esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Perturbating Reactions with FSEOF  
We introduced perturbations on each pFBA optima reaction defined with pFBA to evaluate what happens when the biosynthetic pathway has a bottleneck or competing pathways. The rationale followed is that microorganisms respond to environmental changes, adjusting their metabolic fluxes, and one of these adjustments includes that metabolites overproduced in one part of the metabolic pathway could be redirected to other pathways to maintain homeostasis and growth rate [92]. Our conception requires registering how these perturbations drive the production of the interest metabolites or what happens with the excessive flux when it is not directed to metabolite production. Since FSEOF provided by CAMEO only returns increased fluxes, we modified the instructions so that it returns the fluxes of all reactions when each pFBA optima reaction was simulated to increase in flux (See Equation (3)), creating a redistribution of fluxes divided between biomass and target metabolite production. For each of the pFBA optima reactions obtained in Equation (2), its flux is calculated when the flux in the target reaction is maximized (\(v_{\textit{target}}^{\textit{max}}\)). To avoid an impossible solution, the flux value for the pFBA optima reaction is the minimum between the maximum value that this reaction can achieve (\(v_{\textit{perturbed}}^{\textit{max}}\)), and the value of pFBA optima reaction obtained when the flux of the target reaction is maximized (\(v_{\textit{target}}^{\textit{max}}\)), multiplied by an arbitrary perturbation factor (_PF_).  
The target reaction's minimum (\(v_{\textit{target}}^{\textit{min}}\)) and maximum (\(v_{\textit{target}}^{\textit{max}}\)) fluxes are calculated. The range of both fluxes is divided into \(N\) levels, and each level is used as a value for the target reaction. Thus, \(N\) flux values for each reaction in GEM are obtained by maximizing biomass production with the previous restrictions.  
\[\left\{\begin{array}{l}max\\ v^{\text{ }}_{\text{biomass}}\\ subject\text{ to }\\ S\cdot v=0\\ v_{target}=\frac{n(v^{max}_{target}-v^{min}_{target})}{0\leq n\leq N,\ n\in \mathbb{N}^{+}},\ N\in\mathbb{N}^{+}\\ v_{perturbed}=\min\left(v^{max}_{perturbed},\left(v^{max}_{perturbed} \right)*PF\right),\ perturbed\ \in\Omega V_{Optima}\\ v^{lb}_{i}\leq v_{i}\leq v^{ub}_{i}\end{array}\right. \tag{3}\]  
where  
* \(v^{min}_{target}\): Flux of \(v_{target}\) when it is minimized.
* \(v_{perturbed}\): Flux of a pFBA optima reaction subjected to perturbation.
* \(v^{max}_{perturbed}\): Maximum flux of \(v_{perturbed}\) in each simulation.
* \(v^{max}_{target}\): Flux of \(v_{perturbed}\) when the \(v_{target}\) is maximized.
* \(PF\): Perturbation factor, this was set to 1.5 in simulations.
* \(N\): Number of enforced flux levels for FSEOF (default = 10).
###### Abstract  
The study aimed to assess the effect of two etidronate-containing irrigants and EDTA on the ability of sodium hypochlorite (NaOCI) to remove the smear layer and dissolve organic tissues. This study evaluated the following solutions: distilled water, 3% NaOCI, 3% NaOCI + EDTA, and 3% NaOCI mixed with etidronate powder from two manufacturers [Dual Rinse, (DR); IsraDetn, (ID)] to obtain 9%, 15%, and 18% solutions. To assess the proteolytic activity, bovine muscle tissue samples (56.1 \(\pm\) 5.4 mg: n = 10 per group) were immersed in the tested solutions for 15 min. Absolute weight changes and percentages of weight changes (wt%) were calculated. To assess smear layer removal, the root canals of human wisdom teeth were instrumented, irrigated with the tested solutions (n = 10 per group), and evaluated using SEM. Statistical analysis employed an ANOVA with a post-hoc Tukey test and paired t-test, the Kruskal-Wallis test with a pairwise Wilcoxon rank sum test, and the [PERSON] test with a pairwise Wilcoxon signed-rank test. The mean weight loss in the NaOCI group comprised 17.3 mg (32 wt%). Sequential use of NaOCI and EDTA resulted in a significant increase in proteolytic activity of the former (57 wt%), while mixing these solutions led to a reduction of proteolytic activity (17 wt%). All NaOCI/DR groups exhibited a significantly greater dissolution activity than that of NaOCI alone, with the mean weight loss ranging from 23.3 mg (40 wt%) to 24 mg (41 wt%). ID9% and ID15% significantly decreased the proteolytic activity of NaOCI. In most groups, the apical thirds of the root canals demonstrated a significantly heavier smear layer compared to the middle and coronal thirds. The most effective smear layer removal was observed after irrigation with NaOCI combined with EDTA or DR (all concentrations); however, in the NaOCI + EDTA and DR18% groups, it was accompanied by moderate to severe erosion. Irrigation with ID did not result in smear layer removal or dentin erosion. In conclusion, the smear layer removal and pulp dissolving effects of continuous chelation using etidronate were manufacturer/composition-dependent. DR9% combined with NaOCI demonstrated the most promising results.  
smear layer; continuous chelation technique; etidronate-containing irrigants; dentistry 2024148422[[https://doi.org/10.3390/app14188422](https://doi.org/10.3390/app14188422)]([https://doi.org/10.3390/app14188422](https://doi.org/10.3390/app14188422))
# Fine Characterization Method of Concrete Internal Cracks Based on Borehole Optical Imaging
## 2 Crack Image Acquisition
### Measurement Accuracy Analysis  
The measurement accuracy of the concrete crack is also the resolution of the borehole wall image. The hole wall image is a dot matrix composed of pixels, so it is necessary to analyze the corresponding relationship between the image pixels and the real size of the observed points. The axial resolution of the borehole wall image obtained by the digital borehole camera system can reach 0.1 mm/pix. The circumferential resolution of the image is related to the borehole diameter. The borehole wall images obtained from exploration boreholes with different diameters correspond to different circumferential resolutions. The maximum number of pixels in the circumferential direction of the borehole wall image obtained by the digital borehole camera system is 2560, and the circumferential resolution of the image in the conventional exploration boreholes with different diameters is shown in Figure 3.  
It can be seen from Figure 3 that the circumferential resolution of the hole wall image decreases with the increase in the hole diameter. The resolution is 0.037 mm/pix in 30 mm  
Figure 3: Relationship between borehole diameter and image accuracy.  
diameter holes and 0.16 mm/pix in 130 mm diameter holes. The measurement accuracy is fully sufficient in concrete crack detection.
## 5 Conclusions  
For growers looking to maximize lettuce yields on a static agricultural footprint, _G. diaz_ inoculation may present a beneficial additive to the growing system. The ability to thrive under oxygenated conditions and avoid suppression in a high nitrogen presence make it especially suitable for hydroponic and/or aeroponic setups. For the two cultivars tested in this study, the harvestable biomass was increased in all but one of the trials, with increases from +8.19% in Black Seeded Simpson to +16.16% in Bibb/Limestone. For BIB lettuce, the highest biomass yield was realized with inoculation and an N fertilization level well below the established requirements, potentially allowing growers to reduce their N fertilizer requirements and still maintain production.  
There is seemingly a significant bacterium x cultivar interaction when it comes to root production, allowing growers to tailor their use of _G. diaz_ or their selection of lettuce variety to match their cultivation needs. Luxury consumption trends were not seen in this study, partly because of the non-replenishment fertilizer regime employed in Kratky jars. As this is the first reported case of _G. diaz_ inoculation of lettuce, further research is needed to elucidate the exact mechanisms behind the plant responses. Whether from PGRH production, N fixation, NUE, or alteration in the shuttling of free N _in planta_, _G. diaz_ has the potential to increase the yield of this valuable crop.  
Conceptualization: R.L.S., S.W.D. and R.B.B.; methodology: R.L.S.; validation: S.W.D., R.D.B., J.M.R., J.D.L. and R.B.B.; formal analysis: R.L.S.; resources: S.W.D. and R.B.B.; writing--original draft preparation: [PERSON], S.W.D. and R.B.B.; writing--review and editing: [PERSON], J.M.R. and J.D.L.; supervision: [PERSON] and R.B.B.; project administration: R.B.B.; funding acquisition: R.B.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Sustainable Agriculture Research and Education program, the United States Department of Agriculture, The Pennsylvania State University, Azotic Technologies LLC, and the Jeanne and Charles Rider Endowment. The Pennsylvania State University and the U.S. Department of Agriculture are equal opportunity providers and employers.  
Not applicable.  
We would like to thank Azotic Technologies, LLC for providing the strain of _Glucomacetobacter diazotrophic_ that was used in this research and technical advice for preparing the inoculum.  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 5 Results and Analysis
### Comparison of Loose and Tight Solutions
#### 5.3.2 Segment 2  
The RMS errors of LC and TC architectures for segment 2 are presented in Figure 17. The main remarks deduced from this figure are:  
* The LC architecture does not provide satisfying performance for each tested configuration (with GPS and GLONASS or with motion constraints), showing large errors during GNSS outages;
* The TC architecture shows better performance relative to LC architecture for each tested configuration;
* Including GLONASS observations provides slight performance improvements for TC architecture in terms of position, velocity and azimuth estimation;
* Including the GNSS-derived yaw aiding and velocity and height constraints in the TC GG/INS configuration improves the position, velocity and azimuth estimation.  
From these results, unlike in segment 1, it is clear that a tight integration is still preferred since it offers significantly better performance overall.
## 3 Examination of Machine Learning Analysis Using Simulation Spectra
### Dimensionality Reduction Using Unsupervised Machine Learning Methods  
Figure 3 shows the result of the cumulative contribution evaluation of the transmission spectrum dataset with \(\sigma\) = 0% created in Section 3.1 to evaluate the amount of information lost due to the dimensionality reduction of PCA. The horizontal axis of the figure is the number of principal components after dimensionality reduction by PCA, while the vertical axis is the cumulative contribution rate explained in Section 2.2. From the cumulative contribution rate, it is evident that more than 99% of the original data can be expressed with the first three principal components of the PCA; namely, this dataset can be expected to be sufficiently analyzed by reducing the number of dimensions to three.  
Figure 4 shows the diagram plotting the distribution of the first three principal components of PCA expressing the simulated dataset of \(\sigma\) = 0%. Note that each principal component is computationally found and the unit cannot be defined. In the figure, the point where each plot line converges is the point of a 100% liquid phase; from there, the point of a 100% solid phase appears at the opposite end of the line according to each texture state. Then, the data for each phase fraction are plotted on a straight line connecting both points. This straight line is drawn depending on the solid-liquid phase fraction. The data groups of different preferred orientation vectors are represented by colors, and the  
Figure 3: Cumulative rate of contribution to the number of dimension reductions (Gaussian error \(\sigma\) = 0%).  
Figure 2: Neutron transmission spectra with random errors (\(\sigma\) = 5%) simulating the solid–liquid mixed state when the solid phase had an isotropic texture (MD = 1.0).  
black line in the center of the distribution corresponds to the solid-liquid phase fraction depending on the line of the isotropic texture. These groups are distributed to form fan-shaped planes in different preferred orientations from the black line. That is, as the texture develops (from MD = 1.0 to MD = 0.6), the data are plotted farther from the isotropic texture case (black line). In this way, it was found that by reducing the dimensions with PCA, it is possible to clearly identify the LBE transmission spectra with different solid-liquid phase fractions, preferred orientation vectors, and degrees of development of the texture. With this detailed analysis, it may be possible to obtain not only the solid-liquid phase fraction but also the texture information for the solid phase depending on the position of the transmission spectrum in this diagram.
## 5 Experiment and Evaluation
### Experiment  
We conducted some experiments to validate the effectiveness of the proposed intrusion detection system. In our experimental setup, a testbed consists of a Cisco 7200 VXR router as a target network device connected to the Internet, and a x86 server that hosts the proposed detection system, both of which are connected through a L2 switch equipped with SPAN functionality. The testbed also has a laptop PC, connected to the router through the Internet, acting as a malicious user who delivers the remote code execution attack.  
The Cisco router has an IOS firmware with a version of 12.3(6f), which has a couple of remote code execution vulnerabilities in the FTP server (CVE-2007-2586) [21] and an IP option handling routine (CVE-2007-0480) [22]. Both vulnerabilities allow unauthorized modification of the text segment of the firmware, resulting alteration of the control flow or injection of the shell code.  
The host server is a machine with a 2.4 GHz Intel Core i5 CPU and 8 GB RAM running a 64-bit Linux (Ubuntu 16.04 LTS). It runs the same firmware image as the target router via Dynamips, which is a MIPS emulator for Cisco routers. Dynamips provides a GDB interface for the purpose of debugging internals of the target device. Hence, the memory integrity monitor in our intrusion detection system can directly inspect the target's memory through the interface.  
In our experiment, the laptop PC mounts an attack to the target router by sending crafted packets of the aforementioned vulnerabilities. Throughout the experiment, we measured the average detection time of the attack varying the interval at which the memory integrity monitor takes snapshots, as well as its performance overhead. Figure 4 shows the experimental result. The detection time steadily decreases as the interval of taking a snapshot is getting shorter. However, due to the inevitable delay for the memory dump, we need at least around 1500 ms to detect the attack (Figure 4a). Achieving faster detection comes at the cost of higher CPU use (Figure 4b). From the graph, we can infer that the period of 3000-3500 ms would give the optimal solution in terms of the detection time as well as the CPU consumption. It is worth noting that the attack exploiting the FTP server vulnerability needs more time in detection as well as more CPU than the other. This is because that the crafted packet for the FTP server should be constructed at the TCP layer, which requires the time-consuming operation of TCPSync.  
Figure 4: _Cont._
## 4 The Proposed QoS-Aware Routing
### Parameter Estimations
This per window link reliability is then averaged with the previous measurements using EWMA as follows,  
\[\hat{r}_{i,j}=(1-\beta)\times\hat{r}_{i,j}+\beta\times\frac{SucCounter}{TxCounter} \tag{14}\]  
where \(\beta\) is the moving average smoothing factor, and its value is set equal to 0.4 in our simulation; a higher value is chosen because the current measurement is performed for all of the packets in a window \(W=8\) instead of for a single packet.
# Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey
## 3 Background: iSDVN System
### Cooperative Communication  
SDVNs are aimed at bringing safety and smoothness to the transport system. They can support transportation management and cooperative safety by allowing V2V and V2I connectivity [104]. Vehicles can regularly broadcast safety-related details, e.g., location, speed, heading, and neighbors, through the shortest routes from the source node to the destination node, so-called cooperative communication. Cooperative communication achieves cooperative safety, providing the required means to establish cooperative awareness in the situation [105]. The sharing of real-time data requires connectivity and cooperation between routes. In SDVN communication, we face challenges like frequent link failures and high-mobility issues. Using cooperative communication within the vehicular network, these challenges are reduced, and reliability is improved. Hence, nodes are allowed to cooperate with each other [48]. Next, we focus on various technologies in the field of SDVNs.
## 3 Experimental Results and Analysis
### Change in Temperature Gradient  
Temperature gradient is the main reason for the formation of micro-airflow that transfers heat and promotes the diffusion and redistribution of moisture. Figure 5 shows the temperature gradient curve between the measurement points near the high-temperature boundary wall and those near the low-temperature boundary wall under different pressures. The maximum temperature gradient is 24.3 \({}^{\circ}\)C/m when the vertical pressure is 50 kPa for 72 h, and the minimum temperature gradient is 22.5 \({}^{\circ}\)C/m at 200 kPa. In a constant-temperature field, when the vertical pressure increases from 50 kPa to 200 kPa, the rate of change of the temperature gradient reaches 7.4%. In large-scale storage structures, as the depth of the grain pile increases, the vertical pressure on the grain pile increases and the porosity of grain pile decreases. As a result, the convection heat transfer of the grain pile is hindered, thereby increasing the heat conduction between grains. Hence, vertical pressure affects the temperature field distribution of a grain pile. As the heat conduction between the grains increases, the temperature gradient of the grain pile decreases.
## 2 Materials and Methods
### Experiments  
To experimentally validate the schemes proposed by the simulation study, a subset of the imaging schemes outlined in Section 2.1 were implemented on two 256-channel Ultrasound Advanced Open Platform (ULAOP 256) systems (MSD Lab, University of Florence, Florence, Italy) [27] equipped with a pair of the above-mentioned linear array probes (Esaote LA332). The systems were synchronized in both transmit and receive by sharing the same trigger and sampling times and were used to operate each individual probe [28]. Both probes were mounted on _xyz_ translation and rotation stages (Thorlabs, Newton, NJ, USA) to allow for careful alignment in the elevational plane to enable imaging of a common region of interest. For each probe, in an alternating sequence, a total of 7 PWs with a maximum steering angle (\(\alpha_{max}\)) of 13\({}^{\circ}\) were transmitted at 3 MHz with pulse repetition frequency (PRF) of 1 kHz. Two apodization windows, rectangular and a 50%-Tukey, were tested in transmit. Raw data were acquired simultaneously by both arrays at a sampling frequency of 19.5 MHz and then post-processed in MATLAB to perform the coherent image reconstruction. To further reduce experimental artifacts, two weighted compounding schemes were implemented: a rectangular window, in which all RF datasets acquired are weighted the same (\(\frac{1}{4}[T_{1}R_{1}+T_{1}R_{2}+T_{2}R_{1}+T_{2}R_{2}]\)), and a second case when the trans-received data (data obtained when the transmit and receive arrays are different)is weighted half the weighting applied to the data where the trans-receive array is the same \(\left(\frac{1}{6}[2\times T_{1}R_{1}+T_{1}R_{2}+T_{2}R_{1}+2\times T_{2}R_{2}]\right)\). The latter weighted compounding was used to keep the brightness of the reflections in the common FOV approximately constant, since the backscattered echoes of the trans-received data appear at approximately the same location resulting in a final CoMTUS image with greater brightness in the common FOV.  
In a first experiment, the probes were immersed in water and the resulting direct transmissions were acquired at two different spatial configurations: defined at 40 mm imaging depth with \(\theta\) = 120\({}^{\circ}\) and gap = 9.6 mm, and at 90 mm imaging depth with \(\theta\) = 150\({}^{\circ}\) and gap = 23.7 mm.  
In a second experiment, a calibrated commercial phantom (CIRS Multi-Purpose, Multi-Tissue Ultrasound Phantom model 040 GSE with speed of sound 1540 m/s and attenuation 0.7 dB/cm/MHz) was used to experimentally validate the method by assessing the image metrics (see Section 2.4). Water was inserted between the arrays and the flat surface of the commercial phantom to ensure acoustic coupling. Note that the coupling water had a different speed of sound than the phantom, which may introduce aberrating effects. The two probes were positioned to image a common region of interest located at 70 mm depth and following approximately the two spatial configurations investigated in the tissue-mimicking phantom simulations, i.e., a configuration with minimum-side lobe energy and a configuration with minimum main lobe width.
## 1 Introduction  
In recent years, various products of precision amplifiers for sensor readout applications have been reported [1; 2; 3; 4; 5], e.g., low-noise characteristics essential for low speed and precision-sensing readout circuits. Low-noise techniques such as autozeroning and chopper stabilization are widely implemented in low-noise circuit designs [6; 7; 8; 9; 10]. Autozeroning can achieve a low-noise characteristic; however, the noise level is limited by the high-frequency noise-folding. The chopper stabilization technique can also achieve a low-noise characteristic; however, ripples caused by modulated low-frequency noises occur, and a large low-pass filter (LPF) is needed to reject those ripples. The multipath amplifier scheme with chopper stabilization is suitable for precision-sensing, achieving low-noise and low-offset characteristics with a wide bandwidth [11; 12; 13; 14]. The multipath amplifier scheme, however, has high circuit complexity, causing difficulties in circuit design.  
Bipolar junction transistors (BJTs) can be implemented in the input stage of the amplifier for achieving lower noise characteristics than metal-oxide-semiconductor (MOS) transistors [15]. The implementation of BJTs in a complementary metal-oxide-semiconductor (CMOS) process has been reported in previous works [16; 17; 18; 19; 20; 21]. The implementation of a BJT into the input stage of an amplifier can achieve a low-noise characteristic; however, the biggest disadvantage of BJTs in the input stage of the amplifier is the low input impedance caused by the large input bias current caused by the base current [20].  
In this paper, a low-noise chopper operational amplifier using a lateral PNP input stage with BJT current mirror base current cancellation is presented. The input stage of the amplifier is implemented with a custom-designed lateral PNP in a standard 0.18-\(\upmu\)m CMOS process. The BJT current mirror base current cancellation scheme is implemented to cancel the base current and enhance input impedance. The BJT current mirror base current cancellation scheme is implemented with a simple scheme using NPN transistors with deep n-well. The chopper amplifier scheme is implemented for further noise reduction with the BJT input stage in the low-frequency range by reducing low-frequency components such as 1/f noise terms.