esa-sceva/satcom-chunk-collection huggingface dataset:
# Microalgal Proteins and Bioactives for Food, Feed, and Other Applications
## 1 Introduction
### Microalgae for Food and Functional Food Applications
#### 1.1.4. Pigments  
The presence of pigments such as chlorophylls, phycobilins and carotenoids in microalgae is essential for light harvesting and stress mitigation. The key pigments produced in relation to light harvesting in microalgae include the chlorophylls, absorbing light mainly from the blue and red spectrum of light [83]. As a response to environmental stress, such as oversaturation of light intensity, high salinity or nitrogen limitations, photo-protective carotenoid pigments such as \(\beta\)-carotene, astaxanthin, zeaxanthin, lutein, canthaxanthin, fucoxanthin and lycopene are overproduced [84]. The most important commercial producers of carotenoids are the species _Dunaliella salina_, which produces \(\beta\)-carotene, and _Haematococcus plurialis_, which produces astaxanthin. _Dunaliella salina_ can accumulate up to 14% \(\beta\)-carotene of its DW, and _Haematococcus plurialis_ can accumulate up to 5% astaxanthin of its DW [85]. Carotenoids find application as food colorants, additives for aquaculture feed and, most recently, in cosmetics and pharmaceuticals for their anti-aging, anti-inflammatory and anticancer properties [86]. Phycobilins as phycocyanin that are produced by _Arthospira_ sp. and phycerythrin produced by _Porphyridium_ sp. and _Rhodella_ sp. are another important group of antenna pigments. At present, phycobilins are mainly used as natural food colorants, antioxidants and fluorescent agents [8].
## 3 Experiment and Results

Table 1: SFDR measurement results for all sub channels. Figure 4: SFDR measurement results (**a**) without balanced detection and (**b**) with balanced detection. Figure 3: The output spectrum of the DPMZM. frequency of 8.5 GHz. The broadband signal centered at 7.9 GHz, after down-conversion, should be output by channel 6, while the signal centered at 9.1 GHz should be output by channel 8. After image rejection, the DP-IRR\({}_{1^{\circ}}\)s X-90\({}^{\circ}\) hybrid outputs are expected to deliver channel 6 with 7.9 GHz as the desired signal and 9.1 GHz as the image signal, and channel 8 with 9.1 GHz as the useful signal and 7.9 GHz as the interference signal. The measurement results, as depicted in Figure 5, indicate an image rejection ratio of approximately 29 dB. The experiment also conducted the same tests for channels 9 and 11, as well as for channels 1 and 3. The results, shown in Figures 6 and 7, respectively, demonstrate that broadband signals at different frequencies can be accurately output by their corresponding sub-channels, with image rejection ratios consistently around 28 dB. In order to demonstrate that the proposed channelizer can not only receive the broadband RF signal, but also restore the modulated RF signal, an experiment is carried out. Figure 5: Electrical spectra of (**a**) Ch-6 as the desired channel and (**b**) Ch-8 as the desired channel. Figure 6: Electrical spectra of (**a**) Ch-9 as the desired channel and (**b**) Ch-11 as the desired channel. Figure 7: Electrical spectra of (**a**) Ch-1 as the desired channel and (**b**) Ch-3 as the desired channel. A 16-State Quadrature Amplitude Modulation (16 QAM) signal with a center frequency of 9.5 GHz is modulated by Quadrature Phase Shift Keying (QPSK) with the symbol rate of 50 MSym/s. After channelization, the IF signal is demodulated by the vector signal analyzer; the constellation diagram is shown in Figure 8. When the power of the input RF signal changes from \(-15\) dBm to \(-2\) dBm, the Error Vector Magnitude (EVM) gradually decreases and reaches the minimum value of 5.3%, which is the best point of communication. When the power of the RF signal changes from \(-2\) dBm to 10 dBm, the EVM begins to increase, which is caused by high-order intermodulation distortion.
# Safety Verification of Driving Resource Occupancy Rules Based on Functional Language
## 4 CSP\({}_{\text{M}}\) Definition
### Refinement Relationship  
It is necessary to prove that the semantic model of CSP is a sub-semantic model of CCSP. We first define its sub-semantic model, and then give the theorem proof that CCSP contains CSP semantics, proving that CCSP is extended on the basis of CSP.  
**Definition 5**.: _Suppose a communication sequence process \(\mathrm{P}_{A}\) with semantic \(\mathrm{M}_{A}\) of type \(A\) can obtain another communication sequence process \(\mathrm{P}_{B}\) with semantic \(\mathrm{M}_{B}\) of type \(B\) through a refined relationship, then semantic \(\mathrm{M_{B}}\) is the sub-semantic model of semantic \(\mathrm{M_{A}}\)[33]. The refined models of \(\mathrm{P_{A}}\) and \(\mathrm{P_{B}}\) are as follows:_  
\[\frac{P_{B}\;\text{sat }S_{B}\;\text{in }M_{B}}{P_{A}\;\text{sat }S_{A}\;\text{in }M_{A}}(P_{B} \sqsubseteq P_{A}) \tag{3}\]  
\(\mathrm{S_{A}}\) _is the semantics of \(\mathrm{P_{A}}\), and \(\mathrm{S_{B}}\) is the semantics of \(\mathrm{P_{B}}\)._  
**Definition 6**.: _All acceptable languages of a conditional communication sequence process CCSP are a collection of conditional migration sequences._  
**Theorem 1**.: The semantic model of the communication sequence process CSP is a sub-semantic model of the conditional communication sequence process CCSP.  
Proof of Theorem 1.: Let \(\mathrm{P_{CSP}}\) be a communication sequence process. From **Definition 1** we know that CSP is a migration system \(\mathrm{TS_{CSP}}\) = \(<\)\(NODES\),\(\Sigma\),\(\rightarrow\)\(>\), and its accepted language is \(L\). Construct a conditional communication sequence process according to the communication sequence process. From **Definition 3** we know that \(P_{CCSP}\) is \(\mathrm{TS_{CCSP}}\) = \(<\)\(NODES\),\(\Sigma\),\((C,P)\),\(\rightarrow\)\(>\). Suppose the position time language that \(P_{CCSP}\) can accept is \(L^{\prime}\). At this time, take any conditional transition sequence \(R\) = \(<\)(\(a_{0}\)),(\(a_{1}\))... (\(a_{n\_1}\))\(>\) in \(L\), and there is only one \(R^{\prime}\) = \(<\)(\(c_{0}\),\(a_{0}\)),(\(c_{1}\),\(a_{1}\))... (\(c_{n\_1}\),\(a_{n\_1}\))\(>\) corresponding to it in \(L^{\prime}\). Therefore, \(\sum\subseteq\)\(\sum\)\((\mathrm{C,P})\) and when \(c_{n}=true\), \(\sum_{T}=\sum\)\((C,P)\) is the refined relationship from \(P_{CCSP}\) to \(P_{CSP}\). \(\square\)  
Then, according to **Theorem 1**, all model checking related to functional properties on CCSP can be completed by CSP's model checking tool. Conditional analysis is required for the extended conditions to determine whether the current resource occupancy rules meet the safety. When it is not, perform certain fault-tolerant measures [34].
## 3 Results and Discussion
### Results of Material-Plant Combined Remediation Experiment
Figure 4: The relationship between the reduction rates of As and Cd bioavailability and the variation in mass ratios of components in material formula (SS, Py, and FS refer to steel slag, pyrolusite, and FeSO4, respectively. (**A**): The proportion of steel slag in the ternary component affects the bioavailability of As in the soil; (**B**): The proportion of steel slag in the ternary component affects the bioavailability of Cd in the soil; (**C**): The impact of the proportion of ferrous sulfate in ternary components on the bioavailability of As in soil; (**D**): The impact of the proportion of ferrous sulfate in ternary components on the bioavailability of Cd in soil). The optimization of material dosage in soil remediation was crucial for balancing cost efficiency and environmental impact. In our study, we determined that a 9% material dosage is optimal. This dosage effectively reduced the bioavailability of arsenic (As) and cadmium (Cd) in the contaminated soil from the Zhongshan Hui Long arsenic smelter in Guangxi, achieving reductions of 90.7% and 89.1%, respectively.
## 2 Objectives  
As noted above, the theoretical jet formation and cutting processes of LSCs have rarely been studied together due to the technical difficulty of the problem. The LSC jet in the middle of the cutting process (or the jet-on-target) is not easy to observe, and it is difficult to connect the jet formation theory with the cutting performance. In particular, the equation of motion of the jet will not provide a detailed prediction regarding the penetration since the equation of motion of the jet only predicts a few parameters that affect the penetration performance, and many more must be identified for proper prediction.  
A typical shaped-charge jet (CSCs and LSCs) produces a finite length of jet. Due to significant jet elongation during flight (with a different jet velocity profile along the length of the jet), each individual jet segment will have its own unique penetration capability. This fact is more significant for LSCs because there is no solid jet stage before the participation.  
The main objective of the present research is to understand the penetration capability of each segment (or group) of a particulated jet, and this will be analytically studied in a manner that traces back to the original liner collapse point condition. This information will help to illuminate the penetration capability of LSCs.  
It is important to note that the penetration capability of each segment must be identified in the middle of penetration. Otherwise, all the jet segments hit the target and there is no way to find the penetration capability per segment.
## 3 Results
### CHSP Inhibits Ovarian Cancer Cell Proliferation via the HIF-1\(\alpha\)/VEGF Pathway  
As shown in Figure 4a, levels of VEGF excreted by OVCAR-3 cells were inhibited to 57.12, 36.66, 22.55 and 7.87% by 5, 10, 20 and 40 \(\upmu\)g/mL CHSP treatments, respectively. Also shown in Figure 4, HIF-1\(\alpha\) expression was significantly inhibited by CHSP treatment. Compared to the control level, HIF-1\(\alpha\) expression at 20 \(\upmu\)g/mL of CHSP decreased by 65.8%. Shown in Figure 4b, this inhibition of VEGF was significantly reversed by forced expression of HIF-1\(\alpha\) protein.
# Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series
###### Abstract
With regard to the global relevance of Asian aquaculture, up-to-date spatial information and products for the quantitative assessment of the spatial extent, distribution, and dynamics of aquaculture are of great importance for productive management, climate change adaption measures, food security, and the protection of natural water resources. Figure 1: (**A**) Aquaculture production among continents from 1980–2019, (**B**) Global aquaculture production and capture fisheries production from 1980–2019, and (**C**) Map of aquaculture production in 2019. Data source: [1]. At the continental and national levels, there are very large regional differences in aquaculture production. The five largest aquaculture-producing countries contribute to more than 80 percent of the global volume and all come from Asia: China (48.2 million t), India (7.8 million t), Indonesia (6.0 million t), Vietnam (4.4 million t), and Bangladesh (2.5 million t) (see Figure 2). In 2019, China accounted for more than half (56 percent) of the global aquaculture production, underlining its dominant role in the aquaculture sector in recent years. In Asia, aquaculture mainly takes place in closed, land-based pond systems in inland waters (see Figure 2) and ranges from extensive smallholder pond management to commercial, industrialized pond farms with advanced feeding technologies [17; 18]. Satellite remote sensing enables large-scale, area-wide, and routine monitoring of bodies of water and anthropogenic activities in coastal regions [19; 20] and is a suitable tool for developing geographic information products on a national, regional, or global level. As a result of the rapid global growth of aquaculture in recent years, the spatial recording and mapping of aquaculture in the coastal regions and the collection of relevant environmental data on the effects of aquaculture on natural ecosystems and their spatial and temporal changes represent a focus in coastal research. In this regard, geospatial information on aquaculture can support the management of this growing food sector for the sustainable development of coastal ecosystems, resources, and human health.
# Estimating Double Cropping Plantations in the Brazilian Cerrado through PlanetScope Monthly Mosaics
## 1 Introduction
To the best of our knowledge, there is no study evaluating this high spatial and temporal resolution and NICFI's monthly products to discriminate double crop agricultural fields over the Cerrado biome. Our ultimate goal is to analyze the feasibility of using PS monthly mosaics in an operational way to monitor agricultural areas of the Brazilian Cerrado with double cropping systems. Within this context, this work presents the following scientific contributions: (i) it is the first proposal of mapping the double cropping system in the Brazilian Cerrado using PS monthly mosaics; and (ii) it presents a framework to select the best spectral and textural attributes to identify areas with double cropping based on the machine learning Random Forest classifier. Since there are no other PS monthly mosaics-based studies on mapping double cropping systems in the Cerrado biome, this work is intended to serve as a benchmark to help generate consistent and accurate LULC maps of the Brazilian Cerrado that include, for the first time, double cropping in their legends.
# Real-Time Anomaly Detection with Subspace Periodic Clustering Approach
## 7 Conclusions, Limitations and Lines for Future Works  
### Conclusions  
In this article, a clustering-based method for finding real-time anomalies in a subspace is given. The method first uses a nano-topology-based attribute reduction approach for finding subspace as the core of an attribute set. Then, a dynamic k-means clustering approach is employed to find k-clusters in the subspace. It is to be mentioned here that the clusters obtained by the aforesaid approach will have a k-number of sequences of time intervals, and each cluster will be associated with a sequence of time intervals describing its lifespan. Then, using an interval superimposition method, a superimposed time interval is obtained, and a match ratio for each cluster is also computed. The match ratio determines whether the cluster is fully or partially periodic. Further, from each superimposed time interval, a fuzzy time interval can be computed, and the cluster associated with the fuzzy time interval is termed a fuzzy periodic cluster. At the end, the method supplies fuzzy periodic clusters in the subspace. Since the obtained clusters are periodic in nature, they provide more detailed information about the nature of the data instances. The anomalies would be mostly doubtful instances that either belong to sparse clusters or do not belong to any of the periodic clusters.  
The time-complexity of the method is computed and found to be O(\(n^{3}+n.n_{1}\)3) in the worst-case, where \(n=\) the number of instances and \(n_{1}=\) the maximum number of intervals associated with any cluster. Obviously, \(n_{1}\) is very small in comparison to \(n\). Therefore, the method runs in cubic time. Further, it has also been found that RADSPCA runs linearly with respect to the dimension of the datasets.  
To find efficacy further, ten well-known clustering-based algorithms were taken, and a detailed comparative analysis was conducted against RADSPCA, first using the KDDCUP99 [64] dataset and then the Kitsune [65] network attack dataset. Experimentally, RADSPCA is found to be more efficient than others in terms of recall, precision, and F1-score in high-dimensional data.
## Methods
### Economic Calculations
#### 2.4.2 Wood and Carbon Sequestration Calculations  
[PERSON] ([PERSON] et al., 2016) provided a formulation for economic assessments of the optimal harvest age of a forest stand when both timber value and flows of other services (e.g., carbon sequestration) are considered:  
\[PV_{\text{c}}=P_{\text{cs}}\!\!\int\limits_{0}^{T}\!F^{\prime}(t)\beta_{\text{ c}}\text{e}^{-rt}\text{d}t-P_{\text{cs}}F(T)\text{e}^{-rt} \tag{6}\]  
where \(PV_{\text{C}}\) is the present value of sequestered carbon, \(P_{\text{cs}}\) is the carbon price ($-ton$^{-1}\)-CO\({}_{2}\)), \(F(t)\) is the carbon sequestration function, \(F^{\prime}(t)\) is the integral of \(F(t)\), \(\beta_{\text{c}}\) is the conversion factor (set to 0.3 for the current work) to reduce the total carbon mass to biomass volume which is consistent with an estimated carbon content of wood of approximately 200 kg.m$^{-3}\)([PERSON] et al., 2016), \(r\) is the discount rate (%), and \(t\) is the age of the forest stand. The term to the left of the minus sign in Equation (6) represents the NPV of carbon stored over the period of the standing trees, whereas the term to the right represents the carbon value loss when the stand is harvested, as forest biomass is expected to be set to zero at the time of harvest ([PERSON] et al., 2016). The mass of carbon in standing trees (biomass: ton.ha$^{-1}\)) as a function of stand age, is given by:  
\[F(t)=v_{1}\big{(}1-\text{e}^{-v_{2}t}\big{)}^{v_{3}} \tag{7}\]  
where \(v_{1}\), \(v_{2}\), and \(v_{3}\) are fitted parameters with values of 189.6, 0.0268, and 2.56, respectively. This equation was parameterized by fitting GCM-simulated NEP data over the 1901 to 2100 period.  
Inserting Equation (6) into the Faustmann model (Equation (5)) gives the NPV for both timber production and carbon sequestration:  
\[\mathrm{NPV}=\frac{[PV(t)-C]\mathrm{e}^{-rt}+P_{\mathrm{cs}}\int_{0}^{t}\!\!F^{ \prime}(t)\beta_{\mathrm{c}}\mathrm{e}^{-rt}\mathrm{d}t-P_{\mathrm{cs}}F(t) \mathrm{e}^{-rt}}{1-\mathrm{e}^{-rt}} \tag{8}\]  
We reiterate that the optimal rotation age is that at which NPV is maximized. Importantly, [PERSON] showed that the \"optimal\" decision may be to leave a stand unharvested, depending on details such as growth rates, prices of the values under consideration, and the relevant discount rate.
# Promising Cr-Doped ZnO Nanorods for Photocatalytic Degradation Facing Pollution
###### Abstract
Recently, a new thermal decomposition synthesis technique has been developed to obtain chromium (Cr)-doped ZnO nanorods as cool nano-pigments, which was shown to have good efficiency and properties [27]. Furthermore, dye molecules are one of the most hazardous and polluting waste materials today. Their high stability in normal environmental conditions is the major reason for this. In particular, wastewater effluent that is rich in these molecules remains a challenge facing natural water sources and our ecosystem. The application of these molecules extends to cover various domains, particularly, the optoelectronic domain. This is related to their promising optical response in the visible domain. Such molecules belong essentially to the AZO group of molecules, i.e., components formed by the \"R-N=N-R\" function, such as congo red (CR) and methyl orange (MO). Several studies have addressed the physical and chemical methods of dye molecule hindrance [28]. Nevertheless, these methods remain unable to stop dye contamination of the environment and stop their transformation into adsorbents. One of the promising paths to achieve a complete degradation of these dye molecules is photocatalysis. This latest technique ensures complete destruction of the pollutants by turning them into mineral organic dyes such as H\({}_{2}\)O and mineral acids. A wide range of photocatalysts descend from various semiconductors like TiO\({}_{2}\)[29], SnO\({}_{2}\)[30], Zn\({}_{2}\)SnO\({}_{4}\)[31], CdS [32], and ZnS [33]. Such photocatalyst-based materials are cost-efficient and eco-friendly. Zinc oxide (ZnO) exhibits high efficiency in the photocatalytic degradation of some organic dyes compared to other material [34; 35; 36]. ZnO dye molecules are promising materials for reducing environmental contamination. In this paper, we will consider the performances of Cr-doped ZnO nanorods on photodegradation. We studied the weight ratios of different Zn and Cr precursor salts. These were used as photocatalysts in the UV-photodegradation of methyl orange (MO), as an example of an organic dye pollutant. Different UV-vis studies were developed. This study opens the door to (Cr)-doped ZnO nanorod functionalization for a future optoelectronic application.
## 3 The Proposed Model
### Steps Involved in HMSFO Algorithm
 \tag{16}\]  
\[\beta(t)=\beta_{\text{min}}+\left(\beta_{\text{max}}-\beta_{\text{min}}\right) \times\frac{t}{\text{Max }iter} \tag{17}\]  
Now, \(r_{8}\) indicates a random value within zero and one, \(x_{i,r}\) represents another random value selected from the potential range of that specific problem dimension, and \(\beta_{\text{max}}\) and \(\beta_{\text{min}}\) denote the maximal and minimal values of probability value \(\beta\in[0,1]\), correspondingly. Once the produced solution \(X_{i}^{\prime\prime}\) is superior to the existing solution, \(X_{i}\) can be replaced with \(X_{i}^{\prime\prime}\). The overall process of the ABHC concept is given in Algorithm 1. ```
1:Initialization of the variables \(\beta_{\text{max}}\), \(\beta_{\text{min}}\), and \(K\)
2:Input the existing solution \(X_{i}=\left(x_{i,1},x_{i,2},\ldots,x_{i,D}\right)\)
3:Compute the fitness value \(F(X_{i})\)
4:While \(t\leq\text{Maxiter}\)do
5:Produce the neighbouring solution \(X_{i}^{\prime}\) based on Equation (14)
6:For \(j=1\) to \(D\)do
7:If \(r_{8}<\text{then}\)
8:\(x_{i,j}=x_{i,r}\)
9:Else
10:\(x_{i,j}^{\prime\prime}=x_{i,j}^{\prime}\)
11:End If
12:End For
13:Compute the fitness value \(F(X_{i}^{\prime\prime})\)
14:If \(F(X_{i}^{\prime\prime})<F(X_{i})\) then
15:\(X_{i}=X_{i}^{\prime}\)
16:End If
17:\(t=t+1\)
18:End while
```  
**Algorithm 1**Adaptive \(\beta\)-Hill Climbing Algorithm
# Applications of Wireless Sensor Networks: An Up-to-Date Survey
## 2 Main WSN Applications
### Military Applications
#### 2.1.3 Intruder Detection  
The knowledge of the location of the enemy is considered to be one of the most critical pieces of information during military operations. Whichever side in a conflict has this knowledge is one step ahead and closer to victory. With the utilization of WSNs within a battlefield, intruders can be detected in good time to prevent loss of supplies or territory.  
An intruder can be detected in various ways. The authors of [11] developed a system that can recognize intruders by utilizing Unattended Acoustic and Seismic Sensors to record vibrations and sounds of typical soldier actions. To detect an intruder, with the aid of these sensors, typical military activities, such as equipment handling, walking, rifle loading, etc., have been measured and recorded in a controlled environment and in the field. Although the distance of detection may be small, the information provided by the network can be used to locate an intruder in difficult terrains with large vegetation where visibility is limited.  
In [12] another application of WSNs, developed to detect intruders, is introduced. The authors described a system with dense deployment of a large number of miniaturized wireless sensors, which are small, low cost, and consume low energy. This system utilizes acoustic and seismic sensors to provide detection information to the user and tracking and visual sensors in order to lower the false alarm rate.