esa-sceva/satcom-chunk-collection huggingface dataset:
# Gold Nanoparticles for Drug Delivery and Cancer Therapy
## 4 Gold Nanoparticle-Based Drug Delivery
### Plasmid Deoxynucleic Acids Vector (pDNAAs) Delivery
The Au NPs were synthesized using the free radical polymerization method where the size change can be controlled by increasing the temperature. Au NPs were also synthesized using the sodium dodecyl sulfate surfactant method, seeding the solution which allows it to control the amount of Au NPs by varying the ratio of the nanogels [79]. Liposomes are vesicles that are non-immunogenic efficient and used in gene and drug delivery applications. In targeted DNA delivery, Cationic 1,2 dioleoyltrimethylammoniumpropane (DOTAP) lipid is used due to its ability to form lipoplex by electrostatic interaction. They are ideal for use in antibacterial drug delivery applications. PEG cannot be used with liposome, and the removal of PEG from liposome surface is essential for gene and drug delivery applications. For the Staphylococcus aureus bacteria, DOTAP encapsulated in Au NPs was functionalized using hydrogel, which was used to deliver the drug in bacteria [80]. Another study was carried out to investigate the blocking of specific genes using Bcl-2 DNAi-conjugated Au NPs in MCF-7 cells. The results show that this is a viable choice and has potential to be used in clinical applications [81]. Croatmine is a basic, 42-residue polypeptide obtained from snake venom which possesses cell penetrating properties, so a study was carried out to observe the preparation, purification, biochemical and biophysical analysis of venom. It was studied to be functionalized with Au NPs to deliver DNA [82]. A Y-type DNA-functionalized Au nanoprobe was introduced to identify telomerase and trigger drug release in cancer cells. The system involved Au NPs with the dense modification of a Y-type DNA molecular beacon on the surface. The beacon consists of an FAM-labeled single-strand DNA, telomerase primers, and a single-strand DNA of two templates. An anticancer agent drug molecule was inserted into the DNA double strand. In the presence of the telomerase primers, extend and mismatch DNA is released, leading to the emission of fluorescent light from FAM and releasing Dox [83]. A DNA-based platform that can self-assemble into targeted NIR responsive NP for cancer thermo-chemotherapy was introduced. It has three distinct functional components: Au nanorod, complementary DNA strands, and PEG. This system is capable of delivering DNA and performing heat conversion for PTT [84].
## 2 Materials and Methods
### Methods
#### 2.3.5 The Analysis of Spatial Relationship between GPP with Climate Factors  
To analyse the spatial relationship between GPP variability and local climate factors, the geographic weighted regression (GWR) model was employed. Modelling samples used to run the GWR model were developed based on pixels of the averaged mean annual GPP values from each GPP-LUE model results (CASA, GPP-VPM Canopy, GPP-VPM Chl) and the observed MODIS-GPP (MODI7A2H); and the averaged mean annual values of each climate factor (Prec and LST). GPP pixel values were selected as dependent variables, while climate factors were selected as independent variables. The GWR model was calibrated using the equation suggested by [PERSON] [54] (Equations (17) and (18)).  
\[y_{i}=\beta_{0}+\sum_{k=1}^{p}\beta_{k}x_{ik}+\varepsilon_{i} \tag{17}\]  
\[y_{i}=\beta_{i0}\big{(}u_{j},v_{j}\big{)}+\sum_{k=1}^{p}\beta_{ik}\big{(}u_{j},v_{j}\big{)}x_{ik}+\varepsilon_{i} \tag{18}\]  
where \(y_{i}\) is the dependent variable of \(i\)th sample, which represents GPP value at pixel \(i\); \(x_{ik}\) is the \(k\)th explanatory variable of the \(i\)th sample that denotes the climate factor (Prec or LST) value at pixel \(i\); \(p\) is the total number of explanatory variables; \(\beta_{k}\) is the coefficient of the \(k\)th explanatory variable; \(\beta_{0}\) donates the intercept; \(\varepsilon_{i}\) is the error term; \(\big{(}u_{j},v_{j}\big{)}\) represents the geographic coordinate of the \(i\)th sample. The estimated coefficients \(\beta_{ik}\) for each sample vary depending on the associated location [54]. Finally, the GWR model results were selected in varieties of five GWR stepwise regressions based on the Pearson product moments correlation to verify the effects of climate factors to GPP variability at pixel level [55].
## 4 Discussion
### Influence of Climate Map on Accuracy of Forest Degradation Detection  
Forest structure, function and degradation were sensitive to climate and climate changes [3]. In this study, we developed an approach based on an improved Koppen-Geiger climate classification map to detect the degradated forest. To analyze the influence of this climate map on the accuracy of the results, we also validated the forest degradation area from remote sensing data using the data from the provincial ground survey when the climate map was not used in the classification tree model in Section 2.3.2 (R\({}^{2}\) = 0.40 and RMSE = 12.65 \(\times\) 10\({}^{4}\) hectares from MODIS data, and R\({}^{2}\) = 0.54 and RMSE = 6.58 \(\times\) 10\({}^{4}\) hectares from MODIS-Landsat data). A higher R\({}^{2}\) and lower RMSE as shown in Figure 7 (R\({}^{2}\) = 0.48 and RMSE = 7.37 \(\times\) 10\({}^{4}\) hectares from MODIS data, and R\({}^{2}\) = 0.62 and RMSE = 4.73 \(\times\) 10\({}^{4}\) hectares from MODIS-Landsat data) indicated that the climate classification map could increase the accuracy in forest degradation detecting.
## 3 Comparison of Earthquake Parameters
These periods are different from each other for each geographical location and were calculated using the following equations in the TBEC-2018:  
\[T_{\rm B}=S_{\rm D1}/S_{\rm DS} \tag{3}\]  
\[T_{\rm A}=0.2\cdot T_{\rm B} \tag{4}\]  
S\({}_{\rm s}\), S\({}_{\rm 1}\), PGA, PGV, F\({}_{\rm s}\), F\({}_{\rm 1}\), T\({}_{\rm A}\), T\({}_{\rm B}\), T\({}_{\rm AD}\), T\({}_{\rm BD}\), horizontal, and vertical elastic design spectra were obtained from TEHIWA [14] for each province by using DD-2 earthquake ground motion level and ZC local soil type. The comparison of these earthquake parameters obtained for all provinces is given in Table 6.  
The design spectrum described in the earthquake regulations were used to determine the earthquake loads that will affect an engineering structure [10]. The comparison of horizontal and vertical elastic design spectra obtained for all provinces through TEHIWA [25] are shown in Figure 4. The horizontal and vertical elastic design spectra are completely different for all provinces. The highest spectral acceleration value was obtained for Kocaeli, and the lowest one was obtained for Kirklarelin in both the vertical and horizontal direction. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Province**} & \multicolumn{10}{c}{**Earthquake Parameter**} \\ \cline{2-13}  & **S\({}_{\rm S}\)** & **S\({}_{\rm 1}\)** & **PGA** & **PGV** & **F\({}_{\rm S}\)** & **F\({}_{\rm 1}\)** & **S\({}_{\rm DS}\)** & **S\({}_{\rm D1}\)** & **T\({}_{\rm A}\)** & **T\({}_{\rm B}\)** & **T\({}_{\rm AD}\)** & **T\({}_{\rm BD}\)** \\ \hline Balikesir & 0.880 & 0.219 & 0.372 & 21.591 & 1.200 & 1.500 & 1.056 & 0.329 & 0.062 & 0.311 & 0.021 & 0.104 \\ Bilecik & 0.566 & 0.177 & 0.238 & 15.616 & 1.274 & 1.500 & 0.721 & 0.266 & 0.074 & 0.368 & 0.025 & 0.125 \\ Bursa & 0.854 & 0.228 & 0.356 & 21.807 & 1.200 & 1.500 & 1.025 & 0.342 & 0.067 & 0.334 & 0.022 & 0.111 \\ Canakkale & 0.713 & 0.216 & 0.300 & 19.510 & 1.215 & 1.500 & 0.866 & 0.324 & 0.075 & 0.374 & 0.025 & 0.125 \\ Edirne & 0.424 & 0.132 & 0.180 & 11.663 & 1.300 & 1.500 & 0.551 & 0.198 & 0.072 & 0.359 & 0.024 & 0.120 \\ Istanbul & 0.977 & 0.270 & 0.400 & 24.668 & 1.200 & 1.500 & 1.172 & 0.405 & 0.069 & 0.345 & 0.023 & 0.115 \\ Kirklareli & 0.387 & 0.128 & 0.165 & 11.085 & 1.300 & 1.500 & 0.503 & 0.192 & 0.076 & 0.382 & 0.025 & 0.127 \\ Kocaeli & 1.633 & 0.444 & 0.668 & 55.648 & 1.200 & 1.500 & 1.960 & 0.666 & 0.068 & 0.340 & 0.023 & 0.113 \\ Sakarya & 1.581 & 0.433 & 0.643 & 51.110 & 1.200 & 1.500 & 1.897 & 0.650 & 0.068 & 0.342 & 0.023 & 0.114 \\ Tekirdag & 0.956 & 0.263 & 0.391 & 24.542 & 1.200 & 1.500 & 1.147 & 0.395 & 0.069 & 0.344 & 0.023 & 0.115 \\ Yalova & 1.477 & 0.392 & 0.603 & 42.287 & 1.200 & 1.500 & 1.772 & 0.588 & 0.066 & 0.332 & 0.022 & 0.111 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of earthquake parameters.
# Addressing Sustainability within Public Procurement of Food: A Systematic Literature Review
## 3 Results
### Connecting Sustainability to Focus Words
#### 3.3.1 Framing Environmental Sustainability
While health was primarily identified as relating to social aspects of sustainability, it was also identified in several articles as relating to environmental sustainability. The most frequently identified approach to address human health within the procurement of food was by buying organic produce, which is assumed to additionally contribute to  
Figure 9: Focus words in the context of environmental sustainability. (The star next to some of the words refers to a cut of where different variations of that word are possible, e.g., efficien* can include efficient, efficiency etc.). reduced environmental impacts [29; 71]. Several articles claim that organic foods contain fewer chemicals, less antibiotics, and are more nutritious than conventional food products, thereby contributing to better health for consumers and the environment [49; 67; 68]. Others suggest that healthy diets are promoted through dietary initiatives in public procurements, e.g., through the reduction of the number of meat dishes served [18; 62; 72]. Climate change was addressed both in terms of practices to reduce environmental pressure, mainly by limiting GHG emissions. Several authors addressed adapting more climate-conscious agricultural strategies, see e.g., [19; 55; 73]. Furthermore, a large share of articles focused on reducing GHG emissions by minimizing food miles through procuring local foods, which was included in roughly one-third of the reviewed articles. In several of the articles, quantitative assessments and indicators were used to highlight environmental sustainability, which is primarily related to GHG emissions, and several articles employed life cycle assessment (LCA). 'Low carbon' procurements have also been investigated in other previous articles [69]. For example, [PERSON] et al. [74] quantify the effectiveness of different procurement policies to reduce GHG emissions. Other studies also identify dietary changes as the most effective way to reduce environmental pressure, mainly by limited consumption of ruminant-based products [18]. Furthermore, several articles mentioned strategies to reduce meat consumption to mitigate climate change from food procurement [18; 29; 36; 55; 67; 7; 7; 74; 75; 76; 77]. Seasonal foods, i.e., only purchasing products in season, were also considered environmentally sustainable in many of the articles [40; 55; 62]. This was motivated by the fact that seasonal food was considered 'fresh', having a reduced need for a chemical treatment that is otherwise required for a longer shelf-life [55]. Furthermore, a better understanding of production capacity and seasonality could help to balance supply and demand, additionally reducing waste in the production phase for improved environmental performance [78].
## Materials and Methods
### Synthesis of tiopronin-Capped Gold nanoclusters/Nanoparticles  
The aqueous synthesis of tiopronin-capped gold nanoclusters/nanoparticles began with the formation of gold-tiopronin complexes. The gold precursor solution was prepared by dissolving 0.01 mmol of hydrogen tetrachloroaurate tetrahydrate in 25 mL of double-distilled water. A mixture of 0.01 M tiopronin and sodium hydroxide (1:1) aqueous solution was added to the gold precursor solution, and the reaction led to the formation of gold-tiopronin complex. The amounts of tiopronin and sodium hydroxide mixture solution used in this study were 3, 2, 1, 0.5, 0.2, 0.1, 0.05, and 0.02 mL. During this stage, sodium hydroxide deprotonated the carboxyl group of the tiopronin tail, enabling stable nucleation of the tiopronin-capped gold nanoclusters/nanoparticles during the subsequent reduction process. In the absence of the deprotonation of the carboxyl functional group, the gold nanoparticle seeds tended to aggregate via hydrogen-bonding interactions between the carboxyl groups, resulting in unstable gold nanoparticle growth during the subsequent reduction process. In contrast, an excessive reaction between gold precursors and a mixture of tiopronin and sodium hydroxide for more than an hour led to a spontaneous reduction of gold, resulting in the formation of bare gold microflakes that floated on the surface of the solution.  
The gold ions were reduced by adding 0.1 mmol of fresh sodium borohydride dissolved in 1 mL double distilled water with rapid stirring. The reduction process lasted for 50 min with rapid stirring. This resulted in the formation of tiopronin-capped gold nanoclusters/nanoparticles via metallic bonding between the reduced gold and gold-tiopronin complex. The size of the tiopronin-capped gold nanocluster/nanoparticles was determined by the molar ratio between gold and tiopronin. Finally, boric acid, a byproduct, was removed by adding a small amount of methanol to the resulting colloid. According to Equations (1) and (2), this process yields volatile trimethyl borate, which can be degassed via ultrasonication. Figure 1 illustrates the synthesis method for tiopronin-capped gold nanoclusters/nanoparticles.  
\[2\text{Au}^{3+}+6\text{Na}\text{BH}_{4}+18\text{H}_{2}\text{O} \to 2\text{Au}^{0}+6\text{H}_{3}\text{BO}_{3}+21\text{H}_{2}\uparrow+6 \text{Na}^{+} \tag{1}\] \[\text{H}_{3}\text{BO}_{3}+3\text{CH}_{3}\text{OH} \to B(\text{OCH}_{3})_{3}\uparrow+3\text{H}_{2}\text{O} \tag{2}\]  
Figure 1: Schematic of the synthesis method for tiopronin-capped gold nanoclusters/nanoparticles. (**a**) The experimental process for the synthesis. (**b**) The chemical reaction flow for the process, showing the state of the gold ions, the tiopronin-gold complexes, and the tiopronin-capped gold nanoclusters/nanoparticles.
# Evaluation of IEEE802.15.4g for Environmental Observations
## 4 Analysis
### Smart Agriculture Scenario
#### 4.2.1 RX at 337 m  
With a packet size of 127 B, Figure 9a shows the PDR of almost all radio settings are very close to 100%, including those with the highest data rate possible (800 kbps). Increasing the packet size to 2047 B, Figure 9b shows that the PDR remains 100% for the majority of the radio settings, including OFDM1-800. 2 FSK-50 is 100% in both, with short and long packets, as well as its variations with higher data rates. Therefore we do not highlight any other PHY, since the potential user has many options to choose a radio setting with 100% PDR.  
We can see that the PDR values for some radio settings are higher for long packets than short packets. This is the case for OFDM2-200 and OFDM4-300 at 213 m, and for OFDM4-300 at 337 m. This can be linked to interference. Since we are transmitting packets with only 20 ms separation between them, short packets series are more likely to be affected than longer packets by a single transmission of a different radio technology device using the same frequency and with a lower data rate (a LoRa packet using Spreading Factor 12 with a 13 B payload has a ToA of 925 ms (LoRa Modem Calculator Tool)).  
Figure 9c shows the maximum amount of packets that can be transmitted per hour, and we can see that OFDM1-800 can transmit up to 1531 packets while the reference 2 FSK-50 only 166. We highlight OFDM1-800 since it can transmit at least 25% more packets than any other radio setting, and more that 9 times the amount of the reference. Considering long packets, OFDM1-800 has the capability of sending the maximum amount of packets per hour, up to 167 packets, while the reference roughly 11. Figure 9d shows the maximum amount of 2047 B packets that can be delivered at 337 m. In consequence, if the potential user wants to maximize the throughput of the nodes separated by a similar distance, OFDM1-800 offers the highest throughput.  
charge needed corresponds to OFDM1-800, with just 178 \(\upmu\)C, around ten times less electric charge. Other radio settings are also \"low energy\" such as OFDM2-800 with 215 \(\upmu\)C, and OFDM2-600 with 243 \(\upmu\)C.  
In Figure 9f, we can see the average electric charge consumed by the TX radio in order to transmit a long packet. The radio setting with the smallest electric charge requirement is OFDM1-800, with 1630 \(\upmu\)C on average per a 2047 B transmitted packet. The reference 2 FSK-50 consumes 27.5 mA, 16 times more than OFDM1-800. OFDM2-600 consumes 2200 \(\upmu\)C, more than 12 times less than the reference. If the potential user wants to increase the lifetime of a battery power node, OFDM1-800 is the less electric charge demanding PHY in these conditions.
# A Task-Centric Cooperative Sensing Scheme for Mobile Crowdsourcing Systems
## 6 Conclusions  
Resource consumption efficiency is one of the main problems in mobile crowd sensing systems because smart devices are limited in power, storage and data flow. One valid way to save resources is to select only a portion of the users as active participants, while keeping adequate sensing information integrity. In this paper, we proposed a novel task-centric participant selection scheme under the constraint of sensing data quality. Users are selected to participate as long as the remaining ones are able to predict their data within a given approximation error threshold. Considering the problem of resource consumption imbalance of participant selection and further cutting down the number of active participants, we explore an improved participant selection scheme. It solves the problem of selecting the minimum number of participants with some inaccurate data. Meanwhile, the fairness of selection and resource conservation can be achieved while maintaining adequate sensing data quality. Experimental results validate the effectiveness of both our task-centric cooperative sensing schemes and indicate their performance advantages.  
This work was partially supported by the National Natural Science Foundation of China NSFC (Grant No. 41374088, 41127901-06, 61572370), the Program for Changjiang Scholars and Innovative Research Team in University (Grant No. IRT1278), the National Key Scientific Instrument and Equipment Development Projects of China (Grant No. 2012 YQ10022506), the Development Program of China \"863 Project\" (Grant No. 2015 AA016004), the Natural Science Foundation of Hubei Province of China (Grant No. 2014 CFB191).  
[PERSON] contributed to the original idea, algorithm design and paper writing. [PERSON], [PERSON] and [PERSON] contributed in part to data processing. [PERSON] and [PERSON] supervised the work and helped with editing the paper.  
The authors declare no conflict of interest.
## 3. Key Supporting Technologies for the WSN
### Routing Mechanism  
As stated in Section 2.2, the WSN with multi-parameter monitoring for underground coal mines is composed of two layers. For the perception network, the sensor node can only communicate with its parent node, without routing functions. Therefore, the routing mechanism mentioned here is only applied to the backbone network.  
Because the WSN nodes are limited in electric power and computing capability, in the first place the routing protocol cannot be complex. Secondly, the underground wireless communication condition is so complex that the routing protocol should be robust enough to guarantee the WSN will work stably and reliably. Thirdly, in the WSN, the sensor node mainly transmits environmental parameters, whose destination is the sink node [14, 15, 16]. Based on the above considerations, a kind of routing mechanism with minimum hops, multiple paths and based on link quality indication (LQI) is designed.  
In this routing mechanism, every routing node establishes three routes, _i.e._, parent route, minimum route and backup route, by parsing the parameter collection command which is broadcast periodically. To store the route information, every routing node maintains a routing table, containing items like hops_, _LQI_, _flag_ and _next hop_address_, as described in Table 1. The destination is the sink node, so the destination address item is omitted.  
In order to reduce the power that consumed for transmission, the hops information is carried along in the parameter collection command. The value of hops is zero when the command is first broadcast by the sink node, and adds 1 each time it passes a routing node. After receiving the command, the routing node extracts the hops and LQI information, then the link cost will be obtained based on the definition stated in the ZigBee protocol [6]. The routing node updates the routing table according to the following rules:  
1. Determine whether the command comes from its parent node. If yes, add it into the routing table as the parent route; otherwise, go to 2.
2. Determine whether the minimum route is empty. If yes, add it as the minimum route; otherwise, compare it with the minimum route. If the link cost is less than that of the minimum route, replace it and replace the previous minimum route with the backup route; otherwise, go to 3.
3. Compare it with the backup route. If the backup route is empty, or the link cost is less than that of the backup route, add or replace it as the backup route; otherwise, discard directly.  
At this point, the routing node finishes updating the routing table in a cycle. When the routing node forwards data, it first chooses the minimum route. If it fails to send, it chooses the parent route then. If it fails to send again, it finally chooses the backup route.
## 4 Conclusions  
In summary, using MD simulations, we studied the translocation of PCNT from the aqueous environment to the lipid bilayer interior and compared the behavior of PCNT in the membrane with that of FCNT. We found that the PCNT can readily enter the membrane and preferred to stay therein without specific orientation if the length of PCNT was less than bilayer thickness. With the increasing length of PCNT, the rotation of PCNT in the bilayer center became more and more difficult. With the cooperation of the electrostatic interactions between the functional groups and lipid headgroups, FCNT was kept almost vertical in the membrane and was more suitable than PCNT to deliver drugs. Tentative simulations of the transport properties of CNT embedded in the bilayer center show that both small hydrophobic and hydrophilic molecules could enter the PCNT hollow channels, but urea molecules found it hard to penetrate the FCNT. The PMF profile demonstrated that there was a high energy barrier to prevent the entrance of urea at the two FCNT ends. Further studies should be focused on the specific transportation of small drug molecules through adjusting the radii of CNTs and functionalization.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/xxx/s1](http://www.mdpi.com/xxx/s1)]([http://www.mdpi.com/xxx/s1](http://www.mdpi.com/xxx/s1)).  
**Author Contributions:** [PERSON] and [PERSON] performed the simulations and wrote the paper. [PERSON], [PERSON], and [PERSON] set up the simulation systems. [PERSON], [PERSON], and [PERSON] analyzed the results and prepared all figures. [PERSON] revised the paper. All authors discussed the results and reviewed the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (Grant No. 11875236, 61575178, 11574272, U1832150), the Zhejiang Provincial Natural Science Foundation of China (Grant No. LY18A040001), the Students' Scientific Research and Training Program of Zhejiang A&F University (Grant No. 2013200034) and Zhejiang Provincial Science and Technology Project (Grant No. LGN18C200017).  
**Conflicts of Interest:** The authors declare no conflict of interest.