esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### DZHDZ32 Promotes the Accumulation of Intermediate Metabolites in Diosgenin Synthesis  
DZHDZ32 has been shown to enhance diosgenin accumulation. To further pinpoint the specific stages and regulatory nodes within the diosgenin biosynthetic pathway influenced by DZHDZ32, we analyzed two key intermediate metabolites: cholesterol and \(\beta\)-sitosterol, both of which are critical precursors in diosgenin synthesis [1; 44; 45; 11]. Cholesterol levels were quantified via LC-MS in the roots, stems, leaves, and rhizomes of both  
Figure 4: Levels of _DZHDZ32_ expression, diosgenin, and its biosynthetic intermediates were analyzed in OE lines compared to WT plants. (**a**) Relative _DZHDZ32_ expression was confirmed to be significantly higher in the roots, stems, leaves, and rhizomes of the OE lines than in WT, using _DZACTIN_ as a control housekeeping gene. (**b**) Overexpression of _DZHDZ32_ resulted in increased accumulation of diosgenin across all tested tissues: roots, stems, leaves, and rhizomes. Levels of the intermediate metabolites (**c**) cholesterol and (**d**) \(\beta\)-sitosterol were measured and compared in the leaves, stems, roots, and rhizomes of OE lines and WT. Data represent means \(\pm\) SD (standard deviation) from three biological replicates. Student’s \(t\)-test * \(p<0.05\).  
OE lines and WT plants. The results revealed that _DZHDZ32_ overexpression significantly increased cholesterol accumulation. Notably, the leaves--where diogenin biosynthesis predominantly occurs--exhibited markedly higher cholesterol levels in the OE lines compared to WT. Moreover, cholesterol content was generally higher in leaf tissue than in other examined organs (Figure 4c). A similar trend was observed for \(\beta\)-sitosterol levels (Figure 4d). These findings suggest that DZHDZ32 may regulate diogenin biosynthesis by acting upstream of sterol accumulation or possibly at multiple points that progressively enhance intermediate metabolite production throughout the biosynthetic pathway, ultimately promoting diosogenin synthesis.
## 4 Conclusions  
This paper focused on the separation and recovery of vanadium and chromium from vanadium-chromium reducing residue. The following conclusions were obtained:  
(1) Vanadium and chromium could be efficiently leached out, and the leaching efficiency of vanadium and chromium are up to 99.80% and 97.93% under optimal reaction conditions: reaction temperature of 90 \({}^{\circ}\)C, reaction time of 180 min, dosage of MnO\({}_{2}\) at 50 wt.%, concentration of H\({}_{2}\)SO\({}_{4}\) at 30 wt.%, and liquid-to-solid at 5:1 mL/g.  
(2) The leaching process of vanadium and chromium were controlled by surface chemical reaction, and the leaching kinetic behaviors followed the shrink core model, and the apparent activation energies for vanadium and chromium leaching out are 19.93 kJ\(\cdot\)mol\({}^{-1}\) and 21.26 kJ\(\cdot\)mol\({}^{-1}\), respectively.  
(3) Vanadium and chromium in the leaching solution could be separated by selective adsorption and precipitation. The melamine acted as an efficient absorbent, and nearly 99.99% vanadium was adsorbed by melamine under the initial pH of leaching solution about 1.8 at 90 \({}^{\circ}\)C for 60 min. Then, the V\({}_{2}\)O\({}_{5}\)was obtained by roasting the precipitation at 550 \({}^{\circ}\)C for 150 min in a muffle furnace. The chromium retained in the leaching solution was precipitated as Cr(OH)\({}_{3}\) in pH about 8.5 at 90 \({}^{\circ}\)C. And, the product of Cr\({}_{2}\)O\({}_{3}\) was obtained by drying the refined Cr(OH)\({}_{3}\) at 110 \({}^{\circ}\)C for 2 h.  
Conceptualization, H.P.; Methodology, H.P.; Validation, H.P.; Formal Analysis, L.Y. and Y.C.; Investigation, L.Y. and Y.C.; Resources, H.P.; Data Curation, J.G.; Writing--Original Draft Preparation, H.P.; Project Administration, H.P.; and Funding Acquisition, H.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (No. KJQN201901403) and the Chongqing Science and Technology Commission (No. cstc2018 jcyjAX0018). The authors declare no conflict of interest.
# SENTINEL: A Secure and Efficient Authentication Framework for Unmanned Aerial Vehicles
## 9 Conclusions  
We proposed SENTINEL to verify the authenticity of drones for detecting unauthorized drones in fly zones of the IoD environment. In IoD, it is crucial to authenticate drones in real-time due to drones' fast movements, and lightweight protocols are needed for supporting resource-constrained drones. SENTINEL provides secure and efficient protocols with a lightweight certificate format to reduce traffic and computational overheads to meet such requirements in the IoD infrastructure.  
To demonstrate the feasibility of the proposed framework, we implemented a prototype of SENTINEL and compared it with the conventional \"TLS for IoT\" protocol with client authentication. The experiment results demonstrate that SENTINEL could bring significant benefits concerning performance. The execution time of the authentication protocol in SENTINEL is about 3.1 times faster than the \"TLS for IoT\" protocol on average. We also formally verified the security of SENTINEL using ProVerif, a well-known protocol verification tool.  
**Supplementary Materials:** The source codes of our implementation are available in the public GitHub repository ([[https://github.com/rymuff/drone](https://github.com/rymuff/drone)]([https://github.com/rymuff/drone](https://github.com/rymuff/drone))). The GitHub repository also contains the ProVerif code used for security analysis of SENTINEL.  
**Author Contributions:** Methodology, G.C. and J.C.; Software, J.C.; Formal analysis, G.C.; Writing--original draft, G.C. and J.C.; Writing--review & editing, S.H. and H.K.; Supervision, H.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract (UD190016 ED).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Conclusions  
We presented a numerical approach that utilizes the RT method to model a time-averaged array gain of a 5G BS operating in a macrocell outdoor urban environment. The RT approach provides a more realistic signal propagation and user spatial correlation properties compared to analytical and stochastic approaches. In a realistic scenario, with a BS consisting of 64 antenna elements that serves 5 UEs simultaneously and a 10 s per user connection duration, 95% of the 6-min time-average gain observations fell below 0.22 (more than 6.6 dB reduction), 0.20 (7 dB) and 0.13 (8.9 dB) of the theoretical maximum, using codebook, maximum ratio transmission, and zero-forcing schemes, respectively. With user connection duration of 60 s, the corresponding 95 th percentiles increase to 0.35 (4.6 dB), 0.30 (5.2 dB), and 0.21 (6.8 dB), respectively. In all studied scenarios, increasing the BS element count decreased the normalized time-average gain. With the MRT and ZF transmission schemes, lower time-averaged gain was always observed when the number of multiplexed UEs was increased. With the CB beamforming that was the case only for larger BS arrays. In all multi-user scenarios, the ZF yielded the lowest \(p^{95}\) values of the normalized time-average gain (0.06 or 12.2 dB reduction with 100 BS antennas and 10 UEs), which is more than two times lower than any other studied precoding scheme.  
Conceptualization, S.S. and W.J.; methodology, L.V. and G.T.; software, S.S.; formal analysis, S.S.; resources, L.V. and S.A.; writing--original draft preparation, S.S.; writing--review and editing, A.T., S.A., and W.J.; visualization, S.S.; supervision, W.J., L.M., and P.D.; project administration, W.J., L.M., and P.D.; funding acquisition, W.J., L.M., and P.D. All authors have read and agreed to the published version of the manuscript.  
[PERSON] thanks the ERC for his advanced grant 695495 \"ATTO: A new concept for ultra-high capacity wireless networks\". [PERSON] is an FWO senior postdoctoral fellow. [PERSON] is an FWO postdoctoral fellow.  
The authors declare no conflict of interest.
## 2 Mathematical Formulation
### Theoretical Background
\begin{table}
\begin{tabular}{l l} \hline \hline
**Variable** & **Description** \\ \hline \multicolumn{2}{l}{General Variables} \\ \(X_{i}\) & Total stock of product i, \([IU]\)\([n_{i}\times 1]\) \\ \(A_{i,k}\) & Binary matrix of association of product i to storage location k \([n_{i}\times n_{k}]\) \\ \(S_{k}\) & Total quantities in storage location k, \([IU]\). \\ \(Lim_{k}\) & Limits for storage location k, \([IU]\). \\ \(H_{k}\) & Holding cost at storage k, per inventory unit of product, \([CU/IU]\). \\ \(n_{i}\) & Total number of products. \\ \(n_{k}\) & Total number of storage locations. \\ \(n_{kt}\) & Total number of tanks. \\ \(n_{t}\) & Total number of days considered for the future material requirements. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Required variables for the problem formulation. All units are presented within square brackets. Variables without unit are dimensionless. The decision variables for this problem are the total stocks of each product, i.e., \(X_{i}\). These establish the ideal stocks for a given day, based on the near future conditions. Index \(i\) corresponds to the material, ranging from 1 to \(n_{i}\); index \(k\) corresponds to the storage location, ranging from 1 to \(n_{k}\); index \(t\) corresponds to the day, ranging from 1 to \(n_{t}\). \(n_{i,coll}\); \(n_{i,warehouse}\), and \(n_{i,tanks}\) correspond to the number of products in the cold storage, warehouse, and tanks, respectively, verifying the condition \(n_{i,cold}+n_{i,warehouse}+n_{i,tanks}=n_{i}\).
# Detection of Hidden Communities in Twitter Discussions of Varying Volumes
## 2 Related Work  
### Selecting Community Detection Algorithms  
Community allocation methods can be divided into two categories.  
The first category includes destiny-based clustering (DBSCAN, OPTICS, \(K\)-Means type) [7; 8; 9]. One of the advantages of these methods is the efficiency of the allocation of compacted clusters, but when applying these methods to graph analysis, the following problems arise:  
1. The need to use additional metrics to estimate the distance between vertices, and, in the case of oriented graphs, this choice is very narrow;
2. The limitations of computing on large-dimensional graphs (with more than 50,000 nodes) (in particular, the speed of searching for new clusters is significantly reduced, and, on super-large graphs (with more than 500,000 nodes), this class of algorithms cannot get a result without additional optimization);
3. Limited application to sparse data [10].  
The second type includes graph methods for searching for hidden communities based on graph properties [11] and the structure of connections of graph nodes [12; 13; 14]. The advantages of these methods include the speed and the ability to calculate, including on large-scale graphs. However, when using such approaches, the number of hidden communities found in the graph can vary from several hundred to several thousand (although in many SNA tasks it is required to find up to several dozen). In addition, this class of algorithms is very sensitive to graph data. This is why a separate task within community detection in graphs is used to adapt existing methods to specific real data from the relevant subject area. In [15], the authors received high ratings of the efficiency of the Infomap [12] and LPA [14] algorithms on synthetic data and real web data of large size. In [16], the authors compared their algorithm with the Infomap and OSLOM [17] algorithms, where they obtained the best results in speed and floating quality resultson billing network data. In [18], the authors used the Louvain [13] algorithm as one of two algorithms for clustering large volumes of related data, and, in [19; 20], the authors also used graph algorithms for searching communities on real data, which showed high application efficiency. In studies [21; 22], Infomap and SLPA (GANXiS) [23] were also used in their work, but Infomap proved to be better.  
As we can see from the above studies, there are few works dedicated to the analysis of user discussions on social networks. Individual tasks, such as topic modeling and sentiment analysis, are being investigated; however, user interaction within the discussion has not been investigated using these methods. Therefore, in this work, the main focus is on the application and testing of the most efficient methods for identifying hidden communities on real-data graphs of user discussions.
# Overview of the Research Status of Intelligent Water Conservancy Technology System
## 3 Status Quo of Key Technologies of Smart Water Conservancy
### Knowledge Graph  
The knowledge graph concept is a semantic network in which nodes represent entities or concepts, and edges represent semantic relationships between them [66]. Structured knowledge is extracted from massive heterogeneous data and combined with downstream applications in various industries [67]. In the construction of intelligent water conservancy, the knowledge graph is widely used in spatial query services, and intelligent question and answer [68; 69; 70; 71], as well as in water conservancy business applications such as historical scene replay of water conservancy objects [72]. The construction tasks of water conservancy knowledge include the forecast and dispatching scheme database, knowledge graph database, business rule database, historical scene pattern database and expert experience database. The hydraulic knowledge engine is used for knowledge representation, extraction, fusion, inference and storage [73]. The key technologies of knowledge graph construction include data acquisition, knowledge extraction and knowledge fusion to realize the deep ordering from knowledge processing and knowledge graph construction to knowledge expression [74]. Figure 4 shows the basic construction process of the knowledge graph.  
The research shows that the hydraulic knowledge query and recommendation method based on digital twins adopts the KR-EAR model, collects the hydraulic knowledge map of the digital twin watershed, uses K-means clustering to represent the hydraulic information resources in vector, and forms the candidate resource set. It solves the problems of insufficient resource acquisition and the inaccurate recommendation effect in the water conservancy information inquiry and recommendation in the past [75]. In the process of constructing the water conservancy knowledge map based on the knowledge annotation platform, 43 kinds of water conservancy entities and 110 kinds of entity relationships are covered, which significantly improves the coverage and accuracy of knowledge [76]. However, the existing water knowledge map still has the problems of small scope of knowledge coverage and no close connection with actual business, so the Water Knowledge map for digital twin project (KG4 DT) [77] came into being.
# Enhanced Heat Transfer Performance of the Tube Heat Exchangers Using Carbon-Based Nanofluids
## 2 Performance Calculations of the Tube Heat Exchanger  
The schematic cross-section of a THE is shown in Figure 1. The inner tube is the heat source, and the outer tube is for circulating working fluid. A THE can be used in hot water heaters or for heat dissipation of specific heat sources. The \(HTC\) (W/m\({}^{2}\)\({}^{\circ}\)C) of the working fluid in the outer tube can be expressed as [28, 29]:  
\[HTC=\frac{P_{he}}{A\left(T_{h}-T_{wf,avg}\right)}=\frac{P_{he}}{A\times\Delta T _{h-w}} \tag{1}\]  
\[T_{wf,avg}=\frac{T_{i}+T_{o}}{2} \tag{2}\]  
where \(P_{he}\) is the heating power of the heat source (W), \(A\) is the average surface area between the inner tube and the outer tube (m\({}^{2}\)), \(T_{h}\) is the average surface temperature of the heat source (\({}^{\circ}\)C), \(T_{wf,avg}\) is the average temperature of the working fluid (\({}^{\circ}\)C).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Materials/BFs** & **Concentrations** & **HE Types** & **Main Findings** & **Ref.** \\ \hline Functionaliz-ed & & & & The enhancement of HTC, the average Nusselt & \\ DWCNTs/W & 0.01–0.4 wt\% & Double-pipe HE & number, and \(\Delta P\) was 32\%, 18\%, and 20\% for & [24] \\  & & & & 0.4 wt\% NF, respectively. & \\  & & & & \(HTC\) enhancement far exceeded the enhancement & \\  & & & & in k for NF. & \\ Functionaliz-ed & 0.025, 0.05, 0.075, & Horizontal annular & A maximum enhancement of \(HTC\) was 22.4\% for & \\ MWCNTs/W & and 0.1 wt\% & (tube) HE & 0.1 wt\% NF at Re of 6807 and heating power of & [25] \\  & & & & 1200 W. & \\  & & & & The mean \(\Delta P\) was increased to 38\% due to higher & \\  & & & & \(\mu\) of NF. & \\  & & & & A maximum enhancement of \(HTC\) was 115\% at & \\  & & & & 0.6 vol\% NF at mwf of 0.04 kg/s. & \\ MWCNTs/so-lar & 0.2, 0.4 and, & Double pipe HE with a & \(\Delta P\) increased 1.56 times at mwf of 0.08 kg/s. & \\ glycol & 0.6 vol\% & modified inner surface & Modified inner surface (shot peening process) & \\  & & & & significantly affected on flow behavior and heat & \\  & & & & exchange rate. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._The flow loss (\(P_{f}\), W) of the working fluid flowing through the THE is the product of the (\(G\), m\({}^{3}\)/s) of the working fluid and the \(\Delta P\) (Pa) of THE:  
\[P_{f}=G\times(P_{i}-P_{o})=G\times(\Delta P) \tag{3}\]  
From the cross-section of the THE in Figure 1, Re can be calculated using Equations (4)-(7) [20; 30; 31]:  
\[\text{Re}=\frac{\rho GD_{h}}{\mu A_{c}} \tag{4}\]  
\[D_{h}=\frac{4A_{c}}{WP} \tag{5}\]  
\[A_{c}=\frac{\pi}{4}\left(D_{i}^{2}-d_{o}^{2}\right) \tag{6}\]  
\[WP=\pi(D_{i}+d_{o}) \tag{7}\]  
where \(D_{h}\) is the hydraulic diameter (m), \(\rho\) is the density of the working fluid (kg/m\({}^{3}\)), \(\mu\) is the viscosity of the working fluid (Pa-s), \(A_{c}\) is the cross-sectional area of the working fluid flowing through the tube (m\({}^{2}\)), \(WP\) is the wet perimeter (m), \(d_{o}\) is the outer diameter of the internal tube (m), and \(D_{i}\) is the inner diameter of the outer tube (m).
## 3 Results
### Correlation Analysis between Climate Change and NPP Variation
#### 3.4.2 Partial Correlation Analysis between NPP and Climate Factors  
Partial correlation and T-test were conducted to analyze the pixel-wise correlations between NPP and time-series data of temperature, precipitation, and solar radiation from 2003 to 2020 (Figure 9). The range of partial correlation coefficients between temperature and NPP was from \(-\)0.92 to 0.94, with 76.20% of the regions showing a positive correlation. Among them, 9.54% of the regions exhibited a significant positive correlation, mainly scattered in the upper reaches, Wujiang, the eastern part of Jialingjiang, the western part of Dongting Lake, and the western part of the middle reaches. The negative correlation was observed in 23.80% of the regions, with 0.98% exhibiting a significant negative correlation, mainly distributed in the middle part of the middle reaches and the southern part of Hanjiang.  
The partial correlation coefficients between precipitation and NPP ranged from \(-\)0.95 to 0.93, with 58.44% of the regions showing a positive correlation. Among them, 4.68% exhibited a significant positive correlation, mainly concentrated in the western and southern parts of Wujiang, the upper and middle parts of the middle reaches, and the southeast part of Hanjiang. The negative correlation was observed in 41.57% of the regions, with 2.08% exhibiting a significant negative correlation, mainly scattered in the middle parts of Jialingjiang and Hanjiang.  
Solar radiation had partial correlation coefficients ranging from \(-\)0.94 to 0.93 with NPP. A total of 31.93% of the regions showed a positive correlation, with 1.61% exhibiting a significant positive correlation, scattered in the northwestern part of Jialingjiang and at the junction of Jialingjiang and Hanjiang. A total of 68.07% of the regions exhibited a negative correlation, with 10.89% showing a significant negative correlation, mainly concentrated in the Sichuan Basin, the junction of Taihu Lake and the lower reaches, the middle and southwestern parts of Jialingjiang, and the middle part of Hanjiang.
## 5 DRL-Based Online Resource Allocation
### Mechanism of DRL
#### 5.2.1 Action Space  
The intelligent resource allocation agent decides the terminal's transmission power and channel for S-IoT terminals. More specifically, the joint resource allocation action can be intuitively defined as \(a=\left\{a_{p},a_{c}\right\}\), where \(a_{p}\) and \(a_{c}\) denotes the power and channel allocation action, respectively. Consistent with notation in Section 3, the action space can be denoted as \(\mathcal{C}_{set}\times\mathcal{P}_{set}\), where \(\mathcal{C}_{set}=\left\{c_{m}|m=1,2,\cdots,M\right\}\) and \(\mathcal{P}_{set}=\left\{\frac{1}{N_{p}}P_{k},\frac{2}{N_{p}}P_{k},\cdots, \frac{N_{p}-1}{N_{p}}P_{k},P_{k}\right\}\) represent the available channel set and transmit power set of the terminals, respectively. As a result, the size of the action space is \(\mathcal{A}=M\times N_{p}\).  
Since the online allocation pipeline (as illustrated in Section 5.1) is adopted to sequentially allocate resources to the newly generated data transmission, each decision only focuses on the terminal of the current request, rather than allocating resources to each terminal. Thus, the action space is defined for the current terminal.
## 3 Problem Formulation and Use Case Description  
### Maintenance Scheduling Problem  
Let us consider an industrial asset that contains at least one part that may be replaced. If the considered industrial asset is monitored by sensors whose signals are related to the degradation of the considered part, it is possible to develop an RUL prognostic model for the component, and an estimation of its RUL can be obtained at each day (or working cycle) by the k-LSTM-GFT model. Otherwise, the only data available for maintenance planning are the historical data of the time between failures observed until the current moment.  
Each asset is allocated a set of maintenance slots \(d_{Si}\) that are equally distributed at intervals of \(m\) days. Periodic maintenance tasks and inspections may only be performed in these slots. If a given component \(v\) needs to be replaced at \(d_{Si+1}\), this intervention should be planned at the latest in the previous maintenance slot, \(d_{Si}\), to have a spare part available in stock. If the replacement of a given component is planned at a given slot, \(d_{Si}\), to be performed during \(d_{Si}\), the enterprise incurs an out-of-stock cost, \(C_{os}\), [20] since the replacement was not planned in advance.  
A maintenance action can only be scheduled at each slot \(S_{a}\). Each proactive replacement (before the failure) costs \(C_{p}\), while a corrective action costs \(C_{c}\) (a failure occurred). Note that \(C_{c}>C_{p}\). The objective of the maintenance schedule is to reduce the ratio between the total costs of maintenance and the number of working cycles performed by each component.