esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Aerodynamics Calculation through the Vortex Lattice Method in Aerodynamics
#### 2.5.2 Flow Tangency Condition  
Integral to potential flow theory and VLM is the flow tangency condition. It posits that airflow remains tangent to the body's surface, preventing flow penetration. Mathematically, for a 2D panel \(j\) with vortex strength \(\Gamma_{j}\), the condition is articulated as:  
\[\sum_{j}\Gamma_{j}\cdot\text{Influence of vortex $j$ on panel $i$ }=\ -V_{\infty,i}\cdot n_{i} \tag{4}\]  
and \(\cdot\) is the symbol for the inner product.  
This equation is fundamental in determining the vortex strengths, \(\Gamma_{j}\), across all panels, where \(V_{\infty,i}\) represents the free-stream velocity at panel \(i\), and \(n_{i}\) is the unit normal to panel \(i\)[46].  
After determination of the vortex strengths, the next step is computing the aerodynamic forces on each panel. The lift per unit span for a 2D panel is given by the formula:  
\[L^{\prime}=\rho V_{\infty}\Gamma \tag{5}\]  
In this equation, \(\rho\) signifies air density, \(V_{\infty}\) is the free-stream velocity, and \(\Gamma\) is the circulation around the panel [9; 14; 39; 50].  
The total lift is then the sum of the lift contributions from all the panels. Similarly, other aerodynamic quantities, such as drag, can also be estimated using additional considerations. In conclusion, VLM provides a computationally efficient way to compute aerodynamic properties of wings and aircraft bodies. While it does not capture viscous effects and is typically limited to subsonic flow, it is quite effective for preliminary design and analysis tasks.  
Integrating the horseshoe vortex model and the flow tangency condition, VLM provides a robust framework for predicting the aerodynamic forces on an aircraft. The calculation of lift, through the determination of vortex strengths and the enforcement of flow tangency, showcases the method's capability in simulating complex aerodynamic interactions. Although VLM abstracts away some complexities, such as viscous effects, its computational efficiency and effectiveness in preliminary design and analysis remain  
Figure 2: Neural network layout.  
unparalleled. In our study, applying VLM to UAV aerodynamics allowed for a nuanced understanding of how design and environmental factors influence flight efficiency and responsiveness.
## 1 Introduction
### Innovation, Importance, and the Value of Research  
Considering the importance of cellular networks in everyday life and the fact that they are becoming an inseparable part of modern lives, paying attention to the faults of the network, trying to reduce the time a fault affects the network through early detection of the fault, and, also, the correct identification of the fault caused in the network so as to expedite a return to the network's normal mode are of particular importance.  
The difference between this work and other similar works in this field includes the use of accurate data for modeling the behavior of the network in the face of various types of faults, and examination of all the characteristics and critical statistical indicators that optimization engineers use in the operators to analyze the faults that occur in the network. All features are reviewed simultaneously. Therefore, the effect these indicators have on each other, and the impact of combining the values of different indicators on the network's performance, are studied in this research. In addition, unlike most of the proposed methods, the construction of the proposed model in this field was achieved without the intervention of human resources and experts. Since there is a possibility of human fault in setting model parameters and the assigned values are highly dependent on the expert's experience, the fact that different experts may value the parameters in different ways, under the same circumstances, is pertinent. For this reason, the use of a method that rejects human intervention is significant. Finally, this research used three available sources of information in troubleshooting and optimizing the networks, which, according to our study, is the first case of research considering all sources. In other words, all related work in this field composed our first source of information, namely, the performance support system data. In the second section, these data are introduced in detail.
# Drone SAR Imaging for Monitoring an Active Landslide Adjacent to the M25 at Flint Hall Farm
## 2 Study Area
### Geology  
The geology at Flint Hall Farm is dominated by the Gault Formation, or Gault Clay, with sections of the mid-slope on the Upper Greensand Formation (Figure 5). The Gault Clay is a narrow-band (1-2 km width) sequence of pale to dark blue grey clay, mudstone and siltstone deposited during the Albian stage of the Lower Cretaceous (c. 113-100.5 Ma), spanning from Norfolk through to Devon. Other negligible formations include the Holywell Nodular Chalk and New Pit Chalk (c. 100.5-89.8 Ma), and the West Melbury Marly Chalk and Zig Zag Chalk (c. 100.5-93.9 Ma); however, these formations are rarely cited in geotechnical reports and literature for this location. Boreholes indicate geological contact between the Gault Clay and the Upper Greensand Formation (c. 113-93.9 Ma) at 75 to 125 m upslope to the north of the M25, with some present at the northern sections of Flint Hall Farm [25]. Outcrops of the Aptian to Albian Folkestone Formation (c. 126.3-100.5 Ma) are only present to the south of Flint Hall Farm.  
Figure 4: Landslide extents at the Flint Hall Farm site, including the Flint Hall Farm, Flint Hall Farm South, and Rooks Nest Farm Landslides [25].
# Improved Defect Detection Using Adaptive Leaky NLMS Filter in Guided-Wave Testing of Pipelines
## 5 Results
### Adaption of Filter Weights  
The used filter order in the tests is five, which means only five past samples are required to process the results (Appendix A). Since the number of available samples is limited, observation of the filter's frequency response is not possible. Therefore, in Figure 13b-f, the changes of the first filter value are shown while Figure 13a shows their corresponding results in the time-domain. The red signals illustrate the results of the leaky-NLMS while the black dotted lines represent the normal NLMS algorithms. While normal NLMS depends more on past values and is not fast to adapt to the current iteration, leaky NLMS makes this adaption faster by reducing the filter weights' dependence on their past values. After each adaption to a big sequence such as backward leakage or defect, the magnitude of the filter weight tends to move toward zero; in other words, the weights forget the characteristics of past iterations significantly faster, which allows them a quicker adaption to the characteristic of the current iteration. Furthermore, as illustrated by blue dotted signals, in the region of defects, a high peak is generated in the magnitude of each filter order set by the leaky-NLMS method. Although other techniques and updated algorithms exist, this fast adaption algorithm, where the filter can forget the characteristics of the previous wave, is the main reason why leaky-NLMS is more effective in the guided-wave inspection of pipelines.  
Figure 12: Two sets of results after swapping the inputs to adaptive filter where (**a**) shows the signals received from the backward direction, (**b**) shows the flexural noise, and (**c**) shows the defect signal.
## 3 Problem Formulation  
The main sources of energy consumption in sensor nodes are sensing data, processing data and communication. Generally, sensor nodes can operate in four modes: transmit, receive, idle and sleep. The highest power consumption is in the transmit mode and lowest in the sleep mode, whereas in idle mode nodes consume power almost equal to that of receive mode. In this section, we formulate the hierarchical cooperative MIMO clustering problem as an estimation of number of clusters which offers minimum energy consumption in the network. Let \(E_{total}\) denote total energy consumption in our sensor network (Energy model is discussed in Section 4.6). The total energy is taken as sum of energy consumed in intra-cluster (\(E_{local}\)) and inter-cluster communication (\(E_{CH}\)).  
\[E_{total}=E_{local}+E_{CH} \tag{1}\]  
We assume sensor nodes having a uniform distribution in a square area of size \(2a\times 2a\). Let \(N\) be the total number of sensor nodes in our network and \(k\) be the number of clusters in the cluster set \(K\). There are \(H\) hierarchical levels. For the scope of our problem, the objective is to find number of clusters in the network which minimize energy expenditure for all set of nodes within the network. In this case, our objective function is to minimize total energy consumption subject to the condition that there are \(k\) clusters in the network, each node belongs to a cluster and the distance between a clusterhead and a member sensory node is less than the maximum transmission distance of the node. The first constraint ensures maximum connectivity in the network. Second constraint puts a restriction that all nodes in wireless sensor network should belong to at least one cluster. This is to maximize coverage. The third constraint restricts distance (d) between a CH and an ordinary sensor node (SN) to be less than the maximum transmission distance \(d_{tx}\) of nodes. Once the number of clusters \(k^{*}\) is found, we determine number of backbone/routing clusters (RCHs) (Section 4.4) satisfying minimum total energy constraint within the network while ensuring \(RCHs\leq k\). For a fully connected graph, we aim at minimizing the total cost (energy expenditure). While doing so, the objectives are:  
* Increased network lifetime.
* Minimum energy expended at each node.
# Effective 5G Wireless Downlink Scheduling and Resource Allocation in Cyber-Physical Systems +
## 3 Problem Formulation
Requests can consists of a variety of 5G use cases, such as eMBB, URLLC and eMTC. SRA decisions in this architecture are cross-layer in that decisions are made considering the DLC and PHY layers, as shown in Figure 2. It takes input from the queue state and the CSI, which is three-dimensional information consisting of time (symbols), frequency (number of sub-bands) and space (number of antennas), as shown in Figure 2. At each transmission interval, a CSI exchange takes place between the base station and a mobile station. The mobile station CSI tells the base station about the channel properties of a communication link such as delay-Doppler spread, Signal-Interference-to-Noise-Ratio (SINR), angle-of-arrival, angle-of-departure and PHY layer configurations such as Modulation and Coding Schemes (MCS) and the rank indicator, precoding matrix indicator and channel quality indicator received from the mobile station. The scheduled resources are then transferred to the PHY layer for transmission, as shown in the figure. At the PHY layer, the information goes through the Adaptive Modulation and Coding (AMC) process, which decides the appropriate modulation schemes for individual users. Data modulation is followed by MIMO processing and data transmission using transmit antennas [14; 75; 76] in the base station. In a mobile station, the reverse process takes place: the PHY layer performs multi-carrier post-processing and MIMO equalization on the received signals through the receiving antennas. The AMC and MIMO precoding matrices are exchanged and known by the mobile station during the CSI exchange. Finally, the demodulation process extracts the coded data. In the case of multi-carrier time-slotted downlink architecture, resources, called RBs, are allocated in the time domain across multiple sub-bands. For each transmission time interval \(t\), RBs consist of \(N_{sym}\) symbols for a duration \(T_{p}\), along the time axis and sub-bands of \(\Delta f=\frac{1}{T_{p}}\) in the frequency domain. On the time axis, each RB holds a fixed number of time slots \(T_{s}^{PHY}\). In this paper, PHY represents either orthogonal frequency division multiplexing (OFDM) or filter bank multi-carrier (FBMC) symbols.
# Reliability Analysis of Military Vehicles Based on Censored Failures Data
## 3 Results and Discussion
### Exponential Model of Reliability  
An approximation using Matlab software was used to determine the reliability function of military vehicles as a function of the mileage between failures. The parameter determination \(\lambda\) for the exponential model was done using the nonlinear least-squares method. The objective function is to minimise the sum of squares of the differences between the true values and the values achieved by the approximating function, according to the following equation [59; 60; 61]:  
\[S_{n}(\lambda)=\sum_{i=1}^{n}\Bigl{(}\widehat{R}(t_{i})-f(t_{i},\lambda) \Bigr{)}^{2}, \tag{20}\]  
where \(f(t_{i}\lambda)\) is the value of the exponential reliability function \(R(t)\) with parameter \(\lambda\) for the value of \(t_{i}\).  
\(S_{n}(\lambda)\) takes the minimum value for the critical point, for which the following expression exists:  
\[\frac{\partial S_{n}(\lambda)}{\partial\lambda}=0. \tag{21}\]  
After transformations according to the chain rule, the above relation can be written by the equation:  
\[\sum_{i=1}^{n}\Bigl{[}\widehat{R}(t_{i})-f(t_{i},\lambda)\Bigr{]}\biggl{(}- \frac{\partial f(t_{i},\lambda)}{\partial\lambda}\biggr{)}=0. \tag{22}\]  
Model fitting was performed using the Trust Region algorithm [62; 63] in the Curve Fitting Tool application (Matlab). The models were matched to the estimated values of functions presented in Table 3. The exponential model for approximating the reliability function of the studied sample of technical objects is presented in Equation (26):  
\[R(t)=e^{-0.000235t}. \tag{23}\]  
Figure 5 shows a graphical interpretation of how the exponential model fits the estimated values of the reliability function.  
Figure 4: Kaplan–Meier reliability estimation graph.
# Automatic Extraction of Material Defect Size by Infrared Image Sequence
## 4 Data Post-Processing
### Primary Detection of Defects
#### 4.2.4 Performance Analysis of Primary Detection Results  
To assess the performance of primary detection results, 100 independent experiments were conducted to extract each defect. Each composition image was synthesized by 10 random frames from the interval (90, 224) of the infrared sequence, and the location and size of the sensitive regions in each experiment were randomly selected, too. Figure 17 shows the discrete distribution map of the deviation of the extracted defect area from the ground truth. The symbols \"O\" and \"\(\Delta\)\" in the figure represent defects #1 and #2, respectively. From the figure, we can see that the frame selected by MSDSRM ranges within (155, 205) less than 50 frames. We have obtained a very narrow range from the original image sequence of 1000 frames, so it is a good primary detection. Defect #1 is the shallow defect, and there is a tendency that the relative error gradually increases with time. However, there is no regularity for #2. Moreover, there are serious overlaps of frames for different defects at different depths. There are two reasons for this phenomenon: one is that the edge extracted from the composite image is not the exact boundary of the defect, especially for the deep one shown as Figure 11b, which is smaller than the true; the other is that single sensitive region chosen randomly is not always representative of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Defect Number** & **Frame** & **Threshold** & **Defect Pixel** & **Relative Error** \\ \hline \#1 & 178 & 0.46 & 3443.1 & 2.08\% \\ \#2 & 199 & 0.36 & 3242.1 & 2.91\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of defect parameters using a single sensitive region.  
Figure 16: Defect extraction of primary detection: (**a**) ROI of defect #1 from Frame 178; (**b**) Segmentation of defect #1; (**c**) ROI of defect #1 from Frame 199; (**d**) Segmentation of defect #2; (**e**) Composition segmentation images as a whole.  
the whole standard deviation around the defect due to the non-uniformity of excitation. Under the same excitation, the heat transmission speed in the defect area varies with the burial depth. For the defect with a shallow burial depth, the corresponding surface temperature rises faster, taking less time to form a temperature difference area coinciding with the defect shape. In contrast, the defect with a deeper burial depth takes more time to form a temperature difference area coinciding with the defect shape. The best image for processing defect #1 should be a frame in sequence ahead of that for processing defect #2. So, the results of primary detection should be improved further to maintain the results' robustness.
## 4 Factors Affecting Bioflocculant Production
### Effect of pH  
It has been noted that pH affects the microorganisms, their production of active compounds and activity [5]. The effect of pH on bioflocculant production has been reported by different authors. Alkaline medium support higher flocculating activity in protein bioflocculants while polysaccharide bioflocculants enhances better flocculating activity at a pH range of slightly acidic to alkaline medium [66]. The pH of the medium tends to have an impact on the surface charge and charged state of the colloidal particles, thereby affecting the flocculation effect [67]. The pH conditions for attaining optimum bioflocculant production varies with each organism. For instance, _Aspergillus flavus_ bioflocculant attained its optimum production at pH 7 [5]. In another study, _Terrabacter_ sp. thrives well at a pH range of 7-11, attaining its optimum flocculating activity at pH 8 [68]. Furthermore, bioflocculant produced by _Alteromonas_ sp. was very active at neutral to moderately alkaline conditions and optimum flocculating activity of 600 U/mL was attained [69].
# Using Satellite-Derived Vegetation Products to Evaluate LDAS-Monde over the Euro-Mediterranean Area
## 3 Results
### Evaluation Using Satellite-Derived Vegetation Products
#### 3.2.2 Sun-Induced Fluorescence
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**[PERSON] et al. (2018) (This Study; Figure 10)**} & \multicolumn{3}{c}{**Adapted from Figure 11 in [24]**} \\ \hline
**Veg. Type** & **Slope** & **Intercept** & **R** & **STD** & **No. of Dates** & **Slope** & **Intercept** & **R** \\ \hline deciduous & 12.72 & \(-\)0.10 & 0.97 & 0.37 & 86 & 7.69 & 0.23 & 0.98 \\ coniferous & 16.95 & \(-\)0.74 & 0.96 & 0.51 & 107 & 12.50 & 0.00 & 0.97 \\ C3 crops & 11.28 & \(-\)0.36 & 0.97 & 0.28 & 108 & 7.69 & \(-\)0.31 & 0.99 \\ grasslands & 8.97 & \(-\)0.80 & 0.80 & 0.66 & 108 & 7.14 & \(-\)0.36 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Linear regression between averaged monthly values of observed SIF and analyzed GPP over the pixels covered by at least 50% of one vegetation type: deciduous forests, coniferous forests, C3 crops, grasslands. Slope (in g(C).sr.nm.mW\({}^{-1}\)-day\({}^{-1}\)), intercept (in g(C).m\({}^{-2}\)-day\({}^{-1}\)), correlation coefficient (R), Standard Deviation (STD, in g(C).m\({}^{-2}\)-day\({}^{-1}\)) and the number of dates are indicated (all statistical scores are significant with a \(p\)-value \(<\) 0.05). Slope, intercept and correlation values from [24] are also indicated in the right columns for the same vegetation types. Figure 9: Histogram of correlation values from Figure 8 between the SIF observations and the GPP simulations (open-loop in blue and analysis in red). Figure 10: Pixels where at least 50% is covered by a single vegetation type (deciduous forests,iferous forests, C3 crops, grassland, (**a**) used to investigate the relationship between the analyzed GPP and the observed SIF (**b**). Spatial averages are indicated by colored circles depending on the season: summer in red (June-July-August), autumn in yellow (September-October-November), winter in blue (December-January-February) and spring in green (March-April-May). Dashed purple lines represent the regression with the coefficients and statistics shown in Table 2.
## 3 CubeSats and Inter-Satellite Links (ISLs)
### Future OISL Payloads
#### 3.5.2 Link Budget  
Table 7 presents a performance comparison between the proposed FOCUS link budget and other laser communication systems used on application scenarios [13; 74]. The results indicate that FOCUS is comparable in terms of size, mass, data rates, pointing accuracy, and optical power requirements. Also, the FOCUS link-budget analysis confirms that by adjusting two fundamental parameters, i.e., the beam divergence and the transmit power, the same terminal design could also be adapted to serve a variety of communication scenarios, as also demonstrated by the different models of [13]. The market trend for next-generation laser communication terminals shows a shift toward smaller with extended link acquisition capabilities. However, the key factor for any commercially viable product will be its adaptability to various communication scenarios. The primary distinction  
Figure 4: Mission concept in space, showing positional uncertainties (yellow circles) along the dashed path, with Tx and Rx alignment in green and blue cones.  
Figure 3: Current design of FOCUS.  
between FOCUS and the other models discussed is that FOCUS is specifically designed for inter-satellite communication, which involves a significantly more complex PAT process. FOCUS's ability to autonomously establish a link between two fast-moving satellites is a groundbreaking technological advancement, critical for large satellite constellations that support high-speed global communication networks.
## 3 Results and Discussion
### Practical Applications and Cost Analyses  
#### 3.3.1 Practical Implications and Applications  
The optimized SBR/SBS-modified epoxy asphalt mixture demonstrates significant improvements in high-temperature performance, low-temperature flexibility, and water stability. These properties make it highly suitable for use in bridge expansion joints, particularly in regions with extreme temperature fluctuations or heavy traffic conditions. By mitigating common issues such as high-temperature rutting and low-temperature cracking, the material can significantly enhance the durability of expansion joints, reducing maintenance frequency and associated costs.  
In practical infrastructure projects, this material can be implemented in bridges and highways that require robust and long-lasting expansion joint solutions. For example, in subtropical regions, where the temperature variations and humidity levels are pronounced, the optimized mixture provides superior adaptability, ensuring consistent performance over extended service periods. Additionally, the reduced maintenance require  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Performances**} & **BJ200** & **SSEA-1** & **SSEA-2** & **SSEA-3** & **SSEA-4** & **SSEA-5** \\ \hline \multirow{3}{*}{High-temperature performance} & MS (60 \({}^{\circ}\)C, kN) & 4.72 & 9.2 & 7.94 & 7.1 & 6.12 & 4.96 \\  & Rutting depth (mm) & 1.689 & 1.523 & 1.851 & 3.672 & 5.74 & 7.804 \\  & DS (time/mm) & 2739 & 6300 & 5385 & 3043 & 1620 & 887 \\  & MSR (60 \({}^{\circ}\)C) & 78.78 & 109.76 & 108.26 & 106.34 & 103.61 & 101.65 \\  & TSR (\%) & 69.36 & 98/52 & 97.9 & 97.26 & 96.02 & 95.06 \\  & Bending strength (MPa) & 6.53 & 6.83 & 6.32 & 5.84 & 5.4 & 5.2 \\  & Bending strain (\(\mu\epsilon\)) & 5100 & 2047 & 2363 & 3675 & 3763 & 3811 \\  & Bending failure stiffness (MPa) & 1592.68 & 3335.11 & 2675.13 & 1589.12 & 1474.2 & 1364.29 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comprehensive performance comparison between SSEA mixture and BJ200.  
ments contribute to lower lifecycle costs, making it a cost-effective solution for large-scale infrastructure projects.  
The material's enhanced water stability is particularly advantageous in coastal or rainy regions, where traditional materials are prone to moisture damage. By integrating this material into existing construction practices, it is possible to improve the overall safety and resilience of infrastructure while supporting sustainability goals through extended service life and reduced resource consumption.