esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Comparisons with State of the Art  
Tables 1 and 2 demonstrate the performance of recent advanced weakly supervised methods and ours on Charades-STA and ActivityNet-Captions datasets, respectively. Our method outperformed other algorithms on both datasets except for R@1 [EMAIL_ADDRESS] on Charades-STA dataset. This is because CPL [39] employs Transformer as the backbone. Our runtime was the longest because the strategy selection and implementation in the reinforcement learning part required multi-step iterative calculations with global computation. The superiority of our method on other metrics, especially the more rigorous metrics, e.g., [EMAIL_ADDRESS] and [EMAIL_ADDRESS], verified the effectiveness of the RL-based framework.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**R@1**} \\ \cline{2-5}  & **IoU = 0.3** & **IoU = 0.5** & **IoU = 0.7** & **Runtime/s** \\ \hline CTF [10] & 39.80 & 27.30 & 12.90 & - \\ SCN [11] & 42.96 & 23.58 & 9.97 & - \\ VLANet [12] & 45.24 & 31.83 & 14.17 & 0.3 s \\ LoGAN [14] & 48.04 & 31.74 & 13.71 & - \\ CCL [41] & - & 33.21 & 15.68 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation results on the Charades-STA dataset.
# Blockchain-Based Decentralised Authentication in Closed Environments
## 3 System Model and Methods
### System Model  
This study applied the principles of blockchain to design a decentralised authentication system that would operate in a closed management system; i.e., a private blockchain was utilised in this setting. Figure 1 illustrates the overall architecture and operation of the proposed authentication system. In the system architecture, the nodes are connected to the blockchain network. Figure 1 shows the nodes that can act both as a supplement and an authenticator, where an authenticator is a validator node \(V_{i}\) in the context of the blockchain. In this private architecture, any number of validator nodes can exist, each of which possesses a blockchain (or ledger) containing authentication credentials and validated transactions.  
As illustrated in Figure 1, the proposed decentralised authentication operates as follows: (1) when an authentication request is received by a validator node (nodes \(V_{i}\) in Figure 1), it operates in the way blockchain is designed to, which is to broadcast the request to all other nodes; (2) then, each node processes the authentication independently by validating the received information with the blockchain or ledger held by the node; (3) to confirm whether or not the authentication is a success, a consensus must be reached by all nodes; and (4) finally, the requesting node is informed of the authentication result.
# Performance Analysis of Hybrid PDM-SAC-OCDMA-Enabled FSO Transmission Using ZCC Codes
## 6 Simulation Results
### Impact of WSF and DSF on PDM-SAC-OCDMA-Enabled FSO Transmission  
Furthermore, the performance of the six users of the PDM-SAC-OCDMA-enabled FSO transmission is studied under snowfall conditions. From Figures 8 and 9, the proposed model can reach propagation range under WSF longer than that reached under DSF. This result is expected as DSF causes higher attenuation than WSF. As an example, the FSO link achieved under WSF for User 1 is 1.2 km with log(BER) of \(-10.99\) and Q-factor of 6.70. Under DSF, and at the same values of log(BER) and Q-factor, the range is decreased to 0.255 km.This result is expected as DSF has a large attenuation value when compared to WSF.  
The computed performance values at 1.2 km under WSF and 0.26 km under DSF for the six users are given in Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Users**} & \multicolumn{2}{c}{**Log(BER)**} & \multicolumn{2}{c}{**Q-Factor**} \\  & **WSF (1.2 km)** & **DSF (0.26 km)** & **WSF (1.2 km)** & **DSF (0.26 km)** \\ \hline
1 on \(X_{polar}\) & \(-10.99\) & \(-9.70\) & 6.70 & 6.25 \\
2 on \(X_{polar}\) & \(-12.49\) & \(-10.88\) & 7.19 & 6.66 \\
3 on \(X_{polar}\) & \(-14.43\) & \(-13.61\) & 7.77 & 7.53 \\
4 on \(Y_{polar}\) & \(-10.74\) & \(-9.84\) & 6.62 & 6.30 \\
5 on \(Y_{polar}\) & \(-12.40\) & \(-11.12\) & 7.16 & 6.74 \\
6 on \(Y_{polar}\) & \(-15.59\) & \(-12.44\) & \(8.10\) & 7.17 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance results for snow weather conditions.  
Figure 7: Q-factor for PDM-SAC-OCDMA-enabled FSO transmission versus range under (**a**) LF, (**b**) MF, (**c**) HF weather.
# Iterative MLFMA-MADBT Technique for Analysis of Antenna Mounted on Large Platforms
## 4 Numerical Results  
In this section, several numerical examples are presented to demonstrate the accuracy and efficiency of the proposed iterative MLFMA-MADBT method in this paper. In addition, the numerical results of the proposed method are compared with those of the MLFMA and the MLFMA-PO. In additional, the set of the transition region is the same for the MLFMA-PO method and the proposed method.  
For the MLFMA region in all examples, the mesh sizes are approximately 0.1\(\lambda\). For the BT region used in the iterative MLFMA-MADBT, because of the mesh-independent character of the MADBT method, the mesh sizes are extremely coarse for the BT region and the specific sizes are given in each example, respectively. As for the PO region used in the MLFMA-PO, the mesh sizes are 0.3\(\lambda\).  
All computations were carried out on a workstation with a 2.6 GHz Core E5-4620 v2 CPU equipped with 256 GB RAM and all digits were stored in double precision.
# Tamed Warping Network for High-Resolution Semantic Video Segmentation
## 2 Related Work
### Warping-Based Semantic Video Segmentation  
Researchers have proposed many warping-based approaches [30; 31; 33; 34]. Some works adopt warping as a temporal constraint to enhance features for the sake of accuracy [23; 25; 30]. For acceleration, [PERSON] et al. [34], [PERSON] et al. [33], and [PERSON] et al. [31] proposed to use feature warping to speed up their models. They divided frames into two types: key frames and non-key frames. Key frames are sent to the CNN for segmentation, while non-key frame results are obtained by warping. Recently, [PERSON] et al. [25] proposed to approximate high-level features by composing features from several shallower layers. These approaches speed up the inference phase since the computational cost of warping is much less than that of CNN. However, both the accuracy and robustness of these methods deteriorate due to the following reasons. First, neither optical flows nor motion vectors can estimate the precise motion of all pixels. There always exist unavoidable biases (errors) between the warped features and the expected ones. Second, in the case of consecutive non-key frames, cumulative errors lead to unusable results. To address error accumulation, [PERSON] et al. [32]and [PERSON] et al. [33] proposed to adaptively select key frames by predicting the confidence score for each frame. [PERSON] et al. [31] introduced bi-directional features warping to improve accuracy. However, all these approaches lack the ability to correct warped features.
## 4 In Vitro Susceptibility Testing Methods for _B. burgdorferi_ s.l.
### Microdilution in Combination with Microscopic MIC Determination  
A number of modified microdilution susceptibility testing methods using BSK medium have been developed over the years [52; 53]. In some tests, MIC values are measured by exposing an inoculum with a final density of \(\sim\)10\({}^{5}\) borreliae/mL, as determined in a Neubauer counting chamber, to logarithmic dilutions of antimicrobial agents. Sterile 96-well microtitre plates with the inclusion of positive and negative growth controls are used. The plates are then inoculated under sterile conditions before being sealed with adhesive plastic and incubated at 33 \({}^{\circ}\)C under anaerobic conditions for 72 h. Following incubation, dark-field microscopy is used to examine all wells for spirochete counts, morphology and motility [36; 37; 40; 41; 43; 44; 51]. Here, the MIC is commonly defined as the lowest concentration of an antimicrobial agent at which no motile or only very slightly motile spirochetes are observed in significantly reduced numbers using dark-field microscopy. Antimicrobial agents are normally tested in triplicate. MIC values, in which 50% (MIC\({}_{50}\)) and 90% (MIC\({}_{90}\)) of isolates are inhibited, are determined for each antibiotic used on the isolates tested. However, such experiments are laborious and MIC determination can be very imprecise as it highly depends on the individual investigator. Unfortunately, no internationally accepted standardized MIC breakpoints for antimicrobial agents currently exist for _Borrelia_ spp. [53; 54].
# On the Use of Bright Scatterers for Monitoring Doppler, Dual-Polarization Weather Radars
## 2 Materials and Methods
### The Dual-Polarization, Doppler Radar Located on Monte Lema at an Altitude of 1625 m
#### 2.1.1 The Scan Program with Reference to the Target under Investigation  
Each MeteoSwiss radar performs plan position indicator scans at 20 elevations, that is, from \(-0.2^{\circ}\) to 40\({}^{\circ}\), over a period of 5 min. Figure 2 in Reference [16] (p. 45) shows the Swiss scan strategy: It starts with sweep #1 at an elevation of 6.5\({}^{\circ}\) and ends with sweep #20 at 9.5\({}^{\circ}\) (the first two numbers in the figure). The 3 rd and 4 th numbers represent the number of antenna rotations per minute and the pulse repetition frequency, respectively, which are relevant for the 1\({}^{\circ}\) angular bin that contains the BS at the lowest angle of elevation. Since the PRF at this angle of elevation is 600 Hz, while the antenna takes 20 s to compute a 360\({}^{\circ}\) rotation, the BS is illuminated by a rotating antenna for 1/18 s. This means that each high-range resolution BS \"echo\" (observed every 5 min and referring to the same 1\({}^{\circ}\) azimuthal index) is the result of the spectral analysis of 600/18 \(\cong\) 33 pulses, when azimuthal index 349 is used.  
Physical Radar Observables (and Their Quantization) Coded in High Range Resolution (83.33 m) Radar Bins  
As previously stated, the 1\({}^{\circ}\) radar bin (azimuth-range cell for a given angle of elevation) has a high resolution range (83.33 m); its spectral and polarimetric characteristics are quantized using 1 byte, except for the differential phase shift, which is quantized with 2 bytes (namely 65,536 values). Hence, the Digital Number (DN) can vary in binary format from 0 to 255 (65,535 in the case of the differential phase shift). In the case of the MeteoSwiss high range resolution data, DN = 0 has no quantitative meaning and instead has a qualitative meaning. In fact, if the Log-transformed ratio between the Signal plus the (unknown) Noise and the (estimated) Noise is smaller than 4 dB, then the echo is considered to be below the Minimum Detectable Signal (MDS). The corresponding radar bin isassumed to be WITHOUT (detectable) precipitation, and its DN is set to 0. More details regarding the physical meaning of the moments used in this study (as well as their quantization) can be found in the following six Subsections.
## 5 Experiments and Results
### Comparison with Other Methodologies  
Table 5 summarizes the relevant methods that appear in the bibliography, which use deep learning models for multiclass classification. In Table 5, there are strategies for dynamic modification of the learning rate, such as cyclical learning rates ([PERSON] et al. [10]), polynomial learning rates ([PERSON] et al. [7]) or dynamic learning rates ([PERSON] et al. [9]). There are also static learning rate methods, such as that of [PERSON] et al. [8], and methodologies based on transfer learning (such as that of [PERSON] et al. [14]). The proposed method achieves high accuracy in test images by performing an in-depth study of the optimal strategy for the learning rate. We \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Metrics**} & \multirow{3}{*}{**Class**} & \multicolumn{6}{c}{**VGG19**} \\ \cline{3-8}  & & & \multicolumn{4}{c}{**Learning Rate Scheduler**} \\ \cline{3-8}  & & **Decay 1** & **Decay 2** & **Step Decay** & **Polinomial Decay** & **Piece Cte Decay** \\ \hline \multirow{8}{*}{Precision} & ADI & 0.99 & 0.99 & 0.99 & 0.99 & 0.99 \\  & BACK & 0.99 & 1.00 & 1.00 & 1.00 & 1.00 \\  & DEB & 0.96 & 0.97 & 0.99 & 0.97 & 0.98 \\  & LYM & 0.98 & 0.99 & 0.99 & 0.99 & 0.99 \\  & MUC & 0.93 & 0.96 & 0.96 & 0.99 & 0.98 \\  & MUS & 0.82 & 0.85 & 0.85 & 0.83 & 0.87 \\  & NORM & 0.94 & 0.93 & 0.91 & 0.93 & 0.91 \\  & STR & 0.68 & 0.69 & 0.73 & 0.75 & 0.72 \\  & TUM & 0.94 & 0.96 & 0.96 & 0.98 & 0.97 \\ \hline \multirow{8}{*}{Recall} & ADI & 0.98 & 0.98 & 0.98 & 0.97 & 0.99 \\  & BACK & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\  & DEB & 0.96 & 0.99 & 0.99 & 1.00 & 1.00 \\  & LYM & 0.99 & 0.99 & 0.99 & 1.00 & 1.00 \\  & MUC & 0.98 & 0.98 & 0.99 & 0.98 & 0.98 \\  & MUS & 0.78 & 0.77 & 0.80 & 0.80 & 0.77 \\  & NORM & 0.90 & 0.95 & 0.94 & 0.97 & 0.95 \\  & STR & 0.69 & 0.73 & 0.69 & 0.80 & 0.78 \\  & TUM & 0.94 & 0.96 & 0.95 & 0.96 & 0.95 \\ \hline \multirow{8}{*}{F1-Score} & ADI & 0.98 & 0.99 & 0.99 & 0.98 & 0.99 \\  & BACK & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\  & DEB & 0.96 & 0.98 & 0.99 & 0.99 & 0.99 \\  & LYM & 0.99 & 0.99 & 0.99 & 0.99 & 0.99 \\  & MUC & 0.96 & 0.97 & 0.97 & 0.98 & 0.98 \\  & MUS & 0.80 & 0.81 & 0.83 & 0.82 & 0.82 \\  & NORM & 0.92 & 0.94 & 0.92 & 0.95 & 0.93 \\  & STR & 0.69 & 0.71 & 0.71 & 0.77 & 0.75 \\  & TUM & 0.94 & 0.96 & 0.95 & 0.97 & 0.96 \\ \hline \multirow{2}{*}{Accuracy} & Train & 0.94 & 0.98 & 0.98 & 0.9895 & 0.9830 \\  & Tests & 0.93 & 0.95 & 0.95 & **0.9532** & 0.9494 \\ \hline \multirow{2}{*}{Training time} & 155 m 22 s & 153 m 5 s & 156 m 7 s & 153 m 29 s & 153 m 23 s \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The impact of using different methods of scheduler variation of the LR with respect to epochs on the training and validation of our model believe that the proposed methodology provides excellent accuracy results through a detailed and comprehensive study of the optimal strategy for the learning rate.
## 3 Results
Since in the ionosonde-derived EDPs the topside part is obtained by model extrapolation fitting to the peak electron density value (no real data in the topside), COSMIC-2 experimental data can make an important contribution for investigating the topside ionosphere. Such combinations allow us to avoid fundamental limitations of the ground-based HF ionospheric soundings, which cannot measure plasma density above the F2 layer peak due to the lack of HF reflections from the less dense upper regions. At the same time, the unbiased measurements of plasma density in the bottomside ionosphere and at the F2 layer peak can mitigate GNSS RO retrieving uncertainties. Also, we should note that for RO missions operating in LEOs, there still exist limitations for the maximum altitudes of RO soundings pre-defined by the operational orbit (e.g., \(\sim\)500-550 km for COSMIC-2 missions). The RO soundings from geostationary orbit potentially can solve this limitation as demonstrated by the GOES-based RO retrievals [53].
###### Abstract
Furthermore, these approaches often require extensive infrastructure and chemical inputs, resulting in operational costs in the range of USD 50-200 m\({}^{-3}\) of treated water. Recent advances in ecological engineering have suggested the potential of integrating natural processes with engineered solutions. Hybrid systems combining biological and physical treatments have shown promising results, with documented improvements in both treatment efficiency and cost-effectiveness [10]. However, current hybrid approaches face significant limitations in real-world applications. [PERSON] et al. (2011) reported that existing systems lack the flexibility to adapt to seasonal variations in pollutant loads [11], while [PERSON] et al. (2022) highlighted maintenance challenges that can lead to system failures within 2-3 years of operation [12]. To address these limitations, we propose a novel modular system based on ecological concrete, specifically designed for riparian transition zones. Our approach integrates optimized concrete formulations with modular design principles to create a system that can be easily maintained and adapted to varying environmental conditions. Initial testing has demonstrated that the ecological concrete can maintain structural stability while achieving porosity levels suitable for biological growth and pollutant removal [13]. We hypothesized that carefully designed ecological concrete modules would demonstrate effective heavy metal removal while maintaining structural integrity and supporting ecological functions. To test this hypothesis and address the identified challenges, this study aimed to optimize the composition and properties of ecological concrete for combined structural and environmental functions, evaluate the system's performance in removing multiple heavy metals across different concentration ranges and time scales, and assess the potential for integrating this engineered solution within natural riparian ecosystems. And this research contributes to the development of sustainable water treatment strategies by providing a comprehensive evaluation of an innovative modular approach to riparian zone restoration. The findings offer practical insights for environmental engineers and policymakers seeking effective solutions for heavy metal contamination in aquatic environments while maintaining ecological integrity.
## 3 Results and Discussion
### Total Phenolic Content and Antioxidant Activity
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Sample** & **Hardness (N)** & **Springiness (-)** & **Gumminess (N)** & **Chewiness (Nmm)** & **Cohesiveness (N)** \\ \hline CB \({}^{*}\) & 8.2 \(\pm\) 0.3 \({}^{\text{b}}\)\({}^{**}\) & 0.83 \(\pm\) 0.06 \({}^{\text{a}}\) & 3.4 \(\pm\) 0.2 \({}^{\text{b}}\) & 2.8 \(\pm\) 0.3 \({}^{\text{a}}\) & 0.41 \(\pm\) 0.01 \({}^{\text{a}}\) \\ PB1 & 7.6 \(\pm\) 0.6 \({}^{\text{b}}\) & 0.81 \(\pm\) 0.09 \({}^{\text{a}}\) & 3.2 \(\pm\) 0.1 \({}^{\text{ab}}\) & 2.5 \(\pm\) 0.3 \({}^{\text{a}}\) & 0.42 \(\pm\) 0.01 \({}^{\text{a}}\) \\ PB2 & 6.9 \(\pm\) 0.5 \({}^{\text{ab}}\) & 0.82 \(\pm\) 0.05 \({}^{\text{a}}\) & 2.7 \(\pm\) 0.3 \({}^{\text{a}}\) & 2.2 \(\pm\) 0.4 \({}^{\text{a}}\) & 0.43 \(\pm\) 0.02 \({}^{\text{a}}\) \\ PB3 & 6.9 \(\pm\) 0.4 \({}^{\text{ab}}\) & 0.82 \(\pm\) 0.08 \({}^{\text{a}}\) & 3.1 \(\pm\) 0.2 \({}^{\text{ab}}\) & 2.5 \(\pm\) 0.4 \({}^{\text{a}}\) & 0.45 \(\pm\) 0.02 \({}^{\text{a}}\) \\ PB4 & 6.2 \(\pm\) 0.8 \({}^{\text{a}}\) & 0.87 \(\pm\) 0.03 \({}^{\text{a}}\) & 3.0 \(\pm\) 0.3 \({}^{\text{ab}}\) & 2.6 \(\pm\) 0.3 \({}^{\text{a}}\) & 0.44 \(\pm\) 0.03 \({}^{\text{a}}\) \\ PB5 & 7.8 \(\pm\) 0.7 \({}^{\text{b}}\) & 0.85 \(\pm\) 0.04 \({}^{\text{a}}\) & 3.7 \(\pm\) 0.7 \({}^{\text{b}}\) & 3.1 \(\pm\) 0.6 \({}^{\text{a}}\) & 0.42 \(\pm\) 0.02 \({}^{\text{a}}\) \\ \hline \end{tabular} \({}^{\text{c}}\)CB \({}^{*}\)—control bread, PB1, PB2, PB2, PB3, PB4 and PB4—bread with 1, 2, 3, 4 and 5 \(\%\) of parsley powder, respectively. \({}^{**}\) Values followed by the same letter (a–e) are not significantly different (\(p<0.05\)), \(n=5\).  
\end{table} 
Table 3: Texture parameters and moisture of crumb for control and enriched bread.
# Study on the Increase of the Supporting Capacity of a Cement Milk Pile with Expansive Additives
## 2 Materials and Methods  
### Experiment Outline  
Table 1 shows the experimental plan. The water-binder (W/B) ratio was set to two levels, 70% and 83%, which are commonly used by the Korea Expressway Corporation and the Korea Land and Housing Corporation based on the basic Design Guide for the foundation (2015). An expansive additive, calcium sulfoaluminate, was mixed into the cement in 0%, 5%, 10%, and 15% proportions based on the cement weight after being mixed with water for 1 min. Test specimens were prepared according to each mixing ratio to evaluate the segregation resistance, compressive strength, frictional resistance stress, SEM images, porosity, and economic feasibility of the filling material for each bored pile. All experiments were conducted under an environment of 60 \(\pm\) 5% relative humidity and temperature of 20 \(\pm\) 2 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **W/B** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Ex** \\ **(B \(\times\) wt\%)** \\ \end{tabular} } & \multicolumn{4}{c}{**Unit Weight (g/\(\circ\))**} & \multirow{2}{*}{**Water**} & \multirow{2}{*}{**Evaluation Items**} \\ \cline{3-3} \cline{5-6}  & & & **Binder** & & \\ \cline{3-6}  & & **C 1** & **Ex 1** & & \\ \hline \multirow{4}{*}{70} & 0 & 980 & 0 & \multirow{4}{*}{690} & \multirow{4}{*}{- Segregation resistance} \\  & 5 & 931 & 49 & & \\ \cline{1-1} \cline{5-6}  & 10 & 882 & 98 & & \\ \cline{1-1} \cline{5-6}  & 15 & 833 & 147 & & \\ \hline \multirow{4}{*}{83} & 0 & 880 & 0 & \multirow{4}{*}{730} & \multirow{4}{*}{- Economic evaluation} \\  & 5 & 836 & 44 & & \\ \cline{1-1} \cline{5-6}  & 10 & 792 & 88 & & \\ \cline{1-1} \cline{5-6}  & 15 & 748 & 132 & & \\ \hline \hline \end{tabular}  
* C: Cement, Ex: expansive additive (calcium sulfoaluminate [CSA] type); SEM: scanning electron microscope.  
\end{table}
Table 1: Experimental plan.  
Table 2 shows the chemical properties of the cement and expansive additives used in the experiment.
## 2 Background and Related Works
### Fault Tolerance with Approximate Computing Techniques  
Thanks to the opportunity that AC provides to improve performance and energy efficiency, different researchers have used AC techniques to reduce overheads associated with redundancy in fault mitigation schemes [18]. These works have shown good results by compensating the costs of fault tolerance techniques with the speed-up obtained through AC. However, most of the works presented are at the circuit level [10; 31; 32; 33; 34] or require special architecture [9; 29; 35; 36], so hardware modifications are still necessary. This means that those works are not suitable for COTS processors.  
More recently, fault tolerance proposals have been reported using AC techniques at the software level. In [30], a proposal is presented to reduce overheads in NMR (N-modular redundancy) schemes. The method is based on the use of exact and approximate versions of tasks that are executed in different cores. In [37; 38], the authors analyze the intrinsic fault tolerance that characterizes some algorithms of an iterative nature, and how the approximation affects their fault tolerance. Through experiments based on pulsed laser and fault emulation tests, it was demonstrated that the capability to tolerate errors depends on the algorithm, the number of iterations (approximation level) and the region where the faults occur.  
In [39], the authors propose a technique to detect errors at the software level, with lower overheads. The technique is based on the duplication with comparison (DWC) technique, which consists in duplicating a software task and comparing the generated data to detect errors. To reduce the overheads associated with traditional DWC, the tasks to be duplicated are approximated using software AC techniques.  
In addition, as part of our previous work [40], we recently explored the combination of SIHFT techniques (fully implemented) and AC approaches to reduce overheads in fault tolerant COTS systems. The research showed that it is possible to reduce, and in some cases eliminate, the overheads associated with redundancy by applying AC techniques to the programs before hardening them. The difference with the current work is mainly based on the inclusion of selective hardening techniques, which broaden the design space, unexplored in previous works.