esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
In the study group, there were 14 (48.3%) men and 15 (51.7%) women, while in the control group there were 16 (51.6%) men and 15 (48.4%) women. Both groups had an average age of 62. There were no significant differences regarding sex (\(p\) = 0.796) and age (\(p\) = 0.722) between the two groups (Table 2).  
In the study group, MAS presented a statistically significant decrease, both between the 1 st day and the 10 th day (1.10 \(\pm\) 0.31, \(p\) < 0.001), and between the 1 st day and the 30 th day (0.83 \(\pm\) 0.47, \(p\) < 0.001), and a significant increase between the 10 th and 30 th day (-0.28 \(\pm\) 0.53, \(p\) = 0.011). In the control group, MAS presented a statistically significant decrease between the 1 st day and the 10 th day (0.74 \(\pm\) 0.58, \(p\) < 0.001), and between the 1 st day and the 30 th day (0.23 \(\pm\) 0.50, \(p\) = 0.020), and a significant increase between the 10 th and 30 th day (-0.52 \(\pm\) 0.51, \(p\) < 0.001) (Figure 1).  
There was a significant increase in Barthel Score in both groups between the 1 st and 10 th day of the study (-2.28 \(\pm\) 1.58, \(p\) < 0.001 in the study group and -2.42 \(\pm\) 1.48, \(p\) < 0.001 in the control group), and between the 1 st and 30 th day of the study (-1.93 \(\pm\) 1.60, \(p\) < 0.001 in the study group and -1.87 \(\pm\) 1.09, \(p\) < 0.001 in the control group), but we observed a decrease in Barthel Score in both groups between the 10 th and 30 th day (0.35 \(\pm\) 0.94, \(p\) = 0.064 in the study group and 0.55 \(\pm\) 0.96, \(p\) = 0.005 in the control group) (Figure 2).  
We also analysed the number of patients in each group that had an improved, unchanged, or worsened MAS or Barthel Score on the 10 th and 30 th day. There was a statistically significant difference between the study group and the control group regarding the percentage of participants with an improved MAS from the 1 st day to the 10 th day, with 32.3% more improved cases in the study group (100% vs. 67.7%, \(p\) = 0.004). When comparing MAS from day 1 with day 30, the study group had 59.9% more improved cases than the control group (79.3% vs. 19.4%, \(p\) < 0.001). In the study group, in more than 50% (68,97%) of participants, MAS values improved or remained unchanged until the 30 th day, while in the control group, in more than 50% (51.61%) of cases, MAS values worsened, without statistical significance (\(p\) = 0.087). There were no other significant differences regarding the change of status between the moments of testing (Table 3).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Study Group (\(n\) = 29)** & **Control Group (\(n\) = 31)** & \(p\) \\ \hline Male, no. (\%) & 14 (48.3) & 16 (51.6) & 0.796 ** \\ Female, no (\%) & 15 (51,7) & 15 (48,4) & \\ Age (years old) & 62 (55; 71) & 62 (57.5; 67.5) & 0.722 * \\ \hline \end{tabular}
\end{table}
Table 2: Groups comparison.  
Figure 1: MAS mean comparison.  
Figure 2: Barthel Score mean comparison.
## 4 CRISPR/Cas Mechanism
### Expression, crRNA Generation and Interference
#### 4.2.1 Type I Systems
In addition to the PAM requirement in the target, a non-contiguous 7 nt sequence (nucleotides in position 1-5 and 7-8) within the first 8 nt of the spacer, relative to the 5' end of the spacer within the crRNA, are essential for initial target recognition and hence are termed the'seed sequence' [48,79]. Cascade with bound crRNA specifically binds to negatively supercoiled target DNA [80]. The negative supercoiled topology serves as energy source for formation of an R-loop. Furthermore, Cascade induces DNA bending before Cas3 is recruited for subsequent DNA degradation (Figure 3C) [80]. Cas3 is the signature protein of type I systems and possesses ATP-dependent helicase activity as well as the ability to cleave ssDNA via an HD nuclease domain [81, 82, 83]. Even though the exact mechanism is not yet fully understood, it is hypothesized that once recruited, Cas3 further unwinds and cleaves the target DNA in a 5' to 3' direction, while Cascade dissociates from the target and is recycled [80,83].
## 3 Simulation Studies
### Time Domain Performance Evaluation
#### 3.3.2 Bump Road Profile  
In addition to the random road, the performance of the controllers was also examined when subject to a single bump road input at a vehicle speed of 25 km/h, as shown in Figure 8a. This was generated with the following formula [33]:  
\[x_{r}(t)=\left\{\begin{array}{ll}\frac{a}{2}\Big{(}1-\cos\Big{(}\frac{2\pi V}{ I}\;t\Big{)}\Big{)}&,\;0\leq t\leq\frac{l}{V}\\ 0&,t>\frac{l}{V}\end{array}\right. \tag{12}\]  
where \(a\) and \(l\) denote the height and length of the bump, respectively. In this study the values of \(a\) and \(l\) are 60.0 mm and 2.0 m, respectively.  
Figures 8b and 9 compare the simulated seat acceleration using the proposed active controllers with those of the passive system. From these figures, it can be seen that all proposed active seat suspensions effectively reduce the seat acceleration when compared with both the passive and the PID active systems. Once again, the control strategies A5, A3, and A4 demonstrate the best reduction in the seat acceleration when compared with other strategies.  
Figure 8: Simulated seat suspension acceleration of the passive and active systems when subject to a bump road input. (**a**) Road profile; (**b**) Seat acceleration.  
Figure 7: Simulated performance assessment of the proposed active seat suspension under different vehicle speeds. (**a**) SEAT factor; (**b**) Frequency-weighted RMS acceleration; (**c**) Maximum seat suspension factor.
## 3 Results and Discussion
### Spectroscopic Analysis
#### 3.4.2 FT-IR Spectroscopy  
The FT-IR spectra of TRWP obtained through iPr and DCM extraction are presented in Figure 12. Similar to the observations made by NMR spectroscopy, no significant differences in the qualitative composition of both samples are discernible. The spectra exhibit signals from aliphatic hydrocarbons at 2921 cm\({}^{-1}\), 2852 cm\({}^{-1}\) (C-H stretching), and 1456 cm\({}^{-1}\) (C-H bending). Above 3000 cm\({}^{-1}\), a weakly intense band from aromatic bonds can be observed. Unsaturated bonds (C=C) also appear in the band below 1600 cm\({}^{-1}\). The band around 1700 cm\({}^{-1}\) corresponds to carbonyl groups. These signals align with the anticipated content, primarily composed of residues of micro- and nanoparticles generated due to tire wear (i.e., polymers, such as the previously discussed butadiene, and process oils) [46].
# High Pressure and Pasteurization Effects on Dairy Cream +
## 3 Results and Discussion
### pH and Color  
The initial pH of the cream used in the first and second studies was similar to the ones reported in the literature [27]: 6.74 \(\pm\) 0.05 and 6.91 \(\pm\) 0.14, respectively (Table S1--available in the Supplementary Materials). Regarding the first batch, all treated samples presented similar values to raw cream (\(p>\) 0.05), with small variations throughout storage (Table S1--available in the Supplementary Materials). Contrarily, the pH of HPP samples was higher (\(p\leq\) 0.05) during the first 9 days of storage, decreasing thereafter, which was probably caused by the observed microbial growth and the organic acids produced from their metabolic activity [28]. On the second batch, no significant differences (\(p>\) 0.05) between treatment conditions at each storage day were detected (Table S2--available in the Supplementary Materials).  
Regarding color measurements, detailed results for L*, a*, and b* values are presented in Table S1 (available in the Supplementary Materials) (first batch) and Table S2 (available in the Supplementary Materials) (second batch). In general, the L* parameter on both studies remained stable at all different storage days and conditions, except on 600/5 samples, where a significant increase (\(p\leq\) 0.05) was observed when comparing the value obtained immediately after processing with that obtained on the 51 st day of storage. For the a* parameter, it suffered some variations concerning both batches. On the first batch, compared to the initial raw cream, it was significantly higher (\(p\leq\) 0.05) for both HPP samples and similar (\(p>\) 0.05) to the thermally pasteurized samples. On the contrary, in the second study, initial a* values of all samples, treated and non-treated, were statistically different (\(p\leq\) 0.05) from each other, in the order from the highest a* value to the lowest: raw cream >600/15 > thermally pasteurized. These variations between the first and second studies are probably due to differences between the cream's batch. By looking at every storage period, 450/5 and 600/5 samples remained statistically similar to each other (\(p>\) 0.05), differing only from thermally treated samples (\(p\leq\) 0.05). The same happened with 600/15 and thermally treated samples on the second batch (\(p\leq\) 0.05); the a* parameter on HPP samples was always higher than the thermally treated samples. Despite the small variations obtained for L*, a*, and b* parameters, no statistical differences (\(p>\) 0.05) were observed for the total color change (\(\Delta\)E*) for all treatment conditions at each day of storage on both studies.
# Security Analysis of the Symmetric Cryptosystem Tiny Jambu
## 3 Security of TinyJambu against Cryptanalytic Attacks  
The first security evaluation of TinyJambu was performed by the own designers in [6], the document submitted at the NIST Lightweight Cryptography Standardization Project. Later, a new security evaluation of TinyJambu with additional features was reported in [7]. The authors introduced a method for finding differential trails by means of Mixed Integer Linear Programming (MILP). Examples of the MILP application in cryptanalysis can be easily found in the literature [14; 15; 16]. Now, we analyze such an evaluation in detail.
## 4 Results and Discussions  
After going through the recent studies on reliability analysis on small failure data, it is evident that the CBH test and Meta-analysis test has not been seen as a possible solution for small failure data. Although Meta-analysis has been considered for medical studies, it has not been considered for machines. This paper uses CBH that has not been considered so far as statistical treatment for a small amount of failure data. Additionally, the Meta-analysis used in this paper has been used only in the medical field and not in the industrial sector.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Values of D**} \\ \hline \multirow{3}{*}{**The Subsystems**} & **Values** & \multirow{3}{*}{**Software**} & **Lower Critical** & **Upper Critical** & \multirow{3}{*}{**Results**} \\  & **Numerically** & **Calculated** & & **Value** & \\ \hline Air supply & 0.88 & 0.88 & 0.10 & 5.99 & Passed CBH test \\ Self-starting & 0.78 & 0.79 & 0.10 & 5.99 & Passed CBH test \\ Fuel supply & 0.26 & 0.22 & 0.10 & 5.99 & Passed CBH test \\ Lubrication & 0.21 & 0.21 & 0.10 & 5.99 & Passed CBH test \\ Cooling & 4.45 & 4.45 & 0.10 & 5.99 & Passed CBH test \\ \hline \hline \end{tabular}
\end{table}
Table 4: CBH test values of all five subsystems.
# Damping Assessment of Lightweight Timber Floors Under Human Walking Excitations
## 3 Results and Discussion
### Vibration Dose Values for Timber Floors  
As stated previously, the \(VDV_{day}\) indicator was selected to evaluate the vibratory performance of the floors and analyze the influence of the damping ratio on them. Figure 14 shows the results of \(VDV_{day}\) for the laboratory and in-situ floors, along with the normative limits that define the different probabilities of adverse comments by users.  
From Figure 14, it can be observed that no floor had a vibratory performance in which adverse comments are not expected (\(VDV_{day}<0.2\)). On the other hand, two laboratory floors (Lab-3.0-W and Lab-3.0-S) had average values of \(VDV_{day}\) in the area with the worst vibratory performance, where adverse comments are very likely (\(VDV_{day}>1.6\)). The majority of the floors (six laboratory floors [Lab-2.1-W, Lab-2.1-S, Lab-2.4-W, Lab-2.7-W, Lab-2.7-S] and one in-situ floor [Field-3.1-W]) remained in the area where adverse comments have a high probability of occurrence (\(0.8<VDV_{day}<\)1.6). Finally, the floor with the best vibratory performance (Field-2.4-W) stayed in the area where adverse comments have a medium probability of occurrence (\(0.4<VDV_{day}<0.8\)).  
In order to study the influence of the damping ratio on the vibration performance, floors that have similar resonant frequencies should be compared. For example, in Table 5, it can be seen that floors Lab-2.7-W, Lab-2.7-S, and Field-2.4-W had frequencies that were very similar and close to 26 Hz. However, the mean value of the damping ratio of the Field-2.4-W floor was 2.1 times higher than the Lab-2.7-W and Lab-2.7-S floors (Table 6), which caused the \(VDV_{day}\) indicators to be reduced by 48% and achieve the best vibratory performance of all of the evaluated floors.  
Other evidence of the positive influence of the damping ratio on the vibration performance of the floors was observed for the Field-3.1-W floor. This floor had the most extended span length (3.1 m) and the lowest resonant frequency (approximately 16 Hz), so it was expected to have the worst vibratory performance. However, because this floor had the highest damping ratios (between 9% and 15%), its vibration performance was better than expected, being comparable to floors that had smaller span lengths and higher frequencies (Lab-2.4-S).  
Figure 14: Boxplot of vibration dose value (\(VDV\))day for timber floors.
###### Abstract  
This research aimed to take a next step towards unravelling the CAP inactivation mechanism for mature (_Listeria monocytogenes_ (Gram positive) and _Salmonella_ Typhimurium (Gram negative)) model biofilms, which will support the further optimization this novel technology. More specifically, we examined how the inactivation mechanism was influenced by the applied processing conditions, i.e., by the electrode configuration, the composition of the gas flow, and the power of the discharge. For each combination of plasma characteristics, we examined if the applied CAP treatment had an effect on (i) the cell membrane, (ii) the intracellular DNA, and (iii) the EPS matrix. In addition, we assessed which (reactive) CAP species were responsible for this lethal/damaging effect and whether these species were able to diffuse into the deeper layers of the biofilms. The results indicated that the inactivation mechanism was indeed influenced by the applied processing conditions. Nevertheless, the bactericidal effect of CAP was always a combination of both damage to the membrane and the DNA, caused by (i) the generation of (intracellular) ROS and RNS, (ii) a drop in pH, and/or (iii) the potential generation of a small amount of UV photons. Moreover, the plasma species were able to penetrate into the deeper layers of the model biofilms and some treatment conditions resulted in an increased biofilm porosity.  
cold atmospheric plasma (CAP); inactivation; mechanism; biofilms; _Listeria monocytogenes_; _Salmonella_ Typhimurium; DNA; membrane damage
## 2 OPF Problem Formulation
### Objective Functions
#### 2.1.2 PV Cost Function  
Photovoltaic systems are gaining popularity as a clean energy source due to their affordable cost and simple design. PV characteristics are highly dependent on various factors, including irradiance level, shades, and temperature, which makes it hard to accurately forecast its power production. The generation and penalty costs for PV power can be calculated as follows:  
\[C_{d,pv,i}=d_{pv,i}P_{pv,i} \tag{8}\]  
\[C_{ue,pv,i}=K_{ue,pv,i}\int_{P_{pv,i}}^{P_{pv,i}}\big{(}P-P_{pv,i}\big{)}f(P)dP \tag{9}\]  
\[C_{oe,pv,i}=K_{oe,pv,i}\int_{0}^{P_{pv,i}}\big{(}P_{pv,i}-P\big{)}f(P)dP \tag{10}\]  
where \(i=1,\ldots,n_{v}\) and \(f(P)\) represent the PDF of the PV unit's output power.  
The total cost of PVs is given by the following function:  
\[F_{2}=\sum_{i=1}^{n_{v}}COST_{PV,i}=\sum_{i=1}^{n_{v}}C_{d,pv,i}+C_{ue,pv,i}+C_ {oe,pv,i} \tag{11}\]  
The PDF of the \(i\)th PVs' output power is calculated as follows:  
* Solar cells or PV cells are hypersensitive to the amount of solar radiation. The PDF of solar radiation \(f(R)\) can be modeled by a beta distribution [12]: \[f(R)=\frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha)\Gamma(\beta)}R^{\alpha-1}(1-R)^ {\beta}\] (12) where \(\Gamma(.)\) is the gamma function, \(\alpha\) and \(\beta\) are parameters of the beta distribution, and \(R\) is the solar radiation.
* The relation between power output of PV and output power of solar cell generator which is related to the solar radiation can be calculated as follows: \[P_{pv}(R)=\begin{cases}P_{pv,r}\Big{(}\frac{R^{2}}{R_{C}R_{STD}}\Big{)}&0\leq R \leq R_{C}\\ P_{pv,r}\Big{(}\frac{R}{R_{STD}}\Big{)}&R_{C}\leq R\leq R_{STD}\\ P_{pv,r}&R_{STD}\leq R\end{cases}\] (13) where \(R_{C}\) and \(R_{STD}\) are solar radiation in W/m\({}^{2}\). Usually, a typical solar radiation point is set to 150 W/m\({}^{2}\), and it is set to 100 W/m\({}^{2}\) under standard conditions.
# Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing
## 3 Results  
### Best Thresholds for the Different Combinations of Stations and Species  
The best thresholds varied substantially between the different combinations of stations and species, and also between SOS and EOS (Figure 4). In general, the best thresholds obtained for SOS are around 0.5, with few combinations of station and species being higher, up to 0.7. For EOS, the best threshold is lower than 0.5 for most combinations of station and species, the minimum being 0.2 for _Populus tremula_ at Zibika, _Fagus sylvatica_ at Cerknica, Javor, Lesce, _Betula pendula_ at Cerknica, and _Quercus_ sp. at Celje and Cerknica. The few exceptions (EOS threshold of 0.5 and 0.6) are _Betula pendula_ at Novi and Celje, _Fagus sylvatica_ at Novi and Bohinjska, and _Tilia_ sp. at Novi and Podzemelj. No plant species stands out consistently as having high or low thresholds of SOS and EOS across all stations.  
The Mann-Whitney tests comparing the share of different land uses between two groups of best thresholds (close to 0.5 or different from 0.5) are presented in Figure 5. The results suggest that for SOS, the land share of the species of interest, coniferous species, other broadleaved species, and other land use considered separately does not determine if the best threshold would be different from 0.5. For EOS, however, there is a significant difference of land share of other land uses (agriculture, grassland, bare land, buildings, etc.) for the two groups of best thresholds. It appears that combinations of stations and species  
Figure 4: Best thresholds for SOS (**top panel**) and EOS (**bottom panel**) for each combination of station and species.  
with higher land shares of other land uses are more likely to result in the best thresholds different from 0.5 (i.e., \(\leq\)0.45 and \(\geq\)0.55).
# Energy-Efficient AC Electothermal Microfluidic Pumping via Localized External Heating
## 4 Discussion
### Potential Applications, Limitations, and Future Work Toward Validation  
ACET-driven vortices can be intensified with thin-film heaters and further amplified by interdigitated electrodes that combine Joule heating and AC forcing. This body of work points toward a self-regulated, high-throughput circulation scheme capable, in principle, of removing heat from localized hotspots (e.g., GPU cores) and supports the broader vision of field-tunable, real-time thermal management beyond conventional semiconductor cooling [1; 17; 18; 23; 28; 37].  
By embedding ACET into microfluidic cooling layers, we introduce a testable hypothesis: that controlled, long-range vortices can meaningfully supplement today's passive or pumped microchannel schemes for hotspot mitigation. When combined with centimeter-scale convection that sustains fluid motion over several millimeters, this concept could evolve into an energy-efficient, easily scaled add-on to existing heatsink designs. Past work and extrapolations [5] suggest ACET remains effective even with shear-thinning fluids, and recent modeling [35] outlines how a closed-loop controller might one day adjust the AC drive in response to on-chip temperature feedback.  
Our own results confirm centimeter-scale ACET vortices and show how an external heater plus electrode geometry amplifies convective transport, but we have not yet validated this pump in a realistic heatsink assembly or linked its flow to a chip's actual heat-flux profile. Nor have we measured end-to-end temperature drops in a plausible GPU-cooling stack. Filling those gaps for instance by integrating our ACET module into a full device-level thermal model or performing proof-of-principle experiments on a heated micro-die would directly connect our simulations to real-world cooling performance.  
A logical next step would be to evaluate, in silico, how our ACET pump slots into a complete cooling chain and measurably lowers chip temperatures. To this end, one could begin by embedding the ACET module within a full device-level thermal simulation, incorporating a heated silicon die, thermal interface material (TIM), a microchannel plate, and a bulk heatsink, and then perform hot-spot removal transients under realistic power loads, comparing \(\Delta\)T against a non-ACET baseline. Parallel to that, building a benchtop \"mock GPU\" test vehicle would allow direct experimental validation: the microfluidic pump would be integrated onto a microfabricated heater chip and instrumented with infrared thermography or micro-thermocouples to record temperature drops across a range of power densities. Finally, system-level gains should be quantified by correlating flow rate with \(\Delta\)T and pumping power with cooling capacity, thereby assessing whether this ACET-enhanced approach can meet or exceed the performance of existing microchannel or heat-pipe cooling solutions. A future work that demonstrates each of these scenarios can elegantly link our ACET vortices in silico to tangible improvements in device-cooling performance.
### Contributions  
For the best of the authors' knowledge, there is no literature available evaluating the case of RTK phase-based positioning, and for the application of a weighting function, there is only the work in Ref. [12]. Based on these observations, the contribution of this paper is twofold. On the one hand, we illustrate the design of an exclusion function with a dual polarized antenna and its effects on the performance of the RTK phase-based positioning in real-world conditions. On the other hand, we introduce a novel weighting function based on a dual polarized antenna. This was briefly introduced in Ref. [13]. In this work, though, we provide a more extensive and complete analysis of the developed algorithm. In particular, we give more details about the set-up and hardware used to capture the data within the framework of the FANTASTIC project. This includes the description and properties of the developed dual polarized antenna during the project. Furthermore, in this paper, we describe the procedure carried out to obtain the results presented in this work. Finally, it is worth mentioning that the real-world results presented in Ref. [13] were a summary of the whole set of PVTresults obtained within the FANTASTIC project. In this work, we additionally show the fine-tuning of the proposed algorithm.  
The rest of the paper is organized as follows: Section 2 introduces the set-up and scenarios considered for the measurement collection campaign. We also show some preliminary results before entering into details of the proposed WE function in Section 3. Finally, Section 4 shows the PVT results after applying the proposed WE function, while Section 5 concludes the paper.