esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Cellulose is the most abundant regenerative material in the world, and it has been widely used in many fields such as in medicine, food, packaging, and industrial materials due to its good physical properties [1]. It is a linear polysaccharide composed of p-glucose monomers linked by \(\beta\)-(1,4) glycosidic bonds. Its structure bears a high degree of regularity and exhibits a large number of hydrogen bonds; this renders cellulose insoluble in water and other common organic solvents, resulting in its limited application. Most of the methods available for cellulose processing are nongreen [2]; this necessitated the development of green solutions in order to dissolve cellulose. Recently, _N_-methylmorpholine oxide (NMMO) has been widely studied as the solvent used in the Lyocell process for glues, to regenerate cellulose fibers [3]. In comparison to the traditional method (Viscose process) [4], NMMO is a more environmentally friendly, non-derivtizing solvent. However, it still has some disadvantages, such as the requirement of high temperature to dissolve the cellulose, degradation of cellulose, side reactions of the solvent itself, which are caused without any antioxidant, and large investments in safety [5].  
In contrast, room-temperature ionic liquids (RTILs) are excellent recyclable solvents, owing to their many advantages such as very low vapor pressure, flame retardancy, high thermal stability, and recyclability. Furthermore, RTILs can be easily modified by changing the structure of the cations and anions, which has broadened their range of application. Thus, imidazolium-based ionic liquids, such as 1-butyl-3-methylimidazololium chloride and 1-allyl-3-methylimidazolium chloride (AMIMCI) [1; 6; 7] have been widely studied to dissolve cellulose. However, the application of imidazolium-based ILs is still limited due to the complicated preparation process and expensive raw materials [8; 9; 10; 11; 12; 13; 14; 15]. Herein, we present a facile and cost-effective method for the preparation of quaternary ammonium-based room-temperature ionic liquids (QA RTILs) using quaternary ammonium and acetate groups as the cation and anion, respectively. The prepared QA RTILs were identified by nuclear magnetic resonance (NMR), elemental analysis (EA), and liquid chromatography-mass spectrometry (LC/MS). The physical properties of QA RTILs, such as the melting point, the thermal decomposition temperature, and the viscosity, were also measured. The prepared QA RTILs exhibited excellent cellulose-dissolution ability and thus, they could be applied to prepare the cellulose films. The QA RTILs developed in this work are cheaper and less toxic than imidazolium-based ILs and are halogen-free. The structure and properties of the regenerated cellulose films were investigated in detail to compare the dissolution abilities of different solvents. In addition, the recovery of ILs was also examined to show the practicality of QA RTILs.
## 3 Results
### A Summary of the Results for the Entire Simulation  
Table 2 provides a structured overview of the key findings from the simulation study, organized according to the study's objectives. A column titled \"Figures\" lists the corresponding figures that illustrate the results for each objective. Scenarios with small sample sizes, low SNR, and high correlation are described as having \"limited information\", whereas those with large sample sizes, high SNR, and low correlation are considered to have \"sufficient information\". These terms are used throughout the table and in the discussion section (Section 5).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Study Objectives** & **Key Findings** & **Figures** \\ \hline Effects of initial estimates (OLS, ridge, and lasso) on the prediction accuracy of NNG and ALASSO (Objective 1) & In scenarios with limited information, the three initial estimates yielded different predictions, with ridge estimates producing models with superior predictive performance. In scenarios with sufficient information, all three initial estimates produced models with similar predictions, indicating that they can be used interchangeably. In such cases, lasso estimates may be preferred, as they tend to yield simpler models, which are easier to interpret. & Prediction: Figure 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of results according to the five objectives of the simulation study.  
Figure 5: Comparison of RTE (**upper panel**) and the average number of variables selected (**lower panel**) for classical and penalized methods in scenarios with a high proportion of noise variables (23 noise, and 7 signal) in low-correlation settings (C1). The **left panel** shows results for small sample sizes (n = 100), whereas the **right panel** shows results for large sample sizes (n = 400). Overlapping lines indicate methods with very similar results, such as BE and FS in the lower panel. One standard error bars were added but are not visible due to the small standard errors.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Study Objectives** & **Key Findings** & **Figures** \\ \hline \multirow{6}{*}{Comparison of prediction accuracy of NNLO and ALASSO over the models that generated initial estimates (OLS, ridge, and lasso initial models) (Objective 2)} & \multirow{6}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{6}{*}{
\begin{tabular}{} \end{tabular} } \\  & & \\ \cline{1-1}  & & The main advantage of NNG and ALASSO is their ability to select simpler models than the initial models, which is beneficial for descriptive modeling, where understanding the relationships between the outcome and covariates is more important than prediction. \\ \hline \multirow{6}{*}{Comparison of prediction accuracy of classical and penalized regression methods (Objective
# Density Functional Theory Study on the Adsorption of Co(II) in Aqueous Solution by Graphene Oxide
## 3 GO Model Construction  
In order to study the adsorption process theoretically, it is necessary to first construct the theoretical model of GO and then obtain the corresponding energy, electrostatic potential and charge distribution of the system by calculating and optimizing the structure through Gaussian software so as to complete the analysis of the adsorption mechanism and influencing factors.  
Due to the diversity of graphite structures and the limitations of characterization and analysis techniques, the exact structure of GO has been controversial. At present, [PERSON] [12], [PERSON] [13], Nakajima-Matsuo [14] and other structural models have been proposed successively, among which the \"Lerf-Klinowski model\" [15] proposed by [PERSON] and [PERSON] has been widely recognized. The carbon skeleton of GO is divided into two parts: the unoxidized aromatic region and the oxidized, fat six-membered ring region. Epoxides, hydroxyl groups and carbon-carbon double bonds are randomly distributed on the base level formed by them, and a large number of hydroxyl and carboxyl groups are distributed on the edge of the base level. The Lerf-Klinowski model of GO is illustrated in Figure 1.  
In this paper, the GO structure is constructed according to the Lerf-Klinowski model, the plane plan is drawn by ChemDrawPro8.0, the result file obtained by ChemDraw is converted by GaussianView6.0 and the input file containing the coordinates of each atom in GO in space is obtained. According to DFT, the spatial configuration of the material will have an impact on its energy size, so in the process of building the model, GO was optimized when the number of six-membered rings and the types and quantities of oxygen-containing functional groups were different; their optimal configurations and energies were calculated and the optimization results were tested by frequency calculation. In the optimization of the model, since the system containing only C, H and O involves only strong interactions and main-group elements, the traditional hybrid functional b3 lyp and the polarization basis group 6-31G(d) were used for calculation. Considering the possible \(\Pi\)-\(\Pi\) stacking between GO molecules, the gd3 dispersion correction part was added to the keywords. The subsequent adsorption reaction occurs in the water phase, so the implicit solvent model SMD was also used to consider the effect of water.
## 5 Discussion  
### Research Implications  
The development and implementation of the PRS prototype, utilizing the PBIA and ACTLR, have demonstrated significant achievements in advancing the identification and sorting of PET materials. This project proposes a new residential recycling process, laying a strong foundation for future innovations in waste management.  
The prototype achieved remarkable performance metrics, with the most effective results stemming from the use of balanced datasets (60 images for PET and 60 for non-PET objects), which yielded a precision of 96%, a recall of 100%, and a mAP of 100%.  
The dataset was built using the Trust Exis model 17003 webcam, which has a resolution of 640 \(\times\) 480 pixels. This was connected to the Asus Tinker Board computing system. For training and validation, image acquisition was performed under natural ambient lighting conditions in indoor environments, without the use of directional light sources. The objects were photographed individually against the white background of the basket lid. The authors deliberately varied the objects' positions on the lid of the basket to create various recognition scenarios. Therefore, for each image, the object was manually rotated and positioned differently. Therefore, the data collection process for building the dataset did not  
Figure 17: Confusion matrix.  
use automated methods or detection systems for image preprocessing during the collection stage. The dataset was built manually.  
The employment of the Azure Custom Vision service has highlighted the power of cloud-based AI in real-time image classification. By leveraging Microsoft's advanced ML capabilities, the system processed and analyzed images through the Azure Custom Vision service. This integration of cutting-edge technology into the recycling workflow represents a significant step forward in optimizing waste management processes.  
The introduction of the _CTLRA_ is a noteworthy achievement that quantifies citizens' engagement and reliability in regard to recycling activities. By incorporating a point allocation system based on the number of recycled PET items and penalties for fraudulent recycling attempts, the _CTLRA_ fosters responsible recycling behaviors. To encourage recycling behavior, the proposed system offers users a simulation mechanism that awards points for each recycled PET item. These points are accumulated in the user's account and can be converted into discount vouchers, which can be used in partnerships established with major retail chains.  
Implementing an AIoT framework for monitoring PRS points has enabled real-time reporting when specific thresholds in terms of the amount of recycled PET bottles are reached. This proactive approach ensures that collection devices are emptied promptly, optimizing the operational CEM. The ability to monitor and manage the PRS dynamically represents a significant innovation in recycling logistics.
## 3 Results and Discussion
### Docking Study  
A total number of 262 molecules from the 270 identified molecules were used for the docking study after excluding those that contained inorganic metals, such as silicon. The docking study was conducted using the docking programs Molgro, Autodock Vina, Schrodinger Glide, and the Haddock programs. A total of seven docking scores were generated, two from Molegro (Moledock and Rerank scores), Vina, Glide, and the last three from Haddock, according to the scores obtained for the rigid, flexible, and flexible with water refinement, as described in the methodology.  
Figure 1: The (**a**) chromatogram, (**b**) scan, (**c**) extracted and (**d**) suggested target methoserpidine (CAS number: 865043) mass spectra derived from one of the three samples.  
The 262 potential drug candidates from the traditional herbs were screened for their antileukemia properties against two major leukemia targets BCL-2 and FLT3. The study also included the G101V mutant of BCL-2 (BCL-2-G101V) that has been recently reported to display a resistance to therapy [14]. The docking study included the co-crystallized ligands venetoclax [14; 16] and gilteritinib [11; 12] which are known antileukemia drugs against the target BCL-2 and FLT3, respectively, to serve as a benchmark for our study. The benchmark is used to determine the ability of the docking programs to effectively rank the experimentally proven inhibitor, among the top 10 lead hits and also the possibility of reproducing the experimental orientation. The results of the seven docking scores are analyzed using five consensus ranking methods RbN, RbR, AAS, ZS, and ECR, as described in the methodology.
## 4 Discussion
### Classification of Hybrids Using Machine Learning  
After obtaining the spectral signature of each sorghum hybrid and observing the differentiation of these hybrids in terms of wavelength reflectance and VIs, data was submitted to machine learning analysis, evaluating the performance of six ML algorithms. We also tested different input data for these algorithms: using only SB, only VIs, and using both (SB + VIs), looking for the best performance in classifying the hybrids.  
Considering the three accuracy metrics analyzed, the algorithm with the best performance was ANN, regardless of the input used. ANNs are used in several prediction and classification tasks in agriculture across a wide range of applications. [54] found high accuracy when using the algorithm to predict diameter at breast height (DBH) and total plant height (Ht) in eucalyptus trees. [55] achieved satisfactory accuracy in predicting tannin content in sorghum grains of different genotypes using artificial neural networks.  
Regardless of the input, ANN algorithm achieved satisfactory accuracy in classifying sorghum hybrids. However, the most interesting from a data processing point of view would be using only SB as input variables. [28] stated that performing calculations to generate the VIs was an unnecessary task since using only SB as inputs for classifying soybean genotypes provided higher accuracy. [47] found that spectral bands, especially green and NIR, provide more accurate responses than using VIs for identifying soybean plants that are symptomatic or asymptomatic to nematode attacks.  
Another algorithm that achieved good accuracy in classifying sorghum hybrids was RF using VIs as input set. RF is also used in several studies, such as recognizing the growth pattern of eucalyptus species using multispectral sensing [56]. Although it takes longer to obtain, the use of VIs can improve the image-based assessment of morphological and biochemical parameters of plants, consequently helping to reduce time and improve data processing by algorithms [57].  
This study proposes a way of distinguishing sorghum hybrids using UAV-multispectral imagery, which consists of building a spectral signature model for sorghum hybrids and then processing this data using ML algorithms. Further information on the spectral signature of hybrids can be obtained using data from hyperspectral sensors in more different conditions of environments. The use of other sorghum hybrids, different vegetation indices, or an increased number of VIs can contribute to improving the accuracy of the ML algorithms.
## 3 Results and Discussion
### Land-Use Land Cover (LULC) Changes  
The results of this study expand the work of [PERSON] et al. [56] that conducted research on land-use changes in Multan city from 1992 to 2015. They found that the urban land covered which was 1800 ha in 1992, extended by 112.5% and reached 3825.9 ha in 2002. It should be recalled that Multan is the fifth largest city in Pakistan by population. Land-use land cover changes from 2000 to 2020 are shown in Figure 10. It was found that a decade later, i.e., from 2015, a considerable expansion occurred and the urban part increased significantly. The built-up area, that was about 130.03 km\({}^{2}\) in 2000, gradually expanded over the last two decades, resulting in an increase of 25.46 km\({}^{2}\), to 155.49 km\({}^{2}\) in 2020. On the other hand, the low-vegetation cover decreased from 112.94 km\({}^{2}\) to 105.4 km\({}^{2}\), and the dense vegetation reduced from 31.8 kkm\({}^{2}\) to 12.21 km\({}^{2}\). An analogous decreasing trend  
Figure 8: The projected (**a**) mean precipitation and (**b**) maximum precipitation for the 2040s, 2060s, and 2080s scenarios.  
was also observed in the past, with an agricultural area reducing rate of 0.15% between 1992 to 2002 [56]. Soil characteristics and variability in LULC summarized in Table 3 shows that the major land-use classes of the area are residential land, low and high vegetation, unused or bare soil, and water bodies. The water-covered area increased from 11.36 km\({}^{2}\) (in 2000) to 15.49 km\({}^{2}\) (in 2005) and then decreased to 9.52 km\({}^{2}\) in 2010. It expanded again to 21.08 km\({}^{2}\) (2015) and then finally reduced to 13.03 km\({}^{2}\) (2020) (Figure 10). This happened due to unusual flash floods in the upstream area of the Chenab River basin over the last couple of decades.  
\begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline \multirow{2}{*}{**Symbol**} & \multirow{2}{*}{**HWSD-Soil Group**} & \multirow{2}{*}{**Soil Type**} & \multicolumn{2}{c}{**Area**} & \multicolumn{4}{c}{**Topsoil**} & \multirow{2}{*}{**P\# Water**} \\ \cline{3-3} \cline{5-10}  & & \multicolumn{1}{c}{**(km\({}^{2}\))**} & & & \multicolumn{1}{c}{**(\%)**} & & \multicolumn{1}{c}{**(km\({}^{2}\))**} & & \multicolumn{1}{c}{**(\%)**} & & \multicolumn{1}{c}{**Topsoil**} \\ \hline \multirow{2}{*}{Yh} & \multirow{2}{*}{Haptic Termosols} & Sandy & \multirow{2}{*}{280} & \multirow{2}{*}{97.90} & \multirow{2}{*}{Medium} & \multirow{2}{*}{50.4} & \multirow{2}{*}{29} & \multirow{2}{*}{20.6} & \multirow{2}{*}{6.6} & \multirow{2}{*}{6.8} \\  & & loam & & & & & & & & & \\ \multirow{2}{*}{Jc} & \multirow{2}{*}{Calcario Fluvisols} & \multirow{2}{*}{Loam} & \multirow{2}{*}{6} & \multirow{2}{*}{2.09} & \multirow{2}{*}{Medium} & \multirow{2}{*}{39.6} & \multirow{2}{*}{39.9} & \multirow{2}{*}{20.6} & \multirow{2}{*}{8} & \multirow{2}{*}{8.1} \\  & & Build-up area & & & & & & & & \\ \multirow{2}{*}{Years} & \
## 7 Conclusions  
The paper described the architecture of a command-and-control system supported by two intelligent video systems for multi-sensor fusion aiming at automatic threat detection in crowded spaces. When deployed at different locations near or inside a critical infrastructure, such as an airport or a metro station, the system itself can be a building block of a wider threat detection system for infrastructure protection. The main features of the system, compared to previous works, are the modularity of the architecture, which allows to re-use all or some of the components in different sensor-based deployments, and the integration of smart glasses to deliver the results to the security guards and support them reacting on the field.  
The validity of the architecture has been demonstrated when integrated with two different sensor systems in a large-scale experimentation at a metro station in Roma. Various non-functional aspects of the architecture have been discussed, which also use experimental results. A more accurate assessment of the scalability of the system for high-density crowds and the integration of the command-and-control system with an existing security system of a critical infrastructure are future directions of the work.  
Technological upgrades and use of redundant components to improve the system fault tolerance can be foreseen as future enhancements. The current equipment consists of common-of-the-shelf (COTS) hardware with standardized interfaces, so it is possible to replace the devices with new models. Furthermore, technology enhancement of the command and control with commercial tools, especially to empower the communication and monitoring subsystems, and to better manage security, should be foreseen to achieve higher levels of quality of service, such as when scaling up to more installations of INSTEAD, as illustrated in Section 3.  
Conceptualization, M.L.V., A.D.N., H.B., A.v.R., P.R., J.P., S.T., M.G., C.S. and L.D.D.; Methodology, M.L.V., A.D.N., H.B., A.v.R., P.R., J.P. and M.G.; Software, M.L.V., A.v.R. and P.R.; Data curation, M.L.V., A.v.R. and P.R.; Supervision, H.B., S.T., C.S. and L.D.D.; Validation, C.S. and L.D.D.; Writing--original draft, M.L.V.; Writing--review and editing, M.L.V., A.D.N., H.B., A.v.R., P.R., J.P., S.T., M.G., C.S. and L.D.D. All authors have read and agreed to the published version of the manuscript.  
The INSTEAD (\"INtegrated System for Threats EArly Detection\") project has received funding in the DEXER (Detection of EXplosives and firearms to counter TERrorism) program from NATO Science for Peace and Security (SPS) under grant agreement number G5605 and G5969.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are not publicly available due to project restrictions.  
The authors declare no conflict of interest.
## 3 Identity Authentication and Secure Transmission Based on Blockchain
### Secure Transmission of UAV Data Based on Blockchain
After meeting the conditions, it sends an identity authentication request \(C_{R}\) to the blockchain. In the case of \(C_{B}\), the receiver decrypts and authenticates the blockchain signature. The detailed processing of the received information is shown in Algorithm 1. Figure 3: Secure communication architecture based on blockchain. * The blockchain responds to request; \[B\to R/S:C_{B}=\{E_{PK_{R}/PK_{S}}(DID_{R},DIDs_{S},E_{SK_{B}}(Hash(DID_{R},DIDs _{S},Timestamp_{3},\] \[AuResult/PK_{R})),Timestamp_{3},AuResult/PK_{R})\}\] where \(C_{B}\) represents ciphertext sent by the blockchain, \(PK_{S}\) indicates the public key of the sender, \(SK_{B}\) is the private key of the blockchain, \(Timestamp_{3}\) represents the time when the response message was sent. The blockchain mainly responds to two types of request information, one is the public key request of the sender, and the other is the identity authentication request of the receiver. If the request information is \(C_{S}\), the blockchain finds the PKR requested public key on the chain and returns the \(C_{B}\). If it is \(C_{R}\), the blockchain performs the UAV identity authentication and returns the \(C_{B}\). The detailed processing of the request information is shown in Algorithm 2. ```
1:if information = \(C_{S}\) then then
2:\(P_{S}\) = decrypt (\(C_{S}\))
3: compare \(Timestamp\) and \(Timestamp_{1}\)
4: send \(C_{R}\)
5:else if information = \(C_{B}\) then then
6:\(P_{B}\) = decrypt(\(C_{B}\))
7: compare \(Timestamp\) and \(Timestamp_{3}\)
8: receive \(C_{B}\)
9:else
10: discard data
11:endif
```  
**Algorithm 2** Blockchain processing information.