esa-sceva/satcom-chunk-collection huggingface dataset:
# A Coordinated Supply Contract for a Two-Echelon Supply Chain Considering Learning Effects
## 4 Coordinated Supply Contract under the Learning Curve (LC)
### Model Construction with LC Effects for Maximizing SC Profit  
The key problem here is finding the optimal level of production in a situation where production costs are reduced through learning effects. As previously discussed, the optimal production quantity can be found when we assume the system is centrally integrated. A central decision-maker will determine the production quantity in a way in which the overall profit of the supply chain is maximized. If the production supply quantity is \(Q\), the expected value of the supply-chain profit, \(\pi_{LC}\), can be obtained as follows:  
\[\pi_{LC}=pS(Q)+sI(Q)-c_{Q}Q=\left(p-c_{1}Q^{log_{2}r}\right)Q-(p-s)\int_{0}^{ Q}F(x)dx \tag{7}\]  
The first and second derivatives of Equation (7) with respect to \(Q\) are as follows:  
\[\frac{d\pi_{LC}}{dQ}=p-c_{1}(log_{2}r+1)Q^{log_{2}r}-(p-s)F(Q) \tag{8}\]  
\[\frac{d^{2}\pi_{LC}}{dQ^{2}}=-c_{1}(log_{2}r+1)log_{2}rQ^{(log_{2}r-1)}-(p-s) f(Q) \tag{9}\]  
Based on the assumption \(p>s\), Equation (9) follows that \(\frac{d^{2}\pi_{LC}}{dQ^{2}}<0\). Therefore, to maximize the profit of the supply chain, it is necessary to satisfy the condition \(\frac{d\pi_{LC}}{dQ}=0\), as stated below.  
\[p-c_{1}(log_{2}r+1)Q^{log_{2}r}-(p-s)F(Q)=0 \tag{10}\]  
Equation (10) involves a nonlinear function, making it challenging to find an analytical solution directly.
## 1 Introduction
[PERSON] et al. measured the growth of the _A. flavus_ colony using the Vis/NIR HSI technique. Results showed that the reflectance of the colony surface in different growth periods was significantly different in some wavelength regions [22]. [PERSON] et al. also pointed out that the fungal color, shape, and chemical composition would change with the growth of fungi, which could affect the spectral absorption [7]. They also built fungal growth simulation models based on parameters of the hyperspectral image. This research all showed the potential of hyperspectral imaging to be expanded to detect fungal characteristics grown on agar media. Nevertheless, there were still limited studies for the detection of specific growth characteristics of _A. flavus_ and _A. parasitic_, the main aflatoxin-producing fungi on media, using the HSI technique. The objective of the present work was to investigate the potential of hyperspectral imaging to identify optical features of the two aflatoxin producing fungi, _A. flavus_ and _A. parasitic_, which are inoculated on same rose bengal media under uniform environmental conditions for different days.
## 3 Materials and Methods
### Fusion of Perception Data Based on Ship ST-TF
#### 3.4.2 Visual Localization Frequency Synchronized with Target Ship GPS Frequency  
When performing visual perceptual localization and GPS information fusion, it is critical to ensure that camera and GPS outputs are acquired at the same time to avoid significant errors in the information fusion results. Typically, the time for each sensor is independent, and the sampling frequencies vary. In this experiment, the target ship's GPS operates at frequencies of 5 Hz and 10 Hz, while the camera's operating frequency is set to 5 fps, meaning that it captures 5 frames per second with a 200 ms interval between each frame, which is the same as the GPS working frequency of the host ship. Here, we use the lower frequency camera sensor as a reference and apply systematic sampling to the 10 Hz GPS data to ensure synchronization between the world coordinates calculated by the camera and the GPS coordinates. This frequency synchronization strategy helps effectively integrate information from different sensors, enhancing the accuracy of visual perception localization and GPS information fusion. The frequency synchronization process is shown in Figure 8.
## 3 Materials and Methods
### Architecture of Smart Wound Care Approach
#### 3.1.2 Neural Network Model for Smart Wound Care Approach  
In our proposed approach, we used Neural Networks for classifying current environment to build efficient wound monitoring system. Although, there are many other machine leaning algorithms e.g., SVM, decision tress, KNN etc. which could provide solution of classification problems but, we preferred neural network on other machine learning approaches due to given three reason.  
Figure 3: Hardware components integration design of smart wound monitoring system.  
Figure 2: Architecture of Wound Care Solution.  
* NN can handle large amount of training data, as in our proposed scenario training data size may grow in future if other environmental factors (interior factors i.e., patient body temperature, pulse rate etc.) and patient's personal health profile (other disease i.e., skin, diabetic record) considered in decision making process of proposed system.
* We preferred NN over other machine leaning algorithms due to its flexibility as new features can be adjusted by increasing training data size which ultimately reflect in output, no extra parametric configuration required in case of any change.
* Another reason of choosing NN is its ability to outperform other machine learning techniques as its performance can continue to improve by increasing training data size.
* NN suitable for non-linear problems, as current problem cannot solve by linear separable functions, where two output classes could separate by straight line rather current problem showed convex region, to handle convex region we considered neural network approach.  
Artificial Neural Network first developed in 1950, is famous technique of machine learning. ANN can have produced highly accurate results. They have capability of efficient decisions making like human beings therefore suitable to use in medical applications, like prediction and pattern recognition. Most famous applications of neural network in medical domain are clinical diagnosis, image analysis and interpretation, Further, ANN applied in medicine domain for detection of pathological conditions by analysis of blood and urine samples and detection of glucose [20; 21]. Neural Networks can address the problem of supervised learning i.e., a data set contains all possible examples of input data with their accurate truth values/output are provided. Neural network learns this data set pattern and could be used to predict class/label of incoming test data set where classes/labels are not known. In general, NN provides a classification layer above stored data. Neural Network consist of three components: input layer, hidden layer, and output layer.  
There are many types of NN to use i.e., Conventional Neural Network, Back Propagation, Feed Forward etc. Feed Forward etc. Learning of neural network consists of two steps. First Forward Propagation and then Back Propagation [22; 23].
## 2 SFSE Double-Differenced GPS/Galileo Mathematical Model
### Single GPS/Galileo Code and Phase Observation Equations
Subsequently, the single GPS/Galileo SFSE code and phase double-differenced equations can be expressed as:  
\[\
abla\Delta P_{br,j}^{1G,g}=\
abla\Delta\rho_{br}^{1G,g}+\
abla\Delta \varepsilon_{br,j}^{1G,g} \tag{3}\] \[\lambda_{j}\cdot\
abla\Delta\phi_{br,j}^{1G,g}=\
abla\Delta\rho_ {br}^{1G,g}+\lambda_{j}\cdot\
abla\Delta N_{br,j}^{1G,g}+\
abla\Delta\in_{br,j}^ {1G,g}\] \[\
abla\Delta P_{br,j}^{1E,e}=\
abla\Delta\rho_{br}^{1E,e}+\
abla \Delta\varepsilon_{br,j}^{1E,e}\] \[\lambda_{j}\cdot\
abla\Delta\phi_{br,j}^{1E,e}=\
abla\Delta\rho_ {br}^{1E,e}+\lambda_{j}\cdot\
abla\Delta N_{br,j}^{1E,e}+\
abla\Delta\in_{br,j}^ {1E,e} \tag{4}\]  
where \(\
abla\Delta P_{br,j}^{1G,g}\) denotes the GPS DD code observation; \(\
abla\Delta\phi_{br,j}^{1G,g}\) denotes the GPS DD phase observation; \(\
abla\Delta\rho_{br,j}^{1G,g}\) denotes the real GPS DD baseline range; \(\
abla\Delta N_{br,j}^{1G,g}\) denotes the GPS DD ambiguity; and, \(\
abla\Delta\varepsilon_{br,j}^{g,1x}\) and \(\
abla\Delta\in_{br,j}^{g,1x}\) are, respectively, the DD unmodeled errors of GPS observation equations. Furthermore, Galileo related parameters in (4) have the same meanings as those in (3).
## 2 Experiments
### Materials and Methods
#### 2.1.2 Walnut Shells and Surface Treatment  
The Walnut Shell (WS) used in this research was obtained from a local planter in the south of Shaanxi, China. Prior to the use, they were manually chosen to remove the qualitatively inferior items, cleaned with distilled water to remove the dust and impurities, and then dried in an air dry oven at \(85\pm 2\)\({}^{\circ}\)C for 24 h to reduce the moisture. The dried materials were grounded into powder while using a high-speed rotary cutting mill (CS-700Y, COSUAI).  
The surface treatment of the Walnut Shell Powder (WSP) was carried out with alkali (NaOH), followed by silane, to reduce moisture sensitivity and enhance the interface compatibility with the matrix [33]; Figure 1 shows the treatment procedure. Firstly, the WSP was soaked in a solution of aqueous alkaline (NaOH) for 3 h at room temperature. The ratio between WSP and solution was 1:20 (wt/wt). After the NaOH treatment, in order to remove any traces of NaOH from the surface of WSP, deionized water was used to wash the WSP several times, and acetic acid solution was added to neutralize the solution. The NaOH treated WSP was dried at \(85\pm 2\)\({}^{\circ}\)C for 48 h. Secondly, silane coupling agent (3-amino propyl triethoxy silane, KH550) was used to further treat WSP. Prior to the silane treatment, the acetic acid was added in deionized water to make the pH 3.5-4. Afterwards, 2 wt.%, 4 wt.%, 6 wt.%, and 8 wt.% KH550 was dissolved in deionized water, respectively, for hydrolysis. The solution was continuously stirred while using a magnetic stirring apparatus for 30 min to hydrolyze the silane completely. The NaOH treated WSP was soaked in the prepared silane solution and continuously stirred for 8 h at room temperature. After the treatment, the WSP was washed with deionized water for several times to remove the silane residues from the WSP surface and dried in a laboratory oven at 85\({}^{\circ}\)C to achieve a moisture content of 0-1% measured using a scale with an error of 0.001.  
Figure 2a,b show the micrograph of WSP treated and untreated with NaOH/silane, respectively. The micrographs were examined while using a scanning electron microscopy (SEM, HITACHI SU8020 system). The WSP particles in both two figures display an irregular shape. There were micro pores that were dispersed on the surface of WSP with the shape of an oval and an average size of 1-2 um (white rectangle). In Figure 2a, smaller WSP particles had the tendency to agglomerate together. The incorporation of the NaOH/silane into the WSP, the surface of particles was rougher than untreated one, and the phenomenon of agglomeration was removed. The results showed that the treated WSP is more compatible with the PLA matrix.  
Figure 1: The surface treatment process of Walnut Shell Powder (WSP).
# An Overview of Distributed Firewalls and Controllers Intended for Mobile Cloud Computing
## 3 SDN, NFV, and CASB Technologies
### Integrated Approach
#### 3.5.3 The ANSwer Architecture  
[PERSON] et al. [77] propose the ANSwer architecture, which integrates SDN and NFV capabilities to enhance network resilience strategies. This architecture leverages the programability and flexibility of SDN combined with the virtualisation benefits of NFV to enhance network robustness and fault tolerance. The integration facilitates dynamic reconfiguration, efficient resource allocation, and proactive threat detection and mitigation, thereby improving the overall network resilience. The ANSwer architecture aligns with the evolving landscape of network technologies, where the convergence of SDN and NFV offers fresh opportunities to enhance network management, security, and performance. The proactive approach to resilience strategies, as facilitated by ANSwer, can help in addressing emerging challenges such as botnet detection and mitigation [77].  
The gap in the ANSwer architecture that could be addressed by introducing CASB is the lack of a dedicated security enforcement mechanism for ensuring data protection and access control within the integrated SDN and NFV environment. While the ANSwer architecture focuses on enhancing network resilience strategies through the integration of SDN and NFV features, it does not explicitly address the comprehensive security requirements essential for safeguarding data transmissions and enforcing security policies within the network infrastructure. This gap is critical as the dynamic and virtualised nature of SDN and NFV environments introduces new security challenges related to data privacy, compliance, and threat mitigation. Introducing CASB into the ANSwer architecture could bridge this gap by providing a centralised security control point capable of monitoring, detecting, and enforcing security policies across the SDN and NFV components. CASB solutions provide features like threat detection, data encryption, access control, and compliance monitoring, which are crucial for securing data flow and ensuring regulatory compliance within the integrated system.  
Integrating CASB into the ANSwer architecture allows researchers to investigate the dynamic application, monitoring, and enforcement of security policies in real time, aimed at safeguarding sensitive data and addressing security risks. CASB enhances the network'soverall security by offering insights into data transactions, blocking unauthorised access, and swiftly responding to security incidents.
## 2 Working Conditions Analysis of ASIST
### Working Conditions Analysis  
According to the working principles of ASIST, the primary actuators of the system, including MPU and RSD, mainly undertake the following tasks:  
1. Capture stage Once the helicopter lands on the deck, the ASIST needs to capture the probe rod as quickly as possible to avoid sideshow or rollover accidents. MPU and RSD need to complete the capture actions within no more than 1.5 s [6; 39], including MPU driving RSD to capture the probe rod longitudinal and RSD driving the claw to capture the probe rod lateral, as shown in Figure 3.  
Figure 3: Capture stage diagram.  
Figure 2: Composition diagram of ASIST [6].  
2. Traction stage  
The MPU and RSD are required to tow the helicopter to the specified position under maximum level-3 sea conditions, including the MPU longitudinal towing the helicopter along the deck and the RSD lateral towing the helicopter, as shown in Figure 4.  
As we can see, MPU and RSD play a significant role in the whole process. However, due to the limitation of technical conditions in design, both the longitudinal and lateral speed of the claw cannot be regulated, which leads to the following two problems that are difficult to solve.  
* In the capture stage  
The impact force between the claw and the probe rod is enormous, making ASIST unable to assist the landing of small shipborne aircraft.
* In the traction stage  
The position and attitude of the helicopter must be adjusted repeatedly because the traction speed is not controllable. This process takes a long time, the operation steps are complex, and the operator must be highly experienced.  
Therefore, it is urgent to design an improved ASIST to solve the above-mentioned problems. EASIST, which uses an asynchronous motor as the power source, has become the preferred solution due to its energy-saving characteristics, cleanness, low noise, high power density, and convenient speed regulation.
# Cloning and Characterization of Two Iridoid Synthase Homologs from _Swertia Mussotii_
## 3 Discussion
Further IS sequence phylogenetic analysis may elucidate how ISs have evolutionarily diverged from other P5\(\beta\)Rs. Both SmlS1 and SmlS2 catalyse the conversion of 8-oxogerani, which demonstrates a redundancy of genes encoding IS enzyme activity in _S. mussotii_. IS gene redundancy in _C. roseus_ has also been reported [15]. However, our enzyme assay analysis revealed that SmlS2 has approximately 8-fold reduced affinity than SmlS1 for 8-oxogerani. In addition, phylogenetic analysis revealed that SmlS1 and SmlS2 were clustered in two groups. SmlS1 was clustered with ISs, whereas SmlS2 was clustered with P5\(\beta\)Rs. These results suggested that SmlS2 could potentially be called SmP5\(\beta\)R1 and not be significantly involved in 8-oxogerani conversion. Nevertheless, further experimental data are needed to elucidate its function in _S. mussotii_ cell metabolism. The catalytic efficiency of SmlS1 was reduced compared with CrIS and OeIS. It is possible that the maltose-binding protein fusion observed in SmlS1 affected the conformation of SmlS1. qRT-PCR results revealed that these two genes were co-expressed in three different _S. mussotii_ tissues. The highest gene expression was noted in leaves. In a previous study, we also found that the transcript abundance of the GPPS gene was highest in leaves [21]. Although secoiridoids accumulate in all of the organs of the _S. mussotii_ plant, the highest level was found in its flower [22]. This finding suggests the involvement of a transport mechanism wherein the metabolites are synthesized outside the storage tissue and later transported to floral tissue [23; 24]. CrIS forms dimers or higher-order structures with exclusive cytosolic localization as assessed using fluorescent protein fusions and bimolecular fluorescence complementation assays [13]. In _S. mussotii_, based on prediction programs, both SmlS1 and SmlS2 are likely to be cytosolic (Table 2). Whether SmlSs could also form homo or hetero dimers in _S. mussotii_ requires future investigations.
## 3 Results and Analysis
### Molecular Dynamics Simulation and Analysis
#### 3.3.3 Analysis of Molecular Dynamics Results  
It can be seen from Figure 16 that there are great differences in the interaction energy between different aggregates and CR. It is obvious that limestone has a better interaction relationship with CR, while basalt is worse. At the same time, it can be seen that the addition of water reduces the interaction energy between aggregates and CR. In order to eliminate the influence of contact area on the interaction energy between the aggregate and the asphalt in the molecular model, the interaction energy per unit area between the aggregate and CR in dry and wet conditions was further compared, as shown in Figure 17. It can be seen from Figure 17 that the addition of water has a significant impact on the interaction between aggregate and CR. By comparing the two aggregates, it can be found that the interaction energy density between basalt and CR decreases by only 38.2% after the addition of water, while the interaction energy density between limestone and CR decreases by 64.5% after the addition of water. It can be seen that the interaction relationship of basalt, as an aggregate mineral, decays more slowly after it is combined with CR. From the perspective of interaction energy, the interaction performance between basalt and CR is not as good as that of limestone, but the interaction energy density decreases less after immersion, indicating that basalt has better adhesion to asphalt under the action of water,and it is an aggregate mineral with better resistance to water damage of asphalt mixture. The reason why the performance of limestone is inferior to that of basalt in the presence of water is that the main component of limestone is calcium carbonate, and a small part of it will be hydrolyzed and ionized after encountering water, which destroys its original crystal structure and destroys the interaction relationship between limestone and CR, so it shows instability after encountering water. In addition, the combination of asphalt and aggregate is mainly in the form of hydrogen bond. When water molecules appear at the interface between asphalt and aggregate, the aggregate will be bonded with some water molecules in the form of hydrogen bonds, thus reducing the binding ability between the asphalt and the aggregate. The interaction between the asphalt and the aggregate is weakened. Limestone, as a more polar molecule than basalt, is more likely to bond with water molecules, so its interaction with asphalt decreases more dramatically in the presence of water.
# Characterization of Carrier Phase-Based Positioning in Real-World Jamming Conditions
## 3 Cases for Investigation
### Selected Jamming Incidents
#### 3.2.3 Case 3  
The final case is also from station TA03. It is a single-frequency incident with a duration of about 12 min.  
In this case, the receiver manages to track far more satellites than in Cases 1 and 2. This allows for a better study of the consequences of using heavily attenuated observations for positioning, as both the number of cycle-slips and positioning residuals can be evaluated throughout the incidence--something that is not possible if tracking of a satellite is lost. C/N\({}_{0}\) values for GPS L1 in Case 3 are presented in Figure 6.  
These three cases will be used to evaluate carrier phase positioning solutions for various baselines. Both single-frequency (SF) and dual-frequency (DF) positioning scenarios are evaluated.  
Figure 5: C/N\({}_{0}\) values for Case 2; a multifrequency jamming incident registered at TA10. Figures show Galileo E1 and E5a measurements, but all frequencies are equally affected. The black arrows mark the beginning and ending of the incident. In the period around 17:40–17:50, the receiver is most heavily affected, losing track of several satellites.
## 3 The Results
### Screening and Eligibility, and Inclusion  
A preliminary selection was made, considering only studies in English published after the year 2000. Subsequently, the irrelevant work was identified and excluded through an analysis of the abstracts. To merge the datasets obtained from Scopus and WoS search results for bibliometric analysis and to remove duplicates, EndNote 21(r) software was used. In Table 2, the results of the search are summarized. A total of 1352 papers were identified and selected as worthy of review.  
The final phase focused on reviewing the studies included in the analysis to perform the bibliometric analysis, which included the performance analysis and science mapping. Performance analysis often uses citation counts to gauge the relative importance and influence of publications, authors, journals, institutions, and countries, and it may also involve calculating the average citations per work. Science mapping typically involves co-citation analysis and evolution analysis. Co-citation analysis helps researchers identify prominent authors and related journals within a research area by examining how often they are cited. On the basis of understanding the structure of the research, an evolution analysis trough can be performed to explore the development process in this field.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Search Phrase** & **Selected Papers to** \\  & **Review** \\ \hline (KEY (“vehicle to grid”) OR KEY (“V2G”) OR KEY (“bidirectional charging”)) AND (TITLE-ABS-KEY (“assessment”) OR TITLE-ABS-KEY (“evaluation”) OR TITLE-ABS-KEY (“forecast”)) AND (LIMIT-TO (LANGUAGE, “English”)) & 1352 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of selected studies to review.  
The reviewed documents belong to various types (e.g., journal or conference proceedings papers), and Figure 2 shows the papers per year, divided by type. As the graphs indicate, no relevant studies were found for the years 2000-2004 through the searches performed, confirming that the research interest is quite recent. The diagram also illustrates temporal trends, indicating increased research activity in recent years, and differentiates between source types, showing a dominance of journal papers over books.