esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Water Content and Cementation on the Shear Characteristics of Remolded Fault Gouge
## 2 Methods and Materials
### Experimental Materials  
Based on the analysis in Section 2.1, quartz, montmorillonite, and kaolinite are selected to prepare the simulated fault gouge for this study, with 425-grade Portland cement serving as the cementing material to simulate siliceous or calcareous cementation. The choice of Portland cement is supported by previous experimental studies, which have shown that its hydration products form interlocking crystalline structures resembling those in naturally cemented geomaterials. In particular, Portland cement has been widely used to simulate weak siliceous or calcareous bonding in soft rocks and soils, providing mechanical responses similar to natural fault gouge cementation [39]. Different degrees of cementation in the fault gouge are characterized by varying the cementing ratios. The cementing ratio is defined as the mass ratio of added cement to the mass of dried fault gouge, expressed as a percentage. Quartz particles represent the non-clay components, while montmorillonite and kaolinite represent expansive and non-expansive clays, respectively. To minimize the influence of particle size on the internal friction angle in the experimental results [40], quartz particles with sizes ranging from 250 and 380 \(\upmu\)m are utilized. For montmorillonite and kaolinite, minerals with particle sizes of less than 45 \(\upmu\)m are selected, ensuring their purity is above 90% to maintain the consistency and representativeness of the experimental materials. Although the grain size distribution of the natural fault gouge was not quantitatively measured, visual inspection during field sampling indicated a clay-rich matrix with dispersed silt-sized grains. The selected artificial materials and their particle sizes were chosen to approximate this observed texture as closely as possible under laboratory conditions.  
Statistical analyses from previous studies indicate that the clay mineral content in natural fault gouge typically ranges from 20% to 80%, while the quartz content varies from approximately 10% to 70%. The contents of kaolinite and montmorillonite are generally within the range of 10% and 70%. These statistical ranges align well with the XRD analysis results of rock samples collected from the Liujiang Basin in North China.  
Figure 2: Mineral composition of fault gouge and adjacent mudstone samples collected from the hanging wall and footwall. Note: The sample letters (e.g., A, B, C,...) correspond to the sampling locations indicated in Figure 1.  
By integrating the XRD data from field samples with existing literature, the mass proportions of the three minerals in the simulated fault gouge are determined to be 50% quartz, 20% montmorillonite, and 30% kaolinite. This proportion is comparable to that of the natural fault gouge or adjacent mudstone obtained from the studied profile located in Liujiang Basin, North China, as illustrated in Figure 2.
# Denial-of-Service Attacks on Permissioned Blockchains: A Practical Study
## 5 Experiments and Results
### Testbed Limitations  
The relative performance changes in the latency, throughput, and successful transactions observed in our experiments were obtained on modest hardware and reflected the impact of parameter variation under constrained conditions. While we expect the general trends to hold, performance characteristics may differ on enterprise-grade infrastructure due to differences in resource availability and system bottlenecks. It is also important to note that performance improvements or degradations do not always scale linearly with system resources. For example, the effect of increasing the block size on the latency might be amplified or reduced due to better parallelism or caching on a high-end server.  
We acknowledge that this simplified network and hardware configuration did not capture the full complexity and scalability challenges of real-world, large-scale HLF deployments. In particular, the absence of complex topologies and peer/orderer hierarchies limits the generalizability of quantitative performance findings. Our goal in adopting a con  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Attack Type** & **Variable Pair** & **Metric** & **Spearman \(\rho\)** & \(p\)**-Value Interpretation** \\ \hline \multirow{2}{*}{Batch Timeout} & Batch Timeout (s) vs. Throughput (t/s) & Throughput & \(-1.00\) & Very strong negative correlation \\  & Batch Timeout (s) vs. Delay (s) & Delay & \(1.00\) & Very strong positive correlation \\ \hline \multirow{2}{*}{Block Discard} & Probability vs. Throughput (t/s) & Throughput & \(-1.00\) & Perfect negative correlation \\  & Probability vs. Recorded TXs & Recorded TXs & \(-1.00\) & Perfect negative correlation \\ \hline \multirow{2}{*}{Collusion} & Probability vs. Throughput (t/s) & Throughput & \(-1.00\) & Perfect negative correlation \\  & Probability vs. Recorded TXs & Record TXs & \(-1.00\) & Perfect negative correlation \\ \hline \multirow{2}{*}{Malicious Client} & Malicious Clients vs. Throughput (t/s) & Throughput & \(-1.00\) & Perfect negative correlation \\  & Malicious Clients vs. Delay (s) & Delay & \(1.00\) & Perfect positive correlation \\ \hline \multirow{2}{*}{Consensus Protocol} & Tick Interval (ms) vs. Throughput (t/s) & Throughput & \(-1.00\) & Very strong negative correlation \\  & Tick Interval (ms) vs. Delay (s) & Delay & \(1.00\) & Very strong positive correlation \\ \hline \hline \end{tabular}
\end{table}
Table 3: Spearman correlation coefficients for system performance metrics under various attack types.  
strained testbed was to isolate and clearly observe system behaviors under attack conditions rather than to simulate enterprise-scale operational loads. Previous studies have shown that the CPU core count and memory can significantly alter the latency/throughput trends in HLF, and the performance can change by changing the number of clients, endorsers, orderers, etc., even with controlled test scenarios [75].
## 3 Results and Discussion
### Determination of pH, Viable Cell Count, and Weight during Sourdough fermentation  
As expected, the pH of the samples changed during sourdough fermentation. The LAB in sourdough fermentation hydrolyzed glucose to generate lactic acid and acidify the medium [43] so that the pH of the sourdoughs fermented by LAB decreased compared to the control sourdough, as can be seen in Table 4.  
The reduction in the pH, obtaining a final pH of 3.45-3.80, showed significant sourdough development, as [PERSON] et al. [44] reported. The sourdoughs fermented with the strains _L. plantarum_ TN10 and _P. pentosacous_ TI6 had the largest pH difference between  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{2}{c}{**pH**} \\ \cline{2-3}  & **Before Sourdough Fermentation** & **After Sourdough Fermentation** \\ \hline Control & 6.320 \(\pm\) 0.021 [FOOTNOTE:]Footnote : Strains: _Lucliparthucillas plantarum_ TN10, _Lucliparthucillas plantarum_ T7E, _Policoccus pentosacus_ T7E, _Policococcus cts_, _Policococcus  
the time points. Previous studies such as that of [PERSON] et al. (2018) showed that the strain _P. pentosaccus_ acidified the medium quickly compared to the strain _L. plantarum_ and reported both strains as good starters in sourdough fermentation.  
Regarding microbiological analysis, the number of viable microorganisms grew during the sourdough fermentation. Table 5 shows the viability of the LAB before and after sourdough fermentation and in the dried sourdough. The sourdoughs fermented with _L. plantarum_ TN10, _P. pentosaccus_ TI6, and _P. acidilactici_ TE4 showed the highest count of cells before and after fermentation, reaching 8.85 to 10.60 log CFU/g.  
In dried samples, the viability of the microorganisms decreased in comparison to the fermented sourdoughs due to the conditions of the drying techniques used. [PERSON] et al. (2018) studied the decrease in the viability of LAB after drying sourdough in an oven at low temperature compared to other drying techniques such as lyophilization or spray-drying. Indeed, this study shows less viability of microorganisms after drying in the oven than by spray-drying or freeze-drying.  
The powders were weighed after different drying techniques to determine the yield of the dough. Sourdough fermented with _P. pentosaccus_ TI6 and dried in an oven at 40 \({}^{\circ}\)C had the best yield (Table 6).
## 3 Three Methods for Calculating Satellite Repeat Shift TTime
### Correlation Coefficient Method  
Essentially, the correlation coefficient method is a mathematical analysis algorithm, which is mainly used to analyze the correlation between variables. Let \(X(n)\) and \(Y(n)\) be random variables, and \(n\) is the dimension of each variable. The correlation coefficient of the two variables is given as:  
\[r(X,Y)=\frac{Cov(X,Y)}{\sqrt{Var[X]Var[Y]}}, \tag{12}\]  
where \(Cov(X,Y)\) is the co-variance of the two variables, and \(Var[X]\) and \(Var[Y]\) are the variance of the variable \(X\) and \(Y\), respectively. If \(X\) and \(Y\) are two groups of data of the consecutive observations for the same satellite, the satellite repeat shift time (\(TS\)) can be estimated by:  
\[TS=F_{t}\tau_{m}, \tag{13}\]  
where \(F_{t}\) is the sampling of the observations, and \(\tau_{m}\) is the epoch of the correlation coefficient that keeps the maximum. In order to calculate the repeat shift time accurately, the sample rate of the observation data must be 1 Hz. It is only in this way that the shift time can reach the seconds level.  
It is worth noting that, from the correlation coefficient aspect, the repeat period of the satellite can be estimated by different quantities, such as the residuals of the single difference in the observation domain and the residuals of the position in the coordinate domain. Both of them can achieve the shift time of the repeat period for the same satellite by this method, and there is not much difference between them.
# A Security-Oriented Data-Sharing Scheme Based on Blockchain
## 4 Experiment and Analysis
### Comparisons and Discussions
In addition, based on the uniqueness of the hash value, the data demander \(u_{j}\) can compare the hash value of the data under the chain with the decrypted hash value on the chain to verify whether the data is trustworthy again, which improves the trustworthiness of the shared data. Considerations for lightweight: The scheme proposed in this paper considers lightweight from three perspectives. Firstly, storing data off-chain alleviates the pressure on data storage. Secondly, the use of ECC and ECDHE algorithms reduces the computational overhead of encryption and decryption. Lastly, it does not rely on special hardware, such as Intel SGX. However, it is important to note that data privacy concerns have not been extensively addressed within the scheme proposed in this paper. This is primarily due to the threat model defined in Section 3.1, where attackers are assumed to be interested in the data of specific users rather than having malicious intent towards all data. When data are stored or distributed on IPFS, it becomes challenging for attackers to derive the correlation between the data and the users, thereby making it difficult for them to achieve their intended attack objectives. Consequently, this paper does not offer a corresponding solution for data privacy. Nevertheless, data privacy is a significant aspect of data security, and we will consider this issue more extensively in future work to design a more universally applicable data security sharing scheme with sophisticated techniques, such as zero-knowledge proofs.
# Assessment of a Substandard Reinforced Concrete Frame's Beam-Column Joint Using Shake Table Testing
## 2 Objectives and Novelty of the Current Study  
Despite notable progress across these four research trajectories, significant gaps remain, particularly the limited number of experimental studies that integrate realistic seismic data from recent earthquakes. Most existing experimental frameworks rely on artificially generated or historical seismic data, reducing the relevance and applicability of their findings to current real-world scenarios. Recognizing this critical limitation, the present research uniquely utilizes real seismic records from the catastrophic 2023 Kahramanmaras earthquake in Turkiye within a shake table experimental framework. This innovative approach significantly enhances the practical applicability and empirical robustness of the research outcomes.  
Many existing RC buildings unfortunately do not meet established quality standards. Historically, the performance of these inadequately designed frames during previous earthquakes has been concerning and unsatisfactory. Consequently, it is vital to conduct a comprehensive evaluation and improvement of these substandard buildings to mitigate potential risks and safeguard the safety of their occupants [32].  
In pursuit of the stated ambitious objectives, the significant seismic event that occurred in Kahramanmaras (Pazarcik) in 2023, characterized by a moment magnitude of 7.7, was employed as the basis for our research. Comprehensive shake table tests were conducted on a scaled specimen, representing one-third of the actual size, with a single story and bay configuration. The specimen was meticulously constructed to embody a typical substandard RC. The findings from this comprehensive investigation were meticulously evaluated and  
Figure 2: Different hook positions of crossties.  
contracted with respect to essential global response metrics, particularly the seismic code prerequisites. Additionally, the different damage patterns identified during the shake table tests were examined. This analysis seeks to offer a deeper understanding of the overall efficacy of the alternative design methodology.  
The central objectives of this study are therefore to address these existing knowledge gaps by systematically quantifying the seismic responses of substandard RC beam-column joints under realistic earthquake loading conditions. The investigation specifically targets the shear strength, deformation mechanisms, and failure characteristics of joints prevalent in RC constructions. Additionally, this research critically evaluates the adequacy of contemporary seismic codes in addressing structural vulnerabilities identified experimentally. By employing meticulously scaled experimental models representative of common Turkish construction practices from the 1980s, the research not only contributes valuable insights into structural retrofitting techniques but also influences future policy developments and design regulations aimed at reducing seismic risks associated with older RC infrastructures.
## 2 Applications of HREM
### HREM in Embryo Research
#### 2.1.3 Embryos/Fetuses of Other Species  
HREM is not restricted to research mouse and chick embryos. It also has been shown to work on a broad variety of embryos of biomedical models. This includes quail and _Xenopus_ embryos, where the proof of principle was published [17; 28], but also includes the zebrafish, for which HREM was shown to fit for visualisation and mathematical characterisation of complex developmental processes, such as detentation and cardiac jogging [17; 93].  
Yet, HREM is not restricted to small biomedical models. 3D visualisation of voluminous fetal material is also possible. In particular, joint development was visualised in horse fetuses [94]. Furthermore, the potential of HREM to analyse the heart morphology of human fetal hearts in the first trimester has been shown [95; 96]. Also, other (data not yet published) fetal materials fit as well for HREM imaging, but due to restricted availability of human embryos for morphological studies, collecting such materials is rather problematic and limited to highly specific scientific projects.
## 3 Results and Discussion  
### Liquid Limit  
Figure 3 shows the relationship between the liquid limit of the modified compacted clay and the content of ZP. The liquid limit increases with the increasing ZP, which is higher than the unmodified clay (in Table 1). The liquid limits of the compacted clay were 45%, 49%, 52%, 54%, and 55%, when the corresponding ZP content ranged from 0.2 to 1%. Previous studies have demonstrated [55; 56; 57] that the WRC of clay increases with LL values. This is because clay with high LL can retain more content of water in the soil pore, which in turn can reduce gas permeability.  
To compare the effects of different modification materials on LL, a dimensionless parameter, \(\rm LL_{d}\), is proposed, which was defined as the ratio of LL to \(\rm LL_{ck}\), where LLck is the liquid limit of un-treated clay. The comparison results of \(\rm LL_{d}\) are shown in Figure 3b. The results show that the ZP-modified soil has higher LL\({}_{d}\) values when compared with other modifiers.
## 3 Verification of Noise Reduction Method by Combined Use of Improved Wavelet and EMD
### Noise Reduction Evaluation Index  
The main evaluation indexes of signal noise reduction effect mainly have the Signal-to-Noise Ratio (SNR) and Root Mean Square Error (RMSE). The SNR is higher or RMSE is smaller, the noise reduction effect is better.  
Assuming the original signal is \(x(n)\), the signal after denoising is \(x^{\prime}(n)\), so the SNR Equation is  
\[\mathrm{SNR}=10\mathrm{log}\left[\frac{\sum\limits_{n}x^{2}(n)}{\sum\limits_{ n}\left[x(n)-x^{\prime}(n)\right]^{2}}\right] \tag{9}\]  
The RMSE Equation is  
\[\mathrm{RMSE}=\sqrt{\frac{1}{n}\sum\limits_{n}\left[x(n)-x^{\prime}(n)\right] ^{2}} \tag{10}\]
## 2 Materials and Methods
### Data Processing and Comparison Method
#### 2.3.2 Reference Trajectory: MMS Output  
The 3D post-processed MMS output trajectory was used as a reference for the subsequent data filtering and comparisons. This assumption was the main working hypothesis of the experiment and is based on the following description.  
The POS/LV system provides in output more than fifty data fields. Among the computed data, there are: positioning parameters (latitude, longitude, and ellipsoidal height), run distance, vehicle attitude (roll, pitch, and yaw angles), speed with respect to North, East, and z axes, accelerations, angular speeds, measurements rms.  
The POS/LV system is built to integrate the data acquired from the different sensors, monitoring their health, isolating the sensors showing degraded performances, and re-configuring, conveniently weighting data inputs so as to give, in any case, the best positioning and attitude values. Sensor errors are estimated on a continuous basis using a Kalman filtering technique.  
The system was calibrated thanks to the lever arms computed to give the positioning data of each point of the vehicle. Thanks to this feature, the MMS reference trajectory was referred to GS14  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**n. of Solutions** & **Galileo** & **GPS** & **GLONASS** \\ \hline
4 satellites & 93 & 468 & 205 \\
5 satellites & 34 & 1805 & 582 \\
6 satellites & 9 & 2121 & 586 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of involved solutions for the simulated reduced operativeness (for GPS, GLONASS, and Galileo) in the subsequent computations.  
antenna phase center position. In order to compute the lever arms and the reciprocal positions of the GPS antennas, a reflector-less total station was used.  
The reference trajectory was computed by post-processing the integrated GPS/INS data surveyed by the MMS. To this aim, the Position and Orientation System Post-Processing Package (POSPac(tm)) Mobile Mapping Suite (MMS(tm)) was used [52]. POSPac MMS with IN-Fusion(tm) technology (which provides a deep level of sensor integration and error modeling) allows multiple processing modes to handle different combinations of rover and reference GNSS data. IN-Fusion uses a centralized filter approach to combine the GNSS receiver's pseudo-range and phase observables with the IMU data. As a result, the Applanix IN-Fusion technology has continual access to all GNSS, supplying information even if the GNSS receiver is tracking fewer than four satellites.  
On the basis of the aforementioned MMS features, the formulated working hypothesis can be accepted, considering that: (I) the GS14 trajectories were computed to simulate a reduced operativeness for the GPS and the GLONASS systems (without exploiting the whole number of available satellites); (II) the MMS solution was computed using all the available GPS satellites with certified algorithms able to fuse GNSS L1 + L2 measurements with inertial data. Thus, a more accurate post-processed kinematic solution was ensured with respect to the GS14 one.
# Investigation of Biochars in Terms of Vitamin E Adsorption Capacity
## 3 Results and Discussions
### Limitations of the Study  
Our study indicates that the theoretical calculation of adsorption capacity from surface area and molecular size can be used as a tool to predict adsorption, select an adsorbent, and thus reduce, e.g., the number of trials or analyses. One limitation of this study is that we conducted single-point batch experiments, which is why no isotherm was determined for the adsorption of \(\alpha\)-tocopherol acetate into the pores of the different biochars. Among others, an isotherm could have been calculated using different concentrations of \(\alpha\)-tocopherol acetate, varying temperatures, and/or varying adsorption durations. We used a short adsorption time (10 min) and did not adjust the temperature or the concentration. For instance, [PERSON] et al. [25] varied the adsorption time from 6 to 24 h, whereas [PERSON] et al. [49] varied the \(\alpha\)-tocopherol concentrations from 0.25 to 40 g/L.  
Another limitation of our study is that we did not adjust the moisture content of the biochars to the same level prior to the experimental determination of the adsorption capacity. The reason for this is that the moisture content varies depending on the biomass and production process [75], impacting its properties [64], such as the adsorption capacity. The aim of our study was to find the most suitable biochar to adsorb \(\alpha\)-tocopherol acetate, while considering biochars authorised as a feed additive for dairy cows. Thus, we used commercially available biochars, as purchased, to make the study as application-oriented as possible. With this, we also wanted to enable our results to be applied directly to, e.g., the feed industry.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Mesopore Class (nm)** & \(r\) & \(r^{2}\) & \(r^{2}\)\({}_{adj}\) & **Function** \\ \hline
2.58 to 3.22 & 0.907 & 0.822 & 0.800 & f(x) = \(-24.614+9.163\)x \\
3.22 to 4.03 & 0.954 & 0.910 & 0.899 & f(x) = \(-12.182+8.925\)x \\
4.04 to 5.16 & 0.952 & 0.907 & 0.895 & f(x) = \(-0.547+8.127\)x \\
5.16 to 6.92 & 0.923 & 0.853 & 0.834 & f(x) = \(4.817+8.320\)x \\
6.92 to 10.26 & 0.904 & 0.818 & 0.795 & f(x) = \(10.445+9.510\)x \\
10.26 to 20.01 & 0.887 & 0.787 & 0.761 & f(x) = \(13.327+11.834\)x \\ \hline \end{tabular}
\end{table}
Table 2: An overview of the correlation coefficients \(r\), coefficients of determination \(r^{2}\), and adjusted coefficients of determination \(r^{2}\)\({}_{adj}\), as well as the corresponding function for the Barrett-Joyner–Halenda surface area of each mesopore class, and their dependence on the relative adsorption capacity of \(\alpha\)-tocopherol acetate.