esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Study
### CLD Type and Location  
Figure 2 shows the different types of CLD patches considered. Type A is the original specimen, whereas types B and C are constructed by attaching a rigid (uniform or honeycomb) sheet of aluminium to a layer of viscoelastic polymer. The constraining layer used for specimens B corresponds to an aluminium sheet 2500 mm long, 125 mm wide and 6 mm thick. For the case of specimens C, a 20 mm thick honeycomb is used, built with 0.08 mm aluminium foil and with a hexagonal cell size of 6.4 mm. This honeycomb is sandwiched between two aluminium sheets of different thickness, the thickest being the furthest from the aluminium beam.  
Two different thicknesses (1 mm and 2 mm) of viscoelastic polymer are used for specimens B as well as for specimens C. The length and width of this polymer layer match that of their respective constraining layers. Twelve different cases of partial passive treatment with CLD on the aluminium beam (as shown in Figure 3 and detailed in Table 1) will be evaluated experimentally.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Specimen** & **Polymer** & **Core** & \begin{tabular}{c} **Upper** \\ **Layer** \\ \end{tabular} & \begin{tabular}{c} **Lower** \\ **Layer** \\ \end{tabular} & \begin{tabular}{c} **CLD** \\ \end{tabular} & **CLD** \\ \hline \hline  & **Thickness** & **Layer** & \begin{tabular}{c} **Thickness** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Thickness** \\ **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Length (m)** \\ \end{tabular} &
\begin{tabular}{c} **Added** \\ **Mass (kg)** \\ \end{tabular} \\ \hline A & - & - & - & - & - & - \\ \hline B1 & 2 & ALU6 & - & - & \(1\times 2.50\) & 5.6 \\ \hline B2 & 2 & ALU6 & - & - & \(1\times 2.50\) & 5.6 \\ \hline B3 & 2 & ALU6 & - & - & \(2\times 2.50\) & 11.3 \\ \hline B4 & 2 & ALU6 & - & - & \(4\times 1.25\) & 11.3 \\ \hline B5 & 1 &The viscoelastic polymer is commercially manufactured by Heathcote Industrial Plastics and is designed specifically to provide a maximum and stable loss factor in the frequency range of interest (5-60 Hz). For an ambient temperature of 20 \({}^{\circ}\)C, the corresponding loss factor is about 1.0.
## 4 Simulation and Discussion
### Comparison of Test Results  
To analyze the performance of IYEPDNN, we compare it with the network topology-related algorithms, biological characteristics-related algorithms, and artificial intelligence-related algorithms. Network topology related algorithms mainly include DC [10], SC [13], EC [11], IC [15], local average connectivity (LAC) [36], neighborhood centrality (NC) [37], and BC [11] algorithms.  
Biological characteristics-related algorithms mainly include WDC (based on weighted degree centrality and gene expression data) [38], PeC (based on the integration of protein-protein interaction and gene expression data) [39], UDoNC (based on protein domains and protein-protein interaction networks) [40], LBCC (based on the combination of local density, BC [11], and DC [10]) [41], RSG (based on RNA-Seq, subcellular localization and GO annotation) [42], DEP-MSB (based on multi-source biological information) [43], OGN (based on integrating orthology information, gene expressions data, and PPI networks) [44], and TEGS (based on integrating network topology, gene expression profile, and GO annotation information, and protein subcellular localization information) [45] algorithms.  
Artificial intelligence-related algorithms mainly include RWEP (based on random walk) [46], RWHN (based on randomly walking in the heterogeneous network) [47], EssRank (based on random walk) [48], EPOC (extended Pareto optimality consensus model) [49], ETB-UPPI (based on uncertain networks) [50], EPCS (community significance testing problem) [51], SigEP (local clustering coefficient) [3], RWAMVL (based on local random walk and adaptive multi-view multi-label learning) [6], and AFSO_EP (based on artificial fish swarm optimization) [52] algorithms.  
We used line or histogram charts to compare the correlation algorithms. The top 1-25% or the top 100-600 candidate essential protein data of the correlation algorithms was obtained from the original paper. The top 1% in the DIP dataset contains 61 proteins, 5% contains 255 proteins, 10% contains 509 proteins, 15% contains 764 proteins, 20% contains 1019 proteins, and 25% contains 1273 proteins. The top 1% of the GAVIN dataset contains 19 proteins, 5% contains 93 proteins, 10% contains 186 proteins, 15% contains 278 proteins, 20% contains 371 proteins, and 25% contains 464 proteins.
# Effects of Shading on the Growth and Carbon Storage of _Enhalus acoroides_
## 2 Materials and Methods
### Measurement of Indicators  
The leaf length and leaf width of _E. acoroides_ in all sampling frames were measured with a measuring tape, and the number of leaves was subsequently counted. Plant density was calculated by taking the number of _E. acoroides_ in each sampling frame and dividing it by the area of the sampling frame (0.0625 m\({}^{2}\)) to calculate the plant density of _E. acoroides_ under different light treatments (plants/m\({}^{2}\)). Seagrasses were carefully retrieved, and subsequently dried to constant weight at 60 \({}^{\circ}\)C for the determination of biomass [12].  
Three _E. acoroides_ were randomly selected from each sample frame as the sample plants to be tested, and the middle part of their leaves was taken for the determination of the chlorophyll content. The fresh leaves were cut into small pieces of about 0.20 cm, mixed well and weighed to 0.20 g. The leaves were then put into a stoppered graduated test tube with 80% acetone solution for chlorophyll extraction. When the leaf tissue had all turned white, it indicated that the chlorophyll had been extracted cleanly [19]. After that, the absorbance values of the extracts at the corresponding wavelengths were measured  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site** & **Control/lx** & **Single/lx** & **Intensity** & **Control/lx** & **Double/lx** & **Intensity** \\ \hline  & 76,000 & 28,300 & 62.76 & 78,400 & 6580 & 91.61 \\ ZG1 & 78,700 & 27,800 & 64.68 & 76,900 & 7000 & 90.90 \\  & 75,300 & 29,400 & 60.96 & 76,200 & 7800 & 89.76 \\  & 56,500 & 18,500 & 67.26 & 51,800 & 2530 & 95.12 \\ ZG2 & 59,200 & 20,500 & 65.37 & 52,400 & 3310 & 93.68 \\  & 58,300 & 22,700 & 61.06 & 55,600 & 2890 & 94.80 \\  & 36,900 & 14,500 & 60.70 & 25,800 & 2280 & 91.16 \\ ZG3 & 28,500 & 12,400 & 56.49 & 26,900 & 2560 & 90.48 \\  & 35,200 & 16,700 & 52.56 & 26,200 & 1680 & 93.59 \\ Average & & & 61.32 & & & 92.34 \\ \hline \end{tabular} Note: ZG1, ZG2, ZG3 are three sample plots in study area. Single, the light intensity of the single-layer shading net; Double, the light intensity of the double-layer shading net.  
\end{table}
Table 1: The measurement of light intensity in situ.  
Figure 1: Study area.  
using a UV spectrophotometer (UV-5200), and the content of chlorophyll and chlorophyll a/b value (Chl a/Chl b) were calculated accordingly [20].  
Sediment samples were stored in desiccators prior to analysis. After being naturally air dried, the sediment samples were ground and homogenized with a mortar and pestle and subsequently passed through a 100-mesh sieve for the determination of the sediment organic carbon content. The sediment organic carbon content was determined via the potassium dichronate-sulfur acid oxidation method. The organic carbon of the sediment was oxidized with a potassium dichromate-sulfur acid solution under oil bath heating, and the remaining potassium dichromate was titrated with ferrous sulfate to calculate the organic carbon content from the amount of potassium dichromate consumed [21].
## 1 Introduction
A cross-linking agent is used to synthesize the polymer to form covalent bonds between polymer chains, creating a network structure that helps to stabilize the polymer structure and create specific binding sites for the template molecule. Much effort has been directed into utilizing MIPs to extract a single component from a variety of aqueous samples, such as ketoprofen [11; 25], and abacavir [12] and tenofovir [26]. However, very few studies have been reported on aqueous solutions employing multi-template MIPs, particularly for the simultaneous removal of ARVs and NSAIDs. [PERSON] et al. [14] and [PERSON] et al. [17] presented a study on the synthesis, characterization, and optimizations of MIPs for NSAID elimination. They reported on the detection of ibuprofen, naproxen, and diclofenac in aqueous samples employing a multi-template MIP as a selective adsorbent for solid-phase extraction in different investigations and had recoveries above 98% for each target molecule. Progress on the synthesis of templates that can simultaneously remove drugs from different aqueous mediums is essential for their recovery and environmental protection. While these studies have made significant contributions to the field, challenges such as template leakage, polymerization heterogeneity, and scalability limitations still exist. Recent advancements, including the development of novel functional monomers [27; 28] and the integration of advanced characterization techniques, offer promising solutions to address these challenges and pave the way for the widespread application of MIPs in pharmaceutical formulations and drug delivery systems. Therefore, the objective of this paper is to synthesize a multi-template molecularly imprinted polymer that can selectively adsorb non-steroidal inflammatory and antiretroviraldrugs from aqueous medium. The MIPs were prepared using drugs as templates to afford active sites on the surface for targeting specific drugs. The interaction of the templates with the resultant polymer was evaluated using adsorption isotherms, kinetics, and selectivity. The templates' adsorption behavior was carried out under various parameters, such as pH value and concentration. The extraction efficiencies of the MIP and related non-imprinted polymer (NIP) and the formation of a monolayer or multilayer adsorption medium of all templates were assessed. The prepared templates exhibited high adsorption capacities and selectivity for the investigated drugs. The MIP has huge potential to be employed for the removal of these drugs from the environment as well as in drug purification or recovery processes.
## 3 Results and Discussion
### Synthetic Data
#### 3.2.8 Maximum Mean Discrepancy  
The maximum mean discrepancy (MMD) is a measure for distance between two distributions in a reproducing kernel Hilbert space on the mean embeddings. The measured MMD values for our original and synthetic samples were 0.6 and 0.3, respectively, which evaluated our synthetic data to be better.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{1}{c}{\
\begin{tabular}{c} **Mean** \\ **Correlation** \\ **Coefficient** \\ \end{tabular} } & \multirow{2}{*}{**MAE**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**PRD**} & \multirow{2}{*}{**FD**} & \multicolumn{1}{c}{\multirow{2}{*}{
\begin{tabular}{c} **Mirror** \\ **Column** \\ **Association** \\ \end{tabular} } \\ \hline TGAN & 0.673 & 0.77 & 0.59 & 88.21 & 0.87 & 0.629 \\ TGAN-skip & 0.815 & 0.65 & 0.62 & 71.35 & 0.89 & 0.714 \\ TGAN–WGAN-GP & 0.937 & 0.56 & 0.58 & 61.73 & 0.71 & 0.863 \\ TGAN-skip–WGAN-GP & 0.981 & 0.41 & 0.36 & 51.82 & 0.68 & 0.947 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of various GAN models with our proposed TGAN-skip–WGAN-GP model.  
Figure 10: Mean and standard deviation comparison of different GAN models in our data.
## 2 Inductive Design Exploration Method with Active Learning  
### Limitation of Traditional IDEM  
The IDEM process initially proposed by [PERSON] et al. [7; 8] is briefly explained as follows. In Step 1, a rough design space at each design level is defined and it generates discrete grid points within the spaces. These discrete points are then evaluated using the employed mapping models (e.g., finite element model and surrogate model) in Step 2. This mapping or projection step is performed to relate the input design space (low level) to the output design space (high level) and repeated from the lowest level to the top-most level. Uncertainty associated with design parameters and models is taken into account in this step; thus, the evaluation of a discrete point produces a range of outputs. The evaluated discrete points with the corresponding output ranges are stored so as to help identify the feasible regions in the next step. In Step 3, feasible regions are identified in the solution spaces with given (or identified from a higher level) output ranges that satisfy the requirements. The feasible regions comprise feasible points whose evaluated output ranges are located within the constraint boundary of the requirements. These feasible points are determined based on their hyper-dimensional error margin indices (HD-EMIs). The detailed calculation of HD-EMIs is explained in Refs. [7; 8; 9; 10]. An HD-EMI of a discrete point indicates the quantitative closeness of its output range to the nearest feasible region boundary, i.e., the robustness of a design point so as to not violate the performance constraints under its variability. An HD-EMI greater than unity for a discrete point implies that its output range lies within the constraint boundary of the performance requirements, therefore making it a feasible point. In contrast, an HD-EMI less than unity for a discrete point indicates that its output range is located outside the nearest constraint boundary, which makes it an infeasible point. With increasing HD-EMIs, the output range moves further away from the constraint boundary, thereby making it more robust.  
When implementing the solution strategy of IDEM, there is a difficult question to be asked in Step 1. How many samples should we generate to identify feasible regions with acceptable accuracy? As mentioned, the number of design points to be evaluated has a direct influence on the accuracy of feasible solution spaces. Many existing studies, which utilized the traditional IDEM [6; 7; 8; 9; 10; 11; 12], also appear to show that the sample size is predetermined owing to computational cost. However, it is very difficult to choose the appropriate sample size beforehand and this difficulty is compounded when high cost computational models are involved. To deal with this issue, we present a new method using the active learning technique, which is discussed in Section 2.2.
# Influence of Joint Stiffness and Motion Time on the Trajectories of Underactuated Robots
## 6 Experimental Validation
### Experimental Setup  
The experimental setup is depicted in Figure 13. The geometric dimensions and the mass properties of the 2-DOF robots coincide with the geometric and mass properties of the last two links of the 3-DOF robot, which are reported in Table 1. The first link was actuated by a motor and the second one was connected to the first link using a ball bearing and a torsional spring. The DC Portescap 35 NT2R 82 426 SP motor was adopted. It is driven by an Advanced Motion Control 25A8 and its rotation is measured by an incremental optical encoder LIKA Electronic I58-L-5000 ZCU16R with a resolution of 0.072\({}^{\circ}\). The nonactuated joint was equipped with a stainless steel helical torsional spring with three elastic coils. Torsional stiffness was \(k_{3}=2.6\cdot 10^{-3}\) Nm/rad.  
The position and the orientation of the second link during the motion were measured by means of a vision system since there was not enough room to fit an encoder to the nonactuated joint. An industrial camera Dalsa Genie Nano GM30-M2050 which uses a sensor Sony IMX252 with resolution 2064 \(\times\) 1544 pixels, was used. Three white markers were placed on the second link and were detected by the vision system, see Figure 13b.  
After the camera calibration, the resolution of the measurement was about \(\pm\)0.30 mm/pixel.
# A Hybrid Bio-Optical Transformation for Satellite Bathymetry Modeling Using Sentinel-2 Imagery
## 2 Materials and Methods
### Methodology--Model Development
#### 2.3.1 Sunglint Correction  
The reflectance of the bottom-of-atmosphere (BOA) is a parameter representing how much light is reflected from the sea surface and in which direction. When imagery observations are made above the sea surface, the signal measured by satellites (i.e., the remote sensing signal/reflectance, \(R_{rs}\)) is the sum of the water leaving signal (i.e., water leaving radiance/reflectance) and the sea-surface reflected signal (sunglint).  
Sunglint is related to the specular reflection of sun radiation due to wave undulation and sea-surface foam. According to [PERSON] et al. (2016), high-resolution images uncorrected for the sunlight effect may introduce an error in the water-depth estimation of up to 30%. Modelling the sunlight error was achieved in this work by applying the NIR band (B8) of Sentinel-2A, according to the algorithm (Equation (1)) developed by [PERSON] et al. (2017):  
\[Degl(R_{Bi})=R_{Bi}^{BOA}-slope_{B_{i}}\times(R_{NIR}-minNIR) \tag{1}\]  
where \(Degl(R_{Bi})\) is the resulting deglint image band; \(R_{Bi}^{BOA}\) is the Bottom-of-Atmosphere remote sensing corrected reflectance of \(B_{i}\) band (being either B2, B3, or B4); \(R_{NIR}\) is the B8 band of Sentinel 2A; and \(minNIR\) is the minimum pixel value of the B8 band.  
In order to perform sunlight removal, an area of deep water was selected, with depths more than 50 m. Statistical analysis between B2/B8, B3/B8, and B4/B8 bands resulted in slopes of 0.67 for B2 (blue band), 0.52 for B3 (green band), and 0.31 for B4 (red band), respectively (Figure 3), whereas the \(minNIR\) value in this case was 0.055. The processed image after the sunlight error removal is shown in Figure 4.  
Figure 3: Sunglint correction slope calculation from the Sentinel-2A spectral band ratios of (**a**) B2/B8, (**b**) B3/B8, and (**c**) B4/B8.  
Figure 2: Work-flow chart of the HBT methodology.
# On the Realistic Radio and Network Planning of IoT Sensor Networks
## 6 Result Analysis  
### RSS Coverage  
This section analyses the RSS Coverage results of each sensor deployed in the area of interest. If RSS is accurately estimated, it will realistically affect the outcome of the network layer performance parameters, such as the packet delivery. A comparison between the measured RSS values and the simulation results produced from the three different simulation models is presented below. The comparison is depicted in Tables 3 and 4 and graphically presented in Figures 7 and 8. It can be clearly seen that the simulated RSS values from TruNET wireless highly approximate the measured ones, reaching a correlation level of 73%. On the contrary, both Cooja built-in models deviate significantly from the measured values (correlation level 25% and 26% respectively), hence failing to realistically predict whether a reliable communication link can be established between sensors.  
Figure 5: MRM model simulation.  
Figure 6: Cooja-TruNET interconnected simulation: WSN links.  
In order to further analyse the typical RSS fluctuation behaviour in a deployed WSN, the RSS values of wireless nodes for an indoor and outdoor scenario were monitored for a week. RSS measurements were recorded 24/7, in order to encapsulate any environmental factors, such as  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Location** & **TruNET** & **MRM** & **UGDM** &
\begin{tabular}{c} **Real** \\ **Measurement** \\ \end{tabular} \\ \hline  
\hline \end{tabular}
\end{table}
Table 4: RSS Comparison—WSN Setup II.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Location** & **TruNET** & **MRM** & **UGDM** &
\begin{tabular}{c} **Real** \\ **Measurement** \\ \end{tabular} \\ \hline
1 & \(-\)79.20 & \(-\)97.00 & \(-\)97.00 & \(-\)80.00 \\ \hline
2 & \(-\)80.60 & \(-\)81.77 & \(-\)97.00 & \(-\)75.60 \\ \hline
3 & \(-\)70.70 & \(-\)52.13 & \(-\)89.00 & \(-\)78.00 \\ \hline
4 & \(-\)87.90 &  
temperature changes, etc. The results, as shown in Figure 9, indicate that an RSS fluctuation, up to 12 dB may occur (\(\pm\)6 dB from the average RSS value). However, the overall performance of both indoor and outdoor sensors confirm a generally stable operation of \(\pm\)1-2 dB during 95% of the recorded time. These findings are inline with other scientific work, such as [22; 23]. In the aforementioned papers, authors reported an average standard deviation of RSS values ranging from 1 dB to 12.3 dB depending on the device model. The RSS fluctuation range is also affected by human mobility within the study area [24]. These findings partially justify the errors occurring between the measured and simulated RSS values, as analysed previously. Other deviations can be explained due to the unavoidable introduced inaccuracies between the actual and simulated environment. Such inaccuracies may include material constitutive parameters values, object dimensions, geometry and 3D object placements.  
Figure 8: RSS: Measured vs. Cooja built-in Models Simulation (**a**) Measured vs. MRM Simulation; (**b**) Measured vs. UDMG Simulation.  
Figure 7: RSS: Measured vs. TruNET Simulation.
## 5 Discussion
### Adaptability  
The theme of Adaptability contains the codes and subcodes of preparedness (training and education, technological savvy), current crew, industry mindset, maritime sector, and flexibility. It is generally accepted that onboard positions will be reduced, but the experience, education, and training that Licensed Deck Officers possess will be needed for shoreside positions. Newer skills, such as proficiency in data analysis, will be required in a world with more technologically advanced vessels. This may be attractive to some mariners but may also raise licensing concerns for others. \"Jobs of merchant mariners are not going away, they are going to change. Looking at steam, there were sailors before steam, they harnessed the wind. Then when diesel came, sailors were still there, they changed. History repeats itself.\" Training will have to include more technical skills. Multiple experts who were interviewed agreed that qualified maritime personnel with strong backgrounds in IT and programming will be needed. Most interviewees had a positive outlook compared with the survey responses regarding jobs being available in RCCs for Licensed Deck Officers, agreeing that there will be enough opportunities in the future as long as they are willing to adapt.  
From the survey open-ended question, an echoing theme is that the maritime industry is slow to change and that automation will be an incremental process and will take time to be fully adapted. A mental shift within the industry will have to occur. It was also stated that it will take at least 10 years for delegations from IMO to adjust regulation. This is in-line with most of the interview responses.  
The responses as to which maritime sector will adapt to autonomous shipping first varies from the interview versus the survey answers. Two interviewees were certain that it would be inland shipping, one determined that it would be the ferry industry, two determined deep sea, and two decided on short sea shipping. The survey resulted in almost 50% of participants choosing deep sea shipping.
## 4 Our Work
### System Model
Link quality (LQ) is measured at each time slot to estimate channel conditions as well and if it is found high in the present slot, i.e., greater than predefined threshold \(LQ_{th}\), it will remain high in the following slot with probability \(lq_{on}\) (respectively, \(1-lq_{on}\)) where \(0.5\leq lq_{on}<1\). Whereas, if LQ is estimated as low as \((LQ\ <\ LQ_{th})\) in the current slot, then it will be in such condition in the next slot with probability \(lq_{off}\) (respectively, \(1-lq_{off}\)). Such a two-state model can effectively describe the scenario for many WBAN applications related to healthcare as mentioned in [12]. During each time slot t a node performs action \(a_{t}\) s.t. \(a_{t}\epsilon\{0,1,2,3,...K\}\) which is described in terms of performing data transmission with varying transmission power \(P_{tx}\).