esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### The Colorimetric Detection of ITEC to Al\({}^{3+}\) by Naked Eyes  
The observing method by the naked eyes is the convenient and inexpensive way of detecting metal ions. ITEC could successfully detect Al\({}^{3+}\) by the naked eyes. In Figure 1a, the obviously blue fluorescence was found in the presence of Al\({}^{3+}\) at the excitation wavelength of 365 nm when ITEC (20 umol/L) solutions in ethanol were added various metal ions (40 umol/L) including Co\({}^{2+}\), Cu\({}^{2+}\), Pb\({}^{2+}\), Cd\({}^{2+}\), Ag\({}^{+}\), Zn\({}^{2+}\) Al\({}^{3+}\), Hg\({}^{2+}\), Ca\({}^{2+}\), Na\({}^{+}\), Fe\({}^{3+}\), and Ni\({}^{2+}\). Moreover, the fluorescence intensities of ITEC solutions were gradually strengthened with the increasing concentrations of Al\({}^{3+}\) in the range from 1 \(\times\) 10\({}^{-6}\) mol/L to 4 \(\times\) 10\({}^{-5}\) mol/L (Figure 1b). The limiting concentration (7.41 umol/L) of Al\({}^{3+}\) is just in the range according to WHO regulations on drinking water, which indicates that it is possible to determine the level of Al\({}^{3+}\) by naked eye observation [34].  
Figure 1: (a) Color changes of 3,6-imine-triphenylamine-(9-ethyl) carbazole (ITEC) with various cations under 365 nm UV-light in ethyl alcohol. [ITEC] = 20 μm, [cattions] = 40 μm. (b) Color changes of ITEC with different Al\({}^{3+}\) concentrations (0 μm, 5 μm, 10 μm, 15 μm, 20 μm, 25 μm, 40 μm) under 365 nm UV-light, [ITEC] = 20 μm.
## 3 Results and Discussion
### Histograms of Pore Distribution  
The histograms of pore distribution represent, for each volume range, the number of pores whose volume is within that range. This information can be displayed in absolute values, i.e., the number of pores belonging to each volume range is shown on the ordinate axis, or in relative terms, representing the percentage of pores with respect to the total that belong to each volume range. This second option is more interesting when comparing patterns between different specimens.  
In general, the highest percentage of pores corresponds to very small volumes; therefore, it is usually interesting to represent nested histograms, i.e., on a histogram representing 100% of the pores, histograms are successively generated in which certain ranges of volumes (generally the smallest ones) are observed in greater detail.  
The results of the specimens considered in this work are shown below (Figure 19).  
The results show that almost 100% of the pores have a volume of less than 1 mm\({}^{3}\). If we analyze the data in more detail, we can notice that more than half of the pores have a size of less than 0.01 mm\({}^{3}\), and between 15% and 20% of all pores are smaller than 0.001 mm\({}^{3}\).  
This reveals that it is highly interesting for CT-scan technology to advance towards better resolutions. In this way it will be possible to analyze smaller and smaller pores, which are precisely the most numerous and presumably have the greatest influence on the macroscopic behavior of concrete.
## 4 Evaluation of Stress Integration Algorithms for the e-HAH Model  
In this section, the numerical accuracy and stability of the proposed stress integration algorithms are analyzed for different case problems. The algorithms described in the previous sections were implemented into the static implicit FE ABAQUS/Standard software using the UMAT user material subroutine. For the comparative study, the four algorithms presented in Section 3.1 were implemented into the FE model. Note that the EBM-14R approach represents the fully implicit Euler backward algorithm implemented into the e-HAH model for the first time.  
Two levels of the evaluation procedure are presented in the following sections. The first level consists of a very detailed fundamental analysis on the accuracy of the different algorithms for the investigated e-HAH model. For this, the anisotropic characteristics of the flow stress and r-value are predicted and evaluated under two different loading paths. Only a single element is used in this level to rule out other numerical effects. To clarify the effect of the anisotropic hardening responses of the e-HAH model under different loading paths, different model materials were selectively compared. The investigated model materials exhibit high or low evolution rates under reverse loading and hardening or softening under cross-loading. To represent the initial anisotropy of the material, the non-quadratic anisotropic yield function, YId2000-2d [62; 63], was employed. For the summary of the initial (undistorted) yield function, YId2000-2d, the reader is referred to in Appendix C.  
The second level comprises a real-scale simulation based on the S-rail forming and springback process [45], which was proposed as a benchmark problem and often utilized for the analysis of the constitutive model and numerical algorithm in the sheet metal forming community. The reason for choosing a benchmark is that most of the existing FE simulations are based on hybrid explicit and implicit algorithms. In other words, the forming process is solved via the dynamic explicit FE model as a quasi-static problem, whereas the springback is calculated using the static implicit algorithm. The first is applied to avoid divergence issues typically encountered in contact problems between complex tools and sheet metal, whereas the implicit algorithm is the optimal approach for the springback unloading process to reduce the computational time. In this study, all the stress integration algorithms were implemented into the static implicit FE solver, and the numerical performance of each algorithm was comparatively studied to assess their applicability to industrial-size problems. Two materials, namely stainless steel (STS) and dual-phase steel (DP), were investigated since they have both been used for real automotive parts and exhibit distinctive anisotropic hardening behaviors under loading path changes.
## 3 Conventional Geolocation Techniques
### Tri-Combination Technique of Geolocation Measurements  
More than two conventional positioning measurements can be combined to achieve a more precise estimate of the target's location. There are various applications; each one uses a selected scheme, which depends on the geolocation environment and type of application needed. Demonstrations of a tri-combination of geolocation measurement can be found in several applications, such as TDOA/FDOA/DFS [74], TDOA, FDOA, and differential Doppler rate [75], and TDOA, FDOA and Doppler Rate [76].  
Figure 10: Pair of moving sensors were considered to geolocate a static RFI emitter using the CAF method. The simulation result achieved the peak of CAF that denotes an estimated RFI emitter position. Authors’ own elaboration.  
The improvement in the emitter geolocation method using the Tri-combination of geolocation measurements was the authors' own elaboration, and they compare it to a hybrid measurement, which was achieved in [21]. Figure 11 illustrates the CRLB verification for combining TDOA/FDOA/DDR and comparing it with the hybrid TDOA/FDOA as well as TDOA and FDOA individually.  
Table 2 shows the review of a comparison between different types of measurements used for conventional geolocation.  
Figure 11: CRLB comparison between TDOA, FDOA, hybrid (TDOA/FDOA), and Tri-combination of (TDOA/FDOA/DDR). In this figure, the same scenario and input parameter has been implemented in Figure 9. Additionally, TDOA/FDOA/DDR was computed and its performance compared. Authors’ own elaboration.
# Effect of Methylammonium Iodide (MACI) on MAPbI\({}_{3}\)-Based Perovskite UV-C Photodetectors
## 1 Introduction
In this man-ner, MAPbX\({}_{3}\) allows smooth band gap tuning by varying the material composition. This feature suggests that hybrid perovskites can be applied as light-absorbing layers in vari-ous fields and can enhance the device efficiency. Therefore, research is ongoing to enhance stability by utilizing combinations of monovalent cations like MA\({}^{+}\), FA\({}^{+}\), and CS\({}^{+}\) or by incorporating halides such as Cl\({}^{-}\) and, Br\({}^{-}\) to improve the performance of optical components [24; 25]. In this work, to improve the detection performance of the perovskite based UV-C photo-detector (PD), anions were mixed with MAPbI\({}_{3}\)--based hybrid perovskite film and played a role as a light absorption layer. In a fabrication process (2-step), a precursor solution was prepared by adding 0.10 M MACI to MAPbI\({}_{3}\), and FABr was added as a post-treatment solution. MACI-doped perovskites have flexible bandgaps and exhibit improved crystal-linity. MACI-doped MAPbI\({}_{3}\) thin films have high surface coverage, forming large perov-skite particles, which promotes electron transfer and improves device performance. Cl atoms have a smaller atomic radius compared to I and higher electronegativity of I (2.66) and Cl (3.16), which can improve phase stability, the effect of adding Cl\({}^{-}\) increases the electron mobility of perovskite, resulting in faster reaction. It looks like attempts to improve PD performance by post-treating the CH\({}_{3}\)NH\({}_{3}\)PbX\({}_{3}\) precursor solution with anions have been reported in previous studies. This study presents a new direction by adding anions to the precursor solution. As a result, the fabricated perovskite-based PDs showed more sensitivity and higher detection performance for UV-C than the previously studied FABr-posttreated MAPbI3 precursor solution.
## 7 Results and Recommendations
\begin{table}
\begin{tabular}{l|l l} \hline
**Criteria** & **Results** & **Recommendations** \\ \cline{2-3}
**Topicality** & & \\  & & \(\bullet\) Content is up to date. & \(\bullet\) Watch out for new security breaches. \\  & & \(\bullet\) Watch for changes in IETF standards. \\ \hline
**Completeness** & & \\  & & \(\bullet\) Focuses on techniques used in practice. & \(\bullet\) Increase coverage of IPsec and \\  & & \(\bullet\) Focuses on main specifications. & Cryptographic Methods. \\  & & \(\bullet\) Low overall completeness (0.31). & \(\bullet\) Increase coverage of DHCPv6. \\  & & \(\bullet\) Low weighted completeness (0.32). & \(\bullet\) Increase coverage of DNS security \\  & & \(\bullet\) Low coverage of IPsec, Mobile IPv6, DHCPv6, DNS security issues, Multihoming and Translation. & \(\bullet\) Increase coverage of Dual Stack \\  & & \(\bullet\) No coverage of Cryptographic Methods, IKEv2 and DNSSEC. & \(\bullet\) Watch out for new relevant RFCs. \\ \hline
**Depth** & & \\  & & \(\bullet\) High depth score for main specifications. & \(\bullet\) Generally increase depth by giving practical examples. \\  & & \(\bullet\) Very low overall depth (0.17). & \(\bullet\) Increase depth for IPv6 Specification. \\  & & \(\bullet\) Medium to low depth of covered & \(\bullet\) Increase depth for IPsec. \\  & & \(\bullet\) Increase depth for DHCPv6. \\  & & \(\bullet\) Low content depths due to low completeness. & \(\bullet\) Increase depth for DNS security issues, Multihoming and Routing Protocols. \\  & & \(\bullet\) Increase depth for Dual Stack \\  & & \(\bullet\) Increase depth for Dual Stack and Tunneling. \\ \hline \end{tabular}
\end{table} 
Table 11: BSI—Results and recommendations. The evaluation has shown that the two guides by BSI and NIST differ a lot. While the NIST guide focuses on complete coverage of all relevant topics and sticks close to the IETF standards, it is sometimes overloaded with details and falls short on practical implementation examples. Nonetheless, it provides a very good introduction and explains possible security issues of most of the relevant topics. Practitioners looking for a detailed guide and who do not have much knowledge about IPv6 should choose this guide as a preparation for a real deployment. They can be sure that almost all relevant information is covered or at least referenced. As actual deployment scenarios differ, the guide does not distinguish between scenarios, and practitioners have to decide themselves which parts of the guide are relevant for the individual case. The BSI guide tries to cover only the most relevant topics for an actual deployment and thus leaves out many other issues. The level of detail for covered topics is good, while other relevant information gets neither mentioned, nor referenced in any way. The guide is particularly useful for practitioners with good experience with IPv4 and at least some with IPv6. The main features are the different infrastructure
# An Extended Car-Following Model Considering Generalized Preceding Vehicles in V2X Environment
## 5 Numerical Simulation
### Simulation of Starting Process  
To simulate the car-following behavior of vehicles in the starting process at the intersection when the traffic light turns from red to green in a realistic traffic system, the simulation scenario about vehicle starting process is set as the following: At an intersection with a traffic light, 10 identical vehicles stop and wait in every single of three lanes with the same headway of 10 m between any two consecutive vehicles, and all vehicles are about to start when the traffic light turns from red to green and move in the same direction. The vehicles in the middle of all three lanes are selected as object vehicles and marked as 1 to 10 according to the distance to the intersection from near to far. Considering that GPV is introduced in our model, the first object vehicle of the fleet is following its GPV in the scenario. At the beginning of the simulation, the traffic light turns green, and the vehicles start in sequence. The velocity limit of all object vehicles is set as 5 m/s, and the termination condition of this simulation is set as all object vehicles reach the velocity limit. The velocity and acceleration of all object vehicles are studied, as shown in Figures 6 and 7.  
Figure 6 illustrates the simulated velocity of the two models. As indicated in Figure 5(a), it takes 19 s for all object vehicles to reach the preset velocity (5 m/s) in the simulation with the GPV model. By comparison, it takes 21 s to reach the same state with the FVD model, as shown in Figure 5(b). (The lines with different color in Figure 6 as well as Figures 7-9 respectively represents the object vehicles in the scenario.)  
Figure 6: Comparison of velocity during the starting process between the two models.  
Figure 7 shows simulated acceleration with the two models. One can see that acceleration and accelerating time of the GPV model are less than those of the FVD model during the starting process. As shown in Figure 7a, the maximum acceleration is 1.6 m/s\({}^{2}\), and the acceleration process lasts 19 s with the GPV model. In contrast, the maximum acceleration is 1.8 m/s\({}^{2}\), and it cost an extra 3 s (total 21 s) to complete the acceleration process with the FVD model, as shown in Figure 7b.  
Figure 8: Comparison of velocity during the braking process between the two models.  
Figure 7: Comparison of acceleration during the starting process between the two models.  
Figure 9: Comparison of deceleration during the braking process between the two models.
## 4 Discussion
### The Spatiotemporal Characteristics of the Impact of Environmental Factors on GPP in Arid Regions
Additionally, under sunny, cloudy, and overcast conditions, the correlation coefficients between GPP and various environmental factors in grassland areas are consistently higher than those in other ecosystems. In contrast, in farmland and desert vegetation areas, the correlation coefficients between GPP and various environmental factors fluctuate to varying degrees with weather changes. Desert vegetation areas have the highest fluctuations, indicating that GPP in desert vegetation areas is most affected by weather conditions, followed by farmland areas, and least affected in grassland areas. This conclusion can be gradually confirmed by the relevant research of the scholars mentioned above. Research on the relationship between GPP and environmental factors at the spatial scale in arid regions also shows a strong positive correlation and significant regional characteristics. The high correlation between GPP and PAR, VPD, Tair, and Tosil in the middle of the TM and the Altai Mountains indicates strong spatial attributes. The low value areas of correlation are distributed in areas with sparse vegetation and high evapotranspiration, such as the Gurbantunggput Desert in the Junggar Basin, the Gobi Desert in eastern Xinjiang, and the southern TM. The low-value distribution between GPP and SWC is similar to the spatiotemporal distribution characteristics between GPP and other environmental factors. High values are mainly distributed in farmland areas comprehensively affected by artificial irrigation on the southern and northern slopes of the TM. This indicates that GPP in arid regions is most influenced by environmental factors in areas with high vegetation cover and intense sun radiation periods (summer and spring). This is basically in line with the comprehensive research results of scholars on the interaction between GPP and environmental factors under various weather conditions [62; 65; 68; 69]. PAR has the most significant impact on GPP in arid regions, followed by Tair, Tsoil, and VPD, with SWC having the least impact. This conclusion is consistent with [PERSON] et al. 's [70] study on the spatial-temporal impact mechanisms of GPP in various ecosystems in China using machine learning methods, and with [PERSON] et al. 's [71] study on the comprehensive impact mechanisms of photosynthesis in various ecosystems in China using different models. In addition, there are differences in the expression patterns of the comprehensive impact of various environmental factors on GPP among the three ecosystems in arid regions. Overall, in farmland areas, the comprehensive impact of five environmental factors on GPP can be expressed based on a linear pattern. In grassland and desert vegetation areas, the comprehensive impact of VPD on GPP can be expressed based on a linear pattern, while the comprehensive impact of the standard pole number, Tair, Tsoil, and SWC is based on a non-linear pattern. This is determined by the complex environment of desert and grassland regions [27, 61, 62, 65].
###### Abstract  
The excessive use of pesticides and fertilizers during agricultural production causes water pollution, which is an important type of non-point source pollution (NSP). Large amounts of harmful substances, such as nitrogen and phosphorus, flow into surface water along with farmland runoff, leading to eutrophication and other problems. However, the pollutant discharge capacity of different types of cultivated land varies greatly. Areas sensitive to NSP are areas with rich crop types, large spatial differences in crop growth, and complex planting patterns. These factors can cause different amounts of fertilizer used in and absorbed by the crops to influence the emission intensity of pollutants. NSP intensity mapping can reflect the spatial distribution of lands' pollutant discharge capacity and it can provide a basis for pollution control. However, when estimating NSP intensity, existing methods generally treat cultivated land as a category and ignore how complex crop conditions impact pollution intensity. Remote sensing technology enables the classification and monitoring of ground objects, which can provide rich geographical data for NSP intensity mapping. In this study, we used a phenology-GPP (gross primary productivity) method to extract the spatial distribution of crops in the Yuceheng reservoir catchment area from Sentinel-2 remote sensing images and the overall accuracy reached 85%. Moderate resolution imaging spectroradiometer (MODIS) GPP data were used to simulate the spatial distribution of crop growth. Finally, a new model that is more suitable for farmland was obtained by combining this large amount of remote sensing data with existing mapping models. The findings from this study highlight the differences in spatial distributions between total nitrogen and total phosphorous; they also provide the means to improve NSP intensity estimations.  
remote sensing mapping; multiple cropping extraction; Sentinel-2; decision classification; DPeRS model; time series Article
## 3 Results and Discussion
### The Effect of Airflow Rate on Biochar Surface Properties
The carbonization temperature caused variations in the CEC of the biochar, which decreased from 13.1 to 8.9 cmole kg\({}^{-1}\) as the temperature increased from 350 to 600 \({}^{\circ}\)C. The increase in CEC in biochar from top-lit updraft gasification can also be attributed to the degree of oxidation due to the partial oxidation of the biomass. For instance, [PERSON] [35] stated that biochar could have higher CEC due to long-term natural oxidation. Biochar extracts from the Amazonian have been exposed to oxidation for centuries; as a result, they present high CEC that can range from 0.79 to 21.3 cmol kg\({}^{-1}\)[36]. The use of biomass gasification for biochar production can represent more extreme oxidation of biochar that is due to thermochemical reactions. On the contrary, natural oxidation is the result of exposure to biotic and abiotic interactions [12]. In addition, the AEC of the biochar was found to significantly decrease from 1.38 to 1.01 cmol kg\({}^{-1}\) when the airflow increased from 8 to 16 L/min, whereas it significantly increased from 1.01 to 1.59 cmol kg\({}^{-1}\) when the airflow was further increased from 16 to 20 L/min. The increase in the AEC might be due to the increasing degree of oxidation presented by the H/C ratio reduction from 0.29 to 0.27 when the airflow increased from 16 to 20 L/min. The CEC was positively correlated with the AEC (R\({}^{2}\) = 0.58). A similar correlation of the AEC and CEC was reported in a previous work where biochar was produced from oak wood to test the physiochemical effects of aging on biochar [15]. The results indicated that increasing the pyrolysis temperature from 250 to 650 \({}^{\circ}\)C caused decrease in the AEC from 4.9 to 4.5 cmol kg\({}^{-1}\) and reduced the CEC from 39.9 to 10.2 cmol kg\({}^{-1}\). Based on limited data available in Table 1, there existed a strong positive linear correlation between CEC and basic functional group (Functional group = 31.083 CEC + 2.5018, R\({}^{2}\) = 0.9215), but no correlation was found between AEC and functional group. Because pH was not significantly different among all airflows, the correlation was not considered pH dependent. To compare with other techniques, Table 2 summarizes the properties of biochar from different raw materials produced via pyrolysis and TLUD gasification. It is apparent that the TLUD gasification with low reaction temperatures (small airflow) resulted in biochars of smaller BET surface area and less surface charges than those of pyrolysis biochars. However, at high airflow (20 L/min), the BET surface area of TLUD biochar was much greater than pyrolysis biochars, although surface charges were still smaller. Regardless of airflow, TLUD gasification biochar from the present study was generally more basic (with higher pH) than pyrolysis biochars. H/C and O/C ratios of the biochars were similar based on the available data.