esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Challenges in the Practical Application
[123] developed a subspace system identification algorithm to eliminate spurious modes caused by non-white noise. [PERSON] et al. [124] investigated the effect of non-structural elements on vibration parameters using SSI-COV and SSI-DATA. [PERSON] et al. [125] discussed the modal contribution in each mode to the recorded vibration signal. In some structural systems ambient excitation is the only practical means to excite civil structure as a result; some of the modes are not influenced. [PERSON] et al. [35] introduced injecting auxiliary input to the subspace system identification algorithm to extract the unexcited modes. Several methods are used to introduce uncertainty including adding Gaussian perturbation into natural frequency or damping coefficients, adding independent Gaussian noise at each mode-shape measurement location and adding uncorrelated noise on the extracted vibration response. Some other researchers studied the specific cases that may occur in practice. [PERSON] and [PERSON] [126] investigated the use of correlation-driven SSI to estimate damping ratio from short-length data sets. [PERSON] and [PERSON] [127] studied the effect of environmental variability and short-length measurement data in determining modal parameters. [PERSON] et al. [128] proposed short-time stochastic subspace identification (ST-SSI) to deal with time-variant identification. [PERSON] [129] developed a subspace system identification algorithm for dynamic system with missing data. [PERSON] and [PERSON] [130] compared the degree of uncertainty in black box identification from the author's experiences. [PERSON] and [PERSON] [131] summarized the challenges to extract accurate confidence intervals in the modal identification of civil structures using subspace system identification. As demonstrated above, the most researched challenges in implementation of subspace system identification algorithm deal with merging multi-setup sensor data and improving the performance of the subspace algorithm for the identification of the modal parameters using short-length measurement data. In the next subsection, the use of subspace system identification in the development of software is presented.
## 3 Results
### Model Optimization Based on Feature Wavelength Selection
The differences in variable selection outcomes among UVE, SPA, and CARS are visualized in Figure 4. The UVE-selected bands were concentrated primarily within the 600-900 nm range, which encompasses known absorption features related to water, chlorophyll, and tissue integrity. SPA identified fewer but physiologically meaningful wavelengths--such as 608 nm, 818 nm, and 980 nm--while CARS produced more widely scattered and less interpretable bands, particularly lacking in near-infrared consistency. In summary, both the feature selection method and the classifier architecture jointly influenced the final model performance. The best-performing configuration--UVE-CWT-SVM--combined a stable, physiologically grounded feature set with a flexible and robust  
Figure 4: Spectral feature extraction results using different wavelength selection methods. The reflectance scale is based on CARS-processed spectra. For visual clarity, the SPA and UVE curves were vertically shifted upward by 50% and 100% reflectance, respectively. classifier, providing an optimal strategy for practical bruise detection tasks in prunes. Accordingly, this integrated modeling pipeline was adopted in the following sections to support performance interpretation and guide discussions on real-world applicability.
## 2 Review of Design Provisions and Concepts
### European Masnorry Standards  
The load-carrying capacity of a masonry wall is solely determined by its susceptibility to compression failure. The design methodology for a masonry cross-section, as per Eurocode 6 [24], is based on the principles of rigid-plastic material behaviour and involves a verification process across three distinct sections (top, middle, and bottom). This verification aims to establish the minimum capacity reduction factor, accounting for factors like slenderness and eccentricity. The load-carrying capacity of URM walls, as defined by Eurocode 6 for masonry constructed with general-purpose mortar, is represented by Equation (1).  
\[{f^{\prime}}_{\mathrm{k}}=0.55{f_{\mathrm{b}}^{0.70}}{f_{\mathrm{m}}^{0.3}} \tag{1}\]  
where \({f^{\prime}}_{\mathrm{k}}\) is the characteristic compressive strength of the masonry; \({f_{\mathrm{b}}}\) denotes the normalized compressive strength of the unit; and \({f_{\mathrm{m}}}\) represents the mean compressive strength of the mortar. The thickness of the mortar bed joints is limited to a maximum of 15 mm. It is important to note that Eurocode 6 refers to the characteristic compressive strength of the masonry, diverging from the literature, which often pertains to the mean values. Mean properties are typically used for assessing existing structures, while characteristic values are more appropriate for new structures. For determining the reduction factor values in relation to slenderness and eccentricity at the wall's top or bottom, it is recommended to use a rectangular stress block, as represented in Equation (2).  
\[\Phi_{\mathrm{i}}=1-2\frac{\mathrm{e_{i}}}{\mathrm{t}} \tag{2}\]  
where t represents the wall thickness and \(\mathrm{e_{i}}\) denotes the eccentricity of the wall, calculated using Equation (3), and as shown in Figure 1. \(\mathrm{M_{id}}\) is the design value of the bending moment at the top or bottom of the wall. \(\mathrm{N_{id}}\) is the design value of the vertical load at the top or bottom of the wall; \(\mathrm{e_{he}}\) is the eccentricity at the top or bottom of the wall, if any, resulting from the horizontal load; and \(\mathrm{e_{init}}\) is the initial eccentricity. In cases where the slenderness ratio of the URM wall surpasses 12, second-order effects also need to be taken into account. It is noteworthy that Eurocode 6 [24] only provides design expressions for the compressive strength of unreinforced masonry. Consequently, the evaluation of grouted URM strength can be determined using EN 772-1 [37] or via a compressive strength test on masonry wallette as specified in EN 1052-1 [38]. The recommended minimum compressive strength for grout stands at 12 MPa.  
\[\mathrm{e_{i}}=\frac{\mathrm{M_{id}}}{\mathrm{N_{id}}}+\mathrm{e_{he}}+\mathrm{ e_{init}}>0.05\,\mathrm{t} \tag{3}\]
# Vitamin C in Plants: From Functions to Biofortification
## 6 Vitamin C Biofortification
### Manipulation of Recycling Genes  
Vitamin C enhancement in crops can also be achieved by manipulating the genes coding for MDHAR and DHAR, which are the enzymes involved in the reduction of MDHA and DHA, respectively. Several papers have reported that ASC regeneration by DHAR overexpression could represent an efficient method of vitamin C biofortification in different species, such as corn [216], tomato [217], and blueberry [143]. Recently, a cytosolic DHAR identified in the woody plant _Lriodendron chinese_ was overexpressed in _Arabidopsis_, which led not only to vitamin C enhancement but also to an improvement of growth under stress conditions [218]. In apple, colocation between _DHAR3-3_ and a QTL for browning has been found, showing a relationship between ASC redox state and fruit vulnerability to browning [205].  
Research conducted on MDHAR shows discordant results depending on the species. The overexpression in tobacco of the _Arabidopsis_ cytosolic isoform of MDHAR enhances vitamin C content [219]. Similarly, the acerola MDHAR, overexpressed in tobacco, led to a two-fold increase in vitamin C content and a better tolerance to salt stress [220]. On the other hand, overexpression of the cytosolic-targeted tomato MDHAR caused a 0.7-fold reduction in vitamin C content in tomato fruits [217]. Another study conducted on tomato indicates that transgenic lines overexpressing MDHAR display a reduction in vitamin C content in leaves, while lines with silencing of MDHAR show an increase of vitamin C in both fruits and leaves [221]. The enhancement of vitamin C in silenced MDHAR lines could be due to a decrease in degradation [44]. In cherry tomato, the suppression of AOX was also found to increase vitamin C, lycopene, and caroten contents of the fruits and to confer tolerance to salt stress [222].
# Advances in the Assembly Model of Bacterial Type IVB Secretion Systems
## 3 Mosaic Structure Model of T4 BSS
### Other Functional Proteins
It is involved in the secretion of effector proteins by T4 BSS, and the _dotB_ mutant failed to secrete T4 BSS substrates and productively infect host cells [43]. The IcmQ and IcmR proteins located in the in bacterial cytoplasm are essential for the activity of the Dot/Icm system [45]. IcmQ has a small N-terminal domain and a large C-terminal domain. The purified IcmQ is prone to aggregation, and addition of IcmR inhibits this aggregation, indicating that IcmQ and IcmR have a relationship similar to a substrate and a molecular chaperone. The behavior of IcmR regulates IcmQ is similar to that observed in some chaperones [46; 47]. The crystal structure of IcmR-IcmQ complex shows an amphipathic four-helix bundle (two helices each from IcmR and IcmQ). IcmQ acts on the lipid vesicles that forms and causes them to rupture. The mechanism of action consists of insertion of the N-terminal domain into vesicles of the phospholipid bilayer, and destruction of the membrane structure by the C-terminal domain with targeting of the membrane by electrostatic action. The protein also has an NAD+ binding site that allows IcmR-IcmQ to bind to the membrane, thereby interacting with or modifying the T4 BSS substrate [48]. The IM proteins IcmF and IcmH are present in the T4 BSS of _Legionella_ and _Legionella_-related genera, which are responsible for intracellular growth, immediate cytotoxicity or salt sensitivity [49; 50]. These proteins are essential for the normal function of the Dot/Icm system. When any of them is lost, the amount of core complexes decreases, especially the levels of DotG and DotH, but a partially functional Icm/Dot complex is probably present in the bacteria [50]. Therefore, IcmF and IcmH work together to maintain the stability of the core complex and recruit DotC-H subcomplexes to the poles to initiate assembly because they are the polar localization factors [18]. Lipoprotein DotK has many homologous proteins in other bacteria, is conserved in _Legionella_ and _Coxiella_ species, and is responsible for anchoring the Dot/Icm system to the peptidoglycan layer [51]. When the DotK coding gene is knocked out, _Legionella_ shows partial growth defects into the protozoan host, but it has no effect on the growth on macrophages. [23; 36]. IcmX is a periplasmic protein with a molecular weight of 50 kDa. IcmX is indispensable for the activity of the Dot/Icm system and is conserved in the _Legionellaceae_ family [42]. Even though T4 BSS still assembles in the absence of IcmX, the intracellular growth of the \(\Delta\)_icmX_ mutant is severely affected [18; 42].
## 5 Discussion & Future Work
### Recommendations  
Based on the results from the TARA and the pentests, we believe that human intervention, and hence the vehicle automation level, have a direct impact on the assessed risks. Some attacks, particularly GNSS spoofing, are only applicable if there is no driver who can immediately take control of the vehicle if it goes off the road. In other words, a moderate risk in a vehicle of L4 can be considered severe in an L5 vehicle unless robust and flawless mitigation strategies are implemented.  
To strengthen the entire cybersecurity governance for L4V and support the ACS L5 readiness, the following crucial, yet non-exhaustive, recommendations are delineated:  
* **Physical strengthening:** where LiDARs, cameras, USB ports, and the on-board computer are unreachable and protected from any unwarranted access.
* **Fully automated offline and resilient mode:** providing high protection against jamming attacks and unjustified halt or vehicle stops at a complete connectivity loss.
* **Confidentiality and integrity of communications:** where Private Key Infrastructure (PKI) and digital signatures can be used to secure authentications in addition to HTTPS and IPSec tunnel mode (such as VPN) establishment.
* **Hardening of the on-board computer:** which relies on (i) protecting the BIOS through Root of Trust for Update (RTU) and Trusted Platform Module (TPM) usage during the firmware update [57], (ii) shielding the disk protection through Bitlocker [51], and (iii) adopting operating system best practices, such as the installation of a Host-based Intrusion Prevention System (HIPS) and applying restrictive policies on the listing of files and their modification.
* **Standardised security procedures and certifications:** varying from conducting Cybersecurity Management System (CSMS) [18] and Software Update Management System (SUMS) [58] certifications mandated by the UNECE to comply with ISO/SAE 21434 [17] and ISO/PAS 5112 [59].
* **Security monitoring:** where continuous and frequent assessments are conducted and risks are monitored using the integration of a Security Information and Event Management (SIEM), for example.
# Evaluation of Three-Dimensional Environmental Hydraulic Modeling in Scour Hole
## 1 Introduction
Still, there are not many works focusing on the two models' comparison of the flow near the wall according to the authors' knowledge. Therefore, in the present study, simulation of the turbulence flow through a scour hole in a sand bed channel was conducted by both OpenFOAM-v1712 (released in 2017) and Ansys Fluent 19.1 (in 2018) for comparing their accuracy and performance in simulation. Both models are utilized for 2D modeling of laboratory-scale experiments taken from our own research. The purpose of this work is focusing on the performing of simulations rather than analyzing the inner workings of those two models. This study tries to find out which software shows better simulation for the flow near the bottom wall. The results from this study will contribute as a reference for choosing the appropriate modeling tool and scheme for further research, similar to numerous other previous works [3; 6; 7]. The computational mesh was created by OpenFOAM and then converted to the Ansys Fluent mesh input form. Both simulation models and boundary conditions were set as similar as possible between the two models. The results of water flow behaviors such as streamlines, velocity profiles, and turbulence kinetic energy, were analyzed and compared to our own laboratory experiment data.
## 2 Categorized Overview of Papers (Based on the Areas of Focus)
### Category 2: Marketing and Business Decision Support  
These papers collectively contribute to the field of data-driven applications and predictive modeling across various domains, like a Decision Support System for the Marketing domain [11]. They offer an advanced model for detecting financial statement fraud using XGBoost [12], introduce innovative neural network models for stock price prediction [13], analyze factors influencing tourist offer prices [14], develop predictive models for healthcare patient influx [15], and propose intelligent decision forest models for customer churn prediction in the telecom industry [16]. They also address customer churn prediction in non-contractual B2B settings [17], improve legal judgment prediction through graph neural networks [18], enhance car sales forecasts using online sentiment data and deep learning [19], introduce a reinforcement learning framework for options trading [20], and predict the charge of a legal case using a novel graph convolutional network [21]. These studies showcase the versatility and practical applications of data-driven techniques in diverse fields, underscoring their importance for informed decision making and predictive accuracy.
## 3 Numerical Simulation of Full-Columnar Overburden
### Simulation Results of Models with Different Mining Widths
However, as the mining is conducted in the working face, the separation layer gradually appears in the corresponding overlying strata, and its evolution process consists of three stages roughly: initial generation, reaching peak, and tending to close (stable). In simulation schemes 1-3, when the working face advances past NCL by 100 m, the mining width is fixed at 300 m, and the mining height is 3 m, 4 m, and 5 m, respectively. The cumulative peak volume of separation layer is 0.7 m, 1.8 m, and 2.1 m, respectively, indicating that the development of separation layer is positively correlated with the mining height (Figure 14a). In simulation schemes 4, 3, 5, when the working face advances past NCL by 100 m, the mining height is fixed at 5 m, and the mining width is 250 m, 300 m, and 350 m, respectively. The cumulative peak volume of separation layer is 2.8 m, 2.1 m, and 1.1 m, respectively, indicating that the development of separation layer is negatively correlated with the mining width (Figure 14b). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Scheme** \\ **Number** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **The Highest** \\ **Stratum of** \\ **Separation** \\ **Layer** \\ \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{c} **Cumulative** \\ **Peak Volume of** \\ **Separation** \\ **Layer (m)** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Maximum** \\ **Subsidence (m)** \\ **Coefficient** \\ \end{tabular} } \\ \hline
1 & KS1 & 0.7 & 2.33 & 1.279 \\
2 & KS3 & 1.8 & 3.19 & 1.283 \\
3 & KS3 & 2.1 & 4.34 & 1.289 \\
4 & KS3 & 2.8 & 4.32 & 1.261 \\
5 & KS4 & 1.1 & 4.35 & 1.343 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The statistics of main simulation results of the five schemes. Figure 13: Displacement and stress curves of each measuring point in NLC in Scheme 5: (**a**) Displacement curves; (**b**) Stress curves. The pre-bearing stress concentration will be formed in the mining process at the working face. When the mining width is fixed at 300 m, the increase of mining height from 3 m to 5 m can push the peak concentration coefficient up from 1.279 to 1.289, and the subsidence can go from 2.33 m to 4.34 m (as shown in Figure 15a). When the mining height is fixed at 5 m, the increase of mining width from 250 m to 350 m can trigger the rise of peak concentration coefficient from 1.261 to 1.343, and the subsidence remains virtually unchanged (as shown in Figure 15b). This indicates that the change of mining width affects stress greatly whereas the change of mining height is more influential to subsidence.
## 4 Damage Constitutive Model
### Analysis of Changes in Damage Variables  
Due to the existence of the threshold, the specimen does not initiate damage upon the onset of loading. Figure 13a illustrates that, prior to exceeding this threshold, the mixture remains undamaged. Once the force exerted on the mixture surpasses the threshold, damage accumulation commences, initially rising sharply and then gradually approaching unity, manifesting an \"S\" curve, with all the dosages adhering to this pattern. Figure 13b depicts the rate of increase in the damage variable.  
When \(R=0\%\), the rate of increase in the damage variable for the mixture escalates rapidly, and then promptly declines to zero after surpassing peak strain. Upon the introduction of AOD slag, more damage gradually occurs. At \(R=6\%\), the rate of increase in the damage variable is the slowest. Beyond \(R=6\%\), the rate begins to rise again. Compared with R = 0%, the peak damage growth rate decreased by 23.3%, 40.2%, 36.6%, and 15.2%, respectively, with an increase in AOD slag dosage. This suggests that adding an appropriate amount of AOD slag can inhibit the propagation of microcracks, thereby improving the internal structure of the mixture.  
Through the damage variable, the destruction of the mixture can be segmented into three distinct phases:  
1. Compaction Phase: In this initial phase, the mixture contains voids, preventing immediate damage upon the application of a load. During this loading period, no visible signs of distress are observed, and no microcracks form within the mixture. The material undergoes compaction as the voids are gradually eliminated under stress.
2. Crack Propagation Phase: As strain increases, cracks become visually apparent on the surface of the mixture, eventually extending vertically and penetrating throughout the  
Figure 11: Relationship between parameters and substitution rate. (**a**) \(a\); (**b**) \(b\).  
entire body of the mixture. Corresponding to a decline in stress, aggregate particles on the surface begin to detach. This stage features the highest rate of increase in the damage variable, indicative of rapid crack development and material degradation.
3. Damage Phase: Even after the surface aggregates have detached, the mixture maintains some residual load-bearing capacity. Further loading leads to the gradual flattening of the stress-strain curve until the mixture ultimately fails. During this final stage, the rate of increase in the damage variable decelerates, gradually approaching and eventually reaching 1.0, signifying the completion of the damage process and the material's total failure.  
Figure 12: Test and model curves. (**a**) A-0; (**b**) A-3; (**c**) A-6; (**d**) A-9; and (**e**) A-12.
# Dynamic Characteristics of Sandstone under Coupled Static-Dynamic Loads after Freeze-Thaw Cycles
###### Abstract  
The effect of temperature fluctuation on rocks needs to be considered in many civil engineering applications. Up to date the dynamic characteristics of rock under freeze-thaw cycles are still not quite clearly understood. In this study, the dynamic mechanical properties of sandstone under pre-compression stress and freeze-thaw cycles were investigated. At the same number of freeze-thaw cycles, with increasing axial pre-compression stress, the dynamic Young's modulus and peak stress first increase and then decrease, whereas the dynamic peak strain first decreases and then increases. At the same pre-compression stress, with increasing number of freeze-thaw cycles, the peak stress decreases while the peak strain increases, and the peak strain and peak stress show an inverse correlation before or after the pre-compression stress reaches the densification load of the static stress-strain curve. The peak stress and strain both increase under the static load near the yielding stage threshold of the static stress-strain curve. The failure mode is mainly shear failure, and with increasing axial pre-compression stress, the degree of shear failure increases, the energy absorption rate of the specimen increases first and then decreases. With increasing number of freeze-thaw cycles, the number of fragments increases and the size diminishes, and the energy absorption rates of the sandstone increase.  
freeze-thaw cycles; split Hopkinson Pressure Bar; static-dynamic loads; rock dynamic mechanics; failure mode; energy absorption Article