esa-sceva/satcom-chunk-collection huggingface dataset:
# Game Theory-Based Load-Balancing Algorithms for Small Cells Wireless Backhaul Connections +
## 2 Materials and Methods
### Problem Formulation and System Model
#### 2.1.1 Virtual Tunnel Modeling
We denote by the random variable \(R_{j}\) the residual service time, and according to [34] we have the following relation between the waiting time in the queue, service time (transmission time in the tunnel), residual service time, and the number of packets waiting in the queue:  
\[\mathbb{E}\big{(}W_{j}\big{)}=\mathbb{E}\big{(}n_{j}\big{)}\mathbb{E}\big{(}B_ {j}\big{)}+\rho_{j}\mathbb{E}\big{(}R_{j}\big{)} \tag{11}\]  
and according to [PERSON]'s law, we have:  
\[\mathbb{E}\big{(}n_{j}\big{)}=\lambda_{A^{\prime}}\mathbb{E}\big{(}W_{j}\big{)} \tag{12}\]  
and finally, we obtain:  
\[\mathbb{E}\big{(}W_{j}\big{)}=\frac{\rho_{j}\mathbb{E}\big{(}R_{j}\big{)}}{1- \rho_{j}} \tag{13}\]  
The value of the mean residual service time may be written in the form [34]:  
\[\mathbb{E}\big{(}R_{j}\big{)}=\frac{\mathbb{E}\big{(}B_{j}^{2}\big{)}}{2 \mathbb{E}\big{(}B_{j}\big{)}}=\frac{\sigma_{B_{j}}^{2}+\mathbb{E}\big{(}B_{j }\big{)}^{2}}{2\mathbb{E}\big{(}B_{j}\big{)}}=\frac{1}{2}\Big{(}c_{B}^{2}+1 \Big{)}\mathbb{E}\big{(}B_{j}\big{)} \tag{14}\]  
An important observation resulting from (13) and (14) is that the mean waiting time only depends on the mean and standard deviation of the transmission time random variable and not on the distribution function of this variable. It results that, in practice, it is enough to compute the mean and standard deviation of the service time to estimate the mean waiting time. Based on the previous relations the mean value of the system's response time (the so-called sojourn time) can be expressed as:  
\[\mathbb{E}\big{(}S_{j}\big{)}=\frac{\rho_{j}}{1-\rho_{j}}\frac{1}{2}\Big{(}c_ {B}^{2}+1\Big{)}\mathbb{E}\big{(}B_{j}\big{)}+\mathbb{E}\big{(}B_{j}\big{)} \tag{15}\]  
If the service time has a finite expectation \(\mathbb{E}\big{(}B_{j}\big{)}={\mu_{j}}^{-1}\) and a finite standard deviation, \(\sigma_{B_{j}}={\sigma_{j}}^{-1}\) then the expected sojourn time with Poisson arrivals with rate \(\lambda_{j}\) (obeying condition (4)) is the following:  
\[\mathbb{E}\big{(}S_{j}\big{)}=\frac{\lambda_{j}\Big{(}1+\sigma_{j}^{2}\mu_{j} ^{2}\Big{)}}{2\mu_{j}\big{(}\mu_{j}-\lambda_{j}\big{)}}+\frac{1}{\mu_{j}} \tag{16}\]
## 6 Geosynthetic Applications
A summary of different smart geosynthetics used in various applications is presented in Table 6. The many advantages of using smart geosynthetics in monitoring applications include issuing warnings about imminent infrastructural damage or failure, controlling infrastructure construction, providing the data required to aid in selecting methods of remediating infrastructural damage or problem-solving techniques to manage urban development and transportation infrastructure in smart cities, and elucidating the design of unknown structures by monitoring the behaviour of existing infrastructure in the field during the infrastructure service life. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Application** & **Type of Geosynthetic** & **Sensing Technique** & **Coverage Type** & **References** \\ \hline Seismic reinforcement and monitoring of masonry buildings & Textiles (including reinforced plastics) & Embedded sensors & \begin{tabular}{c} Unidirectional strips \\ and full coverage on external surfaces \\ \end{tabular} & [49; 240; 241; 242] \\ \hline Strengthening and monitoring buildings & Grids (including multi-functional fibre-reinforced plastics) & Embedded sensors & \
\begin{tabular}{c} Full coverage on \\ external surfaces \\ \end{tabular} & [49] \\ \hline Reinforcement, monitoring, and management of earthquake-vulnerable civil infrastructure & Geotextiles (including reinforced and monitoring & FBG and DOFS & Full coverage & [49; 243] \\ \hline Railway embankment reinforcement and monitoring & Geotextiles & OF sensors & Full coverage & [244] \\ \hline Monitoring of dykes and coastal protection structures & Geotextiles & Embedded sensors & Full coverage & [49] \\ \hline Landslides &
\begin{tabular}{c} Tubular braids and \\ epoxy resins \\ \end{tabular} & DOFS & Distributed boreholes & [245] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of different smart geosynthetic applications.
## 5 Discussion  
### Corrosion Impact on Seismic Vulnerability  
The outcomes of this study reveal a significant and progressive increase in seismic risk due to corrosion over time, particularly for moderate and intensive corrosion environments. This finding underscores the critical importance of considering corrosion effects in long-term seismic vulnerability assessments of reinforced concrete (RC) critical infrastructure. While structures subject to minor corrosion levels show only minor increases in vulnerability over a 120-year period, those exposed to moderate corrosion face doubled seismic risk by the end of this timeframe. Most alarmingly, under intensive corrosion conditions, the risk ratio escalates sharply after 40 years, reaching nearly five times the baseline risk at 120 years. This exponential increase in seismic vulnerability suggests that structures in highly corrosive environments may become critically compromised much earlier than their intended design life.  
The cumulative risk ratios provide a comprehensive picture of the long-term impact of corrosion on seismic vulnerability across different locations. For minor corrosion levels, the total seismic risk over the structure's lifecycle increases only marginally, with a 10% to 16% rise depending on the location. However, moderate corrosion levels result in a more substantial increase in the total seismic risk, ranging from 59% in Bikat HaYarden to over 100% in Jerusalem, highlighting the need for more robust maintenance strategies. Most strikingly, intensive corrosion levels dramatically increase the total seismic risk, with cumulative risk ratios reaching 2.91 in Bikat HaYarden, 3.26 in Haifa, and a significant 6.06  
Figure 12: Cumulative risk ratios over 120 years for different corrosion levels.  
in Jerusalem. These findings underscore the critical role of corrosion in the risk assessment process and emphasize the necessity for tailored maintenance strategies, more frequent inspections, and potentially earlier intervention or retrofitting, particularly for structures in moderately to highly corrosive environments, to maintain acceptable levels of seismic safety throughout their design life cycle.  
These findings have profound implications for the management of critical infrastructure. They suggest that current design practices and maintenance schedules, which often do not fully account for the compounding effects of corrosion on seismic vulnerability, may be inadequate for ensuring the long-term resilience of RC critical infrastructures, particularly in moderate to highly corrosive environments. The results underscore the need for more proactive and tailored approaches to infrastructure management, potentially including  
* More frequent and thorough structural health monitoring, especially in corrosive environments;
* Earlier and more aggressive intervention strategies for existing structures showing signs of corrosion;
* The incorporation of time-dependent seismic vulnerability assessments in long-term infrastructure planning and budgeting.
## 3 Influence of Key Parameters on the Response
### On the Cross-Influence between Retained Nodes Number and the Contact Stiffness Values  
Section 3.3.3 proved that the effect of the mesh discretization is negligible with respect to the effect of the number of retained contact nodes. In addition, this section aims at comparing the effect of the retained contact nodes density with that of the global contact stiffness variation. To achieve this goal a cross-comparison of different cases displaying an increasing number of retained nodes along X direction (pattern A) and different values of contact stiffness was performed. In Figure 15, it is shown that the change of the contact stiffness values leads to effects comparable to the ones obtained by changing the number of contact nodes. For instance, as highlighted in Figure 15, the model with a 3.3 node columns/cm and stiffness multiplied by 1 has almost the same frequency of the model with a 6.5 node columns/cm density and stiffness multiplied by 0.5.  
Figure 14: In-phase and out-of-phase behaviors.  
Figure 15: Natural frequency versus the number of retained nodes for different contact stiffness values.  
This observation should serve as a warning to the designer: the contact parameter problem is under-determined as multiple contact parameter combinations can lead to the same result.
# Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images
## 4 Experimental Results and Analysis
### Crop-Wise Analysis
#### 4.5.1 Corn Crops  
The first crop used for the comparison is \"Corn-notill,\" class number 2 from the Indian Pines data. For this crop, the proposed method outperformed the state-of-the-art methods 3 DGSVM, SS3 FC, and WEDCT-MI with higher accuracy of 96.64%. The CNN-MFL method had 94.82% accuracy, which is on par with the proposed method. The second crop used for the comparison is \"Corn-mintill\", class number 3 from the Indian Pines data. For this crop, the proposed method outperformed the state-of-the-art methods 3 DGSVM, SS3 FC, and WEDCT-MI with higher accuracy of 97.12%. The CNN-MFL method had 96% accuracy, which is on par with the proposed method.
# Adaptive Modulation Tracking for High-Precision Time-Delay Estimation in Multipath HF Channels
## 3 MATE Algorithm  
The MATE algorithm first requires initialization of the parameters. Drawing on the basic principle of the signal capture algorithm of the navigation receiver, the carrier frequency difference between the received signal and the transmitted signal, as well as the phase difference of the modulated signal, is estimated. Next, phase-locked loop technology is used to achieve the accurate tracking of the carrier frequency of the received signal and the phase of the modulated signal [15,16]. After the carrier stripping operation is completed, the time-of-arrival (TOA) is accurately estimated by precisely measuring the phase difference between the received and transmitted signals.
# Time-Dependency in the Corrosion Process of Reinforced Concrete Subjected to a Chloride Solution
## 3 Experiments
### Testing for Corrosion Measurement  
Corrosion tests were performed to investigate steel corrosion using HP measurements, the GP technique, and EIS. The HP was measured after each wet-dry cycle. A wet synthetic sponge, squeezed to remove excess water, was placed on the concrete surface for approximately 10 min to facilitate electrical conduction between the reference electrode and the steel within the concrete. After approximately 190 days of concrete ageing, the polarisation resistance was measured using the GP technique after each wet cycle was completed. EIS measurements were obtained at specific times (186, 270, and 382 days), indicated in the legend within the figures or tables.  
A conventional electrode configuration (i.e., a three-electrode setup) was used, including two mild steels placed in concrete as the working and counter electrodes, and a CSE corresponding to the reference electrode. To measure the corrosion potential against the reference electrode, a high-impedance multimeter was used (>10 M\(\Omega\) of acceptable impedance).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of** & **W/C * & **Cement** & **Aggregate** & **Sand** & **Compressive Strength** \\
**Cement** & & **Content** & **(kg/m3)** & **(kg/m3)** & **F28** & **F180** \\ \hline \multirow{3}{*}{CEM 1} & \multirow{3}{*}{0.4} & \multirow{3}{*}{460} & \multirow{3}{*}{1012} & \multirow{3}{*}{650} & 65.32 & 84.23 \\  & & & & & (7.62) ** & (2.20) ** \\ \cline{1-1} \cline{5-7}
52.5N & & & & & & 38.34 & 46.99 \\ \cline{1-1} \cline{5-7}  & & & & & (1.19) ** & (1.35) ** \\ \hline \hline \end{tabular}  
* W/C water to cement ratio, ** values in brackets are one standard deviation for average values in compressive strength.  
\end{table}
Table 2: Mix design and physical properties.  
Figure 2: Schematics of concrete slab samples.  
A potentiostat/galvanostat (Solartron Analytical 1287 Electrochemical Interface) was used in the galvanostat mode for the GP technique. A current of 25 uA was applied for 10 s to the working electrode, equating to 3.32 uC/cm\({}^{2}\), to prevent any disturbance in the system between the concrete and steel and to avoid the diffusion process [13]. The potential bias from the corrosion potential was recorded at a rate of 5 points/s. Before the measurements, the corrosion potential was measured for a minimum of 1 min to verify potential alterations within 10 mV/min.  
EIS measurements were obtained using a frequency response analyser (Solartron 1260A Impedance/Gain-phase Analyser) and a potentiostat (Solartron Analytical 1287 Electrochemical Interface). A sinusoidal wave (root mean square value of 10 mV at the open-circuit potential) was applied within a frequency range of 5-100 kHz based on a logarithmic sweep at 10 frequency points per decade. The open-circuit potential was maintained below 10 mV/min. Corrosion threshold potential and current were \(-\)350 mV vs. CSE and 2 mA/m\({}^{2}\)[23].
## 1 Introduction
This also facilitates independence from the type of data source, making it possible to aggregate heterogeneous modalities ranging from manual inspection data to the output of fully automated scanning systems. Consequently, different sources of information can be effortlessly exchanged and the fusion strategy is readily adapted to unknown future changes of input sources. Although there are recent studies in NDT proposing decision-level fusion, e.g., by weighted averaging [4], hypothesis testing [5], and Bayesian or Dempster-Shafer theory [6; 7], all of these works still rely on image registration and interpolation to perform fusion at common grid points. In fact, we are not aware of any data fusion publication in the field of NDT dealing with scattered decision-level fusion, _i.e._, using the original measurement locations, despite the aforementioned advantages. We propose a new fusion strategy that combines spatially scattered detection locations to improve the detection performance compared to single-source methods. In this paper, we will address the problem of nondestructive surface inspection, although the methodology is quite generic and can be easily extended to the three-dimensional case of volume inspection. Our approach is detailed in Section 2 where the general idea is introduced using a simple example. Subsequently, we formalize the problem, describe our method and provide practical techniques for automatic parameter estimation. The developed approach is then applied to experimental multi-sensor NDT measurements by three different inspection methods. Finally, we quantitatively demonstrate the enhanced performance of multi-sensor crack detection using our technique over single-sensor inspection.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 6 Applications of Machine Learning in FANET  
Fundamentally, FANETs are expected to facilitate end-to-end data delivery using multi-hop (Single-hop transmission is omitted in this study since it is well-addressed in the literature [131; 132]. Hence, they rely on multiple UAVs that are constantly working together in conjunction with the ability to create a system that is far more powerful than any one UAV alone.) transmissions. Therefore, each UAV or node functions by relaying information between nearby drones. When the number of hops increases (and the network becomes larger), the complexity of the system significantly increases. Accordingly, this leads to difficulties in network management and operations due to elevated interference levels, challenging spectrum allocation, demanding trajectory and mobility management, reduced localization accuracy, and more complex energy and multiple access/routing management.  
The utilization of ML techniques has been shown to provide low-complexity solutions in wireless networks [133; 134; 135]. Due to its technology agnostic nature and adaptability, ML is quite flexible and can be used across the different layers of wireless networks [136]. Most notably, AI and ML have led to significant improvements in (i) the physical layer by enhancing digital signal detection [137], (ii) the medium access layer through cognitive radio and spectrum access [138], and (iii) the network layer using network management and optimization [139]. Hence, AI can potentially provide FANETs with the tools necessary in order to overcome some of the different challenges.  
In particular, reinforcement learning (RL) has emerged as a prominent solution to tackle some of the different challenges faced in FANETs [140]. Most notably, Q-learning and deep Q-learning (DQL) are currently the most explored in the literature for FANETs to address their challenges. Owing to its ability to perceive, interpret, and learn from its environment, these techniques enable FANETs to adaptively collect data and optimize network performance. Both Q-learning and DQL rely on states and rewards to understand  
Figure 14: Overhead ratio of different protocols.  
Figure 13: Average number of hops (node exchange) in each protocol.  
and learn the environment and enhance their actions through weighted trial-and-error to optimize the network's overall behavior [140]. Recent works that deploys RL have tackled many avenues for FANETs. In this section, we focus on three main RL-enabled domains; (i) routing, (ii) resource allocation, and (iii) network security.
## 4 Results
### Sensitivity
#### 4.3.2 Analysis of the Pinch Point Temperature Difference  
The pinch point temperature at air preheater 1 was kept at values of 10, 20 and 30 while the other variables were left to vary within their range (10-15 for Cr, 1420-1520 for Gt and 850-950 for Ct). Figure 11 shows that, at Pp = 30, maximum values for the net power, exergy efficiency and CO2 emission were obtained. It also reveals that a change in the Pp from 30 to 20 yields a greater change in the emission of CO2 (compared with that from 20 to 10) with an associated reduction in the net power and exergy efficiency. This suggests that a fairly average value of Pp would be sufficient to produce good results. In comparison with other decision variables, the Pp has the least effect on the objective functions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Optimal Decision Variables**} & \multicolumn{3}{c}{**Optimal Objective Functions**} \\ \cline{2-7}  & **Cr** & **Pp** & **Gt** & **Ct** & **Net Power** & **CO2 Emission** & **Exergy Efficiency** \\ \hline \multirow{6}{*}{Present study} & 10.00 & 10.86 & 1520 & 913.11 & 61.60 & 50.57 & 45.21 \\  & 10.00 & 10.56 & 1520 & 901.58 & 61.47 & 50.31 & 45.28 \\  & 10.00 & 10.46 & 1520 & 895.34 & 61.40 & 50.18 & 45.31 \\  & 10.01 & 10.46 & 1520 & 893.99 & 61.38 & 50.16 & 45.32 \\  & 10.00 & 10.68 & 1520 & 908.14 & 61.55 & 50.45 & 45.24 \\  & 10.00 & 10.68 & 1520 & 907.69 & 61.54 & 50.44 & 45.24 \\ \hline \multirow{6}{*}{
\begin{tabular}{c} Similar study \\ ([PERSON] et al. (2018)) \\ \end{tabular} } & 11.66 & 11.96 & 1470 & 900 & 61.73 & 52.87 & 44.22 \\  & 11.11 & 20.00 & 1470 & 900 & 61.73 & 52.99 & 44.09 \\  & 11.98 & 20.00 & 1470 & 890 & 61.75 & 53.84 & 44.12 \\  & 12.50 & 16.10 & 1484 & 900 & 61.75 & 54.0
# Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization
## 2 Related Work  
The spatial non-uniformity of infrared FPA detector arises from the defects of the focal plane array material and manufacturing process, which leads to the inconsistent response of the infrared focal plane array unit. Generally, this can be corrected by calibration method, but the calibration method is not applicable to stripe noise. The most fundamental NUC technology is based on radiation calibration. For instance, the two-point correction method enables the detector to calculate a set of independent correction factors (gain and offset) through radiation correction at the same temperature. The model is simple and the calculation is convenient. However, this method requires periodic interruption of the system's operation and real-time correction of the system cannot be achieved. In order to overcome this shortcoming, a large number of NUC algorithms based on time-domain filtering and scene information are proposed [7; 11; 12; 13]. However, the limitation of this type of scene algorithm is that it requires stable correction factors for multi-frame images, so it is difficult to implement the algorithm in hardware. Moreover, the scene-based NUC algorithm requires the image to have sufficient scene motion; otherwise the image will be rendered with \"ghosting\" problems.  
Recently, a large number of single-frame NUC algorithms have been proposed [14; 15]. These algorithms usually divide the image into high-frequency parts and smooth parts, and denoise the high-frequency parts. However, in reality, if the texture information of the image is insufficient, it is very difficult to separate the noise and the texture of the image. In general, in the denoising method of filtering (bilateral filtering and guided filtering) [16; 17; 18], a threshold value should be set to distinguish the edge and noise, to achieve the effect of edge preservation. However, when the image texture information is weak, the algorithm may erroneously remove the texture information as noise or save the stripe noise with large amplitude as texture information. In the statistical gray correction algorithm [9; 19; 20], it assumes that the histograms of adjacent columns are the same, and the histogram information of the columns is modified by the adjacent histogram relationship. Conversely, in the constrained optimization method [21; 22], it is assumed that the stripe noise affects the horizontal direction gradient and has little effect on the vertical direction gradient, so, the problem is converted to minimizing the energy function. With the application of convolutional neural networks in image processing in recent years, [PERSON] et al. [23] proposed a deep convolutional neural network (CNN) model to correct the non-uniformity of infrared images, which can accurately remove stripe noise.