esa-sceva/satcom-chunk-collection huggingface dataset:
# Sustainability-Based Analysis of Conventional to High-Speed Machining of Al 6061-T6 Alloy
## 1 Introduction
Further examinations led to a conclusion concerning the significant relation of these distinct SCE regions to the mechanics of the cutting process. The results of the study demonstrated that substantial energy savings can be achieved by selecting optimal cutting parameters. The energy map approach was further extended by [PERSON] et al. [32] who developed SCE-based process maps for Ti-6 Al-4V and related them to tool wear maps. Their results showed that there exists a strong correlation between SCE consumption and tool wear. A detailed review of the literature revealed that although sustainability analyses of the machining process have been investigated by past researchers, there exists no study that measures and compares different sustainability factors in conventional, transitional,and high-speed machining. The present work is aimed at this research gap, and it presents a detailed sustainability analysis for the conventional, transitional, and high-speed turning of Al 6061. Statistical tools such as [PERSON]'s DOE, main effect plots, and ANOVA have been utilized to assess the effect of machining parameters on SCE and Ra. Finally, the results have been compiled to provide a holistic picture of SCE consumption in different cutting speed regimes. As discussed earlier, the sustainability of the machining process depends upon environmental, economic, and social factors. The present study utilizes SCE as a measure to express the environmental impact of the machining process. The economic aspect of the machining process has been covered by considering the surface quality of the finished product. Finally, dry cutting conditions have been used for all experiments, which have multidimensional effects. High-speed machining with a dry cutting condition not only results in better surface quality, but it also eliminates the requirement of coolants, which are unsustainable from the viewpoint of their hazardous effect on the environment and workers.
# Enhanced Scalability and Security in Blockchain-Based Transportation Systems for Mass Gatherings
## 5 Discussion  
This study focuses on State Channels and Rollups in solving transportation challenges during mega-events using blockchain technology. These technologies address the scalability, throughput, security, and latency issues of conventional systems. State Channels enable micropayment applications, whereas Rollups combine multiple small transactions into a single secure one, making use of decentralized systems more efficient. The proposed system achieved a 3% increase in throughput over the reference system, with a throughput of 3500 TPS. It also decreases the latency to 1.5 s compared to the reference systems, which helps transport networks make decisions during significant events in 2.3 s.  
In terms of security, it provides tamper protection, a high data integrity (99.99%), and data encryption. The leveling feature utilizes zero-knowledge proof and elliptic curve cryptography, boasting a 98% success encryption rate. Initially, the State Channel and Rollup infrastructure also required higher synchronization costs than traditional systems; however, Dotseeds are still more cost-effective because they offer a higher transaction per second (TPS) and lower latency. However, the costs of such a structure are justified by the enhanced efficiency it provides, especially in handling and coordinating significant events and transportation networks.  
In conclusion, blockchain technology, utilizing State Channels and Rollups, is the most effective solution for optimizing real-time transportation management during mega-events. It provides a faster, more secure, and more scalable performance than the currently available systems, rendering it the future of transport networks. This research also demonstrates that blockchain can enhance the efficiency and security of transportation systems for mega-events [20,21].
The significance of utilizing green technologies in nanoparticle production lies in their cost-effectiveness, non-toxicity, and ability to generate highly crystalline nanoparticles in various shapes and sizes. These environmentally friendly technologies often use plant or fruit extracts that contain polyphenols with unique properties. These polyphenols can reduce metal salts to create high-purity nanoparticles due to their characteristics, such as reducing abilities, hydrogen bonding, nucleophilicity, acidity, polarizability, and chelating capacity [19; 20; 21]. These exceptional properties have made nanoparticles a valuable resource for biomedical applications due to their high biocompatibility. Their production has increased significantly as a result of their numerous applications in the biomedical field [22; 23; 24]. _Phoenix dactylifera_ L. is a versatile tree that offers a variety of benefits to humans, including food, medicine, fuel, and construction materials. This makes it known as the \"Tree of Life\". It has a broad range of medicinal, nutritional, and economic uses, including antimicrobials, food additives, anti-diabetes, and anti-lipidemic drugs. Researchers have discovered high levels of phytochemicals in this plant species, including polyphenols, flavonoids, terpenoids, aldehydes, alkaloids, fatty acids, etc. In addition, a range of metal oxide nanoparticles (MO-NPs), such as iron, gold, silver, and nickel oxide nanoparticles, have been created using this plant species. This highlights the importance of the phenolic compounds of _Phoenix_ in various fields, including medicine and materials science [25; 26; 27; 28; 19; 29]. _Phoenix dactylifera_ L. materials offer potential pharmaceutical, biomedical, and food protection applications. They can be used as agents for anticancer, antibiotics, and antioxidant treatments. In addition, they can stabilize emulsions and be used in paints, membranes, and water and fertilizer treatment [30; 31; 32; 33; 34; 35; 36]. This study introduces a novel approach to synthesizing zinc salt-dependent zinc oxide nanoparticles (ZS-ZnO-NPs) by employing various zinc salts and a polyphenol-rich extract from _Phoenix dactylifera_ L. We systematically explore the influence of different zinc salts on crucial nanoparticle characteristics, such as morphology, size distribution, crystalline nature, optical properties, and surface chemical groups. This optimization process provides valuable insights into tailoring the green synthesis of ZS-ZnO-NPs for specific attributes. The evaluation of antioxidant capacity through the DPPH free radical assay underscores the potential applications of these environmentally friendly nanoparticles in biomedicine and industries, emphasizing the innovative contributions of this research to the field.
## 1 Introduction
Adsorption is a superficial phenomenon in which mycotoxins are attracted by electrostatic and polar interactions to an adsorbent substance [20]. The most used adsorbents are aluminosilicate clays, with silica, aluminum, oxygen, and hydroxyl groups in their basic composition [22]. These clays may be classified based on their chemical structure, such as phyllosilicates (montmorillonite, bentonite, smectite) and tectosilicates (zeolites) [23]. Examples of these clays are calcium aluminosilicate and hydrated sodium calcium aluminosilicate (HSCAS), bentonite, sepiolite, montmorillonite, diatomic, and zeolite. Activated charcoal, although not an aluminosilicate, is also used as an adsorbent [3; 20]. However, adsorbents usually show wide variation in composition and physical-chemical properties. Thus, it is necessary to assess their efficacy using in vivo protocols, which are generally costly and labor-intensive, as they involve the administration of toxins at different levels, with and without the adsorbent, to assess the effect on animal productivity [16]. Besides, these assays generally require collection of histopathological and clinical data, among other issues that add to the cost and labor. In this context, the use of biomarker approaches to estimate the mycotoxin bioavailability of adsorbent efficiency in in vivo assays may reduce costs, besides being more practical, helping the standardization of the experimental trials, and making it possible to assess the effect of adsorbents in field conditions. Biomarkers are substances that may be detected in biological samples (viscera, milk, eggs, feces, urine, blood), after exposure to a given agent, such as mycotoxins. In the case of exposure to mycotoxins, biomarkers may be the either the structurally unaltered toxin or a biotransformed product, depending on the toxicokinetic of the mycotoxin and the individual characteristics of the species [24; 25; 26; 27]. The European Union has rules for the use of adsorbents in animal feed [28; 29; 30], and these products must be carefully tested to be approved [31]. Therefore, the objective of this systematic review was to evaluate the feasibility of biomarkers as tools to assess the efficiency of mineral adsorbents for mycotoxins in farm animals.
## 3 Results and Discussion
### In Vitro Release Studies and Permeability Studies  
To evaluate the NEs capability to release the entrapped LVX, release studies were performed. In vitro release experiments are commonly employed to predict in vivo behavior, historically with traditional dosage forms such as capsules and tablets (following the dissolution), and more recently with novel dosage forms like injectable, biodegradable, nanocarriers, microspheres, and implants [76].  
In vitro release studies have been performed at 32 \({}^{\circ}\)C (to mimic the physiological temperature). In particular, Figure 9 shows the concentration versus time profiles of LVX released by NEsL during the experiment by cellulose dialysis tubing. The results obtained demonstrate that the sample was characterized by a high LVX percent release (almost 100% in 3 h) probably due to the oil nanodroplet fluidity investigated by anisotropy fluorescence study.  
In vitro diffusive models are crucial tools for screening the penetration of active compounds from various formulations, helping to predict the success of dermal or transdermal therapies [77]. The standard technique for studying in vitro permeation of drugs involves using a Franz diffusion cell device along with a Strat-M(r) artificial membrane [78]. Strat-M(r) is a synthetic, non-animal-based membrane model useful to predict drug permeation capability through human skin. Its use eliminates lot-to-lot variability and addresses common issues related to safety and storage. The membranes are composed of 300 \(\upmu\)m thick polyethersulfone and showed a good agreement in the permeability data (compared with human skin), which allows it to be used as a surrogate for human skin. Therefore, the amount of permeated drug per unit area of Strat-M(r) after 3 h from NEsL was investigated using Franz diffusion cells and was presented in Figure 10. The figure showed the amount of LVX (% and concentration mg/mL) in the acceptor compartment during the experiment (described in Section 2). Probably, the NEs features (together with specific surfactant concentration and drug characteristics) could directly influence the drug per  
Figure 8: Stability study over time of NEs and NEsL at room temperature (**A**,**C**) and 4 \({}^{\circ}\)C (**B**,**D**).  
meation through the Strat-M\({}^{\circledR}\)membrane resulting in non-significant levels of LVX in the acceptor compartment [79]. In addition, the permeation study conducted for 24 h has been performed and, according to the results reported in Figure S1, it is possible to observe that no significant increase in LVX permeation has been detected. In conclusion, Strat-M\({}^{\circledR}\) diffusion experiments suggest that the NEsL could be able, after in vivo administration, to interact only with the biofilm of bacteria and not induce transdermal activity.  
Figure 10: Permeated amount of LVX from NEsL through Strat-M\({}^{\circledR}\) in a Franz cell system.  
Figure 9: LVX release profile from NEsL by cellulose dialysis tubing.
## 4 Results
### Sensitivity Analysis  
Changing selected parameter values from \(-20\) to \(20\%\) had different effects on the estimated output. Figure 8 shows the effects of changes in \(a_{s}\), \(n_{s}\), \(pFS2\), \(pFS20\), \(\alpha_{Cx}\), \(T_{n}\), \(T_{opt}\), \(W_{F}\), and \(W_{s}\). Changes in \(a_{s}\) (Figure 8a) and \(n_{s}\) (Figure 8b) primarily affected \(W_{F}\); when \(a_{s}\) and \(n_{s}\) were changed by \(-20\%\), \(W_{F}\) differed by around \(16\%\) and \(6\%\), respectively. However, the selected outputs were less sensitive to changes in \(pFS20\) (Figure 8d) than \(pFS2\) (Figure 8c). GPP was highly sensitive to \(\alpha_{Cx}\) (Figure 8e) and to \(T_{opt}\) (Figure 8f). \(W_{s}\) was also sensitive to both of those parameters, but not as sensitive as GPP. \(T_{n}\) (Figure 8g) most affected GPP when it was changed by 20 \(\lx@math@degree\). From these results, we recommend that \(a_{Cx}\), \(T_{opt}\), and \(T_{n}\) be carefully measured if accurate results are desired, especially in GPP calculations.  
Figure 7: Comparison of estimated GPP and observed GPP from flux tower at **(a)** Takayama and **(b)** Hitsuijigaoka, respectively. The dash line is the linear regression between the estimated and observed value. At Takayama, the linear equation is y = 0.855x \(-\) 0.339, and at Hitsuijigaoka is y = 1.125x \(+\) 0.217, respectively.  
Figure 9 shows the effects of meteorological data on GPP. Solar radiation (Figure 9b) and \(T_{n}\) (Figure 9a) had significant impacts on GPP, with differences around \(\pm 3\) g C m\({}^{-2}\), but \(T_{x}\) had only a slight effect (Figure 9a). Increased solar radiation and \(T_{n}\) increased GPP. However, GPP was not sensitive to frost days or precipitation, since the study sites are not limited by water. Snow melt increases the soil water content in the summer. The effect of frost days on GPP was large in the winter, as large as the effect of temperature. However, frost days did not affect GPP in the summer, during which GPP was most affected by temperature and solar radiation.  
Figure 8: Sensitivity analysis of selected 3-PG outputs by changed the value of **(a)** stem constant: \(a_{s}\); **(b)** stem power: \(n_{s}\); **(c)** biomass partitioning ratio at DBH = 2 cm: \(p\)_FS2_ and **(d)** DBH = 20 cm: \(p\)_FS20_; **(e)** maximum canopy quantum efficiency: \(\alpha_{cc}\); **(f)** optimum temperature: _Topt_; and **(g)** minimum temperature: _Tmin_. The effect on the following output as stem biomass (black bar), foliage biomass (grey bar) and GPP (open bar) by changed the parameter value differed from \(-20\) to \(20\%\), with \(10\%\) interval. For both of _Topt_ and _Tmin_, they were changed in degree Celsius as \(-2\)\(\mathbb{C}\), \(-1\)\(\mathbb{C}\), \(+1\)\(\mathbb{C}\), and \(+2\)\(\mathbb{C}\).
# Review of Web Mapping: Eras, Trends and Directions
## 3 Web Mapping Eras of Development
### Web Mapping Eras
#### 3.2.4 Interactive Web Mapping Era  
The limitation of the early dynamic maps was that client requests and server responses were synchronized so that users, after submitting a map request, had to wait patiently for the server to assemble and return the response. The technological solution to enhance user interaction with the map was to provide user-client interaction simultaneously with client-server interaction. The interactive web-mapping era equates with [PERSON]'s [26] second significant development and [PERSON]'s [27] third generation web maps and builds on dynamic web mapping technology. The emerging AJAX technology combined with image tiling technology significantly enhanced user interaction with the map by allowing online maps to be delivered to a user in a continuous and responsive manner while the user was simultaneously interacting with the map interface.  
Asynchronous JavaScript and XML (AJAX) are a group of technologies including JavaScript and XML, which allow the client to simultaneously communicate with the server and the user [51].  
The result is a faster response to the user, while the client continues to receive data simultaneously from the server. Google Maps and Google Suggest were early users of AJAX technology. While the downloading of image tiles occurs in the background in an asynchronous fashion, the user experiences no wait time while viewing a continuously refreshing and emerging image [26].  
A second relevant advantage of the interaction was the possibility of geographic mashups of maps. Mashups are the dynamic and personalized combination of data from multiple sources combined into one map [52; 53]. This technology now became achievable, thanks to AJAX, applets and the emerging web map development environments (Java, Flash, Silverlight or customized plugins) or heavier standalone geo-browsers. Consequently, mashups were utilizing the increasing number of available APIs, particularly for growing number of online datasets (including basemap imagery) via map servers, to facilitate improved access to geospatial data and maps by developers and end users.
## 2 Results
### Rapid Identification of SS in Different Regions by HS-GC-IMS  
In general, due to the influence of growing environment, the composition and content of volatile components in the SS of different origins are also different. Therefore, it is  
Figure 3: The OPLS-DA score plots of region multi-classification by HS-GC-IMS (**A**) and HS-SPME-GC-MS (**C**). (**B**) the permutation test results (\(n\) = 200) of the OPLS-DA model of HS-GC-IMS; (**D**) the per-mutation test results (\(n\) = 200) of OPLS-DA mode of HS-SPME-GC-MS.  
particularly important to select SS from different regions. HS-GC-IMS takes less time to obtain analysis results, and data do not require complex processing. Differences between samples can be directly compared through the fingerprint generated by the machine. In this study, HS-GC-IMS was used for rapid identification of the volatile components of SS. The OPLS-DA model established based on the results of HS-GC-IMS had a high prediction ability for the panicles of SS from different habitats, and the samples obtained a good separation degree.  
However, not all VOCs in various samples were significantly different. In order to see the difference, we analyzed the variable importance for the projection (VIP) predictive of VOCs in SS (Figure 4). VIP is generally used to evaluate the contributions of X-variables to a model [29]. Based on the criteria of VIP > 1, 30 (red) important variables were selected in the SS of different regions from the VIP plot of the OPLS-DA model of HS-GC-IMS, but there are unknown compounds in these variables, as shown in Figure 4A. In order to further explore advantageous biomarkers, a random forest model (RF) is established using known compounds. RF is an effective high-dimensional data analysis supervision method, which is a popular ensemble learning algorithm for classification and prediction [30]. The classification trees were set as 1000 in this study. During the construction of the tree, a third of the sample is excluded from the bootstrap sample (out-of-bag data, OOB data). For an unbiased estimation of the classification error (OOB error), the OOB data were used as test samples [31]. Academically, the lower the OOB error, the more accurate the classifier. In this experiment, after several trees, the cumulative OOB error rates dropped to 0.0185. As shown in Figure 4B, the 14 variables with significant differences contributed to the classification of SS in different regions. In A, p-cresol was more abundant. Trans-2-hexenal and 1-menthol are higher in B. J has greater amounts of ethyl acetate, 2-ethyl-5-methylpyrazine, and trans-2-pentenal. 1-phenylethanol was more abundant in the N. Diethyl trisulfide, 1-octen-3-ol, and 2-hexanol are higher in S. The levels of 3-methylbutanoic acid and alpha-phellandrene in Z are higher. These compounds are similar to the analysis results of Figure 1B. Those compounds with significant differences improve the accuracy of random forest classification, which may be a key factor in distinguishing from SS of different origins based on HS-GC-IMS.
# Leveraging Urban Water Distribution Systems with Smart Sensors for Sustainable Cities
## 5 Experimental Results
### Moratalla Scenario  
Figure 8 shows the linear regression between base demand and pipe diameter for Moratalla. We can observe that the slope is negative, which results in lower base demands for larger pipe diameters. Transposing this condition to real conditions would suggest that larger diameter pipes allow for efficient water flows, reducing total demands. It could also  
Figure 6: Linear regression between \(B_{D}\) and \(L_{D}\) in Lugo.  
Figure 7: Resource savings per node optimized in Lugo.  
indicate less resistance to flow, requiring less pressure to move the same quantities of water and experiencing lower friction losses, where less water would be lost due to the internal resistance of the pipes.  
A base demand of 16.15 L/s was calculated, oscillating between [13; 72; 18.57]. The simulation results show sufficient flow for most nodes. Linear regression (Figure 9) predicts node demands based on base demands.  
Unlike Lugo, Moratalla shows a negative correlation between base demand and pipe diameter. This result indicates that smaller pipes are sufficient to meet demand in areas with smaller populations and less complex water networks, while larger pipes may be underutilized. The negative slope (\(-0.0200\)) highlights that as pipe diameter increases, the base demand slightly decreases, potentially due to water distribution across a broader network with less intense demand at individual nodes. In fact, 70.3% of nodes  
Figure 8: Linear regression between \(B_{D}\) and pipe diameter in Moratalla.  
Figure 9: Linear regression between \(B_{D}\) and \(L_{D}\) in Moratalla.  
were optimized, achieving an MAE of 7. The linear regression between base demand and pipe diameter (Figure 9) shows a negative slope, indicating lower base demands for larger diameters.  
\[B_{D}=-0.02003613\times\text{diameter}+18.60030081 \tag{4}\]  
Moratalla's extensive water supply network is largely due to its geographic layout. It serves a dispersed rural population and agricultural areas, contrasting with more concentrated urban populations in the two other cities.  
Figure 10 shows resource savings per node, with the highest saving node achieving 186,840 L/day.
## 3 Results
### Physical Properties
#### 3.3.1 Dry Density  
Analysing the data from Table 8, dry density (\(\rho_{\text{dry}}\)) was lower in all the mixes containing GRC and MRA than in the NAC reference. Figure 4a shows that replacing OPC with GRC lowered \(\rho_{\text{dry}}\) linearly (R\({}^{2}\geq\) 0.998) in the mixes with NA and MRA due to the lower density of the recycled materials (see Section 2.1.1), which also yielded less hydration product (low reactivity) [5; 6]. Dry density was 2.8% lower in N10/0 and 5.8% lower in N25/0 than in NAC and 2.3% lower in R10/50 and 5.3% lower in R25/50 than in R0/50. The vertical distance between the two trend lines in Figure 4a, labelled the 'MRA effect', measures the effect of replacing 50% NA with MRA. The mean 8.0% decline in \(\rho_{\text{dry}}\) in the MRA mixes (R0/50, R10/50 and R25/50) relative to the NA mixes (NAC, N10/0 and N25/0) was attributable to the lower mean dry density of MRA (\(\rho_{\text{dry}}\) = 2069 kg/m\({}^{3}\)) relative to that of NA (\(\rho_{\text{dry}}\) = 2591 kg/m\({}^{3}\)). The joint use of GRC+MRA, in turn, induced declines in dry density relative to NAC of 10.4% in R10/50 and 13.1% in R25/50, findings consistent with earlier observations in concrete made with recycled (coarse and fine) aggregate consisting of crushed waste clay brick [48].