esa-sceva/satcom-chunk-collection huggingface dataset:
# Cascaded Reinforcement Learning Agents for Large Action Spaces in Autonomous Penetration Testing
## 4 Methodology
### Algorithms Pseudocode  
The pseudocode for the main CRLA algorithm is given in Algorithm 1 and the subroutine for initialising the agents given the environment action space \(\mathcal{U}\) and a branching factor \(b\) is provided in Algorithm 2.  
Figure 4: Parallel training and execution. All agents share the replay buffer from which experiences can be sampled in batches and used for training in parallel.  
```
1:Initialise a shared replay buffer \(\mathcal{D}\)
2:Initialise a list of agent \(\mathbf{A}\) = initialise_agents(env)
3:Initialise QMIXER(\(\theta\)) and QMIXER_target(\(\theta\))
4:for i_episode = 0... MAX_EPISODES do
5:Initialise an empty set of compromised hosts \(\mathbf{H}=\emptyset\)
6:Reset the environment \(S_{0}\) = env.reset()
7:\(s=S_{0}\)
8:for step = 0... MAX_STEPS do
9:for agent in agent_list do
10:\(a^{t}=e\_\)greedy(s, \(e^{i}\))
11:endfor
12:Construct final primitive action: \(\mathbf{u}=f(a^{0},a^{1},a^{2},...,a^{k})\)
13:Collect \((s,u,r,s^{t},d)\) by executing action \(\mathbf{u}\) on the environment
14:Perform reward shaping and update \(\mathbf{H}\) if needed:
15:\(r=\) reward_function(state, \(\mathbf{H}\), \(r\))
16:Store \((s,\mathbf{u},r,s^{t},d)\) into \(\mathcal{D}\)
17:Sample a batch of \((s,\mathbf{u},r,s^{t},d)\) from \(\mathcal{D}\) for learning
18:Compute q-value for each agent \(\mathbf{q}^{i}(s,a^{i})\)
19:Compute target q-value for each agent \(\mathbf{q}^{target-i}(s^{t},a^{i})\)
20:\(Q(s,\mathbf{u})=\text{concat}(\mathbf{q}^{i}(s,a^{i}))\) from all agents
21:\(Q^{target}(s,\mathbf{u})=\text{concat}(\mathbf{q}^{target-i}(s^{t},a^{i}))\) from all agents
22:Compute \(\mathbf{Q}_{tot}(s,\mathbf{u})=\text{QMIXER}(Q(s,\mathbf{u}),s)\)
23:Compute \(\mathbf{Q}_{tot}^{target}(s,\mathbf{u})=\text{QMIXER}(Q^{target}(s,\mathbf{u}),s)\)
24:Compute TD target \(y^{tot}=r+\gamma\mathbf{Q}_{tot}^{target}(s,\mathbf{u})\)
25:Perform SGD to minimise \(L(\theta)=MSE(\mathbf{Q}_{tot}(s,\mathbf{u}),y^{tot})\)
26:endfor
```  
**Algorithm 1** CRLA--Cascaded Reinforcement Learning Agents  
```
1:def initialise_agents(env: Environment, b: branching factor)
2:begin
3:\(|U|=\text{env.action\_space}\)
4:\(|T|=\log_{\text{b}}(|U|)\)
5:Build a complete tree with branching factor b
6:Initialise a Dueling DQN agent for each action selection level
7:Return agent_list
8:end
```  
**Algorithm 2** Initialise the agents
###### Abstract  
We propose a probabilistic shaping (PS) scheme based on a single-layer lookup table (LUT) that employs only one LUT for symbol mapping while achieving favorable system performance. This scheme reduces the average power of the signal by adjusting the symbol distribution using a specialized LUT architecture and a flexible shaping proportion. The simulation results indicate that the proposed PS scheme delivers performance comparable to that of the conventional constant-composition distribution-matching-based probabilistic shaping (CCDM-PS) algorithm. Specifically, it reduces the bit error rate (BER) from \(1.2376\times 10^{-4}\) to \(6.3256\times 10^{-5}\), corresponding to a 48.89% improvement. The radial basis function neural network (RBFNN) effectively compensates for nonlinear distortions and further enhances transmission performance due to its simple architecture and strong capacity for nonlinear learning. In this work, we combine lookup-table-based probabilistic shaping (LUT-PS) with RBFNN-based nonlinear equalization for the first time, completing the transmission of 16-QAM OFDM signals over a photonic terahertz-over-fiber system operating at 400 GHz. Simulation results show that the proposed approach reduces the BER by 81.45% and achieves a maximum Q-factor improvement of up to 23 dB.  
p _electronics_2025, 14, 2677
## 2 Supervisory and Control System Based on Real-Time Power Management
### Prediction Layer  
Meteo France provides hourly predictions allowing the calculation of PV power prediction, which is based on solar irradiation (g) and ambient temperature (\(T_{amb}\)) forecast data [45]. The PV power prediction \(p_{PV\_MPPT\_pred}\) is calculated in MPPT mode for each time instant \(t_{i}\)[46] as given in following equations:  
\[\begin{array}{c}p_{PV\_MPPT\_pred}(t_{i})=P_{PV\_STC}.\frac{g(t_{i})}{1000} \cdot[1+\gamma\cdot(T_{PV}(t_{i})-25)]\cdot N_{PV}\\ with_{i}=\{t_{0},t_{0}+\Delta t,t_{0}+2\Delta t,\ldots,t_{F}\},\end{array} \tag{1}\]  
\[T_{PV}(t_{i})=T_{amb}(t_{i})+g(t_{i})\cdot\frac{NOCT-T_{air\_test}}{G_{test}}, \tag{2}\]  
where \(P_{PV\_STC}\) is the PV power under standard test conditions (STC), \(\gamma\) is the power temperature coefficient (\(-0.29\%/^{\circ}\)C), \(T_{PV}\) is the PV cell temperature, \(N_{PV}\) is the number of PV panels, \(t_{0}\), \(\Delta t\), and \(t_{F}\) are the initial time instant, time interval between two samples, and time instant at the end of time operation, respectively, \(NOCT\) is the nominal operating cell temperature (41 \({}^{\circ}\)C), \(T_{air\_test}\) is the fixed air temperature (20 \({}^{\circ}\)C), and \(G_{test}\) is the fixed solar irradiation (800 W/m\({}^{2}\)).
# Forklift Tracking: Industry 4.0 Implementation in Large-Scale Warehouses through UWB Sensor Fusion
## 3 Numerical Analysis
### Effect of Forklift Speed  
The forklift speed might impact the UKF performance. To check it, we simulated the forklift trajectory as that depicted in Figure 3, but we changed the forklift speed among the values \(v=[0.75,1.11,1.67,2.22,2.75]\) m/s. The number of samples for each trajectory was then resized in order to keep unchanged the path length to 75 m for all trials. For each speed value, we performed 100 test cases and then we computed the mean value of \(\epsilon_{d}\), named here as \(\tilde{\epsilon}_{d}\). We show the results in Figure 7. The UWB performance is here unchanged due to the assumed simulation model. Instead, the mean distance error for the dead-reckoning process (circular red markers) increases with the forklift speed, and so, also, the mean distance error of the UKF, due to the fact that the sensor fusion approach relies on both UWB and proprioceptive sensor data. On the other hand, the UKF keeps \"bounded\" the increasing of the mean distance error, which is lower than the error committed by the UWB system, even when the forklift travels at 2.75 m/s. Given the fact that forklifts usually cannot travel faster than 2-3 m/s for safety reasons, we can conclude that the UKF is robust with respect to the increase of the forklift speed.
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Variable** & **Min** & **Max** & **Mean** & **SD** & **Kurtosis** & \multicolumn{3}{c}{**Skewness**} & \multicolumn{3}{c}{**Cronbach Alpha**} \\ \hline Cyber-resilience & 10 & 70 & 58.02 & 5.948 & \(-\)1.137 & 0.087 & 1.997 & 0.204 & 0.846 \\ Cognitive instrumentals & 6 & 42 & 31.57 & 5.054 & \(-\)0.436 & 0.087 & 0.349 & 0.204 & 0.837 \\ Long-term orientation & 8 & 56 & 38.27 & 5.764 & \(-\)0.341 & 0.087 & \(-\)0.572 & 0.204 & 0.871 \\ Flexibility & 5 & 35 & 23.12 & 4.546 & \(-\)0.796 & 0.087 & 0.746 & 0.204 & 0.915 \\ Trust & 5 & 35 & 21.57 & 4.208 & \(-\)0.391 & 0.087 & 0.724 & 0.204 & 0.894 \\ Social influence & 8 & 56 & 41.05 & 5.567 & \(-\)0.468 & 0.087 & 0.486 & 0.204 & 0.952 \\ Perceived ease of use & 3 & 21 & 17.93 & 3.218 & \(-\)0.694 & 0.087 & 0.782 & 0.204 & 0.914 \\ Perceived usefulness & 4 & 28 & 19.81 & 3.957 & \(-\)0.504 & 0.087 & 0.297 & 0.204 & 0.831 \\ Attitude & 3 & 21 & 17.82 & 3.168 & \(-\)0.911 & 0.087 & 1.146 & 0.204 & 0.927 \\ Behavioral intention & 5 & 35 & 31.42 & 4.347 & \(-\)0.964 & 0.087 & 1.108 & 0.204 & 0.931 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Descriptive statistics of the latent variables and the reliability coefficients. The overall fit of the model was assessed with the following goodness of fit indices representing absolute, comparative and parsimonious aspects of fit: Chi square-\(\chi^{2}\)/degree of freedom [53], goodness of fit (GFI): \(>\)0.90 [53; 54], adjusted goodness of fit index (AGFI): \(>\)0.90 and root mean square error of approximation (RMSEA): \(<\)0.05 [54]. The overall model fit statistics indicated an overall acceptable fit between data and the conceptual model, proving the adequacy of the model: GFI = 0.93, AGFI = 0.93, RMSEA = 0.04, x2(127) = 407.26, \(p<0.001\). Attitude interprets 60.4%, facilitated appropriation 21.6%, PU 50.8% and PEOU 51.7% of the variances. In total, the proposed model explains 71.8% of the behavioral intention variances, since the concerned R2 = 0.718. Figure 2 presents the results of the structural model, including path coefficients and variances explained. Table 5 exhibits standardized and unstandardized estimates of the model for each endogenous variable. The model included five exogenous variables: user character, cyber resilience, cognitive instrumental, social influence and trust. The only endogenous outcome in Figure 2 was behavioral intention--the final outcome. The remaining variables, facilitated appropriation, perceived usefulness, perceived ease of use and attitude, were included as endogenous variables that mediate some part of the effect of antecedent variables on subsequent variables. Each of these endogenous variables are explained by other variables in the model (Table 5). Results indicate that PEOU is a stronger predictor of attitude than PU, as well as that PEOU predicts PU stronger than facilitated appropriation (Table 5).
# Dual-Window Superpixel Data Augmentation for Hyperspectral Image Classification
###### Abstract  
Deep learning (DL) has been shown to obtain superior results for classification tasks in the field of remote sensing hyperspectral imaging. Superpixel-based techniques can be applied to DL, significantly decreasing training and prediction times, but the results are usually far from satisfactory due to overfitting. Data augmentation techniques alleviate the problem by synthetically generating new samples from an existing dataset in order to improve the generalization capabilities of the classification model. In this paper we propose a novel data augmentation framework in the context of superpixel-based DL called dual-window superpixel (DWS). With DWS, data augmentation is performed over patches centered on the superpixels obtained by the application of simple linear iterative clustering (SLIC) superpixel segmentation. DWS is based on dividing the input patches extracted from the superpixels into two regions and independently applying transformations over them. As a result, four different data augmentation techniques are proposed that can be applied to a superpixel-based CNN classification scheme. An extensive comparison in terms of classification accuracy with other data augmentation techniques from the literature using two datasets is also shown. One of the datasets consists of small hyperspectral small scenes commonly found in the literature. The other consists of large multispectral vegetation scenes of river basins. The experimental results show that the proposed approach increases the overall classification accuracy for the selected datasets. In particular, two of the data augmentation techniques introduced, namely, dual-flip and dual-rotate, obtained the best results.  
hyperspectral; classification; deep learning; CNN; superpixel; SLIC; data augmentation 2020 10 8833 10.3390/app10248833
## 4 An Indoor Positioning Algorithm Combining IMU and LBP-FFT-CNNs
### Inertial-Based Positioning Algorithm Using IMU
#### 4.1.3 Gravity Compensation  
The measured acceleration includes both linear acceleration \(\overset{\rightarrow}{a}_{linear}\) and gravitational acceleration \(\overset{\rightarrow}{\widetilde{g}}\). Gravity is removed to compute linear acceleration:  
\[\overset{\rightarrow}{a}_{linear}=\overset{\rightarrow}{a}_{raw}- \overset{\rightarrow}{\widetilde{g}} \tag{20}\]  
where \(\overset{\rightarrow}{\widetilde{g}}\) is a gravity vector, typically derived from orientation information.
## 5 Implementation
### Phase 3--Selected Methodology Application
#### 5.3.2 Improve and Deploy/Stabilization/Standardization/Optimization
The comparison of the process before and after removing the first-off inspection manual step is given in Table 8. The operational efficiency was set as 82% by the organization. Thus, the process was optimized with an improvement made to the standardization aspect. After implementing the first-off removal, the validation process included:  
* Creating a change control to manage the implementation in a structured manner. * Completing a risk assessment. * Executing a sectional installation qualification (IQ). * Setting a target or nominal value for the operation qualification (OQ) and performance qualification (PQ). The success of the process validations can be determined by a process capability calculation. A confidence statement needed to be developed for the outcome (pass or fail) of the validation steps. Thus, the manual interaction involving first-off inspection was removed from the process; and removing first-off inspection added value to the process by reducing cycle-time and increasing product output.
## 7 5G Radio Propagation Mechanisms
### CIF Method: MMSE-Based Parameters  
From Equation (24), after changing sides of the equation, assuming Equations (27)-(30):  
\[F=PL^{CIF}(f,d)[\text{dB}]-L(f,d_{0}) \tag{27}\]\[Z=10 log\bigg{(}\frac{d}{d_{0}}\bigg{)} \tag{28}\]  
\[p=n(1-b) \tag{29}\]  
\[q=\frac{nb}{f_{0}} \tag{30}\]  
Equation (31) is obtained:  
\[\sigma^{ABG}=\sqrt{\sum\frac{X_{\sigma}^{ABG^{2}}}{D}}=\sqrt{\sum\frac{\left(A- \alpha L-\beta-\gamma R\right)^{2}}{D}}=F-Zp+qf \tag{31}\]  
The SF standard deviation is given by (32):  
\[\sigma^{CIF}=\sqrt{\sum(S_{\mu,\sigma}^{CIF})^{2}/N}=\sqrt{\sum\left(F-Z(p+qf) \right)^{2}/N} \tag{32}\]  
Minimizing \(\sigma^{CIF}\) means minimizing \(\sum\left(F-Z(p+qf)\right)^{2}\), which in turn results in its derivatives with respect to \(p\) and \(q\) becoming zero, yielding (33) and (34):  
\[\partial\sum\left(F-Z(p+qf)\right)^{2}/\partial p=\sum 2Z(pZ+qZf-F)=2\Big{(}p \sum Z^{2}+q\sum Z^{2}f-\sum ZF\Big{)}=0 \tag{33}\]  
\[\partial\sum\left(F-Z(p+qf)\right)^{2}/\partial p=\sum 2Z(pZ+qZf-F)=2\Big{(}p \sum Z^{2}f+q\sum Z^{2}f^{2}-\sum ZF\Big{)}=0 \tag{34}\]  
Simplification and combination give (35) and (36):  
\[p=\sum Z^{2}f\sum ZF-\frac{\sum Z^{2}f^{2}\Sigma ZF}{\left(\sum Z^{2}f\right) ^{2}}-\sum Z^{2}\sum Z^{2}f^{2} \tag{35}\]  
\[q=\sum Z^{2}f\sum ZF-\frac{\sum Z^{2}\sum ZFf}{\left(\sum Z^{2}f\right)^{2}}- \sum Z^{2}\sum Z^{2}f^{2} \tag{36}\]  
The closed-loop solution of the assumed terms \(p\) and \(q\) was derived in Equations (35) and (36). Inserting \(p\) and \(q\) into Equation (30) yields the standard derivation of the shadow factor. By using the initial definition \(p=n(1-b)\) and \(q=\frac{nb}{f_{0}}\), the values of \(n\) and \(b\) can be computed. B. ABG Model  
To determine the propagation loss at various frequencies, the ABG model, which is a three-parameter multi-frequency model consisting of frequency- and distance-dependent terms can be exploited. The ABG model equation is given by (37)  
\[Z=10 log\bigg{(}\frac{d}{d_{0}}\bigg{)} \tag{28}\]0  
where \(\alpha\) and \(\gamma\) are the link's path length and frequency components of path loss, respectively, \(f\) is the frequency in GHz, and \(\beta\) is a term with no physical significance. The \(X_{\sigma}^{ABG}\big{(}\mu,\sigma^{ABG}\big{)}\) parameter is a Gaussian random variable that describes large-scale variations in the mean path loss between transmitter and receiver. Essentially, the ABG model is a multi-frequency extension of the FI model. It can be shown that when \(\gamma=0\) or \(2\) is used and deployed for a single frequency, it transforms into an FI model. The MMSE-based optimization technique can be used to find the best values for the \(\alpha,\beta\), and \(\gamma\) coefficients. If the ABG model is reduced to the CI, it will resemble the CI model in shape. If \(\alpha=20 log(4\pi/c)\), \(\beta\) will reduce to PLE \(n\), and \(\gamma\) will reduce to 2.
# Position Selection and Collision-Free Path Planning for Fruit Picking Robots
## 6 Conclusions  
In this paper, research on the automation of Kanpei harvesting robots has been conducted. Using the proposed inverse reachability map based algorithm for selecting fruit harvesting positions, it is possible to pre-select advantageous robot system positions for harvesting. This allows for multiple target fruits to be harvested from one fixed location without the overall movement of the robot system, resulting in time and energy savings. Although the fruits to be harvested still need to be determined by humans, it is advantageous to know in advance whether it is possible to harvest using only the manipulator without moving the base. And MPAPF was proposed for collision-free path planning that not only considers the end-effector but also the manipulator as a whole. Since the path is planned in joint space, the end-effector does not exceed the workspace, and the joint limits are also considered. By adding the target angle to the objective function and calculating the potential for multiple points, the problem of falling into local minima, which is a persistent issue encountered with APF, can be mitigated to some extent. Through simulations, successful harvesting of multiple fruits is possible using the position selection and path planning algorithms. Furthermore, experiments showed that collision-free paths are well-planned even in complex environments. However, the computation time increases significantly as the number of obstacles increases; therefore, improvements in the algorithm will be further pursued. Moreover, the entire system, including the mobile base, a vision system, and new types of end-effector, will be developed for experimental validation in a real Kanpei harvesting environment.  
Conceptualization, Y.C.; methodology, Y.C.; software, Y.C.; validation, Y.C.; formal analysis, Y.C.; investigation, Y.C.; resources, Y.C.; data curation, D.C.; writing--original draft preparation, Y.C.; writing--review and editing, J.H.P.; visualization, Y.C.; supervision, J.H.P.; project administration, K.K.; funding acquisition, D.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Ministry of Trade, Industry and Energy (MOTIE) and the Korea Evaluation Institute of Industrial Technology (KEIT) under Grant RS-2024-00443400 and by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, and Forestry (IPET) through the Open Field Smart Agriculture Technology Short-term Advancement Program, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) under Grant 122032-03-2-HD020.  
Figure 24: Clearances between joints and obstacles.
# A Novel Coding Based Dimming Scheme with Constant Transmission Efficiency in VLC Systems
## 5 Conclusions  
In this paper, we have proposed a block coding-based scheme to achieve dimming control, as well as data transmission with constant transmission efficiency, where a large dimming range can be achieved. Meanwhile, the encoding/decoding algorithm was provided accordingly. Via the bitwise AND operation on the block code and each subsection of dimming code to construct the proper codewords satisfying the dimming requirement, the proposed scheme is simple to implement and can maintain constant transmission efficiency under different dimming levels. Simulation results show that the proposed scheme can achieve reliable data transmission via the designed encoding/decoding structure. Therefore, it proves an attractive alternative dimming scheme.  
Figure 3: Error performance of the proposed scheme under different dimming factor.  
**Author Contributions:** All authors contributed equally to writing the paper. Formal analysis, Y.Z.; funding acquisition, J.Z.; writing, original draft, Y.Z.; writing, review and editing, J.Z.  
**Funding:** This work is supported in part by Grant No. 161100210200 from the Major Scientific and Technological of Henan Province, China.  
**Acknowledgments:** The authors wish to thank the anonymous reviewers for their valuable suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Cooperative Reception of Multiple Satellite Downlinks
## 3 Results and Discussion
### The Best Stream Is Repeatedly Injected
In a million-bit length of data streams, the least calculated ED between the message and the received versions \(r_{1:N}\) was 18.0831, whereas it was only 3.1623 between the message signal and the combined stream \(r_{c}\). This reflects how alike the combined stream and the original message are and how well the combined stream approached the original data distribution. The proposed improvement algorithms were tested for consistency to different data sample sizes and showed great scalability by reflecting the same system behavior. Table 2 summarizes the elapsed execution times for the 1-milion data set used in this study in addition to other different data lengths. Figure 9: BER curves from raw and the proposed SP algorithms: (a) linear, (b) logarithmic.
# Assessment of Existing Bond Models for Externally Bonded SRP Composites
## 4 Results and Discussion
### Precision of Predictions Compared to CFRP Composites  
The accuracy of the prediction models for SRP bonded to concrete was not any less than that for CFRP composites. Table 7 shows a comparison of prediction error for selected models (according to mean ratios given in Table 4) and is available in the literature results of similar assessments for CFRP strips. In most cases, the differences did not exceed ten percentage points, and for the previously favored models, [PERSON] et al. [21] and [PERSON] and [PERSON] [28] were practically identical. Similarly to the SRP, can be found models that tended to overestimate and underestimate the results. This proved that SRP and CFRP composites were very similar in terms of bond strength.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Mean** & **SD** & **PCC** & **\% Uncertainty** \\ \hline [PERSON] [14] & 0.882 & 0.344 & 0.049 & 27\% \\ [PERSON] and [PERSON] [15] & 0.984 & 0.277 & 0.526 & 42\% \\ [PERSON] et al. [16] & 1.31 & 0.57 & \(-\)0.174 & 54\% \\ [PERSON] [17] & 0.848 & 0.499 & \(-\)0.241 & 35\% \\ [PERSON] [18] & 0.812 & 0.482 & \(-\)0.264 & 31\% \\ [PERSON] and [PERSON] [19] & 1.043 & 0.619 & \(-\)0.264 & 50\% \\ [PERSON] and [PERSON] [20] & 0.851 & 0.501 & \(-\)0.241 & 35\% \\ [PERSON] et al. [21] & 0.965 & 0.499 & \(-\)0.274 & 50\% \\ [PERSON] et al. [22] & 1.71 & 0.832 & \(-\)0.245 & 69\% \\ [PERSON] and [PERSON] [23] & 1.16 & 0.802 & 0.099 & 46\% \\ [PERSON] et al. [24] & 0.861 & 0.539 & \(-\)0.215 & 27\% \\ [PERSON] et al. [25] & 0.589 & 0.339 & \(-\)0.248 & 8\% \\ [PERSON] and [PERSON] [26] & 2.68 & 1.43 & 0.190 & 96\% \\ [PERSON] et al. [27] & 1.41 & 1.09 & \(-\)0.354 & 54\% \\ [PERSON] and [PERSON] [28] & 1.01 & 0.52 & \(-\)0.274 & 50\% \\ [PERSON] et al. [29] & 1.83 & 0.850 & \(-\)0.210 & 77\% \\ [PERSON] et al. [30] & 0.821 & 0.441 & \(-\)0.275 & 22\% \\ [PERSON] and [PERSON] [31] & 1.39 & 0.52 & 0.146 & 62\% \\ TR55/2000 [33] & 0.554 & 0.332 & \(-\)0.061 & 8\% \\ Fib Bulletin 14/2001 [34] & 0.937 & 0.007 & \(-\)0.261 & 42\% \\ JCI 2003 [35] & 1.02 & 0.604 & \(-\)0.259 & 50\% \\ SIA 166/2004 [36] & 1.01 & 0.541 & \(-\)0.244 & 50\% \\ CNR-DT200R1/13 [37] & 1.02 & 0.595 & \(-\)0.220 & 50\% \\ Fib Bulletin 90/2019 [38] & 1.08 & 0.58 & \(-\)0.273 & 50\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of results—predicted to experimental bond strength ratios for steel-reinforced grout (SRG) composites bonded to concrete.