esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Examples of UV Aging Prevention of Asphalt
### Results Observation  
In the first two years after the trial section of the project was completed and opened to traffic, there were no obvious diseases. In the third year of opening to traffic, there were relatively more transverse cracks and a few pits in trial road 2. The maintenance department repaired the cracks randomly. In addition, the loose disease appeared in all three trial sections, but trial section 2 was the most serious. The disease situation for each trial section is shown in Table 8, and the representative disease photos are shown in Figure 14.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indexes** & **SBS I-C** & **SBS I-C with Anti-UV Aging Agent** \\ \hline Penetration, 0.1 mm & 68.2 & 69.6 \\ softening point, \({}^{\circ}\)C & 60.9 & 59.9 \\ ductility at 5 \({}^{\circ}\)C, cm & 40.1 & 40.7 \\ \hline Penetration loss rate & 157.5 W/m\({}^{2}\), 110 h & 30.8 & — \\ after UV aging, \% & 157.5 W/m\({}^{2}\), 129 h & — & 30.5 \\ \hline Ductility loss rate at 5 \({}^{\circ}\)C & 157.5 W/m\({}^{2}\), 110 h & 42.9 & — \\ after UV aging, \% & 157.5 W/m\({}^{2}\), 129 h & — & 43.1 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The technical indexes of asphalt used in the trial sections.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Trial Sections** & **UV Zones** & **Project Overview** & **Observation Time** & **Disease Situation** & **PCI 1** \\ \hline Trial 1 & I & & & Almost no disease, \\  & & & & light loose disease & 94 \\ \hline Trial 2 & II & & & The beginning of the \\  & & & & third year \\  & & & the same & \\ \hline Trial 3 & III & & & Almost no disease, \\  & & & & light loose disease & 94 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The disease situation of each trial section.  
According to the investigation results of the trial road, the disease degree of trial road 2 was the highest, and the Pavement Surface Condition Index (PCI) of trial road 2 in the third year was lower than that of trial road 3 in the UV zone II, and also lower than that of the trial road 1, which was in the UV radiation Zone I. However, trial section 3, with the anti-UV aging agent, and trial section 1, without the anti-UV aging agent, showed similar pavement performance. This shows that the equivalent performance of asphalt under different UV climate zones can be achieved through the previously discussed design.  
Of course, limited by the number of trial roads, this conclusion still needs to be supported by a large number of engineering data.
# Hydrogen from Depleted/Depleting Hydrocarbon Reservoirs: A Reservoir Engineering Perspective
## 3 Microbial H\({}_{2}\)-Bio-H\({}_{2}\) Generation in DORs
### Reservoir Engineering Aspects
#### 3.2.3 Associated Risks  
An in situ bio-H\({}_{2}\) operation can be impacted by various risks that can violate both technical and economic criteria. The risks that can be relevant to the application of MEOR are of primary importance for potential field applications of bio-H\({}_{2}\)[49]. The presence of sulfate-reducing bacteria (SRB) can lead to bio-souring; H\({}_{2}\)S production is one of the main show-stoppers in all oil and gas operations, making it a particularly concerning risk [50].  
The inhibitory factors on DF are discussed in [34]. This paper provides an in-depth and critical review of various factors that can impact the DF process. These include H\({}_{2}\) consumers and lactic acid bacteria in mixed microflora, as well as slight and heavy metal ions and furan derivatives that can negatively influence H\({}_{2}\) production. These factors can create operational risks that need to be considered when implementing the process. The thermodynamic conditions, namely pressure and temperature, also influence the effectiveness of the DF. It is indicated that higher pressure reduces the production of H\({}_{2}\) whereas the reservoir temperature should be less than the maximum temperature at which the fermentative bacteria can survive [12]. Note that the costs of associated risks for the mitigation and prevention of H\({}_{2}\)S souring and fouling, as well as the separation, transport, and elimination of produced CO\({}_{2}\), were not included in the preliminary cost estimation provided above.  
The key questions in engineering bio-H\({}_{2}\) in DORs are how and why microbial in situ H\({}_{2}\) generation could be competitive with DF bioreactors at surface plants. When considering operational costs alone and based on theoretical and laboratory yields, the estimated cost of bio-H\({}_{2}\) production at a commercial scale is higher than that of any other H\({}_{2}\) production process. Additionally, there is a potential conflict of interest in using high-quality carbohydrates such as glucose and sucrose for H\({}_{2}\) generation instead of using them as food for humans and livestock.
# Dynamic Stability of Orthotropic Viscoelastic Rectangular Plate of an Arbitrarily Varying Thickness
## 1 Introduction
There is a lack of suitable universal numerical methods and unified computational algorithms for solving the obtained equations. It is necessary to carry out research in a geometrically nonlinear formulation and consider the material's viscoelastic properties to obtain a performance of the stress-strain state of thin-walled structural elements of variable thickness. The research object is an orthotropic viscoelastic plate with an arbitrarily varying thickness subjected to a periodic compressive force in the plate's plane. The research subjects were the plate's deformations, the vibration modes of the plate, the vibration amplitudes, and the change in vibration over time depending on the thickness (geometry) of the plate, the ratio of the frequencies of the periodic force, the natural frequencies of the plate, and the viscoelastic properties of the plate material. This research aimed to develop the theory of the orthotropic plate's dynamic stability, considering the plate's materials' viscoelastic properties. The research objectives are as follows:  
* Development of a mathematical model of the dynamic behavior of plates of variable thickness;
* Development of a method for numerical solutions of nondecaying systems of nonlinear integrodifferential equations with weakly singular kernels of an orthotropic viscoelastic plate of variable thickness under the influence of external periodic loads;
* Clarification of the orthotropic viscoelastic plate's dynamic stability with varied mechanical and geometric parameters of the plate.
## 2 Modal-Based Ground Motion Selection Procedure for RC Shear Wall Structures  
### Original Modal-Based Ground Motion Selection Procedure  
The structural dynamics theory is firstly adopted to demonstrate the effect of IGMs on the peak modal responses and modal response combinations in the time domain. The equation of motion of a multi-degree-of-freedom system under the IGM is expressed as [29]  
\[\mathbf{m}\ddot{\mathbf{u}}+\mathbf{c}\dot{\mathbf{u}}+\mathbf{k}\mathbf{u}=-\mathbf{m}\ddot{\mathbf{u}}_{g}(t) \tag{1}\]  
where \(\mathbf{u}\) is story displacements vector, \(\mathbf{k}\), \(\mathbf{c}\) and \(\mathbf{m}\) stand for the stiffness, damping and mass matrices, respectively, and \(\mathbf{i}\) is the influence vector. \(\ddot{\mathbf{u}}_{g}(t)\) is the acceleration history of the input ground motion. As the loading history does not affect the independence of the lateral force, the solution of Equation (1) can be expressed as:  
\[u(t)=\sum_{n=1}^{N}u_{n}(t)=\sum_{n=1}^{N}\Gamma_{n}\mathbf{\Phi}_{n}D_{n}(t)=\sum _{n=1}^{N}\Gamma_{n}\mathbf{\Phi}_{n}D_{n}a_{n}(t) \tag{2}\]  
where  
\[\Gamma_{n}=\frac{L_{n}}{M_{n}},\ L_{n}=\mathbf{\[PERSON]}_{n}^{\mathsf{T}}\mathbf{m}\ \text{and}\ M_{n}=\mathbf{\Phi}_{n}^{\mathsf{T}}\mathbf{m}\mathbf{\[PERSON]}_{n} \tag{3}\]  
and \(u_{n}(t)\) is the displacement history of nth mode; \(\mathbf{\[PERSON]}_{n}\) is the mode shape of \(n\)th vibration mode, and \(N\) is the total number of mode; \(\Gamma_{n}\) is the modal participating factor; \(M_{n}\) is the generalised mass of mode \(n\); \(L_{n}\) is the modal excitation factor of mode \(n\); \(D_{n}(t)\) is the top displacement time history the ESDOF of \(n\)th mode; \(D_{n}\) is the peak displacement of the ESDOF of nth mode; \(a_{n}(t)=\frac{D_{n}(t)}{D_{n}}\) is the normalized top displacement time history of nth mode.  
It is clear from Equation (2) that the seismic demand of tall buildings is determined by both peak modal response \(D_{n}\), and modal response combination in the time domain \(a_{n}(t)\). The \(D_{n}\) and \(a_{n}(t)\) are of the same importance when the higher-mode effect is significant. Spectrum matching-based IGM selecting and scaling procedures only ensure that the value of \(D_{n}\) fulfils specific requirements but ignores the impact of \(a_{n}(t)\) on the structural seismic demand, resulting in significant variations in the computed seismic demand [20,21,24].  
Driven by the need to take the effect of FCCTD of IGMs on the structural seismic demand into account in the selection of IGMs and improve the reliability of NLRHA, the modal-based ground motion selection (MGMS) procedure [30] was developed. The MGMS procedure selects seven IGMs that lead to the most severe interaction of the first three modes from a group of twenty seed IGMs, primarily obtained through the common two-step IGMs selection method. A previous study has shown that with reasonable consideration of the effect of FCCTD of IGMs, the MGMS procedure can select more reasonable IGMs for the NLRHA of frame structures.
## 5 Results
### The Influencing Factors of Marker-Based Methods  
#### 5.1.1 Distance to Marker  
The purpose of this test is to study the relationship between placement accuracy (RMSE) and distance from the marker.  
For marker-based methods, tests using the Vuforia SDK show that the further away from the marker, the placement accuracy of the model deteriorates (Figure 12). At a distance of less than 2 m between the physical marker and the device, the placement error is minimal (millimetric order). Figure 13 reports that the RMSE is significantly augmenting with the distance to marker (reaching 17 cm for a distance of 6m from the marker and 20 cm for a distance of more than 8 m).  
Figure 11: Influencing factors of marker-based methods [13; 60]. (*): The tested factors in our study.  
Figure 10: Distribution of markers outdoors.  
The distance effect is generally negligible when using the ARcore SDK, which is characterized by the anchor functionality in the augmented model (Figure 14).  
Figure 14: 3D model placement using ARcore SDK (Anchor method).  
Figure 12: Degradation of virtual 3D model placement accuracy as we move away (10 m) from the marker (with Vufforia SDK).  
Figure 13: Effect of distance to marker on placement accuracy for Vufforia SDK.  
According to the diagram above, we notice that the effect of the distance to marker follows a linear function, which can be useful for error prediction with this method.
# A Three-Dimensional Electrochemical Process for the Removal of Carbamazepine
## 3 Results and Discussion
### 3.3D Experiments
#### 3.3.3 Effect of the Reaction Time on CBZ Removal  
The effect of the reaction time on the removal of CBZ was examined in the range of 0-45 min for the 2D and 3D electrochemical processes. As shown in Figure 12, a 30 min reaction time was needed to reach a removal efficiency of 95% in the case of the 3D process when using the biochar TN 1-2 mm and the SARATECH(r) activated carbon, after which no significant changes were observed. However, as shown in Figure 12, for the 2D process, after 30 min of reaction, the CBZ removal was only 80% (87% after 45 min).  
[PERSON] et al. [34] examined the effect of the reaction time in the range of 0-60 min to remove CBZ using 2D and 3D electrochemical processes (j = 9 mA/cm\({}^{2}\)). They reported similar conclusions to this study, utilising aluminium anodes and cathodes and a powdered activated carbon particulate electrode, needing 10 min to reach a 90% removal efficiency in the case of the 3D process, which was 18 times shorter than the 2D process.  
The reaction time is closely related to energy consumption and treatment performance; thus, the optimum time is when the efficiency removal is higher with minimal energy consumption. On the other hand, prolonged reaction times would increase the treatment cost [52].  
Figure 12: Effect of the particulate electrode on CBZ removal from aqueous solutions (operational conditions: j = 6.67 mA/cm2; NaCl = 0.1 M; CBZi = 10 mg/L; pH = 7; reaction time = 45 min; interelectrode distance = 7.5 cm; particulate electrode = 1 mg/mL solution; cathode = STS; anode = MMO).  
In Table 5, the CBZ removal efficiency, rate constants, correlation coefficients, and half-lives are presented for the first-order and second-order kinetic models when using the 3D and 2D processes previously described.  
The correlation coefficients showed that for the 3D processes, the model that better described the degradation of CBZ was the first-order kinetics, while for the 2D process, it was the second-order kinetics. The results for the 2D process agreed with the work of [PERSON] et al. (2018), who reported this model as the best to describe the degradation kinetics of CBZ when using Ti electrodes.  
For the removal of CBZ, no kinetic studies were found in the literature regarding the 3D process. The correlation coefficient for the first-order model and the second-order model for the 2D process were comparable, so the first-order model also was valid for describing the reaction kinetics for this process. Comparing the first-order rate constants (k\({}_{1}\)) for the 3D and 2D processes, the 3D process presented a higher k\({}_{1}\), and the highest value was obtained when the biochar TN 1-2 mm was used; regarding the half-lives for these processes, the CBZ concentration was reduced by 50% in a shorter time when using biochar TN 1-2 mm (5.8 min) than in the other conditions.
## 3 Results and Discussion
### Coulometric Method  
Theoretically, the oxidation degree can be determined coulometrically using the galvanostatic mode, knowing the exact mass of the active material and the mass of all additives (PVDF, carbon). The oxidation degree in this case will be the ratio between the practically obtained capacity (calculated for the active material) and the theoretical capacity (170 mAh/g), assuming a 100% yield for material conversion. However, strict intra-diffusion limitations associated with low ionic conductivity of this material make this approach difficult to apply because of low-current necessity and uncertainty associated with an a priori lack of confidence in the 100% conversion during delithiation,/lithiation. In our case, the charge and discharge capacities of the synthesized material reach values of 154.1 and 152.7 mAh/g, respectively, at a current rate of 0.1 C (Figure 5a). Using the above-mentioned formula for the degree of oxidation, a conversion percentage of 90.6% was calculated. This value is close enough to the value determined by Mossbauer spectroscopy (92%). However, one cannot be sure that there is 100% yield of active material at such low currents, and the remaining \(\approx\) 10% of the inactive material should be ferric compound impurities. At higher currents (>1 C), the practical capacity of this material is significantly lower (1 C-145.5; 2 C-132.3; 5 C-113.1; 10 C-91.2; 20 C-65.0) (Figure 5b).  
Figure 4: Morphology of synthesized LiFePO\({}_{4}\)/C: (**a**) High Resolution Scanning Electron Microscopy HRSEM image; (**b**) particle size distribution.  
This is connected with the aforementioned diffusion limitations. Therefore, the coulometric method for determination of the degree of oxidation of the material can be applied only at low current densities.  
Synthesized LiFePO4/C is a good cathode material capable of operating even at high currents. The material was cycled for 20 times at 20C without severe degradation and an average charge/discharge capacity of 65.0 mAh/g was measured (Figure 5b). This material's performance is strictly related to its branched structure and low particle size (Figure 4a,b). It is well-known that the uniform particle size distribution leads to the uniform depth of charge/discharge of each particle, which increase the material's cycling life and enhance the global battery performance.
# A Network Analysis-Based Approach for As-Built BIM Generation and Inspection
## 2 Literature Review  
### As-Designed BIM  
The application of BIM technology has revolutionised various aspects of construction project management, extending beyond traditional inspection tasks. One significant area of impact is in project scheduling and cost estimation. Researchers have demonstrated the effectiveness of BIM in integrating time and cost dimensions, leading to the development of 4D and 5D BIM models. For example, [PERSON] et al. [13] explored the application of 4D BIM for dynamic scheduling and progress tracking in large-scale construction projects, showing improvements in project coordination and timeline adherence. Similarly, [PERSON] [14] utilised 5D BIM for detailed cost estimation and financial management, which enhanced budget accuracy and facilitated better financial planning. Moreover, BIM has been pivotal in improving collaborative workflows among project stakeholders. [PERSON] et al. [15] discussed how BIM fosters enhanced communication and information sharing, leading to fewer misunderstandings and more efficient decision-making processes during social service. This collaborative environment is further supported by cloud-based BIM platforms, as highlighted by [PERSON] and [PERSON] [16], which allow real-time access and updates to project data from multiple users, thus streamlining coordination and reducing delays. In the realm of sustainability, BIM's ability to simulate and analyse building performance has proven invaluable. [PERSON] et al. [17] investigated the use of BIM for energy efficiency assessments, enabling the optimisation of building designs to meet sustainability standards. Additionally, [PERSON] [18] highlighted the role of BIM in lifecycle assessment, where it aids in evaluating the environmental impact of materials and construction methods, thereby promoting green building practices.  
Additionally, the application of BIM technology has greatly enhanced inspection tasks within the civil domain; many researchers implemented practical inspection systems for different structures and scenarios, including beam string structures [19], external walls [20], roads [21], and bridges [22; 23], etc.
## 2 Study Setting  
The South Lhonak Lake (27\({}^{\circ}\)54'41.27\({}^{\prime\prime}\)N, 88\({}^{\circ}\)11'29.38\({}^{\prime\prime}\)E) is a moraine lake situated in the northwest of Sikkim, India. It stretches from east to west and is positioned on the southern Lhonak ice tongue in the Lhonak Valley, with an elevation of 5200 m (Figure 1). It stands out as one of the largest and fastest-growing glacial lakes in the region. In recent decades, global warming has led to an observable trend of retreating and thinning glaciers in high-altitude regions [20; 21; 22; 23; 24; 25]. If this trend persists, it is projected that by 2100, approximately 80% of the current Himalayan glaciers will have vanished [26]. The rapid glacier retreat provides enormous space and sufficient water sources for the expansion of glacial lakes [27]. Sikkim, which has abundant hydraulic resources, experiences substantial rainfall from June to September, leading to frequent natural disasters such as landslides and floods [28]. Landslides triggered by earthquakes and heavy rainfall are common in this area [29].  
The GLOF event studied in this paper occurred in the Teesta River basin area of northern Sikkim. A study by [PERSON] and [PERSON] et al. (2013) [30] suggested that heavy rainfall might induce GLOFs on moraine lakes. Over the 46 years from 1962 to 2008, the South Lhonak Glacier, the parent glacier of South Lhonak Lake, retreated by 1.9 km, leading to the formation of a moraine-dammed lake at the glacier sout. Subsequently, from 2008 to 2019, it further retreated by approximately 400 m. Concurrently with glacier retreat, the lake has exhibited significant expansion over the years, with its area escalating from 0.42 km\({}^{2}\) in 1990 to 1.35 km\({}^{2}\) in 2019 [28]. [PERSON] et al. conducted field investigations of South Lhonak Lake in 2014 and 2016, and based on on-site water depth, the research indicates that the storage capacity of South Lhonak Lake is 65.81 \(\pm\) 2.5 million m\({}^{3}\), with maximum and average depths of 131 \(\pm\) 2.5 and 67.05 \(\pm\) 2.5 m, respectively [31]. As lakes expand, the risk of catastrophic GLOFs escalates. In the Sikkim Himalaya, there are a total of 320 glacial lakes, of which 14 are potentially vulnerable to GLOFs [30]. A substantial inventory of glacial lakes and statewide hazard assessments [32; 30; 33; 34] consistently identified South Lhonak Lake as a potential hazard with a high probability of eruption.
# Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
## 2 WSN Management
### QoS Management  
Coupled with configuration management is performance management. The two main objectives of performance management of a WSN are the Quality of Service (QoS) and the quality of the information obtained [25]. However, there is usually a trade-off between QoS, energy consumption and network lifetime [5; 25]. Depending on the environmental conditions, resource constraints, service demand; the management protocols can permit lower QoS when energy is scarce and thus extending network lifetime. Several network management protocols have been proposed in QoS management of the WSN architecture including the hybrid data dissemination framework protocol RRP, Sensor Network Management System (SNMS), Simple Network Management Protocol (SNMP) and Wireless sensor network Management System (WinMS) [31]. SNMP is a widely used management protocol that provides good management for TCP/IP based networks and is supported by several vendors in managing both wired and wireless networks. It allows management of network performance and enables fault identification. WSNManagement [33] based on SNMP protocols has been proposed as an efficient network configuration, fault and performance management system. The performance of WSNManagement showed efficiency in WSN management with a 5 percent reduced packet loss and a 0.2 s reduced delay time while improving the lifetime of the overall sensor node system. Delay-related QoS is critical for WSNs, especially for real-time applications. Apart from WSNManagement, [PERSON] et al. [34] propose an optimized resource allocation solution for delay constrained Wireless Regional Area Networks (WRANs) to improve delay-related QoS management.