esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Linear Amplitude Sweep Results  
Figure 5 shows the amplitude sweep results, illustrating the relationship between effective shear stress and effective shear strain for various bitumen samples modified with different additives. The unmodified bitumen demonstrates the lowest shear stress levels across the strain range, indicating limited resistance to shear deformation. This lower resistance implies a greater susceptibility to deformation under repeated loading, which could result in premature fatigue and rutting in pavement applications. The results presented in this figure represent the average values for each type of modified binder, as determined from replicate tests.  
Samples modified with styrene-butadiene-styrene (SBS) at concentrations of 4% and 5% show significantly enhanced performance, particularly the 5% SBS sample, which exhibits the highest effective shear stress values among all samples. The enhanced shear resistance offered by SBS highlights its ability to improve the stiffness and elasticity of bitumen, making it particularly suitable for high-stress traffic conditions and environments requiring durable and deformation-resistant pavements.  
Figure 5: Amplitude sweep results.  
Animal bone ash (AB)-modified bitumen samples at 4%, 5%, and 6% concentrations display progressively higher effective shear stresses, with the 6% AB sample achieving the greatest enhancement. This trend confirms that increasing AB content substantially improves stiffness and resistance to deformation, enabling better performance under cyclic loading and reducing the risk of pavement cracking and fatigue.  
The waste cooking oil (WCO)-modified samples demonstrate moderate increases in effective shear stress compared to unmodified bitumen. Although WCO modifications do enhance performance, their improvements are less pronounced compared to SBS and AB modifications. Notably, the 5% WCO sample exhibits an early plateau in effective shear stress and fails to outperform the 3% and 4% WCO samples. This behavior suggests that higher WCO content may introduce instability or reduce stiffness beyond an optimal concentration. Therefore, 3% and 4% WCO concentrations appear more effective in delivering balanced improvements in flexibility and moderate stiffness, making them more suitable for applications with lower traffic loads and where enhanced workability is desired.  
In summary, the amplitude sweep results in Figure 5 clearly indicate that SBS and AB modifications significantly enhance bitumen stiffness and shear resistance, making them highly suitable for demanding pavement conditions. SBS, especially at 5%, delivers the highest overall performance. Animal bone ash modification also shows considerable stiffness improvement, particularly at higher concentrations. WCO provides moderate performance improvements at lower concentrations, balancing flexibility and stiffness effectively, but higher WCO content such as 5% may not yield reliable or optimal performance.
## 4 Experiments and Discussions
### Datasets Comparison Experiment
#### 4.3.2 Test Datasets Composition  
In the practical application process, the image acquisition method was arbitrary in order to verify that the data collected by the system were representative enough and the trained model could identify the data collected under various conditions. Firstly, the test dataset should contain an ideal collection environment, where the camera is facing the fabric and the illumination is uniform, and it is named as the manual test dataset. Secondly, the test dataset should contain complex collection scenes (collection environment built by the system) with complex lighting environments and arbitrary shooting angles,  
Figure 14: The flow of datasets comparison experiment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Acquisition Method**} & \multirow{2}{*}{**Size**} & \multicolumn{2}{c}{**Time-Consuming**} \\ \cline{3-5}  & & & **One Class** & **One Image** \\ \hline dataset1 & Manual acquisition & \(50\times 30\) & 3 min & 3.6 s \\ dataset2 & Dataset1, data augmentation & \(500\times 30\) & - & - \\ dataset3 & System acquisition & \(500\times 30\) & 9 min & 1.1 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Acquisition methods and time-consuming image data.  
and it is named as the system test dataset. Additionally, the shooting equipment will be different in actual applications. For example, people are more accustomed to taking pictures with mobile phones in real life. Therefore, the test dataset should include images taken by mobile phone, which is named as mobile phone test dataset. Some images of the above three test datasets are shown in Figure 15.
## 7 Optimization of the Four-Layer Three-Segment Tube
### Maximization of the Fundamental Natural Frequency \(f_{1}\)
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Training** & **Testing** & **ANN** & **Training** & \multicolumn{3}{c}{**Testing Error**} & **No. of FE** & **Time** \\
**Pattern Sets** & **Pattern Sets** & **Symbol** & **Patterns** & **MSE** & \(\sigma\) & **ARE\({}_{1}\)** & **Calls** & **Consumption** \\ \hline \hline \multicolumn{8}{c}{one-output networks (\(f_{1}\) only)} \\ \hline \(\mathcal{P}_{3}\) & \(\mathcal{P}_{5}+\mathcal{P}_{6}\) & ANN\({}_{3}^{1}\) & 4,096 & 10.45 & 3.23 & 3.53 & 12,096 & 736 min \\ \(\mathcal{P}_{1}+\mathcal{P}_{2}+\mathcal{P}_{3}\) & \(\mathcal{P}_{5}+\mathcal{P}_{6}\) & ANN\({}_{123}^{1}\) & 8,921 & 3.69 & 1.92 & 1.91 & 16,921 & 1,017 min \\ \hline \multicolumn{8}{c}{ten-output networks (from \(f_{1}\) to \(f_{10}\))} \\ \hline \(\mathcal{P}_{1}+\mathcal{P}_{2}+\mathcal{P}_{3}\) & \(\mathcal{P}_{4}\) & ANN\({}_{123}^{10}\) & 8921 & 12.28 & 3.50 & 2.26 & 9921 & 1353 min \\ \(\mathcal{P}_{1}+\mathcal{P}_{2}+\mathcal{P}_{3}+\mathcal{P}_{4}\) & none & ANN\({}_{1234}^{10}\) & 9921 & — & — & — & 9921 & 1353 min \\ \hline \hline \end{tabular} ANN: artificial neural network; ARE: average relative error; FE: finite element; MSE: mean square error.  
\end{table} 
Table 10: Deep network training and testing results (three-segment MODEL2). Figure 8: Optimization of \(f_{1}\) using MODEL2 with four symmetric layers of laminate. versus desired). Obviously, the better the network training, the closer the points--i.e., the networks' results--to the diagonal \(y=x\) line. There are three types of points in the figure: circles represent the results of the pretrained network (trained using the initially generated pattern set \(\mathcal{P}_{3}\)), triangles represent the results of the networks retrained using additional patterns obtained after the first CL loop, and, finally, the upside-down triangles represent the results of the networks retrained again using additional patterns obtained after the second CL loop. Larger values on the horizontal axis in the subsequent steps of the optimization algorithm (CL0, CL1, and CL2) are indicative of additional patterns gathered after successive loops approaching the desired extreme. The decreasing distances from the diagonal \(y=x\) line mean that the retrained networks acquire a higher level of accuracy around the global extreme. Figure 9a presents a visible improvement in ANN\({}_{3}^{1}\) after each of the CL loops, and the ANN\({}_{123}^{1}\) network (see Figure 9b) clearly improves after the CL1 loop. The next loop CL2 does not significantly improve the maximum \(f_{1}\) value, but it increases the network accuracy for the lower values of \(f_{1}\).
## 2 Materials and Methods
### Retreatment Procedures  
After removal of the temporary fillings, two teeth did not undergo any further treatment and were used as positive controls. The remaining eighty teeth were subdivided into four experimental groups (\(n=20\)) according to the systems used: conventional endodontic needle irrigation group (CENI), Irrisafe group (IS), EndoVac group (EV), and XP-endo Finisher group (XPF). The specimens were fixed in a tube stand and placed in a thermostatic bath heated to 37 \({}^{\circ}\)C to simulate the body temperature. In all groups, a master apical file ProTaper Universal size 40 at WL was used to remove the CH, and then, different techniques were applied.  
The subsequent irrigation steps were performed after leaving the samples in the thermostatic bath for at least 20 min.  
In the CENI group, irrigation was performed with a syringe with a 27-gauge side-vented needle placed 2 mm from the WL with 3 mL of 5% NaOCl left in place for 60 s, which was followed by 3 mL of 17% EDTA for 60 s and 5 mL of sterile water. No additional agitation of irrigants was performed.  
In the IS group, the same irrigation protocol as in the CENI group was used, but ultrasonic activation of NaOCl and EDTA were obtained with the P5 Newtron (Satelec) mounting a stainless steel non-cutting 25 tip (Irrisafe, Satelec), which was inserted 2 mm from the WL for 1 min for each irrigant.  
NaOCl was delivered with the master delivery tip in the EV group and simultaneously aspirated by using the macrocannula for 30 s with an up-and-down movement from a point where it started to bind to a point just below the canal orifice. NaOCl was left in place for 60 s. After this, three cycles of microirrigation were performed by inserting the  
Figure 1: **(A)** Buccal surface with the longitudinal groove made with a diamond disk. **(B)** A split half of a sample ready for SEM analysis.  
microcannula at the WL for 6 s, then at 2 mm from the WL for 6 s, and eventually at WL for another 6 s. This was done until a total of 30 s was reached for each cycle. At the end of cycles, the microcannula completely aspirated the irrigant from within the canal. The first and third cycles were performed by using 5% NaOCI, whereas the second cycle used 17% EDTA.  
In the XPF group, the same irrigation protocol as in the CENI group was used, but NaOCI and EDTA were activated using the XP-endo Finisher file. The file, mounted on an endodontic motor (X-Smart Plus, [PERSON], Tulsa, OK, USA) rotated at a speed of 800 rpm and 1 N cm torque, and it was cooled down with ethyl chloride (Crio Spray, [PERSON], IM, Italy). The file was operated for 60 s with vertical movements of 7-8 mm to the WL to activate NaOCI and EDTA.  
The same amount of irrigants was employed in all groups. Sterile paper points were used to dry all canals, and the roots were then split along their longitudinal axis into two halves with a small chisel and a hammer. Division in thirds (apical, middle, and coronal) was determined by marking the roots at 2, 5, and 11 mm from the apex, respectively (Figure 1B).
###### Abstract  
The aim of the study was to develop a semi-automated, computer-aided imaging technique to quantify the amount and distribution of perivascular fat at the level of the iliac arteries (periiilac adipose tissue--PIAT), and to investigate the association of this new computer-aided imaging biomarker with other biomedical imaging biomarkers, which characterize the pelvic adipose tissue (SAT--subcutaneous adipose tissue; VAT--visceral adipose tissue). We included 34 patients with peripheral arterial disease, in whom the volumes of PIAT, SAT and VAT were quantified using a dedicated software, at the level of right and left iliac arteries. Median value of PIAT was five milliliters. Patients with PIAT \(>\) five milliliters were in more advanced Fontaine classes, with more complex arterial lesions, compared to those with low PIAT (\(<\)5 mL) (\(p<\) 0.0001). PIAT volumes presented a gradual increase with the Trans-Atlantic Inter-Society Consensus (TASC) class (2.57 +/- 1.98 in TASC A, 4.65 +/- 1.63 in TASC B, 8.79 +/- 1.99 in TASC C and 13.77 +/- 2.74 in TASC D). The distribution of PIAT between the left and right iliac axis was quasi-uniform (correlation between right and left PIAT: \(r=0.46\), \(p=0.005\)). Linear regression analysis showed that the mean PIAT volume was correlated with VAT (\(r=0.38\), \(p=0.02\)), but not with the SAT at the level of iliac artery origin (\(r=0.16\), \(p=0.34\)). PIAT may represent a novel biomedical imaging derived biomarker, which characterizes the distribution of adipose tissue in the pelvic area and may serve as an indicator of the severity and complexity of lower limb ischemia.  
periiilac adipose tissue; peripheral artery disease; computer-aided imaging 2020 10 4456 10.3390/app10134456
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 7 Preliminary Analysis A: Performance Comparison of ICIC Algorithms
### Evaluation Scenario  
The scenario consists of 3 eNBs positioned in the vertices of an equilateral triangle with side equal to 1000 m, as illustrated in Figure 6, and each eNB is the center of a cell. This scenario was introduced by the authors of [19] to evaluate the ICIC techniques implemented on ns-3.  
Users are distributed uniformly inside circles controlled by a radius that can be configured as needed. There is a circle centered on each eNB and an additional circle in the triangle's centroid, which is the farthest location from each eNB.  
User location within the circles is randomly selected from a uniform distribution, and all circles have the same radius. Given that a smaller radius leads to a more densely populated region (hotspot), it is possible to control user concentration. The number of users vary from 10 to 60 UEs on each circle and different circles always have the same  
Figure 6: Scenario for preliminary analysis a: performance comparison of ICIC algorithms. The Radius R is set to 100 m in Scenario 1 and 500 m in Scenario 2, as described in Section 7.1.  
amount of users. Namely, if there are 30 users in the cell edge area, there are also 30 users on each cell-center, i.e., 120 in total. Therefore, the number of users range from 40 to 240.  
Scheduling is made using the proportional fair algorithm and the link adaptation is based on SINR for a BER of \(5\cdot 10^{-5}\). The system bandwidth is 5 MHz, resulting in 25 RBs for each Transmission Time Interval (TTI). TTIs are the parameters related to the medium access in the radio link layer. At each TTI, the scheduling algorithm allocates RBs for all connected users. It is a parameter strongly related to access latency on LTE systems. Table 1 presents the configuration for each ICIC algorithm, suggested in [19]. Other simulation parameters are presented in Table 2.  
The simulation was conducted in two scenarios:  
* **Scenario 1:** UEs are concentrated in a 100 m radius, representing a classic hotspot scenario;
* **Scenario 2:** UEs are less concentrated, distributed over a 500 m radius circle.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Hard Frequency Reuse** & \\ \hline Bandwidth: cells 1 and 2 & 8 RBs \\ \hline Bandwidth: cell 3 & 9 RBs \\ \hline
**Strict Frequency Reuse** & \\ \hline Bandwidth (center/edge) & 6 RBs each \\ \hline RsrqThreshold & 32 \\ \hline Power offset (center) & \textasciitifootnote 6 dB \\ \hline Power offset (edge) & 3 dB \\ \hline
**Soft Frequency Reuse** & \\ \hline Bandwidth (center) & 25 RBs \\ \hline Bandwidth (edge): cells 1 and 2 & 9 RBs \\ \hline Bandwidth (edge): cell 3 & 9 RBs \\ \hline RsrqThreshold & 32 \\ \hline Power offset (center) & \textasciitifootnote 6 dB \\ \hline Power offset (edge) & 3 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used for the comparison of the ICIC algorithms in preliminary analysis A [19].
## 4 Results and Discussion
### EIS Analysis

Table 5: Fitting values based on the use of an electrical equivalent circuit. electrode. The contact pressure owing to weight is highly influential on the estimation of ohmic resistance. Among contact media, the resistance obtained from saturated Ca(OH)\({}_{2}\) was higher than others. Although it is still unclear, it may be possible that the impedance response is not adequately low to reach the characteristic frequency. Thus, errors were involved in the simulation process. Prior to EIS measurements, corrosion potentials were monitored with a copper-copper sulfate (CSE) electrode (refer to Figure 5). The measured potentials were \(-442.3\) mV and \(-135.5\) mV for the active state of steel (LS part) and the passive state of steel (SS part), respectively. According to ASTM C 876 [16], a corrosion threshold potential is \(-350\) mV for a CSE electrode. Thereby, it was apparent that the LS part was corroded and the SS part was non-corroded with the corrosion threshold potential. The corrosion rate was estimated with the traditional B value (26 mV for the active state), ranging from \(6.9\) mA/m\({}^{2}\) to \(12.9\) mA/m\({}^{2}\). This indicated that corrosion of steel was severe. To reduce errors induced in corrosion measurement, an electrical connection should be made properly between the electrode and concrete. Meanwhile, it is also apparent that the polarized area, i.e., the electrode size, is of importance to estimate the corrosion rate in pitting corrosion (refer to Figure 8). It is very difficult that time-to-corrosion initiation is estimated in reinforced concrete structures. As shown in this paper, steel states were different within the same sample due to the inhomogeneity of steel. In other words, to improve the accuracy of corrosion measurement, the errors caused by the controlled variables should be firstly minimized owing to the presence of uncontrolled factors such as steel and concrete inhomogeneities.
# Rock Indentation Behavior: Effects of Penetration Rates and Indenter Types
###### Abstract  
This paper is an attempt to investigate the rock indentation behaviors of a conical pick under different loading rates (1, 2, 3, and 4 mm/min), indenter types (sharp and blunt indenters), and types of rock (concrete, limestone, granite). Serial indentation tests by indenters were first performed by an automatic universal testing machine and monitored by an i-SPEED high-speed camera to record the peak pick force, indentation depth, rock fracture area, and rock failure process. Accordingly, the effect of loading rates, rock brittleness, and pick type on rock indentation behaviors was subsequently analyzed for a sound understanding of rock fragmentation mechanisms with indenters. It was found that higher loading rates necessitate a higher pick force and indentation depth to achieve rock fragmentation, resulting in a larger fractured area. Notably, a positive linear relationship exists between loading rates, rock-breaking forces, and fracture areas. A sharp indenter induces multiple cycles of repeated crushing and chipping phases, resulting in an arcuate-shaped fracture pattern with a smaller fractured area. Conversely, the rounded blunt indenter leads to a single stage of compression, with cracks propagating directly through the rock specimen, producing a larger fractured area. In addition, rock brittleness is another key factor to control rock failure efficiency, with tensile strength serving as a significant component.  
Keywords:indentation testing; penetration rates; indenter types; peak pick force; fracture area +
Footnote †: journal: Applied sciences
# Grassland Wildfires in the Southern Great Plains: Monitoring Ecological Impacts and Recovery
## 1 Introduction
Specifically, this study provides a framework by which satellite-based indices can inform post-wildfire management decisions for agricultural operations on Great Plains grasslands. Three large wildfires--Anderson Creek, Starbuck, and Perryton--that affected Texas, Oklahoma, and Kansas in 2016 and 2017 were chosen for the study, due both to their size and their effect on livestock operations. This analysis was part of a broad, multi-institutional regional response to the 2016 and 2017 wildfire seasons, led by the U.S. Department of Agriculture and University of Oklahoma, to review the meteorological, climatological, and land use conditions associated with the wildfires, evaluate remote sensing tools to monitor post-wildfire recovery, and understand community needs in advance of and following wildfire occurrence.
# Simultaneous Electrochemical Removal of Selenium and Strontium from Aqueous Solution
## 5 Conclusions  
This study suggests that the conditions required to achieve high removal efficiencies for Se were less stringent compared with those for Sr. Sr removal required more precise control of conditions such as pH, higher currents, or specific electrode materials to reach near-optimal removal rates. These findings highlight that Se could be removed more effectively and with less operational specificity in the EC process compared with Sr.  
The key factors that influenced the efficacy of the EC process included the treatment duration, current density, and the initial pH of the solution, which were critical for forming metal hydroxides and enabling the coagulation and sedimentation of the contaminants. These findings align with prior research, confirming the importance of these variables.  
The novelty of this study lay in the use of electrochemical pH control in the water purification process. This method was found to be feasible and improved the efficiency of the Se and Sr removal without generating byproducts associated with chemical pH control. Alternating the pH optimized the removal by providing ideal conditions for both contaminants: Se in acidic-to-neutral and Sr in alkaline environments. This two-phase process prevents re-dissolution, enhancing the overall efficiency compared with a constant pH. Furthermore, a single-system approach enables the simultaneous removal of both contaminants, eliminating the need for separate treatments and lowering the equipment, energy, and operational expenses. This research confirmed that electrocoagulation, particularly when paired with effective pH management, offers a potent and sustainable alternative to traditional water treatment methods for hazardous elements, balancing the operational efficiency with environmental sustainability.  
Conceptualization, I.H. and K.L.; Methodology, K.L.; Software, H.P.; Validation, H.P. and D.N.; Formal Analysis, I.H. and K.L.; Investigation, K.L.; Resources, I.H.; Data Curation, H.P.; Writing--Original Draft Preparation, I.H. and K.L.; Writing--Review and Editing, D.N. and H.P.; Visualization, D.N.; Supervision, I.H.; Project Administration, I.H.; Funding Acquisition, I.H. All authors have read and agreed to the published version of the manuscript.  
This work has been fully supported by the Croatian Science Foundation under project \"UIP-2020-02-1160--Purification of Microbiology and Chemically Contaminated Drinking Water by Electrochemical and Ultrasonic Processes\".  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All main processed data are contained within this article. The remaining raw datasets from the conducted tests are available upon request from the authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of this study; in the collection, analyses, or interpretation of the data; in the writing of this manuscript; or in the decision to publish the results.