esa-sceva/satcom-chunk-collection huggingface dataset:
# Orderly Charging and Discharging Group Scheduling Strategy for Electric Vehicles
## 4 Electric Vehicle Cluster Optimization Model
### Upper Layer Model
#### 4.1.2 Constraints  
(1) Power Balance Constraints for Distribution Network Nodes  
\[P_{g,m}-P_{j,t}^{\pm}-P_{basic.m}=U_{m}\sum_{m=1}^{k}U_{n}(G_{mn}cos\theta_{ mn}+B_{mn}sin\theta_{mn}) \tag{8}\]  
\[Q_{g,m}-Q_{j,t}^{\pm}-Q_{basic.m}=U_{m}\sum_{m=1}^{k}U_{n}(G_{mn}cos\theta_{ mn}+B_{mn}sin\theta_{mn}) \tag{9}\]  
Here, \(k\) is the total number of nodes; \(P_{g,m}\) and \(Q_{g,m}\) are the active and reactive power of node \(m\); \(P_{j,t}^{\pm}\) and \(Q_{j,t}^{\pm}\) are, respectively, the active and reactive power charged and discharged by electric vehicles; \(P_{basic.m}\) and \(Q_{basic.m}\) are the basic load requirements of the distribution network other than the charging and discharging of electric vehicles; \(U_{m}\) and \(U_{n}\) are the voltage amplitude; \(\theta_{mn}\) is the phase angle difference of voltage; and \(G_{mn}\) and \(B_{mn}\) are the real part and imaginary part of the node conduction matrix.  
(2) Node Voltage Deviation Constraints  
\[|U_{m}U_{n}(G_{mn}\text{cos}\theta_{mn}+B_{mn}\text{sin}\theta_{mn})-U_{m}^{2 }G_{mn}|\leq P_{max.mn} \tag{10}\]  
\[U_{min.m}\leq U_{m}\leq U_{max.m} \tag{11}\]  
where \(P_{max.mn}\) is the maximum active power of the power transmission line \(mn\), and \(U_{max.m}\) and \(U_{min.m}\) are the maximum and minimum voltage constraints.  
(3) The SOC Constraints for Clusters  
The aggregate of the charge and discharge power cannot surpass the total charging demand of cluster j during any period t1 from the start of the optimization period.  
\[D_{j}=\sum_{t=T_{a,j}}^{T_{b,j}}\eta_{j,t}P_{j,t}^{\pm}\Delta t=\sum_{i=1}^{I }\left(S_{e,i}-S_{a,i}\right)B_{i} \tag{12}\]  
\[\sum_{t=T_{a,j}}^{t_{1}}\eta_{j}P_{j,t}^{\pm}\Delta t\leq D_{j} \tag{13}\]  
\[t_{a,i}<t\leq t_{b,i} \tag{14}\]where \(B_{i}\) is the battery capacity; \(\eta_{j}\) is the cluster charge/discharge efficiency; and \(D_{j}\) is the total charge/discharge demand of cluster \(j\) during optimal scheduling.  
(4) Charge/Discharge Power Constraints for Clusters  
\[\sum_{i=1}^{I}P_{i.max}^{-}\leq P_{j.t}^{\pm}\leq\sum_{i=1}^{I}P_{i.max}^{+} \tag{15}\]  
where \(P_{i.max}^{+}\) and \(P_{i.max}^{-}\) are the upper and lower limits of the charging and discharging power that the battery is capable of withstanding to ensure battery safe operation.
## 3 Results and Discussion
### Optimized PSC Device  
Efficiency optimization of the ITO/TiO\({}_{2}\)/MAPbI\({}_{3}\)/Cu\({}_{2}\)O/Au structure was performed using the optimal level of each factor. Figure 13 shows the current density-voltage (\(J\)-\(V\)) graph of the optimized PSC. The initial structure is ITO/TiO\({}_{2}\) (100 nm)/MAPbI\({}_{3}\) (600 nm)/Cu\({}_{2}\)O (400 nm)/Au, whereas the optimized structure is ITO/TiO\({}_{2}\) (100 nm)/MAPbI\({}_{3}\) (1300 nm)/Cu\({}_{2}\)O (400 nm)/Au. The _PCE_ of the optimized structure is higher by 5.51% than that of the initial structure (19.30% and 13.79%, respectively). The key factor for this increase in efficiency is considered to be the increase in PAL thickness. At a PAL thickness of 1300 nm, the _PCE_ showed the highest value of 19.30%. The more sunlight was absorbed, the more hole-electron pairs could be created [76]. Therefore, it is thought that these increased hole-electron pairs have contributed to the enhancement of _PCE_.  
Figure 12: _PCE_–defect density for (**a**) absorber/ETL interfaces and (**b**) absorber/HTL interfaces.
## 4 Recent Progress in Microencapsulation Techniques for Natural Bioactive Compounds
### Micelle and Liposome Based Techniques
Micelle and reverse micelle are thermodynamically stable colloids produced by single layer of one or more surfactants such as phosphatidylcholine, lecithin, and monoglycer and are able to encapsulate only one of either hydrophilic or hydrophobic core materials, the latter owing to the high encapsulation efficiency [153,158]. [PERSON] et al. [159] observed 96% encapsulating efficiency for curcumin (14% load) encapsulated in micelle developed by using monomethylpoly (ethylene glycol)-poly (\(\varepsilon\)-caprolactone)-poly (trimethylene carbonate) and the active ingredient, curcumin, released slowly (20% over 48 h and 36% over 168 h) into phosphate buffer solution (pH 7.4) at 37 \({}^{\circ}\)C as compared to the 92-96% release of curcumin in non-encapsulated form in the same time period [159]. Reverse micelle exhibited lower encapsulation efficiency for hydrophilic core materials such as tetracycline encapsulated in PLGA (poly-d, L-lactide-co-glycoloide) in methylene chloride added to cetyltrimethylammonium bromide. This lower encapsulation efficiency in reverse micelle is attributed to small hollow cavities, the presence of several pores on the surface, as well as the lower molecular weight of the core material [160]. The hydrophilic core material with a molecular weight of more than 500 Da exhibited higher encapsulation efficiency (98%) during the reverse micelle method of encapsulation [161]. For preventing leakage of a low molecular weight core material in the case of the reverse micelle, the core material is incorporated in cyclodextrin, which, due to its higher molecular weight, reduces the chances of the slippage of the core. [PERSON] et al. [162] summarized the approaches to be used for improving micelle stability, which would improve microstructure integrity, prevent the core material from slipping out, and improve encapsulation efficiency.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 4 Data Description
### Data Labeling
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Condition** & **Score** \\ \hline \multirow{3}{*}{TDS (ppm)} & \(\textless{}500\) & 1 \\  & \(500\) \textless{} TDS \(\leq{}1000\) & 0.5 \\  & TDS \textgreater{} 1000 & 0 \\ \hline \multirow{3}{*}{pH} & \(6.5\leq\text{pH}\leq 8.5\) & 1 \\  & \(6.0\leq\text{pH}<6.5\) or \(8.5<\text{pH}\leq 9.0\) & 0 \\  & pH \textless{} 6.0 or pH \textgreater{} 9.0 & 0 \\ \hline \multirow{3}{*}{Temperature (°C)} & Temperature \textless{} 15 & 1 \\  & \(15\leq\text{Temperature}<25\) & 0.5 \\  & Temperature \textgreater{} 25 & 0 \\ \hline \multirow{3}{*}{Turbidity (NTU)} & Turbidity \textless{} 5 & 1 \\  & \(5<\text{Turbidity}\leq 10\) & 0.5 \\ \cline{1-1}  & Turbidity \textgreater{} 10 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Threshold rule for water quality parameters (pH, TDSs, temperature, and turbidity). The following Figure 16 visualizes the distribution of these labels in the dataset. Figure 14: Original dataset. Figure 15: Labeling dataset.
_Article_  
Characterizing Spatiotemporal Patterns of Mangrove Forests in Can Gio Biosphere Reserve Using Sentinel-2 Imagery  
[PERSON] 1, [PERSON] 1,*\(\copyright\), [PERSON] 2\(\copyright\), [PERSON] 1, [PERSON] 3, [PERSON] 4 and [PERSON] 4  
Faculty of Geography, University of Social Sciences and Humanities, VNU-HCM, Ho Chi Minh City 70000, Vietnam; [EMAIL_ADDRESS] (H.T.L.); [EMAIL_ADDRESS] (C.P.T.N.)  
Department of Remote Sensing and Geographic Information System, APECS Consultancy Services, Thimbru 11001, Bhutan; [EMAIL_ADDRESS]  
School of Agriculture and Environment, College of Sciences, Massey University, Palmerston North 4442, New Zealand; [EMAIL_ADDRESS]  
Ho Chi Minh City Institute of Resources Geography, Vietnamese Academy of Science and Technology, Ho Chi Minh City 70000, Vietnam; [EMAIL_ADDRESS] (T.H.L.); [EMAIL_ADDRESS] (M.B.D.)  
Correspondence: [EMAIL_ADDRESS]  
Received: 11 May 2020; Accepted: 10 June 2020; Published: 12 June 2020  
**Featured Application: This study proposes the features of mangrove forest variations in relation to anthropogenic activities under the forms of land use land cover changes around three ecological zones of a Biosphere Reserve area, using Sentinel-2 Imagery and spatial regression approaches that have wide application in natural resources, land use planning, and environmental management.**  
This study aimed at evaluating the spatiotemporal patterns of mangrove forest variations for three ecological zones of the Can Gio biosphere reserve (i.e., core, buffer, and transition zones) and its relation to land use/land cover changes. Time series Sentinel-2 Imagery--which presents the Normalized Different Vegetation Index (NDVI), obtained through the Google Earth Engine and Overlap Similarity Algorithm--was used to characterize vegetation cover in the study area. Furthermore, the Cohen's Kappa agreement was applied to examine the accuracy of mangrove classification, and the Mann-Kendal (MK) significance was used to analyze the spatiotemporal trends of mangrove forests. The results showed that an NDVI value greater than 0.3 recorded the reflected signal of mangrove population in the study area with an \(O\)-index greater than 0.85. A Cohen's Kappa statistic of agreement of 0.7 and an overall classification accuracy of 83% was obtained. Regarding the trend in mangrove forest patterns, an increase in area of 669 ha and 579 ha explored at the buffer and core zones, respectively, while the largest declined mangrove area of 350 ha was investigated at the buffer zone, followed by a transition at 314 ha during the study period due to the interconversion of shrimp farming and the expansion of built-up areas. Moreover, the study also described the negative impacts of the sea-encroached urban-tourism zone on mangrove patterns in the foreseeable future. The results from this study will act as a basic fundamental authentic report for local governments in proposing strategies for the shielding of mangrove forests and economic development from negative consequences in foreseeable future.  
**Keywords: NDVI; overlap similarity algorithm; satellite imagery; LULC; coastal area**
## 2 Materials and Methods
### Approach for Drought Assessment Using Satellite and Meteorological Data  
The aim of the present analysis was to describe the relationship between HTC representing the atmospheric moisture conditions and TCI satellite-derived index characterizing the vegetation response to these conditions for agricultural area in different climatic zones in Poland.  
In this approach, the TCI was used in a time series form, which consists of consecutive observations in each vegetation period. The cross-sectional data structure was adopted to join observations from spatially diversified regions. The samples of meteorological and satellite data, which came directly from evenly distributed meteorological stations (Figure 2) and the agricultural area around the stations, were pooled into a special structure, called _panel data_. Such an approach allowed us to formulate a drought model based on meteorological and satellite data in a spatiotemporal scheme.
# Effects of Atmospheric Turbulence on Lensless Ghost Imaging with Partially Coherent Light
###### Abstract  
Ghost imaging with partially coherent light through two kinds of atmospheric turbulences: monostatic turbulence and bistatic turbulence, is studied, both theoretically and experimentally. Based on the optical coherence theory and the extended Huygens-Fresnel integral, the analytical imaging formulae in two kinds of turbulence have been derived with the help of a tensor method. The visibility and quality of the ghost image in two different atmospheric turbulences are discussed in detail. Our results reveal that in bistatic turbulence, the visibility and quality of the image decrease with the increase of the turbulence strength, while in monostatic turbulence, the image quality remains invariant when turbulence strength changes in a certain range, only the visibility decreases with the increase of the strength of turbulence. Furthermore, we carry out experimental demonstration of lensless ghost imaging through monostatic and bistatic turbulences in the laboratory, respectively. The experiment results agree well with the theoretical predictions. Our results solve the controversy about the influence of atmospheric turbulence on ghost imaging.  
Keywords:ghost imaging; atmospheric turbulence; monostatic turbulence; bistatic turbulence +
Footnote †: journal: Applied Physics Letters
###### Abstract  
With the increasing use of unmanned aerial vehicles (UAVs) in various fields, achieving the precise localization of UAV images is crucial for enhancing their utility. Photogrammetry-based techniques, particularly bundle adjustment, serve as foundational methods for accurately determining the spatial coordinates of UAV images. The effectiveness of bundle adjustment is significantly influenced by the selection of input data, particularly the composition of overlapping image sets. The selection process of overlapping images significantly impacts both the accuracy of spatial coordinate determination and the computational efficiency of UAV image localization. Therefore, a strategic approach to this selection is crucial for optimizing the performance of bundle adjustment in UAV image processing. In this context, we propose an efficient methodology for determining overlapping image sets. The proposed method selects overlapping images based on image similarity, leveraging the complementary strengths of the bag of visual words and number of matches techniques. Essentially, our method achieves both high accuracy and high speed by utilizing a Bag of Visual Words for candidate selection and the number of matches for additional similarity assessment for overlapping image-set determination. We compared the performance of our proposed methodology with the conventional number of matches and bag-of-visual word-based methods for overlapping image-set determination. In the comparative evaluation, the proposed method demonstrated an average precision of 96%, comparable to that of the number of matches-based approach, while surpassing the 62% precision achieved by both bag-of-visual-word methods. Moreover, the processing time decreased by approximately 0.11 times compared with the number of matches-based methods, demonstrating relatively high efficiency. Furthermore, in the bundle adjustment results using image sets, the proposed method, along with the number of matches-based methods, showed reprojection error values of less than 1, indicating relatively high accuracy and contributing to the improvement in accuracy in estimating image positions.  
m 2024 2024 2024 2024 2024 2024 2024 2024 2024 2024 20  
based on bundle adjustment, derive positional information from overlapping images [9; 10]. The composition of an image set with a high proportion of overlaps significantly affects the precision of the bundle adjustment. Hence, identifying and composing overlapping images into image sets is vital to ensuring the accuracy of UAV localization. Moreover, the method used for determining image sets also affects the efficiency of image localization because selecting overlapping images can pose computational challenges [11; 12; 13; 14].
## 2 NDN Background  
In an NDN communication process, as illustrated in Figure 1, there are two types of packets called interest and data packet. On the one hand, the interest packet mainly contains the name of the content; on the other hand, the data packet mainly contains the name of the content, the encrypted information, and the data. As Figure 1 indicates, NDN is based on three essential modules for processing the requested and received packets. These modules are called Content Store (CS), Pending Interest Table (PIT), and Forwarding Information Base (FIB). The CS has a role of caching the data packets with some caching strategies in order to satisfy the next requests for the same data. PIT is used to record the interfaces of the incoming interest packets until the interest packet is satisfied, or the recording time of the same packet is expired. The PIT also has a content name aggregation function, in which the interest packets for the same content received from different interfaces will be registered as a single request to avoid the retransmission of similar requests for the same data. The role of the FIB module is to transmit the packets of interest to the producer(s) of the data using packet transmission mechanisms, which are mainly based on the names of the contents.
## 4 NIST Guidelines for the Secure Deployment of IPv6
### Topicality of the NIST Guide  
The secure deployment guide by NIST was published in 2010. Most but not all of the presented information is up to date. Generally RFCs with the numbers 6000 and up are not covered in the guide as most of these were published after the NIST guide. Five of the covered RFCs have newer versions: RFC 3177 is replaced by RFC 6177, RFC 3484 by RFC 6724, RFC 3697 by RFC 6437, RFC 3775 by RFC 6275, and RFC 4869 by RFC 6379.  
When completeness and depth were evaluated, the content and the extent of the changes in the RFCs were taken into consideration. Thus some of the relevant topics are regarded as covered if the content of the guide is still valid and when changes only had a minor impact. Some covered topics are outdated. The NIST guide presents some methods and techniques that are already moved to historic or obsolete or whose use is discouraged by the IETF, for example, NAT-PT. The NIST guide includes recommendations for the notation of IPv6 addresses to reduce human failure and increase readability while keeping unambiguity; however, these are not fully correct anymore. Last but not least, the NIST guide still mentions IPsec as mandatory part of IPv6. While this holds true for some sub-protocols, the IPv6 specification now only recommends the use of IPsec as the wording was changed from _MUST_ to _SHOULD_ use.
# FEM Modelling Approaches of Bolt Connections for the Dynamic Analyses of an Automotive Engine
## 3 Reference 3D Modelling  
The reference and most accurate option implemented for modelling bolt connections (hereinafter \"full 3D bolt model\") was based on a complete 3D FEM modelling (Figure 2), with the explicit allowance for: pretension in the bolt as caused by the tightening torque (modelled as an imposed deformation in the middle of the bolt that allows interpenetration between the two volumes); nonlinear contact with friction (friction coefficient equal to 0.15) between bolt head and components and between component surfaces to join; a fully clamped condition at the end of the bolt, with nodes of the two coupling parts merged together.  
In another simplified approach, \"Glue surfaces\", a preliminary nonlinear static analysis was run so as to calculate the contact surface in each connection on the basis of the imposed pretension in bolts and friction coefficient on the contact surfaces. Consequently, the nonlinear contact elements were replaced by a continuity condition for the contact areas highlighted by the previous static analysis and the linear dynamic analyses was then performed. Further details are provided in the following sections.  
Figure 1: (a) Full CAD model of the 4-cylinder 4-stroke petrol engine; (b) simplified model adopted for the FEM simulations.  
Figure 2: Full 3D modelling of bolts, with highlight of nonlinear contact and pretension in the bolt shank.  
It is worth mentioning that explicitly modelling each bolt in his three-dimensional nature involves relevant runtimes (one-day calculus) and memory space occupation (up to 60 GB) for the linear modal analysis, despite using a powerful professional workstation.
## 7 Comparison of our Models with Empirical Models  
This section presents the results obtained for 14 different datasets with different path loss prediction techniques. The results obtained by our MLP were compared with those obtained by six empirical models: (Free Space Path Loss Model (FSPL), Walfisch-Ikegami model (WI), ECC-33 Model (ECC-33), COST 231-Hata model (PLCH), EricssonModel (PL999), Stanford University Interim Model (SUI)), and the type of model architecture. The results are shown in Figures 10-19.  
Figure 12: Comparison between empirical models and MLP (with one hidden layer) for Dataset 6.  
Figure 13: Comparison between empirical models and MLP (with one hidden layer) for Dataset 7.  
Figure 14: Comparison between empirical models and MLP (with two hidden layers) for Dataset 3.  
Figure 15: Comparison between empirical models and MLP (with two hidden layers) for Dataset 5.  
Figure 16: Comparison between empirical models and MLP (with two hidden layers) for Dataset 8.  
Figure 17: Comparison between empirical models and MLP (with three hidden layers) for Dataset 11.  
the base station and the mobile station, respectively, which were also used in the experiment. These numerical values were obtained based on the taken measurements. We used different base station heights in our simulations and evaluations. The real data was obtained by controlling these correction variables and the specific properties of the model were used to design and test our models. We divided our datasets into four main environments (urban, suburban, rural, and indoor environments). Based on Figures 10-19, the model proposed in this study gave the best performance compared to other empirical models:  
* Free space model;
* Ericson model;
* ECC-33 model;
* Cost 231 (WI) model;
* Cost 231 Hata model;
* Stanford University Interim model.  
The obtained result can be explained by the fact that these models were developed in a specific domain which was different from that of our datasets. To reach high accuracy, the path loss prediction should be close to the actual path loss value. Furthermore, these changes depend on the parameters of the dataset (distance, frequency, base station antenna height, mobile station antenna height). For example, in Datasets 11 to 14, the parameters were collected in an urban area with the same frequency, distance, and mobile station, but we noticed that the path loss value changed from one set to another because the height of the base station antenna changed; the longer the transmitter antenna, the lower the path loss. This also applies to Datasets 8 to 10, whose parameters were collected in the same situation, except for the base station antenna height.  
On the other hand, the parameters of Dataset 4 were collected inside buildings. The path loss varies depending on the obstacles inside the building, such as wall thickness, room space, and number of windows. Our models showed to be the best in all cases because we used all possible parameters to make predictions almost identical to the actual path loss values.