esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis of the Monitored Results
### Ground Settlement  
Figures 13 and 14 show the distribution of ground surface settlement behind the retaining wall. The locations of JS5.1-JS5.5 and JS21.1-JS21.5 are shown in Figures 2 and 3. Figures 13 and 14 clearly show that the ground settlements increased with the excavation of foundation pit A. This indicates that the excavation and unloading of the soil in the pit will lead to the displacement of the outer soil into the pit. Meanwhile, it can also be found that the maximum settlement occurred approximately 10 m away from the enclosure pile during the excavation of foundation pit A. This was mainly because the friction of the retaining pile helped retain the soil near the pile and, thus, reduced the settlement of the ground surface near the retaining pile.  
Figure 12: Relationship between the maximum wall deflection and excavation depth [24; 25; 26].  
Figure 13: Ground settlements along the distance from the wall (S5.1–S5.5).  
When foundation pit B was excavated, however, the settlements were partially offset. In other words, the excavation of B pit caused great upward movements of the surrounding ground. This may be caused by soil rebound due to the excavation and unloading of foundation pit B. When the second and third layers of foundation pit B were excavated, the excavation of foundation pits A, C, and D had been completed. Under this context, the total weight of soil in foundation pit B became the only factor that caused the deformation of the nearby ground surface. The excavation of the second and third soil layers of foundation pit B reduced the total weight of the overlying soil layer and caused the soil to rebound, which led to the reduction in the settlement on the ground surface.
# Towards a Collection of Security and Privacy Patterns
## 5 Collection Overview and Practical Application
### Collection Expansion
#### 5.1.1 Dependability  
Dependability typically refers to the provision of an expected service towards task accomplishment in a reliable and trustworthy manner, and it entails reliability, safety, availability, and security [91]. Nevertheless, the concept of security is covered separately above, and in modern computer engineering, security is considered to encompass availability (along with confidentiality and integrity). Therefore, in the context of this work, dependability properties mainly focus on reliability, fault tolerance, and safety aspects. These aspects, along with their relationship to some enabling patterns, are depicted in Figure 26.  
In order to guarantee end-to-end dependability properties for a system, suitable component orchestrations should be found. If they do not exist, the substitution or the addition of current components with other atomic ones or orchestrations is required to guarantee dependability. This is also related to the components and the topology of the composition.  
However, it should be noted that the composition of two components which preserve a dependability property does not necessarily guarantee that the composition will also preserve the same property. In addition, if a composition guarantees the conditions of a property, the atomic components may not preserve the property.  
Moreover, certain dependability properties will need to hold at the component level to enable the end-to-end properties to be achieved. One of the most important issues for a system designer is to validate the system dependability of components as a critical condition for the design of complex network infrastructures and to identify the weakest components in order to replace, redesign, and find alternative solutions.  
Figure 26: Hierarchical graph of Dependability properties and relevant patterns.
## 1 Time Domain Measurements  
In 1977, [PERSON] first described the in vivo application of near-infrared spectroscopy (NIRS) [1], also called diffuse optical spectroscopy (DOS). Originally, NIRS was designed for clinical monitoring of tissue oxygenation, and today it has also become a useful tool for neuroimaging studies (functional near-infrared spectroscopy (fNIRS)) [2; 3; 4]. However, difficulties in the selective and quantitative measurements of tissue hemoglobin (Hb), which have been central issues in the NIRS field for over 40 years, are yet to be solved. To overcome these problems, time domain (TD) [5; 6] and frequency domain (FD) [7; 8] measurements have been tried. Presently, a wide range of NIRS instruments are available, including commercial commonly available instruments for continuous wave (CW) measurements based on the modified Beer-Lambert law (steady-state domain measurements). Among these measurements, the TD measurement is the most promising approach, although, compared with CW and FD measurements, TD measurements are less common due to the need for large and expensive instruments with poor temporal resolution and limited dynamic range. However, thanks to technological developments, TD measurements are increasingly being used in research and also in various clinical settings [9; 10].
## 4 Results and Discussion
### Effects of the Higher Sloshing Modes on the Sloshing Response
#### 4.4.2 The First Loading Condition (S1)--The Minor Earthquake Horizontal Component  
As explained above, the minor horizontal component of each considered ground motion was scaled with the scale factor used to scale its major component to 0.4 g. This resulted in different peak ground accelerations (PGAs) for the minor components, as shown in Table 5. According to Table 5, these PGAs ranged from 0.24 to 0.37 g. These PGAs were normalized and plotted in relation to the normalized maximum sloshing responses at point \"b\" on a perpendicular axis to the direction of the minor components. Figure 10 shows the relationship between the PGAs and the maximum sloshing responses using a regression line.  
According to Figure 10, it is safe to assume that the sloshing response is not a function of the PGAs' values of the external excitations (R\({}^{2}\) score = 0.38).  
Similarly, as for the major components, the relationship between the spectral accelerations at the first five sloshing periods and the maximum sloshing responses, perpendicular to the direction of minor horizontal excitations (point \"b\"), is shown in Figure 11.  
Figure 9: Accelerations at the first five sloshing periods versus maximum sloshing responses (S1—major components).  
Figure 11: _Cont._  
Figure 10: Relationship between PGA and sloshing response (S1—minor components).  
Figure 11 indicates that only the spectral acceleration at the first sloshing period has a relatively close relationship with the maximum sloshing response, but not as close as that of the major component. In this case, it should be considered that, unlike the major component, the PGAs of the minor components are different, which may affect the sensitivity of the sloshing response to the higher sloshing odd modes, even though the maximum sloshing response is shown to be affected by the value of spectral acceleration at the first sloshing period.  
Additionally, the results show that the sloshing response was more affected by the spectral acceleration of the applied earthquake at the first sloshing period than PGA.
# An Improved YOLOv8 Model for Strip Steel Surface Defect Detection
## 3 Experiment and Analysis
### Comparison Test of Different Models  
To verify the performance of the improved model, it was compared with the current mainstream models YOLOv5n, YOLOv5s, YOLOX, YOLOv7, YOLOv7-tiny, Faster-RCNN [45], and SSD [46] algorithm, and the experimental results are shown in Table 5.  
The table shows that Faster-RCNN and SSD have slow detection speeds and low accuracy, which makes it difficult to adapt to the current detection requirements. Compared with other methods, the proposed algorithm is better in precision, recall rate, and mean average precision. This indicates that the proposed algorithm addresses the limitations of previous methods, offering a more robust solution for defect detection. Compared with the YOLOv8n, the proposed algorithm is improved by 4.9%, 4.3%, and 4.0%, respectively, which proves the effectiveness of the improved algorithm. This improvement highlights the proposed algorithm's capability to achieve higher detection performance while maintaining efficient processing. Compared with other methods, the accuracy of YOLOv5n, YOLOv6n, and YOLOX was 11.3%, 9.0%, and 4.0% higher, and the parameter amount was reduced by 1.2 M, 2.8 M, and 3.5 M, and the model parameter amount is increased by 2.6 M. Although  
Figure 15: Visualization of detection results: (a) original images; (b) YOLOv8; (c) YOLOv8-BSPB.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **P/\%** & **R/\%** & **mAP@50\%** & **Parameter/M** & **FPS/s\({}^{-1}\)** \\ \hline Faster-RCNN & 67.7 & 66.1 & 69.2 & 108.6 & 21.5 \\ SSD & 68.6 & 70.7 & 71.5 & 93.6 & 42.6 \\ YOLOv5n & 70.1 & 68.2 & 72.2 & 6.7 & 163.9 \\ YOLOv5s & 74.7 & 73.5 & 76.6 & 7.0 & 142.9 \\ YOLOv6n & 72.3 & 73.6 & 74.9 & 8.3 & 126.5 \\ YOLOv7 & 70.4 & 73.7 & 75.5 & 36.5 & 64.9 \\ YOLOv7 & 77.0 & 72.5 & 79.8 & 9.0 & 67.1 \\ YOLOv7-tiny & 71.2 & 69.5 & 73.5 & 6.1 & 143.0 \\ YOLOv8n & 76.4 & 76.0 & 82.9 & 3.0 & 108.7 \\ Our method & 81.3 (\(\uparrow\)) & 80.3 & 86.9 (\(\uparrow\)) & 5.5 & 103.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of different models.  
the model parameter increase may lead to a more complex structure, it contributes to enhanced performance. Due to the introduction of a complex module structure, the FPS of the algorithm in this paper is reduced compared with the detection frame number of YOLOv8, but it is still suitable for real-time detection of strip surface defects. This trade-off between detection performance and FPS is a common characteristic in models that seek to balance accuracy with real-time processing capabilities. In summary, the improved YOLOv8-BSPB model has good detection performance in strip surface defect detection.
###### Abstract  
Biodiesel is nowadays added in 5-10% \(v/v\) to diesel, and its production involves the parallel creation of a vast glycerol amount as a by-product. Despite its many applications, there is a surplus of glycerol (Gly) that has boosted the search for new applications of this compound, now transformed into an industrial synthesis intermediate or platform chemical. Its transcarbonation is a type of reaction that occurs under mild conditions, using weak or moderate basic catalysts, and allows the parallel production of glycoids of industrial interest with high selectivity, such as ethylene glycol. In this research, we have studied the activity of a Tunisian clay rich in inorganic carbonates that give it a weak basic character. The raw clay (RC) has been fully characterized by XRD, FTIR, SEM-EDS and nitrogen porosimetry. Subsequently, it has been employed as a catalyst to react glycerol (G) with ethylene carbonate (EC) to obtain glycerol carbonate (GC) and ethylene glycol (EG). The main operating variables and their effects on glycerol conversion and initial reaction rate were analyzed: catalyst concentration (2-6% \(w/w\) glycerol), reagent molar ratio (EC:G 1.5:1 to 3:1), and temperature (80-110 \({}^{\circ}\)C). Then, an appropriate kinetic model was selected from the results obtained under various reaction conditions, including the total deactivation of order 1 of the catalyst. The kinetic constant activation energy in this reaction using Tunisian smectite was found to be around 183.3 kJ\(\cdot\)mol\({}^{-1}\). In the second phase of the investigation, we explored the reuse of smectite using the kinetic model to appreciate the effect of cycle-to-cycle deactivation. It can be seen that the kinetic constant of the main reaction generally decreases with the number of cycles at low temperature and goes through a maximum at high operating temperature, while the deactivation constant increases with the number of catalytic cycles. The catalyst shows more stability, in general, at higher temperatures.  
Keywords:glycerol valorization; ethylene glycol; glycerol carbonate; smectite +
Footnote †: journal: _Sciences_
# Spatiotemporal Analysis of Evapotranspiration and Effects of Water and Heat on Water Use Efficiency
## 3 Results and Discussion
### WUEs Responds to Temperature and Precipitation Thresholds
From Figure 8,c,e, we can see that the relationship between precipitation and WUEs shows a \"left triangle shape\". This indicates that in the semi-arid zone, an increase in precipitation does not in the meantime increase the water use efficiency. Due to the low fluctuation in the response of semi-arid vegetation to water, there is a lagged effect, which is consistent with the findings of [PERSON] et al. (2018) [PERSON] et al. (2018), who suggested that there is a lagged effect of drought on ecosystem WUE. However, the relationship between air temperature and WUEs showed a \"right triangle\" shape, indicating that air temperature contributes to WUEs. This pattern is currently seen to be specific to semi-arid regions. Figure 8: Relationship between precipitation, temperature, and WUEs. (The black line represents linear fitting; the blue line represents the variation trend of the fitting points. The gray is the 95% confidence interval, and the dashed green line is the threshold.)
## 2 Materials and Methods
### Kinematic and Dynamic Equation of Motion
#### 2.2.2 Kinematic Analysis for Wheel 4 and Caster Wheel 2  
Position of W4 and CW2  
The position of wheel 4 and caster wheel 2 are centroid coordinate of the driving wheel that can be achieved by Equations (13)-(16):  
\[\mathbf{Z_{W4}} =L_{O_{4}D}\ \text{cos}\ \varnothing_{3} \tag{13}\] \[\mathbf{X_{W4}} =L_{O_{4}D}\ \text{sin}\ \varnothing_{3}\] (14) \[\mathbf{Z_{CW2}} =\mathbf{Z_{W4}}+L_{DF}\ \text{cos}\ \varnothing_{4} =L_{O_{4}D}\ \text{cos}\ \varnothing_{3}+L_{DF}\ \text{cos}\ \varnothing_{4}\] (15) \[\mathbf{X_{CW2}} =\mathbf{X_{W4}}+L_{DF}\ \text{sin}\ \varnothing_{4} =L_{O_{4}D}\ \text{sin}\ \varnothing_{3}+L_{DF}\ \text{sin}\ \varnothing_{4} \tag{16}\]  
Speed of W4 and CW2  
By calculating the first-order derivative of Equations (13)-(16), the centroid speed of wheel 4 and caster wheel 2 can be expressed as follows:  
\[\dot{\mathbf{Z_{W4}}} =-\dot{\varnothing_{3}}L_{O_{4}D}\ \text{sin}\ \varnothing_{3} \tag{17}\] \[\dot{\mathbf{X_{W4}}} =\ddot{\varnothing_{3}}L_{O_{4}D}\ \text{cos}\ \varnothing_{3}\] (18) \[\dot{\mathbf{Z_{CW2}}} =\dot{\mathbf{Z_{W4}}}-\dot{\varnothing_{4}}\ L_{DF}\ \text{sin}\ \varnothing_{4} =-\dot{\varnothing_{3}}L_{O_{4}D}\ \text{sin}\ \varnothing_{3}-\dot{\varnothing_{4}}\ L_{DF}\ \text{sin}\ \varnothing_{4}\] (19) \[\dot{\mathbf{X_{CW2}}} =\dot{\mathbf{X_{W4}}}+\ddot{\varnothing_{4}}\ L_{DF}\ \text{cos}\ \varnothing_{4} =\ddot{\varnothing_{3}}L_{O_{4}D}\ \text{cos}\ \varnothing_{3}+\ddot{\varnothing_{4}}\ L_{DF}\ \text{cos}\ \varnothing_{4} \tag{20}\]  
Acceleration of W4 and CW2  
By calculating the second-order derivative of Equations (17)-(20), the centroid acceleration of wheel 4 and caster wheel 2 can be expressed as follows:  
\[\ddot{\mathbf{Z_{W4}}} =-\dot{\varnothing_{3}}^{2}L_{O_{4}D}\ \text{cos}\ \varnothing_{3} \tag{21}\] \[\ddot{\mathbf{X_{W4}}} =-\dot{\varnothing_{3}}^{2}L_{O_{4}D}\ \text{sin}\ \varnothing_{3}\] (22) \[\ddot{\mathbf{Z_{CW2}}} =\ddot{\mathbf{Z_{W4}}}-L_{DF}\Big{(}\ \ddot{\mathcal{A}_{4}}^{2}\ \text{cos}\ \varnothing_{4}\ \Big{)} =-\dot{\varnothing_{3}}^{2}L_{O_{4}D}\ \text{cos}\ \varnothing_{3}-L_{DF}\Big{(}\ \ddot{\mathcal{A}_{4}}^{2}\ \text{cos}\ \varnothing_{4}\ \Big{)}\] (23) \[\ddot{\mathbf{X_{CW2}}} =\ddot{\mathbf{X_{W4}}}-L_{DF}\Big{(}\ \ddot{\mathcal{A}_{4}}^{2}\ \text{sin}\ \varnothing_{4}\ \Big{)} =-\dot{\varnothing_{3}}^{2}L_{O_{4}D}\ \text{sin}\ \varnothing_{3}-L_{DF}\Big{(}\ \ddot{\mathcal{A}_{4}}^{2}\ \text{sin}\ \varnothing_{4}\ \Big{)} \tag{24}\]  
Equations (21)-(24) constitute the kinematic equation for the robot driving wheel and caster wheel in climbing the wind turbine tower capability. The analysis of the W1 and CW1 and W4 and CW2 are relatively the same to W2 and CW1 and W3 and CW2.
# Additive Manufacturing of Variable Density Lenses for Radio Frequency Communications in X-Band
## 2 Dielectric Permittivity Measurements
### Microwave Characterization Methods  
Methods for characterizing the samples can be divided into non-resonant and resonant categories. In non-resonant methods, material properties are determined using reflection or reflection/transmission data in a transmission line. This data is collected via a vector network analyzer (VNA) in a wide frequency range. Transmission/reflection methods require both reflection and transmission data (scattering parameters \(S_{11}\) and \(S_{21}\), respectively) to determine material properties, whereas reflection methods only need reflection data (\(S_{11}\)). Various transmission lines, including coaxial lines, hollow metallic waveguides, dielectric waveguides, and free space, can be utilized in these methods.  
Resonant methods offer greater accuracy and sensitivity compared to non-resonant methods [18], but they are restricted to one or a few discrete frequencies. These methods are divided into the methods of material under test (MUT) as a resonator and the resonant perturbation methods. The main difference between them is that in the resonant perturbation method, the properties of the MUT are determined by observing changes in the properties of a resonant device with and without the MUT, whereas the MUT as a resonator method relies directly on how an MUT behaves as a resonator with its resonant properties. In resonant methods, the dimensions of the MUT are crucial since they impact the measurement, so accurate dimensional control is required.  
Three measurement methods were used in this work to characterize the AM polymeric material samples at a narrow band; all of them are considered resonant perturbation methods: Resonant Coaxial Biorentrant Microwave Cavity at 2.45 GHz, Split-Cylinder Resonant Cavity at 6.25 GHz, and Rectangular Waveguide at 24.5 GHz.
# Making a Case for Hybrid GFRP-Steel Reinforcement System in Concrete Beams: An Overview
## 3 Analytical Models for Hybrid GFRP-Steel Reinforced Beams
### Models Considering Mechanical Effects
#### 3.1.1 Ductility Analysis
A meaningful comparison among hybrid RC beams and other GFRP or steel-only reinforcements is made possible by Equation (7) developed by [PERSON] et al. [101]. In this concept, the effective reinforcement area of GFRP will be converted into equivalent steel. The proposed ductility index \(\mu_{h}\) is designed to satisfy the ductility requirements for conventional steel-reinforced concrete members. \[\mu_{h}=\frac{\Psi D_{uh}}{D_{yh}}\geq[\mu_{D}] \tag{7}\]  
\[\Psi=\frac{U_{H}}{U_{S}}\leq 1.0\]  
where \(U_{H}\) expresses the area under the moment-curvature curve of the beam; \(U_{s}\) represents the area under the moment-curvature of the equivalent steel-RC beam; \(D_{uh}\) is the ultimate deformation of the beam; \(D_{yh}\) is the deformation of the beam at the beginning of steel yielding; \(\Psi\) expresses the ductility reduction factor; and \(\mu_{D}\) is the ductility requirements based on the conventional indices. Although the approaches proposed by these studies were able to compute the ductility of the hybrid RC components, none of them addressed the ductility activation phenomenon. Such information may be useful for understanding the behaviour of both the GFRP and steel reinforcements while designing flexural beams. A ductility model that clarifies the ductility activation phenomenon of the inner steel bar may be helpful information in developing a comprehensive design model for hybrid GFRP-steel RC systems.
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 2 Flying Ad Hoc Networks
### Mobility Models in FANETs
This mobility model is suitable for maximizing coverage under connectivity constraints and avoiding external obstacles and inter-UAV collisions. Many algorithms use APFs to maintain the leader-follower topology [47], target searching [46], and predefined trajectory tracking. However, this type of mobility model can easily be trapped in the local minimum problem [12], particularly in highly confined and dense obstacle mission areas. The adaptive adjustment of the attractive and repulsive force constants of APFs can control the optimal node density in FANETs and minimize interference in inter-UAV communication [12]. In [48; 49], the authors proposed a distributed pheromone repel mobility model to execute reconnaissance and target-searching missions. Each UAV maintains its pheromone map and scans the mission area according to its map. The UAVs exchange information among themselves to assemble a global pheromone map. The UAVs turn right or left or travel straight according to pheromone smell probabilities. UAVs prioritize regions with a low pheromone smell to explore more in the mission area and reduce repetitive coverage. The limitations of this type of mobility model are that they only consider target exploration; they do not consider the connectivity among UAVs in the aerial network. Additionally, the trajectories of UAVs are not smooth and become complex. The realistic mobility models discussed above are summarized in Table 5 according to their applications in FANET.
# A Survey of Vehicular VLC Methodologies
## 2 Visible Light Communication  
### General VLC Architectures  
The VLC system consists of three main components: a VLC transmitter, a VLC receiver, and a VLC channel. The essential work of VLC technology is effortless; on the transmitter side, the white light generated by the LEDs is modulated, and then the optical signal is transmitted through the optical wireless channel. The modified light is extracted using a photodetector (PD) on the receiving side. The most popular method for modulating information on the transmitter side and demodulating it on the reception side is known as Intensity Modulation and Direct Detection (IM/DD). A simplified block diagram of the VLC system is shown in Figure 2[12; 18; 29].