esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Damping Effect of Saturated Sandy Soil  
### Resonant Column Tests on Saturated Sandy Soil  
Saturated sandy soil samples were collected at the confluence of the Guanhe River estuary in Chenjiagang Town, Xiangshu County, Yancheng City. The physical properties of the sandy soil are shown in Table 1.  
Figure 1: Cross-sectional view of a hollow concrete gravity dam containing saturated sandy soil.  
Resonant column tests were conducted on these samples using the DTC-185 model resonant column triaxial apparatus. The specimens, with a diameter of 50 mm and a height of 100 mm, were wrapped in rubber membranes and mounted within a pressure chamber to apply confining and axial stresses. A torsional harmonic excitation force was applied to the top of the specimens, with the excitation frequency adjusted to achieve maximum amplitude at the specimen's crest. The damping ratio \(D\) is derived from the amplitude decay curve of the specimen's free vibrations. After ascertaining the logarithmic decrement, Equations (1) and (2) [16] are employed for the calculation.  
\[D=[\delta(1-e)-e\delta_{0}]/2\pi \tag{1}\]  
\[\delta=\frac{1}{N}\ln\frac{A_{1}}{A_{N+1}} \tag{2}\]  
In the formula, \(\delta\) represents the logarithmic decrement of the specimen's vibration system, \(N\) is the number of wave crests, \(A_{1}\) and \(A_{\text{N+1}}\) are the amplitudes of the first and the \((N+1)\)-th wave crests, respectively, \(\delta_{0}\) is the logarithmic decrement of the instrument's rotating part without the specimen, and \(e\) is the ratio of energy between the specimen and the instrument's rotating part during vibration.  
The experimental data were organized in Figure 2, where the horizontal axis \(\gamma\) represented the shear strain of the sandy soil, and the vertical axis \(D\) represented the damping ratio. The damping ratio, a dimensionless parameter, characterizes the energy dissipation capacity of structures or materials during dynamic responses and serves as an indicator of their energy absorption capabilities. High-damping materials, exemplified by viscoelastic and rubber-based damping materials, generally exhibit damping ratios within the range of 0.2 to 0.6. From the figure, it was observed that under different confining pressures, the damping ratio of saturated sandy soil increased with the increase in shear strain and eventually stabilized around 0.25. This indicated that the saturated sandy soil exhibited a significant damping effect during the testing process and could serve as a damping material.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(d_{50}\) & \(d_{60}/d_{10}\) & Specific Gravity \(G_{\text{s}}\) & \(e_{\text{max}}\) & \(e_{\text{min}}\) \\ \hline
0.18 mm & 2.6 & 2.62 & 0.80 & 0.41 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The physical properties of the sandy soil.  
Figure 2: The damping ratio versus shear strain curve of saturated sandy soil.
## 2 Related Work
### Local Descriptors  
Handcrafted descriptors consist of two groups: one-stage methods and two-stage methods. Typically, one-stage (Non-LRF-based) descriptors compute descriptors from point representations that are defined between pairs of points using 3D point coordinates only [43] and/or surface normals [29]. Point-pair features (PPF) [44] and fast point feature histograms (FPFH) [24] build an oriented histogram by using pairwise combinations of surface normals. They provide accurate feature matching with reasonable computational efficiency. The lack of essential geometrical information for the local frame is the main disadvantage of these methods. The two-stage (based on the LRF) descriptors, such as intrinsic shape signatures (ISS) descriptor [33], signature of histograms of orientations (SHOT) descriptor [30] and rotational projection statistics descriptor [45], are not only able to characterize the geometric patterns of the local support region but also effectively exploit the 3D spatial attributes. However, the LRF-based methods inherently introduce rotation errors, sacrificing the feature robustness.
# The Use of Volcanic Powder as a Cement Replacement for the Development of Sustainable Mortars
## 2 Materials and Methods
### Capillary Absorption Test  
The capillary absorption analysis was done following the standard UNE 83,982 [40], which follows the Fagerlund method to study the capillarity of cementitious materials, such as the concrete. Prior to the test, the samples were oven-dried completely at 105 \({}^{\circ}\)C for 12 h and, then, stored in a container hermetically sealed for 12 h [41], where silica gel was introduced inside. Before the test, the external lateral surface of the samples was sealed with self-adhesive tape, so they would not come in contact with the water [42].  
To implement the tests, a flat-based container base was used, on which the samples were positioned as indicated by UNE 83,982 [40]. During the test, the distilled water level was maintained constant at \(5\pm 1\) mm, keeping the bases of the samples under water throughout. The container was closed during the entire test. The mortars were weighed according to the times established in the standard [40]. The test ended when there was a difference in weight of each sample less than 0.1%, being weighed after a period of 24 h.  
The capillary suction coefficient was determined with the following expression:  
\[\mathrm{K}\ =\frac{\delta_{\mathrm{a}}\cdot\epsilon_{\mathrm{e}}}{10\cdot \sqrt{\mathrm{m}}}\mathrm{\ with\ m\ =\frac{t_{n}}{h^{2}}} \tag{1}\]  
where K is the capillary suction coefficient (kg/(m\({}^{2}\)min\({}^{0.5}\))), \(\delta_{\mathrm{a}}\) is the density of water (1 g/cm\({}^{3}\)), m is the resistance to water penetration by capillary suction (min/cm\({}^{2}\)), \(t_{n}\) is the time necessary to reach the saturation (minutes), h is the thickness of the sample (cm), and \(\epsilon_{\mathrm{e}}\) is the effective porosity obtained from the next expression:  
\[\epsilon_{\mathrm{e}}=\frac{Q_{n}\ -\ Q_{0}}{A\cdot h\cdot\delta_{\mathrm{a}}} \tag{2}\]  
where \(Q_{n}\) is the weight of the sample at the end of the test (g), \(Q_{0}\) is the weight of the sample before starting the test (g), and A is the surface of the sample in contact with water (cm\({}^{2}\)).  
In order to perform this test, for each mortar type, three cylindrical samples 10 cm in diameter and 5 cm thick were studied at 28, 200, and 400 days.  
Figure 5: (**a**) Equivalent circuit used for the fitting of the impedance spectra obtained using the contacting method; (**b**) equivalent circuit used for the fitting of the impedance spectra obtained using the non-contacting method.
## 4 Discussion
Together, we detected nitrifying and denitrifying bacteria in three complex and contrasting environments with gene-specific metagenomic PCR amplification of the unique functional marker genes _hao_, _nirS_, and _nirK_. This provided comprehensive evidence that the microbial community structures of nitrifiers and denitrifiers are influenced by the environmental nitrogen status. The _hao_ genes of the majority of nitrifying bacteria retrieved and the _nirK_ and _nirS_ genes of the denitrifying bacteria were affiliated with those of different genera of uncultured bacteria, but distantly related to the genes of known nitrifiers and denitrifiers, respectively. The high diversities and abundances observed in the Hao and Nir phylogenies suggest high interspecies variety in the three different environments (which differed sharply in their nitrogen and organic carbon status), which caused pronounced shifts in the nitrifying and denitrifying populations. The characteristic nitrifier and denitrifier population structures differed according to the nitrogen source, which was reflected in the predominant potential nitrifying and denitrifying genera present, and especially in the many redundant clones that were present, which may contribute greatly to the nitrogen metabolism in these unique environments. These results extend our knowledge of the biology and diversity of nitrifiers and denitrifiers in natural lakes and polluted rivers, and may have practical utility in the efficient restoration of rivers and lakes. However, the high diversity of marker genes and the changing patterns of bacterial dominance do not really reflect their contribution to nitrogen cycling. Novel experimental approaches such as transcriptome, proteomics, and metabolomics [44] are required to detect active nitrifiers and denitrifiers, to better understand their roles and the mechanisms they use in nitrogen cycling and environmental bioremediation. **Author Contributions:** [PERSON] and [PERSON] wrote the manuscript. Sample collection, processing and analysis were carried out by [PERSON], [PERSON], [PERSON] and [PERSON]. **Funding:** This research was supported by the National Natural Science Foundation of China (Grant 31700078 and Grant 31270724), the Scientific Research Foundation for Talent program of Zhejiang Agricultural and Forestry University (W20170029), Distinguished Scholars of Zhejiang Agricultural and Forestry University (2014 FR064). **Conflicts of Interest:** The authors declare no competing financial interests.
# EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs
## 4 Evaluation Metrics and Results
### Evaluation Metrics
#### 4.1.3 Energy Consumption  
The total energy cost of a UAV (denoted \(E_{total}\)) is the energy consumed to travel the total distance \(\top_{total}\) (denoted \(E(\top_{total})\)) and the energy consumed to perform the turns (denoted \(E(\Phi)\)) [14]:  
\[E_{total}=E(\top_{total})+E(\Phi)=\sum_{i\in V}\sum_{j\in V}\lambda e_{ij}+\sum _{i\in V}\sum_{j\in V}\sum_{k\in V}\gamma\frac{180}{\pi}\Phi_{ijk} \tag{12}\]  
where \(\lambda\) is the energy consumed per unit distance, and \(\gamma\) is the energy consumed per angle degree. In this work, we assume that \(\lambda=0.1164\) KJ/m and \(\gamma=0.0173\) KJ/degree.
## 2 Background and Conventional Approaches Related to RQ1 and RQ2
### NDB Errors in Covariates
(2006) [16] suggest that RC remains valid with GLMs when unbiased reference measurements (i.e., gold standard data) are available, but obtaining such data has been found to be costly and often impractical. In addition, [PERSON] and [PERSON] (2012) [18] demonstrate that parameter estimates from RC can be inconsistent as the size of the error variances \(\sigma_{\epsilon}^{2}\) increases. An alternative method, Simulation Extrapolation (SIMEX), proposed by [PERSON] and [PERSON] (1994) [19], corrects parameter estimates through simulation rather than recovering true covariate values. SIMEX assesses the impact of measurement error by introducing additional artificial noise into the NDB covariate and observing how this affects parameter estimates. By modeling these changes as a function of the noise, optimal parameter estimates can be obtained by setting the noise level back to zero [20]. Unlike RC, SIMEX does not require external reference such as gold standard data for error correction; however, the primary challenge of SIMEX lies in the risk of inaccurate extrapolation due to the complex relationship between the estimated parameters and the error variance \(\sigma_{\epsilon}^{2}\)[20]. Although a simple quadratic curve is often used for extrapolation stability, [PERSON] et al. (2006) [16] show that parameter estimates may remain inconsistent until the extrapolation curve perfectly captures this complex relationship, which remains as a persistent limitation.
# Detection of SQL Injection Attack Using Machine Learning Techniques: A Systematic Literature Review
## 5 Discussion
### Generating SQL Injection Attack Datasets Using Machine Learning Techniques (Related to Q2)  
A high-quality dataset for training is essential for machine learning and deep learning methods to achieve effective detection performance. It is difficult to identify suitable datasets with patterns to train classifiers in SQL injection attack research [30]. The results of the studies reviewed in Section 4 showed that, after automatically generating SQL injection attack payloads from different web applications, machine learning techniques can learn incrementally learn the payloads that are passed or blocked by the firewalls and can be used to efficiently generate additional payloads with high probability of bypassing the firewall. A total of 83% of the reviewed studies used datasets collected from public repositories and HTTP requests. The remaining 17% of the reviewed studies used datasets created by the authors using deep learning models that can be trained to learn the semantic features of SQL attacks to generate new test cases from user inputs.
# Application of Modern Research Methods for the Physicochemical Characterization of Ion Exchangers
## 1 Introduction
### Affinity of Ion Exchangers  
It is well known that the affinity of sulfonic acid resins for cations varies with the ionic size and charge of the cation. Generally, the affinity is greater for large ions with high valency. For dilute solutions the order of affinity for some common cations is approximately:  
\[\mathrm{Hg^{2+}<Li^{+}<H^{+}<Na^{+}<K^{+}\approx NH_{4}^{+}<Cd^{2+}<Cs^{+}<Ag^{+}<Mg ^{2+}<Zn^{2+}<}\] \[\mathrm{Cu^{2+}<Ni^{2+}<Co^{2+}<Ca^{2+}<Sr^{2+}<Pb^{2+}<Al^{3+}<Fe^{ 3+}}.\]  
A corresponding list for the amine based anion exchangers is: \(\mathrm{OH^{-}\approx F^{-}<HCO_{3}^{-}<Cl^{-}<Br^{-}<NO_{3}^{-}<HSO_{4}^{-}<PO_{ 4}^{3-}<CP{O_{4}}^{2-}<SO_{4}^{2-}\).  
The temperature, degree of ionisation of solvent, solute and ion exchanger are important for the ion exchange capacities [33; 34].
# Clinical Brain Monitoring with Time Domain NIRS: A Review and Future Perspectives
## 3 Clinical Brain Monitoring with TD-NIRS
### Traumatic Brain Injury (TBI) Assessment  
The use of NIRS technology in traumatic brain injury (TBI) has been reviewed by [PERSON] and colleagues in 2015 [92]. Here we explicitly review and discuss the use of TD-NIRS systems in the same patient population. We found three recent studies investigating the brain status of TBI patients. [PERSON] and colleagues [58], used the DCE-TD-NIRS method to assess cerebral perfusion in 26 TBI patients and nine controls, using an in-house developed multichannel TD-NIRS system (ID: 15 in Table 2). In this study, the moments of the DTOFs and DTAs were calculated, and their time courses were compared, to track the ICG bolus. Statistically significant differences between the time courses of the ICG bolus calculated using the different moment parameters obtained in healthy subjects and patients with brain haematoma and brain oedema were observed. Moreover, it was noted that the best optical parameter to differentiate between patients and controls was the variance of DTOFs and DTAs.  
[PERSON] and colleagues [93] focused on the changes in light scattering related to TBI. In this study, a total of 21 TBI patients were monitored with a TRS-20 system. In 10 patients suffering from unilateral infarction, even though no significant difference in cerebral saturation or haemoglobin concentration were found, a significant reduction of \(\mu^{\prime}s\) was related to the injury severity.  
Finally, [PERSON] and colleagues [60] used an in-house developed TD-NIRS and DCS system (ID: 16 in Table 2) to continuously monitor the absolute blood flow (i.e., using physiological units, see Section 2) of seven patients with acute brain injury. This absolute assessment was possible by coupling the DCE-TD-NIRS, to extract absolute values, and DCS, to continuously assess the blood flow. Here patients were monitored 8 h a day, four days. This illustrates the use of state-of-the-art TD-NIRS technology and instrumentation to collect information with comprehensive units for a physician.
###### Abstract
In this study, we considered practical cases in which the diffusivities of temperature and salinity were based on the observed data for the Global Ocean Argo gridded dataset. Then, the optical power received by the finite receiving aperture in the presence of pointing error displacements and long-term beam spreading was analyzed. In order to obtain further insights, the approximate expressions of the average BER and the outage probability for the MIMO system employing multiple quadrature amplitude modulation (mQAM) technology over the Malaga turbulence with pointing errors and long-term beam spreading were derived theoretically. We also analyzed the relationships between the long-term beam spreading and the turbulence strength and between the diversity gain and pointing errors. Our results confirmed that both the BER and the outage performance in UWOC systems are drastically influenced by the severity of pointing errors and ocean turbulence strength and that increasing the system receiving aperture diameter to mitigate the effects of long-term beam spreading and ocean turbulence can improve UWOC reliability and efficiency better than adjusting the QAM order. The rest of the paper is organized as follows. In Sections 2 and 3, we discuss the system model and channel model for the MIMO-UWOC. In Section 4, the performance of the MIMO-UWOC system in terms of the average BER and the outage probability is analyzed. In Section 5, we present numerical results and discussions. Finally, the conclusion is given in Section 6.
## Optical Simulation of Light Management in CIGS Thin-Film Solar Cells Using Finite Element Method  
**[PERSON] *, [PERSON] and [PERSON]**  
Institute of Sensor and Actuator Systems, Vienna University of Technology, Floragasse 7, Vienna 1040, Austria; E-Mails: [EMAIL_ADDRESS] (N.S.); [EMAIL_ADDRESS] (N.A.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +43-1-58801-76667; Fax: +43-1-58801-36698.  
Academic Editors: [PERSON], [PERSON] and [PERSON]  
_Received: 15 September 2015 / Accepted: 7 December 2015 / Published: 11 December 2015_  
**Abstract:** In this paper we present an optical simulation of light management in Cu(In,Ga)Se\({}_{2}\) thin-film solar cells with reduced absorber layer thickness, with the goal of absorption enhancement in the absorber layer. The light management was achieved by texturing of the substrate layer, and the conformal growth of all the following layers was assumed. Two texturing shapes have been explored: triangular and convex, with different periods and height aspect ratios. The simulations have shown that significant enhancement of absorption within the absorber layer can be achieved using the proposed geometry. The results showed that the triangular textures with small periods (100-200 nm) and high aspect ratios have the most prominent effect on the enhancement of absorption within the absorber layer, although they are difficult to achieve experimentally.  
**Keywords:** light management; CIGS; thin-film solar cells; simulation; finite element method
## 2 Description of Line Topology
### Definition of Equipment Status  
With increasing use of intelligent measuring equipment such as feeder terminal units (FTUs) and fault indicators (FIs), it makes distribution networks more intelligent. Generally speaking, FTUs are installed at switches, and fault indicators are installed on feeders. In this paper, FTUs and fault indicators are collectively referred to as fault-indicating equipment (FIE). When a fault occurs, there will be a fault current flowing from the substation to the fault point. Then, the fault-indicating equipment can collect the overcurrent information and report it to the control center.  
\[Status=\left\{\begin{array}{ll}1,&\quad\text{FIE is forwardtriggered}\\ 0,&\quad\text{FIE is not triggered}\\ -1,&\quad\text{FIE is reversertriggered}\end{array}\right. \tag{1}\]  
Additionally, distributed generations will also provide a fault current to the fault point. In order to distinguish the direction of fault current, the positive direction is defined as from the substation to the end of the line. When the fault-indicating equipment detects a fault current flowing from the substation to the fault point, it is forward-triggered, and the status of FIE is defined as \"1\". When it detects a fault current flowing in the reverse direction, it is reverse-triggered, and the status is defined as \"-1\". When there is no fault current detected, the status is defined as \"0\". The three kinds of status can be represented as Equation (1).
# Idle Vibration Reduction of a Diesel Sport Utility Vehicle
## 2 Experimental Analysis
A comparative study between the ODS and modal test result is crucial for a deeper understanding of the idling vibration produced by the diesel SUV. In general, the ODS of a system is influenced not only by the excitation frequency and direction, but also by  
Figure 1:0 Mode shapes of the body floor: (**a**) bounce mode (3.2 Hz), (**b**) pitch mode (3.7 Hz), (**c**) bending mode (23.8 Hz), and (**d**) torsional mode (28.4 Hz). Figure 1:1 Mode shapes of the components: (**a**) bounce mode of the powertrain (4.8 Hz); (**b**) pitch mode of the powertrain (13.0 Hz); (**c**) roll mode of the powertrain (18.5 Hz); (**d**) bounce mode of the radiator (20.0 Hz); (**e**) roll mode of the radiator (30.3 Hz); and (**f**) bounce mode of the intercooler (29.3 Hz). the mode shapes corresponding to the natural frequencies proximate to the excitation frequency. Therefore, the direction of the excitation torque (the engine driving torque) and the mode shapes should be analyzed with respect to the natural frequencies proximate to the idling frequency. The ODSs at the idling frequency of 27.7 Hz (refer to Figure 5) were compared with the mode shapes (refer to Figures 7 and 8). First, the ODS of the powertrain, as illustrated in Figure 5a, is almost identical to the pitch mode of the powertrain, as depicted in Figure 7b. However, the natural frequency of this pitch mode, 13.0 Hz, is significantly different from the idle or excitation frequency, 27.7 Hz. Nevertheless, the powertrain possesses an ODS similar to the shape of the pitch mode because the excitation torque of the engine acts in the transverse direction of the vehicle. Conversely, the ODS of the radiator (refer to Figure 5b) corresponds to the roll mode of the radiator (refer to Figure 7e). This roll mode shape was observed in the ODS because the natural frequency of this roll mode, 30.3 Hz, is proximate to the idling frequency, 27.7 Hz. Similarly, the ODS of the intercooler, as depicted in Figure 5c, resembles the bounce mode of the intercooler, as illustrated in Figure 7f, featuring a natural frequency of 29.3 Hz, which is proximate to the idling frequency. Finally, the ODS of the body floor displayed in Figure 5d appears as a combination of the bending and torsional modes of the body floor, which are, respectively, depicted in Figure 8c,d. These modes contribute to the ODS because the idling frequency (27.7 Hz) lies between the natural frequencies of the bending and torsional modes (23.8 and 28.4 Hz, respectively). Notably, because the excitation torque of the engine is applied to the front-center of the body floor, the bending mode exerts a greater influence on the ODS than the torsional mode.