esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Recommendations for Industries and Policymakers  
The integration of Industry 4.0 technologies and ERP systems into sustainable manufacturing offers practical recommendations and case studies for industries and policymakers aiming to effectively manage carbon footprints. Technologies such as IoT, AI, and additive manufacturing play a crucial role in making production processes more sustainable by reducing waste and enabling circular economic flows. This article provides insights into frameworks and models that can be adopted to achieve these goals.  
**Practical Recommendations:**
* Policy Recommendations: Policies should focus on overcoming economic barriers, developing supportive infrastructure, and fostering skill development to facilitate the adoption of Industry 4.0 solutions. Effective governance can pave the way for smart, flexible, and ecologically conscious factories [93].
* Framework Integration: A proposed framework integrates lean manufacturing, LCA, and circular economy principles with digital technologies to ensure environmental sustainability through energy efficiency, waste management, and emissions control [94].  
**Case Studies:**
* Smart EMS Model: A case study conducted in an Indian cement factory demonstrated significant improvements in environmental performance by integrating IoT, cyber-physical systems (CPS), cloud computing, and cognitive computing into Environmental Management Systems (EMS) [84].
* I4.2-GIM Framework: The I4.2-GIM framework, which incorporates smart carbon emission and energy management systems, achieved notable results, saving 10.9% of daily energy consumption and reducing carbon emissions by 12.55%. This provides a practical example for manufacturing enterprises aiming for net-zero goals [95].  
By offering practical recommendations and showcasing successful implementations, this article bridges the gap between theoretical frameworks and actionable strategies, enhancing its relevance and utility for industries and policymakers.
# A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
## 5 Experimental Setup and Methodology
### Battery SOC Estimation  
Herein, the nine drive cycles test datasets at 25 \({}^{\circ}\)C only, provided in [41], and were used to estimate the SOC of the proposed battery both for the model-based or data-driven approaches.  
Each drive cycle has the SOC computed via EKF and UKF for the model-based system. In addition, the ECM terminal voltage \(V_{t}\) is also computed, since it is one of the states of the space-state modeling. For the data-driven PHM calculation, the SOC is computed from the dataset without any prior modeling of the system.  
For the SOC, four estimations are performed using the following:  
* Coulomb-counting method according to (1),
* Extended Kalman Filter (EKF) method as elucidated in Section 3.1,
* Unscented Kalman Filter (UKF) method as elucidated in Section 3.2,
* Data-driven PHM, as described in Section 4.
## 3 Results and Discussion
### Are Additive, Synergistic or Antagonistic Effects in the Combined Stress?
This synergic effect could be due to diverse reasons. First, the improvement of the formation reactive oxygen species (ROS) by drought stress [80] and nutrient deficiency [81] that could sensitize the VOCs response. Indeed, it is known that the VOCs are emitted by early signaling events involving the ROS during the herbivory [82]. Secondly, the abiotic stress and herbivory by _Tuta_ could have increased the biosynthesis of VOCs both via hormone cross-talking and higher resource reallocation to chemical defense. For example, the ABA and JA, the main phytohormones respectively involved in the plant response to the drought and herbivory, interact among each other via molecular cross-talk [72; 73; 74] and the reallocation of plant resources to defense by modification of the gene expression profiles after herbivory was also observed [83]. Third, the increased VOCs biosynthesis in presence of both stresses could have caused their accumulation inside the leaf, leading to the formation of a steep partial pressure gradient between the atmosphere and substomatal cavities along which the VOCs could be highly emitted. Unlike the physiologic and metabolic traits, the results obtained for the morphological traits suggested an early antagonistic effect (at 1 day of treatment) in COMB plants, evolving thereafter towards additivity (Figure 5). This result could be explained by the fact that  
Figure 5: The combined impacts from abiotic (drought and N deficiency) and biotic stress (Infestation of _Tuta absoluta_) on morpho-physiological traits of tomato plants at 1, 3 and 8 days of treatment and VOCs emission at 8 days of treatment. The combined impact of single stressors was estimated as synergist (red color), additive (white color) or antagonistic (blue color) (greater than, equal to or less than expected effects, respectively, based on single stressor effect sizes). The vertical and error bars represent, respectively, the mean and the 95% confidence interval of the overall effect size difference between the observed and expected additive effects from combined abiotic and biotic stress on morpho-physiological and metabolic traits of tomato plants. The zero line represents the expected additive effects from combined stressors. When the means (and their 95% confidence limits) were higher than or less than the zero line, they were considered synergistic or antagonistic, respectively. COMB plants prioritized their responses towards herbivory. Indeed, the COMB plants aimed to reduce their leaf water content more in the presence of _Tuta_ rather than under ABIO ones compared to the CTR through a sharp increase of the leaf dry weight (Figure 1). Why? The water and the dry weight are strictly and negatively linked to the plant palatability towards the pest [84]; hence, in the presence of combined stress, tomato plants might have redirected the allocation of their resources towards the formation of carbon-based secondary compounds such as lignin and fiber contents which contribute to leaf toughness and reduce palatability [85].
# Hard Disk Failure Prediction Based on Blending Ensemble Learning
## 1 Introduction  
### Background  
Hard disks are the primary and most important storage device used in computers today, and many data centers rely on large numbers of hard disks to store vital information. With the rapid development of the Internet and cloud platform, the storage and processing of massive data have brought severe challenges to the relevant personnel and storage systems [1]. A small hard disk failure will cause a significant amount of data loss, thus causing economic losses. To reduce this situation, S.M.A.R.T. (self-monitoring analysis and reporting technology) was developed in the 1990s. Led by Compaq and developed in collaboration with several hard disk manufacturers, this new technology detects all kinds of working information inside the hard disk, such as the number of hard disk reads and writes, the number of head loads/unloads, the seek error rate, and the current ambient temperature of the hard disk, and writes them to the specified register through binary code. These attribute values are usually updated once a day. According to these attribute values, users can compare them with the threshold specified by various manufacturers, so that they can take measures to deal with the failure in advance. Some hard disks can even alarm themselves to remind users to back up data to reduce losses. In an ideal state, as long as some attribute values of the hard disk do not exceed the threshold and are within a reasonable range, the hard disk will not have problems [2].  
There are many reasons why a hard disk fails. Failure can be caused by internal reasons, such as damage to parts caused by an excessively long service time or a sector failure causedby an accident, while indirect and direct effects on hard disks are caused by external reasons, such as vibrations, dust, static electricity, magnetic fields, and unstable voltage. There are thus many factors that can change S.M.A.R.T. attribute values. However, the detection rate achieved by reading the change range and limits of specific attribute values, including the changing S.M.A.R.T. values and the thresholds set by the manufacturer, which can avoid certain losses, is very low, at only about 10%.  
Therefore, scholars have made use of the technology in the computer field that has gradually matured to predict hard disk failures and achieved good results.
# A New Orbiting Deployable System for Small Satellite Observations for Ecology and Earth Observation
## 3 Study Cases
### Application 3: Coastal Area Monitoring  
Coastal zones, i.e., sea areas within \(\sim\)50 km from the mainland, are heavily affected by anthropogenic activities (e.g., [136; 137; 138]). Their continuous observation is thus crucial to formulate recovery plans, as well as the identification of new tools and methods to optimize both monitoring and management. Remote sensing in the spectral range from VIS to MW represents a key to monitoring the coastal zones [139]:  
* _VIS_: 0.46 and 0.54 \(\upmu\)m channels provide chlorophyll and other plankton pigment contents; the 0.7 to 0.8 \(\upmu\)m region detects the presence of sediments and coastal areas pollution and/or erosion;
* _IR_: the \(\sim\)1 to 5 \(\upmu\)m range allows to clearly distinguish water from other surfaces;
* _TIR_: the 8 to 14 \(\upmu\)m spectral range detects sea radiation emissions, allowing for the determination of the sea surficial temperature;
* _MW_: this spectral range provides sea roughness, which allows to obtain information on surface wind, and which affects emissivity, a quantity used to derive sea salinity once the sea surface temperature and the observational conditions are known.  
Coastal oceanography is traditionally focused on measuring several geophysical variables, e.g., ocean color, surface temperature, salinity, sea surface height, and surface wind field. Coastal oceanography would benefit from synoptic observational strategies targeted at the ocean, both to monitor natural marine dynamics, and to track the evolution of anthropogenic phenomena (e.g., pollutant dispersion), in order to address any potential risk for the environment in a timely manner [140]. Combining different spectrum bands allows to: (i) distinguish the original ocean color, by limiting any atmospheric interference; and (ii) define the altimetry, by improving re-tracking techniques, taking into account coastal waveform characteristics [141; 142; 143]. In the following, we separately present the main sea variables.
# Fast Planar Detection System Using a GPU-Based 3D Hough Transform for LiDAR Point Clouds
## 2 Related Works
### Stochastic Methods  
RANSAC is a classic stochastic method for plane detection. To optimise the accuracy of the RANSAC algorithm, the components of inliers in [18] were connected and grew into a corresponding plane surface using a RANSAC method. [PERSON] et al. [19] proposed a normal-coherence CC (NCC)-RANSAC algorithm, where a coherence check was performed to remove the data patches whose normal directions were inconsistent with the fitted plane. The clustered planar surfaces consisted of examined inlier patches, which improved the plane extraction accuracy. To increase time efficiency, the modification of the initial situation and restrictive condition is always employed in the RANSAC algorithm to reduce the iteration times during plane extraction. [PERSON] et al. [20] employed a mean shift algorithm to compute normal vectors of point clouds in different segmented voxels as a rough initialisation in the pre-process. Unlike a fully random selection of the beginning points, the estimation of the normal vectors can reduce the iteration times during optimal plane parameter searching. Because many outlier points exist in collected datasets from real outdoor environment, the accuracy rates of plane estimation were hard to maintain in a steady level. To avoid erroneous results caused by outlier points, [PERSON] et al. [21] adopted a normal distribution transformation algorithm prior to the execution of the plane surface search. Using the normal vector transform algorithm in the pre-process, unorganised point clouds were converted into alignment representations as a down-sampling method to reduce computational cost. After applying the normal vector transform into the RANSAC algorithm, the time efficiency of plane detection was much higher than that of the standard RANSAC algorithm. [PERSON] et al. [22] proposed a parallel extension to the RANSAC algorithm that performed GPU cores to extract planes from point clouds.  
In the stochastic method, geometry features (e.g., normal) are used to determine the optimal fitting plane through one or multiple times of the point cloud iteration. The satisfied point counts for different plane surfaces are recorded during the iteration process, after which the pairs of the parameters of the optimum plane surface are obtained on the basis of the satisfied points of maximum counts. Owing to the uncertainty and randomness characteristics of these RANSAC related algorithms, its performance is still not suitable for real-time data processing.
## 4 Experiments
### Benchmark Comparisons on Public Datasets  
#### 4.6.1 Quantitative Analysis  
To evaluate the effectiveness of our algorithm, we conduct comprehensive \(\times 4\) super-resolution comparisons between the DRFAN and other mainstream lightweight super-resolution models (with a total parameter count of less than 2M) on five benchmark test datasets, including SRCNN [3], FSRCNN [4], VDSR [5], EDSR-baseline [6], CARN [7],  
Figure 5: Visual validation of the ablation experiments. Visual comparisons of different models on Urban100 (\(\times 4\)): img_005 and img_076. Figure 6: Error map visualizations of img_005 and img_076 from Urban100 at \(\times 4\) magnification, where red and blue represent positive and negative deviations, respectively, and white indicates minimal error. Model D (ours) produces the least perceptual distortion, especially along edges and repetitive patterns. IMDN [8], RFDN [9], LatticeNet [31], LAPAR-A [48], EMASRN [49], SMSR [50], DeFiAN [51], ShuffleMixer [52], SAFMN [32], DIPNet [53], GASSL-B [54], and DVMSR [33]. Their results are shown in Table 2.  
The experimental results indicate that our model achieves superior performance across all evaluation metrics, demonstrating particular strength in reconstructing high-frequency details under complex degradation scenarios. Specifically, compared to the runner-up models on the Set5, BSD100, Urban100, and Manga109 test datasets, our model improves PSNR/SSIM by 0.06 dB/0.0005, 0.02 dB/0.0002, 0.03 dB/0.0003, and 0.02 dB/0.0007, respectively. We further analyze the computational efficiency of the DRFAN by comparing the parameter count and FLOPs with mainstream lightweight super-resolution models, as shown in Table 2. The DRFAN achieves consistently competitive or superior performance across most benchmark datasets, with a relatively moderate computational footprint of 1057K parameters and 65.1G FLOPs. While the DRFAN outperforms models like RFDN (550K, 23.9G) and IMDN (715K, 40.9G) in PSNR and SSIM, it also delivers comparable or better reconstruction quality than highly compact models such as SAFMN (240K, 14.0G) and DVMSR (424K, 19.67G), though at the cost of higher complexity. In particular, although GASSL-B surpasses the DRFAN in PSNR by a narrow margin on Set14 and Manga109, the DRFAN still maintains the best SSIM scores on all five benchmarks. Overall, these results highlight that the DRFAN achieves a favorable trade-off between reconstruction accuracy and computational demand, making it suitable for high-fidelity SR tasks under constrained resources.
## 2 Materials and Methods
### Feature Selection and Ranking  
Feature selection approaches reduce the dimensionality of data by choosing a subset of predictor variables. It also enables the machine-learning algorithms to train faster and improves the accuracy of the model with the selection of the right subset. The feature selection techniques are further classified into the filter, wrapper and embedded methods [30]. The main challenge is the redundancy of the features, and the existing dimensionality reduction technique such as principal component analysis (PCA) is not suitable to build the applicable and human understandable models [31]. The filter-based feature selection approaches are investigated and found to be effective in selecting the features that are associated with stroke [32]. In this research, we applied one of the filter-based feature selection approaches, such as ANOVA, to select the optimal feature subset. The computational speed of the filter methods are much faster than the wrapper methods. ANOVA is a statistical test useful in comparing two or more mean values. For each feature, if the \(p\)-value is near to 0, there exists significant differences in the characteristic values for the different categories [33]. Feature importance ranking from the results of the ANOVA F-test are shown in Figure 3. Features that did not achieve sufficient significance (\(p\geq 0.05\)) are omitted. Therefore, we selected three features such as LF/HF, ST and QT that showed statistical significance (\(p<0.05\)) and are the highly ranked features. The correlation analysis was then performed to find the relation between these three features. The correlation coefficient \(r=-0.16\) between LF/HF and QT indicates a weak negative correlation and reveals the inverse relationship between LF/HF and QT. Similarly, the correlation coefficient \(r=-0.19\) between LF/HF and ST represents a weak negative correlation. Further, the ST and QT intervals indicate a moderate positive correlation of \(r=0.44\). As there are no strong correlations between these features, we selected LF/HF, ST and QT as the input features subset for the classification models.  
Figure 2: Fiducial points and features of the ECG signal. PR: time interval measured from the beginning of the P wave to the start of the QRS complex, QRS: time interval of the QRS complex, ST: time interval measured after the S wave to the beginning of the T wave and QT: time interval measured from beginning of the Q wave to the end of the T wave.
## 4 Discussion
Out of eleven predicted consensus rubber tree miRNAs, hbr-miR398 was identified to target ORF1 by four computational algorithms such as miRanda, psRNATarget, RNA22 and RNAhybrid (Figure 6; Table 1 and Table S4). We predicted the evaluation of the free energy of mRNA-miRNA duplex using RNAcofold. The free energy of miRNA-mRNA duplex binding is highly stable in this study. In the previous studies on host-virus interaction, in silico algorithms were used to study host genome-encoded miRNA targets against potato virus Y (PVY) [35], zucchini yellow mosaic virus (ZYMV) [41], cotton leaf curl Kokhran virus-Burewala (CLCuKoV-Bu) [30], rice yellow mottle virus (RYMV) [42], maize chlorotic mottle virus (MCMV) [43], RTV1 [44], Physostegia chlorotic mottle virus (PhCMov) and tomato brown rugose fruit virus (ToBRFV) [45]. We have designed similar studies against sugarcane viruses using computational algorithms to understand the host-virus interaction as well as finding the best target in our previous research [31; 46; 47]. We predicted that hbr-miR396a and hbr-miR398 would be an excellent choice for targeting the RTV1 genome in this work. To establish RTV1 replication experimentally, it is critical to evaluate the function of predicted consensual rubber tree miRNAs for the detection of _Capillovirus_ replication. The designed amiRNA construct has high specificity to the target gene to control the detrimental off-target effects. Therefore, the silencing expression can be stably transformed to future generations [48]. Furthermore, the small size of amiRNA permits the insertion of multiple and distinct miRNAs within a single gene expression cassette, which can then be transformed to develop transgenic plants that are simultaneously resistant to multiple viruses [49]. An amiRNA-based construct was designed to combat RTV1 in rubber tree cultivars that harbor a modified miRNA/miRNA* sequence in a duplex of the precursor (hbr-MIR396a and hbr-MIR398), as shown in Figure 8. Our computational study on RTV1 genome silencing might pave the way for a novel approach to the antiviral effects of host-derived miRNAs against RTV1. Figure 8: Schematic representation of amiRNA-mediated gene-silencing strategy designed to develop transgenic rubber tree cultivars. (**A**) representing the candidate consensus cassava precursors (a) hbr-MIR396a and (b) hbr-MIR398. (**B**) showing designed amiRNA after miRNA/miRNA duplex replacement. (**C**) showing gene expression cassette containing precursor sequence controlled by a 5' promoter and NOS terminator is cloned at 3' end. (**D**) showing pre-amiRNAs processed to develop a mature amiRNA/amiRNA*duplexes. (**E**) representing processing of amiRNAs by RNA induced silencing complex (RlSC). (**F**) showing amiRNAs get incorporated to be released for mRNA translation inhibition or degradation of mRNA.
## 2 Materials and Methods
### Statistical Data Analyses
#### 2.7.2 Random Forest Classification Analysis  
Similar to CART, the RF algorithm is an ensemble-based method which develops multiple decision trees to complete classification-based tasks as opposed to a single decision tree [69; 75; 77]. The algorithm utilizes a deterministic technique to select recursive bootstrapped samples that are drawn with replacements [77]. These samples are then used to build each tree within the decision tree matrix, which is then grown to a user-defined node size [68; 69]. Thereafter, the amount of decision trees (_ntree_) and the total number of predictor variables (_mtry_) to be used were defined. The _mtry_ values were derived by the amount of the square root of the total number of predictor variables (spectral variables) used, whilst _ntree_ was set to 1000 [47; 78]. Next, both _ntree_ and _mtry_ values were optimised to improve model accuracy, and the resultant classification model was run 100 times [68]. Lastly, the samples not included in the bootstrapped samples or the Out Of Bag (OOB) data were used to estimate the importance of the predictor variables [53; 78]. The most important variables for characterising nitrogen-saturated and -unsaturated grassland plots were then derived through a backwards feature elimination method [53; 78]. To avoid over-fitting the data, a 10-fold cross validation approach was applied [68].