown in Table 2.
# Spatial Data Analysis for Deformation Monitoring of Bridge Structures
## 3 Structural Health Monitoring Using Ground-Based Radar
### Spectral Analysis of the Data  
Different methods of spectral analysis can be used when the identification of the bridge's vibration modes is required. Fourier transform (FT) is frequently applied for this purpose. It expresses the signal in the form of time series by continuously differentiable functions. The signal can then be transformed between time and frequency domains, and it can be represented as a continuous or discrete signal.  
In practice, the signal represented by a finite number of measured values is analyzed by FT, known as discrete Fourier transformation (DFT). The mathematical definition of DFT depends on the data analyzed. When the goal is the analysis of the dynamic deformations of bridge structures, the fast Fourier transformation (FFT) is used in most cases. The FFT is described by the formula:  
\[X_{x}(f)=\sum_{k=0}^{M}\gamma_{x}(k)w(k)e^{i2\pi f/f_{s}} \tag{7}\]  
where \(\gamma_{x}(k)\) represents the autocorrelation function and \(w(k)\) is the spectral window function [27]. An alternative to the approach described in the previous section is the so-called Welch method. Using the Welch method, overlapping segments of the signal are used for the determination of the spectral density. FFT is then executed using these overlapping segments. The approach ensures smoother periodograms and improved precision of the estimated frequencies, though the magnitude spectrum's resolution is decreased [28].  
The estimation of the mutual spectral density of synchronized data can be performed by their spectral analysis. By the calculation of an average value of the normalized periodograms, the average normalized spectral density (ANSPD) can be computed. The ANPSD characterizes the spectral density of every time series processed in the data analysis. It allows us to create a complex picture about the static and dynamic behavior of the structure being monitored. Time synchronized measurement of the entire structure by a G-B radar gives the possibility to calculate the average periodograms for the whole structure (Figure 4).  
Figure 3: Radial displacements and their transformation.
# Experimental Validation of a Guaranteed Nonlinear Model Predictive Control
## 3 Guaranteed Controller Design via Validated Simulation
### Validated Nonlinear Model Predictive Control  
The purpose of this section is to apply the validated simulation algorithm presented in the previous section to compute a solution satisfying the NMPC constraints and minimizing an interval cost function.  
The validated NMPC aims to stabilize the nonlinear inverted pendulum in the upright position. It is based on interval analysis tools to compute guaranteed control variables over the receding horizon using the validated numerical integration method of the IVP-ODEs problem (5). The control intervals are calculated such that the convergence to the set-point interval is ensured (i.e., \(y_{i}\rightarrow\left[\mathbf{y}_{r}\right]\), \(\forall i\)), and all the state and input constraints are satisfied (i.e., \(y_{i}\in\left[\mathbf{y}_{i}\right]\) and \(u_{j}\in\left[\mathbf{u}_{j}\right]\), \(\forall i,j\)). The validated NMPC is decomposed into two steps:  
* The IVP-ODEs problem (5) is simulated in a complete and validated way starting from the initial domain of inputs referring to the actuators' saturation. Then, for each tested input set, we check if the constraints on state variables are respected using validated simulation and branching methods (Branch-and-Prune algorithm).  
Figure 2: Prior and tight enclosures computed along one iteration using validated methods.  
Since safe inputs are computed over a finite time horizon, the optimal controller box is calculated by minimizing the interval objective function, optimizing the error between the predicted outputs and the reference set, and the norm of the inputs.
# A Geometric Error Measurement System for Linear Guideway Assembly and Calibration
## 2 Measurement Principles
### Perpendicularity Measurement Module  
Figure 5 shows the proposed perpendicularity measurement system, which was mainly composed of the straightness module and the pentaprism module, as mentioned in previous sections. The perpendicularity measurement process is described as follows:  
First, the deviation angle of the incident beam \(\theta_{1}\) (as shown in Figure 6) was calculated via the least-squares analysis of the measured points detected by the PSD set upon the linear guideway \(A\).  
Secondly, the PSD was moved and remounted on the linear guideway \(B\); thereupon, the pentaprism module was set at the cross-point of linear guideway A and the extended line of linear guideway \(B\), allowing the incident beam be refracted by \(90^{\circ}\) and projected on the re-set PSD.  
Figure 3: The in-lab-developed module for altitude angle calibration.  
Finally, the deviation angle of the refracted beam \(\theta_{2}\) was also calculated via the least-squares analysis of the PSD-measured points.  
The perpendicularity error \(\varepsilon_{per}\) can be obtained by the arctangent relation of \(\theta_{1}\), \(\theta_{2}\), measured distance \(D\), and the pentaprism error \(\alpha\) (\(\pm 30\) arcsec), as shown in Figure 6:  
\[\varepsilon_{per}=D\tan^{-1}[(\theta_{2}-\theta_{1})-\alpha] \tag{3}\]  
Figure 4: Schematic diagram of the straightness module.  
Figure 5: Schematic diagram of the perpendicularity module.
## 2 Collagen Type III Characteristics  
Collagen Type III has a unique molecule structure, giving the connective tissue matrix a specific architecture [5]. Collagen molecules contain three identical or similar polypeptide chains called \(\alpha\)-chains and contain at least one triplet helix collagen domain with repeating (Gly-X-Y) \(n\) sequences. Thus, every third amino acid is a glycine residue with frequently repeated proline and 4-hydroxyproline at the X and Y positions. In addition, all collagen contain non-collagen domains. Collagens type III form fibrils [6]. Type III collagen is a homotrimer of three \(\alpha\)1 (III)-chains and is widely distributed in collagen I-containing tissues with the exception of bone [7]. It is an important component of reticular fibers in the interstitial tissue of the lungs, liver, dermis, spleen, and vessels. This homotrimeric molecule also often contributes to mixed fibrils with type I collagen and is also abundant in elastic tissues [8].  
\"Type III collagen is composed of one collagen \(\alpha\)-chain, unlike most other collagens. This is a homotrimer containing three \(\alpha\)1 (III) chains overlapped in a right triple helix. Type III collagen is secreted by fibroblasts and other types of mesenchymal cells, thus playing a major role in different inflammatory pathological conditions like lung damage, liver diseases, renal fibrosis, and vascular fibrosis diseases\". Both collagen type III and type I are the main components of extracellular matrix (ECM). Biomarkers of Type III collagen turnover have been actively studied and different laboratory methods have been used for the detection of fibrosis [9].
## 2 Experiment and Theory Calculation  
The experimental device has been described in our previous work [17; 18], and here, only a brief description is provided. A mode-locked Ti:sapphire laser was used as an excitation source, with a central wavelength of 800 nm, a pulse duration of 70 fs (FWHM), and a repetition rate of 1 kHz. The output femtosecond laser pulse was focused into the gaseous sample by a lens with a 400 mm focus length, and the laser intensity in the focal volume was estimated between 5.0 \(\times\) 10\({}^{13}\) and 1.5 \(\times\) 10\({}^{14}\) W/cm\({}^{2}\), which was calibrated by the Ar\({}^{2+}\)/Ar\({}^{+}\) yield ratio [19]. The gaseous molecules were seeded into the chamber by Argon with 0.2 atm and then ejected into the ionization region by a pulse valve (Parker General, the frequency of 100 Hz, and the duration time of 150 \(\upmu\)s). Typical pressure in the source chamber was about 1.0 \(\times\) 10\({}^{-7}\) mbar. The supersonic molecular beam passed into the reaction chamber (-4.0 \(\times\) 10\({}^{-9}\) mbar) through a 0.2 mm skimmer, and the laser pulses intersected it at right angles in the region between the repeller and the extractor, where the photo-fragments were guided to fly to the detector. The ion cloud was stretched to meet the slice condition, and detected by a pair of microchannel plates (MCPs) coupled with a P47 phosphor screen. The two-dimensional momentum images of each fragment were the central slice (around P\({}_{Z}\) = 0) of the corresponding three-dimensional momentum distribution, and were obtained by a charge-coupled device camera (PI-MAXII, Princeton Instrument) with 5 ns time resolution. In the whole experimental process, all the timing sequence control was implemented by a DG645 (Stanford Instrument Digital Delay/Pulse Generator).  
To better illustrate the ionization and dissociation processes of the CHBrCl\({}_{2}\) molecule, the related theoretical calculations were performed with GAUSSIAN 16 [20], and the relaxed force constant was obtained with the COMPLIANCE software package [21]. The geometry optimization of the reactants, transition states, and fragments was carried out at the B3 LYP level with a 6-31G* basis set [22]. Vibrational frequencies and relaxed force constants of each optimized structure were computed to ensure that the energies of stable structures and transition states were the local minimum and maximum along the reaction coordinate, respectively, and the unscaled harmonic frequencies were used to obtain the zero-point energy at the G4 level [23]. The potential energy surfaces of the excited state were carried out at the B3 LYP level with the cc-pVTZ basis set [24]. The ionization potential was 10.94 eV for the CHBrCl\({}_{2}\) molecule, which was close to the 10.96 eV [25]. The good agreement between our calculations and previous experiment results can demonstrate the feasibility of the theoretical methodology used in this paper.
# Research on the Pavement Performance of Slag/Fly Ash-Based Geopolymer-Stabilized Macadam
## 2 Experimental
### Testing Methods  
#### 2.4.1 Compaction Test  
In accordance with regulations for tests of stabilization materials of organic binders in Highway Engineering (JTG E51-2009), heavy compaction test was carried out on 6 groups  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Material** & **CaO** & **SiO2** & **Al2O3** & **Fe2O3** & **MgO** & **SO3** & **K2O** & **P2O5** & **TiO2** & **Na2O** \\ \hline Slag & 64.61 & 17.57 & 5.01 & 4.15 & 3.04 & 2.70 & 1.31 & 0.42 & 0.35 & 0 \\ Fly ash & 4.02 & 53.90 & 31.15 & 4.16 & 1.01 & 0.73 & 2.05 & 0.67 & 1.13 & 0.89 \\ Cement & 64.44 & 21.60 & 4.13 & 4.57 & 1.06 & 0.13 & 0.65 & 2.01 & 0.67 & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of raw materials (wt.%).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Samples** & **Stabilizer** & **Dosage of** & **Dosage of** & **Dosage of Fly** & **Ratio of Binder to** & **Gradation** \\  & **Geopolymer (\%)** & **Cement (\%)** & **Ash (\%)** & **Stabilized Material (\%)** & **Type** \\ \hline CFA-3 & Cement/fly ash mixture & - & 3 & 12 & 3:12:85 & JPA \\ \hline CFA-4 & Cement/fly ash mixture & - & 4 & 16 & 4:16:80 & JPA \\ \hline GFA-2 & Geopolymer/fly ash mixture & 2 & - & 8 & 2:8:90 & JPA \\ \hline GFA-3 & Geopolymer/fly ash mixture & 3 & - & 12 & 3:12:85 & JPA \\ \hline GFA-4 & Geopolymer/fly ash mixture & 4 & - & 16 & 4:16:80 & JPA \\ \hline GFA-5 & Geopolymer/fly ash mixture & 5 & - & 20 & 5:20:75 & JPA \\ \hline \hline \end{tabular}
\end{table}
Table 3: The proportions of geopolymer-stabilized macadam.  
of stabilized pellets with different match ratio (CFA-3, CFA-4, GFA-2, GFA-3, GFA-4, GFA-5) and five samples with different water content (5%, 5.5%, 6%, 6.5%, 7%) were prepared for each group. The corresponding dry density with different water content was determined by weighing. The optimal water content (\(\omega_{0}\)) and corresponding maximum dry density (\(\rho_{\rm d}\)) were determined by fitting prior to preparing test pieces.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was conducted using IBM SPSS Statistics (version 25.0, Armonk, NY, USA) and Microsoft Excel. Kinovea software and SPSS Statistics are validated tools that are widely used in biomechanical research, meeting the criteria outlined by the International Committee of Medical Journal Editors (ICMJE). These tools were selected based on their reliability and widespread use in sports science research, as recommended by the ICMJE guidelines. A paired \(t\)-test was used to compare the two measured variables for both throwing techniques, with a significance level of \(p<\) 0.05. Test-retest reliability was assessed using a two-way random Interclass Correlation Coefficient (ICC) with a 95% confidence interval. ICC values were categorized as low (\(<\)0.5), moderate (0.5-0.75), good (0.75-0.9), and excellent (\(>\)0.9). Effect sizes were calculated using Cohen's d, with thresholds of 0.2, 0.5, and 0.8 representing small, medium, and large effects, respectively. The assumptions of the paired \(t\)-test, including normality (assessed with the Shapiro-Wilk test) and homogeneity of variances (assessed with [PERSON]'s test), were verified before analysis. Given the small sample size, future studies should consider using non-parametric tests, such as the Wilcoxon signed-rank test, or bootstrapping methods to improve statistical robustness. Bootstrapping methods might also improve confidence interval estimates for key parameters, providing a more nuanced understanding of the data. Linear mixed models could also be applied  
Figure 4: Flowchart of participants.  
in future research to assess intra- and inter-individual variability, providing a more nuanced understanding of the data. For analyses involving multiple comparisons, Bonferroni correction was applied to minimize the risk of Type I errors. Additional analyses, such as ANOVA, could be considered in future studies to further strengthen statistical rigor and provide additional insights into the biomechanical differences between techniques [17; 18; 19; 20].
# Real-Time Tracking and Position Control of an Elastic Cable-Driven Winch System
## 6 Conclusions  
In this study, a comparative analysis of PD and SMC methods was conducted for an elastic cable-driven winch system through both simulations and real-time experiments. The simulation results demonstrated that the SMC algorithm exhibited superior positioning accuracy, reduced overshoot, and enhanced tracking performance compared to the PD controller.  
Similarly, in real-time experiments, the SMC algorithm achieved higher accuracy than the PD controller in both step displacement and sinusoidal trajectory tracking tasks. Under varying load conditions, the PD controller exhibited instability at maximum load, whereas the SMC algorithm successfully maintained stable system behavior across all tested scenarios. The nonlinear structure of SMC provides increased robustness against modeling inaccuracies and parameter uncertainties, offering a significant advantage for systems operating under variable loads.  
Moreover, the SMC algorithm proved more effective in minimizing torque ripple observed at the motor shaft. The analysis indicated that the SMC systematically reduced acceleration ripple amplitude at specific frequencies, while the PD controller maintained generally higher and more consistent levels of ripple.  
Furthermore, during real-time experiments, only the PD controller failed to maintain control under high-load conditions, despite functioning adequately in simulation. This highlights the limitations of evaluating system performance based solely on simulations and underscores the importance of real-time testing.  
Future research could concentrate on enhancing the SMC algorithm to improve motor torque ripple behavior and investigate its use in more complex systems with multiple degrees of freedom. Specifically, efforts can be focused on developing control strategies for innovative applications that involve multiple elastic cables, such as those used with unmanned aerial vehicles. Additionally, fixed-time control strategies will be compared to the sliding mode control approach used in this study, in terms of convergence time, robustness, and implementation complexity.  
Methodology, D.K.S.; Software, D.K.S.; Investigation, D.K.S.; Writing--original draft, D.K.S.; Writing--review & editing, A.A. and H.G.; Visualization, D.K.S.; Supervision, A.A. and H.G.; Project administration, D.K.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: The original contributions presented in the study are included in the article, and further inquiries can be directed to the corresponding author.  
This study was created bybenefiting from the TUBITAK-2218 National Post-Doctoral Research Fellowship Programme (Project No: 123C313). However, all responsibility for the publication's content rests solely with the authors.  
The authors declare no conflicts of interest.