esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Marine Oil Spill from PlanetScope Images Using CNN and Transformer Models
## 2 Materials  
### PlanetScope Satellite Image  
The PlanetScope satellite is a constellation of more than 430 small cube satellites, with a combined mass of 5.8 kg, operated by Planet Labs Inc. (San Francisco, CA, USA). These satellites maintain a sun-synchronous orbit, crossing the equator between 9:30 and 11:30 am. PlanetScope images cover a range of spectral bands, including blue (455-515 nm), green (500-590 nm), red (590-670 nm), and near-infrared (NIR, 780-860 nm) wavelengths. The spatial resolution is 3 m, with a revisit period of one day, and the positional error is less than 10 m (see Table 1). Due to its one-day revisit period, PlanetScope is less susceptible to the effects of inclement weather compared to other optical sensors. Moreover, the detection of minor oil spills is facilitated by the use of high-resolution images. The 3-m resolution of PlanetScope is a significant advantage in detecting marine oil spills. The enhanced spatial and temporal resolution of PlanetScope increases the probability of detecting marine oil spills and enables the acquisition of a greater quantity of data for deep learning modeling.
## 4 Markov Detection and Prediction (MDP) Model
### Authentic, Suspicious and Malicious Requests  
With the huge data generated by IoT devices, effective classification is essential to extract meaningful insights and facilitate predictive analysis. To streamline this process, a classification framework is proposed, categorizing IoT generated-data into three distinct types of requests:  
1. Authentic requests refer to accurate, reliable, and trustworthy information that has not been manipulated, fabricated, or altered in any way. This type of data reflects the true state of actions without bias or distortion for example a sensor that sends 8 messages per day.
2. Suspicious requests refer to the type of information that raises doubts about its accuracy, reliability, or legitimacy due to inconsistencies, anomalies, or unusual patterns.  
It may indicate potential errors, manipulation, or deceptive practices for example a sensor that exceeds its normal data-rate by sending more than eight messages per day.
3. Malicious requests refer to intentionally crafted or manipulated information designed with harmful intent, with the aim of causing damage, compromising security, or deceiving individuals or systems (e.g., a hacker trying to delete some data or a sensor that sends massive data while exceeding a certain threshold).  
The three types (Authentic, Suspicious, and Malicious) of requests are shown in Figure 2.  
The MDP flowchart depicted in Figure 3 shows the system behavior when it receives Authentic, Suspicious, or Malicious requests.  
Figure 3: MDP flow chart upon the arrival of Authentic, Suspicious, or Malicious requests. Where “C\({}_{1}\)” is the threshold of the Authentic phase, “C\({}_{2}\)” is the threshold of the Suspicious phase, and “\(D_{R}\)” is the number of ongoing Malicious Delete Requests.  
Figure 2: Authentic, Suspicious, and Malicious requests.
## 3 Bacteria-Cathode Interactions  
Cathode-associated electrotrophs are capable of utilising the cathode of MESs as an electron donor, consuming the electron flow generated from the initial electron transfer by electricigens [4]. The composition of cathode-associated communities is less well understood than anode communities [70]. Unlike anode-associated communities, cathode-associated communities have been demonstrated to contain a mix of both aerobic and anaerobic microbes, and one study found a dominance of _Oceanospirillales_, followed by _Bacteroidales_, within cathode-associated communities [70]. Of the _Oceanospirillales_, _Nitrincola_ spp. were most prevalent at the cathode, and being aerobic, it has been suggested that an oxygen gradient may influence cathode community structure [70]. Consideration of the microbial community associated with cathodes may be an important consideration for PMFCs, both in regards to how those communities may affect the plant, and how differing conditions of PMFCs, including the availability of oxygen and water, may affect microbial community composition and function.  
Bacteria-cathode interactions are of particular importance in the synthesis of biocommodities such as biofuels. However, in comparison to bacteria-anode interactions, the mechanisms of cathode interactions and their study are less prevalent. It has been predicted that mechanisms of direct electron transfer, including \(c\)-type cytochromes, are likely similarly important for cathode reactions as anode reactions (Figure 2); however, operating instead at differing redox potentials [71]. Other mechanisms such as flavins, ferredoxins, rubredoxins, hydrogenase, and formate dehydrogenase have also been investigated for the potential of cathode interaction [72]. The acetogenic EAM _Sporomusa ovata_ has been used to demonstrate the production of acetate and 2-oxobutyrate with up to 86% of electrons from the cathode appearing in these products [4]. However, further optimisation of the microbial and industrial processes for biofuel and other biocommodity production will be required if these devices are to provide a viable alternative to fossil fuels [73].
## 5 Interference Distribution Characteristics
### Distance between Electrode and Pipelines  
The interference scope of the HVDC grounding current can reach tens of kilometers. Therefore, the potential and density distribution characteristics were studied in the range from 2 km to 50 km. A schematic diagram for the pipeline and HVDC grounding electrode arrangement is shown in Figure 9. Five distance types (2 km, 5 km, 10 km, 20 km, 50 km) were chosen for study. The HVDC grounding current is 3000 A. The other parameters are  
Figure 11: Corrosion risk area distribution under different grounding currents.  
Figure 10: Calculation results under different HVDC grounding currents. (**a**) leakage current density along the pipeline; (**b**) PSP along the pipeline.  
the same as those listed in Table 1. The relationship between polarization potential and current density is the same as in Section 5.1 and shown in Figure 7.  
The results are shown in Figure 12 for different distances between the electrode and pipelines.  
The maximum leakage current density and PSP reach 25.2 A/m\({}^{2}\) and 8.74 V, respectively, when the distance between electrode and pipeline is 2 km. As the distance increases, the maximum current density and PSP decrease gradually. Regression analysis shows that the relationship between the maximum leakage current density \(J_{\max}\) and the distance \(d\) satisfy negative exponential function, that is \(J_{\max}=a_{2}\cdot d^{b_{2}}\) (\(b_{2}<0\)) where \(a_{2}\) and \(b_{2}\) are fitting constant coefficients. That means that the interference current density increases as power function when the distance between electrode and pipeline decreases.  
Figure 13 shows the interference range for the anode area and cathode area along the pipeline for different distances between the electrode and pipeline.  
The distance between the electrode and pipeline has a significant influence on the anode area range. The anode area range increases as the distance increases. Although the anode area range extends, the influence degree decreases substantially.
# An Overview of the Alternative Use of Seaweeds to Produce Safe and Sustainable Bio-Packaging
## 2 Potential Algal Bioactive Compounds for Bioplastic Production
### Carrageenan  
Carrageenan, mainly found in red algae, is a linear polysaccharide formed from alternating sulfated or non-sulfated galactose units linked with \(\alpha\)-1,3-glycosidic and \(\beta\)-1,4-galactose bonds [61] (Figure 2).  
Depending on the position at which the sulfate group is connected to the galactose unit, carrageenan can be divided into different types (\(\kappa\)-carrageenan, \(\iota\)-carrageenan, \(\lambda\)-carrageenan, \(\gamma\)-carrageenan, \(\
u\)-carrageenan, \(\xi\)-carrageenan, and \(\mu\)-carrageenan). In nature, carrageenan are mostly hybrid; thus, their properties vary based on the bonded sulfate group [62].  
Commercial carrageenan are widely used in ice creams, paints, water gels, and pharmaceuticals. They are normally divided into three structural types (\(\kappa\)-, \(\iota\)-, and \(\AA\)-forms) according to the number of sulfated groups connected to the galactose unit, where the number, chemical location, and arrangement of these groups defines the carrageenan's function and bioactivity power [63]. These different types of carrageenan are obtained from different species, e.g., \(\kappa\)-carrageenan is predominantly extracted from the species _Kappapphycus alvarezii_. \(\kappa\)-carrageenan forms gels that are hard, strong, and brittle; in contrast, \(\iota\)-carrageenan is mainly produced by _Educleuma denticultatum_ (trade name \"_spinosum_\"), and it gives the soft and weak form to gels. Lastly, \(\kappa\)-carrageenan is obtained from different species of the genera _Gigartina_ and _Chondrus_[64].  
Due to their gelling, thickening, and stabilizing properties, carrageenan are widely applied in the food industry [57]. The Food and Drugs Administration (FDA) and the European Food Safety Agency (EFSA) have approved the commercial forms of \(\AA\)-, \(\kappa\)-, and \(\iota\)-carrageenan as food additives [65]. In the last few decades, the biological potential of carrageenan has been explored in medical field, with positive outcomes; in fact, it has been discovered that carrageenan possesses anticoagulant and antithrombotic activity [66], antivirus activity [67], antimuoral activity [68], and antioxidant properties [69].  
All types of carrageenan are soluble in water, although their aqueous solubility is influenced by temperature, pH, ionic strength of the medium, and the presence of cations. The sulfate and hydroxyl groups determine their hydrophilic characteristic, while their hydrophobicity derives mostly from the 3,6-anhydro-\(\alpha\)-D-galactopyranose units [57].  
The hydrophobicity of carrageenan represents a disadvantage for the manufacturing of resistant packaging; however, bonding carrageenan with hydrophobic compounds to reinforce the matrix of the compound might be a solution for enhancing the proper  
Figure 2: Chemical structure of \(\kappa\)-carrageenan.  
ties of carrageenanan, conferring their strength into eco-friendly, cost-effective packaging material [70].
## 6 Applications  
The successful double validation of the 3D BEM tool, against both experiment and high-fidelity CFD, leads to consider the code suitable for systematic applications to multiple test cases representing potential areas of VAWT use/installations.  
The 3D BEM code is employed to assess VAWT performance assuming different machine scale, rotor shape, and blade profiles (mounting both NACA0021 and DU06W200). Comparative analyses have been carried out for the following three categories:  
1. Micro-scale VAWTs: microgeneration H-shaped and DeepWind Demonstrator (rotational speed: 300 rpm);
2. Small-scale VAWTs: H-shaped and Troposkein, referred to rated power range of 10-50 kW, for application in coastal regions;
3. Large-scale VAWT: DeepWind 5 MW.  
The proposed scenarios are summarised in Table 5.
## 2 Related Works  
Owing to the notable strengths of metaheuristic algorithms in addressing NP-hard challenges, an increasing number of researchers are focusing on leveraging these algorithms for cloud manufacturing service selection and composition. This section reviews relevant studies on the application of metaheuristic algorithms to service selection and composition in the setting of cloud manufacturing. [PERSON] et al. [18] proposed a novelistic hybrid method that integrates the grey wolf optimization (GWO) [19] and evolutionary operator of genetic algorithm (GA) [20] to enhance the algorithm's global exploration potential, which is subsequently applied to address the QoS-CSC problem. [PERSON] et al. [21] use the information entropy to dynamically adjust the probability of crossover and mutation in the GA, and confirm the suggested algorithm's efficacy in addressing the service composition challenge. By introducing the sine and cosine functions and the normal cloud model, [PERSON] et al. [22] enhance the particle swarm optimization (PSO) algorithm [23], demonstrating its effectiveness in the CMfg service optimization model. An innovative hybrid TLBO [24] method is presented by [PERSON] et al. [25], combining the advantages of adaptive flower pollination algorithms [26] and uniform mutation, and has better QoS-CSC problem results. [PERSON] et al. [27] propose a valid multi-intelligence ant colony optimization method [28] and validate it using 25 different real datasets and the outcomes demonstrated the algorithm's efficacy. By combining the eagle method and uniform mutation, [PERSON] et al. [29] enhance the whale algorithm's performance in both local and global searches, and they evaluate this enhanced algorithm using both benchmark functions and the QoS-CSC problem. [PERSON] et al. [30] introduce a dual-objective framework targeting service quality and robustness, and issue an enhanced multi-objective GWO to efficiently address the aforementioned model. [PERSON] et al. [31] consider a bi-objective model, focusing on service quality and energy use from a sustainable manufacturing perspective. They improve the multi-objective GWO algorithm by incorporating strategies such as opposition-based learning, and validate its effectiveness through benchmark functions and service composition optimization problems.  
In conclusion, the application of metaheuristic algorithms to cloud manufacturing service combination and selection has shown great potential to address the complexity and multi-objective nature of the QoS-CSC problem. However, given the dynamic and heterogeneous nature of cloud manufacturing environments and the growing need for sustainability, dependability, and efficiency, the development of more advanced and adaptable algorithms remains an urgent task.
# A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks
## 5 Performance Evaluation  
In this section, we analyze the performance of the proposed protocol with numerical simulations. The network model and channel model we used here is presented in Section 2.
## 3 Materials and Methods
### Methods
#### 3.2.5 Reference Data Adjustment  
Considering that the aerial imagery acquisition was recorded in September 2018 and the reference data were recorded from May to November 2017, the provided reference data needed to be adjusted. In the year between the two acquisitions, the study area was modified via planned and salvage logging [61]. This inconsistency was resolved using data from the Austrian Federal Forests database, where all cuttings are registered, to identify already carried out thinnings and salvage logging. Additionally, all commercial forest stands were visually inspected in GIS, and any noticeable errors were corrected.
## 3 Results
### SEDI-Based Drought in the Middle East  
An assessment of the drought characteristics in the Middle East using the SEDI revealed that a total of six significant drought events occurred in 1985, 1989-1990, 1994, 1999-2001, 2008, and 2015 (Figure 6). The very extreme and extreme droughs of 1989-1990 and 1999-2001 covered approximately 8-12% and 12-15.1% of the total area, respectively. Severe droughs dominated more than 15% of the area in 1985, 1990, 1994, 1999-2001, and 2008. The 2000 severe, extreme, and very extreme droughs, which covered roughly 42% of the total area, were the worst observed droughs in terms of intensity, duration, and area, especially over the eastern and southern parts of the region. On the other hand, the 2008 drought event, which was the second-worst recorded event ever recorded in the region, covered about 33.5% of the total area and mainly dominated in the northern parts of the region.  
Figure 5: The left panel shows the spatio-temporal trends of the GPP products (in gC m\({}^{-2}\) yr\({}^{-1}\)) for the Middle East between 1982 and 2016. The small dots denote statistically significant trends at a confidence level of 95% (i.e., \(p\)! 0.05) using the Z-statistic of the M-K test. The upper right panel summarizes the cross-correlation matrix calculated among the models’ trend magnitudes (Sen’s slopes), while the lower right panel shows the distribution density curve of the trend slope for all GPP products.
## 4 Bipolar pqr-Spherical Fuzzy Aggregation Operators
### Bipolar pqr-Spherical Fuzzy Aggregation Operator Method  
Aggregation tools are essential in MCDM issues that utilize this method, as aggregation involves merging multiple numerical values into a single value; thus, we can refer to this method as an aggregator [36; 37; 38]. Numerous researchers have focused on the aggregation operators associated with different types of fuzzy numbers. After exploring certain operations concerning intuitionistic fuzzy numbers (IFNs), both geometric and averaging aggregation operators based on IFNs were developed, as described by [PERSON] [39] and [PERSON] [40]. Subsequently, picture fuzzy geometric and averaging aggregation operators were studied in [41]. Then, spherical aggregation operators were discussed in [25] in the context of MCDM problems. The latter operators were extended to the so-called T-spherical aggregation operators [42]. The concepts of pqr-spherical weighted (geometric and averaging) aggregation operators were proposed in [12; 13], generalizing all earlier operators. The T-spherical aggregation operators were further extended to the bipolar T-spherical aggregation operators that appeared in [23], aggregating the given \(+\)ve and \(-\)ve values. In this study, we build upon the previous ideas by extending bipolar T-spherical aggregation operators to B\({}_{\text{pqr}}\)SF aggregation operators, which are the basis of the new method in MCDM. The stepwise procedure of the proposed method is explained as follows:  
1. The alternatives are evaluated using the \(l\) criterion. The final values of the alternatives with regard to each criterion are used to generate a B\({}_{\text{pqr}}\)SF decision matrix similar to Equation (5).
2. If the criteria are not on the same scale, normalizing is needed, which is the normalizing of the decision matrix according to each criterion's nature, as in Equation (6).
3. The decision-makers give each criterion a weight value in order to accomplish the normalcy condition, _i.e._, regarding the weight vector \(\epsilon=\{\epsilon_{1},\epsilon_{2},\ldots,\epsilon_{l}\}\) with \[\sum_{j=1}^{l}\epsilon_{j}=1.\]
4. The B\({}_{\text{pqr}}\)SFWA operator or B\({}_{\text{pqr}}\)SFWG operator is computed via \(\breve{s}_{i}\), where \[\breve{s}_{i}=\left\{\breve{\theta}_{i}^{+},\breve{\lambda}_{i}^{+},\breve{ \theta}_{i}^{-},\breve{\eta}_{i}^{-},\breve{\lambda}_{i}^{-}\right\}=B_{pqr} SFWA\left(\breve{s}_{i1},\breve{s}_{i2},\ldots,\breve{s}_{il}\right)\] and is obtained by Equation (2), or \[\breve{s}_{i}=\left\{\breve{\theta}_{i}^{+},\breve{\eta}_{i}^{+},\breve{ \lambda}_{i}^{+},\breve{\theta}_{i}^{-},\breve{\eta}_{i}^{-},\breve{\lambda}_ {i}^{-}\right\}=B_{pqr}SFWG\left(\breve{s}_{i1},\breve{s}_{i2},\ldots,\breve{s} _{il}\right)\] is obtained by Equation (4), for \(i=1,2,\ldots,n\).
5. Score function of \(\breve{\tau}_{i}\), denoted by \(scor(\breve{\tau}_{i})\), is calculated using Definition 14.
6. We sort the alternatives in decreasing order using the score values we obtained in step 5.
# Preliminary Validation of Surface Reflections from Fengyun-3C Radio Occultation Data
## 2 Data and Quality Control  
### COSMIC RO Data  
In order to pick the reflected RO events, we used the ROM SAF reflection flag database to extract COSMIC profiles for convenience. The COSMIC occultations with an SVM flag value over 0.25 were likely to be reflections, but some of them may also be disturbed by atmospheric multipaths or other instrumental nuisances to show non-reflections [14]. Hence, for the purpose of this study, we identified the occultations with an SVM flag value greater than 0.25 as reflections and the occultations with an SVM flag value below 0 as non-reflections. The COSMIC refractivity profiles during 2015 were obtained from COSMIC Data Analysis and Archive Center (CDAAC) and used for the consequent comparison with FY-3C refractivity profiles from 2015.
## 3 Methods and Materials
### Field Observation  
In order to investigate the development of the caved zone and fractured zone in the field, the high-pressure water injection method (HWJM) was employed [30; 31; 32]. The principle is that by injecting high-pressure water into the surrounding rock, the water losses (or seepage) at different sections (but pressures are the same) of the borehole are different, as shown in Figure 5. Then the data from along the entire borehole can be plotted. It is obvious that the water losses within the caved zone, the fractured zone, and intact zone must be different. In the intact zone, the water loss must be little. In the fractured zone, the water loss is expected to be much higher than that in the intact zone. In the caved zone, the water loss must be the largest. Therefore, the water loss reflects the degree of fracturing in the surrounding rock, which can define the boundaries of the caved zone and fractured zone.  
For this case study mine, underground boreholes were drilled upward into the caved zone and fractured zone. Compared to surface boreholes, underground boreholes are of less cost. The key component of the apparatus is shown in Figure 6. The entire injection system is shown in Figure 7. The system consists of three parts: inflatable capsules, tubes or pipes, and consoles.  
Figure 5: High-pressure water injection method (HWJM).  
Figure 6: The key components of the apparatus.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Lithology** & **Density/kg m\({}^{-3}\)** & \begin{tabular}{c} **Bulk** \\ **Modulus/GPAs** \\ \end{tabular} & \begin{tabular}{c} **Shear** \\ **Modulus/MPAs** \\ \end{tabular} & \begin{tabular}{c} **Cohesion/MPa** \\ **Friction/\({}^{p}\)** \\ \end{tabular} & \
\begin{tabular}{c} **Internal** \\ **Strength/MPAs** \\ \end{tabular} &
\begin{tabular}{c} **Tensile** \\ **Strength/MPAs** \\ \end{tabular} \\ \hline Silstrone & 2350 & 15 & 9.2 & 3.5 & 31 & 1.7 \\ \hline FGS & 2350 & 20.3 & 11 & 5.8 & 32 & 1.9 \\ \hline Mudstone & 2200 & 6.8 & 6.3 & 4.9 & 31 & 1.5 \\ \hline Limestone & 2700 & 20.7 & 15.5 & 8.1 & 30 & 4.1 \\ \hline Coal & 1400 & 2.3 & 2 & 1.25 & 27 & 1.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical and mechanical properties of the strata.  
The borehole layout plan is shown in Figure 8. Two boreholes were drilled upwards from the 10,105 tail gate. The lengths of the two boreholes were both 120 m, the diameters were 75 mm, and the inclinations were 65\({}^{\circ}\) and 70\({}^{\circ}\). The detailed information about the two boreholes is presented in Table 4.