esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Nanoparticle-Enhanced Cooling Technology for Oil-Based Drilling Fluids
## 3 Results and Discussion
### HTHP Filtration Loss of Nanoparticle-Enhanced OBDFs  
Filtration loss is one of the key indicators of the performance of drilling fluids, which is essential to ensure the stability of the wellbore and prevent fluid intrusion into the well wall. Excessive filtration loss could have led to the formation of thick filter cakes on the wellbore walls, increasing drilling difficulties and even causing wellbore collapse. As shown in Figure 7, the addition of nanoparticles significantly reduced the HTHP filtration loss performance of OBDFs. When the addition of CNTs reached 3%, the filtration loss was reduced to 1.6 mL, which was 42.86% less compared to the OBDFs without nanoparticles. This result indicates that CNTs can significantly improve the sealing performance of drilling fluids under extreme downhole conditions, effectively reduce the filtrate loss, and enhance the integrity of the well wall. Al2O3 nanoparticles also showed effective filtration loss control, with a 3% concentration reducing the filtration loss to 2.0 mL, a 28.57% reduction, indicating good stability and sealing performance in HTHP environments. In contrast, the addition of 3% SiO2 nanoparticles resulted in a filtration loss of 2.2 mL, a 21.43% reduction. Although this reduction was not as significant as with CNTs or Al2O3, it still indicated a noticeable improvement. These results demonstrated that CNTs, Al2O3, and SiO2 can effectively control the filtration loss of drilling fluids, enhancing wellbore stability and showing significant potential for application in HTHP drilling operations.
# Integrated Double-Sided Random Microlens Array Used for Laser Beam Homogenization
## 2 Structure Design of D-rMLA
### Simulation  
The effects of beam homogenization of double periodic MLA and D-rMLA were simulated and calculated by means of the numerical analysis software of Matlab (Version 7.1, MathWorks, Natick, MA, USA). The beam homogenization ability of different structures of MLA is compared. The aperture, radius of curvature and array number of the sub-lens in the periodic MLA are designed as 30 \(\mathrm{\SIUnitSymbolMicro m}\)\(\times\) 30 \(\mathrm{\SIUnitSymbolMicro m}\), 28 \(\mathrm{\SIUnitSymbolMicro m}\), and 8 \(\times\) 8, respectively. Diffraction distance is designed as 20 \(\mathrm{\SIUnitSymbolMicro m}\). According to the design parameters, the surface function Formula (3) is used to establish the MLA model, so that each microlens can be expressed numerically. Meanwhile, the transfer function of the MLA can be calculated by Formulas (4) and (5). The phase component of the MLA can be decomposed from the transfer function to calculate the phase distribution of each microlens. According to Fresnel diffraction theory, the homogenized spot with diffraction distance of 20 \(\mathrm{\SIUnitSymbolMicro m}\) can be calculated. The three-dimensional (3D) structure of the MLA is shown in Figure 2a. Figure 2b is the phase distribution of the periodic MLA. The obtained homogenized spot is shown in Figure 2c. It can be seen that the homogenized spot of the periodic MLA is consisted of periodic dot matrix.  
\[\mathrm{sag}(\mathrm{x_{i}},\mathrm{y_{i}})=\frac{\mathrm{C_{i}}\mathrm{r_{i }}^{2}}{1+\sqrt{1-(\kappa+1)\mathrm{C_{i}}^{2}\mathrm{r_{i}}^{2}}},\ \mathrm{r_{i}}=\sqrt{\mathrm{x_{i}}^{2}+ \mathrm{y_{i}}^{2}} \tag{3}\]  
\[\Phi_{\mathrm{i}}=\frac{2\pi}{\lambda}(\mathrm{n-1})\mathrm{sag_{i}} \tag{4}\]  
\[\mathrm{t_{i}}(\mathrm{x_{i}},\mathrm{y_{i}})=\exp(\mathrm{i}\mathrm{k}\Delta _{0})\exp[\mathrm{i}\Phi_{\mathrm{i}}] \tag{5}\]In order to compare the periodic MLAs, the side length of quadrilateral apertures of the sub-lenses in D-rMLA are designed to vary randomly from 25 \(\upmu\)m to 45 \(\upmu\)m, that is (\(30-5\times\) rand (0, 1), \(30+15\times\) rand (0, 1)) \(\upmu\)m. The array number and the average radius of curvature of the D-rMLA is \(8\times 8\) and 25 \(\upmu\)m, respectively. The 3D structure of the rMLA is shown in Figure 2d. Figure 2e is the phase distribution of the rMLA. The homogenized spot of the laser beam transmitting through the D-rMLA at the distance of 20 mm is shown in Figure 2f. Simulation results show that the D-rMLA can eliminate the interference phenomenon caused by the periodicity of the traditional MLA with better homogenization effect. The homogenized spot of periodic MLA presents periodic lattice distribution, and the energy is only strong diffraction orders. The energy of the homogenized spot of the D-rMLA is more discrete and uniform.
## 2 Materials and Methods
### Methods  
#### 2.3.1 Correlation Analysis  
The Pearson correlation coefficient is a commonly used statistical method for measuring the strength and direction of a linear relationship between two continuous variables [28; 29].  
The Pearson correlation coefficient is calculated using the following formula [30]:  
\[r=\frac{\sum\limits_{i=1}^{n}\left(Xi-\overline{X}\right)\left(Yi-\overline{Y} \right)}{\sqrt{\sum\limits_{i=1}^{n}\left(Xi-\overline{X}\right)^{2}}\sqrt{ \sum\limits_{i=1}^{n}\left(Yi-\overline{Y}\right)^{2}}} \tag{1}\]  
In Equation (1), r is the correlation coefficient between variables \(X\) and \(Y\), and the spatial correlation coefficient range based on pixels is \([-1,1]\). Positive (negative) values indicate a positive (negative) correlation between GPP and its driving factors, whereas zero denotes no statistically significant relationship between these variables; n is the study time, and the number of years is 22. In this study, \(X_{i}\) denotes the annual mean GPP for year \(i\), and \(Y_{i}\) represents the corresponding mean value of a specific driving factor during year \(i\). \(\overline{X}\) represents the mean GPP over n years; \(\overline{Y}\) represents the average driving factor for n years; and n is the total number of years. The strength of the correlation between two variables is represented by the magnitude of the positive or negative values of the correlation coefficient [30]. When the Pearson correlation coefficient approaches 0, it indicates that there is no significant linear relationship between two variables [31]. The Pearson correlation can help study the factors influencing the structure, function, and ecological processes of ecosystems [29].
## 2 System Model  
### OFDM Communication  
OFDM is a multi carrier modulation technique applied in wireless communication for high spectral efficiency and good performance against frequency selective fading. The modulation process for OFDM starts by converting the binary inputs into phase shift Keying (PSK) or quadrature amplitude modulation for the purpose of mapping \(D\) parallel streams. \(O\) is the number of subcarriers in the OFDM system. If the value of \(O\) is increased, the performance of the model is degraded. Thus, the value of \(O\) should be chosen optimally depending on the required system. Let \(X_{i}[q]\) be the \(i\)-th transmit symbol for the \(q\)-th subcarrier, where \(i=0,1,2,\ldots,\infty\) and \(q=0,1,2,\ldots,O-1\). Next, the symbols are converted to the frequency domain from the time domain by using inverse fast fourier transform (IFFT). To avoid inter symbol interference, a cyclic prefix is added in the signal. One single symbol duration is considered as \(T_{s}\), so for \(Q\), symbol transmission requires \(T_{sym}=QT_{s}\). Thus, for the \(i\)-th symbol and the \(q\)-th subcarrier, the OFDM signal \(\text{Y}_{i,q}(t)\) is written as follows:  
\[\text{Y}_{i,q}(t)=\begin{cases}e^{j2\pi f_{q}(t-iT_{sym})},&0<t\leq T_{sym}\\ 0,&elsewhere,\end{cases} \tag{1}\]  
where \(f_{q}\) is the center frequency for the \(q\)-th subcarrier. The continuous time domain signal can be expressed as follows [18]:  
\[X_{i}(t)=\sum_{t=0}^{\infty}\sum_{q=0}^{Q-1}X_{i}[q]e^{j2\pi f_{q}(t-iT_{sym} )}. \tag{2}\]  
The transmitted symbol can be considered as follows:\[X_{i}(n)=\sum_{q=0}^{O-1}X_{i}[q]e^{j2\pi qn/N}\text{for }n=0,1,\ldots,O-1, \tag{3}\]  
where \(f_{q}=q/T_{sym}\).
## 2 Materials and Methods
### Data Collection  
#### 2.2.1 Field Measurements  
Field experiments were performed from July to September, 2014. A total of 20 sample plots (five at each site) were set, of which each plot was 30 m \(\times\) 30 m in size. All sample plots were georeferenced by a global positioning system instrument. Samples of soil were collected consistently in every sample plot and preserved in plastic bags. The sampling position was at the rooting zone and the sampling depth was 20 cm. The soil samples from the same sites were grouped together, dried at room temperature, ground, and then sent to the laboratory for physicochemical characteristics analysis. The measured values of each sample plot are presented in Table 1. Information on the critical growth periods of rice, including transplanting and harvesting date and tillering, heading, and maturity stage was collected for the 10 plots in Sites A and B by interviewing rice farmers. The plot-based rice growth stages were recorded as reference data and the NDVI- and phase-space-based phenometrics for the 10 corresponding pixels on the image were extracted.
### Related Work  
**MP-SPDZ**. The MP-SPDZ framework provides a wide range of SMPC protocols, in which we are particularly interested in the SPDZ2K protocol (in semi-honest mode) that is commonly used as the standard protocol for SMPC-based frameworks. We will also compare our MHE-based methods with SPDZ2K-based methods in this paper;  
**Sharemind**. Sharemind is another SMPC-based framework similar to MP-SPDZ which implements protocols for secure computation over secret shared data. While there has been prior work done to specifically implement the \(t\)-test and \(\chi^{2}\)-test amongst various other statistical tests on the Sharemind framework [12], a thorough evaluation that includes communication costs was not presented. Furthermore, the \(t\)-test took a long time (2.75 min) to finish even with moderately small number of records (2000). We will show that our MHE-based methods have more superior performance compared with SMPC-based methods;  
**FAMHE**. FAMHE is a Federated analytics framework based on multiparty homomorphic encryption [11], which implements secure genome wide association study (GWAS) and secure calculation of the Kaplan-Meier survival analysis. In their paper, they mention a secure division protocol from MHE supposedly using polynomial approximations, which may lose accuracy. Additionally, they present a method for \(p\)-value calculation in the GWAS implementation, but since the distribution was assumed to be standard normal, this computation was approximated using polynomials and was directly calculated. Different from their approaches, we design more accurate secure division and P-value computation protocols, instead of using polynomial approximations;  
**STAR**. The STAR system [13] presents methods for computing the \(t\)-test and \(\chi^{2}\)-test among various other statistical tests using a hybrid MHE-SMPC approach to bypass secure division on encrypted data. The numerators and denominators for the statistics were calculated using MHE and converted into SMPC secret shares for the secure division to be done using SMPC. However, the encryption to share protocol was only simulated and the \(p\)-value computation was done in the clear. On the contrary, our system is end-to-end secure as the entire process including both statistical value computation and \(p\)-value computation is done in the encrypted domain, without the decryption of any intermediate data;  
**Fed-\(\chi^{2}\)**. The Fed-\(\chi^{2}\)[14] protocol recasts the \(\chi^{2}\)-test as a second frequency moment estimation problem and uses secure aggregation and stable projections to reduce the dimensionality of the contingency table and by extension the communication cost of the overall protocol. However, the marginal statistics were leaked and no empirical communication cost data was presented. Our work differs from theirs on the design of more efficient and secure MHE-based computation protocols, which enables more efficient statistical test computations on encrypted data.
# Experimental Assessment of the Effect of Vertical Earthquake Motion on Underground Metro Station
###### Abstract  
This paper presents experimental assessment of the effect of the ratio of vertical to horizontal peak ground acceleration (RVH) on underground metro station. An atrium-style metro station embedded in artificial soil subjected to earthquake loading is examined through shaking table tests. The experimental results for three different RVH, including soil acceleration, soil-structure acceleration difference, dynamic soil normal stress (DSNS), and structural dynamic strain, are presented and the results are compared with the case of horizontal-only excitation. It is found that for an atrium-style metro station, the differences in horizontal acceleration amplitude between the structure and the adjacent soil rise with increasing RVH, which are different at different depths. The most significant differences occur at the depth of the ceiling slab. It is also observed that both the amplitude and distribution of peak DSNS have obvious differences between the left and right side walls at all levels. It is therefore concluded that the RVH has a significant influence on dynamic soil-structure interaction. It is believed that under extreme earthquake loading, such as near fault zones, RVH is a parameter of paramount importance and should be accounted for in the seismic analyses and seismic performance assessments of underground structures, especially for those with zero or near-zero buried depth, such as atrium-style metro stations.  
vertical earthquake motion; seismic response; atrium-style metro station; shaking table test +
Footnote †: journal: Applied
###### Abstract  
This paper investigates a structured sparse SAR imaging algorithm for point scattering model based on tensor decomposition. Several SAR imaging schemes have been developed by researchers for improving the imaging quality. For a typical SAR target scenario, the scatterers distribution usually has the feature of structured sparsity. Without considering this feature thoroughly, the existing schemes have still certain drawbacks. The classic matching pursuit algorithms can obtain clearer imaging results, but the cost is resulting in an extreme complexity and a huge computation resource consumption. Therefore, this paper put forward a tensor-based SAR imaging algorithm by means of multiway structured sparsity which makes full use of the above geometrical feature of the scatterers distribution. The spotlight SAR observation signal is formulated as a Tucker model considering the Kronecker constraint, and then a sparse reconstruction algorithm is introduced by utilizing the structured sparsity of the scene. The proposed tensor-based SAR imaging model is able to take advantage of the Kronecker information in each mode, which ensures the robustness for the signal reconstruction. Both the algorithm complexity analysis and numerical simulations show that the proposed method requires less computation than the existing sparsity-driven SAR imaging algorithms. The imaging realizations based on the practical measured data also indicate that the proposed algorithm is superior to the reference methods even in the severe noisy environment, under the condition of multiway structured sparsity.  
SAR imaging; sparse reconstruction; tensor decomposition; multiway structured sparsity; Kronecker constraint +
Footnote †: journal:  
Article
## 2 Materials and Methods
### Statistical Analysis  
In total, 16 site-years of flux measurements at the two contrasting subtropical forest sites were used to explore the main environmental factors determining the dynamics in GPP of the evergreen needleleaf forest and evergreen broadleaf forest. The Pearson correlation coefficients (\(r\)) jointly with relevant \(p\)-value between these in situ GPP measurements, and the vegetation and climate-related factors including SIF, R\({}_{g}\), T\({}_{a}\), VPD, and P were calculated. When the \(r\) value is close to 1, a strong positive linear relationship is indicated when it passes the significance test (\(p<0.01\)).  
Then, this study aimed to develop an alternative method to retrieve GPP by exclusive use of satellite-based SIF data and evaluated its performance against satellite-based MODIS and BESS GPP products. Here, we used the leave-one-out cross-validation approach to test the reliability and robustness of the developed model. For each forest type, there were a total of 8 years of GPP measurements. One year of the GPP observations were withheld successively as a testing dataset, and then the data from the remaining seven years were used as training samples for model development. Then, we compared the model-estimated GPP including the SIF-based GPP (GPP\({}_{\text{SIF}}\)), MODIS GPP (GPP\({}_{\text{MOD}}\)), and BESS GPP (GPP\({}_{\text{BESS}}\)) with the tower-measured GPP, respectively.  
Two widely used indicators including the coefficient of determination (\(R^{2}\)) and the root mean square error (\(RMSE\)) were used to assess the model's performance. The equations of \(R^{2}\) and \(RMSE\) are as follows:\[R^{2}=1-\frac{\sum\left(\widehat{y}-y\right)^{2}}{\sum y^{2}} \tag{1}\]  
\[RMSE=\sqrt{\frac{\sum\left(\widehat{y}-y\right)^{2}}{n}} \tag{2}\]  
where \(y\) and \(\widehat{y}\) represented the observed and predicted values of GPP; \(n\) is the total number of observations (eight-day periods). Generally, the optimal GPP estimates had the largest \(R^{2}\) and least \(RMSE\). In this study, all statistical analyses are implemented by SPSS 19.0 (IBM, Chicago, IL, USA). When the tower-measured and model-estimated GPP regression was close to the 1:1 line, it indicated a high precision of the model estimation. However, the scatter plots between site-level GPP and modeled GPP can only reflect the overall consistency. Thus, in order to examine the performance of the modeled GPP over time, this study also compared the seasonal consistency between tower-based GPP observations (GPPEC) at the two subtropical forest flux sites and three different satellite-based GPP products.
## 6 Results and Discussion
### Simulation Procedure  
The employed scheme for CHS in WSNs using BEA-SSA was executed in MATLAB. The constraints utilized for simulation are shown in Table 3. Further, the assessment was performed for two node groups, i.e., 100 nodes and 200 nodes. The study was performed for two variants of nodes concerning delay, PDR, distance, RSSI, security, throughput, alive nodes, trust, and energy. Here, the improvement of delay was confirmed over GWO [26], MOFPL [22], PRO, SSA [36], BES [37], ROA, Hunger Games Search (HGS), SSO, RCSO [21], and FCR [20]. Figure 3 represents the node location of the deployed scheme for 100 and 200 nodes. Here, the centre of the node (sink node) is denoted as red color, where the normal nodes are defined in green color.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbols** & **Description** \\ \hline \(E^{i}\) & Initial Energy \\ \hline \(E_{el}\) & Electronic Energy \\ \hline \(E_{ea}\) & Data time aggregation \\ \hline \(di\) & Distance \\ \hline \(di_{0}\) & Threshold energy \\ \hline \(E_{pam}\) & Energy of power allocation \\ \hline \(E_{fr}\) & Requisite Energy \\ \hline \(E_{1}\) & Energy for idle state \\ \hline \(E_{C}\) & Energy Cost \\ \hline \(t_{pi}\) & Predicted Value \\ \hline \(y_{pi}\) & Actual values \\ \hline \(X_{mean}\) & Geometric Mean \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of Symbols.  
Figure 3: Simulation outcomes using BEA-SSA model for node counts of (**a**) 100 and (**b**) 200.
# Digital Pre-Distortion of Carrier Frequency Offset for Reliable Wi-Fi Enabled IoTs
## 2 Wi-Fi Enabled IoTs in Narrowband  
While the IoT does not have a single and widely-accepted definition, it can be described as \"things\" that interact with each other in distributed wireless networks [8]. The IoT will comprise tens to hundreds of billions of heterogeneous and pervasive objects, and will enable an ecosystem with existing networks and software applications [10]. These new applications enable continuous connections with intelligent devices, and IoT solutions are increasingly becoming a staple of modern cities [11]. The IoT changes the requirements for wireless connectivity significantly, mainly with regard to its long range and need for high reliability. However, due to the proliferation of smart devices and sensors, the ISM bands for WLANs are subject to significantly higher interference than other frequency ranges [12]. Therefore, wireless connectivity technologies for IoT devices have been developed to transmit signals in narrowband.  
IEEE 802.11 ah supports narrower bandwidths such as 1, 2, 4, and 8 MHz. Hence, IEEE 802.11 ah is considered to be more than a 10 times down-clocked version of the IEEE 802.11 ac standard, allowing it to improve the receiver sensitivity [4; 13]. These narrowband transmissions offer advantages of high noise immunity because a 10 times down-clocking of the bandwidth reduces the noise floor by 10 dB. Furthermore, narrowband transmission is more robust to interference than is wideband transmission [14]. However, the drawback of narrowband transmission is that high frequency error on the RF crystal leads to an offset on the RF frequency. If the offset gets too big, the desired signal will be located outside the channel and be filtered out by analog low pass filters. Furthermore, the DC canceller at the receiver will filter out data subcarriers near the DC subcarrier, thus noticeably degrading the network performance and damaging the quality of service (QoS).
In Section 2, we introduce fingerprinting-based positioning, discuss mobile-device-based positioning, describe the positioning data aggregation domains, introduce spatial side information, and summarize the key assumptions and scope. In Section 3, we review the literature, identifying previous work proposing the use of spatial side information on the device side with downlink transmission. In Section 4, we explain the concept of fingerprinting with spatial side information and introduce a two-dimensional sensor array, the discretization of the positioning area into area fractions, the positioning process, and the selected pattern-matching methods. The feasibility study using system simulations is presented in Section 5, comprising the generation of the datasets of fingerprints, the implementation of the radio channel model, the simulation process, and simulation execution. The results of the simulations are summarized in Section 6. In Section 7, our conclusions are drawn, the results are discussed, and directions for future work are presented.