esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Physicochemical Analysis  
Table 2 presents the means, standard deviations, and ANOVA (rennet \(\times\) feeding) of physicochemical parameters (F and probability values). The effect of rennet was significant for all physicochemical parameters (\(p<0.01\)) except for pH. The animal coagulant showed the highest average TS and fat content, and the lowest salt level. The pH, fat content, fat/TS value, and sodium chloride level were affected by the type of diet used (\(p<0.05\)). Cheeses from goats fed with a diet based on DOP pellets had higher average pH and salt levels, and lower fat content than the cheeses from goats fed with the control diet. Finally, there was a rennet \(\times\) feeding interaction effect for all the parameters studied (\(p<0.05\), Table 2). Compared to other studies, we found slightly lower values than other Spanish goat milk cheeses [11; 28; 29; 30; 31; 32; 33; 34].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Effect** & **pH** & **TS** & **Fat** & **Fat/TS** & **NaCl** \\  & & **(g/100 g Cheeses)** & **(g/100 g Chees)** & **(g/100 g TTS)** & **(g/100 g Chees)** \\ \hline \multirow{4}{*}{Rennet} & Animal (\(n=9\)) & 4.99 \(\pm\) 0.03 & 75.0 \(\pm\) 3.0 & 36.1 \(\pm\) 3.2 & 48.2 \(\pm\) 3.3 & 1.61 \(\pm\) 0.29 \\  & Vegetable (\(n=9\)) & 4.97 \(\pm\) 0.12 & 72.6 \(\pm\) 3.3 & 32.1 \(\pm\) 3.8 & 44.2 \(\pm\) 4.8 & 1.77 \(\pm\) 0.16 \\  & F & & 42.1 & 144.0 & 11.83 & 28.18 \\  & P & ns & 0.001 & 0.001 & 0.01 & 0.001 \\ \hline \multirow{4}{*}{Diet} & Control (\(n=6\)) & 4.90 \(\pm\) 0.10 \({}^{\circ}\) & 73.6 \(\pm\) 4.0 & 35.7 \(\pm\) 3.2 \({}^{\circ}\) & 48.5 \(\pm\) 2.4 \({}^{\circ}\) & 1.61 \(\pm\) 0.04 \({}^{\circ}\) \\  & DOP40 (\(n=6\)) & 5.02 \(\pm\) 0.03 \({}^{\circ}\) & 73.9 \(\pm\) 2.9 & 32.5 \(\pm\) 5.2 \({}^{\circ}\) & 43.9 \(\pm\) 5.9 \({}^{\circ}\) & 1.91 \(\pm\) 0.27 \({}^{\circ}\) \\ \cline{1-1}  & DOP80 (\(n=6\)) & 5.02 \(\pm\) 0.70 \({}^{\circ}\) & 74.0 \(\pm\) 3.2 & 34.2 \(\pm\) 3.0 \({}^{\circ}\) & 46.1 \(\pm\)3.5 \({}^{\circ}\) & 2.07 \(\pm\) 0.24 \({}^{\circ}\) \\ \cline{1-1}  & F & 36.8 & & 4.82 & 5.26 & 26.36 \\ \cline{1-1}  & P & 0.001 & Ns & 0.01 & 0.05 & 0.001 \\ \hline \multirow{2}{*}{Rennet \(\times\) Diet} & F & 34.3 & 128.0 & 14.2 & 4.06 & 10.82 \\  & P & 0.001 & 0.001 & 0.001 & 0.05 & 0.001 \\ \hline \hline \end{tabular} Values followed by the same letter within the same column are not significantly different (\(p>0.05\)) according to [PERSON]’s multiple range test.  
\end{table}
Table 2: Descriptive measures (mean and standard deviation) and analysis of variance (rennet \(\times\) diet) of physicochemical parameters (F and probability values).  
In addition, we found that both rennet type and diet had an influence on the chemical composition of the cheeses. However, other authors [32] found that diet had a greater effect on physicochemical parameters than rennet.
# Experimental Verification of the Dynamic Model of Turbine Blades Coupled by a Sealing Strip
## 3 The Experimental Results
### Blades with Strips
The moderate differences in the OOP peaks are justified by the fact that a different position of the strip may lead to different extension of the strip-platform contact areas and, as a consequence, a different constraint and sliding contact area. Considering that the strip position could have an influence on the results, the following tests are performed avoiding to switch on and off the compressed air system. At a constant pressure on the strip, the forced response of the system is measured at different excitation levels. The system is tested in IP and OOP conditions. Each set of tests is composed of three frequency sweeps performed at three levels of excitation force amplitude: \(|F_{E}|\) = 0.2 N, \(|F_{E}|\) = 0.15 N, \(|F_{E}|\)= 0.1 N. The \(|F_{E}|\)= 0.2 N test is performed first, in order to try and minimize changes in position during the application of the different excitation levels. Only at the end of the three frequency sweeps (one test set) is the compressed air system switched off. Six different set of tests are performed to account for repeatability. The IP and OOP measured forced responses for blade 1 (results on blade 2 are very similar) are depicted respectively in Figure 7a,b. Different colors correspond to different excitation levels. The IP FRFs are highly repeatable. This is an experimental confirmation of what was proven numerically in [29]: the strip does not influence this mode. On the contrary, the OOP curves show the typical trend in presence of friction damping. By increasing the excitation force levels:  
* sliding in the contact interface increases, thus producing a progressively less stiff system with lowering OOP peaks' frequency;
* the peaks appear increasingly more rounded since, due to the increasing relative displacement between strip and platform, more damping is introduced in the system. Figure 6: (**a**) IP FRFs of blade 1 for three different tests at \(F_{E}\) = 0.1 N; (**b**) OOP FRFs of blade 1 for three different tests at \(F_{E}\) = 0.1 N.
## 3 Results
### Ground Reaction Force  
\(V_{Impact}\): Increasing the V\({}_{\text{Impact}}\) resulted in an increased GRF, shortened impact duration, and shortened t\({}_{\text{max}}\) (Figure 2a). GRF\({}_{\text{max}}\) remained under 8800 N for the typical sideways fall impact velocities (580, 2140, and 3170 mm/s). A higher GRF\({}_{\text{max}}\) (up to 20,500 N) was observed for the higher impact velocities (4310 and 6070 mm/s) (Figure 2a). The area under the curve up to GRF\({}_{\text{max}}\) (I\({}_{\text{GRFmax}}\)) increased with increasing impact velocity, remaining in the range of 43.63-62.24 Ns for V\({}_{\text{Impact}}\) = 580-3170 mm/s, but jumped up to substantially higher values (91.38-111.94 Ns) for higher impact velocities (4310 and 6070 mm/s). Also, by increasing the V\({}_{\text{Impact}}\), the shape of the peak GRF region changed from the plateau to sharp (Figure 2a). The GRF responses were compared with similar previous studies in Appendix A, Figure A6 and Table A4.  
Flooring material: the conventional flooring materials did not affect the GRF\({}_{\text{max}}\), t\({}_{\text{max}}\) or the shape of the GRF curve notably. For almost all of the flooring materials, GRF\({}_{\text{max}}\), t\({}_{\text{max}}\), and impact duration were about 8800 N, 0.0155 s, and 0.0375 s, respectively. Only in the case of very compliant flooring material such as agglomerated cork, the GRF at the early-contact and after passing the GRF\({}_{\text{max}}\) was slightly lower than for the stiffer surfaces. I\({}_{\text{GRFmax}}\) varied from 56.32 to 72.40 Ns depending on the flooring material. Compliant surfaces such as EVA foam and agglomerated cork had a greater I\({}_{\text{GRFmax}}\) than stiff surfaces such as rigid ground and plywood (Figure 2b).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Runs** \\ \hline
**Impact velocity** & 580, 2140, 3170 *, 4310, 6070 mm/s \\ \hline
**Conventional flooring material** & Rigid *, ceramic tile, ethylene-vinyl acetate, lineloum, \\  & concrete, plywood, asphalt pavement, agglomerated cork \\ \hline
**TST stiffness** & Baseline, +6.5\%,+13\%,+19.5\%,+26.0\% \\ \hline \hline \end{tabular}  
* Constant value for simulations of other variables.  
\end{table}
Table 1: Simulated variables.  
_TST stiffness_: increasing the TST stiffness resulted in a slightly increased GRF\({}_{\rm max}\), but it did not affect t\({}_{\rm max}\) and I\({}_{\rm GRF}\)\({}_{\rm max}\) substantially (Figure 2c). While the GRF remained almost unchanged, the GRF remained unchanged.  
Figure 2: Ground reaction force: effect of (**a**) impact velocity; (**b**) flooring material; (**c**) trochanteric soft tissue (TST) stiffness.  
constant for all TST stiffnesses before reaching the peak-impact region (0.007-0.02 s), a stiffer TST resulted in an increase and then a decrease in the GRF during the consecutive intervals (0.025-0.05 s and 0.05-0.1 s), respectively (Figure 2c). For all TST stiffnesses, tmax remained almost the same (0.0115-0.0135 s), and a stiffer TST did not cause a much greater I\({}_{\rm GRFmax}\) in comparison with softer TST (74.47 vs. 49.7 Ns).
## 5 Results
### Results Considering a Single Set of Parameters for All Scenarios  
Initially, the coordination problem of directional OCR was carried out by using only one group of parameters intended to be suitable for all OSes (no clustering). Results are summarized in Table 3. Note that the number of cases in which Equation (2) is not fulfilled is 13, demonstrating that this solution is clearly not adequate. In general terms, this allows us to conclude that it is not possible to guarantee proper coordination when a single set of parameters is considered for all operative scenarios of the MG.
## 5 Analysis of Thermal Loss Results for Granular Assemblies
### Analysis of Frequency-Varying Heating Characteristics of Granular Assemblies  
The temperature change in the 2 mm granular assemblies for different frequencies and reduced accelerations is analyzed. The excitation frequency interval is 50 Hz, with reduced accelerations \(\Gamma\) of 0.8, 1.2, 2.5, and 6. The granular assembly container's surface, middle layer, and bottom layer temperatures are also measured for approximately 1 min. As shown in Figure 7, there is a general heat loss phenomenon in the particle aggregate, and there are differences in the heat loss effects of the internal particles. The temperature changes show that the heating effect of the granular assemblies is evident at low frequencies and large, reduced accelerations. With increasing frequency, the heating effect of the granular assemblies gradually weakens. This is because as the frequency increases, the vibration states of the particles in the aggregate mainly transition from the micro-vibration and intermediate vibrational states, and the collision and friction between particles are not obvious. Interparticle interactions are the key to generating heat loss, so the heating effect of the granular assemblies decreases. When the reduced acceleration increases and the corresponding amplitude increases, the vibration states of the particles gradually transition from the solid-like, micro-vibrational, and intermediate vibrational states to strong vibrational and gas-like states. The interaction between particles becomes more intense, and the heating effect of the granular assemblies becomes evident. The temperature distribution of the internal particles shows a gradually weakening trend from the bottom to the surface. During the first hour of forced vibration, the particles in the strong vibration and gas-like states experience significant temperature increases, while those in the micro-vibrational and intermediate vibrational states experience slow temperature growth. When the vibration time continues to increase to 2 h, the temperature increase in the particles in the strong vibrational and gas-like states decreases, while the particles in the micro-vibrational and intermediate vibrational states continue to experience a significant temperature increase. This reflects a limit to the forced vibration heating effect of the granular assemblies, that is, at this point, the input energy and the temperature dissipated to the environment are in a dynamic equilibrium state.  
Figure 7: _Cont._
## 5 Example Analysis  
### Example Conditions  
The 10 kV system of an oilfield in the Bohai Sea of China was analyzed. The system grid consisted of 16 nodes. Node 16 belonged to the 35 kV distribution network and the other 15 nodes were of 10 kV, as shown in Figure 5. The whole system was connected to the higher-level distribution network through submarine cables. The total load of the network was 103.892 MW and the load of the entire oil field hardly changed over time. Under the existing working conditions, among the eight (total) gas turbines, six were designated for use and two for standby, which were able to meet the power-load consumption of the whole oilfield with the output of the higher-level distribution network. The system structure is shown in Figure 5.  
Based on this topology, this study investigated the rationality of WT/PV ES siting and sizing. Considering the actual demand, Method I and Method II, defined in the previous section, were used to plan the WT/PV ES. Then, comparisons with the original working conditions and analysis were carried out.
## 5 Data Processing and Artificial Intelligence Methods
### First-Level Data Processing
This time interval was chosen because it corresponds to the length of a rumination period. The two characteristics identified during data preprocessing, ICI and JVB, were inputs to the SVM method. According to the authors, these properties were able to make rumination a linearly separable feature; therefore, SVM was used with a linear kernel. In total, 270s of measured data were required to detect rumination. Based on this, rumination can be detected with an accuracy of 89.2% [7]. Determining rumen temperature is not an easy task. Classical studies have concluded that rumen temperature is not suitable for estimating the body core temperature [16]. This is because the rumen temperature is greatly influenced by the external temperature, the temperature of the water consumed, rumen fermentation, and diseases associated with fiber [10; 16; 32; 33; 34; 37; 40; 42; 43; 45]. We hope that by intelligent baseline estimation, we can obtain rumen temperature that reflects the body temperature. The proposed regression algorithm for baseline determination is a robust matching algorithm [72] specifically recommended for handling one-side deviations [73]. The literature discusses patterns of change in rumen temperature, from which an appropriate sampling frequency can be determined and deviations from baseline can be inferred from feed and water intake, whose number can be estimated as additive information. Fuzzy solutions can also be used advantageously in the processing of rumen bolus data, although so far, no such publication has come to our attention. It is worth mentioning the fuzzy solutions in other measurements connected to cattle characteristics. One of them allows measuring the weight of moving animals with 1% accuracy. Based on two variables, the measured actual body weight and the animal's velocity, the model constructs a smoothed body weight curve using a Takagi-Sugeno fuzzy system, the zero frequency Fourier transform of which gives the body weight. Model parameters can be extracted from the ANFIS neuro-fuzzy system and stored in a database [74; 75].
## 3 Estimation of the Tension of the Overhead Wires
### Analysis of the Similarity of the Data  
In order to derive the relationship between acceleration sensor data and the tension of the overhead wires, integrated data were constructed on the basis of the measured data from heterogeneous sensors. In the end, the tension of the overhead wire was derived using only the data of the acceleration sensor, but for the purpose of comparative verification, the tension measurement sensor was installed separately. In addition, the data measured by the acceleration sensor and the tension sensor were analyzed after time synchronization. For the correlation analysis of data, third-order differencing data through DWT was used, and important factors were selected by performing Pearson's correlation and clustering-based similarity analysis. The Pearson's correlation coefficient was calculated on the basis of Equation (2) below, and the results of Pearson correlation analysis from 3 September 2021 to 29 September 2021 are shown in Table 2. As a result of the analysis, it may be seen that in the case of Pearson's correlation analysis, the tension and the \(Y\)-axis acceleration have the highest correlation.  
Figure 6: Example of data conversion using DTW.  
\[\rho=\frac{Cov(X,Y)}{\sigma_{X}\sigma_{Y}}=\frac{\sigma_{XY}}{\sigma_{X}\sigma_{Y}} \ \ (-1\leq\rho\leq 1) \tag{2}\]  
Here, \(\rho\): Pearson's correlation coefficient, \(\sigma\): standard deviation, \(Cov(X,Y)\): covariance between \(X\) and \(Y\).  
In addition, in the case of clustering-based similarity analysis of time series data, three methods were applied: time-series clustering (TS clustering), dynamic time warping clustering (DTW clustering), and normalized dynamic time warping clustering (N-DTW clustering). In addition, six clusters for the cluster analysis were selected and were simulated on the basis of the sum of the squared deviations from each observation and the cluster centroid. An example of the result of detailed analysis for TS clustering and DTW clustering are shown in Figure 7.  
The TS clustering results indicate that the tension of the overhead wire and the \(Y\)-axis and \(Z\)-axis acceleration data have the highest correlation. On the other hand, the results of DTW clustering and N-DTW clustering indicate that tension of the overhead wire and \(Y\)-axis acceleration data had the highest correlation. In other words, it suggests that the \(Y\)-axis acceleration and the tension data had a significant correlation in each correlation analysis method. Thus, the correlation between the tension of the overhead wire and \(Y\)-axis acceleration is required to be analyzed in detail according to regression analysis.
# Post-Quantum Digital Signature: Verkle-Based HORST
## 11 Efficiency Comparison
Hash-based signatures, such as Verkle-based HORST, provide the most conservative security assumptions of any post-quantum approach as they only rely on the binding property of KZG commitments and the collision resistance of cryptographic hash functions. Despite achieving somewhat improved signature sizes and verification times, lattice-based systems such as Dilithium3 rely on more recent mathematical assumptions on the difficulty of learning with errors (LWE) issues, which lack the decades of crypto analysis that have supported hash-based security. Although the multivariate and code-based schemes are intriguing in theory, they are impractical for the majority of real-world deployments. For example, McEliece's 0.9 MB public keys are larger than the storage capacity of many smart cards and Internet of Things devices, while Rainbow-III's 160 KB signatures would take up almost an entire Ethernet frame, which is typically 1500 bytes. Because of its post-quantum security, fewer authentication routes, quicker constant-time verification of vector promises, constant signature size, and lower storage needs, Verkle-based HORST is a more effective option than classic HORST. It is especially useful for resource-constrained situations and applications that need tiny signatures, such as secure boot protocols. Despite its advantages, vector commitments in Verkle-based HORST add complexity, making evaluation and implementation more challenging. Its implementation in practical systems may be slowed down by its complexity. The practical implementation of Verkle-based HORST may be hampered by the limited usage of Verkle trees, a relatively new cryptographic primitive. Efficiency is boosted with Verkle-based HORST, but there may be other trade-offs, including a higher computational cost for creating vector commitments. When selecting Verkle-based HORST for particular applications, these trade-offs need to be carefully taken into account. Verkle-based HORST, with its smaller signatures, quicker verification, and lower storage needs, is a promising advancement over conventional HORST. It is a post-quantum secure signature technique with security predicated on the binding property of vector commitments and the collision resistance of hash functions. Although it adds some complexity,its efficiency improvements make it a solid contender for applications like blockchain, the Internet of Things, and secure communication protocols that demand scalable and small digital signatures.
These data encompass not only machine states, production counts, and cycle times but also energy consumption, temperature profiles, vibration patterns, and other parameters relevant to both operational and environmental performance. Using these data effectively is the key to unlocking the holistic optimization potential. Despite advances in individual technologies, there is still a major gap: few solutions truly integrate real-time Process Mining with dynamic LCA metrics and feed this into a Multi-Objective Optimization engine for both efficiency and sustainability. Most current methods rely on static data, treat goals separately, or lack real-world validation. To address this gap, this study introduces and validates a comprehensive, cloud-based, data-driven framework designed to simultaneously improve both operational efficiency and environmental sustainability in manufacturing. The core innovation lies in the synergistic integration of three key components: (1) real-time Process Mining to discover actual workflows and operational KPIs; (2) dynamic Life Cycle Assessment (LCA) to quantify environmental impacts at the instance level using live sensor data and (3) Multi-Objective Optimization (MOO) to identify Pareto-optimal operating configurations that balance these conflicting goals. The framework is implemented on a scalable cloud infrastructure and incorporates the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to support decision-making. The novelty of this work lies in its integrated, data-driven approach that unifies operational and environmental optimization in real-time. Validated in a real-world tube manufacturing facility, the framework demonstrates how manufacturers can make smarter, more balanced decisions to simultaneously enhance productivity and environmental stewardship. This paper is structured as follows. Section 2 reviews related studies in process mining, LCA, and multi-objective optimization in manufacturing, further refining the identified research gap. Section 3 details the proposed integrated framework and the mathematical formulation of the multi-objective optimization model. Section 4 describes the real-world case study, presents the data analysis, and discusses the optimization results, including Pareto front analysis, TOPSIS ranking, and sensitivity analysis. Finally, Section 5 concludes the paper, summarizes the key findings and contributions, and discusses potential avenues for future research.