esa-sceva/satcom-chunk-collection huggingface dataset:
# Geolocation and Tracking by TDOA Measurements Based on Space-Air-Ground Integrated Network
## 2 Theoretical Performance
### The Effect of System Errors
#### 2.3.2 System Error of Observer Location  
When the system error of observer location is present, the mean of \(\mathbf{n}_{nav}\) is \(E(\mathbf{n}_{nav})=\delta_{nav}\). Hence, the mean of \(\boldsymbol{n}\) is \(E(\boldsymbol{n})=\left[0,\boldsymbol{\gamma}_{nav}^{T}\right]^{T}\) and \(\boldsymbol{G}(\theta_{1})=0\). The localization accuracy is obtained from (48) as  
\[\begin{split}&\text{MSE}(\theta_{1})=4A_{11}I_{1}^{T}C^{-1}I_{1}A_{1 1}^{T}+4A_{11}I_{1}^{T}C^{-1}\bigg{[}\begin{array}{cc}0&0\\ 0&\boldsymbol{\gamma}_{nav}\boldsymbol{\gamma}_{nav}^{T}\end{array}\bigg{]} \boldsymbol{C}^{-1}I_{1}A_{11}^{T}\\ &=4A_{11}I_{1}^{T}C^{-1}J_{1}A_{11}^{T}+4A_{11}B_{2}A_{11}^{T}\end{split} \tag{52}\]  
where \(B_{2}\) is defined as  
\[B_{2}=\begin{bmatrix}0&0\\ 0&J_{nav}^{T}C_{nav}^{-1}\boldsymbol{\gamma}_{nav}\boldsymbol{\gamma}_{nav}^{ T}\boldsymbol{C}_{nav}^{-1}J_{nav}\end{bmatrix} \tag{53}\]  
\(4A_{11}B_{2}A_{11}^{T}\) is the amount of degradation for localization accuracy introduced by the system error of observer location.
# Minimization of Average Peak Age of Information for Timely Status Updates in Two-Hop IoT Networks
## 6 Numerical Studies
### Impact of the Number of Sensors  
So far, we have fixed the number of sensors at \(N=30\). In this subsection, we vary \(N\) from 10 to 200 and investigate its impact on the optimal group size \(K^{*}\) and the optimal blocklengths \((L_{1}^{*},L_{2}^{*})\) for different average SNRs \(\rho\in\{1,3,5\}\) dB. To this end, we repeated the JBG optimization presented in Section 6.3 for each \(N\in\{10,50,100,150,200\}\) and summarize the results in Table 3.  
From the table, we observe that a larger \(N\) generally leads to a larger \(K^{*}\). Conversely, when \(N\) is small (e.g., \(N=10\)), the optimal group size becomes \(K^{*}=1\), indicating that joint packet coding is no longer beneficial. In such cases, optimizing blocklengths alone can be a more effective approach to minimizing the average PAoI. Furthermore, the results in Table 3 show that under low-SNR conditions, a larger group size \(K^{*}\) or longer blocklengths \((L_{1}^{*},L_{2}^{*})\) can help reduce the average PAoI, as they enhance the reliability of short-packet communications.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Average SNR** & **Optimal Group Size** & **Optimal Blocklengths** \\ \(\rho\) **(dB)** & \(K^{*}\) & \((L_{1}^{*},L_{2}^{*})\) \\ \hline
1 & 2 & \((214,407)\) \\
3 & 2 & \((156,298)\) \\
5 & 2 & \((118,226)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimal group size \(K^{*}\) in (13) and optimal blocklengths \((L_{1}^{*},L_{2}^{*})\) in (14) that minimize the average PAoI for each average SNR \(\rho\in\{1,3,5\}\) dB when \(N=30\).
## 3 Mathematical Modeling
### Adsorption Isotherm Model of Type I
#### 3.1.1 Freundlich Model  
The Freundlich adsorption model is an empirical model developed to fit the isothermal variations of gas adsorption on a solid surface. While the original model was created simply to project the experimental data, later, it was derived theoretically as well [37; 38]. [PERSON] et al. [40] expressed that the Freundlich model can successfully describe the sorption process from an aqueous media as well as gas adsorption on porous surfaces. Moreover, [PERSON] et al. [40] pointed out the key feature of this model that it accounts for the intermolecular interactions between adsorbates and can imply heterogeneity of adsorption sites. However, the simplicity of the model cannot correctly comprehend more intricate adsorption systems and, therefore, typically shows significantly lower accuracy than the Toth model or the D-A model. For this reason, it has not frequently been used in recent research. Equation (9) shows the Freundlich model in its typical form:  
\[w=w_{0}\left(\frac{P}{P_{0}}\right)^{1/n} \tag{9}\]  
where \(w\) and \(w_{0}\) are the equilibrium uptake and the maximum uptake in kg/kg, respectively. \(P/P_{0}\) is the relative pressure of adsorbate normally described as the ratio of absolute pressure \(P\) to the saturation pressure \(P_{0}\) at the given temperature.
# A Lunar-Orbiting Satellite Constellation for Wireless Energy Supply
## 3 System Architecture and Key Technologies  
The solution proposed in this paper is a constellation of satellites orbiting the Moon, which generate energy using solar panels, store it on board, and then transmit the energy through the use of laser beams towards specially designed receivers placed on the lunar surface. With this architecture, it is possible to overcome the limitations associated with lunar day and night cycles, enabling lunar missions and applications that require large amounts of energy. A conceptual diagram of the proposed solution is presented in Figure 3.  
LPT needs key technologies to be enabled, such as a thermal management system, an accurate pointing system, an optical component that colli  
Figure 3: Lunar-orbiting satellite constellation solution.  
a laser power converter at the receiving end. The first key technology mentioned could include a combination of two different kinds of cooling: forced convection and heat transfer associated with the oscillating heat pipe closed system, as suggested in [18]. To reach accurate pointing, NASA claims in [19] that with a fine guidance sensor (FGS) could obtain a level of stability and precision comparable to being able to hold a laser beam focused on a dimer that is 200 miles away (an 18 mm target at a distance from Washington D.C. to New York City). FGSs are very-high-precision sensors used in huge satellites such as the James Webb and the Hubble. Given their extreme precision, they have very significant costs and mass that will therefore need to be carefully evaluated.
## 3 ATON Framework
### Presentation Layer
#### 3.6.3 Navigation System
The system maintains a current POV (consistent with all navigation modes) that can be accessed and manipulated by custom routines. One example is the application of navigation constraints (to limit users movements) or adoption of locomotion graphs (move only into specific locations of the virtual environment), depending on the application requirements. These features are specifically designed to _quantize_ the navigation in the 3D space--where needed--for all devices.
## 4 Conclusions  
Two novel FAEs with CGA hydrolytic activity from _Aspergillus aculeatus_ were heterologously expressed in _E. coli_, purified to homogeneity and biochemically characterized. The specific activity of reFAE1 and reFAE2 for CGA hydrolysis was higher than that of the wild strains, respectively. Meanwhile, the fermentation cycle was greatly shortened compared to the original wild strain. The optimum reaction conditions for reFAE1 and reFAE2 were determined in this study. According to substrate specificity assay, both reFAE1 and reFAE2 were recognized as type C FAE, possessing good thermostability and substrate affinity to CGA. With the addition of reFAE2, the color of SSP solution changed noticeably from green to milk-white. This indicated that reFAE2 had potential for application in sunflower processing or another type of processing related to CGA. In the future, the expression of these novel ferulic esterases in fungal microorganisms will be studied further in order to improve their expression efficiency.  
Conceptualization, W.Z. and J.W.; methodology, J.W. and Y.L.; writing--original draft preparation, J.W.; writing--review and editing, J.W. and J.L.; project administration, W.Z. and J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (31401635); the Key R&D program of Jiangsu Province (SZ-YC2018010 and XZ-SZ 201805) and the National First-Class Discipline Program of Food Science and Technology (JUFSTR20180202).  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
###### Abstract  
A novel phosphorus-containing sorbent (CyP(Ph)4-DVB) was prepared by copolymerizing divinylbenzene (DVB) with bis \(\alpha,\beta\)-unsaturated phosphorylated cyclohexene (CyP(Ph)4). ATR-FT-IR indicated that the phosphinoyl group was introduced into the sorbent structure. The thermal properties of the sorbent were investigated using a differential scanning calorimeter (DSC), which revealed that (CyP(Ph)4-DVB) is more stable than poly(DVB). The CyP(Ph)4-DVB was applied for cationic dye removal, such as C.I. Basic Yellow 2 (BY2) and C.I. Basic Blue 3 (BB3). Batch adsorption tests suggested that the Freundlich isotherm model seemed to be the better one for the description of equilibrium sorption data at equilibrium, rather than the Langmuir or Temkin models. The Freundlich constants concerning the adsorption capacity of CyP(Ph)4-DVB, \(k_{F}\), were calculated as \(14.2\) mg\({}^{1-1/n}\)L\({}^{1/n}\)/g for BY2 and \(53.7\) mg\({}^{1-1/n}\)L\({}^{1/n}\)/g for BB3.  
\(\alpha,\beta\)-unsaturated phosphine oxide; phosphorus-containing polymers; polymeric sorbents; basic dye adsorption; Basic Blue 3; Basic Yellow 2 Article
# Applicability Study on Modified Argillaceous Slate as Subgrade Filling for High-Speed Railway
## 3 Experimental Evaluation of Cement/Lime-Modified Soil
### Results
#### 3.1.6. Shearing Characteristics  
The shear strength of soil is one of the important mechanical indexes of soil and a key factor to determine the stability of subgrades and geotechnical structures. The specimen was sieved through a 2 mm sieve, and with OMC at 0.92 compactness, was conducted by direct shear test. Table 10 and Figure 6 show the results.  
For the quicklime-modified WAS, the cohesion and internal friction angle increases and reaches the maximum value when the additive content is 6%. Further addition of additives adversely affects the cohesion and internal friction angle. For the cement-modified soil, the cohesion and internal friction angle increases with the increase in cement content. This can be attributed to the chemical reactions (pozzolanic and cation exchange) which occur between soil and additives. Additionally, under the same conditions, the C and \(\varphi\) values of cement-improved soil are larger than those of quicklime-improved soil. The maximum cohesion and internal friction angle in the case of 6% quicklime-modified soil is therefore 76.5 kPa and 29.6\({}^{\circ}\), while the maximum cohesion and internal friction angle in case of 7% quicklime-modified soil is therefore 100.8 kPa and 31.8\({}^{\circ}\). One important factor is that cement is a quick-setting material and its strength reaches 60-70% of the total strength after 7 days, which makes the internal friction angle and cohesion C of the modified soil increase rapidly. Meanwhile, quicklime is a retarding material whose strength increases slowly after 7 days, and the total strength generally takes 5-10 years to reach a stable value; therefore, the internal friction angle and cohesion C of the quicklime-improved soil increase slowly. Similar observations have been reported while studying the effect of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Soil Sample** & **Mixture Percent (\%)** & **Internal Friction Angle \(\varphi\)(\({}^{\circ}\))** & **Cohesion c (kPa)** &
\begin{tabular}{c} **Fitting Correlation** \\ **Coefficient R** \\ \end{tabular} \\ \hline Natural sample & - & 27.6 & 59.0 & 0.995 \\ \hline \multirow{3}{*}{quicklime} & 5 & 28.2 & 62.1 & 0.99 \\  & 6 & 29.6 & 76.5 & 0.992 \\  & 7 & 29.2 & 67.3 & 0.994 \\ \hline \multirow{3}{*}{cement} & 3 & 29.0 & 68.5 & 0.998 \\  & 5 & 30.6 & 79.9 & 0.998 \\  & 7 & 31.8 & 100.8 & 0.999 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Shear test parameters and fitting correlation coefficients of modified WAS.  
Figure 6: \(\varphi\)-\(\tau\) curve of shear tests for natural and modified WAS.
## Experimental Study on the Precise Orbit Determination of the BeiDou Navigation Satellite System  
**[PERSON] 1,2-*, [PERSON] 2, [PERSON] 1, [PERSON] 2 and [PERSON] 2**  
1 Department of Surveying and Geo-informatics, Tongji University, Shanghai 200092, China; E-Mail: [EMAIL_ADDRESS]  
2 Department of Geodesy and Remote Sensing, German Research Center for Geosciences, Potsdam 14473, Germany; E-Mails: [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON].)  
* Author to whom correspondence should be addressed; E-Mail: 85 [EMAIL_ADDRESS]; Tel.: +86-138-1863-5182.  
_Received: 22 January 2013; in revised form: 25 February 2013 / Accepted: 27 February 2013 / Published: 1 March 2013_  
**Abstract:** The regional service of the Chinese BeiDou satellite navigation system is now in operation with a constellation including five Geostationary Earth Orbit satellites (GEO), five Inclined Geosynchronous Orbit (IGSO) satellites and four Medium Earth Orbit (MEO) satellites. Besides the standard positioning service with positioning accuracy of about 10 m, both precise relative positioning and precise point positioning are already demonstrated. As is well known, precise orbit and clock determination is essential in enhancing precise positioning services. To improve the satellite orbits of the BeiDou regional system, we concentrate on the impact of the tracking geometry and the involvement of MEOs, and on the effect of integer ambiguity resolution as well. About seven weeks of data collected at the BeiDou Experimental Test Service (BETS) network is employed in this experimental study. Several tracking scenarios are defined, various processing schemata are designed and carried out; and then, the estimates are compared and analyzed in detail. The results show that GEO orbits, especially the along-track component, can be significantly improved by extending the tracking network in China along longitude direction, whereas IGSOs gain more improvement if the tracking network extends in latitude. The involvement of MEOs and ambiguity-fixing also make the orbits better.  
**Keywords:** BeiDou; tracking network; precise orbit determination; ambiguity-fixing
# CNN-Based Multi-Factor Authentication System for Mobile Devices Using Faces and Passwords
## 4 Experimental Results and Discussion
### Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Factors**} & \multirow{2}{*}{**FRR**} & \multicolumn{2}{c}{**Number of**} & \multirow{2}{*}{**Imposter Factors**} & \multirow{2}{*}{**FAR**} & \multirow{2}{*}{**CNN**} \\  & & & & **Taposter Factors** & & & \\ \hline \multirow{2}{*}{Experiment 1} & \multirow{2}{*}{Face, Password images} & \multirow{2}{*}{0.012} & \(1\) & Face & 0.144 & CNN \\  & & & \(1\) & Password & 0.186 & (8 layers) \\ \hline \multirow{2}{*}{Experiment 2} & \multirow{2}{*}{Face, Auxiliary images} & \multirow{2}{*}{0.014} & \(1\) & Face & 0.252 & CNN \\  & & & \(1\) & Auxiliary & 0.219 & (8 layers) \\ \hline \multirow{4}{*}{Experiment 3} & \multirow{4}{*}{Face, Password, Auxiliary images} & \multirow{4}{*}{0.002} & \(1\) & Face & 0.45 & \\  & & & \(1\) & Password & 0.458 & \\  & & & \(1\) & Auxiliary & 0.488 & CNN \\ \cline{1-1} \cline{3-6}  & & & \(2\) & Face, Password & 0.086 & (8 layers) \\ \cline{1-1} \cline{3-6}  & & & \(2\) & Face, Auxiliary & 0.064 & \\ \cline{1-1} \cline{3-6}  & & & \(2\) & Password, Auxiliary & 0.092 & \\ \hline \multirow{4}{*}{Experiment 4} & \multirow{4}{*}{Face, Password, Auxiliary images} & \multirow{4}{*}{0.004} & \(1\) & Face & 0.422 & \\  & & & \(1\) & Password & 0.576 & \\ \cline{1-1} \cline{3-6}  & & & \(1\) & Auxiliary & 0.442 & MMCNN \\ \cline{1-1} \cline{3-6}  & & & \(2\) & Face, Password & 0.104 & (4 layers) \\ \cline{1-1} \cline{3-6}  & & & \(2\) & Face, Auxiliary & 0.03 & \\ \cline{1-1} \cline{3-6}  & & & \(2\) & Password, Auxiliary & 0.106 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of the FRR and FAR experiments.
## 3 Wind Tunnel Tests
### Data Processing of Wind Pressure  
The measured wind pressure processes are converted to dimensionless pressure coefficients through:  
\[C_{pi}(t_{\rm m})=\frac{p_{i}(t_{m})-p_{\infty}}{p_{0}-p_{\infty}}, \tag{23}\]  
where \(t_{\rm m}=m/f_{s}\), (\(m=1\), \(2\), \(\ldots\), \(N\)) is the time series, \(N=f_{s}t=312.5\,\times\,60=18\),750 is the sample length, \(p_{\rm i}\) (\(t_{\rm m}\)) refers to the measured wind pressure of tap \(i\) in the freestream, and \(p_{0}\) and \(p_{\infty}\) are total pressure and static pressure at reference height in the freestream, respectively.  
The mean and standard deviation values of wind pressure coefficients are obtained by:  
\[C_{pi,\rm mean}=\frac{1}{N}\sum_{m=1}^{N}C_{pi}(t_{m}),\ \rm{and} \tag{24}\]  
\[C_{pi,\rm rms}=\sqrt{\frac{1}{N-1}\sum_{m=1}^{N}\left[C_{pi}(t_{\rm m})-C_{pi, \rm mean}\right]^{2}}. \tag{25}\]  
Then, the standardized pressure coefficient of tap \(i\), \(C_{pi,\rm st}\) (\(t_{\rm m}\)) can be calculated by:  
\[C_{pi,\rm st}(t_{\rm m})=\left(C_{pi}(t_{m})-C_{pi,\rm mean}\right)/C_{pi,\rm rms}. \tag{26}\]  
It has been established that wind pressure characteristics are closely associated with structures' configuration. Flow at the edge regions of the structures is fluctuant and  
Figure 5: Simulated wind field parameters in the wind tunnel. (**a**) Mean wind speed profile, (**b**) turbulence intensity profile, (**c**) longitudinal turbulence spectrum.  
should be paid more attention. Tap 66, situated at the right edge \(cd\) of BCR, is such a case where the wind flow is complex. As shown in Figure 6a, the pressure history for tap 66 is not symmetrical to the neutral axis where \(C_{pi,st}\) equals 0. Those intermittent negative impulses could lead to predominant sections on the roof. As illustrated in Figure 6b, negative impulses have the precise effect of wind pressure being deviated from the Gaussian distribution and introducing a leftward long tail. Due to this, the suction on the roof is higher than for the Gaussian process though having the same first- and second-order moments.
## 3 Results  
The mean values of UFLs and the bending moments of each of the three configurations can be seen in Table 1 and Figure 2. The c3 (0.38 cm cylindrical endosseous shape and transmucosal convergent neck) showed better and nonsignificant UFLs (421.6 \(\pm\) 12.5 N) than the other two settings i.e., c1 (0.33 cm subrestal abutment implant-level, 324.5 \(\pm\) 5.5 N) and c2 (0.38 cm subrestal abutment implant-level, 396.3 \(\pm\) 5.6). No fracture of the implant was registered even if all evident defects or deformations of the implant necks and bodies were recorded (Figure 3).  
Data showing the number of cycles registered until the specimens failed are givenin Figure 4.  
A Wohler function encoded the dependence of a parameter, i.e., the expected number of cycles before failure under the loading sequences with decreasing load levels. In contrast to the statical performance of fracture results, the c2 setting allowed for survival (5 Megacycles)for a loading force of 169 N (ultimate failure load at 50%). This was better than the c1 (ultimate failure load at 40%: 130 N) and c3 settings (ultimate failure load at 40%: 169 N). Moreover, the function of the 0.38 cm Premium was significantly different from that of the 0.38 cm Prama group (nonparametric one-way ANOVA \(\chi^{2}=6\); degree of freedom = 1; probability = 0.0043) but not from that of the 0.33 cm Premium group (nonparametric one-way ANOVA \(\chi^{2}=0.62\); degree of freedom = 1; probability = 0.4328). The fatigue-life of configuration 2 was one and a halftimes better than that of configuration 3. No conclusionscannot be drawn between configurations 1 and 2, even if the narrow implants showed excellent resistance to shear stress. Examples of failures are shown in Figure 5.  
Figure 4: (**A**) Lateral and occlusal (**B**) views of failures in the statical performance to fracture of a 0.38 cm implant diameter, 0.33 cm subcrestal abutment finishing line, and a cylindrical-shaped coronal emergence profile.  
Figure 3: Cyclical performance to fracture. Significant differences occur between the groups in bold. Red marks arethe mean values; cyan marks arethe maximum values of the force to achieve five million cycles. (**A**) c1 circle; (**B**) c2 square; (**C**) c3 asterisk. Black empty mark: simple sudden fracture of the specimen. Black full mark: both deformation and fracture of the specimen.
## 2 Proposed Method
### Multi-Objective Instance-Weighting Strategy
#### 2.5.1 Kullback-Leibler Divergence  
The KLD measures how different a particular probability distribution is from the reference probability distribution based on information theory. Given two probability distributions \(p\) and \(q\), the KLD of \(p\) and \(q\) can be defined as follows:  
\[KLD(p\parallel q)=\sum_{j=1}^{n}p(j)\log\left(\frac{p(j)}{q(j)}\right), \tag{5}\]  
where \(n\) is the number of class label, \(p(j)\) refers to the true probability of class \(j\), and \(q(j)\) refers to the predicted class probability of class \(j\). It is the expectation of the logarithmic difference between the probabilities \(p(j)\) and \(q(j)\), where the expectation is taken using the probability \(p(j)\). KLD has a nonnegative value. \(KLD(p|q)=0\) if and only if \(p=q\). Because KLD is a nonsymmetric measure, it does not directly indicate the distance between two probability distributions.  
In our case, we calculated the KLD for each source domain instance to be used as weight for that instance. The KLD of \(i^{th}\) source domain instance is formulated as follows:  
\[KLD\left(y_{S_{i}}\parallel\hat{y}_{S_{i}}\right)=\sum_{j=1}^{n}Pr\left(j|y_{S _{i}}\right)\log\left(\frac{Pr\left(j|y_{S_{i}}\right)}{Pr\left(j|\hat{y}_{S_{ i}}\right)}\right), \tag{6}\]  
where \(\hat{y}_{S_{i}}\) denotes the expected probability distribution of \(i^{th}\) source domain instance predicted from the initially trained transfer learning network. The KLD measures the amount of helpful information of each source domain instance for the classification task of the target domain in terms of the classification model. Instances accurately predicted from the target prediction model have small KLD values and are suitable for auxiliary training data of the target diagnosis model.