esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Aeolian Sand Collapsibility Based on Physical Indicators in the Mu Us Desert, China
## 1 Introduction
With the continuous advancement of housing and airport projects in the Luanda area, [PERSON] et al. [39] found that Luanda sand can stand upright under natural conditions and has unique physical and mechanical properties. This includes its collapsibility and the pronounced deterioration of its mechanical properties after immersion in water, based on investigations and laboratory tests. The collapsibility coefficient of Luanda sand increased with an increasing void ratio, load pressure, and clay and silt content and decreased with an increasing water content. [PERSON] et al. [40] showed that the degree of collapsibility of aeolian sand in the Luanda area was mild to moderate based on in situ immersion load tests. [PERSON] et al. [41] showed that the degree of collapsibility of aeolian sand in the Niger region was mild to moderate based on in situ water immersion load tests. In the Thar Desert, [PERSON] et al. [42] showed that the degree of collapsibility of aeolian sand in this area is slight to moderate based on laboratory tests; its influencing factors were the void ratio and the dry density. In northwest China, previous studies have shown that fine silty sands in Yinchuan [43] and northern Shaanxi [44] are collapsible. The collapsibility coefficient of silty fine sand in Yinchuan was negatively correlated with the water content, dry density, and fine particle content. The collapsibility coefficient of silty fine sand in northern Shaanxi was positively correlated with the void ratio and clay content and negatively correlated with the water content. In summary, the research scope of collapsibility regarding aeolian sand is not extensive enough, and the understanding of its collapsibility law is insufficient at present. The studies on aeolian sand have been less systematic than those on loess. Most of them have performed macroscopic experiments, mainly researching the collapsibility degree, collapsibility characteristics, and influencing factors of aeolian sand in various regions. There is a lack of studies that include a comprehensive analysis of the influencing factors and collapsibility mechanism, the establishment of evaluation methods, and foundation treatment. This study considered aeolian sand on the southern edge of the Mu Us Desert as the research object. Based on the results of the water immersion load, standard penetration, and indoor geotechnical tests, four evaluation indicators were selected, the water content, dry density, void ratio, and saturation. They were combined with the support vector machine (SVM) method to establish a method for evaluating the collapsibility of aeolian sand based on physical indicators.
## 7 Security Analysis
### Informal Security Analysis
#### 7.1.3 Replay Attack  
Our protocol withstands replay attack because all transmitted messages are changed in every session. Assuming that \(U_{ma}\) tries to impersonate legal user by resending information transmitted in a previous authentication process, \(U_{ma}\) cannot use the previous messages because \(SD_{i}\) and \(UC_{j}\) check whether \(M_{3}^{*}\stackrel{{?}}{{=}}M_{3}\) and \(M_{6}^{*}\stackrel{{?}}{{=}}M_{6}\), respectively. Thus, our protocol is secure against replay attack.
## 2 Appendix Segmentation with Double-Layered Outlier Rejection FCM  
### Outlier Rejection  
Based on fuzzy set theory, FCM [37] often generates better results in image segmentation than hard clustering due to its tolerance to ambiguity of object membership on the boundaries between several clusters. Like K-means, it computes the distance between pixels and cluster centers and its cost function is minimized when pixels close to the centroid of their clusters are assigned higher membership values than others. However, the intra-cluster variance and the inter-cluster variance are not optimized when there exist overlapping of regions or outliers [35].  
Outlier Rejection FCM(ORFCM) proposed in [35] modified the membership function to overcome the outlier's sensitivity by changing the objective function of FCM as following.  
\[\text{min }ORFCM=\sum_{i=1}^{n}\sum_{j=1}^{k}u_{ij}^{m}\beta\|x_{i}-c_{j}\|^{2} \tag{1}\]  
where \(X=\{x_{i}\}\), \(i\)\(\in\)\(\{1,\,2,\,\ldots,\,n\}\) denotes an image with \(n\) pixels to be partitioned into \(k\) clusters, where \(2\leq k\leq n\), and \(c_{j}\) (for \(j=1,\,2,\,\ldots,\,k\)) is the \(j\)-th cluster. \(U=(u_{ij})\) is a \(k\times n\) fuzzy partition matrix, in which each element \(u_{ij}\) indicates the membership degree of each pixel in the \(j\)-th cluster, \(c_{j}\).  
The exponent variable \(\beta\) limits the partial distribution of the points among the two neighboring clusters rather than to all the clusters.  
\[\beta=\frac{\text{intensity in an cluster}+1}{256} \tag{2}\]  
With this exponent operation of the Euclidean distance in its membership function, ORFCM showed its capability to neutralize the outlier's effect and construct better clusters than the original FCM. There exist several recent results in medical domain using this outlier rejection concept in fuzzy learning process successfully [38; 39; 40].
## 2 Materials and Methods: Ten-Koh Mission Description
### Challenges Faced during the Project
#### 2.7.7 Magnetic Measurement System With No Deployable Boom  
The magnetometer instrument was installed on the top structural panel of the satellite next to the material samples (see Figure 6). Calibration was performed in order to validate whether the instrument was able to perform magnetic field measurements inside the expected range along the satellite orbit, and that it was not influenced beyond acceptable levels by the spacecraft self-induced magnetic noise. A sensitive and linear output inside the expected range was obtained, while confirming that the induced noise (offset from the spacecraft) was an order of magnitude below the instrument's range limits of \(\pm 2\times 10^{-4}\) T (\(\pm 2\times 10^{5}\) nT). Because the different offset values were constant (see Figure 9), it was possible to characterize the different values produced by the different modes of operation of the satellite, thus enabling the removal of magnetic noise from the measurements during post-processing of the data. This calibration and post-processing will be described in detail in a dedicated publication because it requires extensive explanation that cannot be accommodated in this paper.
###### Abstract  
Land subsidence is a prevalent geological issue that poses significant challenges to construction projects. Consequently, the accurate prediction of land subsidence has emerged as a focal point of research among scholars and experts. Traditional mathematical models exhibited certain limitations in forecasting the extent of land subsidence. To address this issue, the sparrow search algorithm (SSA) was introduced to optimize the efficacy of the long short-term memory (LSTM) neural network in land subsidence prediction. This prediction model has been successfully applied to the Guangdong Commercial City project in the Guanghua unit of Wenzhou city, Zhejiang province, China, and has been compared with the predictions of other models. Using monitoring location 1 as a reference, the MAE, MSE, and RMSE of the test samples for the LSTM neural network optimized using the SSA are 0.0184, 0.0004, and 0.0207, respectively, demonstrating a commendable predictive performance. This new model provides a fresh strategy for the land subsidence prediction of the project and offers new insights for further research on combined models.  
sparrow search algorithm (SSA); LSTM; land subsidence prediction; combined models +
Footnote †: journal: Applied Sciences
## 3 Effect of Strain Rate and BRB on Seismic Response of a CFST Frame Structure  
### Description of the Finite Element Model  
A 12-story CFST frame is selected as the exemplar structure. The structure has a height of 4.2 m for the first story and a height of 3.6 m for each of the other stories, totaling 43.8 m. The plan view and elevation of the structure are shown in Figure 6. The seismic fortification intensity is 7 and the designed basic acceleration of the ground motion is 0.15 g (as specified in [40]). The floor/roof dead load and live load are set to be 4.5 kN/m\({}^{2}\) and 2.0 kN/m\({}^{2}\), respectively, which are converted to the density applied on the floor/roof slabs. A 140 mm-thick slab with a concrete strength grade of C30 (i.e., a nominal cubic compressive strength of 30 Mpa) is used for both the floors and the roof. The transverse and longitudinal steel beams are Q235 (i.e., the yield strength of steel is 235 Mpa) I-beams that have cross-sectional dimensions of 700 mm \(\times\) 300 mm \(\times\) 13 mm \(\times\) 24 mm. The CFST column has cross-sectional dimensions of 500 mm \(\times\) 500 mm, with a tube of Q345 steel and infilled concrete of strength grade C30. The thickness of the steel tube is 20 mm for the columns in stories 1 to 5 and 15 mm for the columns in stories 6 to 12. The planar frame along grid line 3 is used for analysis. CSFT-A is an ordinary CFST frame, and CSFT-B is a CFST frame with BRBs in each end bay.  
The FE models of CSFT-A and CSFT-B are established using the ABAQUS software program. The steel beams, steel tubes, and infilled concrete are modeled using the three-dimensional linear interpolation beam element (B31). The beam and column elements are connected by rigid joints. The BRBs are simulated using the three-dimensional two-nodes truss element (T3D2), and the relevant parameters are set based on Reference [49]. The previously mentioned dynamic fiber element model of the CFST member is used for analysis. The constitutive model of square steel tube-confined concrete proposed in Reference [42] is used for the concrete, and the bilinear hardening constitutive model recommended in the \"Code for Design of Concrete Structures (GB50010-2010)\" is selected for the steel. Three sets of real ground motion records, San Fernando (1971), Taft (1952), and Gengmaa (1988) are used as ground motion inputs. It should be noted that the flexibility of beam-column joint may impact the seismic response of the CFST structure [50]. In this case, the rigid beam-column joint is employed in the design of the exemplar CFST structure. Thus, the joint is modeled as rigid in the numerical simulation and its flexibility on the structural seismic performance is neglected.
# Dynamic Single-Electron Transistor Modeling for High-Frequency Capacitance Characterization
## 3 Simulation Results  
### Gate Bias Dependence of Capacitances  
Figure 3a-c represent the \(C_{\text{gg}}\), \(C_{\text{gd}}\), and \(C_{\text{gs}}\), respectively, as a function of gate voltage \(V_{\text{gs}}\) for a low drain voltage of \(e/C_{\text{$\sum$}}/100\) at three different frequencies. The solid line shows the result of the steady-state model [13], which coincides well with the data points (square) of the proposed model at a low frequency (10 kHz).  
The \(C_{\text{gg}}\) shows a sharp peak at \(V_{\text{gs}}=0.5\,e/C_{\text{g}}\), where Coulomb blockade is lifted. The peak \(C_{\text{gd}}\) and \(C_{\text{gs}}\) are almost equal to each other, and are a half of the peak \(C_{\text{gg}}\), indicating that the gate is equally coupled to the drain and source at this low drain voltage.  
Figure 2: Circuit diagrams for the measurement of (**a**) \(C_{\text{gg}}\) and (**b**) \(C_{\text{gd}}\).  
At the cutoff frequency (1 GHz), the peak height is largely reduced, and levels off at 100 GHz due to the disappearance of the transistor action of the SET, i.e., the charge state of the Coulomb island cannot follow the rapid change of the signal. The baseline values of the \(C_{\rm gg}\), \(C_{\rm gd}\), and \(C_{\rm gs}\) are \(C_{\rm g}\)*(\(C_{\rm d}+C_{\rm s}\))/\(C_{\rm\Sigma}\), \(C_{\rm g}\)*\(C_{\rm d}\)/\(C_{\rm\Sigma}\) and \(C_{\rm g}\)*\(C_{\rm s}\)/\(C_{\rm\Sigma}\), respectively, which can be understood by considering the SET as a passive capacitance circuit consisting of \(C_{\rm g}\), \(C_{\rm d}\) and \(C_{\rm s}\).  
Figure 4a-c represent the \(C_{\rm gg}\), \(C_{\rm gd}\), and \(C_{\rm gs}\), respectively, as a function of gate voltage \(V_{\rm gs}\) for a high drain voltage of \(e/C_{\rm\Sigma}/2\) at three different frequencies. The proposed model at a low frequency (square) and the steady-state model (solid line) [13] give the same result even at this large drain voltage.  
The \(C_{\rm gg}\) shows lower and wider plateau compared to the case with the low drain voltage (Figure 3a) reflecting the gentler change in the island charge with respect to \(V_{\rm gs}\), and wider \(V_{\rm gs}\) area where the Coulomb blockade is lifted. In view of the behavior of the \(C_{\rm gd}\) and \(C_{\rm gs}\), it is found that the gate is strongly coupled to the source at low \(V_{\rm gs}\)'s, and to the drain at high \(V_{\rm gs}\)'s due to the large asymmetry in applied voltages to the source and the drain.  
When the frequency is increased to the cutoff frequency (1 GHz), the capacitances come close to the baselines, then level off at 100 GHz due to the loss of the transistor action. The baseline values of the capacitances are the same as those for the low drain voltage (Figure 3) and can be also explained based on the passive capacitance circuit.  
Figure 3: (**a**) SET input capacitance \(C_{\rm gg}\) (**b**) drain-to-gate capacitance \(C_{\rm gd}\) and (**c**) source-to-gate capacitance \(C_{\rm gs}\) as a function of gate voltage \(V_{\rm gs}\) for a low drain voltage \(V_{\rm ds}=0.534\) mV [0.01] at \(T=15.5\) K [0.05].
## 2 Materials and Methods
### Total Phenolic Content and Antioxidant Activity  
The total phenolic content in watermelon flesh, skin, and rind samples after SPE was determined using the modified Folin-Ciocalteu assay in Slinkard and Singleton [32], using gallic acid as standard. Briefly, samples were diluted 50 times in distilled water. After putting gallic acid standards and diluted samples (20 mL) in a 96-well plate, 100 mL of 0.2 N Folin-Ciocalteu reagent and 80 mL of 0.7 M sodium carbonate. The plate was incubated for 2 h at room temperature. The absorbance was read with a microplate reader (Synergy HT multimode microplate reader, BioTek, Winooski, VT, USA) at 760 nm. The results were expressed as milligrams of gallic acid equivalents (GAE) per gram of sample.  
The free radical-scavenging activity of watermelon flesh, skin, and rind samples after SPE was evaluated using an adapted method of [PERSON] et al. (2018) with 2,2-diphenyl-1-picrylhydrazyl (DPPH). Trolox was used as a reference standard. Samples were diluted 50 times in methanol. Trolox standards and diluted samples (10 \(\upmu\)L) were added with 140 \(\upmu\)L of 0.1 mM DPPH in methanol and incubated for 30 min in the dark at room temperature. The results were measured at 517 nm. The free radical-scavenging activity was calculated with the following equation: scavenging effect = [(A0 \(-\) A1)/A0] \(\times\) 100, where A0 was the absorbance of the control, and A1 was the absorbance in the presence of the sample. Data were expressed as micromole of Trolox equivalent (TE) per gram of sample, using the scavenging effect of the Trolox standard curve.  
Ferric reduction antioxidant potential (FRAP) of watermelon flesh, skin, and rind samples after SPE was determined using QuantiChrom(tm) FRAP Assay Kit (BioAssay Systems, Hayward, CA, USA). Samples diluted 100 times were mixed with working reagent and incubated at room temperature for 40 min. The absorbance was measured at 590 nm. The results were expressed as micromole of ferrous (Fe\({}^{2+}\)) equivalent per gram of sample.
# An Exploit Traffic Detection Method Based on Reverse Shell
## 4 Evaluation
### Experimental Setup  
#### 4.1.1 Data Collection  
Since open datasets usually merely consist of shellcode delivery phase traffic, ETDetector is based on shellcode execution results, namely, reverse shell session detection, and it cannot be verified by exploit traffic in open datasets.  
Therefore, we generate the traffic by artificial reverse shell experiment to train the decision tree model and implement ten vulnerability exploitation experiments using the Metasploit penetration testing framework to gather test traffic. Moreover, we collect the laboratory exit traffic as the background traffic. Normal online behaviors include browsing the web, SSH login, transferring files, and remote desktop login. Unencrypted reverse shell traffic is generated by the Metasploit module unencrypted system shell, while encrypted reverse shell traffic is generated by the Metasploit module encryption system shell andMeterpreter advanced shell. Table 1 shows a detailed description of our dataset. It is worth mentioning that the Meterpreter traffic has a dedicated encapsulation format, which contains no plain text. Therefore, in the verification experiment, regardless of whether the traffic is encrypted, Meterpreter traffic is considered encrypted traffic.
## 4 Discussion
Therefore, in this research, an IR-event is defined as one 5-min interval in which the aggregator was unable to respond correctly. An NA-event is defined as one 5-min interval in which the portfolio has insufficient capacity to respond to an extreme (100% portfolio activation) deviation. In addition, the model holds the assumption that an IR-event or NA-event will lead to a fine in all cases. In practice, this might not be the case, since TSOs do not have the capacity to assess and verify every IR- or NA-event and respondaccurately according to the fine regime. With an increasing participation of decentralized small assets in ancillary services markets, it would require automated verification methods to support the financial settlement. To obtain more accurate results, more specific information regarding TSO-regulation is required, as well as a higher resolution and quality of the dataset.
## 3 False Data Injection Attacks on RL-Based Charging Coordination
### False Data Injection Attacks
#### 3.1.1 Attack 1: Random Reduction Factor  
This attack applies a random reduction factor to the actual SoC values, causing the battery's state of charge to be under-reported. This attack is stealthy because it maintains the same pattern of the original SoC data and selects a reduction factor (\(\alpha\)) randomly from a specified range, ensuring that each instance of SoC under-reporting is different and unpredictable. Equation (9) is used to compute the false SoC values using the actual values.  
\[f_{1}(SoC_{i}^{(t)})=\alpha\times SoC_{i}^{(t)},\quad\alpha\in(0.1,0.7) \tag{9}\]  
where:  
* \(SoC_{i}^{(t)}\) is the true SoC value of consumer \(i\) at time \(t\),
* \(\alpha\) is the random reduction factor, with values ranging from 0.1 (more aggressive under-reporting of the SoC) to 0.7 (less aggressive under-reporting of the SoC).  
Figure 1 illustrates the implementation of Attack 1 by a malicious consumer (Consumer 3) in two scenarios: one with \(C_{\max}=6\) and another with \(C_{\max}=10\). The malicious consumer under-reports its SoC using a reduction factor of \(\alpha=0.1\). The figure shows that while the reported SoC follows the same pattern as the actual SoC, the values are significantly lower. The attacker aims to avoid detection by maintaining the same pattern as the actual SoC. Additionally, the figure demonstrates that both the reported and actual SoC values increase as the available charging power rises from \(C_{\max}=6\) to \(C_{\max}=10\).
## 3 Results and Discussion
### Model Evaluation
#### 3.4.1 Model Evaluation along with Actual Missing Data  
Assume that the rainfall values of a station are missing from \(t\) to \(t+k_{1}\) (see Figure 8). The hybrid model was fitted, and these missing values were estimated using the available values from \(1\) to \(t\). To check the validity of the fitted model, rainfall values from \((t+k_{1})\) to \((t+k_{1}+k_{2})\) were estimated based on the same fitted model (refer to Section 3.2) and compared with the actual values. This validation data set was with size \(55\) (here, the total number of observations in all \(k_{2}\) periods). The value \(k_{2}\) was chosen according to the continuous availability of data within the considered time period.  
Spatiotemporal kriging (SPTK) was applied to the same validation set. Finally, a comparison of RMSE, MAE, and R\({}^{2}\) (Table 4) was conducted using the results produced by individual approaches and the hybrid approach.  
Among the considered four models, the minimum RMSE is produced by the hybrid weighted method. It also has an MAE close to its lowest value, which is produced by MLP, and the highest R\({}^{2}\) value as of the spatial kriging (although it still needs to be improved). Thus, in the first phase of model comparison, it was proved that the hybrid model outperformed the other individual models. Moreover, the hybrid model outperformed the spatiotemporal kriging model with respect to all validation measurements.
## 4 Experimental Validations
### Influence of Cycle Slips on PPP Estimation
The improvements of 4.32 cm, 0.25 cm, and 3.09 cm on the positioning RMS are achieved in the east, north, and upward components, respectively. Although the positioning performance can be improved from the quality control, there are still small fluctuations on the positioning error curve since the single GPS observations with low elevation angle are used. Figure 5: The times of the difference between wavelengths on two frequencies for the epoch-difference geometry-free phase combination and the corresponding elevation angle of G07, G05, and G29 satellites. Figure 6: The positioning error of the single GPS PPP solution with quality control and without quality control at station BRST (E: east; N: north; U: upward).