esa-sceva/satcom-chunk-collection huggingface dataset:
# Lemongrass Essential Oil Components with Antimicrobial and Anticancer Activities
## 1 Introduction  
Cymbopogon spp. are fast-growing C4 perennial sedges from the grass family Poaceae and are primarily cultivated for their essential oils. The genus lemongrass comprises about 180 species, such as _Cymbopogon citratus_, _Cymbopogon flexuosus_, _Cymbopogon winterinus_, _Cymbopogon matrinui_, _Cymbopogon nardus_, and _Cymbopogon refractus_. These aromatic grasses are of great commercial interest due to their wide applications in different areas such as the food, pharmaceutical, and cosmetic industries. The plant propagates through seed and slips and has thin and lancolate leaves that appear to emerge directly from the soil without any stem [1]. Although lemongrass cultivation is cosmopolitan, India has a monopoly over its production and export [2, 3]. Given the growing consumer demand and global market for essential oil, few recent reports have suggested various sustainable approaches for enhancing lemongrass production even further, either in field condition or in culture, through the application of elicitors, nanoparticles, and metabolite regulators [3, 4].  
Lemongrass is also called Cochin grass, since 90% of its global export is organised from Cochin port, India [5]. Lemongrass plant has extensive potential as food and foddergiven its richness in vitamins A, C, E, folate, niacin, and riboflavin, protein, antioxidants, and mineral nutrients, such as N (0.74%), P (0.07%), K (2.12%), S (0.19%), Mg (0.15%), Ca (0.36%), Zn (35.51 ppm), Mn (155.82%), Fe (126.73%), and Cu (56.64 ppm) [6; 7]. Figure 1 illustrates the morphological attributes of the _Cymbopogon flexusus_ ([PERSON].) Wats plant and focuses on the characteristics of trichomes and stomata.  
Lemongrass cultivation is a rapidly growing economy. The major drive for lemongrass cultivation is its high industrial potential in the pharmaceutical, food, and cosmetics sectors. It is grown over an area of 16,000 ha on a global scale that produces about 1000 t of lemongrass essential oil (LEO) each year [8]. Out of this, India accounts for one-fourth of total production and area cropped worldwide [9]. Lemongrass cultivation can yield a net profit of about 300 USD per year per hectare [10]. A recent report by Plotshare Market Research pinned the global lemongrass oil market at 247 million dollars in 2019 and expects it to grow to 421 million dollars by 2027, at a compound annual growth rate of 7%. Additionally, lemongrass export of India has risen more than 1250% from 2001 to 2020, which solidifies its demand and importance (The Directorate General of Commercial Intelligence and Statistics, Kolkata, India). Moreover, the size of the essential oil market is estimated by numerous market research firms. One such study by Facts and Factors estimates its size to be 7 billion dollars, which is expected to double by 2027.  
The present study aims to provide a timely and comprehensive review of the antimicrobial and anticancer potential of LEO, as well as to discuss the development of industrial applications based on both feasible and reproducible delivery systems.
## 5 Further Application Examples
### Packet Scheduling in Networks  
Consider a communication network, which is modeled by a directed graph. We have a given system \(P_{1},\ldots,P_{r}\) of directed paths in the network, and we want to send packets along these paths. The paths are edge-simple, which means that no path repeats any edge. Let us consider a synchronous communication model, in which for each time unit, one packet can traverse an edge. Assume a set of packets is given for each path. We would like to achieve that each packet is delivered on its respective path in the shortest possible time. Since the paths and packet sets are given, our only degree of freedom is to decide, whenever several packets compete for an edge, which one goes first, i.e., in which order they will traverse the edge (recall that only one can do it in one time unit). In other words, we are looking for the optimal packet scheduling policy. Furthermore, we would like to achieve it with a constant buffer size at every node.  
Let us define some key parameters for this problem: _congestion_\(c\) is the maximum number of paths that share a directed edge; _dilation_\(d\) is the maximum path length that occurs in the path system. Let \(T\) be the smallest time in which each packet can be delivered, no matter how many packets wait for each path. The time for each individual packet is measured from the instant when it starts traversing the first edge on its path. A _lower bound_ on \(T\) in the worst case is  
\[T\geq\max\{c,d\}.\]The justification for this lower bound is the following:  
* A path can be \(d\) edge long, and traversing such a path clearly takes \(d\) time units, implying \(T\geq d\).
* An edge can be included in \(c\) paths. If a packet arrives at the edge on each of these \(c\) paths at the same time, then one of them will suffer \(c-1\) time units delay, plus its own traversal time, resulting in \(T\geq c\) in the worst case.
* Then, \(T\geq d\) and \(T\geq c\) together yield the worst case lower bound \(T\geq\max\{c,d\}\).  
A remarkable thing is that this essentially trivial lower bound can be achieved, up to a constant factor, no matter how many packets wait for each route. Furthermore, a constant size buffer suffices at every node. Specifically, by a sophisticated use of the Lovasz Local Lemma, [PERSON], [PERSON], and [PERSON] [9] proved the following theorem:  
Theorem 4.1: _In the above model, there is always a packet schedule that achieves \(O(c+d)\) delivery time for each packet, with a constant queue length at every node._  
Observe that \(c+d\leq 2\max\{c,d\}\); so \(O(c+d)\) is indeed optimal up to a constant factor.
## 3 Model Design
### Complexity Analysis  
The main calculation method of the gradient method is to calculate the objective function, \(L\), and its gradient according to the variables. The computational complexity of the objective function, \(L\), is \(O(\rho_{M}l+\rho_{M}\overline{v}l+\rho_{R}l)\), where \(\rho_{M}\) is the number of non-zero terms in the matrix, \(M\), and \(\overline{v}\) is the average number of nodes that have a social relationship with the node. Since there are not many users in a region who have a social relationship value with a user, this indicates that the value of \(\overline{v}\) is relatively small. The computational complexities for the gradients, \(\frac{\partial L}{\partial N_{i}}\), \(\frac{\partial L}{\partial X_{j}}\), and \(\frac{\partial L}{\partial Y_{k}}\), in Equation (25) are \(O(\rho_{M}\overline{s}l+\rho_{M}\overline{s}\overline{v}l+\rho_{R}l)\), \(O(\rho_{M}l+\rho_{M}\overline{v}l)\) and \(O(\rho_{R}l)\), respectively. Among them, \(\overline{s}\) is the average number of devices with social relationships that are close to one device, \(\rho_{R}\) is the number of non-zero terms in the matrix, \(R\). Actually, in a complex social network, the value of \(\overline{s}\) is approximately equal to the value of \(\overline{v}\). Therefore, the total computational complexity of an iteration is \(O(\rho_{M}\overline{s}l+\rho_{M}\overline{s}\overline{v}l+\rho_{R}l)\), which indicates the calculation time of our method is linear with the amount of information collected by the node. If the obtained matrices are all sparse matrices, the algorithm will execute rapidly with a time complexity of \(O(n)\). It also applies when the number of nodes is large. However, if the obtained matrix is a dense matrix, the complexity in the communication range of big data with dense nodes and complex relations is difficult to estimate. Since the complexity of the CAOP, SSIS, and SlaOR algorithms are both \(O(n^{2})\), the complexity analysis shows that the proposed method is simpler than them.
## 1 Introduction
### Literature Review
#### 1.1.2 P2P Market with Network Constraints  
Network constraints play a pivotal role in determining the performance of a P2P market. The authors in [21] suggested network constraints for the P2P market in the form of trading losses and a network utilization fee. However, their implementation was limited to a social welfare maximization, and the individual welfare of each customer was not modeled using the network parameters. A constrained CDA market was developed in [22] for low-voltage (LV) networks using power factor distribution and loss factors for the network. A distribution locational marginal pricing (DLMP)-based formulation for the P2P networks was developed in [23] while developing the optimal-power-flow-based formulation for the LV networks.  
A similar formulation for the P2P markets using the DLMP approach was suggested in [24] for a transactive energy framework for distribution networks. Trading losses have been suggested for the auction-based markets using the Bayesian GT in [25] to model the physical constraints for the P2P network. A DSO-based P2P market was formulated in [26] to model the interactions between the participants using the reserve constraints for the energy transactions. A congestion management for the LV P2P networks was suggested in [27] using the marginal prices of the network. The formulation in [28] models the individual behaviors of the market players of the P2P platform by using the user-centric behavior models while developing the network constraints for the P2P market. However, the implementation in [28] did not consider the optimal P2P clearance for the participants and the sensitivity of the prosumers towards the network coefficients.
# Implementation of an Attribute-Based Encryption Scheme Based on SM9
## 3 Implementation and Evaluation of SM9 Identity-Based Encryption
### Energy Consumption Evaluation  
The energy consumption is a major concern when executing the algorithms on low-power devices. We have evaluated the energy consumption of SM9 IBE scheme on the Raspberry Pi 3 and the OnePlus A6000 smartphone. The power consumption (mAh) is shown in Figure 6, where Enc/Dec (0) represents the KEM-DEM algorithms while using stream cipher, and Enc/Dec (1) represents the algorithms using block cipher.  
Figure 6 shows that the energy consumptions of using two types of DEM are very similar. The decryption algorithms consume more energy than the encryption algorithms. OnePlus A6000 consumes more energy than Raspberry Pi 3 when executing the same algorithm.  
Figure 5: Data encapsulation mechanism’s (DEM’s) encryption and decryption performance (using the stream cipher).  
Figure 6: Algorithms’ energy consumption on Raspberry Pi 3 and OnePlus A6000 smartphone.  
Design, Implementation and Evaluation of the Attribute-Based Encryption Scheme from Identity-Based Encryption  
Based on the idea in [34; 35], we can transform the SM9 IBE scheme into an ABE scheme. With slight modification of the original scheme, we can construct a generic ABE scheme. Roughly speaking, we uniquely convert an access structure into an identity, and then invoke the identity-based encryption scheme with the identity.
## 3 Results and Discussion
### Corrosive Potential of Microbial Communities
The EDS analysis of the coupons submerged in PW_L2 (without stimulation) showed that the main corrosion products included the elements: Fe, S, O, C, Mn, Si, Ca, and Al. The EDS analysis of the coupons immersed in PW_L2 (with stimulation) showed that the main corrosion products consisted of the elements: Fe, S, O, C, Ca, Cu, Mn, Cl, Al, Si, Mg, and P. It should be noted that corrosion products under stimulated conditions contained increased contents of sulfur and oxygen. It can be assumed that the main corrosion products were iron sulfides and oxides. [PERSON] et al. [34] investigated the corrosion of X80 steel coupons after immersion in biotic and abiotic static systems. The products that formed on the coupons immersed in the biotic systems were Fe(OH)\({}_{3}\), Fe(OH)\({}_{2}\), FeOOH, Fe\({}_{3}\)O\({}_{4}\), BaSO\({}_{4}\), CaCO\({}_{3}\), FeS\({}_{2}\), and FeCl\({}_{3}\). The products formed on the coupons immersed in abiotic systems were the following: BaSO\({}_{4}\), SrSO\({}_{4}\), CaCO\({}_{3}\), NaCl, CaCl\({}_{2}\), and cubic body-centered \(\alpha\)-Fe. The authors concluded that the planktonic microorganisms present in the produced water collected from a Brazilian oil production plant increased the corrosion rate of X80 steel. The results of the study conducted by [PERSON] et al. [47] showed that in systems with enriched produced water collected from an Indian crude oil reservoir, coupons were subject to biofouling and greater corrosive effects compared to the abiotic system. The results of SEM confirmed the pitting type of corrosion, while X-ray diffraction analysis indicated the formation of Fe oxides/oxide-hydroxides. [PERSON] et al. [49] studied the corrosion rate of coupons in produced water from a Tuha oilfield under static or dynamic conditions. The results indicate that  
Figure 6: SEM analysis of steel coupons’ surface after 45 days of exposure in unstimulated systems ((**A**)—PW_L1, (**C**)—PW_L2) and stimulated systems ((**B**)—PW_L1, (**D**)—PW_L2). the main corrosion product in the water injection pipe was FeOOH, whereas the corrosion scales were mainly composed of CaCO3 and Mg6 Al2 CO3-(OH)16-4H2O. Produced water usually has a high concentration of dissolved salts, which leads to increased pipe corrosion. It should also be noted that the biogeochemical behavior of iron and the formation of its (oxyhydr)oxides are the subject of not only studies related to both electrochemical and microbial corrosion but also to bioremediation since they act as agents influencing the fate of pollutants [50; 51; 52].
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 4 Limiting Factors of Qualification
### Number of SUT Samples to Test  
According to the component-level tests standards (such as ESCC 22900 and ESCC 25100), the number of samples required for qualification tests is 11 samples and 3 samples for TID tests and SEE tests, respectively, if the samples are from the same lot. For TID, it is required to test 5 samples biased, 5 samples unbiased and have one unirradiated control sample [27]. These recommendations result from the fact that there is always some part-to-part variability of parameters, even for the parts from the same lot, therefore the goal of tests is to obtain a statistical result.
# DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images
## 3 Proposed Approach  
In our proposed approach, the Centernet [36] is used as our baseline, and the framework of the dynamic adjustment network is shown in Figure 2. The detection regards the object as a point and then regress the size and offset of bounding box. The deep convolution network extracts features of the input image and generates a heatmap. Peaks in the heatmap indicate detected objects. Due to the influence of light, obscuration and sharpness, images contain a few objects that are difficult to distinguish. We call them outliers. Dynamic gradient adjustment module is proposed to dynamically refine the loss weight depend on density distribution and minimize the effect of these outliers.  
Deep Layer Aggregation (DLA34) [38] is used as the backbone network of the model, which is illustrated in Figure 3. The upgraded deformable convolutional net (DCN) is used as up sampling to connect features between layers in the network. The shallow layer has spatial-rich information and is more advantageous to detect small targets, while deep layer with semantic-rich information has better robustness and is more suitable for detecting large targets. This approach realizes multi-scale feature fusion and effectively extracts the feature information of objects at different scales. Besides, we introduce the CIoU to improve the convergence speed and accuracy of the model.  
Figure 2: The framework of the dynamic adjustment network. Dynamic gradient adjustment (DGA) module dynamically refine the prediction of heatmap.
## 3 Materials and Methods
### Model Evaluation
#### 3.2.3 Training Setup  
For the multi-label approach, two model architectures were implemented and compared. Both models were based on the DenseNet121 architecture, with the primary difference being the placement of the SE blocks. The first model architecture, referred to as CNN-SE-FFM-BiLSTM, is shown in Figure 2. The second model, termed CNN-FFM-SE-BiLSTM, incorporated a single SE block after the feature fusion layer, followed by a BiLSTM layer to capture temporal dependencies. As for the single-label approach, illustrated in Figure 2, it consists of the second model without the BiLSTM layer due to the absence of temporal dependencies. Table 1 presents a summary of the implemented models.  
The models were trained on an NVIDIA RTX 4090 GPU. The software environment included Python 3.12 with PyTorch 2.7.0 for deep learning model implementation and training, Torchvision for data transformations and image handling, and scikit-learn for computing evaluation metrics. The training process was conducted up to 25 epochs, with early stopping applied based on validation loss to prevent overfitting.
# Assessing Groundwater Level with a Unified Seasonal Outlook and Hydrological Modeling Projection
## 2 Methodology  
### Seasonal Rainfall Outlook  
The Central Weather Bureau (CWB) is responsible for issuing official weather information in Taiwan. The CWB also publicly announces seasonal weather outlooks on the basis of present weather condition, historical statistical records, modeling results (including statistical and numerical weather prediction models), and the subjective judgment of experienced engineers. This study utilized the outcomes of this product to obtain a seasonal perspective (see the first stage in Figure 1). The seasonal weather outlook is the format for prediction of the likelihood of precipitation, as is also the case with many official meteorological service departments and authoritative suppliers of information worldwide, including Japan's Meteorological Agency, the National Institute of Water and Atmospheric Research in New Zealand, Environment Canada, and the South African Weather Service [32]. Seasonal weather outlooks are usually provided on the CWB website for four regions (i.e., northern, western, southern, and eastern regions) of Taiwan near the end of each month. The probabilities of monthly rainfall, in comparison with historical precipitation data, are issued  
Figure 1: Flowchart of the proposed method. CWB, Central Weather Bureau of Taiwan; WGEN, weather generator; HEC-HMS, hydrologic modeling system; WASH123D, watershed systems of one-dimensional (1D) stream–river network, 2D operand regime, and 3D subsurface media.  
(\"below normal\", \"normal\", or \"above normal\") for the coming 3 months. These categories are estimated by ranking the observed monthly data, which are obtained from the meteorological station in descending order and divided into three intervals predicting the probability of occurrence. For example, as shown in Figure 2, the probabilities of being \"below normal\", \"normal\", and \"above normal\" for the upcoming month of June are 20%, 50%, and 30%, respectively, in the northern region of Taiwan. In other words, there is a 50-50 chance of experiencing a normal amount of rainfall this month.
## 3 Results
Furthermore, the modular adaptation of precursor gene expression under phototrophic growth conditions as well as the adjustment of cultivation conditions resulted in an increased sesquiterpenoid formation. Evaluation of Bioactivities of \(\beta\)-Caryophyllene and \(\beta\)-Caryophyllene Oxide against Different Phytopathogenic Organisms  
The agricultural industry is affected by a dwindling number of effective antimicrobial substances. On the other hand, farmers have to control plant pathogenic organisms without damaging non-target organisms. As \(\beta\)-caryophyllene and \(\beta\)-caryophyllene oxide offer a variety of beneficial bioactivities [70; 92; 93; 94; 95], we evaluated the potential use of those two sesquiterpenes as a nature-derived fungicide. To this end, we analyzed the activity against different phytopathogenic fungi, as well as various plant growth-promoting bacteria (PGPB). 3.1 Bioactivities of \(\beta\)-Caryophyllene and \(\beta\)-Caryophyllene Oxide against Phytopathogenic Fungi  
We investigated the bioactivity of \(\beta\)-caryophyllene and \(\beta\)-caryophyllene oxide, which can be formed spontaneously by uncatalyzed processes [96; 97], against the plant pathogenic fungi _S. scleroorum, F. oxysporum, A. brasiscicola, P. lingam_, and _R. solani_. This analysis would additionally reveal whether the compound's oxygenation influences potential antifungal properties. Therefore, PDA agar plates were supplemented with increasing concentrations of both compounds, fungal discs were transferred onto these plates and fungal growth was determined. Evaluation revealed that the degree of growth inhibition due to direct terpene exposure varied depending on the compound and the fungus (Figure 3). Both compounds inhibited the hyphal growth of _S. scleroorum_ when compared to the solvent control. The inhibition reached up to 30% when the fungus was exposed to \(\beta\)-caryophyllene, while it was up to 40% when the fungus was cultivated on medium containing \(\beta\)-caryophyllene oxide. The effect against _F. oxysporum_ was less pronounced. Around 20% inhibition was observed when the fungus was cultivated on the \(\beta\)-caryophyllene-supplemented medium, while it was around 30% in the case of \(\beta\)-caryophyllene oxide. Finally, the presence of \(\beta\)-caryophyllene in the growth medium slightly inhibited the growth of _A. brasiscicola_ while inhibition was higher and reached a maximum of 10% when \(\beta\)-caryophyllene oxide was used. No significant effect of both compounds was observed against _P. lingam_ and _R. solani_ (Figure S7, Supplementary Materials). Our results thus reveal that \(\beta\)-caryophyllene and its oxidized form possess antifungal activity against certain phytopathogenic fungi and that \(\beta\)-caryophyllene oxide tends to be more effective in inhibiting fungal growth.