esa-sceva/satcom-chunk-collection huggingface dataset:
# Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
## 3 Experiment Results
### Implementation Details  
The proposed method is implemented using Pytorch 1.10. The experimental environment is on a computer with an Intel Core i7-10700 CPU and a NVIDIA RTX-3090 GPU. Simple data argumentation is used to enhance the diversity of the data, including rotation and flip. AdamW is used as the optimization algorithm for backpropagation. The learning rate for Stage 1 (building localization) is 0.00015 and for Stage 2 (damage classification) is 0.0002. The number of epochs for Stage 1 is 120 and for Stage 2 is 20. The pre-trained weights with ImageNet are used for initialization.
# Experimental and Numerical Study on UHPC-RC Decks within Hogging Moment Region
## 1 Introduction
[19] conducted an experimental investigation and nonlinear finite element analysis on a UHPC slab instead of conventional concrete; their results showed that the use of a UHPC slab can enhance the cracking performance of composite beams. [PERSON] et al. [20] experimentally studied the flexural behavior of composite slabs and beams under static and fatigue-negative moments. [PERSON] et al. [21] improved a type of ultralight steel UHPC waffle bridge deck used for movable bridges through comprehensive experimental analyses, and the size of the bridge deck and reinforcement configuration were adjusted to make its weight meet the use requirements for movable bridges. [PERSON] et al. [22] experimentally and numerically investigated the flexural behavior of steel-UHPC-RC composite girders and found that the use of UHPC-RC composite concrete deck can save costs on materials, as well as improve the anti-cracking performance. Some specifications provide suggestions for designing structures using UHPC. China's design specification [23] suggests that the design of composite decks using UHPC or SHPLC follow the specifications in the General Code for Design of Highway Bridges and Culverts [24], which does not consider the material characteristics of UHPC. Switzerland's design specification [25] highlights that UHPC has no resistance when a plastic hinge is generated; when the tension strain is over three times the elastic yield strain strength, UHPC no longer works, and all of the tension forces are carried by the rebars. Current research and design specifications on UHPC application in bridge decks mostly focus on full-UHPC decks, with little research having been conducted on composite decks using ultra-high-performance concrete (UHPC) and regular concrete (RC). Since 2015, a large amount of steel-concrete twin-I girder bridges (Figure 1) have been constructed in China, with the advantages of lower material costs and accelerated construction [1; 26]. Within the hogging moment regions, concrete deck cracking is an issue that affects the performance of bridge structures [1]. Double composite action cannot be applied to twin-I girders, since the system uses open sections, and the construction time and cost are increased to attach additional concrete on the bottom flange. Taking the advantages of UHPC, this paper proposes a UHPC-RC composite deck and studies the anti-cracking performance of a steel-concrete composite bridge using a UHPC-RC composite deck within the hogging moment region. Four specimens were tested to investigate the behavior of the UHPC-RC composite deck. A finite element model was established to compare the test results and to investigate the behavior under different conditions. Finally, the crack development law of the UHPC-RC composite deck is summarized, and a reasonable thickness for the UHPC layer is put forward to be suitable for a UHPC-RC composite deck within the hogging moment region, which provide a reference for the design of bridge structures using a UHPC-RC composite deck
## 6 Elimination of Degrees of Freedom
In order to set the surface condition \(\vec{T}\times\vec{n}=0\) on the bases of finite elements, the tangential surfaces associated with the element bases are extruded together with the finite elements, as shown in Figure 6b. Thus, all of the \(T_{i\alpha\beta}\) degrees of freedom belong to one of the created tangential surfaces, as denoted by \(\Gamma_{\text{t}}\). Because setting \(\vec{T}\times\vec{n}=0\) on \(\Gamma_{\text{t}}\) fixes the  
Figure 5: Geometrical decoupling of the edge degrees of freedom for the current vector potential. All of the edges are either parallel to the \(\gamma\)-direction or parallel to the \(\alpha\beta\) plane. The degrees of freedom \(T_{1}\), \(T_{2}\), \(T_{3}\), \(T_{7}\), \(T_{8}\), and \(T_{9}\) are parallel to the \(\alpha\beta\) plane, and are therefore redundant. Figure 6: A sequence of extruded finite elements: (**a**) without tangential surfaces \(\Gamma_{\text{t}}\); (**b**) with tangential surfaces \(\Gamma_{\text{t}}\). values of \(T_{ia\beta}\) degrees of freedom to \(T_{ia\beta}=0\), two-thirds of the total number of \(T_{i}\) degrees of freedom are eliminated from the simulation. In addition, (12) becomes redundant, i.e., the anisotropy of the electrical resistivity is already ensured by setting \(\widetilde{T}\times\vec{n}=0\) on the \(\Gamma_{\mathrm{t}}\) surfaces.
## 3 Results and Discussion
### Durability
The incorporation of EAFS usually improves the chloride diffusion coefficient relative to the reference concrete by about 10% [56], so it is expected that the behaviour of RAC with RCA based on EAFS can show an improvement over mixes with current RCA. Likewise, concrete with current RCA generally have higher chloride diffusion coefficient than concrete produced with NA. [PERSON] et al. [24] obtained average increases of more than 40% in the chlorides diffusion coefficient for total replacement of CRA produced from unsorted CDW. Other studies, however, argue that this property is unrelated with the RA replacement level [10], stating that it depends mostly on the quality of CRA. Drying shrinkage is a significant phenomenon during cement hydration. The difference in relative humidity between the environment and the specimen causes the water adsorbed to the hydrated paste to pass into the atmosphere following [PERSON]'s law. Drying the specimen involves a reduction in volume that can cause cracking of the paste in severe cases. The evolution of drying shrinkage for the different mixes is shown in Figure 11. The drying shrinkage obtained for RAC with total replacement is about 530 um/m at 91 days, while for NAC it is 457 um/m, so there is an increase of 13%. Figure 10: Chloride diffusion coefficient for all mixes and ages. It is well known that there is a strong relationship between drying shrinkage and modulus of elasticity in concrete [65]. The shape and texture of aggregates are variables that affect the drying shrinkage, although modulus of elasticity (or any of the variables that affect the latter) is the parameter that most affects the former [59]. An increase in modulus of elasticity generates a greater restriction against strain, i.e., stiffer aggregates more efficiently oppose the strain generated by shrinkage than more deformable ones. As stated before, the modulus of elasticity drops with increase of the replacement ratio (17% for full replacement) and drying shrinkage decreases similarly. Another factor to keep in mind is that RCA is still shrinking, especially if the source concrete is young concrete, while NA do not shrink. Recycling concrete that has already reached the end of its service life is not a problem, but in the case of laboratory-produced concrete (the case here) it is something to keep in mind. Shrinkage measured from RAC with laboratory-produced source concrete is thus expected to overestimate the shrinkage of RAC. Losses for total RCA replacement range from 10% [10] to 50% [43], the latter being the value established in the Xuping review as a typical value for RAC.
## 2 The Method for Self-Healing Efficiency Estimation
### Adaptive Filter Preparation
#### 2.1.3 Filter Mask  
An adaptive filter mask is created by thresholding averaged wavefield image in the wavenumber-wavenumber domain as:  
\[M\Big{(}k_{x},k_{y}\Big{)}=\left\{\begin{array}{ll}0&if\quad W_{avg}\Big{(}k_ {x},k_{y}\Big{)}>\text{{threshold}}\\ 1&\text{otherwise}\end{array}\right. \tag{4}\]  
The threshold value is set based on a cumulative histogram of averaged wavefield image values to be higher than 98% of image points. In other words, 2% of averaged wavefield image points with the highest values become 0, while all other points are set to 1. In this way, the filter mask is designed to remove the main wave propagation component, leaving information about any abnormalities. An example of a filter mask used in these studies is presented in Figure 4.
# Reliability Analysis of CFRP-Packaged FBG Sensors Using FMEA and FTA Techniques
## 1 Introduction
Currently, the FMEA and FTA techniques have been widely applied to engineering for their well-established and well-understood features, and their effectiveness in improving system reliability and availability has been proven [18; 19; 20; 21]. In this regard, based on the need for an in-depth reliability analysis for CFRP-packaged FBG sensors, this paper will study the reliability and availability of CFRP-packaged FBG sensors applied in WTB full-scale structural testing by using the FMEA and FTA techniques, and provide the foundation for analyzing performance degradation and functional failures in CFRP-packaged FBG sensors. Figure 1: WTB full-scale structural testing: (**a**) static testing; (**b**) fatigue testing. Figure 2: Gauge and gauge wirings in WTB full-scale structural testing. The remainder of this paper is organized as follows. In Section 2, the principle of the FBG sensor and the structure of CFRP-packaged FBG sensors are briefly introduced. In Section 3, reliability analysis of CFRP-packaged FBG sensors using the FMEA technique is conducted, the failure modes of CFRP-packaged FBG sensors as well as the causes are ascertained, the effects of the failure modes are analyzed, and the RPN of each failure item is calculated. Based on FMEA, three key faults with a higher RPN share are further analyzed using the fault tree analysis (FTA) technique in Section 4, the minimum cut set of each key fault is determined, and the failure probability as well as the property weightiness is calculated. In Section 5, the conclusions are established.
## 2 Materials and Methods
### Methods  
#### 2.3.1 Meteorological Drought Indices Calculation  
Based on temperature (\(T\)) and relative humidity (\(RH\)), monthly VPD can be obtained using the following formula:  
\[VPD=0.61078\times e^{(\frac{17.27\times T}{17\times 2373})}\times\left(1-\frac{RH}{ 100}\right)\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Vegetation Types** & **Area Proportions (\%)** \\ \hline Evergreen Forests & 3.3 \\ Deciduous Forests & 5.2 \\ Mixed Forests & 2.2 \\ Woody Savannas & 12.7 \\ Savannas & 8.1 \\ Grasslands & 48.6 \\ Croplands & 19.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vegetation types and their area proportions of the selected study area.  
SPEI is a probability distribution function that fits and normalizes cumulative water scarcity and can characterize the wetness and dryness of a region. It can be calculated using the difference between precipitation and potential evapotranspiration. The SPEI is characterized by multiple time scales. Furthermore, to match the time scale of GPP, this study calculated the SPEI at the 1-month scale. The Hargreaves model, which has strong accuracy in China [35], was used to estimate the monthly PET. The generalized extreme value (GEV) distribution was chosen to normalize the SPEI, as it has been shown to have strong stability in calculating shorter time scales SPEI [42].  
Usually, When SPEI is less than \(-0.5\), drought is indicated [15]. Previous studies have focused on the effect of dryness/wetness on vegetation [19; 43]. In order to investigate the effect of drought on vegetation GPP, all drought months (i.e., months with SPEI less than \(-0.5\)) in each year were selected. The sum of the absolute SPEI values of all drought months, along with the sum of VPD and the mean GPP values corresponding to the drought months, were calculated. The calculation equations are as follows:  
\[SPEI_{drought\_month}=\left|\sum_{i=1}^{n}SPE_{i}\right|(SPEI_{i}<-0.5)\]  
\[VPD_{drought\_month}=\sum_{i}^{n}VPD_{i}\]  
\[GPP_{drought\_month}=\frac{\sum\limits_{i}^{n}GPP_{i}}{n}\]  
where \(SPEI_{drought\_month}\) represents the sum of the absolute values of SPEI for all drought months in a year, \(VPD_{drought\_month}\) represents the sum of the values of VPD for all drought months in a year, \(GP_{drought\_month}\) represents the average GPP of all drought months in a year, \(i\) represents the \(i\)th month, and \(n\) represents the number of drought months in a year.
## 2 Novel LEO Augmented GNSS Receiver System Structure  
To solve the problem of the low sensitivity of GNSS receiver acquisition and tracking under weak signal conditions, we propose a novel LEO augmented system and receiver structure. The proposed system differs from the other LEO augmented system in transmitting the GNSS ephemeris to receivers. As shown in Figure 1, the receiver includes both LEO and GNSS antennas and RF front ends, and the two RF parts share the same oscillator. The receiver extracts Doppler information and GNSS satellite ephemeris information transmitted by LEO satellites through baseband processing of low-orbit satellite signals. The initial positioning of the receiver can be estimated by the Doppler information. Then, the receiver assists the baseband processing of the GNSS signal through the initial position and the received information. In the implementation, the receiver uses the position information and the GNSS satellite ephemeris to calculate the line of sight (LOS) between the receiver and the GNSS satellite. With the help of estimated pseudo-range and Doppler information, the frequency and code search intervals of the GNSS signal can be greatly reduced, which means that the acquisition efficiency can be improved. On the other hand, the receiver can estimate the initial clock frequency using the Doppler positioning algorithm. The receiver takes the orbit, time, Doppler shift, oscillator, and GNSS navigation message as auxiliary information to assist in extending the coherent integration time, thereby effectively compressing the bandwidth and improving the sensitivity of acquisition and tracking. This will eventually improve the positioning performance in the GNSS weak signal environments.
# An Iterative Optimization Algorithm for Planning Spacecraft Pathways Through Asteroids
## 1 Introduction and Related Work
Given the combinatorial nature of the ARP, traditional Gaussian-based Bayesian optimization techniques are not applicable. In [5], two specialized algorithms were used to tackle the ARP: (i) CEGO (Combinatorial Efficient Global Optimization)a distance-based Bayesian optimization method tailored for combinatorial problems; and (ii) UMM (Unbalanced Mallows Model) an estimation distribution algorithm specifically designed for costly permutation optimization problems. Though CEGO and UMM are, to the best of our knowledge, the only algorithms that have been applied to the ARP, it is interesting to mention other algorithmic approaches for related space trajectory optimization problems, such as the Ant Colony Optimization variant proposed in [10], the tree search methods considered in [12; 17], a reinforcement learning approach introduced in [18], the multi-agent genetic algorithm used in [19], and the integer linear programming approach considered in [20]. [PERSON] et al. [21] recently proposed a simple yet effective approach to permutation optimization problems, termed FAT-RLS, that outperformed both UMM and CEGO when a low budget for evaluation was considered. However, FAT-RLS was applied to standard permutation problems such as the Linear Ordering Problem and the Permutation Flowshop Problem. Therefore, the goal of this work is to investigate the application of FAT-RLS to a real costly permutation optimization problem, such as the ARP. The remainder of the article is structured as follows. Section 2 introduces permutation problems along with other preliminary concepts. The computational details of the ARP are provided in Section 3, followed by a description of the FAT-RLS algorithm in Section 4. Section 5 offers a brief overview of the competitor algorithms used in the experiments, while Section 6 discusses the conducted experiments. Finally, conclusions and potential future research directions are presented in Section 7.
# NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio
## 4 Experimental Validation
#### Evaluation Metrics  
For the localization performance assessment, we analyze the pose estimation of the NR5G-SAM method against LIGO-SAM with the average RMSE metric. This metric measures the average deviation of the estimated positions from the ground truth and is expressed by:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(\mathbf{x}_{x,y}^{(i)}-\widehat{\mathbf{x}_{x,y}^ {(i)}})^{2}}, \tag{17}\]  
where \(\mathbf{x}_{x,y}^{(i)}=(x_{i},y_{i})\) are the ground truth positions and \(\widehat{\mathbf{x}_{x,y}^{(i)}}=(\widehat{x_{i}},\widehat{y_{i}})\) are the estimated positions.  
We perform the evaluation considering the 2D pose estimation in our experiments due to the large z error, which is associated with multilateration in radio-based positioning. The reason being that the accuracy of the estimated position is affected by several factors, most importantly by the number and distribution of gNBs, the accuracy of their estimated position, and the measurement noise. The latter is proportional to the signal propagation time as the speed of light is constant and the measurement noise is proportional to the distance traveled by the signal, which is larger in the z-axis than in the x- and y-axes and was identified during our experiments. Additionally, the genetic algorithm may converge to sub-optimal solutions if the noise in the measurements is too large, which can subsequently lead to erroneous z estimates on the z-axis and has been identified through design steps of the proposed method. In the future, we consider benefiting from sensor fusion with other sensors, such as an altimeter, to improve the accuracy of the z estimates in real-world experiments.  
As a second metric, the standard deviation of the errors for each estimated trajectory is given by Equation (18). This represents the expected deviation between the expected error for the estimations performed by the 5G positioning, NR5G-SAM and LIGO-SAM approaches, and the ground truth.  
\[Sn_{(x,y)}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_{i}-\mu_{x})^{2}(y_{i}-\mu_{y})^{2}} \tag{18}\]  
Regarding the mapping process, we propose REM as an initial representation of how the signal fades along the environment for qualitative evaluation. The multi-layer grid map REM provides a comprehensive representation of the radio signal environment and allows the robot to not only identify areas with strong and weak signal strengths, but also provide a unique RSSI feature vector for each position. It is noteworthy that, in this work, the RSSI vector played a key role in improving the robot pose estimation provided by the 5G NR-based approach, even though the REM formed by such a vector over time was not fully explored. Nonetheless, the qualitative assessment of the REMs offered here intends to emphasize the use of the BEB as a critical loop closure component of the proposed architecture.
# CONDA-PM--A Systematic Review and Framework for Concept Drift Analysis in Process Mining
## 6 The CONDA-PM Framework
### Goals Design Phase  
The ultimate goal of this phase is to define the basic aims of the approach for concept drift analysis in process mining. In this phase, two tasks whose outputs represent important determinants of the approach, need to be accomplished:  
* _G01:_ Defining the granularity level at which the approach operates. This task is concerned with specifying whether the approach is going to analyse concept drift at the process type level (variability) or process instance level (flexibility). To evaluate the maturity of an approach in this respect, we use a 3-value scale (1, 2, 3) to indicate whether the approach analyses drifts at process level or process instance level or both, respectively. We tend to give higher values to flexibility, as concept drifts are more captured at less granularity levels, that is, the process instance level in this situation. At the process level, changes tend to be more planned, while capturing changes at the process instance level allows reflecting unplanned changes.
* _G02:_ Configuring the handling mode in which a process instance is analysed. This task requires specifying whether the approach is going to address concept drift in an online mode (e.g., when many running process instances are under execution), or drift analysis is going to be addressed in an offline mode (i.e., after all process instances are complete and recorded in the event log). To evaluate maturity of an approach in this respect, we use a 2-value scale (1, 2) to indicate whether the approach analyses drifts in offline or online mode, respectively. We tend to give higher values to analysing concept drift in an online mode, as the outcomes of conducting online analysis of concept drift might increase robustness, that is, not just responding to change, but also overseeing it and carrying on proactive decision making process. Furthermore, analysing drifts online fosters predictive monitoring and provides more insights into the current status of a running process instance.
## 2 Related Work
### Network Improvements with Sink Mobility  
In examining the most compelling reason for mobilising sink nodes, that being the improvement in energy consumption versus the use of a static sink node, [28] uses a linear mobility model and claims a 500% increase in network lifetime versus a static network. This operates by utilising a sojourn time, which is commonly referenced in other work, where the sink lingers at a node, in this case, to limit time until the first node runs out of power. More recent studies have progressed to novel approaches such as to strategically alter the sink trajectory, such as in [29] where trajectory is altered based on node density, with the aim to pass every single node. This utilises a novel approach of a \"space-filling curve\" [30]. This study suggests a 20% increase in packet delivery ratio (PDR) when a dynamic curve approach is in use. However, given the reliance on node density as a factor, scalability could become problematic. Energy consumption does not appear to be a priority within this study; also, at the MAC layer, 802.11 is used as standard, with no reference to this beyond test parameters. As such, whilst the results produced in this study have merit, the approach is not directly relatable to an MAC layer approach to sink mobility.  
Sink trajectory is again a factor in [31]. A real-world application of an MSN, the FarmBeats platform is developed for precision architecture by using algorithms developed to ensure effective path-planning to extend the battery life of drones--the MSNs in this scenario. This paper also makesreference to the importance of duty cycling in the base stations but does not base this on the mobility of the MSN. This work is extended in [32] by additionally utilising two residual energy thresholds, one for the entire network and one for the path of the MSN. This is then used to modify the existing FarmBeats [31] algorithms in terms of duty cycling and path selection. The authors claim benefits in terms of processing time and energy performance.