esa-sceva/satcom-chunk-collection huggingface dataset:
# Safety Challenges in Battery Swapping Operations of Electric Underground Mining Trucks
## 4 Discussion
The process, including risk scoring criteria and consensus procedures, is described in detail to enable replication in similar operational contexts. While the study focused specifically on battery swapping operations in underground mining environments, where operational constraints such as confined spaces, ventilation challenges, and emergency response limitations are particularly pronounced, the identified hazards and associated risks are, to a significant extent, generalizable. Many of the risk categories (e.g., electrical hazards, thermal events, mechanical failures, and human-machine interaction errors) are inherent to battery swapping technologies across various sectors, including logistics depots, construction sites, and industrial EV fleets. Nonetheless, we acknowledge that the severity and likelihood of specific risks may vary based on environmental, regulatory, and technological conditions, and we have delineated these contextual boundaries where relevant. Moreover, hazard identification and risk analysis underscore the inherent problems associated with both approaches. The meticulous identification of 21 distinct hazards, ranging from crushing injuries and pinch points to electrical risks and confined space issues, is a testament to the potential dangers present in this environment. The use of a 5 \(\times\) 5 risk matrix provides a structured framework for assessing these hazards, assigning probability and consequence scores to help prioritise mitigation efforts. The matrix highlights the high-risk levels associated with several aspects of the battery swapping procedures, notably the handling of heavy batteries, the operation of overhead cranes (in Company 1's method), and the confined nature of the charging chambers. The critical risk ratings assigned to certain hazards, such as those associated with crane operation or the unexpected movement of the vehicle, emphasise the need for stringent safety protocols. The suggested mitigation measures, such as thorough training programs, clear operating instructions, and regular equipment inspections, are practical and demonstrate a proactive approach to managing these risks. The emphasis on restricting access to the charging area is also crucial in controlling the human element, a key factor in preventing accidents. In conclusion, the contrast between Company 1's and Company 2's approaches, coupled with the comprehensive hazard analysis, reveals a critical interplay between technological innovation and operational safety. This study effectively illustrates that even with the promise of improved sustainability through electric vehicles, thorough risk assessment and the implementation of robust safety measures are paramount for successful implementation in a demanding environment like underground mining of mineral resources. Further research will be oriented to quantitative and qualitative analysis of the impact of these technologies in real-life cases, paying particular attention to the disruption EV charging stations can produce on the overall safety of the underground mines.
# Characterization and Optimization of PLA Stereocomplexed Hydrogels for Local Gene Delivery Systems
## Materials and Methods
### DNA Loaded 3 LM Micelle Preparation  
The detailed method for the formation of unique 3-layered micelle (3 LM) system with the ability to entrap high MW DNA using a dual encapsulation approach was reported previously [31]. Briefly, the preparation of the DNA loaded 3 LM is divided into two consecutive steps. Step 1, encapsulation via organo-micelle: the ionic triblock copolymer PLLA-PEI-PLLA (inner polymer) was dissolved in 1 mL DMSO and equilibrated at room temperature. Bulk DNA (1 mg) in 200 \(\upmu\)L of pure water was gradually added to the polymer/DMSO solution under vigorous stirring. The solution was allowed to equilibrate at room temperature for 1 h under gentle stirring and was diluted with 4 mL of THF. The solution was dialyzed against THF using dialysis tubes (MWCO of 3.5-5 kD) to obtain the DNA loaded organo-micelles. Step 2, aqueous stabilization via an amphiphilic layer: To the organo-micelle/THF solution, the amphiphilic triblock copolymer, PLLA-PEG-PLLA (outer polymer, MW: 800-2000-800), was added to obtain a homogenous transparent solution. The mixed solution was then added dropwise to a 10 mL pure H\({}_{2}\)O under vigorous stirring. The solution was placed under a gentle stream of compressed air to firstly evaporate the organic solvent THF, followed by concentrating the aqueoussolution to the desired volume to obtain the targeted concentration of the 3 LM solution. To study and optimize the properties, 3 LMs with different _inner:outer_ polymer weight ratios were prepared.  
PDLA-PEG-PDLA flower type micelle (D-micelle) was also prepared to use for stereocomplexed hydrogel formation. PDLA-PEG-PDLA (MW: 800-2000-800) in THF (20-40 \(w\)/\(\%\)) was added dropwise in DI water under vigorous stirring. THF and excess water were evaporated to obtain predetermined concentration of D-micelle solutions. Alternatively, the asymmetric PDLA-PEG-PDLA micelles with mixture of long/short PEG blocks (D-micelle-(\(\%\))S) were prepared [29]. The long-PEG copolymer, PDLA-PEG-PDLA (MW: 800-3350-800) and the short-PEG copolymer, PDLA-PEG-PDLA (MW: 800-2000-800) were weighed with the ratio of 100:0, 70:30, 50:50, 30:70, and 0:100 (\(\%\)). Each mixture was fabricated into micelle form by same method above.
# Towards an Alternate Evaluation of Moisture-Induced Damage of Bituminous Materials
## 3 Opportunities
### First Scenario  
The moisture-induced damage of asphalt mixtures can be considered as a time-dependent damage due to the combined effects of moisture and traffic loading. The mechanical property \(MP(t)\) at time \(t\) is affected by the initial mechanical property \(MP(0)\) (i.e., dry specimen) and the induced damage \(D(t)\) during conditioning. The progressive damage due to moisture and/or traffic loading can be represented as  
\[MP(t)=MP(0)-D(t). \tag{1}\]  
Based on Equation (1), Figure 1 depicts a schematic of the mechanical property (such as ITS) with respect to the time of conditioning. As examples, the figure compares possible trends of two different mixes.  
This method should involve the conditioning and evaluation steps. In the conditioning step, realistic or field-like conditions should be adopted to induce reasonable effects of moisture and traffic. In a recently published work [9], the authors suggested a conditioning procedure by exposing compacted asphalt specimens to a temperature as high as the highest performance grading (PG) of the used asphalt binder, while the traffic loading was selected based on realistic effects from the literature to simulate the pressure of vehicle wheel on the wet surface. These effects were integrated into the proposed conditioning system in the laboratory. In regard to the evaluation, rather than using a single criterion (i.e., TSR), a model was developed to identify the time-dependent damage of the mixture using the damage rate \(\mathrm{D_{r}}\) concept:  
\[\mathrm{D_{r}}=\frac{\mathrm{ITS_{i}}-\mathrm{ITS_{i+1}}}{\mathrm{T_{i+1}}- \mathrm{T_{i}}}. \tag{2}\]  
In this model, one can quantify the moisture damage over time and determine how much the mixture degrades with respect to time of conditioning. Using the concept of damage rate, the moisture effects can be evaluated at different times, which is more promising as the mixture might have different behaviors over time. Although its usefulness to discriminate between insusceptible and moisture susceptible mixtures in a rational manner, the concept necessitates the development of a threshold to help quantitatively perform the judgment.
# How the Implementation of BREEAM in Hotels Could Help to Achieve the SDGs
## 3 Results and Descriptive Data Analysis
### Upgrades through Additions  
Finally, there are specific elements that will help in achieving a green hotel, which can be classified into two main categories, namely, Ecology and Energy. Certain actions are taken to optimize the ecology of the site, such as shelter boxes for birds and bats and flower boxes located in strategic places in the hotels, which occurred in case studies B, D and E. Moreover, including green roofs improves air and water quality and creates habitat for wildlife. In the category of Energy, solar panels and cooling systems can contribute to the reduction of energy consumption in hotels and are an immediate response that can be included to an existing building to optimize its sustainability [24]. BREEAM promotes the reduction of air pollution and carbon emissions and encourages the production of local energy from renewable sources. Renewable energy plays an important role in reducing the effects of climate change and global warming. Accurate research of renewable energy power is crucial in the completion of the 2030 Agenda [25].  
These findings demonstrate that the measures to be taken are common in almost all hotels. For rehabilitated buildings, the most important categories to consider are Health and Well-being and Land Use and Ecology. In the case of new hotels, the categories Waste and Transportation have been a common factor. In addition to the elements mentioned above, the incorporation of green roofs and the inclusion of new technological measures, such as solar panels or solar cooling systems, are also key in new hotels [24].  
In the case of hotels, it has been proven that the main categories in which BREEAM has the greatest impact are Health and Well-being, Land Use, Ecology and Waste and Transportation. Therefore, if we compare these categories with the SDGs that BREEAM and the UNWTO indicate, we can affirm that the certificate applied in hotels contributes directly to the achievement of SDG 3, 6, 7, 12 and 15 and indirectly in SDG 8, 11 and 13. Moreover, it has been verified that BREEAM supports the inclusion of new technological measures, such as solar panels, which is why it also favors SDG 9. On the contrary, it has not been possible to verify that BREEAM supports SDG 14 in the case of hotels. It should be noted that none of the buildings studied here are near the coast, therefore the sustainable use of marine resources through the sustainable management of fisheries and aquaculture, indicated in SDG 14, is not feasible, but it may be in a coast hotel.
## 3 Finite Element Analysis Results  
### Heating Analysis  
The heating analysis was conducted using an electromagnetic-thermal coupled method, yielding the temperature distribution illustrated in Figure 6. In Region \(a\) of Figure 6, where the coil made contact with the CTBA, the temperature distribution was uneven due to the non-uniform gap caused by the curved surface of the CTBA. The maximum temperature, 879.9 \({}^{\circ}\)C, was observed at the smallest gap of 2 mm between the coil and the CTBA, while the minimum temperature occurred at the largest gap of 3.5 mm. A similar temperature distribution pattern was identified in the symmetric region along the _x_-axis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Properties** & **Copper** & **Borron Steel** \\ \hline Density [g/mm\({}^{3}\)] & 8.69 & 7.85 \\ \hline [PERSON]’s modulus [GPa] & 110 & 210 \\ \hline Poisson’s ratio [-] & 0.34 & 0.30 \\ \hline Specific heat [/g \({}^{\circ}\)C] & 0.39 & 0.45 \\ \hline Thermal conductivity [W/mm \({}^{\circ}\)C] & 0.40 & 0.05 \\ \hline Source: [18] Ansys Grants material data. & & \\ \end{tabular}
\end{table}
Table 1: Thermal and structure properties.  
Figure 5: Electromagnetic properties—electrical conductivity.  
Figure 6: Temperature analysis of CTBA.  
As described in Section 2.4, the results underscore the significant influence of the skin effect on the observed temperature distribution. Figure 7a illustrates the case in which the skin effect was considered, showing that heat was concentrated on the product's surface. In contrast, Figure 7b presents a scenario wherein the skin effect was excluded. Under these conditions, not only the product surface, but also the regions adjacent to the coil were excessively heated, resulting in exaggerated temperature distributions and unrealistically high heating outcomes.
## 2 Materials and Methods
### Ultraviolet-Visible Spectroscopy (UV-Vis)  
A volume of 1 mL of each sample was diluted by 2 mL of IPA immediately after DLS analysis for ultraviolet-visible spectroscopy (UV-Vis). Twenty runs/measurements per sample were taken and averaged. The dilution is necessary to avoid saturation in the UV-Vis spectrum which makes it difficult/impossible to discover distinct peaks especially in highly concentrated nanocolloids. UV-Vis spectroscopy was conducted with a Varian Cary 50 UV-Vis spectrophotometer using a quartz cuvette (10 mm path length, Helma). The scan range was 190-900 nm with a scan rate of 600 nm/min. All the optical spectra were corrected for IPA absorption by subtracting the contribution of IPA from the recorded spectrum. UV-Vis was used to verify the presence of magnesium nanoparticles and as a measure of the nanoparticle count.
## 2 Spectrum Reconstruction Model for Non-Uniform Sampling Signals  
For a finite-length continuous signal \(s_{l}(t)\), where \(t\in[0,T_{l})\), let \(\Delta f_{l}=1/T_{l}\), assuming it satisfies the Dirichlet condition and its spectrum is primarily concentrated within the frequency range of \([-M\Delta f_{l},M\Delta f_{l}]\); with high-frequency components being negligible, we define a signal meeting these conditions as an approximate band-limited signal, which is commonly observed in most real-world sampled analog signals.  
Let \(S_{l}(f)\) be the Fourier transform of \(s_{l}(t)\). Given \(N\) non-uniform sampling positions with known sampling values \(s(i)=s_{l}(t_{i})\), where \(0\leq t_{1}<t_{2}\cdots<t_{N}<T_{l}\), and \(s\) is an \(N\)-dimensional vector, the frequency spectrum of \(s_{l}(t)\) to be reconstructed is denoted as a (\(2M+1\))-dimensional vector \(S\), with the condition that  
\[S(m)=S_{l}[(m-1-M)\Delta f_{l}]\quad m=1,2,\cdots 2M+1. \tag{1}\]  
Define \(A\) as a linear operator, represented by an \(N\times(2M+1)\)-dimensional matrix, satisfying the condition  
\[s=AS+\tilde{n} \tag{2}\]  
where \(\tilde{n}\) is the error term caused by \(s_{l}(t)\) not being a true band-limited signal.  
Equation (2) represents the spectrum reconstruction model for non-uniformly sampled signals, which allows us to estimate the discrete signal spectrum \(S\) given the known vector \(s\) and matrix \(A\). Next, we will derive the expressions for \(A\) and \(\tilde{n}\), and analyze the magnitude of \(\tilde{n}\) through simulations.
# Dynamic Aeroelastic Response of Stall-Controlled Wind Turbine Rotors in Turbulent Wind Conditions
## 1 Introduction
The method works by gradually changing the portions of the blade span that are in the stall or attached regime. Stall starts at the inner portions of the blade and close to the root, and then progresses towards the tip. With the stall method, the elimination of the pitch mechanism lowers the capital cost and reduces maintenance expenses. The elimination of the pitching mechanism is particularly important when considering the systematic increase in turbine size that has been taking place over the last 30 years. This process, sometimes referred to as _upscaling_, is prompted by economies of scale, which reduce the cost of energy production, and this is likely to continue in the future. One of the consequences of upscaling is the substantial increase in the blade mass per unit energy produced, as the blade mass depends on the cube of the rotor's diameter, while the energy captured depends on the rotor's swept area (i.e., the diameter's square). In addition to the aspect of the construction and material costs, this so-called \"square-cube law\" implies that the size and cost of the pitching mechanisms (and the energy required for their actuation) would increase much faster than the increase in the energy produced as the turbine size increases. Thus, the elimination of the pitch mechanisms by adopting some form of stall control would represent a big advantage for the next generation of high-capacity wind turbines with much larger blades [13]. Our current research consists of the investigation of the aeroelastic behavior of a stall-controlled rotor by involving the study of the frequency content and time evolution of the oscillatory behavior in order to gain a better understanding of the underlying physics. To this end, we conducted a wide range of experiments to assess the effects of rapid variations on the rotor's operational conditions, such as gusts and turbulent fluctuations in the wind flow. To systematize this analysis, various gust conditions were tested for different wind speeds. These were represented by pulses of different intensity, occurring suddenly in an otherwise constant wind regime. This allowed us to observe the pure aero-elasto-inertial dynamics of the rotor's response. We then focused on obtaining a reduced-order characterization of the rotor's dynamics as an oscillatory system based on energy-transfer principles. In addition to its intrinsic scientific value, this aspect of the work presented here is of fundamental interest for researchers and engineers working on developing optimized control strategies for wind turbines. It allows for the critical elements of a rotor's dynamic behavior to be described as a reduced-order model that can be solved in real time, an essential requirement for determining predictive control actions.
# Sub-Pixel Displacement Measurement with Swin Transformer: A Three-Level Classification Approach
## 3 Swin Transformer-Based Sub-Pixel Displacement Measurement Method
### Rotation-Relative Labeled Value Method  
The input of the ST-SDM is images with three channels. For each input image, it is generated by overlapping three single-channel grayscale images, which are the reference image, the target image, and the grayscale image generated by the difference between the grayscale values on the corresponding points of the target image and the reference image.  
We define the horizontal rightward and vertical downward sub-pixel displacement values as positive sub-pixel displacement values, which is consistent with the algorithm ST-SDM outputting a nonnegative number of classification labels at each level of classification. However, the displacements can also be negative; thus, at each level of classification, a method called the rotation-relative labeled value method is used to determine the sub-pixel displacement value for that level; and finally, the displacements are integrated to obtain the final sub-pixel displacement value.  
The process of the rotation-relative labeled method is shown in Figure 4. For any level of classification of the ST-SDM, for an input image, rotating it clockwise by 0\({}^{\circ}\), 180\({}^{\circ}\), 90\({}^{\circ}\), and 270\({}^{\circ}\) will result in four new images, as shown in Figure 4b, Figure 4c, Figure 4d, and Figure 4e, respectively. The images in Figure 4b, Figure 4c, Figure 4d, and Figure 4e are inputted into the classifier of this level of the ST-SDM, assuming that the output categorization labels are \(\mathrm{v}_{1},\mathrm{v}_{2},\mathrm{u}_{1},\mathrm{and}\ \mathrm{u}_{2}\), respectively, and it can be concluded that the value of the sub-pixel displacement of this input image is \((\mathrm{u}_{1}-\mathrm{u}_{2},\mathrm{v}_{1}-\mathrm{v}_{2})\). Taking the horizontal component of sub-pixel displacement as an example, since the classifier of the ST-SDM categorizes a negative displacement as category \"0\", when the sub-pixel displacement value in the input image is negative, the classification labels \(\mathrm{u}_{1},\mathrm{v}_{1}\) are both \"0\", and thus the horizontal component of sub-pixel displacement output at this time is \(-\mathrm{u}_{2}\), i.e., a negative value. Similarly, when the sub-pixel displacement value in the input image is positive, the classification labels \(\mathrm{u}_{2},\mathrm{v}_{2}\) are both \"0\", and thus the horizontal component of the output sub-pixel displacement value at this time is \(\mathrm{u}_{1}\), i.e., a positive value. Thus, the rotation-relative labeled value method solves the problem of positive and negative sub-pixel displacement.
## 3 Results and Discussion
### Engine Performance  
The study points out the brake specific fuel consumption (BSFC) overall trend in Figure 6 that is driven to examine the fuel mass consumption per power unit while providing a global perspective of this parameter.  
Figure 5: In-cylinder temperature at a full load rate.  
Figure 6: Diagram of Brake Specific Fuel Consumption (BSFC).  
According to the results, the implementation of oxygenated blends (ABE 5 and ABE 10) promotes higher fuel consumption when compared to the standalone gasoline operation. The latter is a direct consequence of the lower energy density and lower calorific value relative to gasoline. Therefore, a higher fuel amount is required to obtain the same unit power, which escalates the BSFC. For comparison, increasing the ABE ratio fosters the BSFC up to 15%. This pattern is in line with other investigations [12; 43]. Note that the ABE-based blends might influence the octane rating rise, supported by longer ignition delays (Figure 4c) that critically limits the power output, thus magnifying BSFC. The fuel conversion efficiency might be a determinant factor altering the fuel metrics since it was corroborated in the in-cylinder pressure curves that at low engine loads, the combustion center retarded up to 0.6\({}^{\circ}\). Therefore, the altered combustion phasing further deteriorates fuel conversion efficiency, thus increasing fuel consumption. The investigation of Nithyanandan [12] leads to similar findings when implementing blends of ABE 20-40. In this sense, the integration of control strategies that facilitates spark timing could be a feasible solution to avoid the magnification of BSFC in dual-fuel operation [44; 45].  
The highest fuel consumption was achieved by ABE 10, followed by the ABE 5, Gasoline, ABE 10 + HHO, and ABE 5 + HHO. This result demonstrates that HHO enrichment surpasses the increment in the fuel metrics derived from ABE replacement implementation. The latter implies that gaseous fuel promotes chemical conversion efficiency while acting as a heat intensifier, as verified in Figure 5.