esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods for Matrix Suction Measurements  
The water potential sensor used in this study [41] measures both the matric suction, by using two porous discs (i.e., ceramic) with a known pore size distribution (fixed-matrix), and the soil temperature, using a surface-mounted thermistor (Figure 1). In particular, the sensor indirectly measures the water content of the two ceramic discs (dielectrics) spaced apart by a printed electrical circuit board by measuring their electrical resistivity and then returns a suction value of the ceramic discs according to their water retention curve [42]. The obtained value of the suction of the ceramic discs is then supposed to be equal to the soil suction, considering that the water potential equilibrium is established between the sensor and the soil that is in contact with it. According to the manufacturer's specifications, the sensor is calibrated at a saturated state (0 kPa), at a dry state equivalent to 10\({}^{5}\) kPa, and four calibration points between 0 and 100 kPa, resulting in an accuracy of \(\pm\)10% of reading +2 kPa over the range of 5 to 100 kPa [41]. At high suctions, the accuracy of the sensor depends on the water retention property of the ceramic disc [42], established based on mercury intrusion porosimetry [43]. The lower limit of the suction reading range for the sensor (i.e., 9 kPa) corresponds to the AEV of the largest pores in the ceramic discs. The operating temperature of the sensor is between 0 and 60 \({}^{\circ}\)C. A thermistor is located underneath the sensor overmold (resin) that enables monitoring of the temperature. The range, resolution, and accuracy of the temperature sensor are \(-\)40 \({}^{\circ}\)C to 60 \({}^{\circ}\)C, 0.1 \({}^{\circ}\)C, and \(\pm\)1 \({}^{\circ}\)C, respectively [43].  
Figure 1: Schematic view of the TEROS 21 water potential sensor. Top plan view; bottom cross-section view.  
In addition, Whatman No. 42 filter paper and high-capacity tensiometers were used to measure matrix suctions, as described in [PERSON] et al. [28]. The filter paper technique was used in the range 100 kPa-25 MPa. Four in-contact filter discs, two on each side of the sample, were added. The two \"protected-in-contact\" filter paper discs were used to calculate one average suction value [28]. For at least 14 days, suction measurements were taken to allow the filter paper to equilibrate with the suction level in the soil specimen [25; 38]. The HTC was used for suction values of less than 1500 kPa. Measurements involved fixing the tensiometer tip to a Perspex disc on the specimen base, applying kaolin paste for hydraulic contact, and wrapping the specimen in cling film to prevent evaporation. Measurements were often repeated on both specimen bases using different tensiometers, with the reported matric suction value representing the average of these readings.
## 4 Results
### Performance Evaluation in Scenarios with Multiple MC-Capable UEs  
This section considers the performance of the DQN-based solution when there are multiple MC-capable UEs coexisting in the same evaluation environment and applying the learnt DQN-based policy.  
The study consisted of five different simulations, each one with a distinct number of MC-capable UEs, ranging from 5 to 25, all of them with a \(R_{u}\) = 25 Mb/s. During the simulation time each UE moved following a different trajectory and generated traffic during the whole evaluation time, equal to 400 s. The background traffic had a generation rate of 0.2 sessions/s and an exponentially distributed session duration with an average 120s, resulting in an average of 24 background UEs during a simulation.  
The results presented in Figure 10 were obtained by averaging the performance for the different UEs in each evaluation with the SINR-based strategy and with the proposed DQN-based strategy. Figure 10a depicts the performance of our solution in terms of reward. It can be seen that the gain of DQN-based strategy with respect to SINR-based strategy increases from 11.2% for 5 UEs up to 31.7% for 25 UEs.  
Regarding the throughput performance, a similar assessment was done and the results are presented in Figure 10b. They show that the proposed method outperforms the SINR-based approach for all the considered numbers of users., with a gain of around 33% for the case with 25 UEs. Notice how by applying our solution it tends to keep the obtained throughput very close to the requirement of \(R_{u}\) = 25 Mb/s.
## 1 Introduction
### Calculation of CO2 Emissions from the Building Sector  
Life cycle assessments have been widely used to determine buildings' energy consumption and CO2 emissions [5, 6, 7, 8]. The life cycle assessment of buildings was divided into three main parts: the processing and construction, operation, and demolition stages [5, 9].  
Besides estimating CO\({}_{2}\) emissions [10; 11; 12; 13; 14], existing studies analyzed the emissions between residential and commercial buildings using various materials at different stages of the building life cycle [6; 9]. Based on estimating office buildings' emissions at the construction stage, building height positively promoted unit area emissions [15]. In addition to life cycle assessments, previous studies estimated buildings' CO\({}_{2}\) emissions at different stages, such as material input, energy consumption, and human input, using input-output methods [16; 17; 18]. By comparing emissions at different stages, researchers found that energy consumption in the operation stage accounted for the highest proportion in the building's whole life cycle [6; 7; 9; 19].  
The operation stage mainly includes the energy consumed by heating, air conditioning, lighting, and other functions of residential, commercial, and office buildings [20]. The operation stage is the main source of energy consumption and CO\({}_{2}\) emissions in the life cycle of a building. Previous studies focused on estimating energy consumption and CO\({}_{2}\) emissions at the operation stage using China's building energy consumption calculation method (CBECM). This method extracted energy consumption related to building operations from sector statistics in the energy balance sheet [21]. For the fine-scale emissions of building operations in China, a regression model was established between building operation emissions, air temperature, and economic factors at the provincial level and downscaled them to a 1 km resolution [22]. A combined energy balance sheet with POI weights and CO\({}_{2}\) emissions from building operations in Beijing was mapped at a grid scale [23]. Nighttime light data have been widely used to estimate energy consumption and CO\({}_{2}\) emissions [24; 25; 26; 27; 28; 29]. Previous studies have proven its effectiveness in evaluating buildings' material stocks [30; 31] and have also used the linear regression model to downscale the building sector's carbon emissions in the United Kingdom [32]. In addition, nighttime light data could explain more than 90% of the variation in building energy consumption in the United States [26].
# Path Segmentation-Based Hybrid Caching in Information-Centric Networks
## 4 Performances Evaluations
### The Simulation Environment  
We used the Icarus simulator [23] to run the simulation. Icarus is a Python-based ICN cache simulator used to simulate and evaluate the caching performance of various caching strategies in the ICN network environment. It could efficiently complete simulations consisting of millions of requests in a few minutes, and it supports users to implement their designed caching scheme and simulation.
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 3 Review of [PERSON] et al.'s Protocol
### Initialization Process  
The home agent (\(HA\)) selects two prime numbers \(p,q\) and generator \(g\) of a finite field in \(Z_{p}^{*}\), of which \(Z_{p}^{*}\) is a nonsingular elliptic curve \(y^{2}=x^{3}+ax+b\) (mod \(p\)). The \(HA\) calculates \(n=p\times q\) and \(\phi(n)=(p-1)\times(q-1)\). Then, the \(HA\) chooses an integer \(e\), such that \(1<e<\phi(n)\) and \(gcd(e,\phi(n))=1\). After that, the \(HA\) computes the value of an integer \(d\), such that \(d=e^{-1}\), where \(d\) is the secret key of the \(HA\), and \(y=g^{d}\) mod \(n\), where \(y\) is the public key of the \(HA\). The \(HA\) keeps \(\{p,q,d\}\) securely.
# GNSS Real-Time Precise Point Positioning in Arctic Northeast Passage
###### Abstract  
Human activities in the Arctic regions have been increasing in recent years due to the impacts of climate change, such as Arctic Sea ice decline. For example, there has been an increase in Arctic shipping routes. A robust navigation system with a high positioning accuracy is required when traversing the extremely challenging Arctic environment to ensure the safety of human activities. However, the high-precision GNSS navigation and the positioning method, e.g., real-time kinematic (RTK), is not available in the polar regions due to the accessibility issues of the required infrastructures. On the other hand, the International GNSS Service (IGS) enables real-time applications; additionally, quick and convenient satellite communication systems are also available. This offers the possibility of real-time precise point positioning (RT-PPP) with multi-GNSS for high-precision navigation in the Arctic. In our paper, we analyzed the performance of multi-GNSS RT-PPP in the Arctic Northeast Passage (NEP), highlighting the following contributions: First, a GNSS device is installed on the M/V TIANHUL, which passed through the NEP from 10 September 2019; Second, we quantitatively evaluated the collected GNSS signals in terms of the maximum satellite elevations, number of visible satellites (NSAT), position dilution of precision (PDOP) values, signal-to-noise ratio (SNR), and multipath errors. Third, we evaluated the accuracy of the CLK93 real-time products compared with the Deutsches GeoForschungsZentrum (GFZ) final products GBM. Finally, we carried out experiments for both single- (SF) and dual-frequency (DF) RT-PPP in the NEP during the 11-day testing period. Our experimental results show that meter-level positioning accuracy can be achieved with SF RT-PPP, while the DF RT-PPP model reaches sub-decimeter values and even centimeter-level accuracy. In addition, using the multi-GNSS method, we showed that the average RMS values of DF RT-PPP in the horizontal and vertical directions are 0.080 m and 0.057 m, respectively, demonstrating an improvement of approximately 70% over single-GPS solutions.  
Article Northeast Passage; GNSS; RT-PPP; signal quality +
Footnote †: journal: Marine Science and Engineering  
0000-0001-959X  
0000-0001-959X  
0000-0001-959X
## 5 Based on the Data Analysis of a Substation Inspection
### Relationship Between Facular Area and Shooting Distance  
Considering the detection site environment, the color of the facular of the UV imager is set to red. For the red facular UV image, the image segmentation algorithm based on Gaussian mapping is used to extract the facular area. The change trends of the spot area of the three defect types are shown in Figure 17.  
Figure 16: UV image of a zero-value insulator.  
\begin{table}
\begin{tabular}{c c} \hline
**Type** & **Number** \\ \hline pollution & 2 3 4 5 6 9 10 14 15 17 18 19 28 20 21 23 25 26 36 37 \\ rough crack & 7 12 13 16 22 24 31 \\ \(1\) 8 11 27 29 30 32 33 34 35 \\ \hline \end{tabular}
\end{table}
Table 8: Defect types of leakage insulators.  
In order to better study the relationship between the type of insulator defect and the changing trend of the light plate area, this paper introduces two new characteristic parameters, namely, the range ratio \(O_{r}\) and the standard deviation ratio. The range ratio \(O_{r}\) is used to describe the ratio \(O_{s}\) of the range of facular area \(S_{r}\) to the average of facular area \(S_{m}\) over a period of time. The standard deviation ratio \(O_{s}\) is used to describe the ratio of the standard deviation of facular area \(S_{r}\) to the average facular area \(S_{m}\) over a period of time. Those two characteristics can be defined by the following expressions:  
\[\mathrm{S=ae^{bx}} \tag{10}\]  
The two now characteristic parameters can reduce the influence of environmental factors and other non-research objects on the results of the characteristic analysis of the spot area change. And it has a higher reference value to judge the type of defects in which the detection of potential defects and the discharge volume are not uniform, and it improves the robustness and reliability of statistical data analysis. Figure 18 shows the average range ratio \(O_{r}\) and standard deviation ratio \(O_{s}\) of No. 1-37 insulators. Table 9 shows the statistical data of the typical sample facular area of the three types of faulty leakage insulator UV videos. The following conclusions can be drawn from the statistics of the data obtained from this inspection. The facular change range of the surface roughness insulators and the shed crack insulator is relatively small. The range and standard deviation of the facular area caused by the pollution discharge are obviously larger than the other three types of defect insulator discharge.
## 4 Discussion
The results suggest that the five QTLs for NGP detected in Ti52-2 is a result of the integration of NSP and SF. Usually, NGP is determined by NSP multiply SF. Thus, these QTL regions sharing for both NGP and NSP or SF might be due to a pleiotropic effect of one QTL rather than the close linkage of different QTLs. The eight QTLs with large effects detected in three RIL populations were located in six regions on chromosome 1, 2, 3, 5 and 6. Except for _qSF5.2_, all QTLs controlled NGP and/or NSP. The region of RG532\(-\)RM151 on chromosome 1 containing _qNGP1.1_ and _qNSP1_ showed the largest R\({}^{2}\) for NGP and NSP in both of ZM and XM, while the interval of RM6\(-\)RM240 on chromosome 2 covering _qNGP2.2_ and _qNSP2.2_ showed the largest \(R^{2}\) for NGP and NSP in TI. The special significance demonstrated in the given population might reveal the different mechanism of variant ecological type of rice varieties. _Gn1a_, the first cloned QTL for grain number in rice, is located in the former region [18], whereas no QTL for grain number was found to be fine-mapped or cloned in the latter interval. By comparing the physical position, we found a heading date QTL, _DTH2_, situated within this interval [52]. Anyway, no QTL controlling heading date was detected around this region in previous studies [42], which indicated that _DTH2_ could not the candidate gene of _qNGP2.2_ and _qNSP2.2_. In addition, we found that _GS2_, a major QTL for grain size was lain 0.7 Mb upstream of RM6. [PERSON] et al. reported that no significant differences of NGP were found between the recurrent parents and NILs [53], suggesting it would not probably be the pleiotropic effect of _GS2_. Hence, this locus might be a new QTL controlling the grain number. Fine-mapping of _qNGP2.2_ is to be undertaken. The region of RM15303\(-\)RM16 on chromosome 3 was another one for both of NGP and NSP identified in TI. However, it presented the second largest R\({}^{2}\) for NGP, but much smaller effect on NSP. _GL3.1_, a cloned QTL for grain length, was positioned 1.9 cM downstream of RM16 and was reported to have a significant effect on NGP [54]. More work is needed to confirm whether this locus is a new QTL for grain number or just a pleiotropic effect of _GL3.1_. The region of RZ398\(-\)RM217 on chromosome 6 exhibited the second largest R\({}^{2}\) for NGP but no significant effect on NSP in ZM. It also displayed minor effect on NGP and NSP in XM. No QTL controlling grain number had been cloned or fine-mapped in this interval. In the other hand, the region of RM253\(-\)RM276 downstream of RZ398\(-\)RM217 exhibited the second largest R\({}^{2}\) for NSP but no significant effect on NGP in ZM. It was noteworthy that it also displayed the second largest R\({}^{2}\) for NSP and minor effect on NGP in TI. It was previously reported that a QTL controlled NGP and yield-related traits in the same region [55]. Hence, these QTLs provided new candidates for gene cloning and marker-assisted breeding.
## 3 Results and Discussion
### Wettability  
Figures 5 and 6 illustrate the effects of incorporating AgNPs into the electrospun textiles. Contact angle measurements were conducted for all hydrophobic and hydrophilic textiles. The wettability behaviors of the pure PLA fabric with AgNPs added during the pre-and post-electrospinning stages (M1 and M3) are shown in Figure 5. M1 exhibited a stable water droplet with a CA of 128\({}^{\circ}\) after 3 min, demonstrating PLA's hydrophobic nature. By contrast, M3 exhibited a reduced CA of 92\({}^{\circ}\) for the same duration. This decrease in hydrophobicity may be attributed to the direct interaction between the silver colloidal solution and the PLA membrane surface. During the solution casting of colloidal silver, the solution contained low-surface-energy liquids, such as isopropanol, which infiltrated the pure PLA surface and diminished its wettability. Figure 6, however, demonstrates the hydrophilic characteristics of samples M2 and M4, where the surface CA of the water droplet rapidly reached approximately 0\({}^{\circ}\), as the membrane surface completely absorbed the water within 2 s. This phenomenon occurred because the addition of PEG and AgNPs to PLA created a hydrophilic environment that rapidly attracted water molecules [33]. This enhanced hydrophilicity contributes to protecting textiles against bacterial activity, as water can penetrate the fiber matrix and interact with antibacterial agents, such as silver.  
Figure 5: Contact angles of PLA-based textiles: (**M1**) PLA-Ag-E and (**M3**) PLA-Ag-S, initially and after 300 s.  
Figure 6: Contact angles of PLA-based textiles: (**M2**) PLA-PEG-AG-E and (**M4**) PLA-PEG-AG-S, initially and after 5 s.
# A Secure and Efficient Multi-Factor Authentication Algorithm for Mobile Money Applications
## 6 Performance Analysis  
The proposed algorithm contains three main phases, namely, enrolment, authentication, and transaction. These phases are taken into consideration for performance comparison purposes. The performance of the proposed algorithm is evaluated by analyzing the communication overhead and computational cost with existing related algorithms, which helps to understand the efficiency of the proposed algorithm.
## 3 Results and Discussion
### Effect of Maturity on the Macroelement and Microelement Contents of Yellow-Fleshed Cassava Roots  
Figure 1a,b shows the concentrations of macrominerals in yellow-fleshed cassava roots by maturity (harvesting time) for both trials 1 and 2. For trial set 1, the study showed that there was a significant difference (\(p<0.05\)) in the mean values for the Ca and P contents in cassava roots harvested at 9 and 12 months, respectively. However, the concentrations of Ca and P were higher in the cassava roots that were harvested at the age of 9 months than those at 12 months. Thus, harvesting time had a strong influence on the Ca and P content of cassava roots. However, there was no significant difference (\(p>0.05\)) in the concentrations of Mg, Na, K, and S of cassava genotypes harvested at 9 and 12 months. For trial 2, the study showed that there was a significant difference (\(p<0.05\)) between the mean concentrations of Ca, Na, K, S, and P for cassava roots harvested at 9 and 12 months. However, the Mg concentration showed no difference in the mean value for cassava roots harvested at both maturity stages. The concentration of Ca, Na, K, and P were higher in the cassava roots that were sampled at 9 months than those at 12 months except for S, which was higher in the samples that matured at 12 months. It implies that harvesting time plays a major role in the concentration level of some macroelements in cassava roots while showing no effect on some. Additionally, it was established that harvesting time showed different effects on the two trials investigated. Thus, genotype plays a key role in the degree of the effects the harvesting time had on the macroelement concentrations of cassava roots. [PERSON] (1963) evaluated the macroelements of cassava sample materials that were
## 4 Result Analysis
### Hypothesis Testing
#### 4.4.3 Third Hypothesis  
The third null hypothesis states that:  
H30: \(\mu\) (Number of users involved in the SNS-based RE process) = \(\mu\) (Number of users involved in traditional RE process)  
To test this hypothesis, we refer to Figure 8 for data collection.  
\(\mu\) (Number of users involved in SNS-based RE process) = 8373  
\(\mu\) (Number of users involved in traditional RE process) = 172  
Therefore, \(\mu\) (Number of users involved in the SNS-based RE process) \(>\mu\) (Number of users involved in the traditional RE process). Hence, we reject the null hypothesis H30 and accept the alternate hypothesis H3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**t**} & \multirow{2}{*}{**df**} & \multirow{2}{*}{**Sig.(2-tailed)**} & \multirow{2}{*}{**Mean Difference**} & \multirow{2}{*}{**Std. Error Difference**} & \multicolumn{2}{c}{**95 \% Confidence Interval of**} \\  & & & & & **the Difference** \\ \hline \multirow{2}{*}{\(-\)4.310} & \multirow{2}{*}{16} & \multirow{2}{*}{0.001} & \multirow{2}{*}{\(-\)27.44444} & \multirow{2}{*}{6.36784} & \multirow{2}{*}{\(-\)40.94366} & \multirow{2}{*}{\(-\)13.94523} \\ \hline \hline \end{tabular}
\end{table}
Table 12: T-test for Efficiency in determining VR and CR.
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 7 Kaolin in Petrochemical Catalysis: From Fundamentals to Innovations
### Kaolin-Driven Catalysis in Petrochemical Processes
#### 7.4.7 Alkylation
The study demonstrated that the BXE ALKCAT zeolite is an effective catalyst for ethylbenzene production, highlighting the role of kaolinite in enhancing catalytic performance. In 2018, [PERSON] et al. [263] examined the influence of various binders, including kaolin, on the properties of nanocrystalline zeolite beta-based catalysts for benzene alkylation with propylene. They found that the use of kaolin-aluminum hydroxide mixtures as binders increased the number of acid sites without blocking the zeolite's pore structure. The K-1 sample, with aluminum hydroxide binder, exhibited the best catalytic performance, achieving a cumene selectivity of 89.7% at 100% propylene conversion. This study underscored the significance of binder selection in optimizing catalyst performance. More recently, [PERSON] et al. [264] explored the effect of different binders on MWW zeolite catalysts for benzene alkylation with propylene. The study found that aluminum hydroxide as a binder precursor provided the best results, enhancing the yield of cumene without adversely affecting the catalyst's acidic properties. While the addition of kaolin did not impact the strength characteristics, it reduced the number of acid sites, thereby affecting the catalytic performance. This research highlighted the delicate balance between binder selection and catalytic activity in alkylation processes. These investigations collectively underscore the importance of catalyst development in alkylation processes, with a particular focus on optimizing performance through the use of various zeolite structures and binders based on kaolin-based clays. This ongoing research is vital for advancing sustainable and efficient alkylation processes in the petrochemical industry.