esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model Analyses
### Collapse Probability Analysis  
The vulnerability curve of the structure indicates the probability that the structural demand exceeds a specific failure state under the action of different intensity earthquakes. Figures 6-8 are graphs showing the collapse probabilities of structures under earthquake action when different uneven settlement quantities occurred in Zone I, Zone II, and Zone III. It can be seen from the figures that as the earthquake intensity increased, the collapse probability of the structure increased significantly. However, in different uneven settlement areas, there was a certain difference in the collapse probability. When 100 mm of uneven settlement occurred, the peak acceleration of the 50% collapse probability of Zone I corresponded to 1.0 g, and Zone II and Zone III corresponded to 1.30 g and 1.33 g, respectively. This proves that when uneven settlement occurred in Zone I, the risk of structural collapse was higher. Of course, the earthquake collapse may be a partial collapse within a limited range in Zone I.  
Figure 6: Collapse probabilities of structures with different uneven settlement quantities in Zone I.  
Figure 7: Collapse probabilities of structures with different uneven settlement quantities in Zone II.  
In Zone I, due to the different restraining action of the surrounding members on the corner column, it was easily inclined toward the side where the restraining action of lateral restraint was relatively weak when the foundation was unevenly settled. At the same time, the uneven settlement of the foundation and the seismic action caused the interstorey drift ratios to increase significantly, which led to damage of the corner column and its adjacent components. Although the direct damage caused in Zone I was localized, the possibility of progressive collapse of the entire structure cannot be ignored [26,27].  
Compared with Zone I, when the structure had a 50% collapse probability under the same settlement conditions in Zone II and Zone III, the corresponding seismic acceleration peaks were larger. The reason for this is that Zone II and Zone III had more horizontal components connected to them, which had more load transfer paths and were also more constrained. Because the surrounding restraint was more uniform, the inclination of the column caused by uneven settlement in the uneven settlement area was relatively small. Therefore, the interstorey drift ratios of the columns in the area were mainly  
Figure 8: Collapse probabilities of structures with different uneven settlement quantities in Zone III.  
caused by earthquake action. This shows that the risk of collapse caused by the location of uneven settlement areas will be different, and the seismic damage of the components in the uneven settlement area can be effectively reduced by increasing the load transfer paths.
## 4 Advances in Nanotechnology towards Development of Nano-Based Therapies
### AuNPs in the Targeted Treatment of Obesity and Cancer
#### 4.1.3 PHB-Targeted AuNPs as Photothermal Agents  
AuNPs have also shown potential as photothermal agents in that they are able to convert visible or near-infrared (NIR) lights into heat energy through laser excitation, and this property can improve the efficiency of photothermal therapy (PTT) [30]. The morphology of the AuNPs plays a crucial role in PTT [30; 31], as different shapes absorb and emit light differently [31]. The use of gold nanoshells, AuNSs, and AuNRs in PTT has been demonstrated experimentally and showed varying actions in different types of cancers [7; 32; 33; 31] and obesity [16]. AuNSs-induced PTT is suitable for shallow cancers  
Figure 5: Investigation of the specificity of AGK against colon and breast cancer cells. AGK selectively targets and kill cells that express PHB on the cell surface. Adapted from [PERSON] et al. [10]. Abbreviations: AG—cAuNPs + AHP, AGS—cAuNPs + AHP + non-specific peptide, GC—cAuNPs + KLA, AGK—cAuNPs + AHP + KLA. ** The difference is statistical significance at p < 0.001 for Caco-2 cells treated with AGK when compared to MCF-7 and HT-29 cells. Adapted from [PERSON] et al. [10].  
(e.g., skin cancer) and AuNRs for deep tissue penetration. AuNRs can absorb NIR light (650-900 nm) that can penetrate deep tissues up to 10 cm, meaning that they are suitable for clinical applications [7; 34]. These AuNPs systems could be used in place of conventional photosensizers, as they are photostable and biocompatible [30; 31; 32]. As it stands, indocyanine green is the only FDA-approved photosensizer for clinical use [32]. Therefore, these nanosystems would provide an alternative platform for the translation of nano-based PTT into clinical use for the treatment of chronic diseases.  
The BRN investigated the PTT effects of AuNRs functionalized with AHP (AHP-AuNRs) on Caco-2 cells. The AHP-AuNRs by themselves had no effect on cell viability. In contrast, exposure of this treatment to laser therapy resulted in a significant reduction in cell viability [35]. The susceptibility of CRC-derived Caco-2 cells to the PHB-targeted AuNPs treatments (both AuNSs and AuNRs) suggests that theotherapeutic strategies employed by BRN could potentially have dual effects in obese patients with CRC. Since cancer and obesity share similar hallmarks, such as increased cell proliferation, increased angiogenesis, hypoxia, etc., this strategy presents a treatment possibility for both obesity, CRC, and obesity-associated CRC [7].
## 4 Results
### Model Accuracy Verification
#### 4.2.2 Validation Process  
1. Inputting data from the \"102-105 National Survey of Changes in Nutritional Health Status\" into a digitized data semantic analysis model;
2. Model analysis of dishes, portion sizes, and the ingredients in the dishes;
3. Analysis of nutrient intake using the AI Precision Nutrient Analysis Model;
4. Analyze the results against the \"24-h dietary recall nutrient intake sum analysis file\" and the \"24-h dietary recall food weight and nutrient ingredient file\";
5. Compare the accuracy of the model.
## 3 Results
### Future Flood Risk Evolution Driven by Multiple Assessment Factors
#### 3.2.2 Attribution Analysis of Risk increase
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Period**} & \multirow{2}{*}{
\begin{tabular}{c} **Intensity of Risk Change** \\ _(Area Percentage)_ \\ \end{tabular} } & \multicolumn{4}{c}{**Average Contribution Rate of Assessment Factors to**} \\  & & & & \multicolumn{2}{c}{**Risk Increase**} \\  & & & & **(_Area Percentage_)** & **RMAX3** & **Population** & **GDP** & **Land Use** \\ \hline \multirow{8}{*}{SSP2-RCP4.5} & \multirow{3}{*}{2030s} & Significant increase (6.77\%) & 0.56\% & \(-\)0.07\% & 0.59\% & 98.92\% \\  & & Moderate increase (93.23\%) & 49.92\% & \(-\)1.91\% & 20.52\% & 31.47\% \\  & & Total & 46.58\% & \(-\)1.79\% & 19.17\% & 36.04\% \\ \cline{2-7}  & & Significant increase (1.62\%) & 6.87\% & \(-\)0.22\% & 0.64\% & 92.71\% \\  & & Moderate increase (98.38\%) & 87.62\% & \(-\)1.67\% & 8.41\% & 5.64\% \\  & & Total & 86.31\% & \(-\)1.64\% & 8.28\% & 7.05\% \\ \cline{2-7}  & & Significant increase (1.78\%) & 3.30\% & 0.04\% & 1.96\% & 94.70\% \\  & & Moderate increase (98.22\%) & 77.63\% & \(-\)1.56\% & 14.26\% & 9.67\% \\  & & Total & 76.31\% & \(-\)1.53\% & 14.04\% & 11.18\% \\ \hline \multirow{8}{*}{SSP5-RCP8.5} & \multirow{3}{*}{2030s} & Significant increase (1.93\%) & 9.91\% & \(-\)0.11\% & 0.77\% & 89.43\% \\  & & Moderate increase (98.07\%) & 92.18\% & \(-\)0.84\% & 5.13\% & 3.53\% \\  & & Total & 90.59\% & \(-\)0.82\% & 5.04\% & 5.19\% \\ \cline{2-7}  & & Significant increase (1.64\%) & 7.56\% & \(-\)0.25\% & 0.87\% & 91.82\% \\  & & Moderate increase (98.36\%) & 86.08\% & \(-\)1.65\% & 10.64\% & 4.93\% \\  & & Total & 84.79\% & \(-\)1.63\% & 10.48\% & 6.36\% \\ \cline{2-7}  & & Significant increase (1.40\%) & 8.37\% & \(-\)0.19\% & 5.72\% & 86.10\% \\  & & Moderate increase (98.60\%) & 85.23\% & \(-\)2.44\% & 13.29\% & 3.92\% \\  & & Total & 84.15\% & \(-\)2.40\% & 13.18\% & 5.07\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Attribution analysis results of elevated future flood risk in the PLEEZ.
# Construction 4.0: A Systematic Review of Its Application in Developing Countries
## 7 Conclusions  
The findings suggest that there is a discernible, evolving, and continuous dialogue surrounding Construction 4.0 in developing countries. The current analysis underscores the inadequacy of a thorough understanding concerning the ramifications of Industry 4.0 on the construction industry owing to the scarcity of primary research articles available. The authors identified that numerous definitions for Construction 4.0 exist, and these tend to be changed to fit the user requirements. Six main technologies and the roles they play in Construction 4.0 were emphasised, not least the role of BIM in facilitating collaboration between cyber and physical systems. This has led to the development of a comprehensive cyber-planning-physical ecosystem that seamlessly incorporates BIM features throughout all phases of construction project life cycles. Furthermore, there exists a scarcity of scholarly inquiry concerning the awareness, understanding, and application of Construction 4.0 in developing countries. Moreover, there appears to be a lack of enquiry on the barriers that currently exist towards its adoption and ways to overcome these in developing countries. Conducting research in this domain is crucial for transforming the construction sector and its financial landscape while also integrating Construction 4.0 technology and practices. Construction 4.0 can bring about a favourable metamorphosis in developing countries, not least by enhancing the construction life cycle in terms of both quality and performance.  
Bringing Industry 4.0 to the field of construction in emerging economies, also known as Construction 4.0, has vast potential for changing the way traditional construction is implemented and for better integration to embrace sustainability. Although developing nations have various obstacles such as insufficient technological accessibility and poor infrastructure, among others, they have much to gain from implementing Industry 4.0 in their buildings. Through the application of modern technologies such as Building Information Modelling (BIM), Internet of Things (IoT), robotics, and artificial intelligence, emerging countries can optimise project performance, cut costs, and boost overall efficiency. In addition, embracing the principles of Construction 4.0 initiates creativity, encourages ecological consciousness, and enhances infrastructure quality in such areas. AchievingConstruction 4.0's full transformative potential needs joint policies, training, and global partnerships to tackle the outlined challenges. Developing countries can lead the way to a more resilient, productive, and technologically advanced construction sector by putting their money into education in technology, creating a culture of innovation, and putting in place regulatory frameworks in construction, thus ensuring that economic growth is sustainable and beneficial to the people [16].
# The Online Reservation Problem
## Appendix C Proof of Theorem 3
_Statement 4._ Given instance \(L\), define function \(w_{L}:B_{L}\times C_{L}\mapsto\mathbb{R}\) as follows:  
\[w_{L}(b,c)= \begin{cases}\frac{d_{c}}{2\Delta+2}&\text{if $b\in B_{L}\cap C_{L}$ and $b\equiv c$;}\\ \frac{d_{c}}{n(2\Delta+2)}&\text{if $b\text{ and $c$ overlap, and $b\
ot\equiv c$;and}$}\\ 0&\text{otherwise.}\end{cases} \]  
_Claim 7:_\(w_{L}\) is an allocation scheme from the set \(B_{L}\) to the set \(C_{L}\). Proof of Claim 7.: For any job \(b\) in \(B_{L}\), jobs from \(C_{L}\setminus\{b\}\) that overlap with it have total duration strictly less than \(n(2D_{max}+d_{i})\) (Lemma A1), and therefore,  
\[\sum\limits_{c\in C_{L}\setminus\{b\}}w_{L}(b,c)<\frac{n(2D_{max} +d_{b})}{n(2\Delta+2)} =\frac{2\Delta+1}{2\Delta+2}\frac{2D_{max}+d_{b}}{2\Delta+1}=\frac {2\Delta+1}{2\Delta+2}\frac{2D_{max}+d_{b}}{2D_{max}+D_{min}}D_{min}\] \[\leq\frac{2\Delta+1}{2\Delta+2}\frac{d_{b}}{D_{min}}D_{min}=\frac {2\Delta+1}{2\Delta+2}d_{b}.\]Further, if \(b\in B_{L}\cap C_{L}\), \(w_{L}(b,b)=d_{b}/\left(2\Delta+2\right)\). Therefore, for any job \(b\in B_{L}\), \(\sum\limits_{c\in C_{L}}w_{L}(b,c)\leq d_{b}\). Hence, by Definition 1, \(w_{L}\) is an allocation scheme. Claim 8: \(d_{c}/\sum\limits_{b\in B_{L}}w_{L}(b,c)\leq 2\Delta+2\) for any job \(c\) in \(C_{L}\). Proof of Claim 8.: If job \(c\in C_{L}\cap B_{L}\), then \(d_{c}/\sum\limits_{b\in B_{L}}w_{L}(b,c)\leq d_{c}/w_{L}(c,c)=2\Delta+2\). Otherwise if job \(c\in C_{L}\setminus B_{L}\), it must overlap with at least \(n\) jobs in \(B_{L}\) because Algorithm \(A\) could not have declined job \(c\) otherwise. Therefore, \(d_{c}/\sum\limits_{b\in B_{L}}w_{L}(b,c)\leq d_{c}/\left(nd_{c}/\left(n(2 \Delta+2)\right)\right)=2\Delta+2\). Since instance \(L\) was arbitrary, by Lemma 1, \(r_{A}\leq 2\Delta+2\). We will now prove the lower bound. Let \(\beta>0\) be arbitrarily large and \(\epsilon>0\) be arbitrarily small. Consider instance \(L\) in which the jobs arrive in following sequence:  
\[\underbrace{(\beta,D_{min}+2\epsilon),\ldots,(\beta,D_{min}+2 \epsilon)}_{n},\] \[\underbrace{(\beta+\epsilon-D_{max},D_{max}),\ldots,(\beta+ \epsilon-D_{max},D_{max})}_{n},\] \[\underbrace{(\beta+\epsilon+D_{min},D_{max}),\ldots,(\beta+ \epsilon+D_{min},D_{max})}_{n}.\]  
Each 2-tuple represents a job, with first value being the start time and the second being the duration. Then, \(OPT(L)/v_{A}(L)=n(D_{min}+2D_{max})/(n(D_{min}+2\epsilon))\). Therefore, \((D_{min}+2D_{max})/(D_{min}+2\epsilon)\leq r_{A}\). Since \(\epsilon\) was arbitrary, this inequality holds for every \(\epsilon\), letting \(\epsilon\to 0\) gives us the lower bound, \(2\Delta+1\leq r_{A}\).
## 3 Results
### Data Collection and Correction Results
#### 3.2.2 Correction Results  
Table 4 summarizes the loss function of the BP neural network model, and Figure 5 illustrates the relationship between dynamic measurement values of the UAV and the model-corrected values with static measurement values. According to Table 4, the mean squared errors of the BP neural network models for the four air quality indices are all less than 0.1, and the coefficient of determination R\({}^{2}\) exceeds 90%. From Figures 5-8, it is evident that there is a significant deviation between the model-corrected values and the dynamic measurement values, whereas they align closely with the static measurement values overall. These results indicate that the BP neural network model performs well in terms of fitting, accurately capturing patterns within the data. In these figures (Figures 5-8), the green line represents the static measurement values, which are the control group data measured by the sensors in an ideal stationary state. The blue line represents the dynamic measurement values, which are the data measured by the sensors mounted on the UAV during flight in the same series of positions. The red dashed line represents the model correction results, which is the sum of the dynamic measurement values and the corrected values predicted by the neural network model.  
Figure 5: BP neural network model correction results for CO\({}_{2}\).  
Figure 6: BP neural network model correction results for humidity.  
Figure 7: BP neural network model correction results for PM\({}_{2.5}\).
## 4 Proposed QL-Driven ABC-Based Solving Approaches
### Initial Population Generation  
To allow quality and diversity in the search space, we generated an initial population of candidate solutions at random and \"seeded\" it with some good ones. Therefore, in this work, we suggest the following three-step initialization procedure to generate an initial population of \(PopSize\) complete integrated solutions \((S,P)\):  
* **Step 1.** Firstly, we generated the first candidate food source of the initial population with a newly designed heuristic, named INEH, a greedy approximate heuristic based on the well-known NEH heuristic [43], by modifying its second phase.
* **Step 2.** Generate \(\alpha\%\)PopSize production sequences using a modified NEH heuristic [74]. To generate complete food sources, maintenance activities are scheduled using the PHM-based greedy heuristic of [44]. In this heuristic, maintenance operations are inserted according to the current accumulated degradation of the machine estimated by the PHM module, starting from the first machine \(M_{1}\) to the last one \(M_{m}\).
* **Step 3.** The rest of the population is generated randomly with production sequences generated at random and maintenance operations inserted using the heuristic of [44].  
The main idea of the INEH heuristic is to build from scratch the sequence of production jobs and maintenance operations giving priority to jobs with higher processing times to be scheduled first. In the first phase, the jobs are ordered according to the LPT rule in a list \(L\). Then, a partial sequence is generated with the first job of \(L\). The second phase is an insertion procedure to iteratively construct a complete integrated sequence. This phase starts first, by scanning the current partial sequence, and maintenance operations are inserted when necessary with respect to the threshold \(\Delta\)=1 on each machine. Then, by testing the second job of \(L\) in all available positions of the integrated partial sequence. The position that yields the minimum makespan is chosen to be inserted, and the new partial integrated sequence overwrites the last one. This insertion step is repeated iteratively with the rest of the jobs. Detailed steps of the INEH heuristic are given in Algorithm 2.  
```
0: A Set of production jobs Begin  Let \(L\) the list of ranked production jobs in decreasing order of total processing time (LPT rule). Insert the first job of \(L\) in the first position of the current partial empty sequence. Update \(L\) while\(L\) not Empty do  Insert maintenance operations in the appropriate positions of the current partial sequence with respect to the threshold \(\Delta\)  Insert the first job of \(L\) in all the k+1 possible positions of the current partial sequence  Evaluate all of k+1 resulting partial sequences  Keep the best sequence as a new current partial sequence  Update \(L\) endwhile  Return Integrated sequence End
```  
**Algorithm 2** INEH Heuristic
# Topology and Response Surface Optimization of a Bicycle Crank Arm with Multiple Load Cases
## 1 Introduction
It is found that the proposed optimization method shows superiority over other techniques. This method is also found to be effective in the telecommunication field as [PERSON] et al. use this method to optimize the C-RAN backhaul design [17]. [PERSON] et al. [18] propose a novel approach of functor-oriented topology optimization of elastoplastic structures. Several other studies also state that this method is very useful to create lighter components in precision engineering [19], composite material [20], and civil engineering application [21; 22]. Nevertheless, a study on the application of topology optimization on the bicycle crank arm seems to be lacking. It is very important since the crank arm plays a major role in the bicycle component. It is the part where the cycling force is first applied before transmitting into the frame. Most of the research was only focused on the force evaluation or stress distribution on the crank and seldom discussed how to improve the crank arm design. One good approach has been done by [PERSON] et al. [23] where a complete study on the design and manufacture of a bike crank arm is presented in which the topology optimization procedure is included. This paper, therefore, extends the approach of using topology optimization on a bicycle crank arm with an additional application of a response surface optimization method to gain a better improved optimized design. The additional response surface method is used to reduce stress concentrations after the optimized topology has been obtained. Finally, the optimized design is experimentally validated.
## 3 QoS Self-Provisioning and Interference Management Concept
### Step 1--The P-CPICH Power Calculations  
In order to determine the optimal P-CPICH transmit power, \(P_{CPICH}^{opt}\), both the RSCP measurements at the HNB towards the strongest co-channel MNB, \(RSCP_{CPICH}^{macro}\), and the desired femtocell coverage range, \(PL_{femto}\), are used. Optimal P-CPICH transmit power means configuration of pilot channel power in order to be able to obtain the required femtocell range. This range is defined where the users exactly sees the same RSCP from the target femtocell and from the nearest undesired cell (macrocell). We therefore estimate the required P-CPICH transmit power as  
\[P_{CPICH}^{opt}=RSCP_{CPICH}^{macro}\cdot PL_{femto}. \tag{1}\]
## 3 Results and Discussion
### Fatty Acid Profile and Lipid Composition
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Heat** & **Storage** & \multirow{2}{*}{**SFA (\%)**} & \multirow{2}{*}{**MUFA (\%)**} & \multirow{2}{*}{**PUFA (\%)**} & \multirow{2}{*}{**n-3 (\%)**} & \multirow{2}{*}{**n-6 (\%)**} \\
**Treatment** & **Duration** & & & & & \\
**Method** & **(Months)** & & & & & \\ \hline \multirow{4}{*}{FFS} & 1 & 34.55 \(\pm\) 0.01 a,* & 23.85 \(\pm\) 0.46 a & 65.25 \(\pm\) 0.79 a & 7.60 \(\pm\) 0.23 a & 49.30 \(\pm\) 1.49 a \\  & 3 & 34.40 \(\pm\) 0.40 a & 22.40 \(\pm\) 0.60 a & 64.00 \(\pm\) 1.00 a & 8.00 \(\pm\) 0.40 a & 52.60 \(\pm\) 2.40 a \\  & 6 & 33.70 \(\pm\) 0.00 a & 24.20 \(\pm\) 0.00 a & 62.30 \(\pm\) 0.00 a & 6.60 \(\pm\) 0.00 a & 47.00 \(\pm\) 0.00 a \\ \hline \multirow{4}{*}{ESC} & 1 & 15.88 \(\pm\) 0.74 c & 22.93 \(\pm\) 1.77 b & 69.58 \(\pm\) 5.14 b & 7.93 \(\pm\) 0.77 b & 57.44 \(\pm\) 4.90 b \\  & 3 & 18.60 \(\pm\) 1.83 b & 23.18 \(\pm\) 2.22 b & 70.75 \(\pm\) 6.23 b & 8.83 \(\pm\) 0.96 b & 58.58 \(\pm\) 6.20 b \\  & 6 & 22.00 \(\pm\) 0.00 a & 28.00 \(\pm\) 0.00 a & 86.00 \(\pm\) 1.00 a & 11.00 \(\pm\) 0.00 a & 75.00 \(\pm\) 1.00 a \\ \hline \hline \end{tabular}  
*–small superscript letters (a, b) indicate significant differences between months within FFS and ESC according to [PERSON]’s HSD test (\(p\) < 0.05). SFA—saturated fatty acids, MUFA—monousaturated fatty acids, PUFA—polyunsaturated fatty acids, n.3—omega3, n.6—omega6.  
\end{table} 
Table 8: Fatty acid composition (% total FA) of FFS and ESC during 1–6 months of storage. Polyunsaturated fatty acids (PUFAs) represented the dominant lipid fraction in both products. ESC exhibited particularly favorable PUFA levels, ranging from 69.58% to 86.00%, whereas FFS contained PUFA from 62.30% to 65.25%. The PUFA/SFA ratio exceeded 2.5 in ESC throughout storage, which is well above the FAO/WHO minimum recommendation of 0.45 for cardiovascular health benefits [45]. The n-3 (omega-3) fatty acid fraction remained stable in both products but was slightly higher in ESC (7.93-11.00%) than in FFS (6.60-8.00%). Nonetheless, the n-6/n-3 ratio averaged 6.8:1 across both matrices, remaining above optimal nutritional targets. This indicates potential for further improvement via blending with omega-3 rich oils such as flaxseed, chia, or canola to better align with dietary recommendations [46; 47]. The processing-induced lipid modifications observed in ESC are consistent with prior reports highlighting the impact of partial oil removal and thermal expansion on fatty acid partitioning within the protein-fiber matrix [27]. The relative enrichment of PUFA in ESC suggests potential applications in functional foods targeting anti-inflammatory and cardio-protective formulations, while its moderate residual lipid content supports favorable textural and sensory attributes in high-protein products. Overall, both FFS and ESC demonstrated high oxidative stability of their lipid fractions across the 6-month storage period, with no substantial degradation or unfavorable shifts in fatty acid composition.