esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods  
### TOA SW Reflectances  
#### 2.1.1 CERES Edition 4 SSF  
We obtained TOA SW reflectances (0.3-5.0 um) from CERES SSF Edition 4 [15]. Measurements were realized by CERES instruments [16] on board Aqua and Terra satellites and operating in two modes: the Rotating Azimuth Plane Scan (RAPS) mode (performed periodically during 2000-2005) and cross-track scan mode (2007). CERES footprints covered areas of about 20 km diameter at nadir. For \(\theta_{v}\) of up to 69.5\({}^{\circ}\), all footprints were collocated with MODIS imagery as well as products derived from the CERES/MODIS cloud algorithms based on [17]. Cloud information on MODIS pixel-basis was used for statistics across CERES footprints, taking into account the instrument's point spread function [16].  
We screened footprints for conditions typical of marine stratocumulus by selecting mean cloud-top pressures between 700 and 1000 hPa of single-layer clouds, a mean logarithmic cloud optical thickness \(\tilde{\tau}=\exp\left(\overline{\log\tau}\right)\) of 10 (permitting values between 9.75 and 10.25) as well as ocean surface fractions greater 95%. We also screened overcast conditions (selecting cloud fraction \(f\geq 99\%\)), and cases free of horizontal inhomogeneity [18; 19] by using footprints of \(v>10\), where \(v=\overline{\tau}^{2}/\sigma(\tau)^{2}\), \(\overline{\tau}\) is the mean optical thickness and \(\sigma(\tau)\) is the standard deviation of mean cloud optical thickness. Table 1 summarizes extracted footprints after screening.
###### Abstract  
Amphiphilic tetrafluorostyrene monomers (EFS8) carrying in the para position an oligonucleotide glycol chain containing 8 oxyethylenic units on average were synthesized and used for preparation via activator regenerated by electron transfer atom transfer radical polymerization (ARGET-ATRP) of the corresponding amphiphilic homopolymers (pEFS8-x) with different degrees of polymerization (x = 26 and 46). Combining light transmittance and nano-differential scanning calorimetry (n-DSC) measurements revealed that pEFS8-x homopolymers displayed a lower critical solution temperature (LCST) thermoresponsive behavior in water solutions. Moreover, n-DSC measurements revealed the presence in heating scans of a broad endothermic peak ascribable to the dehydration process of the polymer single chains (unimers) and their collapse into aggregates. Consistently, dynamic light scattering (DLS) measurements showed below the LCST the presence of small nanostructures with a hydrodynamic diameter size \(D_{\text{h}}\) of 6-7 nm, which collapsed into concentration-dependent larger multichain aggregates (\(D_{\text{h}}\) = 300-3000 nm) above LCST. Interestingly, n-DSC data showed that the unimer-aggregate transition was reversible up to a specific temperature (\(T_{\text{rev}}\)) of each homopolymer, which in any case was higher than \(T_{\text{max}}\). When heating above \(T_{\text{rev}}\) the transition was no longer reversible, causing the shift of \(T_{\text{onset}}\) and \(T_{\text{max}}\) at lower values, thus suggesting an increase in hydrophobicity of the polymer systems associated with a temperature-dependent dehydration process.  
amphiphilic polymer; thermoresponsive polymer; LCST; DSC; DLS; turbidimetry +
Footnote †: journal: Applied Sciences
# Demonstration of a Hybrid B5G System Integrating VLC and RF-Based Technologies with Access Networks
## 3 Experimental Setup
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Value** \\ \hline  & Power of RF1 @ 25 MHz & \(-\)10 dBm \\  & Power of RF2 @ 2.5 GHz & 0 dBm \\  & Power of RF3 @ 26 GHz & +6 dBm \\ \hline  & Wavelength & 1550 nm \\  & Maximal Output Power & 8 dBm \\ \hline  & Insertion Loss & 9 dB \\  & Bias & 2.3 V \\ \hline  & SMF Length (loss) & 20 km (4.4 dB) \\ \hline  & Maximal Output Power & 15 dBm \\  & Noise Figure & 5 dB \\ \hline  & FSO Length (loss) & 1.5 m (3.4 dB) \\ \hline  & Responsivity & 0.8 A/W @ 1550 nm \\  & Bandwidth & 50 GHz \\ \hline  & Gain and (NF) & 35 dB and (4 dB) \\ \hline  & mm-wave Wireless Length & 2.4 m \\ \hline  & Gain and (NF) & 24 dB and (5 dB) \\ \hline  & Band and (Loss insertion) & 2350–2800 MHz and (\textless{2 dB}) \\ \hline  & Gain and (NF) & 22 dB and (1.4 dB) \\ \hline  & LTE Wireless Length & 3.5 m \\ \hline  & Band and (Loss insertion) & DC-48 MHz and (\textless{1 dB}) \\ \hline  & Gain and (NF) & 51 dB and (2.2 dB) \\ \hline  & VLC Length & 1.3 m \\ \hline  & Responsivity & 0.4 A/W @ 650 nm \\  & Bandwidth & 1.2 GHz \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key parameters of the experimental setup.
## 6 Conclusions  
As the errors in the state vectors from an onboard orbit determination system for a spaceborne SAR affect real-time SAR imaging quality, this paper proposes an analytical approximation model to identify the introduction of QPE by state vector errors. This analytical approximation model can provide approximation results at two granularities, i.e., the approximation with the satellite's true anomaly as the independent variable and the approximation for \(\
u\in[0,2\pi]\). Compared with numerical simulation methods such as Monte Carlo, the proposed analytical approximation model reduces the total calculations and processing time. Another benefit of the proposed analytical approximation model is that it reveals the core of the error transfer, so each parameter's role in the introduced QPE is clear. Spaceborne real-time SAR imaging mission designers can quickly determine QPE with the help of only a calculator and the appropriate parameters. In some ways, this will accelerate the technical parameter iteration period during early-stage development of an onboard real-time SAR imaging mission and provide convincing evidence of the feasibility of spaceborne real-time SAR missions.  
Conceptualization, X.Y. and J.C.; Funding acquisition, J.C. and O.L.; Investigation, X.Y. and H.N.; Methodology, X.Y.; Project administration, J.C. and O.L.; Resources, X.Y., J.C., H.N., and O.L.; Supervision, J.C. and O.L.; Validation, X.Y. and H.N.; Visualization, X.Y. and H.N.; Writing--original draft, X.Y.; and Writing-review & editing, X.Y. and H.N.  
This research was funded by the National Natural Science Foundation of China under Grant 61861136008 and Grant 61671043. The author [PERSON] was funded by China Scholarship Council (CSC) under Grant 201706020033.  
The authors thank [PERSON] from Beihang University for useful discussions. The authors are grateful to [PERSON] from Northwestern University for her understanding and useful suggestions.  
The authors declare no conflict of interest.
# A Comprehensive Review of Acoustic Methods for Locating Underground Pipelines
## 3 Acoustic Methods for Locating Buried Pipelines
### Point Vibration Measurements  
Point vibration measurements for locating pipes buried in shallow depth were first proposed in [90]. The basic rationale implemented by point vibration measurements to determine the location of buried objects is elaborated as follows. When excited by a harmonic load over low frequency range, the ground, which is a homogeneous elastic half space, behaves as a classical single degree of freedom system with three components in terms of mass, stiffness and damping, as shown in Figure 8. This system possesses the intrinsic resonance characteristic at the resonance frequency governed by the elastic properties of the soil and the excitation radius, which are altered by the presence of a buried object with properties differ from the surrounding soil. The location of buried objects can be inferred through analysing these alterations.  
Experimental measurements were conducted on a dedicated pipe rig and a domestic waste pipe. Both of them were buried at a depth of approximately 30 cm. In this paper [90], the procedure of the measurements carried out on the dedicated pipe rig as shown in Figure 9a is introduced as an example. The ground was excited by an electrodynamic shaker with a built-in impedance head as shown in Figure 9b, which enabled the applied force and ground acceleration to be measured straightforwardly. The shaker was driven by a sweep input over a frequency range from 10 Hz to 800 Hz. The sketch of the location of measurement points over the buried pipe rig is depicted in Figure 9c.  
Figure 10 displays the measurement results of three lines. More details about the measurements can be found in [90]. It is clear from the figure that both the resonance frequency and the magnitude of the response at the resonance are changed due to the presence of the buried pipe, from which the location may be identified in both resonance frequencies and accelerate magnitudes at the resonance along the measurement lines, even though the peak magnitude was not always a useful indicator.  
The main advantage of point measurements is that it can be conducted straightforward without resorting to sensor arrays, which is more practical in congested areas. In addition, the measurement results are easy to interpret. Importantly, the detection depth may be improved by increasing the excitation contact radius. The experiment results are encouraging and there is evidence to suggest that point vibration measurement could be a quick and feasible supplement to conventional buried pipe detection techniques.  
Figure 8: The ground as a single-degree of freedom system [90].
# A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks
## 3 Proposed Methodology
### Feature Selection Methods
#### 3.3.2 Filter Feature Selection  
Filter Feature Selection is a method for identifying pertinent features based on their intrinsic characteristics, independent of specific machine learning models. It ranks features through statistical tests measuring their correlation with the target variable. Being algorithm-agnostic and computationally efficient, it offers broad applicability. A popular metric in filter-based methods is the Pearson correlation coefficient [39], defined as:  
\[r=\frac{\sum_{i=1}^{n}(x_{i}-\bar{x})(y_{i}-\bar{y})}{\sqrt{\sum_{i=1}^{n}(x_{i}- \bar{x})^{2}\sum_{i=1}^{n}(y_{i}-\bar{y})^{2}}} \tag{2}\]  
where, \(x_{i}\) and \(y_{i}\) are individual data entries, while \(\bar{x}\) and \(\bar{y}\) represent their means. A large absolute \(r\) indicates a pronounced linear association between the feature and the outcome, suggesting its significance in the model.  
Figure 5 shows the importance of features selected by the filter method for detecting XSS attacks for NF-ToN-IoT-v2 dataset. Highly ranked features, such as L4_DST_PORT, CLIENT_TCP_FLAGS, and TCP_WIN_MAX_IN, demonstrate significant relevance, while others like IN_BTTES and FLOW_DURATION_MILLISECONDS play supporting roles. In comparison, the wrapper method that presented in the previous section selects features based on model performance, favoring attributes like L7_PROTO and IN_BTTES to enhance specific model accuracy. The filter method quickly assigns feature importance based on intrinsic data traits, whereas the wrapper method iteratively adapts features for optimal model accuracy. Figure 6 presents the selected features for the Edge-IIoTset dataset, including Contains Alert, Script Tag, and Contains Less Than, which show high Pearson correlation coefficients with the target variable, indicating their strong association with malicious script patterns. Mid-ranking features, such as Contains Break Line and Contains Greater Than,  
Figure 4: The Selected Features by Wrapper Feature Selection Across Three Algorithms—Edge-IIoTset dataset.  
also contribute meaningfully, while features like Contains HTTP and Contains Semicolon provide additional context for identifying attack vectors.
###### Abstract  
In this study the effect of thermomechanical treatments in chloride induced pitting corrosion is presented for carbon steel redars exposed to synthetic fly ash (FA) pore solution. Due to the likely phase transformations that steel reinforcements in concrete experience during the event of a fire, the understanding of the corrosion behavior of such phases is key in predicting the stability of the structure. The motivation for this study arrives from the scarce literature regarding the corrosion behavior of thermomechanically treated steel reinforcements in FA environments and the need for further investigation to understand its mechanism. In order to better understand the effects on the corrosion behavior electrochemical measurements including cyclic potentiodynamic polarization curves (CPP) and electrochemical impedance spectroscopy (EIS) were used. It was found that quenched specimens showed enhanced corrosion kinetics as their \(i_{\text{corr}}\) values were higher, being of \(3.18\times 10^{-5}\) and \(2.20\times 10^{-5}\) A/cm\({}^{2}\) for water and oil quenched compared to \(2.13\times 10^{-6}\) A/cm\({}^{2}\) for the as-received. Furthermore, the effective capacitance of the double layer (C\({}_{\text{eff,all}}\)) showed the lower stability of the passive film for the quenched specimens, with values of \(1.11\times 10^{-3}\) uF/cm\({}^{2}\) for the as-receive sample that decreased to \(8.12\times 10^{-4}\) uF/cm\({}^{2}\) for the water quenched sample. The anodic charge transfer coefficient in the synthetic FA alkaline pore solution changes from 0.282 to 0.088, for the as-received and water quenched rebars specimens, respectively. These results indicate a lower energy barrier for the anodic dissolution reaction of quenched specimens, indicating that martensite and bainite microstructures promote corrosion process. Enhanced corrosion was found on quenched samples presenting martensite and bainite microstructure as showed by the increased pH depth, with values of 5 um compared to 1 um observed in the as-received samples.  
thermomechanical treatment; fly ash; post-fire corrosion; corrosion of steel; charge transfer coefficient; synthetic fly ash pore solution +
Footnote †: journal: Applied Sci. 2021, 11, 4054. [[https://doi.org/10.3390/app11094054](https://doi.org/10.3390/app11094054)]([https://doi.org/10.3390/app11094054](https://doi.org/10.3390/app11094054))
## 2 Signal Model
The observation model is expressed as follows:  
\[y_{k}=h(x_{k})+v \tag{5}\]  
where \(h(\cdot)\) is the observation matrix, and \(v\) is the observation noise.
## 4 Discussion
### Microscopic Mechanism of the Mechanical Behavior
On the wet side of \(w_{\text{opt}}\), the soil fabric was dominated by dispersed structure (Figure 14c), with water occupying the micro-pores between dispersed clay particles. The shear strength \(q_{\text{max}}\), resilient modulus \(E_{0}\) and cohesion \(c\) of frozen clay soils were observed to increase with \(w\) increasing, which was predominantly governed by the variation of microstructural property of the shear plane. The remolding water content \(w\) exerted its influence through varying the formation of ice cementation within this plane. As remolding water content \(w\) increased, the overall quantity of ice cementation on the shear plane grew, reinforcing the shear plane and leading to an increase in cohesion \(c\). As a result, the \(q_{\text{max}}\) and \(E_{0}\) increased correspondingly. When \(w\leq 27.5\%\), the soil fabric was dominated by clay aggregates. Since water mainly resided inside the aggregate, ice cementation merely reinforced the connections between clay particles inside aggregate. Since numerous large pores (non-cemented zones) existed between aggregates, the effective cemented area induced by the moisture increase remained limited. As a result, cohesion \(c\), \(q_{\text{max}}\) and \(E_{0}\) increased at a relatively low increasing rate. When \(w>27.5\%\), the soil structure transitioned to a homogeneous system dominated by dispersed clay particles with uniformly distributed small pores. In this case, increased moisture content \(w\) induced full development of ice cementation networks within inter-clay space. As a result, cohesion \(c\) as well as \(q_{\text{max}}\) and \(E_{0}\) increased in a high rate. Regarding friction angle \(\varphi\), it was observed to diminish with increasing \(w\), possibly due to the fact that growing ice exerts a lubricating effect within the soil skeleton, thus reducing the friction angle \(\varphi\). And since this lubricating effect was smaller in aggregate-dominated skeletons compared with particle-dominated skeletons, the decreasing rate of \(\varphi\) at \(w\leq 27.5\%\) was smaller. Note that in frozen soils, the shear strength \(q_{\text{max}}\) and \(E_{0}\) were predominantly controlled by cohesion \(c\); this decrease in \(\varphi\) had little effect on \(q_{\text{max}}\) and \(E_{0}\), which still exhibited increasing trends. Figure 14: Schematic illustration of soil fabric: (**a**) \(w=19\%\); (**b**) \(w=27.5\%\); (**c**) \(w=35\%\). Figure 13: Pore size distribution of clay soils at various \(w\) values. When it came to the parameters related to the volumetric variation (Poisson's ratio \(v\) and dilation angle \(\psi\)), both showed a tendency to increase as \(w\) increased. This was due to the involvement of more ice at higher \(w\) values, which introduced more solid materials into the soil structure, favoring horizontal deformation and volumetric expansion.
## 4 Results and Discussion
### Field Data  
Figure 6 shows a sample of data from the PRI sensor for days in May. Top and middle panels show the signal and reference band, respectively, while the bottom panel shows the filtered, gap-filled and smoothed PRI signals, _i.e._, using the 90-samples window. The diurnal PRI signals show a marked recurrent pattern for two sunny days of 8-9 May, and a much smaller variation for measurements obtained from cloudy or partially cloudy days of 11-17 May. The measurements show, moreover, a strong performance under low light conditions as indicated by the steep rise and fall of the PRI signal at dusk and dawn, respectively, and constancy of the PRI signal under cloudy conditions observed for, e.g., 15 May.  
Figure 7 shows a detailed view of the PRI signal for 8 May 2013. In examination of the diurnal PRI signals for sunny days, we observed a behavior that was inverted from results reported in literature. While we expected PRI to decrease with increasing irradiation, we observed a small decrease around 11 a.m. followed by a large increase, and then a decrease again towards the end of day. It is likely that this opposing result is caused by factors other than xanthophyll-induced down-regulation. For example, off-axis behavior of the interference filters, which can influence both bandwidth and position of the center wavelength, can lead to a reduction in the ability to discriminate radiance differences between the signal and reference wavebands. This is an important effect that warrants further study, even though similar previous studies have not explicitly mentioned or investigated this artifact. Other factors impacting the observed signal include changes in the spectral quality of incident radiation, and bidirectional reflectance effects within the sensor FOV as the irradiation conditions changed from diffuse to direct sun and the sun angle changed [25].  
Figure 6: Graphs showing the calibrated radiance data in the 532 nm and 568 nm wavebands and the photochemical reflectance index (PRI). Dates shown are in the Pacific Standard Time zone (_i.e._, UTC-08:00).
# Improvement of Boeing Bump Method Considering Aircraft Vibration Superposition Effect
## 4 Testing Scenarios
### Modifying Process  
We set the conditions and process for optimizing the Boeing Bump method as follows:  
* We set the wavelength range for the Boeing Bump to be 0 m to 120 m, with the step size of 1 m.
* We divided the wavelengths of 1 m-120 m into different bands.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Runway Roughness** & **Peak Value of** & **Peak Value of** & **Peak Value of CGA** \\
**Classification** & **MGDLC** & **NGDLC** & **and PSA (m/s2)** \\ \hline Acceptable zone & \textless{}1.15 & \textless{}1.30 & \\ Excessive zone & 1.15–1.35 & 1.30–1.70 & \textless{}3.92 \\ Unacceptable zone & \textgreater{}1.35 & \textgreater{}1.70 & \textgreater{}3.92 \\ \hline \hline \end{tabular} Note: MGDLC is the main gear dynamic load coefficient; MGDLC is the nose gear dynamic load coefficient.  
\end{table}
Table 6: Aircraft dynamic response control criteria that correspond to Boeing Bump evaluation standard.  
Figure 7: Single and multiple bumps characterized by cosine functions.  
* According to the vibration response characteristics of the different aircraft types, we determined the corresponding wavebands.
* We determined the taxiing speed of the aircraft.
* We determined the bump height that corresponded to each wavelength and input the multiple bumps into ADAMS/Aircraft.
* We simulated five types of aircrafts and found four vibration index values.
* According to the set thresholds of the vibration responses, we determined the height of the bump that corresponded to each wavelength and used that height as the standard for the final modified model.  
Figure 8 presents the entire process for modification of the Boeing Bump method.
## 2 Concepts and Terminologies
### Smart System Terminologies  
In the ICT, we explore essential terms. Smart signifies intelligent technology applications that are often seen in IoT and artificial intelligence. The Internet of Educational Things includes smart education devices. IoT is a network of interconnected devices integrating embedded technology and AI for smart decision-making. MOOCs offer remote learning through platforms like Coursera and edX. LMS aids educational interactions, while Virtual Classrooms refer to online classes. AI powers smart decision-making. VR and AR provide immersive learning; the 5 th Generation ensures real-time communication. MCUs and Fog Computing manage smart sensors, and Cloud Computing offers online resource access through IaaS, SaaS, and PaaS [25; 28].
# Comparison of ANN and ANFIS Models for AF Diagnosis Using RR Irregularities
## 2 Materials and Methods
### Features Extraction
#### 2.4.1 The Coefficient of Variance (\(CV\))  
The \(RR\) interval variation was calculated from the standard deviation of the mean \(RR\) interval, indicating heart function with normal or abnormal beats [30]. The \(RR\) interval can help to assess the risk of primary cardiac arrest without clinically detecting cardiac disease [31]. \(CV\) is one of the typical measurements of \(RR\) interval variation, as shown in (1), where the standard deviation of the \(RR\) interval is \(RR_{r}\) and the mean \(RR\) interval of a group is \(RR_{\mu}\). The \(CV\) value of AF patients is normally higher than that of NSR [32].  
\[CV=\frac{RR_{\sigma}}{RR_{\mu}} \tag{1}\]
# Performance Analysis of NB-IoT Uplink in Low Earth Orbit Non-Terrestrial Networks +
## 2 System Model
### NB-IoT Uplink Transmission Scheme
If the number of HARQ processes is two, 2 \(TBS\) are transmitted in 25.77 ms and the throughput is doubled compared to using a single HARQ process. Increasing the number of HARQ processes raises the problem of growing memory buffer size and decreasing battery life. Therefore, the increase in HARQ processes is not considered in the current NB-IoT NTN system [7]. This study analyzes the throughput results by implementing a simulation including long \(RTT\).