esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Isotopic Analysis in Modern Mammals  
#### 3.2.1 \(\delta^{13}\)C and \(\delta^{15}\)N Values of Various Mammals  
The \(\delta^{13}\)C and \(\delta^{15}\)N values of various herbivores range from \(-\)23.6% to \(-\)19.6% and 3.1% to 8.3%, respectively. The \(\delta^{13}\)C and \(\delta^{15}\)N values of those mammals from Greece confirm that they consumed a terrestrial C3 plant diet (Figure 8), including traces from a C4 diet (e.g., maize). The difference in carbon isotope values between plants and mammals is attributed to fractionation factors ranging from 1% to 5% [44; 45; 74]. In particular, the data indicate an offset for \(\delta^{13}\)C\({}_{\rm co}\) between bone collagen and diet of 5.3% (the difference in median between the carbon of plants and collagen values). This range falls into the offset, from 1% to 5% mentioned in previous studies [46; 75; 76]. Also, a statistical study conducted by [PERSON] et al. [77] determined an offset at 4.8% for carbon collagen isotope with an uncertainty at 0.5%. The statistical study of [PERSON] et al. [77] assessed isotopic data collected by [PERSON] et al. [78] of measurements performed on omnivorous mammals during controlled feeding experiments [44; 45; 75; 76; 79].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fish/Faunal** & **Age** & \begin{tabular}{c} \(\delta^{13}\)**C.** \\ **\%. V-PDB** \\ \end{tabular} & \
\begin{tabular}{c} \(\delta^{15}\)**N.** \\ **\%. V-Air** \\ \end{tabular} &
\begin{tabular}{c} \(\delta^{34}\)**S.** \\ **\%. V-CD** \\ \end{tabular} & **Authors** \\ \hline Pandora, _Pagellus erythrius_ (bones) & Recent & \(-\)13.1 to \(-\)14.1 & 10.3 to 10.1 & \\ \hline Moray eel, _Murarena Helena_ (bones) & Recent & \(-\)13.7 & 10.8 & [68] \\ \hline Barracuda, _Sphraena sphraena_ (bones) & Recent & \(-\)13.7 to \(-\)14 & 7.9 to 7.8 & \\ \hline Dusky grouper, _Epinephrineus marginatus_ (bones) & Recent & \(-\)8.4 to \(-\)8.3 & 11.4 to 10.7 & \\ \hline Marine fish (bones) & Ancient & \(-\)10.1 to \(-\)19.2 & 11.6 to 6.1 & [69] \\ \hline Freshwater fish (bones) & Ancient & \(-\)11.9 to \(-\)20.8 & 10.9 to 4.9 & \\ \hline Marine fish (bones) & Ancient & \(-\)11 to \(-\)14 & 19 to 7 & [71] \\ \hline Marine fish (bass, bones) & Ancient & \(-\)11.4 to \(-\)10.3 & 11 to 8.44 & [69] \\ \hline Faunal (bones) & Ancient & \(-\)18.3 to \(-\)20.5 & 7 to 4.2 & 9.9 to 14.7 & [73] \\ \hline Faunal (bones) & Ancient & \(-\)18.5 to \(-\)20.1 & 8.2 to 2.2 & 8.2 to 2.1 & [71] \\ \hline Faunal (bones) & Ancient & \(-\)19.9 to \(-\)20.4 & 3.9 to 5.7 & 15 to 18.2 & [52] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Stable isotopes of the \(\delta^{13}\)C, \(\delta^{15}\)N, and \(\delta^{34}\)S of fish and faunal, old and recent from Greece.
# Adjustment Algorithm for Free Station Control Network of Ultra-Large Deepwater Jacket
## 2 Materials and Method
### Algorithmic Principle
#### 2.2.5 Coordinate Similarity Transformation  
The coordinate transformation equation for a four-parameter transformation between two coordinate systems is:  
\[\left[\begin{array}{c}x_{2}\\ y_{2}\end{array}\right]=\left[\begin{array}{c}\Delta x\\ \Delta y\end{array}\right]+\text{k}\left[\begin{array}{cc}\cos\theta&\sin \theta\\ -\sin\theta&\cos\theta\end{array}\right]\left[\begin{array}{c}x_{1}\\ y_{1}\end{array}\right] \tag{12}\]  
where \(\Delta_{x}\), \(\Delta_{y}\) are the coordinate translation parameters, k is the scale factor parameter, and \(\theta\) is the rotation angle parameter. By applying \(a=\text{k}\cos\theta\) and \(b=k\sin\theta\), this problem can be converted into a linear model.  
It is known that there are \(N\) common points in the old and new coordinate systems, the coordinates under the target coordinate system are set as the observed values, the coordinates in the source coordinate system are free of error, and the indirect adjustment solution can be further used to find out the adjustment values and the precision of each parameter.
# Effects of Caprolactam Wastewater on Algal Growth and Nutrients Removal by _Arthrospira platensis_
## 2 Materials and Methods
### Effects of Different Wastewater Concentrations on the Growth of A. platensis  
To understand the effects of WCP on algal growth, _A. platensis_ was cultured in diluted mixed WCP for 6 d. The mixed WCP concentrations were 12.5%, 25.0%, and 50.0%, and the culture medium without wastewater was used as a control. The culture medium containing 50% mixed wastewater contained 3.302 g/L of NaNO3 and 0.133 g/L of KH\({}_{2}\)PO4. To avoid the influence of nutrient concentrations on algal growth, 3.30 g/L of NaNO3 and 0.13 g/L of KH\({}_{2}\)PO4 were added to the control medium, and the corresponding amounts of NaNO3 and KH\({}_{2}\)PO4 were added to the culture medium with 12.5% and 25.0% mixed wastewater to ensure that all the culture media had consistent initial concentrations of N and P. Other nutrients were then added by adding each nutrient salt or its concentrated solution, and the formulations of the four culture media are shown in Supplementary Table S1.  
An aerated column photobioreactor was used in the experiment. The bioreactor was composed of glass columns (working volume of 250 mL and inner diameter of 3 cm) and accessory equipment that maintained the culture conditions of a temperature of 30 \({}^{\circ}\)C, the light intensity of 320 umol photons/(m\({}^{2}\)-s), light/dark cycle of 14 h/10 h, and airflow of 250 mL/min (enriched with 2% CO2). The initial optical density of the culture at 560 nm (OD560) was 0.3 \(\pm\) 0.05, and the corresponding biomass dry weight (DW) was approximately 0.15 g/L. The DW of the biomass was measured daily. Three parallel cultures were conducted for each treatment and control, and the experiment was repeated at least twice.
# Late Shelf Life Saturation of Golden Delicious Apple Parameters: TSS, Weight, and Colorimetry
## 2 Materials and Methods
### Measurement of Color  
Color parameters were measured using a Nix Pro wireless color sensor (Nix Sensor Ltd., Hamilton, Ont, Canada, NixPro Mini). The device blocks ambient light, provides its own calibrated LED-based light source during measurement, and works with a proprietary sensor module. The Nix Pro Device has a circular shape with a 15 mm diameter and 45/0\({}^{\circ}\) measuring geometry. Two high-CRI LEDs designed explicitly for color reproduction provided the light source for the measurements. The instrument has <0.1 DE2000 repeatability.  
The color data are used to match physical colors. The results were investigated using the CIELAB color system from the Nix pro color sensor application (App version v1.33, Nix Sensor Ltd., Hamilton, ON, Canada), which displays the RGB, CMYK, HEX, CIELAB, and XYZ values for each color. Three repeated measurement data of 72 apples were obtained for each storage time (from week one to week six) using the HunterLab colorimeter (illuminant D50 and 2\({}^{\circ}\) standard observer). The color was recorded using a CIELAB uniform color space (Lab), where L* indicates lightness, a* indicates chromaticity on a green (\(-\)) to red (\(+\)) axis and b* chromaticity on a blue (\(-\)) to yellow (\(+\)) axis. We calculated color parameters from the L*, a*, and b* data. \(\Delta\)E is the difference between two colors represented by two points in the CIELAB color space and chroma (C*), which expresses the degree of color for an area viewed with its brightness.  
\[\Delta\mathrm{E}=\sqrt{(\mathrm{L}_{0}^{*}-\mathrm{L}^{*})^{2}+\left(\mathrm{a }_{0}^{*}-\mathrm{a}^{*}\right)^{2}+\left(\mathrm{b}_{0}^{*}-\mathrm{b}^{*} \right)^{2}}, \tag{2}\]  
\[\mathrm{C}^{*}=\sqrt{\mathrm{a}^{*2}+\mathrm{b}^{*2}}, \tag{3}\]  
where L\({}_{0}^{*}\), a\({}_{0}^{*}\), and b\({}_{0}^{*}\) represent the color data based on the samples, and L*, a*, and b* indicate the color of the measured instantaneous data.  
At the end of the storage process, the color of the apples will always turn into some shade of brown, which can be considered a final state or limiting color. Since the a* and b* coordinates of green and yellow shades are less than the coordinates of brown shades, the color change process can thus be considered as a saturation or limited growth process, pointing to asymptotic regression analysis.
# Identification of Intrinsically Disordered Protein Regions Based on Deep Neural Network-VGG16
## 2 Materials and Methods
### Performance Evaluation  
The performance of the IDPs prediction is evaluated through sensitivity (_Sens_), specificity (_Spec_), binary accuracy (\(BACC\)), and Matthews correlation coefficient (\(MCC\)). The formulae for computing \(Sens\), \(Spec\), \(BACC\), and \(MCC\) are  
\[Sens=\frac{TP}{TP+FN}, \tag{10}\]  
\[Spec=\frac{TN}{TN+FP}, \tag{11}\]  
\[BACC=\frac{1}{2}(\frac{TP}{TP+FN}+\frac{TN}{TN+FP}), \tag{12}\]  
\[MCC=\frac{(TP\times TN)-(FP\times FN)}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}}\,. \tag{13}\]  
In these equations, \(TP\), \(FP\), \(TN\), and \(FN\), respectively, represent true positives (correctly predicted disordered residues), false positives (ordered residues that are incorrectly identified as disordered residues), true negatives (correctly predicted ordered residues), and false negatives (disordered residues that are incorrectly identified as ordered residues), respectively. The values of \(MCC\) fall in \(-1\) and \(+1\), where \(0\) indicates a lack of correlation. The higher the \(MCC\) values, the better the prediction.
# The Perspective of Large-Scale Production of Algae Biodiesel
## 3 Production of Algal Biodiesel
### The Production Process of Biodiesel from Algae
The solid wall of algal cells is the reason for the problematic release of lipids. The extraction of lipids from the mass of algae is usually done by a mechanical process (expeller press) or by chemicals and solvents. The application of an extrusion press is a simple and efficient process of mechanically crushing algae cells to obtain the oil. The applied pressure should not be too high, as there is a drop in lipid quality. Other mechanical techniques in use include Bead beating. Application of chemical solvent for extraction of algal lipids, like n-hexane, methanol, and ethanol, seems to represent the most proper method, as it is valid and widely used in laboratory research (Folch method is the most popular). Nevertheless, the efficiency of this method highly depends on microalgae strains [60]. By the two-step process, saponification and esterification (homogeneous catalyst method, the efficiency of lipid conversion to biodiesel can be increased to 90%. However, before applying the chemical solvent extraction on a commercial level, several facts need to be considered:* A large amount of chemical solvent is required for efficient lipid extraction. * The problem of solvent toxicity and safety should be addressed. * Additional energy is required to recover the solvent. * Wastewater treatment is required which is an additional cost  
Other technologies that aim to enhance the algae lipid extraction like autoclaving [29], supercritical CO\({}_{2}\)[30; 32; 61], ultrasonication [29; 34; 50], microwave-assisted extraction of lipids from microalgae [54], switchable solvent extraction [56], vacuum shelf drying [57], electroporation technique, and osmotic technique [58] are still in the development phase. Therefore, they need further improvement before being commercially implemented. To eliminate the energy consumption for the dehydration of algae biomass from the slurry, the technology of oil extraction from wet algal biomass has been developed. [PERSON] et al. [59] investigated a novel extraction method that used interchangeable polarity solvents for lipids extraction from diluted algal cultures. The efficiency of this method is about 70-80%. Advanced technologies for extract oil from algal biomass have been described in detail by [60; 61]. The biodiesel production system is shown in Figure 1. Arrow widths are proportional to mass flows.
# A Survey on Robust Video Watermarking Algorithms for Copyright Protection
## 3 Properties of Video Watermarking
### Robustness  
Robustness means that the watermark information can still be extracted completely or recognized correctly from the video which is subject to various normal image processing operations or malicious tampering attacks. As long as the video does not lose its use value after being attacked, the embedded watermark information cannot be destroyed. Similarly, if the embedded watermark information is destroyed, the video quality should be reduced to lose its use value.  
Robustness-related attacks that video watermarking algorithms should be able to resist include three types: normal image processing attacks, geometric attacks, and temporal synchronization attacks. Several typical attacks corresponding to these three types of attacks are listed in Table 1. To evaluate the performance of various algorithms in Sections 4 and 5 conveniently, the corresponding abbreviations of various attacks are also listed.  
In general, normalized correlation (NC) and bit error rate (BER) are used to quantify the robustness of watermarking algorithms. NC is used to estimate the similarity between the extracted watermark and the original one [20]. For watermark images with size of \(M\times N\), the definition of NC \(I_{\text{NC}}\) can be expressed as:  
\[I_{\text{NC}}=\frac{\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{N}W(i,j)\times W^{ \prime}(i,j)}{\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{N}W(i,j)^{2}}, \tag{10}\]  
where \(W(i,j)\) and \(W^{\prime}(i,j)\) denote the pixel values at coordinates \((i,j)\) in original watermark and extracted watermark, respectively. The value range of NC is [0, 1]. The higher the value of NC, the stronger the anti-attack ability of watermarking algorithm.  
BER is used to estimate the error rate between the extracted watermark and the original watermark [21]. The definition of BER \(I_{\text{BER}}\) is shown as:  
\[I_{\text{BER}}=\frac{1}{M\times N}\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{N} \bigl{|}W(i,j)-W^{\prime}(i,j)\bigr{|}\times 100\%, \tag{11}\]  
where \(W(i,j)\) and \(W^{\prime}(i,j)\) denote the pixel points at coordinates \((i,j)\) in original watermark and extracted watermark, respectively. The smaller the BER, the better the robustness.
## 1 Introduction
In Figure 3, the 360-degree view shows how the silhouette and contours of a non-rigid object shift depending on the angle. Object detection and segmentation algorithms must generate a mask for each frame accordingly. The mask results include pixel-level information that separates the object from the background based on its outline. These masks must maintain accurate boundaries even when the object moves or rotates and must adapt to changes in the shape of non-rigid objects. Our proposed method reduces errors in object boundary separation for new object appearances without the need to retrain the segmentation model. This is achieved by integrating the detector's bounding box information into the embedding vector. As a result, the representation of information in AR views for objects exhibiting dynamic poses is significantly enhanced. The key innovation of our approach lies in incorporating prompt-based segmentation, which enables dynamic interaction between the system and the user. Our contributions can be summarized as follows:  
1. We develop a visual prompt selection framework that supports fast prompt processing and accurate mask generation. This significantly improves the user experience and enriches the immersive AR environment. 2. We demonstrated that the user's input in providing ROIs significantly impacts segmentation performance. This can lead to up to a 12.5% difference in object boundary separation performance. 3. We provide a flexible architecture that allows for the replacement of detector or segmentation models based on specific requirements. This enhances adaptability to different AR scenarios and applications. The structure of this article is organized as follows: Section 2 illustrates the related work in the field of AR and image recognition. Section 3 outlines our proposed visual prompt selection framework's structure. Section 4 describes the experimental results, and Section 5 presents the conclusion with our contributions.
## 5 Results and Discussion  
### Hovering Validation  
In hover validation, three directions were tested by sweep inputs under Configuration D. In Figure 9, the simulation results are compared with the helicopter responses in real flights. As observed from the comparisons, the calculations of the three directions follow the flight data well. Variance Accounted For (VAF) was calculated to verify the correctness of the model. All VAF are higher than 91%. The VAF of \(\theta\) and \(\phi\) are in similar range with \(q\) and \(p\), indicating good predictions in hover. Compared with the IDM results in Reference [26], the correctness got from NMM are slightly lower then IDM. For example, in Figure 9a, \(q\) VAF = 92.1%, while in IDM, VAF = 93.4%; In Figure 9b, \(p\) VAF = 91.1%, while in IDM, VAF = 92.9%. Although in hover condition, NMM might not be as accurate as IDM, it is good enough for later research.  
In addition, a large fluctuation in total power was found in the yaw excitation test. The main ESC had notably good functions and was able to record the motor and main battery parameters into a SD card during flights. Using the actual voltage and current of the battery output, the power dissipation of the entire helicopter in flights were obtained. In the simulation, the main rotor power and tail rotor power were calculated separately. The power simulations compared with flight data are shown in Figure 10. It is observed that the calculation of gross power matches well with the recorded power. In hover flight, the main rotor and tail rotor are the main power-consuming components. According to the calculations, the power of main rotor occupied approximately 85% of the gross power. During a yaw maneuver flight, the tail rotor power varied, making the gross power change from 1 kW to 1.7 kW, while power of main rotor was more stable.  
Figure 8: Control diagram in automatic flights.  
Figure 10: Power dissipation comparisons during the yaw maneuver in hover.  
Figure 9: Time domain validation in hover under Configuration D. (**a**) Longitudinal direction; (**b**) Lateral direction; (**c**) Yaw direction.
## 2 Preliminaries  
**Generative Adversarial Networks**: As a generative model, Generative Adversarial Networks (GANs) achieve the implicitly statistical distribution capture of training data through game training. A common analogy used to explain their mechanism is to think of one network as an art forger (generator \(G\)) and the other as an art expert (discriminator\(D\)). \(G\) creates forgeries with the aim of producing realistic images, while \(D\) receives both forgeries and real images and aims to distinguish between them. Both networks are trained simultaneously and in competition with each other [17]. This two-player minimax game is formalized mathematically as follows:  
\[\min_{G}\max_{D}\operatorname*{\mathbb{E}}_{x\sim P_{d}}[\log D(x)]+\operatorname *{\mathbb{E}}_{z\sim P_{z}}[\log(1-D(G(z)))] \tag{1}\]  
where \(p_{z}\) is the prior distribution from which input samples \(z\) to the generator are drawn (usually standard Gaussian) and \(P_{d}\) represents the target distribution associated with the training data. \(\operatorname*{\mathbb{E}}\) represents the expected value with respect to the distribution specified in the subscript.  
In the training stage, GANs always follow an alternating fashion by minimizing the adversarial loss as the object of the discriminator and generator, respectively:  
\[\begin{split} L_{D}&=-\operatorname*{\mathbb{E}}_{x \sim P_{d}}[\log D(x)]-\operatorname*{\mathbb{E}}_{z\sim P_{z}}[\log(1-D(G(z)) )]\\ L_{G}&=-\operatorname*{\mathbb{E}}_{z\sim P_{z}}[D (G(z))]\end{split} \tag{2}\]  
**Fuzzy Concept Modeling**: Fuzzy concept modeling has fundamental importance in Cognitive Psychology and Artificial Intelligence. One prominent work on this topic is prototype theory [11]. The fundamental idea of prototype theory is that concepts are represented by a set of prototypical cases \(P\). These cases correspond to those elements of the underlying conceptual (attribute) space \(\pi\) that are certain to satisfy the concept. Meanwhile, the fuzzy concept \(L\) has the form \"about \(P\)\", \"similar to \(P\)\" or \"close to \(P\)\", and \"about\", \"similar to\" and \"close to\" are fuzzy constraints. Hence, For any element \(x\in\pi\), we can measure the membership degree \(\mu_{L}(x)\in[0,1]\) that it belongs to the fuzzy concept \(L\).  
For the convenience of understanding, we summarized all the notations for the preliminaries and our approach as a quick list in Table 1.
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 4 Development Trend and Key Technology of ISAC towards 6G ISTNs
### Critical Techniques in Integrated Design
#### 4.2.2 Distributed Cooperative Sensing Technology
These cooperative sensing technologies are mainly achieved through dense networks of remote radio units and multiple sensors to gather sensing parameters comprehensively and multidimensionally, thereby enhancing comprehensive detection performance. In [116], the authors utilized the mobility of UAVs, beamforming, and power control technologies to reduce mutual interference. Through communication between UAVs, they share sensing results to extend the sensing range. In [117], the authors introduced satellite ISAC technology into terrestrial ISAC systems and proposed better beam tilting algorithms and pre-coder optimization algorithms. In the future, numerous ISAC nodes will be deployed in the space-ground integration scenario. If other ISAC terminals are also transmitting the same channel signal for environmental sensing, the system performance will be degraded. Therefore, we recommend that the air be divided into regions, and the adjacent sensing areas should be allocated different frequency bands. In addition, CR technology is introduced to optimize spectrum utilization. Specifically, for two ISAC terminals that share the same band resources, we divide one into a primary terminal and one as a secondary terminal. While the primary terminal communicates and senses, the secondary terminal does not work. When the primary terminal is not working, the secondary terminal performs ISAC functions. This further optimizes the performance of collaborative sensing and scheduling of multiple systems. Table 5 summarizes the cooperative sensing technology in the ISAC system. It can be found that future cooperative sensing technology will present the trend of combining centralized and distributed information, spatial and spatiotemporal cooperation, data diversification, and cutting-edge technology. It aims to enhance inter-network connectivity, making it easier for upper management to coordinate. At the same time, it achieves greater communication reliability and sensing precision through internal network collaboration.